PM2.5-bound potentially toxic elements (PTEs) fractions, bioavailability and health risks before and after coal limiting

Ecotoxicology and Environmental Safety 192 (2020) 110249

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PM2.5-bound potentially toxic elements (PTEs) fractions, bioavailability and health risks before and after coal limiting

T

Jiao-Jiao Xiea, Chun-Gang Yuana,b,∗, Jin Xiea, Xiao-Dong Niua, An-En Hea a Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science & Engineering, North China Electric Power University, Baoding 071000, China b MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China

A R T I C LE I N FO

A B S T R A C T

Keywords: PM2.5 Potentially toxic elements Fraction Bioavailability Health risks

Fractions, bioavailability, health risks of fine particulate maters (PM2.5)-bound potentially toxic elements (PTEs) (Pb, Cd, Cr, Cu and Zn) were investigated before and after coal limiting in Baoding city. The winter PM2.5 samples were collected at different functional areas such as residential area (RA), industrial area (IA), suburb (SB), street (ST) and Botanical Garden Park (BG) in 2016 (coal dominated year) and 2017 (gas dominated year). The fractions and bioavailability of PTEs were determined and evaluated based on BCR sequential extraction. Health risks through inhalation exposure were evaluated by US EPA health risk assessment model. The results from different years and functional areas were compared and discussed. The fractions and bioavailability of PM2.5-bound PTEs varied with functional areas. The percentages of cadmium (Cd) and zinc (Zn) in acid-soluble fraction (F1–Cd and F1–Zn) to the total amount of Cd and Zn were low in BG samples (p < 0.05). Bioavailability of Cd were high in SB samples (p < 0.05). Total contents of PM-bound PTEs in 2017 generally decreased compared with 2016. The differences of fraction and bioavailability between 2016 and 2017 depended on the elements and areas. Higher proportions of copper (Cu) in acid-soluble fraction (F1–Cu) and bioavailability of Cu (p < 0.05) were found in 2017 samples. Significant differences were found just at IA and RA for Pb, Cd and Zn. Our results indicated that the health risks from inhalation exposure for PTEs in PM2.5 declined about 11%–52% after the coal limiting in this city.

1. Introduction Haze pollution occurred in China in recent years. Baoding is one of the central cities in Beijing-Tianjin-Hebei region with heavy haze pollution. The incidences and mortality of lung cancer have risen in recent years in heavily air polluted areas (Liang et al., 2018). A high incidence and mortality of lung cancer is closely related to severe haze pollution (Tie et al., 2009; Hassan et al., 2017). According to the data published by National Environmental Protection Agency of China, more than 70% days suffered in serious pollution (Air Quality Index (AQI) > 100), and PM2.5 was the primary pollutant in winter in Baoding. In order to improve the atmospheric environment quality, the policy “Burning gas instead of coal” (BGiC) was promoted in Hebei and implemented in 2017. About 4000 coal-fired boilers have been eliminated or converted to gas-fired boilers until September 2017 in Baoding city according to the announcement from Baoding Municipal People's Government and

could result in the change of pollution sources. Atmospheric fine particulate matters (PM2.5) can cause many negative influences on people like lung cancer, heart diseases, premature death, etc (Samoli et al., 2014; Lelieveld et al., 2015). PTEs were regarded as the important toxic components and big contributions to these adverse health effects (Bollati et al., 2010; Zeng et al., 2016). Some studies have pointed the PTEs pollution caused by atmospheric PM2.5 was serious in Baoding city (Wang et al., 2017; Liang et al., 2019). But these studies just investigated total concentration of PTEs, their fractions, bioavailability and health risks were absent. As we all know, toxicities of PTEs not only relate to the total amount, but also relate to the bioavailability and chemical forms of PTEs (Yuan et al., 2004; Yuan, 2009; Jan et al., 2018). The fractions of PTEs were related to the component of PM2.5 and the pollution sources (He et al., 2020). Coal burning was usually considered as the main pollution source of atmospheric PM2.5. Thus, the contents and fractions of PTEs in PM2.5

∗ Corresponding author. Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science & Engineering, North China Electric Power University, Baoding, 071000, China E-mail address: [email protected] (C.-G. Yuan).

https://doi.org/10.1016/j.ecoenv.2020.110249 Received 28 July 2019; Received in revised form 10 January 2020; Accepted 22 January 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.

