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Page Environmental 1 of 28 Science & Technology

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Environmental Science & Technology

Page 2 of 28

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Development of PM2.5 source profiles using a hybrid

2

chemical transport-receptor modeling approach

3

Cesunica Ivey1†*, Heather Holmes2, Guoliang Shi3, Sivaraman Balachandran4, Yongtao Hu1, and

4

Armistead G. Russell1

5

1

Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta,

6

Georgia, US; 2Atmospheric Sciences Program, Department of Physics, University of Nevada

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Reno, Reno Nevada, USA; 3State Environmental Protection Key Laboratory of Urban Ambient

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Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission

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Research, College of Environmental Science and Engineering, Nankai University, Tianjin

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300071, China; 4Department Biomedical Chemical and Environmental Engineering, University

11

of Cincinnati, Cincinnati Ohio, USA

12

Corresponding Author

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*Cesunica E. Ivey, Atmospheric Sciences Program, Department of Physics, University of

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Nevada Reno. 1664 N. Virginia St., Mailstop 0220, Reno, NV 89557. Phone: (775) 784-6792.

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Fax: (775) 784-1398. Email: [email protected]

16

Present Addresses

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†Now at Atmospheric Sciences Program, Department of Physics, University of Nevada Reno,

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Reno, NV


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Abstract

20

Laboratory-based or in-situ PM2.5 source profiles may not represent the pollutant

21

composition for the sources in a different study location due to spatially and temporally varying

22

characteristics, such as fuel or crustal element composition, or due to differences in emissions

23

behavior under ambient versus laboratory conditions. In this work, PM2.5 source profiles were

24

estimated for 20 sources using a novel optimization approach that incorporates observed

25

concentrations with source impacts from a chemical transport model (CTM) to capture local

26

pollutant characteristics. Nonlinear optimization was used to minimize the error between source

27

profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal

28

variability was seen for coal combustion, dust, fires, metals processing, and other source profiles

29

when compared to the reference profiles, with variability in species fractions over 400%

30

(calcium in dust) compared to mean contributions of the same species. Revised profiles

31

improved the spatial and temporal bias in modeled concentrations of several trace metal species,

32

including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model

33

for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for

34

summer compared with previous studies. Source profile optimization can be useful for source

35

apportionment studies that have limited availability of source profile data for the location of

36

interest.

37

Introduction

38

Receptor-oriented modeling is a widely-used approach for estimating the quantitative

39

impacts of particulate matter sources on ambient concentrations. Receptor-oriented techniques

40

largely rely on surface measurements of total PM2.5 mass as well as individual PM2.5 species.

41

Two of the more popular receptor models are the chemical mass balance (CMB) model1 and


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42

positive matrix factorization (PMF)2. The CMB model relies on inputs of PM2.5 measurements

43

and estimates of source profiles or “fingerprints”—the mass of individual PM2.5 species emitted

44

from a source relative to the total mass emitted1. A well-known repository of source profiles is

45

available through the U.S. Environmental Protection Agency SPECIATE database, a collection

46

of 5,187 volatile organic gas and particulate matter source profiles developed using data from

47

emission studies and laboratory testing (http://www.epa.gov/ttn/chief/software/speciate/). There

48

are uncertainties associated with profiles in the database due to the need for more current

49

speciation data, as noted in the development documentation3. The EPA National Emissions

50

Inventory (NEI) contains hundreds of unique sources, and they are assigned a distinct source

51

classification code (SCC). Source profiles for some SCCs are unavailable and are therefore

52

assigned the profile of a comparable source. Profiles developed from laboratory studies with a

53

sample size of N=1 are assigned a lower quality rating in the SPECIATE database, indicating

54

that the profile has higher uncertainty. Additionally, fuel composition may vary by facility (e.g.

55

gasoline refining), hence a composite profile for gasoline vapors is recommended in the

56

database.

57

Source profiles are subject to uncertainty and several studies have been conducted to

58

address these uncertainties. For example, Reff et al. (2009) addressed uncertainty in trace metal

59

species fractions for 84 unique PM2.5 source profiles by examining PM2.5 emissions in the NEI,

60

and the profiles were developed using existing profiles from SPECIATE (v4.0)4. Authors noted

61

that several profile adjustments were made based on data quality, profile notes, and associated

62

references. The 84 profiles included mass fractions of OC, EC, major ions, non-carbon organic

63

matter, metal-bound oxygen, particulate water, 37 metal elements, and other unspeciated PM2.5.

64

Sources presented included unpaved road dust, residential wood combustion, charbroiling,


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65

aluminum processing, catalytic cracking, fly ash, phosphate manufacturing, urea fertilizer, potato

66

deep-frying, and steel desulfurization.

67

In another study, Balachandran et al. (2013) implemented a Bayesian-based ensemble

68

approach to address the need for season-specific PM2.5 source profiles. Profiles were estimated

69

for a winter and summer month for major sources: gasoline vehicles, diesel vehicles, dust,

70

biomass burning, and coal combustion5. The ensemble members included PMF, CMB, CMB-

71

LGO (Lipshitz global optimizer), CMB-MM (molecular markers), and CMAQ (Community

72

Multiscale Air Quality model)6–9.

73

species, including major ions, carbon species, and trace metals. Certain species in the ensemble-

74

based source profiles for biomass burning and coal combustion showed strong seasonality.

