Characterization of Fine Particle Mass Using Particle ...

Characterization of Fine Particle Mass Using Particle-Phase Organic Compounds as Tracer FINAL REPORT Prepared for: Pat Brewer (VISTAS Technical Coordinator) 2090 US Highway 70, Swannanoa, NC 28778 e-mail: [email protected] and John Hornback (SESARM Executive Director) 526 Forest Parkway, Suite F Forest Park, GA 30297-6140 e-mail: [email protected]

Prepared by Eric M. Fujita, David Campbell, Johann Engelbrecht and Barbara Zielinska Division of Atmospheric Sciences Desert Research Institute, Nevada System of Higher Education 2215 Raggio Parkway Reno, NV 89512

January 21, 2009

2215 Raggio Parkway, Reno, Nevada 89512-1095

(775) 673-7300

ACKNOWLEDGMENTS This work was funded for VISTAS by Southeastern States Air Resources Managers, Inc. Contract #V-2-2004-14. We gratefully acknowledge Patricia Brewer of VISTAS and John Hornback of SESARM for their administrative and technical support. We acknowledge Ralph Morris of ENVIRON for providing the results of their CAMx modeling. We also thank Dr. Ivar Tombach for reviewing Section 4 and Scott Reynold for reviewing the draft report.

i

TABLE OF CONTENTS Page Acknowledgments............................................................................................................................ i List of Table................................................................................................................................... iv List of Figures ..................................................................................................................................v 1. EXECUTIVE SUMMARY ..................................................................................................... 1-1 1.1 Introduction ................................................................................................................... 1-1 1.2 Methods and Approach.................................................................................................. 1-1 1.3 Results and Conclusions................................................................................................ 1-2 2. INTRODUCTION ................................................................................................................... 2-1 2.1 Fundamental of Receptor Models ................................................................................. 2-2 2.2 PM Source Apportionment Studies Conducted in the Southeastern U.S...................... 2-3 2.3 Source Composition Profiles of Primary Emission Sources ......................................... 2-4 2.3.1 Motor Vehicle Exhaust ........................................................................................ 2-5 2.3.2 Vegetative Burning .............................................................................................. 2-6 2.3.3 Meat Cooking....................................................................................................... 2-8 2.3.4 Vegetative Detritus .............................................................................................. 2-9 2.4 Composition and Apportionment of Secondary Organic Aerosols............................... 2-9 2.5 Compilation of Source Profiles and Identification of Molecular Markers for Primary and Secondary PM......................................................................................... 2-11 3. AMBIENT MEASUREMENTS OF MASS AND COMPONENTS OF PM2.5...................... 3-1 3.1 Sampling Locations ....................................................................................................... 3-1 3.2 Sampling and Chemical Analysis Methods................................................................... 3-1 3.2.1 Sampling Methods ............................................................................................... 3-1 3.2.2 Speciation of Particulate Organic Compounds .................................................... 3-2 3.2.3 Particulate Organic Carbon and Positive Carbon Artifact Correction................. 3-3 3.2.4 Isotopic Carbon Analysis..................................................................................... 3-5 3.3 Spatial and Seasonal Variations .................................................................................... 3-5 3.4 Laboratory Comparison for Speciation of Particulate OC ............................................ 3-6 4.

SOURCE APPORTIONMENT OF CARBONACESOUS PARTICLES IN NATIONAL PARKS AND WILDERNESS AREAS OF THE SOUTHEASTERN UNITED STATES................................................................................................................ 4-1 4.1 Methods and Procedures................................................................................................ 4-1 4.1.1 Chemical Mass Balance Method and Procedures................................................ 4-1 4.1.2 Sensitive of CMB Source Contribution Estimates to Alternative Source Profiles ................................................................................................................. 4-4 4.1.3 Application of 14C Data to Apportion Unexplained Carbon from CMB............ 4-5 4.1.4 Positive Matrix Factorization............................................................................... 4-6 4.2 Results ........................................................................................................................... 4-6 4.2.1 Fine (PM2.5) Particulate TC, OC and EC Source Apportionments...................... 4-7 4.2.2 Apportionment of Unexplained Carbon............................................................... 4-8 ii

4.2.3 Reconciliation of CMB and PMF ........................................................................ 4-8 4.2.4 Comparisons with CAMx Modeling.................................................................... 4-9 4.3 Conclusions ................................................................................................................... 4-9 5. POSITIVE MATRIX FACTORIZATION ANALYSIS OF THE VISTAS AMBIENT PM DATA ............................................................................................................................ 5-1 5.1 Initial PMF Analysis...................................................................................................... 5-1 5.2 Final PMF Analysis....................................................................................................... 5-5 6. REFERENCES ........................................................................................................................ 6-1 APPENDIX A. VISTAS Organic Compounds Speciation List

iii

LIST OF TABLES Table No.

Page No.

Table 1-1.

CMB SCEs of primary fossil and modern carbon and estimates of unexplained carbon attributable to modern carbon using 14C data normalized to IMPROVE TC and Accelerator Mass Spectrometry TC................................. 1-4

Table 2-1.

Source composition profiles that may be applied in CMB receptor modeling of VISTAS ambient PM2.5 organic speciation data... ........................................ 2-14

Table 2-2.

Distributions of organic compounds by class in weight percent of total carbon for available source profiles................................................................... 2-16

Table 3-1.

Numbers of filter samples collected and weighed. .............................................. 3-9

Table 3-2.

Numbers of filter samples analyzed for organic compounds............................... 3-9

Table 3-3.

Mean ambient concentrations of PM components and potential marker organic compounds. ........................................................................................... 3-10

Table 3-4.

Fractions of modern carbon derived from isotopic carbon data. ....................... 3-11

Table 3-5.

PM2.5 mass and carbon data for CACHE and VISTAS samples used in the laboratory comparison between Georgia Institute of Technology and Desert Research Institute............................................................................................... 3-11

Table 3-6.

CMB source contribution estimates1 of CACHE and VISTAS laboratory comparison samples2 by Georgia Institute of Technology and Desert Research Institute. ............................................................................................ 3-12

Table 4-1.

CMB source contribution estimates to ambient total carbon by site and season................................................................................................................. 4-11

Table 4-2.

CMB source contribution estimates to ambient organic carbon by site and season................................................................................................................. 4-12

Table 4-2.

CMB source contribution estimates to ambient elemental carbon by site and season................................................................................................................. 4-13

Table 4-4.

CMB SCEs of primary fossil and modern carbon and estimates of unexplained carbon attributable to modern carbon using 14C data normalized to IMPROVE TC and Accelerator Mass Spectrometry TC............................... 4-14

Table 4-5.

Measured TC, OC, and EC concentrations versus CAMx estimates................. 4-15

iv

LIST OF FIGURES Figure No.

Page No.

Figure 1-1.

CMB source contribution estimates of ambient total carbon at the five VISTAS sampling sites........................................................................................ 1-5

Figure 1-2.

CMB source contributions to ambient TC for primary sources and estimates of UCf and UCm normalized to IMPROVE TC. Negative UCf were set to zero with offsetting decreases in UCm. Fossil carbon contributions are shown as dark bars and modern carbon contributions are shown as striped bars....................................................................................................................... 1-6

Figure 1-3.

