Lake Tahoe Visibility Impairment Source Apportionment
Analysis
Final Report
August 16, 2011
Mark Green and Antony Chen, Desert Research Institute
David DuBois, New Mexico State University
John Molenar, Air Resource Specialists
This research was supported by an agreement with the USDA Forest Service Pacific Southwest
Research Station, using funding provided by the Bureau of Land Management through the sale of
public lands as authorized by the Southern Nevada Public Land Management Act (SNLPMA).
1
Table of Contents
List of Figures ................................................................................................................................................ 4
List of Tables ................................................................................................................................................. 8
Executive Summary..................................................................................................................................... 10
1.
Introduction ........................................................................................................................................ 12
1.1 Purpose of the study ......................................................................................................................... 12
1.2 TRPA and Federal visibility management ......................................................................................... 12
1.3 History of visibility related monitoring in the Lake Tahoe Basin ...................................................... 13
1.3.1 Aerosol monitoring .................................................................................................................... 13
1.3.2 Optical monitoring ..................................................................................................................... 16
1.4 Summary of existing analyses ........................................................................................................... 16
1.4.1 Early studies ............................................................................................................................... 16
1.4.2 Causes of Haze Assessment (COHA) .......................................................................................... 17
1.4.3 Western Regional Air Partnership (WRAP) modeling analysis................................................... 21
1.4.4 Lake Tahoe Atmospheric Deposition Study (LTADS).................................................................. 29
1.4.5 DRI Lake Tahoe Source Characterization Study ......................................................................... 29
2.
What aerosol components are responsible for visibility impairment in the Lake Tahoe Basin? ....... 30
2.1 A brief primer on the causes of haze ............................................................................................... 30
2.2 Methodology for reconstructed aerosol fine mass and reconstructed light extinction................... 30
2.3 Reconstructed fine mass results ....................................................................................................... 32
2.3.1 Monthly patterns of aerosol component concentrations ......................................................... 34
2.3.2 Urban increment to aerosol component concentrations by month ......................................... 36
2.3.4 Comparison of aerosol component concentrations for last 5 years to previous 1990-2004
period at Bliss Sate Park ...................................................................................................................... 40
2.4 Reconstructed light extinction results .............................................................................................. 41
2.4.1 Time series of monthly averaged reconstructed light extinction .............................................. 42
2.4.2 Trend analysis of reconstructed light extinction and its chemical component contributions .. 49
2.4.3 Progress toward meeting regional haze rule requirement........................................................ 63
2.5 Analysis of optical data ..................................................................................................................... 66
2.5.1 Monthly patterns in Nephelometer bsp ..................................................................................... 66
2
2.5.2 Diurnal patterns in bsp by month and season ............................................................................ 67
2.5.3 Measured versus reconstructed light scattering by particles .................................................... 71
2.5.4 Monthly patterns in transmissometer light extinction .............................................................. 75
2.5.5 Comparison of nephelometer light scattering and transmissometer light extinction .............. 76
2.6 Relationship of meteorology to haze ................................................................................................ 79
2.6.1 Haze levels as a function of atmospheric stability ..................................................................... 79
2.6.2 Effect of relative humidity on reconstructed light scattering .................................................... 81
3.
Receptor Modeling ............................................................................................................................. 83
3.1 Chemical Mass Balance Solutions ..................................................................................................... 83
3.2 PMF Source Apportionment ............................................................................................................. 85
3.2.1 Input data ................................................................................................................................... 85
3.2.2 Model Procedures ...................................................................................................................... 88
3.2.3 PMF Source Apportionment Results .......................................................................................... 88
3.2.4 Seasonal and Inter-annual Variations ........................................................................................ 94
3.3 EV-CMB Source Apportionment ....................................................................................................... 96
3.3.1 Input data ................................................................................................................................... 96
3.3.2 Sensitivity Testing .................................................................................................................... 100
3.3.3 CMB Source Apportionment Results ....................................................................................... 104
3.4 PMF Weighted backtrajectory analysis .......................................................................................... 105
4.
Case Study Analysis ........................................................................................................................... 113
4.1 May 9, 2008: High sulfate and reconstructed fine soil .................................................................. 113
4.2 July 11, 2008: Highest reconstructed light extinction at Bliss State Park ....................................... 116
4.3 October 15, 2004: Second highest reconstructed light extinction at Bliss State Park.................... 118
4.4 September 11, 2006: Fifth highest reconstructed light extinction at Bliss State Park. .................. 118
4.5 Asian dust episode of April 2001 .................................................................................................... 119
4.6 Asian dust episode of April 1998. ................................................................................................... 121
5.
Comparison of receptor modeling results to source modeling results ............................................ 123
6.
Summary and conclusions ................................................................................................................ 126
7. References ............................................................................................................................................ 130
3
List of Figures
Figure 1-1. Map showing locations of Bliss State Park (■), South Lake Tahoe (●), and Thunderbird
Lodge(▲) monitoring sites. ........................................................................................................................ 14
Figure 1-2. Source regions for the COHA trajectory regression analysis. .................................................. 18
Figure 1-3. COHA trajectory regression analysis attribution for sulfate by mass and percentage
contribution. ............................................................................................................................................... 19
Figure 1-4. COHA trajectory regression analysis attribution for aerosol light extinction by mass and
percentage contribution. ............................................................................................................................ 20
Figure 1-5. WRAP modeling domain and PSAT geographic regions. ......................................................... 24
Figure 1-6. CMAQ Apportionment of organic carbon aerosol to anthropogenic secondary, biogenic
secondary, and anthropogenic and biogenic primary categories for 20% worst visibility days................. 27
Figure 1-7. CMAQ Apportionment of organic carbon aerosol to anthropogenic secondary, biogenic
secondary, and anthropogenic and biogenic primary categories for 20% best visibility days. .................. 27
Figure 2-1. Relative humidity growth factors curves for small sulfate and nitrate -fS(RH), large sulfate
and nitrate- fL(RH), and sea salt – fSS(RH).................................................................................................... 32
Figure 2-2. Pie charts showing percentage contribution to reconstructed fine mass by aerosol
component for Bliss State Park (BLIS), South Lake Tahoe (SOLA), and Thunderbird Lodge (TBLG). .......... 34
Figure 2-3. Monthly contributions to RFM by aerosol component at the South Lake Tahoe and Bliss
State Park monitoring sites (1990-2004). ................................................................................................... 35
Figure 2-4. Monthly percentage contributions to RFM by aerosol component at the South Lake Tahoe
and Bliss State Park monitoring sites (1990-2004). .................................................................................... 36
Figure 2-5. Background and urban increment contributions to fine mass aerosol components and coarse
mass. ........................................................................................................................................................... 40
Figure 2-6. Pie chart of contributions to reconstructed light extinction at Bliss State Park (BLIS) and
South Lake Tahoe (SOLA) for periods with data for both sites (November 1990 – May 2004). Also shown
is data for Thunderbird Lodge (TBLG) for the period May 2001- March 2003. .......................................... 42
Figure 2-7. Monthly average reconstructed light extinction by aerosol component at Bliss State Park. .. 45
Figure 2-8. Monthly average reconstructed light extinction by aerosol component at Bliss State Park
scaled from 0-30 Mm-1. ............................................................................................................................... 48
Figure 2-9. Monthly average reconstructed light extinction by aerosol component at South Lake Tahoe.
.................................................................................................................................................................... 49
Figure 2-10. Aerosol component contributions (Mm-1) to reconstructed light extinction for 20% best,
middle 60%, and 20% worst visibility days at Bliss State Park. ................................................................... 50
Figure 2-11. Reconstructed aerosol light extinction (Mm-1) by components and total aerosol at Bliss
State Park for 20% best days (1991-2009). ................................................................................................. 52
Figure 2-12. Trend of aerosol component and total reconstructed light extinction for 60% middle
visibility days at Bliss State Park. ................................................................................................................ 54
Figure 2-13. Trend of aerosol component and total reconstructed light extinction for 20% worst visibility
days at Bliss State Park................................................................................................................................ 56
Figure 2-14. Aerosol component contributions (Mm-1) to reconstructed aerosol light extinction at South
lake Tahoe for 20% best, middle 60%, and 20% worst visibility days, 1990-2003. Average reconstructed
4
aerosol light extinction is 17.9 Mm-1 for the 20% best days, 33.8 Mm-1 for the middle 60% days and 89.7
Mm-1 for the 20% worst days. ..................................................................................................................... 57
Figure 2-15. Trend in reconstructed extinction and its components for the 20% best visibility days at
South Lake Tahoe 1990-2003. .................................................................................................................... 59
Figure 2-16. Trend in reconstructed extinction and its components for the middle 60% visibility days at
South Lake Tahoe 1990-2003. .................................................................................................................... 61
Figure 2-17. Trend in reconstructed extinction and its components for the 20% worst visibility days at
South Lake Tahoe 1990-2003. .................................................................................................................... 63
Figure 2-18. Percent of days in each month that are classified as 20% best or 20% worst visibility days at
Bliss State Park 2000-2009. Note that the average for each category is 20%. .......................................... 64
Figure 2-19. Acres burned by wildfires in California by year, 2000-2009. Source: California Dept of
Forestry and Fire Protection.
http://www.fire.ca.gov/fire_protection/fire_protection_fire_info_redbooks.php ................................... 65
Figure 2-20. Pie charts showing percentage contribution of each aerosol component to reconstructed
aerosol light extinction for best and worst days 2000-2004 and 2005-2009. ............................................ 66
Figure 2-21. Monthly average nephelometer measured light scattering by particles (bsp) at South Lake
Tahoe and Bliss State Park. ......................................................................................................................... 67
Figure 2-22. Average monthly diurnal patterns of light scattering by particles at Bliss State Park. ......... 68
Figure 2-23. Average diurnal patterns in particle light scattering (bsp) by seasonal group. Winter=
November-February, Spring = March-June, summer-autumn=July-October ............................................. 69
Figure 2-24. Average diurnal patterns in light scattering by particles (bsp) by month............................... 70
Figure 2-25. Average diurnal patterns in light scattering by particles (bsp) by monthly groups. ............... 71
Figure 2-26. Measured and reconstructed bsp for Bliss State Park (n=497)............................................... 72
Figure 2-27. Monthly average measured and reconstructed bsp at Bliss State Park. ................................ 72
Figure 2-28. Measured and reconstructed bsp for Bliss State Park November to February. ..................... 73
Figure 2-29. Measured and reconstructed bsp for Bliss State Park June-August. ...................................... 73
Figure 2-30. Measured and reconstructed bsp for South Lake Tahoe, all months. .................................... 74
Figure 2-31. Monthly average measured and reconstructed bsp at South Lake Tahoe. ........................... 74
Figure 2-32. Measured and reconstructed bsp for South Lake Tahoe November to February. ................. 75
Figure 2-33. Measured and reconstructed bsp for South Lake Tahoe June-August. .................................. 75
Figure 2-34. Monthly average transmissometer light extinction and nephelometer light scattering at
Bliss State Park. Data used are daily average bext and bsp for the period November 1990 – February 2000.
Only days with at least 12 hours of valid data for both instruments are used........................................... 76
Figure 2-35. Comparison of daily average particle light scattering (bsp) and light extinction (bext) at Bliss
State Park. ................................................................................................................................................... 77
Figure 2-36. Comparison of transmissometer measured light extinction and the sum of nephelometer
light scattering, Rayleigh scattering, and reconstructed aerosol light absorption at Bliss State park. ...... 77
Figure 2-37. Comparison of transmissometer measured light extinction aerosol + Rayleigh reconstructed
light extinction at Bliss State Park. .............................................................................................................. 78
Figure 2-38. Comparison of transmissometer measured light extinction aerosol + Rayleigh reconstructed
light extinction at Bliss State Park with 4 outliers removed. ...................................................................... 78
5
Figure 2-39. Comparison of monthly mean measured and reconstructed light extinction at Bliss State
Park. Error bars represent uncertainty in the mean at the 95% confidence level. ................................... 79
Figure 2-40. Temperature at South Lake Tahoe subtracted from temperature at Bliss State Park by time
of day, annually averaged (degrees C). Positive numbers indicate the presence of temperature
inversions. ................................................................................................................................................... 80
Figure 2-41. Temperature at South Lake Tahoe subtracted from temperature at Bliss State Park versus
difference in reconstructed particle light scattering between SOLA and BLIS. .......................................... 81
Figure 2-42. Average reconstructed light extinction (Mm-1) due to water growth of aerosol, by month at
Bliss State Park and South Lake Tahoe monitoring sites. ........................................................................... 82
Figure 2-43. Average percent of reconstructed light extinction due to water growth of aerosol, by
month at Bliss State Park and South Lake Tahoe monitoring sites. ........................................................... 82
Figure 3-1. Profiles of PMF factors for a) BLIS I, b) BLIS II, and c) SOLA. Solid bar and hollow bars indicate
PM2.5 mass fraction of high- and low-confidence species, respectively (high confidence species are those
with value/uncertainty ratio greater than 1). Crosses indicate fractional source contributions to the
species......................................................................................................................................................... 92
Figure 3-2. Seasonal variation of PMF factors based on average PM2.5 contributions, by month, for BLIS I,
BLIS II, and SOLA. ........................................................................................................................................ 94
Figure 3-3. Inter-annual variability of PMF factors for (a) BLIS and (b) SOLA. Biomass burning 1 and 2
represent LCE and HCE burning, respectively. Dust 1 and 2 are road and natural dust. SOLA 2004
contains only 17 samples that may not be representative of annual averages. ........................................ 96
Figure 3-4. Profiles of sources included in the final EV-CMB solutions. Bars and triangles indicate the
profile values and uncertainties, respectively. ......................................................................................... 102
Figure 3-5. Example of a final EV-CMB solution. “% Mass”/”CHI square” is PCMASS/χ2 in the text. ...... 103
Figure 3-6. Modified pseudo-inverse normalized (MPIN) matrix of the example shown in Figure 3-5 . 104
Figure 3-7. Comparison of PMF and EV-CMB source apportionment for BLIS and SOLA. Numbers above
the bars indicate average measured PM2.5 concentration........................................................................ 105
Figure 3-8. Difference map for biomass burning factor 1 at Bliss State Park 2005-2009 period. Positive
numbers indicate a greater frequency during periods of high PMF factor weighting. ............................ 107
Figure 3-9. Ratio map for biomass burning factor 1 at Bliss State Park 2005-2009. Ratios greater than 1
indicate increased frequency of transport associated with the PMF factor. ........................................... 107
Figure 3-10. Difference map for biomass burning factor 2 at Bliss State Park 2005-2009. ..................... 108
Figure 3-11. Difference map for biomass burning factor at Bliss State Park 2000-2004. ........................ 108
Figure 3-12. Difference map for biomass burning factor at South Lake Tahoe 2000-2004. ................... 109
Figure 3-13. Difference map for secondary sulfate factor Bliss State Park 2005-2009. .......................... 110
Figure 3-14. Difference map for secondary sulfate factor South Lake Tahoe 2000-2004. ...................... 110
Figure 3-15. Difference map for secondary nitrate factor Bliss State Park 2005-2009. .......................... 111
Figure 3-16. Difference map for secondary nitrate factor Bliss State Park 2000-2004. .......................... 111
Figure 3-17. Difference map for secondary nitrate factor South Lake Tahoe 2000-2004. ...................... 111
Figure 3-18. Difference map for dust factor2 Bliss State Park 2005-2009............................................... 112
Figure 3-19. Difference map for combustion factor Bliss State Park 2005-2009. .................................... 112
Figure 4-1. Ammonium sulfate 5/9/08 (μg/m3). ..................................................................................... 113
Figure 4-2. IMPROVE Fine soil 5/9/08 (μg/m3)........................................................................................ 114
6
Figure 4-3. Ammonium sulfate 5/12/08 (μg/m3). ................................................................................... 114
Figure 4-4. MODIS optical depth averaged over 10 days period ending May 9, 2008. ........................... 115
Figure 4-5. HYSPLIT backtrajectories starting noon PST 5/9/2008 with 1000 m, 2000 m, and 3000 m AGL
initial heights. ............................................................................................................................................ 115
Figure 4-6. MODIS Aqua visible July 11, 2008. ......................................................................................... 116
Figure 4-7. MODIS Aqua AOD for July 11, 2008. ...................................................................................... 117
Figure 4-8. CO from Aqua AIRS instrument, July 11, 2008....................................................................... 117
Figure 4-9. MODIS Terra image for October 15, 2004. ............................................................................ 118
Figure 4-10. MODIS Aqua image for September 10, 2006. ...................................................................... 119
Figure 4-11. SeaWiFs image for April 11 showing Asian dust cloud approaching the US west coast. .... 120
Figure 4-12. Contour map of IMPROVE reconstructed fine soil (μg/m3) April 13, 2001. ........................ 121
Figure 4-13. Contour map of IMPROVE reconstructed fine soil (μg/m3) April 16, 2001. ........................ 121
Figure 4-14. Contour map of IMPROVE reconstructed fine soil (μg/m3) April 29, 1998. ........................ 122
Figure 4-15. Contour map of IMPROVE reconstructed fine soil (μg/m3) May 2, 1998. ........................... 122
Figure 6-1. Monthly contributions to RFM by aerosol component at the South Lake Tahoe and Bliss
State Park monitoring sites (1990-2004). ................................................................................................. 127
Figure 6-2. Percentage contributions to reconstructed light extinction by aerosol component at Bliss
State Park and South Lake Tahoe. ............................................................................................................ 128
Figure 6-3. Comparison of PMF and CMB source apportionment for BLIS and SOLA. ............................ 129
7
List of Tables
Table 1-1. TRPA Visibility thresholds.......................................................................................................... 12
Table 1-2. PSAT source groups. .................................................................................................................. 23
Table 1-3. Comparison of CMAQ predicted and measured aerosol component concentrations (μg/m3)
and percent bias for all, best 20%, and worst 20% visibility days at Bliss State Park, 2002. Percent bias is
defined as 100*(model-measured)/measured. .......................................................................................... 24
Table 1-4. WRAP apportionment of sulfate at Bliss State Park by source type and geographic region for
all 2002 IMPROVE sampling days, 20% best, and 20% worst visibility days. Source:
http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx ................................................................ 25
Table 1-5. WRAP apportionment of nitrate at Bliss State Park by source type and geographic region for
all 2002 IMPROVE sampling days, 20% best, and 20% worst visibility days. Source:
http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx ................................................................ 26
Table 1-6. CMAQ Apportionment of organic carbon aerosol to anthropogenic secondary, biogenic
secondary, and anthropogenic and biogenic primary categories. ............................................................. 28
Table 1-7. Weighted emissions potential (WEP) for EC by source type for worst and best 20 percentile
visibility days at Bliss State Park, 2002. ...................................................................................................... 28
Table 1-8. Weighted emissions potential by source type for coarse mass at Bliss State Park for worst and
best visibility days. ...................................................................................................................................... 28
Table 2-1. Reconstructed fine mass component (RFM) concentrations and percentage contributions to
RFM at the Bliss State Park (BLIS), South Lake Tahoe (SOLA), and Thunderbird Lodge (TBLG) monitoring
sites. ............................................................................................................................................................ 33
Table 2-2. Average aerosol component concentrations and percentage contribution to reconstructed
fine mass at Bliss State park for 1990-2004 and 2004-2008. ..................................................................... 40
Table 2-3. Reconstructed aerosol light extinction at Bliss State Park and South Lake Tahoe, by aerosol
component, 1990-2004. ............................................................................................................................. 41
Table 2-4. Thiel regression analysis slope (Mm-1/year) and P-value for 20% best visibility days at Bliss
State Park. ................................................................................................................................................... 52
Table 2-5. Thiel regression analysis slope (Mm-1/year) and P-value for middle 60% visibility days at Bliss
State Park. ................................................................................................................................................... 54
Table 2-6. Thiel regression analysis slope (Mm-1/year) and P-value for 20% worst visibility days at Bliss
State Park. ................................................................................................................................................... 56
Table 2-7. Thiel regression results for 20% best visibility days at South Lake Tahoe, 1991-2003. ............ 60
Table 2-8. Thiel regression results for 60% middle visibility days at South Lake Tahoe, 1991-2003. ........ 61
Table 2-9. Thiel regression analysis slope (Mm-1/year) and P-value for 20% worst visibility days at South
Lake Tahoe. ................................................................................................................................................. 63
Table 2-10. Average aerosol component light extinction and deciview at Bliss State Park for the 20%
best and worst visibility days for the 2000-2004 and 2005-2009 time periods. Aerosol component
contributions to light extinction are in Mm-1.............................................................................................. 64
Table 2-11. Average monthly light scattering by particles as measured by nephelometers at South Lake
Tahoe (SOLA) and Bliss State Park (BLIS). Scattering is in inverse megameters (Mm-1). ........................... 67
Table 3-1. Data Groups for PMF model inputs. ......................................................................................... 85
8
Table 3-2. Average and signal-to-noise ratio (SNR) of PM2.5 mass and chemical concentrations (µg/m3)
for each PMF modeling group. ................................................................................................................... 87
Table 3-3. Absolute (in µg/m3) factor contribution to PM2.5 mass (by PMF) for each modeling group.
