Chemosphere 147 (2016) 404e411
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Health risks of children's cumulative and aggregative exposure to metals and metalloids in a typical urban environment in China Suzhen Cao a, 1, Xiaoli Duan a, *, 1, Xiuge Zhao a, Yiting Chen a, b, Beibei Wang a, Chengye Sun c, Binghui Zheng a, **, Fusheng Wei d a
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China Sichuan Academy of Environmental Sciences, Chengdu 610041, China National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing 102206, China d China National Environmental Monitoring Center, Beijing 100012, China b c
h i g h l i g h t s 12 heavy metals and metalloids in an urban environment were investigated. Health risks and pathways of children's exposure to metal(loid)s were assessed. Soil and indoor dust and duplicate food were contaminated by metal(loid)s. Food ingestion was the major pathway for children's exposure to most metal(loid)s. Higher potentially non-cancer and cancer risks happened to the local children.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 October 2015 Received in revised form 29 December 2015 Accepted 30 December 2015 Available online xxx
Rapid development of industrialization and urbanization results in serious environmental contamination by metal(loid)s, which would consequently cause deleterious health effects to the exposed people through multi-pathways. Therefore, total health risk assessment for the population in urban environment is very important. Unfortunately, few studies to date investigate the cumulative health risks of metal(loid)s through aggregative pathways in Children who are often susceptible population. 12 metal(loid)s including Lead(Pb), Cadmium(Cd), Arsenic(As), Chromium(Cr), Zinc(Zn), Copper(Cu), Nickel(Ni), Manganese(Mn), Cobalt(Co), Selenium(Se), Antimony(Se) and Vanadium(V), were analyzed in PM10, drinking water, food, soil and indoor dust in this study. The cumulative and aggregative risks of these metal(loid)s among the local children were then evaluated on a ﬁeld sampling and questionnairesurvey basis. The results showed that the environments were heavily polluted by metal(loid)s. For most metal(loid)s, food ingestion accounted for more than 80% of the total daily exposure dose. The noncancer risks were up to 30 times higher than the acceptable level due to the food ingestion via Pb, Cr, Cu, Zn, As, Se, Cd and Sb, and the PM10 inhalation via Cr and Mn. While, the cancer risks were mainly attributed to Cr via food ingestion and As via food and dust ingestion, and approximately 100 times of the maximum acceptable level of 1.0 104. The study highlights the cumulative and aggregative exposure assessment, instead of pollutant investigation to evaluate the potential health risks and emphasizes concerns to improve indoor hygienic and environmental quality and to decrease the potential harmful health effects of children living in urban area. © 2016 Elsevier Ltd. All rights reserved.
Handling Editor: A. Gies Keywords: Metal(loid)s Children Exposure pathways Health risks Urban
1. Introduction * Corresponding author. ** Corresponding author. E-mail addresses: [email protected]
(X. Duan), [email protected]
(B. Zheng). 1 Suzhen Cao and Xiaoli Duan, these authors contributed equally to this work. http://dx.doi.org/10.1016/j.chemosphere.2015.12.134 0045-6535/© 2016 Elsevier Ltd. All rights reserved.
Researches both from epidemiological and toxicological studies show that some metals and/or metalloids such as Pb, Cd and As do not have any beneﬁcial physiological function and can lead to high damages on human. Pb exposure in high level remains a ubiquitous
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environmental health threat to the human beings, with numerous adverse health effects on nervous system, hematopoietic and reproductive systems (Haeﬂiger et al., 2009). Accumulation of Cd exposure can cause kidney, bone and pulmonary damage (Godt et al., 2006). The exposure of As had been reported to lead to diabetes mellitus, cognitive development and cardiac disorders (Smith et al., 2006). Moreover, many recent researches highlight that detrimental health effects may occur at very low exposure level. For example, the neurological damage happens in children at low blood lead levels (BLLs, 2e10 mg L1), indicating that there is no “safe” threshold for lead exposure (Lanphear et al., 2005). Coincidentally, a recent report states that there is also no safe level for As exposure, since any exposure dose could increase the risks of diabetes, heart disease, immune problems and cancers (Schmidt, 2014). Due to child-speciﬁc phase of growth and development as well as behavior patterns, Children are more prone to metal(loid)s poisoning (Fitzgerald et al., 1998). Thus, children's exposure to metal(loid)s is still common in China (Gao and Xia, 2011). It's accepted that impacts of pollutant exposure are usually attributed to aggregative exposure pathways and sometimes a long-term accumulation. The main pathways include dermal contact, inhalation and/or ingestion of aerosol particles, dust/soils, food, and drinking water (USEPA, 2006). To reduce the exposure, it is important to identify predominant exposure pathways, which include both non-dietary and dietary pathways (Villanueva et al., 2014). With the development of industries and urbanization, the surrounding environments are polluted by contaminants such as metals and organic matters (Luo et al., 2015; Yu et al., 2011). Some previous studies assessed health risks in the vicinities of various industrial areas through the single pathway of soil (Cai et al., 2015; Izquierdo et al., 2015), dust (Kurt-Karakus, 2012), food (Cheng et al., 2013; Lu et al., 2015) or drinking water (Qin et al., 2013; Villanueva et al., 2014), but little is on the cumulative and aggregative exposure of typical toxic heavy metal(loid) via multi-pathways (Cao et al., 2014, 2015; Qu et al., 2012). A cumulative and aggregative exposure over time via