Incorporating bioaccessibility into health risk assessment of heavy metals in particulate matter originated from different sources of atmospheric pollution

Environmental Pollution 254 (2019) 113113

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Incorporating bioaccessibility into health risk assessment of heavy metals in particulate matter originated from different sources of atmospheric pollution* Xinlei Liu a, Wanyue Ouyang a, Yiling Shu a, Yingze Tian b, Yinchang Feng b, Tong Zhang a, *, Wei Chen a a College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, Tianjin, 300350, China b College of Environmental Science and Engineering, State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Nankai University, Tianjin, 300350, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 June 2019 Received in revised form 28 July 2019 Accepted 24 August 2019 Available online 26 August 2019

Rapid industrialization and urbanization have resulted in widespread pollution of airborne particulate matter (PM) containing various heavy metals with adverse human-health effects. Health risk assessment of PM calls for accurate evaluation of the bioaccessibility, instead of the total content, of heavy metals in PM. Here, we demonstrated that the leachable fraction of particle-bound As, Pb, Cr, Mn, Cd, Cu, Ni and Zn in lung fluid within the typical retention duration of particles in human lungs varied drastically among particles originated from different air pollution sources, including coal combustion, biomass combustion, fugitive dust, road dust, construction dust, cement and soil. Moreover, bioaccessibility of heavy metals, particularly in biomass combustion, cement and soil particles, was strongly dependent on pollution sources, and the particulate Cu, Ni, Pb and Cd appeared to be the primary indicators of the source dependence of heavy metal bioaccessibility. Using total rather than bioaccessible concentrations of particle-bound heavy metals not only led to overestimation of the health risk of source particles, but more importantly, inaccurate identification of the high-risk pollution sources and the priority metal pollutants in the source particles. When considering bioaccessibility of particle-bound heavy metals examined in this study, coal combustion products exhibited the highest carcinogenic and noncarcinogenic risks among all source particles, whereas cement particles would be the source with highest risk based on total metal content. As and Mn appeared to be the main drivers for the noncarcinogenic risks of source particles, while As, Ni and Cr were the major contributors to the carcinogenic risks of source particles, significantly different from those based on total contents. This research underlines the importance of incorporating bioaccessibility into health risk indexes of frequently occurring particlebound heavy metals from specific air pollution sources, which will facilitate risk-based assessment of source contribution and hence effective source regulation of airborne PM. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Airborne particulate matter Heavy metal Bioaccessibility Pollution source Health risk assessment

1. Introduction Airborne particulate matter (PM) has received worldwide attention since it is one of the main causes for haze episode occurring frequently in recent years (Lelieveld et al., 2015; Underwood, 2017). Airborne PM tends to accumulate harmful

* This paper has been recommended for acceptance by Prof. Wen-Xiong Wang. * Corresponding author. E-mail address: [email protected] (T. Zhang).

https://doi.org/10.1016/j.envpol.2019.113113 0269-7491/© 2019 Elsevier Ltd. All rights reserved.

substances (e.g., heavy metals, polycyclic aromatic hydrocarbons and viruses) and breathing in such PM leads to adverse health effects to humans (Cheng et al., 2013; Kim et al., 2015). In fact, heavy metals in PM are deemed to be the determining causes of a range of diseases (Franklin et al., 2008; Kodavanti et al., 1997; Pritchard et al., 1996). For example, Heinrich et al. (1999) examined the effects of air pollution on prevalence rates of respiratory and allergic diseases in school-age children, and discovered that children living in the city that was impacted by PM bearing high-concentrations of heavy metals had a 50% higher lifetime prevalence of allergies,

