Heavy metals in street dust in Beijing, China: seasonal and spatial variations, pollution characteristics and health risk assessment

doi:10.21203/rs.3.rs-2377197/v1

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Heavy metals in street dust in Beijing, China: seasonal and spatial variations, pollution characteristics and health risk assessment

https://doi.org/10.21203/rs.3.rs-2377197/v1

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Street dust samples were collected from industrial, residential, educational, commercial, and park areas in Beijing, China, during spring and winter to investigate the contents of heavy metals (Cr, Ni, Cu, Zn, Pb, As, Cd and Hg) and evaluate the source apportionment and degree of environmental pollution according to the geo-accumulation index, ecological indices, and human health risk assessment. The average contents of eight heavy metals (mg/kg) in spring are as follows: Cr (98.17), Ni (26.38), Cu (67.33), Zn (394.87), Pb (64.39), As (6.32), Cd (0.618), and Hg (0.207). In winter they are Cr (94.54), Ni (26.13), Cu (73.03), Zn (341.73), Pb(73.09), As (7.66), Cd (0.635), and Hg (0.279). The rank order of heavy metals concentrations in functional areas is IA (Industrial area) >CA (Commercial area) >EA (Educational area) >RA(Residential area) >PA (Park area). The source apportionment analysis indicates that heavy metals in the street dust are mainly derived from mixed sources of natural, industrial and traffic-related source (54.64%). Both the geo-accumulation index and potential pollution index shows the pollution levels of different heavy metals in different functional areas: from unpolluted, moderately polluted to heavily polluted. A health risk assessment shows no non-carcinogenic and carcinogenic risk of heavy metals to children and adults. Among the five functional areas, the industrial area has the highest non-carcinogenic and carcinogenic risk compared to that of the others.

Street dust

Heavy metals

Seasonal variations

Functional areas

Source apportionment

Health risk assessment

At present, over 50% population in the world lives in urbanized areas. With the rate of urbanization globally, 70% of the people in the world will likely live in urban areas by 2050 (Li et al., 2016). Because of human activities in cities, particularly densely populated metropolises, such as traffic exhaust, garbage, and industrial production activities, are extensive and considerable and can release various contaminants into the environment (Men et al., 2018). For this reason, many environmental problems have appeared, among which heavy metal contamination is one of the important issues of concern, particularly in terms of metal pollution in urban soil and street dust (Lu et al., 2014). Day firstly studied street dust as a normal section of their environment by researching the lead concentration in dust and dirt, which was likely to be encountered by children in Greater Manchester, and analysed the significance of dust as a component of the lead intake of children in city (Day et al., 1975). Street dust is a composite environmental medium, the solid matter of which mainly originates from both natural sources (e.g. re-suspension of soil and weathered materials) and various anthropogenic activities (e.g. vehicular traffic, power generation facilities, industrial plants, residential fossil-fuel burning, construction material, and demolition activities) (Bourliva et al., 2018; Li et al., 2017). Street dust has a comparatively large surface that is easily deposited, re-suspended, and transported or adhered to human skin (Acosta et al., 2015; Keshavarzi et al., 2015). Street dust can cause air pollution by entering the atmosphere through particle resuspension. It can also result in sediment pollution by entering a water body through surface runoff during the rainy season, and eventually entering the food chain (Men et al., 2018). To a certain degree, comparing with single compartmental monitoring such as air, water, and soil, street dust can more readily indicate urban environmental quality because of its multi-media pollutants (Li et al., 2018). Street dust, an environmental medium, typically contains amounts of deleterious contaminants such as heavy metals and polycyclic aromatic hydrocarbons (Shi and Lu, 2018). Heavy metals can result in severe health problems, such as central nervous system damage, renal effects, bone fractures, immune system damage, decreased intelligence quotient (IQ) in children etc., because they are non-biodegradable and become biomagnified in body tissues. (Gope et al., 2017). In summary, street dust is quite significant and useful to identify the mainly harmful metals to implement suitable methods of resolution and quantify the level of pollution (Soltani et al., 2017).

During recent years, numerous researches of heavy metals in street dust have concentrated on metal concentration, particle size, fraction, and pollution characteristic (Aminiyan et al., 2018; Lanzerstorfer, 2018; Najmeddin et al., 2018; Zhang et al., 2019); spatial distribution and seasonal variations (Pan et al., 2017; Skrbic et al., 2018); and risk assessment and source identification (Gunawardana et al., 2012; Dehghani et al., 2017; Li et al., 2018). Health risk assessment is an effective way to assess the risk of heavy metals via different exposure routes such as ingestion, inhalation, and dermal contact (Gope et al., 2018; Yu et al., 2016). In global cities, such as Ahvaz, Iran (Nazarpour et al., 2018); Monterrey, Mexico (Urrutia-Goyes et al., 2018); Durham, Edinburgh, Liverpool, Newcastle upon Tyne, and Sunderland, United Kingdom (Elom et al., 2014); and Shanghai (Shi et al., 2011) and Nanjing, China (Wang et al., 2016), health risk assessment of heavy metals in street dust has been a subject of research. Many researchers have demonstrated that ingestion is the most critical pathway that dust particles enter human bodies than inhalation and dermal contact (Li et al., 2017). Because children are low tolerance to toxins and the accidental ingestion of so much street dust through ingestion, the health risk is particularly higher for children than adults (Zheng et al., 2010). Normally, the geo-accumulation index (I_geo) and ecological indices (E_r and RI) of heavy metals are good indicators of environmental contamination in street dust (Ali et al., 2017; Saeedi et al., 2012). People already have used multivariate statistical analyses like correlation, cluster, and principal component analyses, which can identify possible heavy metal sources (Liu et al., 2014).

Beijing is the capital and political, cultural, and commercial hub of China. The city center is in the ancient Forbidden City and expands outward in six concentric circles, each separated by a ring road for express automobile traffic. Beijing, with a land area of 16,410 km², has exceeded 19.61 million inhabitants (Li et al., 2014; Peng et al., 2011). Beijing is a rapidly developing city, which has experienced substantial economic growth over the past few decades. Because of the hosting of the 2008 Beijing Olympic Games, many industries moved out of the central urban area, and as such, local industrial pollution in central Beijing has dramatically decreased since 2008. However, the number of motor vehicles increased from 3.1 million in 2007 to 5.2 million in 2012, and the use of such large amounts of fossil fuel and the corresponding emission of a large number of air particulates and pollutants has caused many problems of the urban environment in Beijing (Han et al., 2016; Tang and Han, 2017; Yu et al., 2016). Previous studies have largely studied that heavy metal geochemical characteristics in street dust samples collected from one season showed no distinction among different functional areas (Du et al., 2013; Tanner et al., 2008). However, the concentrations, sources, and contamination characteristics of heavy metals in street dust at different functional areas and during different seasons could show high heterogeneity (Duong and Lee, 2011; Skrbic et al., 2018). Therefore, a systematic investigation of the effect of different seasons and functional areas will be significant to obtain more representative and convincing conclusions.

The major purposes of our study were to (1) determine and compare the contents and seasonal variations of eight heavy metals (Cr, Ni, Cu, Zn, Pb, As, Cd and Hg) in street dust collected from different functional areas (industrial, residential, educational, commercial, and park areas) of Beijing, (2) assess heavy metals pollution in street dust of the different functional areas using the ecological indices (Er, RI, and mRI) and a geo-accumulation index (I_geo), (3) identify source apportionment of heavy metals in street dust using multivariate statistical indices, and (4) evaluate the potential carcinogen and non-carcinogen risk of children and adults associated with heavy metals in street dust.

2.1 Study area and sample collection

There is a typical monsoonal climate, which has hot, humid summers because of the East Asian monsoon and generally cold, windy, dry winters because of the vast Siberian anticyclone. The annual temperature of Beijing is approximately 11.5°C and the annual precipitation is approximately 600 mm (Wei et al., 2015; Chen et al., 2016; Chen et al., 2016). We collected 30 street dust samples from five functional areas in Beijing during the winter and spring seasons including industrial area (IA) (n = 6), residential area (RA) (n = 6), educational area (EA) (n = 6), commercial area (CA) (n = 6), and park area (PA) (n = 6). We also recorded the surrounding road characteristics, land use near the sampling sites, and geographic coordinates. Approximately 500 g samples at each sampling site were collected within an area of 5–6 m² using a clean plastic brush and dustpan. The samples were stored in sealed polyethylene bags, labelled, and transported to the laboratory for analysis. We removed impurities and passed the remainder through a 149 µm (100 mesh) sieve after natural-drying, which was ground with an agate mortar and pestle.

2.2 Chemical analysis

Total Cr, Ni, Cu, Zn, Pb, As, Cd and Hg concentrations were measured using an inductively coupled plasma mass spectrometry (ICP-MS), x-ray fluorescence spectrometer (XRF), and atomic fluorescence spectrometer (AFS) at the accredited Ministry of Land and Resources P.R.C. Hefei Mineral Resources Supervision and Testing Center, China. The quality control and quality assessment included the analysis of duplicates and the standard reference material (GSS-27). The accuracy (RE) and precision (RD) are, respectively, as follows: Cr (-3.04% and 2.48%), Ni (-0.70% and 9.70%), Cu (4.07% and 3.76%), Zn (1.26% and 2.46%), Pb (-1.22% and 3.87), As (0.75% and 3.49%), Cd (11.69% and 4.36%), and Hg (4.31% and 13.45%). These show good agreement between the measured and recommended values.

2.3 Pollution assessment

Müller put forward the geo-accumulation index (I_geo) (Müller, 1981), which is used to determine contamination levels. The equation for I_geo is provided in Eq. (1) as follows:

$${I}_{geo}={{log}}_{2}\left[{C}_{n}/\left(1.5\times {B}_{n}\right)\right]$$

where C_n is the measured heavy metal concentrations in the street dust and B_n is the background value of the soil in Beijing, China (Chen et al., 2004). The pollution assessment criteria of I_geo is shown in Table S1.