a variety of multimedia and multi-pathways of environmental pollutants could be applied for assessing the total detrimental risks and identifying hazardous exposure factors. This would be of great importance for the identiﬁcation of predominant exposure pathways and pollutant controls to reduce the risks of detrimental health effects. The objectives of the present study were (1) to quantify the concentrations of 12 metal(loid)s in water, food, PM10, soil/dust in a typical modern urban area in China; (2) quantify the exposure levels and relative contributions from each medium to the local children; and (3) estimate children's health risks due to metal(loid) s exposure from various media. Hazard quotients (HQ) and the incremental lifetime cancer risk (ILCR) were used to assess the noncancer and cancer risks, respectively (Mari et al., 2009). Since risk assessment is inherently linked with uncertainty (Li et al., 2006), Monte Carlo simulation working with probability distributions of each parameter was conducted to determine the inherent uncertainty in predicted risks (Mari et al., 2009). 2. Materials and methods 2.1. Study area The studied city, well-known as a hometown of nonferrous metals, is an industrial city situated to the southeast in Hunan Province, China. Hills are the primary terrains accounting for 75% of the total city area. It has a subtropical monsoon climate, characterized by warm and wet, abundant sunshine and rainfall. The trafﬁc in the city is quite convenient with many expressways, national highways and provincial roads. To accurately assess health
risks of children from the exposure of various metal(loid)s via multi-pathways, a typical modern primary school which includes the students from large part of the city was chosen as a model to select the participants. There was no enterprise or manufacture located near the school, but existed moderate trafﬁc volumes. 2.2. Sample collection and analysis 2.2.1. Human behavior pattern survey After obtaining the ethics approval from ethics committee of the National Center for Disease Control, the study was then conducted. Before the survey and sampling, 70 participants were selected on a personal and parental voluntary basis with written informed consents concerning the behavior pattern survey and the household sampling were obtained. All participants were native-born and aged 5e8 years old. A questionnaire-based survey was then conducted among the participants accompanied with their parents to identify the factors inﬂuencing the exposure risks such as, dietary habits, behavior patterns and nutritional factors (e.g. iron status). 2.2.2. Field sampling To determine the aggregative and accumulative exposure and risk levels to the target metal(loid)s, 20 of the 70 participants were then randomly selected to join the ﬁeld sampling on an informed and voluntary basis. A total of 20 tap water samples were collected in 1 Lacid-washed polyethylene bottles from each volunteer's family. Additional 2 tap water samples were respectively collected from two classrooms of the school to reﬂect the water quality consumed by the children in the school. During sampling, two drops of 65% concentrated HNO3 were added into the sampled water, which was then refrigerated and stored at 20 C until analysis. Since all the children dwelled at buildings, and there is less ground or courtyard around the children's homes. Therefore, 10 soil samples (0e20 cm) were collected in 6 typical parks and gardens, and another 2 soil samples were collected from the school in undisturbed locations. In each sampling site, the soil samples were integrated with 4e5 equal sub-samples in an area of 100 cm2 (Zhang et al., 2010). 4 dust samples from the ﬂoor and stairs of the school and 20 indoor dust samples from the windowsill, furniture surface and the corner of the children's house were collected using a dust-free nylon brush. Each dust sample was mixed with 4 or 5 sub-samples. The inhalable particles (PM10) from 2 representative monitoring sites of inside classrooms and from indoor of the 20 participants' houses were sampled on pre-combusted (500 C, 6 h) quartz microﬁber ﬁlters (MunktellIn C., Sweden) using a low-volume breathing sampler (Buck Libra Plus, AP BUCK In C., U.K) with a ﬂow rate of 2 L min1 for 24 h. Another 2 PM10 samples outside the classrooms of the schools were respectively collected simultaneously. Each sampling lasted for 3 days. Before and after sampling, all ﬁlters were pretreated using previously established methods (Hu et al., 2012). The particle-loaded ﬁlters were then stored at 4 C until analysis. Duplicate daily foods of the 20 volunteers, which were partly locally produced, were directly sampled from each family to represent the actual amount and species of the dietary consumed by each participant. After the food items in each child's diet were weighed separately, a portion of each item consumed in one day was blended for freeze-drying and cryopreservation. 2.2.3. Sample treatment and analysis The water samples were ﬁltered using a ﬁlter membrane (Whatman no.1, Ø ¼ 0.45 mm) before concentration analysis. The PM10-loaded ﬁlter was cut into pieces and then wholly digested (Hu et al., 2012). After being air-dried, ground and sieved, 0.1000 g of
