Heavy metals in road dust from Xiandao District, Changsha City, China: characteristics, health risk assessment, and integrated source identification
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The physicochemical properties and the contents of metals (Cu, Zn, Pb, Cd, Cr, and Fe) in 51 road dust samples from Xiandao District (XDD) were investigated. Enrichment factor (EF), multivariate statistics, geostatistics, and health risk assessment model were adopted to study the spatial pollution pattern and to identify the priority pollutants and regions of concern and sources of studied metals. The mean EFs revealed the following order: Cd > Zn ≈ Pb ≈ Cu > Cr. For non-carcinogenic effects, the exposure pathway which resulted in the highest levels of exposure risk for children and adults was ingestion, followed by dermal contact and inhalation. Hazard index (HI) values for the studied metals at each site were within the safe level of 1 except maximum HICr (1.08) for children. The carcinogenic risk (CR) for Cd and Cr at each site was within the acceptable risk level (1E-06) except CRCr (1.08E-06) for children in the road intersection between the Changchang highway and the Yuelin highway. Cr was identified as the priority pollutant followed by Pb and Cd with consideration of the local population distribution. Spatially, northwest and northeast of XDD were regarded as the priority regions of concern. Results based on the proposed integrated source identification method indicated that Pb was probably sourced from traffic-related sources, Cd was associated with the dust organic material mainly originated from industrial sources, and Cr was mainly derived from both sources.
KeywordsRoad dust Heavy metals Distribution characteristics Health risk assessment Source identification
At present, over half of the global population lives in urbanized areas and, according to world urbanization speed, 70 % of the world’s population is probably urban residents in 2050 (United Nations 2014). Anthropogenic activities associated with industrialization and urbanization are dramatically concentrated in urban areas, especially in Africa and Asia (Tang et al. 2013). As a focus of resource consumption and chemical emissions, cities have been resulting in a variety of problems including ecosystem degradation, public health risk, biodiversity decrease, and so on (Charlesworth et al. 2011; Li et al. 2015a; Liu et al. 2014). Road dust, the primary source and sink of atmospheric pollutants derived from multimedia environment (Chen et al. 2014; Moreno et al. 2013), is regarded as a sensitive indicator of urban environment. Furthermore, road dust often contains high levels of heavy metals and organic contaminants such as polycyclic aromatic hydrocarbons (Saeedia et al. 2012). Heavy metals in road dust may derive from anthropogenic sources such as industrial activities, traffic emissions (exhaust and non-exhaust), pavement wear, municipal solid waste disposal, and construction and demolition activities (Gunawardana et al. 2012; Lu et al. 2010), and from natural sources such as weathering. Hazardous effects or potential risks to civil health can be caused by heavy metal residues in road dust via direct inhalation, ingestion, and dermal contact absorption (Chen et al. 2014; Liu et al. 2014; Lu et al. 2014).
In recent decade, numerous studies on road dust have been conducted on toxic metal concentrations, distributions, and source identification (Apeagyei et al. 2011; Ferreira-Baptista and De Miguel 2005; Gunawardana et al. 2012; Lu et al. 2010; Pathak et al. 2013; Wei et al. 2009), as well as potential ecological risk assessment (Saeedia et al. 2012; Tang et al. 2013) and health risk assessment (Chen et al. 2014; Li et al. 2015a; Lu et al. 2014). However, most of the existing studies were done in the developed countries or the megacities, and little information is available for the developing countries (Keshavarzi et al. 2015; Wei et al. 2015), especially the medium-size cities. In fact, the medium-size cities in developing countries have faced unprecedented environmental issues under its fast industrialization, urbanization, and relatively poor environmental management system. Therefore, it is of significance to systematically study spatial distribution, induced health risk and sources of dust metals, and corresponding dust physio-chemical factors such as pH, dust organic material, and climate which provide a better understanding of pollution mechanism (Li et al. 2013; Luo et al. 2011; Yu et al. 2014) in medium-size cities of developing countries for supporting their environmental management decision making.
