Urban air pollution and health risks of parent and nitrated polycyclic aromatic hydrocarbons in two megacities, southwest China

Accepted Manuscript Urban air pollution and health risks of parent and nitrated polycyclic aromatic hydrocarbons in two megacities, southwest China Shaojie Zhuo, Wei Du, Guofeng Shen, Rui Wang, Xuelian Pan, Tongchao Li, Yang Han, Yungui Li, Bo Pan, Xing Peng, Hefa Cheng, Xilong Wang, Guoliang Shi, Baoshan Xing, Shu Tao PII:

S1352-2310(17)30500-9

DOI:

10.1016/j.atmosenv.2017.07.051

Reference:

AEA 15465

To appear in:

Atmospheric Environment

Received Date: 24 April 2017 Revised Date:

24 July 2017

Accepted Date: 27 July 2017

Please cite this article as: Zhuo, S., Du, W., Shen, G., Wang, R., Pan, X., Li, T., Han, Y., Li, Y., Pan, B., Peng, X., Cheng, H., Wang, X., Shi, G., Xing, B., Tao, S., Urban air pollution and health risks of parent and nitrated polycyclic aromatic hydrocarbons in two megacities, southwest China, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.07.051. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Graphic abstract

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Urban air pollution and health risks of parent and nitrated polycyclic aromatic hydrocarbons

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in two megacities, southwest China

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Shaojie Zhuo1, Wei Du1, Guofeng Shen1,#,*, Rui Wang1, Xuelian Pan1, Tongchao Li1, Yang Han1,

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Yungui Li2, Bo Pan3, Xing Peng4, Hefa Cheng1, Xilong Wang1, Guoliang Shi4, Baoshan Xing5, Shu

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Tao1,**

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1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

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2. School of Environment and Resources, Southwest University of Science and Technology,

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Mianyang 621010, China

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3. Faculty of Environmental Science and Engineering, Kunming University of Science and

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Technology, Kunming 650500, China

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4. College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China

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5. Stockbridge School of Agriculture, University of Massachusetts, Amherst 01003, USA

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*Dr. Guofeng SHEN, Email: [email protected]

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**Prof. Dr. Shu Tao, Email: [email protected]

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# present address: ORISE postdoctoral fellow at National Risk Management Research Laboratory, U.S.

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Environmental Protection Agency, Research Triangle Park, Durham 27709

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The authors declare no competing financial interests.

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Abstract

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Ambient air pollution in China has a significant spatial variation due to the uneven development and

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different energy structures. This study characterized ambient pollution of parent and nitrated polycyclic

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aromatic hydrocarbons (PAHs) through a 1-year measurement in two megacities in southwest China

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where regional PM2.5 levels were considerably lower than other regions. Though the annual average BaP

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levels in both two cities were below the national standard of 1.0 ng/m3, however, by taking other PAHs

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into account, PAHs pollutions were serious as indicated by high BaP equivalent concentrations (BaPEQ)

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of 3.8±2.6 and 4.4±1.9 ng/m3, respectively. Risk assessment would be underestimated by nearly an

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order of magnitude if only using BaP in risk assessment compared to the estimation based on 26 PAHs

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including 16 priority and 10 non-priority isomers targeted in this study. Estimated incremental lifetime

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cancer risks (ILCR) were comparable at two cities, at about 330-380 persons per one million, even

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though the mass concentrations were significantly different. Nitrated PAHs showed distinct temporal

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and site difference compared to the parent PAHs. High cancer risks due to inhalation exposure of PAHs

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and their polar derivatives in the low PM2.5-pollution southwest China suggest essential and effective

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controls on ambient PAHs pollution in the region, and controls should take potential health risks into

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account instead of solely mass concentration.

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Keywords

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Urban air pollution; PAHs; nitrated PAHs; southwest China; non-priority PAHs; risk assessment;

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Introduction Air pollution has significant impacts on human health and climate. The global population-

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weighted PM2.5 (particle with diameter less than 2.5 µm) had increased by 11% from 1990 to 2015, with

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a rapid increase after 2010 (State of Global Air, 2017). Exposure to ambient air pollution in 2010 was

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associated with a globally total of about 3.3 million premature deaths, of which 1.36 million was in China

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(Lelieveld et al., 2015). The latest estimation with new evidences and methodologies found that the air-

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pollution related early death was about 4.2 million in 2015, with about 1.1 million in China (State of

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Global Air, 2017). The overlap of high pollution levels and dense population in China resulted in a large

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number of premature deaths in the country. However, due to uneven development stages and different

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energy structures, air pollution in China has a significant spatial variation. The annual average PM2.5

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concentration ranged from ~10 to ~150 µg/m3 among cities monitored in the National Air Quality

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Monitoring network (Chen et al., 2017; Song et al., 2017). Ambient PM2.5 levels are generally higher in

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mega/large cities than rural area. The north area has higher pollution levels than the south area,

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especially during the cold winter when large amounts of coals and wood were burnt for home heating,

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and the east had high pollutions compared to the west area resulting from high energy consumptions

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under a fast economic development and a fast urbanization. High PM2.5 concentrations are often

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reported in regions such as Northern China Plain (NCP) and Yangtze River Delta (YRD) in east China,

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while the southwest China (including the municipality of Chongqing, provinces of Sichuan and Yunnan,

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and in some studies covering the Tibet area) is a region with considerably low air pollution. For example,

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of the 366 cities covered by the National Air Quality Monitoring network, there were only 96 cities had

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the annual average PM2.5 levels below the national standard of 35 µg/m3 in 2016, and of these 96 cities,

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32 were located in the southwest region.

