National Research Center for
Statistics and the Environment
Final Report
National Research Center
for Statistics and the Environment
FINAL REPORT
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1. SUMMARY
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2. OUTREACH ACTIVITIES
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2.1 Seminars
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2.2 Web site
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2.3 Workshops
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Initial EPA-NRCSE workshop
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Cascadia Tropospheric Ozone Peer Review Workshop
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Combining Information From Programs That Monitor Ecological And Natural Resources 8
Environmental Monitoring Surveys Over Time
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7th International Meeting on Statistical Climatology
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EPA Corvallis
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Particulate matter air pollution
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Quality assurance of environmental models
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EPA Las Vegas
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Slovakia workshop
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Large Data Sets
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Mini-workshops
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Collaborative working group
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EPA site visit
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Spatial moving averages
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NSF/CBMS Regional Conference on Environmental Statistics
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2.4 Conference presentations
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2.5 Professional service and recognition
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2.6 Outreach
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3. RESEARCH ACTIVITIES
3.1 Ecological and environmental impact
Biological monitoring
Hydrologic effect of land use change
Statistical analysis of surface ozone
A review of statistical adjustment of ozone for meteorological variables
A linked toxicokinetic-toxicodynamic model of methylmercury-induced developmental
neurotoxicity in the fetal rat
Analysis of CO data in Spokane
Remote sensing and automobile emissions
Global warming and Pacific Northwest snowpack
Ecological Assessment of Riverine Systems by Combining Information from Multiple
Sources
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Modeling multiple pollutants at multiple sites, with application to acute respiratory studies28
Is there a contradiction between apparent long-term increases in the frequency of extreme
precipitation over the coterminous U.S. and the absence of flood trends?
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3.2 Education and outreach
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Center Computing
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A Bayesian tutorial
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Statistics courses for EPA Region X
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Quantitative Literacy Project
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Scientific method curriculum: The Truth about Science
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3.3 Model assessment
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Operational evaluation of air quality models
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Stochastic precipitation model
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Assessment of environmental fate and transport models
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Assessment of toxicodynamic models
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Developing methodology for assessment of medium and large scale environmental models36
Model assessment using repeated model fitting
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Integrated exposure and uptake biokinetic lead model (IEUBK)
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3.4 Space-time models
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Imputing air pollution exposure over space and time for use in analyses of health effects 40
Use of personal monitors to assess health effects of particulate matter exposure in Slovakia40
Modeling time series of multiply censored data
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Bayesian estimation of nonstationary spatial covariance structure
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Development of an anisotropic global covariance function
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Trend estimation using wavelets
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Receptor modeling for air quality data in space and/or time
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3.5 Sampling and design
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Composite sampling
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Comparison of ranked set sampling to alternative sampling designs and investigation of its
usefulness in environmental monitoring
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Monitoring network design
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3.6 Standards and Regulatory Impact
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Statistical aspects of setting and implementing environmental standards
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Environmental health regulation of particulate matter: Application of the theory of
irreversible investments
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Agricultural modeling for watershed management
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Decision-making under uncertainty: Prioritizing freshwater habitat restoration for salmon
recovery in the Columbia river basin
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3.7 Methodology
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A comparison of SVD and CCA analyses in climate prediction
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ORCA: A visualization toolkit for high-dimensional data
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Semiparametric trend estimation and model selection
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Evaluating the Benefits of an Ecological Study
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Applications of Bartolucci's theorem
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Fast and exact simulation of fractional Brownian motion
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Temporal fallacies in biomarker based exposure inference
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3.8 List of internally funded projects
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3.8 Visitors
3.10 Students
3.11 Research products
Books
Papers
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4. ADMINISTRATION
4.1 Director and Associate Director
4.2 Executive and advisory committees
4.2.1 Executive committee
4.2.2 Advisory committee
4.3 Space
4.4 Hiring
4.5 Members
4.6 Relations to other statistical research groups
NCAR (National Center for Atmospheric Research)
NISS (National Institute for Statistical Sciences)
IMPACT
Other research groups
List of subcontracts
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APPENDIX A. SEMINARS
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APPENDIX B. TECHNICAL REPORTS
1997-98
1998-99
1999-2000
2000-01
2001-02
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APPENDIX C. CONFERENCE PRESENTATIONS
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APPENDIX D. WORKSHOP AGENDAS
ORD-NRCSE Environmental Statistics Workshop
Cascadia Tropospheric Ozone Peer Review Meeting
Environmental Monitoring Surveys Over Time
7th International Meeting on Statistical Climatology
NRCSE/EPA workshop at Corvallis EPA
Particulate Methodology Workshop
Quality Assurance of Environmental Models
EPA Las Vegas
Exposure assessment in environmental and occupational health
Large Data Sets
Internal workshop
Teaching Environmental Statistics at the UW
Course description for EPA Region X Risk Assessment Course
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Spatial moving averages
NSF-CBMS Regional Conference on Environmental Statistics
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1. Summary
In 1996 the US EPA awarded a five-year cooperative agreement with the University of
Washington to create a national center for environmental statistical research. On September 10, 2001, the National Research Center for Statistics and the Environment was notified that its funding would not be renewed. A one-year no-cost extension was allowed,
and on September 30, 2002, the EPA funding of the Center ended. This report describes
the scientific results of the $5,380,207 awarded to the University. The Center has organized or co-organized 14 workshops and two conferences. The 33 Center members, 3
postdoctoral researchers, and 29 graduate students have made 164 presentations at national and international scientific meetings, published 6 books, and 138 scientific papers.
In all, 229 visitors spent time at NRCSE or NRCSE-organized events at the University of
Washington campus. 44 different research projects were pursued at the Center, and 7 outside subcontracts were awarded. 11 doctoral degrees and 9 Master’s degrees have been
earned by NRCSE-funded graduate students.
2. Outreach activities
2.1 Seminars
The first activite of the newly formed Center was to organize a seminar series with speakers including local consultants, EPA Region X and Washington Department of Ecology
staff, Center members and visiting faculty. The seminar series was organized as an official University course, and had about half a dozen registered students. An informal summer seminar series featured mainly Center visitors. Attendance during the first year varied from 20 to 60, with an average attendance of about 40 in Autumn quarter, 35 in Winter quarter, and 30 in Spring quarter .The second year most seminars had 20-30 attending.
During the second year, we added the service of maintaining streaming video and speaker
slides on the web The service requires a special plugin which is available for free download at the web site. The most popular seminars were those of Chris Glasbey, Statistics
Scotland, on image warping (40 requests outside the washington.edu domain) and of Joel
Reynolds, NRCSE, on Pareto optimal model assessment (32 requests). While it is not
generally possible to resolve where each visit originates, out of resolvable addresses 1/3
came from .edu (mainly US educational institutions), slightly fewer from the commercial
.com and .net domains, while about a quarter came from European Union sites and 8%
from .gov (US government sites).
In 1997-98, a committee was formed to study the seminar organization and structure. The
main recommendations were
 to have faculty members organize the seminars with about half the seminars each
quarter falling into a coherent theme
 to develop a Center newsletter
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to use seminars as a focal point for Center communication and interaction
The seminar series during the academic year 1998-99 had a quarterly theme. In Autumn
98 the theme was particulate matter air pollution, while in Winter the theme was assessment of environmental and ecological models. The Spring quarter seminar series focused
on student and post-doctoral researchers presenting their current work, and was carried
out jointly with the graduate program on Quantitative Ecology and Resource Management. During Autumn and Winter all seminars were videotaped and made available on
the web.
Since the University has several weekly statistical seminar series, there had been relatively sparse attendance at NRCSE seminar. A decision was made to limit the NRCSE seminars to three per quarter, and make them joint with other groups.. In addition to the seminars, the Center organized afternoon workshops to cover in more depth areas of interest to
Center members. These workshops started in Winter quarter 2000. A list of all seminars
is in Appendix A.
2.2 Web site
The Center site at the World-Wide Web (http://www.nrcse.washington.edu/) is a key part
of its informational outreach. Much care went into designing the web site to have a consistent look between pages, and be easy to navigate. We maintain as accurate as possible
a description of the work going on at the Center. The following table contains some activity statistics. The Center did not have its own domain name until 1998, and activity statistics before that are not comparable to later ones. During the summer of 2002 the web site
was moved from an NRCSE server to a Statistics Department server. This affected the
basic structure of the site, and activity statistics from 2001-2002 would not be comparable
to those in the table below..
Year
1998-99
1999-2000
Requests
145,905
207,893
Hosts
12,000
14,000
Transfer/day
10 MB
14 MB
2000-2001
293,606
15,000
16 MB
Largest
Largest
foreign
domain
domains
edu (20%) de, fr, ca
edu (26%) it, au, fr,
ca
edu (30%) fr, uk, ca
Most requested
TRS #15
TRS #15
TRS #60
The software page at http://www.nrcse.washington.edu/software of the Center web service was activated in 99-00. It currently contains four links: the Orca visualization software, Doug Nychka’s Funfit package implemented for SPlus, the POMAC package for
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Pareto optimal process model development and assessment, and SPlus code for maximum
likelihood estimation in linear regression with interval or left censored data. Several projects are near completion, but have not yet been posted on the site.
Technical reports, usually the original submitted version of scientific papers, can be
found on the web site at http://www.nrcse.washington.edu/research/reports. During the
period of EPA funding, 72 technical reports were made available. 55 of these resulted in
published papers, and another 8 are still under review or revision. A complete list is in
Appendix B.
2.3 Workshops
One of the most important scientific activities of the Center has been workshops. Over
the years, a large number of workshops have been organized at the Universtiy of Washington campus, and a smaller number organized or co-organized by NRCSE has taken
place in other locations. In this section we describe all of these workshops. Their agendas
are given in Appendix D.
Initial EPA-NRCSE workshop
An initial workshop with EPA personnel and Center researchers was held at the University of Washington January 21-22, 1997. Participants included EPA scientists Guth, Warren, Saint, Benjey, Cox, Eder, LeDuc, Brown, Flatman, Olsen, Setzer, Nussbaum and
Goodman, while Center members present were Guttorp, Sampson, Raftery, Madigan, van
Belle, Cullen, Nyerges, Leroux, Faustman, Sheppard, Hughes, Percival, Ford, Conquest,
Karr and Thompson. In addition Joel Reynolds from the UW Statistics department, Graham Wood from New Zealand, advisory committee members Paul Switzer from Stanford
University and Abdel El-Sharaawi from Canadian Inland Waters, Center consulting associates Marker, Clickner, Millard and Peterson, and several UW graduate students were in
attendance. The format consisted of presentations from EPA researchers and Center
members about interesting research problems.
Cascadia Tropospheric Ozone Peer Review Workshop
A workshop on modeling tropospheric ozone in the Pacific Northwest region with some
50 participants was co-sponsored by the Center and the Washington Department of Ecology Air Quality Section. The main topic were current efforts to model ozone production
and transport in the region, using modern meso-scale atmospheric models together with
the CALPUFF and CALTRANS models.
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Combining Information From Programs That Monitor Ecological And Natural Resources
Organizer: Joe Sedransk, Case Western Reserve University
Co-organizers: Tony Olsen, EPA; Loveday Conquest, U. of Wash./NRCSE
The workshop took place November 21-22, 1997, at the University of Washington Seattle
campus. It was based upon the above theme proposed by Joe Sedransk. Additional participants included Phil Larsen (EPA Corvallis), Steve Rathbun (University of Georgia),
Hans Schreuder (Rocky Mountain Forest and Range Experiment Station), Denis White
(Oregon State University), Nick Chrisman (Univ. of Washington), Mark Kaiser (Iowa
State), Jim Karr (Univ. of Washington), Adrian Raftery, Dale Zimmerman (Univ. of Iowa), Gary Oehlert (University of Minnesota), Mark Handcock (Penn State), Abdel ElShaarawi (National Water Research Institute, Canada). The purpose of the workshop was
to clarify issues and set a research agenda regarding combining multiple sources of information in environmental monitoring programs. Topics included the following: integrating probability samples and judgment samples to evaluate the conditions of the nation's aquatic resources;
adoption of probability-based designs for combining data across time in water quality
monitoring programs; integrating the USFS Forest Inventory and Analysis and NRCS
Natural Resources Inventory to enrich knowledge of the nation's natural resources base;
combining information across a multi-organization biodiversity research program; defining ecological integrity and measuring biological condition to assess ecological health;
tolerance relations as a potential tool for regional monitoring in the absence of probability
based samples; Bayesian synthesis methods for deterministic simulation models; combining environmental time series from multiple measurement systems. A host of attendant
problems were discussed, including issues of missing data, scientific reasons for merging
surveys as well as political burdens of merging; how best to do this; and how to best use
concomitant information. Collaborative relationships were established for continuing this
kind of research. Participants were also urged to apply for grants as NRCSE visitors.
As a follow-up project, the Center funded a graduate student at Penn State University to
work on improved understanding of stream and river systems in the United States by
combining information from separate monitoring surveys, available contextual information on hydrologic systems, and remote sensing information. The project supervisor
was Mark Handcock at Penn State.
Environmental Monitoring Surveys Over Time
Co-organizers: Tony Olsen, EPA; Sarah Nusser, Iowa State University; Ray Czaplewski,
USDA Forest Service Rocky Mountain Research Station; Loveday Conquest, U. of
Washington/NRCSE
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The conference, "Environmental Monitoring Surveys over Time", was held April 20-22,
1998, on the University of Washington campus in Seattle, Washington. The conference
was organized and partially supported by the National Research Center for Statistics and
the Environment (NRCSE); additional funding was provided by the Natural Resources
Inventory and Analysis Institute of the US Dept. of Agriculture's (USDA) Natural Resources Conservation Service, and the Inventory and Monitoring Institute of the USDA
Forest Service. Approximately 65 statisticians, biometricians, and environmental scientists exchanged state-of-the-science information in a series of 14 invited paper sessions.
The objective of the conference was to provide a summary of the current state of statistical methodology for conducting longitudinal natural resource and environmental surveys;
it was organized around design and analysis issues, social science issues pertinent to natural resources.
Invited paper sessions discussed current surveys for a number of natural resources, proposed modifications for surveys, and discussed promising approaches for future surveys.
Sessions addressed terrestrial surveys, human population and institutional surveys, aquatic and avian surveys, remote sensing, watershed surveys, integrating different surveys,
non-sampling errors, database construction and dissemination, and statistical estimation
issues. Also included were perspectives from longitudinal surveys in other subject matter
areas as a means of providing cross-fertilization between natural resource survey scientists and those involved with surveys of agricultural production, economic indicators, and
human populations.
Of particular interest were discussions on potential survey design modifications which
would enable annual estimates to be obtained for Forest Inventory and Analysis (FIA) and
National Resource Inventory (NRI) programs. Selected papers from the conference appeared in a 1999 special issue of the Journal of Agricultural Biological, and Environmental Statistics
7th International Meeting on Statistical Climatology
Program chair: Peter Guttorp, NRCSE
Steering committee chair: Francis Zwiers, Canadian Climate Center
Local organizer: Richard Lockhart, Simon Fraser University, Canada
The 7th International Meeting on Statistical Climatology took place at Whistler resort in
British Columbia, Canada, May 25-29, 1998. About 150 participants from six continents
participated.
The series of meetings on statistical climatology started as an ISI satellite meeting in Japan in 1979. Since 1983 the meetings have been held every three years. These meetings
are unique in that they are not run by any scientific organization. Since 1987 the meetings have been organized by a free-standing steering committee, currently chaired by
Francis Zwiers. Support is sought from a variety of organizations, and co-sponsorship is
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usually sought from national and international statistical and meteorological scientific
societies. The late Allan Murphy of Oregon State University was instrumental in initiating and maintaining this series of talks, and the meeting was dedicated to his memory.
The program of the Whistler meeting was arranged with two plenary special invited sessions per day, in which prominent climatologists and statisticians gave in-depth presentation, followed by two invited discussants (one statistician and one climatologist). The
format with invited discussants, while common in statistics, was a (popular) novelty to
many of the climatologists. There were parallel invited and contributed sessions at other
times of the day.
The statistical special invited talks emerged with a theme: Bayesian hierarchical modeling
as a tool for managing moderately large climatological data sets. Doug Nychka and Mark
Berliner, the present and former directors of the Geophysical Statistics Project at the National Center for Atmospheric Research in Boulder, Colorado, USA, illustrated the approach with some applications, but the tour de force came with some Pacific surface temperature predictions presented by Chris Wikle (NCAR) and Noel Cressie (Iowa State).
The predictions for April-September, 1998, based on data through March, can be seen at
the conference web site, http://www.stat.washington.edu/peter/7IMSC where the abstracts and
some papers also are available.
Among the climatological special invited papers were discussions of neural networks applied to remote sensing problems (Vladimir Krasnopolsky), and the North Atlantic counterpart to El Nino/Southern Oscillation (Tony Barnston).
The NRCSE support for this conference included maintaining the web site, automatic
posting of abstracts, and editing of the abstract booklet and program. Among NRCSE participants were Jim Hughes, Chris Bretherton, Barnali Das and Peter Guttorp.
EPA Corvallis
The Center has initiated a series of workshops at various EPA locations intended to give
EPA researchers a feel for the kind of research being conducted and to initiate new research contacts. The first of these workshops took place at the EPA laboratory in Corvallis, Oregon, on April 13, 1999. Ten Center members and graduate students participated
in the workshop, giving seven talks to a fairly large audience (at most presentations 20-30
EPA and OSU researchers were present).
Particulate matter air pollution
EPA promulgated revised air quality standards for particulate matter on July 18, 1997. At
that time, President Clinton committed EPA to complete the next Particulate Matter
NAAQS review within the five-year statutory period required by the Clean Air Act. Due
to the time required to establish PM2.5 monitoring networks, determine whether or not a
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location is in or out of compliance with the standard(s), and, if out, to develop a State Implementation Plan (SIP) to achieve compliance, implementation of the revised regulations
for particulate matter would not begin for approximately ten years, well after the next
NAAQS review. Also in this time frame, Congress directed EPA to arrange to have the
National Research Council (NRC) to conduct a study to identify research priorities relevant to setting regulatory standards for ambient particulate matter. NRC responded by
forming the Committee on Research Priorities for Airborne Particulate Matter, which
quickly produced its first of four planned reports, Research Priorities for Airborne Particulate Matter: I. Immediate Priorities and a Long-Range Research Portfolio, referred to
here as the NRC Report. Research Topic 10 of this report, Analysis and Measurement,
deals almost exclusively with statistical issues.
Soon after the release of the NRC Report, NRCSE and the EPA Office of Research and
Development decided to organize a workshop focused on Topic 10. With co-sponsorship
from the National Institute of Statistical Sciences and the Health Effects Institute, the
NRCSE/EPA Particulate Methodology Workshop was held October 19–22, 1998, at the
University of Washington in Seattle. The objective of the workshop was to bring together
an interdisciplinary group of statistical and other scientists to illuminate statistical issues
articulated in or raised by Topic 10, to identify priority statistical research bearing upon
these issues, and to organize interdisciplinary research projects on these topics, targeted
for completion prior to the end of the second Particulate Matter NAAQS review. Twenty-five invited speakers, discussants, and session chairs participated together with 36 other attendees.
The workshop was organized around formal presentations and discussion covering
measurement, atmospheric transport, and modeling of particulate matter; understanding
and developing models of particulate matter exposure and health effects; particle transformation; source apportionment; regulatory issues; and new or enhanced statistical research questions and findings stemming from particulate matter studies. The detailed list
of presentations and speakers can be found in Appendix D2. The core of the Workshop
was the deliberations of seven working groups meeting each afternoon. The 19 research
questions raised under Topic 10 of the NRC Report were grouped under six headings for
discussion: time series analysis; assessment of current epidemiological studies; exposureresponse models; study design towards estimation of long and short term effects of exposure; study design and the effects of measurement error in health effects modeling; and
space-time modeling and estimation methods for more accurate estimates of individual
exposures to particulate air pollution. A seventh group addressed Case-Crossover Studies.
The NRCSE/EPA Workshop on Particulate Methodology raised meaningful issues regarding the role of statistical science in the study of particulate matter air pollution.
Leaders from statistical and environmental science shared their expertise and concerns
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and appeared to benefit from the interaction. A summary of the workshop discussions is
given in NRCSE TRS #41. A special issue of Environmetrics (no. 6, 2000) was dedicated
to statistical methods for particulate matter research.
Quality assurance of environmental models
Over the past decade the number of models constructed by EPA scientists has increased
remarkably. Many models are used in policy development and environmental regulation.
Increasingly models are constructed that simulate ecological and environmental processes. The complex structure of these models, and in some cases the limited data associated
with them, has led to concerns about model assessment.
EPA responded to this concern by establishing a committee to produce a White Paper for
the Science Policy Council “Nature and Scope of Issues on Adoption of Model Use Acceptability Guidance.” NRCSE was involved in writing this paper and, as an outcome of
its discussions and deliberations, a joint EPA/NRCSE workshop on Quality Assurance of
Environmental Models was organized at the University of Washington September 7–10,
1999. Some 60 participants from universities, regulatory agencies and consulting firms
listened to 17 presented papers and contributed to discussions in four different areas: Life
cycles of models; Peer review of modes; Very High Order Models; and Tool Chest for
Model Assessment.
The papers read ranged through a wide spectrum of aspects of model assessment. Among
the presenters the first day with theme “Defining the problems of Model Assessment and
Quality Assurance” were Naomi Oreskes, UC San Diego; David Ford, University of
Washington; Jan Rotmans, ICIS, Maastricht, The Netherlands; and Robin Dennis, EPA.
The second day, on “Development of Methodological and Quantitative Techniques ,” featured Andrea Saltelli, EC Joint Research Centre, Italy; Adrian Raftery, University of
Washington; Tony O'Hagan, University of Sheffield, U.K.; Joel Reynolds, Alaska Department of Game and Fish; and Wendy Meiring, UC Santa Barbara. Among the third day
speakers, on
“Assurance of Models Used in Environmental Regulation ,” were David Stanners, European Environment Agency; Tom Barnwell, EPA; Linda Kirkland, EPA; and Helen Dawson, EPA. A summary of theworkshop can be found in NRCSE TRS #42.
EPA Las Vegas
The Center has pursued a series of workshops at various EPA locations intended to give
EPA researchers a feel for the kind of research being conducted and to initiate new research contacts. The second of these workshops took place at the EPA laboratory in Las
Vegas, Nevada, on December 13-14, 1999. Eight Center members and graduate students
participated in the workshop, giving eight talks to a fairly large audience.
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The program was developed jointly with EPA scientists. It consisted of topics ranging
from Tom Lewandowski’s work on toxicodynamic/toxicokinetic modeling to Adrian
Raftery’s methods for incorporating expert opinion in deterministic models. These
presentations alternated with group discussions of statistical issues arising in the EPA
Lab’s work. For example, there was a lively discussion of the best ways to sample to assess pollution levels in industrial sites, and the best ways to handle possibly inadequate
sets of measurements. There have been follow-up contacts between the lab and the Center related to this visit, and we hope that this and future workshops will help bring EPA
and NRCSE researchers closer together.
Slovakia workshop
Alison Cullen was involved in the planning of a NRCSE sponsored workshop in Dovaly,
Slovakia on October 24-26, 1999. The purpose of the workshop was to enhance capabilities to identify, assess and manage high priority environmental and/or occupational health
issues. The workshop had approximately 40 Slovak and Czech participants. The audience
included decision-makers, who must deal with contemporary environmental and occupational health problems, and scientific staff who support the decision-making. The workshop was designed to follow a case where an environmental / occupational issue has been
identified through planning, implementation, analysis and communication of a data collection program in order to support risk management decision making.
Large Data Sets
In July 2000, a workshop on large data sets was held at the National Center for Atmospheric Research in Boulder, CO. This workshop, sponsored by NRCSE, the Geophysical
Statistics Project, and NCAR, acquainted statisticians with substantive scientific problems that hinge on the analysis of large data sets, these can be either observational or generated as the output of numerical models and presented recent statistical advances for
large problems. Topics included visualization strategies, computational algorithms and
new methods, including techniques from data mining. Although the statistical methodology is relevant to wide range of problems, the focus was on continuous variables and
multivariate or spatial-temporal contexts. Funding was available to support attendance
with special emphasis given to graduate students and other young researchers. About 50
researchers participated in the workshop, including 10 from government laboratories or
industry.
Mini-workshops
Internal planning workshop
In order to plan for the expected request for proposals for renewal funding of NRCSE, the
Center held an internal planning workshop to familiarize Center members with the range
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cessful in our organizational structure and communication. Oral presentations as well as
poster presentations were given, and three group discussions were held.
Statistical downscaling of precipitation
On May 24, an NRCSE-sponsored workshop on statistical downscaling of precipitation
was held in Padelford Hall. Attendees heard presentations by Jim Hughes (UW Biostat
and NRCSE), Bryson Bates (CSIRO, Australia), Dennis Cox (Rice University and
NCAR) and Claudia Tebaldi (NCAR). Jim Hughes opened the workshop by giving a review of the downscaling problem (predicting local atmospheric measurements, such as
precipitation, from broad scale measurements such as sea level pressure) and summarizing the various approaches that have been used to solve it. Bryson Bates discussed applications of downscaling in southwestern Australia and future research plans in that area.
Dennis Cox discussed his work on rainfall modeling at five rain gauge stations in the
Southeast U.S. and the development of methods for model goodness of fit assessment.
Finally, Claudia Tebaldi discussed her work using eight rain gauge stations in the southeast U.S., which has focused on the development of atmospheric summary measures at
temporal scales that are useful for downscaling. In addition to these four talks there was
extensive discussion on the utility, strengths and limitations of downscaling.
Environmental Statistics Teaching at UW
A workshop on teaching environmental statistics was organized by Alison Cullen on May
26, 2000. 14 participants from 10 departments discussed current offering of environmental statistics, and identified some areas of need. In particular, undergraduate courses on
correlated data, multivariate analysis, and risk analysis/decision making are lacking on
campus. Among the ideas for improvement were: development of a web site as clearing
house for environmental statistics courses; development of a data set or case repository;
development of a Speakers’ Bureau to bring in researchers to talk about their use of statistics in their work.
Collaborative working group
Following the Joint Statistical Meetings in Indianapolis, Paul Sampson organized a small
but internationally diverse collaborative working group the week of August 21 on spatial
deformation methods for nonstationary spatial covariance modeling. Two of the speakers
at his session at the JSM, Alexandra Schmidt (Brazil), a student at the University of Sheffield, and Olivier Perrin (France), who is now at the University of Toulouse, came to Seattle to present their work and collaborate with our former student Wendy Meiring (South
Africa), now at the University of California, Santa Barbara, and current students Doris
Damian (Israel) and Sinjini Mitra (India). Comparisons of the different deformation
methods were initiated, and data sets to pursue such comparisons were exchanged.
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EPA site visit
On July 19, 2000, Peter Preuss, Jack Puzak and Chris Saint from the EPA National Center of Environmental Research (the EPA office in charge of our funding) conducted a site
visit at NRCSE. Mary Lou Thompson, David Ford, Paul Sampson, June Morita, Thomas
Lumley and Peter Guttorp presented a variety of aspects of NRCSE work. The EPA response was very positive.
Spatial moving averages
A workshop on spatial moving averages was organized by Jay Ver Hoef and Dave
Higdon and held at the NRCSE from May 20 - 22 2001. Spatial moving average models
have surfaced repeatedly in recent years in disparate literatures. They are formed by using a moving average function (or kernel) that operates on an independent spatial process. The goal of the workshop was to bring together authors to share ideas. Talks, followed by discussion, were given by Ron Barry, Nicky Best, Montserrat Fuentes, Dave
Higdon, Katja Ickstadt, Doug Nychka, Jean Thiebaux, Jay Ver Hoef, Chris Wikle, Robert Wolpert on topics relating to basic theory, relationships to other spatial methods, estimation methods (classical and Bayesian, large data sets), univariate and multivariate
models, and stationary and nonstationary models.
NSF/CBMS Regional Conference on Environmental Statistics
The NSF-CBMS regional conference on Environmental Statistics, featuring Richard
Smith from North Carolina, took place June 25-29, 2001, at the University of Washington. There were 59 participants. The format had a lecture by Dr. Smith each morning,
followed by a guest speaker (Paul Switzer, Stanford University, Jim Zidek, University of
British Columbia, Doug Nychka, NCAR Geophysical Statistics Project, and from UW
Tilmann Gneiting, Paul Sampson and Peter Guttorp). In the afternoons Dr. Smith gave
his second lecture of the day, followed by a breakout session in which various topics
were discussed in a roundtable format.
The conference was extremely well received by the audience. Indeed, some of the participants rated this as the best conference they had ever participated in. Dr. Smith’s slides
are available on the web at http://www.stat.unc.edu/postcript/rs/envstat/env.html. His lecture notes will be published by the Institute of Mathematical Statistics.
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2.4 Conference presentations
Center members and graduate students have given 164 seminars and short courses in a
variety of national and international settings. The most frequent meeting was the Joint
Statistical Meetings, at which 27 presentations were given, followed by the Society for
Risk Analysis (13), the International Environmetric Society (11) and the EPA Statistician’s Meeting (9). The following table gives the distribution by year.
Year
Number
96-97
19
97-98
27
98-99
36
99-00
42
00-01
26
01-02
14
A full list is given in Appendix C.
2.5 Professional service and recognition
1996-97:
Paul Sampson and Peter Guttorp reviewed the CASTNET proposal for the Environmental
Protection Agency.
1997-98:
David Ford has made a substantial contribution to the White Paper on Model Assessment
now submitted to the EPA Science Policy Council. The crucial contribution was to illustrate how different programs within EPA were all considering model assessment and developing approaches to it, but each had a different emphasis and/or used different terms.
By defining different components of "uncertainty" it was possible to illustrate to the diverse members of the EPA team involved in preparing and critiquing the White Paper that
an overall EPA policy could be developed that still permitted the necessary flexibility that
the programs needed. The White Paper is being discussed by the Science Policy Council
on 5 November, 1998.
Peter Guttorp participated on a site visit committee for the Superfund Remediation project at University of California at Davis. This resulted in a joint committee report to the
project officers with recommendations for the upcoming renewal application to the EPA
for funding.
Gerald Van Belle has been involved in several activities related to the Health Effects Institute. He is Chair of the Oversight Committee for the National Morbidity and Mortality
Air Pollution Study (NMMAPS). In addition he is a member of the research committee,
and chaired a meeting of NMMAPS and APHEA researchers in London, England, July
15, 16, 1998. (APHEA=Air Pollution and Health–a European Approach)
Alison Cullen, School of Public Affairs, received the Outstanding Young Scientist award
of the International Society of Exposure Analysis at their annual meeting in Boston, 1998.
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Loveday Conquest, Fisheries, was chosen as the first director of the newly formed Teaching Academy at the University of Washington. The Academy consists of winners of the
UW Distinguished Teaching Award.
June Morita, Statistics/Management Science, received the American Statistical Association Chapter Service Award at the national meeting in Dallas, 1998.
1998-99:
Paul D. Sampson was awarded the Distinguished Achievement Medal of the American
Statistical Association Section on Statistics and the Environment.
Loveday Conquest, Peter Guttorp, and Jim Karr were among the recipients of twentieth
century distinguished service awards at the Ninth Lukacs Conference in Bowling Green,
OH, for contributions to the synergistic development and direction of statistics, ecology,
environment and society.
June Morita received the University of Washington Distinguished Teaching Award. She
is the fourth Center member to receive this honor. Previous NRCSE recipients are
Loveday Conquest, Gerald Van Belle, and David Madigan.
Paul Sampson and Alison Cullen participated in peer reviews for the U.S. EPA, Health
Canada and Environment Canada.
Alison Cullen has been commissioned by the Society for Risk Analysis to write a white
paper entitled “Risk and Uncertainty: Quantitative and Precautionary Approaches” for
their Year 2000 Symposium on Risk Analysis. The Symposium will be held in June
2000 and will focus on the discussion of 10 white papers on all aspects of Risk Analysis,
Risk Management and Decision Making.
Peter Guttorp is a member of the Scientific Advisory Board of the recently awarded EPA
Northwest Particulate Matter Center at the University of Washington, and is also a Senior
Statistical Adviser for the PM Center. He is also a member of the Science Advisory
Council for the Geophysical Statistics Project at the National Center for Atmospheric Research in Boulder, Colorado.
Loveday Conquest is the current Chair of the American Statistical Association Section on
Statistics and the Environment. June Morita is Chair-elect of the American Statistical Association Council of Chapters. Adrian Raftery continues as Applications Editor of the
Journal of the American Statistical Association. Alison Cullen is a Council member of
the Society for Risk Analysis.
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1999-2000
Jon Wakefield was awarded the annual Guy Medal in Bronze for 2000 by the Royal Statistical Society for his recent work in research on the development of statistical methods,
particularly for spatial epidemiology and population pharmacokinetic modeling.
Loveday Conquest served as the Chair of the ASA Section on Statistics and the Environment for 1999.
Peter Guttorp is the President-Elect of the International Environmetrics Society. His term
goes for two years. In 2002 he will be President of the society, also for two years.
As institutional members of the International Environmetric Society (TIES), the Center
receives two full memberships, which the executive committee has decided to award to
outstanding research assistants. The 2000 award went to Nicolle Mode and Marianne
Turley.
Ashley Steel won the student methods paper competition at the North American Benthological Association annual meeting 2000 with a paper on horizontal Secchi disks for
measuring water clarity.
Loveday Conquest received a Women Who Make a Difference Award “for outstanding
achievements and contributions to the science, engineering, and technology industries,”
Women of Color Technology Awards, August 2000.
Peter Guttorp is Section Editor for the Spatial/temporal section of Wiley’s Encyclopedia
of Environmetrics, to appear in autumn of 2001. Several NRCSE member have contributed articles to the Encyclopedia.
Peter Guttorp participated in the development of the EPA PM Criteria Document.
2000-2001
The 2001 TIES membership awards for outstanding research assistants went to Doris
Damian, Biostatistics, and Fadoua Balabdaoui, Statistics.
Samantha Bates, Statistics graduate student, was awarded the best student paper award,
and the prize for best risk analysis paper at the Environmetrics 2001 conference in Portland, OR in August, 2001.
Nick Hedley, Geography graduate student, received the Thomas F. Saarinnen Outstanding
Student Paper Award at the Association of American Geographers Meeting in New York.
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Peter Guttorp was named a Fellow of the American Statistical Association in Atlanta, GA
in August 2001. The award citation read: “For major contributions to the growth of
environmetrics; for research on spatial modeling under nonstationary spatial covariance;
for administration of interdisciplinary research groups, especially as Director of the National Research Center for Statistics and the Environment; for service to the profession.”
Dennis Lettenmaier was awarded the American Geophysical Union Hydrology Section
Award at the Fall Meeting Hydrology Section Reception which was held in December,2000, in San Francisco, CA. This award recognized his outstanding contributions to
the science
of hydrology. Dennis has been a key player in the integration of hydrological science with
the atmospheric science community on the one hand, and the water resources engineering
community on the other.
Paul D. Sampson is the webmaster of the International Environmetric Soricetu (TIES).
The TIES web pages are linked to the NRCSE pages.
Richard Smith from University of North Carolina, jointly with Peter Guttorp and Lianne
Sheppard from NRCSE, provided an extensive comment on the draft PM Criteria Document which was made available in March. The comment, together with a public comment
produced by researchers at the EPA NW PM Center and an opinion piece by Guttorp and
Smith, can be found in NRCSE TRS #66.
Peter Guttorp is editing a special issue of International Statistical Review on environmental statistics, and a special issue of Environmental and Ecological Statistics featuring
NRCSE research projects.
2.6 Outreach
One of the important focuses of the Center is on educational outreach. We have assisted
the EPA Region X office with a jointly funded graduate student intern for consulting
help, and have taught short courses at the regional office. We have developed a university
course in Environmental Statistics using a case-based pedagogical approach, and a course
on Spatial Processes in Ecology with laboratory exercises and web page support from
NRCSE. In addition, visitor Michael Phelan taught a course for graduate students in Environmental Statistics with emphasis on economic analysis for the Statistics department
during Summer quarter of 1998. In summer of 1999 Eric Smith taught a course on Multivariate methods in environmental statistics, and in summer of 2001 Richard Smith gave a
CBMS/NSF-sponsored regional conference on Environmental Statistics. Educational research projects are listed in section 3.2.
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We have continuing research links with the Washington State Department of Ecology,
mainly in the area of air pollution (specifically ozone and car exhaust). These research
projects are joint with the University of Washington Statistical Consulting Center.
