Characteristics, Sources and Health Risk of Heavy Metals in Road Dust in the Typical County Town, Central China

Abstract

In this study, to investigate the contamination characteristics and potential health implications of heavy metals in road dust of the typical county in central China, heavy metals (Cd, Co, Cr, Cu, Mn, Ni, V, Pb, Zn) in typical road dust with large traffic flow, in different functional areas of Yangxin County, were determined. The results of the geo-accumulation index (I_geo) showed that Co, Mn, Ni, and V were not polluted, while other heavy metals caused different degrees of pollution. According to principal component analysis (PCA), there were three main sources of heavy metals. The result of statistical analysis showed that heavy metal pollution in road dust mainly comes from traffic activities, industrial production activities, building pollution, and the natural environment. The carcinogenic and non-carcinogenic risks of children and adults were within the safe range, and hand–oral contact was the main exposure route of non-carcinogenic risks. The non-carcinogenic risk and carcinogenic effects of heavy metals in urban road dust were acceptable to children and adults. However, we should still pay attention to the impact of heavy metals on the ecological environment and human health.

1. Introduction

In China, due to dust particles, the rapid growth in urbanization and industrialization has increased the risk of pollution in road areas. The fine particles can be suspended into the atmosphere again through the action of wind, pedestrians, and traffic [1,2]. Most of the pollutants are persistent in road dust, and road dust resuspension is a source of air pollution [3,4]. Several studies mentioned that road dust particle suspension has a significant impact on human health in Kuwait [5], India [6], and Korea [7]. Road dust may cause direct and indirect adverse effects on fauna, flora, and human health on the regional scale [8]. Road dust also has a socioeconomic impact on health [9], the oil sector [10], and photovoltaic energy efficiency [11]. Studies have shown that road fine dust particles (<63 μm radius diameter dust particles) can not only be suspended into the atmosphere but also be exposed more easily to humans [12]. Due to the diverse components and complex sources of urban road dust, it carries a large number of heavy metal elements and has also become the main research object of heavy metal pollution in the urban environment [13], and the content of heavy metals enriched in urban road dust is significantly higher in comparison to that in urban surface soil [14]. Besides, urban road dust has various components and complex sources. It contains many toxic, universal, and persistent heavy metal elements. Heavy metals are absorbed by the human body and will continue to bioaccumulate in key organs (such as the brain, liver and kidney) for a long time, which will have adverse effects on health [15,16,17,18]. Therefore, it is necessary to estimate the harm of heavy metal pollutants caused by urban dust to human health through different exposure pathways. Since the 1970s, research on urban dust pollution has been carried out on a global scale, focusing on the characteristics, sources, and health risks of heavy metals in the dust on urban roads [19,20,21,22,23,24].

The current research concentrates on the distribution characteristics, source identification, accumulation rules, potential risks to human health, and environmental effects of heavy metals in road dust, and it usually focuses on large cities with dense populations, developed economies and industries, and high urbanization. At present, the most concerning heavy metal elements are Cd, Cu, Cr, Ni, Pb, and Zn, among which Cu, Pb, and Zn are also called “urban elements” [25,26,27]. Inevitably, a large volume of wastewater containing Cr, Cd, Cu, Ni, Pb, and other metal elements is discharged by the electroplating industry [28]. Cu may cause serious damages or necrotizing changes to the liver, kidney, and central nervous system [29]. Cr, Cd, Ni, and Pb can induce cancer to a certain extent [30].

Since the implementation of China’s rural revitalization strategy, the economy has achieved great development [31]. For example, Yangxin County, Hubei Province, combined with its advantageous industries, actively docked industrial patents in developed regions and made full use of the national major regional strategy to the county. In addition, it has a leading role in strengthening investment promotion, and thus, the economy of Yangxin County has achieved dramatic development. Yangxin County has a population more than one million. However, with the rapid development of the economy and urbanization, the contradiction between economic development and environmental pollution has become increasingly obvious. Yangxin County is a typical copper-rich and zinc-rich mineral area with frequent smelting activities, which emit significant dust and slag rich in toxic heavy metals into the surrounding environment.

