Proposal for a supplemental
Cooperative Research Agreement
CX 825834
Project Period: May 1, 1998- April 30, 2001
Ozone and PM Air Quality Analysis in Support of Public
Needs
Budget period August 1, 2000-April 30, 2001
Project Officer: Lara Autry OAQPS, MD-19, USEPA
Sub- Project:
Monitoring Network Assessment: Information
Value of Integrated, Multipurpose Networks
Principal Investigator:
Rudolf B. Husar
Center for Air Pollution Impact and Trend Analysis (CAPITA)
Washington University
St. Louis, MO 63130-4899
$90,000 Budget Supplement
Budget period August 1, 2000-April 30, 2001
Submitted to Project Officer:
Richard Scheffe
OAQPS, MD-14
USEPA
Research Triangle Park, NC 27711
July 6, 2000
Monitoring Network Assessment: Information Value of
Integrated, Multipurpose Networks
Background
Recently, EPA/OAQPS has initiated a new program to make the existing air quality
networks more responsive to the needs of air quality management. This may involve
adding and removing stations, and samplers as well as modifying their operating
procedures. New samplers are added to the network when a pollutant needs to be
characterized in more detail, e.g. characterization of ozone precursors through PAMS.
New samplers are also added to the network when a new standard is established as in the
case of PM2.5. In general, all these activities tend to increase the number of monitoring
sites and the associated national expense for maintaining the increasing air quality
monitoring networks.
Removal of monitoring stations or samplers has been avoided by applying the rationale
that:
a) The more data are being collected the better off we are.
b) It is good to have a long-term record from the same site.
c) It is not clear how to evaluate the relative importance of different monitoring sites
and samplers, so might as well keep the stations operating.
Given the limited financial resources available for monitoring the above-described
network growth is not sustainable and results in sub-optimal resource utilization.
Network optimization requires stating the network purposes such as monitoring for
compliance with NAAQS or documenting trends. The network purpose leads to the
specification of the network evaluation criteria. Network optimization may apply
subjective and objective evaluation criteria. Subjective criteria are those that arise during
discussion of the network management process and involve from the subjective
judgements of the participants. Objective methods of network evaluation can be
formulated algorithmically and can be used to support the network management
decisions.
The development of the network optimization algorithms is hampered by several
difficulties including:
1. The network optimization paradox: in order to evaluate the network's ability to
characterize the 'reality', the reality needs to be known. If the reality is fully
known, there is no particular need to monitor.
2. Network integration problem: air quality monitoring networks are generally not
monolithic but they are composed of sub-networks with subtle and sometimes
significant differences in samplers and sampling protocols. Network optimization
requires the integration and fusion of sub-networks (e.g. PM2.5, FRM, IMPROVE,
CASTNET, possibly Supersites)
3. Multipurpose utilization of networks: the air quality data from a given sampler can
be used for multiple purposes where each purpose, may demand different
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evaluation criteria. For example, the evaluation based on NAAQS compliance
needs to adhere to the criteria set by the regulatory standard. On the other hand,
the use of the data for source attribution may apply multiple criteria using many
different source-attribution methods.
Purpose of the Project
The purpose of the project is to develop a set of objective, network evaluation algorithms,
and to apply those algorithms to assess the network performance for the criteria
pollutants (O3, PM2.5, CO, SO2 NOX), with special emphasis on O3 and PM2.5.
Specific Objectives of this Work
The specific objectives set forth for this project are three fold:
1. Develop objective criteria and algorithms by which the network performance can
be evaluated.
2. Integrate the existing network data into a homogeneous datasets with special
emphasis on O3 and PM2.5.
2. Apply the developed algorithms to the best available integrated data sets to
evaluate the network performance from multiple perspectives.
Approach
The approach to the project follows closely the list of specific objectives stated above.
Objective criteria and algorithms for network performance evaluation
A key concept for developing a network evaluation criteria is the information value
contributed by each station. The information value, I, is composed of two parts: E and
W. The parameter E represents the error reduction (uncertainty reduction) that a given
station contributes. The value of E is large for those stations whose data are substantially
different than estimates derived by other means, e.g. extrapolation from neighboring
stations. The value of E is small if the concentration values estimated by other means are
the same as the measured value. In this latter case, the station does not add information,
i.e. does not reduce the uncertainty of concentration estimates.
The magnitude of W represents a weighing factor that weights the importance of a given
measurement from the point of view of pollutant impacts. For example, in case of
population exposure, the value of W may be taken in proportion to the number of people
in the region of influence of a given station. The overall value of the information
provided by a given station is then composed of (1) how much it reduces the overall
concentration uncertainty and (2) how much weight is given to the station due to the
receptor impacts.
The above-illustrated algorithm represents one possible way of evaluating a network
performance. This method has been explored in a brief preliminary study that is
available "Information Value of Air Quality Network Stations: Illustration for Ozone
Monitoring over the Eastern US"
http://capita.wustl.edu/CAPITA/capitareports/NetInfoValue/NetInfoValue.htm
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The above example illustrates the network evaluation based on spatial coverage. It is
clear that evaluation for detecting trends will require a development of additional criteria.