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Of various air pollutants, polycyclic aromatic hydrocarbons (PAHs) are the first air pollutants that

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were considered as suspected carcinogens (Harrison et al., 1996), and receive worldwide interests due

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to the carcinogenic and mutagenic toxicities. PAHs are a group of organic pollutants with two or more

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aromatic rings that are mainly produced from incomplete combustions. Exposure to ubiquitous PAHs in

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environment via inhalation, oral and/or dermal exposure results in many adverse health outcomes like

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lung cancer. There are thousands of PAHs isomers in environment, and 16 priority PAHs listed by U.S.

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EPA (Environmental Protection Agency) are the most widely monitored set of PAHs in most studies and

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programs. Benzo[a]pyrene (BaP) is a Group 1 compound (carcinogenic to humans) classified by the

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International Agency for Research on Cancer and is regulated by many countries and European Union.

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The global total emission of 16 U.S. EPA priority PAHs was 504 Gg, in which 106 Gg and 67 Gg were

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produced from China and India, respectively (Shen et al., 2013a). The global average Incremental

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Lifetime Cancer Risk (ILCR) attributable to PAHs inhalation exposure was ~3.1×10-5, with dominated

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contributions from local emissions (Shen et al., 2014a). In addition to parent PAHs, PAHs derivatives

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such as nitrated, oxygenated and hydroxylated ones are receiving more and more interests owing to

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their potentially higher and direct toxic properties. Studies on these derivatives in sources (e.g. vehicle

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emissions, coal and wood combustions, etc.,) and environment (e.g. air, soil, sediments and water etc.,)

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are growing during the last ten years with the development of advanced analysis technologies and

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chemical standards commercially available.

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It is expected that large geographic variations in China exist not only in ambient PM2.5, but also

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other air pollutants like PAHs and their derivatives. The disparities require distinct pollution control

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strategies in different regions. However, though ambient PAHs pollution in China had been reported in

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numerous past studies, most of them were in relatively developed regions like NCP and YRD in east and

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north China areas, and many were in capital/large cities like Beijing, Tianjin, Shanghai, and Guangzhou. Page 4 of 26

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Ambient pollutions of PAHs in southwest China are rarely studied (Hu et al., 2012; Xu et al., 2011; Yang

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et al., 2015), except for a few studies on ambient persistent organic pollutants in the Tibetan Plateau

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(Ren et al., 2017; Wang et al., 2014, 2016; Gai et al., 2014). Ambient PAHs derivatives in the southwest

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China, to our knowledge, have not been investigated.

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The main objective of this study is to characterize and compare ambient pollutions of parent

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and nitrated PAHs in two megacities in southwest China, through a one-year field measurement.

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Pollution levels, composition profiles and possible sources were discussed. Potential cancer risks due to

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inhalation exposure of parent PAHs were estimated and discussed. Besides 16 priority PAHs, we

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included some non-priority PAHs that could be significant in source apportionments as makers and also

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important in risk assessment owing to relatively high toxic potentials likes some dibenzopyrene isomers.

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2. Method

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2.1 Sites and field sampling

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The field sampling was in two megacities in southwest China: Kunming [KM] and Mianyang

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[MY](Figure 1). [KM] is the capital city of Yunnan province, with an urban population of about 4.7 million.

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It is located at a high altitude (~1900 m above the sea level) and has a temperate climate. The air quality

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in [KM] is considered to be very good in China, with an annual average PM2.5 was only 28 µg/m3 in 2016.

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The city is a famous tour city, known as the “city of Eternal Spring”. Emission inventory on 16 priority

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PAHs estimated that the total emission of 16 PAHs in [KM] was about 331 tones/year with 57% and 39%

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from residential combustions and vehicle emissions, respectively (Shen et al., 2013a). [MY] is the second

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largest city in another province, Sichuan, in southwest China. It has an urban population of about 1.8

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million, and its ambient PM2.5 level in 2016 was about 49 µg/m3. The annual total emission of 16 priority

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PAHs was about 94 tones/year, of which residential combustion sources and vehicle emissions

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contributed 85% and 12%, respectively (Shen et al., 2013a). The sampling campaign was nearly synchronous at two cities, starting biweekly from March

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2014 to February 2015. The sampling in [KM] was done at the top of a 6-floor building in Kunming

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University of Science and Technology. The site in [MY] is in Southwest University of Science and

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Technology. Glass fiber filters (GFFs) and two polyurethane foams (PUFs) in sequence (to avoid potential

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breakthrough, and both were extracted and mixed in laboratory analysis) was used to collect ambient

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PM10 and gaseous samples, respectively (Li et al., 2014; Zhuo et al., 2017a). The sampling flow (~400

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L/min) was recorded automatically every five minutes. Prior to sampling, PUFs were Soxhlet extracted

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using acetone, dichloromethane, and hexane (Beijing Chemical Reagent Company, China, distillated in

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lab before use) in sequence for 8 h each, and GFFs were baked at 450 oC for 12 h. Both PUFs and GFFs

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were sealed separately in aluminum foil bags. After sampling, PUFs and GFFs were sealed in new

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aluminum foil bags separately and stored in a refrigerator under -20 oC prior to laboratory extraction

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and analysis.