CAMx source contributions to TC for primary sources, anthropogenic and biogenic SOA. Estimates of particulate organic aerosol were converted to POC using source-specific OM/OC ratios. Fossil carbon contributions are shown as dark bars and modern carbon contributions are shown as striped bars....................................................................................................................... 1-7

Figure 3-1.

Map of the five VISTAS sampling locations..................................................... 3-13

Figure 3-2a.

Time series of ambient PM2.5 organic carbon, elemental carbon, ammonium, nitrate and sulfate at Shenandoah National Parks. ............................................ 3-14

Figure 3-2b.

Time series of ambient PM2.5 organic carbon, elemental carbon, ammonium, nitrate and sulfate at Mammoth Cave National Parks. ...................................... 3-15

Figure 3-2c.

Time series of ambient PM2.5 organic carbon, elemental carbon, ammonium, nitrate and sulfate at Great Smokey Mountain National Park. .......................... 3-16

Figure 3-2d.

Time series of ambient PM2.5 organic carbon, elemental carbon, ammonium, nitrate and sulfate at Cape Romain National Wildlife Refuge. ......................... 3-17

Figure 3-2e.

Time series of ambient PM2.5 organic carbon, elemental carbon, ammonium, nitrate and sulfate at Millbrook, NC .................................................................. 3-18

Figure 3-3.

Laboratory comparison of organic speciation of CACHE and VISTAS samples............................................................................................................... 3-19

Figure 4-1.

Sensitivity of the CMB source contributions to alternative source profiles. ..... 4-16

Figure 4-2.

Source contributions to CMB fitting species. .................................................... 4-17

Figure 4-3.

Source contributions of individual samples from Great Smoky Mountains National Park and Millbrook by seasons. Samples are ordered by total carbon concentrations within each season. ....................................................... 4-18

Figure 4-4.

CMB Source apportionment of PMF Factors (TC). .......................................... 4-19

Figure 4-5.

Source contributions by CMB versus CMB apportionment of PMF factors..... 4-20

Figure 4-6.

CMB source contributions to ambient TC for primary sources and estimates of UCf and UCm normalized to IMPROVE TC. Negative UCf were set to zero with offsetting decreases in UCm. Fossil carbon contributions are shown as dark bars and modern carbon contributions are shown as striped bars..................................................................................................................... 4-21 v

Figure 4-7.

CAMx source contributions to ambient TC for primary sources, anthropogenic and biogenic SOA. The CAMx estimates of particulate organic aerosol were converted to OC using source-specific OM/OC ratios. Fossil carbon contributions are shown as dark bars and modern carbon contributions are shown as striped bars ............................................................. 4-22

Figure 5-1a.

Abundances of species in PMF factors and average seasonal factor loadings for Cape Romain .................................................................................................. 5-6

Figure 5-1b.

Abundances of species in PMF factors and average seasonal factor loadings for Shenandoah National Park. ............................................................................ 5-7

Figure 5-1c.

Abundances of species in PMF factors and average seasonal factor loadings for Millbrook........................................................................................................ 5-8

Figure 5-1d.

Abundances of species in PMF factors and average seasonal factor loadings for Mammoth Cave National Park....................................................................... 5-9

Figure 5-1e.

Abundances of species in PMF factors and average seasonal factor loadings for Great Smokey Mountain National Park ....................................................... 5-10

Figure 5-1f.

Abundances of species in PMF factors and average seasonal factor loadings for all five sites combined.................................................................................. 5-11

Figure 5-2.

CMB Source apportionment of PMF Factors (TC) ........................................... 5-12

Figure 5-3.

Source contributions by CMB versus CMB apportionment of PMF factors..... 5-12

vi

1.

EXECUTIVE SUMMARY

This report is submitted to SESARM/VISTAS in fulfillment of Contract # V-2004-14, Characterization of Fine Particulate Mass Using Particle-Phase Organic Compounds as Tracers. Desert Research Institute (DRI) applied Chemical Mass Balance and Positive Matrix Factorization analyses to determine the contributions of elemental carbon (EC) and primary (directly emitted) sources of particulate organic carbon (POC), and infer the contributions of secondary organic aerosols (SOA) in samples collected at five sites in the southeastern US. The POC that was not apportioned to primary sources, unexplained carbon (UC), was attributed to fossil (UCf) and modern (UCm) carbon using the measured total carbon (TC), sums of the CMB apportioned primary carbon, and the fractions of fossil carbon (ff) and modern carbon (fm), which were derived by Tanner (2007) from the 14C data obtained by Woods Hole Oceanographic Institute. The spatial and seasonal variations in the contributions of EC, primary sources of POC, UCf, and UCm were compared to results of the CAMx air quality simulation modeling of the VISTAS region by ENVIRON (Morris et al., 2009). 1.1

Introduction

Organic carbon mass is the second largest contributor to fine particle mass and visibility impairment at Class I areas in the southeastern US (ammonium sulfate is the largest contributor). Source attribution of carbon is complicated because mass measured at the monitor is a combination of primary fine particle carbon and secondary organic aerosols (SOA) that are formed from gaseous organic precursors that can be either anthropogenic or biogenic in origin. The primary emissions component of OC in the southeastern US has been attributed in recent studies using the CMB receptor model to mostly vegetative burning and mobile sources, but a large fraction of unexplained OC is presumed to be of secondary origin (Zheng et al., 2002; Yu et al., 2004; Sangil et al., 2007). The spatial and seasonal variations in these source attributions are large and the nature and origin of the unexplained component of ambient particulate carbon are not well understood. VISTAS (Visibility Improvement State and Tribal Association of the Southeast), the regional planning organization for the Southeastern US, initiated a special monitoring study to provide a better understanding of the relative source contributions to ambient concentrations of POC at national parks and wilderness areas in the Southeastern U.S. PM2.5 samples collected at five Class I areas in southeastern US and in Raleigh, NC over one year were analyzed at Desert Research Institute for particulate phase organic compounds and at Woods Hole Oceanographic Institute for 14C isotope to determine fractions of modern carbon (fm) and fossil carbon (ff). The Chemical Mass Balance (CMB) model and Positive Matrix Factorization (PMF) were applied to the ambient data to determine the spatial and seasonal variations in the contributions primary sources of POC and the residual unexplained carbon (UC). UC was further apportioned to modern (UCm) and fossil (UCf), which are assumed to consist of SOA of biogenic and anthropogenic origin, respectively. 1.2

Methods and Approach

PM2.5 samples were collected on quartz fiber filters at Great Smoky Mountains National Park (GRSM), Mammoth Cave National Park (MACA), Shenandoah National Park (SHEN), Cape Romain National Wildlife Refuge (ROMA), (all IMPROVE monitoring sites), and one neighborhood suburban scale site at Millbrook Station in Raleigh, MILL, (Chemical Speciation 1-1

