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Estimating Emissions and Environmental Fate of Di-(2-ethylhexyl) Phthalate in Yangtze River Delta, China: Application of Inverse Modeling Yu Zhan, Jianteng Sun, Yuzhou Luo, Lili Pan, Xunfei Deng, Zi Wei, and Lizhong Zhu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05824 • Publication Date (Web): 10 Feb 2016 Downloaded from http://pubs.acs.org on February 13, 2016

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Environmental Science & Technology

Estimating Emissions and Environmental Fate of Di-(2-ethylhexyl) Phthalate in Yangtze River Delta, China: Application of Inverse Modeling

Yu Zhan,1,2 Jianteng Sun,1,2 Yuzhou Luo,3 Lili Pan,1,2 Xunfei Deng,4 Zi Wei,5 Lizhong Zhu*1,2

1

Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang, 310058,

China 2

Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou,

Zhejiang, 310058, China 3

Department of Land, Air, and Water Resources, University of California, Davis, CA 95616,

USA 4

Institute of Digital Agriculture, Zhejiang Academy of Agricultural Science, Hangzhou,

Zhejiang, 310021, China 5

Analysis and measurement center, Zhejiang University, Hangzhou, Zhejiang, 310058,

China

*Corresponding author Tel: +86 57188273733 Fax: +86 57188273733 E-mail: [email protected]

1 ACS Paragon Plus Environment


1

ABSTRACT

2

A georeferenced multimedia model was developed for evaluating the emissions and

3

environmental fate of di-2-ethylhexyl phthalate (DEHP) in the Yangtze River Delta (YRD),

4

China. Due to the lack of emission inventories, the emission rates were estimated by using

5

the observed concentrations in soil as inputs for the multimedia model solved analytically in

6

an inverse manner. The estimated emission rates were then used to evaluate the

7

environmental fate of DEHP with the regular multimedia modeling approach. The predicted

8

concentrations in air, surface water, and sediment were all consistent with the ranges and

9

spatial variations of observed data. The total emission rate of DEHP in YRD was 13.9

10

thousand t/year (95% confidence interval: 9.4-23.6), of which urban and rural sources

11

accounted for 47% and 53%, respectively. Soil in rural areas and sediment stored 79% and 13%

12

of the total mass, respectively. The air received 61% of the total emissions of DEHP but was

13

only associated with 0.2% of the total mass due to fast degradation and intensive deposition.

14

We suggest the use of an inverse modeling approach under a tiered risk assessment

15

framework to assist future development and refinement of DEHP emission inventories.


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16


TOC art

17



18

INTRODUCTION

19

China produced 74 million tonnes of plastic materials in year 2013, accounting for 24.8% of

20

the global production.1 Plasticizers, mainly phthalates, are emitted to the environment during

21

the life cycle of plastic, including production, residential use, and agricultural use.2, 3 The

22

adverse effects of phthalates, which act as environmental estrogens to humans and organisms

23

in the ecosystem, have been reported previously.4, 5 Di-2-ethylhexyl phthalate (DEHP; CAS

24

No: 117-81-7), the most commonly used phthalate,4 has been classified as Group B2

25

(probable human carcinogen) by the United States Environmental Protection Agency

26

(USEPA).6 DEHP in plastic is not covalently bound and releases to the environment under

27

high surface exposure and warm temperatures.7 The large volume of DEHP production,

28

which is expected to be no less than 0.595 million tonnes per year in the European Union

29

(EU),4 leads to the ubiquitous occurrence of DEHP in the environment.8 DEHP has been

30

found at elevated levels in the air of both urban and rural areas.9 A national soil survey in

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China reported that the median concentration of DEHP in farmland soils (562 µg/kg dry;

32

detection rate: 97.5%)10 was 10-fold higher than the median (50 µg/kg dry) in other

33

countries.11 The application of agricultural plastic films, biosolids, fertilizers, and pesticides,

34

as well as wastewater irrigation are considered to be the major potential sources.2 Moreover,

35

environmental concentrations of phthalates have been reported to be decreasing in Europe but

36

increasing in China.12

37 38

The environmental risk of DEHP has been extensively assessed in Europe and North America,

39

where fugacity-based multimedia models have been used to systematically evaluate the