SOLA results are also separated into UCD and DRI sampling periods. ....................................................... 93
Table 3-4. List of source profiles used for EV-CMB fitting of BLIS and SOLA samples. These profiles are
available upon request. Bold mnemonics indicate source profiles included in the final source
apportionment. Location refers to the place the source profile was measured. ....................................... 98
Table 6-1. Reconstructed fine mass component (RFM) concentrations and percentage contributions to
RFM at Bliss State Park (BLIS1) and South Lake Tahoe (SOLA1). .............................................................. 126
Table 6-2. Reconstructed light extinction by aerosol component at BLIS1 for the 2000-2004 and 20052009 periods. ............................................................................................................................................ 128
9
Executive Summary
The study concentrated on interpretation of 20 years of chemically speciated PM2.5 aerosol data
at Bliss State Park and 15 years worth of data at South Lake Tahoe. Light scattering and absorption by
aerosols are the largest contributions to visibility limitations at all but the cleanest locations where light
scattering by clean air (Rayleigh scattering) can be equally or more significant. On average at Bliss State
Park the calculated light extinction by aerosols of 12.8 Mm-1 was only slightly (35%) higher than the
Rayleigh average of 9.5 Mm-1. Average calculated aerosol light extinction at South Lake Tahoe was 43.7
Mm-1 or about 3½ times that at Bliss State Park. Over a much shorter time period, average calculated
aerosol light extinction at Thunderbird Lodge was 16.8 Mm-1.
Three-fourths of the increased light extinction at South Lake Tahoe compared to Bliss State Park
was due to much higher organic and elemental carbon aerosol at South Lake Tahoe, especially in winter.
Carbonaceous aerosol accounted for 70% of reconstructed light extinction at South Lake Tahoe and 52 %
at Bliss State Park. Coarse mass (mainly dust) contributed 13% to aerosol light extinction at both sites.
While sulfate concentrations were about equal at both sites it contributed 22% of calculated light
extinction at Bliss State Park and only 8% at South Lake Tahoe.
Seasonal patterns in light extinction were reversed for the two sites, with Bliss State Park having
highest average values in summer due to wildfire impacts and South Lake Tahoe having highest average
values in winter due to buildup of residential wood burning and traffic emissions under winter inversion
conditions. Both sites are affected by wildfires in summer, but only the South Lake Tahoe site is
significantly affected by residential wood burning and traffic in winter.
Trend analysis was done for the 20% best, 60% middle, and 20% worst visibility days at Bliss
State Park for 1990-1999 and 2000-2009 and the entire 1990-2009 period and for 1990-2003 at South
Lake Tahoe. For the 1990-2009 period at Bliss State Park, statistically significant decreases (p=0.05) in
sulfate, nitrate, EC, coarse mass and total aerosol extinction occurred on the 20% best days.
Corresponding middle 60% days showed statistically significant decreases in nitrate, EC, coarse mass,
and total light extinction. For the 20% worst days at Bliss State Park for 1990-2009, nitrate and coarse
mass light extinction had statistically significant decreases, while light extinction from organic aerosol
had a statistically significant increase. At South Lake Tahoe on the 20% best days, statistically significant
decreases in sulfate, nitrate, EC, coarse mass and total aerosol extinction occurred. For the middle 60%
of days at South Lake Tahoe sulfate, nitrate, EC, and reconstructed total extinction all had statistically
significant downward trends. Nitrate, organics, EC, and total reconstructed extinction showed
statistically significant decreasing trends on the 20% worst light extinction days at South Lake Tahoe.
Comparison of 20% best and worst visibility days at Bliss State Park for the regional haze rule
(RHR) baseline period of 2000-2004 to 2005-2009 showed the cleanest days getting cleaner and the
haziest days getting hazier, mainly due to increased organic and elemental carbon. The increased haze
on worst days is contrary to the RHR requirement of improved visibility on the worst 20% days.
However the “backsliding” on worst case days appears to be due mainly to increased wildfire emissions
impacting the site.
Receptor modeling was performed using positive matrix factorization (PMF) and chemical mass
balance (CMB). Seven common PMF factors were resolved for the Bliss State Park 2000-2004 (BLIS I) and
2005-2009 (BLIS II) modeling groups: natural dust, road dust, biomass burning, traffic and industrial
emissions, as well as secondary sulfate and nitrate. BLIS II data yielded two biomass burning factors that
10
were attributed to low- and high- combustion efficiency (LCE and HCE) burning. Only 6 factors were
found for South Lake Tahoe (SOLA) including dust, biomass burning, traffic emissions, secondary sulfate,
secondary nitrate, and a salting factor only found at SOLA. CMB was able to separate road and natural
dust and separate LCE and HCE biomass burning for BLIS I, BLIS II, and SOLA. It confirmed that 1)
biomass burning was the dominant source of PM2.5 with increasing importance over time; 2) LCE burning
accounts for most of the biomass burning contribution, though its fraction was lower at SOLA; 3) road
dust and traffic contributions were much higher at SOLA than at BLIS; 4) industrial combustion and
salting were minor sources.
11
1. Introduction
1.1 Purpose of the study
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 sub-regional visibility thresholds in 1982, revised in
1999. 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 study was
proposed in response to the call for proposals in Round 10 funding for Tahoe science projects funded
under the Southern Nevada Public Lands Management Act (SNPLMA) and 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.
A related study (Chen et al., 2011b) provides recommendations for visibility thresholds and future
visibility related monitoring for the Lake Tahoe Basin.
1.2 TRPA and Federal visibility management
For TRPA regional visibility represents averages over the Lake Tahoe Basin while sub-regional visibility
characterizes urban areas such as South Lake Tahoe. The TRPA thresholds were set for median and 90th
percentile visibility as shown in Table 1-1. Evaluations of conditions against the thresholds are to be
done by using chemically speciated aerosol data to obtain “reconstructed” light extinction.
Table 1-1. TRPA Visibility thresholds.
Type of threshold
Regional
Sub-regional
50 percentile light
extinction (bext) Mm-1
25.1
50.2
90 percentile light
extinction (bext) Mm-1
34.0
126.2
The USEPA regional haze rule requires mandatory class I areas such as the Desolation Wilderness to
maintain visibility on the worst days and to improve visibility for the best days to “natural background”
conditions. According to USEPA guidance documents (EPA 2003), natural visibility conditions represent
the long-term degree of visibility that is estimated to exist in a given mandatory Federal Class I area in
the absence of human-caused impairment.
12
Section 51.309(d)(6) requires each State to establish an emissions inventory and tracking system (spatial
and temporal) for VOC, NOX, elemental carbon and organic carbon, and direct fine particulate emissions
from prescribed fire, wildfire, and agricultural burning.
Most importantly, the rule requires the establishment of enhanced smoke management programs for
fire that consider visibility effects, in addition to health and nuisance objectives, and calls for programs
to be based on the criteria of efficiency, economics, law, emissions reduction opportunities, land
management objectives, and reduction of visibility impacts.
Worst days are considered the 20% of days with highest reconstructed light extinction. Best days are
those 20% days with lowest reconstructed light extinction. The baseline period was set as 2000-2004
and the natural background is to be reached by 2064. States were required to prepare State
Implementation Plans (SIPs) to address how they will comply with the regional haze rule. Most states,
including California and Nevada, were required to submit plans by December 17, 2007. California
submitted its plan in March 2009, Nevada in November 2009. States are to prepare comprehensive SIP
revisions every 10 years starting in 2018. States also must submit progress reports every 5 years
beginning in 2013. Progress reports are to give the status of plan implementations, quantify haze levels
over the most recent 5 year period for best and worst visibility days, document changes in emissions
and assess whether the plan is sufficient to meet the SIP goals. The first 5 year interval after the
baseline includes the years 2005-2009. If states can demonstrate that the inability to meet visibility
goals over a period are due to sources over which they have no control such as wildfires or transport
from other countries, then they are essentially held harmless for the visibility effects from these
emissions.
1.3 History of visibility related monitoring in the Lake Tahoe Basin
1.3.1 Aerosol monitoring
Aerosol monitoring has been done routinely at three sites in the Lake Tahoe Basin, in South Lake Tahoe,
Bliss State Park, and Thunderbird Lodge (Figure 1-1).
13
Figure 1-1. Map showing locations of Bliss State Park (■), South Lake Tahoe (●), and Thunderbird Lodge(▲) monitoring sites.
The monitoring at Thunderbird Lodge was relatively short-term compared to the other sites and is thus
given less attention in this report. Some details of the monitoring are given below.
South Lake Tahoe (SOLA1)
Site Specifications:
Site installed March, 1989 by Air Resource Specialists (ARS).
Latitude: 38.947 N
Longitude: 119.969 W
Elevation: 1902 m
Site moved by ARS October, 1999 approximately 200m west of old site
Latitude: 38.946 N
Longitude: 119.971 W
14
Elevation: 1903 m
Aerosol Equipment:
IMPROVE version 1 sampler used throughout monitoring program
Laboratory History
3/25/1989 – 8/1/1998: UC Davis Crocker Nuclear Laboratory using
IMPROVE protocols & sub-contracted laboratories
8/5/1998 – 6/25/2002: UC Davis Delta Group
8/12/2002 – 5/15/2004: Desert Research Institute
Sampling Schedule:
4/1/89 to 8/15/98: IMPROVE schedule - 24hr sample every Wednesday and Saturday
8/15/98 – 6/25/02: Every 6th day
6/26/02 – 8/11/02: NO SAMPLING
8/12/02 – 5/15/04: 24hr sample every 6th day
Site permanently shutdown May, 2004
Bliss State Park (BLIS1)
Site Specifications:
Site installed November, 1990 by Air Resource Specialists (ARS).
Latitude: 38.976 N
Longitude: 120.103 W
Elevation: 2130 m
Aerosol Equipment:
IMPROVE version 1 sampler used 11/17/1990 – 11/14/1999
IMPROVE version 2 sampler installed 11/19/1999 by UC Davis Crocker Lab
Laboratory History
11/17/1990 – 8/1/1998: UC Davis Crocker Nuclear Laboratory using
IMPROVE protocols & sub-contracted laboratories
Note: Paid for by TRPA so NOT an official IMPROVE site.
8/5/1998 – 11/14/1999: UC Davis Delta Group
12/4/1999 – Current: UC Davis Crocker Nuclear Laboratory using
IMPROVE protocols & sub-contracted laboratories
Note: Becomes an official IMPROVE site paid for by IMPROVE.
Sampling Schedule:
11/17/90 to 8/15/98: IMPROVE schedule - 24hr sample every Wednesday and Saturday
8/15/98 – 11/14/99: Every 6th day
15
11/15/99 – 12/3/99: NO SAMPLING
12/4/99 – current: IMPROVE sampling schedule (every third day)
Thunderbird Lodge (TBLG1)
Site Specifications:
Site installed August, 2000 by Air Resource Specialists (ARS).
Latitude: 39.20 N
Longitude: 119.93 W
Elevation: 1905 m
Aerosol Equipment:
IMPROVE version 1 sampler (Old BLIS1 equipment) used entire monitoring
period
Laboratory History
9/3/2000 – 6/28/2002: UC Davis Delta Group
8/15/2002 – 6/10/2004: Desert Research Institute
Sampling Schedule:
9/3/2000 – 6/28/2002: Every 6th day
6/29/2002 – 8/14/2002: NO SAMPLING
8/15/2002 – 6/10/2004: Every 6th day
Site permanently shutdown May, 2004
1.3.2 Optical monitoring
Nephelometer light scattering data is available for Bliss State Park from November 1990- February 2005
and at South Lake Tahoe from March 1989- August 2000. A transmissometer with a sight path from Bliss
State Park to the Zephyr Point fire tower was operated from November 1990- February 2000. The
transmissometer measured light extinction along a 13.3 km path about 180 m above the southern end
of Lake Tahoe. Initially, Belfort 1590 nephelometers were operated at both sites; theses were replaced
with Optec NGN-2 nephelometers in 1996. The transmissometer was an Optec LPV-2. Temperature and
relative humidity were also measured at the nephelometer sites.
1.4 Summary of existing analyses
1.4.1 Early studies
Barone et al (1979) conducted daily and weekly average particulate matter (PM) sampling at numerous
sites in the Lake Tahoe Basin and collected vertical temperature profile data from aircraft flights and
pilot balloons. Study conclusions included: road dust in summer is mainly from unpaved roads and in
winter is due to sanding operations on paved roads; vehicular traffic was responsible for much of the
PM in the basin; and the vertical temperature structure is typically characterized by 3 layers, a strong
16
shallow surface based inversion (≈100 m thick), a better mixed layer extending to about 3000 m MSL,
and a strong inversion above this layer due to large scale subsidence.
Pitchford and Allison (1984) concluded that 70% of the Basin-wide visibility impairment and 30% of the
South Lake Tahoe haze was caused by natural and long range transport of emissions, implying the
remainder of the haze is due to local anthropogenic emissions.
Molenar (2000) did a detailed analysis of optical and speciated PM2.5 data for the Bliss State Park (BLIS)
and South Lake Tahoe (SOLA) sites for the 10-year period 1989-1998. The data were grouped by 20%
best, 20% worst, and all days. Transmissometer light extinction was found to decrease 6-10% for all day
categories. Nephelometer light scattering showed a 45% decrease on 20% clearest days, 5% decrease
over all days, and a slight increase for the 20% haziest days. At South Lake Tahoe the nephelometer
showed from 17-22% decrease in aerosol light scattering for all day types. Molenar (2000) also found
that carbonaceous aerosol (organic and elemental carbon) comprised 55% of fine mass (PM2.5) at Bliss
State Park and from 65% of fine mass on 20% cleanest days to >80% of fine mass on 20% dirtiest days at
South Lake Tahoe. From 1989-1998 significant decreases in elemental and organic carbon and
reconstructed light extinction were noted for South Lake Tahoe. Changes in aerosol concentrations and
reconstructed light extinction were modest at Bliss State Park during this period. The current study (this
report) has findings consistent with the Molenar analysis for the time periods common to both studies.
Cliff and Cahill (2000) present an extensive analysis and discussion of air quality related issues in the
Lake Tahoe Basin including potential effects on visibility, human health, forest health, water quality and
clarity and regulatory issues as well.
1.4.2 Causes of Haze Assessment (COHA)
The Causes of Haze Assessment (coha.dri.edu) was a large data analysis effort in support of the Western
Regional Air Partnership visibility analysis. The study, with its analyses entirely online, summarized
aerosol data representing over 100 Class I areas in the Western United States. Receptor modeling
analysis and backtrajectory analysis were conducted along with narratives of meteorological conditions,
topographic settings, and relationship to emissions sources. Here we briefly present COHA trajectory
regression and positive matrix factorization modeling results for the Desolation Wilderness Area, using
data from Bliss State Park. The full COHA analysis can be seen at http://www.coha.dri.edu/.
COHA Trajectory regression analysis
Trajectory regression analysis is a way to determine a simple relationship between a measured air
quality parameter at a specific receptor location and the amount of time that air is flowing across
potential source regions on their way to the receptor location. The dependent variable in the multiplelinear regression is the air quality parameter and the independent variables are the estimates of time
that air spends over each of a group of specific source regions. Back-trajectory analysis is used to
estimate the amount of time an air parcel spends over each source region.
Implicit in the trajectory regression attribution analysis approach is the concept that the amount of time
air spends over a region determines that region’s contribution to pollutants measured at the receptor
site. Obviously this approach is too simplistic to capture the effects of varying atmospheric factors that
17
are known to be influential in modulating concentrations (e.g. washout by precipitation, enhanced
chemistry in clouds, etc.).
The trajectory regression analysis for the Causes of Haze Assessment is done two ways, with an additive
intercept term and with no intercept. The intercept term is thought to account for both contributions
from beyond the selected source regions (like a global background value) and statistical noise from
imprecise parameters and an imperfect model. Regression without the intercept forces the sources to
account for some of the background contributions, so will overestimate some of the source region
contributions, while regression with an intercept may underestimate some source region contributions
by incorporating statistical noise in the intercept term. The most reasonable attribution results by the
trajectory regression method are likely within a range set by regression with and without an intercept.
Source regions for the Desolation wilderness area trajectory regression analysis are shown in Figure 1-2.
Results for sulfate and aerosol light extinction are shown in Figure 1-3 and Figure 1-4. The largest
contributors to sulfate were the Pacific Coastal area, followed by portions of California southwest of
Desolation wilderness. The largest contributors to aerosol light extinction were California southwest of
Desolation Wilderness, followed by Pacific Coastal areas and California locations northwest of
Desolation Wilderness.
Figure 1-2. Source regions for the COHA trajectory regression analysis.
18
Figure 1-3. COHA trajectory regression analysis attribution for sulfate by mass and percentage contribution.
19
Figure 1-4. COHA trajectory regression analysis attribution for aerosol light extinction by mass and percentage contribution.
20
COHA Positive Matrix Factorization (PMF) modeling
PMF is a receptor model that analyzes historical chemically speciated aerosol data to determine “source
factors”. PMF will be described in more detail in Chapter 3. The COHA PMF modeling formed groups of
sites relatively close to each other. The Desolation Wilderness area (aerosol data from Bliss State Park)
was grouped with other sites in Northern California and Oregon. Later in this report, PMF results done
separately for data at Bliss State Park and South Lake Tahoe will be presented.
The COHA PMF analysis for Northern California and Oregon found the following four source types and
their average contribution to PM2.5 mass:
1) Urban mixture (high in sulfate, OC/EC, nitrate), 6%;
2) Smoke/Mobile sources (OC/EC), 58%;
3) Road dust/mobile sources (soil, OC/EC, nitrate, sulfate, potassium), 22%;
4)Aged sea-salt (sodium, nitrate, sulfate, OC), 14%
1.4.3 Western Regional Air Partnership (WRAP) modeling analysis
The Western Regional Air Partnership (WRAP), is a regional planning organization (RPO) formed to
provide modeling and data analysis in support of regional haze SIPs. WRAP and other RPOs performed
quality modeling for the year 2002 to represent the 2000-2004 baseline period. The WRAP used the
Community Multi-scale Air quality Model (CMAQ) and the Comprehensive Air Quality Model with
Extensions (CAMx). CAMx has a tool, called the PM Source Apportionment Technology (PSAT) that
explicitly tracks for a given emissions source the chemical transformations, transport, and removal of
the particulate matter that was formed from that source. PSAT performs source apportionment based
on user defined source groups. A source group is the combination of a geographic source region and an
emissions source category. Examples of source regions include states, nonattainment areas, and
counties. Examples of source categories include mobile sources, biogenic sources, and elevated point
sources.
21
Table 1-2 shows the groups that used for PSAT for the WRAP modeling. PSAT was only run for sulfate
and nitrate because tracking of other compounds, such as organic carbon was too computationally
expensive. Figure 1-5 shows the modeling domain and geographic regions for which PSAT tracked
impacts to sulfate and nitrate.
22
Table 1-2. PSAT source groups.
23
Figure 1-5. WRAP modeling domain and PSAT geographic regions.
Overall Model performance
Table 1-3 compares the WRAP CMAQ model results to measured data at Bliss State Park. Over all days,
CMAQ was biased high for nitrate and organic aerosol, low for sulfate, soil, and coarse mass, and about
right for EC. Sulfate, organic, EC, and coarse mass were too high on best days and too low on worst
days. Nitrate was a bit high on worst days and far too high on best days. Soil was about right on best
days and too low on worst days.
Table 1-3. Comparison of CMAQ predicted and measured aerosol component concentrations (μg/m3) and percent bias for
all, best 20%, and worst 20% visibility days at Bliss State Park, 2002. Percent bias is defined as 100*(modelmeasured)/measured.
All days
20% best
20% worst
bias
bias
bias
Measured modeled (%)
measured modeled (%)
measured modeled (%)
ammSO4
0.61
0.43
-30
0.19
0.24
26
0.96
0.55
-43
ammNO3
0.24
0.33
38
0.06
0.25
317
0.37
0.44
19
OMC
1.49
1.83
23
0.25
0.43
72
3.58
2.78
-22
EC
0.16
0.16
0
0.04
0.05
25
0.33
0.22
-33
Soil
0.53
0.35
-34
0.12
0.1
-17
1.04
0.48
-54
CM
2.09
0.55
-74
0.65
0.44
-32
3.18
0.53
-83
Attribution of sulfate by source type and region
PSAT attribution of sulfate at Bliss State Park to source type and geographic areas is presented in Table
1-4. On average, modeled sulfate and measured sulfate were similar in concentration with measured
sulfate 8% higher than modeled. Modeled sulfate was 71% of measured sulfate on worst 20% visibility
days and 186% of measured sulfate on best 20% visibility days. Boundary conditions accounted for over
half of the modeled sulfate at Bliss State Park. Boundary conditions are estimated levels of pollutants
such as sulfate that are in the background air entering the modeling domain shown in Figure 1-5.
24
Sources throughout the world may contribute to the boundary conditions. The fractional contribution
from boundary conditions was greatest on the cleanest days and least on the dirtiest days. The source
types contributing most to sulfate were point and area sources, followed by motor vehicles. After
outside of the modeling domain, California and offshore Pacific sources were the greatest contributors
by geographic region, followed by Nevada, Oregon, and Washington State. Virtually all of the
contribution from Pacific offshore was from area sources. Emissions from ships in the eastern Pacific
Ocean burning high sulfur fuel are thought to be responsible for a significant fraction of sulfate in the
western United States (Xu et al., 2006).
