Quantifying Nonlinear Multiregional Contributions to Ozone and Fine

Subscriber access provided by UNIVERSITY OF ADELAIDE LIBRARIES

Article

Quantifying nonlinear multiregional contributions to ozone and fine particles using an updated response surface modeling technique Jia Xing, Shuxiao Wang, Bin Zhao, Wenjing Wu, Dian Ding, Carey J. Jang, Yun Zhu, Xing Chang, Jiandong Wang, Fenfen Zhang, and Jiming Hao Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01975 • Publication Date (Web): 11 Sep 2017 Downloaded from http://pubs.acs.org on September 12, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 30

Environmental Science & Technology

1

Quantifying nonlinear multiregional contributions to ozone and fine

2

particles using an updated response surface modeling technique

3

Jia Xing1, Shuxiao Wang1, *, Bin Zhao1, 4, Wenjing Wu1, Dian Ding1, Carey Jang2, Yun Zhu3, Xing Chang1,

4

Jiandong Wang1, 5, Fenfen Zhang1, Jiming Hao1 1

5 6 7 8 9 10 11 12 13 14 15

State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China 2 The U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA 3 College of Environmental Science & Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China 4 Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA 5 Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany * Corresponding Author: Shuxiao Wang (email: [email protected]; phone: +86-10-62771466; fax: +86-10-62773650)

16

Abstract

17

Tropospheric ozone (O3) and fine particles (PM2.5) come from both local and regional

18

emissions sources. Due to the nonlinearity in the response of O3 and PM2.5 to their precursors,

19

contributions from multiregional sources are challenging to quantify. Here we developed an

20

updated extended response surface modeling technique (ERSMv2.0) to address this challenge.

21

Multiregional contributions were estimated as the sum of three components: 1) the impacts of

22

local chemistry on the formation of the pollutant associated with the change in its precursor

23

levels at the receptor region; 2) regional transport of the pollutant from the source region to the

24

receptor region; and 3) interregional effects among multiple regions, representing the impacts on

25

the contribution from one source region by other source regions. Three components were

26

quantified individually in the case study of Beijing-Tianjin-Hebei using the ERSMv2.0 model.

27

For PM2.5 in most cases, the contribution from local chemistry (i.e., component 1) is greater than

1 ACS Paragon Plus Environment


Page 2 of 30

28

the contribution from regional transport (i.e., component 2). However, regional transport is more

29

important for O3. For both O3 and PM2.5, the contribution from regional sources increases during

30

high-pollution episodes, suggesting the importance of joint controls on regional sources for

31

reducing the heavy air pollution.

32

TOC Art

33

Air pollution

Air pollution

Air pollution

pollution 1 pollution 2

• Chemistry • Transport • Indirect

Specific scenarios

Emission

CTM

34

Full range responses

Emission

RSM

Full range responses to multiregional sources

Multiple pollutant responses to multiregional sources with wellsolved indirect effects

Emission C

Emission C

Emission B

Emission B

Emission A

Emission A

ERSMv1.0

ERSMv2.0

35

Keywords: nonlinear, air pollution, source contribution, response surface modeling, ozone, fine

36

particles


Page 3 of 30

37


1. Introduction

38

Different from primary pollutants (e.g., sulfur dioxides), ozone (O3) and fine particles

39

(PM2.5, particles less than 2.5 micrometers in diameter) have relative longer lifespans in the

40

atmosphere and usually require joint reductions from both local and regional sources to reduce

41

their ambient concentrations1-2. Many studies report that regional emission sources can play a

42

more important role in O3 and PM2.5 concentrations compared to local sources in rural3 and

43

urban areas4. For example, Skyllakou et al.4 found that only 13% of the PM2.5 in Paris originated

44

from local emissions, 36% came from mid-range (50–500 km away from Paris) sources, and 51%

45

came from long range transport (>500 km) during the examined summer and winter periods. In

46

addition, secondary pollutants, such as O3, exhibit strong nonlinear responses to their precursors,

47

making the local control measures inefficient. For example, local NOx controls without regional

48

measures may increase the risk of higher urban O3 levels due to VOC-limited conditions5.

49

Therefore, long-lived pollutants, such as O3 and PM2.5, cannot be controlled effectively without

50

regional measures.

51

In general, pollutant concentrations in receptor areas originate from two pathways: 1)

52

transport precursors (e.g., sulfur dioxides) from source areas that form secondary pollutants in

53

the receptor areas (denoted as “CM”, which is short for “local chemistry formed by transported

54

precursors”) and 2) direct transport of the pollutant (e.g., O3 and PM2.5) from the source area to

55

the receptor area (denoted as “TP”, which is short for “transport the pollutant”). Usually, TP is

56

the dominant pathway in most situations. This is because precursors have a much shorter lifespan

57

than secondary pollutants, so they are unable to be transported long distances. However, the

58

contribution from CM can become larger when the receptor area is small, and thus, the transport

59

of precursors from outside the region can increase. Advanced modeling techniques embedded in



Page 4 of 30

60

atmospheric chemistry transport models such as tagged species6-7 and sensitivity analysis8, were

61

developed to investigate local pollution contributions from certain type of species, sectors, and

62

regions. However, previous studies treat the regional contribution as a total value; thus, none of

63

the previous techniques can separate the regional contributions to TP and CM. In addition, most

64

of the previous studies still suffer from uncertainties associated with the strong nonlinear

65

response of O3 and PM2.5 to precursors5,9-10. To date, simulating the responsiveness of secondary

66

pollutants to multiregional sources is still challenging. The response surface modeling (RSM) technique was initialized in 2006 by the US EPA11-

67 68

12

69

Attainment Assessment System (ABaCAS, http://www.abacas-dss.com) led by an international

70

team of scientists from the United States and China13. The key parameters of RSM development

71

have been tested and determined through computational experiments and validations, confirming

72

the applicability of the RSM technique in the field of air quality modeling for O35 and PM2.59

73

simulations. While, the limitations of traditional RSM were recognized14, for the number of

74

scenarios required to build RSM increases significantly with the increase in the number of

75

control variables (i.e., the specific emission source to be tagged, such as species A from sector B

76

at region C). The idea of a novel extended response surface modeling (ERSM) technique was

77

proposed14. In addition, the ERSM was successfully developed in a study of PM2.5 in the Yangtze

78

River delta (YRD) region of China10. However, previous ERSM (denoted ERSMv1.0) require

79

extra computational efforts to calculate the integrated process rates using the process analysis

80

(PA) method, often doubling the computational time. In addition, indirect effects associated with

81

the interactions among regions were not well represented in ERSM development, resulting in

82

considerable biases when many regions were involved in the ERSM system.

. It has been continuously developed under the framework of the Air Benefit and Cost and


Page 5 of 30


83

To address such limitations, this study aims to improve the ERSM method by adding an

84

estimation of indirect effects to represent the interaction among regions. Furthermore, this study

85

is different from previous RSM studies in that it is the first to develop one RSM system that can

86

calculate the responses of both O3 and fine particles in one model. Understanding the co-benefits

87

from joint controls on multiple pollutants can significantly improve policy design.

