Top Banner
Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study Judith C. Chow * , John G. Watson, Hampden Kuhns, Vicken Etyemezian, Douglas H. Lowenthal, Dale Crow, Steven D. Kohl, Johann P. Engelbrecht, Mark C. Green Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512-1095, USA Received 3 February 2003; received in revised form 18 July 2003; accepted 31 July 2003 Abstract Representative PM 2:5 and PM 10 source emissions were sampled in Texas during the Big Bend Regional Aerosol Visibility and Observational (BRAVO) study. Chemical source profiles for elements, ions, and carbon fractions of 145 samples are reported for paved and unpaved road dust, soil dust, motor vehicle exhaust, vegetative burning, four coal- fired power stations, an oil refinery catalytic cracker, two cement kilns, and residential meat cooking. Several samples were taken from each emitter and source type, and these were averaged by source type, and in source subgroups based on commonality of chemical composition. The standard deviation represents the variability of the chemical mass fractions. BRAVO profiles differed in some respects from profiles measured elsewhere. High calcium abundances in geological dust, high selenium abundances in coal-fired power stations, and high antimony abundances in oil refinery catalytic cracker emissions were found. Abundances of eight thermally evolved carbon fractions [Atmos. Environ. 28 (15) (1994) 2493] differ among combustion sources, and a Monte Carlo simulation demonstrates that these differences are sufficient to differentiate among several carbon-emitters. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Source profiles; Chemical mass balance receptor model; BRAVO; Carbon fractions; Combustion emissions 1. Introduction The big bend regional aerosol and visibility observa- tional (BRAVO) study (Green et al., 2000) was designed to understand the long-range, transboundary transport of visibility-reducing particles from regional sources in the US and Mexico and to quantify the contributions of specific US and Mexican source regions and source types responsible for poor visibility at Big Bend National Park in Texas. Analyses conducted during the study design phase identified several sources of particular interest: the Carbon I and Carbon II coal-fired power plants located about 270 km east-southeast of Big Bend National Park, coal-fired power plants along the lignite belt in Texas, and other urban and industrial sources in east Texas and northern Mexico. The field study conducted from July 1999 through October 1999 included many gaseous, aerosol, meteorological, and optical measurements as well as emission source sampling and analysis. The study also included release of perfluorocarbon gases at four locations, a network of 37 PM 2:5 and SO 2 samplers, and 24 perfluorocarbon sampling sites collocated with the * Corresponding author. Tel.: +1-775-674-7050; fax: +1-775- 674-7009. E-mail address: [email protected] (J.C. Chow). 0045-6535/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.chemosphere.2003.07.004 Chemosphere 54 (2004) 185–208 www.elsevier.com/locate/chemosphere
24

Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Jan 10, 2023

Download

Documents

Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Chemosphere 54 (2004) 185–208

www.elsevier.com/locate/chemosphere

Source profiles for industrial, mobile, and area sourcesin the Big Bend Regional Aerosol Visibility

and Observational study

Judith C. Chow *, John G. Watson, Hampden Kuhns, Vicken Etyemezian,Douglas H. Lowenthal, Dale Crow, Steven D. Kohl, Johann P. Engelbrecht,

Mark C. Green

Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512-1095, USA

Received 3 February 2003; received in revised form 18 July 2003; accepted 31 July 2003

Abstract

Representative PM2:5 and PM10 source emissions were sampled in Texas during the Big Bend Regional Aerosol

Visibility and Observational (BRAVO) study. Chemical source profiles for elements, ions, and carbon fractions of 145

samples are reported for paved and unpaved road dust, soil dust, motor vehicle exhaust, vegetative burning, four coal-

fired power stations, an oil refinery catalytic cracker, two cement kilns, and residential meat cooking. Several samples

were taken from each emitter and source type, and these were averaged by source type, and in source subgroups based

on commonality of chemical composition. The standard deviation represents the variability of the chemical mass

fractions. BRAVO profiles differed in some respects from profiles measured elsewhere. High calcium abundances in

geological dust, high selenium abundances in coal-fired power stations, and high antimony abundances in oil refinery

catalytic cracker emissions were found. Abundances of eight thermally evolved carbon fractions [Atmos. Environ. 28

(15) (1994) 2493] differ among combustion sources, and a Monte Carlo simulation demonstrates that these differences

are sufficient to differentiate among several carbon-emitters.

� 2003 Elsevier Ltd. All rights reserved.

Keywords: Source profiles; Chemical mass balance receptor model; BRAVO; Carbon fractions; Combustion emissions

1. Introduction

The big bend regional aerosol and visibility observa-

tional (BRAVO) study (Green et al., 2000) was designed

to understand the long-range, transboundary transport

of visibility-reducing particles from regional sources in

the US and Mexico and to quantify the contributions of

specific US and Mexican source regions and source types

*Corresponding author. Tel.: +1-775-674-7050; fax: +1-775-

674-7009.

E-mail address: [email protected] (J.C. Chow).

0045-6535/$ - see front matter � 2003 Elsevier Ltd. All rights reserv

doi:10.1016/j.chemosphere.2003.07.004

responsible for poor visibility at Big Bend National Park

in Texas. Analyses conducted during the study design

phase identified several sources of particular interest: the

Carbon I and Carbon II coal-fired power plants located

about 270 km east-southeast of Big Bend National Park,

coal-fired power plants along the lignite belt in Texas,

and other urban and industrial sources in east Texas and

northern Mexico. The field study conducted from July

1999 through October 1999 included many gaseous,

aerosol, meteorological, and optical measurements as

well as emission source sampling and analysis. The study

also included release of perfluorocarbon gases at four

locations, a network of 37 PM2:5 and SO2 samplers, and

24 perfluorocarbon sampling sites collocated with the

ed.

Page 2: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

186 J.C. Chow et al. / Chemosphere 54 (2004) 185–208

aerosol sites. Extensive data analysis and modeling is

being performed; a final report is expected in 2003.

Particulate source profiles (the mass fractions of

designated chemical species) from primary emissions are

needed: (1) to create speciated emissions inventories

used in source-oriented models, (2) as input to receptor-

oriented source attribution models, and (3) to estimate

toxic and hazardous pollutant emissions. US EPA

emission inventory guidance (US EPA, 1999a) requires

speciation, and modeling guidance (US EPA, 2001) uses

a ‘‘weight of evidence’’ approach that normalizes mod-

eled source contributions to the geological, organic

carbon (OC), elemental carbon (EC), sulfate (SO¼4 ), and

nitrate (NO�3 ) components of speciated ambient receptor

samples. The Regional Haze Rule (US EPA, 1999b;

Chow, 2002; Chow et al., 2002a,b; Watson, 2002a,b)

places great reliance on comparable speciated measure-

ments at source and receptor. Receptor models that

derive profiles from ambient measurements (Hopke,

1999; Chow and Watson, 2002; Watson et al., 2002;

Brook et al., 2003) require compatible source measure-

ments for verification.

The profiles in SPECIATE (US EPA, 1999c) are

mostly for tests taken before 1990, even though profiles

representing modern source technology, fuels, and op-

erating conditions have been published. Comparisons of

BRAVO profiles and those from similar SPECIATE

source types are made below. In addition to commonly

measured elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti,

V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr,

Y, Zr, Mo, Pd, Ag, Cd, In, Sn, Sb, Ba, La, Au, Hg, Tl,

Pb, U), ions (water-soluble chloride [Cl�], NO�3 , SO

¼4 ,

sodium [Naþ], potassium [Kþ], and ammonium [NHþ4 ]),

and OC and EC fractions (OC1, OC2, OC3, OC4, OP

[pyrolyzed carbon], EC1, EC2, EC3), specific organic

compounds (e.g., polycyclic aromatic hydrocarbons

[PAHs], hopanes, steranes, guaiacols, syringols, organic

acids, lactones, alcohols, sugars, etc.), isotopic abun-

dances (e.g., 34S, 14C), and individual particle properties

(e.g., morphology, size, elemental composition) are

showing promise to differentiate among source types

and subtypes.

This paper adds to current knowledge of chemical

source profiles for geological material, motor vehicle

exhaust, vegetative burning, coal-fired boilers, oil refin-

ery catalytic cracker, cement kilns, and meat cooking.

These profiles are contemporary, from tests taken in

1999 and 2000. They are specific to the PM2:5 size frac-

tion as well as providing PM2:5, PM10, and PMcoarse

(difference between PM2:5 and PM10) profiles for geo-

logical material. They include species measured at IM-

PROVE (Interagency Monitoring of Protected Visual

Environments) and EPA speciation trends network

PM2:5 monitoring sites, as well as water-soluble Kþ and

Naþ (which are markers for vegetative burning and sea

salt, respectively), and eight thermally derived carbon

fractions that may further discriminate among different

source contributions (Chow et al., 1993, 2001; Watson

et al., 1994).

2. Methods

The potential source types that contribute to PM

concentrations in southwest Texas were identified from

emission inventories and observations. Source samples

were taken from 09/23/99, through 02/25/00, to repre-

sent: (1) Texas fugitive dust (e.g., paved roads, unpaved

roads, and soil) (termed geological material), (2) motor

vehicle exhaust from gasoline- and diesel-fueled vehicles

operated in Texas and Mexico, (3) wildfires simulated by

controlled burns of wood and grass typical of Big Bend

National Park, (4) coal-fired power station effluent and

coal fly ash, (5) a refinery fluidized bed catalyst cracker,

(6) cement kilns fired by coal, coke, scrap tires, and used

oil filters, and (7) residential meat cooking.

Hot stack, dilution stack, airborne plume, ground-

based source-dominated, and grab/resuspension sam-

pling methods have been applied to source profile

characterization (Gordon et al., 1984; Chow et al., 1988;

Hildemann et al., 1989; Houck, 1991; Watson and

Chow, 2001; Watson et al., 2001a, 2002). Dilution stack,

ground-based source-dominated, and grab/resuspension

methods were used for these profiles. Geological samples

were obtained by the grab/resuspension method (Chow

et al., 1994; Carvacho et al., 1996). Geological source

locations representing various soil types and land uses

were chosen from US Department of Agriculture Soil

Conservation Service maps. Site surveys around BRAVO

sampling sites and in major Texas cities identified

additional sampling locations. Samples included paved

road dust from busy traffic intersections, unpaved roads,

and soil. Fifteen geological samples and three fly ash

samples from the electrostatic precipitator residue of a

coal-fired power station were collected by grab sampling

or vacuuming at the locations shown in Fig. 1. Samples

were air-dried in the laboratory under a low-relative-

humidity (approximately 20–30%) atmosphere, sieved

through a Tyler 400-mesh screen (<38 lm geometric

diameter), resuspended in a laboratory chamber, and

collected on filters through PM2:5 and PM10 impactor

inlets at a flow rate of 10 l/min (Chow et al., 1994).

The ground-based source-dominated method ob-

tained motor vehicle exhaust samples at seven locations

in San Antonio and Laredo, Texas (Fig. 1). A portable,

groundbased sampling system was located on a median,

sidewalk or shoulder within 2 m of the nearest high

density traffic lane, with the sampling inlet placed at 1.5

m above ground level. The sampler consisted of a Ben-

dix/Sensidyne 240 cyclone followed by parallel 47-mm

Teflon-membrane and quartz-fiber filter channels. The

flow rate through each channel was 70 l/min, resulting

Page 3: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Fig. 1. Geological and roadside motor vehicle sampling locations for the BRAVO study (source profile mnemonics are noted in

parentheses).

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 187

in a d50 cut point of 2.25 lm. Thirty-four samples of ap-

proximately 2-h sampling durations were obtained be-

tween 09/24/99 and 12/11/99 during morning (0630–0830

CST), noon (1000–1400 CST), late afternoon (1600–

1800 CST), and evening (1800–2400 CST) rush hours.

The Laredo samples contained a larger fraction of

exhaust from Mexican vehicles and fuels than do the

samples from San Antonio, as evidenced by a large

number of vehicles with Mexican license plates. The

sampling locations, dates, periods, and mix of heavy- and

light-duty gasoline and diesel vehicles are presented in

Table 1. Most (�90%) were light-duty, gasoline-powered

Page 4: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 1

Summary of roadside sampling of motor vehicle emissions

Mnemonic Sampling location Sampling

date

Sampling

period

(MST)

10-minute-interval traffic counts (number of vehicles)

Light duty

gasoline

vehicles

Heavy duty

gasoline

vehicles

Heavy duty

diesel vehicle

counts/

% Tot.

Buses,

motorcycles,

RVs/camp-

ers, and other

vehicles

Total number

of vehicles

BVRSMV01 Rittiman and I-410, San Antonio, TX 09/24/99 0642 to 0742 630 0 19/2.9 5 654

BVRSMV02 Rittiman and I-410, San Antonio, TX 09/24/99 0751 to 0851 379 14 14/3.4 1 408

BVRSMV12 Rittiman and I-410, San Antonio, TX 10/18/99 0945 to 1351 1443 16 195/11.8 1 1655

BVRSMV35 Rittiman and I-410, San Antonio, TX 10/18/99 1500 to 1800 1782 10 126/6.6 0 1918

BVRSMV14 Rittiman and I-410, San Antonio, TX 10/23/99 1040 to 1345 1476 4 88/5.6 7 1575

BVRSMV15 Rittiman and I-410, San Antonio, TX 10/23/99 1345 to 1700 1064 3 55/4.9 1 1123

BVRSMV32 Pecos La Trinidad and Commerce,

San Antonio, TX

09/24/99 1056 to 1226 101 0 4/3.8 1 106

BVRSMV04 Pecos La Trinidad and Commerce,

San Antonio, TX

09/24/99 1232 to 1401 103 0 2/1.8 4 109

BVRSMV33 Pecos La Trinidad and Commerce,

San Antonio, TX

09/24/99 1426 to 1556 136 0 0/0 6 142

BVRSMV34 Pecos La Trinidad and Commerce,

San Antonio, TX

09/24/99 1606 to 1734 303 2 29/8.7 0 334

BVRSMV41 Pecos La Trinidad and Commerce,

San Antonio, TX

10/23/99 1035 to 1340 443 1 33/6.9 0 477

BVRSMV42 Pecos La Trinidad and Commerce,

San Antonio, TX

10/23/99 1340 to 1640 526 1 31/5.5 1 559

BVRSMV06 I-410 and I-35, San Antonio, TX 09/25/99 0736 to 0846 487 0 15/3.0 3 505

BVRSMV07 I-410 and I-35, San Antonio, TX 09/25/99 0852 to 0955 530 10 8/1.4 4 552

BVRSMV08 I-410 and I-35, San Antonio, TX 09/25/99 1001 to 1114 578 9 12/2.0 0 599

BVRSMV09 I-410 and I-35, San Antonio, TX 10/18/99 0615 to 1000 659 9 3/0.4 1 672

BVRSMV10 I-410 and I-35, San Antonio, TX 10/18/99 1000 to 1400 506 1 22/4.2 1 530

BVRSMV11 I-410 and I-35, San Antonio, TX 10/18/99 1400 to 1750 579 0 27/4.5 0 606

BVRSMV16 Commerce and I-10, San Antonio, TX 10/20/99 1230 to 1500 591 1 39/6.2 1 632

BVRSMV17 Commerce and I-10, San Antonio, TX 10/20/99 1500 to 1749 554 0 33/5.6 2 589

BVRSMV18 Commerce and I-10, San Antonio, TX 10/21/99 0628 to 0915 348 0 38/9.8 0 386

BVRSMV19 Laredo Bridge #2, TX/Mexico border 12/08/99 1550 to 1740 392 7 47/10.5 0 446

BVRSMV20 Laredo Bridge #2, TX/Mexico border 12/10/99 2115 to 2350 355 7 44/10.8 0 406

BVRSMV37 Laredo Bridge #2, TX/Mexico border 12/11/99 1155 to 1510 541 5 89/14.0 0 635

BVRSMV36 Laredo Bridge #2, TX/ Mexico border 12/11/99 1520 to 1720 174 1 13/6.9 0 188

BVRSMV22 Laredo Bridge #2, TX/Mexico border 12/11/99 1725 to 1930 398 0 3/0.75 1 402

BVRSMV38 Laredo Bridge #2, TX/Mexico border 12/11/99 1930 to 2120 429 0 0/0 0 429

188

J.C.Chowet

al./Chem

osphere

54(2004)185–208

Page 5: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

BVRSMV25

SW

ofSanAntonio,TX

10/21/99

1140to

1430

661

360/8.3

0724

BVRSMV39

SW

ofSanAntonio,TX

10/21/99

1437to

1750

831

044/5.0

0875

BVRSMV40

SW

ofSanAntonio,TX

10/22/99

0630to

0930

621

169/10.0

0691

BVRSMV28

I-35andCarlton,Laredo,TX

12/08/99

1330to

1615

1397

14

165/10.5

01576

BVRSMV29

I-35andCarlton,Laredo,TX

12/08/99

1615to

1900

1296

14

120/8.4

01430

BVRSMV31

I-35andCarlton,Laredo,TX

12/11/99

1245to

1445

1029

758/5.3

01094

BVRSMV30

I-35andCarlton,Laredo,TX

12/11/99

1445to

1645

706

318/2.5

0727

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 189

vehicles, although the cross-border diesel truck volume

was larger in Laredo than in other parts of Texas.

Vegetative burning profiles were obtained by ground-

based source-dominated sampling in the plumes of small

controlled burns of wood debris at Big Bend National

Park. Most of the vegetative cuttings were collected as

part of a habitat restoration project that removed non-

native trees and shrubs from a high-elevation section of

the park that had been used for grazing cattle. These

areas were often subject to wildfires. Fuels included

three abundant species from the area (mesquite, tama-

risk, and huisache), native dry grass, and pine fence

posts treated with creosote. The wood was dried for

several months prior to burning. Approximately 5–10 kg

of fuel was lit using a torch and allowed to burn for 10

min prior to sampling. The sampler inlet was inserted

into the plume �5 m downwind of the fires where the

temperature was slightly above ambient. Twenty-one

simulated wildfire samples of 10–15 min duration each

were acquired.

Dilution stack sampling (Hildemann et al., 1989) was

applied to four coal-fired power plant boilers, a petro-

leum refinery catalytic cracker, and two cement kilns

burning coal, coke, scrap tires, woodchips, and used oil

filters. Between tests of different source types, the dilu-

tion tube and aging chamber were cleaned with a water

power spray followed by an acetone spray, then wrap-

ped with heating blankets, and baked at 150 �C for at

least eight hours with filtered air flowing through it.

