Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons in urban, suburban, and rural ambient air, central China during summer harvest

Ecotoxicology and Environmental Safety 191 (2020) 110219

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Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons in urban, suburban, and rural ambient air, central China during summer harvest

T

Xinli Xinga,b,∗, Zhanle Chena, Qian Tiana,c, Yao Maob, Weijie Liua, Mingming Shia,b, Cheng Chenga, Tianpeng Hua,c, Gehao Zhua, Ying Lia, Huang Zhenga, Jiaquan Zhangc, Shaofei Konga, Shihua Qia,b a

Laboratory of Basin Hydrology and Wetland Eco-restoration, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China School of Environmental Science and Engineering, Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, Hubei Polytechnic University, Huangshi, 435003, China b c

A R T I C LE I N FO

A B S T R A C T :

Keywords: PM2.5 PAHs Source identification Health risk assessment

Characterization and source identification of PM2.5–bound polycyclic aromatic hydrocarbons (PAHs) are conducted in urban Wuhan (WH), suburban Pingdingshan (PDS), and rural Suizhou (SZ) in China during summer harvest. This study analyzes 16 priority PAHs with 38 PM.2.5 samples in June. PAHs had similar physicalchemical properties like polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs), which had been listed as Priority Pollutants. The concentration and detection frequency of OCPs and PCBs were considerably lower than those of PAHs in PM2.5. Results indicate that PDS adjoining the highway has the highest PM2.5–bound PAHs. SZ possesses the lowest concentration of PAHs. Principal component analysis and multivariate linear regression model and molecular diagnostic ratio distinguish the sources. Vehicle emissions and coal combustion are extracted in three sites, while the source of PDS also includes gas combustion. SZ was affected by gas combustion and petroleum. The potential source contribution function and the concentrationweighted trajectory track the potential pollution area. The sampling places might be affected by the local sources and short distance transmission cannot be neglected. The incremental lifetime cancer risks (ILCRs) model evaluates the exposure risk of PAHs. According to the ILCR model, WH and PDS are exposed to harmful PAHs. By contrast, SZ is a substantially safe place.

1. Introduction With the acceleration of urbanization, considerable deterioration of the ambient air has been caused. (Wei et al., 2012; Liu et al., 2015; Jing et al., 2019). Fine fractions (PM2.5) have an aerodynamic diameter of less than 2.5 μm. These fine fractions penetrate into the lungs effortlessly (Chen and Liao, 2006; Taghvaee et al., 2018), where they will remain for a long time and may also enter the blood stream (Wei et al., 2012; Wang et al., 2016; Taghvaee et al., 2018; Chu et al., 2019). PM2.5 increases morbidity and mortality rates, and have various adverse health effects, such as respiratory and cardiovascular diseases (Gilli et al., 2007; Gualtieri et al., 2012; Chu et al., 2019). Carcinogenesis, mutagenesis, and ubiquity of polycyclic aromatic

hydrocarbons (PAHs) are widely recognized in the primary organic compound clusters of PM2.5 (Xing et al., 2011; Villar-Vidal et al., 2014; Ding et al., 2018). PAHs are characterized by reproductive, developmental, immune, neurological, cardiological, and hematological toxicities (Kim et al., 2013).They had similar physical-chemical properties like polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs), which had been listed as Priority Pollutants (WHO,2019). Polycyclic aromatic hydrocarbons (PAHs), PCBs and OCPs are ubiquitous and toxic contaminants(Nežiková et al., 2019). People are exposed to PAHs primarily through inhalation of polluted air or cigarette smoke and ingestion of food containing PAHs (Lee et al., 2018). PAHs are mainly from incomplete combustion of fossil fuels, such as petroleum and coal. PAHs are from anthropogenic sources, such as vehicle

∗ Corresponding author. Laboratory of Basin Hydrology and Wetland Eco-restoration, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China. E-mail address: [email protected] (X. Xing).

https://doi.org/10.1016/j.ecoenv.2020.110219 Received 15 October 2019; Received in revised form 11 January 2020; Accepted 14 January 2020 Available online 20 January 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.

