Introduction

Tight associations between air pollution and human health have been shown in extensive studies. Most researchers observed a statistically significant correlation between air pollution and mortality (Kampa and Castanas 2008; Bell et al. 2009; Han et al. 2015). The rate of hospitalization, symptoms/lung function, mortality were closely linked to PM2.5 levels (Pope 2000). It had been further shown the increase in lung cancer and cardiopulmonary mortality with enhanced PM2.5 concentrations (Pope et al. 2002; Huang et al. 2012).

Although heavy metals only account for a small fraction of PM2.5, heavy metals are carcinogenic and low biodegradable. The heavy metals become toxic if humans are exposed to them in high levels for a long time period (Jarup 2003; Greene and Morris 2006). For example, lead exposure can lead to congenital malformations and lesions of the developing nervous system, causing important impairment in newborn’s motor and cognitive abilities (Bellinger 2005; Garza et al. 2006). Therefore, it is urgent to know the pollution characteristics of heavy metals in PM2.5 and assess their health risks, especially in the polluted urban environment with densely population.

There were more comprehensive studies on the airborne toxic heavy metals in Europe and North America (Diaz and Dominguez 2009; Kim et al. 2005), while limited studies have been conducted in Asia, especially in the countries with rapid economic development such as China and India (Massey et al. 2013; Dubey et al. 2012; Gao et al. 2014). Most studies mainly focused on the major components in PM2.5 and their sources in urban areas of China (Song et al. 2006; Zhou et al. 2014), while only few recent studies had reported particulate heavy metal levels and their health risks (Hu et al. 2012; Sun et al. 2010).

Chengdu (30.67°N, 104.06°E), located in the west of Sichuan basin, is one of the biggest cities in southwestern China with a population of more than 10 million in the area of 12,000 km2. Because it is surrounded by mountains, the wind speed is low and the frequency of calm wind is high and thus blocks the dispersion of air pollutants. Additionally, increasing vehicle continued to generate more traffic emissions, combined with coal combustion, biomass burning and industrial activities, Chengdu is facing severe air pollution. To provide the scientific bases for government and to benefit people, this article mainly studied the characteristics, sources and health risks of eight toxic heavy metals (As, Cd, Cr, Cu, Mn, Ni, Pb and Zn) in Chengdu. The EF analysis was used to assess the pollution level, and CA method was used to analyze the potential sources of heavy metals in PM2.5. Furthermore, US EPA health risk assessment model was applied to assess health risk from these toxic heavy metals.

Materials and methods

Samples collection

The sampling was set on the rooftop of Institute of Plateau Meteorology, Chengdu, China. It was 15 m above ground, surrounded by concentrated residential areas, and there were no industrial pollution sources in 5 km. It can represent the urban environment in Chengdu. The site is shown in Fig. 1. Samples were collected at a flow rate of 5 L min−1 on 47-mm Teflon filters (Whatman PTFE). One hundred and twenty-one PM2.5 samples and 10 blank samples were collected during the periods of April 19, 2009, to January 31, 2010. The sampling days were divided into four seasons: spring (April 19 to May 17, 2009), summer (July 6 to August 6, 2009), autumn (October 26 to November 26, 2009) and winter (January 1 to January 31, 2010). The collection duration for each sample was 24 h (starting at 10:00 a.m. local time each day and ending at 10:00 a.m. next day). The blank samples were collected on the loaded filters without operation for 24 h. Teflon filters were analyzed gravimetrically for particle mass concentrations using an electronic microbalance with a sensitivity of ±1 μg (Sartorius, Göttingen, Germany), and the exposed filters were stored in a freezer at −18 °C before chemical analysis to minimize evaporation of volatile components (Tao et al. 2013).

