Lake Tahoe Visibility Impairment Source Apportionment Analysis
Theme: Air Quality and Meteorology
Sub-theme: Impact and control of atmospheric particulate matter
DRI Principal Investigators: Mark Green, Antony Chen, and Dave DuBois, Desert Research
Institute, 2215 Raggio Parkway, Reno, NV 89521.
Contact: Mark Green, 775-674-7118 (voice), 775-674-7016 (fax), green@dri.edu
Additional Principal Investigator: John Molenar, Air Resource Specialists, 1901 Sharp Point
Dr., Suite E, Fort Collins, CO, 80525, 970-484-7941 (voice), 970-484-3423 (fax)
JMolenar@air-resource.com
Grants contact person: Lycia Ronchetti 775-673-7411 (voice) 775-674-7016 (fax),
lycia.ronchetti@dri.edu
Total funding requested: $99,988
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Proposal narrative
a) Project abstract: The project will provide a comprehensive analysis of causes of visibility impairment
in the Lake Tahoe Basin. We will gather and review existing aerosol, optical, emissions, and
meteorological data and summarize past source apportionment analyses as well as conducting new
source apportionment receptor modeling. We will provide a thorough analysis of chemically speciated
aerosol data, especially the IMPROVE data collected at Bliss State Park and data collected in South Lake
Tahoe. Nephelometer light scattering data, including diurnal and seasonal patterns, and trends will
analyzed and summarized. We will review source and receptor modeling results for the area from the
Western Regional Air Partnership (WRAP) and conduct additional receptor modeling using Chemical
Mass Balance (CMB) and Positive Matrix Factorization (PMF). PMF factors will be weighted by backward
air trajectory residence time frequency to shed light on the likely sources identified by PMF. Receptor
modeling results will be compared to emissions inventories (e.g. WRAP inventory) for consistency. The
conclusions will be based on a weight-of-evidence approach considering the 2 receptor models applied
here, meteorological patterns, source oriented modeling for the WRAP, emissions inventories, and other
relevant information.
b) Justification statement: Visibility impairment (haze) is caused primarily by scattering and absorption
of light by atmospheric particulate matter and is thus an impact of atmospheric particulate matter.
Better definition of sources of haze causing air pollution can lead to effective control of atmospheric
particulate matter.
c) Background and problem statement: Good atmospheric visibility has long been acknowledged to be
a critical attribute for enjoyment of the beauty of the Lake Tahoe Basin. The Desolation Wilderness area
was designated a mandatory Class 1 area with visibility protection pursuant to the Clean Air Act
Amendments of 1977. The Tahoe Regional Planning Authority (TRPA) established regional and subregional visibility thresholds in 1982, revised in 2000. Long-term monitoring conducted at Bliss State
Park and South Lake Tahoe has found that the thresholds were met and visibility has improved since
monitoring began. However, the TRPA 2006 Threshold Evaluation report noted concerns over
backsliding, i.e. potential inability to maintain the improvements made in visual air quality. Although
significant resources have been expended over the past 20 years or so for visibility related monitoring,
there has been no comprehensive analysis of the data. In 2008 TRPA issued an RFP for data analysis
and source apportionment for visibility, selected a contractor, entered contract negotiations, and then
stopped action due to budget cuts. This proposal closely follows the proposal that was selected for
funding by TRPA. In addition to the TRPA, the results of this study will be useful to the State of
California, which is responsible for submitting regional haze plans to USEPA, and the US Forest Service,
which also has responsibilities for protecting its Class 1 areas, including Desolation Wilderness.
d) Goals, objectives, and hypotheses to be tested: The primary goals and objectives for this project
are: 1) Describe levels of visibility impairment in the Lake Tahoe Basin and how they have changed over
time; 2) Assess the contribution of various aerosol component contributions to haze (e.g sulfate, nitrate,
etc.) and how they have changed over time; 3) use receptor modeling techniques to perform source
apportionment for haze and ascertain how source contributions have changed over time; and 4)
reconcile source apportionment results obtained here with emissions inventories and past analyses such
as the WRAP modeling analysis and the WRAP Causes of Haze Assessment. Main hypotheses to be
tested include: 1) numerous sources types and source areas contribute to haze in the Tahoe Basin; 2)
haze has substantial seasonal and shorter-term variability that can be related to variability in source
strengths and transport patterns.