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sample collection (GE, UK). A total of 11, 13, and 14 samples were collected from WH, PDS, and SZ, respectively. The meteorological conditions were acquired from the assimilation system by National Centers for Environmental Prediction. The samples were protected by an aluminum foil at −20 °C a week before weighting. The samples were placed in a thermostatic chamber for three days under constant temperature (20 °C) and relative humidity (45%). Then, these samples were weighed by a microbalance 0.00001 g degree of precision.

exhaust, open-fire straw burning, cigarette smoking, wood combustion, industrial production, including waste incineration, metal production, coke production, iron production, and airplane production (Visser et al., 1998; Ravindra et al., 2008; Mostert et al., 2010; Kim et al., 2013; Xing et al., 2016). Previous studies in China attached great importance to PAHs. These studies concluded that heavy pollution periods occurred only during significant coal combustion for central heating (Du et al., 2018; Kong et al., 2018; Shen et al., 2019). The main pollution sources were emissions from coal-fired boilers and traffic during heating period. During the non-heating period, the main pollution sources were emissions from gasoline engines, traffic, and coal-fired power plants (Kong et al., 2018). Fine particulate PAH concentrations were higher than coarse particulate PAH concentrations. The amounts of PAHs were mostly associated with coarse particles less than 10%. Moreover, low molecular weight PAHs mainly existed in the vapor phase while higher molecular weight PAHs were absorbed on the surface of suspended particles (Kong et al., 2010). Studying the source of PAHs and PM2.5 is essential to comprehend the pollution process. The source can be identified through numerous methods, such as molecular diagnostic ratio (MDR) (Hu et al., 2018), principal component analysis, and multivariate linear regression (PCA–MLR) (Yang et al., 2018), chemical mass balance model (Perrone et al., 2012), UNMIX model (Lang et al., 2015), and positive definite matrix factorization model (PMF) (Lang et al., 2015). The MDR and PMF models disclose that the dominant pollution sources in Wuhan (WH) are coal combustion and vehicles (Zhang et al., 2019). The MDR and PCA–MLR model inferred that traffic as the greatest pollution contributor in the Basque Country (Oleagoitia et al., 2019). Exposure to PM2.5-bound PAHs frequently poses the risk of cancer. Extensive research has been conducted on the toxicity of PAHs in urban areas (Agudelo-Castañeda et al., 2017; Gao and Ji, 2018). The lifetime cancer risk (ILCR) of PM2.5–bound PAHs is relatively infrequent at suburban and rural sites. The concentrations of PAHs were significantly distinct in urban, suburban, and rural areas (Qu et al., 2019). Wuhan in the central of China, is a mega city. Pingdingshan (PDS) and Suizhou (SZ) located in north of Wuhan, are less developed city/ county. Few studies compared the differences of the ambient air in these areas despite plentiful research on seasonal variations of PAHs in urban centers. Thus, the main objective are: (1) to explain the concentration, composition, source and transmission of PM2.5-bound PAHs at rural, suburban, and urban in central China during summer harvest activity which only last about 2 weeks (2) clarifying the potential PAH sources through MDR and PCA–MLR, (3) clustering trajectories by HYSPLIT model for potential pollution source areas, and (4) evaluate the human health risk of PAHs by ILCR model.

2.2. PAH analysis PM2.5 samples were extracted with 150 mL dichloromethane by Soxhlet extraction apparatus for 24 h to move PAHs from the filters to dichloromethane. Five deuterated PAHs consisting of Naphthalene-d8 (Nap-d8), Aceapthene-d10 (Ace-d10), Phenanthrened-d10 (Phe-d10), Chrysene-d12 (Chr-d12), and Perylene-d12 (Pyr-d10) were injected into the samples to rectify the result. The extraction is concentrated into 5 mL by a rotary evaporator at 30 °C (Heidolph Laborota 4000, Germany). Then, 6 mL of n–hexane was added, and this extraction was condensed further to 5 mL. The aliphatic hydrocarbons and PAHs were separated through a glass filled with silica gel, alumina, and amorphous sodium sulfate (V: V: V = 6 mL: 3 mL: 1 mL) cleaning by mixture of DCM and n–hexane (V: V = 2: 3). The solvent repeatedly condensed to 5 mL in 45 °C water. The extractions were shifted into a cell bottle with burette stockpiling under −20 °C. The eluent volume decreased to 2 mL under gentle nitrogen (purity ≥ 99.999%) before the volume was analyzed by GC–MS (GC–MS, Agilent 6890N-5975). The priority list of 16 PAHs (Table 1) recommended by the United States Environmental Protection Agency (US EPA) are detected by gas chromatography–mass by using a spectrometric capillary column (30 m × 0.25 mm × 0.25 μm), absorbing 1 μl of the sample. The GC oven of temperature was set at 80 °C for 2 min initially. Then, this temperature increased to 290 °C at 4 °C/min for the remaining 25 min. The mass spectrometry had an electronic impact source (70 eV) running at SCAN mode (50–500 m/z). The standard curve references were 0.2, 1, 2, 5, and 10 mg/L calibrated and qualified to target PAHs at a certain retention time. The recovery ranged from 80% to 120%. 2.3. Potential geographic origins The NOAA Air Resource Lab HYSPLIT model clusters the well-performing trajectories in transport, diffusion, and sedimentation (Wang et al., 2009). This clustering finds the potential geographic origins of PAHs. In this study, the 48-h backward trajectory clustering was investigated 1 h a day starting on June 3rd, 2017 at 0:00 to June 24th, 2017 at 0:00 with 10 m (ground), 500 m (low altitude), 1000 m (medium altitude), 2000 m (high altitude) as the starting height to explore the vertical distribution and transport. The trajectory calculation points for WH is (N30 31′ 35″ E114 21′ 70″), PDS (N33° 53′34″ E 113° 56′ 1′), and SZ (N32° 21′ 2″, E113′ 53° 11″). The concentration of PM2.5 is obtained from real-time monitoring of the samples. In addition, potential source contribution function (PSCF) and concentrationweighted trajectory (CWT) model explored the potential pollution area. PSCF can reflect the contribution rate of potential source area. The degree of pollution in a potential source area can be exhibited by CWT model. The criterion value was 35 μg/m3 according to the standard of Environmental Air Quality in China (GB-3095-2012).