This study took Yangxin County as the object and selected different types of roads in the old town (OT), the Economic Development Zone (EDZ), and the Chengdong New District (CND). It collected dust samples and tested the content of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, V, Pb, and Zn) in road dust. The date can be used to support theoretical guidance and the decision-making basis for improving regional environmental quality. The main objectives are: (1) to study the distribution of heavy metals in different functional areas; (2) to evaluate heavy metals pollution by the geo-accumulation index (I_geo); (3) to determine the possible sources of heavy metals by multivariate statistical analysis; (4) to evaluate the impact of heavy metal pollution in dust on human health.

2. Materials and Methods

2.1. Study Area

Yangxin County is located on the south bank of the middle reaches of the Yangtze River, which is at the northern foot of the Mufu Mountain Range and in the southeastern part of Hubei Province. With an average annual temperature of 16.8 °C and an average annual rainfall of 1389.6 mm, it is a northern subtropical climate zone. There are a lot of types of mineral deposits and large reserves. Yangxin is famous for its minerals in China, containing over 40 kinds of proven mineral deposits, China’s eight major copper production bases, as well as being one of the hundred key coal mining counties. The main industries include light industry, auto parts, machinery, electronics, logistics, chemical industry, and metallurgy. Additionally, it is an important logistics and commercial distribution center in the adjacent areas of the three provinces (Hunan, Hubei, and Jiangxi Province).

On 15 October 2019, 48 total samples were collected on different types of roads collected in three areas in Yangxin County (Figure 1). There were about 150,000 people in the 10 square kilometers of OT and about 200,000 people in the 12 square kilometers of CND. Population density of the study area was between 15,000 and 16,667 persons/km². Firstly, clean plastic dustpans and brushes were used to collect dust and transfer the collected road dust to polyethylene sealed bags (the five dust samples were mixed into one sample, which was evenly arranged in range of 10 m × 10 m), and the samples were numbered. Secondly, environment and weather information was recorded (temperature, wind speed, humidity, and pressure). Then, the samples were taken to the laboratory. The samples were dried naturally at room temperature, screened through a 200-mesh (75 μm) standard sieve, and dust samples under the sieve were collected for testing.

2.2. Sample Analysis

After natural air drying, 0.2000 g dust samples of the sieve were placed into the 50 mL crucible, and three replicates of dust samples (n = 3) were obtained at every five sampling sites. The HCl–HNO₃–HF–HClO₄ wet digestion method was used to analyze the samples for pretreatment, inductively coupled plasma optical emission spectrometry (ICP-OEC, ICAP7200 Radial, Thermo Fisher, Waltham, MA, USA) was used to determine Co, Cr, Cu, Mn, Ni, V, Pb, Zn in dust samples, and inductively coupled plasma source mass spectrometry (ICP-MS, NexION1000, Perkin-Elmer) was used to determine Cd. In the experiment, various chemical reagents all had high-grade purity. All the utensils were soaked in aqua regia for more than 24 h, and the relative standard deviation (RSD) of the sample analysis was less than 10%. At the same time, the recovery rate of the analysis process was controlled with the national standard soil sample (GSS-3), and the recovery rate of the measured elements was greater than 90%.

2.3. Geo-Accumulation Index

The geo-accumulation index (I_geo) was proposed to evaluate the heavy metal pollution in sediments by Muller [32], and it was also used to identify heavy metal pollution in road dust [33,34]. The function is as shown in Equation (1):

$${\mathrm{I}}_{\mathrm{g}\mathrm{e}\mathrm{o}}=\mathrm{l}\mathrm{o}{\mathrm{g}}_2\frac{{\mathrm{C}}_{\mathrm{n}}}{\mathrm{k}\times {\mathrm{B}}_{\mathrm{n}}}$$

(1)

where C_n is the measured concentration of the nth heavy metal in the road dust, mg/kg; k is the correction coefficient used to consider the changes in the environmental background value that may be caused by diagenesis (usually 1.5); B_n is the metal geochemical background value, mg/kg. This study took the soil environmental background value of Hubei Province [35] as the surface dust background value. The pollution status was classified into seven grades based on I_geo [36].