88

2. Methods

89

2.1 Updated ERSM technique

90

The principle of the RSM approach is to calculate the nonlinear response of O3 and PM2.5

91

ambient concentrations to the change in emissions of gaseous precursors, which are derived from

92

the baseline scenario and a number of controlled scenarios with wide ranges of emission levels

93

(e.g., from controlling all emissions to doubling the emissions) simulated with a chemical

94

transport model (CTM). An advanced nonlinear regression method, i.e., the maximum likelihood

95

estimation of empirical best linear unbiased predictors, is used to create the nonlinear response of

96

concentrations to emissions in the conventional RSM technique. The method used to develop the

97

conventional RSM technique was detailed in our previous paper5. The computational time of

98

RSM mainly depends on the number of samples that are simulated in multiple CTM scenarios.

99

The ERSM is designed to reduce the number of scenarios required to build the RSM by treating

100

each region individually, thus reducing the computational load and avoiding the underfitting

101

problem that occurs when many factors are involved in the statistical regression. Details about

102

ERSM development were provided in our previous paper10. The principle of ERSM is that, rather

103

than building an enormous RSM system with high dimension (in which numerous emission

104

sources from all regions are involved in the statistical regression, as shown in Equation 1), we

105

developed multiple single-region RSM systems with low dimension for each region (in which 5 ACS Paragon Plus Environment


Page 6 of 30

106

only emissions in one region were involved in the statistical regression, as shown in Equation 2)

107

and combined them together mathematically (as shown in Equation 3). With these changes,

108

control variables for each single-region RSM are only defined by species and sectors and not by

109

regions. Therefore, adding many regions to the system will not enlarge the dimension of the

110

RSM system and will not tremendously increase the number of scenarios required for the

111

statistical regression 14. = , , … ,

112 113

(1)

where

114

is the concentration of pollutant X;

115 116

is the enormous RSM system in which numerous emission sources from all regions were involved in the statistical regression; and

117

is the emission ratio of source i (i=1,…,n).

118 = = = ⋯ =

119 120

(2)

where is the single-region RSM systems in which only emissions in region i (i=1,…,n) were involved in the statistical regression.

121 122 123

= , , … , , , … ,

124 125 126 127

(3)

where , , … , is the function of ,…, , representing the mathematical combination of multiple single-region RSM systems.

128 129

However, the key issue in ERSM development is determining how to combine these single-

130

region RSMs together (i.e., how to build Equation 3). As we discussed earlier, emissions in the

131

source region affect pollutant concentrations in the receptor region through two processes, i.e.,

132

TP (direct transport of the pollutant from the source region to the receptor region) and CM

133

(transport precursors from the source region form secondary pollutants in the receptor region). In 6 ACS Paragon Plus Environment

Page 7 of 30


134

the ERSM framework (see Figure 1), the contribution of TP and CM for each region can be

135

calculated directly from the single-region RSMs. To combine the contributions from each region

136

together requires accurate quantification of the indirect effects associated with the interactions

137

among regions, which is not straightforward. In ERSMv1.0, the indirect effects for CM (denoted

138

as CM_IND) were considered, but the indirect effects for TP (denoted as TP_IND) were not well

139

addressed. In ERSMv2.0, this limitation was resolved by adding an explicit representation of

140

indirect effects from a total-region RSM in which emissions in all regions changed

141

simultaneously (denoted as RSM_TT).

142 143

Thus, the total contribution to the pollutant X at receptor region i can be represented as follows: = ∑… → + _ ! + ∑…,$,%,… "#→ + "#_ !

144 145

(4)

where

146

is the concentration of pollutant X at receptor region i;

147 148

→ is the contribution to by transporting precursors from source region j and forming the secondary pollutant at receptor region i, denoted as local chemistry;

149 150

_ ! is the contribution to by the interaction among regions associated with the contribution of local chemistry, denoted as indirect effects of local chemistry;

151 152

"#→ is the contribution to by direct transport of the pollutant from source region j to receptor region i, denoted as regional transport; and

153 154

"#_ ! is the contribution to by the interaction among regions associated with the contribution of regional transport, denoted as indirect effects of regional transport.

155 156

First, the local chemistry represents the contribution caused by changes in the local

157

precursor levels. For instance, the function of pollutant X at receptor region i with precursors A,

158

B, C, D, and E (i.e., NOx, SO2, NH3, and volatile organic compounds (VOC) + intermediate

159

IVOC (denoted as VOC+IVOC) and primary organic aerosol (POA), respectively) from source

160

region j can be described as follows: 7 ACS Paragon Plus Environment


→ = &',(,),*,+ , = - ',(,),*,+ & ,.

161 162

Page 8 of 30

(5)

where

163

is the vector of emission factors for 5 precursors (i.e., A, B, C, D, and E) in source region j;

164

',(,),*,+ is the concentration of 5 precursors (i.e., A, B, C, D, and E) in receptor region i;

165 166 167 168 169

&',(,),*,+ , represents the RSM system that calculates the response of to the changes in ',(,),*,+ based on the single-region RSM model; the principle of RSM is a statistical regression model that quantifies the relationship between one target variable (pollution concentration, /) with other factors (e.g., precursor concentrations in region i, , or emission factors in region j, ); and

170 171

',(,),*,+ & , represents the RSM system that calculates the response of ',(,),*,+ to the change in based on the single-region RSM model.

172 173 174

In addition, the contribution of emissions from the receptor itself is also considered in the local chemistry, i.e., → =

175

(6)

176 177

As described in ERSMv1.0, the contribution of CM from region j to region i depends on

178

the precursor level in region i that is contributed by all regional sources. The sum of the CM

179

from each individual region might not equal the total CM of all the regions, and the difference

180

between these two values is defined as the indirect effect on CM (denoted CM_IND):

181

_ ! = &∑, ',(,),*,+ & ,, − ∑, - ',(,),*,+ & ,.

(7)

182

Additionally, CM_IND will be 0 if there is only one region considered in the RSM system.

183

Second, the regional transport represents the direct transport of the pollutant from the

184

source region to the receptor region. For pollutant X at receptor region i, with precursors A-E and

185

source region j: "#→ = & , − →

186 187

(8)

where 8 ACS Paragon Plus Environment

Page 9 of 30

188 189


& , represents the RSM system that calculates the response of to the based on the single-region RSM model.

190

"#→ = 0 since the contribution from local emissions at the receptor are classified as CM

191 192

(see Equation 6).

193

In ERSMv1.0, we assume that the contributions of regional transport from different source

194

regions do not impact each other (meaning that TP_IND was ignored). However, such an

195

assumption will result in considerable bias when the regional contribution exhibits significant

196

nonlinearity, particularly as is seen for O3. For example, changes in precursor emissions in

197

Region k might affect the formation of pollutant X in Region j. This further affects the transport

198

of X from Region j to Region i. In ERSMv2.0, the TP_IND is estimated as the difference in the

199

response to total emissions of all regions between predictions in RSM_TT and the calculation in

200

ERSMv1.0 (i.e., sum of CM, CM_IND and TP), as shown below: 4444444 − ∑, → − _ ! − ∑,…,$,%,…, "#→ "#_ ! = _22 3

201 202

(9)

where

203 204

4444444 3 is the average of , … , $ , % , … weighted by the contribution to ',(,),*,+ from each source region; and

205 206

4444444 represents the RSM system that calculates the response of to the 4444444 _22 3 3, based on the RSM_TT model.