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each soil and dust powder samples were then pretreated following an acid digestion procedure (Zhang et al., 2010). After the freezedried, 0.5000 g of food sample was digested as soil samples. All digested and made-up solutions were ﬁltered through a ﬁlter membrane (Whatman no.1, Ø ¼ 0.45 mm) and diluted to appropriate concentrations for instrumental analysis. The concentrations of all target metal(loid)s in all samples were detected by ICP-MS (Agilent-7500a, Agilent Scientiﬁc Technology Ltd., USA) under the optimized conditions (Zhang et al., 2010). A solution containing rhodium and rhenium was added online using a Y-type canal as an internal standard and subjected to the concentration measurements. The results were quantiﬁed with an empirical calibration curve using a multi-element calibrations standard material (GNMM27195-2013). To control quality, spiked recoveries of targets for PM10, representative reference materials for soil, food and water were measured in the pretreatments of each matrix. Reagent and analytical blanks and duplicated treatments of each digested batch were also considered to assess the process efﬁcacy. All chemical reagents in the experiments were guaranteed reagents. The instrument and method detection limits for different metal(loid)s ranged from 2.5 to 44.3 ng g1 (shown in Table S1, supplementary data). The recoveries of the elements were 86e112%. The relative standard deviations of the measured elements in parallel treatments were 0.35e2.09%. 2.3. Exposure assessment via ingestion, dermal contact and inhalation According to the recommended exposure assessment models in the U.S. Exposure Factors Handbook (USEPA, 1997), the average daily dose (ADD) (mg kg1 day1) of an element via ingestion, inhalation and dermal contact can be estimated using the following equations, respectively:
C t InhR EF ED PEF BW AT
C SA AF ABS ED EF BW AT
where C is the concentration of the element (mg kg1 or mg m3 and mg L1); IngR is the ingestion rate (mg day1 or L day1); InhR is the inhalation rate (m3 day1); t is the exposure time (h day1); PEF is the inhalation factor for inhalable particles (m3 kg1); SA is the surface area of the skin exposed to pollutants (cm2); AF is the skin adherence factor[mg (cm2 h)1]; ABS is the dermal absorption factor; EF is exposure frequency (days year1); ED is the exposure duration (year); BW is the body weight(kg); and AT is the average exposure time (days). The IngR of consumed drinking water of the children and their BW were obtained through the questionnaire. The IngR of consumed food was obtained by weighing the duplicate food. The other exposure parameters, such as IngRs of the soil/dust were obtained from the literature (Duan et al., 2011; Duan, 2012; USEPA, 1997, 2002), as shown in Table 1. The penetration coefﬁcient factors for human water exposure by dermal contact are listed in Table S2. 2.4. Risk calculation 2.4.1. Non-carcinogenic risk The HQ can be calculated by dividing the ADD from each
where RfD is the estimated maximum permissible risk on humans through daily exposure. If HQ 1, detrimental health effects would unlikely happen, whereas potential non-carcinogenic effects would occur in case HQ > 1 (USEPA, 2011a). To assess the cumulative potential non-carcinogenic effects posed by many contaminants (e.g., i), the HQ value of each target chemical was summed (assuming additive effects) and expressed as an integrated Hazard Index (HI) (USEPA, 1986):
Since the contaminants could enter into human through different pathways, the total exposure hazard index (HIt) was used to reﬂect the aggregative non-cancer risks through multi-pathways and expressed as follow (USEPA, 2011a):
Experiencing chronic risks are assumed unlikely if HIt 1, whereas there may be concern for potential non-cancer risks when HIt > 1. A further analysis segregating the contaminants and separating the HIt would then be preferable if the HIt > 1. 2.4.2. Carcinogenic risk The ILCR deﬁning the incremental probability of an individual developing a cancer over a lifetime was estimated according to the following equation (USEPA, 2011a):
ILCR ¼ ADD SF
C IngR EF ED BW AT
exposure route by a speciﬁc reference dose (RfD) or by a reference concentration for air (RfC). The HQ is deﬁned as follow(USEPA, 2011a):
where SF is the cancer slope factor of carcinogen as listed in Table S2. If there is more than one carcinogenic contaminant, the cancer risks from all carcinogen and exposure routes are summed to assess the total potential cancer risks (assuming additive effects). Risks in the range of 1.0 106 to 1.0 104 are regarded to be acceptable. Cr, Cd, As, Ni and Co were treated as potential carcinogenic contaminants, whereas other metal(loid)s were regarded as non-carcinogenic, on the basis of the order of classiﬁcation group deﬁned by the International Agency for Research on Cancer (IARC, 2011). The exposure factors such as RfD are listed in Table S2. 2.5. Statistical analysis The contents of each metal(loid) were presented as medians and inter-quartile ranges. The correlation coefﬁcients (r) were calculated using the Spearman's method. The statistical analysis was conducted using SPSS 20.0 with a signiﬁcance level of 0.05 for twotailed testing. 3. Results and discussion 3.1. Contents of metal and metalloid in the environmental medium The contents of target metal(loid)s in each environmental medium are listed in Table 2. The levels of all metal(loid)s exceeded the related regulatory values of environmental quality standard for soils (GB 15618-1995). In comparison, the levels of metal(loid)s in soil were 3.0e6.1 times higher than the related natural background
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Table 1 Summary of exposure factors developed for the investigation site. Exposure parameter (age)
InhRambient air(m3 day1) SAwater (cm2) BW (kg) IngRfood(g day1) IngRwater (L day1) toutside (h day1)
e e 21.8 469.1 0.501 2.6
e e 22.6 467.8 0.579 2.6
5.9 0.772 19.5 374.3 0.600 2.3
9.1 0.832 23.5 468.3 0.592 2.6
9.1 0.901 23.8 565.6 0.542 2.8
9.1 0.961 23.6 462.8 0.500 2.1
e e 21.0 470.4 0.426 2.5
6.4 0.756 17.5 491.1 0.500 2.1
8.1 0.811 21.1 530.9 0.517 2.9
8.1 0.869 24.5 452.3 0.411 2.4
8.1 0.938 24.7 407.2 0.313 2.6
Duan, 2012 Duan, 2012 This work This work This work This work
The IngRs of duplicate food and water, as well as the time spent outside are collected based on the validated questionnaires. In the total 70 questionnaires survey, all the selected 70 children would like to take participation in the questionnaire-based survey, the response rate of questionnaires is 100%. After validating, 3 questionnaires have missed answers, the rate of effective questionnaires is 95.71%.