2.4. Health Risk Assessment

In this study, the health risk assessment model issued by the United States Environmental Protection Agency (USEPA) [37,38] was employed to evaluate the health risks caused by dust exposure to urban residents in Yangxin County. The calculation model formulas of the pollutant exposures ADD_ing, ADD_inh, and ADD_derm, through the hand–oral contact intake, respiratory inhalation, and skin contact, are shown as Equations (2)–(4). The calculation model of the lifelong average exposure dose of carcinogenic heavy metal inhalation is shown as Equation (5).

$$\mathrm{A}\mathrm{D}{\mathrm{D}}_{\mathrm{i}\mathrm{n}\mathrm{g}}=\mathrm{C}\times \frac{\mathrm{EF}\times \mathrm{ED}\times {\mathrm{R}}_{\mathrm{ing}}}{\mathrm{AT}\times \mathrm{BW}}\times {10}^{-6}$$

(2)

$$\mathrm{A}\mathrm{D}{\mathrm{D}}_{\mathrm{i}\mathrm{n}\mathrm{h}}=\mathrm{C}\times \frac{\mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}\times {\mathrm{R}}_{\mathrm{i}\mathrm{n}\mathrm{h}}}{\mathrm{A}\mathrm{T}\times \mathrm{B}\mathrm{W}\times \mathrm{P}\mathrm{E}\mathrm{F}}$$

(3)

$$\mathrm{A}\mathrm{D}{\mathrm{D}}_{\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{m}}=\mathrm{C}\times \frac{\mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}\times \mathrm{A}\mathrm{F}\times \mathrm{S}\mathrm{A}\times \mathrm{A}\mathrm{B}\mathrm{S}}{\mathrm{A}\mathrm{T}\times \mathrm{B}\mathrm{W}}\times 10^{-6}$$

(4)

$$\mathrm{L}\mathrm{A}\mathrm{D}{\mathrm{D}}_{\mathrm{i}\mathrm{n}\mathrm{h}}=\frac{\mathrm{C}\times \mathrm{E}\mathrm{F}}{\mathrm{P}\mathrm{E}\mathrm{F}\times \mathrm{A}\mathrm{T}}\times (\frac{{\mathrm{R}}_{\mathrm{i}\mathrm{n}\mathrm{h}}^{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{d}}\times \mathrm{E}{\mathrm{D}}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{d}}}{\mathrm{B}{\mathrm{W}}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{d}}}+\frac{{\mathrm{R}}_{\mathrm{i}\mathrm{n}\mathrm{h}}^{\mathrm{a}\mathrm{d}\mathrm{u}\mathrm{l}\mathrm{t}}\times \mathrm{E}{\mathrm{D}}_{\mathrm{a}\mathrm{d}\mathrm{u}\mathrm{l}\mathrm{t}}}{\mathrm{B}{\mathrm{W}}_{\mathrm{a}\mathrm{d}\mathrm{u}\mathrm{l}\mathrm{t}}})$$

(5)

where ADD_ing, ADD_inh, and ADD_derm are the non-carcinogenic exposures of pollutants in the three routes of hand–oral contact, inhalation, and skin contact, mg/(kg·d); LADD_inh is the average daily exposure amount of carcinogenic heavy metals inhalation, mg/(kg·d); C is the heavy metal content of road dust, mg/kg. The meanings and sources of other parameters were shown in literature [36].

The total non-carcinogenic and carcinogenic risks of various types of heavy metal pollution in dust to the human body can be calculated by Equations (6) and (7).

$$\mathrm{H}\mathrm{I}={\displaystyle \sum \mathrm{H}{\mathrm{Q}}_{\mathrm{i}}}={\displaystyle \sum \frac{\mathrm{A}\mathrm{D}{\mathrm{D}}_{\mathrm{i}\mathrm{j}}}{\mathrm{R}\mathrm{f}{\mathrm{D}}_{\mathrm{i}\mathrm{j}}}}$$

(6)

$${\mathrm{R}}_{\mathrm{T}}={\displaystyle \sum {\mathrm{R}}_{\mathrm{i}}}={\displaystyle \sum \mathrm{L}\mathrm{A}\mathrm{D}{\mathrm{D}}_{\mathrm{i}\mathrm{j}}\times \mathrm{S}{\mathrm{F}}_{\mathrm{i}\mathrm{j}}}$$

(7)

where HI is the total non-carcinogenic risk; RfD is the reference dose; R_T is the total carcinogenic risk; SF is the carcinogenic slope factor, standing for the probability of human exposure to a certain pollutant causing cancer. When HI < 1, the risk is small or negligible. When HI > 1, it indicates that there is a non-carcinogenic health risk. According to the USEPA standard, R_i < 10⁻⁶ is considered to have no carcinogenic risk, and R_i > 10⁻⁴ is considered to have a significant carcinogenic risk [39].