The source profiles included contributions for 15 PM2.5

75

In a study by Lee et al. (2007), authors quantified how the uncertainty of species

76

measurements and source profiles impacts CMB results using Monte Carlo analysis with Latin

77

hypercube sampling10. The percent contributions to uncertainties from individual species in

78

source profiles on CMB results were presented for the following source categories: biomass

79

burning, pulp and paper production, motor vehicles, oil combustion, dust, coal combustion,

80

mineral production, and metal production. Uncertainty contributions of CMB inputs were also

81

presented for ammonium nitrate, ammonium sulfate, and ammonium bisulfate; however, the

82

uncertainties in quantifying impacts from these sources were mainly contributed by the

83

measurement uncertainties for nitrate, ammonium, and sulfate. The findings in the Lee et al.

84

study indicated that the uncertainties in the source impacts are highly species dependent.

85

In a more recent study by Sturtz et al. (2014), authors addressed the uncertainty in

86

wildfire source profiles by employing a weighted PMF-chemical transport modeling approach

87

for source apportionment on total fine particle carbon at receptors impacted by wildfires11. To


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88

improve the source apportionment results, constraints were placed on carbon thermal fractions,

89

K, NO3, and SO4 for the biomass burning profile, and on non-carbonaceous species for the

90

biogenics profile. This study demonstrated that modifications to standard source profiles can be

91

beneficial for source impact studies.

92

This paper addresses the uncertainty of source profiles due to the spatial and temporal

93

variability of source characteristics under ambient conditions. The objective of this work is to

94

develop new source profiles for 20 sources of fine particulate matter using data assimilation with

95

observations and hybrid chemical transport-receptor model results. The revised profiles are

96

calculated using nonlinear optimization and are then used in the CMB-iteration method12

97

(developed to estimate both primary and secondary organic carbon concentrations) to estimate

98

PM2.5 source impacts in two U.S. cities. The revised profiles are uniquely estimated for each

99

monitored location and reflect local conditions by incorporating observed concentrations into the

100

profiles, while the reference profiles were estimated as an aggregate of species contributions

101

determined by in-situ source sampling. Revised profiles are presented for four seasons and are

102

grouped by their location in the administrative regions of the United States Environment

103

Protection Agency (U.S. EPA). Regional and seasonal source profile optimization captures

104

spatiotemporal variations in profiles, where static profiles that are used in traditional modeling

105

may introduce errors by not considering the local variation in emissions.

106

This work is significant in that the proposed methods create an improved or more

107

compatible source profile for the locations of interest. Source profiles are difficult to obtain

108

when considering the sampling and analysis efforts that are required for their construction.

109

Further, the methods presented bring together information from the source profile, source

110

impacts, and the ambient receptor to create a new source profile that reflects local conditions.


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111

Materials and Methods

112

Data

113

Observations from the Chemical Speciation Network (CSN) were used for method

114

development and application. Available data from the CSN network included total PM2.5 mass,

115

organic and elemental carbon, major ions, and 35 metal species. PM2.5 mass and full speciation

116

measurements were available for 121 days during the study year (2006), as measurements were

117

available every third or sixth day depending on the local monitoring schedule. The total number

118

of monitors available on observation days varied from approximately 40 to 150 due to the

119

previously stated scheduling. Carbon species concentrations were converted from TOT to TOR

120

equivalents using methods from a previous study13. In the current study, only 23 chemical

121

species were considered for source profile development due to the low occurrence of

122

concentrations below detection limit for some trace metal species13.

123

CMAQ-DDM Modeling

124

The CMAQ model is a chemical transport model (CTM) that outputs gridded

125

concentrations of atmospheric pollutants and is equipped with the ability to estimate model

126

sensitivities to perturbations in inputs or boundary conditions by implementing the decoupled

127

direct method (DDM) for three-dimensional domains14,15. CMAQ-DDM modeling was

128

performed for one year (2006) at 36 km resolution over the continental U.S., and the modeling

129

domain includes southern Canada and northern Mexico. Meteorological inputs were generated

130

using the Weather Research and Forecasting (WRF) model v3.3.1 with the Pleim-Xiu land-

131

surface model, Kain-Fritsch cumulus parameterization, Morrison 2-moment microphysics,

132

RRTM longwave radiation, Dudhia shortwave radiation, the ACM2 planetary boundary layer

133

scheme, and the Pleim-Xiu surface layer scheme16,17. Emissions inputs were generated using the

134

Sparse Matrix Operator Kernel Emissions (SMOKE) model (v2.6) along with the 2005 U.S. EPA


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135

National Emissions Inventory (NEI)18. The CMAQ-DDM model was used to calculate the

136

sensitivity of PM2.5 concentration to emissions from 20 unique sources categories. These

137

sensitivities represent the initial source impact estimates. The 20 source categories were chosen

138

to provide insightful source impact information, and categories include biogenic emissions, on-

139

road and non-road vehicles, stationary fossil fuel combustion, metals processing, wildfires,

140

agriculture and livestock activities, and several others (Table 1).

141

Spatial Hybrid Source Apportionment

142

A hybrid chemical transport-receptor model (CTM-RM) was applied here, and it uses the

143

initial CMAQ-DDM source impact estimates and observed concentrations to optimize modeled

144

source impact estimates. The approach is detailed in Hu et al. (2014)19 and briefly described

145

here. The hybrid CTM-RM source apportionment model was applied at CSN monitors with

146

available speciated PM2.5 data. The hybrid model is applied for one monitored location and one

147

day at a time. The model optimizes an adjustment factor (for source j), and is applied to

148

base case CMAQ-DDM source impacts to either increase or decrease the impacts so that

149

modeled estimates better reflect observed concentrations (Eq. 1).