In recent three decades, the urban environmental pollution in China has drawn the worldwide attention. Chinese urban atmospheric pollution has become an obvious issue associated with urban economic stability, human safety, and even social equity (Chen et al. 2014; Wei and Yang 2010). Xiandao District (XDD), the pilot district of constructing a friendly environmental society, belongs to Changsha City which is the provincial capital city of Hunan Province, middle China. XDD has experienced rapid urbanization and industrialization with an obvious decline of urban environmental quality in recent 10 years (Chen et al. 2011; Li et al. 2007, 2015a, b; Yang et al. 2012). Except for the distribution and health risk assessment of toxic metals (Ni, Hg, Mn, As) in soil and dust from XDD studied in our published paper (Li et al. 2015a, b), very limited information is available associated with other heavy metal pollution characteristics in road dust and their induced health risk throughout XDD in the previous studies. The objectives of this study were (1) to determine spatial distributions (heavy metals and dust properties) and enrichment degrees of metals (Cu, Zn, Pb, Cd, and Cr) in road dust from XDD using the enrichment factor and geostatistics method of the inverse distance weighted (IDW) interpolation, (2) to assess non-carcinogenic and carcinogenic human health risk of dust heavy metals to determine local priority pollutants and regions of concern, and (3) to identify natural and/or anthropogenic sources of the studied metals jointly using multivariate statistical techniques, spatial distribution maps, and local land use map.
Materials and methods
Xiandao District is a municipal district with inhabitant of over one million, and the average population density is 885 people per square kilometers. XDD belongs to subtropical monsoon climate and its annual average temperature is 16.8–17.2 °C. The urban average annual precipitation is 1358–1552 mm. The area of XDD is totally 1200 km2 and the urban residents per capita disposable income reach US$5300 per year in 2013. From 2007 to 2013, the urbanization rate in Changsha City has increased from 56.5 to 70.6 % (Huang et al. 2016) and the local average annual growth rate of urbanization (2.35 %) obviously exceeds that all over China (1 %). The local vehicular fleet has increased rapidly in recent years with an average increasing rate of over 17 % and probably reaches two million vehicles in 2015. Besides, outdoor air quality of Changsha City is not up to Chinese standard level in 168 days out of the whole year of 2013 (Li et al. 2015a).
Samples collection, preparation, and analytical methods
Dust pH was determined with a dust/water ratio of 1:2.5 (w/v) using HI 3221 pH meter (Hanna Instruments Inc., USA). Dust organic material (DOM) was determined by K2Cr2O7 digestion method (Nelson and Sommers 1982). For the total heavy metal content detection, 0.40-g aliquots from each sample were weighed using an electronic balance (Sartorius TE124S, Germany). Subsequently, the samples were placed in Teflon tubes and digested with HCl, HNO3, HF, and HClO4 (10:6:5:3 ratio) (Xi et al. 2004). Then, the solutions were diluted with 2 % v/v HNO3 to a final volume of 50 ml and analyzed for Fe, Cr, Cu, Zn, Pb, and Cd by an atomic absorption spectrophotometer (AAnalyst700, Perkin-Elmer Inc., US). Quality assurance and quality control were assessed using duplicates, method blanks, and state first-level standard materials (GBW GSS-5) with each batch of samples. The analysis results were reliable when repeat sample analysis error was below 5 %, and the analytical precision for replicate samples was within ±10 %. The recovery of standard samples ranged from 96 to 105 %. Throughout the experimental process, ultra-pure water was utilized for preparing the solutions, dilutions, and blanks. All reagents used in the present investigation were of analytical reagent grade. All the glassware and plastic vessels were treated with 10 % v/v HNO3 for at least 12 h and then washed with distilled and deionized water before use. Contact with metals was avoided during all procedures in order to avert potential cross-contamination of the samples. The results met the accuracy demand of the Chinese Technical Specification for Soil Environmental Monitoring HJ/T 166-2004.
Pollution assessment methodology
Health risk assessment model
The reference dose and slope factor of metals
0.63E + 01
4.2E + 01
Multivariate and geostatistical methods
To explore relationship among metals in road dusts and identify their sources, geostatistics, Pearson’s correlation analysis, and Fourier spectral analysis (FSA) were performed by the software package ArcGIS 9.3, SPSS version 16.0, and Surfer 8.0 for Windows. Geographic information system was used to analyze the spatial characteristics of heavy metals in road dust from XDD with inverse distance weighted (IDW) method. IDW employs a specific number of nearest points that are then weighted according to their distance from the point being interpolated. In this study, the power of 2 and the number of neighboring samples of 12 were chosen to clearly show both spatial variation and spatial patterns of the pollutants. Pearson’ correlation analysis is a method to measure and explore the correlativity among dust metals and dust properties. Fourier spectral analysis (FSA) is widely utilized to obtain how the signals are produced and how many steps are in this procedure based on decomposing these signals into components with different frequency. FSA can also offer spatial correlation analysis with the correlogram. The correlogram makes visual representation about the spatial patterns and spatial correlation of the targeted variables. The final solutions of spectrum analysis are expressed as linear equations of sine and cosine functions known as Fourier transformations (Chen et al. 2009). Therefore, FSA was used to assist geostatistical method and Pearson’ correlation analysis for further indentifying pollution sources (Chen et al. 2009; Li et al. 2014) and decreasing analysis uncertainty (Li et al. 2012).