We are working on furthering links with covernment and local industry through a variety
of joint projects with the Departments of Statistics, Applied Mathematics, and Mathematics. A recently funded NSF proposal (joint between the three departments) is aimed at
vertical integration of education and research, and is providing vehicles for involving undergraduates, graduate students, and postdocs in research group activities.
Center members are actively participating in the University of Washington Program on
the Environment (http://depts.washington.edu/poeweb/), a multidisciplinary undergraduate (in the future also graduate) program focusing on a broad spectrum of environmental
issues. In addition, several Center members are active in the Puget Sound Region Simulation Model (http://www.prism.washington.edu/), a research program to develop a comprehensive model of physical and social development in the greater Puget Sound region,
as well as in the Center for Statistics in the Social Sciences
(http://www.csss.washington.edu), directed by NRCSE member Adrian Raftery, Statistics
and Sociology.
The Center publishes a newsletter about twice a year, with the latest developments, publications, and other items of potential interest to the membership. The newsletters are
available at http://www.nrcse.washington.edu/newsletter
NRCSE was co-sponsoring the Student Paper Awards of the American Statistical Association Section on Statistics and the Environment. This co-sponsorship was motivated by a
desire to ensure that awardees would be able to attend and participate in the Joint Statistical Meetings where the award is presented, something that had not previously been assured.
The first recipient was Deepak K. Agarwal from University of Connecticut.
3. Research activities
NRCSE is developing as a national research center using a strategy with four components. First, guidelines for funding Center members to work on specific research projects
specify the importance of identified EPA contacts to ensure the relevance of the projects
to the EPA mission. Second, the Center is emphasizing its visitors program with researchers from outside the University of Washington visiting the Center to set up joint
research programs with one or more Center members. Third, Center members work on
joint proposals with researchers in other insititutions nationwide. Fourth, we support relevant research at other institutions using subcontracts with the University of Washington.
In addition, the Center computing staff has evaluated and implemented tools for collabo20
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rative research at a distance. In particular, Center members Peter Guttorp and Thomas
Richardson have used these tools when on leave from the University of Washington to
work with some of their PhD students.
In this section we describe the major projects that have been pursued during the EPAfunded period, list our visitors, describe the graduate students supported by EPA, list the
internal funding support, and our contributions to the scientific literature..
3.1 Ecological and environmental impact
Biological monitoring
PI: Peter Guttorp.
EPA researchers: Tony Olsen, Melissa Hughes
Center researchers: Dean Billheimer, Jim Karr.
Research assistants: Mariabeth Silkey, Kristen Ryding, Florentina Bunea.
This project deals with the statistical analysis of compositional data in space and time.
Among the applications are analysis of deep sea benthic macroinvertebrates for the effect
of mining on the ocean floor; realistic simulations of benthic population data for streams
in order to derive statistical properties of measures of water quality such as the Index of
Biotic Integrity (IBI); evaluation of insect repopulation of the Mt. Saint Helens eruption
zone.
The statistical aspects of the project focuses on the Billheimer model of space-time compositional data. This has been applied to biologically based groups, the relative frequency
of which is modeled by a Gaussian distribution in appropriately transformed space. The
model allows for covariates, spatial and temporal dependence, and considerable effort has
been directed towards the display of compositional data.
In order to develop ecologically and statistically sound measures of water quality, a variety of metrics of biological activity and composition as well as of human development
have been proposed. In this work we apply recent tools from the theory of graphical modeling to study the dependence structure of those measures that are included in Karr's Index of Biotic
Integrity (IBI). The resulting pictorial representation of the relationships between the
component metrics and environmental covariates makes it possible to judge which components carry information about different aspects of the stream biology, and which biological measures are most sensitive to specific human activities.
A paper on space-time modeling of bentic invertebrates (Billheimer et al. 1997) appeared
in Environmental and Ecological Statistics. The Master's thesis of Mariabeth Silkey
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(1998) used the same methodology to assess trends and design aspects of the EMAP benthic monitoring project in Delaware Bay. We used graphical models to assess the components and the statistical variability of the IBI (Bunea, Guttorp and Richardson, NRCSE
Technical Report Series (TRS) #36). A paper on the insect repopulation evaluation, Billheimer et al. (2001) appeared in Journal of the American Statistcal Association.
Hydrologic effect of land use change
PI: Dennis Lettenmaier
EPA researcher: Iris Goodman
Research assistant: Laura Bowling
There is a perception in the Pacific Northwest that the frequency and severity of flooding
has increased in the western Cascades due to forest harvest. Field studies have shown
that substantial changes in snowmelt during rain-on-snow events can occur following the
removal of forest cover due to differences in snow accumulation as a result of canopy interception changes, and enhanced latent and sensible heat transfer associated with increased wind at the snow surface. However, field studies are of necessity essentially snapshots; at the watershed scale, the effects of vegetation changes on any particular flood are
complicated by variations in antecedent snow accumulation, spatial differences in temperature and precipitation during the storm, and the area-elevation distribution of the watershed.
From a statistical standpoint, retrospective assessment of the effects of logging on streamflow is a classical trend detection problem. An analysis of changes in annual maxima
(AMS) and peaks-over-threshold (POT), uncorrected for climatic trends, was conducted
for 26 Western Washington basins, ranging in size from 13.8 km2 to 1560 km2 using the
non-parametric Mann-Kendall test. The basins were classified into three categories based
on record length. Statistically significant increases in AMS or POT were found in 5 basins with short records (1960-1996), 4 basins with medium records (1945-1996) and 3
basins with long records (1930-1996). A short record length makes the trend analysis
more sensitive to climate variability. Two techniques were used to correct for the potential influence of climatic trends: paired catchment analysis and analysis of model residuals.
Paired catchment analysis requires that adjacent, similar watersheds be identified that
have had much different logging histories. Since both basins are driven by the same sequence of meteorological events, analysis of the discharge difference series should filter
out systematic climate variations. Seven basin pairs were selected based on vegetation
differences as predicted by Washington Department of Natural Resources canopy cover
classifications. Significantly increasing trends in annual maxima were found for two of
the basin pairs.
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An alternative approach is to control for the effects of climate variability by analyzing the
residuals of flood peaks predicted using a deterministic, spatially distributed hydrologic
model with fixed vegetation. The residual series (simulated less observed discharge)
should filter out any systematic effects due to climate. An analysis of model residuals for
the main stem Snoqualmie River detected a statistically significant increase in the smaller
storms of the POT series. These results are summarized in a journal article (Bowling et
al., 1998) in Water Resources Research.
Statistical analysis of surface ozone
PI: Paul Sampson
Washington Department of Ecology researchers: Cris Figueroa-Kaminsky, Clint Bowman
Center researchers: Peter Guttorp, Joel Reynolds, Mary Lou Thompson
Research assistants: Barnali Das, Ruth Grossman, David Caccia
The NRCSE/ Washington Department of Ecology jointly worked on methodologyt to adjust meteorologically the surface ozone network observations for Western Washington
over the last 20+ yrs and assess time trends. Besides the results of the analysis, of interest
to researchers and agency managers in the region, the project produced a new methodology, canonical covariance analysis (based on using the singular value decomposition), for
meteorological adjustment of surface ozone observations when presented with both a
spatial network of ozone monitors and a spatial network of meteorological stations.
The project has resulted in one presentation (by Barnali Das at 7IMSC) and one technical
report (NRCSE TRS #15) on the methodological developments. As a related project, Joel
Reynolds and David Caccia applied the Canonical Covariance Analysis technique developed for the ozone adjustment project to the Chicago area observations (NRCSE TRS
#25).
A review of statistical adjustment of ozone for meteorological variables
Co-PIs: Joel Reynolds, Peter Guttorp, Paul Sampson, Mary Lou Thompson
Outside collaborators: Hans Wackernagel, Christian Lajaunie, Centre de Géostatistique,
France
EPA researcher: Larry Cox
Research assistants: Barnali Das, David Caccia, Sinjini Gupta
A review paper on meteorological adjustment of ozone (NRCSE TRS 26) appeared in
Atmospheric Environment (Thompson et al., 2001). In conjunction with this work, several
of the approaches suggested in the literature have been applied to Chicago ozone data
from the AIRS database for the period 1981-1991. The work highlights the need for development of techniques for extreme value analysis of space-time processes, as well as
for analysis of networks designed to measure extreme values of a random field.
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In conjunction with the French arm of the EU-funded IMPACT project, we carried out
extensive analyses of the space-time structure of Paris region (Isle-de-France) ozone concentration monitoring data for one year. There were clear indications of increased ozone
on the south-west part of the network in connection with northeasterly winds, indicating a
direct link with the transport of the air mass across the metropolitan region.
Interest in the detailed space-time correlation structure led us to investigate the possible
application of a new family of spatio-temporal correlation models suggested by Tilmann
Gneiting. These models are appropriate when a Lagrangean reference frame is considered for modeling the asymmetric space-time correlations explained (in part) by meteorological systems moving through a region. For hourly ozone concentrations monitored at
different sites in the Paris region, we examined temporally lagged cross-correlations as a
function of wind speed and direction. In fact, these cross-correlations were (surprisingly)
not noticeably temporally asymmetric.
A presentation of the research was made by Peter Guttorp at a special IMPACT session at
the Environmetrics 2001 meeting in Portland. He also presented the material at the NSFCBMS Regional Conference on Environmental Statistics at the University of Washington, at the Fifth Brazilian School of Probability in Ubatuba, Brazil, and at a short course
preceding the Environmetrics 2002 meeting in Genoa.
A linked toxicokinetic-toxicodynamic model of methylmercury-induced developmental neurotoxicity in the fetal rat
Center researchers: Rafael Ponce and Elaine Faustman
UW collaborator: W. C. Griffith
Research assistant: Tom Lewandowski
Previous work conductedat the University of Washington has led to the development of a
toxicodynamic model of methylmercury-induced developmental neurotoxicity. Methylmercury is a naturally occurring organometal that is of concern because of the large population exposed through fish consumption and because epidemiological studies implicate
even low levels, such as those expected among subsistence fish consumers, with adverse
neurobehavioral development. Because the toxicodynamic model that has been developed is biologically based, it may be generally applied to agents that cause developmental
toxicity through interference with cell proliferation. Such models could also allow crossspecies extrapolations based on the incorporation of species-specific rates in model variable parameters; future applications of the pharmacodynamic model to other developmental neurotoxicants such as 5-fluorouracil should allow us to explore these issues. Such
biologically-based models can thus reduce uncertainty, identify research needs, and improve estimates of developmental risks to humans from environmental exposures.
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For risk assessments, there is a need to integrate both exposure information and mechanistic toxicity information to obtain improved risk estimates. Ideally, this requires linkage of both toxicokinetic models describing the absorption, distribution, metabolism and
elimination profiles of toxicants with biologically based dose-response models that describe toxic endpoints/effects.
To complement the existing toxicodynamic model, Mr. Lewandowski and others have
developed a toxicokinetic model to predict maternal and fetal disposition of methylmercury during gestation; this toxicokinetic model is linked with the existing toxicodynamic
model. The idea underlying the development of such a biologically based toxicokinetictoxicodynamic model would be that one could relate a whole body dose, which is delivered by various routes of exposure, to an observable effect on the developing fetus.
The toxicodynamic model describes aspects of the dynamic process of organogenesis,
based on Monte Carlo analysis of branching process models of cell kinetics. The toxicokinetic model demonstrates an adequate fit to experimental toxicokinetic data. For example, 3 days after a dose of 1 mg/kg (given on day 12 of gestation), the model predicts
brain and blood levels within approximately 10% of the values observed by Wannag
(1976). In terms of toxicodynamic effects, the model predicts 15% and 45% decreases in
the number of committed neural cells (on gestational day 15, relative to untreated baseline) at fetal brain concentrations of 0.5 and 1.0 mmol/kg. It is anticipated that the existing model can be extended to address other species (i.e., humans) and other developmental toxicants that act by similar mechanisms (i.e., cell cycle disruption).
Preliminary results of these efforts were presented at the Society of Toxicology Annual
Meeting, 1998 and the IUTOX Congress, 1998, at the, Society of Risk Analysis and at the
NRCSE/USEPA-LV Statistical Conference in Las Vegas, December 1999.
Analysis of CO data in Spokane
PI: Peter Guttorp
Washington Department of Ecology researchers: Chris Bowman, Doug Schneider
A DOE study of CO in downtown Spokane, WA, involved a set of portable samplers in
addition to the permanent monitoring sites in order to evaluate the representativeness of
the permanent sites. Our analysis used kriging techniques to assess the adequacy of the
siting. A report entitled Statistical analysis of Spokane CO data is available as NRCSE
TRS #2. (The original analysis of these data was performed without use of NRCSE facilities).
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Remote sensing and automobile emissions
PI: Paul Sampson
Washington Department of Ecology researchers: Doug Brown, Kerry Swayne, Tom Olsen
Research assistant: Jake Wegerlin
A study of remote sensing technology for field measurement of automobile emissions is
being carried out for the Washington State Department of Ecology. This study aims to
validate remote sensing device (RSD) field measurements of automobile emissions
against EnviroTest measurements taken at Department of Ecology Emissions Check stations. Ideally, if there should be sufficient correlation of the RSD field measurements
with the EnviroTest measurements, adjusting for any possible relevant field measurement
factors such as vehicle acceleration or weather, a statistical calibration (inverse regression) could be used for compliance assessment and/or “clean screening.” A final report
was submitted to DOE.
Global warming and Pacific Northwest snowpack
Center member: Chris Bretherton
UW collaborators: Nate Mantua, Phil Mote
Research assistants: Leslie Bahn, Simon Deszoeke
We studied the past variation of snowpack in the Washington Cascades and Olympic
Mountains and its relation to interannual and interdecadal variations of winter season
temperature, precipitation, and atmospheric circulation. We quickly began to collaborate
with Nate Mantua and Phil Mote of UW's Climate Impacts Group, who were working on
a similar topic.
Our principal findings are as follows. We found that 50% of the interannual variability of
snowpack is associated with variations in one circulation pattern that results in a persistently more northwesterly flow over this area. Both temperature variations (5º C peak-topeak winter mean) and precipitation variations (factor of three variations between extreme winters) contribute almost equally to the historical variability of snowpack. There
is significant (20-30%) interdecadal variability in snowpack due to coupling of the snowproducing atmospheric circulation pattern with long-lived sea-surface temperature anomalies in the central north Pacific Ocean. Anthropogenic climate change will likely overwhelm natural climate change by about 2025, with temperature increases of 2º C by 2050
creating snowpack decrease of 50% or more at 1000-1500 m above sea-level and enormous stresses on summertime water supply. A paper has been submitted for publication,
and the material in this research was part of the foundation for a successful NSF proposal
by Bretherton, Percival and Guttorp.
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Ecological Assessment of Riverine Systems by Combining Information
from Multiple Sources
PI: Mark Handcock, Penn State University
Co-investigators: Joe Sedransk, Case Western
EPA researcher: Tony Olsen
Research Assistant: James McDermott, Penn State University
The objective of the project is to improve understanding of the biological integrity of
stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic
units and remote sensing information. The investigators are collaborating with the MidAtlantic Regional Assessment of Climate Change Impacts (MARA) project at the Pennsylvania State University on the construction of the data sets
(http://lumen.deasy.psu.edu/mara). The MARA study is being conducted as part of the U.S.
National Assessment, under the auspices of the U.S. Global Change Research Program.
The NRCSE project is developing spatial statistical models for measures of biotic integrity on the streams and rivers in the MARA region. The collaboration should ensure that
the case study can be interpreted in the context of the MARA study and easily explored
using the standardized data sets available on the WWW.
This is a collaborative project with co-investigators Mark Handcock, UW, Joe Sedransk,
Case Western, and Tony Olsen, EPA Corvallis. It originated from the NRCSE workshop
on combining information from multiple sources in 1997, and was supported by NRCSE
in 1998-99.
The objective of the project is to improve understanding of the biological integrity of
stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic
units and remote sensing information. We now have developed the heart of the research
program: to complement the mapping presented in the Atlas with new hierarchical spatial
statistical models for environmental indicators on the streams and rivers that capture the
spatial variation in the measures. These models have been used to estimate the indicators
through the riverine system based on the information from multiple sources and aggregate
scales. We quantify the uncertainty based on the information from multiple sources and
aggregate scales, quantify the uncertainty in the estimates, and develop methods to visualize the resulting estimates and uncertainties.
We have developed a general framework for comparative distributional analysis of environmental variables. The methods are based on the “relative spatial distribution.” The
spatial models developed are used to predict spatial distributions and relative spatial distributions. These methods are then used to combine county-level social science data with
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the different sources of environmental data. This makes it possible to investigate questions of environmental justice in a systematic and rigorous way.
Preliminary results of the project were presented at the Joint Statistical Meetings in Indianapolis, Indiana. Based on the preliminary development the project was awarded an NSF
grant under the EPA/NSF Partnership for Environmental Research program for the 20012003 period.
Modeling multiple pollutants at multiple sites, with application to acute
respiratory studies
Center member: Jon Wakefield
Other collaborator: Gavin Shaddick, Imperial College, UK
In cooperation with South East Institute of Public Health in the U.K., a multivariate
Gaussian model was developed to model multiple pollutants measured at a number of
sites over time. This model was applied to four pollutants measured at eight sites in London. We found very little spatial variability in the pollutants; the temporal variability
dominated. This lead to the paper Shaddick and Wakefield, (2002) (NRCSE TRS 70),
which has been published in Applied Statistics.
Is there a contradiction between apparent long-term increases in the frequency of extreme precipitation over the coterminous U.S. and the absence
of flood trends?
Center member: Dennis Lettenmaier
Center researcher: Caren Marzban, National Severe Storms Laboratory
Among the potential consequences of climate change to society, implications for the
availability of water in inhabited areas are among the most often quoted. A particular
concern voiced recently in many scientific for has been the possibility that acceleration of
the global hydrological cycle that is expected to accompany ongoing increases in greenhouse gases might lead to increases in hydrologic extremes, including floods. This possibility is given prominence, for instance, in the recent Third Assessment Report of the Intergovernmental Panel on Climate Change. On the other hand, published studies in the
hydrologic literature that have attempted to determine whether changes in flood frequency
have occurred over the U.S. show varying results, from essentially no evidence of changes in one study to a conclusion of demonstrable links between increases in precipitation
intensity and flood frequency in another.
To address this question in more detail, we have assembled a set of approximately 500
river basins defined by U.S. Geological Survey stream gauges with at least 50 years of
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observations and minimal effects of water management. For each of these river basins,
we have located similarly lengthy precipitation observation records within or close to the
basins. We are attempting to answer questions like 1) for appropriately defined time series of moderate to large floods (defined as having recurrence intervals of roughly 1/3 yr1
; somewhat less than used in previous studies) is there evidence of trends at more stations across the continental U.S. that would be expected by chance, and b) for those river
basins for which there are statistically significant trends in floods, are there identifiable
increases in daily precipitation with similar return periods. In addition to use of the
"peaks over threshold" approach to identify the streamflow and precipitation time series,
we have utilized various methods of attempting to assure that the precipitation and
streamflow events are causally connected. This is accomplished by examining trends in
the subclass of conditioned large precipitation and streamflow events. That is, we examine the conditional probability of a flood, given that a large precipitation event precedes
it. Although the analysis is ongoing, preliminary results suggest an absence of evidence
that trends in precipitation extremes are accompanied by trends in the accompanying
“causally related” streamflow. Examination of possible reasons for this apparently anomalous result is currently ongoing. Among the possible explanations are lack of power
(due both to small sample sizes and high natural variability) to detect modest trends in the
available data, and the predominance of trends in precipitation at times of year (e.g.,
summer) when relatively dry antecedent conditions dictate that relatively few extreme
precipitation events occur. A paper is in preparation.
3.2 Education and outreach
Center Computing
PI: David Madigan
Center researchers: Peter Guttorp, Paul Sampson
Research staff: Erik Christiansen, Peter Sutherland
Research assistant: Tamre Cardoso
The main outreach tool this group maintains is the web page. In addition, the group has
put seminars on the Web, iimplemented long distance collaborative computing tools
(Center members Guttorp and Richardson used these to communicate with their graduate
students while on leave), addied web-based discussion tools to the web site, and developed long-range plans for the Center computing facilities. Center-related software has
been made available to the community and. In particular, graduate student Tamre Cardoso
has ported Doug Nychka's (NCAR) package FUNFITS, a collection of programs based in
S-PLUS (on Unix) for curve and function fitting and spatial design, to run under S-PLUS
version 4.0 for Windows. The Windows version is available as a self-extracting zip file
on the NRCSE web site. S-Plus code for fitting censored multivariate data, and links to
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software for model assessment and for multivariate dynamic graphics are also found on
the software page of the web site.
A Bayesian tutorial
PI: Peter Guttorp
Center researcher:David Madigan
Research staff: Peter Sutherland
Work on a computer-based tutorial in Bayesian Statistics was hampered by the lack of
standardized web-based mathematical representations. A prototype was developed and
shown at the Joint Statistical Meetings in 1997. Several pieces were finished, but the project was eventually put on hold awaiting better computer display tools.
Statistics courses for EPA Region X
Center investigators: Loveday Conques, June Morita, Peter Guttorp, Steve Millard (PSI),
Eliane Faustman
EPA contacts: Diane Ruthruff, Jim Adamski, Patricia Cirione
Research assistant: Kris Ryding
The Center was approached during the summer of 1997 by personnel from the regional
EPA office in Seattle about developing an introductory to intermediate series of lectures
and computing exercises for office personnel. Due to timing problems, the original plan,
which involved Center researchers Loveday Conquest and June Morita, could not be implemented. Instead, center affiliate Steve Millard (Probability, Statistics & Information)
taughtthe course in 1998. Kris Ryding, a QERM graduate student, has been hired jointly
by the Center and the Region to serve as a statistical consultant at the regional office. In
addition a short course on risk analysis was organized at the regional office by Elaine
Faustman, Scott Bartell and Bill Griffith in 2000.
Quantitative Literacy Project
PI: June Morita
Center researcher: Alison Cullen
Research assistant: Lynn Coriano
In conjunction with the outreach activities of the Center, the goal of quantitative literacy
for all citizens is important. The main target group for this project is school children and
their teachers. The Center has offered support to June Morita for her work on activitybased mathematics education. The publication Morita (1999) is a result of this.
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A graduate student at the School of Public Affairs, Lynn Coriano, wrote her MPA degree
project on the topic "An Opportunity in Education: Promoting the Environment". She
explored the approach to environmental education currently in practice in the US and in
particular Washington state. In her conclusions she recommends that environmental education be infused into the classroom in all subject areas. She identified a lack of curriculum materials and teacher preparation as preventing fuller environmental education at the
present. There are many opportunities for web based lessons and quantitative exercises
with the environment as the major theme. As a practicum related to this degree project
she developed 5 lesson plans for use with grades 6-8 in a unit titled “Quantitatively-Based
Watershed Lesson Plans”.
Scientific method curriculum: The Truth about Science
PI: June Morita
Research assistants: Kathryn Kelsey and Ashley Steel
This project, which wais jointly funded by the Discuren Foundation and NRCSE, has developed and implementd a 10 week curriculum for middle school students about the process of scientific research–from hypotheses and research design to statistical analysis and
presentation–using structured activities and long-term independent research projects.
About half the lessons are stand-alone units to teach basic research skills such as developing hypotheses, setting up controls, random selection of observations, calculating an average and a t-statistic, and graphing data. The other half of the lessons apply the concepts to
the Long-Term Research Project (LTRP). Students work in groups to design and carry
out their own LTRP. For example, students investigated whether mushrooms in the
shade were healthier than mushroom in the sun or whether there were more aphids on red
maple than on red alder trees. The curriculum culminates in a celebration night at which
students display posters of their research and give 5-minute presentations to their parents
and classmates.
The curriculum materials has been published (Kelsey et al., 2001) and has been selected
as recommended science curriculum for middle schools in Seattle school district. Teacher
workshops have been attended by teachers from several local school ditricts, and the curriculum is used all over the United States. A variety of resources are available at the
NRCSE web site at http://www.nrcse.washington.edu/truth/. A paper (Steel et al., 2002)
has been submitted to a special issue of Environmental and Ecological Statistics,
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3.3 Model assessment
Operational evaluation of air quality models
PI: Paul Sampson
EPA connections: Sharon LeDuc, Brian Eder, Larry Cox
Center researchers: Peter Guttorp, Joel Reynolds, Wendy Meiring
Research assistants: Ruth Grossman, Doris Damian
This project aims at developing tools for model assessment, using model runs from the
SARMAP air quality model for the San Joaquin Valley in Central California. The model
assessment work focuses on fitting a nonstationary space-time covariance structure to observed data, and using this covariance to estimate (with specified uncertainty) the ozone
levels in the grid squares for which the model produces output. We will pursue these ideas using RADM and MODELS-3, where longer runs of the model will enable us to also
compare the covariance structure of the model output to the covariance structure inferred
from the data. The project was presented at the Novartis symposium on environmental
statistics in London (Sampson and Guttorp, 1999).
Recent work includes empirical modeling of temperature effects on the San. The project
has produced several papers (Meiring et al. 1997, 1998) and technical reports (NRCSE
TRS #6, 20).
Stochastic precipitation model
PI: Jim Hughes
Center researchers: Peter Guttorp, Dennis Lettenmaier
Outside collaborator: Bryson Bates, CSIRO Perth, Australia
Research assistants: Enrica Bellone, Ted Lystig, Tamre Cardoso
In assessment of global warming, much use is made of deterministic models of general
atmospheric and oceanic circulation. These general circulation models generally are on
too coarse a scale to produce realistic precipitation scenarios on local (or meso-) scales.
We are developing stochastic models of precipitation that use atmospheric pressure and
temperature data as input, and produce precipitation forecasts at observation stations or at
unobserved sites as output. The model is based on the concept of weather states, that
summarize the atmospheric behavior, and uses a hidden Markov model with nonstationary transition probabilities.
In Hughes et al (1998) a nonhomogeneous hidden Markov model for relating precipitation occurrences to atmospheric circulation was developed. This work has been extended
to include precipitation amounts. Preliminary results were presented at the Sixth Interna32
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tional Conference on Precipitation (Bellone et al, 1998, contributed poster) Related work
has been carried out by NRCSE member Jim Hughes and colleagues from Australia's
Commonwealth Scientific and Industrial Research Organization (CSIRO). Bryson Bates
and Stephen Charles, both of the CSIRO Land and Water Division in Perth, Australia,
visited the NRCSE in June of 1998 to work with Dr. Hughes on developing models for
downscaling precipitation in western Australia. Preliminary results on this work were
presented at the Sixth International Conference on Precipitation and two paers have been
published (Charles et al., 1999a, Charles et al., 1999b)
Enrica Bellone, a graduate student in the department of Statistics and funded by the
NRCSE, has been working under the supervision of NRCSE members Peter Guttorp and
Jim Hughes to develop such models.Current work focuses on developing a model for precipitation amounts in Washington State, using a small network of 10 stations. Issues of
sensitivity to measurement error, particularly for small precipitation amounts, are important and difficult. The choice of the number of weather states is another technically
challenging question. The work has resulted in a paper (NRCSE TR 21) accepted for publication in Climate Research, and a dissertation by Enrica Bellone entitled Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts, accepted for the PhD degree in Statistics.
A hierarchic Bayesian approach to estimating precipitation rate using data from different
sources, such as rain gauges, weather radar, and distrometers, is being developed by
Tamre Cardoso. Traditionally, rain gauge data has been regarded as “ground truth” for
calibration purposes, although gauges have known biases, particularly in windy conditions. This modeling project will enable researchers to improve radar-gauge calibration
exercises, and will eventually be used to improve precipitation observation networks and
satellite calibration. The model is currently being fitted to data from northern California
.
Two main areas of research on this topic involved the development (by Hughes’ student
Ted Lystig) of vastly improved algorithms for fitting hidden Markov models, including
algorithms for estimating standard errors (Lystig and Hughes, 2001). Hughes gave
presentations of the hidden Markov model for precipitation at the Eastern North American Region of the Biometric Society and at the Eighth International Meeting on Statistical
Climatology in Germany, while Guttorp presented the research as part of his sequence of
talks at the Fifth Brazilian School of Probability.
Assessment of environmental fate and transport models
Co-PIs: Alison Cullen and Adrian Raftery
Center investigator: Chris Bretherton
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Research assistant: Samantha Bates
A Superfund clean-up is underway at the New Bedford Harbor site in Massachusetts,
where marine sediments are contaminated with poly-chlorinated biphenyls (PCBs). Harbor dredging at the site and subsequent transport and deposition may result in human exposure via air, soil and ingestion of locally grown foods. Sampling of households and
farms around the site in 1994 and 1995 yielded produce, air and soil samples which in
turn provided measurements of PCB concentration in soil, outdoor air and root, leafy and
vine plants. A probabilistic exposure assessment in which average annual exposure to
local inhabitants is assessed, is underway at the site. This assessment requires distributions for PCB concentration in soil and in root, leafy and vine plants, and has resulted in
the paper (Vorhees et al., 1997).
The aim of this project is to develop Bayesian methods for assessing uncertainty and variability in risk assessment models, building on the Bayesian melding approach of Poole
and Raftery (2000). There have been four main foci of our work. The first is the development and application of the sampling-importance-resampling (SIR) algorithm for making inference about the parameters of the deterministic simulation models involved given
all available evidence and uncertainty. This has been investigated in the context of three
main examples: a one compartment air-to-soil model developed originally by Alison Cullen for PCBs in the New Bedford Harbor area, a model for the population dynamics of
whales, and a simulated model designed to investigate higher-dimensional situations. The
second focus has been the extension of these methods to multiple-compartment models,
and we have focused on the air-to-soil-to-plant extension of Cullen's air-to-soil model.
The third focus has developed from the observation that the SIR algorithm is inefficient
in high-dimensional models with the ridge-like posteriors characteristic of these models,
and we have been developing an MCMC method as an alternative to the SIR algorithm.
Standard MCMC methods do not work well in this context, and we have developed an
entirely new MCMC method that does perform well for these applications, the nearestneighbor MCMC method. Our fourth focus has been the development of model validation
methods based on the Bayesian melding approach.
In December 1998 at the Society for Risk Assessment (SRA) Annual Meeting in Arizona, Bates presented a paper titled “A Bayesian Synthesis Approach to assessing exposure
to PCBs in New Bedford Harbor,” co-authored by Cullen and Raftery. Bates received a
Student Travel Award from the SRA for this work. Cullen presented a paper entitled
"Developing Distributions of Annual Average Concentration with Dependency among
Daily Values," co-authored by Christopher Bretherton. In 2000 we have published one
paper in the Journal of the American Statistical Association (Poole and Raftery 2000),
and a second paper will appear in the Proceedings of the American Statistical Association
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(Bates, Raftery and Cullen 2000). This latter paper has also been issued as NRCSE TRS
58. Two invited lecturesweregiven atthe Interface meeting and the Joint Statistical Meetings.
Bates’ doctoral thesis, titled “Bayesian Inference for Deterministic Simulation Models for
Environmental Assessment,” was successfully defended in 2001. The major component
of this work was the development and application of Bayesian methodology for making
inference from sequential multicompartment deterministic models, particularly those in
environmental assessment, while accounting for uncertainty in the model inputs. In August of 2001, a talk on this aspect of the research was given at the annual meeting of The
International Environmetrics Society. It received awards for best student paper (joint) and
best risk analysis paper. The paper (Bates, Raftery and Cullen, 2001) has beenaccepted
for publication in Environmetrics.
A paper on tools to assess deterministic models in the Bayesian framework is in preparation and follows on from the thesis work. A paper (Bates and Raftery, 2001) has been
submitted to the Journal of Computational and Graphical Statistics, presenting a Markov
chain Monte Carlo method for sampling distributions, which are ridgelike in high dimensions. Posterior distributions of inputs and outputs to deterministic models may display
this behavior.
Assessment of toxicodynamic models
PI: Elaine Faustmann
EPA researchers: Woody Setzer and Chris Lay
Center researchers: Brian Leroux, Scott Bartell, Rafael Ponce
UW Collaborator: W. C. Griffith
Research assistants: Tom Lewandowski, Julia Hoeft and Scott Bartell
A current project deals with a developmental toxicity models for methylmercury Furthermore the group has met with Woody Setzer and Chris Lau (October 16-17, 1997) to
develop a collaborative research agenda for toxicity modeling of 5-FU and methylmercury. The basic stochastic model describes the cellular processes using Markov processes.
These are then used in conjunction with a toxicokinetic model to generate model predictions for litters.
The chemotherapeutic agent 5-fluorouracil (5-FU), and other fluoropyrimidines, are
known teratogens in a number of species. Among the most prevalent developmental effects of fluoropyrimidine exposure are dose- and stage- dependent hindlimb effects.
Shuey et al reported a sequential biochemical and cellular alterations following 5-FU ex35
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posure in the developing limbs. These effects elicited by 5-FU were later integrated into
an empirical model (Shuey et al., 1994).
A biologically-based dose response model for developmental toxicants was developed by
Leroux et al. (1996). Unlike other empirical models, this model simulates developmental outcomes based on stochastic probability distribution of crucial developmental events
such as cell differentiation, cell cycle and cell death. The pattern of malformation rate is
predicted as a function of critical number of committed cells in a both dose- and timedependent fashion. Because this model incorporates events that are common targets of
many developmental toxicants, the potential application of this model to simulate the toxicity of other developmental toxicants is implicated.
Developing methodology for assessment of medium and large scale environmental models
PI: David Ford
EPA researchers: Sharon LeDuc, Bill Benjey
Center researcher: Joel Reynolds
Research assistant: Marianne Turley
The Environmental Protection Agency develop and use complex multi-parameter models
of
ecological and environmental processes to make predictions about such phenomena as
transport and deposition of pollutants and their effects on public health. Such models
contain many functions not all of which have an undisputed place in the model. Typically,
estimation of multiple parameters during calibration is made from limited data or even
from data which itself has been produced by models). This makes such models vulnerable
to the problems of (1) non-uniqueness, where different models may fit particular data sets
equally well; and (2) accommodation, where an apparently acceptable model calibration
may be achieved due to unrecognized distortion of parameter estimates. As a solution to
these problems we have developed a methodology for the use of simulation models, the
Pareto Optimal Model Assessment Cycle (POMAC) that recognizes: (a) models must be
constructed for a particular purpose from an available knowledge and data base; (b) the
incompleteness of such models.
(i) Development of improved evolutionary search software for the Pareto front:
POMAC_Evolve.
An evolutionary computation optimization program, POMAC_Evolve, was developed
and coded. While some of the data structures and general program outline were adapted
from earlier prototype code (Reynolds, 1997), deficiencies were found which necessitated
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the selection of a new search algorithm and subsequent complete code redevelopment and
writing.
The algorithm used in the prototype code was found to be susceptible to 'genetic drift' the stochastic search became unduly focused on a small region of the parameter space. As
the goal of POMAC is to survey the full Pareto Front rather than just find a restricted region of the Front, it was essential to revise the fitness assignment to each parametrization
in order to avoid restricting the optimization search too quickly to a small region.
(ii) The POMAC manual.
The target audience for the POMAC software is ecological and environmental modelers
many of whom have had little or no instruction in optimization. The manual starts with a
definition of the problem of optimization and illustrates some important features of how
it can be used, e.g., that a single assessment criterion must be selected, and then proceeds
to illustrate why considering multiple assessment criteria can be valuable.
We have developed software implementing an evolutionary computation algorithm for
the solution of the Pareto frontier, i.e., the set of parameterizations for a model that satisfy
a number of model output criteria. A critical requirement for evolutionary computation
is that it makes a comprehensive search of the parameter space and at the same time approaches solution nodes closely. In classical optimization mathematical methods have
been used to define how searches are made, but in evolutionary computation for the Pareto frontier such an approach is not available. In practice what is required is that the repeated “breeding” of new parameterizations must combine refinement to individual solution nodes with maintaining some parameterizations that explore the complete space for
new solution nodes. Our new software improves on previous work by changing the rates
of parameter mutation and parameter cross-over in successive generations of parameterizations.
Ms. Turley’s doctoral dissertation made a comparison of two competing models of plant
competition using multiple criteria. The models were for one- and two-sided competition
where large plants affect small ones but there is no reciprocal influence (one-sided), and
where there is reciprocal influence (two-sided). She has shown the importance of how
multiple criteria are selected and developed in order to calculate a Pareto Set where different model parameterizations satisfy different groups of criteria. Two types of criteria
are important: measures of location for principal output such as mean, median, quartiles;
and measures describing important data characteristics such as frequency distributes, and
metrics of spatial structure. She was also able to compare models with just a single parameter difference and reject the more complex model when that parameter solved as zero. This work has illustrated that selection of assessment criteria, and deciding upon the
range within which a criterion might be considered as satisfied, are as important as model
formulation – though both are frequently relegated to an after thought of model develop-
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ment. This has considerable significance for the development and use of environmental
models.