207 208

Since the emissions in all regions change simultaneously in RSM_TT, Equation 9 is a

209

function of emissions in all regions instead of a function of emissions in specific regions. To

210

address this issue, we assume that, for each source region, its contribution to the indirect effects

211

can be estimated by its contribution to the concentrations of precursors at the receptor area. With

212

this assumption, the response of TP_IND to specific regions is established.

213

In ERSMv1.0, the PA method was used to address the limitation of the minimum 9 ACS Paragon Plus Environment


Page 10 of 30

214

concentration when emissions in all regions were significantly reduced (see Equation 11 in Zhao

215

et al.10). In ERSMv2.0, such treatment is not necessary because the low-emission condition has

216

been included in the TP_IND estimation. Therefore, ERSMv2.0 reduces the additional

217

computational cost associated with PA in ERSMv1.0. The following sections discuss the

218

application of the ERSMv2.0 system in a case study.

219

2.2 Case study of the Beijing-Tianjin-Hebei region

220

The Beijing-Tianjin-Hebei (denoted BTH) region is the most polluted region in China due

221

to extensive emission levels and various emission sources. Strong interregional influences in the

222

BTH region provide a good case for us to investigate the ability of ERSMv2.0 to quantify the

223

nonlinear regional contributions to O3 and PM2.5.

224

The model configuration was described in Zhao et al15. The one-way and triple nesting

225

simulation domains were used, as shown in Figure 2. Domain 1 covers most of East Asia with a

226

grid resolution of 36 km×36 km; domain 2 covers the BTH region with a grid resolution of 12

227

km×12 km. The RSM was performed on domain 2 after it was divided into 5 regions, including

228

Beijing, Tianjin, northern Hebei (denoted HebeiN), eastern Hebei (denoted HebeiE), and

229

southern Hebei (denoted HebeiS). The boundary condition used for simulations in domain 2 was

230

estimated from simulations over domain 1 and represents the impacts of inflow from regions

231

outside the BTH region. The same boundary condition was used in the multiple simulation

232

scenarios to build the RSM. We used a high-resolution anthropogenic emission inventory

233

developed by Tsinghua University15.

234

The Weather Research and Forecasting Model (WRF, version 3.7) and the Community

235

Multi-scale Air Quality modeling system (CMAQ, version 5.0.1) were used to process the

236

multiple simulations. The SAPRC99 gas-phase chemistry module and the AERO6 aerosol 10 ACS Paragon Plus Environment

Page 11 of 30


237

module with detailed treatment of organic aerosols, including POA aging and IVOC

238

photooxidation in the 2D-VBS (two-dimensional volatility basis set) framework 16, 17, were used

239

in this study. The model settings, including physical and chemical options, the geographical

240

projection, the vertical resolution, and the initial and boundary conditions, were consistent with

241

our previous study10. The simulation periods were January and July in 2014. Acceptable model

242

performance that meets the recommended benchmark18 was suggested in the comparison with

243

ground-observed concentrations15.

244

The design of the RSM was summarized in Table 1. First, the traditional RSM method was

245

used to build a RSM to establish the response of O3 and PM2.5 (the combination of primary and

246

secondary organic, inorganic and dust aerosols) to the emissions of 5 group of precursors

247

including NOx, SO2, NH3, VOC+IVOC, and POA from all regions and sectors (i.e., the total

248

emissions for each species). A total of 101 cases were simulated by the CMAQ model and were

249

further used for building the RSM model (i.e., RSM_TT), which included one baseline case and

250

100 control scenarios that were randomly sampled by Latin Hypercube Sampling (LHS) method

251

with a range of 0.0 to 1.2 (baseline =1.0). The RSM_TT was used to calculate the TP_IND for

252

ERSM development. The RSM_TT was built with a traditional RSM method that has been

253

proven to be highly reliable in representing the nonlinear response of pollution concentrations to

254

precursor emissions when the number of control factors are fewer than 8 and the number of

255

samples is reasonable5, 9, 14. Therefore, the RSM_TT was also used as the benchmark for isopleth

256

comparison.

257

Second, the ERSM method was used to create 5 single-region RSMs, which were used to

258

establish the response of O3 and PM2.5 concentrations to the local emissions in each region. In

259

addition, the emissions of NOx/SO2 were further split into LPS (large point sources, including



Page 12 of 30

260

power plants, iron and steel plants, and cement plants) NOx/SO2 and other NOx/SO2; thus, there

261

were 7 control variables (i.e., LPS and other NOx, LPS and other SO2, NH3, VOC+IVOC, and

262

POA) involved in each single-region RSM system. It should be noted that the classification of

263

LPS and other NOx and SO2 was designed for the investigation of sectoral contributions, as

264

discussed in another study15. In this study, our discussion will focus on total NOx and SO2

265

emissions without the sectoral classification. For each single-region RSM, 200 controlled

266

scenarios sampled by LHS were used in RSM development.

267

Third, there are 40 additional cases used for out-of-sample validation. These cases include

268

6 samples in which emissions only change in each region, and 10 samples in which emissions

269

change jointly in 5 regions, with different emission ratios in each region. The additional cases

270

were sampled randomly by LHS to represent a general situation.

271

3. Results

272

3.1 Comparison of the model performance of ERSMv1.0 and ERSMv2.0

273

An out-of-sample comparison was conducted for ERSMv1.0-noPA (with no PA

274

modification), ERSMv1.0-PA (adjustment based on integrated process rates from PA for PM2.5

275

only) and ERSMv2.0 (with consideration of the TP_IND); the results for PM2.5 are shown in

276

Figure S1 and the results for O3 are shown in Figure S2.

277

Across the 40 out-of-sample cases, both ERSMv1.0-PA and ERSMv2.0 had slightly better

278

performance in predicting PM2.5 compared to ERSMv1.0-noPA. The mean normalized errors in

279

ERSMv1.0-noPA, ERSMv1.0-PA and ERSMv2.0 across the 40 cases were 0.57%, 0.38% and

280

0.55%, respectively. Compared to ERSMv1.0-PA, slightly worse performance was found in

281

ERSMv2.0 for the first 30 cases where emissions only change in one single region. This is

282

probably because of the uncertainty in the estimation of TP_IND; the estimation of TP_IND in 12 ACS Paragon Plus Environment

Page 13 of 30


283

ERSMv2.0 is not applicable for cases with no indirect effects, as seen in the first 30 cases where

284

emissions only change in one single region. For cases 31-40, in which emissions change jointly

285

in 5 regions, ERSMv2.0 exhibited better performance compared to both ERSMv1.0-noPA and

286

ERSMv1.0-PA. The mean normalized errors in ERSMv1.0-noPA, ERSMv.10-PA and ERSMv2.0

287

across the cases were 1.62%, 1.51% and 1.12% respectively.

288

Similar results were shown in O3 predictions (Figure S2). The mean normalized errors in

289

ERSMv1.0-noPA and ERSMv2.0 for cases in which emissions change jointly in 5 regions were

290

2.56% and 2.90%, respectively, in January and 1.20% and 1.15%, respectively, in July.

291

Relatively larger normalized errors for O3 occurred in January (Figure S2a) compared to those

292

seen in July (Figure S2b); this is mostly due to the lower O3 concentration (i.e., small

293

denominator). This is consistent with results in our previous study that found that the out-of-

294

sample validation for O3 presents relatively larger biases in scenarios with low ozone mixing

295

ratios5.