Network, CSN, site). PM2.5 samples were collected with Hi-Vol samplers on pre-fired (900 oC for 5 hours) 8 x 10 inch quartz fiber filter media for 24 hours on a one in three day schedule. Samples were collected from April 15, 2004 until May 10, 2005 so as to include all four seasons. Total PM2.5 mass was determined for all samples and selected samples for each site plus laboratory and field blanks were analyzed by gas chromatography/mass spectrometry (GC/MS) for 39 alkanes, 20 PAH’s and oxy-PAHs, 26 hopanes and steranes and 61 polar organic compounds (alkanedioic acids, aromatic acids, alkenedioic acids, alkanoic acids, alkenoic acids, resin acids, methoxy acids, levoglucosan, methoxyphenols, and sterols). The organic speciation database was merged with the elemental and organic carbon data from the IMPROVE or CSN measurements for the corresponding sampling times and locations. The quarter portions of the filters analyzed by DRI were shipped to Woods Hole Oceanographic Institute for determination of the 14C content and the 13C/12C ratios of the aerosol carbon contained in each sample by accelerator mass spectrometry. These data were used by Tanner (2007) to determine the ratios of modern (vegetative emissions, wood burning, agricultural burning, cooking) to fossil (gasoline, diesel, coal, oil) carbon. Version 8 of the DRI/EPA CMB receptor model (Watson et al., 1997) was used to apportion ambient total carbon (TC) to primary sources (gasoline vehicle exhaust, diesel vehicle exhaust, hardwood combustion, softwood combustion, meat cooking, and vegetative detritus). A total of 78 source profiles (18 fossil fuel combustion, 39 vegetative burning, 14 meat cooking, 3 tire wear, 3 brake wear, and 1 vegetative detritus) were compiled from previous emissions characterization studies and evaluated for organic species measured compared to the VISTAS speciation database, differences in methods used to measure carbon fractions by thermal methods (e.g., IMPROVE versus STN/NIOSH), and suitability of the profile for the study region. A subset of four diesel and four gasoline exhaust profiles (Fujita et al., 2007), one softwood and one hardwood combustion and three mixed wood profiles (Fine et al., 2002), three meat cooking (Fitz et al., 2003) and one vegetative detritus (Rogge et al., 1993) profiles was selected from the initial list for further evaluation including a series of sensitivity tests to determine variations of the source contribution estimates and CMB model performance with alternative source profiles for a particular source type. 1.3

Results and Conclusions

Ambient TC was apportioned to diesel and gasoline vehicle exhaust, hardwood and softwood combustion, meat cooking and vegetative detritus. Table 1-1 and Figure1-1 show the average source contribution estimates (SCE) by site and season (winter = December to February, spring = March to May, summer = June to August, and Fall = September to November). Only results with acceptable CMB performance statistics (R2 > 0.8, χ2 < 4.0, % Mass < 120%) were used in the calculation of the average source contributions. Most of the samples with poor performance had very low TC concentrations, especially during the fall and winter at Shenandoah NP. Good model performance was obtained for 75 to 85 percent of the samples except from Shenandoah. The portions of the residual unexplained carbon (UC) that are modern (UCm) and fossil (UCf) are also shown. UCm can result from chemical reactions and gas-toparticle conversion of volatile organic compounds (VOC) and semi-volatile organic compounds (SVOC) emitted directly by vegetation (e.g. terpenes) or during combustion of biological material (e.g., wildfires, prescribed burns, residential wood combustion, meat cooking). UCf is interpreted as secondary organic aerosols (SOA) resulting from gaseous precursor emissions from combustion of fossil fuels. 1-2

The five-site annual mean contributions of unexplained carbon (UC), wood combustion, diesel exhaust, gasoline exhaust, meat cooking and vegetative detritus to ambient TC were 34, 24, 23, 17, 4, and 1 percent, respectively. The variations in relative source contributions were small within any single season despite greater day-to-day variations in ambient TC concentrations. Variations in relative source contributions were related to seasonal changes in emissions and atmospheric conditions. UC was almost exclusively apportioned to modern carbon and its contributions were highest during summer and lowest during winter. Contributions of vegetative burning to ambient TC were highest during winter and fall and coincide with occurrences of prescribed fires from late fall to early spring and increased residential wood combustion during winter. Diesel contributions were higher during winter and fall and gasoline contributions were higher during spring and summer. The PMF factors did not appear to correspond to unique sources. The CMB profiles were applied to the PMF factors to identify and quantify the contributions of sources types to each factor. The factors were found to be mixtures of primary source categories and UC with UC being the largest fraction in four of the PMF factors. This suggests that variation chemical composition measured at the receptor locations depend on transport pattern as well as seasonal variations in emissions. The greater abundance of highly polar organic compounds in the UC fraction and its seasonal variations suggest secondary organic aerosol from either biogenic emissions or transformation of volatile and semi-volatile organic emissions associated with vegetative burning. The CMB and CAMx source contributions are shown in Figure 1-2 and 1-3, respectively. Although the source apportionments are for different years, 2004-5 for CMB and 2002 for CAMx, the overall conclusions are very consistent. Additionally, the CAMx predictions of TC, OC and EC concentrations for 2002 are very similar to the mean ambient concentrations measured during the VISTAS monitoring program in 2004 and 2005. The two main sources of ambient POC for both CAMx and CMB results are SOA from biogenic emissions (UCm from the combined CMB and 14C analysis) and wood combustion. The 5-site annual mean for wood combustion was 31 and 24 percent for CAMx and CMB, respectively and 27 and 33 percent, respectively for SOA-biogenic. Nearly 60 % of the POC, on an annual average basis, is associated with these two categories. SOA-biogenic and wood combustion also account for nearly the entire fraction of modern carbon estimated by 14C analysis (mean fm ~ 0.7 for all sites). The seasonal variations for these two sources were consistent between the source and receptor modeling results. The contributions of vehicle emissions were higher for CMB with 5site annual mean of 23 and 17 percent for gasoline and diesel compared to 17 and 6 percent for CAMx. The difference appears to be assigned in CAMx to point and other (meat and vegetative detritus and other area sources). Aggregation of PMF factors to CMB source categories also showed less contributions of gasoline vehicle exhaust and meat cooking compared to CMB. Although the contributions of motor vehicles were generally greater than 20 percent for both CAMx and CMB, the contributions of SOA-anthropogenic were found to be negligible. This suggests that the contributions of motor vehicles are largely local rather than from long-range transport. This study is the first, to our knowledge, to combine CMB with 14C data to estimate the fractions of modern and fossil UC and to reconcile these results with those of PMF analysis and grid-based air quality simulation modeling. The results from these independent analyses are generally consistent in the magnitude of the source contributions and their seasonal variations.

1-3

1-4

Table 1-1. CMB SCEs of primary fossil and modern carbon and estimates of unexplained carbon attributable to modern carbon using 14 C data normalized to IMPROVE TC and Accelerator Mass Spectrometry TC. TC (µg/m3) 2 1