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2.2 Laboratory analysis and quality controls

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In laboratory, PUFs were Soxhlet extracted with 150 mL hexane/acetone mixture (1:1, v/v) for

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about 8 hours, while GFFs were extracted with 25 mL hexane/acetone mixture using a microwave

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accelerated extraction (MAE) system (CEM Mars Xpress, USA). The temperature program in MAE was

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increased to 110 °C in 10 min and then held for other 10 min. Both PUF and GFF extracts were then

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concentrated to ~1.0 mL using a rotary evaporator (N-1100, EYELA, Japan), and transferred to a

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Silica/alumina gel column for purification. The column was eluted with 70 mL hexane/dichloromethane

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mixture (1:1, v/v). The eluate was then concentrated to ~1 mL using a rotary evaporator, changed to the

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hexane solution and spiked with deuterated internal standards priori to instrument analysis.

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The gas chromatograph coupled with a mass spectrometer (GC-MS) was used to analyze parent

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PAHSs in the Electron Ionization (EI) mode, and nitrated PAHs in Negative Chemical Ionization (NCI)

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mode. HP-5MS capillary columns (30 m × 0.25 mm i.d., 0.25 µm film thickness) were used. Helium was

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used as the carrier gas in PAHs analysis, and the oven temperature program was 50 oC for one minute,

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increased to 150 oC in 10 oC/min, to 240 oC at a rate of 3 oC/min and then ramped to 280 oC for 20

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minutes. In the analysis of nitrated PAHs, high purity helium and methane were used as the carrier and

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reagent gases, respectively. The oven temperature was programmed to increase from 60 oC to 150 oC at

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a rate of 15 oC/min, and then to 300 oC at a rate of 5 oC/min which was held for another 15 minutes.

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Compounds were identified based on the retention time and qualitative ions of the standards in

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Selected Ion Monitoring Mode (SIM), and quantified using internal standard methods (naphthalene-d8,

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acenaphthene-d10, anthracene-d10, chrysene-d12 and perylene-d12 for parent PAHs quantification,

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and 1-nitroanthracene-d9 and 1-nitropyrene-d9 for nitrated PAHs, J&W Scientific, USA).

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Dibenzo[a,h]pyrene, dibenzo[a,i]pyrene, 2-nitro-fluorene, 9-nitro-phenanthrene, 1-nitro-pyrene,

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6-nitro-chrysene and 6-nitro-benzo[a]pyrene were only detected in a few samplers (<20%), thus not

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included in the analysis and discussion here. A total of 26 parent PAHs and 7 nitrated PAHs were

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quantified and discussed in this study: PAHs (naphthalene (NAP), acenaphthene (ACE), acenaphthylene

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(ACY), fluorene (FLO), phenanthrene (PHE), anthracene (ANT), fluoranthene (FLA), pyrene (PYR),

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benz[a]anthracene (BaA), chrysene (CHR), benzo[b]fluoranthene(BbF), benzo[k]fluoranthene (BkF),

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Benzp[a]pyrene

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benzo[g,h,i]perylene (BghiP),) and non-priority PAHs (benzo[c]phenanthrene (BcP), retene (RET),

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perylene

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cyclopenta[c,d]pyrene(CcdP), anthanthrene (AA), Dibenzo[a,l]pyrene (DBalP), and Dibenzo[a,e]pyrene

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(DBaeP), and 1-nitro-naphthalene (1N-NAP), 2-nitro-naphthalene (2N-NAP), 5-nitro-acenaphthene (5N-

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(PER),

(BaP),

dibenz[a,h]anthracene

benzo[e]pyrene

(BeP),

(DahA),

coronene

indeno[1,2,3-cd]pyrene

(COR),

(IcdP),

dibenzo[a,e]fluoranthene

and

(DBaeF),

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ACE), 9-nitro-anthracene (9N-ANT), 3-nitro-phenanthrene (3N-PHE), 3-nitro-fluoranthene (3N-FLA), 7-

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nitro-benzo[a]anthracene (7N-BaA) (Accustandard Inc., via J&W Scientific, USA). Field and procedural blanks were subtracted from the sample results. All solvents used were re-

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distillated and checked for blank before use. All glassware devices were cleaned ultrasonically with

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deionized water and then baked at ~400 oC for 6 h prior to use. Preliminary experiments were

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conducted to determine instrument detection limits, method detection limits and method recoveries.

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Recoveries of parent PAHs ranged from 72% to 148% and from 75% to 136% for the filter and PUF

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samples, respectively. And recoveries for nitrated PAHs ranged from 61-101% for gaseous samples and

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60-98% for particulate compounds. More detailed information on laboratory analysis and quality

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assurance and controls can be found in previous publications (Shen et al., 2012a; Li et al., 2014, 2015).

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2.3 Data analysis

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The total concentration of 26 parent PAHs and 7 nitrated PAHs analyzed in the present study

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were denoted as ∑PPAH and ∑NPAH, respecWvely. Gas-particle partitioning of organics was described by

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calculating a partition coefficient (KP=F/(A×PM)), where F and A are measured concentrations in

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particulate and gaseous phases, respectively (ng/m3) and PM is particle concentration (PM10 in this study,

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μg/m3) (Pankow, 1987).

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Backward air trajectories were calculated using a NOAA Hybrid Single-Particle Lagrangian

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Integrated Trajectory (HYSPLIT, v4.9) model using NCEP/NCAR pressure reanalysis meteorological data

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(Draxler and Rolph, 2013). 72-h backward trajectories were calculated at 6-h intervals of each day. The

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calculated backward trajectories during the whole sampling period were then clustered into four

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clusters using HYSPLIT (Peng et al., 2016).