150 151 152 153

=

! [ ∑ , ( )] ∑0 &+ 1/ ! ! " #" ,

,

%$)*( )!

( ∑./ "

+, (- )

!

(1)

In Eq. 1, is the error to be minimized; 21345 and 21516 are observed and CMAQ-modeled

concentrations of species i, respectively; 78495: is the base-case CMAQ-DDM impact of source 1,

j on species i; ;1,345 , ;1,< , and ;=> ( ) are uncertainties in the observations, modeled

154

concentrations, and source impacts, respectively; and ( (an iterative value equal to first term

155

divided by 20) is a weighting term to balance the optimized output and ensure a physically

156

relevant solution. Refined concentrations and source impacts are calculated using Eqs. 2 and 3:


7

Page 9 of 28

157 158 159 160


21

?:@1*:A

781,

= 21516 + ∑./ 78495: 1, ( − 1)

?:@1*:A

(2)

= 78495: ∗ 1,

(3)

The hybrid CTM-RM method was extended spatially by kriging the adjustment factors

that were calculated at the monitored locations, and then spatially interpolated values were

161

applied to the corresponding gridded 78495: spatial fields20. The gridded values, originally 1,

162

available only on observation days, were temporally interpolated using grid-by-grid linear

163

interpolation. As a result, daily 36 km resolution spatial fields of refined source impacts (spatial

164

hybrid) and concentrations were estimated for the entire continental U.S. Spatial hybrid fields

165

are used as inputs for the source profile study.

166

Source Profile Optimization

167

This study presents a new method for developing source profiles for the 20 previously-

168

mentioned PM2.5 sources. The method employs a nonlinear optimization approach that

169

assimilates CSN observations and spatial hybrid source impacts to generate receptor-trained

170

profiles. The optimization equation is applied at one monitor for one observation day at a time.

171 172 173

The source profile optimization equation is derived from the CMB method: 21345 = ∑./ E1, 7 + F1 ,

(4)

where E1, is the fraction of species i that is emitted from source j, 7 is the impact of source j on

174

total PM2.5, and F1 is the concentration prediction error to be minimized. For the spatial hybrid

175

(SH) impact of source j on total PM2.5, the following equality holds:

176

7G = ∑H1/ 78495: 1, ,

(5)

177

under the assumption that all impacting sources on total PM2.5 are accounted for by the SH

178

approach.


8


To calculate new source profiles, the variable E1, is expressed as the product of an

179 180 181 182

Page 10 of 28

I adjustment ratio and the original or reference species fraction, E1, : I E1, = J1, E1,

(6)

The reference source profiles E1I were derived from the study by Reff et. al (2009) 4 (Table S2).

183

The E1I matrix is composed of source profiles for 19 chemical species (EC, Na, Al, Si, Cl, K, Ca,

184

Ti, Mn, Fe, Cu, Zn, As, Se, Br, Sn, Sb, Ba, and Pb). Now the variable of interest becomes J1, , and the new equality becomes:

185 186

. I ( ∑H1/ 78495: 21345 = ∑/ J1, E1, 1, ) + F1

(7)

187

The source profile ratio is optimized by minimizing the squared prediction error ei, renamed as

188

X2 :

189

I K = ∑H1/L21345 − M∑./ J1, E1, ( ∑H1/ 78495: 1, )NO

(8)

190

Uncertainties and constraints are added to the objective function (Eq. 9) to weight and balance

191

the prediction error:

192

K = ∑H1/

Q P ∑ ?, @, ∑R , S

", ! #",$T !

193

σ[,\] = (∑ic/ σ^Q SAdefg [,c R c )

194

XME1I N =

195

I ∑H1/ J1, E1, ≤1

196

!

+

@ Q M?, N I U( ∑H1/ V∑./ , " XME1, NY Q W,

(9) (10)

_,`

1, jE1I j > 0 0, E1I = 0

(11) (12)

The second term of Eq. 9 serves as the error balancing term. The term ;1, is the uncertainty of

197

the source impacts on species i (Eq. 10); ;@Q is the numerical uncertainty of the reference source

198

profiles; U is a sensitivity term (U =

,

H

in this study); ( is a numerical weighting term

∑R ∑ n(@Q )


9

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199

(( = 0.01 in this application); and XME1I N is a piecewise function used to constrain the

200

optimization and omit species with zero contribution from having numerical influence on the

201

optimization (Eq. 11). After optimization of the source profile ratios (J1 ), revised source profiles

202

(E1*:p ) are derived using Eq. 6. The lower bound for the optimization of J1 is 0.5 and the upper

203

bound is 2, indicating that the optimization allows the initialized J1 to be modified by a factor of

204

2 in both directions.

205

Secondary species OC, NO3, NH4, and SO4 were omitted from the optimization because

206

the method targets species that are mainly inert in the CMAQ model and do not undergo major

207

chemical transformation after being emitted. After optimization, reference fractions of OC, NO3,

208

NH4, and SO4 were reincorporated into the revised source profiles. The original ratios (obtained

209

from Reff et al., 2009) of these four species were maintained. This presents a limitation for this

210

current study, in that ratios of secondary species are expected to change at the receptor. One

211

potential impact of not including secondary species in the optimization is that the fractions of

212

primary metals species estimated in the revised profile are higher than the ambient fractions at

213

the receptor, mainly due to the increased fraction of secondary species (e.g., sulfate, nitrate,

214

ammonium, organic carbon). However, the present focus is placed on improving estimates of

215

the primary species fractions at the source by incorporating observations of trace metal

216

concentrations into the reference profile. The final revised source profiles contain fingerprints

217

for 20 sources and 23 species, including the 19 primary species and 4 secondary species.