Results and discussion
Properties and metal concentrations in road dust
Summaries of measured metals in road dust from different cities at home and abroad mg/kg
(Tang et al. 2013)
(Lu et al. 2010)
(Duzgoren-Aydin et al. 2006)
(Wei et al. 2009)
(Shi et al. 2010)
(Wijaya et al. 2012)
(Apeagyei et al. 2011)
(Rasmussen et al. 2001)
(Saeedia et al. 2012)
(Banerjee et al. 2003)
(Ferreira-Baptista and De Miguel 2005)
Enrichment factors of metals and their spatial distributions
To further quantitatively assess the enrichment degrees of studied metals, EFs of studied metals were calculated based on Eq. (1). Mean EFs of metals (Cu, Zn, Pb, Cd, and Cr) in road dust all exceeded 1. The ranges (arithmetic mean) of EFs for studied metals in road dust were Cu 0.04–4.58 (2.60), Zn 1.24–14.18 (4.00), Pb 1.16–6.57 (3.41), Cd 36.94–353 (128), and Cr 0.86–3.23 (1.71). The ranking of mean enrichment levels of the metals is as follows: Cd (extremely high enrichment) > Zn (moderate enrichment) ≈ Pb (moderate enrichment) ≈ Cu (moderate enrichment) > Cr (minimal enrichment). Besides, for Cu, Cr, Pb, and Zn, there were 70.59, 17.65, 88.24, and 70.59 % sampling sites exceeding EF value 2, and 0, 0, 17.65, and 11.76 % sampling sites exceeding EF value 5, respectively. Moreover, for Cd, 94.12 % sampling sites exceeded EF value 40. Obviously, the enrichment degree of Cd in road dust was higher than that of other metals.
According to Fig. 2d–f, the similar distribution patterns of spatial distribution were observed for Pb, Zn, and Fe to some extent. Pb and Zn from each sampling site decreased in the order of S12 > S15 > S1 > S11 > S5 > S10 > S3 ≈ S6 > S16 > S9 > S17 > S2 > S8 > S4 > S7 > S13 > S14 and S1 > S15 > S16 > S11 > S16 > S5 > S2 > S14 > S17 > S7 > S9 > S6 > S4 > S3 > S10 > S8 > S13. High-enrichment areas for Pb and Zn were in the northwest corner and middle east parts of XDD. Fe, as the most abundant metal and the fourth most common element in the Earth’s crust, was regarded as reference element in calculation of EFs.
Distributions of DOM and Zn also show partly similar spatial distributions (Fig. 2e, g). DOM from each sampling site decreased in the order of S1 > S2 > S16 > S3 > S11 > S15 > S12 > S7 > S10 > S4 > S13 > S5 > S17 > S6 > S8 > S14 > S9. The spatial distribution of Cu was in less spatial variability, and Cu from each sampling site decreased in the order of S16 > S2 > S15 > S12 > S11 > S5 > S6 > S3 > S8 > S9 > S17 > S10 > S1 > S7 > S4 > S13 > S14 (Fig. 2b). And, the high enrichment areas for Cu were found in the upper northwest and middle east parts of XDD.
Combined with the present land use map of XDD in Changsha City (Fig. 1), it was found that the spatial distributions and enrichment levels of studied metals concentrations were in close relationship with regional land use pattern. Further, based on this precondition, the integrated source identification method was proposed in our following sections and the detailed relationship between heavy metals and regional land use pattern was interpreted simultaneously.