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e) Approach, methodology, and location of research: The project does not include additional data
collection. Thus the location of the research will be DRI offices in Reno and Las Vegas, and Air Resource
Specialists Fort Collins, CO office. Long-term aerosol, optical, and meteorological data is available
mainly for the Bliss State Park and South Lake Tahoe monitoring sites. Shorter-term data is also
available at Thunderbird Lodge on the northeastern shore of Lake Tahoe. The Lake Tahoe Atmospheric
Deposition study (LTADS) collected and analyzed data for averaging periods up to two weeks at several
locations around and near the lake.
Data at Bliss State Park (Figure 1) shows the organic carbon is the dominant contributor to light
extinction, followed by sulfate. Analysis of data for Bliss State Park has been done for the Western
Regional Air Partnership Causes of Haze Assessment (www.coha.dri.edu) and air quality modeling
performed using CMAQ by the WRAP Regional Modeling Center. The Causes of Haze Assessment
includes attribution results for the Bliss State Park monitoring site using trajectory regression analysis
and positive matrix factorization (PMF) results. Figure 2 shows the percentage attribution to sulfate by
source region using trajectory regression. The largest contributor was Pacific Coastal areas, thought to
be from shipping emissions (Xu, et al., 2006). Figure 3 shows a summary of PMF light extinction
attribution results for Bliss State Park from the COHA analysis. The COHA analysis grouped several sites
in northern California and Oregon to determine PMF factors. Five factors were identified, with smoke
being the largest, closely followed by an “urban mixture” containing significant amounts of organic and
elemental carbon, sulfate, and nitrate. As described later, we will perform additional PMF analysis.
The South Lake Tahoe site is likely representative of urban areas of the Lake, while the Bliss State Park
site would better represent most other areas. The aerosol monitor at Bliss State Park is located about
220 meters above lake level and may at times miss aerosol trapped in shallow inversions. The South
Lake Tahoe site is located near lake level and will see material trapped in low-level inversions.
The project will use results from previous studies such as the WRAP analyses and the LTADS in addition
to new analyses produced for this study to produce a weight-of-evidence based assessment of causes of
visibility impairment in the Lake Tahoe Basin. A weight -of-evidence approach is appropriate as different
analysis methods may yield conflicting results. When this occurs efforts should be spent to understand
the reasons for different results and evaluation of each method by using independent data to give
greater confidence in one or more methods compared to others. For example a source oriented model
may rely on accurate representation of clouds or fog to enable transformation of gases to particles. If
the clouds/fog are not accurately represented by the meteorological model, this could lead to significant
underprediction or overprediction of aerosol as occurred for the Columbia River Gorge Air Quality Study
(Emery et al., 2007). While quantitative attribution will be done only for sites with sufficient monitoring
data, other Basin locations will be addressed qualitatively based on geographic and topographic
characterization. WRAP modeling results at 12 km grid spacing can be used to try to better define
spatial patterns.
What aerosol components are responsible for visibility impairment? We will use the revised IMPROVE
algorithms (http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm) to
calculate reconstructed light extinction from speciated data at Bliss State Park and South Lake Tahoe.
We will compute daily averaged relative humidity growth factors f(RH) for days with valid RH data rather
than using the IMPROVE monthly average default values. We will summarize the aerosol data in charts
and tables as appropriate “slicing and dicing” the data in many ways, including yearly summaries, best,
worst, and average visibility conditions, seasonal patterns, etc. Charts that can be generated using the
WRAP TSS tool will be used when possible. Rather than automatically using quarterly data as in the TSS
summary, we will objectively group months into seasonal periods that have similar aerosol
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characteristics. Green et al., (1993) used principal component analysis of wind, temperature, and
precipitation data in southern California to group months having similar spatial patterns of these
variables. For temperature and winds, winter was defined as November through February, summer as
May-September and March, April, and October were transition periods. A simple method would be to
use cluster analysis on major aerosol component contributions to light extinction to group months.