Emissions were withdrawn from the stack or duct

through an isokinetic button hook nozzle, then through

a heated copper line to a 12.5 cm diameter · 2.7 m length

u-tube where the exhaust was mixed with clean dilution

air at ambient temperature under turbulent flow condi-

tions. Ambient dilution air was filtered through a high

efficiency particulate air (HEPA) filter to remove parti-

cles and through an activated carbon bed to remove gas-

phase organics. Dilution ratios ranged from 9 to 60, with

a typical ratio of 30 parts of clean air to each part of

exhaust. The diluted air was aged for �80 s to allow for

condensation, coagulation, and rapid reactions to occur

prior to being drawn through three Bendix/Sensidyne

cyclones (2.5 lm cutpoint at 113 l/min) to a multi-port

sampling manifold that accommodated different collec-

tion substrates.

Twenty-six coal-fired boiler samples were acquired

from three electrical generating stations that supplied

power for domestic use and from one station that sup-

plied power to an aluminum processing facility. Exhaust

emission controls included a baghouse at one plant, a

baghouse backed up by a wet limestone scrubber at

another plant, an electrostatic precipitator followed by a

baghouse at a third plant, and a dry limestone scrubber

to remove acidity and fluorine from added potliner

material at the aluminum facility. Bottom fly ash sam-

ples were acquired from three of the power plants to

Page 6: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

190 J.C. Chow et al. / Chemosphere 54 (2004) 185–208

represent a potential fugitive dust source resulting from

ash disposal.

Five samples were taken of emissions from a petro-

leum refinery’s heavy oil catalytic cracker unit equipped

with an electrostatic precipitator. Six samples were taken

of stack emissions from a Texas cement kiln equipped

with an electrostatic precipitator and fueled by coal,

coke, and scrap tires. Five samples were taken of stack

emissions from another Texas cement kiln equipped

with a baghouse and fueled by coal, wood chips, and

used oil filters. Depending on particle loadings, sampling

durations varied from 240 to 1190 min at the coal-fired

power plant boilers, from 300 to 960 min at the refinery,

and from 180 to 900 min at the cement kilns. Dilution

ratios varied from 9 to 60 at the coal-fired power plant

boilers, from 30 to 60 at the refinery, and from 27 to 54

at the cement kilns.

Meat cooking tests were conducted under controlled

conditions at the University of California at Riverside’s

College of Engineering––Center for Environmental

Research and Technology’s (CE-CERT) commercial

kitchen test facility, following its standard testing

methods (Norbeck, 1997). Test protocols specify the

properties of the meats (e.g., fat content, moisture con-

tent, thickness and diameter), cooking conditions (e.g.,

grate and griddle temperature, load capacity), and

cooking procedure (e.g., method of loading the meat,

cooking time) (Welch, 1997). Meat was obtained from a

local supermarket, and its fat and moisture content were

measured (AOAC, 2000). The cooking exhaust was

ducted through cleaned grease baffles in a ventilation

hood operating at a velocity of 8 m/s, corresponding to a

flow rate of 11.3 m3/min. The exhaust stream was iso-

kinetically sampled by the dilution sampler and mixed

with 25–28 times its volume of clean air. Sampling du-

rations ranged from 60 to 152 min for 12 tests with

charcoal and mesquite smoked chicken, propane grilled

chicken, charcoal grilled hamburger, and Chinese-style

pan stir-fry.

Samples were acquired from two parallel sampling

channels fitted with Teflon-membrane and quartz-fiber

filter packs. In the first channel, a Teflon-membrane

front filter (2 lm pore size, R2PJ047, Pall Life Sciences,

Ann Arbor, MI) was followed by a potassium-carbon-

ate-impregnated cellulose-fiber filter (31ET, Whatman,

Clifton, NJ). The Teflon filter was analyzed for mass by

gravimetry and for 40 elements by X-ray fluorescence

spectrometry (XRF, Watson et al., 1999). Sulfur dioxide

(SO2) absorbed by the cellulose-fiber backup filter was

analyzed as SO¼4 by ion chromatography (Chow and

Watson, 1999). In the second channel, a quartz-fiber

front filter (2500 QAT-UP, Pall) was followed by a

citric-acid-impregnated cellulose-fiber filter (Whatman

41ET). The quartz filter was analyzed for Cl�, NO�3 , and

SO¼4 by ion chromatography; for NHþ

4 by automated

colorimetry; for water-soluble Naþ and Kþ by atomic

absorption spectrophotometry; and for OC, EC, and

eight organic, elemental, and pyrolyzed carbon frac-

tions following the IMPROVE thermal/optical protocol

(Chow et al., 1993, 2001). The organic carbon fractions,

OC1–OC4, were measured in a pure helium atmosphere

at 120, 250, 450, and 550 �C, respectively, and the ele-

mental carbon fractions, EC1–EC3, were measured in a

2% oxygen, 98% helium atmosphere at 550, 700, and 800

�C, respectively. A He–Ne laser beam (633 nm) was used

to monitor filter reflectance for pyrolysis correction.

Ammonia (NH3) absorbed on the cellulose-fiber backup

filter was analyzed as NHþ4 by automated colorimetry.

The PM2:5 (fine) fraction was collected for all sources.

The PM10 fraction was also collected and chemically

characterized for the resuspension (geological and fly

ash) samples and a ‘‘coarse’’ fraction was determined as

the difference between PM10 and PM2:5 concentrations.

Gaseous SO2 and NH3 were acquired in all but the re-

suspension samples.

Though dominated by motor vehicle exhaust, road-

side motor vehicle samples also contain suspended road

dust and particles from other ‘‘background’’ sources.

The chemical mass balance (CMB) model (Watson et al.,

1990) was used to adjust the motor vehicle samples for

background source contributions using the technique

described by Watson et al. (1994). Each roadside source

sample was submitted to CMB with the following

sources: paved road dust, secondary ammonium sulfate

and ammonium nitrate, and vegetative (grass) burning.

Only chemical species assumed to come from the back-

ground but not the motor vehicle sources (i.e., Al, Si, K,

Ca, Ti, Rb, and Sr from paved road dust, NHþ4 , SO

¼4 ,

and NO�3 from secondary ammonium sulfate and am-

monium nitrate, and water-soluble Kþ from vegetative

burning) were used as CMB fitting species. The contri-

butions of the background sources to mass and all

chemical species were subtracted from the motor vehicle

profile concentrations and the differences were used to

calculate the adjusted motor vehicle source profiles with

appropriate error propagation.

3. Results and discussion

Chemical abundances for each sample were blank

subtracted and calculated by dividing each chemical

concentration by the mass concentration, with error

propagation by addition in quadrature (Watson et al.,

2001b). Dilution sampling of coal-fired power plant

boilers, cement kilns, residential cooking, open burning,

and roadside sampling for motor vehicle exhaust fre-

quently resulted in low measured masses (typically less

than 0.5 mg/sample). At lower mass concentrations, the

effect of gaseous organic carbon adsorption on quartz-

fiber filters becomes apparent as the sum-of-chemical-

Page 7: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 191

species-to-measured-mass ratios exceed unity. The pro-

files were normalized to the sum of species in these cases.

Because refinery catalytic cracker emissions were color-

less, samples were inadvertently overloaded (16–19 mg/

sample), with consequent inconsistencies between mass

collected on the Teflon-membrane and quartz-fiber fil-

ters. These samples were also normalized to the sum

of species rather than to measured mass.

Individual source profiles are averaged to represent

emissions compositions for a source category. The

variability of the average is estimated as the larger of

the standard deviation of the averaged abundances or

the root mean square of the analytical precisions for the

individual profiles. The rationale for this approach is

illustrated in the extreme case with several values at the

[same] detection limit; the standard deviation of the

average is zero but the analytical uncertainty is 100%.

The individual sample profiles were composited by

source type and sub-type based on commonality of

composition of the chemical species most indicative of

those sources: (1) geological: Al, Si, Ca, Fe, OC; (2)

motor vehicle: sulfur (S), OC, EC, carbon fractions; (3)

vegetative burning: water-soluble Kþ, OC, EC; (4) coal-

fired boilers: S, OC, EC, Se; and (5) cement kilns: Si, K,

Ca, Fe, S, OC. While an average profile for a source type

may be relevant to long-term average emissions inven-

tories, sub-type profiles may be more suitable for re-

ceptor model source apportionment of the short-term

daily samples collected during the BRAVO study and

other air quality studies. The composites reported here

are described in Table 2. Individual profiles are available

(Chow et al., 2002a,b) for alternative composites based

on other criteria. Table 3 presents the average chemical

abundances and their variabilities. Included in these

profiles are eight carbon fractions (OC1, OC2, OC3,

OC4, OP, EC1, EC2, and EC3) (Chow et al., 1993, 2001;

Watson et al., 1994) and ratios of SO2 and NH3 gas

abundances relative to PM2:5 mass emissions. The car-

bon fractions are reported in several ambient data sets

(ARB, 2003; IMPROVE, 2003) and have been used in

receptor models (Engelbrecht et al., 2002). The fractions

are thus relevant for use in CMB modeling where carbon

in the ambient samples and other source profiles was

measured using the same methodology.

The geological profiles are dominated by Si, Ca, and

OC (road dust). PM2:5 Si varies fourfold from 7.8± 0.7%

in unpaved road dust (BVUNPV2) to 29.4 ± 2.5% in

eastern Texas soil (BVSOIL1). Conversely, PM2:5 Ca

varies sixfold from 4.1± 1.3% in soil (BVSOIL1) to

24.0± 1.7% in unpaved road dust (BVUNPV2). Houck

et al. (1989) measured the chemical compositions of

various types of geological material in California and

found no Ca abundances higher than 11.6 ± 1.3% (ag-

ricultural soil from Kern Wildlife Refuge). The carbon

(OC and EC) content is highest in paved road dust

(BVPVRD1) and eastern Texas soil (BVSOIL1), with

large variabilities (i.e., high uncertainties). The major

element compositions do not appear to vary significantly

between the fine and coarse fractions, i.e., they overlap

within twice their uncertainties (Chow et al., 1994).

Perry et al. (1997) found that PM2:5 Al/Ca ratios of

>3.8 were indicators of Sahara dust transport at eastern

US IMPROVE monitors. Al/Ca ratios in the PM2:5

dust samples range from 0.1 in unpaved road dust

(BVUNPV2) to 0.76 in soil from the Purtis Creek and

Big Thicket samples (BVSOIL1) from eastern Texas.

This implies that Sahara dust incursions, if they occur,

should be recognizably different from Texas geological

contributions.

The four vehicle exhaust profiles are dominated

(>95%) by OC and EC, although EC varies from

18.4± 2.9% in BVRDMV3 to 46.6 ± 14.3% in

BVRDMV1. This is similar to the 36± 11% EC and

39± 19% OC in 1989 Phoenix, AZ, profiles (Watson

et al., 1994). The EC-to-OC ratios for BVRDMV1–4

were 0.97, 0.53, 0.23, and 0.66, respectively (average EC-

to-OC ratio ¼ 0.60± 0.30). EC-to-OC ratios in vehicle

exhaust from the Imperial Valley, CA and Las Vegas,

NV, ranged from 0.25 to 1.67 with an average ratio of

0.67± 0.29 (Chow et al., 1999; Watson and Chow, 2001).

There was no apparent relationship between the fraction

of diesel vehicles in BVRDMV1–4 (5.1%, 9.6%, 4.4%,

and 2.5%) and the corresponding EC-to-OC ratios (R-squared ¼ 0.0002).

The vegetative burning chemical abundances are

highly variable, even for the same fuel. For the pine

fence burns, the compositions in BURN1 and BURN2

were 43.4± 5.6% and 87.7± 17.0% for OC, 55.2± 6.4%

and 11.6± 8.3% for EC, and 0.12 ± 0.02% and 0.02±

0.004% for Kþ, respectively. Kþ abundances ranged

from 0.02± 0.004% for pine fence (BURN2) to 13.6 ±

3.1% for Mesquite (BURN3). Fine et al. (2002) reported

profiles for fireplace burns of softwoods and hardwoods.

They found that OC constituted 77% and 100% of PM2:5

mass for white ash and slash pine, respectively. These

abundances may be high relative to those presented

here, as Fine et al. (2002) used transmission, rather than

reflectance to correct for pyrolysis (Chow et al., 2001),

which results higher OC and lower EC concentrations.

The highest total K content (1.75%) was reported for

white ash.

The OC content of coal-fired power plant emissions

range from 0.9± 1.2% in CFPP6 to 62.9 ± 14.8% in

CFPP1. CFPP1 had very low mass emissions, and its

OC content is probably dominated by adsorbed organic

vapors on the quartz filter. CFPP6 is distinguished from

CFPP1 by its low OC and high Se composition. EC

abundances were variable. The highest SO¼4 abundances

were found for CFPP4, CFPP5 and CFPP6 (45.6 ±

18.7%, 46.2± 6.8%, and 62.2± 12.5%, respectively). The

ratios of SO2 relative to PM2:5 mass emissions were high

and variable from sample to sample, ranging from

Page 8: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 2

Composited BRAVO source profiles

Source type Mnemonic Samples useda

Paved road dust BVPVRD1 BVPVRD 1–5, San Antonio and Laredo, TX

Soil BVSOIL1 BVSOIL 1,4 off-road undisturbed soil samples from Purtis

Creek and Big Thicket, TX

Soil BVSOIL2 BVSOIL 2,5,7–8 undisturbed soil samples from Langtree-

Skiles Ranch, San Vincente, Big Bend K-Bar, and Laredo,

TX

Unpaved road dust BVUNPV2 BVUNPV 6,9 unpaved road samples from Guadalupe and

Eagle Pass, TX

Motor vehicle BVRDMV1 BVRSMV 1,2,6–12,14,15,35 roadside samples from San

Antonio and BVRSMV 28–31 roadside samples from

Laredo, TX

Motor vehicle BVRDMV2 BVRSMV 16–18 roadside samples from San Antonio and

BVRSMV 19,20,22,36–38 roadside samples from Laredo,

TX

Motor vehicle BVRDMV3 BVRSMV 4,32–34,41,42 roadside samples from San

Antonio, TX

Motor vehicle BVRDMV4 BVRSMV 25,39,40 roadside samples from San Antonio, TX

Motor vehicle BVRDMV Composite of 34 San Antonio and Laredo TX roadside

samples

Vegetative burning––Pine fence BURN1 BVBURN 1,2

Vegetative burning––Pine fence BURN2 BVBURN 4,13

Vegetative burning––Mesquite BURN3 BVBURN 14,15

Vegetative burning––Mesquite BURN4 BVBURN 5,6

Vegetative burning––Tamarisk BURN5 BVBURN 7,17,18

Vegetative burning––Huisache BURN6 BVBURN 8–10

Vegetative burning––Grass BURN7 BVBURN 11,12,19–21

Vegetative burning BURN Composite of 19 vegetative burning samples

Coal-fired boiler CFPP1 BVCOAL 1–5 stack samples from south central Texas utility

550 MW Unit 1 with baghouse and wet limestone scrubber

(pulverized coal dry bottom boiler with tangential injection)

Coal-fired boiler CFPP2 BVCOAL 6,7,24,25 stack samples from southeastern Texas

utility 600 MW unit fueled by low-sulfur Western coal and

equipped with baghouse (pulverized coal dry bottom boiler

with opposed injection)

Coal-fired boiler CFPP3 BVCOAL 8–10,26 stack samples from southeastern Texas

utility 600 MW unit fueled by low-sulfur Western coal and

equipped with baghouse (pulverized coal dry bottom boiler

with opposed injection)

Coal-fired boiler (aluminum plant) CFPP4 BVCOAL 11–16 stack samples from south central Texas

aluminum processing facility 545 MW Unit 1 equipped

with dry limestone scrubber and potliner material (pulver-

ized coal dry bottom boiler with tangential injection)

Coal-fired boiler CFPP5 BVCOAL 17–21 stack samples from central Texas utility 550

MW Unit 1 with electrostatic precipitator and baghouse

(pulverized coal dry bottom boiler with tangential

injection)

Coal-fired boiler CFPP6 BVCOAL 22–23 stack samples from central Texas utility

550 MW Unit 1 with electrostatic precipitator and baghouse

(pulverized coal dry bottom boiler with tangential injection)

Coal-fired boiler CFPP Composite of 26 coal-fired boiler samples

Coal fly ash BVCLFA BVCLFA 1–3 samples from south central Texas utility 550

MW Unit 1

192 J.C. Chow et al. / Chemosphere 54 (2004) 185–208

Page 9: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 2 (continued)

Source type Mnemonic Samples useda

Catalytic cracker CAT1 BVCAT 1–5 stack samples from southeastern Texas refinery

heavy oil catalytic cracking unit (silica alumina matrix

catalyst with zeolite, regenerated by burning off coke using

natural gas fire) with electrostatic precipitator

Cement kiln CEM1 BVCEMT 1–6 stack samples from northeastern Texas

cement kiln fueled with 70% low-S WY coal, 10% pet coke,

and 20% scap tires

Cement kiln CEM2 BVCEMT 7–10 stack samples from northeastern Texas

cement kiln fueled with 75% low-S NM coal, 25% wood

chips, and used oil filter fluff

Cement kiln CEM Composite of 11 cement kiln samples

Cooking––smoked chicken SMCHICK SMOCKN 1–3 smoked chicken cooking samples

Cooking––charcoal chicken CHCHICK CHACKN 1,2 charcoal charbroiled chicken samples

Cooking––propane chicken PRCHICK PROCKN 1–4 propane charbroiled chicken samples

Cooking––hamburger BURGER CHAHAM 1–2 charcoal charbroiled hamburger samples

Cooking––stir fry steak SFSTEAK STIFRY01 stir fry steak sample

Cooking COOK Composite of 12 cooking samples

a Individual samples are identified in ‘‘BRAVO individual source profiles.xls’’ on the DRI web site.

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 193

219±678% in CFPP4 to 35 017± 32 395% in CFPP1.

NH3 was also detected in three Texas utility pro-

files, ranging from 7.0 ± 3.5% (CFPP3) to 75.7 ± 50.1%

(CFPP1).

These profiles also contained the highest Se levels:

1.3 ± 0.92%, 0.51 ± 0.27%, and 2.2 ± 0.3%, respectively.