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eczema, and bronchitis and about two times the level of respiratory symptoms including wheeze, shortness of breath, and cough compared to children from the control groups (Heinrich et al., 1999). Chronic exposure to PM-associated heavy metals, such as Ni, Cr and Cd substantially compromised lung functions (Rastogi et al., 1991), and resulted in symptoms ranging from asthma, emphysema to lung cancer (Kuo et al., 2006; Nawrot et al., 2006). In addition to respiratory diseases, inhaling PM-associated heavy metals (e.g., Pb and As) may cause other adverse effects, such as neuropathies, increased blood pressure and anaemia, kidney damage and renal cancer (Dai et al., 2015; Kampa and Castanas, 2008). Hence, the toxic effects of heavy metals in the inhaled PM need to be carefully incorporated into the health risk assessment of atmospheric particulate contamination. Heavy metals in the atmospheric particles come from a diverse variety of pollution sources, such as fuel combustion, resuspended dust and geogenic sources, which often release different types and amount of heavy metals (Duan and Tan, 2013; Fang et al., 2005; Popoola et al., 2018). For instance, coal combustion has been considered as one of the major sources of As, Cr, Ni, Pb and Mn emissions (Deng et al., 2014; Kauppinen and Pakkanen, 1990; Sha et al., 2019; Tian et al., 2010; Tian et al., 2012), and the As fraction in PM originated from coal combustion appeared to be 2.6e8.6 times higher relative to other sources in China (Liu et al., 2018b). Vehicle emission led to elevated levels of Pb, Cr and Cu in PM2.5 (i.e., particulate matter with an https://www.sciencedirect.com/topics/ earth-and-planetary-sciences/aerodynamics aerodynamic diameter equal to or less than 2.5 mm) and PM10 (i.e., particulate matter with an https://www.sciencedirect.com/topics/earth-andplanetary-sciences/aerodynamics aerodynamic diameter equal to or less than 10 mm) in urban areas (Pastuszka et al., 2010; Xia and Gao, 2011). Production of steel, plastics and pigments (Tian et al., 2010) as well as tire wearing (Hjortenkrans et al., 2007) greatly contributed to the atmospheric contamination of particulate Cd, while production and recycling of nickel-cadmium batteries was a primary source of Ni-enriched fine particles (Morselli et al., 2003). Urban fugitive dust was enriched with Zn and Pb, and the Zn/Al and Pb/Al ratios in urban fugitive dust were 1.5e5 times of those found in the Gobi desert and in loess soil samples (Zhang et al., 2014). Current assessment of the contribution of different pollution sources to atmospheric heavy metal pollution is largely based on the total content of heavy metals in PM (Fei et al., 2019; Liu et al., 2018a; Pan et al., 2015; Peng et al., 2017). While this approach is useful for source apportionment, it may provide false information when used to prioritize high-risk sources and pollutants for the management of PM pollution. Recent research indicated that the total metal content may not be a reliable parameter for assessing the exposure risks of PM-borne heavy metals (Dias da Silva et al., 2015; Gao et al., 2018; Huang et al., 2018). Once inhaled, it is the fraction of metal contaminants that were readily released into the lung fluid that appeared to be accessible for and toxic to cells (Costa and Dreher, 1997; Ghio et al., 1999; Heal et al., 2005; Kastury et al., 2017; Prieditis and Adamson, 2002). It has been observed that only a fraction (up to ~20% of total metal content) of Ni, Cd, Mn and Pb in PM2.5 collected from three megacities in China appeared to be bioaccessible in Gamble's solution to simulate alveolar lung fluids (Luo et al., 2019). Furthermore, airborne PM emitted from different pollution sources likely contain dissimilar fractions of bioaccessible heavy metal components. For instance, particle-bound heavy metals originated from the residential areas were more bioaccessible in the respiratory system than those originated from the commercial and industrial areas which were impacted by different pollution sources, as these particles exhibited larger fractions of leachable heavy metals in Gamble's solution (Huang et al., 2018). Particulate Cd and Pb from traffic sources showed greater

bioaccessibility than those from coal combustion sources despite the converse trend of their total concentrations (Luo et al., 2019). PM collected near a lead smelter was highly enriched with Pb and Cd, whereas the inhalation bioaccessibility of these metals was relatively low (Xing et al., 2019). Therefore, it is important to accurately assess the bioaccessibility of PM-bound heavy metals originated from various pollution sources, in order to properly identify the high-risk sources as well as the priority contaminants for effective source regulation. In this study, particulate samples that represented seven different sources of airborne PM, including coal combustion (CC), biomass combustion (BC), fugitive dust (FD), road dust (RD), construction dust (CD), cement (C) and soil (S), were collected from Yangzhou and Nanning cities in China. In vitro assays, using simulated epithelial lung fluid (SELF) (Boisa et al., 2014), were performed to estimate the bioaccessible fractions of the particle-bound heavy metals (i.e., As, Pb, Cr, Mn, Cd, Cu, Ni and Zn). The relationship between the pollution sources and the bioaccessible concentrations of heavy metals were analyzed using nonnegative constrained principal component analysis (NCPCA). The carcinogenic and noncarcinogenic risk indexes were calculated to demonstrate that health risks of particle-bound heavy metals were sourcedependent, and yet cannot be accurately predicted using the total metal content. 2. Materials and methods 2.1. Collection, preparation and characterization of particulate samples A total of 81 atmospheric particle samples that represented 7 different sources were collected from 27 sampling sites (Table S1). Samples from the sources, including coal combustion (CC), fugitive dust (FD), road dust (RD), construction dust (CD), cement (C) and soil (S) were collected from Yangzhou city, Jiangsu Province, China in April 2016. Samples from biomass combustion (BC) were collected from Nanning city, Guangxi Province, China in January 2015. Two to six sampling sites were selected for each source particle and three replicate samples were collected at each site. Selection of sampling sites assured the representativeness of samples and prevented interference from other types of sources (MEP, 2013; USEPA, 1995). All sample containers were soaked in an acid bath (ca. 20% HNO3) for 12 h and then rinsed with purified water prior to use. All the samples were dried at 50
C for 24 h to remove moisture and sieved through a 150-mesh sieve to remove stones, coarse materials, and other debris, and then grinded using planetary ball mill (Focucy FY 2000, China). This approach has been utilized in previous research for simulating the environmental weathering processes of source particles (Boisa et al., 2014), and yet it does not represent all the environmental scenarios of how source particles transform to airborne PM, which may influence the physicochemical characteristics of airborne PM. The morphology of particles was characterized using scanning electron microscopy (SEM, Hitachi S3400 N II, Japan). Particle size distribution was assessed using ImageJ software by measuring at least 200 particles in SEM images for each source. The particulate samples exhibited average sizes ranging from 0.16 to 0.50 mm for different sources (Table S1, Fig. S1). 2.2. Determination of total content and enrichment factor of heavy metals in particulate samples Total concentrations of heavy metals were determined in a 0.1 g sample that was digested in a mixture of 6 ml nitric acid, 2 ml hydrogen peroxide and 2 ml hydrofluoric acid in a microwave

