AnAlysishttps://doi.org/10.1038/s41893-020-0565-y

Human impacts on polycyclic aromatic hydrocarbon distribution in Chinese intertidal zonesMin Lv1, Xiaolin Luan1,2,3, Chunyang Liao   2,3 ✉, Dongqi Wang   4,5, Dongyan Liu   6 ✉, Gan Zhang7, Guibin Jiang2,3 and Lingxin Chen   1,3,8 ✉

1CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China. 2 State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China. 3University of Chinese Academy of Sciences, Beijing, China. 4School of Geographic Sciences, East China Normal University, Shanghai, China. 5Key Laboratory of Geographic Information Science of the Ministry of Education, East China Normal University, Shanghai, China. 6State Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming, East China Normal University, Shanghai, China. 7State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China. 8Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China. ✉e-mail: cyliao@rcees.ac.cn; dyliu@sklec.ecnu.edu.cn; lxchen@yic.ac.cn

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S1

Human impacts on polycyclic aromatic hydrocarbons patterns in

Chinese intertidal zones

Min Lv1, Xiaolin Luan

1,2,3, Chunyang Liao

2,3*, Dongqi Wang

4,5, Dongyan Liu

6*, Gan Zhang

7,

Guibin Jiang2,3

& Lingxin Chen1,3,8*

1 CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of

Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China. 2

State Key Laboratory of

Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese

Academy of Sciences, Beijing 100085, China. 3

University of Chinese Academy of Sciences, Beijing 100049,

China.4

School of Geographic Sciences, East China Normal University, Shanghai 200062, China. 5 Key

Laboratory of Geographic Information Science of the Ministry of Education, East China Normal University,

Shanghai 200062, China. 6 State Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming,

East China Normal University, Shanghai 200062, China. 7 State Key Laboratory of Organic Geochemistry,

Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China. 8 Center for

Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China. E-mail: cyliao@rcees.ac.cn (C.

Liao); dyliu@sklec.ecnu.edu.cn (D. Liu); lxchen@yic.ac.cn (L. Chen).

S2

Supplementary Information

Human impacts on polycyclic aromatic hydrocarbons patterns in

Chinese intertidal zones

Min Lv1, Xiaolin Luan

1,2,3, Chunyang Liao

2,3*, Dongqi Wang

4,5, Dongyan Liu

6*, Gan Zhang

7,

Guibin Jiang2,3

& Lingxin Chen1,3,8*

1 CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai

Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China 2 State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for

Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 3 University of Chinese Academy of Sciences, Beijing 100049, China

4 School of Geographic Sciences, East China Normal University, Shanghai 200062, China

5 Key Laboratory of Geographic Information Science of the Ministry of Education, East China

Normal University, Shanghai 200062, China 6 State Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming, East China

Normal University, Shanghai 200062, China 7 State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese

Academy of Sciences, Guangzhou 510640, China 8 Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China

* Corresponding authors

E-mail addresses:

cyliao@rcees.ac.cn (C. Liao);

dyliu@sklec.ecnu.edu.cn (D. Liu);

lxchen@yic.ac.cn (L. Chen).

S3

Supplementary Methods

PAHs extraction and analysis. The sample pretreatment procedures of the sixteen USEPA priority

PAH compounds (naphthalene (Nap), acenaphthylene (Acy), acenaphthene (Ace), fluorene (Flu),

phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), pyrene (Pyr), benzo[a]anthracene (BaA),

chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP),

indeno[1,2,3-cd]pyrene (Ind), dibenzo[a,h]anthracene (DahA), and benzo[g,h,i]perylene (BghiP))

were conducted as described in previous studies1,2

. Briefly, freeze-dried and homogenized sediment

samples were weighed, spiked with deuterated-PAHs as recovery standards (naphthalene-d8

(Nap-d8), acenapthene-d10 (Ace-d10), phenanthrene-d10 (Phe-d10), chrysene-d12 (Chr-d12) and

pyrene-d12 (Pyr-d12)), and extracted by accelerated solvent extraction (ASE) with mixed solvent

(hexane/dichloromethane 1:1 (v/v)). After concentration using rotary evaporator, the extracts were

cleaned up using alumina/silica column, and eluted with hexane/dichloromethane 1:1 (v/v). The

eluant was further concentrated to a final volume of 500 μL and spiked with hexamethylebenzene as

internal standard before instrumental analyses.

Quantification of the 16 priority PAHs was performed using a Thermo Trace 1300 gas

chromatography/TSQ 8000 EVO triple quadrupole mass detector system (GC-MS/MS) equipped

with a DB-5 MS capillary column (30 m length × 0.25 mm i.d. × 0.25 μm film thickness). The

initial oven temperature was set at 70 °C for 1 min and was raised to 140 °C at a rate of 25 °C/min,

to 240 °C at a rate of 10 °C/min, and to 300 °C at a rate of 5 °C/min (held for 4 min). The

temperature of inlet, MS transfer line and ion source temperature were set at 250 °C, 300 °C and

300 °C, respectively. The injection volume was 1 μL in splitless mode. Helium gas was used as

carrier gas with a constant flow of 1.0 mL min−1

. All targets were identified and quantified using

selected reaction monitoring (SRM) mode with an electron ionization (EI) ion source. The precursor

ion/ product ion transition pairs were optimized for all target compounds (Supplementary Table 16).

Supplementary Discussion

Comparison in PAHs concentrations and compositions with previous studies. According to

EPA methods3, the corresponding toxic equivalents (TEQ) for the seven potentially carcinogenic

PAHs were below 138 ng BaP g−1

, indicating low potential carcinogenicity of PAHs. Σ15PAHs

concentrations in this study were comparable to those observed in the intertidal zones of Asaluyeh4,

Bohai Bay5, Daya Bay

6, Beibu Gulf

7 and Kenting coast

8, while lower than those in estuaries of

Daliao River9, Yangtze River

10, Pearl River

11, Jiulong River Estuary

12 and coast of India

13. It is

worth to note that compared with this study, the maximum Σ15PAHs concentrations were lower in

the sediment of continental shelf of China with a narrower concentration range (27.0-224.0 ng g-1

)14

.

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The above results indicated that PAHs were ubiquitous in intertidal zones of China and were mainly

from terrestrial sources.

The PAHs compositions in this study were consistent with previous studies that higher

contributions of HMW PAHs (4 to 6-ring) were also observed in the sediment of Daliao River

estuary9, intertidal zone of Bohai Sea and Yellow Sea

15, and higher contributions of 3 to 4-ring

PAHs were reported in the sediment of Pearl river estuary11

. However, varied results were found in

the intertidal flat surface sediments of Yangtze estuary in 200010

and Jiulong River estuary 12

, with 2

to 3-ring PAHs and 5-ring PAHs predominant, respectively, indicating that the sources in these areas

likely changed.

PAH source analysis. Diagnostic ratios such as Ant/(Phe+Ant), Fla/(Fla+Pyr), BaA/(BaA +Chr),

BaP/BghiP or Ind/(Ind+BghiP) have been widely used for identifying and assessing the origin of

PAHs16,17

. In this study, the ratios of Fla/(Fla+Pyr), BaA/(BaA +Chr), Ant/(Phe+Ant) and

Ind/(Ind+BghiP) were 0.55±0.07, 0.36±0.07, 0.13±0.05 and 0.49±0.11, respectively, indicating

that the sources of PAHs were mainly from coal and biomass combustion, and liquid fuel

combustion (Supplementary Fig. 5 and Supplementary Table 6). Specifically, for intertidal zones of

NEC-DLH, NC-HG, EC-DGH, EC-CJ, EC-HZW, EC-MJ and EC-JLJ, the values of Ant/(Phe+Ant)

were above 0.1 for nearly all sites, while the values of Fla/(Fla+Pyr), BaA/(BaA+Chr), and

Ind/(Ind+BghiP) were above 0.5, 0.35 and 0.5 for most sites and above 0.4, 0.2 and 0.2 for nearly

all sites, respectively, indicating that PAHs in these areas were nearly from pyrogenic sources,

especially coal and biomass combustion. In comparison, the values of Ant/(Phe+Ant) were around

0.1 for BDE, EC-DY, EC-SSLW, EC-YC, SC-ZJ, SC-YLW and SC-DZG, while the values of

Fla/(Fla+Pyr), BaA/(BaA+Chr), and Ind/(Ind+BghiP) were above 0.4, 0.2, and 0.2, implying that

PAHs originated from mixed sources of petrogenic source and pyrogenic source, and were mainly

from pyrogenic sources.