The ecological risk index (RI), defined by Hakanson (1980), is used to calculate the factors of ecological risk potential of heavy metals (Tang et al., 2017; Trujillo-González et al., 2016). The equations for RI and E_r are provided in Eqs. (2–4), where C_s and C_n are, respectively, the heavy metal concentrations in street dust sample and soil background values; E_r is the ecological risk of each element, and RI is the ecological risk of multiple elements. T_r is toxic-response factor and the values of different heavy metals are as follows: Zn (1) < Cr (2) < Ni (5) = Cu (5) = Pb (5) < As (10) < Cd (30) < Hg (40). The risk index assessment criteria of E_r and RI are shown in Table S1.

However, according to the suggestions of Skrbic et al. (2018), we also should think the modified RI (mRI) to resolve the lack of an exact agreement between the number of contaminants that need to be included in the RI classification. The equation of mRI is provided in Eq. (5) as follows:

$$RI=\sum _{i=1}^{n}{E}_{r}$$

$${E}_{r}={T}_{r}\times {C}_{f}$$

$${C}_{f}={C}_{s}/{C}_{n}$$

$$mRI={\sum }_{i=1}^{8}{E}_{r}^{i}/{\sum }_{i=1}^{8}{T}_{r}^{i}=\frac{{\sum }_{i=1}^{8}{T}_{r}^{i}\times {C}_{f}^{i}}{{\sum }_{i=1}^{8}{T}_{r}^{i}}$$

The modified risk index assessment criteria of mRI are shown in Table S1.

2.4 Statistical analysis

We used paired t-test, one-way analysis of variance (ANOVA), Pearson’s correlation coefficient analysis, principal component analysis (PCA), and cluster analysis (CA) to identify the relationship among heavy metals in street dust and their possible sources performing by the commercial statistics software package SPSS version 20.0 for Windows. Paired t-test (p < 0.05) and one-way ANOVA (p < 0.05) were respectively calculated to identify the differences of heavy metal contents among different seasons and functional areas (Li et al., 2017; Skrbic et al., 2018). Pearson correlation analysis was used to measure the strength of the relationships between pairs of analysed heavy metals (Luo et al., 2012). CA, undertaken according to the Ward-algorithmic method, was subsequently studied for defining metal groups with similarities (Christoforidis and Stamatis, 2009; Ordóñez et al., 2015). PCA is widely used to reduce data and extract a small number of latent factors (principal components, or PCs) for analysing relationships among the variables (Aminiyan et al., 2018; Zhang et al., 2015). To simplify the results interpretation, PCA with a varimax normalized rotation was also used, which can maximize the variances in the factor loadings across variables for each factor (Lu et al., 2010). In this study, all principle factors extracted from the variables after varimax rotation had eigenvalues exceeding 1 (Han et al., 2006). CA and PCA are often coupled to check results and group individual parameters and variables (Wang et al., 2016; Kim et al., 2016).

After the study of PCA, source apportionment analysis was conducted by the PCA-MLR method, which was utilized to calculate the percentage of various sources of heavy metals in street dust. For MLR, the PCA factor scores were used as independent variables and the measured total concentrations of different heavy metals as dependent variables (Pan et al., 2017). The equation of the multiple linear regression model is shown by Eq. (6) as follows:

$$\text{y}\text{=}\sum {m}_{i}{X}_{i}+b$$

where y is the total concentrations of different heavy metals (ΣHMs), X_i is the PCA factor score, m_i is the regression coefficient, and b is the residual error. If one normalizes y as Eq. (7), the MLR model is represented as Eq. (8) as follows:

$$Z=\left(y-\stackrel{-}{y}\right)/s$$

$$Z=\sum {B}_{i}{X}_{i}$$

where y is the total concentrations of different heavy metals (ΣHMs), ‾y is the mean of y, S is the standard deviation of y, Z is the standardized normal deviate of y, X_i is the PCA factor score, and B_i is the regression coefficient. MLR was calculated by the SPSS software and a default level of significance of 0.05 was used.

The mean percent contribution of each source was calculated as in Eq. (9) as follows:

$$i\left(100\%\right)=100\times \left({B}_{i}/\sum {B}_{i}\right)$$

where i is the percentage mean contribution of each source, B_i is the regression coefficient, and ΣB_i is the total of B_i (LARSEN et al., 2003; Salim et al., 2019).

2.5 Health risk assessment model

Human exposure to heavy metals in street dust mainly occurs through four ways: direct ingestion, inhalation, dermal absorption, and exposure through inhalation of Hg vapor (Eqani et al., 2016; Keshavarzi et al., 2015; Phi et al., 2017). The aims of our study were to evaluate the non-carcinogenic and carcinogenic risks from exposure of street dust of different functional areas which are contaminated by heavy metals, to children and adults. The daily intake for each heavy metal via different exposure pathways was also calculated using Eqs. (10)–(13) (USEPA, 1989, 1996; Najmeddin et al., 2018).

$${ADD}_{ing}=C\times \frac{IngR\times EF\times ED}{BW\times AT}\times {10}^{-6}$$

$${ADD}_{inh}=C\times \frac{InhR\times EF\times ED}{PEF\times BW\times AT}$$

$${ADD}_{dermal}=C\times \frac{SL\times SA\times ABS\times EF\times ED}{BW\times AT}\times {10}^{-6}$$

$${ADD}_{vapor}=C\times \frac{InhR\times EF\times ED}{VF\times BW\times AT}$$

For carcinogens of Cr, Ni, As, and Cd through inhalation exposure route, the lifetime average daily dose (LADD) is used to assess the cancer risk (Eq. (14)) (USEPA, 1996, 2001c; Li et al., 2013).

$${\text{L}\text{A}\text{D}\text{D}}_{inh}=\frac{C\times EF}{AT\times PEF}\times (\frac{Ingchild\times EDchild}{BWchild}+\frac{Ingadult\times EDadult}{BWadult})$$

The exposure factors for the mathematical models are listed in Table S2.

The potential non-carcinogenic and carcinogenic risks for each metal from different functional areas were calculated using Eqs. (15)-(17) (USEPA, 1989):

$$\text{H}\text{Q}=ADD/RfD$$

$$\text{H}\text{I}=\sum {HQ}_{i}$$

$$\text{R}\text{I}={LADD}_{inh}\times SF$$

The values of reference dose (RfD, mg/kg d^− 1) (USEPA, 1993, 2010) and slope factor (SF) (USEPA, 2001a, 2001b) are listed in Table S3.

The hazard index (HI), the total of hazard quotients (HQs), refers to the cumulative non-carcinogenic risk. An HI > 1 indicates there is a chance that non-carcinogenic risks may occur and will increase as the value of HI increases, while an HI < 1 indicates there are lower or no non-carcinogenic risks (USEPA, 2001c). RI denotes the carcinogenic risk that was calculated by the slope factor (SF) and reference dose (RfD). The acceptable risk for regulatory purposes is within the range of 1×10^− 6–1×10^− 4 (Praveena and Aris, 2018).

3.1 Characteristics of heavy metal concentrations in street dust

According to the paired t-test, there is no significant difference (p < 0.05) of heavy metal concentrations in street dust between spring and winter. As a result, the concentrations adopted are the average value of the spring and winter values. The statistical value of heavy metal concentrations in street dust in Beijing are listed in Table 1. The arithmetic average of 30 street dust samples of Cr, Ni, Cu, Zn, Pb, As, Cd and Hg is 96.35, 26.26, 70.18, 368.30, 68.74, 6.99, 0.63, 0.27 mg/kg, respectively. Zn has the highest concentration compared to that of the other elements. Zn may originate from anthropogenic sources such as the wear and tear of tires, mechanical corrosion, and oil leaks from automobiles (Jadoon et al., 2018). The results indicate that the average values of Cr, Cu, Zn, Pb, Cd, and Hg in street dust are substantially higher than the background values of the soil in Beijing except for Ni and As. Zn and Cd concentrations are extremely high and are the 6.41 and 5.27 times the background values. These results indicate that Zn and Cd may be derived from anthropogenic sources (Skrbic et al., 2018). Each element shows a relatively wide concentration range and extremely high standard deviation except for Cd and Hg. This indicates an obvious change in the concentrations of street dust. The skewness values are approximately positive except for those of Cr and Cd which indicated that these metals positively skew towards lower concentrations. The kurtosis values are mostly larger than 0 except for those of Cr and Ni, which showed that the distributions of these elements are steeper than a normal distribution while the high coefficient of variation (CV) of Zn, Pb, and As indicate a high inhomogeneity of elements in street dust in Beijing.

Table 1

Concentrations (mg/kg) of eight heavy metals in street dust from study areas
Element	Minimum	Maximum	Mean	Median	Std.deviation	Skewness	Kurtosis	CV	Background values (chen et al., 2004)
Cr	65.05	121.70	96.35	93.75	16.91	-0.11	-0.75	0.18	29.80
Ni	19.25	33.80	26.26	26.50	4.26	0.24	-0.84	0.16	26.80
Cu	36.60	145.80	70.18	62.05	29.66	1.27	1.66	0.42	18.70
Zn	128.60	1149.10	368.30	330.95	241.19	2.64	8.53	0.65	57.50
Pb	28.75	200.75	68.74	64.25	39.71	2.88	9.84	0.58	24.60
As	4.15	24.55	6.99	6.05	4.95	3.64	13.70	0.71	7.09
Cd	0.19	1.01	0.63	0.62	0.22	-0.29	0.02	0.35	0.12
Hg	0.13	0.71	0.27	0.25	0.13	2.49	8.12	0.48	0.12

Heavy metal concentrations in street dust in Beijing compared to data studied in other cities in the world are presented in Table 2. Comparing our previous data in 2013 (Yu et al., 2019) and a study in 2005 in Beijing (Liu and Cen, 2007; Tanner et al., 2008) to this study in 2017, we find that the Cr, Cu, Zn, and Pb concentrations in street dust in 2017 are much higher than those of 2005 and 2013, while those of Hg and Ni are the opposite. The As and Cd concentrations are between the concentrations of 2005 and 2013. The mean Ni, Cu, Zn, and Pb concentrations in Beijing street dust are evidently lower than those in other cities except for Wuhan (China), Ottawa (Canada), and Luanda (Angola). In conclusion, heavy metal concentrations in Beijing are universally at low or medium levels in comparison to other cities in the world.