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digestion system (COOLPEX, PreeKem Scientific Instruments, China) at 210
C for 30 min. The digests were diluted into a 50 ml volumetric flask and filtered through an 0.22-mm polyethersulfone membrane prior to analysis. Concentrations of As, Pb, Cr, Mn, Cd, Cu, Ni, Zn, Hg and Se were determined using inductively coupled plasma mass spectrometry (ICP-MS, PerkinElmer NexION 350, USA) with the kinetic energy discrimination (KED) mode (using helium as the collision gas) to eliminate the mass spectrometric determination of multiatom spectra interference. The total concentrations of heavy metals were compared against the regulatory guidance values (RGVs) established for metals and metalloids in residential soils by the USEPA (2013), which were 24, 270, 230, 390, 30, 800, 1560 and 11700 mg/L for As, Pb, Cr, Mn, Cd, Cu, Ni and Zn, respectively (Pena-Fernandez et al., 2014). The contamination levels of heavy metals in atmospheric particles were also evaluated by calculating the geo-accumulation index (Igeo) (Muller, 1969). This method evaluates the enrichment of metal levels above the background values (i.e., concentration in background soils), and has been widely applied in studies on heavy metal pollution in soil, sediment (El Azhari et al., 2017; Tang et al., 2015; Zhuang et al., 2013) and dust (Doabi et al., 2017; Doabi et al., 2018; Lu et al., 2009; Wei et al., 2015). Details of the calculating method for Igeo were included in the supplementary information.

2.3. Measurement of bioaccessibility of heavy metals in particulate samples Bioaccessibility of heavy metals in atmospheric particles was evaluated in vitro using a previously established simulated epithelial lung fluid (SELF) (Boisa et al., 2014) with modification. Briefly, 6.02 g of NaCl, 0.26 g of CaCl2, 0.15 g of Na2HPO4, 2.7 g of NaHCO3, 0.30 g of KCl, 0.20 g of MgCl2, 0.072 g of Na2SO4, 0.018 g of ascorbic acid, 0.016 g of uric acid, 0.030 g of glutathione, 0.12 g of cysteine and 0.38 g of glycine were dissolved in 1000 ml of deionized water, and the pH of the solution was adjust to 7.4 ± 0.2 using HCl. 0.1 g of particulate sample was added into 10-mL plastic centrifuge tube containing 8 mL of SELF. The tubes were sealed and kept on a rotational incubator (QB-128, Kylin-Bell Lab Instruments, China) at 37
C for 5 days (Wragg and Klinck, 2007), and then centrifuged at 15,000 rpm for 10 min. The supernatant in each vial was filtered with 0.22-mm polyethersulfone membrane, digested with 2% (v/v) HNO3 and then stored at 4
C prior to subsequent analysis. Heavy metal concentrations were measured using ICP-MS with KED mode. The residual heavy metal concentrations in atmospheric particles were estimated based on mass balance. The bioaccessibility of each metal was calculated as the percentage of the leachable metal concentration in SELF relative to the total metal content in particles (Guney et al., 2016; Luo et al., 2019). The bioaccessible concentrations of Hg and Se in simulated lung fluid were below detection limit, i.e. < 0.03 mg/kg for Hg, and < 0.006 mg/kg for Se. Hence, the total and bioaccessible data of eight heavy metals (i.e., As, Pb, Cr, Mn, Cd, Cu, Ni and Zn) were utilized for further quantitative analysis.

. DI ¼ ðCHM  Fbioa  TR  PM  Vresp Þ ðBW  109 Þ

3

(1)

where CHM (mg/kg) is the concentration of heavy metals in source particles; Fbioa (%) is the bioaccessible fraction of particle-bound heavy metals (assessed in SELF in this study and subject to the reaction matrix used for bioaccessibility assays), and it was assumed to be 100% when estimating the health risks based on total metal content; TR (%) is the tracheobronchial retention, and a value of 75% was applied in this study (SFT, 1999); PM (mg/m3) is the concentration of airborne particles, and the standard of 75 mg/ m3 for PM2.5 recommended by the new national ambient air quality standard of China (MEP, 2012) was used for calculation; Vresp (m3/day) is the inhalation rate and a value of 20 m3 air per day for adults was used (USEPA, 1991); BW (kg) is the body weight, and an average adult body weight of 70 kg was assumed (Gulkowska et al., 2006). According to USEPA's guideline on risk assessment of inhaled heavy metals (USEPA, 2009), carcinogenic and non-carcinogenic risks of atmospheric particles were assessed using separate models. For estimating noncarcinogenic effects, USEPA has established reference dose (RfD), an estimate of daily exposure of human population to contaminants that would unlikely cause adverse effects (USEPA, 1989). The non-carcinogenic risk of inhalation of atmospheric particle was assessed based on the hazard quotient (HQ):