Selenium is often used as a marker for coal-fired power

plant emissions (Thurston and Spengler, 1985; Malm

et al., 1990). The Se compositions of BRAVO coal-fired

power plant profiles CFPP4–6 were unusually high

compared with Se in other coal-burning profiles (0.001–

0.044%) (US EPA, 1999c). The PM10 coal fly ash profile

(BVCLFA) contained 10.7± 2.7% Al, 13.4± 3.6% Si,

19.8± 2.7% Ca, and 3.7± 0.7% Fe, and resembled geo-

logical material with the addition of SO¼4 (4.3 ± 2.0%).

For comparison, a PM10 coal fly ash profile (#43303,

Ray and Parker, 1977) reported by US EPA (1999c)

contained 13.2% Al, 21% Si, 4.2% Ca, and 6.2% Fe

(uncertainties were not reported).

The catalytic cracker profile (CAT1) were enriched in

SO2 (716± 310%), SO¼4 (59.2 ± 10.7%), Ni (1.9 ± 0.44%),

V (1.0 ± 0.21%), and La (1.9 ± 0.38%). This profile also

contains the highest concentration antimony (Sb) mea-

sured in this study (0.58± 0.11%) and is the only profile

in which Sb was detected. For comparison, a composite

catalytic cracker profile (#26209, Cooper et al., 1987)

contained 6.2% SO¼4 , 0.1% Ni, 0.16% V, 0.3% La, and

0.06% Sb (uncertainties were not reported), or approxi-

mately one-tenth of the chemical abundances reported in

CAT1.

The cement kiln profiles (CEM1 and CEM2) are

dominated by SO2 (19 014± 16 482% and 49 783±

16 033%, respectively), NH3 (27.4 ± 15.1% and 23.6±

31.8%, respectively), and SO¼4 (31.4 ± 4.5% and

29.7± 13.5%, respectively). The 1.06–1.14% Al, 3.4–

5.8% Si, 17.3–18.8% Ca, 1.10–1.75% Fe, 11.7–14.6%

OC, and 2.4–4.2% EC compositions of these two profiles

are similar. Total K and Kþ are three times higher in

CEM1 (14.6 ± 1.5% and 12.5 ± 1.3%, respectively) com-

pared with CEM2 (4.5 ± 1.8% and 3.9± 0.7%, respec-

tively). The ratios of SO2 to PM2:5 emissions in the

cement kilns are comparable and higher than those

found in the coal-fired power plants. Another coal-fired

cement kiln profile (#27203, Cooper et al., 1985) con-

tained 4.3± 0.4% Al, 8.4 ± 0.8% Si, 10 ± 1.3% Ca,

0.9 ± 0.14% Fe, 5.4 ± 0.6% OC, 0.2± 0.4% EC, and

5.4± 1.5% total K. The abundances of Ca and OC in

Texas cement kiln profiles are two to three times higher

than those in Cooper et al. (1985), and the differences

in EC are 10- to 20-fold.

The meat-cooking emissions consist primarily of or-

ganic carbon (76–96%), with significant NH3 abundances

in smoked chicken (SMCHICK, 128± 56%), chicken

fired by propane (PRCHICK, 51.2± 58.2%), charbroiled

chicken (CHCHICK, 42.4± 14.0%), and charbroiled

hamburger (BURGER, 36.4± 3.4%). While there is con-

siderable overall variability in the relative abundances

of the chemical species, the charbroiled chicken

(CHCHICK) and hamburger (BURGER) profiles are

similar with respect to OC, EC, and the carbon fractions.

This suggests that at this level of chemical speciation,

cooking conditions, i.e., charbroiling in this case, may

determine the carbon composition, including that of the

carbon fractions. Detailed organic speciation of these

samples (to be separately reported) may provide a greater

degree of discrimination among different categories of

cooking and other combustion emissions (Schauer and

Cass, 2000; McDonald et al., 2003).

Page 10: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 3

BRAVO source profile composites

Paved road dust (BVPVRD1) Soil (BVSOIL1) Soil (BVSOIL2)

PM2:5 Coarse PM10 PM2:5 Coarse PM10 PM2:5 Coarse PM10

Cl� 0.0511± 0.2117 0.0965± 0.1406 0.0837± 0.1143 0.0190± 0.4161 0.0144±0.1495 0.0160± 0.1445 0.0041± 0.0790 0.0156± 0.0501 0.0130± 0.0424

NO�3 0.1104± 0.2066 0.0447± 0.1317 0.0584± 0.1077 0.1094± 0.4164 0.0651±0.1500 0.0754± 0.1449 0.0000± 0.0686 0.0000± 0.0413 0.0000± 0.0353

SO¼4 0.9993± 0.2203 0.6230± 0.2741 0.7127± 0.2393 0.6322± 0.4232 0.1282±0.1511 0.2297± 0.1797 0.1329± 0.0694 0.0759± 0.0420 0.0899± 0.0360

NHþ4 0.2033± 0.2222 0.0364± 0.1419 0.0817± 0.1161 0.4850± 0.4787 0.0898±0.1819 0.1670± 0.1723 0.1224± 0.0820 0.0488± 0.0547 0.0663± 0.0457

Naþ 0.1403± 0.1618 0.1036± 0.1041 0.1111± 0.0847 0.1087± 0.3259 0.0514±0.1173 0.0629± 0.1134 0.0857± 0.0658 0.0839± 0.0806 0.0852± 0.0651

Kþ 0.2708± 0.1636 0.2313± 0.2161 0.2419± 0.1987 0.1406± 0.3267 0.0670±0.1175 0.0825± 0.1137 0.2643± 0.0584 0.1380± 0.0389 0.1661± 0.0285

OC1 0.7470± 1.2946 0.5008± 0.9414 0.5570± 0.7425 6.8116± 8.7495 0.5260±2.0748 1.7239± 2.1549 0.1281± 0.4067 0.0000± 0.2036 0.0247± 0.1810

OC2 1.8907± 1.2384 1.2464± 0.9197 1.4066± 0.7288 3.9814± 3.5319 0.6008±0.9882 1.2646± 1.0124 0.4625± 0.4015 0.0604± 0.2143 0.1367± 0.1870

OC3 6.6879± 1.8142 5.6501± 3.4360 5.8017± 2.8477 7.2977± 7.1030 1.6533±1.2327 2.7175± 1.1706 1.7607± 0.5614 0.7320± 1.1446 0.9334± 0.8772

OC4 6.2708± 1.5464 6.9826± 3.7036 6.6222± 2.5854 1.6045± 2.3167 1.5808±0.8482 1.5968± 0.8153 5.0062± 2.8320 2.8876± 3.1386 3.2861± 2.1341

OP 0.2699± 1.2550 0.2053± 1.1041 0.2279± 0.8118 0.0650± 2.2758 0.5591±0.9887 0.4579± 0.9052 0.5487± 0.8042 0.2747± 0.5475 0.3233± 0.4445

OC 15.8664± 3.1864 14.5852± 7.6192 14.6155± 5.8452 19.7601± 18.9875 4.9199±2.9284 7.7607± 4.0833 7.9063± 2.8450 3.9546± 4.6422 4.7043± 3.1571

EC1 1.6682± 0.8977 1.2417± 1.0204 1.3395± 0.7754 0.1927± 0.6498 0.5746±0.3958 0.5037± 0.3415 0.2417± 0.3334 0.1464± 0.1563 0.1603± 0.1135

EC2 0.8986± 0.6425 0.6405± 0.8291 0.6781± 0.5757 0.0761± 0.6740 0.3528±0.3356 0.2995± 0.2992 0.3185± 0.2508 0.1419± 0.1765 0.1763± 0.1427

EC3 0.0536± 0.3599 0.0000± 0.1785 0.0000± 0.1522 0.0000± 0.5425 0.0000±0.1947 0.0000± 0.1884 0.0000± 0.0895 0.0000± 0.0467 0.0000± 0.0411

EC 2.3505± 1.7110 1.6769± 1.7264 1.7896± 1.2710 0.2038± 2.5200 0.3684±1.1323 0.3453± 1.0295 0.0115± 0.8716 0.0136± 0.5927 0.0132± 0.4817

TC 18.2169± 3.6461 16.2621± 8.2100 16.4052± 6.2533 19.9639± 18.6994 5.2882±3.1425 8.1060± 4.4058 7.9178± 2.8357 3.9682± 4.6350 4.7175± 3.1471

Na 0.0358± 0.2012 0.0136± 0.1085 0.0199± 0.0947 0.0612± 0.4320 0.0373±0.1237 0.0421± 0.1295 0.0657± 0.0796 0.0436± 0.0354 0.0482± 0.0438

Mg 0.3072± 0.0830 0.2474± 0.0988 0.2603± 0.0908 0.1161± 0.1716 0.1417±0.1138 0.1405± 0.0561 0.7056± 0.0586 0.3880± 0.0372 0.4625± 0.0324

Al 2.5891± 0.5548 3.9558± 1.3396 3.5633± 0.9495 3.0922± 0.7519 4.4543±1.3835 4.1799± 1.0924 4.8185± 0.3429 7.0440± 2.1544 6.5193± 1.6422

Si 8.6604± 2.2933 13.6808± 5.5603 12.2295± 4.0591 29.3881± 2.5233 50.0467±16.7039 45.9004± 13.3172 17.3734± 1.3302 23.6443± 7.6731 22.1809± 5.8855

P 0.1007± 0.1079 0.1194± 0.0952 0.1133± 0.0988 0.0029± 0.0534 0.0131±0.0767 0.0107± 0.0619 0.0565± 0.0312 0.0797± 0.0392 0.0742± 0.0377

S 0.6212± 0.1196 0.5858± 0.2182 0.5897± 0.1613 0.1838± 0.0819 0.0765±0.0610 0.0987± 0.0621 0.0693± 0.0264 0.0668± 0.0357 0.0675± 0.0333

Cl 0.0221± 0.0962 0.0231± 0.1922 0.0219± 0.1453 0.0324± 0.0837 0.0182±0.0489 0.0209± 0.0421 0.0012± 0.0574 0.0000± 0.1125 0.0000± 0.0862

K 0.8377± 0.2589 0.9716± 0.3582 0.9314± 0.3168 1.3156± 0.7827 1.5608±0.7629 1.5096± 0.7596 1.8098± 0.2230 1.9312± 0.4068 1.9003± 0.3125

Ca 16.4838± 1.8273 23.5120± 4.3578 21.7346± 3.2736 4.0857± 1.3142 3.5428±0.8812 3.6899± 0.6085 10.1650± 1.4324 13.5759± 2.4420 12.7807± 1.8502

Ti 0.3082± 0.1626 0.2422± 0.0937 0.2564± 0.0796 0.4853± 0.3506 0.5949±0.1443 0.5756± 0.1180 0.3770± 0.0636 0.3233± 0.0367 0.3360± 0.0317

V 0.0065± 0.0686 0.0055± 0.0435 0.0053± 0.0360 0.0022± 0.1505 0.0090±0.0541 0.0076± 0.0522 0.0095± 0.0273 0.0086± 0.0196 0.0089± 0.0162

Cr 0.0098± 0.0154 0.0088± 0.0102 0.0088± 0.0083 0.0105± 0.0350 0.0063±0.0120 0.0072± 0.0118 0.0031± 0.0067 0.0025± 0.0054 0.0026± 0.0045

Mn 0.0443± 0.0105 0.0388± 0.0070 0.0399± 0.0060 0.0951± 0.0230 0.0614±0.0091 0.0685± 0.0086 0.0677± 0.0336 0.0605± 0.0268 0.0621± 0.0281

Fe 3.3420± 0.4743 2.5720± 0.5403 2.7493± 0.5249 1.8994± 0.1669 1.5474±0.3233 1.6244± 0.2695 3.6725± 0.2611 3.1830± 0.2600 3.2951± 0.2112

Co 0.0000± 0.0509 0.0000± 0.0620 0.0000± 0.0462 0.0020± 0.0326 0.0000±0.0328 0.0000± 0.0269 0.0000± 0.0547 0.0000± 0.0668 0.0000± 0.0523

Ni 0.0039± 0.0063 0.0047± 0.0035 0.0045± 0.0030 0.0097± 0.0136 0.0011±0.0042 0.0027± 0.0042 0.0010± 0.0025 0.0016± 0.0015 0.0014± 0.0012

Cu 0.0284± 0.0098 0.0204± 0.0042 0.0219± 0.0041 0.0135± 0.0143 0.0051±0.0044 0.0067± 0.0045 0.0033± 0.0024 0.0026± 0.0011 0.0028± 0.0010

Zn 0.1680± 0.0426 0.1363± 0.0225 0.1429± 0.0264 0.0294± 0.0175 0.0151±0.0107 0.0180± 0.0117 0.0212± 0.0101 0.0161± 0.0090 0.0173± 0.0093

Ga 0.0003± 0.0122 0.0002± 0.0069 0.0001± 0.0059 0.0001± 0.0264 0.0004±0.0081 0.0003± 0.0082 0.0003± 0.0044 0.0006± 0.0020 0.0005± 0.0018

As 0.0013± 0.0153 0.0003± 0.0114 0.0005± 0.0092 0.0005± 0.0302 0.0005±0.0096 0.0006± 0.0097 0.0008± 0.0052 0.0010± 0.0026 0.0010± 0.0023

Se 0.0005± 0.0074 0.0001± 0.0041 0.0001± 0.0035 0.0003± 0.0159 0.0001±0.0049 0.0001± 0.0049 0.0003± 0.0026 0.0000± 0.0012 0.0000± 0.0011

Br 0.0020± 0.0070 0.0020± 0.0037 0.0019± 0.0033 0.0028± 0.0152 0.0013±0.0044 0.0016± 0.0046 0.0019± 0.0025 0.0008± 0.0010 0.0011± 0.0010

Rb 0.0048± 0.0063 0.0046± 0.0035 0.0047± 0.0030 0.0076± 0.0140 0.0076±0.0043 0.0076± 0.0044 0.0091± 0.0024 0.0090± 0.0013 0.0090± 0.0011

194

J.C.Chowet

al./Chem

osphere

54(2004)185–208

Page 11: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Sr 0.0851± 0.0455 0.0714± 0.0312 0.0742±0.0336 0.0128± 0.0153 0.0129± 0.0048 0.0129± 0.0048 0.0411± 0.0091 0.0459± 0.0121 0.0449± 0.0114

Y 0.0016± 0.0087 0.0019± 0.0048 0.0018±0.0041 0.0032± 0.0187 0.0028± 0.0057 0.0029± 0.0058 0.0036± 0.0030 0.0033± 0.0014 0.0034± 0.0013

Zr 0.0090± 0.0102 0.0089± 0.0058 0.0088±0.0050 0.0151± 0.0220 0.0207± 0.0070 0.0196± 0.0070 0.0155± 0.0039 0.0147± 0.0021 0.0149± 0.0018

Mo 0.0018± 0.0182 0.0015± 0.0099 0.0010±0.0086 0.0009± 0.0392 0.0017± 0.0118 0.0016± 0.0121 0.0007± 0.0065 0.0005± 0.0029 0.0004± 0.0027

Pd 0.0000± 0.0572 0.0000± 0.0318 0.0000±0.0273 0.0000± 0.1232 0.0000± 0.0379 0.0000± 0.0385 0.0000± 0.0206 0.0000± 0.0094 0.0000± 0.0087

Ag 0.0011± 0.0690 0.0002± 0.0383 0.0002±0.0329 0.0000± 0.1488 0.0032± 0.0456 0.0026± 0.0465 0.0006± 0.0249 0.0002± 0.0113 0.0002± 0.0104

Cd 0.0007± 0.0733 0.0006± 0.0407 0.0005±0.0350 0.0000± 0.1581 0.0019± 0.0484 0.0015± 0.0494 0.0006± 0.0264 0.0002± 0.0120 0.0002± 0.0110

In 0.0004± 0.0819 0.0000± 0.0455 0.0000±0.0391 0.0000± 0.1768 0.0000± 0.0541 0.0000± 0.0552 0.0000± 0.0295 0.0000± 0.0134 0.0000± 0.0123

Sn 0.0070± 0.1040 0.0040± 0.0581 0.0046±0.0498 0.0191± 0.2241 0.0063± 0.0685 0.0090± 0.0699 0.0023± 0.0374 0.0033± 0.0168 0.0031± 0.0156

Sb 0.0102± 0.1184 0.0032± 0.0655 0.0049±0.0563 0.0255± 0.2552 0.0000± 0.0783 0.0000± 0.0798 0.0011± 0.0425 0.0014± 0.0192 0.0012± 0.0178

Ba 0.1703± 0.4115 0.1088± 0.2275 0.1237±0.1957 0.1904± 0.8908 0.0328± 0.2717 0.0608± 0.2776 0.0480± 0.1492 0.0502± 0.0657 0.0495± 0.0612

La 0.0262± 0.1438 0.0082± 0.2088 0.0102±0.1498 0.1790± 0.2942 0.0011± 0.2470 0.0057± 0.2074 0.0102± 0.0517 0.0079± 0.0620 0.0082± 0.0477

Au 0.0005± 0.0073 0.0000± 0.0098 0.0000±0.0073 0.0075± 0.0101 0.0000± 0.0088 0.0000± 0.0074 0.0002± 0.0019 0.0000± 0.0024 0.0000± 0.0018

Hg 0.0004± 0.0042 0.0002± 0.0061 0.0002±0.0044 0.0001± 0.0082 0.0019± 0.0071 0.0016± 0.0059 0.0005± 0.0015 0.0000± 0.0018 0.0001± 0.0014

Tl 0.0002± 0.0042 0.0009± 0.0060 0.0007±0.0043 0.0018± 0.0081 0.0000± 0.0068 0.0001± 0.0057 0.0011± 0.0015 0.0001± 0.0018 0.0002± 0.0014

Pb 0.0396± 0.0100 0.0397± 0.0117 0.0391±0.0104 0.0184± 0.0166 0.0104± 0.0105 0.0120± 0.0115 0.0071± 0.0055 0.0051± 0.0049 0.0056± 0.0050

U 0.0004± 0.0040 0.0008± 0.0058 0.0007±0.0042 0.0007± 0.0078 0.0005± 0.0069 0.0005± 0.0058 0.0013± 0.0019 0.0003± 0.0023 0.0004± 0.0018

SO2 0.0000± 0.0000 0.0000± 0.0000 0.0000±0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000

NH3 0.0000± 0.0000 0.0000± 0.0000 0.0000±0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000

Unpaved road dust (BVUNPV2)