40

environmental fate of DEHP.4, 11, 13 The emission rates of DEHP, estimated from the emission

41

inventories, were used with other model parameters to predict the environmental

42

concentrations, intermedia transfer, advection, and degradation using a system of mass


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balance equations. A regional population-based model for DEHP demonstrated that air is the

44

primary medium of emission while soil and sediment are the main sinks, based on the use of

45

the emission factors and degradation rates as the key parameters to the model predictions.13

46

The model also suggested that DEHP was not persistent in the environment, with a reaction

47

residence time of 47 days. The predicted environmental concentrations agreed well with the

48

monitoring data, implying that the multimedia model was sufficiently parameterized to link

49

the monitoring data with the given emission inventories.

50 51

Environmental monitoring of DEHP has been conducted extensively in China.10, 14-16 A

52

systematic and large-scale evaluation of emissions and environmental fate of DEHP for

53

China, however, has not yet been performed. Conducting a robust assessment of

54

environmental fate requires detailed understanding of emission inventories, which is poorly

55

understood for China. A key challenge towards developing emission inventories for a large

56

region requires collaborative efforts across multiple governmental and industrial

57

organizations. As a first step, estimates of DEHP emission rates would be useful in better

58

directing research activities. While DEHP emission inventories developed in the EU provide

59

valuable information for estimating the emission rates in China,13 they do not account for a

60

possible important emission source in China, agricultural plastic film (thickness: 10 - 40 µm).

61

More than two million tonnes of agricultural plastic film was consumed per year in China.17

62

The DEHP concentrations in the soils covered with plastic film were found to increase by 9-

63

fold in 45 days,18 and were reported to be more than 2-fold higher than those without plastic

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film.19 In addition, a provincial-scale study found a significant correlation between the DEHP

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concentrations in soils and the use of agricultural plastic film.10 It is thus suggested to better

66

characterize the importance of this material as a source of DEHP to rural soils based on the

67

use of an inverse model.



68

A feasible solution to the problem of lacking emission inventory data is to run a multimedia

69

model inversely. In such a model, observed environmental concentrations are treated as

70

known variables in the system of mass balance equations, which are solved for the emission

71

rates. The estimated emission rates are then used to predict the environmental fate, including

72

reactions and intermedia fluxes. Inverse models have been applied to evaluate the emissions

73

and environmental fate of persistent organic pollutants (POPs), with concentrations in air or

74

sediment as the starting point.20-23 However, more studies are needed to illustrate the

75

applicability of the inverse modeling method for spatially distributed cases as well as

76

chemicals other than POPs. Furthermore, the systems of mass balance equations for inverse

77

models have traditionally been solved numerically, with a tradeoff between estimation

78

accuracy and computing time. In contrast, analytic solutions to inverse models are

79

computationally efficient and are better suited to the sophisticated sensitivity or uncertainty

80

analyses for model evaluation.

81 82

This study aims to evaluate the emissions and environmental fate of DEHP in the Yangtze

83

River Delta (YRD) region of China, which is one of the most densely populated regions in

84

the world. A georeferenced multimedia model was developed for modeling DEHP in YRD.

85

The emission rates were estimated from the observed concentrations in soil by using the

86

analytic solutions to the system of mass balance equations solved in an inverse manner. The

87

estimated emission rates were then used to predict environmental fate of DEHP. The results

88

of this study provide valuable information for assessing the exposure of humans and wildlife

89

to DEHP in YRD. Under a tiered framework for risk assessment, the inverse modeling

90

approach can be a useful tool in the early stages of screening analyses and can assist in

91

developing or refining the emission inventories.

92


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MATERIALS AND METHODS

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Multimedia Model. The study region (29.58-32.15oN, 118.70-122.14oE; 45800 km2)

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representing YRD includes Shanghai municipality, southern Jiangsu Province, and northern

96

Zhejiang Province (Figure 1). A gridded Level-III multimedia model (steady-state but non-

97

equilibrium) was developed to systematically simulate the spatially distributed emissions and

98

environmental fate of DEHP in YRD. Although a Level-IV (dynamic) model may provide

99

more details on the seasonality and trends of emissions and fate, it would require input data

100

and field measurements at higher temporal resolution. Due to the lack of historical emission