218

The source profile optimization method was applied for the year 2006 at CSN sites on

219

days when species measurements were available (every 3rd or 6th day). After optimization, new

220

source profiles were grouped by season (winter: December, January, February; spring: March,

221

April, May; summer: June, July, August; and fall: September, October, November) and their


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222

associated EPA administrative region (Fig. 1). Region 4 had the greatest number of monitors (N

223

= 56), and Region 7 had the least number of monitors (N = 4) (Table S1).

224

The CMB-iteration (v3.0) method12 was used to test the revised source profiles for

225

Atlanta, GA (Region 4) and St. Louis, MO (Region 7) case studies for one winter (January 2006)

226

and one summer (July 2006) month. The CMB-iteration method is a modification of the EPA

227

CMB method and estimates both primary and secondary organic carbon impacts. Sources were

228

selected to match results from the previous studies for Atlanta and St. Louis21,22. Negative

229

source contributions from CMB-iteration were excluded from the monthly averages, with the

230

exception of the impacts for the SOC category. In that case, increased NOx emissions can lead to

231

a reduction in SOC as the yields change and radical levels can be decreased. Negative source

232

contributions occur when the CMB-iteration model over fits the minimization of the error for

233

that time instance. Positive contributions were obtained for all sources for the majority of the

234

CMB-iteration calculations. For consistency, the sources chosen were kept the same for each

235

calculation. Implementation of the updated source profiles in CMB-iteration demonstrated the

236

ability to use CTM-revised profiles in traditional receptor models.

237

Variability Analysis

238

A total of 1955 source profiles were calculated in the application of the optimization

239

method, and profiles were stratified by EPA region and season. All revised profiles were also

240

averaged to calculate a national average profile. Species fractions and standard deviations for

241

the U.S. average for all sources are presented in the supplemental information (Tables S3-S10).

242

Variability of the regionally and seasonally averaged revised profiles was analyzed by examining

243

two metrics: cosine similarity and coefficient of variability.


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244 245 246


First, the cosine similarity for source j was calculated to contrast the regional-seasonal revised profile with the reference profile, which was derived in Reff et al. (2009) 4 (Eq. 13). qrsrtuJrvw =

@xy ∙@Q

{@xy {{@Q {

=

∑R @xy @Q

|∑R(@xy )! |∑R(@Q )!

(13)

247

A similarity of 1 indicates that the profiles are the same, -1 indicates opposite profiles, and 0

248

indicates orthogonality23. The second metric analyzed was the coefficient of variation (CoV),

249

which is the ratio of the standard deviation and mean species fraction (Eq. 14).

250

"Wxy

}~ (%) =

Wxy

100

(14)

251

The CoV metric characterizes the spread of the revised species fractions, where higher values

252

indicate more variability in the species fractions over space and time. The CoV was calculated

253

for the annual U.S. averaged profile for all 20 sources.

254

Results and Discussion

255

Source Profile Ratios

256

The ratios rij that were calculated as a result of the source profile optimization are

257

presented in Figs. S1-S20 (see Supplemental Information) in the form of seasonal distributions

258

for each source and species. Distributions ranged from 0.5 to 2, as reflected by the lower and

259

upper constraints on the optimization. An rij value of 0.5 indicates a 50% decrease in the species

260

contribution compared to the reference profile, and an rij of 2 indicates a doubling in species

261

contribution in that source profile compared to the reference profile. Species fractions for

262

agriculture/livestock (Fig. S1) and biogenic (Fig. S3) sources were less than 1 for EC, Na, Al,

263

and Si (further designated at EC-Si), and were near one for all other species, indicating little

264

variability in optimized ratios. Note that the reference contributions are zero for


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265

agriculture/livestock and biogenics, and contributions remain zero after optimization (no primary

266

emissions of PM2.5 from these sources).

267

Distributions for the other 18 sources had common characteristics. For example, ratios for

268

EC-Si were less than 1 for all sources, indicating a reduction in these species’ contributions after

269

optimization. For fuel oil combustion (Fig. S7) and non-road gasoline (Fig. S12) ratios for the

270

all other metals were mostly near one, indicating little change in species contribution for these

271

sources. For coal combustion (Fig. S4), dust (Fig. S5), fires (Fig. S6), metals processing (Fig.

272

S9), on-road gasoline combustion (Fig. S15), other PM2.5 (Fig. S16), and other combustion (Fig.

273

S17), the distributions for most metals (Ca, Ti, Mn, Zn, Cu, Se, Br, and Sn) spanned the

274

constrained range for rij, from 0.5 to 2. This indicated that metals contributions for these sources

275

underwent more change than for the other sources. Each source had unique patterns of

276

distributions for each species, indicating that the source profile optimization captures local

277

variabilities that are unique to each source.