296

The isopleth validation of ERSMv1.0-noPA, ERSMv1.0-PA and ERSMv2.0 is compared in

297

Figure 3 for PM2.5 and Figure 4 for O3. The RSM_TT is used as the benchmark for isopleth

298

comparison due to its reliability in representing nonlinearity5, 9, 14. The different colors in Figure

299

3 represent different PM2.5 concentrations. The x- and y-axes show the emission ratio, which is

300

defined as the ratio of the changed emissions to the emissions in the baseline case for the entire

301

region.

302

The isopleth suggests the precursor emissions of NOx, SO2, NH3, VOC+IVOC, and POA

303

substantially contribute to PM2.5. The responses of PM2.5 to VOC+IVOC and POA are nearly

304

linear. However, strong nonlinearity is shown in the PM2.5 responses to NH3 and NOx, which is

305

due to the complex formation of particle nitrate9. The ERSMv1.0-noPA generally reproduced this



Page 14 of 30

306

nonlinearity, except when noticeable biases were observed at low emission levels of NOx and

307

NH3 (Figure 3b). The ERSMv1.0-PA reduced the bias at low levels of NOx but not for other

308

species (Figure 3c). After the inclusion of TP_IND in ERSMv2.0 (Figure 3d), the isopleth

309

predicted in ERSMv2.0 improved significantly, presenting consistent PM2.5 responses to the five

310

precursors based on the benchmark (i.e., RSM_TT, see Figure 3a).

311

Similar results were found for O3 responses to NOx and VOC+IVOC in July for all regions.

312

The nonlinearity in O3 formation is stronger than in PM2.5. Compared to Beijing and HebeiN, the

313

isopleth was not well reproduced at Tianjin, HebeiE and HebeiS which exhibited even stronger

314

nonlinear responses to precursors than in Beijing and HebeiN (see Figure 4). This is probably

315

because too few cases were used to build the RSM. According to Xing14, the number of cases

316

required to build a RSM with 7 control variables is approximately 330, which is a slightly more

317

than the 200 used in the current study. However, it is still clear that the inclusion of TP_IND in

318

ERSMv2.0 significantly improved the model’s ability to reproduce the observed nonlinearity,

319

presenting an isopleth comparable with the benchmark. Specifically, the bias at low NOx levels

320

was reduced significantly in ERSMv2.0.

321

3.2 Interpretation of components in ERSMv2.0

322

The responses of PM2.5 and O3 to precursors were further separated into components (i.e.,

323

CM and TP) in Figures 5 and 6 respectively. ERSMv1.0 includes CM, CM_IND and TP.

324

TP_IND is newly added into ERSMv2.0.

325

In July in Beijing, the PM2.5 contributed by local chemistry stemmed from NH3 (denoted as

326

CM-NH3), the largest contribution compared to other species (Figure 5a). CM-NH3 is also larger

327

than the PM2.5 contributed by regional transport stemming from NH3 (denoted as TP-NH3, see

328

Figure 5b), indicating that controlling of NH3 will most likely reduce the PM2.5 formed inside the 14 ACS Paragon Plus Environment

Page 15 of 30


329

receptor region. NOx exhibits the opposite behavior, and regional transport stemming from NOx

330

(i.e., TP-NOx) was larger than that from NH3 and SO2, as well as local chemistry stemming from

331

NOx, indicating that controlling NOx will result in the reduction of PM2.5 formed outside the

332

receptor region. Similarly, controlling VOC+IVOC would reduce the PM2.5 formed outside the

333

region. TP_IND (Figure 5c) indicated that biases resulted from a simplification of the treatment

334

of regional transport (TP), which was assumed to be regionally independent in ERSMv1.0.

335

However, when NH3 was substantially reduced in all regions but NOx and SO2 emission levels

336

were still high (implying a strong NH3-poor condition), the TP of each region will be

337

overestimated if the effects of reduced NH3 in surrounding regions is not considered. A similar

338

situation was found when NOx was substantially reduced in all regions but NH3 emission levels

339

were still high (implying a strong NH3-rich condition). The reduced contribution of TP_IND to

340

PM2.5 compensates for such high biases of TP when the impacts from surrounding regions on TP

341

cannot be ignored (e.g., strong NH3-limited or NH3-rich conditions). Since TP_IND was not

342

considered in ERSMv1.0, the ERSMv1.0 might underestimate the reduction in PM2.5 caused by

343

greater control efforts on NH3 and NOx.

344

For O3 in July, we focused on the O3 response to two precursors, i.e., NOx and

345

VOC+IVOC, in two regions, i.e., Beijing and HebeiN, since they present two different types of

346

O3 chemistry. The NOx level is high in Beijing, resulting in a strong VOC-limited scheme. The

347

nonlinearity is evident in O3 contributed by local chemistry (CM+CM_IND, see Figure 6a),

348

suggesting the control of NOx will result in a transition from increasing to reducing O3. However,

349

the response of O3 contributed by regional transport (TP, see Figure 6b) suggests greater O3

350

reduction from the control of NOx compared to VOC+IVOC. In HebeiN where NOx levels are

351

not as high as in Beijing, the O3 chemistry exhibits a NOx-limited scheme. Compared to



Page 16 of 30

352

VOC+IVOC, NOx control results in a greater reduction of O3 through both local chemistry and

353

regional transport. Substantial contribution from TP_IND is suggested under low NOx levels for

354

both the Beijing and HebeiN regions (Figure 6c), implying the additional control of NOx under

355

low NOx levels will facilitate the reduction of O3. Without the consideration of TP_IND, the

356

original ERSMv1.0 might underestimate the benefits of NOx control when NOx emissions are

357

largely reduced.

358

3.3 Apportionment of responses to precursor emissions

359

Contributions from individual components of PM2.5 and O3 responses to precursors are

360

further quantified in Figure 7. From Equation 4, the regional contribution can be split into local

361

chemistry (CM), regional transport (TP) and the indirect effects due to the interregional

362

influences of CM (i.e., CM_IND calculated by Equation 7) and TP (i.e., TP_IND calculated by

363

Equation 9). Therefore, multiple regional contributions to O3 and PM2.5 can be apportioned into

364

CM and TP by region (i.e., CM_Beijing, CM_Tianjin, CM_HebeiN, CM_HebeiE, CM_HebeiS,

365

and TP_Beijing, TP_Tianjin, TP_HebeiN, TP_HebeiE, and TP_HebeiS), and the indirect effects

366

of CM and TP can be identified (i.e., CM_IND and TP_IND). The apportionment of multiple

367

regional contributions was conducted for monthly averages and daily averages grouped by

368

different pollution levels for PM2.5 (see Figure 7a-b) and O3 (see Figure 7c). The response was

369

defined as the change in O3 and PM2.5 concentrations caused by the reduction in total emissions

370

in each region. The 2014 emissions of major air pollutants in 5 regions are summarized in Table

371

S1. Greater amounts of emissions were located in HebeiS, which suffers from heavier pollution

372

contributed by local sources, while fewer emissions were found in HebeiN and pollution

373

concentrations were relative low compared to other regions (Figure 7-8).