Table 2

Heavy metal concentration in street dust of different cities (mg/kg)
City	Cr	Ni	Cu	Zn	Pb	As	Cd	Hg	References
Beijing (China)	96.35	26.26	70.18	368.3	68.74	6.99	0.63	0.28	This study
Beijing (China)	89.07	27.27	57.67	273.2	63.49	7.82	0.6	0.37	Yu et al., 2019
Beijing (China)	87	34	46	219	54	6	1.1	0.34	Liu and Cen, 2007; Tanner et al., 2008
Wuhan (China)	75.3	27.7	62.1	224.2	102.6	-	-	-	Yang et al., 2010
Baoji (China)	126.7	48.8	123.2	715.3	433.2	19.8	0.47	1.1	Lu et al., 2009; 2010
Shanghai (China)	159.3	83.98	196.8	733.8	294.9	-	1.23	-	Shi et al., 2008
Xian (China)	167.28	-	94.98	421.46	230.52	10.62	-	0.64	Han et al., 2006
Thessaloniki (Greece)	187.3	95.71	526.2	671	191	13.2	0.59	-	Bourliva et al., 2016
Shiraz (Iran)	67.16	77.52	136.34	403.46	115.71	6.58	0.5	1.05	Keshavarzi et al., 2015
Tokyo (Japan)	52.3	29.6	-	1888	245	1.16	0.98	-	Wijaya et al., 2012
Massachusetts (American)	95	-	105	240	73	-	-	-	Apeagyei et al., 2011
Seoul (Korea)	151	-	396	795	144	-	-	-	Kim et al., 2007
Luanda (Angola)	25.65	-	41.78	316.6	351.3	-	1.15	-	Ferreira-Baptista and De Miguel, 2005
Birmingham (England)	-	41.1	466.9	534	48	-	1.62	-	Charlesworth et al., 2003
Coventry (England)	-	129.7	226.4	385.7	47.1	-	0.9	-	Charlesworth et al., 2003
Ottawa (Canada)	59	19	188	184	68	1	0.6	0.02	Rasmussen et al., 2001

3.2 Spatial and seasonal variations in heavy metals concentration characteristics in street dust

With seasonal changes in climatic characteristics such as temperature, rainfall, and humidity, there are significant changes in human activities and production, such as seasonal fluctuations in tourist traffic, temperature regulation in residential areas, fertilizer and pesticide usage in agricultural areas, and even energy and other raw materials required for production processes in industrial enterprises. This might be the important reason for the changes in the seasonal distribution characteristics and sources of heavy metals in urban street dust (Men et al., 2018).

The concentration of heavy metal in street dust has proven to be extremely variable (Tang et al., 2013). Those from different functional areas are shown in Fig. S1. The statistic values are listed in Table S4. These parameters are calculated using the average of the spring and winter. The concentrations of all elements except for Ni and As in industrial area (IA), residential area (RA), educational area (EA), commercial area (CA) and parking area (PA) are higher than the background values of soil in Beijing (Chen et al., 2004), indicating that heavy metals in street dust may be of natural sources and anthropogenic emissions, and Ni and As may come from natural origin. The order of the total concentrations of eight heavy metals in each functional area is IA > CA > EA > RA > PA. The highest value is found in the IA, which is in line with previous research in Novi Sad, Serbia (Skrbic et al., 2018). The spatial distribution of heavy metals in street dust is influenced both by the urban terrain and the emission source distribution such as industrial activities, traffic, higher population density, and diversity of human activities (Tang et al., 2013; Wang et al., 2016).

The highest values of Cr, Cu, Pb, and Cd were found in the CA. The Cr, Cu, Pb, and Cd in street dust come mainly from traffic and sources such as street coatings. There are three sampling sites in CA. Bai Rong Trade Center is a mega wholesale market dealing mainly with clothing and small commodities, and is the largest modern commodity trading center in Asia. Wangfujing Street is a century-old commercial pedestrian street in Beijing with a daily flow of up to one million people. Zhongguancun Mall Square is a comprehensive shopping center integrating leisure, entertainment, shopping, and dining. And it has the largest underground shopping center in China, with huge daily traffic and pedestrian flow. The huge traffic and pedestrian flow in CA make it possible for the highest values of Cr, Cu, Pb, and Cd to occur in CA. Although Beijing's urban area is only 8.2% of the total area of Beijing, its traffic accounts for 37.2% of the city's traffic burden (Cen, 2019). Compared to other regions, Beijing's congested traffic is most likely the main reason for the enrichment of these elements (Men et al., 2018).

The highest value of Ni was found in the EA. The content of Ni in this study was lower than the background values, indicating that Ni was less disturbed by humans and most likely originated from natural sources. According to a previous paper that Ni content in the topsoil of Beijing is lower than its background value, and is content mainly influenced by the parent material and natural processes (Dong et al., 2020). The highest values of Ni were found in EA, indicating that it was more disturbed by anthropogenic interference in EA than in other areas. Parent material is still the main factor dominating its content.

The highest values of Zn and As were found in the IA. There are more industrial enterprises distributed around the sampling sites in the IA. Yuquanying building materials market, Yongdingmenwai Street, and Dazhongsi good home building materials market around a lot of large integrated building materials market. Building materials factory around the road is open, has a huge traffic flow and pedestrian flow. Meanwhile, the industrial raw materials piled up by these industrial enterprises, the materials used in the production process, and the waste gas generated in the production process all affect the spatial distribution and content of heavy metals in the surrounding street dust. Furthermore, former industrial production activities can affect the distribution of elements in the topsoil, also the content and distribution of heavy metals in the street dust. As is most likely to be of natural origin due to its content being lower than the background values. And the highest value of As was found in IA indicating a greater anthropogenic disturbance in these areas. Therefore, in this study, Zn in the IA might originate from traffic sources and previous industrial activities.

The highest value of Hg occurs in PA. Previous studies have reported that Hg pollutants in urban topsoil are mainly from coal combustion used for heating in northern China (Zhao et al., 2015; Huang et al., 2016). The expansion of urban boundaries is mainly through the integration of surrounding rural land, and agricultural activities such as the application of pesticides and fertilizers can cause long-term accumulation of heavy metals in the soil. And new parks built on this basis may affect human health. Due to the expansion of Beijing's urban areas, agricultural land is gradually becoming urban land, but the heavy metal pollution problems left by agricultural activities still exist (Liu, 2020). The accumulation of heavy metals in the northern areas of Chaoyang District and Haidian District may be related to former agricultural activities. Hg emissions in agricultural activities mainly include irrational application of Hg-containing fertilizers and pesticides, and sewage irrigation (Huang et al., 2014). Furthermore, the fertilizers and pesticides applied to manage the park also carry Hg into the topsoil. This affects the content and distribution of heavy metals in the street dust of the area. Therefore, in this study, the highest Hg value was found in the PA most likely due to coal combustion and the application of pesticides and fertilizers.

In addition to the above analysis, as far as Beijing is concerned, a large number of factories were established in the 20th century. Including chemical plants, coking plants, paper mills, power plants, and glass factories. Before 2008, a large number of factories were gradually converted, closed, or relocated to successfully host the 2008 Beijing Olympics. The former industrial production activities led to contamination of the local urban soil, including heavy metal contamination.

According to the current list of industrial heritage protection issued by the government, Beijing's existing industrial heritage can be divided into three levels: national, municipal, and general industrial heritage. There are four national industrial heritage sites, five industrial heritage sites, and thirteen general industrial heritage sites in the study area. Therefore, the impact of the former industrial activities cannot be ignored as well.

Additionally, the differences in heavy metal concentrations among five functional areas are calculated by one-way ANOVA, which indicates Cu values in CA and PA are significantly different and the others have no obvious difference in the five functional areas.

The heavy metal concentrations in street dust during different seasons are shown in Fig. 2. The mean Cu, Pb, As, Cd, and Hg concentrations during spring are lower than those during winter. The difference between Pb and As is greater than that of any other metals from spring to winter. Thus, the increased concentrations of these elements during winter are most probably result from vehicle exhaust, lubricants oil, tire wear, and coal combustion resources in the study areas (Men et al., 2018; Pan et al., 2017; Najmeddin et al., 2018). During winter, the increased amount of coal combustion in cities increases the As content (Zhang et al., 2014). The Cr, Zn, and Ni concentrations are higher during spring. These results could be from frequent transportation and industrial activities. At the same time, traffic emission is much greater during spring than during winter. Many tourists visit Beijing during the spring of every year. Additionally, climatic events such as dust storms during winter might affect the heavy metal concentrations in street dust (Men et al., 2018).

The results of the PCA of the heavy metal of street dust in spring showed that three eigenvalues (47.54%, 15.25%, and 15.16%) were greater than 1.00, accounting for 77.95% of the total variance. Based on the results of the PCA of street dust heavy metals in spring, the spatial distribution of factor scores (F1, F2, F3) was produced (Fig. 3.). F1, which includes Cr, Ni, Cu, Zn, and Cd, showed the highest values in IA, indicating that the contribution of IA to these elements in heavy metals in spring street dust was the largest compared to other areas (Fig. 3.1.). This is also related to the large number of industrial enterprises and wholesale markets distributed around IA. F2, which includes only As shows the highest value in IA (Fig. 3.2.). F3, which includes Hg and Pb, shows the highest value in PA (Fig. 3.3.). The highest value of F3 occurs in PA is most likely related to coal burning in Beijing in winter.