Another method would be to use cluster analysis of factor scores for PCA of aerosol chemical
component concentrations. Aerosol data would then be summarized for each seasonal grouping and
related to changes in meteorological conditions (e.g. different transport patterns, mixing depths,
washout by precipitation) and emissions (e.g wildfires, wood burning, biogenic) between the
determined seasons.
In addition to using reconstructed light extinction from aerosol data, we will use nephelometer data,
when available to help understand haze in the Lake Tahoe Basin. We will compare reconstructed light
scattering with nephelometer measured light scattering to evaluate the appropriateness of the
IMPROVE algorithms at the Lake Tahoe area sites. The nephelometer data will also give us more insight
into haze at the Lake by providing high time resolution data that can help distinguish between local
sporadic sources with rapid changes in haze levels and more distant sources whose contributions to
haze varies more slowly.
The 2002 WRAP emissions inventory will be used to compare aerosol component concentrations to
emission rates of primary aerosol and precursor gases. The WRAP 2002 inventory exists as a highly
quality controlled database of point, mobile and area source emissions in the western US. Tahoe basinwide emissions of paved road, burning and residential wood combustion from recent research such as
Kuhns et al. (2004) and Fitz and Lents (2004) will also be used to compare with the receptor modeling
results. Unpaved road emissions in the basin will be based on the WRAP estimates since there are no
published studies on unpaved road emissions to our knowledge.
We will use Chemical Mass Balance and Positive Matrix Factorization for source apportionment of haze.
Positive matrix factorization (PMF) (Kim et al., 2003, Liu et al., 2003) is similar to Principal Component
Analysis (PCA) in that both are factor analysis methods that identify patterns in a dataset. The methods
essentially take a dataset consisting of a large number of observations and reduce the dimension into a
few patterns that best represent the original data. PMF is basically a refinement of PCA that allows only
positive factor scores. PMF uses as input chemically speciated particulate data that that represents a
combination of primary and secondary particles. This makes it problematic to match up to measured
source profiles which are for primary particles. At times source profiles of PMF factors are very
distinctive and appear to isolate individual source types. Frequently the PMF factors are given rather
generic names like “secondary sulfate” because they are a mix of different sources high in sulfate that
cannot be separated based upon trace element concentrations or other features. However at times
they can be given more definitive descriptions such as “oil combustion” or “paper mill” or “dust” based
upon trace elements in the profile –see Figure 4. From Figure 4 the “paper mill” factor was so-named
because of the abundance of both sodium and sulfate as sodium sulfate particulate is directly emitted
from Kraft paper mills (Lind et al., 2006). Oil combustion was called as such due to the noted presence
of vanadium as well as sulfur, etc. The dust profile shows an abundance of crustal elements such as
calcium, silicon, and iron. We will do PMF analysis of data from Bliss State Park and South Lake Tahoe.
A comprehensive collection of source profiles at DRI can help verify the PMF factors like those done in
Chen et al. (2007). Results will be compared to the analysis done for the Causes of Haze Assessment.
Contribution of PMF factors for year-to-year, seasonal, episodic, and best and worst case visibility will be
summarized. PMF results give scores for each factor for each measurement period and these will be
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used to show the temporal variability of the contribution of each factor to haze. An example PMF factor
score time series plot for the Columbia River Gorge oil combustion source is shown in Figure 5. The
source is important mainly in summer when westerly winds are very dominant, transporting emissions
from ships coming up the Columbia River from the Pacific Ocean and at the Port of Portland are
transported eastward to the Mt. Zion monitoring site.