Table 1. Reference doses and slope factors for different exposure routes of heavy metals.

Project	Cd	Co	Cr	Cu	Mn	Ni	V	Pb	Zn
RfDing	1.00 × 10−3	3.00 × 10−4	3.00 × 10−3	4.00 × 10−2	4.60 × 10−2	2.00 × 10−2	7.00 × 10−3	3.50 × 10−3	3.00 × 10−1
RfDinh	1.00 × 10−3	5.71 × 10−6	2.86 × 10−5	4.02 × 10−2	5.00 × 10−5	2.06 × 10−2	7.00 × 10−3	3.52 × 10−3	3.00 × 10−1
RfDderm	1.00 × 10−5	1.60 × 10−2	6.00 × 10−5	1.20 × 10−2	1.84 × 10−3	5.40 × 10−3	7.00 × 10−5	5.25 × 10−4	6.00 × 10−2
SF	6.3	9.8	42	-	-	0.84	-	-	-

showed the reference doses and carcinogenic slopes of various heavy metals in different exposure routes.

2.5. Statistical Analyses

Descriptive statistics (maximum, minimum, mean, standard deviation, coefficient of variation, Pearson’s correlation analysis (P’CA), principal component analysis (PCA), and Cluster analysis) of the research data used SPSS26 (IBM SPSS). KMO (Kaiser–Meyer–Olkin) test and Bartlett sphericity test were used to test the applicability of the original PCA data set. ArcGIS 10.5 (ESRI, Redlands, CA, USA) was applied to mapping, and ordinary Kriging was adopted for interpolating the concentrations of all metals.

3. Results and Discussion

3.1. Distribution Characteristics of Heavy Metals

The contents of heavy metals in road dust in Yangxin County were shown in

Table 2. Heavy metals contents in road dust in Yangxin County, mg·kg⁻¹.

Sampling Area		Cd	Co	Cr	Cu	Mn	Ni	V	Pb	Zn
EDZ	Mean	1.91	12.09	195.47	122.39	595.53	27.54	85.41	49.04	182.01
	Max	11.11	15.63	462.23	478.51	879.44	55.51	126.83	77.69	308.77
	Min	0.05	8.97	53.59	32.09	350.42	13.82	42.56	13.72	66.87
	SD	2.59	1.93	116.18	140.64	138.46	10.74	20.57	19.84	80.68
	CV	136	16	59	115	23	39	24	40	44
OT	Mean	1.30	11.88	161.36	122.13	549.70	34.51	85.29	57.17	230.71
	Max	8.71	28.69	262.27	381.47	1214.71	75.92	124.29	204.11	429.98
	Min	0.01	8.24	63.85	53.10	354.93	18.60	66.14	17.09	92.20
	SD	2.00	4.80	58.54	97.04	186.99	14.66	14.76	40.28	80.23
	CV	154	40	36	79	34	42	17	70	35
CND	Mean	1.24	14.27	200.85	124.83	618.09	42.22	92.71	38.69	202.98
	Max	11.25	46.06	559.05	832.94	966.42	222.54	142.11	169.96	511.56
	Min	0.06	8.32	71.42	18.99	419.12	13.08	71.10	11.94	55.26
	SD	2.61	8.54	126.90	188.61	133.39	50.19	15.08	34.98	101.19
	CV	209	60	63	151	22	119	16	90	50
SUN	Mean	1.48	12.75	185.89	123.11	587.78	34.75	87.80	48.30	205.23
	Max	11.25	46.06	559.05	832.94	1214.71	222.54	142.11	204.11	511.56
	Min	0.01	8.24	53.59	18.99	350.42	13.08	42.56	11.94	55.26
	SD	2.44	5.87	106.37	146.94	157.43	31.40	17.36	33.72	90.15
	CV	164	46	57	119	27	90	20	70	44
Background value of soil		0.17	15.40	86.00	30.70	712.00	37.30	110.20	26.70	83.60