Figure 3 Spatial distribution of the factor score (F1, F2, F3) in spring obtained by factorial analysis (n = 30)

The results of the PCA of the heavy metal of street dust in winter showed that three eigenvalues (45.66%, 16.91%, and 13.05%) were greater than 1.00, accounting for 75.62% of the total variance. Based on the results of the PCA of street dust heavy metals in winter, the spatial distribution of factor scores (F1, F2, F3) was produced (Fig. 4.). F1, which includes Cr, Ni, Cu, Zn, Cd, and As, showed the highest values in IA (Fig. 4.1.). F2, which includes only Hg, showed the highest values in IA, EA, PA, and CA (Fig. 4.2.). F2's high values over a large area in these areas are most likely related to winter coal combustion in Beijing (Fig. 4.3.). The increased coal combustion in Beijing in winter and the large area of burned coal carbon particles entering street dust through atmospheric deposition increased the Hg content in street dust compared to spring. F3, which includes only Pb, showed high values in CA.

Figure 4 Spatial distribution of the factor score (F1, F2, F3) in winter obtained by factorial analysis (n = 30)

3.3 Assessment of heavy metals’ environmental quality in street dust

The geo-accumulation index (I_geo) of heavy metals in street dust from five functional areas is shown in Fig. 5 and presented in Table S5. The average of I_geo increases as follows: As < Ni < Hg < Pb < Cr < Cu < Cd < Zn. The rank order of the I_geo in different functional areas is as follows: IA Ni < As < Hg < Pb < Cr < Cu < Cd < Zn; RA As < Ni < Pb < Hg < Cr < Cu < Cd < Zn; EA As < Ni < Hg < Pb < Cr < Cu < Cd < Zn; CA As < Ni < Hg < Cr < Pb < Cu < Cd < Zn; PA Ni < As < Pb < Cu < Cr < Zn < Hg < Cd. According to Table S1 based on the mean value of I_geo to assess the heavy metal contamination in Beijing, Ni and As are unpolluted; Pb and Hg are unpolluted to moderately polluted; and Cr, Cu, Zn, and Cd are moderately polluted. According to the addition of I_geo of eight heavy metals of each functional area, the I_geo in different functional areas decrease as follows: CA > IA > EA > RA > PA. This result may be owing to the socioeconomic activities in the commercial and industrial area, where great quantity of tall structures and a high pedestrian volume occur (Li et al., 2017). The range of I_geo of each heavy metal in different functional areas is as follows: Cr 0.76–1.28 (PA-CA) is from unpolluted to moderately polluted to moderately polluted; Ni -0.81 – -0.43 (RA-EA), is unpolluted; Cu 0.63–1.98 (PA-CA) is from unpolluted to moderately polluted to moderately polluted; Zn 0.99–2.96 (PA-IA) is from unpolluted to moderately polluted to moderately to heavily polluted; Pb 0.32–1.65 (RA-CA) is from unpolluted to moderately polluted to moderately polluted; As -1.06–0.16 (PA-IA) is from unpolluted to unpolluted to moderately polluted; Cd 1.27–2.10 (PA-CA) is moderately polluted to moderately to heavily polluted; and Hg 0.20–1.11 (IA-PA) is from unpolluted to moderately polluted to moderately polluted.

The Er, RI, and mRI of heavy metals in street dust from different functional areas are shown in Fig. 6 and presented in Table S6. The mean value of Er increased as follows: Ni < Zn < Cr < As < Pb < Cu < Hg < Cd. The rank order of Er in different functional areas is as follows: for the IA, Ni < Cr < Zn < Pb < As < Cu < Hg < Cd; for the RA, EA and CA, Ni < Zn < Cr < As < Pb < Cu < Hg < Cd; and for the PA, Zn < Ni < Cr < Pb < As < Cu < Hg < Cd. According to the criteria of the ecological risk indicator in street dust based on the mean value of Er to assess the heavy metal ecological risk in Beijing, Cr, Ni, Cu, Zn, Pb, and As are of low risk and Cd and Hg are of considerable risk. The RI and mRI of each different functional area all decrease in the order of RA < PA < EA < IA < CA. According to the criteria of the RI indicator to evaluate the heavy metal ecological risk in different functional areas, the RA and PA are at a moderate risk and the IA, EA, and CA are at considerable risk. Meanwhile, using mRI to assess the heavy metal ecological risk in different functional areas indicates that all functional areas are at considerable risk. Using the range of Er values of each heavy metal in different functional areas and the criteria of the ecological risk indicator show that Cr, Ni, Cu, Zn, Pb, and As are all low risk in five functional areas, while the range of Cd is from considerable to high risk (PA-CA) and Hg is from moderate to considerable risk (IA-PA).

3.4 Source apportionment analysis of heavy metals in street dust

Pearson’s correlation coefficient is calculated to investigate the inter-element relationships (Table S7). As the t-paired test indicates, there is no significant difference between spring and winter; the average of the concentrations during spring and winter is applied for correlation analysis. In accordance with the data of the Pearson’s correlation significant relationships (P < 0.05), Cr-Ni (0.70), Cr-Cu (0.72), Cr-Zn (0.57), Cr-Cd (0.81), Cd-Ni (0.70), Cd-Cu (0.62), Cd-Zn (0.64) are found to significantly relate. Based on published studies (Li et al., 2016), these heavy metals may have a same source, mutual dependence, and common behaviour during transport, which correlation coefficient between the factors is positive. The results of cluster analysis (CA) are shown in Fig. S2. Three clusters are classified: (1) Cr-Cd-Ni-Cu-Zn, (2) As, and (3) Pb-Hg. The results obtained by CA are identical with results using Pearson’s correlation analysis of heavy metals.

For further researching the contamination characteristics and identifying the sources of heavy metals, PCA-MLR is applied. PCA-MLR is among the major ways of receptor models that utilize the chemical composition of receptors for identification and apportionment of sources. PCA-MLR is especially useful in cases in which detailed source profiles are not available (Pan et al., 2017). We apply PCA-MLR to identify the source apportionment of heavy metals in street dust. The PCA results are provided in Table S8 and Fig.S3. The results show that there are three eigenvalues (43.98%, 18.15%, and 12.52%) lager than 1.00, in proportion of 74.65% of the total variability.

PC1 explains 43.98% of the total variance. It includes important loadings for Cr, Ni, Cu, Zn, and Cd. As the average Ni content is lower than the background value and the I_geo of Ni indicates that it is practically unpolluted, the Er shows the low risk of Ni. According to this, Ni may originate from a natural source. The mean Cr, Cu, Zn, and Cd concentrations are all higher than the background values, respectively, and the results of I_geo show that they all have a different degree of contamination. The Er values of Cr, Cu, Zn are all low risk, while Cd is of considerable to high risk.

Cr in street dust mainly from such as tire wear and aging, vehicle exhaust (Gunawardena et al., 2014; Hini et al., 2019), street coatings (Lee et al, 2018), vehicle parts and motor bodies (Rahmana et al., 2019). Ni can originate from car bodies and parts (Khashman., 2004), nickel-cadmium batteries, and electronics waste (Rahman and Islam., 2010; Kabadayi and Cesur., 2010). Cu in street dust comes mainly from traffic sources such as vehicle exhaust and tires, engine and other mechanical wear, and automotive lubricants (Cerda et al. 2011; Gunawardena et al. 2014; Świetlik et al. 2015; Bourliva et al. 2017; Budai and Clement., 2018; Hini et al. 2019; Zhong et al. 2020). Zn is primarily a traffic source, including tire and brake wear, diesel exhaust emissions, lubricants, carburetors, mechanical parts wear, vehicle wear, galvanized automotive parts corrosion, etc. ( Salim Akhter and Madany., 1993; Ellis and Revitt., 2008; Cerda et al., 2011; Świetlik et al., 2015; Bourliva et al., 2017; Budai and Clement., 2018; Rahmana et al., 2019). Cd can come from transportation sources including diesel exhaust emissions, lubricants, and tire wear (Men et al., 2018), and other sources including cadmium-nickel battery production, plastic production and paint (Kabadayi and Cesur., 2010), protective alloys, and surfaces of construction materials, electroplating materials, fertilizers (Zhong et al., 2020; Rahman and Islam., 2010). The highest Cr, Cu, Zn, and Cd concentrations are all shown in the IA and CA. At the sampling sites in IA and CA, in addition to congested traffic, there are a large number of industrial enterprises in IA. The industrial raw materials piled up in many building material plants will enter the surrounding road dust with air, water, and soil, thus affecting the spatial distribution and content of heavy metals in road dust. Meanwhile, according to previous studies, the sources of Cr, Cu, Zn, and Cd have been preliminarily identified to be a mixture of anthropogenic sources, such as industrial activities, the iron/steel industry, industrial plants such as ceramic and tile factories, vehicular emissions, brake linings, lubricating oils, tire and brake wear, diesel exhaust emissions, and corrosion of safety fences (Aminiyan et al., 2018; Bourliva et al., 2018; Li et al., 2016; Men et al., 2018; Pan et al., 2017). At the sampling sites in IA and CA, in addition to congested traffic, there are a lot of industrial enterprises. The industrial raw materials piled up in many building material plants will enter the surrounding road dust with air, water, and soil, thus affecting the spatial distribution and content of heavy metals in street dust. Beyond that, a large number of industrial sites exist in the study area. Including sites of chemical plants, coking plants, paper mills, power plants, and glass factories. Previous industrial activities would have influenced the content and distribution of heavy metal elements in the topsoil of the area over a long period, which in turn would have influenced the content and distribution of heavy metals in the street dust. Thus, PC1 originates from mixed natural, industrial, and traffic related sources.

PC2 explains 18.15% of the total variance. It includes only As. In the present study, the average As concentration is lower than the background value, which indicates it may be derived from a natural source. The I_geo of As is from practically unpolluted to unpolluted to moderately polluted and the Er of As is low risk. The As hot spots are in the IA and PA and the mean As concentration during spring is lower than that during winter. Non-exhaust vehicle emissions contribute the most proportion to heavy metals during the spring while fuel combustion accounts for the most ratio of heavy metals during winter (Men et al., 2018). Coal is the major fuel in Beijing. Obviously, the content and distribution of As are anthropogenically disturbed, but its main source is still the parent material. Thus, the source of PC2 is mainly a natural source.