Calculation of the Pareto set, on which multi-criteria assessment depends, requires an efficient evolutionary algorithm that is fast and does find the complete possible set. Ms
Komuro has compared our algorithm, Pareto_Evolve, originally developed by Dr. Joel
Reynolds, with some other algorithms. Performance for some simple tests was satisfactory (Komuro and Ford 2001; NRCSE TRS 62). However, the standard tests used are for a
two criterion problem whereas in multi-criteria model assessment the number is likely to
be greater than two. Further, most tests use continuous functions–frequently with well
known solutions. Our recent work has concentrated on the segmentation of a known data
stream into multiple criteria, each representing different characteristics, and to calculate
the effectiveness with which solutions are found as the number of criteria are changed.
This work is showing that the choice of criteria must be designed to test particular aspects
of model function.
Model assessment using repeated model fitting
PI: David Ford
Research assistant: Zoe Edelstein, University of Chicago undergraduate.
An important problem in the assessment of ecological and environmental models is that
of repetition of the complete process of model fitting to new data sets. Where second data sets are available they are not treated as replicates but typically the question is asked:
"Using the same set of parameters as obtained from fitting to the model to the first data
set (i.e., obtained during calibration) does the model fit the second set?" This is referred
to as validation. That such a procedure is not validation, in the sense that a successful fit
renders the model to be true is now generally accepted. But the question remains of what
value a second data set is. A similar problem is faced when modelers take a single data
set and break it in two and fit the model to one segment and seek to test the model against
the second.
We have available a process model of plant competition that is fit to experimentally obtained data. The model has many characteristics of typically ecological and environmental models: it is stochastic and it predicts changes in the system modeled over time. Over
the summer we conducted glasshouse experiments to provide two further instances of the
data so that we now have four. We can now fit the model to obtain four sets of fitted parameters. We intend to treat these parameter sets as members of an ensemble and explore how such ensembles can be treated, both statistically and in interpretation of the
system being modeled. This approach, of considering repetitions of the data as each producing a set of fitted parameters, brings a different perspective to the concept of model
assessment that we will develop in future. The situation is similar to fitting a time series
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model to repeated realizations of a time series, though with an important difference. In
time series from ecological systems the variation is often such that the order of the model
changes, not necessarily by a great deal but sufficient to complicate comparison between
model fits. In process models the structure of the model remains constant. A paper is
under revision.
Integrated exposure and uptake biokinetic lead model (IEUBK)
Center member: Elaine Faustman
UW collaborators: JH Shirai, AC Pierce, and JC Kissel
Research assistant: Scott Bartell
The last twenty years have seen the development of numerous models for predicting the
kinetics of lead in the human body (US EPA, 1994). These models are necessary because
health effects have historically been linked to specific blood lead concentrations, while
pollution control and industrial hygiene efforts are most easily directed at environmental
(e.g. air, water, soil, and food) lead concentrations. Exposure and toxicokinetic models
provide the quantitative link between environmental concentrations and biomarkers such
as blood lead concentration.
EPA requested that the NRCSE review childhood toxicokinetic lead models and suggested additional validation strategies. The agency is particularly interested in validation of
its own model, the Integrated Exposure and Uptake BioKinetic lead model (IEUBK),
which predicts blood lead concentrations for children ages 0 to 7 years old based on environmental lead concentrations.
One of the most controversial parameters in childhood exposure models is the soil ingestion rate. Experimental estimates are usually determined from tracer studies, in which
aluminum, silicon, titanium, and other rare earth elements are measured in the diet, urine,
and feces. Steady state conditions are assumed, and mass balance approaches are used to
estimate the rate of soil ingestion. Soil ingestion rate estimates derived in these studies
vary by several orders of magnitude, appear to fluctuate daily for each monitored individual, and are highly dependent on the tracer and statistical model selected. An alternative
to the tracer study is the use of pollutant biomonitoring studies which include environmental measurements. We have obtained data from one such study, the Urban Soil Lead
Abatement Demonstration Project (USLADP), in which children’s blood lead concentrations were monitored for two years following the replacement of contaminated yard soil
with soil with lower lead content. A perturbation analysis was performed using a simplified toxicokinetic lead model to estimate a soil ingestion rate for each child in the
USLADP study. The model includes a probabilistic uncertainty analysis component
which assesses the impacts of toxicokinetic parameter uncertainty on each child’s estimated soil ingestion rate.
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Estimates of soil ingestion rates have a mean of 10 mg/day and a 95th percentile of 93
mg/day. Uncertainty regarding individual soil ingestion rates is clearly large and is primarily due to uncertainty in the lead absorption fraction. Model uncertainty is not accounted for in these estimates and would be expected to increase the variance in the individual estimates.
We have recently completed the model runs for this analysis, and are now compiling the
results. We have presented these results at an EPA workshop June 1999. We are preparing a manuscript, “Estimation of soil ingestion rates from observed blood lead loss following soil remediation”, for submission to Environmental Health Perspectives by January 31, 2000. Results were also presented as “Uncertainty and variability in childhood
soil ingestion rates estimated from USLADP blood lead levels” at the International Society for Risk Analysis annual meeting in December 1999.
3.4 Space-time models
Imputing air pollution exposure over space and time for use in analyses of
health effects
PI: Lianne Sheppard
EPA researchers: Larry Cox, Dave Holland, Sharon LeDuc, Jim Quackenboss
Center researchers: Peter Guttorp, Paul Sampson
Research assistant: Doris Damian
A Bayesian approach to imputing air pollution exposure data is applied to monitoring data from Seattle. We are developing methods that allow for data missing at random due to
temporary equipment failure and for data missing by design over time. Our focus is on
methods that are computationally feasible for multiple years of daily observations from
multiple monitoring stations. We have evaluated the air pollution predictions both using
cross-validation techniques to assess the accuracy of the prediction when a single location
is left out, and also in terms of improvements to the health effects analysis. In order to
assure that improvements in the health effects analysis are not due to hidden biases, these
evaluations will be conducted on simulated as well as observed data. The work resulted in
a methodological paer (Sheppard and Damian, 1999) in the Environmetrics special issue
on particulate matter air pollution.
Use of personal monitors to assess health effects of particulate matter exposure in Slovakia
PI: Alison Cullen
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EPA researcher: John Vandenberg
Collaborators: Michael Brauer, UBC, Canada; Eleanora Fabianova, Eva Mikhalikova,
Peter Miskovic, Frantiska Hruba, SUHE, Slovakia.
Recent interest in the levels of and health effects associated with airborne particulate matter exposure have sparked studies in the US and worldwide. Working with local scientists we are examining new measurements of PM2.5 taken by personal monitors in occupational settings, both industrial and office type, and in the home, by researchers at the
SUHE (Institute for Epidemiology and Hygiene) in Banska Bystrica, Slovakia. This work
also involves Michael Brauer at UBC and John Vandenberg at HERL, EPA, and has received funding from the Joint Fund for US/Czech/Slovak Science and Technology. Regression analyses will be carried out in the coming year to identify factors influencing
particulate matter exposure in Slovakia and to support the standard setting process.
The research team visited Seattle in August 1998 to discuss preliminary results and to
plan next steps. At this meeting the group prepared a talk for the ISEA (International Society of Exposure Analysis) annual meeting in Boston. The talk entitled "US-Slovak Cooperation in Environmental Health Risk Assessment: Preliminary Estimates of Personal
Exposure to Particles and NO2 in Banska Bystrica, Slovakia " was presented by Eva Mikhalikova. Further analysis was planned and a talk describing additional analyses was
presented by Frantiska Hruba at the Society of Risk Analysis Annual Meeting in Phoenix
in December 1998.
At the request of EPA's Vandenberg, NCRSE hosted additional meetings between the visitors and researchers from UW involved in PM work including: Jane Koenig, Tim Larsen,
Lianne Sheppard, Sally Liu, and Dave Kalman, as well as Tim Nyerges of UW Dept. of
Geography's GIS in decision making group. During these sessions Vandenberg highlighted the interests and needs of EPA anticipated in this area.
.Working with Slovakian scientists we have examined ambient exposure to inhalable particulate matter (PM10 and PM2.5), nitrogen oxides, sulfates, and nicotine in occupational
settings, both industrial and office type, and in the home. 49 subjects were selected from
those residing in either the Banska Bystrica city center or the Sasova residential area, because earlier studies in both areas suggested that ambient levels of particulate matter were
significantly lower in the residential area than the city center.
Results indicate that central site monitors underpredict actual human exposures to PM10
and PM2.5. Personal exposure to sulfates was found to be predicted by outdoor sulfate
levels, location of receptor residence and time activity information. From these results we
concluded that personal exposure measurements and precise daily activity data are crucial
for accurate evaluation of exposure.
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A workshop in Slovakia took place in October 1999. The paper “Personal Exposure to
particles in Banska Bystrica Slovakia” (authors: M. Brauer, F. Hruba, E. Mihalikova, E.
Fabianova, P. Miskovic, A. Plzikova, M. Lendacka, J. Vandenberg and A. Cullen) which
was presented at the PM 2000 conference in South Carolina in January, 2000, has appeared in Exposure Analysis and Environmental Epidemiology.
The EPA has approved additional funding for this project, which is funneled through the
Nortwest PM Center due to the cessation of NRCSE funding from EPA.
Modeling time series of multiply censored data
PI: Mary Lou Thompson
EPA researcher: John Warren
Center researcher: Bruce Peterson (Terastat)
Research assistant: Kerrie Nelson
The statistical practices of chemists are designed both to minimize the probability of misidentifying a sample compound and the probability of falsely reporting a detectable concentration. In environmental assessment, trace amounts of contaminants of concern are
thus often reported by the laboratory as "non-detects" or "trace", in which case the data
may be multiply left-censored. We consider the observations on each individual as being
a nonhomogeneous Markov chain with three states: "non-detect", "trace" and "detect".
Given the presence of "detect", the distribution of the observed measurements is modeled
by some appropriate parametric form. This allows estimation of the parameters of the
"detects" distribution and the proportion of censored values as a function of covariates
(such as time, rural vs. urban etc.).
We have developed a maximum likelihood approach to point and interval estimation for
multiple linear regression in the presence of Type I interval and left censoring. We have
evaluated and compared the characteristics of the ML estimates to those obtained from
simple midpoint substitution under different assumptions as to the degree of censoring,
strength of correlation and sample size. The methodology has been implemented in Splus
and a program for general implementation is available from the NRCSE website
http://www.nrcse.washington.edu/research/projects/software/smmcd.txt.
Bayesian estimation of nonstationary spatial covariance structure
Co-PIs:: Paul D. Sampson, Peter Guttorp
Research assistant: Doris Damian
Undergraduate student: Gabriel Johnson
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The approach to modeling nonstationary (or non-homogeneous) spatial covariance structure through a deformation of the geographic coordinate system, as implemented first by
Sampson and Guttorp and then by Meiring, has left the calculation of uncertainty in the
estimated structure exceedingly difficult using bootstrap methods. We have long recognized the appeal of a formal Bayesian estimation of this spatial covariance model assuming a Gaussian model for the space-time process, and have now completed the specification of a Bayesian estimation paradigm for the spatial deformation model and its implementation using MCMC methods. In the process of this investigation, a number of results
concerning likelihood-related estimation of variograms and spatial deformations have
been revealed. The first manuscript on this methodology has been published in Environmetrics. Preliminary results were presented by Sampson as invited plenary talks at two
recent international meetings: the joint meeting of TIES and SPRUCE in Sheffield, and
the Third European Conference on Geostatistics for Environmental Applications (geoEnv
2000) in Avignon this November.
Doris Damian has completed her Ph.D. thesis on a Bayesian approach to modeling and
estimation of the spatial correlation structure of spatio-temporal environmental monitoring data using the spatial deformation model of Sampson and Guttorp. The parametrization of the thin-plate splines used for the spatial deformation allows specification of prior
probability models on both the affine and non-affine components of the spatial deformation. In addition, the modeling accommodates the (temporal) variance of the spacetime process varying spatially according to a nonstationary pattern according with the
same spatial deformation assumed to underlie the spatial correlations.
This project has also employed an undergraduate major from the U.W. program in Applied, Computational and Mathematical Sciences, Gabriel Johnson, to port the code for
model estimation using McMC algorithms from her Unix version to a version running
under a Windows PC operating environment. In addition to the porting of the computational algorithms, with substitution and testing of mathematical support libraries as necessary, Johnson implemented a user interface that will greatly benefit our release of the
software.
Invited presentations on this work were given by Paul Sampson at the NSF/CBMS Environmental Statistics lecture series sponsored by NRCSE here in Seattle, June 25-29, and
at the First Spanish Workshop on Spatio-Temporal Modeling of Environmental Processes
in Benicassim, Spain, October 27-30. Peter Guttorp gave an invited presentation on this
subject at the 2001 annual meeting The International Environmetric Society (TIES) in
Portland, Oregon, Aug 13-17, at the Canadian Statistical Society meeting in Halifax
2002, and the Royal Statistical Society International meeting in Portsmouth, 2002. Publications based in part on this work include Sampson et al. (2001a,b), Damian et al. (2001)
and Sampson (2001). The paper Damian et al. (2002) is under revision for Journal of Geophysical Research.
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Development of an anisotropic global covariance function
PI: Peter Guttorp
NCAR collaborator: Doug Nychka
Center researchers: Paul Sampson, Tillman Gneiting
Research assistant: Barnali Das
When dealing with global stochastic processes (such as temperature), most work to date
has (implicitly or occasionally explicitly) been focusing on covariance structures that are
isotropic, or rotation invariant. In many situations such an assumption is not reasonable, if
only for the fact that Earth has a rotational direction. This project involves finding methods to simulate nonstationary processes on the globe as well as estimating covariances
from real data.
We study anisotropies that occur in atmospheric variables, at least partly related to the
rotation of the Earth. Such covariance structures can be developed by deforming the globe
(a sphere with a natural orientation) into itself, with an isotropic covariance applied to the
deformed globe. A parametric description of the deformation is combined with a likelihood approach to estimate the covariance for global temperature data, characterized by
measurement stations coming and going according to the vagaries of national policies in
different parts of the world.
We have developed computationally rather demanding tools for analyzing meteorological
time series on a global scale, taking into account spatial heterogeneity and the fact that
data are collected on a globe (an oriented sphere). A flexible class of parametric nonstationary global covariance functions has been developed, and applied to global temperature data with likelihood tools that enable use of incomplete monitoring data without requiring imputation. The methodology enables realistic estimates of prediction variance
for regional and global averages, and allows comparison of gridded model output data to
suitably processed observational data. This work would not have been possible without
the generous cooperation of the Geophysical Statistics Projects at NCAR in Boulder, CO.
The main result of this effort was the Ph.D. dissertation by Barnali Das, entitled Global
covariance modeling: a deformation approach to anisotropy.
Trend estimation using wavelets
Center members: Don Percival, Peter Guttorp
Research assistant: Peter Craigmile
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A common problem in the analysis of environmental time series is how to deal with a
possible trend component, which is usually thought of as large scale (or low frequency)
variations or patterns in the series that might be best modeled separately from the rest of
the series. Trend is often confounded with low frequency stochastic fluctuations, particularly in the case of models that can account for long memory dependence (slowly decaying auto-correlation) and non-stationary processes exhibiting quite significant low frequency components.
We have developed both an approach to estimating trend at a given temporal scale and
procedures for testing the presence of a trend, valid for a large range of assumptions.
This work is described briefly in Section 9.4 of the book Percival and Walden (2000),
and two NRCSE Technical Reports (NRCSE TRS #47 and #49). This work forms the
basis for the central part of Peter Craigmile's doctoral dissertation Wavelet Based Estimation for Trend Contaminated Long Memory Processes, which was completed in December 2000. The submitted papers Craigmile et al. (2000) and Craigmile and Percival
(2001) are based on the dissertation research.
. His thesis focuses on a topic in time series analysis, namely estimating a trend component (large scale variations) in the presence of long memory (LM) errors (slowly decaying
autocorrelations). Craigmile has also investigated wavelet-based approximate maximum
likelihood estimators for fractionally differenced processes, and established the validity of
an exact method for simulating these - and related - processes. The work is a mix of theoretical, methodological and applied statistics (e.g. analyzing Northern Hemisphere temperatures since the mid 1800s). The trend estimation procedure has also been used in
health effects studies for particulate matter air pollution (NRCSE TRS #54)
Receptor modeling for air quality data in space and/or time
Center members: Peter Guttorp, Dean Billheimer
Outside collaborators: Ron Henry, USC; Cliff Spiegelman, Texas A&M
Center postdoc: Eun Sug Park
An important problem in environmental science is to identify where pollution comes from
given air pollution data. Multivariate receptor modeling aims to achieve this goal by decomposing ambient concentrations of pollutants to components associated with source
emissions. This is a difficult problem in its most general form and typically restrictive
assumptions are required. One assumption is that the observations are temporally independent, which is inappropriate for most of hourly measurements. We have developed a
multivariate receptor model for temporally correlated data, which can incorporate extra
sources of variability due to dependence in estimation of model parameters and uncertainty. The work has resulted in a paper (Park et al., 2001) in Journal of the American Statis-
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tical Association.. An invited session at the Joint Statistical Meetings in Atlanta in August 2001, organized by Peter Guttorp of NRCSE, heard a presentation of this work.
Assumptions on the number of pollution sources and identifiability conditions are the
main source of model uncertainty in multivariate receptor models, which is often overlooked. A Bayesian approach based on the marginal likelihood for assessing model uncertainty in multivariate receptor models has been developed. The work resulted in a paper (Park et al., 2002) in Chemometrics and Intelligent Laboratory Systems. A different
approach (Billheimer, 2000) uses earlier NRCSE work on spatio-temporal models for
compositional data to analyze air pollution data from Alaska.
We are currently focusing on extending receptor models to spatially correlated data obtained from multiple monitoring sites. Two cases, measurements on a single species from
multiple monitoring sites, and measurements on multiple species from multiple monitoring sites, are being investigated. The first type of data can be used to locate the major
pollution sources by estimating their spatial profiles, while the second type of data is ideal
for characterizing spatial structure of source contributions and errors. The first approach
has been applied to an analysis of PM10 data for Seoul, Korea, and yielded physically
meaningful results, i.e., the resulting estimates for the source spatial profiles seemed to be
consistent with our prior expectation about the PM10 sources in Seoul. The paper from
this research, Multivariate receptor modeling for air quality data in space and/or time,
was invited to be presented at International Statistical Institute meeting held in August,
2001, Seoul, Korea. The paper will be part of a special issue of Environmental and Ecological Statistics. The second approach is the topic of NRCSE TRS #71, and uses nonparametric regression on wind direction to infer the source of PM air pollution from data
at two locations. This work was also presented at the invited session on Statistical analysis of multivariate air quality data at the Joint Statistical Meetings in Atlanta.
3.5 Sampling and design
Composite sampling
PI: Gerald Van Belle
Center researcher: Steve Edland
Collaborator: David Marker, WESTAT
Composite sampling, defined as the pooling of field samples prior to measurement or laboratory analysis, is a simple and straightforward method of enhancing sampling programs in situations where estimates of variability are less important. We will extend the
methodology from the log-normal case to a variety of distributions, and examine the
composite sampling strategy in assays with limits of detection. Among important applications is routine monitoring of ground water for presence of metals (and other toxic sub46
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stances) at the Hanford Reservation’s tank farm. If the tanks are in stable condition there
should be no leakage or contamination. Samples are taken at regular intervals and could
be pooled. If there is no leakage then the pooled sample should be negative. A paper by
Griffith et al. (1999) appeared in Ecological and Environmental Statistics.
The Department of Housing and Urban Development and the National Institute of Environmental Health Sciences are sponsoring a national survey of dust hazards in housing.
Westat developed the survey and was to conduct the data collection between June and
October 1998. The survey assessed children's potential household exposure to lead and
allergens by estimating the levels of lead in dust, soil, and paint, the prevalence of hazardous levels of lead, and levels and patterns of allergens in dust in homes. The survey is
an area probability sample of 1,000 homes representing the entire U.S. housing stock.
The survey collected multiple floor dust samples from every house, all of which were to
be measured individually. The dust samples were sent to analytical laboratories for
chemical analysis for lead and selected allergens.
NRCSE funded an add-on to generate empirical data on matched individual samples and
composites for lead, as follows. After the acid digestion of a sample was completed, extracts from two to four of the floor dust samples from each home in the sample were
drawn and composited. The composite samples were then analyzed for lead. The composited extract would match what would have been obtained if the same four floor samples had been composited in the field. In addition, the two-to-four individual results
werel still available. In about half the housing units, the maximum lead loading from the
composite sample was in the same range as the maximum lead loading from individual
measurements. In the other half, about equally many were higher as were lower. Using
the composite samples, a 95% confidence interval for prevalence of lead hazard was
(5.5%,12.3%), while the corresponding interval for individual measurements was (7.7%,
16.2%).
Comparison of ranked set sampling to alternative sampling designs and
investigation of its usefulness in environmental monitoring
PI: Loveday Conquest
EPA researcher: Barry Nussbaum
Center researcher: David Marker (Westat)
Center postdoc: Jean-Yves Courbois
Research assistants: Nicolle Mode, Rebecca Buchanan
Ranked set sampling (RSS) is a two-phase sampling procedure involving initial ranking
of each of m samples of size m (often via a relatively cheap or fast method of measurement), followed by observing (often using a more accurate and more expensive method of
measurement) the first order statistic from the first sample, the second order statistic from
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the second sample, and so on, until the mth order statistic from the mth sample yields a
secondary sample of size m from the initial m2 data points. The goal of our research is to
determine a set of conditions under which RSS is the appropriate statistical methodology
to implement when trying to collect environmental data.We focused upon cost analysis of
RSS for normal and skewed data (with and without errors in ranking) including a real data set of stream habitat. A paper based on this work (Conquest et al., 1998) has been published in Environmetrics.
In September, 1999, Nicolle Mode and Loveday Conquest met with NRCSE visitors Bimal Sinha (U. Maryland Baltimore) and Barry Nussbaum for two days to discuss collaborative research. Areas for collaborative research include extending the cost ratio inequality for unbalanced ranked set samples, and for the case where the interest is on estimating
a quantile
of interest (rather than the population mean). Known distributions can be investigated
(e.g., normal, exponential) in addition to a distribution-free approach.
The paper, "Incorporating Human Judgment into Ecological Sampling" by Mode, Conquest and Marker had been presented at the Fourth International Chemometrics/Environmetrics Meetings in Las Vegas, Nevada, in September, 2000. This paper has
since been published in Environmetrics. QERM graduate student Rebecca Buchanan developed extensions of the balanced design cost models presented in Mode et al. (1999).
These extensions include considerations for unbalanced designs.
Dr. Conquest was successful in participating in an EPA STAR grant with Dr. Don Stevens of Oregon State University. The UW portion is "Model-assisted Design for Ecological Sampling". The research will be done by QERM graduate student Rebecca Buchanan,
post-doc J.-Y. Courbois, and Dr. Conquest. Designing sampling schemes for sampling
river networks must take into account such network processes as correlation running
downstream (flow direction) and also upstream (biological processes, such as salmon migration). Using model-assisted designs, we intend to develop sampling strategies that estimate model parameters and, at the same time, address traditional monitoring purposes,
tracking biological, chemical, and geological responses through time.
Monitoring network design
Center member: Paul Sampson, Peter Guttorp
EPA collaborator: Dave Holland
Research assistant: San-San Ou
Undergraduate assistants: Brooke Hoem, Friedrich Kuchling and Lean Richmond
U.S. EPA guidelines for air quality monitoring network design specify four explicit aims
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1. to determine highest concentrations expected to occur in the area covered by the network;
2. to determine representative concentrations in areas of high population density;
3. to determine the impact on ambient pollution levels of significant sources or source
categories;
4. to determine general background concentration levels.
However, the statistical literature on optimal network design seems far removed from
these aims, considering almost exclusively the optimization of a single criterion, such as
(some function of) kriging predictive variances. In view of the fact that practical policy
decisions require consideration of (at least) these four aims, we initiated a project to develop a methodology for “Pareto optimal” monitoring network design for multiple objective criteria. We argue that an attractive alternative to optimization of a single (possibly
composite) design criterion is the identification and consideration of the space of Pareto
optimal designs for a set of objective functions. Consideration of this “Pareto frontier” of
designs will allow better understanding of the trade-offs necessary to obtain greater relative efficiency with respect to the optimization of a single criterion such as a (possibly
weighted) spatial average of kriging variances. We have successfully employed a sequence of three different undergraduates (Brooke Hoem (ACMS, now graduated), Friedrich Kuchling (computer engineering), and Leah Richmond (ACMS)) to adapt for this
purpose the “Pareto Evolve” software developed at NRCSE for multi-criteria assessment
of ecological process models. Pareto-Evolve uses genetic algorithms (evolutionary computation) to identify candidate parameterizations in the “Pareto Frontier”. In this context,
each parameterization represents a monitoring network.
Work to date has involved (a) coding of simple geostatistical design criteria such as maximum and average kriging variances, as well as a spatial coverage criterion for use with
the Pareto-Evolve software; (b) modification of some details of the evolutionary computation algorithm, and (c) a preliminary demonstration of the successful application of the
evolutionary computation algorithm for a toy design problem. This work was the subject
of invited presentations by Paul Sampson at the 2001 Joint Statistical Meetings the First
Spanish Workshop on Spatio-Temporal Modeling of Environmental Processes in Benicassim, Spain, and the Spatial Data Analysis Technical Exchange Workshop in RTP, NC.
The methodology is discussed in the proceedings publication Sampson et al. (2001b).
Current research plans include the application of this methodology to practical network
(re)design calculations using, first, the example of the CASTNET monitoring network.
Modeling of data from this network in preparation for estimation of a spatial covariance
model to be used as a basis for spatial estimation criteria was carried out by graduate R.A.
San-San Ou.
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3.6 Standards and Regulatory Impact
Statistical aspects of setting and implementing environmental standards
PI: Mary Lou Thompson
EPA researcher: Larry Cox
Center researchers: Peter Guttorp, Paul Sampson
Other collaborators: Ronit Nirel, Israel, and Bruno Sanso, Venezuela
Research assistants: David Caccia and San San Ou
Undergraduate assistant: Anthony Nguyen
The debate surrounding the change in ozone standards illustrates many of the difficulties
in translating scientific studies into practical policy decisions. This project studies ways
of setting standards that makes use of the information available in a way that takes proper
account of uncertainties in knowledge and understanding of the process, in measurement
of the pollutants, and in enforcement rules. While the initial work focuses on ozone and
particulate matter data, the intent is to produce a methodology that can be applied to a variety of environmental concerns.
The typical environmental standard is what may be called an ideal standard. Based on
various health effects studies, a target value not to be exceeded is determined, and the
standard may be that this value not be exceeded, or only be exceeded with a certain probability, or a certain number of times per year.
Products of this project include presentations at the Novartis workshop on Environmental
Statistics in London (Larry Cox), at the Joint Statistical Meetings in Dallas (Mary Lou
Thompson), and at the Newton Institute workshop on Environmental Statistic and Technology (Larry Cox). A paper has been produced for the proceedings of the Novartis workshop (Cox et al., 1998)..
Work on hypothesis testing approaches to air quality environmental standardsresulted in a
paper in Environmental and Ecological Statistics (Thompson, et al., 2002). A technical
report by Guttorp (NRCSE TRS #48) was presented at the 75th birthday conference for C.
R. Rao. Guttorp’s work benefited from collaboration with undergraduate student Anthony
Nguyen. Current research plans include the development of explicitly spatial standards
(in contrast to current air quality standards that do not address issues of spatial variation).
In this context we initiated a project to incorporate the scientific information encoded in
deterministic photochemical modeling predictions as prior information in a Bayesian spatial estimation methodology. This project, begun while Ronit Nirel and Bruno Sanso
were visiting NRCSE in Summer of 1999.
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Environmental health regulation of particulate matter: Application of the
theory of irreversible investments
PI: Michael J. Phelan
Environmental health policy decisions are characterized by irreversibility and uncertainty
of an economic, ecological and biomedical nature. Economic analysis of problems of this
kind fall within the framework of the theory of irreversible investments as applied to the
sunk costs and sunk benefits of environmental regulation. The proposed research describes an application of the basic theory to problems associated with the regulation of
particulate matter in environmental health policy.
The first line of investigation models the social costs of regulation in light of current scientific, medical and economic understanding of problems associated with particulate matter. A particular emphasis is given to representation of health effects. All such models
involve however some uncertain parameters, so a second line of investigation integrates
modern practices of stochastic inference with sequential policy designs. An important
goal is to characterize fully the role of uncertainty and information on the design and implementation of policy, particularly learning strategies designed to address key uncertainties. The research has produced two publications (Phelan, 1999 and 2000).
Agricultural modeling for watershed management
Center member: Alison Cullen
EPA Region X collaborators: Chris Feise and Karl Arne
Research assistant: Valerie Lertyaovarit
The deliverables of this project are: (i) to build a model using Stella software (by Region
X request) to represent agricultural inputs to the environment at the watershed scale level,
(ii) to identify the interrelationships between inputs to and outputs of the agricultural system in order to gain a more accurate picture of which are having the greatest impact on
watershed-scale ecosystems, (iii) to describe the tradeoffs involved in managing a system
via assessment of maximum contaminant loading vs. managing for the overall health of
the watershed, and (iv) to make recommendations regarding the prioritization of policy
options that will make the most efficient use of limited agency resources.
A web site was needed to fully understand the relationships different departments within
EPA have with agriculture issues. It was believed that many of the departments shared
the same agricultural issues and were not collaborating with each other to find further in51
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formation. The project was to create and design a site for staff within EPA to find out
which EPA departments and state agencies had common agricultural interests. Access to
this web site is restricted since EPA has not yet decided whether it should be made available to the public or kept internal.
Decision-making under uncertainty: Prioritizing freshwater habitat restoration for salmon recovery in the Columbia river basin
Center member: Ray Hilborn
Research assistant: Jody Brauner
This research focused on data collection and model development to better understand the
linkages between riparian management/restoration and habitat carrying capacity for salmonids. Regional data were collected on riparian management regulations, stream surveys
for LWD, channel morphology, and pool characteristics, as well as age-specific salmonid
habitat preferences (coho and steelhead). An existing wood recruitment model (Riparian
Aquatic Interaction Simulator) was linked to a forest growth and yield model (Organon)
to generate a matrix of wood loading in streams (pieces/m) as a function of channel
width, riparian management practices, stand age and density. The resulting wood recruitment profiles were subsequently used as input to a model of pool formation and habitat carrying capacity for salmonids. Parameters in the pool formation model were estimated using standard linear regression techniques and then compared to parameter estimates based on a posterior probability distribution. The purpose of this comparison was
to illustrate the effects of incorporating estimation uncertainty on the distribution of consequences under different riparian management scenarios. Ongoing work is focused on
the incorporation of three additional types of uncertainty in the wood recruitment and
pool formation models - process, observation and model uncertainty.
3.7 Methodology
A comparison of SVD and CCA analyses in climate prediction
Center member: J. M. Wallace
Research assistant: Mary Fishel
For her MS thesis, Mary Fishel compared the performance of three different linear statistical techniques for predicting patterns of seasonal mean surface air temperature anomalies over the contiguous United States a season in advance based on knowledge of patterns of sea surface temperature anomalies over the world ocean. One of the methods,
canonical correlation analysis (CCA), is in operational use at NOAA's National Centers
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for Environmental Prediction. Singular value decomposition analysis (SVDA) and redundancy analysis (RA) were the other methods considered in the study. Of the three
methods, SVDA is the simplest to apply in practice because it requires the least 'tuning',
and CCA is the most involved.
Fishel found that if the sea surface temperature anomalies were assumed to be perfectly
known for the season in which the surface air temperature anomalies over the United
States were being predicted and if the methods were applied to the data in an a posteriori
manner (i.e., without cross validation), CCA and RA outperformed SVDA by a substantial margin. However, when the three methods were applied in a realistic forecast setting
in which the sea surface temperature anomalies for the previous season were used to predict the surface air temperature pattern over the United States, their performance was
found to be quite comparable. For all three methods, most of the forecast skill was derived from the El Niño–related sea surface temperature anomalies over the tropical Pacific. These results suggest that the labor intensive tuning required to adapt the CCA methodology to new forecast applications may not be worth the effort. A publication based on
Fishel's work is in preparation.
ORCA: A visualization toolkit for high-dimensional data
PI: Thomas Lumley
Center postdoc: Pip Courbois
Other investigators: Dianne Cook, Nicholas Lewin-Koh and Zach Cox (Iowa State), Peter
Sutherland (Neomorphic, Inc.), and Tony Rossini (UW)
Undergraduate assistants: Renata van Dienst,, Zach Frazier
A main goal of the Orca project is to make interactive and dynamic graphics programming accessible to researchers from many different backgrounds. It arises from years of
research in statistical graphics, and takes advantage of the object-oriented nature of Java
to 'open up
the data pipeline' allowing developers greater flexibility and control over their applications. The Orca framework separates different aspects of data processing and rendering
into segments of a pipeline. New types of dynamic graphics that adhere to a few simple
Orca design
requirements can easily integrate with existing pipe sections. This integration will allow
access to sophisticated linking and dynamic interaction across all Orca view types. Orca
pipes can be called from data analysis packages such as Omegahat (an AT&T product) or
R. Considerable effort has been made to facilitate graphical tools for space and time dependent data. Dr. Courbois supervised two undergraduate students in developing interface
modules and stochastic process representations for a hematologic model.
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A paper describing the structure and development of ORCA has been published (Sutherland et al., 2000). A presentation of ORCA dynamic graphics was a main part of NRCSE
direction Guttorp’s Hunter lecture at the Environmetrics conference in Athens 1999, and
Lumley presented the latest developments at the Fourth International Chemometrics/Environmetrics Meetings in Las Vegas, 2000. The latter presentation resulted in the
publication Lumley et al. (2002). The web page for the project is linked from the Center
software page.
Semiparametric trend estimation and model selection
Center member: Peter Guttorp
Research assistant: Florentina Bunea
The partially linear regression model is a semiparametric extension of the linear regression model, in which the mean of the observations are the sum of a linear function of
some covariates and an arbitrary nonlinear function of another set of covariates. In the
trend estimation framework, this second set of covariates would be time and/or space.
This work deals with optimal estimation of the nonlinear function in the presence of
model selection for the linear part. A method has been devised allowing for adaptive estimation of the nonlinear function and simultaneous selection of variables. The method
has been applied to an analysis of the ozone level at Chicago O’Hare airport, yielding results that are quite comparable to other studies of the same data. For 1981–1985, very few
meteorological variables are needed (temperature and possibly relative humidity) to explain the ozone variation. Also, based on this 5 year period of observation, we could not
detect a rend. This work also resulted in a Ph.D. dissertation by F. Bunea entitled A Model Selection Approach to Partially Linear Regression.
Evaluating the Benefits of an Ecological Study
Center member: Jon Wakefield
Jon Wakefield has been working on a framework for ecological studies and in particular
to aid in determining the benefits of a specific study. Ecological bias is discussed with
respect to confounding, both within and between areas, and within-area variability in risk.
It is argued that more energy should be placed on such issues, rather than refining models
for spatial dependence. The paper Wakefield (2002) (NRCSE TRS 72) will appear in a
special issue of Environmental and Ecological Statistics.
Applications of Bartolucci's theorem
Center member: Julian Besag
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NRCSE funding has enabled Julian Besag to establish a fruitful collaboration with Francesco Bartolucci at the University of Perugia, Italy, and this contributed to the latter's very
recent tenure promotion. One development has been perfect block Gibbs sampling for
synergistic autologistic models and this is being incorporated into our Biometrika paper
(Bartolucci and Besag, 2002).
Fast and exact simulation of fractional Brownian motion
Center member: Tilmann Gneiting
Research assistant: Peter Craigmile
Outside collaborator: Martin Schlather
Long-memory dependence plays crucial roles in the assessment of environmental concerns such as global warming. In this context, fast and exact simulations of long-memory
processes are desirable. The best technique presently available is the Davies-Harte algorithm. Craigmile (2000) validates this algorithm for broad classes of long-memory processes. Schlather (2001) made software publicly available; Gneiting has been a consultant
on this project, which is still being developed. Gneiting and Schlather (2001) develop
new classes of long-memory processes, simulate from these processes, and suggest new
statistical tools for their analysis. Theoretical background material motivated by this project is discussed in Gneiting, Sasvári and Schlather (2001).