Motor vehicle

(BVRDMV1),

PM2:5

Motor vehicle

(BVRDMV2),

PM2:5

Motor vehicle

(BVRDMV3),

PM2:5

Motor vehicle

(BVRDMV4),

PM2:5

Motor vehicle

composite (BVRDMV),

PM2:5PM2:5 Coarse PM10

Cl� 0.0346± 0.0661 0.0518± 0.0338 0.0479±0.0301 0.5180± 1.2323 0.2860± 0.4982 0.0425± 0.7830 0.4601± 0.4203 0.3676± 0.9508

NO�3 0.2368± 0.3348 0.3072± 0.2755 0.2921±0.2874 0.3652± 1.4590 0.3452± 0.6710 0.2470± 1.0098 0.6749± 1.1440 0.3664± 1.1901

SO¼4 0.2313± 0.1222 0.1617± 0.1027 0.1774±0.1081 2.9606± 4.2303 1.7383± 1.4932 1.1808± 2.7286 1.0722± 1.2643 2.1563± 3.2352

NHþ4 0.0897± 0.0737 0.0420± 0.0427 0.0525±0.0371 0.0000± 1.7760 0.0000± 0.6657 0.0000± 1.1906 0.0000± 0.6955 0.0000± 1.3764

Naþ 0.0481± 0.0519 0.0349± 0.0248 0.0379±0.0264 0.2971± 0.2321 0.4942± 0.3534 0.2424± 0.1499 0.1108± 0.0902 0.3232± 0.2649

Kþ 0.1642± 0.0537 0.0792± 0.0237 0.0982±0.0279 0.1668± 0.1695 0.0433± 0.2125 0.2212± 0.1393 0.0523± 0.0781 0.1336± 0.1684

OC1 0.1279± 0.3945 0.0000± 0.1610 0.0143±0.1533 10.5493± 14.6543 17.8564± 7.8898 26.3442± 12.9293 19.4108± 7.2960 16.0528± 12.3180

OC2 0.3106± 0.3773 0.0538± 0.1636 0.1110±0.1525 18.5465± 8.7039 19.2988± 4.5912 16.1592± 7.8103 14.4980± 3.5528 17.9671± 7.0030

OC3 1.4732± 0.6914 0.6459± 0.5308 0.8308±0.5802 12.9848± 6.8366 12.3537± 3.4517 16.3513± 4.9224 12.4096± 2.7413 13.3611± 5.4852

OC4 7.5438± 2.0017 3.5684± 0.4196 4.4697±0.3723 6.1875± 4.0493 6.2211± 2.2864 9.1455± 4.9247 8.6300± 1.8421 6.9339± 3.3074

OP 0.0535± 0.3689 1.0971± 1.2592 0.8582±0.9699 3.7260± 3.8639 2.3690± 2.8662 10.3791± 6.6335 1.7355± 1.3325 4.3652± 4.6221

OC 9.5089± 3.0288 5.3651± 1.3891 6.2839±1.6633 48.1044± 22.4003 61.9398± 11.7226 78.8867± 15.7445 57.8763± 8.3032 58.0611± 17.8053

EC1 0.0000± 0.1010 1.0651± 0.5465 0.8244±0.4211 16.4093± 10.9335 19.3351± 8.3802 9.1856± 2.5736 12.9324± 5.1421 15.6022± 9.2854

EC2 0.0798± 0.1162 0.1953± 0.1496 0.1689±0.1180 29.8368± 8.0995 15.0408± 10.6902 17.5529± 4.6737 24.7041± 5.8792 23.2996± 10.4132

EC3 0.0000± 0.0860 0.0259± 0.0816 0.0199±0.0656 4.0438± 5.9594 0.6796± 0.6732 2.0033± 1.3402 2.0580± 1.2245 2.6180± 4.3094

EC 0.0263± 0.4089 0.1891± 1.3806 0.1549±1.0646 46.5555± 14.3398 32.7353± 11.8172 18.3743± 2.8589 37.9605± 4.1738 37.1657± 15.5277

TC 9.5352± 2.9916 5.5542± 1.9595 6.4387±1.5340 94.1655± 23.8921 95.1887± 12.7765 96.9224± 16.7158 95.8624± 9.9639 95.0726± 19.2330

Na 0.0000± 0.0703 0.0000± 0.0346 0.0000±0.0311 1.5357± 2.9587 1.4643± 1.0570 1.7007± 1.7837 1.0234± 1.0141 1.5007± 2.2472

Mg 0.4843± 0.0901 0.3048± 0.1031 0.3445±0.1032 0.1396± 0.3308 0.1940± 0.1395 0.1501± 0.2073 0.0436± 0.1289 0.1474± 0.2563

Al 2.4251± 0.1730 3.6601± 1.1142 3.3809±0.8635 0.0999± 0.2282 0.1460± 0.2747 0.0123± 0.0709 0.0000± 0.0647 0.0878± 0.2117

J.C.Chowet

al./Chem

osphere

54(2004)185–208

195

Page 12: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 3 (continued)

Unpaved road dust (BVUNPV2)

Motor vehicle

(BVRDMV1),

Motor vehicle

(BVRDMV2),

Motor vehicle

(BVRDMV3),

Mo r vehicle

(BV DMV4),

Motor vehicle

composite (BVRDMV),

PM2:5 Coarse PM10 PM2:5 PM2:5 PM2:5 PM PM2:5

Si 7.7908± 0.6772 11.5266± 3.7007 10.6855± 2.8749 0.3575± 0.3502 0.3564± 0.5285 0.2967± 0.1820 0.3 1± 0.1740 0.3430±0.3707

P 0.0427± 0.0303 0.0529± 0.0267 0.0506± 0.0273 0.0089± 0.0884 0.0000± 0.0433 0.0000± 0.0494 0.0 0± 0.0311 0.0042±0.0685

S 0.1522± 0.0167 0.1738± 0.0614 0.1689± 0.0478 1.6162± 1.4715 1.3533± 0.9329 0.8334± 0.8696 0.7 6± 0.3345 1.3358±1.1940

Cl 0.0000± 0.1243 0.0000± 0.2336 0.0000± 0.1841 0.0000± 0.2517 0.0763± 0.1634 0.0000± 0.1549 0.0 0± 0.0783 0.0202±0.1949

K 0.8706± 0.1790 1.0375± 0.3973 0.9987± 0.3461 0.0000± 0.1258 0.0020± 0.0792 0.0000± 0.0695 0.0 0± 0.0426 0.0005±0.1006

Ca 24.0198± 1.7061 29.8027± 5.2089 28.5589± 4.0727 0.0369± 0.7585 0.4648± 0.7276 0.0000± 0.3500 0.7 2± 0.7775 0.2105±0.6048

Ti 0.1607± 0.0541 0.1283± 0.0397 0.1351± 0.0303 0.0618± 0.2689 0.0300± 0.2143 0.0169± 0.1816 0.0 2± 0.0954 0.0419±0.2298

V 0.0037± 0.0283 0.0071± 0.0277 0.0061± 0.0227 0.0307± 0.1188 0.0247± 0.0911 0.0088± 0.0787 0.0 5± 0.0454 0.0234±0.1005

Cr 0.0046± 0.0056 0.0051± 0.0045 0.0049± 0.0037 0.0361± 0.0543 0.0028± 0.0201 0.0062± 0.0128 0.0 5± 0.0237 0.0209±0.0403

Mn 0.0209± 0.0070 0.0192± 0.0032 0.0195± 0.0041 0.0278± 0.0228 0.0237± 0.0130 0.0471± 0.0195 0.0 7± 0.0066 0.0295±0.0202

Fe 1.6978± 0.1207 1.5336± 0.1964 1.5683± 0.1511 0.7203± 0.3612 0.5032± 0.4537 0.4451± 0.0944 0.6 3± 0.1216 0.6096±0.3545

Co 0.0000± 0.0254 0.0000± 0.0310 0.0000± 0.0248 0.0037± 0.0437 0.0017± 0.0357 0.0028± 0.0196 0.0 4± 0.0267 0.0027±0.0370

Ni 0.0047± 0.0020 0.0100± 0.0010 0.0088± 0.0009 0.0401± 0.0794 0.0056± 0.0089 0.0062± 0.0074 0.0 7± 0.0375 0.0248±0.0572

Cu 0.0030± 0.0021 0.0025± 0.0009 0.0026± 0.0008 0.0393± 0.0147 0.0468± 0.0453 0.0309± 0.0128 0.0 1± 0.0150 0.0409±0.0258

Zn 0.0131± 0.0023 0.0100± 0.0011 0.0106± 0.0010 0.3324± 0.3322 0.2282± 0.1651 0.1746± 0.0593 0.2 9± 0.0819 0.2657±0.2492

Ga 0.0000± 0.0039 0.0000± 0.0016 0.0000± 0.0015 0.0007± 0.0375 0.0002± 0.0196 0.0007± 0.0251 0.0 6± 0.0129 0.0006±0.0298

As 0.0010± 0.0045 0.0011± 0.0017 0.0010± 0.0017 0.0030± 0.0218 0.0001± 0.0215 0.0012± 0.0142 0.0 0± 0.0092 0.0019±0.0197

Se 0.0004± 0.0024 0.0000± 0.0010 0.0000± 0.0009 0.0017± 0.0092 0.0008± 0.0097 0.0034± 0.0057 0.0 4± 0.0034 0.0018±0.0085

Br 0.0063± 0.0041 0.0074± 0.0069 0.0072± 0.0063 0.0275± 0.0182 0.0160± 0.0092 0.0206± 0.0141 0.0 4± 0.0035 0.0224±0.0150

Rb 0.0043± 0.0020 0.0045± 0.0009 0.0044± 0.0008 0.0001± 0.0101 0.0011± 0.0087 0.0007± 0.0068 0.0 4± 0.0037 0.0005±0.0088

Sr 0.0477± 0.0156 0.0508± 0.0187 0.0502± 0.0180 0.0435± 0.1234 0.0000± 0.0097 0.0000± 0.0071 0.0 0± 0.0040 0.0205±0.0860

Y 0.0022± 0.0027 0.0019± 0.0011 0.0019± 0.0010 0.0001± 0.0145 0.0005± 0.0119 0.0013± 0.0096 0.0 3± 0.0055 0.0004±0.0125

Zr 0.0083± 0.0033 0.0075± 0.0032 0.0076± 0.0014 0.0044± 0.2947 0.0021± 0.0970 0.0019± 0.1980 0.0 5± 0.0957 0.0032±0.2260

Mo 0.0008± 0.0058 0.0000± 0.0023 0.0000± 0.0022 0.0007± 0.0311 0.0005± 0.0249 0.0008± 0.0205 0.0 4± 0.0112 0.0009±0.0266

Pd 0.0000± 0.0183 0.0000± 0.0074 0.0000± 0.0071 0.0064± 0.0668 0.0002± 0.0744 0.0103± 0.0467 0.0 0± 0.0280 0.0049±0.0634

Ag 0.0012± 0.0220 0.0000± 0.0088 0.0000± 0.0084 0.0047± 0.0800 0.0024± 0.0899 0.0113± 0.0559 0.0 9± 0.0340 0.0050±0.0762

Cd 0.0002± 0.0234 0.0000± 0.0093 0.0000± 0.0090 0.0022± 0.0840 0.0039± 0.0953 0.0027± 0.0587 0.0 0± 0.0356 0.0026±0.0802

In 0.0000± 0.0262 0.0000± 0.0104 0.0000± 0.0100 0.0229± 0.0969 0.0067± 0.1068 0.0263± 0.0666 0.0 2± 0.0408 0.0178±0.0915

Sn 0.0033± 0.0331 0.0029± 0.0129 0.0029± 0.0124 0.0239± 0.1374 0.0088± 0.1367 0.0204± 0.0924 0.0 6± 0.0559 0.0174±0.1250

Sb 0.0013± 0.0377 0.0011± 0.0149 0.0011± 0.0143 0.0326± 0.1584 0.0111± 0.1559 0.0131± 0.1078 0.0 4± 0.0634 0.0206±0.1437

Ba 0.2883± 0.3178 0.3459± 0.4302 0.3341± 0.4062 0.0481± 0.6922 0.0086± 0.5764 0.0606± 0.4566 0.0 7± 0.2650 0.0357±0.5970

La 0.0258± 0.0468 0.0008± 0.0497 0.0050± 0.0401 0.0263± 0.9237 0.0171± 0.3496 0.0174± 0.5996 0.0 9± 0.2986 0.0216±0.7108

Au 0.0004± 0.0017 0.0000± 0.0019 0.0000± 0.0015 0.0012± 0.0412 0.0033± 0.0178 0.0016± 0.0266 0.0 2± 0.0150 0.0020±0.0320

Hg 0.0004± 0.0014 0.0000± 0.0015 0.0000± 0.0012 0.0013± 0.0185 0.0002± 0.0078 0.0010± 0.0123 0.0 0± 0.0054 0.0008±0.0144

Tl 0.0006± 0.0014 0.0001± 0.0015 0.0002± 0.0012 0.0002± 0.0183 0.0000± 0.0078 0.0000± 0.0123 0.0 0± 0.0054 0.0001±0.0142

Pb 0.0054± 0.0008 0.0031± 0.0016 0.0035± 0.0013 0.0248± 0.0372 0.0456± 0.0465 0.0130± 0.0176 0.0 1± 0.0081 0.0279±0.0362

U 0.0009± 0.0014 0.0000± 0.0016 0.0000± 0.0013 0.0009± 0.0257 0.0000± 0.0097 0.0018± 0.0163 0.0 4± 0.0081 0.0008±0.0197

196

J.C.Chowet

al./Chem

osphere

54(2004)185–208

to

R

2:5

18

00

92

00

00

94

21

09

23

20

67

00

37

51

04

00

03

02

18

00

00

00

02

03

00

01

00

07

02

00

00

18

03

00

00

21

00

Page 13: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

SO2 0.0000± 0.0000 0.0000± 0.0000 0.0000±

0.0000

1331.6875±

10572.8809

826.4444±

5166.7930

1537.3333±

10307.6611

2009.3333±

5248.2661

1294.0294±

8991.7529

NH3 0.0000± 0.0000 0.0000± 0.0000 0.0000±

0.0000

63.1250±

23.9858

55.1111±

10.5409

46.3333±

14.2501

45.6667±

14.3643

56.5000±

19.5700

Pine fence burn

(BURN1),

PM2:5

Pine fence burn

(BURN2),

PM2:5

Mesquite burn

(BURN3),

PM2:5

Mesquite burn

(BURN4),

PM2:5

Tamarisk burn

(BURN5),

PM2:5

Huisache burn

(BURN6),

PM2:5

Grass burn

(BURN7),

PM2:5

Vegetative burning

composite (BURN),

PM2:5

Cl� 0.2669± 0.0762 0.1028± 0.0404 18.4191± 3.8197 11.3913± 4.4226 17.9047± 5.8879 4.0206±0.9031 4.8616± 2.9729 7.6145± 7.2976

NO�3 0.0066± 0.0429 0.0451± 0.0174 0.4757± 0.1457 0.4194± 0.0833 0.1817± 0.0406 0.4429±0.0896 0.1530± 0.0977 0.2365± 0.1817

SO¼4 0.2902± 0.1030 0.2935± 0.0592 2.0975± 0.4422 1.4770± 0.3374 6.4306± 1.7063 1.4394±0.5482 3.2539± 2.0391 2.3889± 2.2702

NHþ4 0.0331± 0.0468 0.0393± 0.0179 3.7437± 0.3750 1.6461± 1.9100 6.7925± 1.5679 0.4093±0.4255 0.0355± 0.0481 1.6478± 2.5274

Naþ 0.0210± 0.0086 0.0063± 0.0033 0.1730± 0.0923 0.0695± 0.0160 4.1879± 1.4918 0.1072±0.0352 0.0231± 0.0098 0.6489± 1.5548

Kþ 0.1152± 0.0193 0.0190± 0.0036 13.6511± 3.1483 4.9853± 3.2342 2.2059± 0.9791 3.4528±0.5886 6.2976± 1.9151 5.4085± 6.5236

OC1 19.2680± 5.5336 39.7851± 17.1758 8.4841± 2.1364 16.3230± 3.9705 29.3748± 6.6446 35.3848±11.1619 20.5371± 6.0676 23.8845± 12.4374

OC2 7.9390± 1.2510 12.2484± 3.2296 7.1925± 1.1158 12.4002± 1.9327 10.1671± 1.6442 16.4389±3.4366 14.7574± 4.0596 11.8593± 4.2028

OC3 8.2322± 6.1347 5.8299± 1.0302 17.0703± 2.0216 22.0037± 8.7806 9.5479± 1.2576 14.9457±5.6736 16.4167± 6.9996 13.7939± 6.5631

OC4 3.7110± 1.4955 4.2555± 1.1661 13.1832± 1.7183 13.1153± 2.5184 8.5238± 1.6182 9.3995±1.4009 9.0723± 4.6109 8.7887± 3.8284

OP 4.3025± 1.1177 25.5985± 21.9489 0.0119± 0.1616 1.6564± 2.3424 2.5665± 1.4588 2.3722±2.0805 7.4249± 6.8810 6.0800± 9.3231

OC 43.4527± 5.5761 87.7173± 16.9684 45.9419± 4.0042 65.4985± 11.3237 60.1802± 7.8838 78.5411±10.9289 68.2084± 14.2931 64.4064± 16.4483

EC1 18.6894± 4.1176 11.4926± 2.5841 14.7930± 8.2367 10.2793± 1.8945 7.0445± 2.0160 12.9544±4.1315 14.5665± 5.2888 13.9258± 7.2847

EC2 40.7083± 4.6715 25.6340± 22.0619 0.2899± 0.1744 0.3166± 0.1559 0.1629± 0.0493 0.1843±0.1173 0.2769± 0.2028 7.7746± 15.0999

EC3 0.0892± 0.1053 0.0465± 0.0284 0.1869± 0.2643 0.1301± 0.1736 0.1047± 0.0867 0.1639±0.1427 0.1858± 0.1630 0.1301± 0.1293

EC 55.1843± 6.3893 11.5745± 8.2651 15.2580± 7.7814 9.0696± 2.9703 4.7457± 1.4865 10.9306±6.2537 7.6043± 3.6228 15.7505± 15.4458

TC 98.6370± 9.7451 99.2919± 19.9199 61.1999± 5.8682 74.5681± 8.3535 64.9259± 8.1932 89.4716±11.7307 75.8127± 14.7385 80.1569± 15.2717

Na 0.0996± 0.1408 0.0401± 0.0481 0.0000± 0.3314 1.3822± 1.9547 0.0000± 0.1525 1.7584±1.5790 1.7688± 1.8186 1.1345± 1.8367