% ff

% fm

CMB Primary Carbon (PC) SCE (% of TC) Veg Burn Modern

Others Modern

Diesel Fossil

4

Gasoline Fossil

% Unexplained Carbon IMPROVE UCf

IMPROVE UCm

5

AMS UCf

AMS UCm

ROMA

Winter

8

3.0 ± 0.4

3.3 ± 0.2

22.2%

77.8%

30.7 ± 2.5

3.9 ± 0.3

28.8 ± 4.1

14.6 ± 2.0

-21.2 ± 2.4

47.6 ± 4.5

-13.1 ± 2.6

61.8 ± 5.0

Spring

10

2.1 ± 0.2

3.4 ± 0.2

20.8%

79.2%

14.7 ± 1.3

3.0 ± 0.2

18.8 ± 2.6

34.4 ± 4.1

-34.5 ± 1.8

62.3 ± 4.5

-10.8 ± 2.0

76.7 ± 5.8

Summer

12

2.3 ± 0.4

3.7 ± 0.1

30.6%

69.4%

9.3 ± 0.9

4.8 ± 0.6

12.8 ± 2.4

19.5 ± 3.6

-1.4 ± 1.4

55.0 ± 1.6

10.0 ± 1.6

61.1 ± 3.6

Fall

8

3.4 ± 0.6

4.2 ± 0.2

29.2%

70.8%

24.3 ± 2.4

6.5 ± 0.8

22.6 ± 4.0

10.2 ± 2.0

-6.6 ± 1.4

44.0 ± 4.8

0.8 ± 1.5

48.2 ± 3.7

Annual

38

2.7 ± 0.4

3.7 ± 0.2

25.7%

74.3%

19.8 ± 1.9

4.5 ± 0.5

20.8 ± 3.4

19.7 ± 3.1

-15.9 ± 1.8

52.2 ± 4.1

-3.3 ± 1.9

61.9 ± 4.6

Winter

3

1.6 ± 0.2

2.4 ± 0.1

35.3%

64.7%

26.6 ± 2.9

3.1 ± 0.4

30.3 ± 4.6

10.2 ± 1.7

-7.3 ± 0.5

37.0 ± 1.3

6.8 ± 1.3

47.5 ± 3.6

Spring

6

1.4 ± 0.2

2.1 ± 0.1

38.1%

61.9%

18.1 ± 1.7

1.3 ± 0.1

26.6 ± 3.1

18.9 ± 2.2

-13.8 ± 1.5

52.3 ± 4.1

2.9 ± 2.2

56.2 ± 5.9

Summer

11

2.8 ± 0.5

4.3 ± 0.2

22.0%

78.0%

20.1 ± 2.4

5.6 ± 0.6

21.9 ± 3.8

14.1 ± 2.4

-20.0 ± 1.8

58.9 ± 6.7

-3.5 ± 2.5

64.2 ± 7.2

Fall

4

1.8 ± 0.3

2.1 ± 0.1

50.0%

50.0%

44.4 ± 4.8

7.7 ± 0.9

28.0 ± 4.6

9.6 ± 1.6

12.6 ± 1.7

-2.4 ± 2.2

15.6 ± 1.1

9.4 ± 4.0

Annual

24

1.9 ± 0.3

2.8 ± 0.1

36.3%

63.7%

27.3 ± 3.2

4.4 ± 0.6

26.7 ± 4.1

13.2 ± 2.0

-7.1 ± 1.5

36.4 ± 4.1

5.4 ± 1.9

44.3 ± 5.4

1-5 GRSM

MILL

AMS

3

Season

MACA

IMPROVE

C Analysis

Site

SHEN

No.

14

Winter

6

1.9 ± 0.3

2.9 ± 0.1

33.4%

66.6%

39.2 ± 4.1

6.1 ± 0.6

34.0 ± 5.5

13.0 ± 2.1

-15.0 ± 0.7

23.8 ± 2.0

1.3 ± 0.9

38.5 ± 2.9

Spring

9

1.8 ± 0.2

1.8 ± 0.1

24.4%

75.6%

22.6 ± 2.1

2.2 ± 0.2

29.7 ± 4.0

42.4 ± 5.8

-47.0 ± 3.5

50.5 ± 3.2

-24.4 ± 2.1

42.2 ± 6.2

Summer

15

2.9 ± 0.5

3.5 ± 0.1

22.3%

77.7%

14.0 ± 1.5

4.9 ± 0.6

16.1 ± 2.9

18.7 ± 3.4

-11.4 ± 1.1

61.9 ± 4.1

-3.7 ± 1.3

68.0 ± 4.8

Fall

9

2.3 ± 0.4

3.1 ± 0.1

30.8%

69.2%

26.2 ± 2.5

5.1 ± 0.5

31.1 ± 5.1

10.9 ± 1.8

-8.9 ± 0.5

37.0 ± 1.5

4.2 ± 0.6

49.5 ± 2.1

Annual

39

2.2 ± 0.4

2.8 ± 0.1

27.7%

72.3%

25.5 ± 2.7

4.6 ± 0.5

27.8 ± 4.5

21.3 ± 3.6

-20.6 ± 1.9

43.3 ± 2.9

-5.6 ± 1.4

49.6 ± 4.3

Winter

7

1.7 ± 0.3

2.2 ± 0.1

23.1%

76.9%

53.6 ± 5.4

6.5 ± 0.7

25.8 ± 4.3

9.2 ± 1.5

-5.8 ± 0.9

16.5 ± 1.8

4.6 ± 0.9

39.5 ± 2.1

Spring

8

1.4 ± 0.2

2.7 ± 0.1

23.8%

76.2%

26.1 ± 2.4

2.9 ± 0.3

24.5 ± 3.7

35.0 ± 5.2

-36.9 ± 1.7

50.9 ± 2.3

-10.1 ± 2.3

65.7 ± 7.1

Summer

15

2.7 ± 0.5

3.4 ± 0.1

31.9%

68.1%

12.5 ± 1.4

4.7 ± 0.6

21.1 ± 3.7

11.1 ± 2.0

-2.6 ± 1.7

53.3 ± 2.8

0.2 ± 1.4

59.9 ± 3.8

Fall

9

2.4 ± 0.4

3.0 ± 0.1

26.3%

73.7%

22.9 ± 2.5

5.4 ± 0.6

25.6 ± 4.3

7.5 ± 1.4

-2.7 ± 0.7

44.7 ± 2.3

5.8 ± 0.6

55.8 ± 3.2

Annual

39

2.0 ± 0.4

2.8 ± 0.1

26.3%

73.7%

28.8 ± 3.3

4.9 ± 0.6

24.2 ± 4.0

15.7 ± 3.0

-12.0 ± 1.3

41.3 ± 2.3

0.1 ± 1.4

55.2 ± 4.4

Winter

8

6.3 ± 1.1

6.2 ± 0.9

46.4%

53.6%

24.3 ± 3.3

6.4 ± 2.0

16.6 ± 3.7

13.6 ± 3.2

18.9 ± 14.2

23.7 ± 8.9

18.1 ± 12.7

23.2 ± 7.9

Spring

11

3.8 ± 0.4

4.5 ± 0.8

24.3%

75.7%

16.5 ± 1.8

3.1 ± 1.2

12.1 ± 2.0

20.2 ± 3.0

-6.4 ± 3.4

56.2 ± 7.0

2.5 ± 7.5

67.4 ± 13.1

Summer

14

5.8 ± 0.5

5.2 ± 0.5

28.9%

71.1%

8.1 ± 0.9

3.5 ± 0.4

11.1 ± 1.8

8.0 ± 1.0

8.5 ± 1.2

61.6 ± 8.5

6.0 ± 0.9

60.8 ± 8.2

Fall

9

6.3 ± 0.8

5.0 ± 1.3

25.4%

74.6%

17.3 ± 1.8

10.7 ± 3.7

17.2 ± 3.8

9.4 ± 2.5

6.9 ± 1.5

41.7 ± 6.8

12.0 ± 3.6

59.6 ± 8.7

Annual

42

5.6 ± 0.7

5.2 ± 0.9

31.3%

68.7%

16.6 ± 2.1

5.9 ± 2.2

14.2 ± 3.0

12.8 ± 2.6

7.0 ± 7.4

45.8 ± 7.9

9.6 ± 7.6

52.8 ± 9.7

2

2

1. Numbers of samples with acceptable CMB performance statistics (R > 0.8, X < 4.0, Pct Mass < 120%) out of about 50 samples per site. 2. IMPROVE subtracts network mean positive artifact determined from afterfilters. Accelerator Mass Spectrometry TC was determined from high-volume quartz filters that were subtracted for field blanks. 3. Isotopic carbon data reported by Tanner, 2007. Fraction of modern carbon, fm determined as the ratio, (14C/12C)obs/(14C/12C)std. 4. Averages of the individual sample percent mass apportioned normalize to IMPROVE TC. % SCE normalize to AMS TC will be lower by approximately the ratios of the IMPROVE TC/AMS TC. Uncertainties are standard errors of the mean percentage contributions. 5. UCm = fm x TC - ΣPCm and UCf = ff x TC - ΣPCf normalized to IMPROVE or AMS TC.