374

The largest contributor for PM2.5 is the local chemistry associated with local emissions 16 ACS Paragon Plus Environment

Page 17 of 30


375

(CM_Beijing for Beijing, CM_Tianjin for Tianjin, etc.), which account for more than 50% of the

376

total contribution. In HebeiS, the local chemistry associated with local emissions (i.e.,

377

CM_HebeiS) accounted for more than 90% of the total contributions from the BTH region to

378

PM2.5. Regional contributions are also evident in both January and July, mostly through the direct

379

transport of PM2.5 (i.e., TP). The PM2.5 concentrations in Beijing, Tianjin and HebeiN are

380

considerably impacted by regional transport from HebeiE and HebeiS (i.e., TP_HebeiE and

381

TP_HebeiS). The regional transport from Tianjin (i.e., TP_Tianjin) substantially contributes to

382

PM2.5 in Beijing and HebeiN and in HebeiE in July. The contribution of transporting precursors

383

(i.e., local chemistry associated with precursors transported from other regions) was also

384

noticeable, as seen by PM2.5 concentrations in Beijing and Tianjin, which originated from local

385

chemistry associated with precursors transported from HebeiE and HebeiS (i.e., CM_HebeiE,

386

CM_HebeiS). In addition, it becomes more significant when comparing January to July,

387

probably because precursors exhibit longer lifespans in winter, which is beneficial for long-range

388

transport, than in summer. In Beijing and Tianjin, the CM_IND in January was much larger than

389

in July. The TP_IND indicated a positive contribution in January but a negative contribution in

390

July. That might be influenced by seasonal variations in the atmospheric oxidation capacity and

391

the neutralization with NH3. In winter, when a VOC-limited regime is evident, joint regional

392

controls can provide extra benefits by reducing the atmospheric oxidation capacity, resulting in a

393

slower transition rate from NOx and SO2 to nitrate and sulfate, respectively. Therefore, both

394

CM_IND and TP_IND in Beijing and Tianjin are positive in winter. In summer, not enough NH3

395

is available to neutralize nitrate due to the large amount of NH3 that is taken over by sulfate,

396

resulting in the large sensitivity of nitrate to precursor emission perturbations. The sum of

397

regional contributions calculated individually by region is larger than the total regional



Page 18 of 30

398

contribution calculated by simultaneous controls for all regions9. Therefore, in Beijing and

399

Tianjin, the positive contribution of CM_IND and TP_IND is reduced in the summer.

400

In July, the O3 contribution from local chemistry varies significantly across the regions,

401

presenting the largest positive contributions to Beijing, HebeiN and HebeiS, the smallest

402

contribution to HebeiE and a negative contribution to Tianjin. This can be explained by the

403

variation of O3 chemistry in different regions (Figure 4), i.e., the strongest VOC-limited regimes

404

are in Tianjin and HebeiE, moderate VOC-limited regimes are found in Beijing and HebeiS, and

405

a strong VOC-limited regime is found in HebeiN. NOx controls in strong VOC-limited regimes

406

increase O3 concentrations, resulting in negative contributions by local chemistry, as shown in

407

Tianjin. Different from PM2.5, regional transport contributes more than local chemistry in most

408

regions, with the exception of HebeiS, where more is contributed by local sources. The TP_IND

409

is much larger than that for PM2.5, and the CM_IND in most regions provides positive

410

contributions, implying the additional benefit in O3 reduction from joint control on regional

411

emissions.

412

Daily apportionment results vary significantly for both PM2.5 (Figure S3) and O3 (Figure

413

S4). The daily data were grouped into five PM2.5 concentration levels (i.e., 150 µg m-3) (Figure 7). One interesting finding is that the proportion of regional

415

contributions increases (up to 40% of total contributions) with an increase in PM2.5

416

concentrations, especially in January, implying the importance of regional contributions during

417

high-pollution episodes. Similar results were also found for O3 in July. The positive contribution

418

from regional sources increases during high-pollution days (up to 80% to total contributions). In

419

addition, the contribution of local chemistry exhibits a transition from negative to positive. The

420

growth of the regional contribution with the increase in pollution levels implies that joint control


Page 19 of 30

421


on regional sources is important for reducing O3 and PM2.5 during high-pollution events.

422

In conclusion, the successful development of ERSMv2.0 addressed the indirect effects that

423

were not well resolved in the previous version of the model. The nonlinearity in the response of

424

O3 and PM2.5 to precursors was reproduced well in ERSMv2.0, which significantly reduced the

425

low biases that existed in the simulated responses to large changes in precursors in ERSMv1.0.

426

In most regions, the PM2.5 formed locally in the receptor region was greater than the

427

amount of PM2.5 that was formed outside of the receptor region and transported in. The opposite

428

trend was found for O3, and it was concluded that regional transport contributed more than local

429

chemistry. Regardless of NOx-limited or VOC-limited chemistry, the effects of regional transport

430

suggest that greater O3 reduction would be achieved through the control of NOx compared to the

431

control of VOC+IVOC, implying the importance of NOx control in reducing regional O3 levels.

432

Moreover, during high-pollution episodes, the contribution from regional sources increases,

433

indicating the importance of joint control over regional sources for reducing O3 and PM2.5 during

434

high-pollution events.

435

Though our model had acceptable levels of agreement with observations (discussed in

436

Zhao et al.15), it might still suffer from uncertainties inherent in the CMAQ simulations and

437

emission inventory, which substantially influence the apportionment results. Further

438

improvements in better representing the chemical formation and precursor emissions are

439

important. It is also necessary to investigate the impact of meteorology on PM2.5 and O3

440

responses to precursors.

441

442 443

Acknowledgements This work was supported in part by National Key R & D program of China 19 ACS Paragon Plus Environment


Page 20 of 30

444

(2016YFC0207601), National Science Foundation of China (21625701) and Strategic Pilot

445

Project of Chinese Academy of Sciences (XDB05030401). This work was completed on the

446

“Explorer 100” cluster system of Tsinghua National Laboratory for Information Science and

447

Technology.

448

449

Supporting Information Available

450

Description about model configuration; emissions of major air pollutants in Beijing,

451

Tianjin, HebeiN, HebeiE, HebeiS regions; Comparison of out-of-sample validations;

452

Apportionment of daily O3 and PM2.5 response to the reduction in emissions from multiple

453

regions.