Mg 0.0000± 0.0621 0.0009± 0.0077 0.0351± 0.0400 0.0916± 0.0243 0.0259± 0.0220 0.0143±0.0188 0.0985± 0.0275 0.0449± 0.0436

Al 0.0024± 0.0070 0.0026± 0.0037 0.2160± 0.0907 0.1593± 0.0168 0.0407± 0.0589 0.0734±0.0174 0.1943± 0.0995 0.1061± 0.0975

Si 0.2206± 0.1524 0.0924± 0.0799 0.2387± 0.0538 0.1724± 0.1231 0.0505± 0.0561 0.0651±0.0298 0.7777± 0.2830 0.3017± 0.3222

P 0.0000± 0.0028 0.0000± 0.0019 0.0000± 0.0068 0.0000± 0.0062 0.0000± 0.0127 0.0000±0.0040 0.0145± 0.0201 0.0035± 0.0110

S 0.2273± 0.0204 0.2580± 0.0391 0.7225± 0.1811 0.4651± 0.0561 1.9164± 0.6500 0.4785±0.1973 1.0034± 1.0273 0.7842± 0.7505

Cl 0.2505± 0.0262 0.1105± 0.0404 18.6148± 2.9080 12.0179± 4.4149 15.0086± 2.0210 4.1719±0.7412 7.9226± 5.5499 8.1245± 6.8032

K 0.1319± 0.0115 0.0244± 0.0037 12.5964± 3.5484 9.0065± 1.9086 2.0158± 1.0054 3.6503±0.7316 10.2542± 6.5858 5.7343± 5.6264

Ca 0.0991± 0.0788 0.0417± 0.0206 0.2553± 0.1863 0.1986± 0.1314 0.0979± 0.1190 0.0933±0.0357 1.2278± 0.2645 0.4023± 0.5008

Ti 0.0019± 0.0116 0.0000± 0.0045 0.0094± 0.0124 0.0053± 0.0202 0.0025± 0.0056 0.0012±0.0084 0.0262± 0.0156 0.0088± 0.0126

V 0.0002± 0.0049 0.0000± 0.0019 0.0011± 0.0057 0.0002± 0.0086 0.0005± 0.0026 0.0002±0.0036 0.0016± 0.0061 0.0006± 0.0054

Cr 0.0004± 0.0008 0.0008± 0.0003 0.0013± 0.0007 0.0068± 0.0068 0.0012± 0.0003 0.0003±0.0005 0.0013± 0.0008 0.0015± 0.0024

Mn 0.0008± 0.0004 0.0002± 0.0002 0.0028± 0.0005 0.0027± 0.0007 0.0009± 0.0002 0.0004±0.0003 0.0071± 0.0007 0.0027± 0.0028

Fe 0.0264± 0.0206 0.0071± 0.0074 0.0602± 0.0152 0.0707± 0.0218 0.0142± 0.0179 0.0177±0.0125 0.1772± 0.0860 0.0688± 0.0790

Co 0.0000± 0.0006 0.0000± 0.0002 0.0003± 0.0011 0.0003± 0.0013 0.0000± 0.0004 0.0000±0.0004 0.0000± 0.0031 0.0001± 0.0017

Ni 0.0012± 0.0004 0.0010± 0.0003 0.0019± 0.0005 0.0027± 0.0007 0.0001± 0.0002 0.0000±0.0003 0.0016± 0.0005 0.0011± 0.0004

Cu 0.0013± 0.0006 0.0004± 0.0003 0.0133± 0.0078 0.0061± 0.0011 0.0017± 0.0003 0.0006±0.0004 0.0015± 0.0006 0.0030± 0.0043

Zn 0.0381± 0.0240 0.0223± 0.0042 0.0659± 0.0045 0.0382± 0.0173 0.0092± 0.0064 0.0159±0.0104 0.0340± 0.0178 0.0305± 0.0203

Ga 0.0002± 0.0016 0.0000± 0.0005 0.0003± 0.0017 0.0000± 0.0027 0.0000± 0.0008 0.0001±0.0011 0.0000± 0.0017 0.0001± 0.0016

J.C.Chowet

al./Chem

osphere

54(2004)185–208

197

Page 14: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 3 (continued)

Pine fence burn

(BURN1),

PM2:5

Pine fence burn

(BURN2),

PM2:5

Mesquite burn

(BURN3),

PM2:5

Mesquite burn

(BURN4),

PM2:5

Tamarisk burn

(BURN5),

PM2:5

Huisache burn

(BURN6),

PM2:5

Grass burn

(BURN7),

PM2:5

Vegetative burning

composite (BURN),

PM2:5

As 0.2198± 0.1252 0.0134±0.0081 0.0000± 0.0016 0.0003± 0.0013 0.0044± 0.0006 0.0002± 0.0007 0.0009± 0.0008 0.0243±0.0711

Se 0.0000± 0.0005 0.0000±0.0002 0.0036± 0.0004 0.0009± 0.0006 0.0031± 0.0003 0.0009± 0.0002 0.0002± 0.0004 0.0012±0.0004

Br 0.0000± 0.0053 0.0021±0.0004 0.1964± 0.0282 0.0966± 0.0347 0.2148± 0.0678 0.0248± 0.0129 0.0443± 0.0395 0.0827±0.0900

Rb 0.0003± 0.0007 0.0000±0.0002 0.0112± 0.0025 0.0063± 0.0014 0.0035± 0.0078 0.0037± 0.0005 0.0028± 0.0008 0.0037±0.0035

Sr 0.0003± 0.0004 0.0001±0.0002 0.0033± 0.0005 0.0032± 0.0007 0.0007± 0.0002 0.0017± 0.0003 0.0218± 0.0067 0.0064±0.0094

Y 0.0000± 0.0006 0.0000±0.0002 0.0009± 0.0013 0.0004± 0.0011 0.0002± 0.0012 0.0000± 0.0005 0.0000± 0.0008 0.0001±0.0009

Zr 0.0000± 0.0124 0.0000±0.0043 0.0014± 0.0142 0.0008± 0.0218 0.0002± 0.0059 0.0009± 0.0089 0.0029± 0.0128 0.0012±0.0129

Mo 0.0000± 0.0012 0.0000±0.0005 0.0010± 0.0012 0.0002± 0.0021 0.0000± 0.0007 0.0000± 0.0009 0.0000± 0.0016 0.0001±0.0013

Pd 0.0000± 0.0024 0.0000±0.0009 0.0014± 0.0024 0.0000± 0.0043 0.0000± 0.0012 0.0000± 0.0017 0.0000± 0.0029 0.0001±0.0026

Ag 0.0001± 0.0030 0.0002±0.0011 0.0009± 0.0034 0.0000± 0.0053 0.0000± 0.0015 0.0007± 0.0021 0.0008± 0.0035 0.0004±0.0032

Cd 0.0025± 0.0031 0.0006±0.0012 0.0015± 0.0034 0.0000± 0.0053 0.0026± 0.0014 0.0005± 0.0022 0.0003± 0.0036 0.0010±0.0033

In 0.0012± 0.0036 0.0002±0.0014 0.0023± 0.0039 0.0027± 0.0063 0.0003± 0.0018 0.0006± 0.0026 0.0007± 0.0043 0.0010±0.0039

Sn 0.0021± 0.0053 0.0000±0.0021 0.0010± 0.0062 0.0054± 0.0075 0.0008± 0.0028 0.0008± 0.0039 0.0013± 0.0064 0.0014±0.0055

Sb 0.0010± 0.0060 0.0004±0.0023 0.0007± 0.0070 0.0028± 0.0105 0.0001± 0.0031 0.0003± 0.0043 0.0010± 0.0073 0.0009±0.0066

Ba 0.0000± 0.0283 0.0019±0.0110 0.0000± 0.0321 0.0111± 0.0491 0.0027± 0.0146 0.0008± 0.0204 0.0022± 0.0345 0.0034±0.0307

La 0.0000± 0.0379 0.0011±0.0147 0.0000± 0.0430 0.0000± 0.0653 0.0012± 0.0195 0.0006± 0.0273 0.0000± 0.0459 0.0004±0.0410

Au 0.0001± 0.0022 0.0000±0.0010 0.0010± 0.0030 0.0000± 0.0031 0.0000± 0.0009 0.0002± 0.0013 0.0001± 0.0022 0.0002±0.0021

Hg 0.0000± 0.0011 0.0000±0.0003 0.0006± 0.0008 0.0008± 0.0012 0.0005± 0.0003 0.0008± 0.0004 0.0007± 0.0007 0.0005±0.0007

Tl 0.0000± 0.0070 0.0000±0.0005 0.0001± 0.0008 0.0002± 0.0012 0.0001± 0.0004 0.0000± 0.0005 0.0000± 0.0008 0.0000±0.0023

Pb 0.0105± 0.0088 0.0008±0.0004 0.0059± 0.0035 0.0015± 0.0018 0.0018± 0.0007 0.0016± 0.0008 0.0001± 0.0015 0.0025±0.0039

U 0.0000± 0.0011 0.0000±0.0004 0.0003± 0.0048 0.0000± 0.0030 0.0006± 0.0048 0.0000± 0.0011 0.0000± 0.0019 0.0001±0.0029

SO2 6.3073±

913.4648

5.9053±

320.1829

46.2498±

1045.7844

16.3571±

1615.0380

113.7057±

434.6846

27.5726±

654.9965

6.8115±

948.3355

32.6666±

951.5014

NH3 0.5203±

0.2220

1.6362±

0.3852

12.3088±

12.4892

34.7671±

4.2325

20.0665±

18.8168

25.6610±

17.7356

19.6718±

10.9509

16.1732±

14.7677

550 MW utility

coal-fired boiler

(CFPP1),

PM2:5

600 MW utility

coal-fired boiler

(CFPP2),

PM2:5

600 MW utility

coal-fired boiler

(CFPP3),

PM2:5

545 MW alumin-

um plant coal-fired

boiler (CFPP4),

PM2:5

550 MW utility

coal-fired boiler

(CFPP5),

PM2:5

550 MW utility

coal-fired boiler

(CFPP6),

PM2:5

Coal-firedboiler

composite

(CFPP),

PM2:5

Cl� 3.0578± 1.0603 0.3132±0.5237 0.0680± 0.1927 0.0287± 0.0616 1.1291± 2.4369 0.3017± 0.2130 0.8937± 1.5668

NO�3 2.5397± 1.0346 0.9998±0.6741 0.0735± 0.1819 0.0676± 0.0514 0.0904± 0.0844 0.0000± 0.0802 0.6865± 1.0917

SO¼4 11.4070± 2.0918 5.7416±1.9133 9.4809± 2.7967 45.6284± 18.6921 46.2367± 6.8494 62.2238± 12.5122 28.7433± 22.5634

NHþ4 0.6778± 0.6024 0.4654±0.5648 0.2780± 0.2759 0.6595± 0.2337 5.1146± 0.5016 5.3068± 0.5913 1.7887± 2.1280

Naþ 4.4339± 0.7313 0.4267±0.1073 0.5269± 0.2015 0.5770± 0.5289 0.1233± 0.0647 0.0719± 0.0168 1.1618± 1.6832

Kþ 0.5435± 0.1075 0.0946±0.0607 0.1168± 0.0387 0.2565± 0.1129 0.1009± 0.0207 0.1180± 0.0166 0.2247± 0.1809

OC1 22.0535± 10.6466 13.7789±8.0167 3.5677± 3.0068 0.0366± 0.3779 3.2015± 6.1608 0.0000± 0.9418 7.5339± 10.1529

OC2 16.0144± 5.4063 24.2508±8.3395 12.2643± 2.3013 1.0910± 0.7891 2.1621± 2.0245 0.0720± 0.5470 9.3705± 9.6312

OC3 12.8867± 4.1185 10.7561±6.1436 4.1661± 1.8714 0.8772± 0.3556 0.9090± 0.7468 0.0000± 0.4438 5.1512± 5.8271

OC4 6.5048± 2.3423 4.2146±2.1548 1.3902± 0.6525 1.1480± 0.3861 1.5422± 0.7923 0.1891± 0.2703 2.6892± 2.4109

OP 5.3910± 3.4418 2.6746±3.1625 1.4200± 1.8571 1.0172± 0.3889 2.4896± 0.9384 0.6664± 0.3168 2.4314± 2.5173

198

J.C.Chowet

al./Chem

osphere

54(2004)185–208

Page 15: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

OC 62.8505± 14.8165 55.6750± 11.9389 22.8083± 4.1552 4.1699± 1.5348 10.3044± 9.7720 0.9274± 1.2479 27.1762±25.7663

EC1 5.5433± 1.5570 2.2629± 1.5241 0.7469± 0.3275 1.4687± 0.5374 1.2774± 0.4543 0.2399± 0.1368 2.1321±1.9696

EC2 2.5633± 1.2231 2.5596± 1.6272 0.7340± 0.3697 0.9974± 0.4134 1.1659± 0.2927 0.5051± 0.1689 1.4929±1.0761

EC3 0.0082± 0.2310 0.2191± 0.3117 0.0000± 0.0725 0.1298± 0.0989 0.1785± 0.1115 0.0174± 0.0360 0.1009±0.1646

EC 2.7238± 3.2386 2.3686± 4.0871 0.6386± 0.9373 1.5787± 0.7776 0.1321± 0.7674 0.0961± 0.3806 1.3836±2.2247

TC 65.5743± 15.3061 58.0435± 12.2885 23.4469± 4.1237 5.7486± 2.2608 10.4366± 9.6614 1.0235± 1.3047 28.5598±26.6490

Na 4.2961± 1.5036 0.3003± 1.1496 0.2380± 0.8367 0.0000± 1.8103 0.0000± 0.4494 0.0000± 0.4669 0.9090±1.7783

Mg 1.4472± 0.4279 1.2988± 0.5808 1.9432± 0.8313 0.2009± 0.4504 0.1067± 0.0887 0.0000± 0.1592 0.8439±0.8588

Al 1.3110± 0.3101 5.5772± 2.1390 10.1194± 0.7965 4.8791± 3.4158 6.5301± 0.5525 3.2475± 0.6585 5.2985±3.2609

Si 1.9782± 0.2905 6.1001± 1.9984 11.5479± 0.7746 16.0087± 7.4458 13.8981± 1.6025 16.0140± 10.6714 10.6944±6.8068

P 0.0652± 0.0665 0.7205± 0.2446 1.0909± 0.3310 0.0000± 0.1204 0.0000± 0.0787 0.0000± 0.0987 0.2912±0.4551

S 5.9929± 1.4767 1.8907± 0.7542 3.1472± 0.7571 11.7983± 5.1870 12.3575± 1.2370 15.6001± 1.3245 8.2267±5.3390

Cl 3.8831± 0.6009 0.1403± 0.1653 0.1093± 0.0878 0.0000± 0.2669 0.0000± 0.2446 0.0000± 0.3050 0.7851±1.5625

K 0.7465± 0.1705 0.1875± 0.0502 0.4699± 0.1108 0.7083± 0.2957 0.4604± 0.0418 0.3016± 0.0265 0.5199±0.2561

Ca 5.1179± 1.0183 16.1149± 5.4475 32.3265± 2.1607 23.9882± 7.8824 11.0561± 1.2695 5.9274± 0.7637 16.5546±10.5262

Ti 0.1454± 0.1221 0.6489± 0.1881 1.2442± 0.1242 1.4200± 0.4632 0.8370± 0.1010 0.5496± 0.0936 0.8501±0.5164

V 0.0085± 0.0615 0.0454± 0.0695 0.0707± 0.1152 0.1217± 0.1140 0.1261± 0.0141 0.0937± 0.0221 0.0790±0.0810

Cr 0.0086± 0.0099 0.0155± 0.0092 0.0195± 0.0091 0.0530± 0.0205 0.0246± 0.0029 0.0201± 0.0070 0.0255±0.0191

Mn 0.0314± 0.0063 0.0319± 0.0097 0.0416± 0.0126 0.2711± 0.0908 0.1503± 0.0207 0.0807± 0.0069 0.1150±0.1063

Fe 0.8920± 0.1255 2.8893± 0.9272 6.4850± 0.6362 4.7477± 1.7648 3.6774± 0.2629 2.5575± 0.3831 3.6133±2.0193

Co 0.0004± 0.0152 0.0024± 0.0475 0.0013± 0.1025 0.0099± 0.0792 0.0044± 0.0582 0.0040± 0.0406 0.0041±0.0651

Ni 0.0189± 0.0219 0.0158± 0.0048 0.0167± 0.0028 0.0394± 0.0163 0.0071± 0.0009 0.0086± 0.0051 0.0198±0.0166

Cu 0.0798± 0.0504 0.1049± 0.0906 0.0724± 0.0053 0.1810± 0.0594 0.0232± 0.0061 0.0131± 0.0016 0.0898±0.0746

Zn 0.5105± 0.1473 0.3602± 0.2571 0.2494± 0.0882 0.4567± 0.5717 0.0525± 0.0076 0.0367± 0.0151 0.3103±0.3334

Ga 0.0008± 0.0195 0.0221± 0.0163 0.0065± 0.0076 0.0526± 0.0190 0.0162± 0.0101 0.0145± 0.0037 0.0209±0.0217

As 0.0000± 0.0109 0.0020± 0.0122 0.0086± 0.0052 0.0039± 0.0954 0.0000± 0.0271 0.0000± 0.0976 0.0025±0.0550

Se 0.0142± 0.0068 0.0079± 0.0032 0.0114± 0.0035 1.3310± 0.9220 0.5081± 0.2677 2.1721± 0.3282 0.5776±0.8325

Br 0.1385± 0.0353 0.0012± 0.0048 0.0003± 0.0021 0.0000± 0.0049 0.0001± 0.0030 0.0000± 0.0028 0.0269±0.0573

Rb 0.0005± 0.0079 0.0022± 0.0044 0.0036± 0.0011 0.0078± 0.0031 0.0031± 0.0007 0.0007± 0.0010 0.0034±0.0039

Sr 0.0761± 0.0217 0.3501± 0.1191 0.6707± 0.0449 1.0430± 0.3445 0.3049± 0.0379 0.1572± 0.0133 0.4831±0.3969

Y 0.0017± 0.0077 0.0050± 0.0048 0.0094± 0.0021 0.0035± 0.0050 0.0022± 0.0015 0.0000± 0.0056 0.0038±0.0049

Zr 0.0029± 0.1525 0.0159± 0.1319 0.0360± 0.0510 0.0453± 0.0156 0.0250± 0.0208 0.0152± 0.0225 0.0250±0.0879