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quartz microfiber filters (QMA, Ø = 90mm, Whatman) were used in this study. The sampling was started at 9:00 a.m. every sampling day and lasted for 23 h with 100 L/min of the flow rate. All filters were dried at 100 °C for 1 h, placed in a desiccator for 24 h and weighted before sampling. After sampling, all filters were equilibrated moisture for another 24 h in the same desiccator and weighted again. Eight -PM2.5 samples were collected at five sampling sites each year, and the total sampling number was eighty. A quarter of filter was cut before digestion and extraction. Three balanced filters without particles were used as blank samples for background check.

might be changed after the BGiC. Therefore, it is necessary to compare their differences before and after the BGiC. Furthermore, the primary pollution sources were different in different functional areas. Investigating the atmospheric PM2.5-bound PTEs in different functional areas can give more complete information about the pollution situation in a city. It is interesting to investigate the spatial distribution of PM2.5bound PTEs before and after coal limiting. At present, in vivo test, in vitro test and sequential extraction are the most widely used methods to estimate the bioavailability of PTEs in solid matrix (Yuan et al., 2010; Huang et al., 2018a). Compared to in vivo test and in vitro test, BCR sequential extraction method (BCR) is more practical, efficient and convenient for our daily work (Li et al., 2015a; Xie et al., 2019b). BCR method has been widely applied in many previous studies (Bielicka-Giełdoń et al., 2013; Xie et al., 2017). According to this scheme, PTEs are divided into four fractions (F1–F4), and the first two fractions (F1 and F2) are regarded as the bioavailable fractions (Nemati et al., 2011). Health risk assessment model promoted by US Environmental Protection Agency (EPA) has been widely accepted (Bolan et al., 2017; Xie et al., 2019a). The model includes some parameters like exposure concentration (EC) (μg/m3), chemical daily intake (CDI) (mg/kg/day) and dermal absorption dose (DAD) (mg/kg/day), Hazard Quotient (HQ), Hazard Index (HI) and carcinogenic risk (CR) (US EPA, 1989; 2009, 2013) (Xie et al., 2019a). EC, CDI and DAD are used when the exposure risk occurs through inhalation, ingestion and dermal adsorption, respectively. HQ and HI present the non-carcinogenic exposure risk, while CR refers to the carcinogenic exposure risk. It's cold and dry with static/stable weather days in winter in Baoding city (Li et al., 2015b; Gao et al., 2018; He et al., 2019; Xie et al., 2019c). Haze pollution always becomes more serious in winter (Xie et al., 2019a). Coal combustion has been limited in 2017 in order to improve air quality in Baoding. In this study, PM2.5 was sampled from five functional areas in Baoding in Dec 2016 and Dec 2017. The PM2.5-bound PTEs (fractions, bioavailability and health risks) were studied. The purposes of this study are: (1) study the fractions, bioavailability and health risks of PM2.5-bound PTEs (Pb, Cd, Cr, Cu and Zn) from five functional areas; (2) to compare the variation of the fraction and health risks of PM2.5-bound PTEs before and after coal limiting. Our research will provide some useful information for the study of air pollution control policy in the future.

2.2. Total digestion and analysis The total concentrations of PTEs in solution were detected by an inductively coupled plasma mass spectrometer (ICP-MS) (7700cs, Agilent Technologies, Palo Alto, CA). 100 μg/L of 115In was used as the internal standard. Before determination, the solutions were refined through 0.22 μm water nylon filters. In order to get the total concentrations of PM2.5-bound PTEs, each cut subsample and 4.0 mL of concentrated HNO3, 1.0 mL of H2O2 and 1.0 mL of HF were added into a Teflon container and digested. The microwave digestion procedure was: Stage-1, heating to 100 °C over 5 min and keeping for 1 min; Stage-2, heating to 140 °C over 4 min and keeping for 1 min; Stage-3, heating to 180 °C over 10 min and incubating for 15 min. The solutions were concentrated through evaporation and then diluted to 50 mL in colorimetrical tubes after digestion. At the same time, a certified reference material (GBW-07405) was disposed to test the loss of PTEs during the procedure. The recoveries of PTEs for total analysis ranged from 95.5% to 106.7% (n = 3), which showed that the procedure was accurate enough in this study. Here, the recovery means the detection value to the reference value. 2.3. The procedure of BCR The fraction distributions of PTEs in PM2.5 were studied using the BCR sequential extraction method. The details of BCR method were shown in Table S1 (Supporting Information). After each step, the mixture was centrifuged for 15 min at 4000 rpm and then the solution was transferred to a clean tube, while the residue was used for the next step. After each step, wash step was also applied. The residue fraction was extracted using 5.0 mL of aqua regia. In this study, the recoveries of Pb, Cd, Cr, Cu and Zn were in the ranges of 87.65–104.48%, 85.38–103.56%, 81.26–95.64%, 90.02–104.53% and 91.17–105.34%, respectively. The results meant the BCR sequential method was suitable to study the fractions of PTEs in PM2.5. The recovery means the sum of heavy metals in five fractions divided by the total amount of the heavy metals from digestion.