Table 2 Contents of the target metals and metalloids in environmental media. Contaminant
Soil mg kg1
Dust mg kg1
PM10 mg m3
Drinking water mg L1
Duplicate food mg kg1
Median 25% 75% Median 25% 75% Median 25% 75% Median 25% 75% Median 25% 75%
116.09 88.71 127.97 72.07 31.06 119.67 0.11 0.09 0.13 0.60 0.51 0.72 0.49 0.07 1.06
367.16 244.97 413.50 130.14 39.32 212.73 0.05 0.03 0.06 6.67 6.16 7.19 0.26 0.05 0.33
1754.91 632.38 1927.44 444.97 145.58 747.28 0.13 0.07 0.15 2.36 1.98 2.88 2.34 1.36 4.27
18.03 11.69 25.52 5.52 1.41 9.92 0.01 0.01 0.02 0.18 0.16 0.27 0.08 0.01 0.11
85.41 71.21 128.81 119.31 45.74 182.77 0.07 0.05 0.11 1.43 1.44 1.79 0.45 0.34 0.92
108.09 87.08 126.00 34.36 12.40 221.33 0.18 0.13 0.23 1.63 1.47 1.98 5.07 2.09 83.68
300.08 185.34 403.02 250.22 135.36 345.30 0.15 0.01 0.82 157 178.03 199.55 11.88 2.72 37.21
0.59 0.34 0.89 2.15 0.56 4.11 0.02 0.02 0.03 0.04 0.03 0.04 0.02 0.01 0.16
434.38 375.21 479.07 19.75 5.40 67.21 0.05 0.02 0.09 2.90 2.67 3.47 0.17 0.06 0.22
44.17 12.53 84.00 75.82 39.00 156.79 0.38 0.32 0.48 0.11 0.06 0.11 0.07 0.02 0.26
79.41 62.03 169.46 180.84 79.41 253.21 0.03 0.01 0.06 0.33 0.24 0.37 0.01 0.01 0.20
15.82 12.77 53.45 9.81 3.75 15.78 0.37 0.31 0.40 0.95 0.86 1.02 0.001 0.002 0.002
levels in the soils of Hunan province (Pan and Yang, 1998). The result was in accordance with some previous studies, which reported that there was a heavier pollution in soil of Hunan province by metals and metalloids due to the frequent reﬁning, smelting and mining activities (Liu, 2011). The soil in the studied area is inevitably contaminated by metal(loid)s during the manufactures of orerelated activities. It could also be part of the explanation that why the contamination of metal(loid)s in soil of this study was more serious than that from the urban areas without ore-related manufactures (Cao et al., 2014). As typical “urban metals”(AjmoneMarsan et al., 2008), the contamination of Cd, Cu, Pb and Zn in soil was found to be heavier, which could be attributed to the trafﬁc source in urban environments (Cui et al., 2005), since these metals are ubiquitously found to be present in fuels, lubricants, oils and engines in vehicles (Dao et al., 2010). Similarly, heavier contamination of metal(loid)s was found in indoor dust. The contents of all the metal(loid)s in dust samples were 1.3e21.9 times of the related natural background levels in Hunan province (Pan and Yang, 1998). Bivariate correlation analysis showed that there was no signiﬁcant correlation in contents of the metal(loid)s between indoor dust and soil, indicating outside soil might be not the main pollution source for metal(loid)s in indoor dust. As one of the most pervasive and important factors affecting human health and well-being, soil could receive plenty of metal(loid)s inputs from a variety of mobile or stationary sources. For the metal(loid)s pollution in urban soils and dusts, except for the contribution originated primarily from the earth's crust, the anthropogenic sources such as trafﬁc emission, industrial emission, domestic emission, weathering of building and pavement surface and atmospheric deposited (Sezgin et al., 2003) could also be great contributors. While for the indoor dust, the pollution sources for different metal(loid)s could be partly attributed to the outside soil,
atmospheric deposition (Zheng et al., 2010) and long-term accumulation from various pollution sources (Leung et al., 2008). Take Cd for example, it is worthy to note that the level of Cd in indoor dust was much higher than that in soil. Since combustion of coal, oil waste and cigarettes is a typical source of Cd accompanying with its use in industry (ATSDR, 2008), combustion of fuel and cigarettes indoor would lead to a higher level of Cd indoor when compared to the broad outdoor. In the PM10, As, Sb, Cu, Zn, Mn and Pb were the most abundant pollutants (Table 2), which was in accordance with the results reported in Nanjing city (Hu et al., 2012) and a battery manufacture area in Hunan (Cao et al., 2015). Because of its respiratory, neurological and other health