Table 1-4. WRAP apportionment of sulfate at Bliss State Park by source type and geographic region for all 2002 IMPROVE
sampling days, 20% best, and 20% worst visibility days. Source:
http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx
Source type
Measured SO4
concentration (μg/m3)
Modeled SO4
concentration (μg/m3)
Point sources %
Anthropogenic fires %
Motor vehicles %
Natural fires & biogenic
%
Area sources %
Outside domain all
types %
Geographic region %
Outside domain
California
Pacific offshore
Nevada
Oregon
Washington
Canada
Other WRAP states
Mexico
Central US
Eastern US
All IMPROVE days
California in ()
0.474
20% worst days
California in ()
0.742
20% best days
California in ()
0.187
0.439 (0.078)
0.524 (0.11)
0.347 (0.070)
18.2 (8.9)
0.5 (0.3)
5.6 (3.5)
3.7 (1.7)
22.4 (10.3)
0.3 (0.3)
6.5 (3.9)
9.0 (2.3)
13.0 (11.4)
0.3 (0.2)
4.1 (3.6)
0 (0)
16.9 (3.5)
54.9
20.1 (3.5)
41.4
13.6 (4.8)
69.2
54.9
17.8
9.6
4.1
3.7
3.0
2.7
1.7
1.1
0.7
0.7
41.4
20.3
8.9
4.8
7.3
3.2
3.2
3.8
1.9
0.8
0.6
69.2
20.2
6.3
3.0
0.4
0.4
0.3
0.2
0
0
0
Attribution of nitrate by source type and region
PSAT attribution of nitrate at Bliss State Park to source type and geographic areas is presented in Table
1-5. Average model nitrate was far higher than measured nitrate for all days and especially so for 20%
best visibility days. Motor vehicles (mainly from California) accounted for more than half of the
25
modeled nitrate. California accounted for almost 60% of the modeled nitrate, with boundary conditions
21% and Nevada about 10%.
Table 1-5. WRAP apportionment of nitrate at Bliss State Park by source type and geographic region for all 2002 IMPROVE
sampling days, 20% best, and 20% worst visibility days. Source:
http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx
Source type
Measured NO3
concentration (μg/m3)
Model NO3
concentration (μg/m3)
Point sources %
Anthropogenic fires %
Motor vehicles %
Natural fires & biogenic
%
Area sources %
Outside domain all
types %
Geographic region %
Outside domain
California
Pacific offshore
Nevada
Oregon
Washington
Canada
Other WRAP states
Mexico
Central US
Eastern US
All IMPROVE days
California in ()
0.193
20% worst days
California in ()
0.293
20% best days
California in ()
0.057
0.515 (0.301)
0.313 (0.176)
0.944 (0.657)
8.6 (4.1)
0.8 (0.6)
53.8 (43.0)
4.0 (3.0)
10.6 (3.5)
0.6 (0.5)
52.7 (40.5)
5.3 (4.3)
6.6 (5.0)
0.9 (0.9)
56.4 (51.0)
3.3 (3.0)
11.8 (7.8)
21.0
10.8 (7.6)
19.5
13.5 (9.8)
19.4
21.0
58.5
4.1
9.6
2.1
2.7
0.8
1.3
0
0
0
19.5
56.2
2.6
16.3
0.9
1.1
1.3
1.8
0.3
0
0
19.4
69.6
5.0
4.2
0.6
0.5
0.1
0.6
0
0
0
Carbon apportionment
For the WRAP modeling CMAQ kept track of organic particulate by three categories of sources:
1) Anthropogenic secondary organic aerosol resulting from aromatic VOCs, such as xylene, toluene, and
cresols;
2) Biogenic secondary organic aerosol resulting from biogenic VOCs, such as terpenes; and
3) Primary organic aerosol (anthropogenic and biogenic sources), resulting from direct organic aerosol
emissions
26
Table 1-6 shows the attribution of organic aerosol to each of the three source categories. On average
Anthropogenic secondary accounted for 32% of the organic aerosol, biogenic secondary 64%, and
primary 4%. Figure 1-6 shows the contribution of each organic aerosol source category by day for 20%
worst visibility days. Figure 1-7 shows the contribution of each organic aerosol source category by day
for 20% best visibility days.
Figure 1-6. CMAQ Apportionment of organic carbon aerosol to anthropogenic secondary, biogenic secondary, and
anthropogenic and biogenic primary categories for 20% worst visibility days.
Figure 1-7. CMAQ Apportionment of organic carbon aerosol to anthropogenic secondary, biogenic secondary, and
anthropogenic and biogenic primary categories for 20% best visibility days.
27
Table 1-6. CMAQ Apportionment of organic carbon aerosol to anthropogenic secondary, biogenic secondary, and
anthropogenic and biogenic primary categories.
All IMPROVE days
(μg/m3)
0.59
20% worst
(μg/m3)
0.81
20% best
(μg/m3)
0.17
Anthropogenic
secondary
Biogenic secondary
1.17
1.87
0.25
Anthropogenic and
0.07
0.10
0.02
biogenic primary
Elemental carbon was not tracked by source type. However the WRAP weighted emissions potential
(WEP) gives insight into potential contributions from each source category. The WEP weights emissions
by frequency of transport toward the area of concern (Bliss State Park) and inversely by distance from
the area. Table 1-7 shows the WEP for elemental carbon at Bliss State Park for best and worst days in
2002. Wildfire accounted for about 60% of EC, mobile sources over 20%, and area sources about 10%.
California accounted for about 80% of the total, with Nevada about 15%.
Table 1-7. Weighted emissions potential (WEP) for EC by source type for worst and best 20 percentile visibility days at Bliss
State Park, 2002.
Source type
worst 20%
best 20%
Point
1.0
1.0
Anthropogenic Fire
2.7
2.9
Natural Fire
60.5
61.7
Area
12.3
10.3
Off-Shore
1.2
0.9
On-Road Mobile
8.8
10.0
Off-Road Mobile
13.3
13.0
Road Dust
0.1
0.2
Fugitive Dust
0.1
0.1
Table 1-8 shows the WEP for coarse mass at Bliss State Park for best and worst days in 2002. Road dust
has the highest potential impact, followed by miscellaneous fugitive dust sources, and windblown dust.
Wildfires also potentially contribute significantly to coarse mass. California sources account for about
70% of the coarse mass WEP at Bliss State Park and Nevada sources about 25% for best and worst days.
Table 1-8. Weighted emissions potential by source type for coarse mass at Bliss State Park for worst and best visibility days.
Source
Point
Anthropogenic Fire
Natural Fire
Area
On-Road Mobile
Off-Road Mobile
Road Dust
Fugitive Dust
Windblown Dust
worst 20%
best 20%
2.6
0.5
14.4
3.7
1.3
0.8
31.4
26.4
19.0
2.5
0.6
15.6
3.4
1.3
0.7
33.8
24.2
17.9
28
1.4.4 Lake Tahoe Atmospheric Deposition Study (LTADS)
The Lake Tahoe Atmospheric Deposition Study (LTADS) was a multi-agency sampling program designed
to help improve the clarity of Lake Tahoe by determining source contributions to atmospheric
deposition to the lake. The core program elements included sampling and chemical speciation of total
suspended particulates (TSP), PM10, and PM2.5 at five sites for continuous 2-week periods from
November 2002- January 2004. The five sites included two in South Lake Tahoe - SOLA and Sandy Way
(SW), both near US-50; one near Tahoe City (Lake Forest-LF), 20 m south of Highway 28; one at
Thunderbird Lodge (TB), on the northeast shore of Lake Tahoe; and an upwind background site (Big HillBH) 40 Km SW of Bliss State Park.
As expected the two South Lake Tahoe sites had the highest average TSP, PM10, and PM2.5
concentrations (20-22 μg/m3 for TSP, 16-19 μg/m3 for PM10 and 6.5-9 μg/m3 for PM2.5). The relatively
remote Thunderbird Lodge site had the lowest levels of TSP, PM10, and PM2.5 (6.2 μg/m3, 6.0 μg/m3, and
3.6 μg/m3, respectively). The Lake Forest site had the second lowest average PM2.5 (4.3 μg/m3), but the
highest TSP (16.5 μg/m3) and PM10 (14.0 μg/m3) after the South Lake Tahoe sites. The relatively high
PM10 and TSP were likely mostly due to road dust from nearby Highway 28. The background site Big Hill
had TSP, PM10, and PM2.5 concentrations intermediate between the South Lake Tahoe sites and
Thunderbird Lodge (11.4 μg/m3, 8.8 μg/m3, and 5.0 μg/m3, respectively).
All five sites had lowest TSP, PM10, and PM2.5 in March –April. The South Lake Tahoe sites had similar
concentrations in winter as summer; the other sites had winter concentrations about half of the
summer concentrations. The excess concentrations at South Lake Tahoe sites in winter compared to
other sites were due to higher concentrations of organic and elemental carbon. Organic carbon
compounds dominated the PM2.5 mass at all sites, while for TSP and PM10 soil elements constituted the
largest component, followed by organic material.
1.4.5 DRI Lake Tahoe Source Characterization Study
This study investigated the chemical composition and emission factors of selected particulate matter
(PM) sources in the Lake Tahoe basin. Particulate matter (PM) samples directly relevant to major PM
sources in Lake Tahoe were collected and analyzed as part of this study. Sources sampled included
residential wood combustion, motor vehicle exhaust, and entrainment of road dust, traction control
material, and road deicing material. In addition, several new emission measurement technologies were
applied during this study to investigate residential wood combustion, motor vehicle exhaust, and reentrained road dust. The major chemical components of wood-burning PM emissions were organic
carbon (OC) and elemental carbon (EC). Total carbon (TC) accounted for 15% to 74 % of PM2.5 mass.
Between 40% and 90% of PM mass emissions due to motor vehicles were composed of road dust
material (i.e. silicon, aluminum, iron, and organic carbon). The study concluded that road dust
entrainment may be the dominant source of coarse organic carbon PM in the Lake Tahoe Basin. For
particulate matter, the combined emission inventory indicated that residential wood combustion,
unpaved road dust, and paved road dust are the largest sources.
29
2. What aerosol components are responsible for visibility impairment in
the Lake Tahoe Basin?
Chemically speciated aerosol data are available for Bliss State Park (11/17/1990-9/28/2009) and South
Lake Tahoe (4/1/1989-5/15/2004). Contributions of each of the aerosol components to light extinction
are estimated using the method recommended by the IMPROVE program.
2.1 A brief primer on the causes of haze
Atmospheric aerosols are solid or liquid particles suspended in the atmosphere. Aerosols affect
atmospheric visibility by scattering light out of a sight path or by absorbing light and thus removing it
from the sight path. Generally scattering of light dominates over light absorption. The total light
extinction is given by the sum of light scattering and absorption by particles and light scattering and
absorption by gases. Light extinction in this document is expressed in units of inverse megameters (Mm1
), where a megameter is 1 million meters or 1000 km. The light extinction coefficient is the distance at
which the intensity of a beam of light would drop to 1/e of its original strength over the distance implied
by the extinction coefficient. For example for an extinction of 100 Mm-1, the distance is 10-4m or one per
10 km, so the remaining light intensity would drop by 1/e or 63% of its original intensity at a distance of
10 Km.
Light scattering by gases is mainly due to the gases (mostly nitrogen and oxygen) that compose clean air.
This natural light extinction (called Rayleigh scattering) depends upon air density and is typically about
12 Mm-1 at sea-level. The value at the elevation of Lake Tahoe is about 10 Mm-1. Absorption of light by
gases is usually small compared to scattering by gases. The main gas absorbing light is nitrogen dioxide
(NO2), which is mostly formed when nitrogen oxide (NO) is produced during high temperature
combustion and reacts with ozone (O3) to produce NO2 and O2. NO2 absorbs light preferentially at the
shorter wavelengths allowing longer wavelengths to dominate, resulting in a yellow-brown
discoloration. This discoloration is sometimes noted over urban areas and in the plumes of power
plants.
In this document, the effects of light scattering and absorption by particles are considered in detail. All
particles scatter light, while only some particles, such as diesel soot or smoke significantly absorb light.
The amount of light a single particle scatters depends upon its composition and size, with larger
particles scattering more light than smaller particles because of the greater chance of their interaction
with a photon of light. However, smaller particles often scatter more light per unit mass than large
particles because of their higher surface area to mass ratio. Some particles such as sulfate, nitrate, and
sea salt absorb water at high relative humidity (RH) and increase in size and thus scatter more light at
high RH. Carbonaceous particles are generally considered to absorb little water; their light scattering
properties do not change significantly with RH.
2.2 Methodology for reconstructed aerosol fine mass and reconstructed light
extinction
A reasonable estimate of the contribution of each major aerosol component to haze (light extinction)
can be made based on the mass concentration of each component and the relative humidity. The USEPA
30
Regional Haze Regulations require the use of reconstructed light extinction from aerosol data to track
trends in haze at mandatory Class I areas, such as the Desolation Wilderness area in the Lake Tahoe
Basin. The Bliss State Park IMPROVE monitoring site is located very close to the Desolation Wilderness
Class I area and was located to represent this Class I area.
The IMPROVE recommended methods for reconstructed aerosol fine mass and reconstructed extinction
are used here. They are briefly described below.
Reconstructed fine mass (RFM)
RFM=1.29*NO3 + 1.375*SO4 + 1.8*OC + EC + fine soil + sea salt
Where NO3 and SO4 are nitrate and sulfate ion, respectively, OC= organic carbon from the thermal
optical reflectance (TOR) analysis, EC=elemental carbon from TOR.
Fine soil is calculated as 2.2*Al+2.49*Si+1.63*Ca+2.42*Fe+1.94*Ti
Sea salt is calculated as 1.8*Cl- or 1.8*Cl if Cl-=0, where Cl-=chloride ion from ion analysis.
The 1.8 factor for OC is to account for hydrogen associated with carbon in organic aerosols. OC is the
organic mass or OM.
It should be noted that the equations used here to represent reconstructed fine mass and following
equations to estimate contributions to haze from each reconstructed fine mass component are based on
relationships established using many sites across the US. Differences in soil composition from that
assumed in the fine soil equation contributes uncertainty to the results at any particular site. Also, the
1.8 factor to convert organic carbon to organic may vary considerably among sites and even over
different seasons at a given site. Sea salt is defined by the equation above and could actually be due to
salting of roads rather than true sea salt.
Reconstructed light extinction
Reconstructed light extinction (bext) is given by:
b ext
2.2
f S(RH)
Small Sulfate
4.8
2.4
f S(RH)
Small Nitrate
5.1
2.8
Small Organic Mass
10
Elemental
Carbon
1
Fine Soil
1.7
f SS(RH) Sea Salt
0 .6
Coarse Mass
Rayleigh
Scattering
0.33
NO (
2 ppb)
6.1
(S ite Specific)
where
31
f(
L RH)
f(
L RH)
L arge Sulfate
L arge Nitrate
L arge Organic
Mass
Small Sulfate Total Sulfate L arge Sulfate
L arg e Sultate
Total Sulfate , for Total Sulfate
L arge Sulfate
Total Sulfate
20 g / m 3
20 g / m 3
Total Sulfate , for Total Sulfate
20 g / m 3
The same equations are used to apportion total nitrate and total organic mass concentrations into the
small and large size fractions.
Fine mass is the PM2.5 mass. Coarse mass is defined as the difference between PM10 and PM2.5.
The fS(RH), fL(RH) are relative humidity growth factors for small and large sulfate and nitrate particles;
fSS(RH) is the relative humidity growth factor for sea salt. Figure 2-1 shows the relative humidity growth
factors.
Figure 2-1. Relative humidity growth factors curves for small sulfate and nitrate -fS(RH), large sulfate and nitrate- fL(RH), and
sea salt – fSS(RH).
2.3 Reconstructed fine mass results
Reconstructed fine mass compared well to measured fine mass at both sites (r2=0.93 at Bliss State Park
and 0.83 at South Lake Tahoe). Average reconstructed fine mass was 3.9% higher than measured fine
mass at Bliss State Park and 8.9% higher than measured fine mass at South Lake Tahoe. The
reconstructed fine mass likely averaged slightly higher than measured fine mass due to the 1.8 factor
used to obtain organic mass from organic carbon. This IMPROVE method multiplication factor had been
1.4 which would have resulted in reconstructed fine mass less than measured. All in all, the good
32
relationship between reconstructed and measured fine mass gives confidence in the overall quality of
the data and supports conclusions drawn from it.
Table 2-1 shows the average reconstructed fine mass by component for Bliss State Park and South Lake
Tahoe for periods where both sites had data (11/17/1990-5/15/2004). Also shown are data at
Thunderbird Lodge (only from 5/19/2001-3/25/2003). The “sea salt” component on average is highest at
Thunderbird Lodge, but compared over the same time period as data available at TBLG, SOLA had higher
average sea salt concentrations. The reconstructed sea salt is actually thought to be due mainly to
salting of area roads in winter and appears at the near-lake elevation TBLG and SOLA sites, but not
significantly at the elevated BLIS site. Reconstructed fine mass on average is about 2.5 times higher at
SOLA than at BLIS. This may be considered the urban increment over the background concentration.
Most of the excess fine mass at SOLA is due to carbon (organic and elemental) with fine soil also
contributing to the excess (probably road dust). Although the time period is somewhat different, the
concentrations of aerosol components at Thunderbird Lodge are similar to those at Bliss State Park.
Figure 2-2 shows graphically the component contributions to RFM at BLIS, SOLA, and TBLG.
Component
Bliss
South Lake
3
(μg/m ) Tahoe (μg/m3)
0.63
Thunderbird % of
Lodge(μg/m3) RFM
Bliss
0.66
19.4
% of RFM
South Lake
Tahoe
7.0
% of RFM
Thunderbird
Lodge
15.2
Ammonium
sulfate
Ammonium
nitrate
Organic mass
Elemental carbon
Fine soil
Sea salt
Reconstructed
fine mass
0.69
0.27
0.46
0.29
7.6
5.1
6.6
1.87
0.18
0.50
0.03
3.53
5.41
1.09
1.36
0.10
9.06
2.52
0.20
0.40
0.25
4.31
52.9
5.1
14.3
0.7
59.8
12.0
15.0
1.1
58.4
4.7
9.3
5.8
Table 2-1. Reconstructed fine mass component (RFM) concentrations and percentage contributions to RFM at the Bliss State
Park (BLIS), South Lake Tahoe (SOLA), and Thunderbird Lodge (TBLG) monitoring sites.
33
Figure 2-2. Pie charts showing percentage contribution to reconstructed fine mass by aerosol component for Bliss State Park
(BLIS), South Lake Tahoe (SOLA), and Thunderbird Lodge (TBLG).
2.3.1 Monthly patterns of aerosol component concentrations
Figure 2-3 shows the monthly average contributions to reconstructed fine mass (RFM) by aerosol
component at the SOLA and BLIS monitoring sites. BLIS shows highest fine mass in the summer to early
autumn, mainly due to higher organic mass (OM), while SOLA has highest fine mass in the winter, again
due mainly to organic mass. Figure 2-4 shows the same data, except by percentage contribution to RFM
from each aerosol component. Monthly percentage contributions by component do not vary much,
with a few exceptions. In April and May, at both sites, organic mass percentage contributions are lowest
and soil contributions are highest. The increased soil contributions may be due to transport of Asian
dust into the region, which has been noted to peak in the springtime. This will be explored in more
detail later. Also, sulfate percentage contributions are higher at both sites in the warmer months.
34
Figure 2-3. Monthly contributions to RFM by aerosol component at the South Lake Tahoe and Bliss State Park monitoring
sites (1990-2004).
35
Figure 2-4. Monthly percentage contributions to RFM by aerosol component at the South Lake Tahoe and Bliss State Park
monitoring sites (1990-2004).
2.3.2 Urban increment to aerosol component concentrations by month
Next the monthly variation in difference of concentrations between the background site (BLIS) and
urban site (SOLA) are considered. The difference in concentrations between these two sites may
reasonably be expected to represent the “urban contribution”.
Figure 2-5 shows the background concentrations (BLIS) and the urban increment (SOLA-BLIS) for each
aerosol component by month. Sulfate is essentially the same at the urban and background sites. Nitrate
36
shows a considerable urban increment in winter and little in summer when nitrate levels are low.
Organic mass shows a large urban increment in winter and much less in summer. The pattern is similar
for EC, except that EC has higher urban increment than background for all months. For soil, the largest
urban increment is in winter months and for all months except April and May the urban increment
exceeds the background value. The high background soil in spring may be due to Asian dust and the high
urban increment in winter may be due to applying sand to roads during winter. The “sea salt” urban
increment is highest in winter and may be due to applying salt to roads.
37
38
39
Figure 2-5. Background and urban increment contributions to fine mass aerosol components and coarse mass.
2.3.4 Comparison of aerosol component concentrations for last 5 years to previous 19902004 period at Bliss Sate Park
Consideration of urban increment could only be done for the 1990-2004 period where samples were
taken at both Bliss State Park and South Lake Tahoe. It is of considerable interest to see if, and how,
aerosol component concentrations and percent contribution to reconstructed fine mass (RFM) have
changed over time. In this section the aerosol component concentrations and percent contribution to
fine mass are compared for the last 5 year of available data (2004-2008) to the 1990-2004 period
considered previously. Table 2-2 compares the average fine mass aerosol component concentrations
and percentage contribution to RFM for the 1990-2004 and the 2004-2008 periods.
Table 2-2. Average aerosol component concentrations and percentage contribution to reconstructed fine mass at Bliss State
park for 1990-2004 and 2004-2008.
Component
1990-2004
(μg/m3)
2004-2008
(μg/m3)
% of RFM
1990-2004
% of RFM
2004-2008
Ammonium
sulfate
Ammonium
nitrate
Organic mass
Elemental carbon
Fine soil
Sea salt
Reconstructed
fine mass
0.69
0.78
19.4
21.1
0.27
0.23
7.6
6.1
1.87
0.18
0.50
0.03
3.53
2.02
0.17
0.49
0.02
3.70
52.9
5.1
14.3
0.7
100.0
54.6
4.6
13.1
0.5
100.0
40
There is little change in reconstructed fine mass and aerosol component composition over this time
period for the Bliss State Park site. There are slight increases in RFM, sulfate and organic mass, a slight
decrease in nitrate, and essentially no change in EC and fine soil. The make-up of RFM changes a bit,
with sulfate and organic mass fractions increasing, and fractions of the other components decreasing. In
a later section, trend analysis will be performed to look at the trends in each aerosol component at BLIS
and SOLA over their periods of record and over shorter time frames and the statistical significance of
any trends will be calculated.