HQ ¼ DI=RfD

(2)

where DI is the average daily dose of each heavy metal and RfD is the reference daily dose via inhalation. RfD values for As, Pb, Cr, Mn, Cd, Cu, Ni and Zn were 4.29  106, 3.52  103, 2.86  105, 5.00  105, 1.00  103, 4.02  102, 2.06 102 and 3.00 101 mg kg1BW day1 respectively (Li et al., 2018). To assess the overall noncarcinogenic risk posed by more than one heavy metal elements, the hazard index (HI) was calculated as the sum of the HQ values of individual metal, as following:

HI ¼

8 X

HQ

(3)

i¼1

The values of HQ or HI < 1 indicate that the levels of exposure are unlikely to cause adverse effects. The values of HQ or HI > 1 indicate a great chance of non-carcinogenic effects, with a probability increasing with the increasing values of HQ or HI (Doabi et al., 2018). Carcinogenic risk (CR) was estimated by calculating the incremental probability of an individual developing cancer over a lifetime as the result of exposure to the potential carcinogen. The carcinogenic risks associated with a single and multiple heavy metal element were calculated using Eqs. (4) and (5), respectively:

 CRi ¼ DI  URF  BW Vresp  103

CRT ¼

8 X

CRi

(4)

(5)

i¼1

2.4. Health risk assessment of particle-bound heavy metals Human exposure risk to airborne contaminants is often estimated by comparing respiratory intake or inhalable dose with an acceptable dose (Liu et al., 2019). In this research, the daily exposure of heavy metals (daily intake (DI), mg kg1BW day1) was estimated using a modified version of a previously published approach (Betha et al., 2013; Boisa et al., 2014):

where CRi denotes the probability of a person exposed to a specific carcinogenic metal developing cancer during his lifetime, CRT is the sum of the cancer risks for all carcinogenic heavy metals (assuming additive effects), and URF is the unit risk factor, (mg/m3)1. As, Pb, Cr (VI), Cd, and Ni have been identified as human carcinogens and the URF values for these metals were 4.3  103, 1.2  105, 1.2  102, 4.2  103 and 2.4  104 (mg/m3)1, respectively (Pena-Fernandez

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et al., 2014). The URF value of Cr(VI) was used for calculating the Crinduced risk index based on total Cr measurements (i.e., Cr with different valence states) and may lead to overestimation of Crinduced risks. In general, the USEPA recommends that an CR values lower than 1  106 are considered negligible and CR values above 1  104 indicate harmful effects to human beings (USEPA, 1989). 2.5. Statistical analysis Statistical analysis was conducted using SPSS 19.0 (IBM, USA) and MATLAB R2015b (MathWorks, USA). Pearson product-moment correlation analysis was conducted to estimate the linear dependence between the total and bioaccessible contents of heavy metals. One-way analysis of variance (ANOVA) followed by Duncan's multiple range test at the level of significance of 95% (p < 0.05) was used to determine the significances in concentrations, bioaccessibilities, health risks and Euclidean distances of heavy metals in particles from different sources. A nonnegative constrained principal component analysis (NCPCA) model was conducted using NCAPCA1.0 (Shi et al., 2016a) in MATLAB program to identify latent factors (Shi et al., 2016b; Shi et al., 2011). Specifically, an m by n matrix (m is the number of samples and n is the number of elements) of bioaccessibility of heavy metals was generated, normalized and then analyzed by principal component analysis (PCA). Because of the different scales of the measured variables, each individual variable was normalized by subtracting its mean and then divided by its standard deviation before extracting principal components. After varimax rotation, three factors (with eigenvalue of at least 1) were extracted. The obtained factor loadings and scores from original matrices were rotated with nonnegative constraints until the final score matrix contains no negative values (Shi et al., 2009). The results of the NCPCA model calculations were examined in terms of the performance indices, such as regression coefficient, correlation coefficient (R) and percent variance explained. To examine how sample source influences the bioaccessibility of heavy metals, the extracted nonnegative scores were then used for similarity analysis by calculating Euclidean distance within each source (Brodny and Tutak, 2019), and for hierarchical cluster analysis with Ward's method as the amalgamation rule and squared Euclidean distance as metric (Qiao et al., 2018). Hierarchical cluster analysis comprises an unsupervised procedure that involves measuring the distance between samples to be clustered. Samples are grouped in clusters in terms of their similarity. The initial assumption is that nearness of samples in the space defined by the variables reflects the similarity of their properties (Charron and Harrison, 2005). 2.6. Quality assurance and quality control To avoid background contamination, all containers were acidcleaned prior to use and all chemicals used in this study were trace metal grade. The blank samples contained all reagents and no particulate samples, and the measurements from the blanks were subtracted from the results of the particulate samples. The concentrations of heavy metals in reagent blanks ranged from below detection limit to 0.5 mg/L, all < 1% of the average analyte concentrations. Detection limit of As, Pb, Cr, Mn, Cd, Cu, Ni and Zn was 0.00132, 0.000802, 0.000658, 0.000367, 0.000645, 0.00187, 0.00101 and 0.000524 mg/kg, respectively. The correlation coefficient of the calibration curve was > 0.999, and the relative standard deviations in the repeated experiments (n ¼ 3) were within 5% for all heavy metals. To assess the recovery of heavy metals during sample extraction, two standard reference materials (SRM 1648a and SRM 1633c, obtained from the National Institute of Standards