effects, Pb is regulated with a seasonal threshold of 1.0 mg m3 and an annual threshold of 0.5 mg m3 in the National Ambient Air Quality standard. The Pb concentration was found to be within the related thresholds in our study. However, the contents of Cu, Pb, As, Zn, etc., in PM10 were relatively higher than those of other sites (Hu et al., 2012), especially for As pollution. The observation that plenty of arsenic and nonferrous mining induced environmental metal(loid)s pollution in Hunan could be an important reason (Liu, 2011). Furthermore, based on the threshold regulated through the WHO guidelines with a maximum value of 5 ng m3 for Cd, the concentration of Cd were nearly 4 times higher. This may suggest that inhalation exposure of Cd in PM10 might bring about detrimental health risk to the local children signiﬁcantly. Additionally, Pb in PM10 signiﬁcant positively correlated with Cu in PM10 (Spearman r ¼ 0.573, p < 0.05), whereas Pb showed signiﬁcant negatively correlation with Cd, As, Sb and Ni in PM10 (Spearman r ¼ 0.660, 0.619, 0.643 and 0.552, p < 0.01, respectively). It was then speculated that Cd, As, Sb and Ni in PM10 might have similar pollution source, which was different from that of Cu and Pb.
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Comparable with the previous study (Cao et al., 2015), the drinking water in this study was also considered to be safe for local people, as the contents of the metal(loid)s in tap water were lower than the related thresholds in the National Drinking Water Quality Standard (GB 5749-2006). However, the concentrations of most metal(loid)s in tap water in these area were higher than those previously found in the coking area (Cao et al., 2014). The different outcomes between these two situations could be explained by the fact that the water source of former is somewhat polluted by metal(loid)s during industrial activities (Liu et al., 2010). However, the water of the latter is provided by an ancient spring, the concentrations of metal(loid)s in which are low, and the pilot process which relied on the transport pipes is quite short reducing the potential for pollution during transportation. Compared with the threshold of Pb (ranging from 0.1 to 0.5 mg kg1) for the main food sources according to the National Food Quality Standard, the duplicate food in our study showed a much severely pollution in Pb, which was 0.4e8.3 times higher than the threshold. Most metal(loid)s in food correlated with one another in the concentration (Table S3), indicating similar pollution sources are responsible for the contamination of metal(loid)s in food. This observation in our study was in accordance with the ﬁnding previously conducted in the same city (Liu et al., 2005). It is worthy to be mentioned that, there were no signiﬁcant correlations found between contents of metal(loid)s in food and those in soil or in PM. This outcome could occur because of part of food was not locally produced. However, soil and PM could not be excluded as the pollution source, since various vegetable (as part of daily diet food) showed certain adsorption properties of metals in the soil (Xu et al., 2008), and the accumulation of a series of metal(loid)s such as Pb could be attributed to the contaminated atmosphere (Zheng and Chang, 1989).
3.2. Daily exposure doses of metals and metalloids Considering that aerosol particles, dust/soil, food and drinking water are the main exposure pathways for human exposure to contaminants (Diaz-Somoano et al., 2009; USEPA, 1997), the contribution of each exposure pathway via different routes to children's ADD was evaluated and shown in Fig. 1. In line with a pervious study conducted near a battery area in Hunan province (Cao et al., 2015), the result in this study indicated the ADD of children's exposure to most metal(loid)s was attributed to food ingestion. Particularly for Zn, Cu, Cd, Se and Co, it accounted
Fig. 1. The contribution of drinking water, food, PM10 and soil/dust via ingestion, inhalation and dermal contact to the average daily dose of the children exposure to metal(loid)s.