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Principal Component Analysis after Varimax rotation and data statistical analysis were done using SPSS (IBM, Armonk, NY, USA). A significance level of 0.05 was adopted. Health risks due to inhalation exposure of parent PAHs were assessed by calculating the

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Incremental Lifetime Cancer Risk (ILCR) (OEHHA, 2015; Jia et al., 2011; WHO, 2010) following the

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equation: (1)

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where Ci and TEFi are the mass concentration (ng/m3) of species i and its corresponding Toxic

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Equivalent Factor (TEF). URBaP ((ng/m3)-1) is the unit cancer risk factor for BaP, which describes the excess

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cancer risk associated with the inhalation exposure to BaP with an air concentration of 1 ng/m3. A unit

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value of 8.7×10-5 (ng/m3)-1 was adopted for URBaP in the present study (Jia et al., 2011; WHO, 2010).

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Many different TEF values are reported in literature (Boström et al., 2002; Nisbet and LaGoy, 1992;

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Larsen and Larsen 1998; U.S. EPA, 2010), resulting in high variances and uncertainties in the estimated

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BaPEQ (BaP equivalent concentration) and risks. In this study, we adopted TEF values from Nisbet and

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LaGoy (1992) for priority PAHs, and for non-priority PAHs the TEF values are from a recent review (U.S.

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EPA, 2010). The TEFs values and PAHs concentrations are summarized in Table S1.

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3. Results and Discussion

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3.1 Pollution levels and site difference

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The average annual concentration of ∑PPAH in [KM] was 71.6±39.6 ng/m3, that was significantly

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higher (p<0.05) than 36.7±11.8 ng/m3 in [MY] (Figure 2A). In [KM], relatively higher concentrations were

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measured in summer and autumn, while in [MY], the seasonal difference is small with comparable levels

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among the four seasons (Figure S1). For ∑NPAH, interesWngly (Figure 2B), higher concentrations were Page 9 of 26

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found in [MY] with an annual average of 164±95 pg/m3, compared to that in [KM] (344±167 pg/m3). The

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seasonal variations were not obvious in both two cities, although apparently high levels were measured

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in winter (Figure S1).

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The previous emission inventory estimated that the total emissions of 16 priority PAHs in [KM]

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and [MY] were 331 and 94 tons/year, respectively (Shen et al., 2013a). High emissions in [KM]

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contributed to high ambient levels of parent PAHs measured in the city compared to [MY]. However,

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primary emissions of PAHs in south and southwest China had very small seasonal/monthly variations

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(Zhang and Tao, 2008). Thus, relatively high parent PAHs levels during the summer and autumn periods

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are thought to be affected by the regional transport rather than local emissions. Back trajectories during

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the sampling period were analyzed and the cluster analysis result was shown in Figure 3. The occurrence

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of each cluster in different seasons and calculated average PAHs concentrations in each cluster are also

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illustrated in the figure. As seen, during the summer period, [KM] is obviously affected by air masses

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from the south area, bringing pollutions from south Asia area to the studied region. The measured PAHs

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concentrations in days affected by air masses from the south areas were generally higher than other

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periods.

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Different from parent PAHs that are mainly produced from primary combustion sources,

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photochemical formations also contribute to ambient nitrated PAHs. The mass ratio of nitrated PAHs to

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the corresponding parent PAHs ranged from 5.6×10-4 (3N-PHE) to 0.56 (1N-NAP/NAP) in [KM], and

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1.2×10-3 (3N-PHE) to 0.31 (9N-ANT/ANT) in [MY]. These values were much higher than ratios measured

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in combustion emission exhausts (Keyte et al., 2016; Tang et al., 2005; Shen et al., 2012a, 2013b),

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suggesting obvious secondary formations in environment. Relative contributions of primary and

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secondary sources to ambient PAHs derivatives vary in sites and study periods (Lin et al., 2015; Ringuet

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et al., 2012; Zhuo et al., 2017b), but are not well understood due to limitations like few field data,

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lacking of reliable makers for primary sources, complicated photochemical formation pathways, as well

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as appropriate source apportionment methods. So far, there is still no inventory on primary emissions of

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PAHs derivatives, although emission factors had been reported for sources including vehicle emissions,

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coal and biomass combustions (Karavalakis et al., 2010a, 2010b; Keyte et al., 2016; Shen et al., 2012a,

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2013b; Tang et al., 2005).

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As mentioned above, though [MY] had lower levels of parent PAHs compared to [KM], the levels

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of nitrated PAHs were much higher in [MY] than that in [KM]. Figure 4 shows the mass ratio of

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∑NPAH/∑PPAH in these two ciWes. [MY] had much higher mass raWos than [KM], with annual means of

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9.24±3.39 and 2.96±2.35 pg/ng in these two cities (p<0.05). In comparison with [KM], [MY] had

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relatively higher temperature, lower moisture and stronger ultraviolet radiation (Figure S2), that may

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promote photochemical reactions to form nitrated PAHs. Interestingly, ∑NPAH was posiWvely correlated

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with ∑PPAH in [MY] (p<0.05), whereas in [KM] there is no significant correlation between ∑NPAH and

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∑PPAH. It is inferred that nitrated PAHs in [KM] may largely affected by secondary formations, while in

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[MY] both primary emissions and secondary reactions had obvious impacts on ambient nitrated PAHs.