and Technology, NIST, USA) were subject to the same extraction procedure as all the particulate samples. The recoveries were 111 ± 4.5% and 102 ± 6.0% (As), 97 ± 4.8% and 87 ± 5.4% (Pb), 104 ± 2.0% and 98 ± 9.3% (Cr), 103 ± 8.6% and 98 ± 6.2% (Mn), 97 ± 3.3% and 96 ± 4.7% (Cd), 91 ± 4.8% and 98 ± 5.2% (Cu), 101 ± 6.0% and 96 ± 3.0% (Ni), 100 ± 6.3% and 99 ± 6.0% (Zn) for SRM 1648a and SRM 1633c, respectively. For the bioaccessibility assays, the mass balance of heavy metals was examined by summarizing the content of the leached metals in SELF as well as the metals remained in the particles after leaching. After incubated in SELF, a subset of samples were digested in a microwave digestion system at 210
C for 30 min using 10 ml acid solution (HNO3:H2O2:HF ¼ 3:1:1, v/v), and then centrifuged at 15,000 rpm for 10 min. The concentrations of heavy metals in the supernatant were analyzed using ICP-MS with KED mode. In comparison with the total content of heavy metals in source particle samples, the recoveries of As, Pb, Cr, Mn, Cd, Cu, Ni and Zn were 90.4% ± 5.2%, 92.1% ± 6.6%, 97.8% ± 6.2%, 96.3% ± 3.9%, 93.5% ± 4.7%, 103.7% ± 6.2%, 99.4% ± 4.9% and 88.0% ± 7.2%, respectively, for the bioaccessibility assays. 3. Results and discussion 3.1. Total content of heavy metals in different source particles The contents of the eight types of heavy metals that frequently occur in airborne PM and induce health risks (i.e., As, Pb, Cr, Mn, Cd, Cu, Ni, Zn) (Betha et al., 2014) were analyzed for the particulate samples that represented seven different sources of airborne PM, including coal combustion, biomass combustion, fugitive dust, road dust, construction dust, cement and soil (Fig. 1 and Table S2). The heavy metal concentrations fell in relatively wide ranges and the variations of total metal content among different source particles were up to two orders of magnitude. Specifically, the total particulate contents of As, Pb, Cr, Mn, Cd, Cu, Ni, Zn were 5.15e56.6, 7.92e464, 10.8e236, 140-1770, 0.0474e1.56, 14.3e148, 13.0e81.7 and 28.0e858 mg/kg, respectively (Fig. 1 and Table S2). The abundance of particulate heavy metals appeared to be dependent on the particle sources. For example, coal combustion samples generally exhibited higher heavy metal contents relative to biomass combustion samples (Fig. 1). Indeed, combustion of fossil fuels, such as coal, is one of the major anthropogenic sources of heavy metal pollution (Duan and Tan, 2013), whereas biomass combustion has been suggested as a “cleaner” approach of energy production (Jagustyn et al., 2017). The geo-accumulation indexes (Igeo) of all eight heavy metal pollutants were calculated to assess the contamination level of particles from different sources (Fig. S2). Among the total 81 samples, 78 showed positive Igeo values for at least one type of heavy metal, suggesting that these particles likely contributed to the atmospheric heavy metal pollution. The mean values of Igeo decreased in the order of Cd > Zn > Cu > Pb > As > Ni > Cr > Mn. The largest Igeo values of Cd in fugitive dust (3.05), Zn in road dust (3.19) and Pb in cement (3.56) were all greater than 3.0, indicating heavy contamination (Muller, 1969). Thus, these sources may significantly contribute to the corresponding heavy metals in the airborne PM. Our result is consistent with previous observations that Cd, Zn and Pb were enriched in dust samples, and their contents in PM sampled around cement plants were apparently elevated (Bi et al., 2018; Huang et al., 2014; Neitlich et al., 2017; Patel et al., 2017; Yang et al., 2018). 3.2. Bioaccessibility of heavy metals in different source particles The leachable concentrations of the eight particle-bound heavy