for 99.11%, 99.65%, 99.34%, 97.28% and 95.81% of the total ADD, respectively. Furthermore, the exposure via soil and dust was the second predominant contributor for most metal(loid)s, especially for As and V, which accounted for 12.13% and 9.47% of the ADD, respectively. The relatively higher As and V levels in dust and soil could be partly responsible for it. Additionally, PM10 exposure was the third contributor to the children's ADD, especially for Sb in which PM10 inhalation could attribute to 6.81% of the total ADD of Sb. 3.3. Risk characteristics 3.3.1. Non-cancer risks The HI and HIt of various metal(loid)s through different exposure pathways in this study are listed in Table 3. The mean combined HIt for all metal(loid)s were 32.9, indicating a higher potential non-carcinogenic risk to the local children when compared to some previous ﬁndings (Cao et al., 2014; Qu et al., 2012). However, the mean combined HIt in this urban area was much lower in comparison with our previous study in a typical lead-acid battery plant (Cao et al., 2015). The main explanation for this difference would be that the heavier pollution played an important role in the previous study, while the daily behavior patterns and intake rate of food, drinking water, etc., had no signiﬁcant variance between these two areas. From Table 3, it indicated that the total non-cancer risk was largely attributed to the ingestion of Pb, As, Sb, Cr, Se, Cd, Cu and Zn and inhalation of Mn and Cr, while the non-cancer risk of children through dermal contact would be negligible. Moreover, the non-cancer risk of children's exposure to the metal(loid)s decreased in the order of Pb > As > Sb > Mn > Cr > Se > Cd > Cu > V > Zn > Co > Ni, indicating Pb, As, Sb, Mn and Cr would pose potentially serious adverse health effects to the local children, similar to the phenomenon found in a lead-acid battery area in Hunan province (Cao et al., 2015). In order to ﬁgure out the main exposure routes of ingestion pathway, the ingestion exposure was divided into different routes as shown in Table S4. It showed that the potential non-cancer risks via soil and water ingestion would be negligible since the HIs were less than 1 at the 75th percentile, while the HIs of food and dust ingestion were higher than 1 even at the 25th percentile. The ingestion of Pb, Cr, Cu, Zn, As, Se, Cd and Sb via foods were largely responsible for the non-cancer risks, followed by the dust ingestion of As, with an median HQ value of 5.88, 1.76, 1.69, 1.31, 2.66, 2.26, 1.93, 3.09 and 1.39, respectively. This corresponded to the result on the ADD of children's exposure to various metal(loid)s, supporting that food ingestion was the largest contributor to the children's daily intake. For most metal(loid)s, the HQs via soil ingestion were lower than those of dust, showing the non-carcinogenic adverse health effects posed by soil were lower than those of dust, mainly due to a heavier contamination level in dust than the soil (seen Section 3.1). Thus, to keep an interior sanitation would be a more effective measure to reduce the detrimental health effects. Considering inhalation exposure was another important pathway for children suffering from potential non-carcinogenic risks, the HIs via inhalation exposure through PM10, soil and dust were divided and shown in Table S5. It indicated that the PM10 was the predominant exposure pathway to the HI via inhalation, as the HIs were higher than 1.0 even at the 25th percentile. The potential adverse health effects posed by PM10 were largely attributed to V, Cr and Mn exposure, and was nearly a hundred to a ten thousand times of those from soil and dust inhalation. While, the inhalation of soil and dust contributed equally to the non-cancer risks for most metal(loid)s. To determine the predominant exposure pathways leading to the potential non-cancer risks, Cr was taken as an example to reveal
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Table 3 Summary of non-carcinogenic risk via dermal contact, ingestion and inhalation exposure of soil, dust, drinking water, food and ambient air on children (5e8) based on the total element concentrations and ﬁeld measurements of the exposure factors at the 25th, median and 75th percentiles. Metal(loid)s
Pb V Cr Mn Co Ni Cu Zn As Se Cd Sb Total
5.22Eþ00 4.03E01 1.67Eþ00 3.81E01 4.84E01 4.21E01 1.39Eþ00 1.08Eþ00 3.95Eþ00 1.92Eþ00 1.59Eþ00 2.77Eþ00 2.13Eþ01
1.30E02 9.22 E01 1.42Eþ00 3.10Eþ00 1.58E01 2.80E03 6.24E04 5.39E04 3.13E01 6.12E02 9.20E02 5.93E01 6.68Eþ00
5.35E05 1.06 E05 1.39 E03 1.92E05 1.68E03 6.19E05 1.11E06 1.61E06 5.69E04 9.48E06 1.09E05 1.50E04 3.96E03
5.23Eþ00 1.33Eþ00 3.09Eþ00 3.48Eþ00 6.44E01 4.24E01 1.39Eþ00 1.08Eþ00 4.26Eþ00 1.98Eþ00 1.68Eþ00 3.36Eþ00 2.80Eþ01
6.31Eþ00 4.82 E01 2.00Eþ00 4.62E01 5.85E01 5.09E01 1.70Eþ00 1.32Eþ00 4.61Eþ00 2.33Eþ00 1.94Eþ00 3.30Eþ00 2.55Eþ01
1.40 E02 1.03Eþ00 1.56Eþ00 3.30Eþ00 1.74E01 3.12E03 6.93E04 5.87E04 3.51E01 6.66E02 1.03E01 7.23E01 7.33Eþ00
5.82 E05 1.15 E05 1.51 E03 2.09E05 1.83E03 6.74E05 1.21E06 1.75E06 6.19E04 1.03E05 1.19E05 1.64E04 4.31E03
6.32Eþ00 1.51Eþ00 3.56Eþ00 3.76Eþ00 7.61E01 5.12E01 1.70Eþ00 1.32Eþ00 4.96Eþ00 2.40Eþ00 2.04Eþ00 4.02Eþ00 3.29Eþ01
8.03Eþ00 5.95 E01 2.51Eþ00 5.90E01 7.50E01 6.54E01 2.19Eþ00 1.70Eþ00 5.32Eþ00 2.98Eþ00 2.49Eþ00 4.22Eþ00 3.20Eþ01
1.50 E02 1.11Eþ00 1.80Eþ00 3.53Eþ00 1.90E01 3.41E03 7.66E04 6.42E04 3.79E01 7.29E02 1.11E01 8.17E01 8.03Eþ00
6.37 E05 1.26 E05 1.65 E03 2.28E05 2.00E03 7.38E05 1.32E06 1.92E06 6.77E04 1.13E05 1.30E05 1.79E04 4.71E03
8.05Eþ00 1.71Eþ00 4.31Eþ00 4.12Eþ00 9.42E01 6.57E01 2.19Eþ00 1.70Eþ00 5.70Eþ00 3.05Eþ00 2.60Eþ00 4.95Eþ00 3.98Eþ01
The value in bold indicates that the health risk from the exposure pathway is higher than the maximum acceptable level, which means it might pose detrimental health effect to the human body.