PCA-MLR was further used to quantify the contributions of the identified PAH sources. Using

varimax with Kaiser normalization, three main factors, accounting for 93.4% of the variability, were

extracted, and they were heavily weighted by 4-, 5-, and 6-ring PAHs, 3-, and 4-ring PAHs, 3-ring

PAHs, respectively (Supplementary Table 9). Based on previous studies18-22

, they have been mainly

associated with coal, biomass and oil combustion, coal and biomass combustion, combustion and

spill of petroleum, respectively. Therefore, after further quantification by MLR analysis, a relative

contribution of 40.2% was attributed to coal, biomass and fossil fuel combustion, and another 33.8%

was attributed to coal and biomass combustion, while 26.0% was attributed to mix sources of

petrogenic PAHs and petroleum combustion (Supplementary Table 10-11).

S5

Supplementary Tables

Supplementary Table 1 Information of the 14 intertidal zones spanning all 11 coastal

provinces/municipalities of China

Intertidal zone Province (Municipality) Geographical region Transects abbreviation

Corresponding sea

area

Daliao He Liaoning Northeast China (NEC) 4 NEC-DLH Bohai Sea

Beidaihe Hebei North China (NC) 3 NC-BDH Bohai Sea

Hangu Tianjin North China (NC) 4 NC-HG Bohai Sea

Dongying Shandong Eastern China (EC) 5 EC-DY Bohai Sea

Sishili Wan Shandong Eastern China (EC) 3 EC-SSLW Yellow Sea

Dagu He Shandong Eastern China (EC) 5 EC-DGH Yellow Sea

Yancheng Jiangsu Eastern China (EC) 4 EC-YC Yellow Sea

Chang Jiang Shanghai Eastern China (EC) 4 EC-CJ East China Sea

Hangzhou Wan Zhejiang Eastern China (EC) 3 EC-HZW East China Sea

Min Jiang Fujian Eastern China (EC) 4 EC-MJ East China Sea

Jiulong Jiang Fujian Eastern China (EC) 4 EC-JLJ East China Sea

Zhu Jiang Guangdong Southern China (SC) 5 SC-ZJ South China Sea

Yingluo Wan Guangxi Southern China (SC) 3 SC-YLW South China Sea

Dongzhai Gang Hainan Southern China (SC) 3 SC-DZG South China Sea

S6

Supplementary Table 2 The anthropogenic and climate parameters

Year NEC-DLH NC-BDH NC-HG EC-DY EC-SSLW EC-DGH EC-YC EC-CJ EC-HZW EC-MJ EC-JLJ SC-ZJ SC-YLW SC-DZG

Total population (10,000

person) 2014 1860.6 306.5 2293.6 1762.2 700.2 1604.9 722.3 11345.8 3058.2 1256 1136 7190.5 1450.5 275.1

2015 1858.3 307.3 2327.1 1774.4 701.4 1611.1 722.9 11422.2 3077.2 1267 1147 7332.6 1454.5 277.8

Unbanization rate (%) 2014 67.1 52 60.9 62.9 58.6 64.1 58.5 67.6 66.6 61.8 65 71.4 43.8 71.2

2015 67.4 54.1 62.1 64.6 60.4 65.8 60.1 67.9 67.4 62.5 65.7 71.9 44.2 71.9

GDP (100 million yuan) 2014 14493.1 1200 22228.3 15570.6 6002.1 14694.2 3835.6 111228.7 21820.5 8022.9 7401.5 59224.8 4430.1 1251.5

2015 14532.8 1250.4 22940.9 16036.4 6446.1 15746.2 4212.5 118862.2 23292.7 8670.6 7971.9 63876.4 4717.9 1331.6

Total consumption of energy

(10000 tons SCE) 2014 9024.5 1216.8 11229.6 6365.8 2529.6 5797.5 2709.5 41250.3 10256.4 3996.3 3614.5 16238.8 3185 554.2

2015 8702.1 1216.7 11348.6 6623.6 2618.3 6014.2 2740.1 42000.1 10683.6 4019.8 3639.1 16530.2 3198.4 591

Coal consumption (10000

tons SCE) 2014 5604.2 1076.4 6319.6 5140.4 2042.6 4681.5 1744.2 23292.2 5491 2118 1915.7 7541.9 1524.3 215.6

2015 5325.7 1053 5915.2 5264.5 2081.1 4780.1 1761.3 23354.4 5491.9 2030 1837.7 7179.5 1408.6 234.6

Oil consumption (10000

tons SCE) 2014 2544.9 84.9 2505.6 947.9 376.7 863.2 455.1 7637.8 2094.4 1071 968.7 3837.5 703.9 188.1

2015 2697.7 97.2 2556.4 1051.2 415.5 954.5 495 8352.5 2408.8 988.9 895.2 4047.7 734.9 203.6

Coal ratio (%) 2014 62.1 88.5 56.3 80.8 80.8 80.8 64.4 56.5 53.5 53 53 46.4 47.9 38.9

2015 61.2 86.6 52.1 79.5 79.5 79.5 64.3 55.6 51.4 50.5 50.5 43.4 44 39.7

Oil ratio (%) 2014 28.2 7 22.3 14.9 14.9 14.9 16.8 18.5 20.4 26.8 26.8 23.6 22.1 33.9

2015 31 8 22.5 15.9 15.9 15.9 18.1 19.9 22.5 24.6 24.6 24.5 23 34.4

Other energy ratio (%) 2014 9.7 4.5 21.4 4.3 4.3 4.3 18.8 25 26.1 20.2 20.2 30 30 27.2

2015 7.8 5.4 25.4 4.6 4.6 4.6 17.6 24.5 26.1 24.9 24.9 32.1 33 25.9

Sewage (100 million tons) 2014 11.5 1.3 12.2 12.1 3.2 8.6 4.6 89.2 22.5 7.4 10.6 65.8 3.5 1.4

2015 11 1.3 12.6 13.1 3.7 9 4.5 92.1 23.3 7.4 10.1 63.9 3.7 1.4

Per capital GDP

(10000/person ) 2014 7.79 3.92 9.69 8.84 8.57 9.16 5.31 9.80 7.14 6.39 6.52 8.24 3.05 4.55

2015 7.82 4.07 9.86 9.04 9.19 9.77 5.83 10.41 7.57 6.84 6.95 8.71 3.24 4.79

Per capital energy

consumption (tons SCE/

person)

2014 4.85 3.97 4.90 3.61 3.61 3.61 3.75 3.64 3.35 3.18 3.18 2.26 2.20 2.01

2015 4.68 3.96 4.88 3.73 3.73 3.73 3.79 3.68 3.47 3.17 3.17 2.25 2.20 2.13

Per capital coal consumption

(tons SCE/person) 2014 3.01 3.51 2.76 2.92 2.92 2.92 2.41 2.05 1.80 1.69 1.69 1.05 1.05 0.78

2015 2.87 3.43 2.54 2.97 2.97 2.97 2.44 2.04 1.78 1.60 1.60 0.98 0.97 0.84

Per capital oil consumption

(tons SCE/ person) 2014 1.37 0.28 1.09 0.54 0.54 0.54 0.63 0.67 0.68 0.85 0.85 0.53 0.49 0.68