278

Revised Profile Variability

279

Regional-seasonal profiles for coal combustion, dust, metals processing, and others had

280

the most deviation (lowest similarity) from the original profiles (Tables S11-S14). For the coal

281

combustion profiles, the minimum similarity was 0.97 for the Region 10 summer profile (Table

282

S11). In general, spring and summer revised profiles were less similar to the reference profile

283

than the fall and winter profiles. This indicated that the coal combustion profiles were more

284

variable during warmer months, which is consistent with the peak in coal combustion impacts

285

during warmer months24. For the dust profiles, most similarities were 1.00, indicating no

286

significant difference in the regional-seasonal profile compared to the reference profile (Table

287

S12). According to the similarity calculations, the most variable dust profiles were for fall (0.98)


13

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288

for Region 1, spring (0.98) and fall (0.98) for Region 9, and winter (0.95) and summer (0.99) for

289

Region 10. Regions 1, 9, and 10 are all coastal regions for which the dust content of the

290

atmosphere can be influenced by African or Asian dust.

291

For the metals processing profiles, regional-seasonal profile variability was highest for

292

Regions 3,4, 5, and 7, which fall in the mid-Atlantic, southeastern, and midwestern regions of the

293

United States (Table S13). These areas are concentrated with manufacturing and smelting

294

facilities, where variability in emissions composition is likely22.

295

processing profiles were as low as 0.94 (fall for Region 4), and similarities for each Region were

296

similar across the seasons. For the others category most similarities were 0.99, where only the

297

similarities for the summer profiles of Region 7 and 10 were less (0.98) (Table S14).

298

Considering that the similarity metric is dependent on the magnitudes of the numbers in

299

question, values less than 1.0 are significant in this study due to the relatively small magnitudes

300

of the species fractions (~10-3). It is important to note that the similarities for the sea salt profiles

301

were highly variable; however, this variability was ignored due to the low variability of the

302

observed composition of sea spray.

Similarities for metals

303

Five sources had relatively high CoV values: coal combustion, dust, fires, metals

304

processing, and others (Fig. 2, Tables S15-S19). CoV values for coal combustion were highest

305

for Regions 1 and 2, which are located in the northeastern U.S. (Table S15). The species with

306

significantly high values included Ca (264%), Fe (115%), Si (93%), and Se (65%) for Region 1

307

and Ca (139%) and Si (72%) for Region 2. The CoV values for dust were high for Si (range: 49-

308

272%) and Ca (41-379%) for all Regions (Table S16). The CoV for Fe in Region 1 (115%) and

309

Region 4 (59%) was also high, and these Regions are in the eastern U.S. For fires, the CoV was

310

high for Cl and K in most regions, and values were highest for Region 9 (Cl: 204%, K: 153%) in


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311

the western U.S., which has high occurrences of wildfires (Table S17). The highest CoV values

312

for the metals processing profile occurred for Cl and Fe for several regions: 717% (Region 5),

313

265% (Region 3), 182% (Region 9), and 143% (Region 2) for Cl; and 243% (Region 1), 171%

314

(Region 9), 153% (Region 2), 123% (Region 8), 113% (Region 3), 106% (Region 10), and 95%

315

(Region 4) for Fe (Table S18). The others profile had high CoV values for Cl (range: 7%

316

(Region 10) to 197% (Region 7)) and Na (range: 3% (Region 10) to 90% (Region 7)) (Table

317

S19). The results from the species fraction variability analysis highlighted the most variable

318

species for the source profiles with respect to location and season over the continental U.S. The

319

variability analysis indicates that source profiles are highly variable in space and time, and the

320

optimization method is useful for capturing and addressing this variability.

321

Revised Concentrations

322

Concentrations of metals were compared for observations, SH concentrations derived

323

from the reference source profile (SHreference), and SH concentrations derived from the revised

324

source profiles (SHrevised) (Table S20). On average, mean normalized biases (MNB) between

325

observed and modeled concentrations of some species did not improve, namely: Si (obs v.

326

SHreference = 7.62, obs v. SHrevised = 9.40); K (obs v. SHreference = 9.79, obs v. SHrevised = 13.7); Fe

327

(obs v. SHreference = 23.96, obs v. SHrevised = 36.06); and Zn (obs v. SHreference = 1.87, obs v.

328

SHrevised = 2.97) (Table S21). On the other hand, estimates and MNBs for some species did

329

improve, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb, which represents 50% of all trace

330

metal species considered in this study. For the improved species, Figures S21a-c and S22 show a

331

tighter spread for revised biases compared to reference biases.

332

Receptor Model Application


15

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333

The CMB-iteration method was applied for Atlanta, GA and St. Louis, MO for January

334

and July 2006 to determine if the revised profiles were suitable for use in traditional receptor

335

models. Source impacts calculated with revised profiles were within the expected range of

336

values for the sources that were analyzed (Fig. 3, Tables S22 and S23). Of the revised profiles,

337

the Region 4 winter and summer profiles were used for Atlanta, and the Region 7 winter and

338

summer profiles were used for St. Louis.

339

compared with results from CMB-gas constraint (CMB-GC)25 applications and showed similar

340

results. Source impacts are also presented for the application of the spatial hybrid method using

341

the reference (SHreference) and revised (SHrevised) sources profiles. In general, the source impacts

342

from CMB-GC and CMB-iteration had similar trends, and the SHreference and SHrevised impacts

343

had similar trends; however impacts for certain sources and seasons for all four methods were

344

similar.