the contribution of each pathway to the total HI. As shown in Fig. 2, the contribution from food ingestion and PM10 inhalation to the potential non-carcinogenic health risk was nearly comparable, accounting for 56.18% and 43.82% to the total Cr HI, respectively. Different from Fig. 1, which indicated that food ingestion was the largest contributor to the children's Cr ADD, food ingestion and PM10 inhalation were both predominant contributors to the Cr HI. This result revealed that slight environmental pollution still had a great possibility to pose potential adverse health effects to human beings if the RfD value is low. Whereas, though the environment was heavily polluted by contaminants, detrimental effects to human would be unlikely to happen due to the low intake. Except from the neglected contribution from ingestion through water and dust/soil and inhalation through dust and soil, the harmful health effects through dermal contact were regarded to be acceptable. Among the PM10 inhalation, the outdoor air was the larger contributor, which accounted for 67.47%. This could be mainly attributed to that the Cr in indoor air was less polluted compared to that of outdoor. In addition, home indoor air contributed less than that of school indoor air. The latter was 1.5 times higher than the former. One explanation is that the school was surrounded by trafﬁcs and typical urban activities, and thus school interior air was more polluted than the home interior air. On the basis of home investigation, the dwelling houses of most volunteer participants were situated in the residential area with little trafﬁc, which might
be an important reason for the less contamination in home interior air than in school interior air. To assess the uncertainties associated with the exposure factors, the cumulative probability distribution of the calculated HQ of Cr exposure via outdoor PM10 inhalation was depicted as an example using a Monte Carlo simulation, and shown in Fig. 3. The mean and median values of the HQ were 0.96 and 0.93, respectively, which
Fig. 3. Cumulative probability distribution of the Cr HQ from outdoor PM10 inhalation. The health risk was evaluated by means of a Monte Carlo simulation based on Crystal ball soft for 10000 iterations.
Fig. 2. Multi-pathway analysis of HQ (Cr). Each pathway's contribution to the total Cr exposure of the local children.
S. Cao et al. / Chemosphere 147 (2016) 404e411
Table 4 Summary of carcinogenic risks via dermal contact, ingestion and inhalation exposure of soil, dust, drinking water, food and ambient air on children (5e8) based on the total element concentrations and ﬁeld measurements of the exposure factors at the 25th, median and 75th percentiles. Metal(loid)s 5%
Cr Co Ni As Cd Total
1.64E¡02 NA 6.83E04 2.99E¡03 NA 2.01E¡02
1.70E04 4.15E06 4.62E06 1.31E04 3.07E06 3.13E04
∑¼1.64E¡02 P ¼ 4.15E06 P ¼ 6.89E04 ∑¼3.16E¡03 P ¼ 3.07E06 ∑¼2.04E¡02
2.11E¡02 NA 8.66E¡04 3.84E¡03 NA 2.58E¡02
1.74E04 4.42E06 4.93E06 1.45E04 3.39E06 3.32E04
∑¼2.13E¡02 P ¼ 4.42E06 ∑¼8.71E¡04 ∑¼3.98E¡03 P ¼ 3.39E06 ∑¼2.61E¡02
2.46E¡02 NA 1.01E¡03 4.47E¡03 NA 3.01E¡02
1.84E04 4.82E06 5.66E06 1.57E04 3.67E06 3.55E04
∑¼2.46E¡02 P ¼ 4.82E06 ∑¼1.00E¡03 ∑¼4.62E¡03 P ¼ 3.67E06 ∑¼3.05E¡02
7.21E07 NA 4.32E09 2.48E08 NA 7.50E07
7.67E07 NA 4.59E09 2.64E08 NA 7.98E07
8.46E07 NA 5.06E09 2.91E08 NA 8.80E07
NAdnot applicable, since this exposure pathway has less possibility to pose carcinogenic effects to the human beings. The value in bold indicates that the health risk from the exposure pathway is higher than the maximum acceptable level, which means it might pose detrimental health effect to the human body.