2. Methodology 2.1. Sampling locations and procedure The surrounding environment of sampling sites namely, WH, SZ, and PDS are showed in Fig. 1. The sampling site in WH is the roof of the 8th floor in Hubei Environmental Monitoring Center Station, adjoining Bayi Road (N30° 31′ 35″ E114° 21′ 70″) (Fig. 1). This location is approximately 25 m above the ground with intensive human activities of the large city. The second site (PDS) is situated on the roof of the residents' committees in Baofeng (N33° 53′34″ E 113° 56′ 1′). This site is located in the central and western of Henan province (Fig. 1). The third site is selected in SZ (N32° 21′ 2″, E113′ 53° 11″), the north of Hubei province (Fig. 1) and is surrounded by hills with no industrial emissions. Samples were collected by TH-150C intelligent medium flow air total suspended particle sampler (Tianhong Co. Ltd, China). The sampling period is from June 3rd to 22nd, 2017, from 9:00 a.m. to 8:00 a.m. of the next day. The rate is 100 L/min on quartz fiber filter membrane, disposed by a muffle furnace under 800 °C 2 h before

2.4. Principal component analysis and multivariate linear regression Principal component analysis (PCA) of PAHs sources in different periods was conducted by SPSS 22. On the basis of source identification, in order to further determine the contribution rates of different pollution sources to PAHs, multivariate linear regression (MLR) was used for analysis after PCA (Larsen and Baker, 2003). The basic 2

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Fig. 1. Map of sampling sites. Pingdingshan (suburban), Suizhou (rural), and Wuhan (urban).

Average contribution of source k(%) = Bk ∑n Bi i=1

equation (Eq) of PCA model is:

Y= L× X

where, Y is the concentration matrix of PAHs, X is the factor score variable obtained by PCA analysis, and L is the factor loading matrix. Mass apportionment of possible sources to the total PAHs in each sample is obtained through MLR analysis, which used the total concentrations of PAHs as the dependent variable and the factor scores of each source as the independent variable. Eq. (2) was used to obtain the best correlation between observed and predicted total PAH concentration for each sample.

2.5. Cancer risk assessment The ILCR model is widely applied to calculate the exposure risk of PAHs (Taghvaee et al., 2018). The toxicity equivalent factor (TEF) of Benzo(a)pyrene (BaP) is the reference at home and abroad. This reference obtains the TEF of other PAHs with different ring numbers (Hu et al., 2017; Liu et al., 2017). Table 1 shows the TEF of each monomer PAH. The cancer risk of PAHs includes three pathways: ingestion, inhalation, and dermal contact. The formula is in the supplementary material.

n

Z=

∑ Bi × Xi i=1

(3)

(1)

(2)

2.6. Quality assurance and quality control (QA/QC)

where, Bi is the regression coefficient of MLR, X is the factor score matrix and Z is the normal standard deviation. The average contribution of source k can be calculated as follows:

Quality assurance and quality control include method blank, 3

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Table 1 TEF value and PAH concentrations (ng/m3) in three places. Compound

Nap Ace Acy Flu Phe Ant Fla Pyr BaA Chr BbF BkF BaP Inp DBA BghiP ∑PAHs

Ring

2 2 2 2 3 3 3 4 4 4 4 4 5 5 5 6

TEFa

0.001 0.001 0.001 0.001 0.001 0.010 0.001 0.001 0.100 0.010 0.100 0.100 1.000 0.100 1.000 0.010

WH (urban)

PDS (suburban)

SZ (rural)

Min

Max

Mean

SD

Min

Max

Mean

SD

Min

Max

Mean

SD

0.090 N.D N.D 0.090 0.230 0.020 0.030 0.170 0.060 0.140 0.110 0.040 0.020 N.D N.D 0.020 1.020

0.240 0.040 0.220 0.130 0.510 0.060 0.690 0.470 0.330 0.760 1.460 0.400 0.550 0.540 0.090 0.630 7.120