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Fig. 1. Total concentrations of heavy metal As (a), Pb (b), Cr (c), Mn (d), Cd (e), Cu (f), Ni (g) and Zn (h) in the particulate samples from different sources, including coal combustion, biomass combustion, fugitive dust, road dust, construction dust, cement and soil. Different letters denoted a significant difference (p < 0.05).

metal elements in the lung fluid varied by three to five orders of magnitude (Table S2), and the bioaccessibility ranged from below the detection limit (< 0.001%) to 76.5%. The averaged bioaccessibility values of As, Pb, Cr, Mn, Cd, Cu, Ni, Zn were 6.91%, 4.45%, 0.819%, 0.836%, 13.1%, 11.1%, 10.7% and 1.46%, respectively (Fig. 2). In the majority of the samples, bioaccessibilities of Cr, Mn and Zn were relatively low (Fig. 2c, d, h), while bioaccessibilities of Cd and Cu were generally greater than other heavy metals (Fig. 2e and f). For As, Pb and Ni, occasional samples exhibited significantly larger bioaccessibilities compared to the other particles, such as biomass combustion samples for As (Fig. 2a), cement samples for Pb (Fig. 2b), and fugitive dust samples for Ni (Fig. 2g). These results pointed to the potential risks of these particle-bound heavy metals posing on human health. It is worth noting that the trend of the bioaccessible fractions of heavy metals did not agree with the trend of the total heavy metal content among different source particles (Fig. 3). Moderate to weak correlations between total concentrations and bioaccessible concentrations were observed for Pb (R2 ¼ 0.53, Fig. 3b), Zn (R2 ¼ 0.32, Fig. 3h) and Ni (R2 ¼ 0.25, Fig. 3g), whereas no correlation was observed for As, Cr, Mn, Cd or Cu (R2 < 0.10, Fig. 3a, c-f). In particular, coal combustion and cement samples showed substantially higher levels of total As content than the other samples

(Fig. 1a), however, the bioaccessibility of As in these two source particles appeared to be similar, if not lower relative to the other source particles (Fig. 2a). Our results well corroborated previous research, showing that the bioaccessibility of heavy metals in airborne PM varied significantly with sampling site and time, which were impacted by different pollution sources (Guney et al., 2017; Hernandez-Pellon et al., 2018; Huang et al., 2018; Luo et al., 2019; Mbengue et al., 2015; Niu et al., 2010; Tang et al., 2019; Witt et al., 2014; Xie et al., 2019; Xing et al., 2019). For example, HernandezPellon et al. (2018) demonstrated that the heavy metal bioaccessibility was higher on average in the industrial area affected by a ferromanganese alloy plant, relative to the urban area mostly comprised of residential and commercial sectors. Moreover, the bioaccessibility of As in PM2.5 samples collected in Baoding, China was found to be greater in the summer than that in the winter, although samples collected in the winter contained higher concentration of As due to the coal burning for heat generation (Xie et al., 2019). This may be explained by the variation in As speciation in airborne particles. In the summer, As in PM2.5 was mainly derived from vehicle and industrial emissions, and mostly present in soluble and adsorbed phases. In the winter, the primary As pollution sources changed to coal combustion and fugitive emissions that released As in well-crystalline forms (Lee et al., 2015).

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Fig. 2. Bioaccessibility of heavy metal As (a), Pb (b), Cr (c), Mn (d), Cd (e), Cu (f), Ni (g) and Zn (h) in the particulate samples from different sources, including coal combustion, biomass combustion, fugitive dust, road dust, construction dust, cement and soil. Different letters denoted a significant difference (p < 0.05).

3.3. Source dependence of bioaccessibility of particulate heavy metals The impact of pollution sources on the bioaccessibility of particulate heavy metals was further evaluated using NCPCA (Fig. 4), considering that negative values of the factor score or loading of PCA do not hold reasonable physical meanings. The first three components (i.e., PC1, PC2, PC3) with eigenvalues above 1.0 were extracted and explained 63.2% of the data variance (Fig. 4a). The regression coefficient and correlation coefficient (R) between the estimated and measured values of metal bioaccessibility were 0.97 and 0.95, respectively (Fig. S3), proving that the established model for NCPCA analysis was valid. The bioaccessible fractions of heavy metals appeared to be source-dependent, particularly for biomass combustion, cement and soil samples, which appeared to cluster in the plots of the PCA factor scores (Fig. 4a) and exhibited small Euclidean distances among sample points from the same source (Fig. 4b). The particulate samples were further grouped according to their similarity in heavy metal bioaccessibility using clustering analysis and the resulted dendrogram is shown in Fig. 4c. All the biomass combustion samples were clustered together while most of the soil