PC3 explains 12.52% of the total variance. It includes Hg and Pb. In the present study, the average Hg content is much bigger than the background value, the I_geo of Hg is unpolluted to moderately polluted to moderately polluted, and the Er of Hg is moderate to considerable risk. The mean Pb concentration is higher than the background value, and the I_geo shows it is unpolluted to moderately polluted. The values of Er for Pb show that it is of low risk.

For one thing, Hg in street dust may come from pesticides and fertilizers, which have characteristics such as easy migration and volatility (Dong et al., 2016; Giersz et al., 2017). By the beginning of this century, the farmland in the northern areas of Chaoyang District and Haidian District had a hundred-year history, and the long-term farming environment caused a certain degree of soil contamination by fertilizer or pesticide application. Due to the expansion of Beijing's urban areas, the farmland has gradually become urban land, but heavy metal contamination left by agricultural activities still exists (Liu, 2020). For another, Hg mainly comes from coal combustion, industrial flue gas, and industrial wastewater (Men et al., 2018; Wang et al., 2019). In China, Hg may account for up to 70% of total Hg emissions from coal combustion (Lv., 2019), and thus its atmospheric transport and deposition lead to Hg contamination in soils (Luo et al., 2012; Streets et al., 2005). Beijing's coal consumption in 2015 was about 10 million tons of standard coal (Beijing Statistics Bureau., 2016), which shows that coal is still the main fuel in Beijing. High levels of Hg from long-term coal combustion have also been demonstrated in urban soils in Beijing (Chen et al., 2010). The hot spots of Hg are in the PA and CA. In this study, the enrichment of Hg most likely came from the winter coal combustion in Beijing through atmospheric deposition and from Hg-contaminated soil on the original land.

Global concentrations of Pb in street dust range from 18.99-597.93 mg/kg (Rahmana et al., 2019). And Pb, which is an indicator of vehicle-related metal in urban areas, has not been used as a gasoline additive since it was banned in China during the 2000s. Although leaded gasoline is being phased out, the long half-life and low leaching rate of Pb lead to long-term enrichment in the urban environment. Besides, the Pb element in street dust could be linked to painted road lines because PbCrO₄ is frequently used as a pigment in paints. Furthermore, the carbonate host minerals of Pb such as cerussite (PbCO₃) and Pb hydroxyl carbonate have been widely used as a white pigment in road surface markings (Hajar Merrikhpour and Shahriar Mahdavi., 2016; Gope et al., 2018; Li et al., 2016; Li et al., 2018; Men et al., 2018). Sources of Pb in street dust are associated with sources such as smelting and coal combustion, in addition to traffic sources (Zhong et al., 2020; Lee et al., 2018). The highest average concentration of Pb from different functional areas is in the CA and EA. Beijing's congested traffic and winter coal combustion could explain why this occurs in CA and EA. Therefore, the source of PC3 is a mixed source of fertilizer, pesticides, traffic, and coal combustion.

In conclusion, the PCA findings agree well with the results of the Pearson correlation coefficient and CA analyses. We identify the major sources of heavy metals in street dust and the heavy metals of each source through geochemical and statistical analysis. Thus, MLR is used following PCA to study the percentage contribution of each contamination source (Fig. 7).

According to Eqs. (6–8), the resultant equation is expressed as Eq. (18).

Z = 0.736PC1 + 0.518PC2 + 0.093PC3 (= 0.819, P < 0.005) (18)

So, the result is 54.64% for mixed natural, industrial, and traffic-related sources; 38.46% for mixed natural and coal combustion sources; and 6.9% for mixed fertilizers, coal combustion, and traffic-related sources.

3.5 Health risk assessment of heavy metals in street dust

Results obtained for the daily intake of each heavy metal via different exposure pathways and non-carcinogenic and carcinogenic human health risk are listed in Table S9 and Table S10, respectively. According to the HI values of the heavy metals all lower than the safety level of 1, inhabitants (children and adults) have no non-carcinogenic risk of the heavy metals. The order of main route of exposure of both children and adults in street dust is as follows: ingestion > dermal contact > inhalation. This result is also consistent with previous research (Ali et al., 2017; Skrbic et al., 2018). For a non-carcinogenic effect, inhalation of Hg vapor is the major exposure route, which is coincide with previous research (Keshavarzi et al., 2015), because Hg has the significant vapor pressure at ambient temperatures. The decreasing order of HI for children is Cr > As > Pb > Hg > Cu > Zn > Ni > Cd, while the decreasing order of HI for adults is As > Cr > Pb > Hg > Cu > Cd > Zn > Ni. The highest non-carcinogenic risks for both children and adults are As and Cr. The rank order of HI from different functional areas for both children and adults are the IA > CA > EA > PA > RA. This result indicates that there could be more non-carcinogenic risk to the resident in the IA and CA than in the other functional areas. The non-carcinogenic risk HI values of Cr and Ni for inhabitants in the EA are higher than those of other functional areas; Cu and Pb in CA; Zn, As, and Cd in IA; and Hg in PA. The non-carcinogenic risk for children is higher than for adults.

The exposure route and its specific toxicity decide if a metal can cause carcinogenic risk (Li et al., 2017). Cr, Ni, As, and Cd are considered a carcinogenic risk when inhaled or ingested. The values of the RI of heavy metals are not all within the risk level between 10^− 6 – 10^− 4, which indicates that there is no carcinogenic risk of the heavy metals to inhabitants (children and adults). The rank order of RI of the heavy metals for both children and adults are all Cr > As > Ni > Cd. The highest value of RI is for Cr. The decreasing order of RI for children from different functional areas is IA > EA > CA > PA > RA, while the order of RI for adults from different functional areas is IA > EA > CA > RA > PA. The carcinogenic risk RI values of Cr and Ni for inhabitants in the EA are higher than those of the other functional areas, and those for As and Cd for inhabitants in the IA are higher than those of the other functional areas. This result indicates that there could be more carcinogenic risk to the resident in the IA and EA than in the other functional areas. The carcinogenic risk for adults is higher than that for children.

(1) Except for Ni and As, the contents of the other elements in street dust all exceed the background values of soil in Beijing. The obtained concentrations are at low or medium levels compared to those researched in other cities where street dust was investigated.

(2) According to the paired t-test indicate that heavy metal contents between spring and winter has no significant difference. The rank order of the heavy metal concentrations among the five functional areas can be classified as follows: IA > CA > EA > RA > PA.

(3) The mean I_geo values decrease in following manner: Zn > Cd > Cu > Cr > Pb > Hg > Ni > As, while those of the mean Er are Cd > Hg > Cu > Pb > As > Cr > Zn > Ni. According to the addition of I_geo, RI, and mRI from different functional areas, higher values are all found in the CA and IA compared to those of the other functional areas.

(4) Pearson’s correlation coefficient, PCA-MLR, and CA indicate that eight heavy metals from Beijing mainly occur from three sources: for PC1-Cr, Ni, Cu, Zn, and Cd (43.98%), mixed natural, industrial, and traffic-related sources; for PC2-As (18.15%), mixed natural and coal combustion sources; and for PC3-Hg and Pb (12.52%), mixed fertilizers, coal combustion, and traffic-related sources.

(5) There is no non-carcinogenic and carcinogenic risk for children and adults. As and Cr have higher non-carcinogenic and carcinogenic risks for habitants than those of the other elements. Among the five functional areas, the IA has the highest non-carcinogenic and carcinogenic risk compared to that of the others.

Statements & Declarations

Funding

“This work was supported by Fundamental Research Funds for the Central Universities (Grant number [2-9-2012-080]) and National Innovation and Entrepreneurship Training Program for College Students of China University of Geosciences, Beijing (grant number [201711415013] ). ”

Competing Interest

“The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript. ”

Author Contributions

“Haiyan Feng contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by Xiangnan Yu and Xinhao Chen, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”