Fig. 1
figure 1

PM2.5 sampling site in Chengdu, China

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Chemical analysis

The energy-dispersive XRF was used to determine the concentrations of toxic heavy metals, including As, Cd, Cr, Cu, Mn, Ni, Pb and Zn. Each of samples is analyzed for 30 min, and it produces a spectrum of X-ray counts versus photon energy in the process, the peak energies matching eligible elements and peak areas corresponding to elemental concentrations. In addition, quality assurance/quality control (QA/QC) procedures were carried out and had been maintained throughout years, such as regular instrument calibration with standard reference materials (SRMs), followed the standard QA/QC requirements of the Chinese government. The detailed XRF analysis procedure can be found in the study conducted by Xu et al. (2012).

Contamination assessment

Enrichment factor was calculated for each detected element to evaluate the influences of anthropogenic emissions to atmospheric trace elements (Wang et al. 2006). In this study, it was used to assess the contamination characteristics. The calculation formula is:

$${\text{EF}} = \frac{{\left( {C_{\text{i}} /C_{\text{ref}} } \right)_{\text{aerosol}} }}{{\left( {C_{\text{i}} /C_{\text{ref}} } \right)_{\text{crustal}} }}$$
(1)

where C i is the concentration of the element considered in aerosol and C ref is the concentration of reference element in crust. The reference element in crust is based on Chengdu background soil conditions (Tang et al. 2005). The Al, Fe, Sc and Ba are commonly used as reference elements of crustal materials (Gao et al. 1992). In this study, Al is used as the reference element, because many cement and steel factories built in Chengdu which could produce significant amounts of Fe and Si (Tao et al. 2013). If EF is <1, then its dominant source is natural sources (such as crust and soil); if EF exceeds 10, then the element may mainly come from anthropogenic sources (such as industry and waste incineration) (Yang et al. 2009).

Source apportionment methods

Correlation analysis is used to represent the variation pattern of the various elements in PM2.5 and correlation structure among the measured variables. The correlation coefficient is used to analyze the degree of correlation between two elements. If the correlation coefficient value was closer to ±1.0, it indicated the two variables were more likely derived from the same pollution source. Inversely, the elements came from the different source (Paatero et al. 2005). CA method was performed by using SPSS 20.0 software.

Health risk assessment

People are easy to expose to heavy metals in their daily lives. We divided them into two groups: children (0–15 years) and adults (>15 years) in this study. The exposure can generally occur via direct inhalation of atmospheric particles through mouth and nose. According to the US Environmental Protection Agency’s Superfund program (USEPA), the exposure concentration can be calculated as follows:

$${\text{EC}} = {{\left( {C \times {\text{ET}} \times {\text{EF}} \times {\text{ED}}} \right)} \mathord{\left/ {\vphantom {{\left( {C \times {\text{ET}} \times {\text{EF}} \times {\text{ED}}} \right)} {\text{AT}}}} \right. \kern-0pt} {\text{AT}}}$$
(2)

where EC: exposure concentration (μg m−3), ET: exposure time (24 h day−1), EF: exposure frequency (365 days year−1), ED: exposure duration (6 years for children and 24 years for adults), AT: average time (for non-carcinogens, AT = ED × 365 days × 24 h day−1 and for carcinogens, AT = 70 years × 365 days year−1 × 24 h day−1).

The contamination concentration C is considered to yield an estimate of the “reasonable maximum exposure,” the upper limit of the 95 % confidence interval for the mean (95 % UCL). Since the concentrations of most studied heavy metals approximated lognormal distributions, the 95 % UCL was calculated as follows:

$$C_{{95\% {\text{UCL}}}} = \exp \left[ {\bar{X} + 0.5 \times s^{2} + \frac{s \times H}{{\sqrt {n - 1} }}} \right]$$
(3)

where \(\overline{X}\) is the arithmetic mean of the log-transformed data, s is the standard deviation of the log-transformed data, H is the H-statistic, and n is the number of samples.