samples were closely clustered. The majority of the cement samples were clustered with exception of samples collected from Weilai cement plant in Yangzhou (Fig. 4c). On the contrary, distinctive clustering pattern was not observed for the coal combustion, fugitive dust, road dust or construction dust samples, probably due to the heterogeneous nature of the particles from each of these sources. The type of coal and the combustion technique both strongly affect the characteristics of the coal combustion products (Chen et al., 2019; Wang et al., 2018), while the heavy metal components in the dust samples were highly susceptible to the location of sampling sites (Middleton et al., 2017). To further elucidate the dominant factors of the source dependence of heavy metal bioaccessibility, the extracted factors and the corresponding loading coefficients of PCA were identified as follows (Fig. 4a and d): the first principal component (PC1) explained 25.4% of the data variance, with high loadings (i.e., loading coefficient > 0.8) for Cu, Ni and Pb; the second principal component (PC2) explained 20.5% of the data variance, with moderately high loading (i.e., 0.6 < loading coefficient < 0.8) for As; and the third principal component (PC3) explained 17.3% of the data variance, with high loading for Cd, which indicated that the three extracted principal components of bioaccessibility were mainly determined

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Fig. 3. Correlation of total concentrations and bioaccessible concentrations of heavy metal As (a), Pb (b), Cr (c), Mn (d), Cd (e), Cu (f), Ni (g) and Zn (h) in the particulate samples from different sources.

by these heavy metals among the eight targeted metal pollution in this research. Altogether, the NCPCA results demonstrated that pollution sources, particularly biomass combustion, cement and soil, strongly affected the bioaccessible fractions of heavy metals in source particles. Cu, Ni, Pb and Cd were the dominant factors affecting the source dependence of heavy metal bioaccessibility, and thus may serve as indicators for identifying the primary sources responsible for the bioaccessible heavy metal components of atmospheric PM. This information provided the basis for better understanding the source contributions to the heavy metal components in the airborne PM that actually led to human health risks.

3.4. Bioaccessibility-based risk assessment of heavy metals in different source particles The health risks of the particle-bound heavy metals were assessed via calculating the HI and CR values that represent the non-carcinogenic and carcinogenic risks, respectively. In general, the risks estimated using the bioaccessible metal fractions were lower than the risks estimated using the total metal content (Tables S3 and S3, Fig. 5 and S4) due to the fact that only a fraction of

the particulate heavy metals were leachable within the retention time of the atmospheric particles in the lung fluid environment (Boisa et al., 2014; Wiseman, 2015). According to the bioaccessibility of As, Pb, Cr, Mn, Cd, Cu, Ni, Zn (Fig. 2), the HI and CR values for the source particles lay in the range of 0.0002e0.05 and 1.14  108-2.61  106, respectively (Fig. 5). These values were in accordance with the result of previous research that evaluated the noncarcinogenic and carcinogenic risks of heavy metals by considering their bioaccessibility in urban park dust in Nanjing, China (Wang et al., 2016). More importantly, the relative health risks among particles from different sources drastically changed when substituting the total concentrations with the bioaccessible concentrations of heavy metals in risk assessment (Fig. 5). For instance, considering the large total content of heavy metals in cement particles, they should exhibit the highest noncarcinogenic risk among all samples (Fig. 5a). However, the low bioaccessibility of these heavy metals rendered the noncarcinogenic risk of cement particles lower than the other source particles after incorporating metal bioaccessibility into the HI indexes (Fig. 5c). The coal combustion samples appeared to be relatively ‘safe’ with respect to the HI indexes based on total metal content (Fig. 5a), whereas these particles showed the

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Fig. 4. Nonnegative constrained principal component analysis (NCPCA) of bioaccessibility of particulate heavy metals with respect to particle sources: (a) factor score, (b) Euclidean distance, (c) clustering pattern, and (d) principal component loading. Different letters denoted a significant difference (p < 0.05) (panel b).

greatest bioaccessibility-based noncarcinogenic risk relative to all source particles (Fig. 5c). In addition to the averaged HI and CR values, the variability of the risk indexes also depended on whether total or bioaccessible metal concentrations were utilized in the

assessment. The carcinogenic risks of the fugitive dust samples based on total metal content appeared to be highly variable (Fig. 5b), while the relative standard deviation of the CR indexes based on the bioaccessible fractions was apparently smaller

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Fig. 5. Hazard indexes for assessing noncarcinogenic risks based on total concentrations (a) and bioaccessible concentrations (c) of heavy metals, and carcinogenic risk indexes based on total concentrations (b) and bioaccessible concentrations (d) of heavy metals in the particulate samples from different sources. Different letters denoted a significant difference (p < 0.05). TC ¼ total concentrations of heavy metals; BAC ¼ bioaccessible concentrations of heavy metals; HI ¼ hazard index; CRT ¼ carcinogenic risk. CC ¼ coal combustion, BC ¼ biomass combustion, FD ¼ fugitive dust, RD ¼ road dust, CD ¼ construction dust, C ¼ cement and S ¼ soil.