0.170 0.030 0.010 0.080 0.370 0.040 0.440 0.320 0.180 0.430 0.630 0.210 0.200 0.220 0.040 0.260 3.630

0.046 0.007 0.004 0.026 0.089 0.015 0.152 0.126 0.095 0.226 0.498 0.141 0.184 0.186 0.031 0.223 2.049

0.140 0.020 N.D N.D 0.320 0.050 0.560 0.500 0.200 0.360 0.240 0.110 0.060 N.D N.D N.D 2.560

1.130 0.160 0.090 0.520 1.300 0.530 3.250 3.090 1.040 3.970 3.790 1.600 1.820 1.690 0.090 1.040 25.110

0.291 0.060 0.027 0.161 0.610 0.098 1.248 1.080 0.600 1.461 1.452 0.486 0.495 0.397 0.049 0.403 8.918

0.274 0.042 0.025 0.151 0.245 0.130 0.682 0.667 0.300 1.142 1.159 0.318 0.480 0.487 0.055 0.398 6.555

0.107 N.D N.D 0.038 0.157 N.D 0.217 0.150 0.047 0.161 0.037 0.029 0.019 N.D N.D N.D 0.962

0.402 0.314 0.053 0.151 0.581 0.048 0.513 0.525 0.360 0.913 0.623 0.765 0.229 0.577 0.072 0.620 6.746

0.210 0.045 0.016 0.093 0.197 0.029 0.311 0.258 0.122 0.335 0.360 0.209 0.108 0.128 0.017 0.142 2.580

0.087 0.078 0.011 0.031 0.124 0.010 0.104 0.111 0.075 0.195 0.175 0.212 0.063 0.159 0.019 0.170 1.624

ND: not detected. a TEF values are derived from (Nisbet and LaGoy, 1992).

3.2. PM.2.5-bound PAH concentration

procedural blanks, and sample duplicates to eliminate human error and maintain the actual atmospheric environment and accurate concentrations of PAHs. Two field blanks were prepared in each sample site at the same conduction with the sample. The concentrations of PAHs were generally below the limits of detection in the blank assay. Duplicates of every 10 samples were investigated, in which the relative standard deviation must remain at 5%. Furthermore, a method blank is inserted into every 5 samples without probing the target PAHs. The average recoveries of Nap-d8, Ace-d10, Phe-d10, Chr-d12, and Pyr-d10 are 88% ± 11%, 84% ± 38%, 95% ± 38%, 95% ± 14%, and 92% ± 22%, respectively. Minimum detection limits (MDLs) for the 16 PAHs range from 0.001 to 0.009 ng (Table S4).

The concentration and detection frequency of OCPs and PCBs were considerably lower than those of PAHs in PM2.5(Kim et al., 2019). The research in Korea shows that the total concentration of PAHs (13.52 ± 6.62 ng/m3) was significantly higher than OCPs (1.40 ± 1.18 ng/m3) and PCBs (2.99 ± 1.41 pg/m3) (Kim et al., 2019). Yu observed that the concentrations of OCPs in PM2.5 ranged from 29.9 pg/m3 to 103.3 pg/m3(Yu et al., 2019). The samples of PM2.5 in the east China sea has been collected by Ji and the results show that the concentration of OCPs in PM2.5 is very low(Ji et al., 2015).The PAH concentrations in the three sites are shown in Table 1. The average concentration of PM2.5-bound PAH is 3.7 ± 2.05 ng/m3 in WH. Four compounds, namely, Benzo(b)fluoranthene (BbF) (0.626 ± 0.498 ng/ m3), Fluorene (Flu) (0.444 ± 0.152 ng/m3), Chr (0.428 ng/m3), and Phe (0.371 ng/m3), account for approximately 49.1% of the total PAHs. The average concentration of PM2.5-bound PAHs in PDS is 8.36 ± 6.06 ng/m3 while SZ is 2.58 ± 1.62 ng/m³. Compared with other cities in Table S1, the concentrations of the three sites are higher than those in Taiwan (1.7 ng/m3) (Zhu et al., 2019) and Croatian/Zagreb (0.45 ± 0.19 ng/m3) (Pehnec and Jakovljević, 2018). The concentrations are lower than those in China/Wuhan (11.3 ± 10.2 ng/ m3) (Zhang et al., 2019), China/Bei Gangcun (31.3 ± 3.49 ng/m3) (Zhang et al., 2018a), China/Xi'an/Guangyun (67.5 ng/m3), China/ Xi'an/Sanqiao (104.7 ng/m3) (Xu et al., 2016), and China/Shandong (25.9 ± 3.95 ng/m3) (Lu et al., 2016). Obvious trends can be observed from the PAH concentrations in the three sites. Results show that PDS has the highest concentration and SZ has the lowest. The reasons may be the expressway in PDS and the urbanization in WH. The monomeric compounds of PAHs and its daily variation of particles during the sampling period showed that the PAHs in WH are dominated by 3-4-ring (71.0%) (Fig. S1). The major ring of PAHs in PDS and SZ are 4-5-ring, 81.4% and 75%, respectively. The high ring (4-, 5-, and 6-ring) PAHs are likely affected by motor vehicle emissions, industrial activity, and heating combustion (Zhang et al., 2019). PAHs with high molecular weight can combined with particle undemanding, whereas those with low molecular weight are easily volatilized and degraded. The higher Relative Humidity is, the higher concentrations of PM2.5–bound PAHs will be. The increase of temperature and thermal radiation facilitates the gas phase transition of the 2-ring and 3-ring PAHs (Nap, Ace, Acenaphthylene (Acy), Flu, Phe, Anthracene (Ant), Fluoranthene (Fla)). Increasing particle concentrations can be good

