Environmental Science and Pollution Research

, Volume 23, Issue 13, pp 13100–13113 | Cite as

Heavy metals in road dust from Xiandao District, Changsha City, China: characteristics, health risk assessment, and integrated source identification

Research Article

Abstract

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.

Keywords

Road dust Heavy metals Distribution characteristics Health risk assessment Source identification 

Introduction

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

Study area

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

With the comprehensive consideration of the local land use map (Fig. 1), the local wind rose map (Fig. 1), the population distribution (Li et al. 2015a), and the research budget, a total of 51 samples (17 sampling sites and 3 parallel samples for each sampling site) were collected at the trunk roads throughout XDD, shown in Fig. 1. Sampling was carried out 1 week after the last precipitation event. Samples were collected in October 2013 by gently sweeping, using a clean plastic dustpan and brush, an area of about 2 m2 adjacent to the curb of the road and transferring about 300 g of dust to a polyethylene bag for transport to the lab (Huang et al. 2016). All samples were naturally air-dried in the lab for more than 2 weeks and then sieved through a 100-mesh nylon sieve (diameters 149 μm) to remove stones, dead organisms, and coarse debris. Owing to the road dust with diameters below 100 μm which can be considered to mainly arise from atmospheric deposition and be transported by re-suspension (Nicholson 1988; Zhao et al. 2013), all the samples were sieved through a 200-mesh nylon sieve (diameters 74 μm) before analysis (Keshavarzi et al. 2015).
Fig. 1

Sampling sites in Xiandao District. The right images are maps of China and the present land use map of Changsha City; the left remote sensing image is detailed land use map in which different functional zones and urban roads are labeled (Huang et al. 2016; Li et al. 2015a)

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

An enrichment factor (EF) approach is widely utilized to assess anthropogenic impact of metal pollution on environmental media (El Nemr et al. 2006; Li and Feng 2012). EF is defined mathematically as
$$ EF={\left({X}_{\mathrm{i}}/R\right)}_{\mathrm{dust}}/{\left({X}_{\mathrm{i}}/R\right)}_{\mathrm{background}} $$
(1)
where (Xi/R)dust and (Xi/R)background are the concentration ratios of metal i and the normalizer in road dust and background material, respectively. Elements Fe (Fang et al. 2006; Kükrer et al. 2014) and Al (Liu et al. 2014) are most common references when calculating the enrichment factor of environmental toxic metal pollutants. Fe was selected as the background material in this study. Degrees of metal enrichment were classified into five categories (Sutherland 2000): (1) EF < 2, minimal enrichment; (2) 2 ⩽ EF < 5, moderate enrichment; (3) 5 ⩽ EF < 20, significant enrichment; (4) 20 ⩽ EF < 40, very high enrichment; and (5) EF ⩾ 40, extremely high enrichment.

Health risk assessment model

Exposure evaluation

The models to assess exposure risk of adults or children to metals in road dust refer to the recommended models sourced from the US Environmental Protection Agency (USEPA 1996, 2001) and the Dutch National Institute of Public Health Agency (Van den Berg 1994). Under the specific scenario analysis based on the corresponding literatures (Keshavarzi et al. 2015; Li et al. 2013, 2015a) and the present land use map (Fig. 1), the corresponding receptors were exposed to road dust through three main pathways: ingestion of dust, inhalation of dust particles through mouth and nose, and dermal contact absorption. The corresponding dose received through each of the three pathways was evaluated by Eqs. (2)–(4) respectively for non-carcinogenic risk (USEPA 1996, 2001). For carcinogens, the lifetime average daily dose (LADDinh) for Cd and Cr through inhalation exposure pathway was used in the assessment of cancer risk expressed by Eq. (5) (USEPA 1996, 2001):
$$ {D}_{\mathrm{ing}}=\frac{C\times {R}_{\mathrm{ing}}\times EXF\times ED}{BW\times AT}\times {10}^{-6} $$
(2)
$$ {D}_{\mathrm{inh}}=\frac{C\times {R}_{\mathrm{inh}}\times EXF\times ED}{PEF\times BW\times AT} $$
(3)
$$ {D}_{\mathrm{der}}=\frac{C\times SA\times SL\times ABS\times EXF\times ED}{BW\times AT}\times {10}^{-6} $$
(4)
$$ LAD{D}_{\mathrm{inh}}=\frac{C\times EXF}{PEF\times AT}\times \left(\frac{R_{\mathrm{inh}\mathrm{child}}\times E{D}_{\mathrm{child}}}{B{W}_{\mathrm{child}}}+\frac{R_{\mathrm{inh}\mathrm{adults}}\times E{D}_{\mathrm{adults}}}{B{W}_{\mathrm{adults}}}\right) $$
(5)
where C is the concentration of trace element in road dust, mg/kg; Ring is the ingestion rate, estimated to be 200 mg/day for children and 100 mg/day for adults; Rinh is the inhalation rate, estimated to be 7.6 m3/day for children and 20 m3/day for adults; EXF is the exposure frequency, assumed in this study to be 350 d/a; ED is the exposure duration, in this study taken as 6 years for children and 24 years for adults; SA is the exposed skin area, in this study, 2448 cm2 for children and 5075 cm2 for adults; SL is the skin adherence factor, estimated to be 0.2 mg/m2·day for children and 0.07 mg/cm2·day for adults; ABS is the dermal absorption factor (unitless), defined to be 0.001 for all studied metals; PEF is particle emission factor, in this study, 1.36E + 09 m3/kg; AT is the average contact time, defined ED × 365 days for non-carcinogens and 74 × 365 days for carcinogens; and BW is the average bodyweight, considered to be 15.9 kg for children and 56.8 kg for adults. All the parameters refer to these literatures (MEPPRC 2014; Van den Berg 1994; USEPA 1996, 2001), and to decrease the corresponding parameter uncertainty, the local parameters BW, SA, EXF, ED, SL, and AT included were preferentially adopted (MEPPRC 2014). The following assumption underlying the model applied in XDD needs to be declared: Intake rates and particle emission for street dust can be approximated by those developed for soil.