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Future quantitative studies with more ambient data and detailed information on primary sources are

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interesting to look into source contributions and fates of these derivatives.

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3.2 Gas-particle partitioning

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The average concentration of ∑PPAH in the gaseous phase 65.9±38.3 and 26.2±11.1 ng/m3 in

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[KM] and [MY], while the particulate ∑PPAH were 5.69±3.27 and 10.6±7.0 ng/m3 in these two cities,

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respectively. For the ∑NPAH, the gaseous and parWcle-bound concentrations were 122±57 and 52±77

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pg/m3 in [KM], and 261±132 and 97±104 pg/m3 in [MY], respectively. Therefore, a majority of both

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parent and nitrated PAHs presented in the gaseous phase. There would be large underestimations of the

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mass concentrations of PAHs and their derivatives when only particles were collected and analyzed.

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Generally, the mass percentage of gaseous phase to the total decreased with the increase of compound

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MW (Figure 5)-which is a trend widely reported in literature for many organic pollutants. Calculated

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gaseous fractions for the same compound were similar at two cities (Figure S3), despite of different

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pollution levels between these two cities.

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The calculated gas-particle partitioning coefficients (Kp) for nitro-PAHs were expectedly higher

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than the Kp for the corresponding parent PAHs, due to the polar propriety of derivatives and thus

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preferable presence in particle. For example, the log-transformed Kp values of 9N-ANT were -2.05±0.87

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and -2.24±0.81 in [KM] and [MY], while the log-transformed Kp values of ANT were -3.69±0.55 and -

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2.98±0.52 in these two sites, respectively.

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It is usually believed that the gas-particle partitioning of organics in air can be controlled by

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absorption into organic matter, and/or adsorption on adsorbents like elemental carbon (Goss and

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Schwarzenbach, 1998; Li et al., 2016; Tomaz et al., 2016). Figure 6 illustrates relationship between Kp

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and the octanol-air coefficient (KOA), and between Kp and subcooled liquid vapor pressure (Vp). The

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positive correlations between Kp and KOA suggested the partitioning process is strongly affected by the

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absorption. Regarding the relationship between Kp and Vp, it was suggested that the slope of the

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regression may provide insights on the partition mechanism(s), with a steeper slope <-1.0 indicating the

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adsorption dominance and a shallower slope >-0.6 suggesting the absorption governance (Goss and

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Schwarzenbach, 1998; Lohmann and Lammel, 2004; Shen et al., 2011). As shown in Figure 6, the slopes

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in the present study were in the range of -0.44 to -0.32, which again suggested a dominant impact of

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absorption in the gas-particle partitioning of parent and nitrated PAHs here.

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The gas-particle partitioning of volatile and semi-volatile organics is a complex and dynamic

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process, and affected by a variety of factors including physicochemical properties of compounds, mixing Page 12 of 26

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of fresh emissions and aged air, loss or new formation of compounds in air, properties of particle (e.g.

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size and contents of absorbents and adsorbents) and meteorological conditions. Several models like

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Junge-Pankow adsorptive model, KOA absorption model, and the dual-model were also developed to

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predict the partitioning. However, the comprehensive process and mechanism(s) are still not well

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understood. In a study on PAHs and their nitrated and oxygenated derivatives in north China, Li et al.,

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(2016) found considerable underestimation of Kp in all three partitioning models tested, and results

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from the dual model were closer to the measured one. In comparison with a single linear free energy

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relationship (spLFER), a multiple-phrase poly-parameter model (ppLFER) was found to improve the

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prediction of Kp of nitrated and oxygenated PAHs at an urban site in France (Tomaz et al., 2016), and

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suggested that absorption attributable to soluble organic matter and organic polymers was the main

278

process controlling the partitioning. With respect to a lacking of key parameters and potential

279

uncertainties in sampling artifacts due to chemical reactions during the field campaign, we did not

280

further run and compare models to predict the partitioning in this paper. A future study is interesting to

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enrich the knowledge of gas-particle partitioning of PAHs and their derivatives with inputs from a high-

282

time resolution database and improved models.

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3.3 Composition profiles and source identification

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PHE, FLA and PYR had higher concentrations than other parent PAHs, and for nitrated PAHs, 9N-

286

ANT and nitro-NAP were higher than other species. The profiles were generally similar in different

287

seasons (Figures S4 and S5). Some differences in the composition profile of parent PAHs were found

288

between [KM] and [MY]. As seen in Figure 7, in [KM], in addition to relatively high levels of had high

289

levels of PHE, FLA and PYR, a high amount of RET was observed with an annual mean of 19.7 ng/m3,

290

contributing 26±8% of the ∑PPAH. While in [MY], though again PHE, FLA and PYR were dominant species, Page 13 of 26

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the fractions of low-volatile High Molecular Weight (HMW) PAHs were higher than the percentages in

292

[KM]. The mass percentages of PAHs with 2-3 ring, 4 ring and ≥5 ring were 78±5, 19±4 and 3±1% in [KM],

293

but

294

(http://inventory.pku.edu.cn/) reported that residential solid fuel combustion contributed nearly 85% of

295

the total PAHs emissions in [MY] while in [KM] the contribution of residential sector was 57% (Shen et

296

al., 2013a). Fractions of HMW PAHs in low efficient residential combustion sources are typically higher

297

than other sources like vehicle emissions and combustions in industrial boilers (Xu et al., 2006; Shen et

298

al., 2013a). High fractions of those HMW PAHs would probably lead to high risks on human health

299

induced by PAHs inhalation exposure, even though the total mass concentration of ∑PPAH was lower in

300

[MY] compared to [KM]. Health risk attributable to PAHs inhalation exposure is present in the next

301

section.