3. Results and discussion 3.1. Ambient levels of PM2.5 The descriptive statistics of the observed PM2.5–bound PAH concentrations are presented by Table S1. The average PM2.5 concentration is 63.5 ± 31.8 μg/m3 in WH (average ± standard deviation, which is the same hereafter), 70.4 ± 26.45 μg/m3 in PDS, and 53.8 ± 10.8 μg/m3 in SZ. The PM2.5 concentrations are higher than the daily average air quality standard (75 μg/m3) set by the Ministry of Environmental Protection of China. Comparing urban to urban, and regional to regional, Table S1 shows that the average level of PM2.5 in WH are higher than those of China/Huangshi in 25th Jul — 9th Aug 2013 (52.9 μg/m3) (Hu et al., 2018) and China/Taiwan in Jun 2014 (14.1 μg/m3) (Zhu et al., 2019), but lower than those of WH/China in 12th Jun — 22nd Jul 2014 (Zhang et al., 2019). The results showed that emission reduction measures including optimization energy mix, adjustment of industrial structure, environmental inspection and supervision system (Lv et al., 2019) in WH was effective recent years. When it comes to PDS, the PM2.5 concentrations are lower than those in China/ Bei Gangcun (47.4 ± 4.96 μg/m3) (Zhang et al., 2018a) under the periods of Jul–Aug 2014, but lower than those in China/Xi'an/Guangyun (80.5 μg/m3), China/Xi'an/Sanqiao (85.3 μg/m3) (Xu et al., 2016) during 15th Jun — 21st Jun 2013. The fact in SZ is that the concentrations of PM2.5 exceed those in China/Xiamen in the period of Jul 2013 (22.1 μg/m3) (Zhang et al., 2018b) while below those in China/ Shandong/Yuchen during 6th Jun—29th Jun 2013 (70.1 ± 23.8 μg/ m3) (Lu et al., 2016). 4

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Fig. 2. Correlation between PM2.5 and PAHs concentrations with wind speed and direction.

meteorological monitoring parameters (wind speeds and directions), varying porosity, such as surface area, electrical charge, and elemental mapping were indispensable to accurately describe and predicted PM behaviors(Assaf et al., 2016; Oluwoye et al., 2017).Hence, this is important to characterize PM combined PAHs with morphological, mineralogical and elemental information.

carriers to transport PAHs. In Kong's research, 5-ring or 6-ring PAHs (BaP, BbF, Indeno[1,2,3-cd] pyrene (InP), Dibenz[a, h]anthracene (DBA), and Benzo[ghi]perylene (BghiP)) are gasoline vehicles. In addition, the major source of 3-ring and 4-ring PAHs (Phe, Flu, Pyr, Benzo (a)anthracene (BaA), and Chr) is coal combustion (Kong et al., 2010). The extreme and formidable capture of atmospheric particles leads to the low level in PM2.5 Fig. S1 shows a significant correlation between PM2.5 with PAHs and PM2.5 -bound PAHs are at diverse PM2.5 levels. Fluctuation of PAHs are similar when PM2.5 fluctuates during the sampling period. These findings indicate that the major source of WH is coal combustion. In the meanwhile, PDS and SZ are influenced by gasoline vehicles and coal combustion. What's more, the highest Relative Humidity in PDS is one of the reasons explaining the high PAHs concentrations. The figure below (Fig. 2) illustrates some of the main characteristics of the concentrations of PM2.5 and PAHs under different wind speeds and directions in three places. The most interesting aspect of this graph is high concentration of PM2.5 in three places mainly under low speeds. A comparison of the three places reveals that high PM2.5 in three places always from northwest (NW). The most striking result to emerge from the figure is that PAHs concentrations in under low speeds are higher than those in high speeds. It is apparent from this picture that very few 2- and 5-ring PAHs can be found in WH and SZ. However, 2-ring PAHs can be seen in PDS with high wind speeds from NW and 5-ring is under low wind speeds from southeast (SE). If we now turn to the result of 3-, 4- and 5-ring PAHs, we will find a remarkable outcome that the gap between three places has narrowed. The concentrations mainly came from east, south and west. Together these results provide important insights into that PM2.5 and PAHs come from different directions and under a stable weather condition. The source of the PM was different for various cities, except for