Risk characterization

Based on calculation of exposure dose at each exposure pathway, a hazard quotient (HQ) for each metal and for each exposure pathway and hazard index (HI) which represents the magnitude of adverse effect with total exposure pathways were yielded as follows:
$$ HI=H{Q}_{\mathrm{ing}}+H{Q}_{\mathrm{inh}}+H{Q}_{\mathrm{der}}=\left({D}_{\mathrm{ing}}/Rf{D}_{\mathrm{ing}}\right)+\left({D}_{\mathrm{inh}}/Rf{D}_{\mathrm{inh}}\right)+\left({D}_{\mathrm{der}}/Rf{D}_{\mathrm{der}}\right) $$
(6)
where RfDi is the corresponding reference dose for each heavy metal and for exposure pathway i. HI is presented as the sum of HQ for each exposure pathway to certain heavy metal. And, non-carcinogenic risk is accepted when the HI value is below 1 and the degree of risk increases as HI increases (MEPPRC 2014; USEPA 2001).
For carcinogenic risk, the exposure doses at each exposure pathway were multiplied by the corresponding carcinogenic slope factor (CSF) ((kg·day)/mg) to produce a level of cancer risk. The CSF is used to estimate the risk of cancer associated with exposure to a carcinogenic or potentially carcinogenic substance. Carcinogenic risk (CR), the probability of an individual developing any type of cancer from lifetime exposure to carcinogenic hazards, was yielded as follows:
$$ CR= LADD\times CSF $$
(7)
The acceptable risk level for regulatory purposes is 1E-06, i.e., one over one million of the population (MEPPRC 2014). The CSF and RfD values of the studied metals are shown in Table 1 which were taken from the US Department of Energy’s Risk Assessment Information System (RAIS) compilation (Ferreira-Baptista and De Miguel 2005; U.S. Department of Energy 2004; Van den Berg 1994). The only exception was Pb whose reference doses have been taken from the World Health Organization Guidelines for Drinking Water Quality (WHO 1993). Inhalation-specific toxicity data are available only for Cd and Cr. Further, for the other three studied metals included in the risk assessment, the toxicity values considered for the inhalation route are the corresponding oral reference doses, on the assumption that, after inhalation, the absorption of the particle-bound toxicants will result in similar health effects as if the particles had been ingested (Ferreira-Baptista and De Miguel 2005; Van den Berg 1994).
Table 1

The reference dose and slope factor of metals

 