Veg Burn - Hardwood Meat Cooking Gasoline Unidentified

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Figure 1-1. CMB source contribution estimates of ambient total carbon at the five VISTAS sampling sites.

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Figure 1-2. CMB source contributions to ambient TC for primary sources and estimates of UCf and UCm normalized to IMPROVE TC. Negative UCf were set to zero with offsetting decreases in UCm. Fossil carbon contributions are shown as dark bars and modern carbon contributions are shown as striped bars.

1-7

% Contributions

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Figure 1-3. CAMx source contributions to TC for primary sources, anthropogenic and biogenic SOA. Estimates of particulate organic aerosol were converted to POC using source-specific OM/OC ratios. Fossil carbon contributions are shown as dark bars and modern carbon contributions are shown as striped bars.

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2.

INTRODUCTION

In 1999, the U.S. Environmental Protection Agency announced a major effort to improve air quality in national parks and wilderness areas. The Regional Haze Rule (40 CFR Part 51) requires the states, in coordination with the U.S. Environmental Protection Agency, the National Park Service, U.S. Fish and Wildlife Service, the U.S. Forest Service, and other interested parties, to develop and implement air quality protection plans to reduce the pollution that causes visibility impairment in 156 national parks and wilderness areas. VISTAS (Visibility Improvement State and Tribal Association of the Southeast), the regional planning organization for the southeastern states, is evaluating visibility and sources of fine particle mass in the region and developing the technical basis for state implementation plans. Analysis of existing monitoring and visibility data has shown that sulfate is the dominate contributor to light extinction in the southeastern US and organic carbon (OC) is the second most important cause of light extinction. Particulate organic matter is a complex mixture of directly emitted (primary) particles from different combustion sources and secondary organic aerosols (SOA) from atmospheric transformations of volatile and semi-volatile organic compounds emitted by both anthropogenic and natural sources. Both primary and secondary components of organic air pollutants may partition between the gas and particulate phases with relative amounts that vary by region and season. The primary emissions component of OC in the southeastern US has been attributed in recent studies using the CMB receptor model to mostly vegetative burning and mobile sources, but a large fraction of unexplained OC is presumed to be of secondary origin (Zheng et al., 2002; Yu et al., 2004; Sangil et al., 2007). The spatial and seasonal variations in these source attributions are large and the nature and origin of the unexplained component of ambient particulate carbon are not well understood. Laboratory and theoretical studies of organic PM formation from precursor gases indicate that secondary compounds are comprised of aliphatic and aromatic compounds, including carboxylic acids, alcohols, carbonyls, nitrates, and other single and multifunctional oxygenated compounds. In addition to VOC, both gasoline- and diesel-powered vehicles emit semi-volatile organic compounds (SVOC) that are largely ignored in existing emission inventories. As a result, their potential contributions to the formation of SOA may be underestimated by current air quality simulation models. Although many studies have examined the relationships between source emissions and ambient levels of carbonaceous aerosols in receptor areas, the uncertainties associated with the results obtained by current receptor analysis methods have not been adequately addressed. VISTAS initiated a special monitoring study to provide a better understanding of the relative source contributions to ambient concentrations of OC. 24-hour PM2.5 samples were collected using high volume samplers on quartz fiber filters every third day at five sites (Mammoth Cave National Park, Great Smoky Mountains National Park, Shenandoah National Park, Cape Romain Wildlife Refuge, and Millbrook Station, Raleigh, shown in Figure 1-1) during April 2004 to May 2005. Subsets of samples were selected for carbon analysis assuring that samples on selected dates were available from all sites and that selected samples represented a range of conditions from low to high PM mass. The samples were analyzed by gas chromatography with mass spectrometry (GC/MS) at Desert Research Institute for organic compounds that may be used in subsequent source attribution analysis using the Chemical Mass Balance (CMB) receptor model and Positive Matrix Factorization (PMF). Fractions of the same 2-1

samples were analyzed at Woods Hole Oceanographic Institute’s National Oceanographic Sciences by Accelerator Mass Spectrometry (NOSAMS) for 14C isotope to determine the ratios of modern (vegetative emissions, wood burning, agricultural burning, cooking) to fossil (gasoline, diesel, coal, oil) carbon. The information from this special monitoring study is intended to support the planning process for regional haze in the VISTAS region, as well as to address other regional and local air quality issues. 2.1

Fundamental of Receptor Models

Receptor models have been widely used to estimate the contributions of various sources to measured airborne particulate matter concentrations (Hopke, 1997; Henry, 1997; Watson et al. 2001). The general mass balance receptor model can be stated in terms of the contribution from p independent sources to all measured chemical species in a given sample as follows: p

xik = ∑ g ij f jk + ε ik

(2-1)

j =1

where for airborne particles xik is the kth species concentration (μg/m3) measured in the ith sample, gij is the particulate mass concentration (μg/m3) from the jth source contributing to the ith sample, fjk is the kth specie mass fraction in particles emitted from the jth source, and εik is the model residual. Traditionally, the U.S. EPA has recommended using the effective variance weighted chemical mass balance (CMB) receptor model (Watson et al., 1984) in conjunction with emissions inventories, for making these source contribution estimates. This approach requires knowledge of the number of sources contributing to the observed airborne concentration of particle mass and chemical species, and also the composition of the particles emitted from each source. More recent applications of this method have explored the use of particulate organic tracers (Schauer et al., 1996; Watson et al., 1998; Fujita et al., 1998, Fujita et al., 2007; Lough et al., 2007; Lough and Schauer, 2007). In its most basic form, this model assumes that the composition of the particles does not change from source to receptor. Therefore its ability to resolve secondary particulate matter is limited. The same model (equation 2-1) can be solved for g without prior knowledge of f using several different factor analytic algorithms. In principle, there are an infinite number of possible solutions of equation (1), that is, the model is non-identifiable (Henry, 1987). To lessen this ambiguity, these multivariate algorithms impose positive constraints on F and G. The algorithms used in practice include the UNMIX algorithm (Henry, 2003; Henry et al., 1999), the positive matrix factorization (PMF) algorithm (Paatero, 1997), and the multi-linear engine (ME2) algorithm (Paatero, 1999). The latter two algorithms provide a solution that minimizes an object function based upon the value of each observation and its corresponding uncertainty Paatero, 1997). In practice (Poirot et al., 2001; Kim et al., 2003; Maykut et al., 2003; Polissar et al., 1998; Ramadan et al, 2000; Song et al., 2001; Xie et al., 1999; Yakovleva et al., 1999), the results of the latter two models are scaled to the measured mass concentration by a constant, sj as follows:

2-2

⎛ f jk ⎞ ⎟ xik = ∑ (s j g ij ) ⎜ ⎜ ⎟ j =1 ⎝ sj ⎠ p

(2)

where sj is determined post hoc by regressing measured total PM2.5 mass concentration against gij. These analyses require multiple sampling periods and therefore, unlike CMB, cannot be implemented with only one or a few ambient samples. For a simple, positively constrained bilinear model, there must be at least [n-1] samples that have no or very low impact from each of the n sources (a total of n[n-1] samples) in order to minimize rotational ambiguity. In principle, to the extent that additional constraints can be added to the simple, bilinear model, these samples requirements can be reduced. 2.2

PM Source Apportionment Studies Conducted in the Southeastern U.S.