3.3. Source identification 3.3.1. Molecular diagnostic ratio Diagnostic ratios are shown in Fig. 3, including Fla/(Fla + Pyr) versus BaP/BghiP and BaA/(BaA + Chr) versus InP/(InP + BghiP) to discriminate the PAH sources(Qu et al., 2018). In this study, the Fla/ (Fla + Pyr) ratios of 0.50–0.70 indicate that the wood and coal combustion (Hu et al., 2018) lead to the results in the three places. The ratios of Fla/(Fla + Pyr) range from 0.55 to 0.61 in WH, 0.50 to 0.61 in PDS, and 0.49 to 0.60 in SZ. These ratios manifest the effects of coal and wood burning (Li et al., 2016). Meanwhile, the ratio of BaP/BghiP ranges from 0.29 to 3.34 in WH, 0.40 to 0.50 in PDS, and 0.37 to 5.40 in SZ. Results indicate the source of motor vehicles, biomass or coal combustion (Bootdee et al., 2016). Thus, the domination of motor vehicles contributes to PAHs in WH. By contrast, SZ and PDS are influenced by motor vehicles, biomass or coal combustion. When the ratio of BaA/(BaA + Chr) is greater than 0.3, the source of PAHs must be come from combustion (Yunker et al., 2002). According to the result in our article, the numerical value of BaA/(BaA + Chr) is almost less than 0.3. Combustion sources are not abundant in the study area. The ratio of InP/(InP + BghiP) in WH ranges from 0.40 to 0.48, which is generated by petroleum combustion (Yunker et al., 2002). The ratio in PDS ranges from 0.37 to 0.62, which is equally reflected in petroleum, biomass or coal combustion. The ratio in SZ ranges from 5

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Fig. 3. Diagnostic ratios for emission sources of PAHs.

Fig. 4. Results of backward trajectory in WH under different starting height.

vehicles. The PAHs in SZ are influenced by coal, petroleum combustion, and motor vehicles.

0.33 to 0.63, which is also accounted for by petroleum, biomass, or coal combustion. The ratio of BaA/(BaA + Chr) in WH ranges from 0.24 to 0.35, which elucidates that PAHs are from petroleum and combustion (Yunker et al., 2002). The ratio in PDS ranges from 0.17 to 0.53, which indicates that the source of PAHs is a mixture of petroleum and combustion. The ratio in SZ ranges from 0.21 to 0.33, similar to that of WH. To summarize, petroleum and motor vehicles are the main sources in WH, similar to the findings of other studies (Zhang et al., 2019), and PDS is affected by petroleum, coal or biomass combustion, and motor

3.3.2. Principal component analysis and multivariate linear regression More importance should be attached to PCA-MLR because this method can reduce the number of variations and retain the original information. PCA–MLR has high efficiency and is widely used to transform the large variable into a smaller number. This transformation can work with unknown emission sources. Results of PCA–MLR are 6

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Fig. 5. Results of backward trajectory in SZ under different stating height.