Cu

Zn

Pb

Cd

Cr

RfDing

4.00E-02

3.00E-01

3.50E-03

1.00E-03

3.00E-03

RfDdermal

1.20E-02

6.00E-02

5.25E-04

2.50E-05

6.00E-05

RfDinh

5.71E-05

2.86E-05

CSFinh

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

The descriptive statistics of dust properties and metal concentrations in road dust are given in Table 2, as well as the background values in Hunan province and the guideline values of the Chinese Environmental Quality Standard for Soils. The soil properties covered a wide range of values in pH (8.1–11.3, alkaline state) and DOM (2.3–12.6 %). pH and organic matter may influence transformation, absorption, and desorption of heavy metals in dust. Application of the Shapiro–Wilk test confirmed that the concentration data of Cu and Pb were normally distribution (significance >0.05), whereas other metals were non-normally distribution. Therefore, log transformation was utilized and the distribution of Cd, Cr, Fe, and DOM conformed to normally distribution after log transformation, namely, they conformed to logarithmic normal distribution. Arithmetic mean and geometric mean were the mathematical expectation of normally distribution and logarithmic normal distribution, respectively. The ranges (arithmetic or geometric mean concentrations) of the studied metals in road dust were Cu 0.60–90.5 (43.9) mg/kg, Zn 83.8–722 (171) mg/kg, Pb 19.4–133 (66.6) mg/kg, Cd 2.60–25.2 (7.48) mg/kg, Cr 36.0–238 (71.6) mg/kg, and Fe 1.12–4.54 (2.15) %. The mean concentrations of Cu, Zn, Pb, Cd, and Cr exceeded their corresponding background values in Hunan Province soils at 1.76, 1.78, 2.22, 107, and 1.05 times, respectively. The coefficients of variation (CV = 100SD/mean) were found for Cu, Zn, Pb, Cd, and Cr indicating their medium spatial variability which was probably effected by human activities. According to the current Chinese Environmental Quality Standard for Soils (15618-1995), the second class values generally are developed for protecting agricultural production and corresponding human health (NEPAC 1995) which are often used for identifying pollution state of soil toxic metals. Compared with the Chinese Environmental Quality Standard for Soils, Cd at each site exceeded class III value. Cu, Pb, and Cr at each site were within corresponding class II values, while Zn was generally lower than class II value in most urban areas.
Table 2

Descriptive statistics of metal concentrations (mg/kg) and dust properties in road dust from XDD (Huang et al. 2016; Li et al. 2015a)

Elements

Cu

Zn

Pb

Cd

Cr

Fe (%)

DOM (%)

pH

AM

43.9

215

66.6

9.11

80.7

2.26

3.0

9.4

GM

33.3

171

59.4

7.48

71.6

2.15

1.9

9.4

SD

21.7

180

30.2

6.28

47.9

0.80

0.03

0.84

CV (%)

49.4

83.6

45.3

68.9

59.3

35.3

1

8.9

S-W Sig.

0.964

0.001

0.544

0.008

0.001

0.018

0.018

0.147

BVChinaa

22.6

74.2

26.0

0.097

61.0

2.94

2.54

4.8

BVHunanb

25

96

30

0.07

68

NA

NA

NA

Calss Ic

35

100

35

0.2

90

NA

NA

NA

Calss IIc

100

300

250

0.6

250

NA

NA

NA

Calss IIIc

400

500

500

1.0

300

NA

NA

NA

aCNEMC (1990)

bSoil background value of Hunan Province, China (Pan and Yang 1988; Zhou et al. 2008)

cEnvironmental quality standard secondary grade for soils, soil limitations to ensure agricultural production and human health (National Environmental Protection Agency of China 1995)

AM arithmetic mean, GM geometric mean, SD standard deviation, CV coefficient of variation, S-W Sig. significance based on Shapiro-Wilk test, NA not available

Comparison of mean concentrations of the studied metals in road dust from XDD with that from different cities at home and abroad is summarized in Table 3. In general, the dust metal concentrations were of relatively moderate pollution degree compared with the concentrations reported for cities in other countries. Comparing the metal concentrations in road dust among Chinese cities, the road dusts from XDD also showed a generally moderate level of heavy metals. Especially, Cd in road dust from XDD was higher than that in other cities except Cd in Delhi City of India. Detailed comparisons were clearly presented in Table 3.
Table 3

Summaries of measured metals in road dust from different cities at home and abroad mg/kg

Sites

Cu

Zn

Pb

Cd

Cr

Reference

Beijing (China)

64.23

NA

50.40

0.47

77.45

(Tang et al. 2013)

Baoji (China)

123.20

715.30

433.20

NA

126.70

(Lu et al. 2010)

Guangzhou (China)

79

492

185

1.3

50.3

(Duzgoren-Aydin et al. 2006)

Urumqi (China)

94.54

294.47

53.53

1.17

54.82

(Wei et al. 2009)

Shanghai (China)

257.63

753.27

236.62

0.97

264.32

(Shi et al. 2010)

Tokyo (Japan)

NA

1888

245

0.98

52.3

(Wijaya et al. 2012)

Massachusetts (USA)

105

240

73

NA

77

(Apeagyei et al. 2011)

Ottawa (Canada)

65.84

112.5

39.05

0.37

43.3

(Rasmussen et al. 2001)

Tehran (Iran)

225.3

873.2

257.4

10.7

33.5

(Saeedia et al. 2012)

Delhi (India)

174.43

241.73

284.55

17.92

189.62

(Banerjee et al. 2003)

Luanda (Angola)

42

317

351

1.1

26

(Ferreira-Baptista and De Miguel 2005)

Present study

43.9

171

66.6

7.48

71.6

 

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.