Chemical mass balance (CMB) uses measured speciated aerosol data and measured source profiles to
attribute aerosol to sources. This has the inverse of the problem with PMF – much of the aerosol is
often secondary particulate where the profiles are for primary particles. It is, however, possible to
mitigate the problem by introducing artificial secondary aerosol source profiles, i.e., ammonium sulfate
[(NH4)2SO4; AMSUL], ammonium bisulfate (AMBSUL), ammonium nitrate (NH4NO3; AMNIT), and organic
carbon (SOC) were constructed in which primary species are completely absent. We will perform CMB
analysis as well as PMF.
In December 2006, the U.S. EPA upgraded the SPECIATE database from Version 3.2 to Version 4.0
(http://www.epa.gov/ttn/chief/software/speciate/), which contains 2,865 PM source profiles, and
provided an interface for identifying, examining, and formatting source profiles. Most of the newly
assembled profiles resulted from eighteen aerosol and source characterization studies conducted by the
DRI from 1987 to 2006 (Chow et al., 2006). These include 549 geological profiles (e.g., paved and
unpaved road dust, soil, and fly ash, including several of those acquired during the Lake Tahoe Source
Characterization Study [Kuhns et al., 2004]) and 876 combustion profiles for motor-vehicle exhaust (e.g.,
diesel, gasoline, or mixed), vegetative burning (e.g., agriculture, residential, and wildfire), industrial
boilers (e.g., coal, oil, gas, manure), residential meat cooking, and miscellaneous (e.g., residential coal
combustion, fluidized catalyst cracker, aircraft exhaust). These source profiles will be useful for the
source apportionment applications in Lake Tahoe. They will serve as inputs for the CMB and used to
verify factor profiles derived from PMF.
Receptor modeling methods are more convincing when they are used in conjunction with transport
information. For example if a given source type is identified as contributing to the haze at a receptor on
a given day, we want to demonstrate that the wind patterns were consistent with this conclusion (were
the winds blowing from the identified source toward the receptor?). We will run backward air
trajectories from Bliss State Park for each aerosol sample day. (We have already computed these for the
1997-2002 period for the Causes of Haze assessment). We will weight the backtrajectory residence
times by the PMF factor score. This then shows transport patterns weighted by the contribution of the
“source” for a given day. Due to uncertainties in modeled wind fields in complex terrain such as the
Lake Tahoe area, there are significant limitations in the value of the results. However, past applications
of this technique have been quite illuminating and we expect it will be useful. Figure 6 is an example of
the mobile source PMF weighted backtrajectory analysis for the Wichita Mountain Wilderness in
Oklahoma. The analysis shows an enhancement of frequency when the trajectories go over the DallasFort Worth urban area. As this is consistent with mobile source emissions inventories, this combination
of receptor model results with transport information gives confidence in the PMF results and the
naming of the “source” factor as mobile emissions.
To consider highly sporadic events such as forest fires and dust storms, we will do case study analysis for
days with suspected fires or dust storms. High organic carbon days are expected to be associated with
wildfires; high fine soil and coarse mass days are typically associated with dust events. The highest fine
soil concentration at the Bliss State Park site was April 13, 2001, during the well documented Asian Dust
event. Most of the other high fine soil days also occurred in April, suggesting Asian dust transport. We
will use the chemical profile method of Kavouras et al. (2005) to identify Asian dust episodes.
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Additionally, backtrajectory analysis can help confirm suspected Asian dust episodes. Smoke from forest
fires is likely a contributor during many summer days – we will focus on identifying periods with major
visibility impacts. Nephelometer data, when available will be helpful to identify highly sporadic events
because the particulate data is averaged over 24-hours while hourly nephelometer data is available and
will show much more temporal structure. We will investigate reported fire activities to confirm
suspected high fire impact days. Backtrajectory analysis can help confirm suspected Asian dust
episodes. Data sources that have been useful in case study analysis are shown in the following table.