(Fig. 5d). Conversely, the coal combustion samples appeared to be much more heterogeneous in both noncarcinogenic and carcinogenic risk assessment when using the bioaccessible concentrations of heavy metals (Fig. 5c and d) as opposed to the total heavy metal content (Fig. 5a and b). These inconsistent trends may, at least in part, be explained by the variation in metal speciation of these source particles. Fugitive dusts are mostly originated from natural geological sources, such as soil, that contain heavy metals mainly in non-labile phases (e.g., sulfidic or residual) (Duong and Lee, 2009), whereas the metal speciation in coal combustion products tends to be more diverse, possibly due to the large variety of metal species present in coal and the transformation reactions of these species during combustion (Chen et al., 2019; Wang et al., 2018). The priority heavy metal pollutants in the source particles were determined as the heavy metals that contributed the most to the health risk indexes (Fig. 6). According to the total particulate heavy metal content, Mn and Cr were the main drivers for noncarcinogenic and carcinogenic risks, respectively, for all the source particles (Fig. 6a and b). Nevertheless, As became the priority pollutant in a good number of source particles when taking the bioaccessibility of heavy metals into account (Fig. 6c and d). In fugitive dust, road dust and construction dust particles, Ni significantly contributed to the CR indexes when considering the bioaccessibility of heavy metals (Fig. 6d), but the Ni-induced carcinogenic risks were consistently minimal according to the total metal content (Fig. 6b). After substituting the total content with the bioaccessible fractions of heavy metals, apparent increases were observed in the contribution of Cd to the carcinogenic risks of construction dust, road dust and coal combustion samples, as well

as the contribution of Pb to both carcinogenic and noncarcinogenic risks of cement samples. As a matter of fact, Cd in atmospheric particles, particularly those originated from anthropogenic sources, was largely in soluble-exchangeable forms and thus appeared to be bioaccessible in lung fluid (Wang et al., 2019). 4. Conclusions The present study quantified both total and bioaccessible concentrations of As, Pb, Cr, Mn, Cd, Cu, Ni and Zn in particles from various pollution sources, including coal combustion, biomass combustion, fugitive dust, road dust, construction dust, cement and soil. Our results revealed that a widely varying fraction of total heavy metal content was readily released in the synthetic lung fluid within the duration of particle retention in this environment. The bioaccessible fractions of heavy metals were strongly dependent on the sources of particles, particularly for biomass combustion, cement and soil, and the particulate Cu, Ni, Pb and Cd appeared to be the primary indicators of the source dependence of heavy metal bioaccessibility. Neither the averaged level nor the variability of the bioaccessible fractions followed the same trend as the total concentrations of particulate heavy metals. As a result, when taking metal bioaccessibility into account, both carcinogenic and noncarcinogenic risk indexes for different source particles and for different heavy metals changed by a large extent, relative to the risk indexes based on total metal content. In particular, the noncarcinogenic risk of cement particles and the carcinogenic risk of fugitive dust particles significantly decreased when using metal bioaccessibility

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Fig. 6. Relative contribution of each heavy metal to the hazard indexes for assessing noncarcinogenic (a and c) and carcinogenic (b and d) risks based on total (a and b) and bioaccessible concentrations of heavy metals (c and d) in the particulate samples from different sources. TC ¼ total concentration of heavy metal; BAC ¼ bioaccessible concentration of heavy metal; HQ ¼ hazard quotient; HI ¼ hazard index; CRi ¼ carcinogenic risk due to metal i; CRT ¼ total carcinogenic risk. CC ¼ coal combustion, BC ¼ biomass combustion, FD ¼ fugitive dust, RD ¼ road dust, CD ¼ construction dust, C ¼ cement and S ¼ soil.

versus total metal content. The priority metal pollutants changed from Mn to As and Mn for noncarcinogenic risks, and changed from Cr to As, Ni and Cr for carcinogenic risks of source particles after substituting the total content with the bioaccessible fractions of heavy metals. All in all, this research underlined the necessity of considering the bioaccessibility of heavy metal pollutants in the source particles in order to properly assess the source contribution to the health risks induced by the atmospheric particulate pollution. This research also provided quantitative information regarding the bioaccessibility as well as the bioaccessibility-based health risk indexes of typical heavy metal pollutants from a number of main sources of airborne PM. To deepen the mechanistic understanding of the health risks induced by PM pollution and establish effective regulation of air pollution sources, future research ought to focus on assessing the speciation-dependent bioaccessibility and risks of particulate heavy metals and benchmarking the risks induced by leachable heavy metals against other PM components (e.g., insoluble fractions).

Conflict of interest The authors declare no conflict of interest. Acknowledgements This research was supported by the National Natural Science Foundation of China (Grants 41603099 and 21425729), the National Key Research and Development Program of China (2018YFC1800705), Tianjin Municipal Science and Technology Commission (17JCYBJC23100), and the 111 Program of the Ministry of Education of China (T2017002). We thank Linlan Su and Zixuan Shi for the assistance with sample preparation, and Guoliang Shi, Tingting Du for the helpful discussion regarding statistical analysis. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.envpol.2019.113113.

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