101

rates and long-term environmental monitoring data for DEHP in the study region, a

102

parsimonious Level-III model, which balances model complexity and data availability, is

103

more suitable for policy making.24, 25 The study region was divided into 141 cells (each 20 x

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20 km2). Each cell has six environmental compartments: air, water, rural soil, urban soil,

105

urban film, and sediment. Soil was separated into two compartments in order to account for

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different emission scenarios in rural and urban areas. Urban film that coats impervious

107

surfaces is a transient sink that accelerates chemical cycling in urban areas.26 A generic mass

108

balance equation was applied to compartment i as follows (i = 1, 2, …, n, where n = 846),

109

QRi −

m

∑ (Q

ij

− Q ji ) = Ei

(1)

j =0, j ≠i

110

where QRi is the reaction rate (kg/h) in compartment i; Qij and Qji are inter-compartment

111

fluxes from compartment j to i and from i to j, respectively, with i or j=0 indicating areas

112

outside of the simulation domain; and Ei is the emission rate (kg/h) to compartment i. The

113

mass balance equations for each cell are adapted from the generic Level-III model and the

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multimedia urban model.26, 27 Air advection between neighboring air compartments was

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derived from the wind field, including the wind direction and speed. Water advection



116

between adjacent water compartments was modelled using hydrological data of the main

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streams.

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118 119

In the multimedia model, the flux Q is expressed as the product of fugacity (F) and mass

120

transfer coefficient (MTC). The system of mass balance equations is represented in a block

121

matrix form (Eq. 2).The detailed mathematical formulations are listed in the supporting

122

information (SI).

123

[ D11 ] [ D ]  21 [ D31 ]  [ D41 ] [ D51 ]  [ D61 ]

124

where subscript i = 1, 2, 3, 4, 5, and 6 represent compartments of air, water, rural soil, urban

125

soil, urban film, and sediment, respectively. [Fi] is the fugacity array (n elements) of

126

compartments i, and [Ei] is the array (n elements) of emission rates in compartments i. [Dij] is

127

the matrix (n x n size, n is the number of cells, i.e., 141) of MTC from compartments j to i;

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when i=j, [Dii] represents the sink term, i.e., degradation (for all cells) and flux out of the

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simulation domain (only for cells on the border of the simulation domain). Note that [D10]

130

and [D20] represent inflows via air and water, respectively, from areas outside of the

131

simulation domain.

[ D12 ] [ D13 ] [ D14 ] [ D15 ] [ D16 ] [ F1 ]   [ E1 ] + [ D10 ]  [ D22 ] [ D23 ] [ D24 ] [ D25 ] [ D26 ] [ F2 ] [ E2 ] + [ D20 ] [ D32 ] [ D33 ] [ D34 ] [ D35 ] [ D36 ] [ F3 ]  [ E3 ]   =  [ D42 ] [ D43 ] [ D44 ] [ D45 ] [ D46 ] [ F4 ]  [ E 4 ]  [ D52 ] [ D53 ] [ D54 ] [ D55 ] [ D56 ] [ F5 ]  [ E5 ]      [ D62 ] [ D63 ] [ D64 ] [ D65 ] [ D66 ] [ F6 ]  [ E6 ] 

(2)

132 133

Inverse Modeling. Without available [Ei] data, an inverse modeling approach was applied to

134

solve the mass balance equations (Eq. 2) in order to estimate [Ei] from available [Fi] values.

135

Specifically, the measured concentrations of DEHP in rural and urban soils were used to

136

inversely estimate the emission rates. We initially assumed that the rates of DEHP emission


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to urban film and sediment are negligible (E5=E6=0), and separated the rates of emission to

138

air and water by rural (“r”) and urban (“u”) areas to reflect different emission scenarios.

139

E1 = E1r + E1u

(3)

140

E2 = E 2 r + E 2 u

(4)

141

where E1r and E1u are the rates of emission to air from rural and urban sources, respectively;

142

and E2r and E2u are the rates of emission to water from rural and urban sources, respectively.

143

In rural areas agricultural plastic film use is considered to be the main source of DEHP.19

144

Given that dumping used film directly into streams or lakes rarely occurs, we assumed the

145

rates of DEHP emission to water from agricultural plastic film use are negligible (E2r=0).