were close to the calculated value (1.05) based on the health risk assessment model from U.S. EPA. This suggested that there was probably a little bias in the risk evaluation and there were higher potential non-cancer effects on the local children attributable from the cumulative and aggregative exposure.
the no absolutely accurate risk assessment, this study scored the health effects based on a well-deﬁned investigation on total exposure pathways and various metal(loid)s to the local children in a urban area. 4. Conclusion
3.3.2. Cancer risks The calculated cancer risks of children exposed to the carcinogenic metal(loid)s are shown in Table 4. The ILCR from dermal contact was much lower than the maximum acceptable level (1.0 104). It may conclude that the potential carcinogenic health effects from dermal contact would not occur since one to one hundred in a million chance of additional human cancer over a 70 year lifetime (ILCR ¼ 1.0 106 ~ 1.0 104) is regarded as an acceptable or inconsequential risk (USEPA, 2011b). However, it is of high priority to concern potential cancer risks among the local children from the ingestion exposure, since the total ILCR from this pathway was approximately 100 times of the acceptable level even at the 25th percentile. Similar to the previous study in Hunan province (Cao et al., 2015), the potential cancer risks through ingestion pathway were mainly from Cr and As exposure in this study, and the ILCR of Cr from ingestion exposure pathway were nearly 10 times higher of that of As. Additionally, the potential carcinogenic risks from inhalation pathway could not be neglected, as the total ILCR from this pathway was nearly 3 times higher than the maximum acceptable level (1.0 104) even at the 25th percentile. Similar to the ingestion exposure pathway, Cr and As were the equally main contributors to the ILCR from inhalation exposure, which were both little higher than 1.0 104 at the 25th percentile. Compared to As, the potential cancer risk from Cr was slight higher. Although PM10 was not heavily polluted by Cr and As, the risks due to Cr and As exposure via PM10 could be detrimental to the local children (Table S5), which again implied it is appropriate to evaluate the potential health risks considering the cumulative and aggregative exposure, instead of a single evaluation on environmental contamination status. 3.4. Uncertainly analysis The health risk assessment of metal(loid)s in our study remained some uncertainties which are inherent in quantitative risk assessment. Firstly, the bioavailable or bioaccessible concentration of pollutants is regarded to be more reliable and accurate during the human risk assessment (Oomen et al., 2002). Secondly, certain uncertainties might exist during the toxic experiment for the dose-effect simulation (Xia et al., 2010). Therefore, the noncancer and cancer risks based on the total pollutant concentration in this study would be little overestimated. However, despite
The environmental media such as soil, indoor dust and food in the urban area were heavily contaminated by metal(loid)s. Food ingestion was the largest contributor to the children's ADD for most metal(loid)s. Because of the food ingestion via Pb, Cr, Cu, Zn, As, Se, Cd and Sb, and the PM10 inhalation via Cr and Mn, the local children were likely under higher non-cancer risks, which were up to 30 times higher than the acceptable level. Furthermore, the integrated carcinogenic risks to the local children were approximately 100 times higher than the acceptable level and were attributed mainly to Cr via food ingestion and As via food and dust ingestion. The study strengthens concerns to improve indoor hygienic and environmental quality, so as to decrease potential harmful health effects on children living in urban area. Acknowledgement The founding of this study was provided by the Ministry of Environmental Protection of China (201109064) and the Open Foundations of State Key Laboratory of Environmental Criteria and Risk Assessment (SKLECRA2013OFP05, SKLECRA2015OFP02). The authors are indebted to Nan Huang, Ting Dong, Jianbin Jiang and others who helped in ﬁeld sampling. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2015.12.134. References ATSDR, 2008. Toxicological Proﬁle for Cadmium. US Department of Health and Human Service, Public Health Service, Agency for Toxic Substances and Disease Registry, Atlanta GA. Ajmone-Marsan, F., Biasioli, M., Kralj, T., et al., 2008. Metals in particle-size fractions of the soils of ﬁve European cities. Environ. Pollut. 152, 73e81. Cai, L.M., Xu, Z.C., Qi, J.Y., et al., 2015. Assessment of exposure to metals and health risks among residents near Tonglushan mine in Hubei, China. Chemosphere 127, 127e135. Cao, S.Z., Duan, X.L., Zhao, X.G., et al., 2014. Health risks from the exposure of children to As, Se, Pb and other heavy metals near the largest coking plant in China. Sci. Total. Environ 472, 1001e1009. Cao, S.Z., Duan, X.L., Zhao, X.G., et al., 2015. Health risk assessment of various metal(loid)s via multiple exposure pathways on children living near a typical lead-acid battery plant, China. Environ. Pollut. 200, 16e23. Cheng, Z., Wang, H.S., Du, J., et al., 2013. Dietary exposure and risk assessment of mercury via total diet study in Cambodia. Chemosphere 92, 143e149.
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