The exposure risk of inhalation method was calculated as follows:

$${\text{HQ}} = {{\text{EC}} \mathord{\left/ {\vphantom {{\text{EC}} {\left( {{\text{RfC}}_{\text{i}} \times 1000\;\mu {\text{g}}\cdot{\text{mg}}^{ - 1} } \right)}}} \right. \kern-0pt} {\left( {{\text{RfC}}_{\text{i}} \times 1000\;\upmu {\text{g}}\,{\text{mg}}^{ - 1} } \right)}}$$
(4)
$${\text{CR}} = {\text{IUR}} \times {\text{EC}}$$
(5)

where HQ is the non-cancer risk of a single contaminant in an exposure way. The RfCi is the inhalation reference concentrations (mg m−3) below which adverse non-cancer health effects are unlikely to happen. If HQ < 1, it is believed that the non-cancer effect is not significant and sometimes may be neglected. If HQ ≥ 1, adverse health effect is possible and more attention should be paid. IUR is the inhalation unit risk (per μg m−3). CR is the probability of cancer, which showed by the cancer individual number among a certain number of people. The acceptable or tolerable risk is 1 × 10−6–10−4, which is the basis to assess health risk.

According to the classification given by International Agency for Research on Cancer (IARC 2014), As, Cd, Cr, Ni and their related compounds are classified as class 1 carcinogenic agents, whereas Pb and its associated compounds are assigned as class 2A carcinogenic agents. From the full list of integrated risk information system (IRIS) chemicals and the US EPA regional screening level (RSL) resident air supporting table, the RfCi for As, Cd, Cr, Mn and Ni and the IUR for As, Cd, Cr, Ni and Pb were provided. The RfCi for Cu and Zn was not available, so the health risk characterization of them was not discussed.

Results and discussion

Concentrations and seasonal variations of heavy metals in PM2.5

The average concentration of PM2.5 in Chengdu was 165.1 ± 84.7 µg m−3 (Table 1), ranging from 49.2 to 425.0 µg m−3, far exceeding the NAAQS (35 µg m−3 in annual average). Moreover, 91 % daily PM2.5 concentrations exceeded the daily standard (75 µg m−3). Table 1 lists the PM2.5 levels in various urban atmospheres. The PM2.5 concentrations in Beijing, Shanghai and Guangzhou, representative megacities located in three different climate regions of China, were 118.2 ± 40.6, 103.1 and 81.7 ± 25.6 µg m−3, respectively (Wang et al. 2013a, b; Yang et al. 2011). They were all lower than that measured at Chengdu. Comparing with other cities, the PM2.5 concentration in Chengdu was similar to that in Agra (India), but dramatically higher than that in Jeddah (Saudi Arabia), Tampa (America) and Seoul (Korea). On the whole, the ambient PM2.5 pollution was serious in urban Chengdu.

Table 1 Heavy metals (ng m−3) and PM2.5 concentrations (µg m−3) in different cities
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The total mass concentration of the eight toxic heavy metals in this study was 1.4 µg m−3, only accounting for 0.85 % of PM2.5 mass concentrations. Each element level at different seasons is shown in Fig. 2. The concentrations from high to low were Zn > Pb > Mn > Cu > As > Cr > Cd > Ni, in which 27.9 % mass concentration of heavy metals was carcinogenic. Comparing with the ambient air quality standard of As (6.0 ng m−3), Cd (5.0 ng m−3) and Pb (500 ng m−3) in China, As and Cd concentrations were much higher than the standards, suggesting that their concentrations were at dangerous levels. The highest concentrations of most heavy metals except for Cd occurred at winter. This may be affected by the meteorological factors such as precipitation and wind speed. Frequent rainfall can efficiently scavenge atmospheric particulate matter and inhibit fugitive dust resuspension. The high wind speed can fasten the diffusion of pollutants in atmosphere.