Temporal fallacies in biomarker based exposure inference
Center researchers: Rafael Ponce and Elaine Faustman
EPA collaborator: Anne Jarabek
UW collaborator: W. C. Griffith
Research assistant: Scott Bartell
Biomarker measurements from single time points are often used to make inferences about
longer periods of toxicant intake. However, toxicant exposures rarely, if ever, occur under steady-state conditions, and biomarkers are typically most sensitive to recent toxicant
exposures. Moreover, toxicant exposures are typically episodic and vary in magnitude
over time. While it is often believed that the error introduced by the steady-state assumption is minimal and can safely be ignored, no rationale is typically presented to support
this belief. Moreover, no guidelines have been established for determining a de minimus
error level or for estimating the degree of error potentially introduced by a fallacious temporal assumption in biomarker interpretation. A framework for evaluating the potential
magnitude of temporal fallacy error has been developed along with applications of this
framework.
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The magnitude of error depends on many factors, including the exposure frequency, exposure magnitude, exposure duration, baseline biomarker value, and exposure inference
duration. Graphical presentation of the error as a function of those factors provides insight into the design and interpretation of biomarker sampling programs. In addition,
these results can be combined with a stated de minimus error level to determine whether
or not the potential error introduced by temporal fallacy is acceptable. We developed statistical methods for evaluation of errors in special cases and simulation tools for evaluation of other cases. Application of these methods has been made for a recognized model
relating longitudinal mercury exposure to mercury blood and hair concentrations in human adults.
It was found that blood mercury biomarkers are strongly weighted towards the most recent exposures, while hair mercury biomarkers are weighted more toward previous exposures. Temporal error bias increases as the exposure duration decreases and as the exposure inference period increases, and the bias approaches zero with sufficiently long exposure. Blood mercury biomarkers appear to be superior for reflecting the most recent exposures in that they reduce the potential for bias. However, hair mercury may be superior
for measuring longer term or historic exposures. While these characteristics are already
qualitatively recognized, a statistical approach allows for optimization and adjustment
based on the goals of the exposure analysis. These results were presented at the Society
for Risk Analysis meeting in December,1999.
We are currently examining temporal error under less restrictive conditions. Statistical
methods for inference based on multiple biomarker samples are of particular interest. We
also plan to apply these methods to the analysis of biomarker data sets for mercury and
other heavy metals. We are collaborating with Anne Jarabek, NCEA, USEPA and her
colleagues on this project and have been asked by her to submit a publication for the special issues of Risk Analysis that she has organized on temporal issues for environmental
models. She participated in our NRCSE mini-workshop focused on temporal biomarker
issues Some results have been submitted in Barell et al. (2001) and were presented in
Bartell and Johnson (2002).
3.8 List of internally funded projects
P.I.
Dept
PI payroll
Title
RA's
56
Other payroll
National Research Center for
Statistics and the Environment
1997-98
Loveday
Conquest
Fish
1 mo.
Sum
Comparison of Ranked Set Sampling to Alternative Sample Designs
and Investigation of Its Usefulness
In Environmental Monitoring
N. Mode
Alison Cullen
Pub Aff
20%
Application of Bayesian and NonBayesian Methods to Development
and Assessment of Environmental
Fate and Transport and Toxicodynamic Models
S. Bates
A. Raftery S.
Bartell B.
Leroux New
Postdoc
E. David Ford
Forestry
2 mo.
Developing Methodology for AsSum. 3 sessment of Medium and Large
mo @ Scale Environmental Models
50% Spr
M. Turley
J. Reynolds
Ecological Assessment of Benthic
Populations in Estuaries and
Streams
M. Silkey
F. Bunea
D. Billheimer
Jim Karr
Fish/ Zool
1/2 mo.
Sum
Lettenmaier
Civ. Eng.
1 mo
Assessing the Hydrologic Effects of
LandUse Change in the Pacific
Northwest
L. Bowling
Joel Reynolds
Stat
6mo @
50%
Completion of meteorological adjustment of surface ozone data
from Western Washington, including an investigation of the association between adjusted ozone and
estimates of total VOC and total
NOx emissions
B. Das
Paul Sampson
Stat
1 mo
Sum
Spatio-Temporal Modeling and the
Operational Evaluation of Air Quality Models
R. Grossman
Lianne Sheppard
Biostat
None
Imputing Ambient Air Pollution Exposures over Space and Time for
use in Analyses of Health Effects
and Compliance Rules for Standards
D. Damian M. L.
Thompson
Mary Lou Thompson
Biostat
2 mo @
50%
Modelling Time Series of Multiply
Censored Data
Dennis
Gerald van Belle Env Health
None
Composite Sampling
S. Edland
1998–99
Chris Bretherton
Atm Sci
1 mo @ Global Warming and Pacific
50% Sum Northwest Snowpack
57
W. Meiring
S.
Deszoeke
National Research Center for
Statistics and the Environment
Loveday Conquest
Fish
1 mo
Sum
Ranked Set Sampling: Costs and
Applications
N. Mode
E. David Ford
Forest
1 mo
Sum
Developing Methodology for Assessing Medium and Large Scale
Environmental Models
M. Turley
Peter Guttorp
Stat
25%
Three Research Projects:
1. Nonhomogeneous covariance
estimation on the sphere
2. A
tutorial in Bayesian environmental
statistics
3. Graphical Modeling of Factors
Influencing Benthic Populations in
Streams
B. Das
F. Bunea
Patrick Heagerty
Biostat
10%
Transition Models for Categorical
Space-Time Data with Application
to Gypsy Moth Defoliation
C. Zhou
Jim Hughes
Biostat
5%
Development and Evaluation of a
Stochastic Precipitation Model
E. Bellone
Thomas Lumley
Biostat
20%
Multiple Time Scale Regression
Modelling of Air Pollution
June Morita
Stat
None
Proposal to Design and Implement
an Environmental Research Curriculum
A. Steel
Tim Nyerges
Geograph
None
Visualizing Uncertainty in Environmental Data
N. Hedley
Eun Sug Park
NRCSE
100%
Time Series Aspects of Receptor
Modeling
Mary Lou Thompson
Biostat
25%
Three Research Projects:
Gerald van Belle
Env.Heal
20%
T. Lumley
Guttorp
1. Statistical Aspects of Setting
Environmental Standards
2. Modeling Time Series of Multiply
Censored Data
3. Methods for the Adjustment of
Ozone for Meteorological Variables
David
Caccia
K. Nelson
Three Research Projects
1. Completion of Primer on the
Design of Experiments
2. Completion of Book on Statistical
Rules of Thumb
3. An Empirical Investigation of
Composite Sampling for Environmental Contaminants
RA
58
J. Reynolds
National Research Center for
Statistics and the Environment
Mike Wallace
Atm Sci
None
A Comparison of SVD and CCA
Analysis in Climate Prediction
2 5% RA M.
Fischel
Dean Billheimer
Stat
25%
PM Air Pollution
Chris Bretherton
Atm Sci
1 mo
@50%Sum
Global Warming and Pacific
Northwest Snowpack
Stat
VIGRE
TBD
Conquest
Fisheries
20%
A Comprehensive View of Ranked
Set Sampling for Ecological Research
N. Mode
Alison Cullen
Publ Aff
20%
Application of Bayesian Methods to
Development and Assessment of
Environmental Risk Assessment
Models
S. Bates
None
Temporal Fallacies in Biomarker
Based Exposure Inference
1999–00
Pip Courbeis
Loveday
Elaine Faustman Env Health
E. David Ford
Forestry
None
Peter Guttorp
Patrick Heagerty
Two Research Projects:
1. Assessing Process Based Models Using Multiple Criteria
S.
Deszoeke
S. Bartell
M. Turley
1 mo
2. A Research Strategy for Assessment of Process Based Models
Stat
10%
Nonhomogeneous Covariance Estimation on the Sphere
B. Das
Biostat
10%
Transition Models for Categorical
Space-Time Data with Application
to Gypsy Moth Defoliation
C. Zhou
59
Raftery
Programmer
Student Hrly
Sampson
National Research Center for
Statistics and the Environment
Jim Hughes
Biostat
5%
Development and Evaluation of a
Stochastic Precipitation Model
Thomas Lumley
Biostat
10%
Transition Models for Categorical
Space-Time Data with Application
to Gypsy Moth Defoliation
Stat
None
Science Truth
Eun Sug Park
NRCSE
100%
Spatio-temporal Receptor Modeling
Don Percival
APL
20%
Semiparametric and Nonparametric
Trend Estimation for Environmental
Measurements
F. Bunea
Guttorp
Adrian Raftery
Stat
20%
Application of Bayesian Methods to
Development and Assessment of
Environ mental Risk Assessment
Models
S. Bates
Cullen
Paul Sampson
Stat
15% @ 9 Spatio-Temporal Modeling and the
mo.
Operational Evaluation of Air Quality Models
S. Mitra
Guttorp
June Morita
Lianne Sheppard
Biostat
Mary Lou Thompson
Biostat
None
5%
15%
A. Steel
Guttorp Billheimer
D. Damian
1. Statistical Aspects of Setting
Environmental Standards
2. An Exposition on Partial Least
Squares Methodology in an Environmental Health Setting
3. Statistical Modelling of Multiply
Censored Data
None
Composite Sampling
20%
Compositional Receptor Modeling
1 mo
A Comprehensive View of Ranked
Set Sampling for Ecological Research
D. Caccia
K. Nelson
2000-01
Dean Billheimer
Loveday
Conquest
Stat
Guttorp
Three Research Projects:
15%
Gerald van Belle EnvHealth
Statistical Modeling of Ambient Air
Pollution in the Greater Seattle
Area for use in Analyses of Health
Effects
E. Bellone
60
N. Mode
Sampson
/Guttorp
Sampson
National Research Center for
Statistics and the Environment
E. David Ford
Forestry
1 mo
Assessing Process Based Models
Using Multiple Criteria
Elaine Faustman Env Health
None
Analysis of microchip array data to
identify gene responses underlying
diseases from environmental
expsures
Tilmann Gneiting
Stat
1 mo
Fast and exact simulation of fractional Brownian motion
Peter Guttorp
Stat
1 mo +
15%
Ray Hillborn
Fisheries
None
Decision-Making Under Uncertainty: Prioritizing Freshwater Habitat
Restoration for Salmon Recover in
the Columbia River Basin
Jim Hughes
Biostat
10%
Stochastic precipitation models
Dennis Lettenmaier
Civil Eng
1 mo
Is there a contradiction between
apparent long-term increases in the
frequency of extreme precipitation
over the conterminous U.S. and the
absence of flood trends?
Rafael Ponce
Biostat
None
Temporal Information in Biomarker
Based Expsure Inference
Adrian Raftery
Stat
None
Bayesian Analysis of Deterministic
Simu lation Models for Environmental Risk Assessment
S. Bates
Paul Sampson
Stat
35%
IMPACT Assessment of Air Quality
Trends
D. Damian
S. Mitra
Jon Wakefield
Stat
1 Qtr
Modeling multiple pollutants at multiple sites, with application to acute
respiratory studies
Julian Besag
Stat
1 mo
Applications of Bartolucci's theorem
Loveday Conquest
Fish
! mo
Further Cost Models for Ranked
Set Sampling
Buchanan3mo
Publ Aff
1 mo
+3 wks
Su02
Evaluating the Benefits of an Ecological Study
Groves
Slovakia 1
mo.
Network Design Bias
R. Komuro
25% stu- Griffith 10%
dent
1 qtr.
2 RAs
J. Brauner
T. Cardoso Guttorp 10%
Marzban
S. Bartell Griffith 5%
hourly up to
500 hrs
SUMMER 01
Alison Cullen
61
Percival
1
mo
National Research Center for
Statistics and the Environment
Tilmann Gneiting
Stat
1 mo
Space-Time Covariance Models for
Dynamic Processes
Jon Wakefield
Stat
1 mo
Evaluating the Benefits of an Ecological Study
RA for 3
mos
3.8 Visitors
It is the stated intend of the Center to have a vigorous and stimulating visitors program.
The full list of the 229 Center visitors is given here. Long-term visitors (staying at least
one weekl) are in boldface.
NAME
Arrival
Departure
Organization
Purpose
Alegria, James
98 04 20
98 04 22
Conference 4/98
Allard, Denis
Almasri, Abdullah
Arner, Stanfod
Azuma, David
Balabdoui, Fadoua
Barnwell,
Thomas
Barring, Hans
97 06 09
01 01 01
97 06 16
01 06 15
USDI Bur of Lnd Mngment
Avignon
Lund University
98 04 20
98 04 20
99 11 01
98 04 22
98 04 22
00 08 01
Conference 4/98
Conference 4/98
Visitor
98 08 12
98 08 13
USDA Forest Serv
US Forest Service
Ecole des Mines de
Paris
EPA
99 12 01
99 12 01
Visitor
Barry, Ronald
01 05 19
01 05 22
Bates, Bryson
98 05 01
7 wks
Bates, Bryson
00 04 30
00 05 28
Bates, David
Beck, Bruce
Beck, Bruce
Befort, William
98 10 19
99 03 08
99 09 06
98 04 20
98 10 22
99 03 10
99 09 10
98 04 22
Bellone, Enrica
Bengtson, Thomas
Benjay, William
Berhane, Kiros
Berman, Mark
Best, Nicky
01 06 25
01 06 25
01 06 29
01 06 29
U of AmsterdamNetherlands
Enviro. System Research Institute
CSIRO Land & Water,
Australia
CSIRO Land & Water,
Australia
UBC
U of Georgia
U of Georgia
Minn Dept of Natural
Res
NCAR
NCAR
97 01 20
98 10 19
99 03 03
01 05 19
97 01 21
98 10 22
99 03 07
01 05 22
EPA
USC
CSIRO, Australia
Imperial College
62
Visitor
Visitor
Visitor
Workshop 5/01
Visitor
Visitor
Workshop 10/98
Seminar speaker
Workshop 9/99
Conference 4/98
Conference 6/01
Conference 6/01
Workshop 1/97
Workshop 10/98
Visitor w/Stat
Workshop 5/01
National Research Center for
Statistics and the Environment
Bevilacqua, Eddie
Biemer, Paul
Bilisoly, Roger
Bird, Sandra
Biswas, Atanu
Bjørkestol,
Kirsten
Brand, Kevin
Breidt, F. Jay
Bright, Doug
01 06 25
98 04 20
98 04 21
97 06 20
01 06 25
00 08 21
01 06 29
98 04 22
98 04 23
97 08 16
01 06 29
01 06 30
98 05 20
98 04 20
97 10 28
98 05 24
98 04 22
97 10 29
Brown, Jennifer
Brown, Robert
Bruno, Francesca
Busch, David
Carr, Daniel
Carriquiry, Alicia
Cass, Glenn
Charles, Stephen
Choi, Sungwoon
01 06 25
97 01 20
02 01 07
01 06 29
97 01 21
02 06 01
98 04 20
98 04 20
98 04 20
98 10 19
98 05 01
98 04 22
98 04 22
98 04 22
98 10 22
7 wks
02 03 18
03 09 15
Choo, Louise
Chu, David
01 06 25
01 06 25
01 06 29
01 06 29
Claiborn, Candis
Clickner, Bob
Clyde, Merlise
Clyde, Merlise
Collins, Steve
98 10 19
97 11 20
98 06 09
98 09 28
01 06 25
98 10 22
97 11 23
98 06 11
99 07 15
01 06 29
Cook, Di
Cook, Di
Courbois, Pip
Cox, Larry
Cox, Larry
98 10 12
99 06 14
98 04 20
97 01 20
97 09 30
98 11 20
99 07 18
98 04 22
97 01 21
97 10 03
Cox, Larry
00 01 19
00 01 21
02 04 01
02 04 05
Ohio State University
Visitor
98 06 06
01 05 19
98 04 20
01 06 25
98 04 20
98 10 19
98 04 20
98 06 12
01 05 22
98 04 22
01 06 29
98 04 22
98 10 22
98 04 22
Iowa State
Ohio State University
US Forest Service
U of Idaho
Natl Wetlands Res Cntr
Iowa State
Iowa State
Visitor
Workshop 5/01
Conference 4/98
Conference 6/01
Conference 4/98
Workshop 10/98
Conference 4/98
Craigmile,
Peter
Cressie, Noel
Cressie, Noel
Czaplewski, Ray
Dakins, Maxine
Dale, Rassa
Daniels, Mike
Dodd, Kevin
SUNY-ESF
Res Triangle Inst.
Ohio State--interview
EPA
Indian Stat Institute
Agder University College, Norway
Iowa State
Royal Military College,
BC
U of Canterbury, NZ
EPA
Univ of Bologna, IT
US Geological Surv
George Mason U
Iowa State
CSIRO Land & Water,
Australia
Hanyang University,
Seoul
U of Bath, UK
U College of Fraser Valley, BC
Westat
Duke
Duke Univ
WV Div of Environ Protection
Iowa State
Iowa State
Oregon State U
EPA
EPA
Conference 6/01
Conference 4/98
Postdoc Appl
Visitor
Conference 6/01
Visitor
Postdoc appl
Conference 4/98
Seminar Spkr
Conference 6/01
Workshop 1/97
Visitor
Conference 4/98
Conference 4/98
Conference 4/98
Workshop 10/98
Visitor
Visitor
Conference 6/01
Conference 6/01
Workshop 10/98
Workshop 11/97
Visitor
Visitor
Conference 6/01
Visitor
Visitor
Conference 4/98
Workshop 1/97
Workshop 1/97
EPA Internal Review 1/00
63
National Research Center for
Statistics and the Environment
Dominici, Francesca
Eder, Brian
El-Shaarawi, Abdel H.
El-Shaarawi, Abdel H.
El-Shaarawi, Abdel H.
Elsaadany, Susie
Eltinge, John
Faucher, Manon
Ferson, Scott
Fink, Barry
Flatman, George
Frissell, Chris
98 11 23
98 11 25
97 01 20
97 01 20
97 01 21
97 01 23
97 11 20
97 11 23
00 01 19
00 01 22
98 04 20
98 04 20
98 04 13
99 03 01
98 04 20
97 01 20
97 01 13
98 04 22
98 04 22
98 04 14
99 03 03
98 04 22
97 01 21
97 01 15
Fuentes,
Montserrat
Fuller, Wayne
Galt, Jerry
Geissler, Paul
Gelfand, Alan
01 05 19
01 05 22
98 04 20
99 02 02
98 04 20
01 03 04
98 04 22
99 02 02
98 04 22
01 03 24
Genton, Marc
02 05 20
02 05 23
Andrew
Glasby, Chris
Goebel, Jeff
98 04 20
98 04 22
98 06 01
98 04 20
98 06 06
98 04 22
Golinelli, Daniela
Golinelli, Daniela
Goodman, Iris
Goovaerts,
Pierre
Gove, Jeffrey
Gray, Brian
Green, Peter
Green, Roger
Gregoire, Tim
01 07 05
Guenni, Lelys
Gurney, David
Guth, Dan
Hampson,
George
Handcock, Mark
Gillespie,
Johns Hopkins
Seminar Spkr
EPA
Workshop 1/97
Natl Water Research
Workshop 1/97
Inst
Natl Water Research
Workshop 11/97
Inst
Natl Water Research Internal Review 1/00
Inst
Bureau of Infect Dis
Conference 4/98
Texas A & M
Conference 4/98
UBC
Seminar Spkr
Applied Biomath, Inc.
Seminar speaker
Bureau of the Census
Conference 4/98
EPA
Workshop 1/97
Flathead Lake Bio Sta,
Workshop 1/97
Univ of MT
North Carolina State
Workshop 5/01
University
Iowa State
Conference 4/98
NOAA
Seminar Spkr
US Geological Survey
Conference 4/98
University of ConnectiVisitor
cut
North Carolina State
Seminar
University
USDA Forest Serv
Conference 4/98
Visitor
Conference 4/98
01 08 01
U of Edinburgh
Natural Res Conserv
Serv
USC
02 07 06
02 07 31
USC
Visitor
97 01 20
99 12 02
97 01 21
99 12 06
EPA
Univ of Michigan
Workshop 1/97
Visitor
98 04 20
01 06 25
98 05 17
01 06 25
98 04 20
98 04 22
01 06 29
98 05 24
01 06 29
98 04 22
Conference 4/98
Conference 6/01
Visitor
Conference 6/01
Conference 4/98
01 06 25
01 06 25
97 01 20
97 11 17
01 06 29
01 06 29
97 01 21
97 11 19
97 11 20
97 11 23
USDA Forest Serv
U of S. Carolina
U of Bristol
U of Western Ontario
Virginia Polytech &
State U
U of New Hampshire
SE Louisiana Univ.
EPA
Woods Hole Oceanographic Inst
PSU
64
Visitor
Conference 6/01
Conference 6/01
Workshop 1/97
Seminar Spkr
Workshop 11/97
National Research Center for
Statistics and the Environment
Handcock, Mark
Hansen, Mark
98 04 19
98 04 20
98 04 23
98 04 22
Penn State
N. Cntrl Forest Exper
Sta
Victoria University, New
Zealand
U of Missouri
USC
Duke University
Natl Inst of Stat Sci
Conference 4/98
Conference 4/98
Harte, David
00 08 23
00 08 28
He, Zhuqiong
Henry, Ronald
Higdon, David
Hilden-Minton,
James
Hoffman, Annette
98 04 20
00 05 31
01 05 19
97 11 03
98 04 22
00 06 01
01 05 22
97 11 05
98 05 05
98 05 06
Seminar Spkr
01 05 22
97 01 22
98 04 22
01 05 22
Wash St. Dept. Fish &
Wildlife
Lund Univ, Sweden
Lund Univ, Sweden
Vancouver, BC
Clarkson Univ
USDA NASS
Academic Sinica, Taiwan
Darmstadt Univ of Tech
USDA NASS
Ecole des Mines de
Paris
Iowa State
Iowa State
Duquesne Univ
Seoul National University
U Conn
Westat
USDA NASS
Env. Sys Res Inst.
Holst, Jan
Holst, Ulla
Hoover, Sara
Hopke, Phil
House, Carol
Huang, HsinCheng
Ickstadt, Katja
Iwig, Bill
Jamet,
Philippe
Kaiser, Mark
Kaiser, Mark
Kern, John
Kim, Ho
01 06 25
01 06 25
98 02 03
98 10 19
98 04 20
99 03 29
01 06 29
01 06 29
98 02 03
98 10 22
98 04 22
99 04 02
01 05 19
98 04 20
98 03 08
01 05 22
98 04 22
98 03 15
97 11 20
98 10 19
01 05 19
00 08 22
97 11 23
98 10 22
01 05 22
00 08 25
Kim, Hyon-Jung
Klicker, Bob
Kott, Philip
Krivoruchko,
Konstantin
Krivoruchko,
Konstantin
Lagona,
Francesco
Larsen, Phil
Lawson,
Lawrence
Le Duc, Sharon
Lesser, Virginia
Lesser, Virginia
Li, Ta-Hsin
Liggett, Walter
Linder, Ernst
01 05 19
97 01 21
98 04 20
01 05 19
01 06 25
01 06 29
Env. Sys Res Inst.
Conference 6/01
01 06 25
01 06 29
Univ. Roma Tre, Italy
Conference 6/01
98 04 19
01 06 25
98 04 23
01 06 29
EPA
U of Pittsburgh
Conference 4/98
Conference 6/01
97 01 20
98 04 19
01 06 25
99 06 30
98 04 20
01 02 15
97 01 21
98 04 22
01 06 29
99 07 02
98 04 22
01 06 15
Workshop 1/97
Conference 4/98
Conference 6/01
Seminar speaker
Conference 4/98
Visitor
01 06 25
01 06 29
EPA
Oregon State U
Oregon State U
IBM-Matson Res. Centr
Oregon State U
University of New
Hampshire
Lund Univ, Sweden
Lindstrom,
Torgny
65
Visitor
Conference 4/98
Visitor
Workshop 5/01
Seminar Spkr
Conference 6/01
Conference 6/01
Seminar Spkr
Workshop 10/98
Conference 4/98
Visitor
Workshop 5/01
Conference 4/98
Visitor
Workshop 11/97
Workshop 10/98
Workshop 5/01
Visitor
Workshop 5/01
Workshop 1/97
Conference 4/98
Workshop 5/01
Conference 6/01
National Research Center for
Statistics and the Environment
Lophaven, Søren
MacNab, Ying
Malmberg, Anders
Marcus, Allan
Marker, David
Marker, David
Marker, David
Marker, David
Marker, David
Marzban, Caren
01 06 25
01 06 25
01 06 25
01 06 29
01 06 29
01 06 29
Tech U of Denmark
UBC
Lund Univ, Sweden
98 09 21
97 01 21
97 07 16
98 04 20
98 09 16
00 05 02
00 09 01
98 12 20
97 01 22
97 07 18
98 04 22
98 09 17
00 05 05
02 09 01
Matthews, Robin
McBride,
Sandra
McDonald,
Lyman
McRoberts, Ron
97 12 09
01 06 25
97 12 09
01 06 29
EPA
Visitor
Westat
Workshop 1/97
Westat Visitor
Westat Conference 4/98
Westat
Visitor
Westat
Visitor
Natl. Severe Storms
Visitor
Lab
WWU
Seminar Spkr
Duke Univ
Conference 6/01
98 04 20
98 04 22
WEST, Inc.
Conference 4/98
98 04 20
98 04 22
Conference 4/98
Meiring, Wendy
Meiring, Wendy
Meiring, Wendy
Meiring, Wendy
Miller, Stephen
Mohapl, Jaroslav
Moisen, Gretchen
Monestiez, Pascal
Moriarty, Tim
MunozHernandez, Breda
Murtaugh, Paul
Nair, Gopalan
97 07 07
98 01 21
99 09 07
00 08 20
98 04 20
97 05 08
98 04 20
98 07 01
97 08 06
98 01 27
99 09 10
00 08 24
98 04 22
97 05 09
98 04 22
98 07 07
N. Cntrl Forest Exper
Sta
NCAR
NCAR
UC Santa Barbara
UC Santa Barbara
Bureau of Labor Stat
Interview for Post doc
USDA Forest Serv
Avignon
98 04 20
98 04 20
98 04 22
98 04 22
Bureau of Indian Affairs
Oregon State U
Conference 4/98
Conference 4/98
00 09 01
01 06 25
01 07 15
01 06 29
Visitor
Conference 6/01
Nirel, Ronit
99 07 13
99 08 30
Nirel, Ronit
Nott, David
Nussbaum, Barry
Nusser, Sarah
Nychka, Doug
Nychka, Doug
Nychka, Doug
O'Hagan, Anthony
O'Hagan, Tony
00 07 01
98 06 02
97 01 20
98 04 20
99 06 21
01 05 19
01 06 25
99 09 06
00 08 15
98 06 02
97 01 21
98 04 22
99 06 22
01 05 22
01 06 29
99 09 10
97 05 06
97 05 06
Oehlert, Gary
97 11 20
98 11 23
Oregon State University
Curtin U of Tech, Australia
Hebrew Univ of Jerusalem
Hebrew University
Univ of NS Wales
EPA
Iowa State
NCAR
UCAR
NCAR
School of Math & Sci,
UK
School of Math & Sci,
UK
U of Minnesota
66
Conference 6/01
Conference 6/01
Conference 6/01
Visitor
Visitor
Workshop 9/99
Visitor
Conference 4/98
Applicant
Conference 4/98
Visitor
Visitor
Visitor
Visitor
Workshop 1/97
Conference 4/98
Visitor
Workshop 5/01
Conference 6/01
Workshop 9/99
Seminar
Workshop 11/97
National Research Center for
Statistics and the Environment
Olsen, Tony
Olsen, Tony
Oosterbaan, Jasha
Oosterbaan, Jasha
Opsomer, Jean
Oreskes, Naomi
Otto, Mark
Paciorek, Christopher
Park, Eun Sug
Park, Eun Sug
Peng, Liang
Perrin, Oliver
Workshop 1/97
Conference 4/98
Visitor
00 08 24
EPA
EPA
Ecole des Mines de
Paris
Ecole des Mines
98 04 20
99 09 07
98 04 20
01 06 25
98 04 22
99 09 10
98 04 22
01 06 29
Iowa State
UC San Diego
US Fish & Wildlife
Carnegie Mellon U
Conference 4/98
Workshop 9/99
Conference 4/98
Conference 6/01
98 06 11
98 10 18
01 06 25
00 08 17
98 06 13
98 10 22
01 06 29
00 09 01
Postdoc appl
Workshop 10/98
Conference 6/01
Visitor
Michael
Pollak, Moshe
98 06 01
98 08 06
Texas A & M
Texas A & M
Georgia Inst of Tech
University of Toulouse,
France
Chapman Univ
99 10 10
99 10 13
Shared visitor w/Stat
Pontius, Jeffrey
Pope, C. Arden
Preisler,
Haiganoush
Pye, John
Rashid, Sammy
Rathbun,
Stephen
Reams, Gregory
Reynolds, Joel
Rigdon, Steveen
Ritter, Kerry
Ritter, Kerry
Rotmans, Jan
98 04 20
98 10 19
98 04 20
98 04 22
98 10 22
98 04 22
Hebrew Univ of Jerusalem
Kansas State U
Brigham Young
98 04 20
01 06 25
97 11 20
98 04 22
01 06 29
97 11 23
USDA Forest Serv
U of Sheffield
U of Georgia
Conference 4/98
Conference 6/01
Workshop 11/97
98 04 20
99 09 07
01 06 25
98 04 20
01 06 25
99 09 07
98 04 22
99 09 10
01 06 29
98 04 22
01 06 29
99 09 10
Conference 4/98
Workshop 9/99
Conference 6/01
Conference 4/98
Conference 6/01
Workshop 9/99
Rykiel, Ed
Saint, Chris
Saltelli, Andrea
99 02 23
97 01 20
99 09 07
99 02 24
97 01 21
99 09 10
Sanso, Bruno
00 07 08
00 08 10
Schmidt, Alexandra
Schreuder, Hans
Schreuder, Hans
Scott, Charles
Sedransk, Joe
Sedransk, Joe
Seibel, John
00 08 17
00 08 25
USDA Forest Serv
Fish & Wildlife
S. Illinois U
Oregon State U
S. Calif CWRP
Maastricht Univ, Netherlands
WSU-Tri=Cities
EPA
Joint Research Cntr,
Italy
Universidad Simon Bolivar
University of Sheffield
97 11 20
98 04 20
98 04 20
97 11 20
98 04 20
98 04 18
97 11 23
98 04 22
98 04 22
98 11 23
98 04 22
98 04 23
US Forest Service
US Forest Service
USDA Forest Serv
Case Western
Case Western
PBS&J
Phelan,
97 01 20
98 04 19
98 03 08
97 01 21
98 04 23
98 03 29
00 08 19
67
Visitor
Visitor
Conference 4/98
Workshop 10/98
Conference 4/98
Seminar speaker
Workshop 1/97
Workshop 9/99
Visitor
Visitor
Workshop 11/97
Conference 4/98
Conference 4/98
Workshop 11/97
Conference 4/98
Conference 4/98
National Research Center for
Statistics and the Environment
Setzer, Woody
Shaddick, Gavin
97 01 20
00 11 01
97 01 21
00 12 01
Sinha, Bimal
Smith, Eric
99 09 29
98 04 20
99 10 03
98 04 22
Smith, Eric
Smith, Eric
Smith, Graham
Smith, Martha
Smith, Richard
Smith, Robert
Smith, Steve
Smith, William
Smythe, Robert
Sørensen, Per
98 10 17
99 06 11
98 04 20
01 06 25
01 06 25
01 06 25
98 04 28
98 04 20
01 06 25
98 09 01
98 10 23
99 08 09
98 04 22
01 06 29
01 06 29
01 06 29
98 04 29
98 04 22
01 06 29
98 11 01
Spiegelman, Cliff
Stanner, David
Stehman, Steve
Stein, Alfred
99 10 20
99 09 07
98 04 20
00 05 17
99 10 22
99 09 10
98 04 22
00 05 19
Stein, Michael
Stevens, Donald
Stokes, Lynne
Streett, Sarah
Switzer, Paul
Switzer, Paul
Switzer, Paul
Switzer, Paul
Tahsoh, Joseph
Tassone, Eric
Tebaldi, Claudia
Thalib, Lukman
Thiebaux, Jean
Thompson, Dean
Thurston, George
Tøgersen A,
Frede
Urquhart, N.Scott
Usner, Dale
Van Deusen, Paul
van Storch, Hans
Vega, Silvia
Ventura, Valerie
Ver Hoef, Jay
01 05 19
98 04 20
98 04 20
01 06 25
97 01 20
98 11 20
00 01 19
01 06 25
01 06 25
01 06 25
00 04 09
01 06 25
01 05 19
98 04 20
98 11 19
00 03 01
01 05 22
98 04 22
98 04 22
01 06 29
97 01 21
98 11 22
00 01 20
01 06 29
01 06 29
01 06 29
00 05 31
01 06 29
01 05 22
98 04 22
98 11 22
00 07 01
98 04 20
98 04 20
98 04 20
00 01 06
01 05 19
01 06 25
01 05 19
98 04 22
98 04 22
98 04 22
00 01 07
01 05 22
01 06 29
01 05 22
EPA
Imperial College School
of Medicine
Univ of Maryland
Virginia Polytech &
State U
Virginia Tech
Virginia Tech
US Fish & Wildlife
U of Texas
U of N. Carolina
Workshop 1/97
Visitor
Visitor
Conference 4/98
Visitor
Visitor
Conference 4/98
Conference 6/01
Conference 6/01
Conference 6/01
Visitor
Conference 4/98
Conference 6/01
Visitor
NOAA
USDA Forest Serv
Oregon State U
Inst. For Water EnvDenmark
Texas A & M
Visitor
Denmark
Workshop 9/99
SUNY
Conference 4/98
Waginengen University,
Visitor
The Netherlands
U of Chicago
Workshop 5/01
Dynamic, Inc
Conference 4/98
U of Texas
Conference 4/98
NCAR
Conference 6/01
Stanford
Workshop 1/97
Stanford
Workshop 11/98
Stanford Internal Review 1/00
Stanford U
Conference 6/01
Alabama A & M
Conference 6/01
Emory
Conference 6/01
NCAR
Visitor
Kuwait Univ
Conference 6/01
NOAA
Workshop 5/01
Iowa State
Conference 4/98
NYU
Workshop 10/98
Danish Inst. Of Agricul.
Visitor
Research
Oregon State U
Conference 4/98
Oregon State U
Conference 4/98
Tufts U
Conference 4/98
GKSS, Germany
Visitor
Insightful Corporation
Workshop 5/01
Carnegie Mellon
Conference 6/01
Alaska Dept of Fish and
Workshop 5/01
Game
68
National Research Center for
Statistics and the Environment
Vere-Jones, David
Warren, John
Welty, Leah
White, Denis
Whittemore, Ray
Wikle, Chris
Wikle, Chris
00 08 23
00 08 28
97 01 20
01 06 25
97 11 20
99 09 06
98 04 20
01 05 19
97 01 21
01 06 29
98 11 22
99 09 10
98 04 22
01 05 22
Michael
Winters, Franklin
Wolpert, Robert
Wright, Bill
98 04 20
98 04 22
98 04 20
01 05 19
98 04 07
98 04 22
01 05 22
98 04 07
Yang, Yuhong
Yap, Christina
York, Jeremy
Zhai, Jun
Zhang, Lianjun
Zidek, Jim
Zimmerman, Dale
Zwiers, Francis
98 04 20
01 06 25
99 01 26
97 05 05
01 06 25
97 05 06
97 11 20
97 04 08
98 04 22
01 06 29
99 01 26
97 05 06
01 06 29
97 05 06
98 11 23
97 04 09
Williams,
Victoria University, New
Zealand
EPA
U of Chicago
Oregon State U
Tufts Univ
NCAR
University of
Missouri-Columbia
USDA Forest Serv
Bureau of the Census
Duke U
Montgomery Watson
Americas
Iowa State
U of Glasgow, UK
Cartia, Inc
Interview for Post doc
SUNY ESF
UBC-Seminar panel
Univ of Iowa
Canadian Cntr for Climate Modelling
Visitor
Workshop 1/97
Conference 6/01
Workshop 11/97
Workshop 9/99
Conference 4/98
Workshop 5/01
Conference 4/98
Conference 4/98
Workshop 5/01
Visitor
Conference 4/98
Conference 6/01
Seminar speaker
Applicant
Conference 6/01
Seminar
Workshop 11/97
Seminar Spkr
3.10 Students
The following table describes all graduate students who have received more than one
quarter of support from NRCSE’s EPA funding, as well as their educational outcomes.