Mo 0.0028± 0.0163 0.0130± 0.0112 0.0036± 0.0059 0.0223± 0.0077 0.0056± 0.0018 0.0044± 0.0018 0.0097±0.0091

Pd 0.0081± 0.0373 0.0132± 0.0338 0.0000± 0.0125 0.0000± 0.0022 0.0002± 0.0041 0.0000± 0.0046 0.0036±0.0218

Ag 0.0118± 0.0446 0.0062± 0.0402 0.0023± 0.0144 0.0015± 0.0024 0.0096± 0.0190 0.0012± 0.0055 0.0059±0.0259

Cd 0.0039± 0.0466 0.0056± 0.0426 0.0013± 0.0154 0.0027± 0.0019 0.0020± 0.0049 0.0014± 0.0055 0.0029±0.0272

In 0.0198± 0.0538 0.0158± 0.0484 0.0030± 0.0174 0.0010± 0.0030 0.0015± 0.0059 0.0010± 0.0066 0.0073±0.0312

Sn 0.0135± 0.0739 0.0043± 0.0648 0.0101± 0.0244 0.0216± 0.0073 0.0054± 0.0079 0.0112± 0.0076 0.0117±0.0425

Sb 0.0243± 0.0850 0.0041± 0.0750 0.0073± 0.0277 0.0163± 0.0072 0.0040± 0.0098 0.0015± 0.0111 0.0111±0.0490

Ba 0.2246± 0.3393 0.8469± 0.4498 2.0975± 0.2309 2.2461± 0.7696 0.2166± 0.0379 0.1169± 0.0397 1.0651±1.0098

La 0.0125± 0.4760 0.0000± 0.3934 0.0000± 0.1583 0.0004± 0.0310 0.0069± 0.0632 0.0000± 0.0687 0.0038±0.2694

Au 0.0025± 0.0285 0.0187± 0.0212 0.0020± 0.0111 0.0054± 0.0256 0.0015± 0.0033 0.0031± 0.0043 0.0054±0.0200

Hg 0.0015± 0.0110 0.0091± 0.0073 0.0039± 0.0033 0.0000± 0.0025 0.0206± 0.0200 0.0084± 0.0040 0.0069±0.0112

Tl 0.0000± 0.0103 0.0013± 0.0102 0.0007± 0.0036 0.0025± 0.0147 0.0018± 0.0049 0.0010± 0.0171 0.0013±0.0107

Pb 0.0081± 0.0169 0.0221± 0.0170 0.0271± 0.0048 0.1834± 0.1324 0.0148± 0.0532 0.0099± 0.2080 0.0551±0.0895

U 0.0017± 0.0131 0.0034± 0.0106 0.0023± 0.0052 0.0000± 0.0043 0.0011± 0.0021 0.0011± 0.0024 0.0015±0.0077

J.C.Chowet

al./Chem

osphere

54(2004)185–208

199

Page 16: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 3 (continued)

550 MW utility

coal-fired boiler

(CFPP1),

PM2:5

600 MW utility

coal-fired boiler

(CFPP2),

PM2:5

600 MW utility

coal-fired boiler

(CFPP3),

PM2:5

545 MW aluminum

plant coal-fired

boiler (CFPP4),

PM2:5

550 MW utility

coal-fired boiler

(CFPP5),

PM2:5

550 MW utility

coal-fired boiler

(CFPP6),

PM2:5

Coal-firedboiler

composite

(CFPP),

PM2:5

SO2 35016.4922±

32394.8867

29575.0625±

44712.1484

29473.2324±

19096.9492

219.1606±

678.1803

3286.1223±

4062.8457

13687.1816±

9277.8418

17553.6758±

26048.8730

NH3 75.6945±

50.1126

46.0722±

13.5468

7.0390± 3.5178 0.0779± 0.0842 0.0750± 0.2082 0.0000±0.2245 22.7600± 37.1492

Coal fly ash (BVCLFA)

Catalytic cracker

(CAT1),

Cement kiln

(CEM1),

Cement kiln

(CEM2),

Cement kiln

composite (CEM),

PM2:5 Coarse PM10 PM2:5 PM2:5 PM2:5 PM2:5

Cl� 0.0120± 0.0658 0.0000± 0.0489 0.0000± 0.0389 0.5207± 0.4975 0.8779± 0.4878 16.9140±17.8163 7.1209± 12.5454

NO�3 0.7303± 0.5646 0.2710± 0.1007 0.4207± 0.2396 0.0000± 0.0043 11.0513± 8.5797 6.7598±5.9519 8.9073± 7.3390

SO¼4 6.3898± 3.7301 3.5568± 1.5201 4.2992± 2.0212 59.2005± 10.6883 31.4319± 4.4640 29.7282±13.5478 31.3778± 8.3698

NHþ4 0.0782± 0.0738 0.0080± 0.0480 0.0256± 0.0397 0.1433± 0.0557 1.4614± 0.9388 4.0008±2.1325 2.3589± 1.8725

Naþ 0.2061± 0.1022 0.1070± 0.0620 0.1337± 0.0717 0.0713± 0.0318 1.5711± 0.1962 1.6441±1.2573 1.5483± 0.7243

Kþ 0.0499± 0.0518 0.0161± 0.0327 0.0249± 0.0270 0.0143± 0.0070 12.5458± 1.2978 3.9003±0.7065 10.0849± 5.4653

OC1 0.2929± 0.4300 0.0668± 0.2708 0.1076± 0.2233 0.1117± 0.0847 1.8306± 3.3808 0.0000±2.6308 0.9985± 2.5747

OC2 0.5044± 0.3889 0.0097± 0.2401 0.0678± 0.1989 0.0023± 0.0287 2.0917± 1.2319 1.9767±1.6988 2.0192± 1.3690

OC3 0.4143± 0.4484 0.0252± 0.2812 0.1204± 0.2325 0.0408± 0.0254 3.1217± 1.4695 4.9470±1.4533 3.9615± 1.6264

OC4 0.3196± 0.3679 0.0636± 0.2310 0.1087± 0.1910 0.1578± 0.0641 3.9404± 2.5127 5.8139±4.1316 4.5321± 3.0649

OP 0.0010± 0.3596 0.0012± 0.2266 0.0009± 0.1872 0.1606± 0.0642 0.7623± 0.5554 1.8435±1.1741 1.2692± 0.9501

OC 1.5322± 0.8982 0.1664± 0.5611 0.4055± 0.4640 0.4732± 0.2141 11.7467± 6.7648 14.5810±6.1887 12.7805± 6.0340

EC1 0.6435± 0.8295 0.5779± 1.0009 0.5653± 0.9792 0.0772± 0.0394 2.0654± 1.2938 3.7717±1.4824 2.7048± 1.4884

EC2 0.7470± 0.8570 0.0000± 0.2363 0.0882± 0.2326 0.1470± 0.0527 1.1394± 0.6801 2.0916±1.5876 1.4529± 1.1202

EC3 0.0000± 0.0858 0.0000± 0.0540 0.0000± 0.0447 0.0067± 0.0045 0.0000± 0.0617 0.1844±0.3689 0.0671± 0.2224

EC 1.3894± 1.6839 0.5767± 0.9989 0.6526± 1.1235 0.0703± 0.0502 2.4426± 1.5262 4.2041±3.6240 2.9555± 2.5007

TC 2.9216± 2.3719 0.7431± 1.0548 1.0581± 1.3191 0.5435± 0.2380 14.1892± 7.7593 18.7850±9.5990 15.7360± 7.9843

Na 0.2232± 0.2291 0.2123± 0.1918 0.2128± 0.1988 0.0036± 0.2293 1.9377± 0.6982 2.8366±2.6416 2.3068± 1.5913

Mg 1.5464± 0.2137 0.8708± 0.1313 1.0632± 0.1916 0.0000± 0.0391 0.0358± 0.1141 0.0691±0.1167 0.0475± 0.1167

Al 6.8116± 0.6640 12.1941± 3.7695 10.6914± 2.6922 7.6692± 1.9152 1.0608± 0.2778 1.1351±0.5040 1.0636± 0.3522

Si 9.0606± 2.4365 15.1178± 4.9805 13.4493± 3.6297 21.6578± 6.8473 3.4259± 0.9798 5.7924±3.0656 4.2610± 2.1863

P 0.6428± 0.5727 0.3615± 0.3524 0.4446± 0.4241 0.0000± 0.1213 0.0000± 0.0688 0.0000±0.0682 0.0000± 0.0711

S 2.3807± 1.2292 1.3955± 0.6398 1.6548± 0.7670 26.4961± 2.4829 10.7285± 1.3433 9.9963±3.5528 10.8349± 2.5635

Cl 0.0000± 0.1108 0.0000± 0.2004 0.0000± 0.1444 0.0000± 0.4753 0.4234± 0.2979 7.0674±9.3977 3.1586± 6.0992

K 0.2664± 0.1710 0.4999± 0.1534 0.4342± 0.1703 0.0775± 0.0156 14.6460± 1.4704 4.4704±1.7771 11.5920± 6.1836

Ca 18.8276± 3.6996 20.1180± 3.7960 19.7790± 2.7060 0.2698± 0.0414 18.8088± 4.8466 17.2633±5.7045 17.4756± 5.2635

Ti 0.9592± 0.0944 0.9938± 0.1694 0.9873± 0.1317 0.7326± 0.1749 0.1396± 0.0435 0.1754±0.1149 0.1494± 0.0737

V 0.0628± 0.0585 0.0521± 0.0753 0.0553± 0.0548 1.0347± 0.2128 0.0169± 0.0144 0.0177±0.0266 0.0164± 0.0201

Cr 0.0105± 0.0093 0.0118± 0.0117 0.0115± 0.0090 0.1366± 0.0286 0.0095± 0.0065 0.0184±0.0049 0.0129± 0.0069

200

J.C.Chowet

al./Chem

osphere

54(2004)185–208

Page 17: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Mn 0.0796± 0.0572 0.0845± 0.0592 0.0830± 0.0588 0.0637±0.0132 0.1262± 0.0338 0.0662± 0.0261 0.0972± 0.0438

Fe 3.4730± 0.7845 3.8094± 0.6614 3.7187± 0.6889 3.5040±0.8139 1.1028± 0.2672 1.7524± 0.6454 1.3419± 0.5164

Co 0.0000± 0.0525 0.0000± 0.0815 0.0000± 0.0598 0.1041±0.0226 0.0024± 0.0179 0.0035± 0.0290 0.0029± 0.0226

Ni 0.0080± 0.0020 0.0105± 0.0016 0.0098± 0.0013 1.8999±0.4411 0.0154± 0.0131 0.0367± 0.0141 0.0243± 0.0160

Cu 0.0564± 0.0212 0.0507± 0.0164 0.0521± 0.0175 0.0019±0.0094 0.0219± 0.0130 0.0239± 0.0046 0.0242± 0.0109

Zn 0.0493± 0.0188 0.0366± 0.0090 0.0397± 0.0109 0.1963±0.0243 0.1838± 0.0491 0.8102± 0.9052 0.4090± 0.5902

Ga 0.0133± 0.0086 0.0080± 0.0075 0.0095± 0.0074 0.0014±0.0006 0.0000± 0.0054 0.0000± 0.0080 0.0002± 0.0066

As 0.0059± 0.0062 0.0063± 0.0068 0.0062± 0.0067 0.0000±0.0024 0.0007± 0.0057 0.0014± 0.0105 0.0009± 0.0165

Se 0.0114± 0.0023 0.0044± 0.0014 0.0064± 0.0012 0.0286±0.0034 0.0055± 0.0012 0.0058± 0.0018 0.0061± 0.0026

Br 0.0000± 0.0024 0.0000± 0.0016 0.0000± 0.0013 0.0021±0.0002 0.0446± 0.0061 0.1254± 0.0778 0.1113± 0.1281

Rb 0.0025± 0.0019 0.0022± 0.0014 0.0023± 0.0012 0.0008±0.0001 0.0834± 0.0074 0.0405± 0.0123 0.0780± 0.0451

Sr 0.4763± 0.0489 0.4787± 0.0444 0.4791± 0.0441 0.0155±0.0035 0.0919± 0.0215 0.0845± 0.0203 0.0857± 0.0220

Y 0.0062± 0.0026 0.0047± 0.0016 0.0052± 0.0013 0.0079±0.0006 0.0017± 0.0042 0.0023± 0.0033 0.0020± 0.0046

Zr 0.0219± 0.0071 0.0252± 0.0192 0.0245± 0.0129 0.0172±0.0037 0.0080± 0.0419 0.0198± 0.0585 0.0122± 0.0494

Mo 0.0056± 0.0055 0.0008± 0.0038 0.0014± 0.0031 0.0075±0.0007 0.0019± 0.0044 0.0055± 0.0063 0.0040± 0.0052

Pd 0.0000± 0.0177 0.0000± 0.0109 0.0000± 0.0091 0.0000±0.0018 0.0005± 0.0092 0.0000± 0.0134 0.0002± 0.0111

Ag 0.0006± 0.0213 0.0003± 0.0129 0.0003± 0.0109 0.0004±0.0022 0.0033± 0.0103 0.0049± 0.0167 0.0044± 0.0129

Cd 0.0020± 0.0226 0.0003± 0.0137 0.0002± 0.0116 0.0001±0.0023 0.0009± 0.0113 0.0058± 0.0163 0.0054± 0.0133

In 0.0013± 0.0253 0.0000± 0.0153 0.0000± 0.0129 0.0010±0.0029 0.0086± 0.0124 0.0083± 0.0195 0.0083± 0.0156

Sn 0.0017± 0.0319 0.0028± 0.0194 0.0023± 0.0162 0.0005±0.0043 0.0065± 0.0195 0.0052± 0.0297 0.0054± 0.0240

Sb 0.0016± 0.0363 0.0011± 0.0220 0.0010± 0.0185 0.5754±0.1086 0.0010± 0.0224 0.0050± 0.0333 0.0038± 0.0271

Ba 1.0788± 0.3719 0.8098± 0.3916 0.8822± 0.3926 0.0777±0.0274 0.0224± 0.0999 0.0640± 0.1554 0.0395± 0.1242

La 0.0145± 0.0434 0.0079± 0.0734 0.0100± 0.0518 1.9463±0.3801 0.0000± 0.1395 0.0030± 0.2089 0.0011± 0.1692

Au 0.0003± 0.0025 0.0000± 0.0036 0.0000± 0.0026 0.0000±0.0072 0.0000± 0.0088 0.0003± 0.0491 0.0005± 0.0304

Hg 0.0005± 0.0016 0.0000± 0.0025 0.0000± 0.0018 0.0002±0.0006 0.0001± 0.0026 0.0037± 0.0055 0.0018± 0.0036

Tl 0.0009± 0.0023 0.0000± 0.0031 0.0000± 0.0023 0.0039±0.0003 0.0024± 0.0023 0.0075± 0.0037 0.0081± 0.0125

Pb 0.0322± 0.0315 0.0175± 0.0173 0.0215± 0.0200 0.0063±0.0029 0.0284± 0.0090 0.0564± 0.0186 0.0635± 0.0812

U 0.0014± 0.0027 0.0008± 0.0041 0.0007± 0.0030 0.0001±0.0010 0.0000± 0.0144 0.0000± 0.0096 0.0000± 0.0157

SO2 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 715.6211±

310.4723

19013.8027±

16482.1797

49783.2539±

16032.7744

31628.2852±

21004.8320

NH3 0.0000± 0.0000 0.0000± 0.0000 0.0000± 0.0000 0.0109±0.0120 27.3699± 15.1342 23.6171± 31.7870 24.3957± 21.0925

Smoked

chicken

(SMCHICK),

PM2:5

Charbroiled

chicken

(CHCHICK),

PM2:5

Chicken over

propane

(PRCHICK),

PM2:5

Charbroiled

Hamburger

(BURGER),

PM2:5

Steak

Stirfrya

(SFSTEAK),

PM2:5

Cooking

composite

(COOK),

PM2:5

Cl� 1.6723± 1.2367 0.0634± 0.0462 0.7348± 1.0601 0.0349±0.0246 0.9539± 0.7603 0.7589± 0.8405

NO�3 0.5177± 0.1480 0.0892± 0.0406 0.0771± 0.9656 0.0777±0.0223 1.0348± 0.6765 0.2691± 0.5956

SO¼4 0.5366± 0.3296 0.1320± 0.0931 0.5776± 2.2596 0.1974±0.0635 0.0000± 1.5343 0.3816± 1.3882

NHþ4 0.1927± 0.2136 0.0000± 0.0399 0.0000± 0.9744 0.0000±0.0212 0.0000± 0.6644 0.0482± 0.5991

Naþ 0.2871± 0.2036 0.0726± 0.0174 0.3922± 0.2210 0.1360±0.0496 0.0000± 0.1286 0.2373± 0.1957

Kþ 0.4558± 0.2991 0.0218± 0.0054 0.2392± 0.1645 0.1094±0.0366 0.0000± 0.0804 0.2155± 0.2298

OC1 14.7155± 6.8316 23.1569± 5.3829 7.6801± 12.5182 18.3299±4.1816 0.0000± 8.1916 13.1534± 9.0165

OC2 12.3563± 2.2613 40.2446± 6.6374 22.5011± 8.6983 43.1296±6.4724 10.7612± 5.3108 25.3819± 13.8554

OC3 32.8816± 12.2873 25.4606± 3.3897 44.6889± 18.3532 29.3652±3.9450 64.7025± 12.1844 37.6462± 15.8667

J.C.Chowet

al./Chem

osphere

54(2004)185–208

201

Page 18: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 3 (continued)