2015 1.45 0.32 1.10 0.59 0.59 0.59 0.68 0.73 0.78 0.78 0.78 0.55 0.51 0.73

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Per GDP energy

consumption (tons SCE/

10000 yuan)

2014 0.62 1.01 0.51 0.41 0.42 0.39 0.71 0.37 0.47 0.50 0.49 0.27 0.72 0.44

2015 0.60 0.97 0.49 0.41 0.41 0.38 0.65 0.35 0.46 0.46 0.46 0.26 0.68 0.44

Gross industrial output value

above designed size (100

million yuan)

2014 26925 488 29272 37152 14618 30574 7238 187323 36133 12050 10630 121196 5283 518

2015 18249 465 29424 36820 15298 32109 8254 191538 36032 12841 11425 126122 5705 533

Ratio of heavy industry (%) 2014 79.5 77.1 79.0 69.0 78.4 69.5 66.4 74.3 61.0 56.0 62.1 65.5 61.5 50.1

2015 79.9 75.2 77.2 68.0 77.4 68.5 65.1 73.2 60.3 53.9 59.8 66.0 60.6 46.1

Ratio of light industry (%) 2014 20.5 22.9 21.0 31.0 21.6 30.5 33.6 25.7 39.0 44.0 37.9 34.5 38.5 49.9

2015 20.1 24.8 22.8 32.0 22.6 31.5 34.9 26.8 39.7 46.1 40.2 34.0 39.4 53.9

Industrial waste gas

emission (1000 m3) 2014 42932200 38728048 87999769 11607000 32237770 20848630 13645000 130074256 63655669 36616640 15124753 16934493 6565200 643260

2015 49849500 33915869 83551525 13460000 40520104 22136842 27016000 128017194 62689892 31348024 9533789 20456118 7485503 780194

Industrial dust emission

(ton) 2014 96395.57 59221 112187 7194 34691 32196 52532 131433 30577 105713 4561 12972 3313 998

2015 84653.3 3049.6 32588.3 5155.7 30316 28767 55380.2 28767.1 103306 90911 2414 10446 4692 854

Household sulphur dioxide

emission (ton) 2014 3857.5 8030 13767 1721 9837 27089 2318 32765 1877 1279 181 0 1282 15

2015 3195.2 288 734.4 1721 15664 27089 12732.9 27088.6 16633 1726 265 0 1282 20

Household nitrogen oxides

emission (ton) 2014 774.9 1668 9517 864.7 2059 2648 351 18734 480 169 66 3 145 19

2015 557.7 98.3 201.6 864.7 5369 2648 1650.6 2647.9 3255.3 306 92 0 145 27

Household dust emission

(ton) 2014 14787.7 17856 21072 926 6651 9806 1281.1 4017 3000 547 85 0 408.5 229

2015 1769 180.6 270 925.5 6458 9806 10206.7 9805.8 14900 1096 85 0 408 241

Annual average

concentration of SO2 (ug/m3) 2014 32.5 54 49 72 28 38 20 18 17 8 16 11 13 6

2015 29.5 38 29 53.4 21 28 19 17 15 6 10 9 9 5

Annual average

concentration of NO2

(ug/m3)

2014 34.6 49 54 47 40 45 25.4 45 41 36 37 33 14 16

2015 30.7 45 42 40 33 36 23 46 43 33 31 29 14 14

Annual average

concentration of PM10

(ug/m3)

2014 72 113 133 148 84 107 93 71 73 65 59 53 58 42

2015 77 99 117 136 80 98 85 69 69 56 48 51 48 40

95th percentile daily average

concentration of CO (ug/m3) 2014 2.2 3.5 2.9 2.2 1.6 1.6 1.3 1.3 1.4 1.3 1 1.4 1.9 0.9

2015 2.1 3.6 3.1 2.2 1.6 1.8 1.4 1.5 1.4 1 0.9 1.6 1.7 0.9

90th percentile daily

maximum 8 hours average

concentration of O3 (ug/m3)

2014 162.4 114 157 213 152 154 140 149 140 137 128 138 144 102

2015 190 107 142 197.4 150 146 166.4 161 152 119 95 142 132 103

Annual average

concentration of PM2.5

(ug/m3)

2014 40 61 83 77.5 52 58 58 52 46 34 37 34 29 23

2015 48.6 48 70 79.4 48 52 49 53 45 29 29 31 29 22

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Days of air quality equal to

or above Grade II (day) 2014 298 239 175 144 246 236 270 278 302 310 344 321 322 346

2015 239 259 216 158 190 263 263 252 302 344 355 323 344 349

Volume of maritime goods

transported (10000 t) 2014 13810 4041 9749 9226 9226 9226 23725 44231 51724 22186 22186 39328 5211 11433

2015 13439 4544 9850 9542 9542 9542 22278 47215 54212 25308 25308 37841 5608 10124

Volume of maritime goods

turnover (100 million t-km) 2014 7980 1482 2734 1009 1009 1009 6582 18274 7603 3634 3634 10771 680 1334

2015 7963 1553 1729 1171 1171 1171 4016 19149 7858 4284 4284 10894 727 1068

International standardized

containers handled by

coastal seaports (10000

TEU)

2014 1860 184 1406 2256 2256 2256 511 3529 2136 1271 1271 4752 112 162

2015 1838 253 1411 2402 2402 2402 518 3654 2257 1364 1364 4915 142 154

International standardized

weight handled by coastal

seaports (10000 T)

2014 30834 2732 15904 24845 24845 24845 5074 35335 22224 16502 16502 51871 1960 2784

2015 31044 3588 15492 26851 26851 26851 5091 35850 22914 17754 17754 53957 2549 2698

Temperature (oC) 2014 19 20.1 22.5 21.6 15.4 16 17.8 6.8 7.7 12 13.5 21.1 21.7 24.1

2015 25 25.2 26.9 27.4 25.5 25.5 22.7 27.3 27.7 28 28.3 28.6 27.9 29

Rainfall (mm) 2014 22.7 56.2 48 49.6 34.5 38.2 14.2 102.1 147.1 40 40 22 33.1 26.7

2015 106.3 76.7 122.3 181.2 60.6 69.9 91.2 149.4 268 200 140 249 424.5 72.9

Notes:

1. The data were mainly collected and calculated from governmental statistical yearbooks, bulletins and reports. Specifically, air quality parameters (annual average concentration of SO2, annual average concentration

of NO2, annual average concentration of PM10, 95th percentile daily average concentration of CO, 90th percentile daily maximum 8 hours average concentration of O3, annual average concentration of PM2.5 and days

of air quality equal to or above Grade II) were obtained from https://www.aqistudy.cn/historydata/. Maritime traffic parameters (volume of maritime goods transported, volume of maritime goods turnover,

international standardized containers handled by coastal seaports and international standardized weight handled by coastal seaports) were obtained from Marine Statistical Yearbook of China. Waste gas emission

parameters (industrial waste gas emission, industrial dust emission, household sulphur dioxide emission and household dust emission) were obtained from City Statistical Yearbook of China. Most other parameters

were obtained from Provincial or Municipal Bureau of Statistics. Total population is total permanent population and urbanization rate is the ratio of urban population and total population. Coal ratio and oil ratio are

the ratios of coal consumption, oil consumption and total consumption of energy, respectively. Other energy ratio was calculated as 1- Coal ratio - oil ratio. Per capital GDP, per capital energy consumption, per

capital coal consumption and per capital oil consumption were calculated with GDP, energy consumption, coal consumption and oil consumption divided by total population, respectively. Per GDP energy

consumption was calculated using energy consumption divided by GDP. Ratio of heavy industry and ratio of light industry represented the ratio of heavy industry and light industry compared with gross industrial

output value above designed size.