2. Material and method 2.1. Locations and sampling As shown in Fig. S1, five sampling sites, residential area (RA), industrial area (IA), suburb (SB), street (ST) and Botanical Garden Park (BG), were chosen in this study to collect the PM2.5 samples. Eighty samples were obtained during sixteen days before (Dec 2016) and after (Dec 2017) coal limiting. In 2016, the sampling dates were Dec 16, Dec 17, Dec 19, Dec 21, Dec 22, Dec 24, Dec 27, Dec 28. In 2017, the sampling dates were Dec 16, Dec 19, Dec 20, Dec 21, Dec 22, Dec 25, Dec 28, Dec 30. RA site was placed on a roof of a residential building in a community (115°522′N, 38°880′E) where was no factories within 2 km and was about 500 m far from the traffic streets. IA site was set on a roof of a building (115°452′N, 38°889′E), which was nearby many plants including power plant, chemical factories and printing plants, etc. SB site (115°544′N, 38°830′E) was about 15 km far from the city center and in a village. ST site (115°509′N, 38°882′E) was located beside one street with heavy traffic. The sampler was placed on about 5 m beside the street. Botanical Garden Park (115°491′N, 38°925′E) (BG) was another site, where the important pollution source was natural source like soil, dust, etc. Five samplers worked simultaneously every day during the sampling period. Intelligent air suspended particle samplers with medium flow rate (TH-150C, Wuhan Tianhong Instruments Co., Ltd., Wuhan, China) and

2.4. Estimated bioavailability PTEs in different fractions (F1–F4) indicated different bioavailability and toxicity (Xie et al., 2019b). Bioavailability factor (BF) (Xie et al., 2019b) can be estimated by equation (1) based on BCR method. 4

BF = (F1 + F2)/ ∑ Fi 1

(1)

where Fi is the content of PTE in Fi. 2.5. Health risk assessment Inhalation is the most important exposure route to PM2.5 compared with ingestion and dermal absorption. Health risks of PM2.5-bound PTEs through inhalation were calculated using the models mentioned above (US EPA, 2009). HI was applied to estimate the non-carcinogenic risk (NCR) induced by mix exposure. The risk occurs when the HI below 2

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1.0. There is cancer risk when CR is higher than 1 × 10−4 but it doesn't exist when CR is lower than 1 × 10−6. The relevant calculations show as following (2)–(5) (US EPA, 1989; 2009; Xie et al., 2019a).

EC = (C×EF × ET×ED)/ATn

(2)

HQ = EC/(RfCi×1000μg/mg)

(3)

HI  =

∑ HQ

CR = IUR× (C× EF × ET×ED)/ATc

(4) (5)