2.4 Reconstructed light extinction results
Table 2-3 shows reconstructed light extinction by aerosol component at Bliss State Park and South Lake
Tahoe for periods with data at both sites (November 1990 – May 2004). Aerosol light extinction is 3.4
times higher at South Lake Tahoe than at Bliss State Park. At South Lake Tahoe 70% of the aerosol light
extinction is due to carbonaceous aerosol (organic and elemental carbon). The fraction due to carbon
aerosol at Bliss State Park is about ½. Coarse mass contributes 13% to aerosol light extinction at both
sites. Sulfate contributes over 20% of aerosol extinction, but only about 8% at South Lake Tahoe. This is
because the sulfate light extinction is about the same at both sites, but with total aerosol extinction at
South Lake Tahoe 3 times as high at as Bliss State Park, the percent extinction from sulfate is only about
1/3 as high as at Bliss State Park.
Table 2-3. Reconstructed aerosol light extinction at Bliss State Park and South Lake Tahoe, by aerosol component, 19902004.
Component
Ammonium
sulfate
Ammonium
nitrate
Organic mass
Elemental carbon
Fine soil
Sea salt
Coarse mass
Total
reconstructed
aerosol extinction
Bliss light South Lake
extinction Tahoe light
(Mm-1)
extinction
(Mm-1)
% of
reconstructed
extinction
Bliss
% of
reconstructed
extinction
South Lake Tahoe
2.76
3.33
21.6
7.6
1.14
4.80
1.78
0.51
0.10
1.68
2.27
18.81
11.97
1.11
0.48
5.73
8.9
37.6
13.9
4.0
0.8
13.1
5.2
43.0
27.4
2.5
1.1
13.1
12.76
43.71
100
100
Figure 2-6 shows monthly average contributions to reconstructed light extinction at Bliss State Park,
South Lake Tahoe and Thunderbird Lodge by aerosol component.
41
Figure 2-6. Pie chart of contributions to reconstructed light extinction at Bliss State Park (BLIS) and South Lake Tahoe (SOLA)
for periods with data for both sites (November 1990 – May 2004). Also shown is data for Thunderbird Lodge (TBLG) for the
period May 2001- March 2003.
2.4.1 Time series of monthly averaged reconstructed light extinction
Monthly average reconstructed light extinction at Bliss State Park by aerosol component is shown in
Figure 2-7. A few months stand out, particularly July 2008, as having much higher average
reconstructed extinction, mainly from organic mass. The high average light extinction in is due to
persistent smoke from forest fires. The Angora fire caused high reconstructed light extinction on the
June 26, 2007 sampling day only, so it did not result in a particularly high monthly average.
42
Figure 2-8 shows the same data but with uniform y-axis scales for ease of comparison over time.
43
44
Figure 2-7. Monthly average reconstructed light extinction by aerosol component at Bliss State Park.
45
Figure 2-8 shows the same data but with uniform y-axis scales for ease of comparison over time.
46
47
-1
Figure 2-8. Monthly average reconstructed light extinction by aerosol component at Bliss State Park scaled from 0-30 Mm .
Figure 2-9 shows monthly averaged reconstructed light extinction at South Lake Tahoe..
48
Figure 2-9. Monthly average reconstructed light extinction by aerosol component at South Lake Tahoe.
2.4.2 Trend analysis of reconstructed light extinction and its chemical component
contributions
This section considers trends in reconstructed light extinction and in light extinction causes by each
aerosol component. Data are segregated by 20 percentile best, middle 60 percentile, and 20 percentile
worst reconstructed light extinction days for each year. First, trends over the 19 years of record at Bliss
State Park are presented, then trends for the periods 1990-1999 and 2000-2009 at Bliss State Park are
presented. The Bliss State Park analysis is followed by analysis for South Lake Tahoe.
Thiel regression (Thiel, 1950) is used to determine the slope and statistical significance of trends. A
trend is considered statistically significant if the is different from zero at the 95% confidence level
(p=0.05 or less).
Bliss State Park
49
Figure 2-10 shows the aerosol component contributions to reconstructed light extinction at Bliss State
Park for 20% best, middle 60%, and 20% worst visibility days, 1990-2003. For the 20% best days, organic
mass and sulfate each had about ¼ of the aerosol light extinction which averaged only 4 Mm-1. For the
middle 60% days average total reconstructed aerosol light extinction was 11.0 Mm-1. On the 20% worst
days average total reconstructed aerosol light extinction was 26.5 Mm-1.
20% best
60% middle
20% worst
-1
Figure 2-10. Aerosol component contributions (Mm ) to reconstructed light extinction for 20% best, middle 60%, and 20%
worst visibility days at Bliss State Park.
20% best case days
Figure 2-11 shows plots of annual average aerosol component and total aerosol contributions to best
20% reconstructed light extinction days for Bliss State Park from 1990-2009. A best fit ordinary least50
square (OLS) regression line is also shown to help visualize any trend. It should be noted however that
the slope of the OLS will, in general be different from the Thiel regression slope.
51
-1
Figure 2-11. Reconstructed aerosol light extinction (Mm ) by components and total aerosol at Bliss State Park for 20% best
days (1991-2009).
Table 2-4 shows the Thiel regression results for 20% best days for the entire 1991-2009 period, for 19911999, and 2000-2009. For the entire period, all component and total reconstructed extinction showed
decreases. The decreases in sulfate and soil extinction were not statistically significant at the P=0.05
level (but were at the P=0.10 level). The largest decrease was from organic carbon, then coarse mass.
For the 1991-1999 period, sulfate, nitrate, coarse mass and total reconstructed extinction showed
statistically significant decreases. EC had a statistically significant increase. For the 2000-2009 period,
nitrate, EC, and total reconstructed extinction had statistically significant decreases.
-1
Table 2-4. Thiel regression analysis slope (Mm /year) and P-value for 20% best visibility days at Bliss State Park.
20% best days
19912009
19911999
20002009
Slope
P-Value
Slope
P-Value
Slope
P-Value
SO4 ext
-0.019
0.062
-0.093
0.038
-0.025
0.242
NO3 ext
-0.012
0.003
-0.034
0.006
-0.023
0.014
OMC ext
-0.049
0.001
-0.009
0.381
-0.038
0.146
EC ext
-0.020
0.001
0.043
0.038
-0.034
0.005
Soil ext
-0.003
0.093
0.008
0.179
-0.003
0.146
CM ext
-0.038
0.000
-0.082
0.022
0.013
0.300
Recon
bext
-0.138
0.000
-0.209
0.022
-0.081
0.023
60% middle days
Figure 2-12 shows plots of annual average aerosol component and total aerosol contributions to middle
60% reconstructed light extinction days for Bliss State Park from 1990-2009. The 60% middle days may
be considered as representing “typical” days without high or low outliers.
52
53
Figure 2-12. Trend of aerosol component and total reconstructed light extinction for 60% middle visibility days at Bliss State
Park.
Table 2-5 shows the Thiel regression results for middle 60% days for the entire 1991-2009 period, for
1991-1999, and 2000-2009. For the entire 1991-2009 period, organic carbon, nitrate, EC, and coarse
mass, and total extinction showed statistically significant drops. Organic aerosol extinction showed the
greatest drop. Figure 2-12 shows that OC and EC light extinction on middle 60% days had distinctly
higher levels from 1991-2000 and then lower levels afterwards. For the 1991-1999 period sulfate and
nitrate extinction showed statistically significant declines. During 2000-2009, nitrate, EC, and coarse
mass extinction had statistically significant decreases.
-1
Table 2-5. Thiel regression analysis slope (Mm /year) and P-value for middle 60% visibility days at Bliss State Park.
Middle 60% days
19912009
19911999
20002009
Slope
P-Value
Slope
P-Value
Slope
P-Value
SO4 ext
-0.002
0.473
-0.170
0.000
0.010
0.500
NO3 ext
-0.018
0.004
-0.083
0.001
-0.047
0.005
OMC ext
-0.067
0.006
0.113
0.090
-0.039
0.300
EC ext
-0.038
0.002
0.021
0.090
-0.042
0.036
Soil ext
-0.001
0.365
0.003
0.460
0.010
0.300
CM ext
-0.031
0.040
0.003
0.460
0.052
0.023
Recon
bext
-0.170
0.000
-0.173
0.060
-0.091
0.300
20% worst days
Figure 2-13 shows plots of annual average aerosol component and total aerosol contributions to the
20% highest reconstructed light extinction days for Bliss State Park from 1990-2009.
54
55
Figure 2-13. Trend of aerosol component and total reconstructed light extinction for 20% worst visibility days at Bliss State
Park.
Table 2-6 shows the Thiel regression results for 20% worst days for the entire 1991-2009 period, for
1991-1999, and 2000-2009. For the entire period (1991-2009) nitrate and coarse mass extinction had
statistically significant decreases, while organic aerosol extinction had statistically significant increases.
For the 1991-1999 period, the only change at the P=0.05 level was an increase in organic aerosol
extinction. For the 2000-2009 period, none of the components or total reconstructed extinction had
statistically significant trends.
-1
Table 2-6. Thiel regression analysis slope (Mm /year) and P-value for 20% worst visibility days at Bliss State Park.
20% worst days
19912009
19911999
20002009
Slope
P-Value
Slope
P-Value
Slope
P-Value
SO4 ext
0.061
0.082
-0.232
0.130
0.112
0.190
NO3 ext
-0.062
0.012
-0.101
0.179
-0.070
0.146
OMC ext
0.328
0.012
0.441
0.012
0.425
0.190
EC ext
-0.010
0.418
0.088
0.179
0.010
0.431
Soil ext
0.007
0.203
0.004
0.460
-0.046
0.364
CM ext
-0.034
0.025
-0.079
0.179
0.038
0.364
Recon
bext
0.175
0.184
0.370
0.060
0.788
0.300
South Lake Tahoe
Figure 2-14 shows the aerosol component contributions to reconstructed light extinction at South Lake
Tahoe for 20% best, middle 60%, and 20% worst visibility days, 1990-2003. Organic and elemental
carbon dominate light scattering at South Lake Tahoe for all 3 categories. For the clearest days, organic
light extinction and EC light extinction are about equal. For the dirtiest 20% of days organic light
extinction is about twice EC light extinction. After carbon, coarse mass gives the greatest contribution
to reconstructed light extinction for each set of days.
56
20% best
20% worst
60% middle
-1
Figure 2-14. Aerosol component contributions (Mm ) to reconstructed aerosol light extinction at South lake Tahoe for 20%
-1
best, middle 60%, and 20% worst visibility days, 1990-2003. Average reconstructed aerosol light extinction is 17.9 Mm for
-1
-1
the 20% best days, 33.8 Mm for the middle 60% days and 89.7 Mm for the 20% worst days.
57
20% best case days South Lake Tahoe
Figure 2-15 shows the time series of reconstructed light extinction and its components for the annual
20% best visibility days at South Lake Tahoe for the period 1990-2003. Table 2-7 shows the Thiel
regression analysis. Statistically significant decreases (at P=0.05) in sulfate, nitrate, EC, coarse mass and
total aerosol extinction occurred. Decreases in organic and soil extinction were not statistically
significant.
58
Figure 2-15. Trend in reconstructed extinction and its components for the 20% best visibility days at South Lake Tahoe 19902003.
59
Table 2-7. Thiel regression results for 20% best visibility days at South Lake Tahoe, 1991-2003.
20% best days
19912003
Slope
P-Value
SO4 ext
-0.096
0.011
NO3 ext
-0.049
0.005
OMC ext
-0.134
0.126
EC ext
-0.341
0.000
Soil ext
-0.013
0.218
CM ext
-0.053
0.038
Recon
bext
-0.680
0.003
Middle 60% days South Lake Tahoe
Figure 2-16 shows the time series of reconstructed light extinction and its components for the annual
middle 60% visibility days at South Lake Tahoe for the period 1990-2003. Table 2-8 shows the Thiel
regression analysis. Sulfate, nitrate, EC, and reconstructed total extinction all had statistically significant
downward trends.
60
Figure 2-16. Trend in reconstructed extinction and its components for the middle 60% visibility days at South Lake Tahoe
1990-2003.
Table 2-8. Thiel regression results for 60% middle visibility days at South Lake Tahoe, 1991-2003.
Middle 60% days
19912003
Slope
P-Value
SO4 ext
-0.126
0.001
NO3 ext
-0.073
0.003
OMC ext
-0.312
0.082
61
EC ext
-0.604
0.000
Soil ext
0.021
0.064
CM ext
-0.094
0.082
Recon
bext
-1.468
0.001
20% worst case days South Lake Tahoe
Figure 2-17 shows the time series of reconstructed light extinction and its components for the annual
20% best visibility days at South Lake Tahoe for the period 1990-2003. Table 2-9 shows the Thiel
regression analysis. Nitrate, organics, EC, and total reconstructed extinction showed statistically
significant decreasing trends.
62
Figure 2-17. Trend in reconstructed extinction and its components for the 20% worst visibility days at South Lake Tahoe
1990-2003.
-1
Table 2-9. Thiel regression analysis slope (Mm /year) and P-value for 20% worst visibility days at South Lake Tahoe.
Worst 20% days
19912003
Slope
P-Value
SO4 ext
-0.053
0.102
NO3 ext
-0.318
0.005
OMC ext
-2.623
0.029
EC ext
-1.816
0.000
Soil ext
-0.002
0.383
CM ext
-0.205
0.218
Recon
bext
-4.737
0.005
2.4.3 Progress toward meeting regional haze rule requirement
The USEPA regional haze rule requires mandatory class I areas such as the Desolation Wilderness to
maintain visibility on the worst days and to improve visibility for the best days to “natural background”
conditions. Worst days are considered the 20% of days with highest reconstructed light extinction. Best
63
days are those 20% days with lowest reconstructed light extinction. Figure 2-18 shows the frequency of
best and worst days by month at Bliss State Park for 2000-2009.
Figure 2-18. Percent of days in each month that are classified as 20% best or 20% worst visibility days at Bliss State Park
2000-2009. Note that the average for each category is 20%.
The baseline period was set as 2000-2004 and the natural background is to be reached by 2064. States
are to evaluate progress at 5 year intervals. The first 5 year interval after the baseline includes the years
2005-2009. The guidelines for assessing reasonable progress prescribe that the daily reconstructed
extinction values be converted to deciviews (dV) by the formula dV=10*ln(bext/10), where bext is in Mm-1.
The bext is a sum of the reconstructed aerosol extinction and Rayleigh scattering of 10 Mm-1. For each
year, the average deciviews for the 20% best days (lowest dV) and the 20% worst days (highest dV) are
calculated. The 5 year deciview for the best and worst days is the average over each 5-year period.
Table 2-10 summarizes the results.
Table 2-10. Average aerosol component light extinction and deciview at Bliss State Park for the 20% best and worst visibility
-1
days for the 2000-2004 and 2005-2009 time periods. Aerosol component contributions to light extinction are in Mm .
Sea salt
ext
aerosol
ext
SO4 ext NO3 ext OC ext EC ext
soil ext
CM ext
dV
2000-2004
best
1.07
0.39
0.98
0.50
0.14
0.18
0.40
3.66
3.07
2005-2009
best
1.02
0.26
0.71
0.30
0.11
0.09
0.42
2.92
2.53
2000-2004
worst
4.61
1.63 12.02
3.24
1.08
0.11
2.03
24.72
11.15
2005-2009
worst
5.45
1.56 17.22
3.65
0.79
0.07
2.16
30.90
12.71
The best days did improve, with all component contributions to bext except coarse mass decreasing.
However, the worst days got worse, mainly due to a 43% increase in extinction from organic mass and
lesser increases in sulfate, EC, and coarse mass light extinction. It should be noted that the values
64
presented here are slightly different from those obtained using the USEPA guidance document for
tracking progress on regional haze. Monthly averaged relative humidity growth factors f(RH) for BLIS1
were computed using RH data collected at each site rather than the EPA default RH values for
Desolation Wilderness. Also, methods for substitution of missing data differed and quarterly and annual
data completeness requirements were not applied here.
The 43% increase in organic mass light extinction on 20% worst visibility days from 2000-2004 to 20052009 deserves a closer look. It is expected that most of the organic carbon on worst visibility days is
from wildfires. Figure 2-19 shows the number of acres burned per year by wildfires in the State of
California from 2000-2009. The 5-year average increased from 497,455 acres for 2000-2004 to 942,923
acres for 2005-2009, a 90% increase. While is it unknown how many burned acres affected the monitor
at Bliss State Park, it seems reasonable to assume that fires would have caused greater impacts for the
2005-2009 period compared to the 2000-2004 period.
Figure 2-19. Acres burned by wildfires in California by year, 2000-2009. Source: California Dept of
Forestry and Fire Protection.
http://www.fire.ca.gov/fire_protection/fire_protection_fire_info_redbooks.php
Figure 2-20 shows pie charts of aerosol component contribution to light extinction for the data in Table
2-10. Best day light extinction is dominated by sulfate and organic mass, with coarse mass, EC, and
nitrate giving lower contributions that are similar in magnitude to each other. For worst days, organic
mass dominates light extinction followed by sulfate and EC.
65
Figure 2-20. Pie charts showing percentage contribution of each aerosol component to reconstructed aerosol light extinction
for best and worst days 2000-2004 and 2005-2009.
2.5 Analysis of optical data
Optical data for the Lake Tahoe Basin includes light scattering measured with nephelometers at South
Lake Tahoe and Bliss State Park and light extinction measured by a transmissometer with a sight path
from Bliss State Park to Zephyr Point. This section summarizes the optical data and compares to light
scattering and light extinction as reconstructed from the aerosol data.
2.5.1 Monthly patterns in Nephelometer bsp
Nephelometer data is available for Bliss State Park from November 1990- February 2005 and at South
Lake Tahoe from March 1989- August 2000. Monthly average light scattering by particles (bsp) is shown
66
for both sites in Table 2-11 and Figure 2-21. South Lake Tahoe (SOLA) has higher scattering than does
Bliss State Park (BLIS) every month and sees a winter peak in scattering and spring minimum. The winter
peak at SOLA is due to build up of local emissions during winter stagnation conditions (little vertical
mixing of pollution). The smaller summer peak for both sites is due to forest fires. These annual
patterns are consistent with the monthly average aerosol concentrations at the two sites.
Table 2-11. Average monthly light scattering by particles as measured by nephelometers at South Lake Tahoe (SOLA) and
-1
Bliss State Park (BLIS). Scattering is in inverse megameters (Mm ).
SOLA
BLIS
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
45.5
37.8
25.5
23.3
20.9
23.1
25.0
31.4
28.3
32.3
46.1
51.2
8.4
8.8
9.4
13.7
15.6
13.6
16.6
22.3
15.5
15.8
13.5
11.6
Figure 2-21. Monthly average nephelometer measured light scattering by particles (bsp) at South Lake Tahoe and Bliss State
Park.
2.5.2 Diurnal patterns in bsp by month and season
Diurnal patterns in light scattering by month at Bliss State Park are shown in Figure 2-22. The monthly
averages by time of day are shown for 3 monthly groupings within which the diurnal patterns were
similar.
67
Figure 2-22. Average monthly diurnal patterns of light scattering by particles at Bliss State Park.
Winter (Nov-Feb) see highest light scattering near noon. This may be due to mixing of low lying
pollution over the Lake up to the monitoring site. Spring and summer see morning maximums and
scattering decreasing through much of the day, with an increase toward evening in the spring. This can
68
likely be explained by mixing depth and wind speed patterns. Morning is highest due to limited mixing
depths and light wind speeds and as the day progresses the pollution is dispersed as vertical and
horizontal mixing increase. The spring evening increase in scattering may be due to the decrease in
mixing late in the day. Figure 2-23 summarizes the seasonal patterns by averaging the months from
each chart from Figure 2-20 into one plot.
Figure 2-23. Average diurnal patterns in particle light scattering (bsp) by seasonal group. Winter= November-February, Spring
= March-June, summer-autumn=July-October
Figure 2-24 shows the monthly average diurnal patterns of light scattering by particles at South Lake
Tahoe. Months are grouped by 4 month periods as in Figure 2-22 for Bliss State Park. Values and
patterns within each plot are similar, except March and October have somewhat higher values and a
greater diurnal variation than the other months plotted with them. Winter months have large diurnal
variation in bsp while summer diurnal variations are small. March and October are transition months
between the large and small diurnal range months and very closely match the annual average diurnal
patterns in bsp. Lowest particle light scattering occurs in spring in near midday and is about 15 Mm-1.
Highest particle light scattering is in evening hours in winter and is about 100 Mm-1. It is interesting to
note that from about noon to 4 pm, average bsp is about the same (near 20 Mm-1) for all months. At 8
pm however, monthly average bsp ranges from under 30 Mm-1 from May-July to over 110 Mm-1 in
December.
69
Figure 2-24. Average diurnal patterns in light scattering by particles (bsp) by month.
70
Figure 2-25 shows average bsp by hour of day for winter (Nov-Feb), spring-summer (Apr-Sep), and
transition months (Oct, Mar). All periods show morning and evening peaks, presumably due to traffic
patterns, although they are much more pronounced in winter than summer. The winter morning peak is
later than other seasons due to a delay in increased mixing with later sunrise. The evening peak gets
later as the sun sets later due to increased dispersion during evening rush hour.
Figure 2-25. Average diurnal patterns in light scattering by particles (bsp) by monthly groups.
2.5.3 Measured versus reconstructed light scattering by particles
In this section particle light scattering reconstructed from aerosol composition and concentration to that
measured by a nephelometer are compared. As shown above, measured bsp often varies significantly
over the course of a day. Because the reconstructed scattering is for 24-hour averages, daily averaged
measured bsp is compared to reconstructed bsp. There are multiple reasons for measured and
reconstructed bsp to differ for a given sampling day, including:
1) Less than 24 hours of valid nephelometer data (e.g. RH>95% not included)
2) variations of actual aerosol scattering efficiencies from those assumed (IMPROVE default equations)
3) errors in measured aerosol component concentrations and bsp.