2. The data of temperature and rainfall were corresponding to the sampling region in the sampling month. Other parameters were provided on an annual basis, and since the dry season was from September in 2014 to

February in 2015, we used the statistic data of 2014 for them. Among them, waste gas emission parameters (industrial waste gas emission, industrial dust emission, household sulphur dioxide emission, household

nitrogen oxides emission, household dust emission) and air quality parameters (annual average concentration of SO2, annual average concentration of NO2, annual average concentration of PM10, 95th percentile daily

average concentration of CO, 90th percentile daily maximum 8 hours average concentration of O3, annual average concentration of PM2.5 and days of air quality equal to or above Grade II) were corresponding to the

sampling region, while the data for the rest parameters were collected and calculated based on the administrative regions described below. Whether a parameter is based on the sampling region or administrative

regions is depended on if the parameter could influence intertidal pollution through riverine systems. For example, sewage could transport through riverine into intertidal zones, so it is based on administrative

regions.

3. The administrative regions for data collection and estimation of every intertidal zones were mainly determined by the principle that if the intertidal zone is located in an estuary, then the data of the main cities located

S9

in the basin were collected for this intertidal zone. If the intertidal zone is not located in an estuary, then the data of city it is located or nearby were collected. The following are the detailed cities considered for each

intertidal zone.

NEC-DLH: The intertidal zone is located in the estuary of Daliao river and the cities includes Yingkou, Anshan, Liaoyang, Shenyang and Fushun prefectures in Liaoning province.

NC-BDH: The intertidal zone is not located in an estuary and includes Qinhuangdao prefecture in Hebei province.

NC-HG: The intertidal zone is in the estuary of Dou river and Sha river. The cities include Tangshan prefecture in Hebei province, and Tianjin municipality.

EC-DY: The intertidal zone is in the estuary of Zhimai river and includes Dongying, Zibo, Binzhou, and Jinan prefectures in Shandong provinces.

EC-SSLW: Several rivers in Yantai prefecture flow into the sea through the SSLW intertidal zone. The city includes Yantai in Shandong provinces.

EC-DGH: The intertidal zone is in the estuary of Dagu river and includes Qingdao and Yantai prefectures in Shandong provinces.

EC-YC: The intertidal zone is in the estuary of Xingyanjie river and includes Yancheng prefecture in Jiangsu province.

EC-CJ: The intertidal zone is in the estuary of Yangtze river and includes Shanghai municipality, Nantong, Yangzhou, Nanjing, Wuxi, Changzhou, Suzhou,Taizhou, and Zhenjiang prefectures in Jiangsu province;

Maanshan, Qianghu, Tongling, Anqing, and Hefei prefectures in Anhui province; Huangshi, Wuhan, and Yichang prefectures in Hubei provinces; Yueyang and Changsha prefectures in Hunan province.

EC-HZW: The intertidal zone is in the estuary of Qiantang river and Yong river. Cities include Hangzhou, Shaoxing, Ningbo, Jinhua, and Quzhou prefectures in Zhejiang province; Huangshan prefecture in Anhui

province.

EC-MJ: The intertidal zone is in the estuary of Min river and includes Fuzhou, Sanming and Nanping prefectures in Fujian province.

EC-JLJ: The intertidal zone is in the estuary of Jiulong river and includes Xiamen, Zhangzhou and Longyan prefectures in Fujian province.

SC-ZJ: The intertidal zone is in the estuary of Pearl river and includes Zhuhai, Shenzhen, Shaoguan, Zhaoqing, Jiangmen, Foshan, Zhongshan, Huizhou, Dongguan, Heyuan, Qingyuan and Guangzhou prefectures in

Guangdong province; Nanning, Liuzhou, Wuzhou, Guigang, Baise, Laibin, and Chongzuo prefectures in Guangxi Zhuang Autonomous Region; Qujing prefecture, Yuxi prefecture and Honghe Hani and Yi Autonomous

prefecture prefecture in Yunnan province.

SC-YLW: The intertidal zone is in the estuary of Jiuzhou river and includes Beihai, Zhanjiang and Yulin prefectures in Guangxi Zhuang Autonomous Region.

SC-DZG: The intertidal zone is besides the bay of Dongzhai. Cities include Beihai and Wenchang in Hainan provinces.

S10

Supplementary Table 3 Concentrations (ng g-1

) of PAHs in intertidal sediments from various

regions

Region Year Season

Number

of

samples

Mean Median Range Predominant

composition Reference

NEC-DLH,

China

2014-2015

Dry

Wet

12

12

174.3

171.2

145.0

156.9

43.4-326.9

80.6-291.7 4-ring

This study

NC-BDH, China Dry

Wet

9

9

14.0

12.4

9.9

11.6

3.3-44.6

8.4-18.3 3-ring

NC-HG, China Dry

Wet

12

12

206.5

244.7

185.4

206.6

92.4-364.5

157.0-416.0 4-ring

EC-DY, China Dry

Wet

15

15

19.1

21.9

12.7

21.5

7.8-46.8

7.6-45.7 3 to 4-ring

EC-SSLW,

China

Dry

Wet

7

9

5.8

11.7

6.1

12.1

2.3-11.8

6.5-18.0 3-ring

EC-DGH, China Dry

Wet

15

13

132.9

147.2

122.9

150.3

30.3-332.8

25.8-248.0 4-ring

EC-YC, China Dry

Wet

12

12

23.1

27.5

18.3

24.9

3.1-60.5

11.6-49.8 3-ring

EC-CJ, China Dry

Wet

11

11

156.8

159.3

179.0

149.1

19.4-269.5

61.2-278.8 4-ring

EC-HZW, China Dry

Wet

9

9

124.9

116.0

120.8

102.5

60.4-195.8

54.2-167.7 4-ring

EC-MJ, China Dry

Wet

12

12

145.2

133.6

93.1

107.0

5.0-442.6

8.2-348.0 3 to 4-ring

EC-JLJ, China Dry

Wet

12

12

320.9

232.9

282.7

238.7

154.6-1031.7

160.2-289.0 4-ring

SC-ZJ, China Dry

Wet

13

14

88.3

136.6

34.0

161.3

4.3-296.2

7.1-480.0 3 to 4-ring

SC-YLW, China Dry

Wet

9

9

19.9

31.1

11.0

17.5

5.7-74.4

12.0-74.3 3-ring

SC-DZG, China Dry

Wet

9

9

32.9

19.4

23.9

18.4

10.9-97.1

9.1-33.5 3 to 4-ring

Daliao River

estuary 2013 August 27 3700.3 374.8-11588.9a 4 to 6-ring 9

Intertidal zone,

Asaluyeh,

Persian Gulf

2016 September 15 NA 1.8-81.2b 4 to 6-ring 4

Intertidal zone,

Bohai Sea and

Yellow Sea

1997 October -

November 20 877.2 20.4-5734.2c 4-ring 15

coastal zones of

China 2014

January

August

12

12

2220.6

607.9

195.9-4610.2a

98.2-2796.5a Mostly 4-ring 23

Intertidal

sediment,

Shuangtaizi

Estuary, China

2013 May 60 115.9 28.8–282.0a 2 to 3-ring 24

Intertidal zone,

Bohai Bay,

Northeast China

Not

available

Not

available 10 134.2 144.8 37.2-206.6a 3 to 4-ring 5

Yangtze estuary,

China 2000

May and

August 14 1662.0 263.0-6372.0d 2 to 3-ring 10

Jiulong River

Estuary and

Western Xiamen

Sea, China

1999 June 19 334 59-1177 a 5-ring 12

Pearl River

estuary, China 2012 December 17 563.8 119.5-3828.6a 3 to 4-ring 11

Inter-tidal areas 2005 April to 20 1171.4 266.2 65.7-14844.3a 25

S11

of Dar es

Salaam,

Tanzania

May

Daya Bay, China 2003 November 14 126.2 137 42.5-158.2a 2- and 5-ring 6

Beibu Gulf,

China 2010

August and

September 41 95.5 3.0-388.0e 3 to 4-ring 7

Continental shelf

sediment, China 2007

August and

September 31 81.0 57.0 27.0-224.0f 2 to 4-ring 14

Kenting coast,

Taiwan 2006 Monthly 0.2-493.0g 4 to 6-ring 8

Bhavnagar coast,

India 2013-2014 Quarterly 345000 5020-981180a 2 to 3-ring 13

Continental

shelf, Swedish 2006

Not

available 120 900 120-9600h 4 to 5-ring 26

Notes:

a: Σ15PAHs plus naphthalene.