where C is the total content of PTE in the bioavailable fractions (μg/ m3). The rest of the parameters in equations and their values of each element were shown in Tables S2 and S3. 2.6. Statistics analysis Statistical analysis was accomplished by Excel (2010) (Microsoft Corporation., Redmond, WA, USA) and Origin 8.0 (OriginLab Corporation., Northampton, MA, USA). The differences of years and functional areas were tested by t-test. 3. Results and discussion 3.1. Total contents of PTEs Five PTEs were studied, including Pb, Cd, Cr, Cu and Zn, because they are the elements in the priority control list with potentially higher toxicity. The concentrations of these PTEs in each functional area were shown in Fig. 1, Table S4. Zn was the most abundant element in atmospheric PM2.5 in both five functional areas with the concentration range of 141.7–1279.2 ng m−3. Pb or Cu was the next followed by Cr and Cd. The concentrations of Cd in PM2.5 during sampling periods ranged from 0.7 ng m−3 to 26.4 ng m−3. The highest concentration of Pb was 814.5 ng m−3 which occurred at SB in 2016. The concentration of Cu ranged from 44.9 ng m−3 to 623.7 ng m−3 with an average value of 251.9 ng m−3 during the entire sampling period. The concentrations of Zn, Cd, Cr and Pb in PM2.5 at SB were higher than those at BG, IA, ST and RA. The concentration of Zn at SB was obviously higher than those at IA (p = 0.01), ST (p = 0.028) and RA (p = 0.01) in 2016, while in 2017, the significant higher concentration of Zn also was found at BG than those at IA (p = 0.03) and ST (p = 0.02). At BG, the use of fertilizer contained Zn probably was the main reason of higher Zn level. The concentration of Cd in SB samples was obviously higher than those in IA samples (p = 0.02) and ST samples (p = 0.03). The concentrations of Pb in PM2.5 samples at SB also were significantly higher than those at IA (p = 0.04). Coal burning was considered as the main source of Zn, Cd, Cr and Pb in atmospheric PM2.5 (Huang et al., 2018b; Wang et al., 2018). Compared with IA, although less coal consumed at SB, coal burning provided bigger contributions to atmospheric PM2.5, probably because of the insufficient combustion of small boiler and without any end-of-pipe controls (Liu et al., 2016). Furthermore, desulphurization, denitrification and dust removal in factories and power plants can remove some PTEs and bring about the lower concentration of PTEs in PM2.5 samples at IA. The concentrations of Cu at ST were higher, while these at SB, BG, RA and IA were lower during the sampling periods. Significantly higher concentrations of Cu in PM2.5 samples were found at ST than those at BG (p = 0.02), SB (p = 0.02) and RA (p = 0.02) in 2016. While in 2017, the concentrations of Cu at ST were obviously higher than those at BG (p = 0.02), SB (p = 0.001) and IA (p = 0.03). Furthermore, the concentrations of Cu were obviously higher in IA samples than those in SB samples (p = 0.03). Cu has been considered mainly from tire abrasion, vehicle emission and fuel combustion (Bolan et al., 2017; Jiang et al., 2017; Liu et al., 2018). The atmospheric PM2.5 in ST mainly comes from road dust and automobile exhaust. The huge traffic volume

Fig. 1. Total concentration of PTEs at different functional areas in 2016 and 2017 (a: Pb, Cu, Zn; b: Cd, Cr).

at ST also made a higher tire abrasion and corrosion of vehicular parts, which may lead to high concentration of Cu. Most of the concentrations of PTEs in PM2.5 samples in 2017 were reduced compared to those in 2016, resulting from the decreasing of PM2.5 concentration. The average concentrations of PM2.5 in Baoding city in December 2016 and December 2017 were 190.13 and 98.42 μg m−3 according to the data of National Environmental Monitoring Center of China. Compared with 2016, the concentration of PM2.5 was significantly decreased (p = 0.0001) in 2017 (Fig. S2), which indicated that BGiC made a great contribution to the decrease of PM2.5 pollution. The concentrations of Pb significantly decreased at SB (p = 0.02), IA (p = 0.04) and RA (p = 0.01) in 2017. The concentrations of Cd in PM2.5 obviously decreased at SB (p = 0.01) and IA (p = 0.01) in 2017. The replacement of coal by clean natural gas resulted in the lower concentrations of PTEs in 2017. PTEs reduction suggested that limiting coal utilization was an effective way to control atmospheric pollution. However, there was no significant decrease in 2017 for the concentrations of Zn and Cr at RA, ST and BG. The main pollution sources of PM2.5 at RA were vehicle emission and domestic cooking fumes. Cr was the main PTE in PM2.5 from domestic Chinese cooking (Zhang et al., 2017). Zn was one of the main elements from vehicle emission (Arhami et al., 2017). Furthermore, the liquefied petroleum gas was the main energy source for cooking at RA and central heating was applied with lower air pollution because of the endof-pipe controls. The main pollution sources at ST and BG were not coal combustion but vehicle emission and natural sources like soil, dust and the activity/metabolism of animals and vegetations, while the pesticides, fertilizers used to maintain the garden at BG were also the main 3

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Table 1 Mass concentrations of PTEs in PM2.5 at different functional areas in 2016 and 2017 (mg g−1 for Cr, Cu, Zn, Pb; μg g−1 for Cd). 2016

BG

SB

IA

ST

RA

a b

Min Max Av ± SDa RSDb Min Max Av ± SDa RSDb Min Max Av ± SDa RSDb Min Max Av ± SDa RSDb Min Max Av ± SDa RSDb