Bliss State Park
Measured and reconstructed bsp for Bliss State Park are compared in Figure 2-26. There is only moderate
correlation between measured and reconstructed bsp (r=0.57). Average reconstructed bsp is 28.3% lower
than measured bsp. Looking at seasonal comparisons, the relationships vary between summer and
winter. Figure 2-27 compares monthly average measured and reconstructed bsp. For every month
reconstructed bsp is greater than measured bsp. Figure 2-28 and Figure 2-29 compare measured and
reconstructed bsp for November to February and June through August. The correlations improve
somewhat when comparing by seasons because the seasons show different relationships between
71
measured and reconstructed bsp, as can be noted by the difference in slope and intercept of the
regression equations between the seasons. Winter shows a low bias of 40% for reconstructed bsp while
summer is biased low by only 24%.
Figure 2-26. Measured and reconstructed bsp for Bliss State Park (n=497).
Figure 2-27. Monthly average measured and reconstructed bsp at Bliss State Park.
72
Figure 2-28. Measured and reconstructed bsp for Bliss State Park November to February.
Figure 2-29. Measured and reconstructed bsp for Bliss State Park June-August.
South Lake Tahoe
Figure 2-30 compares measured and reconstructed bsp at South Lake Tahoe. The comparison between
measured and reconstructed bsp at South Lake Tahoe is better than at Bliss State Park. The correlation
coefficient is 0.78 and reconstructed scattering is biased 8.3% low. Figure 2-31 compares monthly
average measured and reconstructed bsp at South Lake Tahoe. For all months except December through
March reconstructed bsp is less than measured bsp. Figure 2-32 and Figure 2-33 compare daily measured
and reconstructed bsp for June to August and November – February. Reconstructed bsp is biased low by
23% in June-August and high by 6% in November to February.
73
Figure 2-30. Measured and reconstructed bsp for South Lake Tahoe, all months.
Figure 2-31. Monthly average measured and reconstructed bsp at South Lake Tahoe.
74
Figure 2-32. Measured and reconstructed bsp for South Lake Tahoe November to February.
Figure 2-33. Measured and reconstructed bsp for South Lake Tahoe June-August.
2.5.4 Monthly patterns in transmissometer light extinction
Figure 2-34 compares monthly average transmissometer light extinction data (bext) to nephelometer
particle light scattering (bsp) at Bliss State Park. Monthly patterns track well for the two instruments
with the transmissometer light extinction on average about 14 Mm-1 greater than nephelometer light
scattering. Excluding Rayleigh scattering of 9.5 Mm-1 from clean air from the transmissometer data gives
a difference of about 4.5 Mm-1. Some of this difference would be expected to be due to light absorption
mainly from elemental carbon (EC) and there could be some due to light absorption from nitrogen
dioxide as well.
75
Figure 2-34. Monthly average transmissometer light extinction and nephelometer light scattering at Bliss State Park. Data
used are daily average bext and bsp for the period November 1990 – February 2000. Only days with at least 12 hours of valid
data for both instruments are used.
2.5.5 Comparison of nephelometer light scattering and transmissometer light extinction
Figure 2-35 compares daily average transmissometer light extinction data (bext) to nephelometer particle
light scattering (bsp) at Bliss State Park for days with aerosol data. There is reasonable correlation among
the measurements (r=0.80) and the intercept of 8.54 Mm-1 is close to the Rayleigh value of 9.48 Mm-1.
The slope is significantly less than 1. Some the slope difference from 1 may be explained by light
absorption. Figure 2-36 compares transmissometer light extinction to the sum of nephelometer light
scattering, Rayleigh scattering and reconstructed aerosol light absorption (10*EC). This comparison
shows a slope closer to, but still less than 1 and an intercept near zero. Forcing the intercept to zero
does not significantly affect the correlation and gives a slope of 0.92. These results seem reasonable
since the nephelometer measurement is essentially a point measurement whereas the transmissometer
is a measurement of several km over which the light extinction coefficient may vary. Thus we would not
expect a perfect correlation between the two measurement methods.
76
Figure 2-35. Comparison of daily average particle light scattering (bsp) and light extinction (bext) at Bliss State Park.
Figure 2-36. Comparison of transmissometer measured light extinction and the sum of nephelometer light scattering,
Rayleigh scattering, and reconstructed aerosol light absorption at Bliss State park.
Figure 2-37 compares reconstructed light extinction with transmissometer measured light extinction.
There is a poor correlation (r=0.46). Also, the reconstructed light extinction is biased low compared to
transmissometer light extinction by 18%. When removing 4 outliers (Figure 2-38), the correlation is
much improved (r=0.74). Figure 2-39 shows monthly average reconstructed and measured light
extinction comparisons. Seasonal patterns of measured and reconstructed bext mostly track each other,
with measured higher than reconstructed except for December.
77
Figure 2-37. Comparison of transmissometer measured light extinction aerosol + Rayleigh reconstructed light extinction at
Bliss State Park.
Figure 2-38. Comparison of transmissometer measured light extinction aerosol + Rayleigh reconstructed light extinction at
Bliss State Park with 4 outliers removed.
78
Figure 2-39. Comparison of monthly mean measured and reconstructed light extinction at Bliss State Park. Error bars
represent uncertainty in the mean at the 95% confidence level.
2.6 Relationship of meteorology to haze
2.6.1 Haze levels as a function of atmospheric stability
Under highly stable atmospheric conditions (i.e. inversions) vertical mixing of the atmosphere is
suppressed. Temperature data collected at Bliss State Park (BLIS) and South Lake Tahoe (SOLA) can be
used to give a measure of stability. The Bliss State Park site is located about 200 m above Lake Tahoe,
while the South Lake Tahoe monitoring site is located near lake level. If an inversion exists the Bliss
State Park temperature should be higher than the temperature at the South Lake Tahoe monitoring site.
Figure 2-40 shows the annual average temperature difference between Bliss State Park and South Lake
Tahoe by time of day. Typical of mountain basins, inversions usually are present in morning due to
radiational cooling and cold air drainage. In afternoon, increased mixing from solar insolation usually
causes a temperature decrease with height due to decreased atmospheric pressure.
79
Figure 2-40. Temperature at South Lake Tahoe subtracted from temperature at Bliss State Park by time of day, annually
averaged (degrees C). Positive numbers indicate the presence of temperature inversions.
Winter was shown to have highest levels of haze at the South Lake Tahoe site. It was speculated that
this is due to trapping of urban emissions within a shallow mixing layer as well as increased local
emissions due to wood burning for home heating. It can be expected that during periods with strong
inversions that persist much of the day, highest haze levels will result than during periods with weak or
short-lived inversions. Figure 2-41 plots the daily average temperature difference between Bliss State
Park and South Lake Tahoe versus the difference in reconstructed light extinction between the sites for
winter days (November- February). While there is considerable scatter in the data, a clear relationship
does exist between inversion strength and enhanced light extinction at South Lake Tahoe compared to
Bliss State Park. The squared correlation coefficient of 0.38 means that approximately 40% of the
wintertime day-to-day variability in relative reconstructed light extinction at the two sites can be
explained by the vertical temperature gradient. Relationships between stability and reconstructed light
extinction were weak in other seasons.
80
Figure 2-41. Temperature at South Lake Tahoe subtracted from temperature at Bliss State Park versus difference in
reconstructed particle light scattering between SOLA and BLIS.
2.6.2 Effect of relative humidity on reconstructed light scattering
Sulfate, nitrate, and sea salt aerosol take on an increasing amount of water as relative humidity
increases above about 40% (Figure 2-1). These effects are most dramatic in areas with high relative
humidity, such as much of the eastern United State. Here the amount and percent of light scattering due
to water growth effects in the Lake Tahoe Basin are considered. Monthly averaged relative humidity
growth factors were applied to each day’s aerosol component concentrations for sulfate, nitrate, and
sea salt to calculate light extinction from each component. The effect of water growth was calculated by
setting the water growth factor equal to one to obtain dry particle light extinction and subtracting the
calculated dry extinction from the reconstructed total aerosol light extinction.
Figure 2-42 shows the monthly average light extinction (Mm-1) due to aerosol water growth for Bliss
State Park and South Lake Tahoe. The values range from about 1 Mm-1 at Bliss State park in summer to
over 4 Mm-1 at South Lake Tahoe from January-March. For every month, water growth extinction at
SOLA is greater than at BLIS, although they are nearly equal April-June. This greater amount at SOLA is
essentially due to the generally higher concentrations of aerosol at South Lake Tahoe. Figure 2-43
shows the percent contribution to reconstructed aerosol light extinction due to water growth, by
month. Bliss State Park shows a strong seasonal pattern, with over 20% of its aerosol light extinction due
to water growth in January- April, but only about 7% in July-August. Compared to Bliss State Park, South
Lake Tahoe has a smaller percentage of its aerosol light extinction from water growth in every month,
although they are nearly equal in summer.
81
-1
Figure 2-42. Average reconstructed light extinction (Mm ) due to water growth of aerosol, by month at Bliss State Park and
South Lake Tahoe monitoring sites.
Figure 2-43. Average percent of reconstructed light extinction due to water growth of aerosol, by month at Bliss State Park
and South Lake Tahoe monitoring sites.
82
3. Receptor Modeling
Source apportionment for PM2.5 was achieved through receptor modeling in this study. Receptor models
include several multivariate analysis methods that infer source contributions and atmospheric processes
from air quality and meteorological measurements. This chapter describes the receptor modeling
methodology, particularly the Positive Matrix Factorization (PMF) and effective variance (EV) solutions
to the chemical mass balance (CMB) equation, explains how the models were applied to the Tahoe
monitoring data sets, and presents the source apportionment results.
3.1 Chemical Mass Balance Solutions
Most receptor models present solutions to the following equation:
J
C
F T
ijm
iklmn
j 1
ijklmn
S
jklmn
for i = 1 to I
(1)
The indices are defined as:
i = a quantifiable chemical element, compound, or physical property that is expected to have
markedly different proportions to other characteristics in different sources.
j = a group of emitters with similar emissions compositions that differ from the compositions of
other source types.
k = the sampling period, a part of the day, day of the week, season, period before or after a
control measure has been implemented, or a special event such as a fire or dust storm.
l = the receptor location. Monitoring sites are usually selected to determine human exposure,
but are most useful for receptor models when they also include sites that represent different
spatial scales (Chow et al., 2002), including those near suspected pollution sources such as
roadways and industries, as well as regional background sites representing mixtures from many
emitters.
m = particle size fraction, the most useful being the ultrafine (<0.1 µm), fine (<~2.5 µm, or
PM2.5), and coarse (2.5 to 10 µm, PM10-2.5) fractions.
n = transport direction, which can be a simple wind direction for local sources, or a more
complex set of curvilinear trajectories for long-range transport.
Using these indices, the variables in Eq. (1) are:
Ciklmn = Concentration (unit of µg/m3, ng/m3, ppm, or ppb) of pollutant i for time period k at
location l corresponding to particle size range m and transport direction n. This is the measured
receptor concentration.
Fijm = Fractional quantity of pollutant i in source type j for size range m (unitless). For PM
measurements, profile abundances are often normalized to mass emissions from a source in the
desired size range and averaged over several source tests.
Sjklmn = Contribution from source type j in size range m from wind sector n for time period k at
location l (µg/m3, ng/m3, ppm, or ppb). Source contributions are calculated by the receptor
model.
Tijklmn = Changes in Fijm during transport from source to receptor.
83
In its most common use, Eq. 1 is solved for Sjklmn using Fijm and Ciklmn as input data. Uncertainties of the
input data are designated as σCiklmn (usually determined by replicate analysis and propagation of
analytical and flow rate uncertainties) and σFijm (usually estimated as the standard deviation of the
average from several source tests). Uncertainties of the source contribution estimates are designated as
σSjklmn and are estimated by error propagation or Monte Carlo simulation (Javitz and Watson, 1988; Javitz
et al., 1988).
Eq. 1 reduces to the CMB of Hidy and Friedlander (1971) for single samples taken at a single location and
time period. Hidy and Friedlander (1971) assumed that Tijklmn was equal to one, meaning that the
proportions of the different elements they used as source markers for southern California did not
change between source and receptor. In their example, Hidy and Friedlander (1971) used individual
elements as sole markers for selected source types. Friedlander (1970) recognized the limitations of
available measurements, and conceptualized the requirements of measurement devices that would
provide information detailed enough to bring the full potential of Eq. 1 into reality.
Most of the receptor models are solutions to Eq. 1, which can be derived from physical principles with
simplifying assumptions (Watson, 1984). PMF, Unmix, and effective variance (EV) are three common
solutions to the CMB equations (Eq. 1). The effective variance CMB (EV-CMB) solution uses measured
source profiles and ambient concentrations as inputs and calculates source contribution estimates
(SCEs) and their uncertainties for individual samples. Source profiles should be determined using the
same analytical methods as the ambient samples. There is no guarantee that source profiles measured
at other times and places represent emissions in the study region for the EV-CMB analysis. As
recommended by Watson (2004), sensitivity tests should be performed on several samples to evaluate
how different source profiles and combinations of EV-CMB fitting species affect SCEs. The initial source
profile combination is modified in subsequent trials to examine changes in the SCEs and EV-CMB
performance measures. An acceptable solution requires 0.8 < PCMASS < 1.2, r2 > 0.8, and χ2 < 4. The
modified pseudo-inverse normalized (MPIN) matrix indicates the most influential species (e.g., MPIN
value >0.5) for each source type. For most tests, five to ten different source combinations are attempted
until the best solution, in terms of EV-CMB fitting performance and MPIN, is attained.
Unmix (Henry, 1997; Henry, 2003) and PMF (Paatero, 1997; Paatero et al., 2002) solve the CMB
equations by identifying source-related “factors” in the dataset. Nonnegative factor loadings and scores
are derived simultaneously and interpreted as source profiles (F) and source contributions (S),
respectively. Unmix relies on “edge points” that signify missing or small contributions from one or more
sources. In higher dimensions, edges become hypersurfaces. Unmix may find too few or too many such
surfaces and reports no solution for a particular problem. This could be due to noise in the dataset or
unsuitable assumptions (e.g., linearity). PMF attempts to minimize the weighted difference between
measured and calculated concentrations for many samples taken in time and space, subject to the
constraint that factor scores and factor loadings are non-negative. There are many PMF solutions that
meet these conditions, and not all of them are physically reasonable. Uncertainties of the PMF solutions
are usually calculated using a bootstrapping method (Reff et al., 2007).
84
Although measured source profiles are not used as inputs to Unmix and PMF, measured profiles are
needed to justify the assignment of a source factor to a source type or emitters. There is no guarantee
that mathematically-derived Unmix and PMF factors represent real sources; they often appear to be
mixtures of source types that have correlated SCEs in space and time (Chen et al., 2010a). A needed
demonstration of any Unmix or PMF application is that at least one measured source profile can be
reasonably associated with each derived source factor (Chen et al., 2007b; Watson et al., 2008). PMF (or
Unmix) typically require large ambient datasets, e.g., more than 100 samples distributed across time and
space, for which the underlying source profiles are relatively constant and for which there is large
independent variability among actual source contributions.
The EV, Unmix, and PMF solutions to the CMB equations have many restrictive assumptions and are
limited by uncertainties in the input data. Unmix and PMF are good precursors to the EV-CMB solution
as they provide an idea of the types and magnitudes of the source contributions. This provides guidance
in selecting existing profiles, or measuring region-specific profiles. Comparison of source contributions
from the EV, Unmix, and PMF CMB solutions provides evidence of the extent to which these
assumptions are complied with for a given application (Chen et al., 2011a).
3.2 PMF Source Apportionment
3.2.1 Input data
Speciated PM2.5 measurements from the BLIS and SOLA sites in the Lake Tahoe Basin were used for the
PMF multivariate receptor modeling. Though PMF does not specify a minimum number of samples, the
stability of the solution usually increases with the number of samples. The data must have a large
variation in source contributions among different samples, and the chemical profiles of the contributing
sources should remain reasonably constant within a source type but differ substantially between source
types. Preferably this would require 200 – 500 samples acquired from all four seasons.
Data has been collected at BLIS since 1991. However, data over a long period (e.g., > 10 years) probably
experiences changes in average source profiles owing to emission controls and land use changes. For
consistency in this analysis, only recent data acquired between January 1, 2000 and December 31, 2009
were used. U.S. EPA assigned year 2000-2004 as the “baseline” period, based on which progress in
visibility recovery will be evaluated. Moreover, the carbon analysis protocol was switched from
IMPROVE to IMPROVE_A on January 1, 2005 (Chow et al., 2007a), causing changes in thermal carbon
fractions. For these reasons, BLIS data were separated into 2000–2004 and 2005–2009 periods and
analyzed independently (Table 3-1). This provides an opportunity to examine the robustness of PMF
solution by comparing factor loadings between the two periods.
Table 3-1. Data Groups for PMF model inputs.
Modeling
Group
Location Code
(Network)
Period
Number of
Samples
Reported
Number of
Valid
Samples a
Fraction of Data
Used for Receptor
Modeling
PM2.5 Concentration
Range (Mean) in
µg/m3
I
BLIS (IMPROVE)
1/1/00 – 12/29/04
598
521
87.1%
0.2 – 70.2 (3.2)
85
II
BLIS (IMPROVE)
1/1/05 – 12/30/09
609
553
90.1%
0.1 – 79.9 (3.3)
SOLA (UC-Davis)
1/1/00 – 6/25/02
143
113
79.0%
1.2 – 24.1 (6.5)
SOLA (DRI)
8/12/02 – 5/15/04
107
85
79.4%
1.8 – 15.8 (6.3)
250
198
79.2%
1.2 – 24.1 (6.4)
III
Total
a
Validation includes removing incomplete data and data with poor material balance.
The operation of SOLA site was stopped in May 2004, and therefore only one period (2000–2004) is
available for PMF analysis. It should be noted that SOLA samples were (chemically) analyzed by the
Crocker Nuclear Laboratory at University of California-Davis (UCD) before July, 2002 but by Desert
Research Institute (DRI) after July, 2002 (Table 1). The two laboratories employed equivalent analytical
techniques but reported different precisions and detection limits for several species including As, Cr, Ni,
Pb, Rb, Se, Sr, and V. These species contribute little to visibility impairment but could be important
source markers. Larger uncertainties are therefore associated with sources identified through these
species. Considered an already smaller number of samples at SOLA than at BLIS, we decided not to
separate SOLA data further.
Missing and questionable data could bias source apportionment results. Several strategies have been
proposed for coping with incomplete data (i.e., one or more species missing) in PMF (e.g., Reff et al.,
2007), but biases introduced by these strategies have not been evaluated. In this study, samples lacking
complete elemental, ionic, and/or carbon analyses were eliminated.
A PM2.5 material balance (PM2.5,matbal) was calculated as follows for comparison with the measured
gravimetric mass (PM2.5,measured):
PM2.5,matbal = 1.38 [SO42-] + 1.29 [NO3-] + OM + [EC] + Salt + CM
where
OM = Organic Matter=1.8 [OC]
Salt = 1.8 [Cl-]
CM = Crustal Material= 2.2×[Al] + 2.49×[Si] + 1.63×[Ca] + 2.42×[Fe] + 1.94×[Ti]
(2)
The SO42- and NO3- multipliers assume NH4(SO4)2 and NH4NO3 compositions (Malm et al., 1994). The 1.8
OC multiplier intends to account for unmeasured hydrogen and oxygen in organic compounds, and the
CM elemental multipliers intend to account for unmeasured mineral oxides (Pitchford et al., 2007).
Bounds for mass closure should be established to minimize outliers in receptor modeling (Lewis et al.,
2003; Watson et al., 2002; Chen et al., 2010c). Considering the substantial uncertainty in the OM and
CM multipliers (e.g., El Zanan et al., 2005), samples were included as long as PM2.5,matbal accounted for
45–220% of PM2.5,measured. The large range is in part due to low concentrations (close to MDL) of PM2.5 at
the two sites. More than 79% of the samples passed the screening (Table 3-1). For these validated
samples, BLIS reported higher signal-to-noise ratios (SNR) than SOLA for most species (Table 3-2).
86
Na and Mg are the lightest elements to be analyzed by X-ray fluorescence (XRF) and their analytical
results suffer from self-absorption effects. Na and Mg therefore were excluded from all PMF analysis.
Since H and Sr data were completely missing for SOLA (UCD) and SOLA (DRI), respectively, they were not
included in PMF source apportionment for SOLA. In addition, Cr, Rb, P, and Zr were excluded due to
large uncertainties and/or frequent (>75%) missing data.
3
Table 3-2. Average and signal-to-noise ratio (SNR) of PM2.5 mass and chemical concentrations (µg/m ) for each PMF
modeling group.
Site/Network
I
BLIS (IMPROVE)
II
BLIS (IMPROVE)
SOLA (UC-Davis)
SOLA (DRI)
Period
1/1/00 – 12/29/04
1/1/05 – 12/30/09
1/1/00 – 6/25/02
8/12/02 – 5/15/04
521
553
113
85
Modeling Group
# of Valid
Samples
Species
PM2.5 mass
Cl
NO3
2SO4
H
OC1
OC2
OC3
OC4
OP
EC1
EC2
EC3
b
OC
b
EC
Na
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mg
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Avg.