Owing to spatial distributions of metals and dust properties are a useful aid to assess the possible pollution sources and to identify hotspot area with high metal concentration (Chen et al. 2014; Li et al. 2013; Liu et al. 2014), the corresponding distribution maps were obtained using IDW and are shown in Fig. 2. Figure 2a shows that Cd from each sampling site decreased in the order of S2 > S1 > S12 > S15 > S13 > S14 > S3 > S6 > S11 > S5 > S10 > S7 = S16 > S4 > S17 > S9 > S8. And, the high-value zones of Cd appeared on the northwest and south parts of XDD. Moreover, the distribution pattern of Cd was similar to that of Cr. Cr from each sampling site decreased in the order of S6 > S15 > S16 > S5 > S11 > S12 > S13 > S14 > S2 > S17 > S8 > S9 > S3 > S14 > S4 > S7 > S1 (Fig. 2c).
Fig. 2

Spatial distribution of metals and dust properties in road dusts from XDD

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

After investigating the spatial environmental enrichment of the studied metals, health risk assessment to adults and children from exposure to metals in road dust from XDD through possible exposure pathways were calculated based on Eqs. (2)–(7), and results are shown in Tables 4, 5, and 6.
Table 4

Daily dose and hazard indices of metals in road dust for children

Elements

Ding

Dderm

Dinh

HQing

HQder

HQinh

HI

Cu

Max

1.10E-03

2.67E-06

3.05E-08

2.74E-02

2.22E-04

7.59E-07

2.76E-02

Min

6.90E-06

1.68E-08

1.92E-10

1.72E-04

1.40E-06

4.78E-09

1.74E-04

Mean

5.31E-04

1.29E-06

1.48E-08

1.33E-02

1.08E-04

3.68E-07

1.34E-02

Zn

Max

8.73E-03

2.13E-05

2.43E-07

2.91E-02

3.55E-04

8.11E-07

2.95E-02

Min

1.01E-03

2.47E-06

2.82E-08

3.38E-03

4.12E-05

9.41E-08

3.42E-03

Mean

2.60E-03

6.34E-06

7.24E-08

8.67E-03

1.06E-04

2.41E-07

8.77E-03

Pb

Max

1.61E-03

3.92E-06

4.48E-08

4.59E-01

7.46E-03

1.27E-05

4.67E-01

Min

2.35E-04

5.72E-07

6.54E-09

6.71E-02

1.09E-03

1.86E-06

6.82E-02

Mean

8.06E-04

1.96E-06

2.24E-08

2.30E-01

3.74E-03

6.37E-06

2.34E-01

Cd

Max

3.05E-04

7.43E-07

8.49E-09

3.05E-01

2.97E-02

1.49E-04

3.35E-01

Min

3.15E-05

7.67E-08

8.76E-10

3.15E-02

3.07E-03

1.53E-05

3.45E-02

Mean

1.10E-04

2.69E-07

3.07E-09

1.10E-01

1.08E-02

5.38E-05

1.21E-01

Cr

Max

2.88E-03

7.02E-06

8.02E-08

9.60E-01

1.17E-01

2.80E-03

1.08E + 00

Min

4.36E-04

1.06E-06

1.21E-08

1.45E-01

1.77E-02

4.24E-04

1.63E-01

Mean

9.76E-04

2.38E-06

2.72E-08

3.25E-01

3.97E-02

9.50E-04

3.66E-01

Table 5

Daily dose and hazard indices of metals in road dust for adults

Elements

Ding

Dderm

Dinh

HQing

HQder

HQinh

HI

Cu

Max

1.53E-04

5.43E-07

2.24E-08

3.82E-03

4.53E-05

5.58E-07

3.87E-03

Min

9.63E-07

3.42E-09

1.41E-10

2.41E-05

2.85E-07

3.52E-09

2.44E-05

Mean

7.42E-05

2.63E-07

1.09E-08

1.85E-03

2.19E-05

2.71E-07

1.88E-03

Zn

Max

1.22E-03

4.33E-06

1.79E-07

4.07E-03

7.22E-05

5.97E-07

4.14E-03

Min

1.42E-04

5.03E-07

2.08E-08

4.72E-04

8.38E-06

6.93E-08

4.81E-04

Mean

3.63E-04

1.29E-06

5.33E-08

1.21E-03

2.15E-05

1.78E-07

1.23E-03

Pb

Max

2.24E-04

7.97E-07

3.29E-08

6.41E-02

1.52E-03

9.36E-06

6.57E-02

Min

3.28E-05

1.16E-07

4.81E-09

9.37E-03

2.22E-04

1.37E-06

9.59E-03

Mean

1.13E-04

3.99E-07

1.65E-08

3.21E-02

7.61E-04

4.69E-06

3.29E-02

Cd

Max

4.26E-05

1.51E-07

6.25E-09

4.26E-02

6.05E-03

1.09E-04

4.87E-02

Min

4.39E-06

1.56E-08

6.45E-10

4.39E-03

6.24E-04

1.13E-05

5.03E-03

Mean

1.54E-05

5.47E-08

2.26E-09

1.54E-02

2.19E-03

3.96E-05

1.76E-02

Cr

Max

4.02E-04

1.43E-06

5.90E-08

1.34E-01

2.38E-02

2.06E-03

1.60E-01

Min

6.08E-05

2.16E-07

8.93E-09

2.03E-02

3.60E-03