. The average contents of Cd, Co, Cr, Cu, Mn, Ni, V, Pb, and Zn were 1.48, 12.75, 185.89, 123.11, 587.78, 34.75, 87.80, 48.30, and 205.23 mg/kg, respectively, which were 8.63, 0.83, 2.16, 4.01, 0.83, 0.93, 0.80, 1.81, and 2.45 times the soil background value of Hubei Province.

Obviously, these nine heavy metals were enriched in road dust to varying degrees. These results showed that there was heavy metal pollution in road dust in Yangxin County. The coefficient of variation (CV) can reflect the influence of human activities on the content of heavy metals to some extent. The larger the CV, the greater the change of heavy metal content in the spatial scale and the impact of human interference. If the CV was greater than 50%, it was considered that the spatial distribution of heavy metals was heterogeneous, and the existence of local point pollution sources should be considered [40]. The order of CV was Cd (164%) > Cu (119%) > Ni (90%) > Pb (70%) > Cr (57%) > Co (46%) > Zn (44%) > Mn (27%) > V (20%). The CV of Cd, Cr, Cu, Ni, and Pb exceeded 50%, indicating that these elements were unevenly distributed in different pavement areas of Yangxin County, and they were considered as local pollution sources or artificial non-point pollution sources. The CV of Mn and V were 27% and 20%, and it was considered that the two metals were homogeneously distributed.

The statistical analysis of heavy metal content in road dust samples in Yangxin County was carried out, and the analysis results were shown in Figure 2. Research showed that the sources of heavy metals in road dust were affected by human factors [27,41]. The spatial distribution patterns were obviously different in the study area. Generally, the high contents of Pb, Zn, and Ni coincided with metal smelting, auto parts, machinery manufacturing, and other leading industries [42]. The relatively high Cu content may be related to the development of the modern logistics industry in the region because the wear of automobile brake pads will lead to the input of copper in dust [43,44]. The copper mine in Yangxin County accounted for 25.23% of the identified reserves in Hubei Province. Therefore, the influence of natural factors, such as wind, was also one of the reasons for the enrichment of copper [45]. The high content of Cd and Cr in OT was mainly due to the impact of traffic activities. The OT was the main living area of residents [46,47]. There were many public places, such as shops, schools, and hospitals, as well as crowded people and high traffic flow, so car braking frequency was high, and car brakes, brake pads, tire wear, and other human activities will cause Cd, Cr, and Cu to fall on the ground [48,49]. There was no significant difference in the content of V in the three different functional areas, which may be related to the dense greening on both sides of the road and more soil particles in the road dust. At the same time, as a major mining County, Yangxin County had high Zn and Cr contents and large reserves of common and associated minerals. Therefore, mining and other activities were also the reasons for the high content of Zn and Cr in road dust [50,51].

From the perspective of different road types in the three regions, the content of Cr, Cu, and Zn in the main roads of OT and EDZ was higher than that of CND. This may be related to the industry, modern logistics, smelting, parts manufacturing, etc. in the special economic zone. The high content of Cr, Cu, and Zn in the trunk roads of OT is associated with the large traffic flow, the large number of people, and intersections with traffic lights. Cu, Zn, and other elements entered the road environment due to the frequent start and stop times of the vehicle and the frequent use of brakes. The highest content of Cd and Ni were in the branch road of OT, while Cd and Ni were related to people’s daily travel and coal combustion. At present, OT is the main living area of residents in Yangxin County. Dense population, daily travel, heating, and other activities will lead to high contents of Cd and Ni on the branch. The content of each element in the three types of roads in CND was high, which was mainly because CND was in the stage of infrastructure construction, with relatively high passenger and vehicle flows and frequent construction activities.