146 147

Emission ratios (e) were then introduced to relate the emission rates to air and water to the

148

rates to soils, which were quantified from field measurements in this study.

149

E1r = e1r ⋅ E 3

(5)

150

E1u = e1u ⋅ E 4

(6)

151

E 2 u = e2 u ⋅ E 4

(7)

152

where e1r (0.54) was derived from a field experiment studying DEHP release from plastic

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film (see the SI for the details).18 The emission factors developed for industrialized regions in

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the EU were 1.25, 0.056, and 0.07 for emissions to air, water, and urban soil, respectively,

155

which were multiplied by the production and consumption tonnages to estimate the emission

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rates in the EU.13 The emission ratios were the ratios between the emission rates to different

157

environmental compartments, which were equal to the ratios between the emission factors.

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For the YRD, e1u and e2u were set to 17.86 (17.86=1.25/0.07) and 0.8 (0.8=0.056/0.07) based

159

on the emission factors from the EU scenario.

160



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With the above substitutions, Eq. (2) can be transformed to Eq. (8). The detailed derivation

162

procedures are presented in the SI.

163

[ D11 ] [ D ]  21 [ D51 ]  [ D61 ] [ D31 ]  [ D41 ]

[ D12 ] [ D15 ] [ D16 ] - e1r I [ D22 ] [ D25 ] [ D26 ] [ D52 ] [ D55 ] [ D56 ] [ D62 ] [ D65 ] [ D66 ]

0 0 0

[ D32 ] [ D35 ] [ D36 ] [ D42 ] [ D45 ] [ D46 ]

-I 0

- e1u I   [ F1 ]   [ D10 ] − [ D13 ][ F3 ] − [ D14 ][ F4 ]  - e2u I  [ F2 ] [ D20 ] − [ D23 ][ F3 ] − [ D24 ][ F4 ] 0  [ F5 ]   − [ D53 ][ F3 ] − [ D54 ][ F4 ]    = 0  [ F6 ]  − [ D63 ][ F3 ] − [ D64 ][ F4 ]  0  [ E3 ]  − [ D33 ][ F3 ] − [ D34 ][ F4 ]      - I  [ E4 ]  − [ D43 ][ F3 ] − [ D44 ][ F4 ] 

(8)

164

where I is an identity matrix. Eq. 8 was solved in R to obtain the values of arrays [F1], [F2],

165

[F5], [F6], [E3], and [E4].28 The concentration ([Ci]) and total mass (Ni) of DEHP in each

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compartment and each cell were also calculated from the corresponding [Fi] values.

167 168

Input Data. The parameters for the multimedia model include DEHP concentrations in soil,

169

chemical properties, and environmental conditions (Table S1). The cell-level DEHP

170

concentrations in rural and urban soil were interpolated from the sampling data. See the SI for

171

the descriptions of sampling, instrumental analysis, and interpolation. The chemical

172

properties were obtained from EPISuite and the European Union risk assessment report.4, 29

173

The site-specific data, e.g., atmospheric particle matter (PM) content retrieved from the air

174

quality data platform,30 were interpolated to the predefined grid cells using the block Kriging

175

method. With this method, the average of each grid cell can be predicted with higher

176

accuracy than that estimated from the prediction(s) of one (e.g., the cell center) or a few

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points within the cell.31 The land cover data32 (a raster file of resolution of 30 x 30 m2) was

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resampled to the predefined grid cells. The annual streamflow data for main rivers within

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YRD were obtained from the hydrological yearbook.33 The SI includes details for preparing

180

the spatially distributed data.

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Sensitivity and Uncertainty Analyses. The sensitivity of model predictions to input

183

parameters was analyzed with the Sobol method, in which the variances of model predictions

184

are decomposed and attributed to predictor parameters.34, 35 The Sobol method, as a global

185

sensitivity analysis method, is superior to one-at-a-time sensitivity analysis methods that

186

explore sampling spaces locally and cannot detect interactions.36 The samples, log-normal

187

distributed with coefficient of variance (CV) of 0.2 for targeted parameters, were generated

188

with the Sobol pseudorandom sequence.37 The main effects (Si) and the total effects (STi) of

189

predictor parameters are reported, with 95% confidence intervals calculated by

190

bootstrapping.38 Si indicates the effect of parameter i alone, and STi denotes the effect of

191

parameter i plus its interaction effects with all other parameters. In total, 12000 base samples,

192

resulting in (49+2)x12000=612000 model runs for 49 parameters, were generated to assure

193

the estimation convergence. Additionally, the signs of the relationships (i.e., positive or

194

negative correlation) between the predictor and response variables were evaluated with the

195

Spearman’s rank correlation coefficients. The information on the response and predictor

196

variables is listed in Table S2.