b: Σ15PAHs subtract acenaphthylene, plus naphthalene and benzo[e]pyrene.

c: 10 PAHs including naphthalene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo[a]anthracene, chrysene,

benzo[e]pyrene, benzo[a]pyrene.

d: Σ15PAHs subtract benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, indeno[1,2,3-cd]pyrene, dibenzo[a,h]anthracene,

and benzo[g,h,i]perylene, plus naphthalene.

e: Σ15PAHs.

f: Σ15PAHs plus biphenyl, 11H-benzo[b]fluorene, benzo[e]pyrene.

g: Σ15PAHs plus 2-Methylnaphthalene, 1-Methylnaphthalene, 3-Dimethylnaphthalene, 1,6-Dimethylnaphthalene,

1,4-Dimethylnaphthalene, 1,5-Dimethylnaphthalene, 1,2-Dimethylnaphthalene, 2,3,5-Trimethylnaphthalene, 1-Methylfluorene,

Dibenzothiophene, 4,6-Dimethyldibenzothiophene, 3,6-Dimethylphenanthrene, 2-Methylfluoranthene, Retene, Benzo[a]fluorine,

Benzo[b]fluorine, 1-Methylpyrene, Triphenylene, 1-Methylbenz[a]anthracene, 4/6-Methylchrysene, Benzo[e]pyrene, Perylene,

Coronene.

h: Σ15PAHs subtract acenaphthylene, plus naphthalene.

S12

Supplementary Table 4 Correlation analysis between TOC-normalized PAHs and various factors

Spearman correlation

(n = 261) TOC

Population

size and

economic

development

Industrial

structure

and air

quality

Urbanization

Waste

gas

emissions

Per capital

oil

consumption

Traditional

energy

ratio

Temperature Rainfall

Acy R -0.22 0.13 -0.07 -0.18 -0.04 0.06 0.33 0.14 0.22

Sig. 0.00 0.04 0.26 0.00 0.55 0.30 0.00 0.03 0.00

Ace R -0.48 0.24 0.12 -0.25 0.08 -0.03 0.31 0.01 0.19

Sig. 0.00 0.00 0.05 0.00 0.22 0.61 0.00 0.84 0.00

Flu R -0.40 0.22 0.21 -0.25 -0.00 -0.03 0.19 0.11 0.24

Sig. 0.00 0.00 0.00 0.00 0.96 0.63 0.00 0.07 0.00

Phe R -0.08 0.33 0.10 -0.18 0.05 0.05 0.29 0.09 0.27

Sig. 0.17 0.00 0.12 0.00 0.42 0.41 0.00 0.14 0.00

Ant R 0.30 0.37 0.07 -0.05 0.24 0.22 0.31 -0.00 0.14

Sig. 0.00 0.00 0.29 0.38 0.00 0.00 0.00 0.94 0.02

Fla R 0.41 0.35 0.12 0.00 0.28 0.29 0.34 -0.05 0.05

Sig. 0.00 0.00 0.06 0.98 0.00 0.00 0.00 0.43 0.38

Pyr R 0.44 0.34 0.02 0.03 0.25 0.29 0.31 -0.05 0.06

Sig. 0.00 0.00 0.71 0.60 0.00 0.00 0.00 0.42 0.33

BaA R 0.41 0.37 0.06 0.05 0.36 0.25 0.29 -0.14 0.04

Sig. 0.00 0.00 0.36 0.46 0.00 0.00 0.00 0.02 0.54

Chr R 0.38 0.42 0.15 0.04 0.30 0.22 0.29 -0.13 0.06

Sig. 0.00 0.00 0.02 0.50 0.00 0.00 0.00 0.04 0.35

BbF R 0.41 0.38 0.12 0.08 0.35 0.25 0.27 -0.12 0.04

Sig. 0.00 0.00 0.05 0.19 0.00 0.00 0.00 0.05 0.48

BkF R 0.42 0.39 0.05 0.14 0.29 0.28 0.24 -0.15 0.00

Sig. 0.00 0.00 0.41 0.03 0.00 0.00 0.00 0.01 0.97

BaP R 0.45 0.40 0.02 0.09 0.34 0.28 0.26 -0.13 0.06

Sig. 0.00 0.00 0.80 0.13 0.00 0.00 0.00 0.03 0.36

DahA R 0.22 0.37 0.16 0.13 0.24 0.14 0.23 -0.14 0.02

Sig. 0.00 0.00 0.01 0.03 0.00 0.02 0.00 0.02 0.74

Ind R 0.41 0.34 0.07 0.08 0.37 0.26 0.22 -0.19 -0.01

Sig. 0.00 0.00 0.27 0.19 0.00 0.00 0.00 0.00 0.84

BghiP R 0.41 0.43 0.06 0.11 0.23 0.25 0.22 -0.00 0.17

Sig. 0.00 0.00 0.37 0.07 0.00 0.00 0.00 1.00 0.01

PAHs R 0.30 0.39 0.11 -0.02 0.28 0.22 0.32 -0.07 0.09

Sig. 0.00 0.00 0.07 0.77 0.00 0.00 0.00 0.25 0.13

S13

Supplementary Table 5 The relative ratios of PAHs in intertidal sediments from various regions

(%) Region 3-ring PAHs 4-ring PAHs 5-ring PAHs 6-ring PAHs

NEC-DLH, China 17.9±3.4e 46.0±2.5a 24.0±3.4ab 12.2±1.7cd

NC-BDH, China 55.5±16.9ab* 25.8±12.0d* 11.0±5.4de 7.7±3.3e

NC-HG, China 18.8±3.1e 44.1±2.5ab 23.1±3.6ab* 14.0±3.2b

EC-DY, China 34.2±8.6cd 33.6±5.0c 20.0±4.7bc 12.2±2.3c

EC-SSLW, China 63.4±18.3a 20.8±9.8e 9.3±7.9e 6.5±6.0e

EC-DGH, China 17.3±3.5e 43.0±2.9ab 25.8±2.3a 13.9±1.7bc

EC-YC, China 40.8±14.3c 29.4±6.8d 18.5±6.6c 11.4±4.2cd

EC-CJ, China 20.0±4.0e 40.2±1.8b 25.9±2.9a 13.9±1.7bc

EC-HZW, China 20.5±2.7e 39.6±2.1bc 25.4±2.2ab 14.6±0.8a

EC-MJ, China 34.4±20.0cd 35.2±10.7c 18.7±7.6c 11.7±3.9cd

EC-JLJ, China 17.3±3.6e 46.8±2.7a 22.6±3.8b 13.3±2.0bc

SC-ZJ, China 39.1±17.8c 33.4±10.6c 17.0±6.4c 10.5±3.9d

SC-YLW, China 49.6±18.9b 26.5±11.6d 13.4±5.7d 10.7±3.9cd*

SC-DZG, China 29.5±12.3d 31.9±7.8cd 22.4±6.1b 16.2±3.7a

Notes:

Different letters (a, b, c, d, e) in column represent differences are significant at P ﹤ 0.05. The symbol “*” indicate significant

temporal variations.