454

455

References

456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479

(1) Stevenson, D.S.; Dentener, F.J.; Schultz, M.G.; Ellingsen, K.; Van Noije, T.P.C.; Wild, O.; Zeng, G.; Amann, M.; Atherton, C.S.; Bell, N.; Bergmann, D.J. Multimodel ensemble simulations of presentXday and nearXfuture tropospheric ozone. J. Geophys. Res. 2006, 111(D8), DOI 10.1029/2005JD006338 (2) He, H.; Vinnikov, K.Y.; Li, C.; Krotkov, N.A.; Jongeward, A.R.; Li, Z.; Stehr, J.W.; Hains, J.C.; Dickerson, R.R. Response of SO2 and particulate air pollution to local and regional emission controls: A case study in Maryland. Earth’s Future 2016, 4, 94–109. (3) Brook, J.R.; Lillyman, C.D.; Shepherd, M.F.; Mamedov, A. Regional Transport and Urban Contributions to Fine Particle Concentrations in Southeastern Canada. J. Air Waste Manage. Assoc. 2002, 52, 855-866. (4) Skyllakou, K.; Murphy, B.N.; Megaritis, A.G.; Fountoukis, C.; Pandis, S.N. Contributions of local and regional sources to fine PM in the megacity of Paris. Atmos. Chem. Phys. 2014, 14, 2343-2352 (5) Xing, J.; Wang, S.X.; Jang, C.; Zhu, Y.; Hao, J.M. Nonlinear response of ozone to precursor emission changes in China: a modeling study using response surface methodology. Atmos. Chem. Phys. 2011, 11, 5027-5044. (6) Wu, Q. Z.; Wang, Z.F.; Gbaguidi, A.; Gao, C.; Li, L.N.; Wang, W. A numerical study of contributions to air pollution in Beijing during CAREBeijing-2006. Atmos. Chem. Phys. 2011, 11, 5997-6011. (7) Kwok, R.H.F.; Baker, K.R.; Napelenok, S.L.; Tonnesen, G.S. Photochemical grid model implementation and application of VOC, NOx, and O3 source apportionment. Geosci. Model Dev. 2015, 8,99-114. (8) 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, 6739–6748. (9) Wang, S. X.; Xing, J.; Jang, C.; Zhu, Y.; Fu, J.S.; Hao, J. Impact assessment of ammonia emissions on inorganic aerosols in east China using response surface modeling technique. Environ. Sci. Technol. 2011, 45, 9293–9300. (10) Zhao, B.; Wang, S.X.; Xing, J.; Fu, K.; Fu, J.S.; Jang, C.; Zhu, Y.; Dong, X.Y.; Gao, Y.; Wu, W.J.; Wang, J.D. Assessing the nonlinear response of fine particles to precursor emissions: development and application of an extended response surface modeling technique v1.0. Geosci. Model Dev. 2015, 8, 115-128. (11) US Environmental Protection Agency. Technical support document for the proposed PM NAAQS rule:


Page 21 of 30

480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504


Response Surface Modeling, Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC, US, 48, 2006. (12) US Environmental Protection Agency: Technical support document for the proposed mobile source air toxics rule: ozone modeling, Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC, US, 49, 2006. (13) Xing, J.; Wang, S.; Jang, C.; Zhu, Y.; Zhao, B.; Ding, D.; Wang, J.; Zhao, L.; Xie, H.; Hao, J. ABaCAS: an overview of the air pollution control cost-benefit and attainment assessment system and its application in China. The Magazine for Environmental Managers - Air & Waste Management Association 2017, April. (14) Xing, J. Study on the nonlinear responses of air quality to primary pollutant emissions. Doctor thesis, School of Environment, Tsinghua University, Beijing, China, 2011 (in Chinese). (15) Zhao, B.; Wu, W.; Wang, S.; Xing, J.; Chang, X.; Liou, K.-N.; Jiang, J. H.; Gu, Y.; Jang, C.; Fu, J. S.; Zhu, Y.; Wang, J.; Hao, J. A modeling study of the nonlinear response of fine particles to air pollutant emissions in the Beijing-Tianjin-Hebei region, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-428, in press, 2017. (16) Zhao, B.; Wang, S.; Donahue, N.M.; Chuang, W.; Hildebrandt Ruiz, L.; Ng, N.L.; Wang, Y.; Hao, J. Evaluation of one-dimensional and two-dimensional volatility basis sets in simulating the aging of secondary organic aerosols with smog-chamber experiments, Environ. Sci. Technol. 2015, 49, 2245-2254, DOI 10.1021/es5048914. (17) Zhao, B.; Wang, S.; Donahue, N.M.; Jathar, S.H.; Huang, X.; Wu, W.; Hao, J.; Robinson, A.L. Quantifying the effect of organic aerosol aging and intermediate-volatility emissions on regional-scale aerosol pollution in China, Sci. Rep. 2016, 6, 10.1038/srep28815. (18) US EPA. Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze. US EPA Office of Air Quality Planning and Standards. 2007, available at https://www3.epa.gov/ttn/naaqs/aqmguide/collection/cp2_old/20070418_page_guidance_using_models.pdf



Table 1 Scenarios for RSM design

505 506

Short name Baseline RSM_TT

ERSM

Out-ofsample

507 508 509 510

Page 22 of 30

Objective

Control factor

Baseline case Using the traditional RSM method, as the benchmark for isopleth comparison

5 precursors, including NOx, SO2, NH3, NMVOC+IVOC and POA

Number of cases

1 100 in addition to the baseline case (total 101), Latin Hypercube Sampling between 0.0 to 1.2 (baseline =1.0) Extend RSM method to 7 precursors, including 1000 in addition to the create individual LPS/other* NOx, baseline case (total 1001), single-region RSM for LPS/other SO2, NH3, 200 samples for each each of 5 regions NMVOC+IVOC and POA region, Latin Hypercube Sampling between 0.0 to 1.2 (baseline =1.0) Out-of-sample 40, 6 samples in each validation region, and 10 samples for 5 regions together, Latin Hypercube Sampling between 0.0 to 1.2

*The classification of LPS (large point sources, including power plants, iron and steel plants, and cement plants) and others for NOx and SO2 was designed for the investigation of sectoral contributions, which has been discussed in another paper (Zhao et al., 201715). In this study, we only focused on the total NOx and SO2 without the sectoral classification

511


Page 23 of 30


512 513

ERSMv2.0 Indirect effects

RSM_TT

ERSMv1.0 Transport

+

Transport

Transport

Transport

Precursor

+

Precursor

Precursor

Precursor

Single-region RSM

Single-region RSM

Single-region RSM

Single-region RSM

Region 1

Region 2

…

Region i

…

Region n

514 515

Figure 1. Conceptual framework of extend response surface model (ERSM)



Page 24 of 30

516 517

Figure 2. WRF-CMAQ simulation domain (left) and region defined in the RSM (right)

518


Page 25 of 30


519 NOx(y) vs NH3(x)

1.2 1.2

1.0 1.0

1.0 1.0

0.8 0.8

0.8 0.8

SO SO22

0.6 0.6

0.6 0.6

0.4 0.4

0.4

0.2

0.2 0.2

0.4

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NH3 NH3

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

POA POA

1.0 1.0

0.8 0.8

0.8 0.8

VOC+IVOC IVO

1.0 1.0

SO SO22

1.0 1.0

NO NOx x

1.2 1.2

0.6

0.6

0.4 0.4

0.4 0.4

0.2 0.2

0.2 0.2

0.8 0.8 0.6 0.6

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

POA POA

1.2 1.2

1.2 1.2

1.2 1.2

1.0

1.0

1.0

1.0 1.0

0.8 0.8

0.8 0.8

VOC+IVOC IVO

SO SO2 2

NO NOx x

1.0

0.6

0.6

0.4 0.4

0.4 0.4

0.2 0.2

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NH3

0.6 0.6

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

POA POA

1.2 1.2

1.0 1.0

1.0 1.0

0.8 0.8

0.8 0.8

SO SO22

VOC+IVOC IVO

1.2 1.2

1.0 1.0

NO NOx x

1.2 1.2

0.6 0.6

0.4

0.2 0.2

0.2 0.2

0.4

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 NH3 NH 3

51.25 55.63 60.00

0.4 0.4 0.2 0.2

NH3

0.4 0.4

25.00 25.00 29.38 33.75 29.38 38.13 33.75 42.50 46.88 38.13 51.25 42.50 55.63 46.88 60.00

0.8 0.8

NH3

0.6 0.6

51.25 55.63 60.00

0.4 0.4

NH3

0.6 0.6

25.00 25.00 29.38 33.75 29.38 38.13 33.75 42.50 46.88 38.13 51.25 42.50 55.63 46.88 60.00