197 198

The uncertainties of the model predictions were estimated by using the Monte Carlo

199

method.39 The Latin hypercube sampling method was used to generate 8000 samples with the

200

same settings used for the sensitivity analysis. Note that the samples for spatially distributed

201

parameters, including DEHP concentrations in soil, temperature, precipitation, wind speed,

202

aerosol content, and soil organic carbon (SOC) content, were generated using the sequential

203

Gaussian simulation, in which samples that vary stochastically between data points follow a

204

Gaussian covariance function while being conditioned by the observed data points.40 The 95%

205

confidence intervals of the model predictions were reported as the results of uncertainty

206

analysis.



207 208

RESULTS AND DISCUSSION

209

Model Evaluation. The ranges of predicted DEHP concentrations in air, surface water, and

210

sediment were generally consistent with the ranges measured in YRD (Table 1). Note that we

211

used the monitoring data for soil (collected by ourselves) as the model inputs, and then used

212

the monitoring data for air, surface water, and sediment (compiled from the literature) to

213

evaluate the model predictions. Given the independence of the two monitoring datasets (one

214

for soil; the other for air, surface water, and sediment), this validation approach is feasible.

215

The good agreement between predicted and observed data suggests that this model

216

adequately links the observed environmental concentrations with DEHP properties,

217

environmental conditions, and estimated emission rates.

218 219

In addition, it is worth noting that the predicted data presented in Table 1 are statistics of

220

modeling results over the whole study region, while observations are only available at some

221

locations within the region. Therefore, differences between the predicted and observed data

222

might be attributed to sampling bias, given the limited number of monitoring studies in YRD.

223

For instance, gaseous-phase monitoring studies were only available in Nanjing (northwestern

224

area of the study region).9, 14 In the model, the gaseous-phase concentrations were predicted

225

to be lower in Nanjing than in other areas within the study region (Figure S3a). The observed

226

gaseous-phase concentrations in Nanjing (3.4-28.7 ng/m3, Table 1) were indeed much lower

227

than the predicted median value over the whole YRD (93 ng/m3), but similar to the predicted

228

values (37-54 ng/m3, not presented in Table 1) for the cells enclosing Nanjing. Moreover, soil

229

samples were mainly collected from rural or suburban areas. Therefore, cells dominated with

230

urban land use are associated with larger uncertainties in the interpolated mass in soil and the

231

predicted environmental concentrations.


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232 233

Regional evaluation of DEHP in YRD requires additional monitoring studies for multiple

234

environmental media. The modeling results obtained here are expected to effectively direct

235

future environmental monitoring projects. For example, the predicted spatial distribution of

236

the multimedia concentrations of DEHP could be useful to optimize resources in the planning

237

of future monitoring projects. More measurements in the areas with low (southern Jiangsu),

238

intermediate (northern Zhejiang), and high (Shanghai) predicted concentrations will help to

239

generate regionally representative environmental observations for YRD, and further validate

240

the predictions of this multimedia environmental model.

241 242

The results of the sensitivity analysis show that the model predictions including emission

243

rates (Eu and Er) and environmental masses (N1, N2, N5, and N6) were generally sensitive to

244

DEHP half-lives and the emission ratios (Table 2; only the parameters with at least one

245

Si>0.01 are presented). Interaction effects were not found to be significant (as ∑Si≈1 and

246

Si≈STi). All model predictions were sensitive to the half-life in soil (HLS), with negative

247

correlations. This finding was related to the use of DEHP inventories in soils as known

248

variables for the inverse model, as shown in Eq. (8). HLS directly determined the effective

249

inventories in soils that were available for inter-compartment fluxes and subsequent mass

250

balance. Total emission rates (Eu and Er in Table 2) were linear functions of emission rates to

251

soils, E3 and E4 in Eq. (8), and thus very sensitive to the linear coefficients (represented by

252

emission ratios) and DEHP fate in soils (represented by HLS). These effects were transmitted

253

to inventories in the other environmental compartments. For example, N5 was sensitive to e1u

254

as the urban film received a large amount of DEHP via atmospheric deposition. In addition to

255

the half-lives and emission ratios, the model predictions were sensitive to other parameters.