Supplementary Table 6 The diagnostic ratios of PAHs in intertidal sediments Region Fla/(Fla+Pyr) Ind/(Ind+BghiP) Ant/(Phe+Ant) BaA/(BaA+Chr)

NEC-DLH 0.56±0.01

0.57±0.02

0.55±0.10

0.49±0.03

0.19±0.04

0.16±0.04

0.40±0.03

0.39±0.02

NC-BDH 0.59±0.11

0.60±0.03

0.53±0.20

0.47±0.06

0.10±0.04

0.07±0.02

0.36±0.13

0.30±0.04

NC-HG 0.58±0.04

0.54±0.08

0.55±0.11

0.42±0.05

0.17±0.02

0.17±0.01

0.36±0.03

0.35±0.04

EC-DY 0.58±0.07

0.57±0.06

0.51±0.11

0.43±0.04

0.09±0.01

0.10±0.02

0.26±0.04

0.28±0.04

EC-SSLW 0.58±0.08

0.59±0.08

0.47±0.16

0.43±0.07

0.10±0.04

0.08±0.02

0.37±0.12

0.36±0.04

EC-DGH 0.54±0.03

0.54±0.02

0.54±0.10

0.47±0.03

0.18±0.02

0.18±0.03

0.38±0.03

0.37±0.02

EC-YC 0.51±0.10

0.54±0.17

0.51±0.11

0.46±0.02

0.11±0.03

0.10±0.02

0.31±0.07

0.32±0.02

EC-CJ 0.50±0.04

0.55±0.02

0.54±0.11

0.47±0.01

0.16±0.03

0.17±0.05

0.39±0.02

0.39±0.02

EC-HZW 0.51±0.03

0.54±0.02

0.50±0.11

0.45±0.03

0.15±0.02

0.14±0.01

0.37±0.02

0.36±0.01

EC-MJ 0.55±0.04

0.53±0.03

0.55±0.08

0.43±0.14

0.14±0.05

0.14±0.05

0.37±0.04

0.39±0.04

EC-JLJ 0.52±0.02

0.52±0.02

0.52±0.10

0.45±0.02

0.16±0.01

0.17±0.01

0.38±0.02

0.37±0.02

SC-ZJ 0.50±0.05

0.51±0.07

0.49±0.20

0.42±0.06

0.09±0.03

0.10±0.03

0.33±0.07

0.33±0.04

SC-YLW 0.53±0.06

0.59±0.09

0.49±0.13

0.45±0.18

0.09±0.02

0.09±0.02

0.42±0.13

0.42±0.13

SC-DZG 0.48±0.13

0.54±0.03

0.51±0.12

0.49±0.02

0.12±0.05

0.10±0.02

0.36±0.09

0.37±0.02

S14

Supplementary Table 7 Correlation analysis between diagnostic ratios and energy consumption

Spearman correlation

(n = 261) Fla/(Fla+Pyr) BaA/(BaA+Chr) Ant/(Phe+Ant) Ind/(Ind+BghiP)

Fla/(Fla+Pyr) R 1.00

Sig. 0.00

BaA/(BaA+Chr) R -0.04 1.00

Sig. 0.51 0.00

Ant/(Phe+Ant) R -0.16 0.48 1.00

Sig. 0.00 0.00 0.00

Ind/(Ind+BghiP) R 0.09 0.24 0.17 1.00

Sig. 0.10 0.00 0.00 0.00

Coal consumption R -0.14 -0.01 0.29 -0.08

Sig. 0.02 0.85 0.00 0.19

Oil consumption R -0.22 0.09 0.34 -0.05

Sig. 0.00 0.11 0.00 0.42

TOC R -0.29 0.32 0.65 0.04

Sig. 0.00 0.00 0.00 0.54

Supplementary Table 8 Correlation analysis between PAHs and TOC of sediment within each

intertidal zone Spearman correlation

(n = 9-15)

3-ring

PAHs

4-ring

PAHs

5-ring

PAHs

6-ring

PAHs Sum

Fla/(Fla+P

yr)

BaA/(BaA

+Chr)

Ant/(Phe+

Ant)

Ind/(Ind+B

ghiP)

Dry

NEC-DLH 0.89** 0.90** 0.85** 0.91** 0.90** 0.34 -0.15 0.08 -0.15

NC-BDH -0.63 0.23 0.41 0.27 0.13 -0.08 -0.19 0.23 -0.08

NC-HG 0.81* 0.71* 0.63* 0.66* 0.70* -0.30 -0.06 -0.11 -0.01

EC-DY -0.20 0.05 0.21 0.09 -0.01 -0.01 0.13 -0.55* 0.33

EC-SSLW 0.68 0.68 0.71* 0.25 0.79* 0.11 -0.25 0.25 0.50

EC-DGH 0.73** 0.68** 0.72** 0.69** 0.68** 0.14 0.01 0.43 0.64**

EC-YC 0.73** 0.93** 0.89** 0.90** 0.88** 0.61* 0.37 0.78** 0.45

EC-CJ 0.77** 0.65* 0.69* 0.72* 0.70* 0.46 -0.09 0.55 -0.41

EC-HZW 0.60 0.62 0.55 0.50 0.55 0.45 -0.68* 0.15 -0.52

EC-MJ 0.86** 0.97** 0.90** 0.90** 0.91** -0.46 0.78** 0.75** -0.13

EC-JLJ -0.28 0.03 0.05 0.08 -0.02 -0.20 0.28 0.42 0.32

SC-ZJ 0.27 0.26 0.36 0.30 0.37 -0.02 0.62* 0.51 0.35

SC-YLW 0.54 0.75* 0.55 0.58 0.50 -0.06 -0.44 0.05 -0.18

SC-DZG 0.17 0.08 0.12 0.13 0.30 -0.18 -0.10 -0.55 0.17

Wet

NEC-DLH 0.41 0.46 0.47 0.46 0.58* -0.06 -0.11 0.06 -0.15

NC-BDH -0.27 0.27 0.78* 0.48 0.00 -0.47 -0.27 -0.33 0.42

NC-HG -0.01 -0.04 -0.07 0.03 0.06 -0.35 0.09 0.04 -0.32

EC-DY 0.31 0.59 0.47 0.48 0.46 0.15 -0.38 -0.36 0.52*

EC-SSLW 0.22 0.17 0.15 0.15 0.20 -0.35 -0.07 -0.03 -0.13

EC-DGH 0.91** 0.87** 0.74** 0.74** 0.74** 0.28 0.15 0.91** 0.53

EC-YC 0.53 0.73** 0.89** 0.90** 0.71* 0.04 -0.04 0.30 0.33

EC-CJ 0.43 0.45 0.51 0.60 0.49 0.22 0.15 0.36 -0.12

EC-HZW 0.80** 0.95** 0.95** 0.92** 0.95** 0.08 -0.27 0.47 0.40

EC-MJ 0.88** 0.95** 0.94** 0.94** 0.94** -0.52 0.73** 0.86** 0.46

EC-JLJ -0.01 -0.11 -0.13 -0.13 -0.13 0.20 0.25 -0.11 -0.18

SC-ZJ 0.80** 0.68** 0.70** 0.68** 0.71** -0.73** 0.15 0.82** -0.65**

SC-YLW 0.37 0.90** 1.00** 0.98** 0.70* -0.62 -0.45 0.73** -0.20

SC-DZG 0.78* 0.77* 0.80** 0.93** 0.95** -0.25 0.47 0.78* -0.42

Notes:

* and ** represent differences are significant at P﹤0.05 and P <0.01, respectively.