Degree quarters in parentheses are anticipated degree quarters.
NAME
APPOINTMENT
DATE
DEPT
RA
SUPERVISOR
DE QUAR- TITLE
GR TER
EE
BALABDAOUI, 00 09 16
Fadouah
Stat
Guttorp/
Sampson
PhD (SP03)
BATES, Samantha
97 09 16
Stat
Raftery
PhD SU01
Bayesian Inference for Deterministic
Simulation Models for Environmental
Assessment.
BELLONE,
Enrica
97 03 16
Stat
Hughes/
Guttorp
PhD SU00
Nonhomogeneous hidden Markov
models for downscaling synopticatmospheric patterns to precipitation
amounts
69
National Research Center for
Statistics and the Environment
BOWLING,
Laura
97 02 01
BRAUNER,
Jodie
00 06 16
Civil Eng Lettenmaier
QERM
Hilborn
Fisheries
BUCHANAN,
Rebecca
BUNEA,
Florentina
01 06 16
97 09 16
QERM
Stat
MS
SU97
MS
(WI03)
Evaluation of the effects of forest
roads on streamflow in Hard and
Ware Creeks, Washington
PhD (SU03)
Conquest
MS
AU02
Conquest
PhD (SP04)
Richardson
PhD SU00
Non-thesis
A model selection approach to partially linear regression
CACCIA, David 97 12 16
QERM
Thompson /
Sampson
CARDOSO,
Tamre
98 03 16
QERM
Guttorp
PhD (SU03) A hierarchical Bayes Model for combining precipitation measurements
from different sources
CRAIGMILE,
Peter
00 03 16
Stat
Guttorp
PhD AU00
Wavelet-Based Estimation for Trend
Contaminated Long Memory Process
Biostat
Sampson
/Guttorp
PhD SU02
A Bayesian approach to estimating
heterogeneous spatial covariances.
Stat
Guttorp/
Sampson
PhD SU00
Global covariance modeling: a deformation approach to anisotropy
DAMIAN, Doris 97 06 16
DAS, Barnali
97 09 16
DESZOEKE,
Simon
98 08 16
Left the program
Atmos Sci Bretherton PhD (SU03) Large-eddy simulation of boundary
layer clouds and convection
FISHEL, Mary 98 11 01
Atmos
Wallace
MS
SU99
A comparison of statistical methods
used to predict U.S. temperatures
from sea surface temperatures
FREEMAN,
Elizabeth
96 12 16
QERM
Ford
MS
AU97
The Effects of Data Quality on Spatial
Statistics
GOLINELLI,
Daniella
00 06 16
Stat
Guttorp
PhD SU00
Geog
Nyerges
PhD (WI03)
HEDLEY, Nick 98 09 16
KOMURO, Rie 00 06 16
App Math Ford
Bayesian inference in hidden stochastic population processes
PhD (SU03) Using a Pareto optimization algorithm
with model assessment criteria to
improve a model's structure by investigating parameter and criteria uncer-
70
National Research Center for
Statistics and the Environment
tainty
MODE, Nicolle 97 06 16
QERM
NELSON, Ker- 98 10 01
rie
Stat
NOTHSTEIN,
Greg
97 06 16
OU, San-San
00 09 16
Biostat
RYDING, Kris
98 06 16
QERM
Conquest
PhC
Left the program
Thompson PhD SU02
Env Health Van Belle
MS
SP98
Sampson/
Guttorp
MS
(SP03)
Guttorp
MS
AU98
Estimation in Generalized Linear
Mixed Models: Comparison of
maximum likelihood with iterative
bias correction
Public willingness to pay for improvements in visibility and air quality
Analyzing adult returns to assess
ocean effects and salmon survival
relationships
PhD AU02
Estimation of demographic parameters used in assessing wildlife population trends
STEEL, E. Ash- 98 09 16
ley
QERM
Guttorp
PhD SP99
In-stream factors affecting juvenile
chinook salmon migration
SILKEY, Mari- 96 10 01
abeth
Stat
Guttorp
MS
AU97
Evaluating a stochastic model of the
benthic macro-invertebrate population of Delaware Bay, Delaware
SULLIVAN,
Erin
Stat
Guttorp
MS
SU00
Estimating the Association Between
Ambient Particulate Matter and Elderly Mortality in Phoenix and Seattle
Using Bayesian Model Averaging
99 06 16
TURLEY, Mari- 97 06 16
anne
QERM
Ford
PhD AU00
Investigating alternative ecological
theories using multiple criteria assessment with evolutionary computation
ZHOU, Chuan 98 10 01
Biostat
Heagerty
MS
Non-thesis
SP00
PhD (W04)
3.11 Research products
Over the EPA-funded period, Center members and visitors published six books and 138
scientific papers. Seven papers are currently under review. The most common journals for
publishing NRCSE research has, not surprisingly, been Environmetrics, with 11 papers,
followed by Journal of the American Statistical Association (7), Environmental and Eco71
National Research Center for
Statistics and the Environment
logucal Statistics (5), Epidemiology (4) and Ecology (4) . Papers were published in 55
different scientific journals, illustrating the cross-disciplinary nature of the Center.
NRCSE members produced 11 scientific entries in the Encyclopedia of Environmetrics,
published by Wiley.
Books
Cullen, A. and Frey, H. (1998): Probabilistic Techniques in Exposure Assessment: A
Handbook for Dealing With Variability and Uncertainty in Models and Inputs,
New York: Plenum.
Ford, E. D. (2000): Scientific Method for Ecological Research. Cambridge, U.K.: Cambridge University Press.
Jankowski, P. and Nyerges, T. (2001) Geographic Information Systems for Group Decision Making London: Taylor & Francis.
Kelsey, K., Steel A. E. and Morita, J. (2002): The Truth About Science: A Curriculum for
Developing Young Scientists. Arlington: NSTA Press.
Percival, D. B. and A. T. Walden (2000): Wavelet Methods for Time Series Analysis.
Cambridge, U.K.: Cambridge University Press.
van Belle, G. (2002): Statistical Rules of Thumb. New York: Wiley Interscience.
Papers
Assunção, R.and P. Guttorp (1999): Robustness for Inhomogeneous Poisson Point Processes, Annals of the Institute of Statistical Mathematics, 51: 657–678.
Arnold, R. A., I. Diamond, and J. C. Wakefield (2000): Population denominator data. In
Spatial Epidemiology. Elliott, P., Wakefield, J.C., Best, N.G. and Briggs, D. (editors), Oxford University Press.
Aylin, P., Maheswaran R., J. Wakefield, S. Cockings, L. Jarup, R. Arnold, G. Wheeler, P.
Elliott (1999): A national facility for small area disease mapping and rapid initial
assessment of apparent disease clusters around a point source: the UK Small Area
Health Statistics Unit. Journal of Public Health Medicine 21: 289–98.
Bartell, S. M. and E. M. Faustman (1998): Comments on “An approach for modeling
noncancer dose responses with an emphasis on uncertainty” and “A probabilistic
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framework for the reference dose(probabilistic R&D).” Risk Analysis 18(6): 663664.
Bartell, S. M., T. K. Takaro, R. A. Ponce, J. P. Hill, E. M. Faustman, and G. S. Omenn
(1999): Risk assessment and screening strategies for beryllium exposure. Technology 7 241–249.
Bartell, S.M., R. Ponce, R. Sanga and E. Faustman (2000): Human Variability in Mercury
Toxicokinetics and Steady State Biomarker Ratios. Environmental Research Section A 84: 127–132.
Bartell, S. M., Ponce, R. A., Takaro, T. K., R. O. Zerbe, G. S. Omenn, and E. M. Faustman (2000). Risk estimation and value-of-information analyses for three proposed
genetic screening programs for chronic beryllium disease prevention. Risk Analysi
20: 87–99.
Bartell, S. M., T. K. Takaro, R. A. Ponce, J. Hill, E. M. Faustman, and G. S. Omenn
(2000): Risk assessment and screening strategies for beryllium exposure. Environment International, In press.
Bartell, S., Griffith, W.C. and. Faustman, E.M (2001) Temporal fallacy in biomarker
based average exposure inference. Submitted to Journal of Exposure Analysis and
Environmental Epidemiology.
Bartell S. M. and Johnson W. O. (2002): Statistical methods for non-steady state exposure estimation using biomarkers. Epidemiology 13: 228.
Bartolucci, F. and Besag, J.E. (2002). A recursive algorithm for Markov random fields.
Tentatively accepted by Biometrika.
Bates, S.C., Cullen, A.C. & Raftery, A.E. (2003) Bayesian Uncertainty Assessment in
Multicompartment Deterministic Simulation Models for Environmental Risk Assessment. To appear, Environmetrics.
Bates, S.C. & Raftery, A.E. (2001) An Efficient Markov Chain Monte Carlo Proposal
Distribution for Ridgelike Target Distributions Using Nearest Neighbors. Submitted to Journal of Computational and Graphical Statistics.
Bellone, E., J. P. Hughes and P. Guttorp (2000): A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Climate Research 15:
1–12.
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Besag, J.E. (2001) Invited discussion of ``Conditionally specified distributions'', by Arnold, Castillo and Sarabia. Statistical Science 16: 265–267.:
Besag, J.E. (2002) Likelihood analysis of binary data in space and time. Volume edited by
P.J. Green, N. Hjort and S. Richardson. In press.
Besag, J. and D. Higdon (1999): Bayesian Inference for Agricultural Field Experiments.
Journal of the Royal Statistical Society B, 61, 691-746.
Best, N. G. and J. C. Wakefield (1999): Accounting for inaccuracies in population counts
and case registration in cancer mapping studies. Journal of the Royal Statistical
Society, Series A 162: 363–382.
Billheimer, D. (2001a): Compositional Receptor Modeling. Environmetrics 12: 451–467.
Billheimer, D. (2001b): Space-time modeling of compositional data. In A. El-Shaarawi
and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London: Wiley.
Billheimer, D, Cardoso T., E. Freeman, P. Guttorp, H.-W. Ko and M. Silkey (1997): Natural variability of benthic species in the Delaware Bay. Environmental and Ecological Statistics 4: 95-115.
Billheimer, D., Guttorp, P. and Fagan, W. F. (2001): Statistical Interpretation of Species
Composition. Journal of the American Statistical Association 96: 1205–1214.
Bowling, L. C., P. Storck and D. P. Lettenmaier (2000): Hydrologic effects of logging in
Western Washington. Water Resources Research 36: 3223–3240.
Brillinger, D. R., P. Guttorp and R. P. Schoenberg (2001): Point process, temporal. In A.
El-Shaarawi and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London:
Wiley.
Brauer, M., F. Hruba, E. Mihalikova, E. Fabianova, P. Miskovic, A. Plzikova, M.
Lendacka, J. Vandenberg and A. Cullen (2000): Personal exposure to particles in
Banska Bystrica, Slovakia. Journal of Exposure Analysis and Environmental Epidemiology 10: 478–487.
Bunea, F. and J. Besag (2000): MCMC for contingency tables. In N. Madras (ed.): Monte Carlo Methods: 25–36. Fields Institute Communications. Providence, RI:
American Mathematics Sociaety.
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Charles, S. P., B. C. Bates and J. P. Hughes (1999a): A spatio-temporal model for
downscaling precipitation occurrence and amounts. Journal of Geophysical Research–Atmospheres 104: 31657–31669.
Charles, S. P. B. C. Bates, P. H. Whetton, and J. P. Hughes (1999b): Validation of
downscaling models for changed climate conditions in southwestern Australia.
Climate Research 12: 1-14.
Charles, S. P., B. C. Bates and J. P. Hughes (2000): Statistical Downscaling from Numerical Climate Models for Southwest Australia. Proc. 3rd International Conference
on Water Research and Environmental Research, the Institution of Engineers,
Australia.
Clyde, M., P. Guttorp and E. Sullivan (2000): Effects of ambient fine and coarse particles
on mortality in Phoenix, Arizona. Submitted to Journal of Exposure and Environmental Epidemiology.
Conquest, L. (2000): Environmental monitoring: investigating associations and trends.
Statistics in Ecotoxicology, John Wiley & Sons, 179-210.
Conquest, (L.2002): Biomonitoring. In A.H. El-Shaarawi and W.W. Piegorsch: Encyclopedia of Environmetrics. John Wiley & Sons, Ltd., Chichester.Vol. 1, 199–205.
Cox, Lawrence H., Peter Guttorp, Paul D. Sampson, David C. Caccia and Mary Lou
Thompson (1998) A Preliminary Statistical Examination of the Effects of Uncertainty and Variability on Environmental Regulatory Criteria for Ozone . In Environmental Statistics: Analyzing Data for Environmental Policy. Novartis Foundation.
Chichester: John Wiley & Sons, Ltd. 122–143.
Craigmile, P. F. (2002) Simulating a class of stationary Gaussian processes using the Davies-Harte algorithm, with application to long memory processes.To appear,
Journal of Time Series Analysis.
Craigmile, P. F., D. B. Percival, and P. Guttorp (2000): Wavelet-based parameter estimation for trend contaminated fractionally differenced processes. Submitted to Journal of Time Series Analysis.
Craigmile, P. F. and D. B. Percival (2001): Wavelet-based trend detection and estimation. In A. El-Shaarawi and W. Piegorsch (eds.): Encyclopedia of Environmetrics.
London: Wiley.
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Cullen, A. (1999): Addressing Uncertainty–Lessons from Exposure Analysis. Inhalation
Toxicology 11:603-610.
Cullen, A. C., P. Guttorp and R. L. Smith (2000): EDITORIAL: Special issue on statistical analysis of particulate matter air pollution data. Environmetrics 11: 609–610.
Damian, D., Sampson, P. D. and P. Guttorp (2002): Variance modeling for nonstationary spatial temporal processes. Under revision for Journal of Geophysical
Research.
Damian, D., Sampson, P. D. and P. Guttorp (2001): Bayesian Estimation of NonStationary Semi-Parametric Spatial Covariance Structures. Environmetrics 12:
161–178.
Diggle, P. J., Morris, S. E., and J. C. Wakefield (2000): The analysis of matched casecontrol studies in spatial epidemiology. Biostatistics 1: 89–105.
Doberstein, C. P., J. R. Karr, L. L. Conquest (2000): The effect of fixed-count subsampling on macroinvertebrate biomonitoring in small streams. Freshwater Biology
44: 1–17.
Elliott, P., Arnold, R., S. Cockings, N. Eaton, L. Jarup, J. Jones, M. Quinn, M. Rosato, I.
Thornton, M. Toledano, E. Tristan and J. Wakefield (2000): Risk of mortality,
cancer incidence and stroke in a population potentially exposed to cadmium. Occupational and Environmental Medicine 57: 94–97.
Elliott, P., Wakefield, J. C. , N. G. Best, and D. Briggs (2000): Spatial Epidemiology:
methods and applications. In Spatial Epidemiology. Elliott, P., Wakefield, J.C.,
Best, N.G. and Briggs, D. (editors), Oxford University Press.
Elliott P. and J. C. Wakefield (2000): Bias and confounding in small-area studies. In
Spatial Epidemiology. Elliott, P., Wakefield, J.C., Best, N.G. and Briggs, D. (editors), Oxford University Press.
Faustman, E. M. (1999): Implications of research for remediation technology design.
Risk Excellence Notes 1(9): 9.
Faustman, E. M. and S.M. Bartell (1997): Review of noncancer risk assessment: Applications of benchmark dose methods. Human and Ecological Risk Assessment
3(5): 893-920.
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Faustman, E. M., T. A. Lewandowski, R. A. Ponce, and S. M. Bartell (1999): Biologically based dose-response models for developmental toxicants: Lessons from
methylmercury. Inhalation Toxicology 11(6): 559-572.
Faustman, E. M. S. M. Silbernagel, R. A. Ponce, T. Burbacher and R. Fenske (2000):
Mechanisms underlying children’s susceptibility to environmental toxicants. Environmental Health Perspectives 108: 13–21.
Ford, E.D., M. Turley and J. Reynolds (2000): Users Manual: the Pareto optimal model
assessment cycle using evolutionary computation.
Freeman, E. A. and E. D. Ford (2002). Effects of data quality on analysis of ecological
pattern using the K(d) statistical function. Ecology 83: 35–46.
Gertler, N. and A. C. Cullen (2000): Effects of a Transient Cancer Scare on Property
Values: Implications for Risk Valuation and the Value of Life. Human and Ecological Risk Assessment. 6: 731–745.
Gneiting, T. (1999a): Correlation functions for atmospheric data analysis. Quarterly
Journal of the Royal Meteorological Society 125: 2449-2464.
Gneiting, T. (1999b): Isotropic correlation functions on d-dimensional balls. Advances
in Applied Probability 31: 625–631.
Gneiting, T. (1999c): The correlation bias for two-dimensional simulations by turning
bands. Mathematical Geology 31: 95-211.
Gneiting T. (2000a): Criteria of Pólya type for radial positive-definite functions. To appear in Proceedings of the American Mathematical Society 128: 1721–1728.
Gneiting, T. (2000b): Power-law correlations, related models for long-range dependence,
and their simulation. Journal of Applied Probability 37: 1104–1109.
Gneiting, T. (2000c): Addendum to “Isotropic correlation functions on d-dimensional
ball”. Advances in Applied Probability 32: 960–961.
Gneiting T. (2002): Nonseparable, stationary covariance functions for space-time data.
Journal of the American Statistical Association 97: 590–600.
Gneiting, T. (2002): Compactly supported correlation functions. Journal of Multivariate
Analysis, in press.
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Gneiting, T. and Z. Sasvari (1999): The characterization problem for isotropic covariance
functions. Mathematical Geology 31: 105-111.
Gneiting, T., Sasvári, Z. and Schlather, M. (2000) Analogies and correspondences between variograms and covariance functions. Advances in Applied Probability 33:
617–630.
Gneiting, T. and M. Schlather (2001a): Space-time covariance models.In A. El-Shaarawi
and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London: Wiley.
Gneiting, T. and Schlather, M (2001b) Stochastic models which separate fractal dimension and Hurst effect”, submitted to SIAM Review.
Gove, N.E., R.T. Edwards, L.L. Conquest (2001). Effects of scale on land use and water
quality relationships: a longitudinal basin-wide perspective. ¬Journal of the American Water Resources Association, 37: 1721–1734
Guttorp, P. (2000): Environmental Statistics. Journal of the American Statistical Association 95: 289–292.
Guttorp, P., Meiring, W., and P.D. Sampson (1997): Contribution to discussion of R.J.
Carroll, R. Chen, T.H. Li, H.J. Newton, H. Schmiediche, N. Wang and E.I.
George (1997): Trends in ozone exposure in Harris County, Texas. Journal of the
American Statistical Association 92: 405-408.
Guttorp, P., D. R. Brillinger and R. P. Schoenberg (2001): Point process, spatial. In A. ElShaarawi and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London:
Wiley.
Heagerty, P. J. and T: Lumley (2000): Window subsampling of estimating functions
with application to regression models. Journal of the American Statistical Association 95: 197–211.
Hedley, N. R. and B. D. Campbell (1998). Collaborative GeoScientific Visualization Project Final Report. Human Interface Technology Laboratory Technical Report R99-3). Seattle: Human Interface Technology Lab.
Hedley, N. R. (1999): Uncertainty in Environmental Research: Beyond Conceptual Difficulties and Synthetic Frameworks. Submitted to Journal of Risk and Uncertainty.
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Hedley, N. R., C. H. Drew , E. A. Arfin, and A. Lee (1999): Hagerstrand Revisited: Interactive Space-Time Visualizations of Environmental Data. Informatica 23: 155–
168.
Henry, R. C., E. S. Park, and C. H. Spiegelman (1999): Comparing a new algorithm with
the classic methods for estimating the number of factors. Chemometrics and Intelligent Laboratory Systems 48, 91-97.
Henry RC, Chang YS, Spiegelman CH (2002): Locating nearby sources of air pollution
by nonparametric regression of atmospheric concentrations on wind direction.
Atmospheric Environment 3: 2237-2244.
Hruba F., Fabianova E., Koppova K., Vandenberg J. (2001) Childhood respiratory symptoms, hospital admissions and long-term exposure to particulate matter. Journal of
Exposure Analysis and Environmental Epidemiology; 11:33–40.
Hughes JP, Guttorp P, Charles SP (1999) A nonhomogeneous hidden Markov model for
precipitation. J. Royal Stat. Soc., Series C 48: 15–20.
Kang, S.H. and E.S. Park (2000): The actual size of the chi-squared and the likelihood
ratio test of independence in a contingency table. Submitted to Journal of Statistical Computation and Simulation.
Kelsall, J. E., Morris, S. E. and J. C. Wakefield (2000): Disease surveillance and cluster
detection. In Spatial Epidemiology. Elliott, P., Wakefield, J.C., Best, N.G. and
Briggs, D. (editors), Oxford University Press.
Kirkland, L., Hoffmeyer, D., Allender, H., L. Zaragoza, J. LaVeck, T. Barnwell, J. Fowle,
J. Rowe and D Ford (1988): Science Policy Council Model Acceptance and Peer
Review White Paper Working Group. White Paper on the Nature and Scope of Issues on Adoption of Model Use Acceptability Guidance. Environmental Protection
Agency Science Policy Council. Available at
http://epa.gov/osp/crem/documents/whitepaper.pdf
Knorr-Held, L. and Besag, J. (1998): Modelling risk from a disease in time and space.
Statistics in Medicine 17: 2045-2060.
Levy, D. , Lumley, T. , L. Sheppard, J. Kaufman, H. Checkoway (2001a): Referent selection in case-crossover analyses of health effects of air pollution. Epidemiology
12: 186–192
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Levy, D. , Sheppard, L., Checkoway, H., Kaufman, J., Lumley, T. , Koenig, J. and Siscovick, D. (2001b) A case-crossover analysis of particulate matter air pollution
and out-of-hospital primary cardiac arrest. Epidemiology, 12:193-199.
Lewandowski, TA, Bartell, SM, Pierce, CH, Ponce, RA, and Faustman, EM (1998a) Toxicokinetic and toxicodynamic modeling of the effects of methylmercury on the fetal rat [abs.]. The Toxicologist, 42(1-S), No. 683.
Lewandowski, TA, Bartell, SM, Pierce, CH, Ponce, RA, and Faustman, EM (1998b) Effect of tissue binding uncertainty on a PBTK model of methylmercury in the fetal
rat [abs.]. Toxicological Letters, 95(Suppl. 1), No. P2F148.
Lumley, T. and D. Levy (1999): Bias in the Case--Crossover Design: Implications for
Studies of Air Pollution. Environmetrics 11: 689–704.
Lumley, T. and L. Sheppard(1999): Assessing Seasonal Confounding and Model Selection Bias in Air Pollution Epidemiology Using Positive and Negative Control
Analyses. Environmetrics 11: 705–717.
Lumley T, Sutherland P, Rossini A, Lewin-Koh N, Cook D, Cox Z (2002):Visualising
high-dimensional data in time and space: ideas from the Orca project. Chemometrics and Intelligent Laboratory Systems 60: 189–195.
Lystig, T. C, Hughes, J. P. (2002) Exact computation of the observed information matrix
for hidden Markov models. Journal of Computational and Graphical Statistics
11: 678–689.
Maheswaran, R., Morris, S. E. , S. Falconer, A. Grossinho, J. C. Wakefield and P. Elliott,
(1999). Magnesium in drinking water supplies and mortality from acute myocardial infarction in North West England. Heart 82: 455–460.
Meiring, W., Guttorp, P., and P. D. Sampson (1997): Computational Issues in Fitting
Spatial Deformation Models for Heterogeneous Spatial Correlation. Computing
Science and Statistics 29: 409-417.
Meiring, W., Guttorp, P., and P. D. Sampson (1998): Space-time estimation of grid-cell
hourly ozone levels for assessment of a deterministic model. Environmental and
Ecological Statistics 5:197–222..
Melvin, E.F., Parrish, J.K., Conquest, L.L. (1999): Novel Tools to Reduce Seabird Bycatch in Coastal Gillnet Fisheries. Conservation Biology 13: 13861–397.
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Mode, N., Conquest, L. and Marker D. (1999): Ranked set sampling for ecological research: Accounting for the total cost of sampling. Environmetrics 10: 179–194
Mode, N. A., Conquest, L. L., Marker, D. A (2002) Incorporating prior knowledge in
environmental sampling: ranked set sampling and other double sampling procedures. Environmetrics13: 513–521
Morris, S. E. , R. Sale, J. C. Wakefield, S. Falconer, P. Elliott and B. J. Boucher (2000):
Hospital admissions for asthma and chronic obstructive airways disease in east
London hospitals and proximity to major roads. Journal of Epidemiology and
Community Health 54: 75–76.
Morita, J. G. (1999): Capture and Re-Capture Your Students’ Interest in Statistics. Mathematics Teaching in the Middle School, Mar 1999, 412–18.
Murtaugh, P. A. (2002): Journal quality, effect size, and publication bias in meta-analysis.
Ecology 83: 1162–66.
Murtaug, P. A. (2002):On rejection rates of paired intervention analysis. Ecology 83:
175261.
Park, E. S. , Spiegelman, C. H., and R. C. Henry (2000): Estimating the number of factors
to include in a multivariate bilinear model. Communications in Statistics, B 29:
723–746.
Park, E. S. , Spiegelman, C. H. and R. C. Henry (2002), Bilinear estimation of pollution
source profiles and amounts by using multivariate receptor models. Environmetrics 13: 775–798.
Park, E. S. , Guttorp, P. and R. Henry (2001): Multivariate receptor modeling for temporally correlated data by using MCMC. Journal of the American Statistical Association 96: 1176–1183.
Park, E. S. , Man-Suk Oh and P. Guttorp (2002), Multivariate Receptor Models and
Model Uncertainty. Chemometrics and Intelligent Laboratory Systems 60: 49–67.
Pascutto, C. , J. Wakefield, N. Best, L. Bernardinelli, P. Elliott, S. Richardson and A.
Staines, (2000). Statistical issues in the analysis of disease mapping data. Statistics in Medicine 19: 2493–2519
Percival, D. B. (2001): Wavelet methods. In A. El-Shaarawi and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London: Wiley.
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Percival, D. B., Overland, J E. and Mofjeld, H. O. (2001), Interpretation of North Pacific
Variability as a Short and Long Memory Process, Journal of Climate 14: 4545–
4559
Phelan M. J. (2000): Timing and scope of emission reductions for airborne particulate
matter: a simplified model Environmetrics 11: 627–649.
Phelan, M. J. (1998): Environmental health policy decisions: the role of uncertainty in
economic analysis. Journal of Environmental Health 61: 8–12.
Ponce, R. A., S. M. Bartell, R. C. Lee, T. J. Kavanagh, J. S. Woods, W. C. Griffith, T. K.
Takaro, and E. M. Faustman (1998): Uncertainty analysis methods for comparing
predictive models and biomarkers: A case study of dietary methylmercury exposure. Journal of Regulatory Toxicology and Pharmacology 28(2): 96–105.
Ponce, R. A., S. M. Bartell, E. Wong, D. LaFlamme, C. Carrington, R. Lee, D. Partrick,
E. Faustman and M. Bolger (2000): Use of Quality-Adjusted Life Year Weights
with Dose-Response Models for Public Health Decisions: A Case Study of the
Risks and Benefits of Fish Consumption. Risk Analysis, Vol. 20, No. 4, 529-542.
Poole, D. J. and A. E. Raftery (2000): Inference for Deterministic Simulation Models:
The Bayesian Melding Approach. Journal of the American Statistical Association
95: 1244–1255.
Reynolds, Joel H. (1998): Causal Systems in Ecology. Letter in reply to Science's Compass essay. Science, 15 May, 280: 988–989
Reynolds, J. H., E. D. Ford (1999): Multi-Criteria Assessment of Ecological Process
Models. Ecology 80: 538–553.
Sampson, P. D. (2001): Nonstationary spatial covariance modeling. In A. El-Shaarawi
and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London: Wiley.
Sampson, P. D. and Guttorp, P. (1998): Operational Evaluation of Air Quality Models. In
Environmental Statistics: Analyzing Data for Environmental Policy. Novartis Foundation. Chichester: Wiley: 33–45.
Sampson, P.D., Damian, D., and Guttorp, P. (2001a). Advances in Modeling and Inference for Environmental Processes with Nonstationary Spatial Covariance. In: GeoENV 2000: Geostatistics for Environmental Applications, P. Monestiez, D. Allard, R. Froidevaux, eds., Dordrecht: Kluwer, pp. 17-32.
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Sampson, P.D., Damian, D., Guttorp, P., and Holland, D.M. (2001b). Deformation-based
nonstationary spatial covariance modelling and network design. In: SpatioTemporal Modelling of Environmental Processes, Colecció «Treballs
D’Informàtica I Tecnologia», Núm. 10., J. Mateu and F. Montes, eds., Castellon,
Spain: Universitat Jaume I, pp. 125-132.
Schlather, M. (2001) Random Fields: Simulation and Analysis of Random Fields. Package on random field simulation for R. Posted at http://cran.r-project.org/.
Schoenberg, R. P. , Brillinger, D. R. and P. Guttorp (2001): Point process, spatialtemporal. In A. El-Shaarawi and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London: Wiley.
Shaddick, G. and Wakefield, J (2002) Modelling Daily Multivariate Pollutant Data at
Multiple Sites. Applied Statistics (JRSS C) 51: 351–372.
Sheppard L. (2001): Ecological study design. In A. El-Shaarawi and W. Piegorsch (eds.):
Encyclopedia of Environmetrics. London: Wiley.
Sheppard, L. and Damian, D. (1999) Estimating short-term PM effects accounting for
surrogate exposure measurements from ambient monitors Environmetrics 11:
675–687.
Sheppard, L., T. Lumley (2000): Comments on Combining evidence on air pollution and
daily mortality from the 20 largest U.S. cities: a hierarchical modeling strategy by
Francesca Dominici, Jonathan M. Samet and Scott L. Zeger. Journal of the Royal
Statistical Society Series B 163: 297.
Sheppard, L, D. Levy, H. Checkoway (2001): Correcting for the effects of location and
atmospheric conditions on air pollution exposure analysis in a case-crossover
study. Journal of Exposue Analysis and Environmental Epidemiology 11: 86–96
Sheppard L, Levy D, Norris G, Larson TV, Koenig JQ (1999). Effects of ambient air pollution on non-elderly asthma hospital admissions in Seattle, Washington, 19871994. Epidemiology 10: 23–30.
Silkey. M., Nur, N. and Geupel, G. R.(1999): The use of mist-net capture rates to monitor
annual variation in abundance: A validation study Condor 101: 288-298
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Smith, E. P., K. Ye, C. Hughes and L. Shabman (2001): Statistical Assessment of Violations of Water Quality Standards Under Section 303 (d) of the Clean Water Act.
Environmental Science and Technology 35: 606–612.
Steel, E. A. and S. Neuhauser (1999): A Comparison of Methods for Measuring Water
Clarity. Journal of the North American Benthological Society 21: 326–335.
Steel, E. A., P. Guttorp, J. J. Anderson, and D. C. Caccia (2001): Modeling juvenile
salmon migration using a simple Markov chain. Journal of Agricultural, Biological, and Environmental Statistics 6: 80-88.
Steel, E. A., Kelsey, K. and Morita, J. (2002): The Truth about Science:A middle school
curriculum teaching the scientific method and data analysis in an ecology context.
To appear, Environmental and Ecological Statistics.
Thompson M. L. (2001): Meteorological adjustment of air quality data. In A. El-Shaarawi
and W. Piegorsch (eds.): Encyclopedia of Environmetrics. London: Wiley.
Thompson, M. L., Reynolds, J., L. H. Cox, P. Guttorp and P. D. Sampson (2001): A review of statistical methods for the meteorological adjustment of tropospheric
ozone. Atmospheric Environment 35: 617–630.
Thompson, M.L., Cox, L.H., Sampson, P.D. and Caccia D. C. (2002) Statistical Hypothesis Testing Formulations for U.S. Environmental Regulatory Standards for
Ozone. Environmental and Ecological Statistics 9: 321–339.
Tjelmeland, H. and Besag, J.(1998): Markov random fields with higher-order interactions. Scandinavian Journal of Statistics 25: 415–433
van Belle, G., Griffith, W.C. and Edland, S.D. (2001) Contributions to composite sampling. Environmental and Ecological Statistics, 8:171-180.
Vorhees , D.V., Cullen, A.C. and L.M. Altshul (1997) Exposure to Polychlorinated Biphenyls in Residential Indoor Air and Outdoor Air Near a Superfund Site, Environmental Science & Technology, 31:3612-3618.
Vorhees, D. V. , A. C. Cullen, and L. M. Altshul (1999): Polychlorinated Biphenyls in
House Dust and Yard Soil Near a Superfund Site. Environmental Science & Technology 32:2151-2156.
Wakefield J. C. and P. Elliott (1999). Issues in the statistical analysis of small-area health
data. Statistics in Medicine 18: 2377–2399.
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Wakefield J. C.and S. E. Morris (1999). An application of spatial errors-in-variables
modelling: investigating the relationship between ischaemic heart disease and water constituents. In Bayesian Statistics 6; Proceedings of the Sixth Valencia International Meeting, Bernardo, J.M., Berger, J.O., Dawid, A.P. and Smith, A.F.M.
(editors), p. 657–684, Oxford University Press.
Wakefield, J. C., N. G. Best and L. A. Waller (2000): Bayesian approaches to disease
mapping. In Spatial Epidemiology. Elliott, P., Wakefield, J.C., Best, N.G. and
Briggs, D. (editors), Oxford University Press.
Whitcher, B., P. Guttorp and D. B. Percival (2000a): Wavelet analysis of covariance with
application to atmospheric time series Journal of Geophysical Rsearch – Atmospheres 105 (D11): 14941–14962.
Whitcher, B., P. Guttorp and D. B. Percival (2000b): Multiscale detection and location of
multiple variance changes in the presence of long memory. Journal of Statistical
Computing and Simulation 68: 65–88.
Whitcher, B., Byers, S. D., Guttorp, P,and Percival, D. B. (2002): Testing for homogeneity of variance in time series: Long memory, wavelets, and the Nile River. Water
Resources Research 38: art. no. 1054.
M. Widmann and C. S. Bretherton (2000): Validation of Mesoscale Precipitation in the
NCEP Reanalysis Using a New Gridcell Dataset for the Northwestern United
States. Journal of Climate 13: 1936–1950.
M. Widmann, C.S. Bretherton and E.P. Salathe Jr. (2002): Statistical precipitation
downscaling over the Northwestern United States using numerically simulated
precipitation as a predictor. In press, Journal of Climate,
4. Administration
Much of the first year of Center activities was spent setting up administrative routines,
such as proposal submission dates, evaluation criteria, payroll coordination etc. The executive committee was instrumental in setting these policies, and the administrative details were expertly handled by our Secretary Supervisor.
4.1 Director and Associate Director
The NRCSE director, Peter Guttorp, spent Autumn quarter of 1998 in Sweden, developing contacts with European researchers in environmental statistics. During his absence,
Paul Sampson was acting director. Due to the heavy administrative load for the director,
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the executive committee decided to add an associate director position to the Center administrative staff. This is a 25% position, and Paul Sampson was selected by the executive committee to fill it. The duties of the associate director include maintaining the web
sites and other external informational issues and coordinating the visitors program.
4.2 Executive and advisory committees
4.2.1 Executive committee
The executive committee prioritizies research proposals in order to advise the director on
funding decisions; elects new members of the Center; and assists the director in setting
goals and directions of Center activities. This committee is elected by the membership,
generally to three year terms. The first committee consisted of Alison Cullen (Public Affairs), David Ford (Forestry), Paul Sampson (Statistics), and Gerald Van Belle (Environmental Health). The agendas and decisions of the executive committee meetings are recorded on the web site http://www.nrcse.washington.edu/ people/execcom.html
A the end of the second year, the executive committee saw the conclusion of two terms of
service: Gerald Van Belle and Paul Sampson. In a membership election Mary Lou
Thompson (Biostatistics) and Paul Sampson were voted in for three-year terms on the executive committee. After three years, the terms of Alison Cullen and David Ford ended.