Journal article Acosta, J.A., Gabarrón, M., Faz, A., Martínez-Martínez, S., Zornoza, R., Arocena, J.M. (2015). Influence of population density on the concentration and speciation of metals in the soil and street dust from urban areas. Chemosphere, 134, 328–337. https://doi.org/10.1016/j.chemosphere.2015.04.038.
Journal article Ali, M.U., Liu, G.J., Yousaf, B., Abbas, Q., Ullah, H., Munir, M.A.M., Fu, BA. (2017). Pollution characteristics and human health risks of potentially (eco) toxic elements (PTEs) in road dust from metropolitan area of Hefei, China. Chemosphere, 181, 111–121. https://doi.org/10.1016/j.chemosphere.2017.04.061.
Journal article Aminiyan, M.M., Baalousha, M., Mousavi, R., Aminiyan, F.M., Hosseini, H., Heydariyan, A. (2018). The ecological risk, source identification, and pollution assessment of heavy metals in road dust: a case study in Rafsanjan, SE Iran. Environmental Science and Pollution Research-International, 25 (14), 13382–13395. https://doi.org/10.1007/s11356-017-8539-y.
Journal article Al-Khashman, O.A. (2004). Heavy metal distribution in dust, street dust and soils from the work place in Karak Industrial Estate, Jordan. Atmospheric Environment, 38, 6803–6812. https://doi.org/10.1016/j.atmosenv.2004.09.011.
Journal article Bourliva, A., Kantiranis, N., Papadopoulou, L., Aidona, E., Christophoridis, C., Kollias, P., Evgenakis, M., Fytainos, K. (2018). Seasonal and spatial variations of magnetic susceptibility and potentially toxic elements (PTEs) in road dusts of Thessaloniki city, Greece: A one-year monitoring period. Science of the Total Environment, 639, 417–427. https://doi.org/10.1016/j.scitotenv.2018.05.170.
Journal article Bourliva A, Christophoridis C, Papadopoulou L, Giouri, K., Papadopoulos, A., Mitsika, E., Fytianos, K. (2017). Characterization, heavy metal content and health risk assessment of urban road dusts from the historic center of the city of Thessaloniki, Greece. Environmental Geochemistry and Health, 39:611–34. https://doi.org/10.1007/s10653-016-9836-y.
Journal article Budai, P., & Clement, A. (2018). Spatial distribution patterns of four trafc-emitted heavy metals in urban road dust and the resuspension of brake-emitted particles: fndings of a feld study. Transportation Research Part D: Transport and Environment, 62, 179–185. https://doi.org/10.1016/j. trd.2018.02.014.
Book chapter Beijing Statistics Bureau, B.S. (2016). Beijing Statistical Yearbook. China Statistics Press, Beijing.
Journal article Cen, B. (2019). Study on the Spatial Characteristics and Protection Countermeasures of Beijing Urban Industrial Heritage. Journal of Beijing Union University, 33 (4):30–37. https://doi.org/10.16255/j.cnki.ldxbz.2019.04.006.
Journal article Chen, X., Xia, X., Wu, S., Wang, F., Guo, X. (2010). Mercury in urban soils with various types of land use in Beijing, China. Environmental Pollution, 158, 48–54. https://doi.org/10.1016/j.envpol.2009.08.028.
Journal article Cerda E, Reyes L, Manuel J, Elizondo-Martinez P, Acuna-Askar K. (2011). Contamination and chemical fractionation of heavy metals in street dust from the Metropolitan Area of Monterrey, Mexico. Environmental Technology, 32: 1163–1172. https://doi.org/10.1080/09593330.2010.529466.
Journal article Chen, H.Y., Teng, Y.G., Lu, S.J., Wang, Y.Y., Wu, J., Wang, J.S. (2016). Source apportionment and health risk assessment of trace metals in surface soils of Beijing metropolitan, China. Chemosphere, 144, 1002–1011. https://doi.org/10.1016/j.chemosphere.2015.09.081.
Journal article Chen, T.B., Zheng, Y.M., Chen, H., Zheng, G.D. (2004). Background Concentrations of Soil Heavy Metals in Beijing. Environmental Science, 25(1), 117–122. (in Chinese). https://doi.org/10.13227/j.hjkx.2004.01.026.
Journal article Chen, Y., Schleicher, N., Fricker, M., Cen, K., Liu, X.L., Kaminski, U., Yu, Y., Wu, XF., Norra, S. (2016). Long-term variation of black carbon and PM2.5 in Beijing, China with respect to meteorological conditions and governmental measures. Environmental Pollution, 212, 269–278. https://doi.org/10.1016/j.envpol.2016.01.008.
Journal article Christoforidis, A., Stamatis, N. (2009). Heavy metal contamination in street dust and roadside soil along the major national road in Kavala's region, Greece. Geoderma, 151, 257–263. https://doi.org/10.1016/j.geoderma.2009.04.016.
Journal article Day, J.P., Hart, M., Robinson, M.S. (1975). Lead in urban street dust. Nature, 253,
https://doi.org/343-345
. 10.1038/253343a0.
Journal article Dehghani, S., Moore, F., Keshavarzi, B., Beverley., Hale, A. (2017). Health risk implications of potentially toxic metals in street dust and surface soil of Tehran, Iran. Ecotoxcology Environmental Safety, 136, 92–103. https://doi.org/10.1016/j.ecoenv.2016.10.037.
Journal article Duong, T.T.T., Lee, B.K. (2011). Determining contamination level of heavy metals in road dust from busy traffic areas with different characteristics. Environmental Management, 92, 554–562. https://doi.org/10.1016/j.jenvman.2010.09.010.
Journal article Du, Y.R., Gao, B., Zhou, H.D., Ju, X.X., Hao, H., Yin, S.H. (2013). Health risk assessment of heavy metals in road dusts in urban parks of Beijing, China. Procedia Environmental Sciences, 18, 299–309. https://doi.org/10.1016/j.proenv.2013.04.039.
Journal article Dong, H., Lin, Z., Wan, X., Feng, L. (2016). Risk assessment for the mercury polluted site near a pesticide plant in Changsha, Hunan, China. Chemosphere, 169, 333–341. https://doi.org/10.1016/j.chemosphere.2016.11.084.
Journal article Dong, S.Z., Zhang, S.W., Wang, L.J., Ma, G., Lu, X.W., Li, X.P. (2020). Concentrations, Speciation, and Bioavailability of Heavy Metals in Street Dust as well as Relationships with Physiochemcal Properties: A Case Study of Jinan City in East China. Environmental Science and Pollution Research, 27(28): 35724–35737. https://doi.org/10.1007/s11356-020-09761-6.
Journal article Ellis, J. B., & Revitt, D. M. (2008). Quantifying diffuse pollution sources and loads for environmental quality standards in urban catchments. Water, Air, & Soil Pollution: Focus, 8(5–6), 577–585. https://doi.org/10.1007/s11267-008-9175-9.
Journal article Elom, N.I., Entwistle, J., Dean, J.R. (2014). Human health risk from Pb in urban street dust in northern UK cities. Environmental Chemistry Letters, 12, 209–218. https://doi.org/10.1007/s10311-013-0436-0
Journal article Eqani, S.A.M.A.S., Khalid, R., Bostan, N., Saqib, Z., Mohmand, J., Rehan, M., Ali, N., Katsoyiannis, IA., Shen, HQ. (2016). Human lead (Pb) exposure via dust from different land use settings of Pakistan: A case study from two urban mountainous cities. Chemosphere, 155, 259–265. https://doi.org/10.1016/j.chemosphere.2016.04.036.
Journal article Gope, M., Masto, R.E., George, J., Balachandran, S. (2018). Tracing source, distribution and health risk of potentially harmful elements (PHEs) in street dust of Durgapur, India. Ecotoxcology Environmental Safety, 154, 280–293. https://doi.org/10.1016/j.ecoenv.2018.02.042.
Journal article Gope, M., Masto, R.E., George, J., Hoque, R. R. (2017). Bioavailability and health risk of some potentially toxic elements (Cd, Cu, Pb and Zn) in street dust of Asansol, India. Ecotoxcology Environmental Safety, 138, 231–241. https://doi.org/10.1016/j.ecoenv.2017.01.008.
Journal article Gunawardana, C., Goonetilleke, A., Egodawatta, P., Dawes, L., Kokot, S. (2012). Source characterisation of road dust based on chemical and mineralogical composition. Chemosphere, 211, 674–683. https://doi.org/10.1016/j.chemosphere.2011.12.012.
Journal article Gunawardena, J., Ziyath, A. M., Egodawatta, P., Ayoko, G. A., & Goonetilleke, A. (2014). Mathematical relationships for metal build-up on urban road surfaces based on trafc and land use characteristics. Chemosphere, 99, 267–271. https://doi.org/10.1016/j.chemosphere. 2013.10.068.
Journal article Giersz, J., Bartosiak, M., Jankowski, K. (2017). Sensitive determination of hg together with Mn, Fe, Cu by combined photochemical vapor generation and pneumatic nebulization in the programmable temperature spray chamber and inductively coupled plasma optical emission spectrometry. Talanta, 167, 279–285. https://doi.org/10.1016/j.talanta.2017.02.018.
Journal article Hakanson, L. (1980). An ecological risk index for aquatic pollution control. A sedimentological approach. Water Research, 14, 975e1001. https://doi.org/10.1016/0043-1354(80)90143-8.
Journal article Han, L.F., Gao, B., Wei, X., Xu, D.Y., Gao, L. (2016). Spatial distribution, health risk assessment, and isotopic composition of lead contamination of street dusts in different functional areas of Beijing, China. Environmental Science and Pollution Research-International, 23, 3247–3255. https://doi.org/10.1007/s11356-015-5535-y.
Journal article Huang, W., Fan, H., Qiu, Y., Cheng, Z., Qian, Y. (2016). Application of fault tree approach for the causation mechanism of urban haze in Beijing-considering the risk events related with exhausts of coal combustion. Science of the Total Environment, 544, 1128–1135. https://doi.org/10.1016/j.scitotenv.2015.10.009.
Journal article Hajar Merrikhpour & Shahriar Mahdavi. (2016). Heavy metal contamination and solid phase speciation in street dusts, Archives of Environmental & Occupational Health. https://doi.org/10.1080/19338244.2016.1219300.
Journal article Hini G, Eziz M, Wang W, Ili A, Li X. (2019). Spatial distribution, contamination levels, sources, and potential health risk assessment of trace elements in street dusts of Urumqi city, NW China. Human and Ecological Risk Assessment: An International Journal, 1–17. https://doi.org/10.1080/10807039.2019.1651629.