The Southeastern Aerosol Research and Characterization Study (SEARCH) in coordination with the Atlanta Supersite Study have provided the most recent and comprehensive information on the nature and origin of ambient PM2.5 in the region. The SEARCH measurements were taken at four paired urban-rural sites: Atlanta-Yorkville, BirminghamCentreville, Urban Pensacola-Suburban Pensacola, and Gulfport-Oak Grove. A daily PM2.5 mass and composition data set was collected at all SEARCH sites during the calendar year 1999. Thereafter, filter samples were taken daily only in Atlanta and every third day at the other seven sites, except for the July 2001 and January 2002 Supersite intensives, when daily samples were collected at all sites. The SEARCH data provided the basis for source apportionment analysis by Zheng et al. (2002), Kim et al., (2003) and Liu et al., (2005, 2006). A more recent source apportionment study utilized data from the Speciation Trends Network (STN) sites in the Southeastern U.S. (Lee et al., 2007). Zheng et al. (2002) applied the CMB receptor model to the PM data set from the four paired urban-rural sites during April, July, and October 1999 and January 2000. Source composition profiles used in the CMB analysis included diesel truck, catalyst and noncatalystequipped gasoline vehicles, meat cooking (Hildemann et al, 1991; Schauer et al., 1999a; Schauer et al., 1999b) and vegetative detritus and natural gas combustion (Rogge et al., 1993a; Rogge et al., 1993b). The biomass burning source profile was generated by averaging six source tests of southern hardwoods and softwoods reported by Fine et al. (2002). The paved road dust profile used organic species from Schauer and concentrations of Al and Si from Alabama road dust samples (Cooper, 1981). The major contributors to PM2.5 organic carbon concentrations at the SEARCH sites include wood combustion (25-66%), diesel exhaust (14-30%), meat cooking (512%), gasoline exhaust (0-10%) and smaller contributions of natural gas combustion, paved road dust, and vegetative detritus. The contribution of wood combustion was higher during October and January and unexplained OC concentrations were greater in rural sites and during July, which the authors attribute the likely source to SOA. While almost all of the OC was attributed to primary sources during the winter, 30-70% of OC is attributed to SOA during the summer. The spatial and temporal distributions of primary OC and secondary OC over the continental US during summer 1999 were estimated by using observational OC and EC data from the IMPROVE and SEARCH monitoring sites, coupled with primary OC/EC ratios obtained from air quality modeling using the U.S. EPA Models-3/Community Multiscale Air Quality (CMAQ) model (Yu et al., 2004). Results showed that primary and secondary OC made

2-3

equal contributions over the West and West Pacific areas whereas SOA contributions were dominant in the Northeast (77 ± 3%). The fraction of SOA to total OC at the SEARCH sites are consistent with the high percentage of unexplained PM OC concentrations observed in July by Zheng et al. (2002). Lee et al., (2007) applied CMB to the speciated PM data from 23 STN sites in six southeastern states for the period between January 2002 and November 2003. Most STN sites are located in urban areas and are focused primarily on human health exposure. The source composition profiles for vehicle exhaust were those used in the analysis by Zheng et al. (2002). The biomass burning source profile was generated by averaging six source tests of southern wood reported by Fine et al. (2002). The apportionment of primary OC by CMB was combined with estimates of the relative contributions of primary and secondary OC using the Deming linear least square regression EC tracer method (Chu, 2005). Motor vehicle and biomass burning are the two main primary sources apportioned. Motor vehicles were the greatest primary source contributor in urban areas whereas biomass burning was dominant in less urbanized areas. Biomass burning contributed more in the colder seasons and the contributions of inferred SOA were greatest during the summer. PMF was applied to PM speciation data from the Atlanta Supersite by Kim et al. (2003) consisting of 662 samples and 26 variables (ions, OC, EC and elements). The analysis did not include organic compounds. The PMF-derived factors were associated with eight source categories and were normalized to PMF-apportioned mass concentrations to obtain quantitative contributions for each source. The labeled factors included sulfate-rich secondary aerosol (56%), motor vehicle (22 %), wood smoke (11%), nitrate-rich secondary aerosol (7%), mixed source of cement kiln and organic carbon (2%), airborne soil (1%), metal recycling facility (0.5%) and mixed source of bus station and metal processing (0.3%). PMF was used to identify possible source-related factors contributing to PM2.5 mass at two pairs of urban-rural sites in Georgia (Atlanta-Yorkville) and Alabama (BirminghamCentreville) using SEARCH data for January 2000 to December 2002 (Liu et al., 2005). 19 chemical species (ions, OC, EC and elements) were used in the analysis in addition to PM2.5 mass. Eight factors were resolved for the two urban sites and seven factors for the rural sites. Common factors included: 1) secondary sulfate with strong seasonal variation peaking in summer; 2) nitrate with seasonal peak in winter; 3) “coal combustion/other” with presence of sulfate, EC, OC, and SE; 4) soil with predominance of Al, CA, FE, K, SI and Ti; and 5) wood smoke with high concentrations of EC, OC and water soluble K. The motor vehicle factor with high EC and OC and presence of some soil dust components was found in urban sites, but could not be resolved for the two rural sites. 2.3

Source Composition Profiles of Primary Emission Sources

The main sources of primary carbonaceous aerosols in urban areas include motor vehicle exhaust (diesel and gasoline), wood combustion, and restaurant grills and residential cooking. Inorganic constituents including trace elements, sulfate, nitrate, and ammonium, and total particulate organic carbon (OC) and elemental carbon (EC) are typically measured in PM source apportionment studies. However, source contributions of carbonaceous particles are difficult to distinguish on the basis of these kinds of constituents alone. Elemental and organic carbon are present in motor vehicle exhaust, wood-smoke, and other combustion-related emissions in varying proportions within the same source type. Lowenthal et al. (1992) demonstrated the 2-4

difficulty in distinguishing contributions of gasoline- and diesel-powered vehicles in complex airsheds using traditionally-measured species. Since organic compounds are emitted from all combustion sources certain organic compounds such as polycyclic aromatic hydrocarbons (PAH), hopanes, steranes, sterols, methoxyphenols and other types of organic species have been used to obtain more selective apportionment of various combustion sources. Highly specific molecular markers exist for wood combustion (e.g., levoglucosan and methoxyphenols) and meat cooking (sterols). In contrast, particle emissions from gasoline and diesel-powered vehicles share many molecular markers. The apportionments for these sources are based upon differences in the relative amounts of the molecular markers and elemental carbon. However, the variations in abundances of these markers among profiles within a source category can vary greatly resulting in a range of source contribution estimates and uncertainties depending upon the profiles selected. Ideally, the profiles should be contemporary and geographically representative of the receptor location. In addition to emissions from combustion sources, OC can be directly entrained into the atmosphere by abrasion of leaf and plant wax (during summer months) and decomposition of vegetative detritus (fall). High molecular n-alkanes with strong odd carbon number predominance may serve as markers for this source. The organic compounds most suitable for serving as source tracers in receptor modeling studies should be: •

Emitted in relatively high concentration, to allow small sample sizes and short sampling times;

•

Relatively easy to distinguish from other classes of organic compounds;

•

Relatively easy to identify and quantify on the basis of the chromatographic and spectral properties of its members;

•

Either be chemically stable or of quantifiable reactivity;

•

Emitted in reasonably stable proportion to the amount of PM2.5 and total carbon mass.