3.3149
0.0105
0.1604
0.4705
0.1787
0.0750
0.2382
0.3082
0.1469
0.2786
0.3735
0.0570
0.0014
1.0469
0.1533
0.0252
0.0387
0.1022
0.0000
0.1807
0.0008
0.0300
0.0245
0.0025
0.0002
0.0000
0.0052
0.0006
0.0279
0.0000
0.0003
0.0015
0.0000
0.0001
0.0013
a
SNR
24.6
1.2
17.6
23.3
17.2
3.3
5.5
6.6
5.5
3.7
7.4
3.3
1.4
9.4
1.3
5.7
15.3
19.2
1.6
19.6
14.4
19.5
19.5
17.1
3.5
4.3
4.0
14.3
19.8
2.8
9.8
17.8
2.1
4.7
17.3
Avg.
3.2182
-0.0119
0.1939
0.4804
0.1449
0.0855
0.1728
0.4214
0.2177
0.1282
0.2251
0.0648
0.0057
1.0256
0.1674
0.0302
0.0385
0.1235
0.0001
0.1676
0.0005
0.0305
0.0305
0.0037
0.0004
0.0002
0.0068
0.0008
0.0332
0.0000
0.0003
0.0015
0.0001
0.0001
0.0013
III
a
SNR
22.8
1.3
14.3
20.3
18.4
2.9
5.2
6.5
6.5
3.1
7.1
2.9
2.0
10.4
3.9
2.7
13.9
18.5
1.6
19.0
3.9
18.2
18.5
10.2
2.5
2.7
2.2
4.8
19.8
3.0
8.5
15.8
2.4
4.5
15.9
87
Avg.
6.4903
0.0533
0.2949
0.4876
0.1989
0.2801
0.3959
0.9325
0.4964
0.1089
0.7347
0.1438
0.0072
2.2138
0.7595
0.0290
0.1923
0.3982
0.0046
0.1608
0.0112
0.0732
0.0889
0.0141
0.0007
0.0009
0.0203
0.0031
0.1199
0.0002
0.0016
0.0039
0.0002
0.0001
0.0010
a
SNR
31.8
2.9
19.6
19.9
19.7
3.5
5.8
6.3
8.3
2.5
9.1
4.4
0.8
10.3
7.5
1.8
12.5
15.0
5.1
16.3
11.0
15.0
15.4
12.9
2.0
2.8
2.6
6.4
15.2
1.5
6.2
9.5
0.3
0.6
2.9
Avg.
6.3494
0.2434
0.2755
0.5125
N/A
0.4849
0.6445
1.4305
0.5721
0.0680
0.6921
0.1739
0.0164
3.1999
0.8146
0.1591
0.0574
0.1741
0.0004
0.2105
0.0065
0.0469
0.0377
0.0027
0.0003
0.0001
0.0233
0.0012
0.0640
0.0001
0.0012
0.0045
0.0001
0.0001
0.0013
a
SNR
17.3
11.3
7.0
14.7
N/A
6.8
11.5
12.0
8.3
5.1
9.9
8.1
1.4
11.4
8.8
1.7
9.8
18.5
0.3
19.5
2.4
17.3
17.4
0.4
0.1
0.2
1.2
3.8
19.6
0.6
5.2
15.7
0.2
0.3
6.7
Rb
Sr
Zr
Pb
a
0.0001
0.0003
0.0001
0.0007
3.5
8.6
2.2
13.5
Signal-to-noise ratio (SNR) is calculated by
0.0001
0.0003
0.0000
0.0007
4.5
11.9
1.9
12.9
Ci2 /
i
2
i
i
0.0003
N/A
0.0000
0.0005
where Ci and
i
3.4
N/A
1.1
0.2
0.0003
0.0005
0.0003
0.0010
0.7
1.2
0.5
1.1
are value and uncertainty of each
measurement.
b
OC and EC were measured by the IMPROVE_TOR or IMPROVE_A (after 2005) protocol.
3.2.2 Model Procedures
A receptor modeling procedure was established to exploit the strengths of EPA UNMIX v6.0 and EPA
PMF v3.0 software (http://www.epa.gov/scram001/receptorindex.htm). UNMIX v6.0 contains principle
component analysis (PCA) with a Varimax rotation and the NUMFACT algorithm that help to determine
the number of source factors. It also provides tools for analyzing relations among different species,
suggesting possible species combinations for PMF. According to Chen et al. (2010a), the number of
factors should be just adequate to explain the variance of input data (i.e., r2 > 0.95 for all species with
relatively high SNR). In this study, species with SNR < 5 were not considered for Unmix analysis. Carbon
fractions (OC1-OC4, EC1-EC3, and OP) were not used, either, as they are sensitive to thermal analysis
protocols before and after CY2005. With other species Unmix suggested 7 or 8 factors for Group I and II
and 5-6 factors for Group III.
PMF v3.0 can use all species as inputs because species with lower SNR are automatically weighted less in
PMF fitting and usually do not influence the results significantly. Additional uncertainties were
introduced into species that produced “singlet” factors, i.e., factors consisting of nearly 100% single
species that are not unique source markers. These factors more likely resulted from inaccurate
uncertainty assignments than from real/natural causes (Chen et al., 2010a). Factor rotations were also
attempted through adjusting the FPEAK parameter (-1 to 1) in PMF v3.0, but no significant change to
solutions were observed. Our “final” PMF solutions were sought that 1) contained the number of factors
suggested by Unmix; 2) showed robustness in bootstrapping analysis (i.e., insensitive to the random
initial fitting values); 3) yielded factor profiles that are consistent with known sources; and 4) yielded
SCEs that are consistent with a conceptual model established from emission inventories and previous
studies. These final solutions are presented in the next section.
3.2.3 PMF Source Apportionment Results
Seven common factors were resolved for the BLIS I and II modeling groups: natural dust, road dust,
biomass burning, traffic and industrial emission, as well as secondary sulfate and nitrate. BLIS II data
yielded two biomass burning factors that were attributed to low- and high- combustion efficiency (CE)
burning. Only 6 factors were found for SOLA (Group III), including dust, biomass burning, traffic
emissions, secondary sulfate, secondary nitrate, and a salting factor only found at SOLA. Chemical
profiles of these factors are shown in Figure 1(a)-(c) with their contributions to PM2.5 presented in Table
3-3.
88
Dust factors at BLIS were identified by rich crustal elements such as Si, Al, Fe, Ca, and K. OC in these
factors could be substantial but not exceed the crustal contribution (calculated by eq. [2]). The Si and Fe
fractions are pretty consistent across road and natural dust factors, i.e., 0.15-0.19 for Si and 0.037-0.044
for Fe, but the natural dust factors generally contain much higher Ca and K concentrations. In fact, a Ca
fraction of ~4.5% and Ca/Si ratio of 0.24-0.30 in the BLIS I and II natural dust factor are consistent with a
geological source profile derived to represent Asian dust (4.0% and 0.28, this study). VanCuren (2003)
showed frequent impact of Asian dust on visibility at Crater Lake National Park (CRLA) and Lassen
Volcano National Park (LAVO). Asian dust could also be a major source of natural dust for BLIS. Road
dust samples collected in the Lake Tahoe region showed Ca fractions of only 2.3-2.4% and Ca/Si ratio of
0.12-0.15 (Kuhns et al., 2004).
At BLIS, natural dust contributed more than road dust to ambient PM2.5, especially after 2004 (Group II).
The decreasing road dust contribution supports the effectiveness of recent control strategies. There is
only one dust factor resolved for SOLA (Ca/Si ratio ~0.22) and so it is expected to represent a mixture of
natural and road dust, with higher contributions from road dust at this site. Assuming a similar natural
dust level between BLIS and SOLA, the road dust contribution at SOLA could average ~1 g/m3 for 2000–
2004 or ~3.5 times that at BLIS I.
89
1
Biomass Burning
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Combustion/Industrial
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Secondary Sulfate
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Dust (Road)
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Dust (Natural)
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Traffic
0.1000
0.8
0.6
Series
3
0.0100
0.0010
0.4
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Secondary Nitrate
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Frac. Contribution
H
Frac. Contribution
NO3- SO4=
Frac. Contribution
Cl-
Frac. Contribution
PM2.5
Frac. Contribution
0.1000
Frac. Contribution
PM2.5 Mass
Fraction
1.0000
Frac. Contribution
BLIS I
Pb
(a)
1
Biomass Burning (LCE)
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Biomass Burning (HCE)
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Combustion/Industrial
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Secondary Sulfate
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Dust (Road)
90
0.1000
0.0100
0.8
0.6
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
ass
n
1.0000
0.1000
Zr
Pb
1
Dust (Natural)
Frac. Contribution
NO3- SO4=
Frac. Contribution
Cl-
Frac. Contribution
PM2.5
Frac. Contribution
0.1000
0.8
ribution
PM2.5 Mass
Fraction
1.0000
Frac. Contribution
BLIS II
0.0001
0
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Secondary Nitrate
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Frac. Contribution
PM2.5
Pb
(a)
1
Biomass Burning (LCE)
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Biomass Burning (HCE)
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Combustion/Industrial
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Secondary Sulfate
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Dust (Road)
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Dust (Natural)
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Traffic
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
PM2.5 Mass
Fraction
1.0000
1
Secondary Nitrate
0.1000
0.8
0.6
0.0100
0.4
0.0010
0.2
0.0001
0
PM2.5
Cl-
NO3- SO4=
H
OC
EC
Al
Si
P
S
Sl
K
Ca
Ti
V
91
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Frac. Contribution
OC
Frac. Contribution
H
Frac. Contribution
NO3- SO4=
Frac. Contribution
Cl-
Frac. Contribution
PM2.5
Frac. Contribution
0.1000
Pb
Frac. Contribution
PM2.5 Mass
Fraction
1.0000
Frac. Contribution
BLIS II
(b)
(c)
Figure 3-1. Profiles of PMF factors for a) BLIS I, b) BLIS II, and c) SOLA. Solid bar and hollow bars indicate PM 2.5 mass fraction
of high- and low-confidence species, respectively (high confidence species are those with value/uncertainty ratio greater
than 1). Crosses indicate fractional source contributions to the species.
Combustion sources such as gasoline exhaust, diesel exhaust, and biomass burning smoke contain much
higher OC and EC abundances than is present in the geological materials. OC and EC by themselves are
insufficient to distinguish among the sources. Both BLIS I and II data yielded a high OC (52-54%) and EC
(7.1-7.3%) factor that also contains a significant K fraction of 0.2–0.5%. The K/OC ratios are at the low
end of reported values for biomass burning (e.g., Turn et al., 1997; Chen et al., 2007a; McMeeking et al.,
2009). This likely results from relatively low combustion efficiency (LCE), since the OC emission factor is
anti-correlated with CE but the K emission factor shows little to no correlation with CE (Carrico et al.,
2010). Fuel moisture content is critical to CE as high-moisture fuels tend to burn incompletely through
LCE smoldering (Chen et al., 2010b). The second biomass burning factor from BLIS II features a much
higher K fraction (5.8%), consistent with values from dry fuel combustion. It should be noted that EC/OC
ratio usually increases with CE (Chen et al., 2007a) but these two factors (BLIS II) show the opposite. EC
92
abundance in the high CE (HCE) factor is highly uncertain, as suggested by the PMF bootstrapping
analysis (Figure 3-1b).
Biomass burning represents the most important PM2.5 source for BLIS. Most of it should be attributed to
wildfires and/or prescribed burns because the site is far away from residential areas. The increase of
biomass burning PM2.5 from 1.27 g/m3 (or 39% PM2.5) for 2000-2004 to 1.68 g/m3 (or 51% PM2.5) for
2005-2009 is consistent with an upward trend of wildfire occurrence in the western U.S. (Westerling et
al., 2006) and California in particular (Figure 2-19). On the other hand, SOLA is greatly influenced by
residential wood combustion (RWC) emissions. The only biomass burning factor found at SOLA accounts
for 3.05 g/m3 (or 48% PM2.5). This factor is more similar to the LCE biomass burning factor in terms of
K/OC ratio, although it contains the highest EC/OC ratio among all biomass burning factors.
Another factor dominated by OC and EC has lower or more uncertain K contents, and is attributed to onroad traffic emissions. Additional markers in this factor include Fe (related to brake wear and/or
resuspended road dust) and Br (fuel additive). Diesel and gasoline contributions could not be separated
in this study. The traffic contribution is 2-3 times higher at SOLA than at BLIS (Table 3-3. Absolute (in
µg/m3) factor contribution to PM2.5 mass (by PMF) for each modeling group. SOLA results are also
separated into UCD and DRI sampling periods.). It explains 7-11% of PM2.5 mass.
The industrial combustion factors feature S (SO42-) and industrial elements such as Zn, Pb, and Se,
potentially originating from stacks of coal- or oil-fired plants/factories. This is a relatively minor factor,
and factor profiles between BLIS I and II are substantially different. For SOLA, the industrial elements are
mostly associated with the secondary sulfate factor; the industrial combustion contribution might be
mixed into the secondary sulfate factor (due to large uncertainties associated with some marker
species). Instead, a unique factor found in SOLA was linked to salting (i.e., de-icing) activities due to high
Cl- concentration. (Note Cl- SNR is much higher during the SOLA [DRI] sampling period than any other
periods (Table 3-2).
Ideally, secondary sulfate and secondary nitrate factors contain only the “secondary” inorganic species
such as SO42-, HSO42-, NO3-, and NH4+, but this is seldom achieved by PMF (Chen et al., 2010a). In this
study, the secondary factors also contain OC, and EC as well as trace elements at low levels (Figure 1).
Primary sulfate and nitrate, though minor, might be included in these factors as well. Secondary sulfate
appears to be more uniform between BLIS and SOLA than primary sources (i.e., dust, biomass burning,
and traffic).
3
Table 3-3. Absolute (in µg/m ) factor contribution to PM2.5 mass (by PMF) for each modeling group. SOLA results are also
separated into UCD and DRI sampling periods.
Source Type
Factor
BLIS I
BLIS II
Factor
SOLA III
Dust
Road
0.27±0.05
0.14±0.02
Natural
0.33±0.03
0.26±0.01
Road
Natural
1.27±0.06
Biomass Burning
Low CE
High CE
1.27±0.08
1.47±0.02
0.21±0.01
Low CE
High CE
3.05±0.36
93
Fossil Fuel
Traffic
Industry
0.34±0.04
0.10±0.04
0.26±0.03
0.16±0.04
Secondary
Sulfate
Nitrate
0.61±0.04
0.25±0.03
0.60±0.03
0.15±0.01
Traffic
0.74±0.17
Sulfate
0.77±0.14
Salting
0.10±0.07
Nitrate
0.24±0.08
SOLA (UCD)
SOLA (DRI)
1.88±0.08
0.46±0.04
2.55±0.32
3.72±0.42
0.83±0.18
0.64±0.15
0.06±0.05
0.15±0.10
0.61±0.11
0.99±0.17
0.26±0.08
0.21±0.06
3.2.4 Seasonal and Inter-annual Variations
Monthly averages of PMF factor contributions were calculated for BLIS I, BLIS II, and SOLA (Figure 3-2).
Biomass burning factor peaked in summer for both BLIS I and II, consistent with a dominant wildfire
contribution. In August, the biomass burning PM2.5 reached 2.8 µg/cm3 for both BLIS I and SOLA, but
then higher contributions were observed for SOLA towards winter. The reversed seasonal trend
corroborates a strong influence of RWC (up to 6 µg/cm3) at SOLA.
Figure 3-2. Seasonal variation of PMF factors based on average PM2.5 contributions, by month, for BLIS I, BLIS II, and SOLA.
The seasonal variation of natural dust factors agrees with Asian dust outbreaks that frequently occur in
spring (VanCuren and Cahill, 2002). The road dust factor somewhat picked up in spring as well (more so
for BLIS I). Weather patterns associated with Asian dust outbreaks (e.g., cyclone, windy conditions) may
also lead to more resuspended road dust. Nonetheless, the road dust factors at BLIS showed a second
peak in summer corresponding to seasonal tourist traffic. The only dust factor in SOLA behaves similarly
to the BLIS I road dust factor, confirming an overwhelming road dust impact.
It is interesting to see the industrial combustion factor tracking the natural dust well during both BLIS I
and II. Due to the lack of industrial sources within the Tahoe basin, the industrial combustion particles
94
probably came into the basin with Asian dust when weather patterns allowed long-range transport. The
potential sources of these particles include coal-fired power plants in California and China (cross-Pacific
transport) and freighters on the U.S. West Coast.
The secondary sulfate factors exhibit a summer high and winter low, as a result of stronger
photochemical conversion in summer (Chen et al., 2002; 2003). Nitrate is generally higher in winter due
to lower temperatures shifting HNO3(g)-NO3-(s) equilibrium towards particulate nitrate. The salting factor
at SOLA does not show appreciable seasonal variation, though more de-icing activities are expected in
winter. Most high Cl- days occurred during the SOLA (DRI) period. The origin of this factor warrants
further investigation.
BLIS
(mg/m3)
3
2
Biomass Burning 2
1
Biomass Burning 1
0
PM2.5
Contribution
(mg/m3)
PM2.5 Contribution
Figure 3-3 shows inter-annual variability of the PMF factors. The most significant decreasing trends (at
BLIS and SOLA) were identified for traffic, road dust, and secondary nitrate, all of which are related to
motor vehicle emissions. The reduction in motor vehicle contribution, however, is mostly canceled by
increasing biomass burning emissions− there is little net change between average PM2.5 concentrations
for BLIS I and BLIS II and for SOLA (UCD) and SOLA (DRI) (see Table 3-2). There is also substantial interannual variability for the combustion and secondary sulfate factors, but with no clear long-term trends.
2000
PM2.5
Contribution
(mg/m3)
2002
2003
2004
2005
2006
2007
2008
2009
BLIS
1
Combustion
Secondary Sulfate
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
BLIS
1
Dust 2
0
2000
PM2.5
Contribution
3
(mg/m )
2001
2001
2002
2003
2004
2005
2006
2007
2008
2009
BLIS
1.5
Dust 1
1
Traffic
0.5
Secondary Nitrate
0
2000
2001
2002
2003
2004
2005
(a)
95
2006
2007
2008
2009
PM2.5
Contribution
3
(mg/m )
SOLA
5
4
3
2
1
0
Biomass Burning 1
PM2.5
Contribution
(mg/m3)
2000
2001
2002
2003
2004
SOLA
1.5
1
Secondary Sulfate
0.5
0
2001
(mg/m3)
2003
2004
0.15
0.1
Salting
0.05
0
2000
2001
2002
2003
2004
SOLA
4
3
2002
SOLA
0.2
(mg/m )
PM2.5 Contribution
PM2.5 Contribution
2000
3
Dust 1
2
Traffic
1
Secondary Nitrate
0
2000
2001
2002
2003
2004
(b)
Figure 3-3. Inter-annual variability of PMF factors for (a) BLIS and (b) SOLA. Biomass burning 1 and 2 represent LCE and HCE
burning, respectively. Dust 1 and 2 are road and natural dust. SOLA 2004 contains only 17 samples that may not be
representative of annual averages.
3.3 EV-CMB Source Apportionment
3.3.1 Input data
PMF receptor models suggested the potential number of sources contributing to the ambient
concentration in samples from the BLIS and SOLA sites. However, quantitative analysis is often limited
by the rotational ambiguity inherent in such factor analyses. EV-CMB analysis that uses measured
source profiles reduces the rotational degree of freedom, thus often leading to more accurate source
contribution estimates (SCEs). All the data summarized in Table 1 were also subject to EV-CMB analysis.
The EV-CMB solution highly relies on major crustal components (e.g., Al, Si, Ca, and Fe) to determine
fugitive dust SCEs, and imprecise ratios of other trace elements (e.g., K, Mn, Se, and Pb) to these dust
components can decrease their utility as markers for other sources. In this analysis, we used dust
profiles developed by Kuhns et al. (2004). Two geological samples were collected near locations where
roadside sampling for motor vehicle exhausts took place (Village-Lakeshore and Southwood-Mays) by
vacuuming a section of the roadway and gutter to fill a high-efficiency filter bag. The two collected
samples were dried at 40 C and 20% relative humidity and sieved through a Tyler 400 mesh screen (<38
m geometric diameter) prior to resuspension in the laboratory chamber following the procedures
96
described by Chow et al. (1994). Filter samples were drawn through PM10 and PM2.5 inlets. Chemical
analysis was performed on the filters to determine source profiles. These profiles are representative of
road dust composition. Mnemonics TahoeNRD and TahoeSRD were assigned to the resuspension sample
from Village-Lakeshore and Southwood-Mays, respectively.
Geological source profiles acquired from Las Vegas, central California, Texas, and Minnesota were also
considered for CMB fitting (Table 4). We developed the source profile (AD_IMPRO) from ambient
samples taken from two IMPROVE sites on days when Asian dust dominated PM2.5. Three samples at
Crater Lake National Park (CRLA) and Lassen Volcanic National Park (LAVO) with the highest Asian dust
scores (Kavouras et al., 2006) were retrieved, normalized to the PM2.5 mass concentration, and
composited to produce a single Asian dust profile. The chemical profiles of the three samples are
remarkably similar, thus corroborating a common origin and yielding a low uncertainty in AD_IMPRO.
Kuhns et al. (2004) also developed two composite source profiles (LTMV01 and LTMV02) representing
motor vehicle emissions in the Tahoe basin. This was achieved by ground-based roadside sampling at
traffic intersections in Incline Village (Village-Lakeshore and Southwood-Mays) where the sampled air
was dominated by emissions from motor vehicle exhaust. The In-Plume Sampling System (Nussbaum et
al., 2009) was located on the sidewalk or shoulder within 2 m of the nearest traffic lane, with the
sampling inlet placed beneath a rubber bumper on the road and across the lane. Video images collected
during the source sampling campaign revealed a fleet composition of 98% gasoline and 2% diesel.