Fig. 2
figure 2

Seasonal concentrations of eight toxic heavy metals in PM2.5

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In comparison with other urban atmosphere in China (Table 1), the particulate heavy metal levels in Chengdu were comparable to those in Beijing. However, the concentrations of heavy metals were lower in Chengdu than those in Guangzhou, except for As. In particular, the concentrations of Cu and Cr in Guangzhou were 3.5 and 3.9 times of those in Chengdu, indicating that the pollution of heavy metals in Chengdu was not the worst in China. When compared to Shanghai, only the concentrations of Cr and Ni were higher than those in Chengdu, while the rest were lower. When compared with foreign cities, the levels of most heavy metals in Chengdu were much higher than those in Tampa, Seoul and Jeddah, while lower than those in Agra. The huge disparity among these cities might be due to the difference of economic structure. In Jeddah, Tampa and Seoul, the tertiary industry was the most flourished, so the pollution of heavy metals was at a low level. While in Agra, the secondary industry with some big refineries was developed, leading to the serious environmental problems of heavy metals. In short, the particulate toxic heavy metals in Chengdu were at a moderate level in comparison with the four Chinese megacities, even though the concentrations of As and Cd surpassed the standards, while the pollution levels in Chengdu were significantly higher than those in most foreign cities.

Contamination assessment of toxic heavy metals

The seasonal EF values for each toxic metal are shown in Fig. 3. The EF values of heavy metals were typically higher than 10, suggesting that they were mainly influenced by anthropogenic sources. For Cr, Mn and Ni, their EFs fell within the range of 1–40, indicating that they were slightly enriched. A similar result was obtained in Jinan (Gu et al. 2014), in which the EFs of Cr, Mn and Ni were between 1 and 40. The EF for Cu was much more than 100, indicating that it was highly enriched. For As, Cd, Pb and Zn, they were highly enriched and most of their EFs were more than 1000, especially for Cd which even reached up to 10,000 in summer and autumn. It suggested that the extremely enriched metals were predominantly controlled by anthropogenic sources. Comparable EFs of As and Cd were also found at Foshan, an industrial city of China, in which the daily maximum EFs of As and Cd reached 674.35 and 18,357, respectively (Tan et al. 2014).

Fig. 3
figure 3

Seasonal enrichment factor values of eight toxic heavy metals in PM2.5

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The seasonal variation order of EFs was spring < autumn < winter < summer for As, Cr, Mn, Pb and Zn. For Cd, the EFs order were winter < spring < autumn < summer; for Cu, the order was spring < winter < summer < autumn; and for Ni, the order was autumn < spring < winter < summer. Thus, the highest enrichment degrees were in summer for most toxic heavy metals except for Cu, while the EFs were relatively low in spring.

Possible sources of heavy metals

The correlation analysis results are listed in Table 2. Pb, As, Cu, Zn, Cr and Mn were significantly correlated, indicating that they may have a common source or be controlled by meteorological factor such as mixing layer height. Previous studies have suggested that Cu, Zn and Pb can be served as markers for traffic sources (Xia and Gao 2011; Xu et al. 2012). Smichowski et al. (2004) pointed out that Pb mainly came from tire wear after the Pb was banned in gasoline in 1996. In the study conducted by Johansson et al. (2009), more than 90 % of the road traffic emissions of Cu were due to brake wear, and 40 % of Zn were estimated to be derived from exhaust emissions. In atmospheric particles, As was usually used as coal combustion indicator in China (Tian et al. 2011). The correlation coefficient (R 2) value between Ni and Cr was 0.598 (P < 0.01), suggesting that part of them were probably derived from the same source. The coal combustion was the leading source of Ni contamination (Tian et al. 2012). Thus, the significant correlation among As, Cr, Cu, Mn, Ni, Pb and Zn may come from traffic emissions and coal combustion. It should be noted that Cd had not shown significant correlation with any other metals. The metallurgy industries and mechanical manufactures were found to be the largest source of Cd (Thomaidis et al. 2003). They may also contribute significant amounts of particulate Cd in Chengdu, since more than 78 nonferrous metal smelting enterprises were set to the northeast part of Chengdu (around 20 km). Therefore, the CA results suggested that Zn, Pb, Mn, As, Cu, Cr, Ni and Cd in PM2.5 were mainly associated with anthropocentric sources such as traffic emissions, coal combustion, metallurgy and mechanical manufacturing emissions.