256

For instance, N5 was sensitive (Si=0.299) to the urban film-to-water mass transfer coefficient



257

(kFW), while N1 was sensitive (Si=0.147) to the precipitation rates (UR). kFW and UR were

258

associated with the processes of surface water runoff and wet deposition, both of which

259

showed relatively large fluxes.

260 261

Furthermore, DEHP masses in soil were estimated on the basis of measured total

262

concentrations (determined by the solvent extraction method) rather than bioavailable

263

concentrations, which tended to underestimate the half-life in soil used in the inverse model.

264

Consequently, the emission rates and masses in other environmental compartments might be

265

over-predicted according to their negative correlations with the half-life in soil as suggested

266

by the sensitivity analyses (Table 2). Biodegradation was considered as the main degradation

267

path for DEHP in soil,41 and the degradation rate constant in soil was found to be negatively

268

correlated with the SOC content.42 It has been reported that the degradation rate constants in

269

soil for organic pollutants are largely determined by the bioavailable fractions, which are

270

influenced by the SOC content, soil texture, and aging.43 Nevertheless, the methods for

271

extracting bioavailable fractions of organic chemicals from soil vary greatly, such as using

272

the Tenax extraction and sequential ultrasonic extraction methods.43, 44 Using bioavailable

273

concentrations only when analysis methods are standardized is recommended for emission

274

estimation and fate evaluation in the future.45

275 276

Environmental Emissions. The DEHP emission rate in YRD was predicted to be 13.9

277

thousand t/year (95% CI: 9.4-23.6) in total, of which urban and rural sources accounted for

278

47% and 53%, respectively. By environmental media, emissions to air, soil, and water

279

accounted for 61%, 37%, and 2% of the total emission, respectively. Urban areas were the

280

main source of the emissions to air (70%), while rural areas contributed the majority of the

281

emissions to soil (94%). The spatial patterns of the total emissions were distinct between the


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urban and rural sources (Figure 2). The emission rates from the urban sources were predicted

283

to be the highest in the Shanghai metropolitan areas (> 100 t/year), when compared to those

284

in Hangzhou (southwestern study region; 75-100 t/year) and Nanjing (northwestern study

285

region; 50-75 t/year) urban areas. In contrast, the high-emission areas for the rural source

286

were predicted to be mainly located in Zhejiang province, which suggests more intensive

287

agricultural plastic film use there.

288 289

The predicted emission rates from agricultural plastic film use were generally consistent with

290

the rates estimated from provincial-level statistics. On the basis of the published statistics,17

291

the average use rates of agricultural plastic film for all farmlands in Zhejiang, Jiangsu, and

292

Shanghai were estimated to be 25, 14, and 53 kg/ha, respectively. By setting the content of

293

DEHP in agricultural plastic film to be 14.7% (based on a field measurement which was also

294

used to derive the emission ratio),18 the DEHP emission rates were estimated to be 3.7, 2.1,

295

and 7.7 kg/ha for the three regions. These estimates were similar to model predictions in

296

Zhejiang and Jiangsu (3.3 and 1.5 kg/ha, respectively), while a much lower rate was predicted

297

in Shanghai (1.5 kg/ha). Farmlands in Shanghai are highly fragmented, thus the spatial

298

variability of DEHP concentrations in rural soils may not be sufficiently captured by the

299

limited sampling sites used to generate the model input data. Compared to the available data

300

of agricultural plastic film use provided at the provincial scale, the inverse model provided

301

estimates at finer spatial resolution, which is important for identifying hotspots for

302

environmental management. According to the observed heterogeneity and land use

303

compositions, the sampling network especially in Shanghai should be optimized for

304

monitoring and modeling work in the future.