2017

Cr

Cu

Zn

Cd

Pb

Cr

Cu

Zn

Cd

Pb

0.20 0.65 0.39 38.9 0.21 0.93 0.46 50.5 0.26 0.63 0.41 31.8 0.22 0.62 0.36 35.8 0.29 0.70 0.46 34.6

0.55 1.51 0.93 32.3 0.27 0.97 0.66 33.8 0.40 1.37 0.89 37.1 0.92 2.79 1.71 34.1 0.57 1.50 0.97 37.6

2.49 7.26 4.14 34.3 2.79 6.22 4.22 24.7 2.22 3.55 2.64 16.4 1.79 5.89 3.52 40.7 2.22 5.56 3.83 28.9

23.3 61.2 41.8 ± 34.1 29.8 93.3 59.5 ± 35.8 19.4 58.1 34.9 ± 37.3 18.7 62.4 40.3 ± 40.8 26.0 100.5 52.8 ± 47.6

0.65 1.45 1.08 30.3 1.09 2.88 1.72 36.4 0.63 1.39 1.11 25.2 0.74 2.19 1.27 43.2 1.13 2.31 1.51 28.7

0.13 0.92 0.44 57.3 0.21 0.63 0.42 38.1 0.18 1.58 0.62 77.7 0.22 0.50 0.35 26.0 0.19 1.53 0.58 78.5

0.31 2.50 1.16 55.8 0.33 1.18 0.61 58.8 0.75 1.76 1.16 32.5 1.24 3.10 1.65 36.4 0.54 1.78 1.18 33.4

2.67 8.30 4.53 37.4 2.35 7.78 3.81 45.8 1.72 6.12 2.88 48.0 1.30 3.40 2.49 26.6 1.05 6.22 3.94 43.0

20.6 71.1 37.3 42.4 17.5 65.9 34.1 50.5 13.4 35.9 23.6 37.6 10.3 50.4 25.6 49.5 3.42 56.4 36.9 51.1

0.64 1.39 0.94 26.1 0.57 1.73 1.08 43.4 0.63 1.83 1.06 45.9 0.59 1.64 0.87 40.5 0.38 1.28 0.96 30.2

± 0.15

± 0.23

± 0.13

± 0.13

± 0.16

± 0.30

± 0.22

± 0.33

± 0.58

± 0.37

± 1.42

± 1.04

± 0.43

± 1.43

± 1.11

14.3

21.3

13.0

16.4

25.1

± 0.33

± 0.63

± 0.28

± 0.55

± 0.43

± 0.25

± 0.16

± 0.48

± 0.09

± 0.46

± 0.65

± 0.36

± 0.38

± 0.60

± 0.39

± 1.70

± 1.75

± 1.38

± 0.66

± 1.69

± 15.8

± 17.2

± 8.87

± 12.7

± 18.9

± 0.25

± 0.47

± 0.49

± 0.35

± 0.29

SD means standard deviation. RSD means relative standard deviation, RSD (%) = SD ÷   Average. × 100 .

pollution sources of heavy metals. Therefore, it was not difficult to understand why there was no obvious decrease at those areas in 2017 after limiting coal burning. The mass concentrations of PM2.5-bound PTEs samples were calculated and detailed in Table 1. Most of the mass concentrations of PTEs at different functional areas decreased in 2017. The significant decreases were just found at SB for Pb (p = 0.02) and Cd (p = 0.01), and it was not found at the other functional areas. In contrast, the concentrations of Cr, Cu and Zn at BG, IA and RA increased a little bit in 2017, but not obviously (p > 0.05). This meant that the significant decrease of total PTEs concentrations mainly resulted from the decrease of PM2.5 (decreased 48.2% in December 2017). The significant decreases at SB for Pb and Cd could give an indicator that coal burning was the important pollution source of Pb and Cd at SB. But at the other areas (BG, IA and RA), coal burning was the main pollution source of PM2.5, but probably not the main pollution source of Cr, Cu and Zn. The conclusive reasons for that need much more studies to support in the future.

Fig. 2. BF and species distribution of Cd in PM2.5 samples at different functional areas in 2016 and 2017.