63±11,

29±7

and

9±3%

in

[MY].

The

emission

inventory

on

parent

PAHs

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at

PAHs isomer ratios had been widely used to identify main sources of PAHs (Chen et al., 2015;

303

Katsoyiannis et al., 2011; Liu et al., 2007; Shen et al., 2013c; Yunker et al., 2002) under the assumption

304

that the paired isomers are diluted to a similar extent and the ratios remain constant from sources to

305

receptors. Several commonly used isomer ratios are ANT/(ANT+PHE), FLA/(FLA+PYR), BaA/(BaA+CHR)

306

and IcdP/IcdP+BghiP. The ANT/(ANT+PHR) of 1.0 is suggested to distinguish emissions from petro- or

307

pyro-genic sources. A ratio of FLA/(FLA+PYR) higher than 0.5 suggests emissions from coal and biomass

308

burning, while FLA/(FLA+PYR) below 0.4 suggests petro-genic sources and the ratio between 0.4 and 0.5

309

is considered to be an indicator of petroleum combustion sources like vehicle emissions. BaA/(BaA+CHR)

310

of 0.35 and IcdP/(IcdP+BghiP) of 0.5 are suggested to distinguish petroleum combustion sources and

311

coal/biomass combustion sources. Emissions from coal and biomass burnings usually have similar

312

profiles, and thus few ratios are used to further separate these two source types. As seen in Figure 8,

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results of isomer ratios suggested that both petro- and pyrogenic sources had contributions to ambient

314

parent PAHs in these two cities, and these two cities had different source profiles. Table 1 lists component loadings in each factor extracted from the PCA. These factors accounted

316

for over 80% of the variance. In [KM] the first factor (F1) was associated with high loadings of high

317

molecular weight PAHs including BaA, PER, BeP, DahA, IcdP, BghiP, COR, AA and dibenzopyrenes, that

318

were usually abundant in vehicle emissions. The second factor (F2) with high loadings of PHE, ANT, FLA,

319

PYR, and moreover RET, indicated emissions from coal and biomass burning (Shen et al., 2012b; Zhuo et

320

al., 2017a). The third factor (F3) was mainly loaded with 1N-NAP, 2N-NAP and 3N-PHE, and thus was

321

thought to indicate secondary formations from photochemical reactions. The fourth factor (F4) was

322

abundant in low molecular weight PAHs including NAP, ACE, FLO as well as some nitrated PAHs. These

323

low molecular weight PAHs probably suggested this factor was associated with petro-genic sources

324

(Kong et al., 2015), while nitrated PAHs could be from vehicle emissions or secondary formation. Thus,

325

this factor is thought to be associated with mixed sources of petro-genic, vehicle emissions and also

326

secondary formation. The fifth factor (F5) was thought to indicate other unidentified primary sources

327

based on its high loadings of NAP, CcdP and AA that were widely reported in many combustion sources

328

(Eisenstadt and Gold, 1978; Shen et al., 2013b, 2015). Accordingly, these five factors are considered to

329

indicate vehicle emission, solid fuel (coal and biomass) combustion, secondary formation, mixed sources

330

of petro-genic sources, vehicle emissions and secondary formation, and other unidentified primary

331

sources, accounting for 34, 16, 15, 12 and 8% of the total variance, respectively.

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In [MY], the factor loadings were a little bit different in the first two factors compared to that in

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[KM]. The first factor was associated with CHR, BaA, PER, BbF, BkF and IcdP, while the second factor had

334

high loadings of DahA, COR, BghiP and dibenzopyrenes in addition to PHE, FLA, PYR and RET. Typically,

335

DahA, BghiP, COR and dibenzopyrenes were highly associated with gasoline emissions while diesel Page 15 of 26

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emissions had more proportions of CHR, BaA, BbF and IcdP compared to gasoline vehicle emissions

337

(Ravindra et al., 2008; Shen et al., 2014b; Zhuo et al., 2017b). Thus, identified sources in [MY] are diesel

338

vehicle emission, mixed sources of gasoline and solid fuel combustion emissions, mixed source of petro-

339

genic source and secondary formation, other unidentified primary sources and secondary formation,

340

accounting for 31, 20, 13, 10 and 9%, respectively.

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Note that the explained variations from PCA does not mean source contributions. Here we only

342

qualitatively discussed possible sources in these two cities, and did not quantify contributions of each

343

source type. To do a quantitative analysis, other receptor models like PMF and ME2 are preferable (Liu

344

et al., 2015), however these models usually rely on a large sample size to obtain reliable results. The

345

minimum sample size in PMF was suggested to be 50 or 30+(3+v)/2 where v is the number of species

346

(Pant and Harrison, 2012). We acknowledged the limitation in the present analysis on possible sources.

347

For instance, the use of isomer ratios is based on the assumption of constant isomer ratios in sources

348

and receptors, and requires distinct source signatures among different source types, which are not also

349

valid in many circumstances. All receptor models need objective interpretation on source types based

350

on available information on source profiles and some biomarkers, leading to uncertainties and potential

351

bias in source contributions. However, the qualitative results clearly suggested the source contributions

352

in these two cities are distinct, and vehicle emissions had high contributions to the variability.