345

Atlanta, GA

Monthly-averaged CMB-iteration results were

346

For Atlanta, GA, the same sources as studied by Balachandran et al. (2012)21 were

347

chosen for analysis in CMB-iteration: gasoline and diesel impacts, dust, biomass burning, coal

348

combustion, sulfate, nitrate, and SOC (Fig. 3). The profiles used in the 2012 study originated

349

from Marmur et al. (2007)26. For gasoline vehicle impacts, the winter-summer difference was

350

largest for CMB-iteration, and SH impacts were the lowest. Wintertime gasoline impacts were

351

higher than summer impacts for all methods. For diesel vehicle impacts, the SH methods

352

apportioned more mass, and the summer impacts were higher than winter impacts for all

353

methods except CMB-GC. Overall, diesel vehicle impacts were higher than gasoline vehicle

354

impacts for all methods and seasons, with the exception of the wintertime impacts from CMB-

355

iteration.


16


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356

For Atlanta dust impacts, CMB-iteration had the highest impacts followed by CMB-GC,

357

and the impacts from the SH applications were significantly lower than the impacts from the

358

CMB applications. For all methods, dust impacts were higher during the summer season than

359

winter. The CMB methods apportioned larger dust impacts during summer, and SH methods

360

apportioned larger impacts during winter. For biomass burning impacts, the CMB impacts were

361

again higher than SH impacts, and summertime burning impacts from SH applications were

362

especially low. The discrepancy in dust and burn impacts between CMB and SH applications

363

could be attributed to the higher number of sources analyzed in the SH applications, leading to

364

more primary mass being apportioned to the “others” category (Fig. 3). Also, the application of

365

the SH methods (see previous publications) tended to reduce the impacts from biomass burning

366

and dust during SH optimization due to the high positive bias in tracer species concentrations for

367

the sources (e.g., K for biomass burning and Si for dust)19,20,24.

368

For coal combustion, impacts from SH methods were much higher than impacts from

369

CMB methods, and SH impacts in summer were higher than winter impacts. The source-oriented

370

approaches capture both primary and secondary impacts from coal combustion while the receptor

371

models do not specifically identify the secondary impacts. For the secondary sources, CMB

372

methods apportioned higher impacts compared with impacts from SH methods. For all methods,

373

sulfate impacts are higher than nitrate impacts. As a seasonal comparison, sulfate impacts were

374

higher in summer, and nitrate impacts were higher in winter. For Atlanta SOC, results were

375

similar for all methods with the exception of wintertime SOC for SH methods. Summertime

376

SOC was highest for the CMB-iteration method. The winter SOC impacts for SH methods were

377

negative due to low bias in CMAQ-modeled SOC in winter for this study, which was not seen in

378

the receptor-oriented methods.

The CMAQ-modeled PM2.5 for Atlanta was biased low in


17

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379

summer, which may be attributed to the low bias in dust and biomass burning impacts. Note that

380

standard deviations for SOC were unavailable for this version of CMB-iteration.

381

St. Louis, MO

382

For St. Louis, MO, the same sources as studied by Maier et al. (2013)22 were chosen for

383

analysis in CMB-iteration: mobile (sum of gasoline and diesel impacts), dust, biomass burning,

384

metals, sulfate, nitrate, and SOC (Fig. 3, Tables S24 and S25). The Maier et al. (2013) study

385

included metals processing as a source for St. Louis due to the large presence of metal-working

386

industries in the area, while the other profiles originated from the Marmur et al. (2007) study26.

387

In the present analysis, impacts from the two CMB methods had similar trends, and impacts from

388

the two SH methods had similar trends. Overall, the SH impacts were lower than the CMB

389

impacts because the CSN site used in the SH application is located farther from the city center

390

than is the Blair Street monitor used in the comparison study. The Blair Street monitor is

391

strongly influenced by pollutant sources near the city center.

392

The seasonality of the mobile source impacts was opposite for CMB and SH methods, in

393

that CMB impacts are higher in winter and SH impacts are higher in summer. For dust, impacts

394

are highest for CMB-iteration and are higher in the summer for all four methods. For biomass

395

burning, CMB impacts are significantly higher than SH impacts, and CMB-iteration impacts are

396

highest. As a seasonal comparison, summertime burning impacts are higher for CMB methods

397

than winter impacts, and wintertime burning impacts are higher for SH methods than summer

398

impacts. Similar to Atlanta, SH dust and biomass burning impacts are lower due to a high bias in

399

modeled tracer species concentrations. Also, primary mass could be apportioned to the “others”

400

category for the SH methods instead of dust and biomass burning. Metals impacts were similar

401

for all methods but slightly higher for CMB-iteration. For secondary sources (sulfate and nitrate),


18


Page 20 of 28

402

CMB impacts were higher than SH impacts. Seasonality was similar for all methods for sulfate

403

and nitrate, where summertime sulfate and wintertime nitrate impacts were higher than those in

404

winter and summer, respectively. For SOC, impacts were similar for all methods and higher in

405

the summer. SOC impacts were negative in winter for SH methods due to low bias in CMAQ-

406

modeled SOC in winter in this study.