S15

Supplementary Table 9 Rotated component matrix of PAHs in intertidal sediments

F1 F2 F3

Acy 0.354 0.765 0.449

Ace 0.335 0.321 0.842

Flu 0.331 0.374 0.776

Phe 0.417 0.613 0.574

Ant 0.534 0.607 0.514

Fla 0.578 0.654 0.407

Pyr 0.551 0.659 0.392

BaA 0.764 0.504 0.386

Chr 0.741 0.483 0.395

BbF 0.748 0.484 0.348

BkF 0.848 0.320 0.324

BaP 0.780 0.478 0.357

DahA 0.819 0.152 0.288

Ind 0.822 0.405 0.267

BghiP 0.554 0.495 0.379

Variance (%) 85.4 5.7 2.3

Notes:

Rotation Method: Varimax with Kaiser Normalization

Supplementary Table 10 Multiple linear regression of principle component scores against

∑15PAHs in intertidal sediments of China coastal zones

Standardized regression equation Probability of parametric test Determined coefficient after correction

Z= 0.667F1+ 0.560F2+ 0.432F3 P1= 0.000, P2= 0.000 , P3= 0.000 0.998

Supplementary Table 11 Contribution of ΣPAHs sources in intertidal sediments of China coastal

zones

F1 F2 F3

Sources Contributions Sources Contributions Sources Contributions

B, O & V 40.2% C 33.8% O & Os 26.0%

B: biomass combustion; C: coal combustion; O: oil combustion; V: vehicular emissions; Os: oil

Supplementary Table 12 Correlation analysis between PAHs and sediment properties

Spearman correlation

(n = 261) Clay Silt Sand TOC

Acy R 0.61 0.30 -0.44 0.72

Sig. 0.00 0.00 0.00 0.00

S16

Ace R 0.60 0.41 -0.52 0.61

Sig. 0.00 0.00 0.00 0.00

Flu R 0.67 0.42 -0.55 0.70

Sig. 0.00 0.00 0.00 0.00

Phe R 0.73 0.47 -0.60 0.81

Sig. 0.00 0.00 0.00 0.00

Ant R 0.77 0.49 -0.64 0.83

Sig. 0.00 0.00 0.00 0.00

Fla R 0.77 0.49 -0.62 0.86

Sig. 0.00 0.00 0.00 0.00

Pyr R 0.78 0.48 -0.62 0.86

Sig. 0.00 0.00 0.00 0.00

BaA R 0.77 0.50 -0.64 0.84

Sig. 0.00 0.00 0.00 0.00

Chr R 0.78 0.52 -0.67 0.85

Sig. 0.00 0.00 0.00 0.00

BbF R 0.81 0.50 -0.68 0.86

Sig. 0.00 0.00 0.00 0.00

BkF R 0.77 0.50 -0.65 0.82

Sig. 0.00 0.00 0.00 0.00

BaP R 0.77 0.52 -0.65 0.83

Sig. 0.00 0.00 0.00 0.00

DahA R 0.75 0.51 -0.65 0.79

Sig. 0.00 0.00 0.00 0.00

Ind R 0.80 0.52 -0.67 0.86

Sig. 0.00 0.00 0.00 0.00

BghiP R 0.80 0.52 -0.67 0.86

Sig. 0.00 0.00 0.00 0.00

Clay R 1.00 0.64 -0.89 0.80

Sig. 0.00 0.00 0.00 0.00

Silt R 0.64 1.00 -0.87 0.47

Sig. 0.00 0.00 0.00 0.00

Sand R -0.89 -0.87 1.00 -0.65

Sig. 0.00 0.00 0.00 0.00

TOC R 0.80 0.47 -0.65 1.00

Sig. 0.00 0.00 0.00 0.00

Supplementary Table 13 Rotated component matrix of anthropogenic parameters

Components

1 2 3 4 5 6

Total population 0.983 -0.032 0.100 0.031 -0.042 -0.040

urbanization 0.238 -0.238 0.745 0.011 0.272 0.113

S17

GDP 0.979 0.020 0.108 0.093 -0.046 -0.052

Total consumption of energy 0.975 0.055 0.093 0.141 0.044 -0.070

Coal consumption 0.948 0.145 0.138 0.170 0.011 -0.023

Oil consumption 0.968 0.009 0.079 0.109 0.188 -0.049

Coal ratio -0.173 0.721 -0.122 0.091 -0.391 0.504

Oil ratio -0.075 -0.593 0.337 -0.131 0.639 -0.201

Other energy ratio 0.313 -0.666 -0.054 -0.045 0.138 -0.615

Sewage 0.984 -0.058 0.103 0.020 -0.066 -0.025

Per capital GDP 0.453 0.536 0.576 0.218 0.170 -0.014

Per capital energy consumption 0.048 0.681 -0.199 0.341 0.542 0.140

Per capital coal consumption -0.111 0.828 -0.212 0.227 0.029 0.428

Per capital oil consumption 0.015 -0.019 0.172 0.118 0.973 -0.020

Per GDP energy consumption -0.337 0.068 -0.896 0.063 0.033 0.188

Gross industrial output value above

designed size 0.967 0.062 0.156 0.090 -0.101 -0.016

Ratio of heavy industry 0.317 0.724 -0.324 0.206 0.343 0.263

Ratio of light industry -0.317 -0.724 0.324 -0.206 -0.343 -0.263

Industrial waste gas emission 0.739 0.175 -0.140 0.438 0.274 -0.247

Industrial dust emission 0.164 -0.029 -0.065 0.711 0.442 -0.065

Household sulphur dioxide

emission 0.452 0.131 0.227 0.674 -0.300 0.223

Household nitrogen oxides

emission 0.485 0.162 0.065 0.647 -0.075 -0.241

Household dust emission 0.039 0.256 -0.101 0.779 0.077 0.137

Annual average concentration of

SO2 -0.138 0.931 0.004 -0.020 -0.119 0.096

Annual average concentration of

NO2 0.306 0.697 -0.016 0.350 -0.092 -0.022

Annual average concentration of

PM10 -0.137 0.968 0.034 0.049 -0.150 -0.079

95th percentile daily average

concentration of CO -0.056 0.735 -0.530 0.062 0.125 0.036

90th percentile daily maximum 8

hours average concentration of O3 0.166 0.705 0.376 -0.094 0.159 0.116

Annual average concentration of

PM2.5 0.056 0.951 0.045 0.112 -0.068 -0.175

Days of air quality equal to or

above Grade II -0.032 -0.966 -0.097 -0.005 0.050 0.075

Volume of maritime goods

transported 0.645 -0.423 0.150 0.159 -0.018 -0.106

Volume of maritime goods

turnover 0.888 -0.225 -0.026 0.161 0.197 0.092

International standardized

containers handled by coastal

seaports

0.765 0.229 0.487 -0.023 -0.018 0.200

International standardized weight

handled by coastal seaports 0.672 0.216 0.488 -0.060 0.198 0.337

Variance (%) 31.7 28.0 9.6 8.3 8.1 4.4

Rotation Method: Varimax with Kaiser Normalization

S18

Supplementary Table 14 Correlation analysis between climate parameters and anthropogenic

factors

Spearman correlation (n = 261) Temperature Rainfall

Population size and economic development R -0.11 0.28

Sig. 0.07 0.00

Industrial structure and air quality R 0.01 -0.21

Sig. 0.89 0.00

Urbanization R 0.01 -0.05

Sig. 0.91 0.45

Waste gas emissions R -0.33 -0.24

Sig. 0.00 0.00

Per capital oil consumption R 0.07 -0.10

Sig. 0.28 0.11

Traditional energy source ratio R -0.03 -0.21

Sig. 0.60 0.00

Supplementary Table 15 Correlation analysis between urbanization and PAHs in intertidal

sediments

Spearman correlation (n = 261) Unbanization

Acy R 0.21

Sig. 0.00

Ace R 0.26

Sig. 0.00

Flu R 0.18

Sig. 0.00

Phe R 0.23

Sig. 0.00

Ant R 0.25

Sig. 0.00

Fla R 0.27

Sig. 0.00

Pyr R 0.30

Sig. 0.00

BaA R 0.31

Sig. 0.00

Chr R 0.28

Sig. 0.00

BbF R 0.30

Sig. 0.00

BkF R 0.32

Sig. 0.00

BaP R 0.33

Sig. 0.00

DahA R 0.33

Sig. 0.00

Ind R 0.31

Sig. 0.00

BghiP R 0.33

Sig. 0.00

S19

Supplementary Table 16 The MS parameters, matrix spike recovery and MDLs for the PAH

analytes

Name Q1a/Q3b,Q3c Matrix spike Recoveryd (%) MDLs (ng/g)