Table 2 Correlation analysis among heavy metals in PM2.5
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Health risk assessment of heavy metals in PM2.5

The carcinogenic and non-carcinogenic risks from toxic heavy metals in PM2.5 are listed in Table 3. For non-carcinogenic risks, the HQ values of As, Cd, Cr, Mn and Ni were investigated and the order from high to low was As > Mn > Cd > Ni > Cr. The HQ values of As, Mn and Cd were 3.07, 3.06 and 1.20, respectively, exceeding the safe level (HQ = 1), indicating that these metals would result in non-carcinogenic health effects to both children and adults. Compared with the results obtained in Nanjing, China (Hu et al. 2012) and Mexico (Diaz and Dominguez 2009), both Mn and As posed no non-carcinogenic risks to children and adults. The HQ values of As, Cd, Mn and Ni were even higher than those of vehicle inspection workers in Beijing (Li et al. 2013). Thus, people in Chengdu suffered much more harm from atmospheric toxic heavy metals. Exposure to elevated levels of inorganic As is associated with irritation of the skin and mucous membranes (ATSDR, USEPA). Although Mn was nutritionally essential in humans at low level, bad effects may be caused on the nervous system when people are exposed to high level (ATSDR, IRIS). Further studies are required to investigate the high non-carcinogenic risk of As, Mn and Cd in Chengdu.

Table 3 Cancer and non-cancer health risk values of toxic heavy metals in PM2.5
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As can be seen in Table 3, the order of carcinogenic risk values for both children and adults was Cr > As > Pb > Cd > Ni. Only the values of Cr were beyond the tolerable risk limit; the values were 1.44 × 10−4 and 5.76 × 10−4 for children and adults, respectively. It was similar to the results obtained in India (Yadav and Satsangi 2013) and Tianjin, China (Zhang et al. 2014). The carcinogenic values of As, Cd and Pb for both children and adults were all higher than 1 × 10−6, but within the acceptable level (1 × 10−4), and the values of Ni were 1.23 × 10−7 and 4.94 × 10−7 for children and adults, respectively. The carcinogenic values of As, Cd and Pb were also in the range of 10−6–10−4 in Nanjing, China (Sun et al. 2014), but the values of Ni (10−7) were lower than that in Chengdu. The carcinogenic values of As, Cd, Cr, Ni and Pb were all higher for adults than for children.

On the whole, the non-carcinogenic risk of As, Mn and Cd and the carcinogenic risk of Cr were beyond the safe levels in Chengdu; therefore, more attention and action should be paid on the pollution control of toxic heavy metals in PM2.5, especially As, Mn, Cd and Cr.

Conclusion

The PM2.5 samples were collected from April 2009 to January 2010, and eight toxic heavy metal elements (As, Cd, Cr, Cu, Mn, Ni, Pb and Zn) were analyzed and discussed in the study. Some results are obtained:

The average concentration of PM2.5 in Chengdu was at a very high level, far exceeding the NAAQS. The concentrations of Zn and Pb were always high in the year round, far more than other metals. The levels of heavy metal in PM2.5 in urban Chengdu were at a moderate level compared with other cities in China, while significantly higher than most cities in other countries.

The EF analysis results showed that these eight toxic heavy metals were all enriched at high levels. More specifically, Cr, Mn and Ni were slightly enriched, Cu was highly enriched, while As, Cd, Pb and Zn were severely enriched.

The source apportionment results indicated that As, Cr, Cu, Mn, Ni, Pb and Zn were derived from traffic emissions and coal combustion, while Cd was probably influenced by the metallurgy and mechanical manufacturing.

The results of health risk assessment showed that As, Mn and Cd would pose a significant non-carcinogenic health risk to both children and adults, while Cr would cause carcinogenic risk for both children and adults. Other toxic heavy metals were within a safe level.