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3.4 BaPEQ and Incremental Lifetime Cancer Risks

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The annual average BaP concentrations in [KM]and [MY] were 0.27±0.13 and 0.67±0.50 ng/m3,

356

respectively. The level was generally lower than most results reported in north and east China where

357

ambient BaP can exceed the national limit of 1.0 ng/m3 by 2-10 times (Zhu et al., 2015, Zhuo et al., Page 16 of 26

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2017a and references therein), but much higher than that in the Tibetan Plateau area (0.003-0.034 ng/3

359

with an overall mean of 0.011±0.008 ng/m3) (Wang et al., 2014). The level in [KM] from the present

360

study was apparently lower than that of 0.50±0.38 ng/m3 in a previous study during March-April, 2012 in

361

this city (Yang et al., 2015). In a past study at a background site in Yunnan province in 2005-2006, BaP in

362

particle was 0.43 ng/m3 (Xu et al., 2011). These studies, though very limited now, probably suggest a

363

declining trend in ambient PAHs. This is, to some extent, consistent with reduced particle pollutions

364

(Figure S6) and alleviated air quality owing to a series of intensive and strict controls during the last

365

several years. The decline in ambient PAHs and sometimes nitrated PAHs had also been reported in

366

some other cities like Beijing (Tang et al., 2017) and Nanjing (Zhuo et al., 2017a). This is believed to be a

367

co-benefit from air pollution control measures of criteria air pollutants like SO2, PM2.5 and ozone in the

368

country for which the sources are usually major contributor to primary PAHs and nitrated PAHs.

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Though in both cities, ambient BaP levels are lower than the national standard of 1.0 ng/m3.

370

However, if taking other priority PAHs into consideration, the calculated BaPEQ concentrations for the

371

total 16 priority PAHs were 1.3±0.8 and 1.5±0.7 ng/m3 in [KM] and [MY], respectively. And, if including

372

other non-priority PAHs targeted in this study, the BaPEQ concentrations were 3.8±2.6 and 4.4±1.9

373

ng/m3, respectively. Individuals such as DBalP, BaP, CcdP, RET and FLA contributed largely to the overall

374

BaPEQ (Figure 9). Higher contributions of DBalP, BaP and CcdP were mainly due to their relatively higher

375

toxic properties (high TEF values), and for FLA, it is mainly because of its relatively high mass

376

concentration. Since most HMW PAHs had higher TEFs values and these PAHs are preferably present in

377

particles, the calculated BaPEQ for only particulate PAHs were 2.3±1.9 and 4.1±2.0 ng/m3 in [KM] and

378

[MY], respectively, contributing to about 60% and 90% of the BaPEQ including both gaseous and

379

particulate PAHs.

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The overall ILCRs due to PAHs inhalation exposure were 3.3x10-4 and 3.8x10-4 in [KM] and [MY],

381

respectively, that are higher than the benchmark of 1.0 x 10-6 and even higher than the acceptable level

382

of 1.0×10-4, indicating high risks in lung cancer among the population due to PAHs inhalation exposure

383

(Figure 10). Though the mass concentration of total parent PAHs was higher in [MY] than [KM], high

384

fractions of HMW PAHs resulted in a comparable or even slightly higher (p>0.05) risks owing to PAHs

385

inhalation exposure in [MY]. The present study did not include risks due to exposure of nitrated PAHs

386

that are probably more toxic than parent PAHs. Taking high levels of nitrated PAHs in [MY] into account,

387

health risks due to inhalation exposure of ambient polycyclic aromatic compounds would be even more

388

higher in [MY] compared to [KM].

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Risk assessment based on only particulate PAHs underestimated the overall risk by about 40% in

390

[KM] and 7% in [MY], whereas if only 16 priority PAHs were analyzed, the risk would be underestimated

391

by about 66%, and if only BaP is used, the risk was nearly one order of magnitude lower. Therefore, the

392

study clearly suggested to include other PAHs in addition to BaP, especially some non-priority high toxic

393

ones into risk assessment. The point was also highlighted in some past studies (Andersson and Achten,

394

2015; Sauvain et al., 2003; Jia et al., 2011; Zhuo et al., 2017a). It is important to note that the estimation

395

of incremental cancer risk here is a simple one-point estimation, without considering of different

396

indoor/outdoor exposure and individual susceptibility in this analysis. We did not analyze risk

397

attributable to exposure of nitrated PAHs due to a lack of toxic data and exposure-dose relationship for

398

these derivatives. Future experiments are interesting and essential to develop a systematic database for

399

the toxic effects of PAHs derivatives.

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4. Conclusions

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In this study, we evaluated ambient air pollution of parent and nitrated PAHs in two megacities

403

located in southwest China, where regional air quality was considerably better than those in north and

404

east China area. [KM] had higher pollution levels of parent PAHs, but lower ambient levels of nitrated

405

PAHs compared to [MY]. The seasonal differences were not obvious in these two megacities, except in

406

[KM] relatively high parent PAHs were found during the summer period which is partly explained by the

407

impacts of air transport from south Asia area. Results from isomer ratios and PCA suggested that both

408

petro- and pyrogenic sources contributed to ambient PAHs. In [KM], five source groups including vehicle

409

emission, solid fuel (coal and biomass) combustion, secondary formation, mixed sources of petro-genic

410

source and secondary formation, and other unidentified primary sources accounted for 34, 16, 15, 12

411

and 8% of the total variance, respectively. In [MY], diesel vehicle emission, mixed sources of gasoline

412

and solid fuel combustion emissions, mixed source of petro-genic source and secondary formation,

413

other unidentified primary sources and secondary formation, accounting for 31, 20, 13, 10 and 9%,

414

respectively.