•

Uniquely abundant in emission from one or more of the sources of interest.

2.3.1

Motor Vehicle Exhaust

The emission rate and chemical composition of gaseous and particulate pollutants from diesel and gasoline vehicles depend upon many factors, which include vehicle age and mileage, fuel, brand and age of lubricating oil, emission control technology, vehicle operating mode (e.g., cold start, hot stabilized), load, ambient temperature, and state of maintenance. The Desert Research Institute has participated in several relevant studies during the past five years. They include the Kansas City Light-Duty Gasoline Vehicle PM Emissions Characterization Study (Kishan et al., 2007), Gas/Diesel Split Study (Fujita et al., 2007a; Fujita et al., 2007b; Lough et al., 2007a; Lough et al., 2007b) and Heavy-Duty Vehicle Chassis Dynamometer Testing for Emission Inventory, Air Quality Modeling, Source Apportionment, and Air Toxic Inventory (CRC E-55), EC Diesel Fuel Emission Characterization Study (Lev-On et al., 2002) and Comparative Toxicity Study (Zielinska et and Sagebiel., 2001). These studies were preceded by earlier investigations by DRI such as the Characterization of PM Emissions from DoD Sources (Zielinska et al., 2002), Northern Front Range Air Quality Study (Watson et al., 1998; Fujita et

2-5

al., 1998; Zielinska et al., 1998) and several highway tunnels studies. Data from some of these and similar studies by other researchers (e.g., Schauer et al., 1996; Schauer et al., 1999) as well as many other studies (over 140 literature reports) have been compiled recently for the Coordinating Research Council in Project E-75. The project report and assembled database are under review and are not currently available. This database is simply a compilation of the available regulated and unregulated emissions from diesel vehicles and composite profiles were not derived as part of this project. Organic carbon and elemental carbon are the most abundant particle species in motor vehicle exhaust, accounting for over 95% of the total PM mass. The abundances of organic and elemental carbon can be quite variable in motor vehicle exhaust profiles. Elemental carbon is dominant in diesel exhaust, but is lower in newer technology diesel engines. The abundance of EC is generally less at lower engine load. While most gasoline vehicles are relatively clean, especially in hot-stabilized mode, high emitters can have particulate emission rates that are comparable to or exceed most diesel vehicles. We have found that gasoline vehicles emit higher amounts of elemental carbon during cold starts and during hard accelerations. Gasoline exhaust measured during the NFRAQS (Watson et al, 1998) had an average split of 75% organic carbon and 25% elemental carbon with higher abundance of EC during cold starts (based on TOR/IMPROVE carbon measurements). Because of the variability of OC/EC splits, gasoline and diesel vehicles cannot be apportioned by carbon analysis alone, and EC is not a unique tracer for diesel exhaust. Hopanes and steranes are present in lubricating oil with similar composition for both gasoline and diesel vehicles and are not present in gasoline or diesel fuels. Emission rates of hopanes and steranes are the highest for both gasoline and diesel “high emitting” vehicles. While hopanes and steranes are useful markers for motor vehicle emission, they cannot be used to distinguish between gasoline and diesel exhaust. Data from NFRAQS, NREL Comparative Toxicity Study, Gas/Diesel PM Split, and Kansas City Vehicle PM Emissions Characterization Study show that gasoline vehicles emit certain PAHs in greater abundance relative to other PAHs than do diesel vehicles. Gasoline vehicles, whether low or high emitter, emit greater amounts of high molecular-weight particulate PAHs (e.g., benzo(b+j+k)fluoranthene, benzo(ghi)perylene, ideno(1,2,3-cd)pyrene, and coronene). These PAHs are found in used gasoline motor oil, but not in fresh oil and not in diesel engine oil. Diesel vehicles also emit particulate PAHs, but in lower relative proportions relative to other PAHs, especially the semi-volatile methylated PAHs. Diesel emissions also contained higher proportions of dimethylnaphthalenes, methyl- and dimethylphenanthrenes, and methylfluorenes. These compounds are distributed between the gas and particle phase and thus require back-up traps to be quantitatively collected. 2.3.2

Vegetative Burning

A wide range of volatile, semi-volatile and particulate organic compounds (McDonald et al., 2000; Schauer et al. 2001) are emitted from wood combustion by the release of resinous compounds and decomposition of cellulose, hemicelluloses and lignin. The emission rates and chemical composition of wood combustion depend on many factors related to burn conditions (burn rate, wood moisture content), fuels (hardwood versus softwood), and type of appliance (catalytic and noncatalytic woodstove versus fireplace). Particulate carbon emissions are predominantly organic with OC/EC ratios for softwood of about 4 compared to 8 to 9 for hardwood. Several organic compounds have been proposed as tracer for wood smoke aerosols,