Database Name
Database Description
Purpose
National Weather Service
weather maps
Daily maps of synoptic weather for
surface and at standard upper levels
heights (850, 700, 500 mb, etc)
Describes the large scale meteorological
conditions across the region
Hourly surface
meteorological data
The Western Regional Climate Center
database houses hourly data archive of
airport ASOS, RAWS data for the
western US
Useful to identify fogs and clouds that
might enhance particle formation,
surface stagnation; helps in identifying
sites impacted from smoke and dust
Air quality databases
Hourly and daily air quality data from
CARB and NV monitoring sites
Provides current and past air quality
data in both Nevada and California; to
potentially show episodes of regional
transport
MODIS satellite imagery
Provides twice daily satellite imagery in
true color and infrared at 250 meter
resolution
For use in locating sources of dust, fires
and haze
GOES satellite imagery
Provides twice-hourly visible satellite
imagery at 1 km resolution
For use in locating sources of dust, fires
and haze
Navy Aerosol Analysis and
Prediction System model
maps
Shows daily global predictions of dust,
smoke and sulfate
For use in locating sources of dust, fires
and haze; mainly for large events and
international transport episodes
NIFC wildfire reports and
maps
There are several sources of wildfire
activity information
These databases show locations, sizes
and times of current and past wildfires
NOAA Hazard Mapping
System Fire and Smoke
Products
Daily maps of smoke outlines over
North America. Also shows fire
locations
Useful for locating plumes of smoke as
well as wildfires
NOAA-UMBC IDEA aerosol
maps
Daily satellite derived aerosol optical
depth over the US
Large scale transport from wildfires,
urban haze, Asian dust
What is the role of meteorology in the causes of visibility impairment? Meteorology may be expected
to affect visibility in many ways. Perhaps most basic is the water growth of hygroscopic aerosols at high
relative humidity. This affects mainly sulfate and nitrate particles. During a haze episode, aerosol water
can easily contribute to ~40% of the light extinction coefficient (Chen et al., 2003). Meteorology also
affects the vertical mixing of aerosols and horizontal dilution which is proportional to wind speed.
Periods with cloud or fog cause enhanced gas to particle conversion. Wind direction affects which
sources have their emissions transported to the receptor site. Some emissions are meteorologically
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driven, such as increase of biogenic emissions with temperature increase or increase in dust at high
wind speeds.
By computing reconstructed extinction using dry light extinction efficiencies and extinction efficiencies
using daily averaged f(RH) water growth we will calculate the additional amount of haze contributed by
elevated relative humidity. We will use temperature differences between Lake level (SOLA monitoring
site or South Lake Tahoe airport) and the Bliss State Park site (about 220 meters above the Lake) or
other elevated meteorological data sites for an indicator of atmospheric stability. We will compute a
stability parameter and see how it relates to visibility. A highly stable atmosphere reduces vertical
mixing and may lead to high haze levels. For specific episodes or poor visibility that cannot be easily
explained by fires, etc. we will inspect surface and upper level weather maps to see if any obvious
patterns occur (e.g. wintertime high pressure over the Great Basin leading to strong inversions
Are there any detectable and/or statistically significant multi-year trend in the causes of visibility
impairment in the Lake Tahoe Basin? We will use Thiel regression analysis (Thiel, 1950;Emerson et al.,
1983) to assess the trends in each aerosol component to haze and reconstructed extinction for 20%
worst, 60% middle, and 20% best visibility days. This was the methodology used for the Causes of Haze
assessment (http://www.coha.dri.edu/web/general/tools_trendanaly.html) The analysis gives a slope
per year and the P-value level of significance. We will do the analysis for the Bliss State Park and South
Lake Tahoe monitoring sites. We will also do trend analysis for the nephelometer data to see if the
trends in reconstructed extinction are confirmed by trends in nephelometer measured light scattering.