Integration of network data with special emphasis on O3 and PM2.5.
In order to evaluate the performance of the existing networks for the criteria pollutants
the most recently available data need to be accessed including the NAMS/SLAMS,
IMPROVE, and CASTNET networks. In the past CAPITA has integrated these data sets.
The available ozone data sets from NAMS/SLAMS and CASNET were integrated in
order to provide science support to the OTAG process. The report that describes the
ozone data integration effort can be found at the website below: "Ozone Data Integration
for OTAG Air Quality Analysis and Model Evaluation"
http://capita.wustl.edu/CAPITA/Awma98/HTTP/98_A929.htm
The available PM2.5 data sets were also integrated during Year 1 of this cooperative
agreement (CX 825834). The report describing the integrated fine particle data sets can
be found on the website: "North American Integrated Fine Particle Data"
http://capita.wustl.edu/datawarehouse/Datasets/CAPITA/NAMPM_25/Data/NAMPM25.html
"North American Integrated Fine Particle Data Set"
http://capita.wustl.edu/CAPITA/CapitaReports/Awma99/NamPM/NAMPMdata.htm
The integrated data sets will need to be updated. This is particularly important for the
PM2.5 data since the PM2.5 network has been expanding rapidly since the introduction
of the PM2.5 standard in 1998.
Application of network performance evaluation algorithms to the best available
integrated data sets
In this task we will apply the developed network evaluation algorithms to the integrated
data sets, with particular emphasis on ozone and PM2.5. An example of algorithm testing
is given in the report "Information Value of Air Quality Network Stations: Illustration for
Ozone Monitoring over the Eastern US"
http://capita.wustl.edu/CAPITA/capitareports/NetInfoValue/NetInfoValue.htm
The network evaluation will be conducted for the entire conterminous US territory.
In developing and applying the algorithms we will seek close interaction with analysts
within OAQPS as well as with interested participants from the states.
Output of the Work
The output of this activity will roughly coincide with the three tasks outlined in the
Approach Section:
1. New algorithms for evaluating integrated networks using multiple evaluation
criteria.
2. Integrated data sets, updated through at least 1999 containing data on criteria
pollutants from NAMS/SLAMS, CASTNET, and IMPROVE networks.
3. Network performance evaluations will be provided that illustrate the information
value contributed by individual stations. The evaluations will be made for multiple
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pollutants and evaluation criteria will include compliance with NAAQS,
determination of trends, delivery of the information to the public and source
identification.
The entire network evaluation plan as well as appropriate intermediate results will be
posted on the CAPITA website for purposes of interaction with interested collaborating
analysts and managers.
Schedule
The work will be conducted during August, 2000 – April, 2001. Intermediate outputs will
be discussed with the Project Officers, Laurel Schultz and Richard Scheffe.
Budget
The budget for the project is $90,000. The detailed budget is listed in the attachment. The
project will be conducted through incremental funding to the Cooperative Agreement CX
825834 between EPA and CAPITA “Ozone and PM Air Quality Analysis in Support of
Public Needs”
Personnel
The project will be conducted by Professor Rudolf B. Husar, director of the Center for
Air Pollution Impact and Trend Analysis. One or two graduate students will participate in
the project. Drs. Stefan Falke and Bret Schichtel will serve as consultants to the
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project.
PROJECT TITLE: OZONE AND PM AIR QUALITY ANALYSIS IN SUPPORT OF PUBLIC NEEDS
PROJECT PERIOD: 05/01/1998 - 04/30/2001
EPA ASSISTANCE ID NO. CX 825834-01-2
PRINCIPAL INVESTIGATOR: RUDOLF HUSAR
SUPPLEMENTAL FUNDING REQUEST
Monitoring Network Assessment: Information Value of Integrated, Multipurpose Networks
BUDGET PERIOD: 08/01/2000- 04/30/2001
Salaries
Rudolf B. Husar, PI
Graduate Research Assistant
Undergraduate Lab Asst.
EPA
WU
TOTAL
31,079
6,750
1,238
2,575
33,654
6,750
1,238
39,066
2,575
41,641
6,141
509
6,650
45,208
3,084
48,292
Travel
1 person trip to a technical meeting
1,350
0
1,350
Equipment
1 pentium grade computer and peripheral equipment
partial charge
Other Expenses
Software library charges
Telephone long distance / fax charges
Computer network charges
Publication charges
3,094
0
3,094
1,502
0
1,502
Consulting
Bret A. Schichtel
Bryan Van Hook
Stefan Falke
8,100
0
8,100
Total Direct Costs
59,253
3,084
62,337
Total Indirect Cost Base @56.0%
Total Indirect Cost Base @ 54.5%
Indirect Cost @56.0% MTDC, current
Indirect Cost @54.5% MTDC, effective 7/1/00
Total Indirect Costs
9,360
46,799
5,242
25,505
514
2,570
288
1,401
9,874
49,369
5,530
26,906
Total Direct and Indirect Costs
90,000
4,773
94,773
Total Salaries
Fringe Benefits
Total Salaries and Fringe Benefits
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