305



306

As suggested by the sensitivity analyses, the two emission ratios for urban and rural sources

307

played an important role in predicting the emission rates. The individual emission ratios (e’s

308

in Eq. 8) were invariable over grid cells, but the composite emission ratio on the cell scale

309

exhibited spatial variation that was a function of the percentage of urban and rural areas. The

310

emission ratio for urban sources was calculated as the ratio of the emission factors from the

311

EU scenario, where the emission rates in a region were estimated by multiplying the

312

population-normalized emission factors by the population.13 By using the gridded population

313

density data for YRD46 and the emission ratio derived from the EU scenario, the inverse

314

model predicted that the urban emission rate was 124 t/year per one million people in YRD,

315

which was around 5.5-fold higher than the rate predicted the EU scenario (19 t/year per one

316

million people).13 In other words, if the emission rates in this study were estimated by

317

multiplying the population-normalized emission factors from the EU scenario by the

318

population in YRD (i.e., a regular modeling approach), the predicted environmental

319

concentrations in YRD would be about 85%, calculated as 1-[1/(5.5+1)], lower than the

320

current predictions. Given the good agreement between the current predictions and the

321

observations, the emission ratio was therefore considered to be more transferable from the

322

EU scenario to the YRD than the emission factors.

323 324

Environmental Fate. The total mass of DEHP in YRD was predicted to be 7,948 t. The

325

majority of the inventory was predicted to reside within rural soil (79.2%), followed by

326

sediment (12.5%) (Figure 3). Rural soil and air were determined to be the primary sinks for

327

DEHP in the study area, accounting for 54% and 31% of total DEHP degradation,

328

respectively. Although the air compartment received 61% of predicted total emissions, it is

329

associated with only 0.2% of the total mass, due to fast degradation and intensive

330

atmospheric deposition. Similarly, the study for evaluating the fate of DEHP in Europe and


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Page 17 of 35


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North America predicted that 91% of the emission goes to air which however only stores 1.6%

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of the total mass, while soil and sediment store 79% and 8.6% of the total mass,

333

respectively.13 In addition to atmospheric deposition (448 t/year), surface water received a

334

large amount from surface runoff (934 t/year), most of which (98%) was contributed by

335

urban film. Urban film that coats impervious surfaces has low retention of water and DEHP,

336

causing intensive transport to surface water by wash-off. Due to high hydrophobicity (log

337

KOW=7.5), a large amount of DEHP settled to sediment (116 t/year). DEHP is persistent and

338

accumulated in sediment with a reaction residence time (i.e., ratio of the mass to the overall

339

reaction and advection rate) of 9.1 years. With short residence times of DEHP in air (0.003

340

year), rural soil (0.91 year), urban soil (0.90 year), urban film (0.002 year), and water (0.02

341

year), however, the overall residence time in the whole system was determined to be 0.51

342

year.

343 344

The aerosol contents determined the ratios of particulate/gaseous concentrations and may

345

affect the overall reaction rates in air. Given high aerosol contents in the study region (116-

346

239 ng/m3), the particulate DEHP accounted for 46% of the total mass in air. A nonlinear

347

relationship between the particulate and the gaseous concentrations was observed (Figure 4a).

348

The particulate/gaseous ratio, determined by the aerosol content, was higher in the

349

northwestern region, where the aerosol contents were high and the DEHP concentrations in

350

air were predicted to be low compared with other areas in YRD. Furthermore, the aerosol

351

content might affect the degradation in air, which is dominated by photodegradation.41 The

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half-life in air used by the model was shorter than one day, representing the overall

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degradation including both gaseous and particulate phases. However, the reaction rates of

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particulate organic chemicals were found to be influenced by the substrate types and reaction

355

conditions.47 For instance, particulate polycyclic aromatic hydrocarbons (PAHs) were more



Page 18 of 35

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persistent than gaseous PAHs, which might be due to incorporation in a particle matrix

357

limiting their accessibility to atmospheric oxidants. As the content of fine particulate matter

358

in eastern China was more than five times as high as that of western countries,48 degradation

359

and migration of particulate DEHP should be the subject of more research efforts in the

360

future.

361 362

The predicted DEHP concentrations in air and soil are generally well correlated (r=0.58,

363

p

Phthalate in Yangtze River Delta, China - ACS Publications

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