Human health assessment of children and adults exposure to road dust
Daily dose and hazard indices of metals in road dust for children
1.08E + 00
Daily dose and hazard indices of metals in road dust for adults
Daily dose and cancer risk of metals in road dust for children
For non-carcinogenic risk, ingestion of dust particles appeared to be the main exposure pathway for metals to children and adults, followed by dermal contact (Tables 4 and 5), which was similar to other reports (Ferreira-Baptista and De Miguel 2005; Shi et al. 2010; Liu et al. 2014). HIs for the studied metals in each sample were higher for children than for adults. Specially, HQs for children through ingestion were averaged 7.2 times higher than those for adults, with dermal contact 4.9 times higher and inhalation 1.4 times higher. Mean HIs for the analyzed metals to both children and adults decreased in the following order: Cr > Pb > Cd > Cu > Zn. HIs of Cr, Pb, and Cd were more than 1 order of magnitude higher than those for other metals. For children, the mean HIs were generally within the safe level, whereas the maximum HI for Cr (1.08) exceeded the safe level (Table 4). Besides, the HIs for adults in all sites were within the safe level (Table 5). Spatially, the areas of high resident population density (Li et al. 2015a) to Cr, Pb, and Cd exposure decreased in the following order: Pb > Cd > Cr (Fig. 1). Contacting by children in enough doses of Pb can trigger neurological and developmental disorders (Ferreira-Baptista and De Miguel 2005). Cd is a cumulative toxic metal, and the kidney is the main target for Cd toxicity. Cr is a neurological, renal, and developmental toxicant at certain concentrations (Jones et al. 2010).
For carcinogenic risk (Table 6), the carcinogen risk from inhalation exposure pathway was considered in the model based on Eqs. (5) and (7). By comparison, CRCr ranged from 1.63E-07 to 1.08E-06 and the mean CRCr was 1.70E-07 which have the two times the order of magnitude than corresponding CRCd. The results show that the cancer risks of Cd and Cr were generally within the internationally accepted precautionary criterion (1E-06). However, for children, CRCr at S6 exceeded 1E-06 indicating an unacceptable level resulting in high carcinogenic risk and hazard that would happen at any time. Relatively fortunately, high-risk areas around S6 were not in the high density of resident population (Fig. 1). Therefore, based on results of spatial EFs and health risks for the studied metals, Cr should be regarded as priority control pollutants. Moreover, the potential non-carcinogenic risk of Pb and Cd cannot be overlooked for children and typically occupational receptors such as taxi drivers and street cleaners who may also be at health risk due to the long-term exposure to the road dust.
The evaluation of uncertainty is an important step accompanying the health risk assessment. Some sources of uncertainty are well emphasized in the literature (Li et al. 2012) and are inherent like the reference toxicity values and PEF. And, the exposure parameters used to characterize the risks adapt to the people of whole country. Therefore, it is recommended that a clinical toxicological research is carried out in the obtained priority regions of concern, and the precise epidemiological consequences on children and adults living in different communities with exposure to the road dust should be performed. Moreover, accurate spatial population density and seasonal changes of studied metal concentration were also not considered. However, although there are some uncertainties, the present study would be a useful tool to assess the human health risk due to exposure to road dust metals in urban environments and could help to supply key information for the public and government to establish scientific receptor protection measures and efficient regional environment management strategies.
Integrated metal source identification
Pearson correlation matrix for metal concentrations and dust properties in road dust from XDD
Integrated metal source identification
Multivariate statistical analyses mainly are frequently and widely utilized in source apportionment of environmental pollutants in aquatic environment sediments (Li et al. 2014), soils (Li et al. 2012; Luo et al. 2011; Yang et al. 2012), dusts (Han et al. 2006; Kartal et al. 2006; Lu et al. 2010; Chen et al. 2011), waters, etc. However, just like the findings in above sections, there were uncertainties to a certain extent to identify sources of pollutants merely by mathematical and statistical methods for reasons of insufficient data, model resolution itself, and even incorrect way of data processing (Huang et al. 2015). Therefore, to reduce corresponding uncertainties, the integrated source identification method based on the multivariate statistical methods including Pearson’s correlation analysis and FSA (Table 7 and Fig. 3), spatial distribution maps based on geostatistical interpolation technology (Fig. 2), and the present land use map based on remote sensing technology (Fig. 1) was proposed and performed. As a result, three main sources of the studied heavy metals in road dust from XDD could be finally identified, i.e., (1) Pb, Cu, and Zn were probably sourced from traffic-related sources, (2) Cd associated with DOM mainly originated from industrial sources, and (3) Cr is mainly derived from both sources above.