In a membership election they were each voted in for another three-year term on the executive committee. Due to Dr. Cullen’s sabbatical leave during 2000–2001, Loveday
Conquest (Fisheries) was chosen as a substitute committee member.
4.2.2 Advisory committee
The Center advisory committee, as outlined in the original Center proposal, was intended
to consist of three representatives of statistical professional organizations, and three representatives of the US Environmental Protection Agency. The main purpose of this committee is to assist the Center director and its executive committee to extend the vision and
scope of the Center activities. The advisory committee eventually only had three members: Paul Switzer (Stanford University) representing the Institute for Mathematical Statistics; Abdel El-Shaarawi (Canadian National Water Research Institute) representing the
International Environmetric Society; and Lawrence Cox, representing the American Statistical Association. The US EPA never chose any representatives to the committee.
The advisory committee had its first meeting during the ORD-NRCSE workshop in January, 1997. It met with acting director, Paul Sampson, during the Joint Statistical Meetings in Dallas in August, 1998. On the subject of the current EPA focus on particulate
matter research, Larry Cox noted that NRCSE might be considered as a "sixth PM center"
in addition to the 5 PM centers that were to be established. It was noted that if the Center
had plans to move beyond its current situation as a primarily EPA-based (or EPA-limited)
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center, this was the year to do the planning. The Advisory committee finally participated
in the internal workshop in January, 2000. The discussions focused on directions of
change of Center structure, outreach, and research topics.
4.3 Space
The Center started with off-campus space about 10 minutes walk from campus. Sooner
than expected, after about two months, surge space in Bagley Hall on campus was made
available. This space was shared with the cross-disciplinary graduate program in Quantitative Ecology and Resource Management. Another group with space adjacent to ours
was the Program on the Environment, which is developing undergraduate and graduate
curricula in Environmental Science.
The intent of the Center is that all researchers (including research assistants) who so wish
may have access to a desk at the Center, adequate computing equipment and support, and
reasonable office support. In addition, most visitors would be housed at the Center, although long-term visitors working with a Center member who does not use Center facilities may be housed in the member’s department or laboratory.
The Center was allocated permanent space on the fourth floor of Bagley Hall in 1998.
This space was again shared with the Program on the Environment and the graduate program in Quantitative Ecology and Resource Management. Unfortunately, the allocation
was revoked the following year, and the Center had to move into space made available by
the Statistics department on the second floor of Padelford Hall.
4.4 Hiring
The Center office was competently managed by our Secretary Supervisor, Gerri Goedde.
She was selected from a group of four interviewed candidates. She remained with the
Center until May, 2002.
During the first year it became increasingly obvious that the Center was in need of a
computer systems specialist. After advertising and interviewing the four top candidates,
one was hired but resigned after a few weeks. We were allowed to fill the position from
the original pool of applicants without readvertising, and hired Erik Christianson, who
remained in the Center until July, 2002.
The third staff position was a research programmer/software engineer. We hired Peter
Sutherland, who was our Web master during the first year of the Center. When he resigned in 1999, the funding for this position was moved to the Associate Director position.
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In 1998, the Center hired its first postdoctoral research fellows: Eun Sug Park from Texas A&M, and Kevin Brand from Harvard University. Dr. Park (from Texas A&M), working on receptor modeling, arrived in January of 1999, and stayed through February of
2001... Kevin Brand from Harvard University, working on risk analysis, arrived in May,
but left shortly after his arrival for a permanent position in Canada. The Departments of
Statistics, Mathematics, and Applied Mathematics at the University of Washington were
awarded a VIGRE grant from the National Science Foundation, and as part of this grant
Pip Courbois from Oregon State was hired as a postdoc. He arrived in December of 1999,
and washoused in NRCSE, while also having teaching duties in the Statistics department.
Dr. Courbois is a specialist in environmental sampling techniques, particularly modelbased design. He is staying through December, 2002.
4.5 Members
At this point, the Center has 30 members (listed below) from 12 departments in 6 schools
or colleges. Members are original members (listed in the original proposal) unless otherwise indicated.
Julian Besag, Statistics (elected 00–01)
Chris Bretherton, Applied Mathematics and Atmospheric Science (elected 97–98)
Loveday L. Conquest, Fisheries
Alison C. Cullen, Public Affairs
Elaine M. Faustman, Environmental Health
E. David Ford, Foresty
Tilmann Gneiting, Statistics (elected 98–99)
Peter Guttorp, Statistics
Mark Handcock, Sociology and Statistics (elected 00–01)
Patrick Heagerty, Biostatistics (elected 97–98)
Ray Hilborn, Fisheries (elected 99–00)
Jim Hughes, Biostatistics
James R. Karr, Zoology and Fisheries
Bill Lavely, Sociology (elected 96–97)
Brian G. Leroux, Biostatistics
Dennis P. Lettenmaier, Civil Engineering
Thomas Lumley, Biostatistics
June Morita, Management Science and Statistics (elected 97–98; from 2000–2002 at Interdisciplinary Arts and Sciences, UW Bothell, from 2002 Statistics, UW Seattle)
Timothy Nyerges, Geography
Donald B. Percival, Applied Physics Lab
Rafael Ponce, Environmental Health (elected 98–99)
Adrian Raftery, Statistics and Sociology
Thomas Richardson, Statistics (elected 98–99)
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Paul D. Sampson, Statistics
Lianne A. Sheppard, Biostatistics
John R. Skalski, Fisheries
Mary Lou Thompson, Biostatistics
Gerald van Belle, Environmental Health
Jon Wakefield, Biostatistics and Statistics (elected 99-00)
John Michael Wallace, Atmospheric Sciences (elected 97–98)
Original member David Madigan left for AT&T in 1999. Joel Reynolds, Statistics, was
elected member in 97–98 and left for the State of Alaska Department of Fish and Game in
1999. Dean Billheimer, Statistics, was elected in 98–99 and left for Vanderbilt University
in 2001.
4.6 Relations to other statistical research groups
NCAR (National Center for Atmospheric Research)
Many of the activities in the Geophysical Statistics Project at NCAR in Boulder, Colorado, directed by Doug Nychka, are related to Center research activities. For example, work
on precipitation modeling, covariance modeling, and global climate modeling is closely
related to work at the Center. An NRCSE research assistant, Barnali Das, spent Autumn
and Winter quarters 1999–2000 at the Geophysical Statistics Project at NCAR working
on the development of statistical methods for data collected on a globe. This visit was
jointly funded by NRCSE and NCAR/GSP. A reciprocal visit to NRCSE by NCAR/GSP
postdoc Claudia Tebaldi in April–May 2000 was also jointly funded by the two groups.
They organized a joint workshop on large data sets at NCAR in July 2000 (see workshop
list).
NISS (National Institute for Statistical Sciences)
There are close research links, particularly in the area of air quality modeling, between
NRCSE and NISS. David Ford (Forestry) spent some time at NISS in order to pursue research on model assessment. The Center participated in a proposal to the National Science Foundation for funding for a Mathematics Research Center in Statistics, housed at
and organized by NISS. A subcontract with NISS on particulate matter work funded a
research assistant to Richard Smith during the summer of 1999.
IMPACT
During Guttorp’s visit to Europe in Autumn 1998, a collaboration with European Union
scientists was initiated. This resulted in a joint proposal “Estimation of human impact in
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the presence of natural fluctuations” to the European Commission from researchers at
University of Linköping (Sweden), Lancaster University (UK), the Finnish Meteorological Institute, The European Commission Joint Research Center (Italy), GKSS (Germany),
and
ARMINES (France). The proposal was funded at the level of 900,000 euro, and is aimed
at creating tools for times series decomposition into meteorologically induced fluctuations and estimates of human impact; significance tests permitting retrospective impact
assessment; and model reduction procedures that facilitate merging of statistical and
mechanistic approaches. The NRCSE part of the project (receiving no funding from the
EU) focuses on the singular value decomposition as a tool for meteorological adjustment
of spatio-temporal air quality data (cf. sec. 3.1.4). The project is directed by Anders
Grimvall at University of Linköping, and co-investigators include Hans Wackernagel,
Peter Young, Peter Diggle, Ulrich Callies, Peter Guttorp, Jari Walden, and Andrea Saltelli.
NRCSE contributions to the project are summarized in the project’s first integrated annual report available from http://www.mai.liu.se/impact/index.html. Peter Guttorp presented
NRCSE research on meteorological adjustment of air quality data at the project’s group
meeting in Linköping, Sweden, June 19-20, while Paul Sampson presented results of preliminary analyses of Paris regional air quality data at the project’s group meeting in Fontainebleau, France, Nov 20-21. RA Fadoua Balabdaoui developed further space-time
models of these data in order to relate them to meteorological data and for assessing the
output of an air quality model providing air quality model predictions for the summer of
1999. An invited session at the TIES meeting in Portland 2001 presented some results
from the project. Due to lack of funding, a planned workshop in Seattle in 2002 was cancelled.
Other research groups
Our long-standing collaboration with Westat (particularly with David Marker) on sampling issues was the subject of our largest and longest-lasting subcontract.
The Center had a subcontract with Penn State to work on the follow-up from the 1997
workshop on combining data from multiple sources. The investigator was Mark Handcock, and the subcontract covered a research assistant.
A subcontract with the University of British Columbia covered particulate matter work
under the leadership of Jim Zidek and John Petkau. Other collaborators in this context
included Mark Kaiser at Iowa State, Noel Cressie at Ohio State, Merlise Clyde at Duke,
and the NISS group.
A subcontract with Ron Henry at USC allowed continuation of our work in receptor
modeling, focusing on issues of spatio-temporal dependence and source allocation.
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List of subcontracts
Title
Company
Amount
Dates
Support for Statistical Analysis of Spatial Data (Henry)
Support for Statistical Analysis of Spatial Data (Hruba)
USC
State Health
Inst,Slovakia
Univ Corp for Atmos Res
Westat
53,303
9450
10/1/00-9/30/01
6/1/01-9/30/01
6401
6/1/97-8/31/97
82,215
6/29/98-9/30/02
Penn State
11,579
11/98-9/30/01
Natl Inst of Stat
Sciences
UBC
19,813
6/1/99-9/30/01
25,325
3/1/00-4/30/00
Analyze California Ozone Data for Comparison with
Model Output (Meiring)
Comparison of Ranked Set Sampling to Alternative
Sampling (Marker)
Ecological Assessment of Riverine Systems (Handcock)
Particulate Matter Research (Smith)
Spacial Temporal Models for PM Fields with Application to Health Impact Analysis (Zidek)
Appendix A. Seminars
The following is a list of seminar presentations.
Autumn 1996
October 8 Peter Guttorp, NRCSE: The National Research Center for Statistics and the
Environment (attendance approximately 50)
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October 15 Joe Felsenstein, Genetics, University of Washington: Evolutionary trees of
genes within species: how to use them, whether to use them. (60)
October 22 Patricia Cirone, John Yearsley, Bruce Duncan, Joe Goulet and Julius Nwosu,
EPA Region X, Seattle: The Use of Statistical Techniques when Evaluating Uncertainty
and Variability in Human Health and Ecological Risk Assessments. (50)
October 29 Jim Hughes, NRCSE: Modeling rainfall in SW Australia. (40)
November 5 David Ford, NRCSE: Developing Ecological Models for Practical Use (50)
November 12 Paul D. Sampson, NRCSE: Spatio-Temporal Analysis and Modeling of
Tropospheric Ozone (40)
November 26 Jim Karr, NRCSE: Attaining Environmental Goals: Biological Monitoring
in Theory and Practice. (40)
December 3: Milton Smith, Remote Sensing Laboratory , University of Washington: Remote sensing of the Amazon basin. (30)
December 10: Joel Reynolds, Statistics, University of Washington. How good is your
model? or Process Model Assessment using Pareto Optimality (30)
Winter 1997
January 7 Steve Millard, Statistics, Probability and Information: Environmental Statistics
package for S-Plus. (35)
January 14 Chris Frissell, University of Montana: Spatial Assessment of Biological Status and Biodiversity Loss (40)
January 21 Dennis Lettenmaier, NRCSE: Effects of Forest Management on flooding in
the Western Cascades (50)
January 28 Ray Hilborn, Fisheries, University of Washington: Using hierarchic Bayesian
meta-analysis to synthesize the existing knowledge on the recruitment dynamics of
fish stocks (30)
February 4 Rick Edwards, Fisheries, University of Washington: Predicting watershed
effects of human actions: the need for new statistical approaches at the land-river interface (35)
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February 11 David Montgomery, Geology, University of Washington: Alluvial and bedrock channels, forests, and river incision: never mind climate change, what about erosion change? (25)
February 18 Tim Nyerges, NRCSE: Toward a Theory of GIS-supported Collaborative
Decision Making: Enhanced Adaptive Structuration Theory (35)
February 25 Alta Turner, CH2M Hill: Superfund Cleanup in a residential area: Digging
out the bad dirt (20)
March 4 Bruce Peterson, Terastat: Calibration and the effect of measurement uncertainty
on environmental decisions. (30)
March 11 Gerald van Belle, NRCSE: The Bivariate Normal—A Willing Suspension of
Disbelief. (25)
Spring 1997
April 1 Alison Cullen, NRCSE: PCB Congener Levels and Profiles in Environmental
Media near New Bedford Harbor - Measurements and Model Estimates (35)
April 8 Francis Zwiers, Environment Canada: Interannual variability and predictability
in an ensemble of six weather models. (25)
April 22 (Earth Day) Maria Silkey, NRCSE: Developing the tools to meet the nations
monitoring needs —a report on the Environmental Monitoring and Assessment Program's research symposium in Albany, New York. (25)
April 29 Pat Sullivan, International Pacific Halibut Commission: Individual Growth as a
Factor Affecting Estimates of Halibut Abundance and Model Development Using
Fournier's ADModelBuilder. (30)
May 6 Panel Discussion on Federal Ozone Standards. Tony O'Hagan, University of Nottingham, Peter Guttorp, NRCSE, Jim Zidek, University of British Columbia, Larry
Cox, EPA, and Clint Bowman, Washington Department of Ecology. (45)
May 13 Lianne Sheppard, NRCSE: Hospital Admissions during Ozone Excesses: the
Seattle Story (35)
May 20 Peter Ward, Geology, University of Washington: Fluctuations in Biodiversity
over Geologic Time (25)
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May 27th Nancy Neuerburg, King County Metro: Transit and Statistics—A Sampler. (20)
June 3 Paul Sampson, NRCSE. Where the Center is going (30)
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Summer 1997
July 2 Sandra Bird, EPA Athens: ReVA – Ecological Assessment in NERL (attendance
was not taken during the summer)
July 9 Denis Allard, University of Avignon, France: Spatial modeling of temperatures
using land use data
July 16 Wendy Meiring, NCAR: Statistical challenges in analyzing stratospheric ozone
data at mid-latitude
July 23 Peter Guttorp, NRCSE: The future of environmental statistics
July 30 Jan Beirlant, University of Leuwen, Belgium: Practical analysis of extreme
values (with applications to earthquakes, windspeed modeling etc.)
Autumn 1997
October 7: James B. Hatfield, Hatfield and Dawson Broadcast and Communications
Consulting Engineers. “Electromagnetic Fields and Human Health”
October 14: Tony Rossini, University of South Carolina, “ESS and Literate Programming: Computer Environments for Effective Statistical Programming and Data Analysis”
October 21: Jeffrey Richey, UW Oceanography, “PRISM and the NRCSE: A (Spatial)
View to the 21st Century in Puget Sound”
October 28: Doug Bright, Royal Roads University, Victoria, BC. “Environmental Science and Management in the Georgia Basin Coastal Zone: Tales of a Bottom Feeder”
November 4: James Hilden-Minton, National Institute of Statistical Sciences, “Multilevel Monitoring of Drinking Water Systems”
November 18: George Hampson, Woods Hole Oceanographic Institute. “Land Development vs. Environmental Health of a Small New England Island. Nantucket Harbor
Study: Benthic Animal Communities and Habitat Quality”
November 25: William Lavely, UW International Studies and Sociology, “Infant Mortality in China: A Multilevel Model”
December 2: Marina Alberti, UW Urban Design and Planning, “Measuring Urban Sustainability”
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Statistics and the Environment
December 9: Robin Matthews, Western Washington University, “Problems and Issues
in Quantifying Ecological Risk”
Winter 1998
January 13: Richard C. Pleus, Ph.D., Senior Toxicologist and Principal, Delta Toxicology/Intertox, “Foul odor or adverse health effect? Odor investigation of a Portland cement plant”
January 27: Dr. Robert Francis, Professor, UW School of Fisheries, “Climate and Large
Marine Ecosystems: A Statistical View”
February 3: Dr. Thomas M. Leschine, Assoc. Prof, UW School of Marine Affairs,
“Ranking and Rating Systems in Environmental Management: An Organizational Learning Perspective”
February 10: Dr. Russell P. Herwig, Research Assist. Prof., UW School of Fisheries,
“Knock, Knock, Who’s There? Microbial Communities in Contaminated Puget Sound
Sediments”
February 17: Thomas Lumley, Ph.C., UW Department of Biostatistics, “Marginal regression modelling of space and time data”
February 24: Dr. Joel Reynolds, UW Department of Statistics, “Pareto Optimal Model
Assessment and Statistics: Thoughts from applying POMAC to a stochastic process model”
March 3: Dr. Charles Fowler, NOAA, “Sustainability: Empirical Examples and Management Implications”
March 10: Dr. Philippe Jamet, Ecole des Mines de Paris, “Macroscopic phenomenologies and implicit approaches in the quantification of mass transport in the geosphere”
March 24: Jasha Oosterbaan, Ecole des Mines de Paris, “Application of geochemical
prospecting and exploratory data analysis methods in characterization at contaminated
sites”
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Statistics and the Environment
Spring 1998
April 7: Bill Wright, Montgomery Watson Americas. “Application of Statistics and
Probability to Environmental Problem Solving With An Emphasis on Risk Assessment:
Case Histories and Reflections.”
April 14: Manon Faucher, Environmental Adaptation Research Group, University of
British Columbia. “The Climatology of Surface Marine Winds Near the Western Coast
of Canada.”
April 21: Elaine Faustman, Ph.D., Environmental Health, UW.
April 28: Steve Smith, Ph.D., National Marine Fisheries Service. “Factors Affecting
Survival and Travel Time of Migrating Juvenile Salmonids in the Lower Snake River.”
May 5: Annette Hoffman, Ph.D., Washington State Department of Fish and Wildlife. “A
Statisticians Role in Resource Management.”
May 12: Andrew Gelman, Department of Statistics, Columbia University, “Statistical
issues in home radon mapping and remediation decisions”
May 19: Loveday Conquest, Ph.D., and Nicolle Mode, Quantitative Ecology and Resource Management (QERM), UW. “Ranked Set Sampling—What is it and is it any
good?”
May 26: Samantha Bates, Department of Statistics, UW. “The Bayesian Synthesis Approach to Environmental Risk Assessment: Separating Uncertainty and Variability.”
June 2: Chris Glasbey, Biomathematics and Statistics Scotland, Edinburgh, “Problems in
image warping”
Autumn 1998
Sept 29: Tim Larson, Environmental Science, UW
Smoke, Dust and Haze
Oct 6: Paul Sampson, Statistics, UW
Monitoring Network Design and Air Quality Standards
Oct 13: Per Settergren Sørensen, Institute of the Water Environment, Denmark
Mapping Mussels at the Sea Bottom by Use of Hydroacoustics: Some Advances of
Traditional Methods
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Statistics and the Environment
Oct 20: Kiros Berhane, Department of Preventive Medicine, University of Southern
California
Flexible multi-stage modeling of Pulmonary Function in Children
Oct 22: Eric Smith, Virginia Polytechnic Institute (Joint seminar with Biostatistics)
Evaluating Model Goodness of Fit for Complex Environmental Models
Nov 3: Allan Marcus, US EPA
Particulate Matter Measurement Error, Correlation, and Confounding: How Serious a
Problem?''
Nov 10: Suresh Moolgavkar, Fred Hutchinson Cancer Research Institute.
Air Pollution and hospital admissions for COPD in King County
Nov 17: Drew Levy, Epidemiology, and Thomas Lumley, Biostatistics, UW
A case-crossover study of air pollution and primary cardiac arrest: challenges and
some results'
Nov 24: Francesca Domenici, Biostatistics, The Johns Hopkins University
National Mortality, Morbidity and Air Pollution Study: Statistical challenges
Dec 1: Merlise Clyde, Statistics, Duke University
Does Particulate Matter Particularly Matter?
Winter 1999
January 12: Peter Guttorp, Department of Statistics and NRCSE, UW.
Displaying Uncertainty in Contour Lines
January 19: David Poole, Department of Statistics, UW.
Bayesian Inference for a Non-invertible Deterministic Model for Bowhead Whales
January 26: Jeremy York, Cartia Inc.
Analyzing Textual Data using a Map Metaphor
February 2: Jerry Galt, Hazardous Materials Response Division, NOAA.
Statistical issues encountered when dealing with hazardous materials accidents
February 9: John Yearsley, EPA.
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Statistics and the Environment
Temperature Inputs of Dams on the Columbia and Snake Rivers
February 16: Brian Mar, Environmental Engineering, University of Washington
Uses and Abuses of Environmental Engineering models
February 23: Ed Rykiel, Washington State University.
Validating Ecological Models: What's Scale Got To Do With It?
March 2: Scott Ferson, Applied Biomathematics.
Why probability is insufficient for handling uncertainty in risk analysis
March 9: Bruce Beck, University of Georgia.
Assuring the Quality of Models Designed to Fulfill Predictive Tasks
Spring 1999
April 6: Eun Sug Park, Research Associate, NRCSE,
Multivariate Receptor Modeling from a Statistical Science Viewpoint
April 13: David Caccia, QERM,
Toward a Method for Design of Air Pollution Sampling Networks
April 20: Mary Fishel, Atmospheric Sciences,
A Comparison of Statistical Methods Used to Predict US Surface Temperatures from
Sea Surface Temperatures
April 27: Kevin Brinck, QERM,
Adding Biological Information on a Multivariate Analysis to Measure Biological
Condition
May 4: Alison Cullen, Public Affairs,
Elicitation and Calibration
May 11: Enrica Bellone, Statistics,
A Hidden Markov Model for Downscaling Synoptic Atmospheric Patterns to Precipitation Amounts
May 18: Susan Crane Lubetkin, QERM,
Improving Age Estimates of Bowhead Whales
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National Research Center for
Statistics and the Environment
May 25: Kevin Brand, Research Associate, Environmental Health,
Interpreting Bioassays for Policy: Simulating Calibration
June 1: Heather Caffoe, QERM,
Describing and Modeling Early Growth in Managed Stands of Douglas-Fir
Summer 1999
July 1: Ta-Hsin Li, Statistics and Applied Probability, University of California, Santa
Barbara
Multiscale Representation and Analysis of Spherical Data by Spherical Wavelets
Autumn 1999
October 11, 1999: Moshe Pollak, Department of Statistics, The Hebrew University of Jerusalem (Joint with Statistics)
“A Likelihood Approach to Control Charts”
October 21, 1999: Peter Guttorp, Department of Statistics, University of Washington
(Joint with the Departments of Statistics and Biostatistics)
“Picture the future—graphical innovation in environmental statistics”
December 3, 1999: Chris Bretherton, Departments of Atmospheric Sciences and Applied
Mathematics (Joint with Department of Atmospheric Sciences)
“Statistical Methods for Downscaling GCM Precipitation Predictions over Complex Terrain”
Winter 2000
January 13, 2000: Thomas Lumley, Department of Biostatistics and NRCSE, University
of Washington
“Case-Pseudocontrol Studies – A Free Lunch?”
Spring 2000
April 27: Stanley Barone Jr., PhD., Research Biologist, Cellular and Molecular Toxicology Branch, Neurotoxicology Division, U.S. Environmental Protection Agency, Research
Triangle Park, NC
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Statistics and the Environment
“Preliminary efforts at incorporating developmental effects of exposure to chlorpyrifos
into a biologically-based dose response model”
May 11: Richard J. Jackson, MD, MPH., Director, National Center for Environmental
Health Centers for Disease Control
“Public Health and Environmental Protection: Unfortunate Rivals, Unrivaled Partners”
May 18: Douglas Bell, PhD., Head, Genetic Risk Group, Laboratory of Computational
Biology and Risk Analysis, National Institute of Environmental Health Sciences
“Polymorphism in Carcinogen Metabolism and DNA Repair: Modulation of Exposure
Induced Damage and Disease”
May 25: Rob McConnell, MD, Associate Professor Division of Occupational and Environmental Health, Department of Preventive Medicine, University of Southern California
“Asthma, Lung Function Growth, and Air Pollution: Results from the Southern California
Children’s Health Study”
June 1: Dan Costa, Sc.D., Pulmonary Toxicology Branch, Experimental Toxicology Division, National Health and Environmental Effects Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC
“The Toxicology of Ambient Particle Matter: Links to the Epidemiology”
Summer 2000
August 24: David Vere-Jones, Victoria University of Wellington and Statistics Research
Associates, and David Harte, Statistics Research Associates, New Zealand
“Modeling for Earthquake Forecasts: Point Process Models and Associated Software”
Autumn 2000
Friday, October 20, 2000.
Montserrat Fuentes, Statistics Department at NCSU and US EPA
“Spatial Modeling and Prediction of Nonstationary Environmental Processes”
Monday, October 30, 2000. Joint with Statistics and Atmospheric Sciences.
Caren Marzban, NRCSE and the National Severe Storms Lab, NOAA and Department of
Physics, University of Oklahoma.
“On the Correlation Between U.S. Tornadoes and Pacific Sea Surface Temperatures”
Thursday, November 30, 2000. Joint with Biostatistics and Statistics.
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Statistics and the Environment
Jon Wakefield, NRCSE
“Another Solution to the Ecological Inference Problem”
Winter 2001
Monday, January 8, 2001. Joint with Biostatistics and Statistics.
Paul Murtaugh, Oregon State University
“Before-After-Control-Impact Analysis in Ecology”
Spring 2001
Monday, April 30 2001. Joint with Statistics.
Ernst Linder, University of New Hampshire
"Estimating local trends in large environmental spatial temporal databases"
Summer 2001
Tuesday, July 3, 2001. Joint with Statistics.
Gopalan Nair, Curtin University of Technology and University of California, Santa Barbara
"Directed Markov Point Processes"
Spring 2002
Monday, May 20, 2002. Joint with Statistics.
Marc Genton, North Carolina State University:
“Robust Indirect Inference”
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Statistics and the Environment
Appendix B. Technical reports
1997-98
TRS number 16
A Preliminary Statistical Examination of the Effects of Uncertainty and Variability on
Environmental Regulatory Criteria for Ozone
Lawrence H. Cox, Peter Guttorp, Paul D. Sampson, David C. Caccia and Mary Lou
Thompson (Published by Novartis Foundation)
TRS number 15
Meteorological Adjustment of Western Washington and Northwest Oregon Surface
Ozone Observations with investigation of Trends
Joel H. Reynolds, Barnali Das, Paul D. Sampson and Peter Guttorp
TRS number 14
Modeling Juvenile Salmon Migration Using a Simple Markov Chain
E. Ashley Steel and Peter Guttorp (Published in Journal of Agricultural, Biological and
Enviornmental Statistics 2002)
TRS number 12
Environmental Health Policy Decisions. The Role of Uncertainty in Economic Analysis
Michael J. Phelan. (Published in Environmental Healt 1998)
TRS number 11
Statistical Analysis and Interpretation of Discrete Compositional Data
Dean Billheimer, Peter Guttorp, and William F. Fagan (Published in Journal of the American Statistical Association 2001)
TRS number 10
Multi-Criteria Assessment of Ecological Process Models
Joel H. Reynolds and E. David Ford (Published in Ecology 1999)
TRS number 9
Testing for Homogeneity of Variance in Time Series: Long Memory, Wavelets and the
Nile River
Brandon Whitcher, Simon D. Byers, Peter Guttorp and Donald B. Percival (Published in
Water Resources Research 2002)
TRS number 8
103
National Research Center for
Statistics and the Environment
On the Validity and Identifiability of Spatial Deformation Models for Heterogeneous Spatial Correlation Structure
W. Meiring, P. Guttorp and Paul D. Sampson (Published in Proceedings of the 29th Interface Conference)
TRS number 7
Space-time Estimation of Grid-cell Hourly Ozone Levels for Assessment of a Deterministic Model
W. Meiring, P. Guttorp, and P. D. Sampson (Published in Environmental and Ecological
Statistics 1998)
TRS number 6
Computational Issues in Fitting Spatial Deformation Models for Heterogeneous Spatial
Correlation
W. Meiring, P. Guttorp, and P. D. Sampson
TRS number 5
Modelling Risk from a Disease in Time and Space
Leonhard Knorr-Held and Julian Besag (Published in Statistics in Medicine 1998)
TRS number 4
A Nonhomogeneous Hidden Markov Model for Precipitation
J. P. Hughes, P. Guttorp, and S. P. Charles (Published in Applied Statistics 1999)
TRS number 3
Discussion of the paper by Carroll et al
P. Guttorp, W. Meiring, and P. D. Sampson (Published in Journal of the American Statistical Association 1997)
TRS number 2
Analysis of Spokane CO data
Peter Guttorp
TRS number 1
Natural variability of benthic species in the Delaware Bay
D. Billheimer, T. Cardoso, E. Freeman, P. Guttorp, H.W. Ko and M. Silkey (Published in
Environmental and Ecological Statistics 1997)
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National Research Center for
Statistics and the Environment
1998-99
TRS number 33
Interpolating Vancouver's Daily Ambient PM10 Field
Li Sun, James V. Zidek, Nhu D. Le and Haluk Ozkaynak (Published in Environmetrics
2000)
TRS number 32
Environmental Statistics
Peter Guttorp (Published in Journal of the American Statistical Association 2000)
TRS number 31
Bias in the Case--Crossover Design: Implications for Studies of Air Pollution
Thomas Lumley and Drew Levy (Published in Environmetrics 2000)
TRS number 30
Assessing Seasonal Confounding and Model Selection Bias in Air Pollution Epidemiology Using Positive and Negative Control Analyses
Thomas Lumley and Lianne Sheppard (Published in Environmetrics 2000)
TRS number 29
Timing and Scope of Emission Reductions for Airborne Particulate Matter: A Simplified
Model
Michael J. Phelan (Published in Environmetrics 2000)
TRS number 28
A Poisson Process Approach for Recurrent Event Data with Environmental Covariates
Anup Dewanji and Suresh H. Moolgavkar (Published in Environmetrics 2000)
TRS number 27
Model Uncertainty and Health Effect Studies for Particulate Matter
Merlise Clyde (Published in Environmetrics 2000)
TRS number 26
A review of statistical methods for the meteorological adjustment of tropospheric ozone
Mary Lou Thompson, Joel Reynolds, Lawrence H. Cox, Peter Guttorp and Paul D.
Sampson (Published in Atmospheric Environment 1999)
TRS number 25
105
National Research Center for
Statistics and the Environment
Meteorological Adjustment of Chicago, Illinois, Regional Surface Ozone Observations
with investigation of Trends
Joel H. Reynolds, David Caccia, Paul D. Sampson, Peter Guttorp (1999)
TRS number 24
Wavelet analysis of covariance with application to atmospheric time series
Brandon Whitcher, Peter Guttorp, Donald Percival (Published in Journal of Geophysical
Research—Atmospheres 2000)
TRS number 23
A Comparison of Methods for Measuring Water Clarity
E. Ashley Steel, Steve Neuhauser (Published in Journal of the North American Benthological Society 2002)
TRS number 22
Examination of U.S. Environmental Regulatory Criteria for Ozone from a Statistical Perspective
Lawrence H. Cox (Published in Proceedings of the ISI, Helsinki 1999)
TRS number 21
A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation
amounts
Enrica Bellone, James P. Hughes, Peter Guttorp (Published in Climate Research 2000)
TRS number 20
Identifiablility for Non-Stationary Spatial Structure
Olivier Perrin and Wendy Meiring (Published in Journal of Applied Probability 1999)
TRS number 19
Bilinear estimation of pollution source profiles in receptor models
Eun Sug Park, Clifford H. Spiegelman, Ronald C. Henry (Published in Environmetrics
2002)
TRS number 18
Operational Evaluation of Air Quality Models
Peter Guttorp and Paul D. Sampson (Published by Novartis Foundation 1999)
TRS number 17
Ranked Set Sampling for Ecological Research: Accounting for the Total Costs of Sampling
106
National Research Center for
Statistics and the Environment
Nicolle A. Mode, Loveday L. Conquest and David A. Marker (Published in Environmetrics 1999)
1999-2000
TRS number 55 (2000)
Influence of Large Scale Circulation Measures on Precipitation at Local Stations in the
South East of the US
Claudia Tebaldi
TRS number 54 (2000)
Estimating the Association between Ambient Particulate Matter and Elderly Mortality in
Phoenix and Seattle Using Bayesian Model Averaging
Erin M. Sullivan (MSc thesis, Depeartment of Statistics)
TRS number 53 (2000)
The Method of Synthesis in Ecology
E. David Ford and Hiroaki Ishii (Published in Oikos 2001)
TRS number 52 (2000)
Limitations to Empirical Extrapolation Studies: The Case of BMD ratios
Kevin P. Brand, Paul J. Catalano, James K. Hammitt, Lorenz Rhomberg and John S. Evans (Published in Risk Analysis 2001)
TRS number 51 (2000)
Compositional Receptor Modeling
Dean Billheimer (Published in Environmetrics 2001)
TRS number 50 (2000)
A Comparison on Consistency of Parameter Estimation Using Optimization Methods for
a Mixture
Marianne C. Turley and E. David Ford
TRS number 49 (2000)
The Impact of Wavelet Coefficient Correlations on Fractionally Differenced Process Estimation
Peter F. Craigmile, Donald B. Percival and Peter Guttorp (Published in European Congress of Mathmaticians, vol. II, 2001)
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Statistics and the Environment
TRS number 48 (2000)
Setting environmental standards: A statistician's perspective
Peter Guttorp
TRS number 47 (2000)
Wavelet-Based Parameter Estimation for Trend Contaminated Fractionally Differenced
Processes
Peter F. Craigmile, Donald B. Percival and Peter Guttorp (Published in Journal of Time
Series Analysis 2002)
TRS number 46 (2000)
ORCA: A Visualization Toolkit for High-Dimensional Data
Peter Sutherland, Anthony Rossini, Thomas Lumley, Nicholas Lewin-Koh, Dianne Cook,
Zach Cox (Published in Journal of Computational and Graphical Statistics 2000)
TRS number 45 (2000)
Compactly Supported Correlation Functions
Tilmann Gneiting (Published in Journal of Multivariate Analysis 2002)
TRS number 44 (2000)
Developing an Efficient Surveillance Scheme for Assessing Compliance with Air Quality
Standards
Ronit Nirel
TRS number 43 (2000)
Multivariate Receptor Modeling for Temporally Correlated Data by Using MCMC
Eun Sug Park, Peter Guttorp and Ronald C. Henry (Published in Journal of the American
Statistical Association 2001)
TRS number 42 (2000)
Quality Assurance of Environmental Models
Alice Shelly, E. David Ford and Bruce Beck
TRS number 41 (2000)
Statistical Issues in the Study of Air Pollution Involving Airborne Particulate Matter
Lawrence H. Cox (Published in Environmetrics 2000)
TRS number 40 (2000)
Effects of Ambient Fine and Coarse Particles On Mortality In Phoenix, Arizona
Merlise A. Clyde, Peter Guttorp and Erin Sullivan
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National Research Center for
Statistics and the Environment
TRS number 39 (2000)
Bayesian Estimation of Semi-Parametric Non-Stationary Spatial Covariance Structures
Doris Damian, Paul D. Sampson and Peter Guttorp (Published in Environmetrics 2000)
TRS number 38 (2000)
Mathematical Background for Wavelet Estimators of Cross-Covariance and CrossCorrelation
Brandon Whitcher, Peter Guttorp and Donald B. Percival (Mathematical background for
paper published in Journal of Geophysical Research 2000)
TRS number 37 (2000)
MCMC in I x J x K contingency tables
Florentina Bunea and Julian Besag (Published by Fields Institute 2001)
TRS number 36 (1999)
Ecological Indices and Graphical Modeling of Factors Influencing Benthic Populations in
Streams
Florentina Bunea, Peter Guttorp and Thomas Richardson
TRS number 35 (1999)
Estimating Short-term PM Effects Accounting for Surrogate Exposure Measurements
from Ambient Monitors
Lianne Sheppard and Doris Damian (Published in Environmetrics 2000)
TRS number 34 (1999)
Determining the Number of Major Pollution Sources in Multivariate Air Quality Receptor Models
Eun Sug Park, Ronald C. Henry and Clifford H. Spiegelman (Published in Comminications in Statistics C–Simulation 2000)
2000-01
TRS number 71
Locating Nearby Sources of Air Pollution by Nonparametric Regression of Atmospheric
Concentrations on Wind Direction
Ronald C. Henry, Yu-Shuo Chang and Clifford H. Spiegelman (Published in Atmospheric
Enviroment 2002)
TRS number 70
Modelling Daily Multivariate Pollutant Data at Multiple Sites
Gavin Shaddick and Jon Wakefield (Published in Journal of the Royal Statistical Society,
Series C 2002)
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National Research Center for
Statistics and the Environment
TRS number 69
Stochastic models which separate fractal dimension and Hurst effect
Tilmann Gneiting and Martin Schlather
TRS number 68
Journal Quality, Effect Size and Publication Bias in Meta-analysis
Paul Murtaugh (Publixhed in Ecology 2002)
TRS number 67
On Rejection Rates of Paired Intervention Analysis
Paul Murtaugh (Published in Ecology 2002)
TRS number 66
Comments on the Criteria Document for Particulate Matter Air Pollution
Richard Smith, Peter Guttorp, Lianne Sheppard, Thomas Lumley and Naomi Ishikawa
(Public comment on the EPA Criteria Docuent on Particulate Matter Air Pollution)
TRS number 65
Interpretation of North Pacific Variability as a Short and Long Memory Process
Donald B. Percival, James E. Overland and Harold O. Mofjeld (Published in Journal of
Climate 2001)
TRS number 64
A Markov Chain Model of Tornadic Activity
Caren Marzban and Peter Guttorp
TRS number 63
Nonseparable, Stationary Covariance Functions for Space-Time Data
Tilmann Gneiting (Published in Journal of the American Statistical Association 2002)
TRS number 62
Application of POMAC to the Multiobjective 0/1 Knapsack Problem
Rie Komuro and E. David Ford
TRS number 61
Advances in Modeling and Inference for Environmental Processes with Nonstationary
Spatial Covariance
Paul Sampson, Doris Damian and Peter Guttorp (Published in Geo-ENV 2000 2001)
TRS number 60
Multivariate Receptor Models and Model Uncertainty
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Statistics and the Environment
Eun Sug Park, Man-Suk Oh and Peter Guttorp (Published in Chemometrics and Intelligent Laboratory Systems 2002)
TRS number 59
Statistical Hypothesis Testing Formulations for U.S. Environmental Regulatory Standards
for Ozone
Mary Lou Thompson, Lawrence H. Cox, Paul D. Sampson and David C. Caccia (Published in Environmental and Ecological Statistics, 2002)
TRS number 58
Bayesian Uncertainty Assessment in Deterministic Models for Environmental Risk Assessment
Samantha Bates, Adrian E. Raftery and Alison Cullen (Inpress, Environmetrics)
TRS number 57
Simulating a Class of Stationary Gaussian Processes Using the Davies-Harte Algorithm,
with Application to Long Memory Processes
Peter F. Craigmile (In press, Journal of Time Series Analysis)
TRS number 56
Analogies and Correspondences Between Variograms and Covariance Functions
Tilmann Gneiting, Zoltán Sasvári and Martin Schlather (Published in Advances in Applied Probability 2001)
2001-02
TRS number 73
A Recursive Algorithm for Markov Random Fields
Francesco Bertolucci and Julian Besag
TRS number 72
A Critique of Ecological Studies
Jonathan Wakefield (In press, Environmental and Ecological Statistics)
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Statistics and the Environment
Appendix C. Conference presentations
1996-97
Alison Cullen: A comparison of model estimates and measurements of PCB levels in soil
and produce near New Bedford harbor. Society of Risk Analysis annual meeting,
New Orleans, Louisiana.