Because these samples were collected from the roadside, they are likely to be affected by vehiclerelated resuspended road dust. The geological contribution was reduced by using a PM 2.5 inlet on the
sampling system. The remaining geological components were subtracted from each of the roadside
sample profiles by using the CMB model to estimate the contributions from geological material (i.e.,
TahoeNRD and TahoeSRD) to the concentrations of chemical species (Chow et al. 1988). It was
concluded, however, in Kuhns et al. (2004) that LTMV01 and LTMV02 contain much higher SO42- and NO3fractions than mobile source profiles derived from dynamometer tests, likely due to contamination from
ambient air. Pure gasoline and diesel source profiles acquired from the Northern Front Range Air Quality
Study (NFRAQS), NREL Gas/Diesel Split Study, and Las Vegas Source Apportionment Study (LVSAS) were
also considered in this analysis.
B
B
P
Biomass burning source profiles were developed from a laboratory combustion experiment supported
by SNPLMA Round 7 (Chen et al., 2010b). Litter and duff were collected from the Tahoe forest floor.
Common aboveground shrubs (e.g., Bitterbrush, Green Leaf Manzanita, and Squaw Carpet) were also
collected and separated into leaves and stems of different diameters. To prepare fuels for desired/with
different moisture contents, litter and duff samples from various locations were first homogenized and
air dried for a week at 30% relative humidity. Calculated amounts of water were added into these
samples to achieve fuels with moisture contents of 10-20% of dry mass. Shrub leaf and stem samples
were also composited, and either air dried or soaked in water for 24 to 96 h. Dry and wet samples were
burned in a combustion facility at DRI. Particles emissions were collected on filters and analyzed for
chemical composition. These experiments showed that fuel moisture lowers the combustion efficiency,
shortens the flaming phase, and prolongs the smoldering period before flames start. Emission factors of
97
PM2.5, carbon monoxide (CO), and ammonia (NH3) increase with the fuel moisture content; the effect is
generally larger for plant leaves and duff materials than for stems and litter.
This analysis tested 8 wet and dry biomass burning source profiles, seeking to explain LCE and HCE
contributions. Other biomass burning source profiles considered in the CMB fitting included RWC
profiles from the Lake Tahoe Source Characterization Study (Kuhns et al., 2004) and California Regional
PM10/PM2.5 Air Quality Study (CRPAQS, see Chow et al., 2007b) and open burning source profiles from
Big Bend Regional Air Visibility Observation (BRAVO) study (Chow et al., 2004).
Point source profiles, such as coal or oil-fired power plant, cement factory, and restaurant, should be
quantified from real-world emission tests of exhausts diluted to ambient temperatures followed by
collection of filter samples amenable to chemical speciation. Such profiles specific to the Lake Tahoe
region were not available, so profiles from other studies (e.g., BRAVO and CRPAQS) relevant to this
study and available from U.S. EPA (2008) were used. Secondary NO3- and SO42- sources were represented
by pure ammonium nitrate (AMNIT; NH4NO3) and ammonium sulfate [AMSUL; (NH4)2SO4] profiles,
respectively. Secondary OC was represented by a profile (SOC) containing exclusively (100%) OC. This is
equivalent to using the OC/EC ratio method to estimate secondary organic carbon (Gaffney et al., 1984).
Table 3-4 lists all the source profiles tested in the EV-CMB analysis.
Table 3-4. List of source profiles used for EV-CMB fitting of BLIS and SOLA samples. These profiles are available upon request.
Bold mnemonics indicate source profiles included in the final source apportionment. Location refers to the place the source
profile was measured.
Category
Subcategory
Mnemonic
Year
Location
TahoeNRD
2003
Lake Tahoe
Resuspension soil dust (PM2.5) from Village Lakeshore
TahoeSRD
2003
Lake Tahoe
Resuspension soil dust (PM2.5) from Mays/Southwood
GPVRDC
1995
Las Vegas
Composite of 7 paved road dust samples
FDPVRD
1997
Central
California
PVRD1
1999
Texas
Composite of 2 paved road dust samples from San
Joaquin Valley
Composite of 5 paved road dust samples from San
Antonio and Laredo, TX
GUPRDC
1995
Las Vegas
Composite of 2 unpaved road dust samples
FDUNPVRD
1997
Central
California
UNPV2
1999
Texas
Composite of 2 unpaved road dust samples from San
Joaquin Valley
Composite of 2 unpaved road dust samples from
Guadalupe and Laredo, TX
GSOILC
1995
Las Vegas
Composite of 5 desert soil samples
AGRI
1997
Central
California
SOIL1
1999
Texas
Construction
CONST
1997
Asian Dust
AD_IMPRO
2001
Composite of agricultural soil samples from San
Joaquin Valley
Composite of 2 soil samples from Purtis Creek and Big
Thicket, TX
Composite of 2 construction & earthmoving dust
profiles
Composite of 3 IMPROVE samples (4/29/1998 at
CRLA1 and LAVO1 and 4/16/2001 at LAVO1)
representing Asian dust impact
Paved Road
Dust
Geological
Unpaved
Road Dust
Surface Soil
Central
California
California
and
Oregon
Description
98
Reference
Kuhns et al.
(2004)
Green et al.
(2004)
Chow et al.
(2003)
Chow et al.
(2004)
Green et al.
(2004)
Chow et al.
(2003)
Chow et al.
(2004)
Green et al.
(2004)
Chow et al.
(2003)
Chow et al.
(2004)
Chow et al.
(2003)
2007
SALT
1997
NWHDc
1997
LVOnRDIE
LVOffRDIE
2003
2003
MDD
2001
HDD
2001
DIESEL
2001
LVOnRGAS
2003
NWnSPC
1997
NWSCPC
1997
SI_LC
2001
SI_LW
2001
SI_HC
2001
SI_HW
2001
GAS
2001
Mixed
Vehicle
Exhaust
LTMV01
2003
Lake Tahoe
LTMV02
2003
Lake Tahoe
Brake Wear
BrakeWare
2002
Tire Wear
TireWare
2002
CR_HWOOD
2001
CR_SWOOD
2001
LTRWSC
2004
Lake Tahoe
LTRWHC
2004
Lake Tahoe
LTRWCC
2004
Lake Tahoe
BVBURN
1999
Texas
LTDuffDry
2009
LTLittDry
2009
LTLeafDry
2009
Salting
Diesel
Exhaust
Mobile
Minneapoli
s,
Minnesota
Central
California
Northern
Colorado
Las Vegas
Las Vegas
Southern
California
Southern
California
Southern
California
MNSalt
Gasoline
Exhaust
Residential
Wood
Combustion
Biomass
Burning
Open
Burning
Las Vegas
Northern
Colorado
Northern
Colorado
Southern
California
Southern
California
Southern
California
Southern
California
Southern
California
Southern
California
Southern
California
Northern
Nevada
Northern
Nevada
Northern
Nevada
Northern
Nevada
Northern
From the Minnesota Department of Transportation
de-icing stockpile at the Minneapolis Camden Truck
Station.
Composite of two disturbed land salt buildup samples
Winter, composite of 14 heavy-duty diesel vehicles
Winter on-road diesel exhaust
Winter off-road diesel exhaust
Medium heavy-duty diesel vehicles, dynamometer
test, mixed mode
Heavy heavy-duty diesel vehicles, dynamometer test,
mixed mode
Chen et al.
(2010)
Chow et al.
(2003)
Watson et
al. (2)
Green et al.
(3)
Fujita et al.
(2007)
Composite of MDD and HDD
On road vehicle exhaust sampled at the intersection of
Cheyenne and Losee, Las Vegas
Winter, non-smoker, Federal Test Protocol (FTP)
composite.
Winter, smoker, FTP composite.
Green et al.
(3)
Watson et
al. (2)
Cold start mode of low emitting vehicles
Warm start mode of low emitting vehicles
Cold start mode of high emitting vehicles
Fujita et al.
(2007)
Warm start mode of high emitting vehicles
Composite of SI_LC, SI_LW, SI_HC, SI_HW
On road vehicle exhaust sampled by the DRI In-Plume
system at Southwood-Mays
On road vehicle exhaust sampled by the DRI In-Plume
system at Village Lakeshore
Kuhns et al.
(2004)
Brake wear, dynamometer test
?
Tire wear,, dynamometer test
California hardwood (oak, eucalyptus, almond)
combustion in a fireplace
California softwood (pine) combustion in a fireplace
Residential softwood combustion (fireplace and wood
stove)
Residential hardwood combustion (fireplace and wood
stove)
Composite of LTRWSC and LTRWHC
Composite of 21 profiles of open burning of vegetative
material to simulate wildfire emissions.
Open laboratory burning, duff composite, low
moisture, collected from Lake Tahoe forest floor
Open laboratory burning, litter composite, low
moisture, collected from Lake Tahoe forest floor
Open laboratory burning, leaf (Manzanita, Bitterbrush,
99
Chow
2007b
Kuhns et al.
(4)
Chow et al
(2004)
Chen et al.
(2010)
Coal
Combustion
Oil
Combustion
Cement
Coal-Fired
Power Plant
Oil-Fired
Plant
Cement
Factory
Charbroil
Cooking
Cooking
Meat
Cooking
Secondary
Secondary
Bisulfate
Secondary
Sulfate
Secondary
Nitrate
Secondary
OC
Nevada
Northern
Nevada
Northern
Nevada
Northern
Nevada
Northern
Nevada
Northern
Nevada
Northern
Colorado
Squaw Carpet) composite, low moisture
Open laboratory burning, stem (Manzanita,
Bitterbrush, Squaw Carpet) composite, low moisture
Open laboratory burning, duff composite, high
moisture, collected from Lake Tahoe forest floor
Open laboratory burning, litter composite, high
moisture, collected from Lake Tahoe forest floor
Open laboratory burning, leaf (Manzanita, Bitterbrush,
Squaw Carpet) composite, high moisture
Open laboratory burning, stem (Manzanita,
Bitterbrush, Squaw Carpet) composite, high moisture
LTStemDry
2009
LTDuffWet
2009
LTLittWet
2009
LTLeafWet
2009
LTStemWet
2009
MZPPC
1995
BVCFPP
1999
Texas
BVCLFA
1999
Texas
IMGPEC
1992
Southern
California
Border
Composite of six Mexicali oil-fueled glass plant
emission profiles collected on 12/17/92.
BVCAT1
1999
Texas
Composite of 5 profiles of stack emissions from a
Texas petroleum refinery’s catalytic cracker
Watson et
al. (6)
Chow et al
(2004)
Chow et al
(2004)
Watson
and Chow
(7)
Chow et al
(2004)
CR_REFIN
2002
Central
California
Oil refinery emissions
?
RS472
1997
Mexico City
Cement from Tolteca Cement factory in Tula, Hidalgo,
Cementera Tolteca (9/24/97).
BVCEM
1999
Texas
Composite of 11 profiles of cement kiln emissions
IMTSAC
1992
Composite of 11 Mexicali charbroil cooking emission
profiles from the Asadero El Nerivl Ciclon restaurant).
Samples collected on 12/16/92–12/18/92.
NMc
1997
BVCOOK
2000
Southern
California
Border
Northern
Colorado
Southern
California
Vega et al.
(8)
Chow et al
(2004)
Watson
and Chow
(7)
Watson et
al. (2)
Chow et al
(5)
Composite of ten coal-fired boiler emission samples
Composite of 26 profiles of stack emissions from coalfired boilers in Texas.
Composite of 3 profiles of resuspended coal fly ash
from coal-fired boilers
Composite of chicken grilling.
Composite of charcoal chicken, propane chicken, and
charcoal hamburger cooking profiles.
AMBSUL
Ammonium bisulfate
AMSUL
Ammonium sulfate
AMNIT
Ammonium nitrate
SOC
Secondary organic carbon
Lowenthal
et al. (10)
3.3.2 Sensitivity Testing
Sensitivity tests evaluate the performance of different source profile combinations in terms of
correlation (r2), root mean square difference (χ2), and percent of mass explained (PCMASS). Usually, only
one profile in each source group may be included since similar profiles result in collinearity, nonconvergence, and/or negative source contributions. The CMB v8.2 software does allow sources with
negative SCEs to be eliminated iteratively; Wang and Hopke (1989) showed that this approach provided
more precise estimates than did an unconstrained solution for sources whose profiles might be
collinear.
100
The sensitivity tests started with source profiles most relevant to the study region and then replaced
them, usually one by one, with other source profiles until the best performance measures were reached.
The final solution involves 8 profiles for the BLIS I and II group (i.e., TahoeSRD, AD_IMPRO, SI_LC,
LTStemDry, LTStemWet, MZPPC, AMSUL, AMNIT) and 8 profiles for the SOLA group (TahoeSRD,
AD_IMPRO, MPNSalt, SI_LC, LTStemDry, LTStemWet, AMSUL, AMNIT). These source profiles are
presented in Figure 3-4. The missing of MZPPC in SOLA is due to the low SNR of Se, a marker for coal
combustion. SI_LC that represents low-emitting gasoline vehicles best explains the motor vehicle
emissions at BLIS and SOLA. LTStemDry and LTStemWet correspond to the PMF HCE and LCE biomass
burning factor, respectively. SOC cannot be incorporated into the model successfully (resulting in source
elimination), suggesting low secondary organic contents.
101
LTStemWet
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
0.1000
LTStemDry
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
MZPPC
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
AMSUL
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
TahoeSRD
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
AD_IMPRO
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
SI_LC
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
MNSalt
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
AMNIT
0.1000
Fraction
PM2.5 Mass
1.0000
0.0100
0.0010
0.0001
Cl-
NO3- SO4= OC
EC
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Zr
Pb
Figure 3-4. Profiles of sources included in the final EV-CMB solutions. Bars and triangles indicate the profile values and
uncertainties, respectively.
Figure 3-5 and Figure 3-6 show an example (BLIS 5/5/2006) of EV-CMB fitting and the modified pseudoinverse normalized (MPIN) matrix. Eight sources explain 97.6% of PM2.5 mass with r2 = 0.98 and χ2 = 1.62.
MPIN calculates the sensitivity of SCEs to each species’ abundance in the corresponding source profiles.
MPIN values are normalized such that they range from -1 to 1. Species with MPIN values of 0.5–1 are
considered influential species (U.S. EPA, 2004). The MPIN matrix indicates Sr, Ca, K, OC, and Se are the
most important species for TahoeSRD, AD_IMPRO, LTStemDry, LTStemWet, and MZPPC, respectively.
102
Influential species for SI_LC include EC, Br, Cu, Ni, and Zn. Unsurprisingly, AMSUL and AMNIT are mostly
influenced by SO42- and NO3-, respectively.
2
Figure 3-5. Example of a final EV-CMB solution. “% Mass”/”CHI square” is PCMASS/χ in the text.
103
Figure 3-6. Modified pseudo-inverse normalized (MPIN) matrix of the example shown in Figure 3-5
3.3.3 CMB Source Apportionment Results
SCEs determined by EV-CMB are presented in Table 5 and compared with the PMF results in Figure 7.
EV-CMB was able to separate road and natural dust and separate LCE and HCE biomass burning for BLIS
I, BLIS II, and SOLA. It confirms that 1) biomass burning is the dominant source of PM2.5 with increasing
importance over time; 2) LCE burning accounts for most of the biomass burning contribution, though its
fraction is lower at SOLA; 3) road dust and traffic contributions are much higher at SOLA than at BLIS; 4)
industrial combustion and salting are minor sources. However, EV-CMB generally apportions less PM2.5
to biomass burning and secondary sulfate and more PM2.5 to dust and traffic emissions than PMF.
Industrial and salting contributions become negligible (<< 0.1 g/m3) under EV-CMB.
Table 5. Absolute (in µg/m3) factor contribution to PM2.5 mass (by EV-CMB) for each modeling group.
SOLA results are also separated to UCD and DRI periods.
Source Type
Factor
BLIS I
BLIS II
Factor
SOLA III
SOLA (UCD)
SOLA (DRI)
*
Dust
Road
Natural
0.10±0.07
0.62±0.11
0.15±0.08
0.42±0.10
Biomass Burning
Low CE
High CE
1.00±0.35
0.10±0.09
1.07±0.36
0.11±0.11
Fossil Fuel
Traffic
Industry
0.47±0.13
0.02±0.02
0.45±0.13
0.02±0.02
Secondary
Sulfate
Nitrate
0.54±0.06
0.21±0.04
0.58±0.07
0.18±0.03
Road
0.64±0.26
0.74±0.30
0.51±0.20
Low CE
2.40±0.78
1.80±0.60
3.13±0.99
Traffic
1.19±0.37
1.13±0.34
1.26±0.41
Sulfate
0.44±0.11
0.35±0.09
0.55±0.12
Natural
0.99±0.30
1.51±0.39
0.35±0.18
High CE
0.47±0.27
0.35±0.18
0.61±0.38
Salting
0.05±0.02
0.03±0.01
0.08±0.03
Nitrate
0.29±0.06
0.28±0.05
0.30±0.06
*
Road Dust: TahoeSRD; Natural Dust: AD_IMPRO; Low CE: LTStemWet; high EC: LTStemDry; Traffic: SI_LC;
Industry: MZPPC; Sulfate: AMSUL; Nitrate: AMNIT; Salting: MNSalt.
EV-CMB predicted nearly the same biomass burning contribution between BLIS I and II (i.e., 1.10 and
1.18 g/m3) while PMF suggested much higher BLIS II values (by 0.41 g/m3). It also came to our
attention that there is a ~0.3 g/m3 gap (~10% of PM2.5) between measured and CMB-calculated PM2.5
concentration for BLIS II (Figure 3-7). The selected biomass burning source profiles may not fit emissions
from particularly large wildfires (such as those in 2007 and 2008) well, thus leading the observed
discrepancy. With regard to SOLA biomass burning contribution, EV-CMB agreed with PMF to within +/104
5%. Higher HCE fractions at SOLA might just reflect a larger RWC contribution. The combustion efficiency
of RWC in the Tahoe basin has not been examined thoroughly, although it is believed that combustion
efficiency increased in recent years from implementing licensed fireplaces and woodstoves.
8
Source Contribution ( g/m3)
7
6.4 g/cm3
Traffic
Secondary Nitrate
Road Dust
Salting
Natural Dust
Secondary Sulfate
Industrial
HCE Burning
LCE Burning
6
5
4
3.3 g/cm3
3.2 g/cm3
3
2
1
0
PMF
CMB
BLIS I
PMF
.
CMB
BLIS II
PMF
.
CMB
SOLA (All)
Figure 3-7. Comparison of PMF and EV-CMB source apportionment for BLIS and SOLA. Numbers above the bars indicate
average measured PM2.5 concentration.
A 20-40% higher (total) dust contribution was reported by EV-CMB (Figure 7). In addition, EV-CMB
suggested natural dust to exceed road dust even at SOLA contradicting the suggestions from PMF results
and our conceptual model. Since road/natural dust partition is so different between the SOLA (UCD) and
SOLA (DRI) period according to EV-CMB, the actual dust partition at SOLA seems highly uncertain.
According to Table 2, SO42- concentration ranges from 0.47 to 0.51 g/m3 at BLIS and SOLA, and this
translates to (NH4)2SO4 concentrations of 0.65–0.70 g/m3. EV-CMB suggests lower secondary
ammonium sulfate contributions, only 0.35–0.54 g/m3. Since AMSUL represents a “pure” secondary
source profile, some of the observed SO42- may not be secondary. PMF secondary sulfate factors are not
pure, and they generally give higher SCEs than EV-CMB (Table 3). For NO3-, both PMF and EV-CMB
secondary nitrate factors do not fully explain the observed NO3-, suggesting possibility of primary
sources for NO3- as well.
3.4 PMF Weighted backtrajectory analysis
PMF analysis gives us an estimate of the contribution of each factor (source type) to PM2.5 and
reconstructed light extinction (haze) but tells us nothing about the location of the sources contributing
to each factor. Backward air trajectory analysis (backtrajectory analysis) estimates the pathway air
travels before arriving at a receptor location such as Bliss State Park. Backtrajectories passing over areas
with low pollution emission sources are likely to coincide with low pollutant concentrations at the
receptor site. Conversely, air that had previously been over highly polluted areas is likely to correlate
with higher receptor pollution levels. In reality the situation is more complicated. For example if air
passes over an area with high emissions but the pollution is removed by precipitation, then air quality
105
may be quite good at the receptor. Strong winds will also cause significant dilution of the pollution
(Green et al., 1996). During strong inversions, air passing over a polluted area such as a city within a
valley, may stay above the polluted layer and result in clean conditions downwind.
Backtrajectories are typically computed using 3 dimensional wind fields from a meteorological model
such as those used for weather forecasting. For this study the NOAA HYSPLIT model (Draxler and Hess,
1997) was used with the Eta Data Assimilation System (EDAS) 40 km resolution meteorological fields.
Five-day duration backtrajectories were started from 500 m AGL at Bliss State Park every 1 hour during
aerosol sampling days.
For each IMPROVE sampling day a PMF factor score is obtained which gives the importance of each PMF
factor for that particular day. These scores then were used to weight backtrajectories generated with
HYSPLIT. The PMF weighted backtrajectories weight the residence time over a given area during each
backtrajectory by the PMF factor score for the day when the backtrajectory started. These weights can
only be done for backtrajectories starting on days with chemically speciated aerosol data. The weighting
gives higher weights to regions the air passed over on days with high impacts from a particular source
(factor) and low weights to areas associated with low impacts from that factor. Two types of PMF
weighted backtrajectory contour maps were produced, difference maps and ratio maps. The PMF
weighted backtrajectory difference map is the PMF weighted residence time minus the unweighted
residence time. The difference then represents the enhancement due to the PMF weighting. This has
the effect of emphasizing geographical areas contributing significantly to a PMF factor. The ratio maps
give the ratio of the PMF weighted residence time divided by the unweighted residence time. This is
more of a multiplicative effect. However ratio maps can be misleading when the residence time over an
area is low. The relatively coarse resolution of the meteorological field (40 Km) will not properly
represent local flows and care must be taken when interpreting the results. Difference maps are mainly
presented rather than ratio maps.