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The annual average BaP levels in both two cities were below the national standard of 1.0 ng/m3,

416

however, by taking into account of other PAHs, severe PAHs pollution lead to high cancer risk over than

417

1 in one million populations. The estimated ILCR levels were about 330-380 people per one million.

418

Though [MY] had a low mass concentration of the total parent PAHs, estimated incremental lifetime

419

cancer risk owing to PAHs inhalation exposure was comparable, or even slightly higher than, that in [KM],

420

owing to more HMW PAHs. Estimated risk levels would be an order of magnitude lower if only using BaP

421

compared to the estimated cancer risks based on 26 PAHs including 16 priority PAHs and 10 non-priority

422

PAHs in this study.

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Acknowledgement

426

Funding for this study is supported by China National Natural Science Foundation (41629101, 41390241

427

41301554 and 41571130010) and project 111 (B14001).

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Table 1. Component loadings including both parent and nitrated PAHs in each factor extracted from the PCA analysis for Kunming and Mianyang.

F1

F2

Mianyang [MY]

F3

F4

F5

NAP

.567

.606

ACE

.930

.915

PHE

.760

ANT

.839

FLA

.724

PYR

.788

.574

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BcP CcdP CHR

.844 .871

RET PER

.679

BeP

.964

BbF

.936

BkF

.580

BaP

.703

DahA

.848

F2

F3

F4

.903 .906 .810

.656

.632 .726

.893 .480

.864 .824

F5

.809

.862

EP

.900

.580

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BaA

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FLO

F1

SC

.709

ACY

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Kunming [KM]

.850 .767 .572 .953 .980 .948 .887 .938 .797 Page 25 of 26

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Kunming [KM]

BghiP

.783

COR

.520

DBaeF

.709

DBalP

.828

DBaeP

.822

AA

.559

F4

F5

F1

F2

F4

F5

.914

.632 .804

.621

1N-NAP

.848

2N-NAP

.858

5N-ACE

.647 .752 .736

.445

.656 .835 .850 .700

.849

3N-PHE .695

.704

TE D

.666

9N-ANT

3N-FLA

F3

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.853

F3

SC

IcdP

F2

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F1

Mianyang [MY]

.687 .627

.870

EP

7N-BaA

.732

13.5

6.7

3.2

2.3

2.0

11.4

8.2

3.5

2.5

2.1

% of Variance

33.8

16.0

14.5

12.3

7.5

30.9

20.4

13.1

10.4

9.3

Cumulative %

33.8

49.7

64.2

76.5

84.0

30.9

51.3

64.4

74.8

84.0

vehicle emissions

coal/biomass combustion

secondary formation

petrogenic sources and secondary

other primary

diesel vehicle emissions

gasoline and coal/biomass combustions

petrogenic sources and secondary

other primary

secondary and other primary

Sources

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Eigenvalues

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Figure captions Figure 1. Locations of two megacities (KM: Kunming, and MY: Mianyang) in this study and their

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population numbers and annual emissions of the total 16 U.S. EPA priority PAHs from the previous inventory.

Figure 2. Ambient ∑PPAH (le* panel) and ∑NPAH (right panel) concentra,ons from both gaseous and particulate phases in Kunming [KM] and Mianyang [MY]. Data shown are monthly

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means and standard deviations.

Figure 3. Cluster analysis results of back trajectories during the sampling periods at two cities,

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and occurrences of each cluster in different seasons. Average concentrations of ∑PPAH and ∑NPAH in days under the influence of air from the same cluster are calculated and present as well.

Figure 4. Comparison of the mass ratio of ambient ∑NPAH/∑PPAH in two ci,es.

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Figure 5. Dependence of the mass percentage in gaseous phase on molecular weight. Figure 6. Relationship between gas-particle partitioning coefficient (Kp) and octanol-air coefficient (KOA), and between Kp and subcooled liquid vapor pressure (Vp) for parent PAHs (yellow circles) and nitrated PAHs (blue circles). KOA and Vp are modeled values at 25 OC using

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U.S. Environmental Protection Agency’s EPISuiteTM (https://www.epa.gov/tsca-screening-tools).

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Figure 7. Normalized composition profiles of parent and nitrated PAHs in the two cities. Figure 8. Isomer ratios of parent PAHs in two cities. Figure 9. BaPEQ concentrations including both gaseous and particulate phases for the two cities and distributions between gaseous and particulate phase for PAHs individuals. Figure 10. Distribution of estimated ILCR due to inhalation exposure of total PAHs (solid lines) and particulate PAHs (dotted lines) in two cities.

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Highlights Southwest China is a relatively low PM2.5-pollution region but few is known about air pollution of polycyclic aromatic compounds

•

Ambient PAHs and nitrated derivatives were investigated through a 1-yr field measurement in two megacities, southwest China

•

The site difference in ambient parent PAHs was in contrast to that of nitrated PAHs

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Comparable risk levels, although the mass concentration was significantly different, indicate pollution controls take risk into account instead of solely mass concentration

•

Significant underestimation in health risk if only BaP or priority PAHs were involved suggests importance of non-priority isomers in risk assessment

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Cancer risks attributable to PAHs inhalation exposure were 330-380 persons per million calling for effective controls on PAHs in the low PM2.5-pollution southwest China

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