Alison Cullen: Exposure to polychlorinated biphenyls in residential indoor air and outdoor air near a Superfund site. Society of Risk Analysis annual meeting, New Orleans, Louisiana, contributed talk.
Peter Guttorp: Panel discussant on Cooperative Agreements, EPA Statisticians meeting,
Richmond, Virginia.
Maria Silkey: Poster on Evaluating a model of the benthic macroinvertebrate distribution
in Delaware Bay. EMAP research conference, Albany, New York.
Peter Guttorp: A National Research Center on Statistics and the Environment, 29th Symposium on the Interface: Computing Science and Statistics, Houston, Texas.
Wendy Meiring: Computational Issues in Fitting Spatial Deformation Models for Heterogeneous Spatial Correlation.. 29th Symposium on the Interface: Computing Science
and Statistics, Houston, Texas.
Julian Besag, Florentina Bunea, and Thomas Richardson: Exact MCMC p-values for
multi-dimensional contingency tables. American Mathematical Society Conference
on Graphical Models, Seattle, Washington.
Paul Sampson: Spatio-temporal modeling for an hourly air quality monitoring network.
Joint Statistical Meetings, Anaheim, California.
Peter Guttorp: A national research center on statistics and the environment. Invited poster, Joint Statistical Meetings, Anaheim, California.
Peter Guttorp: Panel discussant on The future of environmental statistics. Joint Statistical
Meetings, Anaheim, California.
Peter Guttorp: Weather states, hidden Markov models, and precipitation modeling. Joint
Statistical Meetings, Anaheim, California
Gerald Van Belle: Discussant, Environmental Epidemiology, Joint Statistical Meetings,
Anaheim, California.
Julian Besag: Disease mapping and risk assessment for public health decision making.
EU/WHIO workshop, Rome, Italy.
Loveday Conquest: Effects of commercial salmon net fisheries on protected seabirds. Environmetrics, Vienna, Austria.
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National Research Center for
Statistics and the Environment
Mary Lou Thompson: Partial least squares analysis of neurotoxic effects of agrochemical
exposure. SPRUCE IV, Enschede, Holland, contributed talk.
Gerald Van Belle: Composite sampling. SPRUCE IV, Enschede, Holland, contributed
talk.
Joel Reynolds: Adjusting surface ozone for meteorology and emissions prior to the investigation of time trends. Cascadia Tropospheric Ozone Peer Review Workshop, Seattle, Washington.
Alison Cullen: Approaches to Uncertainty Analysis in Risk Assessment and Risk Communication. Institute of Epidemiology and Hygiene, Banska Bystrica, Slovakia.
T. A. Lewandowski, C. H. Pierce, S. M. Bartell, R. A. Ponce, and E. M. Faustman. Toxicokinetic and toxicodynamic modeling of the effects of methylmercury in the fetal
rat. Fourteenth Annual Meeting of the Pacific Northwest Association of Toxicologists, Ocean Shores, Washington, poster.
1997-98
Peter Guttorp: A National Research Center on Statistics and the Environment. University
of California at Berkeley Statistics colloquium, February, 1998.
T. A. Lewandowski: Toxicokinetic and toxicodynamic modeling of the effects of
methylmercury on the fetal rat. Society of Toxicology annual meeting, March 1998.
Peter Guttorp: Some research problems in environmental statistics and Some research
approaches in environmental statistics. Centre de Recherches Mathématiques Workshop on Applications of Spatial Statistics in Earth, Environmental and Health Sciences, Montréal, April 1998.
Paul Sampson: Tropospheric Ozone, Air Quality Standards, Photochemical Models and
Air Quality Monitoring Data and Spatio-Temporal Statistical Modeling of Hourly
Tropospheric Ozone Data for Operational Evaluation of a Photochemical Model.
Centre de Recherches Mathématiques Workshop on Applications of Spatial Statistics
in Earth, Environmental and Health Sciences, Montréal, April 1998.
Dean Billheimer: Natural Variability of Benthic Species Composition I & II. Centre de
Recherches Mathématiques Workshop on Applications of Spatial Statistics in Earth,
Environmental and Health Sciences, Montréal, April 1998.
Paul D. Sampson: Operational evaluation of air quality models. Novartis Foundation
Symposium on Environmental Statistics: Analyzing Data for Environmental Policy,
and RSS Open Meeting, London, May 1998.
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Statistics and the Environment
Paul D. Sampson: Spatio-temporal models and methods for the operational evaluation of
air quality models. Society for the Interface on Computer Science and Statistics,
Minneapolis, May 1998.
Jim Hughes: Statistical downscaling of precipitation: An example using the AMIP simulations. 7th International Meeting on Statistical Climatology, Whistler, BC, Canada
May 1998.
Chris Bretherton: Effective Degrees of Freedom and Significance Testing for Data with
Strong Spatial and Temporal Correlations . 7th International Meeting on Statistical
Climatology, Whistler, BC, Canada May 1998.
Barnali Das: Adjusting Surface Ozone for Meteorology: Incorporating Regional Information using the SVD. 7th International Meeting on Statistical Climatology, Whistler, BC, Canada May 1998.
Enrica Bellone: A stochastic model for precipitation amounts at multiple stations. Sixth
International Conference on Precipitation, Hawaii, June 1998
Stephen Charles: A spatio-temporal model for downscaling precipitation occurrence and
amounts. Sixth International Conference on Precipitation, Hawaii, June 1998
Lianne Sheppard: Estimation of Exposure Effects in Occupational Studies with Multiplicative Measurement Error in Grouped Exposures. WNAR annual meeting, San Diego, June 1988.
Don Percival: Wavelet variance and covariance analysis of processes with stationary increments. IMS Western Regional meeting, San Diego, June 1988.
Simon Byers, Julian Besag: Bayesian mapping of risk, with an application to prostate
cancer. WNAR annual meeting, San Diego, June 1988.
Ford, E.D. Tales from the Frontier. Keynote Address to the North American Forest Biology Workshop, July 1998.
T. A. Lewandowski: Effect of tissue binding uncertainty on a PBTK model of methylmercury in the fetal rat. IUTOX congress, July, 1998.
Reynolds, Joel H. Assessing Ecological Process Models using the Pareto Optimal Model
Assessment Cycle. Contributed paper, VII International Congress of Ecology
(INTECOL). Florence, Italy, July 1998.
Ford, E.D. and Turley, M. Model Assessment at EPA Athens Georgia, and the National
Institute for Statistical Sciences. North Carolina, June, 1998.
Paul Sampson: Monitoring Network Design and Air Quality Standards . Joint Statistical
Meetings, Dallas, Texas, August 1998.
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Statistics and the Environment
Mary Lou Thompson: Setting Environmental Standards: A Statistical Evaluation of the
US Ozone Standard . Joint Statistical Meetings, Dallas, Texas, August 1998.
Gerald Van Belle: Statistics and Mandated Science. Joint Statistical Meetings, Dallas,
Texas, August 1998.
Loveday Conquest Effects of Commercial Salmon Net Fisheries on Protected Seabirds.
Joint Statistical Meetings, Dallas, Texas, August 1998.
Gerald Van Belle: Environmental Epidemiology and Environmental Policy. International Society for Clinical Biostatistics, Dundee, Scotland, August 1998.
Lianne Sheppard Health effect estimates with multiplicative exposure error and grouping.
Contributed paper, International Symposium On Epidemiology In Occupational
Health meeting, Helsinki, Finland, September 1998.
Peter Guttorp: Some research problems at NRCSE. Seminar at Department of Statistics,
University of Stockholm and contributed paper at Statistics Sweden Methodology
Conference, September-October 1998.
Ford, E.D. Purpose and Problems involved in long term ecological and environmental
research. Final Summary Paper for the Conference: Long-term Silvicultural Research Sites: Promoting the Concept —Protecting the Investment. Victoria, British
Columbia, October 1998.
1998-99
Oct. 1998 P. Guttorp: Using non-stationary hidden Markov models to downscale general
circulation models. Zürcher Kolloquium über anwendungsorientierte Statistik. ETH,
Zürich, Switzerland.
Oct. 1998 P. Guttorp: State-space models for species compositions. Seminar für Statistik,
ETH, Zürich, Switzerland.
Nov. 1998 P. Guttorp and J. Morita: Case-based teaching in statistics and business. Univ.
of Stockholm, Depts. of Statistic and Mathematical Statistics, Sweden
Nov. 1998 P. Guttorp: Some research topics in environmental statistics. Dept. of Statistics, Univ. of Linköping, Sweden.
Dec. 1998 P. Guttorp: Some research topics in environmental statistics. Dept. of Mathematical Statistics, Univ. of Lund, Sweden.
Dec. 1998 P. Guttorp and T. Polfeldt: Displaying uncertainty in contour lines. Dept. of
Statistics, Univ. of Stockholm, Sweden
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Dec. 1998 P. Guttorp: State-space models for species compositions. Dept. of Mathematical Statistics, Univ. of Stockholm, Sweden.
Dec. 1998 A. Cullen and C. Bretherton: Developing Distributions of Annual Average
Concentration with Dependency among Daily Values. Society for Risk Analysis annual meeting, Phoenix, AZ.
Dec. 1998 S. Bates: A Bayesian Approach to Assessing Exposure to PCBs in New Bedford Harbor. Society for Risk Analysis annual meeting, Phoenix, AZ.
Dec. 1998 F. Hruba: Personal Exposure to Particles and NO2 in Banska Bystrica, Slovakia. Society for Risk Analysis annual meeting, Phoenix, AZ.
Dec. 1998 S. B. Curtis: An index of harm for exposure to a combination of radiation and
chemical pollutants. Society for Risk Analysis Annual Meeting, Phoenix, AZ.
Dec. 1998 E. M. Faustman: New Approaches to Temporal Issues in Human Health Risk
Assessment. Society for Risk Analysis Annual Meeting. Phoenix, AZ.
Dec. 1998 R. C. Lee: The value of biomarker information in aflatoxin risk management.
Society for Risk Analysis 1998 Annual Meeting. Phoenix, AZ.
Feb. 1998 C. Bretherton: Northwest Mountain Snowpack, the Pacific Decadal Oscillation, and Implications for Regional Climate Change. Pacific Northwest Climate
Workshop, Seattle, WA.
Mar. 1999 N. Hedley: Exploring Cognitive Domain Structures of Geographic Visualization in Multidimensional Space: Perturbing Synergetic Stability with Uncertainty.
Association of American Geographers Annual Meeting. Honolulu, HI.
Mar.1999 N. Hedley, C. H. Drew, E. A. Erfin, and A. Lee: A Space-Time Trajectory of
100-K Area Workers at Han ford. Association of American Geographers Annual
Meeting, Honolulu, HI.
Mar. 1999 N. Hedley, T. L. Nyerges, C. H. Drew, and C. Hendricksen: Empirical Research Strategies for Investigating Risk Evaluation with Stakeholder Participation at
Hanford. Association of American Geographers Annual Meeting, Honolulu, HI.
Apr. 1999 A. Raftery: Statistical inference for deterministic simulation models. H. O .
Hartley Memorial Lecture, Texas A&M University.
May 1999 P. D. Sampson: Sampling and Monitoring: Network Design and Air Quality
Standards. HSSS/SPRUCE 99 Workshop: Complex Models and Methods for Environmental Problems, Willersley Castle, UK.
May 1999 S. Bates, A. Raftery and A. Cullen: Bayesian Model Assessment. EPA Conference on Environmental Statistics and Information, Philadelphia, PA.
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May 1999 M. L. Thompson: Statistical modeling of multiply censored data. EPA Conference on Environmental Statistics and Information, Philadelphia, PA.
May 1999 P. Craigmile: Trend estimation using wavelets. EPA Conference on Environmental Statistics and Information, Philadelphia, PA.
May 1999 J. Reynolds: Meteorological adjustment of ozone. EPA Conference on Environmental Statistics and Information, Philadelphia, PA.
May 1999 R. Ponce: Development of a linked pharmacokinetic-pharmacodynamic model
of methylmercury induced developmental neurotoxicity. EPA Conference on Environmental Statistics and Information, Philadelphia, PA.
May 1999 M. Turley: Pareto optimal multi-criteria model assessment. EPA Conference
on Environmental Statistics and Information, Philadelphia, PA.
May 1999 D. Marker, WESTAT: Sample designs for environmental data collection:
Ranked set sampling and composite sampling. EPA Conference on Environmental
Statistics and Information, Philadelphia, PA.
May 1999 P. D. Sampson: Monitoring network design with applications to regional air
quality. EPA Conference on Environmental Statistics and Information, Philadelphia,
PA.
May 1999 S. M. Bartell: Human variability in steady state blood-to-hair, blood-to-intake,
and hair-to-intake ratios for mercury: Implications for health risk assessment. Poster.
5th International Conference on Mercury as a Global Pollutant. Rio de Janeiro, Brazil.
June 1999 P. Guttorp: Stochastic modeling using hidden Markov models. Statistical Society of Canada annual meeting, Regina, Canada.
June 1999 S. M. Bartell: Estimation of childhood soil ingestion rates using a probabilistic toxicokinetic model and lead biomonitoring data. Presentation and Poster. US
EPA Workshop on Lead Model Development: Probabilistic Risk Assessment and
Biokinetic Modeling, Research Triangle Park, NC.
July 1999 M. L. Thompson: Statistical modelling of multiply censored data. International
workshop on statistical modelling. Graz, Austria.
Aug. 1999 E. Park: Statistical science for receptor modeling. Joint Statistical Meetings,
Baltimore, MD.
Aug. 1999 L. Conquest, N. Mode and D. Marker: Climbing over slippery rocks and fallen trees: statistical points to ponder while sampling streams. Joint Statistical Meetings, Baltimore, MD.
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Aug. 1999 L. Sheppard: Modeling Short-Term Air Pollution Health Effects Using Surrogate Exposure Measurements from Ambient Monitors. TIES/SSES meeting (ISI satellite), Athens, Greece.
Aug. 1999 P. Guttorp: Picture the Future—graphical innovation in environmental statistics. Hunter lecture, TIES/SSES meeting (ISI satellite), Athens, Greece.
Sep. 1999 A. Raftery: Statistical Inference for Deterministic Simulation Models: The
Bayesian Melding Approach. Clifford C. Clogg Memorial Lecture, Penn State University.
1999-2000
Oct. 1999 T. Gneiting: Matheron's Hankel group - an algebraic gem in geostatistics.
Fields Institute, Toronto (Canada).
Dec. 1999 T. Gneiting: Correlation models in spatial statistics and positive definite functions. Portland State University, OR.
Dec. 1999 S. M. Bartell, R. P. Ponce, W. C. Griffith, and E. M. Faustman: Temporal Fallacies in Biomarker Based Exposure Inference. Society for Risk Analysis Annual
Meeting, Atlanta, GA.
Dec. 1999 S. M. Bartell, J. H. Shirai, C. H. Pierce, and J. C. Kissel: Estimation of Childhood Soil Ingestion Rates Using a Probabilistic Toxicokinetic Lead Model. Society
for Risk Analysis Annual Meeting, Atlanta, GA.
Dec. 1999 S. M. Silbernagel, D. A. Grace, and E. M. Faustman: Nuclear Waste Transportation—A Case Study on Identifying Risk Information Needs. Society for Risk Analysis Annual Meeting, Atlanta, GA.
Dec. 1999 W. C. Griffith: Use of semiparametric statistical methods to model environmental transport of contaminants. Society for Risk Analysis Annual Meeting, Atlanta, GA.
Dec. 1999 W. C. Griffith, K. McCarthy, E. Faustman, J. Moore: Evaluation of Hanford
Cleanup Certification Packages to Support Records of Decision. Society for Risk
Analysis Annual Meeting, Atlanta, GA.
Jan. 2000 S. Liu, J. Koenig, D. Kalman, J. Kaufman, T. Larson, L. Sheppard: PM exposure assessment in high-risk subpopulations. PM 2000: Particulate Matter and
Health. Charleston, SC.
Jan. 2000 T. Lumley, D. Levy, L. Sheppard: Design Bias in Case-Crossover Analyses of
Acute Health Effects of Air Pollution. PM 2000: Particulate Matter and Health.
Charleston, SC.
Jan. 2000 L. Sheppard, D. Levy, H. Checkoway: Teasing Apart the Role of Location and
Atmospheric Conditions in Air Pollution Exposures for Health Effect Analyses: Re-
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sults from the CABS Air Pollution Exposure Substudy. PM 2000: Particulate Matter
and Health. Charleston, SC.
Jan. 2000 M. Clyde, P. Guttorp and E. Sullivan: Effects of Ambient Fine and Coarse Particles on Mortality in Phoenix, Arizona. PM 2000: Particulate Matter and Health.
Charleston, SC.
Jan. 2000 L. Sheppard, D. Damian, M. S. Kaiser, M. Daniels: Incorporating Spatial Predictions of Ambient Particulate Matter into an Analysis of Asthma Hospital Admissions. PM 2000: Particulate Matter and Health. Charleston, SC.
Jan. 2000 J. Vandenberg, M. Brauer, A. Cullen, E. Fabianova, F. Hruba, M. Lendacka, E.
Mihalikova, P. Miskovic, A. Plzikova: Measuring human exposures to priority air
pollutants in Slovakia. PM 2000: Particulate Matter and Health. Charleston, SC.
Jan. 2000 O. Yu, L. Sheppard, T. Lumley, J. Q. Koenig, G. G. Shapiro: Effects of Ambient Carbon Monoxide and Atmospheric Particles on Asthma Symptoms: Results
from the CAMP Air Pollution Asthma Study. PM 2000: Particulate Matter and
Health. Charleston, SC.
Jan. 2000 T. F. Mar, J. Q. Koenig, T. V Larson, L. Sheppard, R. A. Stier and C.S.
Claiborn The association between air pollution and peak expiratory flow in asthmatics in Spokane, Washington. PM 2000: Particulate Matter and Health. Charleston,
SC.
Feb. 2000: P. Guttorp: Environmental standards: A statistical approach. UC Santa Barbara, CA.
Mar. 2000 L. Conquest: Incorporating Judgement into Ecological Sampling. University
of Uruguay in Montevideo, Uruguay.
Mar. 2000 J. Wakefield: Modeling spatial variation in risk, ENAR meeting, Chicago, IL.
Apr. 2000 J. Wakefield: Modeling spatial variation in risk. Pacific Northwest Statistics
Meeting, UBC, Vancouver, Canada.
Apr. 2000 A. Raftery: Inference for Deterministic Simulation Models: The Bayesian
Melding Approach. Conference on the Statistical Analysis of Computer Codes,
Gregynog, Wales.
Apr. 2000 S. Bates: Bayesian Assessment of Uncertainty in Deterministic Environmental
Exposure Models. The Utility of Bayesian Decision Analysis and Environmental
Problems. Interface 2000.
Apr. 2000 T. Gneiting: Covariance functions for spatial and spatio-temporal data: recent
developments. 6th International Geostatistics Congress, Cape Town (South Africa).
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May 2000 P. Guttorp: Setting environmental standards–A statistician's approach. Statistics: Reflections on the past and visions for the future. Conference in honor of C. R.
Rao's 80th birthday.
May 2000 P. Craigmile: Wavelet Based Parameter Estimation of Trend Contaminated
Long Memory Processes. Bernoulli World Congress, Guanajuato, Mexico.
May 2000 B. Das: Estimating Global Temperature using Anisotropic Global Covariance
Functions. Bernoulli World Congress, Guanajuato, Mexico.
May 2000 F Bunea: A Model Selection Approach to Partially Linear Regression. Bernoulli World Congress, Guanajuato, Mexico.
Jun. 2000 L. Conquest: Analysis of Short Repeated Measures Series from Designed Experiments. Oceanic Institute, Waimanalo, HI.
Jun. 2000 T. Gneiting: Criteria of Pólya type for radial positive definite functions. Université d'Angers, France.
Jul. 2000 P. Craigmile: Decorrelation Properties of Wavelet Based Estimators for Fractionally Differenced Processes. Wavelet Applications in Signal Processing minisymposium, 3rd European Congress in Mathematics, Barcelona, Spain.
Jul. 2000 T. Gneiting: Covariance functions for spatial and spatio-temporal data: recent
developments. International Conference on Spatial Statistics in the Agro-, Bio- and
Geosciences, Freiberg (Germany).
Aug. 2000 S. Bates and A. Raftery: Assessing Deterministic Environmental Exposure
Models. Joint Statistical Meetings, Indianapolis, IN.
Aug. 2000 P. Guttorp, P. D. Sampson, D. Damian, S. Mitra and W. Meiring: A Covariance-Based Approach to Assessment of Environmental Air Pollution Models. Joint
Statistical Meetings, Indianapolis, IN.
Aug. 2000 E. D. Ford: Assessment of deterministic models in the ecological and environmental sciences. Joint Statistical Meetings, Indianapolis, IN.
Aug. 2000 M. Handcock, J. Sedransk and A. Olsen: Ecological Assessment of Riverine
Systems by Combining Information from Multiple Sources. Joint Statistical Meetings, Indianapolis, IN.
Aug. 2000 J.-Y. Courbois: Horvitz-Thompson based estimators for finite population variance components. Part I: The population variance. Joint Statistical Meetings, Indianapolis, IN.
Aug. 2000 T. Lumley: Is it true, is it kind, is it necessary? International Society for Environmental Epidemiology meeting, Buffalo, NY.
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Sep. 2000 P. Sampson: Developments in the Modeling of the Nonstationary Spatial Covariance Structure of Environmental Processes. TIES/SPRUCE 2000. Sheffield, UK.
Sep. 2000 J. Wakefield: A critique of ecological studies. Imperial College, London, UK.
Sep. 2000 J. Wakefield: A critique of ecological studies. European meeting on spatial and
computational statistics, Ambleside, UK.
Sep. 2000 E. Park: Multivariate Receptor Models and Model Uncertainty. Fourth International Conference on Environmetrics and Chemometrics, Las Vegas, NV.
Sep. 2000 T. Lumley: Visualising high-dimensional data in time and space: ideas and
tools from the Orca Project. Fourth International Conference on Environmetrics and
Chemometrics, Las Vegas, NV.
Sep. 2000 L. Conquest: Incorporating Judgment in Ecological Sampling. Fourth International Conference on Environmetrics and Chemometrics, Las Vegas, NV.
2000-2001
December 2000. T. Gneiting: Positive definite functions: basic facts, applications, and
challenges. Universität Tübingen, Germany,
January 2001. T. Gneiting: Criteria of Pólya type for positive definite functions, with applications in analysis, numerical analysis, and statistics. Universität Erlangen, Germany)
March, 2001 J. P.Hughes: Weather simulation methods. Plenary talk at 8th International
Meeting on Statistical Climatology, Lüneburg, Germany,
March 2001. J. P. Hughes: Hierarchical models for studying climate variability and climate change in SW Australia. ENAR Annual Meeting, Charlotte, SC,
May 2001. T. Gneiting: Nonseparable covariance models for space-time data. Technical
University of Vienna, Austria.
June 2001. T. Gneiting: Nonseparable covariance models for space-time data. GSF Research Center for Environment and Health, Munich, Germany.
June 2001. P. D. Sampson and T. Gneiting: Issues in geostatistical space-time modelling.
NSF-CBMS Regional Conference on Environmental Statistics, University of Washington, Seattle.
June, 2001. P. Guttorp: Meteorological adjustment of air pollution data. NSF-CBMS Regional Conference on Environmental Statistics, University of Washington, Seattle.
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July 2001. S. M. Bartell, W. C. Griffith, R. A. Ponce, and E. M. Faustman. Temporal
fallacy in biomarker based exposure inference. Poster, Environmental Protection
Agency STAR Fellowship Conference, Silver Spring, Maryland,
July 2001. T. Gneiting: Correlation models in spatial statistics and positive definite functions. SIAM Annual Meeting, Minisymposium on Spatial Statistics, San Diego, CA.
July 2001. T. Gneiting: Nonseparable, stationary covariance functions and space-time geometry. French Mathematical Research Institute, Luminy, France.
July-August 2001. P. Guttorp: Six lectures on Inference for Stochastic Processes in Environmental Science. Fifth Brazilian School in Probability, Ubatuba, Brazil.
August 2001. Besag, J.E. and Higdon, D.M.: Bayesian analysis of agricultural field experiments. Joint Statistical Meetings, Atlanta, GA.
August 2001. M. Handcock: A Two-part Model for Semicontinuous Spatial Data. Joint
Statistical Meetings, Atlanta, GA.
August 2001. T. Lumley: Window Subsampling for Spatially Correlated Censored Data.
Joint Statistical Meetings, Atlanta, GA.
August 2001. E. S. Park. Multivariate receptor modeling for temporally correlated data by
using MCMC, Joint Statistical Meetings, Atlanta, GA.
August 2001. P. D. Sampson,: Air Quality Monitoring Network Design Using Pareto Optimality Methods for Multiple Objective Criteria. Joint Statistical Meetings, Atlanta,
GA.
August 2001. S. Bates: Bayesian Inference for Deterministic Simulation Models for Environmental Assessment. Environmetrics 2001, Portland OR. This received awards for
best student paper (joint) and best risk analysis paper.
August 2001. L. Conquest: Ranked Set Sampling and Other Double Sampling Procedures: Incorporating Judgement into Ecological Sampling; Environmetrics 2001,
Portland OR.
August 2001. P. Guttorp: Meteorological Adjustment of Air Pollution Data. Environmetrics 2001, Portland OR.
August 2001. P. Guttorp: Bayesian Estimation of Non-stationary Spatial Processes Using
the Sampson-Guttorp Deformation Approach. Environmetrics 2001, Portland OR
August 2001. A. Steel: Applications of Ratios in Monitoring Salmonid Populations: The
Problem with Random Denominators. Environmetrics 2001, Portland OR.
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August, 2001; P. Craigmile: Wavelet-Based Maximum Likelihood Estimation for Trend
Contaminated Long Memory Processes, Recent Developments in Time Series section, European Meeting Of Statisticians 2001, Funchal , Madeira,
August 2001. S. M. Bartell, W. C. Griffith, R. A. Ponce, and E. M. Faustman. Temporal
fallacy in biomarker based exposure inference. Poster, Third Annual UC Davis Conference for Environmental Health Scientists, Napa, California
August, 2001. E. S. Park: Multivariate receptor modeling for air quality data in space
and/or time, International Statistical Institute meeting, Seoul, Korea
September, 2001. Drimal M., Hruba F., Koppova K.: Analyses of relationship between air
pollution and health with use of GIS. Seminar “Air Pollution and Health”, Belusske
Slatiny.
2001-2002
February 2002. P. Guttorp: Some visualization problems in environmental statistics. University of Idaho.
April 2002. P. Guttorp: Setting environmental standards–a statistician's approach. RAND,
Santa Monica.
June, 2002. P. Guttorp: Bayesian estimation of nonstationary spatial covariance. Statistical Society of Canada annual meeting, Hamilton, ON, Canada.
June, 2002. P. Guttorp: Meteorological adjustment of air quality data. IMPACT short
course, Environmetrics 2002, Genoa, Italy.
June 2002. P. D. Sampson: A geostatistical approach to assessment of regional air quality
models. Environmetrics 2002, Genoa, Italy.
June 2002. F. Bruno: A simple nonseparable space-time covariance model for ozone. Environmetrics 2002, Genoa, Italy.
August 2002: P Courbois: Model-Aided Sampling Designs for Spring Chinook Salmon in
the Middle Fork Salmon River. Joint Statistical Meetings, New York.
August 2002. T. Lumley: Generalised Linear Models for Sparsely Correlated Data. Joint
Statistical Meetings, New York.
August 2002. A. Raftery: An Efficient Markov Chain Monte Carlo Proposal Distribution
for Ridgelike Target Distributions Using Nearest Neighbors. Joint Statistical Meetings, New York.
August 2002. L. Conquest: Model-assisted and Design-based Sampling Approaches in
Sampling of Natural Resources. Joint Statistical Meetings, New York.
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August 2002. T. Cardoso: A Hierarchical Bayes Model for Combining Precipitation
Measurements from Different Sources. Joint Statistical Meetings, New York.
August 2002. P. Heagerty: Longitudinal Categorical Data and Likelihood Inference. Joint
Statistical Meetings, New York.
September, 2002. P. Guttorp: Recent advances in estimating nonstationary spatial covariance. Royal Statistical Society International Meeting, Plymouth, UK.
September, 2002. P. Courbois: Model-Aided Sampling Designs for Salmon Population
Status. Annual Conference: Statistical Survey Design and Analysis for Aquatic Resources. Colorado State University Ft. Colins Co., Department of Statistics.
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Appendix D. Workshop agendas
ORD-NRCSE Environmental Statistics Workshop
309 Parrington Hall (the Forum), University of Washington
January 21-22, 1997
Tuesday, January 21
8:30 Welcome and Introductions
Peter Guttorp/Larry Cox
8:45 About the Center
Peter Guttorp
9:00 Technology Issues
David Madigan
9:30-12:00 I. Space-Time and Meteorological Models
9:30 Space-time covariance
Paul Sampson
9:50 Spatial/temporal modeling
- spatial/temporal structures
- multicomponent geochemical fingerprint analyses of anion/cation mixtures
George Flatman
10:10 Spatial design and analysis
- spatial methods for design and evaluation of monitoring networks
- combining spatial and GIS methods for environmental assessment
Larry Cox
10:30 Combining ecological data over spatial and temporal scales
Tony Olsen
10:40 Aggregation techniques for decision support
Brian Eder
10:55 Floor discussion
11:45 Lunch and Small Group Discussions I
1:00-3:15 II. Ecological Assessment
1:00 Ecological indicators
Jim Karr
1:20 Space-time models for proportions
Peter Guttorp
1:40 Ecological indicators
-compositional data: its use in constructing ecological indicators
Tony Olsen
1:55 QA for regional scale assessments
Iris Goodman
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2:10 Ecological/landscape systems
- integration of ecological process models to assess consequences
of landscape pattern
- statistical approaches to compare expected to observed values in
landscape indicators
- statistical approaches to assess accuracy and confidence in various
landscape composition and pattern indicators
Bob Brown
2:40 Floor discussion
3:15 Break
3:30-4:45 NRCSE Weekly Seminar (in Smith 211)
3:30 Effects of Forest Management on Flooding in the Western
Cascades
Dennis Lettenmaier
4:40 Small Group Discussions II
5:30 Adjourn
7:30 Dinner at Ivar’s Salmon House
Wednesday, January 22
8:00-11:30 III. Model Assessment
8:30 Assessing model uncertainty
Adrian Raftery
8:50 Model choice using Pareto optimality
David Ford
9:10 Model validation
- development of statistical methods for model validation when
input variables are subject to error
- model validation and transport
Larry Cox
9:30 Estimation from data bases having differing quality assurance
parameters
John Warren
9:45 The need for statistical tools to quantify uncertainties in
inventories of emissions to the atmosphere
William Benjey
10:00 Floor discussion
10:45 Break
11:00 Small Group Discussions III
11:30 Lunch
12:30-2:15 IV. Environmental Sampling and Analysis
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12:30 Environmental sampling
Loveday Conquest
12:50 Sampling methods, quality assessment and human exposure
David Marker and Bob
Clicker
Bob Brown
1:10 Sampling designs
- efficient immunochemical measurement screens
- remote sensing sampling designs
- field sampling designs
- hazardous waste identification rule
- human exposure surveys
1:35 Meta-analytic methods for site characterization
Larry Cox
1:40 Floor discussion
2:00 Break and Small Group Discussions IV
2:30-5:00 V. Toxicology and Risk Assessment
2:30 Risk assessment
Alison Cullen
2:50 Ambient air pollution and health -- what can we learn about
risks?