distinguished by different colors. The major of the trajectories is below 2000m. As Fig. 4 and Table S6 shows, there is a significant difference between these four groups in WH. The trajectory comes from Hubei who is the local sources accounts for 30.4% of the total air flow with the second PM2.5 concentration at the height of 10 m. The trajectory with highest PM2.5 concentration (134.1 μg/m3) comes from Guizhou but only occupied 8.93% of the total air flow. At the height of 500 m, the largest proportion of the trajectories and PM2.5 come from Hubei. The trajectory originating from Anhui whose concentration of PM2.5 is 60.65 μg/m3 represents 26.8% of the total air flow. The next section was concerned with the height of 1000 m. The most polluted track (72.40 μg/m3) is from Jiangxi and the percentage is 25.0%. The largest number of the trajectories (38.1%) come from Anhui and the concentration of PM2.5 is 65.76 μg/m3. Last but not least, at the height of 2000 m, the contamination is from Hubei. The ratio is 54.8% and the concentration of PM2.5 is 73.30 μg/m3. Turning now to Fig. 5 and Table S7. What is interesting about the result in this figure is that the consequence at the height of 10 m, 500 m, and 1000 m are very similarity. At the height of 10 m, 500 m, 2000 m, the largest percentage and most polluted of the trajectories is form Henan account for 42.19%, 44.79%, 79.32%, and 74.66 μg/m3, 74.46 μg/m3, 74.59 μg/m3, respectively. At the height of 1000 m, the trajectory from Jiangsu provide 42.97% with the total air flow and the concentration of PM2.5 is 74.27 μg/m3. In the final part, the results obtained from the preliminary analysis of Fig. 6 and Table S8. The single most striking observation to emerge from the data comparison is at different height the clusters are 4 which is discrepant with WH and PDS. This result is somewhat counterintuitive that the concentration of PM2.5 at the height of 500 m is much higher than that in other height, especially in medium altitude and high altitude. At the height of 1000 m and 2000 m, the air is always clean. SZ has demanding impact from Hubei at the height of 10 m and 500 m. The ratio of the trajectories is 38.06% and 41.11% whose concentration of PM2.5 is 52.31 μg/m3 and 206.0 μg/m3 respectively. Models of PSCF and CWT identify the potential geographic origins

shown in Fig. S2, and Tables S2 and S3. Results illuminate that the total variability are expressed by the three factors in WH and PDS and four factors in SZ. These factors can explain the variances of 88.68% in WH, 81.02% in PDS, and 88.18% in SZ. According to Fig. S2 and Table S3, the FAC1 in WH predominated by Bghip, DBA, Inp, BaP, BkF, BbF, Chr, BaA, Fla, Ant, and Ace, which are the fingerprints of vehicle emissions, including diesel and gasoline, and coal combustion (Rocha and Palma, 2019). Thus, we speculate that FAC1 is vehicular emissions and coal combustion accounts for 75.7% of the sources. The status of WH as a highly developed and industrialized city is similar to the findings of several studies (Deka et al., 2016) (Deka et al., 2016; Zhang et al., 2019). FAC2 had high Pyr associated with gas combustion contributing 20.1% of the emission. FAC3 without dominant substance declares that the correlation with the mixed emission occupy 4.2% in the original data (Mostert et al., 2010). The FAC1 in PDS and SZ are similar with WH and their FAC1 can be associated with vehicular emissions with 56.9% and 44.9% of the emission, respectively. PDS is mainly influenced by the highway, while SZ is possibly affected by external emissions. In addition, the FAC2 in PDS is abundant in Ant and Flu. This emission is from coal combustion, which represents only 1.1% because coal use decreases during the summer. FAC3 is predominated by Pyr from gas combustion (Oleagoitia et al., 2019) with 42.1%. Gas is widely used in PDS (Feng et al., 2018). Nevertheless, the FAC2 in SZ is from gas combustion with 23.3% and coal combustion with 28.0%. FAC4 reveals that petroleum accounts for 3.8% of the emission. The complex phenomenon in SZ is due to the industrial park near the sampling site. In summary, the main emissions in WH and SZ are vehicular emissions and coal combustion while PDS and SZ are also influenced by gas combustion. 3.4. Potential transport pathway and geographic origins The HYSPLIT model of the NOAA Air Resources Laboratory clusters the trajectories at different starting height of the three areas (Figs. 4–6, and Tables S6 and S7 S8). The height of the trajectories can be 7

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Fig. 6. Results of backward trajectory in PDS under different stating height.

Fig. 7. Incremental lifetime cancer risk (ILCR) of PAHs.

transmission cannot be neglected.

of PM2.5 by analyzing trajectory transport pathways. The WPSCF and WCWT results are displayed in Figs. S3, S4, S5, S6, S7 and S8. Pink indicates serious pollution and blue represents lightly polluted areas. The most surprising aspect of the data in Figs. S3 and S4 is that high scores are adjacent to WH. Figs. S5 and S6 below illustrates some of the main characteristics of the sources in PDS. Most of the pollution are the local sources at all height but some contamination come from SZ, as well. From Figs. S7 and S8 we can see that the low altitude (10 m and 500 m) reported significantly different with the other two groups. The potential polluted area of SZ is Xiaogan and WH at the height of 10 m and 500 m. At the height of 1000 m and 2000m, SZ was subjected to neighboring area. Overall, these results indicate that the sampling places might be affected by the local sources and short distance

3.5. Cancer risk assessment The concentration of BaP ranges from 0.021 to 0.553 ng/m3 in WH, 0.059 ng/m3 to 1.818 ng/m3 in PDS, and 0.011–0.229 ng/m3 in SZ. The average concentrations of these three places are 0.204 ng/m3, 0.495 ng/m3, and 0.102 ng/m3, respectively. These concentrations are lower than the standard stipulated by world health organization (1 ng/ m3). According to the US EPA, the cancer risks are categorized into no risk (ILCR < 10−6) and minimal risk (10−4 < ILCR < 10−6). Fig. 7 shows the results of these three places. The median, percentile 75, percentile 25, maximum, minimum, and average of the ILCR from child 8