Smoked

chicken

(SMCHICK),

PM2:5

Charbroiled

chicken

(CHCHICK),

PM2:5

Chicken over

propane

(PRCHICK),

PM2:5

Charbroiled

Hamburger

(BURGER),

PM2:5

Steak

Stirfrya

(SFSTEAK),

PM2:5

Cooking

composite

(COOK),

PM2:5

OC4 11.2084± 1.9312 5.1214± 0.7628 8.6626± 3.9870 3.9532± 0.5925 15.2853±3.7129 8.4758± 3.6762

OP 5.1356± 3.2240 1.0432± 0.7040 0.7809± 3.1702 0.8446± 1.1822 1.4073±2.1935 1.9761± 2.4533

OC 76.2974± 8.5231 95.0265± 10.8492 84.3136± 23.3608 95.6222± 10.4533 92.1563±18.3577 86.6333± 16.3090

EC1 16.8639± 5.8421 3.9175± 0.7575 11.1508± 5.9113 3.6708± 1.3707 3.5978±1.3147 9.4974± 6.8350

EC2 4.7832± 3.0342 0.6310± 0.1098 1.9849± 2.1016 0.3203± 0.0564 3.3371±1.4101 2.2941± 2.3874

EC3 0.6999± 0.5599 0.1169± 0.0693 0.3701± 0.5306 0.2247± 0.1355 0.0000±0.2638 0.3553± 0.4058

EC 17.2115± 3.7279 3.6221± 1.1771 12.7248± 4.9287 3.3712± 2.4315 5.5276±2.9468 10.1707± 6.2895

TC 93.5089± 10.0374 98.6486± 11.0729 97.0384± 24.9788 98.9934± 10.6572 97.6839±19.1030 96.8040± 17.4052

Na 1.1624± 0.8656 0.1225± 0.1008 2.1295± 2.9941 0.1813± 0.0590 1.3453±2.1478 1.1632± 1.8156

Mg 0.1924± 0.2132 0.0121± 0.0177 0.3735± 0.3730 0.0270± 0.0172 0.1363±0.2374 0.1905± 0.2633

Al 0.0678± 0.0580 0.0411± 0.0248 0.0792± 0.1375 0.0218± 0.0054 0.1358±0.0560 0.0651± 0.0825

Si 0.7119± 0.5636 0.2700± 0.2555 0.6082± 0.5385 0.0948± 0.0616 0.4401±0.0722 0.4782± 0.4460

P 0.0000± 0.0075 0.0003± 0.0038 0.0087± 0.0460 0.0101± 0.0013 0.0020±0.0328 0.0048± 0.0285

S 0.3115± 0.0908 0.0730± 0.0134 0.3225± 0.2646 0.1086± 0.0204 0.3581±0.1077 0.2455± 0.1843

Cl 2.1727± 1.8394 0.0726± 0.0182 0.4265± 0.2534 0.0866± 0.0267 0.0743±0.0234 0.7180± 1.1944

K 0.6630± 0.4633 0.0538± 0.0151 0.2981± 0.1891 0.1347± 0.0486 0.0845±0.0353 0.3036± 0.3243

Ca 0.2194± 0.2550 0.2357± 0.1935 0.0923± 0.3408 0.1344± 0.0380 0.3038±0.2454 0.1726± 0.2115

Ti 0.0135± 0.0350 0.0044± 0.0115 0.0048± 0.2477 0.0016± 0.0061 0.0077±0.1752 0.0066± 0.1528

V 0.0008± 0.0149 0.0000± 0.0056 0.0001± 0.1053 0.0004± 0.0026 0.0000±0.0748 0.0003± 0.0650

Cr 0.0069± 0.0020 0.0003± 0.0013 0.0047± 0.0172 0.0000± 0.0004 0.0010±0.0118 0.0034± 0.0106

Mn 0.0695± 0.0520 0.0141± 0.0179 0.0113± 0.0100 0.0090± 0.0069 0.0072±0.0057 0.0256± 0.0355

Fe 0.7409± 0.6247 0.2053± 0.2285 0.2129± 0.1814 0.0764± 0.0301 0.1450±0.0316 0.3152± 0.3914

Co 0.0020± 0.0143 0.0003± 0.0041 0.0012± 0.0092 0.0025± 0.0035 0.0005±0.0061 0.0014± 0.0092

Ni 0.0076± 0.0051 0.0042± 0.0051 0.0061± 0.0085 0.0023± 0.0003 0.0061±0.0057 0.0055± 0.0052

Cu 0.0732± 0.0882 0.0125± 0.0064 0.0175± 0.0167 0.0046± 0.0033 0.0097±0.0088 0.0278± 0.0476

Zn 0.0620± 0.0540 0.0188± 0.0096 0.0396± 0.0574 0.0070± 0.0007 0.0394±0.0094 0.0363± 0.0428

Ga 0.0005± 0.0047 0.0000± 0.0017 0.0000± 0.0327 0.0000± 0.0007 0.0000±0.0231 0.0001± 0.0201

As 0.0022± 0.0031 0.0002± 0.0137 0.0001± 0.0156 0.0002± 0.0004 0.0000±0.0108 0.0007± 0.0111

Se 0.0002± 0.0010 0.0000± 0.0004 0.0001± 0.0074 0.0000± 0.0002 0.0000±0.0051 0.0001± 0.0046

Br 0.0226± 0.0239 0.0007± 0.0009 0.0016± 0.0067 0.0013± 0.0002 0.0056±0.0042 0.0070± 0.0139

Rb 0.0010± 0.0013 0.0003± 0.0004 0.0004± 0.0081 0.0000± 0.0002 0.0000±0.0056 0.0004± 0.0050

Sr 0.0016± 0.0013 0.0021± 0.0004 0.0004± 0.0104 0.0008± 0.0002 0.0015±0.0072 0.0011± 0.0064

Y 0.0000± 0.0018 0.0000± 0.0009 0.0000± 0.0126 0.0000± 0.0004 0.0000±0.0087 0.0000± 0.0078

Zr 0.0054± 0.0376 0.0322± 0.0455 0.0000± 0.2596 0.0000± 0.0058 0.0000±0.1839 0.0067± 0.1602

Mo 0.0000± 0.0037 0.0000± 0.0024 0.0000± 0.0267 0.0000± 0.0007 0.0000±0.0190 0.0000± 0.0165

Pd 0.0009± 0.0073 0.0000± 0.0022 0.0008± 0.0512 0.0002± 0.0012 0.0087±0.0364 0.0012± 0.0316

Ag 0.0036± 0.0090 0.0000± 0.0027 0.0085± 0.0633 0.0000± 0.0015 0.0082±0.0446 0.0044± 0.0390

Cd 0.0022± 0.0092 0.0001± 0.0029 0.0006± 0.0645 0.0000± 0.0016 0.0000±0.0461 0.0007± 0.0398

In 0.0009± 0.0108 0.0002± 0.0034 0.0027± 0.0771 0.0005± 0.0018 0.0000±0.0543 0.0012± 0.0475

Sn 0.0066± 0.0150 0.0028± 0.0050 0.0011± 0.1142 0.0007± 0.0028 0.0000±0.0815 0.0026± 0.0704

202

J.C.Chowet

al./Chem

osphere

54(2004)185–208

Page 19: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Sb

0.0784±0.1187

0.0026±0.0058

0.0044±0.1291

0.0004±0.0031

0.0000±0.0912

0.0216±0.0797

Ba

0.0091±0.0850

0.0146±0.0269

0.0491±0.6046

0.0054±0.0147

0.0000±0.4267

0.0220±0.3728

La

0.0000±0.1135

0.0063±0.0358

0.0302±0.8056

0.0004±0.0196

0.0000±0.5692

0.0112±0.4968

Au

0.0000±0.0055

0.0000±0.0016

0.0038±0.0327

0.0000±0.0008

0.0000±0.0231

0.0013±0.0202

Hg

0.0003±0.0020

0.0000±0.0007

0.0000±0.0148

0.0000±0.0004

0.0000±0.0102

0.0001±0.0091

Tl

0.0001±0.0021

0.0000±0.0023

0.0000±0.0141

0.0000±0.0004

0.0000±0.0097

0.0000±0.0087

Pb

0.0105±0.0061

0.0629±0.0880

0.0000±0.0260

0.0000±0.0007

0.0000±0.0184

0.0131±0.0357

U0.0000±0.0033

0.0000±0.0010

0.0000±0.0222

0.0000±0.0006

0.0000±0.0154

0.0000±0.0137

SO

22.7212±

2780.2695

0.8895±

799.6980

9.7416±

19219.9922

0.5429±

429.9691

10.4354±

13619.6494

5.0358±

11860.1777

NH

3128.4314±

55.9488

42.4342±

14.0024

51.0845±

58.2171

36.4371±

3.3950

0.0000±

1.8473

62.2812±

57.4136

aOnly

onesample

wasacquired.

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 203

The species abundances for each category of the

composite profiles are summarized in Table 4. The

coarse geological profiles are dominated by Si and Ca.

Paved road dust (BVPVRD1) also contains high levels

of OC (14.6 ± 7.6%), especially OC2 (1.3 ± 0.9%), OC3

(5.6 ± 3.4%), and OC4 (7.0 ± 3.7%). Fly ash is dominated

by the crustal elements (Al, Si, Ca, and Fe) as well as

SO¼4 (3.6 ± 1.5%). Both motor vehicle (BVRDMV) and

vegetative burning (BURN) emissions are dominated by

OC, EC, and carbon fractions, but vegetative burning

emissions are also enriched in Cl and SO¼4 . Coal power

plant (CFPP) emissions consist of SO¼4 (28.7 ± 22.6%),

OC (27.2 ± 25.8%), Ca (16.6 ± 10.5%), and Si (10.7 ±

6.8%). The cement kiln (CEM) profile is dominated by

SO¼4 (31.3 ± 8.4%), Ca (17.5 ± 5.3%), OC (12.8 ± 6.0%),

and K (11.6 ± 6.2%), most of which is soluble Kþ

(10.1 ± 5.5%). The cooking (COOK) profile is dominated

by OC (86.6 ± 16.3%) and EC (10.2 ± 6.3%). Fig. 2

compares the abundances of the composited profiles for

motor vehicle (BVRDMV), vegetative burning (BURN),

coal-fired power plant (CFPP), and meat cooking

(COOK) emissions. Trace element abundances in the

CFPP profile are enriched compared with the BURN

and COOK profiles.

3.1. Carbon Fractions

The carbon fraction abundances in Table 5 are con-

sistent within a source category, but differ between cat-

egories; these differences may be useful for source

discrimination. OC1 ranges from 0.11± 0.08% in CAT1

to 23.9± 12.4% in BURN, OC2 varies from 0.0± 0.03%

in CAT1 to 25.4 ± 13.9% in COOK, OC3 varies from

0.04± 0.03% in CAT1 to 37.6± 15.9% in COOK, OC4

varies from 0.16± 0.06% in CAT1 to 8.8± 3.8% in

BURN, and OP (pyrolized carbon) varies from 0.16±

0.06% in CAT1 to 6.1± 9.3% in BURN. Among the OC

fractions, OC1 is enriched (37%) in the vegetative

burning (BURN) profile, and OC3 is enriched (43%)

in the cooking (COOK) profile. Yu et al. (2002) observed

a larger OP fraction for polar organic compounds ex-

tracted in water than for non-polar compounds ex-

tracted in hexane for aerosols in China. Because

pyrolized carbon (OP) is 2% and 30% of total organic

carbon (OC) in COOK and CAT1, respectively (from

Tables 3 and 5), it might be inferred that source emis-

sions from catalytic cracking are more enriched in polar

organic compounds than those from meat cooking.

High-temperature EC (EC3) is low and variable. 63% of

EC in the motor vehicle (BVRDMV) profile is found in

EC2, whereas 93% of EC in the cooking (COOK) profile

is found in low-temperature EC1.

The degree to which the carbon fractions might dif-

ferentiate among combustion source contributions was

determined using a Monte Carlo approach (Watson,

Page 20: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 4

Summary of chemical species abundancesa in BRAVO source profiles

Composite source

profilebParticle size Chemical abundances in percent mass

<0.1% 0.1–1% 1–10% >10%

Paved road dust

(BVPVRD1)

Coarse Rb, Ni, Zr, Cu,

Mn, Pb, Sr

P, Zn, Kþ, Ti, S,

SO¼4 , K

EC1, OC2, Fe, Al,

OC3, OC4

Si, OC, Ca

Soil (BVSOIL1) Coarse Cu, Rb, Sr, Zn, Zr,

Mn, S

EC1, EC2, Ti Fe, K, OC3, OC4,

Al, Ca, OC

Si

Soil (BVSOIL2) Coarse Ni, Cu, Y, Pb, Rb,

Zr, Zn, Sr, Mn, S,

SO¼4 , P

Kþ, Ti K, Fe, Al Ca, Si

Unpaved road dust

(BVUNPV2)

Coarse Y, Cu, Pb, Rb, Cr,

Br, Zr, Ni, Zn, Mn,

Sr, Cl�, P, Kþ

Ti, SO¼4 , S, EC2,

NO�3 , OC3

K, EC1, Fe, OC4,

Al, OC

Si, Ca

Coal fly ash

(BVCLFA)

Coarse Rb, Se, Y, Ga, Ni,

Cr, Pb, Zr, Zn, Cu,

Mn

Naþ, NO�3 , P, Sr,

K, Ba, Ti

S, SO¼4 , Fe Al, Si, Ca

Coal fly ash

(BVCLFA)

Fine Rb, Mo, Y, Cr, Se,

Ga, Zr, Pb, Zn, Cu,

V, NHþ4 , Mn

Naþ, K, Sr, OC2,

P, NO�3 , Ti

Ba, OC, S, Fe,

SOþ4 , Al, Si

Ca

Motor vehicle

composite

(BVRDMV)

Fine Br, Mn, Cu Naþ, Zn, Fe S, OC4 OC1, OC2, OC3,

OC, EC1, EC2, EC

Vegetative burning

composite (BURN)

Fine Ni, Se, Rb, Zn Al, EC3, NO�3 , S SO¼

4 , K, Cl�, Cl,

OC4

OC1, OC2, OC3,

OC, EC1, EC

Coal-fired boiler

composite (CFPP)

Fine Mo, Ni, Cr, Cu Mn, Kþ, Sr, K, Ti Ba, EC1, EC2,

OC4, Fe, Al, S

Si, Ca, OC, SO¼4

Cement kiln com-

posite (CEM)

Fine Se, Cr, Cu, Ni, Rb,

Sr, Mn

Ti Al, OP, Fe, EC2,

Naþ, NHþ4 , EC1,

EC, OC3, Si, OC4,

NO�3

Kþ, S, K, OC, Ca,

SO¼4

Cooking composite

(COOK)

Fine Ni S, Si OC4, EC1 EC, OC1, OC2,

OC3, OC

Catalytic cracker

(CAT1)

Fine Rb, Ga, Br, Tl, Pb,

EC3, Mo, Y, Kþ,

Sr, Zr, Se, OC3,

Mn, EC, EC1, Ba

Co, OC1, Cr, NHþ4 ,

EC2, OC4, OP, Zn,

Ca, OC, Cl�, Sb, Ti

V, Ni, La, Fe, Al Si, S, SO¼4

a For species with abundances greater than their variabilities.b Samples in composite source profiles are identified in Table 2.

204 J.C. Chow et al. / Chemosphere 54 (2004) 185–208

1979; Chow, 1985; Javitz et al., 1988; Lowenthal et al.,

1992). Synthetic ambient data sets were generated from

the BVRDMV, BURN, CFPP, CEM, COOK, and

CAT1 profiles, ‘‘true’’ contributions of 10 lg/m3 from

each source, and random ambient and source uncer-

tainties were generated according to Eq. (1) in Lowen-

thal et al. (1992). Ambient measurement uncertainty for

all chemical species was assumed to be 10%.

The CMB receptor model with effective variance

weighting (Watson et al., 1984) was applied to each of

two synthetic data sets of 100 samples each, one con-

structed with random lognormal uncertainties corre-

sponding to a coefficient of variation of 30% for all

species for comparison with Javitz et al. (1988), and the

second with the measured source uncertainties in Table

3. The source contribution estimates (SCE) were con-

strained to values greater than or equal to zero. The es-

timated and true source contributions were compared

using the average absolute error (AAE), i.e., the average

of the absolute difference between the estimated and true

values divided by the true value, expressed as a percent

(Javitz et al., 1988). CMB analysis was conducted with all

Page 21: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

BVRDMV

Species

Cl-

NO3

SO4

NH4

Na+ K+ O1

O2

O3

O4

OP

OC E1 E2 E3 EC TC Al Si S Cl K Ca Ti V Cr

Mn Fe Ni

Cu Zn Ga As Se Br

Rb Sr Y Zr In Sn Sb Ba La Hg Tl Pb

Percent

0.001

0.01

0.1

1

10

100

BURN

Species

Cl-

NO3

SO4

NH4

Na+ K+ O1

O2

O3

O4

OP

OC E1 E2 E3 EC TC Al Si S Cl K Ca Ti V Cr

Mn Fe Ni

Cu Zn Ga As Se Br

Rb Sr Y Zr In Sn Sb Ba La Hg Tl Pb

Percent

0.001

0.01

0.1

1

10

100

CFPP

Species

Cl-

NO3

SO4

NH4

Na+ K+ O1

O2

O3

O4

OP

OC E1 E2 E3 EC TC Al Si S Cl K Ca Ti V Cr

Mn Fe Ni

Cu Zn Ga As Se Br

Rb Sr Y Zr In Sn Sb Ba La Hg Tl Pb

Percent

0.001

0.01

0.1

1

10

100COOK

Species

Cl-

NO3

SO4

NH4

Na+ K+ O1

O2

O3

O4

OP

OC E1 E2 E3 EC TC Al Si S Cl K Ca Ti V Cr

Mn Fe Ni

Cu Zn Ga As Se Br

Rb Sr Y Zr In Sn Sb Ba La Hg Tl Pb

Percent

0.001

0.01

0.1

1

10

100

Fig. 2. BRAVO composite source profiles for motor vehicle exhaust (BVRDMV), vegetative burning (BURN), coal-fired power plant

(CFPP), and residential meat cooking (COOK). Eight-fraction carbon is defined in Table 5.

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 205

species, including the eight carbon fractions and without

the carbon fractions. The results are shown in Table 6.

For 30% source error, including the carbon fractions

produced significant improvements in the BVRDMV

(motor vehicle) and COOK (cooking) SCEs. With the

measured source errors, significant improvements in the

SCEs for these sources and BURN (vegetative burning)

source were realized when the carbon fractions were in-

cluded in the CMB.While it is unlikely in practice that an

individual ambient sample would be affected by all six

combustion sources, including the carbon fractions in

CMB analysis should significantly improve the estima-

tion of their contributions.

4. Conclusions

Source profiles for emissions inventories and receptor

source apportionment modeling were determined as part

of the BRAVO study. The profiles represent emissions

from paved road, unpaved road, natural soil dust, motor

vehicles, vegetative burning, coal-fired power plants,

coal fly ash, a petroleum refinery catalytic cracker, ce-

ment kilns, and meat cooking. The individual sample

compositions were averaged by source type and source

subgroups where systematic differences in composition

were observed.

Geological material in the BRAVO domain was un-

usually enriched in Ca: up to 29% in unpaved road dust.