407

Implications

408

This analysis demonstrated that CTM-RM-derived source profiles can be used in

409

traditional receptor modeling studies. However, the traditional methods were unable to make use

410

of all of the source categories at once due to source profile similarities, which led to negative

411

source contributions, due in part to over-fitting by the CMB-iteration model. It is suggested that

412

the most appropriate source categories be chosen when performing traditional receptor modeling

413

with revised source profiles. Additionally, as source profiles greatly affect source-oriented model

414

results such as CMAQ-DDM, revised source profiles can be utilized to re-speciate emissions and

415

can provide transport-based information into the calculation of primary trace species

416

concentrations. By optimizing source profiles over several monitors, the understanding of the

417

uncertainty in each profile increases. This study produced fairly consistent profiles over the

418

entire U.S., where concentrations and source impacts have significant spatial and temporal

419

variability. Results also imply that spatial variability in source profiles is an important factor to

420

consider when choosing profiles for a source apportionment study. Profiles with high spatial

421

variability should be carefully considered for source apportionment studies, as location-specific

422

source characteristics may not be represented in the traditional static profiles. Additional work

423

to be done includes modifying the optimization configuration to further improve modeled


19

Page 21 of 28


424

estimates of trace metal concentrations and incorporating the receptor-based secondary fraction

425

into the revised profile.

426

The CMB-iteration and source profile optimization methods have been formatted for

427

wide-scale use in the form of user-friendly graphic interfaces (Figs. S23 and S24). The programs

428

(CMB-iteration 3.0 and SSAPO (Simultaneous Source Apportionment with Profile

429

Optimization)) can be used to apply the methods for one location and one time. Both programs

430

are

431

(http://russellgroup.ce.gatech.edu/node/16). The source profile optimization method is applicable

432

for any location where initial source impact estimates, speciated observations, and reference

433

profiles are available. The hybrid adjustment factor, Rj, can be omitted for profile optimization

434

applications that are initialized with receptor model source impacts (78495: 1, ), or when CTM

435

resources are unavailable. The reference profile serves as an initial guess for the calculation of a

436

locally-based source profile.

437

especially for developing regions such as China and India, observed and modeled data are

438

relatively easier to obtain. New revised source profiles may better reflect local emissions

439

sources and pollutant characteristics.

440

Supporting Information

441

Tables: number of CSN monitors per region; tables containing source profiles for each region

442

and season; cosine similarities; coefficients of variance; mean observed and modeled

443

concentrations; correlations and normalized mean biases. Figures: distributions of source profile

444

ratios; normalized bias vs. observations; box plots of normalized bias; screenshots of CMB-

445

iteration and SSAPO.

available

for

download

from

Georgia

Tech’s

Russell

group

website

While locally-based profiles are traditionally difficult to obtain,


20


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446

Author Contributions

447

The manuscript was written through contributions of all authors. All authors have given approval

448

to the final version of the manuscript.

449

Acknowledgment

450

This publication was made possible in part by USEPA STAR grants R833626, R833866,

451

R834799 and RD83479901, STAR Fellowship FP-91761401-0, and by NASA under grant

452

NNX11AI55G. Its contents are solely the responsibility of the grantee and do not necessarily

453

represent the official views of the US government. Further, US government does not endorse the

454

purchase of any commercial products or services mentioned in the publication.

455

acknowledge the Southern Company and the Alfred P. Sloan Foundation for their support.

456

References

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Watson, J. G.; Cooper, J. A.; Huntzicker, J. J. The Effective Variance Weighting for Least Squares Calculations Applied to the Mass Balance Receptor Model. Atmos. Environ. 1984, 18 (7), 1347–1355 DOI: 10.1016/0004-6981(84)90043-X.

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Reff, A.; Bhave, P. V.; Simon, H.; Pace, T. G.; Pouliot, G. A.; Mobley, J. D.; Houyoux, M. Emissions Inventory of PM2.5 Trace Elements across the United States. Environ. Sci. Technol. 2009, 43 (15), 5790–5796 DOI: 10.1021/es802930x.

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Balachandran, S.; Chang, H. H.; Pachon, J. E.; Holmes, H. A.; Mulholland, J. A.; Russell, A. G. Bayesian-Based Ensemble Source Apportionment of PM 2.5. Environ. Sci. Technol. 2013, 47 (23), 13511–13518 DOI: 10.1021/es4020647.

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Byun, D.; Schere, K. L. Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Appl. Mech. Rev. 2006, 59 (2), 51 DOI: 10.1115/1.2128636.

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Cass, G. R. Organic Molecular Tracers for Particulate Air Pollution Sources. TrAC Trends Anal. Chem. 1998, 17 (6), 356–366 DOI: 10.1016/S0165-9936(98)00040-5.

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Zheng, M.; Cass, G. R.; Schauer, J. J.; Edgerton, E. S. Source Apportionment of PM2.5 in

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the Southeastern United States Using Solvent-Extractable Organic Compounds as Tracers. Environ. Sci. Technol. 2002, 36 (11), 2361–2371 DOI: 10.1021/es011275x.

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Zheng, M.; Cass, G. R.; Ke, L.; Wang, F.; Schauer, J. J.; Edgerton, E. S.; Russell, A. G. Source Apportionment of Daily Fine Particulate Matter at Jefferson Street, Atlanta, GA, during Summer and Winter. J Air Waste Manag Assoc 2007, 57 (2), 228–242 DOI: 10.1080/10473289.2007.10465322.

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Lee, S.; Russell, A. G. Estimating Uncertainties and Uncertainty Contributors of CMB PM2.5 Source Apportionment Results. Atmos. Environ. 2007, 41 (40), 9616–9624 DOI: 10.1016/j.atmosenv.2007.08.022.