Journal article Han, Y.M., Du, P.X., Cao, J.J., Posmentier, E.S. (2006). Multivariate analysis of heavy metal contamination in urban dusts of Xi’an, Central China. Science of the Total Environment, 355, 176–186. https://doi.org/10.1016/j.scitotenv.2005.02.026.
Journal article Jadoon, W.A., Khpalwak, W., Chidya, R.C.G., Abdel–Dayem, S.M.M.A., Takeda, K., Makhdoom, M.A., Sakugawa, H. (2018). Evaluation of Levels, Sources and Health Hazards of Road–Dust Associated Toxic Metals in Jalalabad and Kabul Cities, Afghanistan. Archives Environmental Contamination and Toxicology, 74, 32–45. https://doi.org/10.1007/s00244-017-0475-9.
Journal article Keshavarzi, B., Tazarvi, Z., Rajabzadeh, M.A., Najmeddin, A. (2015). Chemical speciation, human health risk assessment and pollution level of selected heavy metals in urban street dust of Shiraz, Iran. Atmospheric Environment, 119, 1–10. https://doi.org/10.1016/j.atmosenv.2015.08.001.
Journal article Kim, J.A., Park, J.H., Hwang, W.J. (2016). Heavy Metal Distribution in Street Dust from Traditional Markets and the Human Health Implications. International Journal of Environmental Research & Public Health, 13 (8), 820. https://doi.org/10.3390/ijerph13080820.
Journal article Kabadayi F, Cesur H. (2010). Determination of Cu, Pb, Zn, Ni, Co, Cd, and Mn in road dusts of Samsun City. Environmental Monitoring and Assessment, 168: 241–253. https://doi.org/10.1007/s10661-009-1108-1.
Journal article Lanzerstorfer, C. (2018). Heavy metals in the finest size fractions of road-deposited sediments. Environmental Pollution, 239, 522–531. https://doi.org/10.1016/j.envpol.2018.04.063.
Journal article Larsen, R.K., Baker, J.E. (2003). Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere: A comparison of three methods. Environmental Science & Technology: ES&T, 37 (9), 1873–1881. https://doi.org/10.1021/es0206184.
Journal article Liu, C.H., Cen, K. (2007). Chemical composition and possible sources of elements in street dusts in Beijing. Acta Scientiae Circumstantiae, 27 (7), 1181–1188. https://doi.org/10.1109/ICBBE.2010.5516593.
Journal article Liu, E.F., Yan, T., Birch, G., Zhu, Y.X. (2014). Pollution and health risk of potentially toxic metals in urban road dust in Nanjing, a mega-city of China. Science of the Total Environment, 476–477, 522–531. https://doi.org/10.1016/j.scitotenv.2014.01.055.
Article by Liu, L.L. (2020). Heavy metal(loid)s pollution in surface soil of Beijing urban park and their risk assessment. Anhui University. https://doi.org/10.1016/j.envpol.2020.114083
Journal article Li, F.I., Jinxu, Y.F., Shao, L.Z., Zhang, G.X., Wang, J.M., Jin, Z. F. (2018). Delineating the origin of Pb and Cd in the urban dust through elemental and stable isotopic ratio: A study from Hangzhou City, China. Chemosphere, 211, 674–683. https://doi.org/10.1016/j.chemosphere.2018.07.199.
Journal article Li, F., Zhang, J.D., Huang, J.H., Huang, D.W., Yang, J. (2016). Heavy metals in road dust from Xiandao District, Changsha City, China: characteristics, health risk assessment, and integrated source identification. Environmental Science and Pollution Research-International, 23, 13100–13113. https://doi.org/10.1007/s11356-016-6458-y.
Journal article Li, H.H., Chen, L.J., Yu, L., Guo, Z.B., Shan, C.Q., Lin, J.Q., Gu, YG., Yang, ZB., Yang, YX., Shao, JR. (2017). Pollution characteristics and risk assessment of human exposure to oral bioaccessibility of heavy metals via urban street dusts from different functional areas in Chengdu, China. Science Total Environment, 586, 1076–1084. https://doi.org/10.1016/j.scitotenv.2017.02.092.
Journal article Li, H.M., Qian, X., Hu, W., Wang, Y.L., Gao, H.L. (2013). Chemical speciation and human health risk of trace metals in urban street dusts from a metropolitan city, Nanjing, SE China. Science of the Total Environment, 456–457, 212–221. https://doi.org/10.1016/j.scitotenv.2013.03.094.
Journal article Li, R.P., Cai, G.Q., Wang, J., Ouyang, W., Cheng, H.G., Lin, C.Y. (2014). Contents and chemical forms of heavy metals in school and roadside topsoils and road-surface dust of Beijing. Journal of Soils and Sediments, 14, 1806–1817. https://doi.org/10.1007/s11368-014-0943-z.
Journal article Li, X.P., Zhang, M., Gao, Y., Zhang, Y.C., Zhang, X., Yan, X.Y., Wang, S., Yan, R., Liu, B., Yu, HT. (2018). Urban street dust bound 24 potentially toxic metal/metalloids (PTMs) from Xining valley-city, NW China: Spatial occurrences, sources and health risks. Ecotoxicology Environmental Safety, 162, 474–487. https://doi.org/10.1016/j.ecoenv.2018.07.006.
Journal article Lu, X.W., Wu, X., Wang, Y.W., Chen, H., Guo, P.P., Fu, Y. (2014). Risk assessment of toxic metals in street dust from a medium-sized industrial city of China. Ecotoxicology Environmental Safety, 106, 154–163. https://doi.org/10.1016/j.ecoenv.2014.04.022.
Journal article Lu, X.W., Wang, L.J., Li, L.Y., Lei, K., Huang, L., Kang, D. (2010). Multivariate statistical analysis of heavy metals in street dust of Baoji, NW China. J. Hazardous Materials, 173, 744–749. https://doi.org/10.1016/j.jhazmat.2009.09.001.
Journal article Lee, P. K., Chang, H. J., Yu, S., Chae, K. H., Bae, J. H., Kang, M.J., & Chae, G. (2018). Characterization of Cr (VI)–containing solid phase particles in dry dust deposition in Daejeon, South Korea. Environmental Pollution, 243, 1637–1647. https://doi.org/10.1016/j.envpol.2018.09.127.
Journal article Lv, J. (2019). Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. Environmental Pollution, 244, 72–83. https://doi.org/10.1016/j.envpol.2018.09.147.
Journal article Luo, X.S., Yu, S., Zhu, Y.G., Li, X.D. (2012). Trace metal contamination in urban soils of China. Science of the Total Environment, 421–422, 17–30. https://doi.org/10.1016/j.scitotenv.2011.04.020.
Journal article Men, C., Liu, R.M., Xu, F., Wang, Q.G., Guo, L.J., Shen, Z.Y. (2018). Pollution characteristics, risk assessment, and source apportionment of heavy metals in road dust in Beijing, China. Science of the Total Environment, 612, 138–147. https://doi.org/10.1016/j.scitotenv.2017.08.123.
Journal article M.Safiur Rahmana, M.D.H.Khan, Y.N.Jolly, J.Kabir, S.Akter, A.Salam. (2019). Assessing risk to human health for heavy metal contamination through street dust in the Southeast Asian Megacity: Dhaka, Bangladesh. Science of the Total Environment, 660(1): 1610–1622. https://doi.org/10.1016/j.scitotenv.2018.12.425.
Article by Müller, G. (1981). Die Schwermetallbelastung der Sedimente des Neckars und seiner Nebenflüsse Eine Bestandsaufnahme. Chemiker-Zeitung, 6, 157–164.
Journal article Najmeddin, A., Keshavarzi, B., Moore, F., Lahijanzadeh, A. (2018). Source apportionment and health risk assessment of potentially toxic elements in road dust from urban industrial areas of Ahvaz megacity, Iran. Environmental Geochemistry and Healthy, 40, 1187–1208. https://doi.org/10.1007/s10653-017-0035-2.
Journal article Najmeddin, A., Moore, F., Keshavarzia, B., Sadegh, Z. (2018). Pollution, source apportionment and health risk of potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) in urban street dust of Mashhad, the second largest city of Iran. Journal of Geochemical Exploration, 190, 154–169. https://doi.org/10.1016/j.gexplo.2018.03.004.
Journal article Nazarpour, A., Ghanavati, N., Watts, M.J. (2018). Spatial distribution and human health risk assessment of mercury in street dust resulting from various land-use in Ahvaz, Iran. Environmental Geochemistry and Healthy, 40, 693–704.
Journal article Ordóñez, A., Álvarez, R., De Miguel, E., Charlesworth, S. (2015). Spatial and temporal variations of trace element distribution in soils and street dust of an industrial town in NW Spain: 15 years of study. Science of tne Total Environment, 524–525, 93–103. https://doi.org/10.1016/j.scitotenv.2015.04.024.
Journal article Pan, H.Y., Lu, X.W., Lei, K. (2017). A comprehensive analysis of heavy metals in urban road dust of Xi'an, China: Contamination, source apportionment and spatial distribution. Science of tne Total Environment, 609, 1361–1369. https://doi.org/10.1016/j.scitotenv.2017.08.004.
Journal article Peng, C., Chen, W.P., Liao, X.L., Wang, M., Ouyang, Z.Y., Jiao, W.T., Bai,Y. (2011). Polycyclic aromatic hydrocarbons in urban soils of Beijing: Status, sources, distribution and potential risk. Environmental pollution, 159, 802–808. https://doi.org/10.1016/j.envpol.2010.11.003.
Journal article Phi, T.H., Chinh, P.M., Cuong, D.D., Ly, L.T.M., Thinh, N.V., Thai, P.K. (2018). Elemental Concentrations in Roadside Dust Along Two National Highways in Northern Vietnam and the Health–Risk Implication. Archives of Environmental Contamination and Toxicology, 74, 46–55. https://doi.org/10.1007/s00244-017-0477-7.
Journal article Praveena, S.M., Aris, A.Z. (2018). Status, source identification, and health risks of potentially toxic element concentrations in road dust in a medium-sized city in a developing country. Environmental Geochemistry and Healthy, 40, 749–762. https://doi.org/10.1007/s10653-017-0021-8.
Journal article Rahman, M.S., Islam, M.R. (2010). Adsorption of Cd(II) ions from synthetic wastewater using maple sawdust. Energy Sources, Part A, 32 (3), 222–231. https://doi.org/10.1080/15567030802459297.
Journal article Saeedi, M., Li, L.Y., Salmanzadeh, M. (2012). Heavy metals and polycyclic aromatic hydrocarbons: Pollution and ecological risk assessment in street dust of Tehran. Journal of Hazardous Materials, 227–228, 9–17. https://doi.org/10.1016/j.jhazmat.2012.04.047.
Journal article Salim, I., Sajjad, R.U., Paule-Mercado, M.C., Memon, S.A., Lee, B.Y., Sukhbaatar, C., Lee, C.H. (2019). Comparison of two receptor models PCA-MLR and PMF for source identification and apportionment of pollution carried by runoff from catchment and sub-watershed areas with mixed land cover in South Korea. Science of tne Total Environment, 663, 764–775. https://doi.org/10.1016/j.scitotenv.2019.01.377.