For any trends statistically significant at the P=0.05 level or better, we will examine emissions
information to determine if the trends are consistent with emissions changes.
f) Relationship of the research to previous and current relevant research, monitoring, and/or
environmental improvement efforts: The Western Regional Air Partnership performed modeling with
the Community Multiscale Air Quality model (CMAQ) using MM5 meteorological fields. Results have
been summarized on the WRAP Technical Support System (TSS) for the Desolation Wilderness area. We
will compare WRAP haze source apportionment with results from the CMB and PMF analyses.
Reconciliation between source modeling and receptor modeling results is not necessarily
straightforward. Source models give attribution to specific source types and source regions, while
receptor modeling techniques such as PMF and CMB give attributions only to source types. In addition,
PMF “source” factors sometimes combine a variety of different sources such as in a “secondary sulfate”
factor. However, source model results can be aggregated to compare with more general PMF results
(see Pitchford et al., 2008). We will also compare study results with the Causes of Haze Assessment
(COHA) results, and conclusions from the Lake Tahoe Atmospheric Deposition Study (LTADS).
g) Strategy for engaging with managers and obtaining permits. As we are not collecting additional
data, no permits are required. We will coordinate the project with the following agencies: TRPA, US
Forest Service, California Air Resources Board, Nevada Division of Environmental Protection, Washoe
County Dept. of Health, and Eldorado and Placer counties. Coordination will be mostly through sending
copies of draft report sections to each of the agencies and requesting comments. A meeting will be
scheduled after the submittal of the draft report to present findings to the agencies and any other
interested stakeholders. The meeting will most likely be at the TRPA offices in Stateline, NV.
h) Description of deliverables/products and plan for how data and products will be reviewed and
made available to end users.
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Task 1: Obtain data and summarize existing reports. We will first gather all data, reports, and other
information needed to complete the project. This will include aerosol, meteorological, optical, and
emissions data and inventories. We will provide a thorough review of applicable results from other
studies. The deliverable is a report section summarizing the data to be used and results of other studies.
Tasks 2 (initial data analysis) and 3 (multivariate statistical procedures) will be combined for efficiency.
This involves summarizing and explaining the data. Deliverable: a report section that describes the
chemical characteristics of the aerosol, measured light scattering, reconstructed extinction, and
emissions and how they vary in time (and in space to the extent allowable). This task will include the
trends analysis.
Task 4: Compile source profiles for Chemical Mass Balance (CMB) analysis. We will review the DRI Lake
Tahoe source characterization study (Kuhns et al., 2004) and other studies to compile source profiles for
CMB analysis. DRI has an extensive collection of source profiles. Deliverable: report section giving
review of source profile options, choices made, and their justification.
Tasks 5 and 6. Receptor modeling and related analysis. We will provide results of the CMB and
Positive Matrix Factorization (PMF) modeling analyses and related analysis such as PMF factor score
backtrajectory analysis. Reconciliation of the receptor models against each other and emissions
inventories will be included in task 7. Deliverable : report section describing the CMB and PMF results
and their interpretation.
Tasks 7 and 8. Compare receptor modeling results with emissions inventories and summarize results.
In this task we will reconcile the CMB and PMF results with each other, emissions inventories (e.g. WRAP
and CARB inventories), the WRAP modeling analysis, and any other applicable attribution results.
Deliverable: report section on model reconciliation and reconciled source attribution results
Task 9: Prepare written draft and final reports to present study results. Deliverables draft and final
reports .
Peer review will be accomplished through submittal of a manuscript to a peer-reviewed journal for
publication.
Upon completion of the study, key data gathered for the project will be posted to the Tahoe Integrated
Information Management System (www.tiims.org). Of most importance is aerosol and light scattering
data collected for many years at the South Lake Tahoe and Bliss State Park sites.