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Sturtz, T. M.; Schichtel, B. A.; Larson, T. V. Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires. Environ. Sci. Technol. 2014, 48 (19), 11389–11396 DOI: 10.1021/es502749r.

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Shi, G. L.; Tian, Y. Z.; Zhang, Y. F.; Ye, W. Y.; Li, X.; Tie, X. X.; Feng, Y. C.; Zhu, T. Estimation of the Concentrations of Primary and Secondary Organic Carbon in Ambient Particulate Matter: Application of the CMB-Iteration Method. Atmos. Environ. 2011, 45 (32), 5692–5698 DOI: 10.1016/j.atmosenv.2011.07.031.

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Malm, W. C.; Schichtel, B. A.; Pitchford, M. L. Uncertainties in PM2.5 Gravimetric and Speciation Measurements and What We Can Learn from Them. J. Air Waste Manage. Assoc. 2011, 61 (11), 1131–1149 DOI: 10.1080/10473289.2011.603998.

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Cohan, D. S.; Hakami, A.; Hu, Y.; Russell, A. G. Nonlinear Response of Ozone to Emissions: Source Apportionment and Sensitivity Analysis. Environ. Sci. Technol. 2005, 39 (17), 6739–6748 DOI: 10.1021/es048664m.

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Napelenok, S. L.; Cohan, D. S.; Hu, Y.; Russell, A. G. Decoupled Direct 3D Sensitivity Analysis for Particulate Matter (DDM-3D/PM). Atmos. Environ. 2006, 40 (32), 6112– 6121 DOI: 10.1016/j.atmosenv.2006.05.039.

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Hu, Y.; Balachandran, S.; Pachon, J. E.; Baek, J.; Ivey, C.; Holmes, H.; Odman, M. T.; Mulholland, J. A.; Russell, A. G. Fine Particulate Matter Source Apportionment Using a Hybrid Chemical Transport and Receptor Model Approach. Atmos. Chem. Phys. 2014, 14 (11), 5415–5431 DOI: 10.5194/acp-14-5415-2014.

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Ivey, C. E.; Holmes, H. A.; Hu, Y. T.; Mulholland, J. A.; Russell, A. G. Development of PM2.5 Source Impact Spatial Fields Using a Hybrid Source Apportionment Air Quality Model. Geosci. Model Dev. 2015, 8 (7), 2153–2165 DOI: 10.5194/gmd-8-2153-2015.

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Maier, M. L.; Balachandran, S.; Sarnat, S. E.; Turner, J. R.; Mulholland, J. A.; Russell, A. G. Application of an Ensemble-Trained Source Apportionment Approach at a Site Impacted by Multiple Point Sources. Environ. Sci. Technol. 2013, 47 (8), 3743–3751 DOI: 10.1021/es304255u.

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Nguyen, H.; Bai, L. Cosine Similarity Metric Learning for Face Verification. Comput. Vision–ACCV 2010 2011, No. Figure 1, 1–12.

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Ivey, C. E.; Holmes, H. A.; Hu, Y.; Mulholland, J. A.; Russell, A. G. A Method for Quantifying Bias in Modeled Concentrations and Source Impacts for Secondary Particulate Matter. Front. Environ. Sci. Eng. 2016, 10 (5), 1–12 DOI: 10.1007/s11783016-0866-6.

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Marmur, A.; Unal, A.; Mulholland, J. A.; Russell, A. G. Optimization-Based Source Apportionment of PM2.5 Incorporating Gas-to-Particle Ratios. Environ. Sci. Technol. 2005, 39 (9), 3245–3254 DOI: 10.1021/es0490121.

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Marmur, A.; Mulholland, J. A.; Russell, A. G. Optimized Variable Source-Profile Approach for Source Apportionment. Atmos. Environ. 2007, 41 (3), 493–505 DOI: 10.1016/j.atmosenv.2006.08.028.

538


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539 540

Figure 1. U.S. EPA administrative regions and CSN monitors used for model development and

541

evaluation.


24


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542 543

Figure 2. Seasonal revised source profiles averaged over all available monitors in the U.S.

544

Profiles are presented for (top to bottom) coal combustion, dust, fires, metals processing, and

545

other sources.

546


25

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547

548 549

Figure 3. Averaged results from CMB-GC, CMB-iteration, original spatial hybrid (SH), and

550

spatial hybrid with revised profiles (new SP) for a winter and summer month for Atlanta, GA

551

and St. Louis, MO. CMB-GC results are obtained from an application of methods from

552

Balachandran et al. (2012) and Maier et al. (2013)21,22. PM2.5 mass concentration data is obtained

553

from the CSN network. The arrows indicate the direction of the appropriate y-axis for the bars

554

on either side of the dashed line. Note that data from all methods represent the same time

555

periods.


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556

Table 1. Source categories and abbreviations used in the source profile study. Note that note that

557

the abbreviations are used in the tables found in the Supplementary Information.

source

abbrev.

source

abbrev.

agricultural activities and livestock ag operations

non-road diesel

nrd

aircraft

air

non-road gasoline

nrg

biogenics

biog

non-road others

nro

coal combustion

coal

on-road diesel

ord

dust

dust

on-road gasoline

org

fires (wildfires, prescribed burns)

fire

others

ot

fuel oil combustion

foil

other combustion

otc

meat cooking

meat

solvents

slv

metals processing

metal

sea salt

ss

natural gas combustion

ng

wood burning

wood

558


27

Development of PM2.5 source profiles using a ... - ACS Publications

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