Lianne Sheppard
3:10 Toxicology I
- predictive quantitative dose-response models in neurotoxicology
- quantitative models of developmental toxicity
- correlation of immune system function data with resistance to diseases
Woody Setzer
3:35 Toxicology II
- modeling the relationship between exposure and toxic severity
using regression on ordinal response data
- ratios analysis for RfD uncertainty analysis
- exposure factor distributions and dermal exposure activity patters
Dan Guth
4:05 Risk assessment
- stochastic systems analysis of physiologically based pharmacokinetic
(PBPK) and microenvironmental exposure/dose models
- Monte Carlo confidence bounds
- parameter estimation for compartmental models
- optimal status and trends monitoring using Bayesian analysis
George Flatman
4:20 Extensions of meta-analysis: hierarchical methods for
combining studies
Larry Cox
4:25 Floor discussion
5:00 Closing Observations and Next Steps
Peter Guttorp/Larry Cox
5:15 Small Group Discussions V
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6:00 Adjourn
Cascadia Tropospheric Ozone Peer Review Meeting
Day 1
7:45 Overview/Introduction
8:15 Mesoscale Modeling with MM5
Synoptic Scale Meteorology during Ozone Episodes
Cliff Mass and Ernie Recker, Atmospheric Sciences, UW
9:15 Photochemical Grid Model Simulations
Brian Lamb, Laboratory for Atmospheric Sciences, WSU
10:15 Break
10:30 Questions
10:50 Adjusting surface ozone for meteorology and emissions
prior to the investigation of time trends
Joel H. Reynolds, Statistics Department, UW
11:35 Spatial Distribution of Ozone Dosages in Western Washington
Dave Peterson, College of Forest Resources, UW
12:20 LUNCH (on your own)
1:25 Questions
1:45 Hydrocarbon and Carbonyl Measurements
Hal Westberg, Laboratory for Atmospheric Sciences, WSU
2:30 Analysis of Ozone Precursor Data Sets
Halstead Harrison, Atmospheric Sciences, UW
3:15 Break
3:30 Tunnel Measurements and Chemical Mass Balance Analysis
Eric Fujita, University of Nevada, Desert Research Institute
4:15 Questions
4:35 Key Issues for Discussion
Peer review panel
5:00 Adjourn
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6:30 Dinner Cruise and Reception
Day 2
Session 1.
9:00-12:00 Workgroups meet (Closed meeting)
12:00 LUNCH (on your own)
1:00 Round-table discussion (Closed meeting for scientists in workgroups)
Session 2.
9:00-10:00 Results from the Interview Process conducted throughout Cascadia
Regarding the Policy and Science Issues of Ozone and Fine Particulate
Jay Hayney, Systems Applications International
10:00 Discussion and Questions
10:45 Break
11:00 Important Cross Boundary Issues: Canadian Perspective
Bruce Thompson, Environment Canada
12:00 LUNCH (on your own)
Combined Sessions 1 and 2:
2:30 Summary and Findings of the Meeting
Peer Review Panel
Environmental Monitoring Surveys Over Time
MONDAY, APRIL 20 ___________________________________________________________
Opening Remarks: 8:30–8:45 a.m.
Chair: Loveday Conquest
Peter Guttorp, NRCSE and University of Washington
Loveday Conquest, NRCSE and University of Washington, Workshop logistics
SESSION 1: 8:45–9:45 a.m.
PLENARY SESSION
Chair: Loveday Conquest
Environmental Surveys Over Time
Wayne Fuller, Iowa State University
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SESSION 2: 10:00–11:45 a.m.
TERRESTRIAL SURVEYS
Chair: Ray Czaplewski
Overview of National Natural Resource Monitoring Programs
Anthony R. Olsen, US EPA Western Ecology Division
Design and Estimation for the National Resources Inventory
Sarah Nusser, Iowa State University
USDA Forest Service Strategic Level Forest Inventory and Monitoring
Andy Gillespie, USDA Forest Service
Discussant: Steve Stehman, SUNY College of Environmental Sciences and Forestry
SESSION 3: 1:00–2:15 p.m.
PERSPECTIVES FROM HUMAN POPULATION AND INSTITUTIONAL SURVEYS
Chair: Sarah Nusser
Design Features of the Survey of Income and Program Participation (SIPP) and the Survey of
Program Dynamics (SPD)
Franklin Winters, US Census Bureau
The Agricultural Management Study: A Multi-Purpose Survey of Resource and Economic
Management of Farms
Carol House, USDA National Agricultural Statistics Service (NASS)
Discussant: John Eltinge, Texas A&M University
SESSION 4: 2:30–3:45 p.m.
AQUATIC AND AVIAN SURVEYS
Chair: Tony Olsen
Surveying Breeding Duck Populations in North America
Graham Smith, US Fish & Wildlife Service
Spatially Restricted Surveys Over Time for Aquatic Resources
Donald L. Stevens, Jr., Dynamac Corporation; Anthony R. Olsen, US EPA Western Ecology
Division
Discussant: Lyman McDonald, WEST, Inc.
SESSION 5: 4:00–5:15 p.m.
REMOTE SENSING AND SURVEYS
Chair: Ray Czaplewski
Organizing and Interpreting Statewide Satellite Imagery Over Time
Bill Befort and Jim Rack, Forestry Division, Minnesota Department of Natural Resources
Merging Forest Inventory Data with Satellite-Based Information in Utah
Gretchen Moisen, USFS Intermountain Research Station; Thomas C. Edwards, Jr., USGS
BRD Utah State University; Tracey Frescino, USFS Intermountain Research Station
Discussant: Dean Thompson, NRCS Natural Resources Inventory and Analysis Institute
Joint Conference Dinner at Ivar’s Salmon House (optional)
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TUESDAY, APRIL 21 ___________________________________________________________
SESSION 6: 8:00–9:45 a.m.
DESIGN ISSUES IN AQUATIC AND WATERSHED SURVEYS
Chair: Tony Olsen
A Multi-Year Lattice Sampling Design for Maryland-Wide Fish Abundance Estimation
Douglas Heimbuch, John Seibel, Harold Wilson, PBS&J; Paul Kazyak, Maryland Department of Natural Resources
Current Applications of Sampling for Watershed and Riparian Health Assessment
Jean Opsomer, Iowa State University
Trend Detection in Repeated Surveys of Ecological Resources
N. Scott Urquhart, Oregon State University; Thomas M. Kincaid, Dynamac Corporation
Discussant: Joe Sedransk, Case Western Reserve University
SESSION 7: 10:00–11:45 a.m.
ANNUALIZED MODIFICATIONS TO TERRESTRIAL SURVEYS
Chair: Ray Czaplewski
The Annual Forest Inventory System
Ronald E. McRoberts, USFS North Central Forest Experiment Station
The Southern Annual Forest Inventory System
Gregory A. Reams, USDA Forest Service Southern Research Station; Paul C. Van Deusen,
NCASI, Northeast Regional Center, Tufts University
Annualizing the National Resources Inventory
F. Jay Breidt and Wayne A. Fuller, Iowa State University
Discussant: Scott Urquhart, Oregon State University
SESSION 8: 12:45–2:00 p.m.
SURVEY INTEGRATION PANEL DISCUSSION
Chair: Loveday Conquest
Hans Schreuder, USFS Rocky Mountain Research Station
Jeff Goebel, NRCS
Carol House, USDA NASS
Anthony Olsen, US EPA Western Ecology Division
Paul Geissler, USGS BRD
Bill Williams, BLM
Discussant: Tim Gregoire, Virginia Polytechnic and State University
SESSION 9: 2:15–3:30 p.m.
NONSAMPLING ERRORS
Chair: Sarah Nusser
Some Methods for Evaluating the Quality of Survey Data
Paul P. Beimer, Research Triangle Institute
Nonsampling Errors in EMAP: How Large, How Intractable?
Virginia Lesser, Oregon State University
Discussant: Lynne Stokes, University of Texas at Austin
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SESSION 10: 3:45–5:30 p.m.
DATABASE CONSTRUCTION AND DISSEMINATION
Chair: Tony Olsen
Database Design Considerations for the Forest Inventory and Analysis Program
Mark H. Hansen, USDA, Forest Service
Imputation in the National Resources Inventory
F. Jay Breidt and Kevin W. Dodd, Iowa State University
Processing and Analyzing Data from the Survey of Income and Program Participation (SIPP)
Barry Fink, US Census Bureau
Discussant: Dan Carr, George Mason University
WEDNESDAY APRIL 22 ________________________________________________________
SESSION 11: 8:00–9:45 a.m.
STATISTICAL ESTIMATION: APPROACHES
Chair: Loveday Conquest
Composite Estimation: An Example from the Current Population Survey
Stephen M. Miller, US Bureau of Labor Statistics
Modeling Time Series of Small Area Survey Estimates
Mark Otto and Bill Bell, US Census Bureau
Small Area Estimators for Environmental Surveys
David A. Marker, Westat, Inc.
Discussant: John Eltinge, Texas A&M University
SESSION 12: 10:00–11:45 a.m.
STATISTICAL ESTIMATION: APPLICATIONS
Chair: Sarah Nusser
Combining Results from Different Surveys Drawn Using a Coordinated Design
Phillip S. Kott, USDA National Agricultural Statistics Service
Bayesian Inference for Estimating Hunting Success Rates Based on Survey Data
Zhuoqiong He and Dongchu Sun, Missouri Department of Conservation and University of
Missouri-Columbia
Spatio-Temporal Modeling and Design: Applications to Environmental Data
Christopher K. Wikle, National Center for Atmospheric Research
Discussant: Mark Handcock, Pennsylvania State University
SESSION 13: 1:00–2:15 p.m.
STATISTICAL ESTIMATION: ANNUALIZED INVENTORIES
Chair: Tony Olsen
A Comparison of Annual Survey Design Alternatives and Estimation Methods
Charles T. Scott, US Forest Service; Michael Köhl, Swiss Federal Institute for Forest, Snow
and Landscape Research
Forest Monitoring with Multivariate Time-Series of Remotely Sensed Areal Estimates, Field
Observations and Prediction Models under Dependent and Heteroscedastic Measurement and
Prediction Errors
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Raymond L. Czaplewski, USDA Forest Service Rocky Mountain Research Station
Discussant: Paul Van Deusen, NCASI, Northeast Regional Center, Tufts University
SESSION 14: 2:30–3:30 p.m.
CONCLUDING PANEL DISCUSSION
Chair: Organizing Committee
Summary comments from Chairs of prior sessions
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NRCSE/EPA workshop at Corvallis EPA
9:30 Peter Guttorp, Director of NRCSE:
Overview of the Center
10:00 Loveday Conquest, Professor, Fisheries:
Integrating judgment into ecological sampling
10:40 Ashley Steel, Graduate student, Quantitative Ecology:
In-stream factors controlling juvenile chinook migration
11:20 David Ford, Professor, Forest Resources:
Multi-criteria assessment of ecological process models
1:30 Dennis Lettenmaier, Professor, Civil Engineering:
Hydrologic effects of logging in Western Washington
2:10 Dean Billheimer, Assistant Professor, Statistics:
Measures of environmental quality and compositional data
2:50 Peter Guttorp, Professor, Statistics:
Graphical modeling as a tool to study the components of the IBI
Particulate Methodology Workshop
October 19 (Monday)
Evening session chair: Tim Larson (Univ of Washington)
7:00 pm
Physical and Chemical Characteristics of Atmospheric
Particulate Matter
Glen Cass (Cal Tech)
7:50 pm
Instrumentation and Measurement
Candis Claiborn (Washington State Univ)
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October 20 (Tuesday)
Morning session chair: Clare Weinberg (NIEHS)
8:40 am
Design Considerations for Air Pollution Exposure Effect
Studies
Lianne Sheppard (Univ of Washington)
Discussant: David V. Bates (Univ of British Columbia)
10:30 am
Modeling Vancouver PM Fields for Health Impact Analysis
Jim Zidek (Univ of British Columbia)
Discussant: Mark Kaiser (Iowa State)
Evening session chair: Phil Hopke (Clarkson Univ)
7:00 pm
Meteorology and Particle Transport
Jason Ching (NOAA/EPA)
7:50 pm
Source Apportionment
Ron Henry (Univ of Southern California)
October 21 (Wednesday)
8:30 am
Working group reports and discussion
Morning session chair: Gerald van Belle (Univ of Washington)
9:15 am
Statistical Approaches to Handling Exposure Measurement
Error in the Children's Health Study
Kiros Berhane (Univ of Southern California)
11:00 am
Models for Improved Exposure Quantification
Haluk Ozkaynak (U.S. EPA)
Discussant: Paul Switzer (Stanford)
Evening session chair: George Thurston (NYU)
7:00 pm
Health Effects of Air Pollution: "Particles in the Air: Guilty as
Charged?"
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Joe Mauderly (Lovelace Respiratory Research Inst)
7:50 pm
Regulatory Issues
Terence Fitz-Simons (U.S. EPA)
October 22 (Thursday)
8:30 am
Working group reports
Morning session chair: Jerry Sacks (NISS)
9:15 am
Single Pollutant Effects in Multiple Pollutant Data
Suresh Moolgavkar (Univ of Washington)
Discussant: Arden Pope (BYU)
11:00 am
Assessment of Statistical Models
Merlise Clyde (Duke Univ)
Discussant: Adrian Raftery (Univ of Washington)
12:30 pm
Conference summary
Larry Cox (U.S. EPA)
Quality Assurance of Environmental Models
Tuesday, September 7, 1999
Defining the problems of Model Assessment and Quality Assurance
Session Chair: Tom Barnwell
8:45am
Naomi Oreskes, Department of History, University of California,
San Diego
Model Assessment: Where Do We Go From Here?
9:30am
David Ford, College of Forest Resources and NRCSE,
University of Washington
Defining Similarities and Differences in Quality Assurance Requirements
for Classes of Environmental Models
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10:45am
Ray Whittemore, National Council of the Paper Industry for Air and
Stream Improvement, Inc.
EPA's BASINS MODEL - Is it good science or serendipitous modeling?
11:30am
Jan Rotmans, International Centre for Integrative Studies (ICIS), Faculty
of General Sciences, Maastricht University
Uncertainty in Integrated Modeling: a Multi-Perspective Approach
12:15pm
Robin L Dennis, Atmospheric Sciences Modeling Division, US Environmental Protection Agency
Facing Prediction and Multimedia Modeling, Model Evaluation is a Sci-
ence
and Knowledge Task: Recommendations from Air Quality Modeling
2:00pm
Iris Goodman, Landscape Ecology Branch, National Exposure Research
Laboratory, EPA Las Vegas
Ecological modeling to assess the effect of land cover on water resources:
A
summary of approaches and modeling issues
2:30pm
William McDonnell, EPA Chapel Hill
Exposure-Response Modeling of Ozone-Induced FEV1 Changes in
Humans: Effects of Concentration, Duration, Minute Ventilation, and Age.
Wednesday, September 8, 1999
Development of Methodological and Quantitative Techniques
Session Chair: Peter Guttorp
8:30am
Andrea Saltelli, Institute for Systems, Informatics and Safety, The European Commission Joint Research Centre
Sensitivity analysis and the quality assessment of environmental models
9:10am
Adrian Raftery, Statistics and NRCSE, University of Washington
Statistical Inference for Deterministic Simulation Models: The Bayesian
Melding Approach
9:50am
Tony O'Hagan, University of Sheffield
Bayesian Calibration and Model Correction
11:00am
Joel Reynolds, Statistics and NRCSE, University of Washington
Open Questions in Applying the Pareto Optimal Model Assessment Cycle
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11:40am
nia,
Wendy Meiring, Statistics and Applied Probability, University of CaliforSanta Barbara
Thursday, September 9, 1999
Assurance of Models Used in Environmental Regulation
Session Chair: Robin Dennis
8:45am
David Stanners, Integrated Assessment and Prospective Analysis, European Environment Agency
“Best Available Information” to support European policy making
(what is good enough and sufficient, and how do we get there)
9:30am
William L. Richardson, ORD, NHEERL, MED-Duluth, Large Lakes Research Station, US EPA
Modeling Quality Assurance Plan for the Lake Michigan Mass Balance
Project
10:45am
Tom Barnwell, Bruce Beck, Lee Mulkey, Environmental Protection Agency, Athens, Georgia
Model Use Acceptability Guidance:
Part 1) Model Validation for Predictive Exposure Assessments: A Draft
Protocol
Linda Kirkland, Environmental Protection Agency, Washington D.C.
Model Use Acceptability Guidance:
Part 2) Updating the Protocol for General Agency Use: Stakeholder Input
11:30am
12:15pm
Helen Dawson, Superfund Program Support, U.S. Environmental Protection Agency
Evaluating Performance and Reliability of Intermedia Transfer Models
Used in Probabilistic Human Health Risk Assessment
Friday, September 10, 1999
The Way Ahead: Linking Research and Practice on Model Assurance
Session Coordinator: Bruce Beck
8:45am
Discussion Groups
11:00am
Discussions at the Workshop - A Synthesis
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4th yr:
EPA Las Vegas
Tuesday, Dec 14
10:15 Peter Guttorp (Statistics): Research at NRCSE
11:00 Thomas Lumley (Biostatistics): Orca: A toolkit for visualizing structured
high-dimensional data
11:45 Eun Sug Park (NRCSE): Multivariate receptor modeling for temporally correlated data using MCMC
2:00 David Ford (Forestry): Pareto optimal model assessment
2:45 Adrian Raftery (Statistics): Inference for deterministic simulation models:
The Bayesian melding approach
3:30 Discussion of statistical issues for site characterization and assessment
Wednesday, Dec 15
9:15 Loveday Conquest (Fisheries): Use of ranked set sampling in stream research
10:00 Discussion of statistical issues for landscape ecological assessments
1:00 Alison Cullen (Public Affairs): Exposure assessment - A tale of two surveys
1:45 Tom Lewandowski (Environmental Health): Linked toxicodynamic and toxicokinetic model for developmental neurotoxicity
2:30 Discussion of toxicokinetic and toxicodynamic modeling
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Exposure assessment in environmental and occupational health
Donovaly, Slovakia, October 25-26, 1999
Organizers
Michael Brauer, UBC; Alison Cullen, NRCSE; John Vandenberg, U.S. EPA
Program
Monday, Oct. 25
Session 1: Problem definition and study design
Alison Cullen, Michael Brauer and Frantiska Hruba
Session 2: Data collection and chemical analysis
Michael Brauer, Eva Mihalíkovâ and Peter Miskovic
Session 3: Data analysis
Alison Cullen, Michael Brauer and Kaja Hruba
Session 4: Poster display
Tuesday, Oct. 26, 1999
Session 5: Results interpretation and risk characterization
Alison Cullen, Eleonora Fabianova and John Vandenberg
Session 6: Reporting results and risk communication
Eleonora Fabianova and John Vandenberg
Session 7: Discussion and implications
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Large Data Sets
NCAR, Boulder, Colorado, July 24-26, 2000
Organizers:
Di Cook, Iowa State, Chris Wikle, University of Missouri, David Madigan, Soliloquy
Inc., Doug Nychka, NCAR GSP, Peter Guttorp, NRCSE
Program
Monday, July 24
8:35 - 9:35 - Jerry North, Texas A&M University: “Some Estimation Problems Utilizing
Large Climate Data Sets”
9:35 - 10:05 - Lawrence Buja, NCAR “Community Climate System Model (CCSM) Data”
10:30 - 11:30 - Di Cook, Iowa State University “Issues and Approaches for Visualization
of Large Multi-Dimensional Data”
1:00 - 2:00 - Padhraic Smyth, U. California-Irvine “Part I: What is Data Mining?”
2:00 - 2:45 - Alexey Kaplan, Lamont Doherty Earth Observatory, Columbia U. “Leastsquares optimal analyses of historical climate data sets I: Problem set-up and existing
solutions”
3:15 - 4:00 - Alexey Kaplan, Lamont Doherty Earth Observatory, Columbia U. “Leastsquares optimal analyses of historical climate data sets II: Difficulties and prospects”
Tuesday, July 25
8:30 - 9:30 - Dan Carr, George Mason University “Several Templates for Looking at
Large Georeferenced Data Sets”
9:30 - 10:30 - Marina Meila, Carnegie Mellon University and the University of Washington, “Fast Algorithms for Learning Tree Graphical Models in High Dimensions”
11:00 - Noon - Hsin-Cheng Huang, Institute of Statistical Science, Academia Sinica,
“Fast Spatial Prediction of Global Processes from Satellite Data”
1:30 - 2:30 - Mark Gahegan, Penn State University “Using Expertise to Guide Geoscientific Visualization”
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Wednesday, July 26
8:30 - 9:30 - Dave Higdon, Duke University “Building Dependence Structure for Large
Space-Time Datasets”
9:30 - 10:00 - Tim Hoar, NCAR “Getting to know a large dataset: Satellite Observations
of surface quantities.”
10:30 - 11:30 - Padhraic Smyth, University of California, Irvine “Part II- Data mining:
The potential role of data mining in atmospheric and environmental sciences”
Internal workshop
January 20, 2000
SPEAKER SCHEDULE
8:30 am--8:45
8:45
9:10
Introduction
Paul Sampson
Thomas Lumley
Peter Guttorp
Covariance modeling
Extending data visualization to structured
data: the Orca project
Standards
9:35
Mary Lou Thompson
10:00--10:45
10:45 am
11:15--12:15
Coffee break and posters
Lianne Sheppard
Health effects of PM
Small group discussion
1:45 pm
2:15
Alison Cullen
Adrian Raftery
2:45
Ashley Steel
3:15--4:00
4:00--5:00
5:00--5:30
Coffee break and posters
Small group discussion
Wrap-up
Larry Cox
The Slovakia project
Statistical Inference for Deterministic Simulation Models: The Bayesian Melding Approach
The Truth About Science: A Hands-On Scientific Research Curriculum
POSTER PRESENTATIONS:
Enrica Bellone, Jim Hughes and Peter Guttorp
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A stochastic model for precipitation amounts at multiple stations
Dean Billheimer
Compositional Receptor Modeling
Elaine Faustman
Linking Toxicokinetic and Toxicodynamic Models for Methylmercury Developmental Toxicity
William Griffith
Temporal Fallacies in Biomarker Based Exposure Inference
Patrick Heagerty
Spatial Transition Models and Forecasting of Gypsy Moth Defoliation
Nick Hedley, Tim Nyerges
Thomas Lumley
Air pollution time series: case-crossover analyses and other difficulties.
Nicolle Mode, Loveday Conquest, and David Marker
Ranked Set Sampling for Ecological Monitoring: Costs, Comparisons and Compromises
Kerrie Nelson
Statistical methods for modeling multiply censored data
Don Percival, Peter Craigmile and Peter Guttorp
Wavelet-based trend detection and estimation
Eun Sug Park
Multivariate Receptor Modeling for Temporally Correlated Data by Using
MCMC
Chris Bretherton
Variations in Pacific Northwest Snowpack and Regional Climate—Past and Future
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Teaching Environmental Statistics at the UW
Attendees: Joyce Cooper (Mechanical Engineering), Peter Guttorp (Statistics), June Morita (Bothell Interdisciplinary Arts and Sciences), Don Percival (Applied Physics Laboratory), Marcia Ciol (CQS), Mary Lou Thompson (Biostatistics), Bruce Bare (College of Forest Resources), Loveday Conquest (Fisheries), Craig Zumbrunnen (Geography), Sally Liu
(Public Health), Suzanne Withers (Geography), Alison Cullen (Public Affairs), Christy
Howard (CQS)
Who is the audience for Environmental Statistics courses?




Undergraduate Program on the Environment Students
- Required to take one basic statistics course and one capstone course
Evans School Master’s students
- Required to take one course in analysis beyond Evans 2 introductory stat courses
- Roughly 20 students each year are environmental concentrators.
Natural Sciences grad students - CFR, Fisheries, SMA, Atmospheric Science, QERM,
etc.
Environmental Management Certificate Program graduate students from throughout
UW
- Must take 2 electives in some area of environmental management, analysis, etc.
- Need abilities in critical study design and critiquing design, not just methods.
- Develop skills in strategic planning and decision making
I. Offerings at the UW
A. Geography 426 - intro to use of stat in geography emphasize what's inappropriate to
use in analysis weekly computer lab session with instructor who demonstrates
how to run computer analysis and interpret the data. Students are given data, they
run analysis, and interpret results.
B. Geography 326 - elementary statistics up to regression, focused on research design.
C. Statistics Department - Environmental Statistics - taught by Peter Guttorp in past years
1. Case-based course - ASARCO smelter in Tacoma, Port Townsend Paper Mill,
among others.
2. Students expected to propose research design based on cases, propose
research questions, analyze data.
3. Can be effective as undergraduate or graduate course - Uses same
cases for both, but adjusts the depth of the issues explored accordingly
4. Not offered recently due to lack of student interest
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II. Areas of Need
A. Spatial Statistics - No undergraduate courses offered currently. Not offered at a low
enough level to be accessible.
B. Correlated Data - There are only 2 biostat courses at the graduate level on correlated
data, but they are very specific to medicine.
C. Time Series
D. Temporal & spatial Correlation
E. Multivariate -with applications in Natural Sciences: Ecology, Biology, Fisheries,
Forestry
F. Risk Analysis/Decision-making – proposed as a future course by EM certificate program and also the Evans School of Public Affairs
G. GIS with integration of spatial statistics and GIS, many GIS courses are offered however
III. Problems in Teaching Statistics
A. Teach out of date theory from decades ago
B. Courses can be boring unless examples and cases are aptly chosen
C. Many courses rely on basic statistical knowledge, for which students tend to be illprepared.
D. In CQS, they teach service courses in statistics
1. Students are heterogeneous - with varied backgrounds and interests
2. These are a requirement for many students, not all want to be there
3. It is hard to make course relevant and interesting for everyone.
4. Instructors seek more connections with researchers/faculty in other department
to help find relevant data and research topics for student's term projects.
IV. Suggestions for improvements
A. Develop web site as a clearinghouse for statistics course information
1. Each department/faculty who teach statistics course submit
comprehensive syllabus describing what is taught in the course. The
syllabi could then be combined into one master listing and posted on the web.
B. Make up an information packet with techniques and application topics as a resource
for instructors.
C. More outreach to advisors so they know what statistics courses are available and inform their advisees about them.
D. Develop a repository of data set on environmental topics and/or relevant published articles.
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E. Especially in the CQS stat course, connect students with someone on campus who has
done statistical research in the student's area of interest. Students could receive
guidance from this person in conducting their own analyses for their course term
project.
F. Develop a Speakers Bureau to bring in researchers to give talks on how they apply statistics in their work.
1. Make this available and accessible to undergraduate students to give
them a sense of why statistics is important and how it is utilized.
G. Need more money to implement many of these ideas!
1. NRC just released a report that strengthens the link between statistical sciences
and mathematical sciences.
2. Important to know about this report and cite it in grant applications, as leverage
verifying the importance of statistics at the University.
V. Proposed Program Level Changes
A. Discussion of offering an Environmental Statistics Certificate Program for graduate
students.
B. CFR is probably offering 3 new courses - one in GIS/Intro to ArcView, and two in
spatial analysis.
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Course description for EPA Region X Risk Assessment Course
Instructors: Elaine Faustman, Scott Bartell and Bill Griffith
Each of the 21 sessions below will devote the first 20-25 minutes to a discussion of the
concept followed by 10 minutes of examples of applications, 10-15 minutes for the class
members to apply what they have learned to an exercise, and 10-15 minutes of discussion
August 7
Introduction (45 min)
Risk Assessment Framework
How to formulate questions that statistical methods can assist in answering
Describing Populations
1. General Methods (1 hr 30 min)
Sampling
Measures of central tendency and variability
Graphical Techniques
2. Parametric/Nonparametric Methods (1 hr)
General Tools
Detection Levels
Graphical Techniques
Comparison of Populations
3. Multiple Populations (1 hr)
ANOVA
Corrections for multiple corrections
4. Statistical power to make comparisons (1 hr)
5. Estimating differences between populations and power (1 hr)
6. Upper Confidence limits and interpretations (1 hr)
August 8
Combining Distributions of Populations
7. Monte Carlo Simulation methods (50 min)
8. Applications in Risk Assessment (50 min)
9. Two Dimensional Monte Carlo (50 min)
Nonparametric Methods
10. Classical methods for comparing 2 populations (50 min)
11. Randomization methods for making comparisons (50 min)
12. Graphical methods for describing distributions of measurements (50 min)
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Regression
13. Classical regression methods (50 min)
14. Confidence limits on regression (50 min)
August 9
Regression
15. Evaluating Regression models using residuals (50 min)
Toxicology
16. Survival Analysis for censored data (50 min)
17. Estimating age specific rates using Kaplan-Meier methods (50 min)
18. Comparing two populations using Cox models (50 min)
19. Meta analysis of multiple studies (50 min)
20. Analysis of noncancer studies with multiple endpoints (50 min)
21 Benchmark Dose(50 min)
Summary and evaluation
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Spatial moving averages
Sunday, May 20, 2001
9:15-9:30
Registration
9:30 – 10:00
Welcome/Overview
10:00 – 11:45
Jean Thiebaux and Discussion
11:45 – 1:00
Lunch
1:00 – 2:45
Ron Barry and Discussion
2:45 – 3:00
Break
3:00 – 4:45
Jay Ver Hoef and Discussion
Monday, May 21, 2001
8:00 – 9:45
Dave Higdon and Discussion
9:45 – 10:00
Break
10:00 – 11:45
Montserrat Fuentes and Discussion
11:45 – 1:00
Lunch
1:00 – 2:45
Doug Nychka and Discussion
2:45 – 3:00
Break
3:00 – 4:45
Chris Wikle and Discussion
Tuesday, May 22, 2001
8:00 – 9:45
Robert Wolpert and Discussion
9:45 – 10:00
Break
10:00 – 11:45
Katja Ickstadt & Nicky Best and Discussion
11:45 – 12:30
Final Discussion and Wrap-up
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NSF-CBMS Regional Conference on Environmental Statistics
Monday, June 25, 2001
8:30 am—9:00
Continental breakfast
9:00 – 10:00
Richard Smith,
Lecture #1
10:00-10:15
Discussion
10:15-10:45
Break
10:45 – 11:45
Paul Switzer
Introduction: Motivated by the question "Is global
warming really happening?", I introduce the three principal methodological themes of the series - spatial statistics, time
series analysis and extreme values - in the context of
climatological time series and some simple questions about
the nature of trends.
Air Pollution Epidemiology Using Daily Time Series:
Recent studies have tried to relate daily variations in air
Pollution monitoring data to daily variations in mortality,
using data from a number of U.S. cities. The goal is to
estimate the effect on longevity of putative changes in
pollutant levels. Because the relative pollution effects are
very small, the modeling of the data plays a critical role in the analysis.
The principal tool is a Poisson regression with a
mean function that varies daily with pollutant concentrations
and important confounding weather variables. Challenging
inferential problems arise because of variable selection,
linearity and additivity assumptions, measurement error, and seasonality. Pollution effects estimated for different cities
show variations that are geographically modeled to account for demographic differences. This lecture will discuss strengths
and weaknesses of published reports as well as
directions for further research.
11:45 – 12:00
Discussion
12:00 – 1:30
Lunch
1:30 p.m. – 2:30
Richard Smith,
Geostatistical methods I: Classical methods of
Lecture #2
spatial statistics (a.k.a. geostatistics) using stationary,
isotropic models for spatial processes. Definitions: stationary
and intrinsically stationary processes, the variogram, simple parametric
models. Estimation of the variogram, and methods
of fitting parametric models: Cressie's WLS method,
maximum likelihood, REML, Bayesian methods.
2:30 – 2:45
Discussion
2:45 – 3:15
Break
3:15 – 4:30
Roundtable discussions
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Tuesday, June 26, 2001
8:30 am—9:00
Continental breakfast
9:00 – 10:00
Richard Smith,
Geostatistical methods II: Spatial prediction and interpolation
Lecture #3
(kriging). Derivation of the basic equations: allowing for
parameter uncertainty: extensions, e.g. reconstructing a surface
from observations with measurement error. Applications to
atmospheric pollution and meteorology.
10:00-10:15
Discussion
10:15-10:45
Break
10:45 – 11:45
Jim Zidek
Professor,
Head of Statistics,
University of British
Columbia
Mapping Urban Pollution Fields From Ambient Monitoring Data: Some of the problems encountered by my coinvestigators (in particular, Nhu D Le and Li Sun) and I in mapping pollution fields, notably in Vancouver and Philadelphia.
Interest in mapping such fields stems from the desire to avoid the
deleterious effects of measurement error through the prediction
of pollution levels down to fairly fine scales of resolution, especially in estimating human exposure and its health impacts.
Among the problems are: (1) the inclusion of meteorological
effects; (2) systematically and monotone missing data patterns;
(3) the inseparability of spatial and temporal correlation in fields
corresponding to short time aggregates (hours for example). I
will describe approaches to the solution of these problems and
illustrate them with applications to both of the cities referred to
above. Particulate air pollution will be a focus of attention.
11:45 – 12:00
Discussion
12:00 – 1:30
Lunch
1:30 p.m. – 2:30
Richard Smith,
Nonstationary spatial processes: Various approaches
Lecture #4
to spatial modeling that do not assume the standard stationarity
and isotropy conditions. Haas's moving windows approach. EOF
analysis. Deformation models. Kernel models.
2:30 – 2:45
Discussion
2:45 – 3:15
Break
3:15 – 4:30
Roundtable discussions
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Wednesday, June 27, 2001
8:30 am—9:00
Continental breakfast
9:00 – 10:00
Richard Smith,
Lecture #5
Models defined by conditional probabilities:
Markov random fields and the Hammersley-Clifford theorem;
estimation by likelihood and pseudo-likelihood methods. Modern developments in which MRF models are used as priors within a
broader hierarchical structure. The primary emphasis in this
section will be on the contrast between models of this structure
and the geostatistical approaches more commonly used in
environmental statistics
10:00-10:15
Discussion
10:15-10:45
Break
10:45 – 11:45
Paul Sampson
11:45 – 12:00
Discussion
12:00 – 1:30
Lunch
1:30 p.m. – 2:30
Richard Smith,
Design of monitoring networks I: The problem of locating
Lecture #6
monitors within a network to optimize prediction- or
estimation-based criteria. Bayesian approaches to spatial data
analysis and their application to network design through entropy
criteria. Methods based on optimal design theory. Other
approaches. Designs for data assimilation.
2:30 – 2:45
Discussion
2:45 – 3:15
Break
3:15 – 4:30
Roundtable discussions
Spatial Covariance Modeling
Thursday, June 28, 2001
8:30 am—9:00
Continental breakfast
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National Research Center for
Statistics and the Environment
9:00 – 10:00
Richard Smith,
Lecture #7
10:00-10:15
Discussion
10:15-10:45
Break
10:45 – 11:45
Doug Nychka
Design of monitoring networks II
Wavelet representations for nonstationary
spatial fields.: Spatial analysis for large nonstationary
processes poses challenges in both modeling and
computation. A promising way to represent nonstationary covariance structure is by expanding the field in terms of
a wavelet basis and then building a simple, sparse model for correlations and variances among the wavelet coefficients.
In this talk a nonorthogonal wavelet basis (the W-transform)
is presented that not only appears to fit a variety of standard covariance models but is well suited to the computation of
Kriging estimates and conditional distributions. From a more conventional perspective, this wavelet-based model provides
an reasonable blending between an EOF representation
(principle components of the sample covariance matrix) and
a stationary, parametric family. This approach is illustrated
using output from a run of the Regional Oxidant Model, an
EPA pollution simulation.
11:45 – 12:00
Discussion
12:00 – 1:30
Lunch
1:30 p.m. – 2:30
Richard Smith,
Trends in Time Series: An overview of various
Lecture #8
strategies for estimating and testing trends in time series,
built around the theme of testing the significance of
observed trends in global temperature series. ARMA and
fractional ARIMA models; spectral approaches; long-range dependence. Extensions to multiple time series.
2:30 – 2:45
Discussion
2:45 – 3:15
Break
3:15 – 4:30
Roundtable discussions
Friday, June 29, 2001
8:30 am—9:00
Continental breakfast
9:00 – 10:00
Richard Smith,
Lecture #9
Extreme Values I:
The last two lectures have a somewhat different
emphasis, where we look specifically at rare events,
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National Research Center for
Statistics and the Environment
their estimation and prediction. However, the
discussion will also take in such issues as whether
extreme meteorological events are becoming more
frequent, and the spatial integration of information
about extreme events, thus providing a link with the
rest of the course. Specific topics are:
the three principal approaches to extreme value
analysis based on annual maxima, threshold
exceedances and point processes; estimation by
moment-based, maximum likelihood and Bayesian
methods; diagnostics. Extensions: extreme value
regression and trend detection, spatial models for
extremes.
10:00-10:15
Discussion
10:15-10:45
Break
10:45 – 11:45
Peter Guttorp,
Director, NRCSE
11:45 – 12:00
Discussion
12:00 – 1:30
Lunch
1:30 p.m. – 2:30
Richard Smith,
Meteorological adjustment of air pollution data:
A variety of statistical methods for meteorological
adjustment of ozone have been proposed in the literature
over the last decade or so. These can be broadly classified
into regression methods, extreme value methods,
and space-time methods. Among the crucial issues are
questions of variable selection and trend estimation. The end
use of the adjustment (e.g., monitoring trend, assessing
health effects, etc.) largely determines these issues. I will
illustrate the methods with ozone data
from the Paris region in France, and particulate matter
data from Phoenix, AZ.
Extreme Values II
Lecture #10
2:30 – 2:45
Discussion
2:45 – 3:00
Closing remarks
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