3.2. Fraction distribution and bioavailability of PTEs 3.2.1. Spatial distribution of fractions The fraction distributions of PTEs were pictured in Figs. 2–4 and Figs. S3–S4. Most of Pb, Zn and Cd elements in PM2.5 samples were distributed in F1 and took percentages of 64.6–98.8%, 46.4–94.9% and 40.6–95.9%, respectively. A large percentage of Cr existed in F3 and took an average percentage of 68.5%. High percentage of Cu accumulated in F1 and F3 and accounted for 38.1–57.0% and 20.9–44.4%, respectively. The main species of F1–Cu were CuSO4 and Cu(NO3)2 in particles (Huang et al., 2018a) and probably came from the second particulate formation. These species of Cu were easily to be dissolved in water solution at low pH and presented with high bioavailability. Cu has strong affinity with organic particles and its coating (Thuong et al., 2015), and has higher complexation stability constant than other heavy metal elements (Li et al., 2001), which resulted in higher percentage of F3–Cu. Thus, most of F3–Cu probably came from the primary particles like soil, dust, coal burning and combined with organism and existed as complex states. The percentages of F1–Cd and F1–Zn were significant lower (p < 0.05) in BG samples, while, F2–Zn was significant higher at BG than those at RA (p = 0.02) and ST (p = 0.02). F1–Cd took a larger

Fig. 3. BF and species distribution of Cr in PM2.5 samples at different functional areas in 2016 and 2017.

4

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was obviously higher than those at BG (p = 0.04 in 2016, p = 0.02 in 2017), which could because that the main pollution source of Cr in RA was domestic cooking as mentioned above, while it was natural source for the BG samples. The BF of Cd was significantly lower (p < 0.05) in BG samples, while higher in SB samples (p < 0.05). Coal burning was the most important source of Cd, and high temperature and high concentrations of sulfur, nitrogen and ammonia during coal burning probably lead to many chemical reactions, which can make Cd transport easier. Furthermore, The BFs of Cd in SB were higher than those in IA, indicating low-rank coal combustion without any tail gas treatment probably cause higher toxicity of PTEs. The bioavailability of Pb in IA and SB samples were significantly higher than those in BG samples (2016), ST samples (2017) and RA samples (2017) (p < 0.05). The results indicated that the toxicity of Pb at IA and SB were higher (Tan et al., 2006). Bioavailability of Cu obviously increased in 2017 than 2016 except at BG (p = 0.00002 at RA, p = 0.002 at ST, p = 0.000006 at IA, p = 0.007 at SB). In 2017, the bioavailability of Pb (p = 0.02) and Cd (p = 0.007) increased obviously at IA, while the Zn bioavailability decreased obviously (p = 0.006) at RA. Comparing the fraction distribution of PTEs in 2016 and 2017, we found that the variation was related with the elements and areas.

Fig. 4. BF and species distribution of Zn in PM2.5 samples at different functional areas in 2016 and 2017.

percentage in SB samples than those in the other samples indicated that the more soluble fractions existed in the particles.

3.3. Health risks of PTEs

3.2.2. Temporal distribution of fractions Proportions of F1–Pb were obviously higher (p = 0.01) in IA samples in 2016, while the proportions of F2–Pb were obviously lower (p = 0.02). The percentages of F1–Cr in IA samples were lower than those in SB samples in both 2016 and 2017. The results might because that the F1–Cr (acid-soluble fraction) was eliminated during the flue gas desulphurization in factories at IA, whose pH ranged from 5.5 to 6.0. The percentages of F1–Cu in BG and RA samples were lower than those in the other functional areas. The F2–Cu in SB samples took a larger proportion, while F3–Cu in SB samples took a lower percentage. In IA, the percentages of F1–Cd and F2–Pb in 2017 were significant higher (p = 0.01) than that in 2016, while, the percentages of F1–Pb in 2017 were obviously lower (p = 0.01) than that in 2016. The obvious differences of Zn between 2016 and 2017 were just found at RA (p = 0.007 for F2–Zn, p = 0.02 for F3–Zn). The percentage of F2–Zn and F5–Zn decreased but the percentage of F3–Zn increased in 2017. Considering that coal burning was the main pollution source of Pb and Zn, these variations mentioned above possibility ascribed to the limitation of coal combustion. The percentages of F2–Cr in 2016 were obviously higher (p = 0.03) than those in 2017, while the percentages of F3–Cr in 2016 were obviously lower (p = 0.01) than those in 2017. The percentage of F1–Cu in 2017 were obviously higher (p = 0.04 at BG, p = 0.01 at SB, p = 0.001 at IA, p = 0.007 at ST, p = 0.00002 at RA) than those in 2016, which was opposite with F3–Cu.