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Acknowledgment

to adult are calculated. The cancer risk of ILCRing and ILCRderm varies from 1.00 × 10−8 to 1.00 × 10−6, which is approximately 104 to 105 times more than the risk of ILCRinh. The primary risks of exposure to PM are ingestion and dermal contact, as compared with inhalation. Several differences are observed between child and adult. The risks in the three places also have imparity. The exposure risk of ingestion, dermal contact, and inhalation are all lower than 10−6 in WH. However, a portion of TILCR is higher than 10−6. The concentrations of PAHs in PDS is the highest among the three places. Therefore, the recorded risks of ingestion and dermal contact are higher. The summit reaches 10−5 and TILCR has risk of cancer. Meanwhile, neither ILCR nor TILCR is estimated to be lower than 10−6 in SZ, which means that no cancer risk exists in SZ. The cancer risk of adults is higher than children's in these three places due to frequent exposure and unhealthy lifestyle.

This study was supported by the National Key Research and Development Program of China (No. 2017YFC0212603); the National Natural Science Foundation of China (No. 41773124); the Open Research Program of Laboratory of Basin Hydrology and Wetland Ecorestoration, China University of Geosciences (No. BHWER201503A); the Open Research Fund of Hubei Key Laboratory of Mine Environmental Pollution Control & Remediation, Hubei Polytechnic University (No. 201702); the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUGL170208). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110219.

4. Conclusion

References

This study calculates the concentrations of PM2.5 and PAHs in the 38 air samples from three sites (WH, PDS, and SZ) during summer. The average concentrations of PM2.5 and PAHs are 63.5 ± 31.8 μg/m3 and 3.7 ± 2.05 ng/m3 in WH, 70.4 ± 26.45 μg/m3 and 8.36 ± 6.06 ng/ m3 in PDS, and 53.8 ± 10.82 μg/m3 and 2.58 ± 1.62 in SZ the PAHs in WH are dominated by 3-4-ring (71.0%). The major ring of PAHs in PDS and SZ are 4-5-ring, 81.4% and 75%, respectively. Results of the source apportionment showed that the PAHs in WH probably come from vehicular emissions, coal combustion, and gas combustion. Furthermore, the PAHs in PDS are from vehicular emissions, biomass, or coal combustion. The PAHs in SZ are rooted from vehicular emissions, coal and gas combustion, and petroleum. The overall combustion of fossil is the dominant source of these three places. The HYSPLIT model clusters the trajectories. Models of PSCF and CWT cast the potential origins of PM2.5 and PAHs. The wind from the sea attenuates the concentration of PM2.5. Therefore, the high scores in WH are adjacent to the high scores in PDS. The neighboring areas of Hubei, Suizhou, Wuhan, and Zhejiang affect the score of SZ. External sources predominated in PDS, while WH and SZ are contaminated by local sources and nearby places. In addition, the pollutants in PDS are consequently from WH and SZ. According to the ILCR model, the average total rates of cancer risk in WH, PDS, and SZ are 8.53 × 10−7, 2.26 × 10−6 and 5.60 × 10−7 for children, and 9.46 × 10−7, 2.51 × 10−6, and 6.21 × 10−7 for adults, respectively. Almost 50% are dangerously exposed in WH and approximately 75% are harmfully exposed in PDS. Meanwhile, SZ is substantially the safest place compared with the others. The parameters of the model, with reference to other publications, have a powerful correlation with regional factors, which made a deviation of the consequence compared to the research in the other place. Thus, further research is necessary.

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CRediT authorship contribution statement Xinli Xing: Conceptualization, Methodology, Software, Investigation, Writing - original draft, Writing - review & editing. Zhanle Chen: Methodology, Validation, Formal analysis, Visualization, Investigation, Software, Data curation, Writing - review & editing. Qian Tian: Validation, Formal analysis, Writing - review & editing. Yao Mao: Resources, Writing - review & editing, Supervision. Weijie Liu: Resources, Writing - review & editing, Supervision. Mingming Shi: Writing - review & editing. Cheng Cheng: Writing - review & editing. Tianpeng Hu: Writing - review & editing. Gehao Zhu: Writing - review & editing. Ying Li: Writing - review & editing. Huang Zheng: Writing review & editing. Jiaquan Zhang: Writing - review & editing. Shaofei Kong: Writing - review & editing. Shihua Qi: Writing - review & editing. 9

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