Journal article Salim Akhter, M., & Madany, I. M. (1993). Heavy metals in street and house dust in Bahrain. Water, Air, and Soil Pollution, 66(1), 111–119. https://doi.org/10.1007/BF00477063.
Journal article Shi, D.Q., Lu, X.W. (2018). Accumulation degree and source apportionment of trace metals in smaller than 63 µm road dust from the areas with different land uses: A case study of Xi'an, China. Science of tne Total Environment, 636, 1211–1218. https://doi.org/10.1016/j.scitotenv.2018.04.385.
Journal article Shi, G.T., Chen, Z.L., Bi, C.J., Wang, L., Teng, J.Y., Li, Y.S., Xu, S.Y. (2011). A comparative study of health risk of potentially toxic metals in urban and suburban road dust in the most populated city of China. Atmospheric Environment, 45, 764–771. https://doi.org/10.1016/j.chemosphere.2018.04.124.
Journal article Skrbic, B.D., Buljovci, M., Jovanovic, G., Antic, I. (2018). Seasonal, spatial variations and risk assessment of heavy elements in street dust from Novi Sad, Serbia. Chemosphere, 205, 452–462. https://doi.org/10.1016/j.chemosphere.2018.04.124.
Journal article Soltani, N., Keshavarzi, B., Moore, F., Tavakol, T., Lahijanzadeh, A.R., Jaafarzadeh, N., Kermani, M. (2015). Ecological and human health hazards of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in road dust of Isfahan metropolis, Iran. Science of tne Total Environment, 505, 712–723. https://doi.org/10.1016/j.scitotenv.2014.09.097.
Journal article Świetlik, R., Trojanowska, M., Strzelecka, M., & Bocho-Janiszewska, A. (2015). Fractionation and mobility of Cu, Fe, Mn, Pb and Zn in the road dust retained on noise barriers along expressway-a potential tool for determining the efects of driving conditions on speciation of emitted particulate metals. Environmental Pollution, 196, 404–413. https://doi.org/10.1016/j.envpol. 2014.10.018.
Journal article Tang, R.L., Ma, K.M., Zhang, Y.X., Mao, Q.Z. (2013). The spatial characteristics and pollution levels of metals in urban street dust of Beijing, China. Applied Geochemistry, 35, 88–98. https://doi.org/10.1016/j.apgeochem.2013.03.016.
Journal article Tang, Y., Han, G.L. (2017). Characteristics of major elements and heavy metals in atmospheric dust in Beijing, China. Journal for Geochemical Exploration, 176, 114–119. https://doi.org/10.1016/j.gexplo.2015.12.002.
Journal article Tang, Z.W., Chai, M., Cheng, J.L., Jin, J., Yang, Y.F., Huang, Q.F., Li, Y.H. (2017). Contamination and health risks of heavy metals in street dust from a coalmining city in eastern China. Ecotoxicology Environmental Safety, 138, 83–91. https://doi.org/10.1016/j.ecoenv.2016.11.003
Journal article Tanner, P., Ma, H.L., Yu, P.N. (2008). Fingerprinting Metals in Urban Street Dust of Beijing, Shanghai, and Hong Kong. Environmental Science & Technology, 42, 7111–7117. https://doi.org/10.1021/es8007613.
Journal article Trujillo-González, J.M., Torres-Mora, M.A., Keesstra, S., C.Brevik, E., Jiménez-Ballesta, R. (2016). Heavy metal accumulation related to population density in road dust samples taken from urban sites under different land uses. Science of tne Total Environment, 553, 636–642. https://doi.org/10.1016/j.scitotenv.2016.02.101.
Journal article Urrutia-Goyes, R., Hernandez, N., Carrillo-Gamboa, O., Nigam, K.D.P., Ornelas-Soto, N. (2018). Street dust from a heavily-populated and industrialized city: Evaluation of spatial distribution, origins, pollution, ecological risks and human health repercussions. Ecotoxicology Environmental Safety, 159, 198–204. https://doi.org/10.1016/j.ecoenv.2018.04.054.
Book chapter USEPA. (1989). Risk Assessment Guidance for Superfund Vol. I: Human Health Evaluation Manual. EPA/540/1–89/002. Office of Solid Waste and Emergency Response.
Book chapter USEPA. (1993). Reference Dose (RfD): Description and Use in Health Risk Assessments. Background Document 1 A. Integrated risk information system (IRIS).
Book chapter USEPA. (1996). Soil Screening Guidance: Technical Background Document. EPA/540/R-95/128. Office of Solid Waste and Emergency Response.
Book chapter USEPA. (2001a). Child-Specific Exposure Factors Handbook. National Center for Environmental Assessment. EPA-600-P-00-002B.
Book chapter USEPA. (2001b). Risk Assessment Guidance for Superfund: Volume III-Part A, Process for Conducting Probabilistic Risk Assessment. Washington, D.C. EPA 540-R-02-002.
Book chapter USEPA. (2001c). Supplemental Guidance for Developing Soil Screening Levels for Superfund Sites. OSWER 9355.4–24. Office of Solid Waste and Emergency Response.
Book chapter USEPA. (2010). Estimation of Relative Bioavailablity of Lead in Soil and Soil-like Materials Using in Vivo and in Vitro Methods. OSWER 9285.7–77. Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency, Washington, DC.
Journal article Wang, J.H., Li, S.W., Cui, X.Y., Li, H.M., Qian, X., Wang, C., Sun, Y.X. (2016). Bioaccessibility, sources and health risk assessment of trace metals in urban park dust in Nanjing, Southeast China. Ecotoxicology Environmental Safety, 128, 161–170. https://doi.org/10.1016/j.ecoenv.2016.02.020.
Journal article Wang, Q., Lu, X.W., Pan, H.Y. (2016). Analysis of heavy metals in the re-suspended road dusts from different functional areas in Xi’an, China. Environmental Science & Pollution Research, 23, 19838–19846. https://doi.org/10.1007/s11356-016-7200-5.
Journal article Wang S, Cai LM, Wen HH, Luo J, Wang QS, Liu X. (2019). Spatial distribution and source apportionment of heavy metals in soil from a typical county-level city of Guangdong Province, China. Science of the Total Environment, 655:92–101. https://doi.org/10.1016/j.scitotenv.2018.11.244.
Journal article Wei, X., Gao, B., Wang, P., Zhou, H.D., Lu, J. (2015). Pollution characteristics and health risk assessment of heavy metals in street dusts from different functional areas in Beijing, China. Ecotoxicology Environmental Safety, 112, 186–192. https://doi.org/10.1016/j.ecoenv.2014.11.005.
Journal article Yu, X.N., Li, F.Y., Fu, G.X., Feng, H.Y., Xiong, F. (2019). The Spatial-Temporal Distribution Characteristics and Contamination Assessments of Heavy Metals in the Road Dusts of Beijing. Geoscience, 33(1), 169–175. (in Chinese). https://doi.org/10.19657/j.geoscience.1000-8527.2019.01.16
Journal article Yu, Y., Li, Y.X., Li, B., Shen, Z.Y., Stenstrom, M.K. (2016). Metal enrichment and lead isotope analysis for source apportionment in the urban dust and rural surface soil. Environmental Pollution, 216, 764–772. https://doi.org/10.1016/j.envpol.2016.06.046.
Journal article Yu, Y., Ma, J.H., Song, N.N., Wang, X., Wei, T., Yang, Z.F., Li,Y.X. (2016). Comparison of metal pollution and health risks of urban dust in Beijing in 2007 and 2012. Environmental Monitoring & Assessment, 188, 657. https://doi.org/10.1007/s10661-016-5658-8.
Journal article Zhao, N., Lu, X., Chao, S., Xu, X. (2015). Multivariate statistical analysis of heavy metals in less than 100µm particles of street dust from Xining, China. Environmental Earth Sciences, 73, 2319–2327. https://doi.org/10.1007/s12665-014-3578-x
Article by Zhang, Z.H. (2014). A Research of PM10, Temporal-Spatial Distraction And Analysis Of Human Exposure Level To It In Beijing[D]. Inner Mongolia University.
Journal article Zhang, M.M., Lu, X.W., Chen, H., Gao, P.P., Fu, Y. (2015). Multi-element characterization and source identification of trace metal in road dust from an industrial city in semi-humid area of Northwest China. Journal of Radioanalytical and Nuclear Chemistry, 303, 637–646. https://doi.org/10.1007/s10967-014-3300-1.
Journal article Zhang, Z.Y., Mamat, A., Simayi, Z. (2019). Pollution assessment and health risks evaluation of (metalloid) heavy metals in urban street dust of 58 cities in China. Environmental Science & Pollution Research, 26(1), 126–140. https://doi.org/10.1007/s11356-018-3555-0.
Journal article Zheng, N., Liu, J.S., Wang, Q.C., Liang, Z.Z. (2010). Health risk assessment of heavy metal exposure to street dust in the zinc smelting district, Northeast of China. Science of the Total Environment, 408, 726–733. https://doi.org/10.1016/j.scitotenv.2009.10.075.
Journal article Zhong, P., Zhang, J.Q., Xu, D.M., Tian, Q., Hu, T.P., Gong, X.Y., Zhan, C.L., Liu, S., Xing, X.L., Qi, S.H. (2020). Contamination characteristics of heavy metals in particle size fractions from street dust from an industrial city, Central China. Air Quality, Atmosphere & Health, 13(7): 871–883. https://doi.org/10.1007/s11869-020-00846-2.

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Heavy metals in street dust in Beijing, China: seasonal and spatial variations, pollution characteristics and health risk assessment

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Abstract

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1 Introduction

2 Materials And Methods

2.1 Study area and sample collection

2.2 Chemical analysis

2.3 Pollution assessment

2.4 Statistical analysis

2.5 Health risk assessment model

3 Results And Discussion

3.1 Characteristics of heavy metal concentrations in street dust

3.2 Spatial and seasonal variations in heavy metals concentration characteristics in street dust

3.3 Assessment of heavy metals’ environmental quality in street dust

3.4 Source apportionment analysis of heavy metals in street dust

3.5 Health risk assessment of heavy metals in street dust

4 Conclusions

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References

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