For non-carcinogenic risk assessment via inhalation exposure, the EC, HQ and HI of Pb, Cd, Cr, Cu and Zn in each functional area's PM2.5 samples were detailed in Fig. 5 and Table S5. The HQ values for a single heavy metal (Pb, Cd, Cr, Cu and Zn) ranged from 1.16E-03 to 1.14. Most of the values were lower than the safe criterion excepted for the Cd in SB samples in 2016, whose HQ value was 1.14. Most of the NCR values of Cd were 10–100 times higher than the other PTEs. The integrated non-carcinogenic risk or HI values were ranged from 4.46E-01 to 1.42, and the highest value was occurred in SB in 2016. These results indicated that there was non-carcinogenic risk from inhalation for people living in SB. In 2016, the integrated NCR values from Pb, Cd, Cr, Cu and Zn in descending order were SB, RA, ST, IA, BG, while in 2017, it was SB, RA, BG, ST, IA. Overall, the non-carcinogenic risk of Cd via inhalation exposure was the most serious, and people in SB suffered to the highest integrated non-carcinogenic risk from inhalation exposure. For cancer risk assessment via inhalation exposure, the CR values of Pb, Cd, Cr, Cu and Zn in each functional area's PM2.5 samples were detailed in Fig. 6 and Table S6. It should be mentioned that the CR of Cr is probably not accurate enough, because only Cr (IV) is classified as

3.2.3. Spatial and temporal distribution of bioavailability The bioavailability (BF value) of Pb (89.3–97.3%), Zn (81.4–93.4%) and Cd (67.4–97.9%) were higher, followed by Cu (53.2–79.1%) and Cr (5.8–17.9%). According to the bioavailability (BF value), PTEs in the environment can be classified into three grades: bioavailable element (BF > 60%), potential bioavailable element (60% ≥ BF ≥ 20%) and unbioavailable element (BF < 20%) (Fang et al., 2015). Pb, Zn and Cd in PM2.5 samples were bioavailable element, indicating they were easy to transform and transport in ecological environment and pose health risks to human beings. Cu in PM2.5 samples was regarded as the potential bioavailable element, which meant that it could be released when the environmental conditions (pH, temperature, etc.) were changed. Cr could be regarded as an unbioavailable element and should be difficult to transfer to organism in the environment. The bioavailability of Cu in SB samples was significant higher (p < 0.05) than those at the other functional areas, which indicated more soluble species like CuSO4 and Cu(NO3)2 might take a large percentage in the PM2.5 samples (Huang et al., 2018a). The bioavailability of Cr at RA

Fig. 5. Non-carcinogenic health risk of PTEs in PM2.5 samples at different functional areas in 2016 and 2017 (the unit of Cd is 1, the unit of Pb and Cr was 10−1, the unit of Cu and Zn was 10−2). 5

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4. Conclusion Fractions, bioavailability, health risks of PM2.5-bound PTEs (Pb, Cd, Cr, Cu and Zn) and their spatial distribution were investigated in Baoding city in December 2016 (coal dominated year) and December 2017 (gas dominated year) at different functional areas, including residential area (RA), industrial area (IA), suburb (SB), street (ST) and Botanical Garden Park (BG). The fractions of PTEs were determined using BCR sequential extraction and their bioavailability was also estimated. Health risk from inhalation exposure was calculated by US EPA health risk assessment model. Zn was the most abundant element in PM2.5 followed by Pb, Cu, Cr and Cd. Pb, Zn and Cd elements in PM2.5 samples were bioavailable elements and mainly distributed in F1. Cu was a potential bioavailable element and mainly accumulated in F1 and F3. Cr was an unbioavailable element and mainly existed in F3. The concentrations of Zn, Cd, Cr and Pb were high in the SB samples, while the concentrations of Cu were high in the ST samples. The fractions and bioavailability of PM2.5-bound PTEs varied obviously with functional areas. Percentages of F1–Cd and F1–Zn were lower in the BG samples (p < 0.05). Bioavailability of Cd was high in the SB samples and low in the BG samples (p < 0.05). The total concentrations of PM-bound PTEs in 2017 generally decreased, while there was no significant decrease for Zn and Cr at RA. The variation of fraction and bioavailability between 2016 and 2017 was related with elements and areas. Obviously high proportions of F1–Cu and bioavailability of Cu (p < 0.05) in 2017 samples were found. However, the significant variation was just found at IA and RA for Pb, Cd and Zn, respectively. The integrated non-carcinogenic risks ranged from 4.44E-01 to 1.42. The combined carcinogenic risks ranged from 5.10E-06 to 2.14E-05 for children and 2.04E-05 to 8.57E-05 for adults, which indicated that the carcinogenic risk was possible. Health risk at SB was higher than that at the other functional areas. The health risk from inhalation exposure for PTEs in atmospheric PM2.5 declined about 11%–52% after limiting the coal combustion, especially at SB area.