2-6

including resin acids, retene, and methoxylated phenols (Edye and Richards, 1991; Ramdahl, 1983; Hawthorne et al., 1988, 1989; Standley and Simoneit, 1990; Simoneit et al., 1993; and Simoneit and Mazurek, 1982). Resin acids are biosynthesized mainly by conifers (gymnosperms) in temperate regions. The unaltered resin acids, such as dehydroabietic, abietic or pimaric acid were found in coniferous (mostly pine) wood smoke but were not detected in deciduous tree wood smoke (Rogge, 1993 and references therein). Retene (1-methyl-7-isopropylphenanthrene) is most probably derived by thermal degradation of diterpenoid resin acid, abietic acid (Ramdahl, 1983 and references therein). Retene was proposed as a unique molecular marker for coniferous wood combustion (Ramdahl, 1983). Guaiacol (2-methoxyphenol), syringol (2,6 dimethoxyphenols) and their derivatives are commonly found in wood burning emissions. Upon combustion of wood, lignin breakdown products include hydroxylated and methoxylated phenols that often preserve the original substituents on the phenyl ring. Phenolic compounds with two substituents are typically favored in pine smoke, whereas phenolic compounds with three substituents are mainly found in oak wood smoke (Rogge et al., 1993). Hawthorne et al. (1989) analyzed the samples collected near chimneys where different woods were being burned for 27 different organic compounds and recommended the guaiacol and syringol series of methoxylated phenols as reliable markers. The guaiacol series was fairly consistent no matter what type of wood was being burned, while the syringol series was almost two orders of magnitude higher in hardwoods. These wood lignin pyrolysis products are emitted in distinctive amounts and constitute as much as 21 percent of the total fine particle mass emissions (MacDonald et al., 2000). A study of high volume samples collected in urban areas (Miller et al., 1990) showed that methoxyphenols averaged 24% of the total organics in the samples. The samples were collected in cities and showed more methoxyphenols in residential neighborhoods than in business districts. Parts of these samples were analyzed for 14C, and the percent of guaiacol was correlated with the percent of 14C. The predominant PAHs in wood smoke emissions are typically acenaphthylene, naphthalene, anthracene, phenanthrene, benzo[a]pyrene, and benzo[e]pyrene (Khalil et al. 1983). Other methylated phenanthrene derivatives, dimethylphenanthrene isomers, can also be utilized as potential markers for wood combustion. Ambient particulate matter samples collected in Boise, ID, were apportioned between motor vehicle emissions and residential wood combustion (Benner et al., 1995) as part of the U.S. EPA Integrated Air Cancer Project. It was observed that 1,7-dimethylphenanthrene was the most prominent dimethylphenanthrene in the samples while, in the Baltimore Harbor Tunnel, the 1,7-isomer was present at comparable levels to other dimethylphenanthrene (DMPs). The enrichment of the 1,7-isomer with respect to other DMPs in the Boise samples, attributed to input from soft wood burning, was used in calculating an enrichment factor representing the multiplier of the residential wood combustion contribution over that of motor vehicles. The resulting apportionments using the enrichment of 1,7-DMP were compared with results obtained by radiocarbon measurements (14C/13C) of the same extracts from Boise, with good correlations between the two techniques, suggesting that the methods are comparable when used to distinguish emissions of motor vehicles from residential wood combustion of soft woods. Levoglucosan (1,6-anhydro-β-D-glucose) is a product of the decomposition of cellulose, which is the primary structural component of green plants. The emission rate of levoglucosan per kg of biomass burned can be substantially higher than other organic species with a reported 2-7

particulate emission rate of 40-1200 mg kg-1 of wood burned compared to 0.1-3 mg kg-1 of benzo[a]pyrene (Locker, 1988). Consequently, levoglucosan has been commonly used as a tracer for vegetative burning in source apportionment studies. However, the abundances of levoglucosan can vary substantially depending on the vegetative fuel that is burned. The abundance of levoglucosan in PM emissions from vegetative burning is much higher for pine needles and grasses than for wood. This has important implications for source attribution of ambient PM samples that contain contributions particulate carbon from wildfires and prescribed burns. PM emissions from such fires can vary due differences in fuel, season, time of day, and the nature of the combustion. The differences in emission rates and chemical composition of emissions from combustion of wildland fuels and residential woods were examined by Mazzoleni et al. (2007). A high degree of variability was found in the emissions of OC, EC, levoglucosan, methoxy phenols, and organic acids. The variability of emissions of levoglucosan did not correlate with PM2.5 gravimetric mass and may affect source apportionment estimates. Residential wood combustion emissions contained much lower abundances of levoglucosan than emissions from combustion of wildland fuels. Using profiles for residential wood combustion in CMB may lead to significant overestimation of the contributions of biomass combustion emissions in ambient samples affected by wildland fires or prescribed burns. Another important factor to consider in receptor modeling is the variation in phase distribution of semi-volatile organic compounds in both source and ambient samples. The distributions of the key guaiacol species between the filters and the backup adsorbent traps showed that guaiacol and 4-methylguaiacol are almost exclusively in the vapor phase. Other guaiacol derivatives are distributed between the vapor and particulate phases, with the least volatile associated exclusively with the filters. Retene also partitions between gas and particle phases. Thus, the use of a filter followed by solid adsorbent is necessary to account for total ambient concentration of these compounds. Semi-volatile organic compounds should not be used in receptor modeling unless both source and ambient samples are collected with both filter and adsorbent trap. Ambient PM samples are commonly collected on filters only. In such case, only organic species with sufficiently low vapor pressures should be used in receptor modeling to avoid the effects of partial partitioning to the gas phase during the warmer seasons. This has direct implications for the VISTAS samples that were collected by high volume samplers on filters without adsorbent trap. 2.3.3

Meat Cooking

Meat charbroiling and frying may be significant sources of carbonaceous particles in urban areas. The emissions from meat cooking depend strongly on the cooking method used, fat content of the meat, and the type of grease eliminator used in the cooking facilities. It has been reported (Rogge et al., 1991; Rogge, 1993) that charbroiling regular hamburger meat produces up to 40 g of fine aerosol per 1 kg of meat cooked, whereas charbroiling of extra-lean meat produces only 7 g/kg. Frying the same two types of meat produces only 1 g of fine aerosol per 1 kg of meat. The identified compounds belong to the following compound classes: alkanes, alkanoic and alkenoic acids, dicarboxylic acids, alkanals and alkenals, ketones, alkanols, furans, lactones, amides, nitriles, polycyclic aromatic hydrocarbons, pesticides, and steroids. Among these compound classes, alkanoic and alkenoic acids (normal and unsaturated fatty acids) are the most abundant. Charbroiling produces a much higher amount of these compounds and their emission rates increase with the increasing fat content of the meat. Palmitic (hexadecanoic) acid and oleic (cis-9-octadecenoic) acid are emitted in the highest quantities. However, these fatty 2-8

acids can be emitted from other sources in addition to meat cooking; they are the major constituents of seed oils that are widely used in all cooking processes, not only meat cooking. Analysis of meat smoke aerosol show that certain fatty acids (e.g. palmitic acid, stearic acid and oleic acid) as well as cholesterol could be used as possible markers for meat smoke (Schauer et al., 1999; McDonald et al., 2003). Long-chain γ-lactones are formed by lactonization of β-hydroxy fatty acids normally found in triacylglycerols. They also result from the oxidation of alkenals and oleic acid. These compounds are emitted in small amounts relative to PM2.5, but may be useful molecular markers for meat cooking. Furans and thiophenes are important flavor constituents of cooked beef (Baines and Mlotkiewicz, 1983; Bailey, 1983). Since the furan-type compounds are derived from the non-fatty portion of the beef, their emission rates are independent of the fat content of the meat. Furans, which to our knowledge are not emitted from other sources, could be important tracers for meat cooking. Two furans, namely 2-pentylfuran and 3-methyltetrahydrofuran, were identified in small quantities in aerosols emitted during meat cooking (Rogge et al., 1991; Rogge, 1993). Thiophenes were not identified in either Rogge study. However, both furans and thiophenes have relatively high vapor pressure and, under ambient conditions, should be present predominantly in the gas phase, not in the aerosol phase. Polycyclic aromatic hydrocarbons (PAH) are reported to be emitted in limited quantities during meat cooking, with charbroiling producing more particle-bound PAH than frying (approximately 5-fold increase — Rogge et al., 1991; Rogge, 1993). Emissions of PAHs from meat cooking are dominated by 2-3 ring unsubstituted compounds, especially naphthalene, acenapthtylene, phenanthrene, and fluorene. 2.3.4

Vegetative Detritus

It has been reported (Rogge et al., 1993; Simoneit, 1989) that carbon preference index (CPI) is a measure of biologically synthesized compounds. Rogge et al. (1993) suggest that the groups of n-alkanes in the range of C27 to C33 with their strong odd carbon number predominance may serve as markers for green and dead leaf abrasion products released to the atmosphere. Gasoline and diesel powered vehicles emit n-alkanes mainly in the carbon range