3.12E-04

2.42E-02

Mean

1.36E-04

4.84E-07

2.00E-08

4.54E-02

8.07E-03

6.99E-04

5.42E-02

Table 6

Daily dose and cancer risk of metals in road dust for children

Elements

LADD

CR

Cd

Max

2.72E-09

1.71E-08

Min

2.80E-10

1.77E-09

Mean

9.83E-10

6.19E-09

Cr

Max

2.57E-08

1.08E-06

Min

3.88E-09

1.63E-07

Mean

4.04E-09

1.70E-07

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

Correlation matrix

Pearson’s correlation analysis needs all analysis variables according with the normal distribution which has been probably overlooked in some published reports (Pathak et al. 2013; Li et al. 2012; Saeedi et al. 2012). Therefore, in this study, the log function was used and the transformed results were in accordance with the normal distribution. Pearson’s correlation coefficients of metals and dust properties in road dust from the XDD were performed, and the results are shown in Table 7. The element pairs Fe–Cr and Zn–Pb had a significantly positive correlation at P < 0.01 significance level (Table 7). The element pairs Cu–Pb, Cu–Cr, Pb–Fe, pH–Cr, Zn–DOM, and Cd–DOM had a significantly positive correlation at P < 0.05 significance level (Table 7). Based on the published studies (Lu et al. 2010; Saeedi et al. 2012), if the correlation coefficient between the heavy metal factors is positive, these factors may have a common source, mutual dependence, and identical behavior during the transport.
Table 7

Pearson correlation matrix for metal concentrations and dust properties in road dust from XDD

 

Cu

Pb

pH

Zn

Cd

Cr

Fe

DOM

Cu

1

       

Pb

0.505*

1

      

pH

0.113

0.182

1

     

Zn

0.397

0.633**

0.006

1

    

Cd

0.087

0.353

0.275

0.459

1

   

Cr

0.598*

0.316

0.599*

0.071

0.018

1

  

Fe

0.447

0.556*

0.126

0.228

0.200

0.643**

1

 

DOM

0.384

0.418

0.016

0.559*

0.560*

−0.061

−0.090

1

*Correlation is significant at the 0.05 level (two-tailed); **correlation is significant at the 0.01 level (two-tailed)

FSA

To further clarify the possible sources of heavy metals in road dust from XDD, FSA was performed on these interrelated variables and the spatial correlograms were plotted (Fig. 3). Based on the theory of FSA (Bai et al. 2012; Chen et al. 2009), if the spatial correlation distribution characteristic between the factor variables is similar, these factors may have a common source, mutual dependence, and correlative behavior during the transport. Figure 3 shows that the spatial correlation distributions of Cd-DOM and Zn-pH are mainly located at same direction. Moreover, the spatial correlation distributions of Fe, Pb, Cu, Zn, DOM, and pH were partly similar to each other indicating their mutual dependence to the certain extent. Different from other factors, the spatial correlation distribution of Cr was mainly located at relatively special direction. In addition to Cr, the above results for Pb, Cu, Zn, and Cd were accorded with the results of Pearson’s correlation analysis (Table 7). Therefore, there were proved to be fatal uncertainties in identifying sources of Cr merely by mathematical and statistical methods in present study.
Fig. 3

Spatial correlograms of the studied metals and dust properties based on FSA

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.

Conclusions

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.

Notes

Acknowledgments

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  2. 2.College of Environmental Science and EngineeringHunan UniversityChangshaChina
  3. 3.South China Institute of Environmental Sciences, Ministry of Environmental ProtectionGuangzhouChina

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