Acy 152/102, 126 78±1 0.03

Ace 154/153, 152 86±7 0.01

Flu 166/165, 115 99±12 0.03

Phe 178/151, 152 100±8 0.13

Ant 178/151, 152 103±13 0.21

Fla 202/200, 152 106±9 0.04

Pyr 202/200, 199 105±10 0.11

BaA 228/226, 226/224 100±9 0.15

Chr 226/200, 224 99±13 0.38

BbF 126/113, 252/250 101±11 0.17

BkF 252/250, 226 95±12 0.36

BaP 250/248, 252/250 94±9 0.34

DahA 276/274, 278/276 101±12 0.47

Ind 138/125, 276/274 100±13 0.60

BghiP 276/274, 138/137 89±9 0.31

Ace-D10 162/160, 160/132 80±13

Phe-D10 188/158, 160 89±12

Chr-D12 240/236, 212 99±10

Pyr-D12 132/118, 264/260 100±12

hexamethylebenzene 162/147, 147/77 internal standard

Notes:

a, precursor ion (m/z); b, product ion 1; c, product ion 2; d, Data were presented as mean ± standard deviation.

Supplementary Table 17 Rotated component matrix of TOC normalized PAHs in intertidal

sediments

Components

1 2

Acy 0.041 0.896

Ace 0.030 0.951

Flu 0.049 0.967

Phe 0.273 0.834

Ant 0.663 0.638

Fla 0.910 0.188

Pyr 0.912 0.161

BaA 0.943 0.207

Chr 0.805 0.291

BbF 0.957 0.134

BkF 0.871 -0.112

BaP 0.954 0.087

DahA 0.814 -0.072

Ind 0.903 0.218

BghiP 0.830 0.165

Variance (%) 56.4 27.0

Rotation Method: Varimax with Kaiser Normalization

S20

Supplementary Figures

Sampling sites

NEC-D

LH

NC-B

DH

NC-H

G

EC-D

Y

EC-S

SLW

EC-D

GH

EC-Y

C

EC-C

J

EC-H

ZW

EC-M

J

EC-J

LJ

SC-Z

J

SC-Y

LW

SC-D

ZG

To

tal P

AH

s C

once

ntr

atio

n (

ng

g-1

)

0

200

400

600

800

1000

1200

Dry Season

Wet Season

North South

A

CDDD CD C CDB

BB BBBA

CDCDD

CDCDE

C

BC

B

BC

BAB

AB

A

*

Supplementary Fig. 1 Spatiotemporal variations of Σ15PAHs in the intertidal sediments (The box represents

the data between the 25th and 75th percentile. The solid and dashed lines represent the median and mean values,

respectively. Different letters (A, B, C, D, E) represent differences are significant at P ﹤ 0.05, and one -way

ANOVA was conducted separately for dry season and wet season. The symbol “*” indicate significant temporal

variations.)

0 5 10 15 0 10 20 30 40 50 60 0 2 4 6 8 10 12

0 20 40 60

TO

C c

onte

nt

(%)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 20 40 60 0 5 10 15 20 25 30 0 10 20 30 40 0 10 20 30 40 50 60

PAH concentration (ng g-1

)

0 5 10 15 20

TO

C c

onte

nt

(%)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 10 20 30 0 2 4 6 8 10 0 10 20 30 0 10 20 30 40 50

0 1 2 3 4 50 1 2 3 4

TO

C c

onte

nt

(%)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

r = 0.72

P < 0.01

n = 261

r = 0.61

P < 0.01

n = 261

r = 0.70

P < 0.01

n = 261

r = 0.81

P < 0.01

n = 261

r = 0.83

P < 0.01

n = 261

r = 0.86

P < 0.01

n = 261

r = 0.86

P < 0.01

n = 261

r = 0.84

P < 0.01

n = 261

r = 0.85

P < 0.01

n = 261

r = 0.86

P < 0.01

n = 261

r = 0.82

P < 0.01

n = 261

r = 0.83

P < 0.01

n = 261

r = 0.79

P < 0.01

n = 261

r = 0.86

P < 0.01

n = 261

r = 0.86

P < 0.01

n = 261

Acy Ace Flu Phe Ant

Fla Pyr BaA Chr BbF

BkF BaP DahA Ind BghiP

Supplementary Fig. 2 Spearman correlations between PAH concentration and total organic carbon (TOC)

content.

S21

Sampling sites

NEC-DLH

NC-BDH

NC-HGEC-D

Y

EC-SSLW

EC-DGH

EC-YCEC-C

J

EC-HZW

EC-MJ

EC-JLJSC-ZJ

SC-YLW

SC-DZG

PA

Hs r

ela

tive

ab

und

ance

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Six RingFive RingFour RingThree Ring

North South

Supplementary Fig. 3 The composition patterns of PAHs by ring size in the intertidal sediments. For each

intertidal zone, the left and right bars represent dry season and wet season.

Supplementary Fig. 4 PCA analysis of the PAH concentrations in the intertidal sediments of the 14

intertidal zones. a-c represent the seasonal variations, spatial variations among different level of tides and

different intertidal zones, respectively.

S22

0.0 0.2 0.4 0.6 0.8

Ind

/(In

d+

Bg

hiP

)

0.0

0.2

0.4

0.6

0.8

1.0

NEC-DLH-Dry

NC-BDH-Dry

NC-HG-Dry

EC-DY-Dry

EC-SSLW-Dry

EC-DGH-Dry

EC-YC-Dry

EC-CJ-Dry

EC-HZW-Dry

EC-MJ-Dry

EC-JLJ-Dry

SC-ZJ-Dry

SC-YLW-Dry

SC-DZG-Dry

NEC-DLH-Wet

NC-BDH-Wet

NC-HG-Wet

EC-DY-Wet

EC-SSLW-Wet

EC-DGH-Wet

EC-YC-Wet

EC-CJ-Wet

EC-HZW-Wet

EC-MJ-Wet

EC-JLJ-Wet

SC-ZJ-Wet

SC-YLW-Wet

SC-DZG-Wet

0.0 0.2 0.4 0.6 0.8

Ant/(A

nt+

Phe

)

0.0

0.1

0.2

0.3

Fla/(Fla+Pyr)

0.0 0.2 0.4 0.6 0.8

BaA

/(B

aA

+C

hr)

0.0

0.2

0.4

0.6

Grass, wood

or coal combustion

Petrogenic sources

or combustion

Petrogenic sources

Petrogenic sources

Pyrogenic sources

Petrogenic sources

Liquid fossil

fuel combustion

Grass, wood

or coal combustion

Petrogenic sourcesLiquid fossil

fuel combustion

Grass, wood

or coal combustion

Supplementary Fig. 5 Plots of PAH isomer pair ratios for source identification.

S23

Coal consumption (10000 tons SCE)

0 5000 10000 15000 20000 25000

Mo

lecula

r d

iag

no

stic r

atio

s

0.0

0.2

0.4

0.6

0.8Fla/(Fla+Pyr)

BaA/(BaA+Chr)

Ant/(Phe+Ant)

Ind/(Ind+BghiP)

r = -0.14

P < 0.05

r = 0.29

P < 0.01

P > 0.05

P > 0.05

Supplementary Fig. 6 Spearman correlations between molecular diagnostic ratios and coal consumption.

S24

Supplementary Fig. 7 A priori SEM used in this study.

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