0.2 0.2

NH3

NH3

520 521 522 523 524 525 526 527 528

0.4 0.4

1.2 1.2

NH3 NH 3

(d) ERSMv2.0

0.6 0.6

1.2 1.2

0.6

25.00 29.38 33.75 38.13 42.50 46.88 51.25 55.63 60.00

0.8 0.8

NH3

0.6

25.00 29.38 33.75 38.13 42.50 46.88 51.25 55.63 60.00

0.2 0.2

NH3

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

(c) ERSMv1.0PA

VOC+IVOC IVO

1.2 1.2

1.0 1.0

0.2

(b) ERSMv1.0noPA

VOC+IVOC(y) vs POA(x)

1.2 1.2

NO NOx x

(a) RSM_TT

SO2(y) vs NH3(x)

25.00 25.00 29.38 33.75 29.38 38.13 33.75 42.50 38.13 46.88 51.25 42.50 55.63 46.88 60.00

0.8 0.8 0.6 0.6

51.25 55.63 60.00

0.4 0.4

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NH3

NH3

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 POA POA

Figure 3. Comparison of the 2-D isopleths of PM2.5 in Beijing in response to the simultaneous changes of precursor emissions in all regions, as derived from the (a) RSM_TT (benchmark), (b) ERSMv1.0-noPA (with no PA modification), (c) ERSMv1.0-PA (adjustment based on integrated process rates from PA) and (d) ERSMv2.0 (with consideration of the TP_IND). The x and y-axes show the emission ratio, defined as the ratio of the change in emissions to the emissions in the baseline case for the entire region. The different colors represent different PM2.5 concentrations (monthly averaged in July, unit: µg m-3).

529 25 ACS Paragon Plus Environment


Page 26 of 30

530 (a) RSM_TT

(b) ERSMv1.0-noPA 1.0 1.0

0.6 0.6

0.4

0.4

0.8 0.8

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

1.0 1.0

1.0 1.0

1.0 1.0

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

1.2 1.2

1.2 1.2

1.2 1.2

1.0 1.0

1.0 1.0

1.0 1.0

0.4 0.4

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NOx NO x

NOx NO x

VOC+IVOC VOC

0.6 0.6

0.8 0.8 0.6 0.6

0.4 0.4

0.2 0.2

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.6 0.6

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 NOx NO x

1.2 1.2

1.0 1.0

1.0 1.0

1.0 1.0

64.00 64.00 66.50 69.00 66.50 71.50 74.00 69.00 76.50 71.50 79.00 81.50 74.00 84.00

VOC+IVOC VOC

1.2 1.2

VOC+IVOC VOC

1.2 1.2

0.4 0.4

69.50 71.75 74.00

0.4 0.4

NOx

0.6 0.6

56.00 56.00 58.25 60.50 58.25 62.75 60.50 65.00 67.25 62.75 69.50 65.00 71.75 74.00 67.25

0.8 0.8

NOx

0.8 0.8

78.25 81.50 84.75 88.00

0.4 0.4

0.4 0.4

0.0 0.0

VOC+IVOC VOC

0.8 0.8

0.8 0.8

0.6 0.6

0.6

0.6

76.50 79.00 81.50 84.00

0.4 0.4

0.4 0.4

0.2 0.2

0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

1.2 1.2

1.2 1.2

1.2 1.2

1.0

1.0

1.0 1.0

NOx NO x

NOx NO x

NOx

NOx

1.0

VOC+IVOC VOC

1.0

0.8 0.8 0.6 0.6 0.4 0.4

VOC+IVOC VOC

VOC+IVOC VOC VOC+IVOC VOC

0.6 0.6

0.6 0.6

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

VOC+IVOC VOC

0.8 0.8

0.8 0.8

0.2 0.2

0.8 0.8

62.00 65.25 62.00 68.50 65.25 71.75 75.00 68.50 78.25 71.75 81.50 84.75 75.00 88.00

VOC+IVOC VOC

1.2 1.2

VOC+IVOC VOC

VOC+IVOC VOC

1.2 1.2

0.2 0.2

0.8 0.8

0.6 0.6

0.4 0.4 0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NOx

64.00 64.00 66.75 69.50 66.75 72.25 69.50 75.00 77.75 72.25 80.50 83.25 75.00 86.00

0.8 0.8 0.6 0.6

77.75 80.50 83.25 86.00

0.4 0.4 0.2 0.2

0.2 0.2

NOx

531

NOx NO x

NOx

NOx

HebeiS

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NOx

NOx

HebeiE

0.2 0.2

1.2 1.2

0.8 0.8

85.00 90.00 95.00

0.4 0.4

0.2 0.2

NOx NO x

HebeiN

0.6 0.6

0.4 0.4

NOx

Tianjin

0.8 0.8

0.6 0.6

NOx

55.00 55.00 60.00 65.00 60.00 70.00 65.00 75.00 80.00 70.00 85.00 75.00 90.00 95.00 80.00

VOC+IVOC VOC

1.2 1.2

1.0 1.0

VOC+IVOC VOC

1.2 1.2

1.0 1.0

VOC+IVOC VOC

1.2 1.2

0.8 0.8

Beijing

(c) ERSMv2.0

NOx

NOx

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 NOx NO x

Figure 4. Comparison of the 2-D isopleths of O3 in response to the simultaneous changes of 26 ACS Paragon Plus Environment

Page 27 of 30

532 533 534 535 536 537


precursor emissions in all regions, as derived from the (a) RSM_TT (benchmark), (b) ERSMv1.0-noPA (with no PA modification), and (c) ERSMv2.0 (with consideration of the TP_IND). The x and y-axes show the emission ratio of NOx and VOC+IVOC, respectively, defined as the ratio of the change in emissions to the emissions in the baseline case for the entire region. The different colors represent different O3 concentrations (monthly averaged 1 h-max O3, July; unit: ppb)



NOx(y) vs NH3(x)

0.8 0.8

0.4

0.4 0.4

0.2 0.2

0.2 0.2

0.8 0.8 0.6 0.6

0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.0

POA POA

NH3 1.2 1.2

1.0

1.0 1.0

0.8 0.8

0.8 0.8

1.2 1.2

0.8 0.8

NO NOx x

0.6 0.6

0.6 0.6

25.00 25.00 29.38 33.75 29.38 38.13 33.75 42.50 46.88 38.13 51.25 42.50 55.63 60.00 46.88

1.0 1.0

SO SO22

1.0

51.25 55.63 60.00

0.4 0.4

NH3

1.2 1.2

0.6 0.6

51.25 55.63 60.00

0.4 0.4

0.4

0.2 0.2

0.2 0.2

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NH3

NH3

POA POA

NH3 NH 3

1.2 1.2

1.2 1.2

1.2 1.2

1.0 1.0 0.8 0.8

SO SO22

0.8 0.8

0.6

0.6

0.6

0.6

25.00 29.38 25.00 33.75 29.38 38.13 33.75 42.50 46.88 38.13 51.25 42.50 55.63 60.00