The first group of elements, including Pb, Cu, and Zn, had a generally positive correlation between each other (Table 7) and shared a similar spatial distribution with that of their hotspot areas mainly associated with main roads where high traffic density was identified (Fig. 1). Further, main roads included second ring road and Jinzhou highway of XDD, and their crossroads were of higher pollution (Fig. 1). Therefore, Pb in road dust might originate from roadside soil deposited materials from previous usage of leaded fuel, whereas Cu and Zn from tire abrasion, lubricants, and corrosion of vehicular parts (Pathak et al. 2013; Wei et al. 2009). Moreover, their hotspot areas around S1 and S15 were in high population density with dominant residential land use and public facility land use (Fig. 1).
The second group consisting of Cd and DOM was poorly correlated with other metals in the dust samples, suggesting the existence of different sources compared to other metals. According to Figs. 1 and 2, Cd had typical spatial distribution with that of their hotspot areas, northwest of XDD, associated with industrial land use to high extent. Besides, their hotspot areas around S1 and S2 were in moderate population density with dominant industrial land use (Fig. 1). The dominant industrial land use included including almost 50 industrial enterprises (including Sany Industrial Park, Ningxiang YinTai Textile Co., Ltd., Special Metal Factory in Hunan Province, East Non-ferrous Metal Company, Hunan Heng He Paper Co., Ltd., Changsha Sanyou Building Materials Technology Co., Ltd., etc.) near upper Ningxiang County Quanmin township.
The third group of element consisted of Cr might have a different spatial distribution with medium variability (Table 7 and Fig. 2). Their hot-spot areas were associated not only with main roads but also with industrial land use (Fig. 2) which was accorded with the results from correlation matrix and FSA, indicating that Cr may in some extent relationship with the first and second groups simultaneously (Table 7 and Fig. 3). The higher enrichment areas contained main roads including the second ring road, Jingzhou avenue and Xiangjiang 1st and 2nd river bridge of XDD, and the industrial land use around S2, S3, and S8 including almost 10 industrial parks (Zoomlion industrial park, Hunan instrument industrial park, Hunan nuclear 304 industrial park, etc.) near Ningxiang County (mainly including Quanmin township, Lijingpu township, and Xiayipu township) and Wangcheng County (Figs. 1 and 2).
Consequently, with the help of the spatial distribution maps and the local land use map, the sources for Cr were finally identified and simultaneously the relationship between heavy metals and the local land use pattern was scientifically interpreted. At last, it’s worth noting that the proposed method is based on the reality of the local instrument and equipment conditions. And, although it would reach a more accurate level combined with the isotope ratio analysis (too expensive for conventional use), the integrated source identification method is of significance, especially in the developing countries, to be an efficient and feasible technology of regional pollutant source identification.
The elevated concentrations of Cu, Zn, Pb, Cd, and Cr in road dusts from XDD were observed compared with their local soil background values, indicating an anthropogenic input. Results based on the enrichment factors and health risk assessment showed that Cr in road dusts of XDD should be identified as the priority metal pollutant of concern followed by Pb and Cd. And, the exposure pathway which resulted in the highest levels of exposure risk for children and adults was ingestion, followed by dermal contact and inhalation. The local children receptors had higher health risks than adults. Spatially, northwest and northeast of XDD were regarded as the priority regions of environmental monitoring and management. Integrated source identification showed that Pb, Cu, and Zn were probably sourced from traffic-related sources, Cd was associated with DOM mainly originated from industrial sources, and Cr was mainly derived from both sources. Integrated sources identification method was proved to be more reliable. Therefore, the corresponding departments should establish scientific receptor protection measures according to the primary exposure pathways and formulate efficiently regional environment management strategies for the priority pollutants based on their probable sources. Furthermore, it is suggested that the fraction of the priority metal pollutants in the dust, the metal concentrations in different size dust, the regional receptor exposure parameters and the environmental pollution mechanism should be further studied in the priority regions under cost-efficient consideration.
This study was financially supported by the National Natural Science Foundation of China (51578222, 51178172, 51308076, 51521006 and 51378190) and the Fundamental Research Funds for the Central Universities (2015062; 2722013JC095).
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