Roadside motor vehicle emissions compositions were

variable, with EC-to-OC ratios ranging from 0.23 to

0.97. These ratios did not appear to be related to the

relative abundances of on-road heavy-duty diesel and

light-duty gasoline-powered vehicles. Profiles represent-

ing open burns of various types of wood debris con-

tained water-soluble Kþ, a marker for vegetative

burning, at levels ranging from 0.02% to 13.6%. Coal-

fired power plant emissions contained levels of SO¼4 and

Page 22: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

Table 5

Carbon fractiona composition of BRAVO combustion profiles

Source Carbon fraction (%)

OC1 OC2 OC3 OC4 OP EC1 EC2 EC3

Motor vehicle

composite

(BVRDMV)

16.0± 12.3 18.0± 7.0 13.4± 5.5 6.9± 3.3 4.4± 4.6 15.6± 9.3 23.3± 10.4 2.6 ± 4.3

Vegetative burning

composite (BURN)

23.9± 12.4 11.9± 4.2 13.8± 6.6 8.8± 3.8 6.1± 9.3 13.9± 7.3 7.8± 15.1 0.13± 0.13

Coal-fired boiler

composite (CFPP)

7.5± 10.1 9.4± 9.6 5.2± 5.8 2.7± 2.4 2.4± 2.5 2.1± 2.0 1.5± 1.1 0.10± 0.16

Cement kiln composite

(CEM)

1.0± 2.6 2.0± 1.4 4.0± 1.6 4.5± 3.1 1.3± 1.0 2.7± 1.5 1.5± 1.1 0.07± 0.22

Cooking composite

(COOK)

13.2± 9.0 25.4± 13.9 37.6± 15.9 8.5± 3.7 2.0± 2.5 9.5± 6.8 2.3± 2.4 0.35± 0.40

Catalytic cracker

(CAT1)

0.11± 0.08 0.00± 0.03 0.04± 0.02 0.16± 0.06 0.16± 0.06 0.08± 0.04 0.15± 0.05 0.007± 0.005

a Eight carbon fractions including: OC1 (120 �C), OC2 (250 �C), OC3 (450 �C), and OC4 (550 �C) combusted in a 100% helium

atmosphere; and EC1 (550 �C), EC2 (700 �C), and EC3 (800 �C) combusted in a mixture of 2% oxygen and 98% helium atmosphere. A

630 nm helium–neon laser is used to monitor pyrolyzed carbon (OP) based on the IMPROVE thermal/optical reflectance (TOR)

carbon analysis protocol (Chow et al., 1993, 2001).

Table 6

Effect of using eight carbon fractionsa to resolve combustion source contributions by CMB

Source 30% source errors Actual source errors

AAEb (without

carbon fractions)

AAE (with carbon

fractions)

AAE (without

carbon fractions)

AAE (with carbon

fractions)

Motor vehicle composite (BVRDMV) 20.9 13.5 53.0 38.4

Vegetative burning composite (BURN) 15.1 14.0 54.5 45.0

Coal-fired boiler composite (CFPP) 10.1 9.9 22.9 22.0

Cement kiln composite (CEM) 10.6 10.2 25.5 24.1

Cooking composite (COOK) 40.4 24.4 74.1 44.5

Catalytic cracker (CAT1) 8.6 8.6 11.1 11.0

aCarbon fractions are identified in Table 5.b Percent.

206 J.C. Chow et al. / Chemosphere 54 (2004) 185–208

Se as high as 62% and 2.2%, respectively. An oil-refinery

catalytic cracker profile was characterized by relatively

high levels of SO¼4 (59%), V (1.03%), Ni (1.90%), La

(1.95%), and Sb (0.58%). Cement kiln emissions were

comprised mainly of SO¼4 (30–31%), Ca (17.3–18.8%),

total K (4.5–14.6%), and OC (11.7–14.6%). Meat

cooking emissions were dominated by OC (76–96%).

The eight carbon fractions of organic and elemental

carbon provide additional discrimination between

combustion source emissions.

Acknowledgements

This study was jointly sponsored by the US Envi-

ronmental Protection Agency (EPA) and the National

Park Service as part of the BRAVO study through an

agreement with the Cooperative Institute for Atmo-

spheric Sciences and Terrestrial Applications (CI-

ASTA). The results and conclusions presented in this

paper are solely those of the authors and do not repre-

sent BRAVO study findings. The authors are grateful to

Mr. James Yarbrough of EPA for coordinating the

source testing; to the rangers at Big Bend National Park

for assisting with the logistics of the source testing; to

Dr. Marc Pitchford of the National Oceanic and At-

mospheric Administration and Dr. Lowell Ashbaugh of

the University of California at Davis for performing site

surveys and collecting samples; and to Mr. William

Welch at the University of California at Riverside,

CE-CERT, for the use of their cooking test facility.

References

AOAC, 2000. Official Methods of Analysis of AOAC Interna-

tional, 17th ed. Association of Analytical Chemists, Arling-

ton, VA.

Page 23: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

J.C. Chow et al. / Chemosphere 54 (2004) 185–208 207

ARB, 2003. Central California air quality studies––CCOS and

CRPAQS. California Air Resources Board, Sacramento,

CA. Available from <http://www.arb.ca.gov/airways/ccaqs.

htm>.

Brook, J.R., Vega, E., Watson, J.G., 2003. Chapter 7: Receptor

methods. In: Particulate Matter Science for Policy Makers,

A NARSTO Assessment, Part 2. NARSTO, Pasco, WA,

pp. 7-1–7-48.

Carvacho, O.F., Ashbaugh, L.L., Matsumura, R.T., Southard,

R.J., Flocchini, R.G., 1996. Measurement of PM10 potential

from agricultural soils using a dust resuspension test

chamber. In: Proceedings, International Conference on Air

Pollution from Agricultural Operations. Midwest Plan

Service, Kansas City, MO.

Chow, J.C., 1985. A composite modeling approach to assess air

pollution source/receptor relationships. Sc.D. Dissertation,

Harvard University, Cambridge, MA.

Chow, J.C., 2002. 2002 Critical review introduction––visibility:

science and regulation. J. Air Waste Manage. Assoc. 52 (6),

626–627.

Chow, J.C., Watson, J.G., 1999. Ion chromatography in

elemental analysis of airborne particles. In: Landsberger,

S., Creatchman, M. (Eds.), Elemental Analysis of Airborne

Particles, vol. 1. Gordon and Breach Science, Amsterdam,

pp. 97–137.

Chow, J.C., Watson, J.G., 2002. Review of PM2:5 and PM10

apportionment for fossil fuel combustion and other sources

by the chemical mass balance receptor model. Energy Fuels

16 (2), 222–260.

Chow, J.C., Frazier, C.A., Watson, J.G., 1988. A survey of

existing fugitive/area source characterization methods for

receptor modeling. In: Hoffnagle, G.F., Lebowitz, M.

(Eds.), Proceedings, Particulate Matter/Fugitive Dusts:

Measurements and Control in Western Arid Regions. Air

Pollution Control Association, Pittsburgh, PA, pp. 176–

189.

Chow, J.C., Watson, J.G., Pritchett, L.C., Pierson, W.R.,

Frazier, C.A., Purcell, R.G., 1993. The DRI thermal/optical

reflectance carbon analysis system: description, evaluation

and applications in US air quality studies. Atmos. Environ.

27 (8), 1185–1201.

Chow, J.C., Watson, J.G., Houck, J.E., Pritchett, L.C., Rogers,

C.F., Frazier, C.A., Egami, R.T., Ball, B.M., 1994. A

laboratory resuspension chamber to measure fugitive dust

size distributions and chemical compositions. Atmos. Envi-

ron. 28 (21), 3463–3481.

Chow, J.C., Watson, J.G., Green, M.C., Lowenthal, D.H.,

DuBois, D.W., Kohl, S.D., Egami, R.T., Gillies, J.A.,

Rogers, C.F., Frazier, C.A., Cates, W., 1999. Middle- and

neighborhood-scale variations of PM10 source contributions

in Las Vegas, Nevada. J. Air Waste Manage. Assoc. 49 (6),

641–654.

Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H.,

Merrifield, T., 2001. Comparison of IMPROVE and

NIOSH carbon measurements. Aerosol Sci. Technol. 34

(1), 23–34.

Chow, J.C., Bachmann, J.D., Wierman, S.S.G., Mathai, C.V.,

Malm, W.C., White, W.H., Mueller, P.K., Kumar, N.,

Watson, J.G., 2002a. 2002 Critical review discussion––

visibility: science and regulation. J. Air Waste Manage.

Assoc. 52 (9), 973–999.

Chow, J.C., Watson, J.G., Crow, D., Kohl, S.D., Kuhns, H.D.,

Etyemezian, V., Engelbrecht, J.P., 2002b. Source profiles for

the big bend regional aerosol visibility and observational

(BRAVO) source characterization study, Draft report.

Prepared for CIASTA, NOAA, Las Vegas, NV, by Desert

Research Institute, Reno, NV.

Cooper, J.A., Frazier, C.A., Houck, J.E., 1985. Seattle-Tacoma

Aerosol Characterization Study (STACS): Final report.

NEA, Inc., Tigard, OR.

Cooper, J.A., Redline, D.C., Sherman, J.R., Valdovinos, L.M.,

Pollard, W.L., Scavone, L.C., West, C.R., 1987. PM10

source composition library for the South Coast Air Basin.

vol. I: Source profile development documentation. Final

report. South Coast Air Quality Management District, El

Monte, CA.

Engelbrecht, J.P., Swanepoel, L., Chow, J.C., Watson, J.G.,

Egami, R.T., 2002. The comparison of source contributions

from residential coal and low-smoke fuels, using CMB

modeling, in South Africa. Environ. Sci. Policy 5 (2), 157–

167.

Fine, P.M., Cass, G.R., Simoneit, B.R.T., 2002. Chemical

characterization of fine particle emissions from the fireplace

combustion of woods grown in the southern United States.

Environ. Sci. Technol. 36 (7), 1442–1451.

Gordon, G.E., Pierson, W.R., Daisey, J.M., Lioy, P.J., Cooper,

J.A., Watson, J.G., Cass, G.R., 1984. Considerations for

design of source apportionment studies. Atmos. Environ. 18

(8), 1567–1582.

Green, M.C., Kuhns, H.D., Etyemezian, V., 2000. Final

Program Plan for the Big Bend Regional Aerosol and

Visibility Observational Study (BRAVO). Desert Research

Institute, Las Vegas, NV.

Hildemann, L.M., Cass, G.R., Markowski, G.R., 1989. A

dilution stack sampler for collection of organic aerosol

emissions: design, characterization and field tests. Aerosol

Sci. Technol. 10 (10–11), 193–204.

Hopke, P.K., 1999. An introduction to source receptor mod-

eling. In: Landsberger, S., Creatchman, M. (Eds.), Elemen-

tal Analysis of Airborne Particles. Gordon and Breach

Science, Amsterdam, pp. 273–315.

Houck, J.E., 1991. Source sampling for receptor modeling. In:

Hopke, P.K. (Ed.), Receptor Modeling for Air Quality

Management. Elsevier, Amsterdam, The Netherlands,

pp. 45–82.

Houck, J.E., Chow, J.C., Watson, J.G., Simons, C.A., Pritch-

ett, L.C., Goulet, J.M., Frazier, C.A., 1989. Determination

of particle size distribution and chemical composition of

particulate matter from selected sources in California.

Report no. A6-175-32. Prepared for California Air Re-

sources Board, Sacramento, CA, by OMNI Environmental

Services Inc. and Desert Research Institute, Beaverton, OR

and Reno, NV.

IMPROVE, 2003. Interagency monitoring of protected visual

environments––data resources. National Park Service, Ft.

Collins, CO. Available from <http://vista.cira.colostate.edu/

IMPROVE/Data/data.htm>.

Javitz, H.S., Watson, J.G., Robinson, N.F., 1988. Performance

of the chemical mass balance model with simulated local-

scale aerosols. Atmos. Environ. 22 (10), 2309–2322.

Lowenthal, D.H., Chow, J.C., Watson, J.G., Neuroth, G.R.,

Robbins, R.B., Shafritz, B.P., Countess, R.J., 1992. The

Page 24: Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study

208 J.C. Chow et al. / Chemosphere 54 (2004) 185–208

effects of collinearity on the ability to determine aerosol

contributions from diesel- and gasoline-powered vehicles

using the chemical mass balance model. Atmos. Environ. 26

(13), 2341–2351.

Malm, W.C., Iyer, H.K., Gebhart, K.A., 1990. Application of

tracer mass balance regression to WHITEX data. In:

Mathai, C.V. (Ed.), Transactions, Visibility and Fine

Particles. Air Waste Management Association, Pittsburgh,

PA, pp. 806–818.

McDonald, J.D., Zielinska, B., Fujita, E.M., Sagebiel, J.C.,

Chow, J.C., Watson, J.G., 2003. Emissions from charbroil-

ing and grilling of chicken and beef. J. Air Waste Manage.

Assoc. 53 (2), 185–194.

Norbeck, J., 1997. Standardized test kitchen and screening tools

evaluation for South Coast Air Quality Management

Districtn Proposed Rule 1138. Report No. S-C95073.

Prepared for South Coast Air Quality Management District,

El Monte, CA, by University of California CE-CERT,

Riverside, CA.

Perry, K.D., Cahill, T.A., Eldred, R.A., Dutcher, D.D., 1997.

Long-range transport of North African dust to the eastern

United States. J. Geophys. Res. 102 (D10), 11225–11238.

Ray, S.S., Parker, F.G., 1977. Characterization of ash from

coal-fired power plants. Report No. EPA-600/7-77/010. US

Environmental Protection Agency, Washington, DC.

Schauer, J.J., Cass, G.R., 2000. Source apportionment of

wintertime gas-phase and particle-phase air pollutants using

organic compounds as tracers. Environ. Sci. Technol. 34 (9),

1821.

Thurston, G.D., Spengler, J.D., 1985. A quantitative assess-

ment of source contributions to inhalable particulate matter

pollution in metropolitan Boston. Atmos. Environ. 19 (1),

9–25.

US EPA, 1999a. Emissions inventory guidance for implemen-

tation of ozone and particulate matter National Ambient

Air Quality Standards (NAAQS) and regional haze regula-

tions. Report no. EPA-454/R-99-006. US Environmental

Protection Agency, Research Triangle Park, NC. Available

from <http://www.epa.gov/ttn/chief/eidocs/eidocfnl.pdf>.

US EPA, 1999b. 40 CFR Part 51––Regional haze regulations:

Final rule. Federal Register, vol. 64, no. 126, pp. 35714–

35774. Available from <http://frwebgate.access.gpo.gov/

cgi-bin/getpage.cgi?position¼ all&page¼ 35714&dbname¼1999_register>.

US EPA, 1999c. SPECIATE: EPA’s repository of total organic

compound and particulate matter speciated profiles for a

variety of sources for use in source apportionment studies.

US Environmental Protection Agency, Office of Air Quality

Planning and Standards, Research Triangle Park, NC.

Available from <http://www.epa.gov/ttn/chief/software/spe-

ciate/>.

US EPA, 2001. Draft guidance for demonstrating attainment of

air quality goals for PM2:5 and regional haze. US Environ-

mental Protection Agency, Research Triangle Park,

NC. Available from <http://vistas-sesarm.org/tech/draftpm.

pdf>.

Watson, J.G., 1979. Chemical element balance receptor model

methodology for assessing the sources of fine and total

suspended particulate matter in Portland, Oregon. Ph.D.

Dissertation, Oregon Graduate Center, Beaverton, OR.

Watson, J.G., 2002a. Visibility: science and regulation. J. Air

Waste Manage. Assoc. 52 (6), 628–713.

Watson, J.G., 2002b. Visibility: science and regulation––a

summary of the 2002 critical review. EM (June), pp. 36–43.

Watson, J.G., Chow, J.C., 2001. Source characterization of

major emission sources in the Imperial and Mexicali valleys

along the US/Mexico border. Sci. Total Environ. 276 (1–3),

33–47.

Watson, J.G., Cooper, J.A., Huntzicker, J.J., 1984. The

effective variance weighting for least squares calculations

applied to the mass balance receptor model. Atmos.

Environ. 18 (7), 1347–1355.

Watson, J.G., Robinson, N.F., Chow, J.C., Henry, R.C., Kim,

B.M., Pace, T.G., Meyer, E.L., Nguyen, Q., 1990. The

USEPA/DRI chemical mass balance receptor model, CMB

7.0. Environ. Software 5 (1), 38–49.

Watson, J.G., Chow, J.C., Lowenthal, D.H., Pritchett, L.C.,

Frazier, C.A., Neuroth, G.R., Robbins, R., 1994. Differ-

ences in the carbon composition of source profiles for diesel-

and gasoline-powered vehicles. Atmos. Environ. 28 (15),

2493–2505.

Watson, J.G., Chow, J.C., Frazier, C.A., 1999. X-ray fluores-

cence analysis of ambient air samples. In: Landsberger, S.,

Creatchman, M. (Eds.), Elemental Analysis of Airborne

Particles, vol. 1. Gordon and Breach Science, Amsterdam,

pp. 67–96.

Watson, J.G., Chow, J.C., Houck, J.E., 2001a. PM2:5 chemical

source profiles for vehicle exhaust, vegetative burning,

geological material, and coal burning in northwestern

Colorado during 1995. Chemosphere 43 (8), 1141–1151.

Watson, J.G., Turpin, B.J., Chow, J.C., 2001b. The measure-

ment process: precision, accuracy, and validity. In: Cohen,

B.S., McCammon, C.S.J. (Eds.), Air Sampling Instruments

for Evaluation of Atmospheric Contaminants, 9th ed.

American Conference of Governmental Industrial Hygien-

ists, Cincinnati, OH, pp. 201–216.

Watson, J.G., Zhu, T., Chow, J.C., Engelbrecht, J.P., Fujita,

E.M., Wilson, W.E., 2002. Receptor modeling application

framework for particle source apportionment. Chemosphere

49 (9), 1093–1136.

Welch, W., 1997. Standardized test kitchen and screening tools

evaluation for South Coast Air Quality Management Dis-

trict proposed rule 1138. Report no. S-C95073. Prepared for

South Coast Air Quality Management District, El Monte,

CA, by CE-CERT, University of California, Riverside, CA.

Yu, J.Z., Xu, J.H., Yang, H., 2002. Charring characteristics of

atmospheric organic particulate matter in thermal analysis.

Environ. Sci. Technol. 36 (4), 754–761.