1.0 1.0

VOC+IVOC IVO

1.0 1.0

NO NOx x

0.4 0.4

0.4

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.8 0.8 0.6 0.6

46.88 51.25 55.63 60.00

0.4 0.4

0.4 0.4

0.2 0.2

0.2 0.2

0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NH3

NH3

1 2 3 4 5

0.6

0.4

25.00 25.00 29.38 33.75 29.38 38.13 33.75 42.50 46.88 38.13 51.25 42.50 55.63 60.00 46.88

1.0 1.0

0.6

0.6 0.6

NH3 NH 3

(c) TP_IND

1.2 1.2

VOC+IVOC IVO

0.8 0.8

SO SO2 2

1.0 1.0

NO NOx x

1.0 1.0

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

(b) TP

VOC+IVOC(y) vs POA(x)

1.2 1.2

1.2 1.2

(a) CM+CM_IND

SO2(y) vs NH3(x)

VOC+IVOC IVO

Component

Page 28 of 30

0.0 0.0

NH3 NH 3

0.4 0.4

POA POA

Figure 5. Components of ERSM of (a) CM+CM_IND, (b) TP, and (c) TP_IND (CM - transport precursors from source region that form the secondary pollutants at receptor region; CM_IND-indirect effects in CM; TP -direct transport of the pollutant from source region to receptor region; TP_IND- indirect effects in TP; The x and y-axes show the emission ratio, defined as the ratio of the change in emissions to the emissions in the baseline case for the entire region. The different colors represent different PM2.5 concentrations (monthly averages in Beijing in July; unit is µg m-3)

6 28 ACS Paragon Plus Environment

Page 29 of 30


Beijing

VOC+IVOC VOC

1.0 1.0 0.8 0.8

(a) CM+CM_IND

0.6

0.6

80.00 85.00 90.00 95.00

0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

55.00 55.00 60.00 65.00 60.00 70.00 65.00 75.00 80.00 70.00 85.00 75.00 90.00 95.00

1.0

VOC+IVOC VOC

0.8 0.8

(b) TP

0.6

0.6

80.00 85.00 90.00 95.00

0.4 0.4 0.2 0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 NOx NO x

1.0

VOC+IVOC VOC

0.8 0.8

(c) TP_IND

0.6

0.6

80.00 85.00 90.00 95.00

0.4

0.4

0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NOx

NOx

67.25 69.50 71.75 74.00

0.4 0.4 0.2 0.2

1.2 1.2

56.00 56.00 58.25 60.50 58.25 62.75 65.00 60.50 67.25 62.75 69.50 71.75 65.00 74.00

1.0 1.0 0.8 0.8 0.6 0.6

67.25 69.50 71.75 74.00

0.4 0.4 0.2 0.2

NOx NO x

55.00 55.00 60.00 65.00 60.00 70.00 65.00 75.00 80.00 70.00 85.00 75.00 90.00 95.00

1.0

0.6 0.6

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0

1.2 1.2

0.8 0.8

NOx NO x

NOx

1.0

56.00 58.25 56.00 60.50 58.25 62.75 65.00 60.50 67.25 62.75 69.50 71.75 65.00 74.00

1.0 1.0

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NOx

1.2 1.2

1.2 1.2

VOC+IVOC VOC

55.00 55.00 60.00 65.00 60.00 70.00 65.00 75.00 80.00 70.00 85.00 75.00 90.00 95.00

VOC+IVOC VOC

1.2 1.2

HebeiN

1.2 1.2

56.00 58.25 56.00 60.50 58.25 62.75 65.00 60.50 67.25 62.75 69.50 71.75 65.00 74.00

1.0 1.0

VOC+IVOC VOC

Component

0.8 0.8 0.6 0.6

67.25 69.50 71.75 74.00

0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NOx NO x

1 2 3 4 5 6

Figure 6. Components of ERSM of (a) CM+CM_IND, (b) TP, and (c) TP_IND (CM - transport precursors from source region that form the secondary pollutants at receptor region; CM_IND-indirect effects in CM; TP -direct transport of the pollutant from source region to receptor region; TP_IND- indirect effects in TP; The x and y-axes show the emission ratio of NOx and VOC+IVOC respectively, defined as the ratio of the change in emissions to the emissions in the baseline case for the entire region. The different colors represent different O3 concentrations (monthly averaged 1 h-max O3, July; unit: ppb) 29 ACS Paragon Plus Environment


Page 30 of 30

1 -3

daily averages grouped by pollution levels(%)

monthly averages (µg m ) CM_Beijing

CM_Tianjin

CM_HebeiN

CM_HebeiE

CM_HebeiS

CM_IND

CM_Beijing

CM_Tianjin

CM_HebeiN

CM_HebeiE

CM_HebeiS

CM_IND

TP_Beijing

TP_Tianjin

TP_HebeiN

TP_HebeiE

TP_HebeiS

TP_IND

TP_Beijing 100%

TP_Tianjin

TP_HebeiN

TP_HebeiE

TP_HebeiS

TP_IND

60 Apportionment of response

Apportionment of response

70 50 40 30 20 10 0 -10 Beijing (90.2)

Tianjin (123.7)

HebeiN (33.8)

HebeiE (113.5)

80% 60% 40% 20% 0% -20%

HebeiS (127.9)

L1: 150

Beijing

(a) PM2.5 in January

CM_Beijing

CM_Tianjin

CM_HebeiN

CM_HebeiE

CM_HebeiS

CM_IND

CM_Beijing

CM_Tianjin

CM_HebeiN

CM_HebeiE

CM_HebeiS

CM_IND

TP_Beijing

TP_Tianjin

TP_HebeiN

TP_HebeiE

TP_HebeiS

TP_IND

TP_Beijing

TP_Tianjin

TP_HebeiN

TP_HebeiE

TP_HebeiS

TP_IND

100%

40



50

30 20 10 0 -10 Beijing (54.3)

Tianjin (73.0)

HebeiN (33.8)

HebeiE (76.4)

80% 60% 40% 20% 0%

-20%

HebeiS (71.4)

L1: 150

Beijing

(b) PM2.5 in July

CM_Beijing

CM_Tianjin

CM_HebeiN

CM_HebeiE

CM_HebeiS

CM_IND

TP_Beijing

TP_Tianjin

TP_HebeiN

TP_HebeiE

TP_HebeiS

TP_IND

CM_Tianjin

CM_HebeiN

CM_HebeiE

CM_HebeiS

CM_IND

TP_Beijing

TP_Tianjin

TP_HebeiN

TP_HebeiE

TP_HebeiS

TP_IND

100%



30

CM_Beijing

20 10 0

80% 60% 40% 20% 0%

-20% -40%

-10

-60% -80%

-20 Beijing (86.1)

Tianjin (78.2)

HebeiN (71.1)

HebeiE (81.6)

HebeiS (80.2)

-100% L1: 100

(c) O3 in July 2 3 4 5 6 7 8

Figure 7. Apportionment of PM2.5 and O3 responses to the reduction in emissions from multiple regions: (a) PM2.5 in January, (b) PM2.5 in July, (c) O3 in July (CM - transport precursors from source region that form the secondary pollutants at receptor region; CM_IND-indirect effects in CM; TP -direct transport of the pollutant from source region to receptor region; TP_INDindirect effects in TP; left: monthly averages in 5 regions, the number at the x- axis shows the baseline concentration: daily averages in Beijing grouped by different pollution levels) 30 ACS Paragon Plus Environment

Quantifying Nonlinear Multiregional Contributions to Ozone and Fine

Recommend Documents