The reader should note that the maps have difference scales for each color. While red always indicates
greater than average frequency (positive difference) and deep blue is always less than average
frequency (negative), the zero and near zero difference colors vary. It should also be noted that high
positive values do not necessarily indicate an area to be a source area, rather that when air flows over
the area the factor is important. The true source may be upwind of the area with high difference values.
Figure 3-8 shows the PMF weighted difference map for the biomass burning factor at BLIS1 for 20052009. Areas near Bliss State Park to the northwest had higher than normal frequency of transport
associated with the biomass burning factor 1. This is likely due in large part from the large lightning
caused fires in northern California during summer 2008 (see case study section 4.2 for July 11, 2008).
The ratio map for biomass burning factor 1 is shown in Figure 3-9. Areas to the north-northwest of Bliss
State Park (roughly along the heavily forested Cascade Range) show ratios significantly greater than one.
The difference (Figure 3-10) and ratio maps for biomass burning factor 2 at Bliss State Park for 20052009 are similar to the biomass burning factor 1 maps. The biomass burning difference map for Bliss
State Park for 2000-2004 (Figure 3-11) shows a similar area as for 2005-2009 biomass burning, but
appears to extend further south along the Sierra Nevada Range. The biomass burning factor for South
106
Lake Tahoe for 2000-2004 shows less effect from northwest of Lake Tahoe and enhanced transport from
the south compared to the Bliss State Park results. This may just be due to the reduced sampling
frequency at South Lake Tahoe compared to Bliss State Park, thus representing a different set of days.
Figure 3-8. Difference map for biomass burning factor 1 at Bliss State Park 2005-2009 period. Positive numbers indicate a
greater frequency during periods of high PMF factor weighting.
Figure 3-9. Ratio map for biomass burning factor 1 at Bliss State Park 2005-2009. Ratios greater than 1 indicate increased
frequency of transport associated with the PMF factor.
107
Figure 3-10. Difference map for biomass burning factor 2 at Bliss State Park 2005-2009.
Figure 3-11. Difference map for biomass burning factor at Bliss State Park 2000-2004.
108
Figure 3-12. Difference map for biomass burning factor at South Lake Tahoe 2000-2004.
Figure 3-13and Figure 3-14 are difference maps for the secondary sulfate factor at Bliss State Park for
2005-2009 and South Lake Tahoe 2000-2004. The difference maps show enhanced transport from the
nearby western areas and enhanced transport from a strip along the Pacific Coast. While short of
conclusively demonstrating such a link, this is consistent with previous conclusions (e.g. Xu et al., 2006)
of increased particulate sulfate associated with shipping emissions. These could also be associated in
part with transport of sulfate from Asia.
109
Figure 3-13. Difference map for secondary sulfate factor Bliss State Park 2005-2009.
Figure 3-14. Difference map for secondary sulfate factor South Lake Tahoe 2000-2004.
Figure 3-15, Figure 3-16, and Figure 3-17 are difference maps for the secondary nitrate factor. All maps
show enhanced frequency of transport from the San Joaquin Valley associated with this factor. The San
Joaquin Valley has high wintertime particulate nitrate so this may be representing transport of nitrate
from that area.
110
Figure 3-15. Difference map for secondary nitrate factor Bliss State Park 2005-2009.
Figure 3-16. Difference map for secondary nitrate factor Bliss State Park 2000-2004.
Figure 3-17. Difference map for secondary nitrate factor South Lake Tahoe 2000-2004.
Figure 3-18 is a difference map for dust factor 2 at Bliss State Park for 2005-2009. Because of its source
profile and its peak frequency in April, the dust 2 factor was suggested in section 3.2 to be associated
with Asian dust. Figure 3-19 is a difference map for the combustion factor at Bliss State Park for 2005111
2009. Section 3.2 showed that this factor also has a spring peak and is expected to be due in part to
transport of combustion emissions from coal-fired power plants in China. PMF weighted
backtrajectories for the dust 2 and combustion factors are consistent with long-range transport from
Asia.
Figure 3-18. Difference map for dust factor2 Bliss State Park 2005-2009.
Figure 3-19. Difference map for combustion factor Bliss State Park 2005-2009.
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4. Case Study Analysis
4.1 May 9, 2008: High sulfate and reconstructed fine soil
Of the highest 25 fine soil days at Bliss State Park from 1990-2009 (n=1764), all but one were between
the dates of March 30 and May 16. May 9, 2008 had the highest sulfate concentration recorded at Bliss
State Park over the period of record= 3.25 μg/m3 (equivalent ammonium sulfate of 4.47 μg/m3). This
date also had the fifth highest reconstructed fine soil concentration (5.55 μg/m3). Reconstructed
aerosol light extinction was 41.5 Mm-1, which is in the 98th percentile of days.
The CMB analysis identified this day as having the highest contribution from Asian dust during the 20052009 period. This period is considered in more detail to better understand the causes of poor visibility
days and whether this particular day was dominated by intercontinental transport of which local air
pollution control agencies would have no control over.
Figure 4-1 shows IMPROVE ammonium sulfate concentrations for May 9, 2008. A band of elevated
concentrations is seen along the higher elevation sites in California, with lower concentrations at low
elevation sites. This pattern is consistent with long-range transport of material higher in the
atmosphere. The IMPROVE fine soil for the same day shows a similar pattern (Figure 4-2). By the next
IMPROVE sampling day (May 12), the sulfate peak is shifted to the south and east (Figure 4-3). Aerosol
optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) averaged over
the 10-day period ending May 9, 2008 is shown in Figure 4-4. AOD is the vertical integration of the
aerosol light extinction coefficient and represents the total aerosol light extinction in a column from the
earth’s surface to the top of the atmosphere. Figure 4-4 shows high optical depth in eastern China
extending downwind across the Pacific Ocean. Figure 4-5 shows backward trajectories starting the
middle of the sampling day. Backtrajectories roughly track the areas of high AOD shown in Figure 4-4
and are consistent with the concept of transport of Asian aerosol to the Bliss State Park IMPROVE site.
Figure 4-1. Ammonium sulfate 5/9/08 (μg/m3).
113
Figure 4-2. IMPROVE Fine soil 5/9/08 (μg/m3).
Figure 4-3. Ammonium sulfate 5/12/08 (μg/m3).
114
Figure 4-4. MODIS optical depth averaged over 10 days period ending May 9, 2008.
Figure 4-5. HYSPLIT backtrajectories starting noon PST 5/9/2008 with 1000 m, 2000 m, and 3000 m AGL initial heights.
115
4.2 July 11, 2008: Highest reconstructed light extinction at Bliss State Park
July 11, 2008 has the highest reconstructed light extinction over the period 1990-2009 at Bliss State
Park. It also had the highest concentration of organic carbon. On June 20-21, 2008 mostly dry
thunderstorms resulted in over 6000 lightning strikes and over 2000 fires started in northern California.
These fires continued to burn for several weeks with full containment reported August 11, 2008. The
Bliss State Park IMPROVE site had three of its four highest reconstructed light extinction days during this
period (6/26/08, 7/11/08, and 7/14/08). Visibility at the South Lake Tahoe airport ranged from 1.5 to 6
miles on July 11.
Figure 4-6 shows the MODIS Aqua visible image for July 11, 2008. Numerous smoke plumes can be seen
and nearly the entire area has some amount of smoke visible. Figure 4-7 shows the MODIS Aqua
derived AOD for July 11 which is high over much of central to northern California and western Nevada.
Figure 4-8 shows the carbon monoxide (CO) from the Atmospheric Infrared Sounder (AIRS) on Aqua for
July 11. Due to incomplete combustion, wildfires often release large quantities of CO.
Figure 4-6. MODIS Aqua visible July 11, 2008.
116
Figure 4-7. MODIS Aqua AOD for July 11, 2008.
Figure 4-8. CO from Aqua AIRS instrument, July 11, 2008
117
4.3 October 15, 2004: Second highest reconstructed light extinction at Bliss
State Park
October 15, 2004 has the second highest reconstructed light extinction and second highest organic
carbon concentrations over the period of record at Bliss State Park. The MODIS Terra image for this day
(Figure 4-9) clearly shows smoke over the region. For more on the October 2004 fires in Northern
California see http://alg.umbc.edu/usaq/archives/000940.html.
Figure 4-9. MODIS Terra image for October 15, 2004.
4.4 September 11, 2006: Fifth highest reconstructed light extinction at Bliss
State Park.
September 11, 2006 was another fire impacted day with the fifth highest organic carbon and
reconstructed light extinction levels art Bliss State Park for the period of record. The MODIS Aqua visible
image from the afternoon of September 10, 2006 (Figure 4-10) shows a smoke plume being transported
toward Lake Tahoe from the west (probably from the Ralston fire near Foresthill).
118
Figure 4-10. MODIS Aqua image for September 10, 2006.
4.5 Asian dust episode of April 2001
April 2001 had a well documented Asian dust event that impacted much of the United States. Figure
4-11 is a Sea-viewing Wide Field-of-view Sensor (SeaWiFS) image showing the cloud approaching the
U.S. west coast on April 11. Figure 4-12 shows the IMPROVE reconstructed fine soil for April 13, 2001,
which had the highest fine soil on record at Bliss State Park. By April 16 the cloud had spread over much
of the western US with the IMPROVE network showing highest concentrations at Death Valley National
Park and at national parks in southern Utah (Figure 4-13). April 16, 2001 was the second highest
reconstructed fine soil day at Bliss State Park.
119
Figure 4-11. SeaWiFs image for April 11 showing Asian dust cloud approaching the US west coast.
120
Figure 4-12. Contour map of IMPROVE reconstructed fine soil (μg/m3) April 13, 2001.
Figure 4-13. Contour map of IMPROVE reconstructed fine soil (μg/m3) April 16, 2001.
4.6 Asian dust episode of April 1998.
This was the first extensively documented Asian dust episode affecting the western US. Figure 4-14
shows the IMPROVE reconstructed fine soil for April 29, 2008. Maximum concentrations were in the
Pacific Northwest. By the next IMPROVE sampling day (May2) the dust cloud had moved over the
Intermountain West (Figure 4-15).
121
Figure 4-14. Contour map of IMPROVE reconstructed fine soil (μg/m3) April 29, 1998.
Figure 4-15. Contour map of IMPROVE reconstructed fine soil (μg/m3) May 2, 1998.
122
5. Comparison of receptor modeling results to source modeling results
In this section some broad comparisons are made between the WRAP source-oriented modeling results
for 2002 at Bliss State Park (described in section 1) to the PMF receptor modeling results. Comparisons
are difficult because the different ways of resolving sources for each modeling methodology. For
sulfate (Table 1-4) and nitrate (Table 1-5), the WRAP PSAT apportionment was to point sources,
anthropogenic fires, natural fires & biogenic sources, area sources, and sources outside the domain. For
organic carbon apportionment is only by anthropogenic secondary, biogenic secondary, and
anthropogenic and biogenic primary (
123
Table 1-6). Potential elemental carbon impacts were characterized by a weighted emissions potential
(Table 1-7) for point area, and off-shore sources, anthropogenic fire, natural fire, on-road mobile, offroad mobile, road dust, and fugitive dust. For the PMF analysis most of the sulfate and nitrate are
contained within generic secondary sulfate and secondary nitrate factors. The PMF factors were:
biomass burning, combustion/industrial, road dust, natural dust, and traffic.
Sulfate
WRAP apportioned 5.6% of sulfate to motor vehicles and 4.2% to fires, while PMF attributed zero
percent of sulfate to the traffic and biomass burning categories. However, PMF attributed 5.5% of the
sulfate to road dust which is arguably due to motor vehicles and similar to the 5.6% motor vehicle
contribution from WRAP. PMF contributed 18.2% of sulfate to combustion/industrial sources
comparable to the 19% WRAP contribution from point sources. The largest categories for PMF and
WRAP sulfate were 68.3% “secondary sulfate” for PMF and 54.9% “out-of-domain” for WRAP.
Nitrate
Most of the PMF nitrate (80.5%) was in the general secondary nitrate source. About 54% of the WRAP
nitrate was from motor vehicles. PMF had 5.4% from biomass burning compared to 4.8% from WRAP
fires and biogenic. Except for biomass burning the source categories are too dissimilar to make
meaningful comparisons.
Organic carbon
PMF attributed 80% of the organic carbon to biomass burning and 15% to traffic. WRAP CMAQ
attributed 64% to biogenic secondary OC, 32% to anthropogenic and biogenic primary, and 4% to
anthropogenic secondary OC. The 80% of PMF OC from biomass is not inconsistent with the 64-96% of
OC from biomass from CMAQ (64% from secondary biogenic, 0-32% from primary biomass). The 15%
from traffic in PMF is consistent with 4% from anthropogenic primary and 0-32% from anthropogenic
secondary from CMAQ.
Elemental carbon
For EC we can only compare the PMF results to the weighted emissions potential (WEP) from WRAP.
THE WEP attributed 63.2% of EC to fire compared to 71.8% from PMF biomass burning. WEP attributed
22.1% of EC to on and off road mobile sources compared to the 15.8% attribution to traffic from PMF.
In short, the categories for which we have attribution for by WRAP and PMF are rather different, making
comparisons difficult, especially for sulfate and nitrate where generic secondary sulfate and nitrate
categories dominate the PMF attribution. For organic carbon the WRAP CMAQ had only 3 categories,
one of which included biogenic and anthropogenic OC sources, again making comparisons difficult. For
EC both techniques provided similar attribution to the two main EC sources (burning and vehicles). In
summary comparisons are difficult but generally consistent when comparing similar source categories is
possible; also no obvious large discrepancies were found.
124
125
6. Summary and conclusions
This Study was performed to help inform TRPA and other agencies regarding the trends in visibility
impairment in the Lake Tahoe Basin and the causes of haze in the basin. In addition to the TRPA, the
results of this study will be useful to the states of California and Nevada which are responsible for
submitting regional haze plans to the US Environmental Protection Agency, and to the US Forest Service,
which also has responsibilities for protecting its Class 1 areas, including Desolation Wilderness.
Chemically speciated PM2.5 and PM10 mass was collected at an urban location near lake level in South
Lake Tahoe (SOLA1) from March 1989 to May 2004 using an IMPROVE sampler. Speciated PM2.5 and
PM10 mass have been collected with an IMPROVE sampler since November 1990 at Bliss State Park
(BLIS1) at the edge of the Desolation Wilderness area, in a rural location about 200 m above the
elevation of Lake Tahoe, and about 8 km WNW from South Lake Tahoe. At BLIS1 sampling followed the
IMPROVE schedule with sampling every Wednesday and Saturday until November 1999 and then every
third day afterwards. At SOLA1 sampling was reducing to 1 day in 6 starting in August 1998.
Reconstructed fine mass and light extinction calculations used the most recent IMPROVE recommended
algorithms (Pitchford, et. al., 2007). Monthly averaged relative humidity growth factors f(RH) were
computed using RH data collected at BLIS1 and SOLA1.
Reconstructed fine mass- Table 6-1 shows the aerosol component concentration contributions to
reconstructed fine mass and percent contribution by component for the period with data at both sites
(Nov. 1990- May 2004).
Table 6-1. Reconstructed fine mass component (RFM) concentrations and percentage contributions to RFM at Bliss State
Park (BLIS1) and South Lake Tahoe (SOLA1).
BLIS1
(μg/m3)
SOLA1
(μg/m3)
% of RFM
BLIS1
% of RFM
Ammonium
sulfate
0.69
0.63
19.4
7.0
Ammonium
nitrate
0.27
0.46
7.6
5.1
Organic mass
1.87
5.41
52.9
59.8
Elemental carbon
0.18
1.09
5.1
12.0
Fine soil
0.50
1.36
14.3
15.0
Sea salt
0.03
0.10
0.7
1.1
Total
Reconstructed
fine mass
3.53
9.06
100.0
100.0
Component
126
SOLA1
Table 6-1 shows that the urban site SOLA1 has over twice the fine mass as the rural site BLIS1. Sulfate
concentrations are about the same at both sites, while organic and elemental carbon and fine soil is
much higher at SOLA1. The higher concentrations of carbonaceous aerosol are expected to be due
mainly to higher residential wood burning and traffic impacts at the urban site. Higher fine soil would
be expected at the urban site due to higher road dust emissions (Zhu et. al.; 2009). At both sites
carbonaceous aerosol dominate the fine mass (57% at BLIS1 and 72% at SOLA).
Figure 6-1 shows the monthly average contribution to reconstructed fine mass by aerosol component at
each site.
Figure 6-1. Monthly contributions to RFM by aerosol component at the South Lake Tahoe and Bliss State Park monitoring
sites (1990-2004).
BLIS1 shows highest fine mass in the summer to early autumn, mainly due to higher organic mass (OM)
associated with wildfires. By contrast, SOLA1 has highest fine mass in the winter due mainly to organic
mass associated with residential wood burning although smoke from wildfires also affects SOLA1 in
summer. The BLIS1 summer peak in organic mass is due to enhanced wildfire activity in summer.
Reconstructed light extinction- Reconstructed aerosol light extinction for the period 1990-2004
averaged 12.76 Mm-1 at BLIS1 and 43.71 Mm-1 at SOLA1. Percentage contributions from each aerosol
component are shown in Figure 6-2. Carbonaceous aerosol accounted for 70% of reconstructed light
extinction at SOLA1 and 52 % at BLIS1. Coarse mass contributed 13% at both sites. While sulfate
concentrations were about equal at both sites, they contributed 22% of reconstructed light extinction at
BLIS1 and only 8% at SOLA1.
127
Figure 6-2. Percentage contributions to reconstructed light extinction by aerosol component at Bliss State Park and South
Lake Tahoe.
Progress toward meeting regional haze rule goals- According to the regional haze rule, at mandatory
Federal Class 1 areas the cleanest 20% of days need to not get hazier and the 20% worst visibility days
need to improve. The years 2000-2004 are the baseline period and progress is to be evaluated at 5 year
intervals (the first of which is 2005-2009). Table 6-2 compares reconstructed light extinction at BLIS1
(representing Desolation Wilderness) for 20% best and worst visibility days for the 2000-2004 and 20052009 periods.
Table 6-2. Reconstructed light extinction by aerosol component at BLIS1 for the 2000-2004 and 2005-2009 periods.
SO4 ext
NO3 ext
OC ext EC ext
soil ext
Sea salt
ext
CM ext
aerosol
ext
dV
2000-2004
best
1.07
0.39
0.98
0.50
0.14
0.18
0.40
3.66
3.07
2005-2009
best
1.02
0.26
0.71
0.30
0.11
0.09
0.42
2.92
2.53
2000-2004
worst
4.61
1.63
12.02
3.24
1.08
0.11
2.03
24.72
11.15
2005-2009
worst
5.45
1.56
17.22
3.65
0.79
0.07
2.16
30.90
12.71
For the best visibility days, reconstructed light extinction decreased at Bliss State Park mainly from
decreased carbonaceous aerosol light extinction. However, the worst 20% visibility days saw increased
light extinction, mainly from increased carbonaceous aerosol but also due to increased sulfate. The
increased carbonaceous aerosol is thought to be due to increased effects from wildfires (Figure 2-19).
The reason for increased sulfate is unknown, but could be due in part to increased effects from shipping
and/or transport from Asia.
128
Receptor modeling results- Data was segregated by 2000-2004 (BLIS I) and 2005-2009 (BLIS II) and 20002004 for SOLA. Seven common PMF factors were resolved for the BLIS I and II modeling groups: natural
dust, road dust, biomass burning, traffic and industrial emission, as well as secondary sulfate and
nitrate. BLIS II data yielded two biomass burning factors that were attributed to low- and highcombustion efficiency (LCE and HCE) burning. Only 6 factors were found for SOLA including dust,
biomass burning, traffic emissions, secondary sulfate, secondary nitrate, and a salting factor only found
at SOLA. CMB was able to separate road and natural dust and separate LCE and HCE biomass burning
for BLIS I, BLIS II, and SOLA. It confirms that 1) biomass burning is the dominant source of PM2.5 with
increasing importance over time; 2) LCE burning accounts for most of the biomass burning contribution,
though its fraction is lower at SOLA; 3) road dust and traffic contributions are much higher at SOLA than
at BLIS; 4) industrial combustion and salting are minor sources. However, CMB generally apportions less
PM2.5 to biomass burning and secondary sulfate and more PM2.5 to dust and traffic emissions than PMF.
Industrial and sating contributions become negligible (<< 0.1 μg/m3) under CMB. Figure 6-3 compares
attribution to source type for PMF and CMB.
Figure 6-3. Comparison of PMF and CMB source apportionment for BLIS and SOLA.
Receptor modeling results were compared to the extent possible to source-oriented modeling done for
WRAP. The categories for which we have attribution for by WRAP and PMF are rather different, which
makes comparisons difficult, especially for sulfate and nitrate since generic secondary sulfate and nitrate
categories dominate the PMF attribution. For organic carbon the WRAP CMAQ had only 3 categories,
one of which included biogenic and anthropogenic OC sources again making comparisons difficult. For
EC both techniques provided similar attribution to the two main EC sources (burning and vehicles). In
summary, comparisons are difficult but generally consistent when source categories are comparable; in
addition; no obvious large discrepancies were found.
Six case studies of poor visibility were done. Three were attributed to local/regional wildfires, two to
transport of Asian Dust, and one combined Asian dust, high sulfate (likely Asian in origin). There is little
that can be done by local agencies to mitigate the poor visibility for these types of episodes.
129
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