Fig. 6. Carcinogenic health risk of PTEs in PM2.5 samples at different functional areas in 2016 and 2017 (the unit of Cr (Adult) is 10−5, the unit of the other items is 10−6).

carcinogenic. When the bioavailable Cr was applied, there must be some deviation between the concentration of Cr (IV) and bioavailable Cr. The cancinogenic risks of Cr to both child and adult were higher, followed by Pb and Cd. The CR values of Pb, Cd and Cr were higher in SB than the other functional areas. The combined CR values ranged from 5.10E-06 to 2.14E-05 for children and 2.04E-05 to 8.57E-05 for adults. The values were lower than 1.00E-04 but higher than 1.00E-06 indicated that the Ccancer risk of Cd, Pb and Cr via inhalation were possible. The results mean that about 6–22 children and 21-86 adults may get cancer in every million children and adults, respectively. Overall, the cancer risk of Cr via inhalation exposure was the most serious. People in SB also suffered the highest non cancinogenic risk from inhalation exposure. We also compared the difference of health risk between 2016 and 2017 and found that it declined about 11%–52% after limiting the coal combustion. For NCR, the HI value at SB fell furthest, about 52%, followed by ST (38%), IA (34%), RA (31%) and BG (15%). In terms of each element, the HQ of Pb, Cd, Cr and Zn declined about 17–51%, 11–53%, 2–50% and 12–32%, respectively. The NCR of Cu also decreased at SB (10%), but increased at other functional areas. The risks of Pb, Cd and Cr declined most obviously at SB, while Zn declined most obviously at ST. The HQ of Pb, Cd and Zn decreased slightly at BG, while Cr decreased slightly at RA. For CR, the combined risk declined about 11%–51% after limiting the coal combustion. The maximum decrease value occurred at SB, while the minimum was at RA. In terms of each heavy metal (Pb, Cd and Cr), the situation was the same as the NCR. The non-cancer and cancer risks from inhalation exposure for PTEs in PM2.5 at SB were higher than those at the other sampling sites, and they decreased after limiting the burning of coal. We could conclude that the health risk occurred by low-rank coal combustion without any tail gas treatment was the most serious. Thus, decreasing the low-rank coal combustion, reducing the unorganized emissions, promoting center heating and increasing the tail gas treatment would be effective ways to reduce the health risk of PTEs in atmospheric PM2.5. It should be mentioned that the CR assessment and NCR assessment in this study were just occurred by inhalation exposure and Pb, Cd, Cr, Cu and Zn. As we all know, the health risks can also be posed by ingestion exposure and dermal exposure. Furthermore, the toxic components in PM2.5 are not only the studied elements in this study but include the other toxic elements and organic compounds like As, Hg, polycyclic aromatic hydrocarbons (PAHs), etc. Overall, it must be mentioned that the health risk assessment based on the studied elements in this study was limited and underestimated.

Credit author statement Jiao-Jiao Xie, Methodology, Formal analysis, Investigation, Data curation, Writing - Original Draft. Chun-Gang Yuan, Conceptualization, Writing- Reviewing and Editing, Methodology, Supervision, Project administration, Funding acquisition. Jin Xie, Investigation. Xiao-Dong Niu, Investigation. An-En He, Investigation. Declaration of competing interest None. Acknowledge This work was kindly funded by the National Natural Science Foundation of China (91543107) and the Fundamental Research Funds for the Central Universities (2017ZZD07, 2017XS126). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110249. References Arhami, M., Hosseini, V., Shahne, M.Z., Bigdeli, M., Lai, A., Schauer, J.J., 2017. Seasonal trends, chemical speciation and source apportionment of fine pm in Tehran. Atmos. Environ. 153, 70–82. Bielicka-Giełdoń, A., Ryłko, E., Żamojć, K., 2013. Distribution, bioavailability and fractionation of metallic elements in allotment garden soils using the BCR sequential extraction procedure. Pol. J. Environ. Stud. 22, 1013–1021. Bolan, S., Kunhikrishnan, A., Seshadri, B., Choppala, G., Naidu, R., Bolan, N.S., Ok, Y.S.,

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