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Schedule
Milestone/Deliverables
Start Date
End Date
Report section summarizing
data to be used and existing
reports
June 1, 2010
July 31, 2010
Initial data analysis report
section
July 15, 2010
September 15, 2010
Report section containing
CMB source profiles
September 1,
2010
September 30, 2010
Report section describing
receptor modeling results
October 1, 2010
November 30, 2010
Report section on model
reconciliation and reconciled
source attribution results
December 1,
2010
December 31, 2010
January 1, 2011
January 31, 2011
Draft report
Final report
Progress reports
April 30, 2011
July 1, 2010; October
1, 2010; January
1,2011; April 1, 2011
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Literature Cited
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Analysis of summertime PM2.5 and haze episode in the mid-Atlantic region. J. Air Waste
Manage. Assoc., 53(8):946-956.
Chen, L.-W.A.; Watson, J.G.; Chow, J.C.; and Magliano, K.L. (2007). Quantifying PM2.5 source
contributions for the San Joaquin Valley with multivariate receptor models. Environ. Sci.
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Chow, J.C.; Watson, J.G.; Kuhns, H.D.; Etyemezian, V.; Lowenthal, D.H.; Crow, D.J.; Kohl, S.D.;
Engelbrecht, J.P.; and Green, M.C. (2004). Source profiles for industrial, mobile, and area
sources in the Big Bend Regional Aerosol Visibility and Observational (BRAVO) Study.
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Chow, J.C.; Watson, J.G.; and Chen, L.-W.A. (2006a). Contemporary inorganic and organic speciated
particulate matter source profiles for geological material, motor vehicles, vegetative burning,
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VA, by Desert Research Institute, Reno, NV.
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http://www.swcleanair.org/gorgereports.html
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Kavouras, I.G., V. Etyemezian, D. DuBois, J. Xu, M. Pitchford, and M. Green (2005), Assessment of the
Principal Causes of Dust-Resultant Haze at IMPROVE Sites in the Western United States. Final
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factorization, J. Air & Waste Manage. Assoc., 53:731-739, 2003.
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Lind, T., Hokkinen, J., Jokiniemi, J., Hillaino, R., Makkonen, U., Raukola, A., Rintanen, J., Saviharju, K.,
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Atmospheric Environment, 40, 3433-3447, 2006.
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Figures
Figure 1. Average contribution to light extinction, by year at the Bliss State Park monitoring site (from
WRAP TSS: http://vista.cira.colostate.edu/TSS/
http://vista.cira.colostate.edu/TSS/)
Figure 2. Sulfate attribution to source area by trajectory regression analysis.
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CM
10%
Aged Sea Salt
9%
Urban Mixture
34%
Road
Dust/Mobile
10%
Smoke
37%
Figure 3. COHA PMF extinction attribution results for Bliss State Park (average over all sampling days
from 2000-2004.
1
Paper mill
0.1
0.01
0.001
0.0001
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU
H
FE PB MG MN NI NO3 P
K
RB SE SI NA SR
S
TI
V
ZN ZR
K
RB SE SI NA SR
S
TI
V
ZN ZR
K
RB SE
S
TI
V
ZN
Oil combustion
1
0.1
0.01
0.001
0.0001
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU
H
FE PB MG MN NI NO3 P
Dust
1
0.1
0.01
0.001
0.0001
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU
H
FE PB MG MN NI NO3 P
SI
NA SR
ZR
Figure 4. Example PMF profiles for Mt. Zion monitoring site, Columbia River gorge. Y-axis gives relative
abundance of each element- note logarithmic scale. Source: Green et al., 2007.
13
3
Oil Combustion
Concentration (ug/m3)
2.5
2
1.5
1
0.5
0
12/30/2002
5/29/2003
10/26/2003
3/24/2004
Date
8/21/2004
1/18/2005
Figure 5. Example time series of PMF factor scores for the oil combustion factor at the Mt. Zion site.
Figure 6. Residence time analysis for Wichita Mountains Wilderness – PMF weighted minus unweighted
residence time. Source:
(http://www.coha.dri.edu/web/state_analysis/Oklahoma/WIMO1/products/pmf_weighted_backtraj/p
mf5_diff_0500m.jpg).
14