3.2. Evaluation of the I_geo

It can be seen from Figure 3 that the order of the average I_geo of heavy metals in road dust in Yangxin County was Cd (2.52) > Cu (1.42) > Zn (0.71) > Cr (0.53) > Pb (0.27) > Ni (−0.69) > Mn (−0.86) > Co (−0.86) > V (−0.91). According to I_geo classification, Cd presented moderately to heavily contaminated, Cu presented moderately, Cr, Pb, and Zn presented uncontaminated to moderately contaminated, and Co, Mn, Ni, and V presented not polluted. The average I_geo of nine types of heavy metals in the EDZ, OT, and CND areas were 1.11, 1.05, and 0.79 respectively, indicating that the heavy metal pollution in high-density human activity areas was significantly higher than that in low-density human activity areas. In addition, heavy metal pollution of road dust was related to human production and life [52,53,54]. The average I_geo of the heavy metals in the whole area was 0.98, and the overall pollution level was slightly polluted, indicating the low pollution level of road dust in the urban area of Yangxin County. However, there were also some pollution phenomena that need to be paid attention. Combined with the distribution characteristics of road dust emission, the distribution characteristics of heavy metal content in road dust, and the evaluation of heavy metal pollution degree, it can be concluded that most heavy metal elements showed obvious differences in high and low concentrations. In addition, this difference was consistent with the high-density and low-density ranges of human activity areas.

3.3. Source Identification of Heavy Metals

3.3.1. Pearson’s Correlation Analysis

The Pearson’s correlation analysis results of road dust in Yangxin County were shown in

Table 3. Correlation coefficients for heavy metals in road dust in Yangxin County.

Elements	Correlation Coefficient
	Cd	Co	Cr	Cu	Mn	Ni	V	Pb	Zn
Cd	1
Co	0.059	1
Cr	0.245	0.53 **	1
Cu	0.558 **	0.177	0.312 **	1
Mn	0.019	0.576 **	0.431 **	0.265	1
Ni	0.534 **	0.303 *	0.535 **	0.65 **	0.236	1
V	−0.014	0.623 **	0.126	0.349 *	0.617 **	0.08	1
Pb	0.619 **	0.008	0.159	0.728 **	0.019	0.477 **	0.122	1
Zn	0.379 **	0.003	0.225	0.639 **	0	0.472 **	0.051	0.662 **	1

** Reflects a significance level of 0.01. * Significance level of 0.05.

. Positive correlations existed between the heavy metals. Cd, Cu, Ni, Pb, and Zn showed significant positive correlation at p < 0.01. Except Cr-V (r = 0.126), Co, Cr, Mn, and V showed significant positive correlation at p < 0.01. There were positive correlations, suggesting an analogous origin of these elements. In the road dust samples with heavy traffic, the contents of Cd, Cu, Ni, Pb, and Zn were high, and their sources may be related to the exhaust emissions, tire wear, and the wear and corrosion of alloys [55].

3.3.2. Principal Component Analysis

In order to explore the relationship between the contents of heavy metals in road dust and the sources of pollutants in Yangxin County, principal component analysis (PCA) was carried out by SPSS. KMO (Kaiser–Meyer–Olkin) test and Bartlett sphericity test were used to test the heavy metal content (KMO = 0.708, Bartlett = 0.000). After rotating by the Kaiser normalization maximum variance method, three principal components with eigenvalues greater than 1 were extracted, and they accounted for 77.478% of the total variance (

Table 4. Eigenvalues and cumulative contribution rates obtained from a heavy metal factor analysis of road dust in Yangxin County.

Factor	Initial Eigenvalues			Before Rotation			After Rotation
	Eigenvalues	Variance Percentage (%)	Accumulative Contribution Rate (%)	Eigenvalues	Variance Percentage (%)	Accumulative Contribution Rate (%)	Eigenvalues	Variance Percentage (%)	Accumulative Contribution Rate (%)
1	3.733	41.479	41.479	3.733	41.479	41.479	3.119	34.658	34.658
2	2.222	24.690	66.169	2.222	24.690	66.169	2.236	24.843	59.502
3	1.018	11.309	77.478	1.018	11.309	77.478	1.618	17.977	77.478
4	0.618	6.866	84.344
5	0.435	4.836	89.180
6	0.373	4.141	93.321
7	0.257	2.851	96.171
8	0.216	2.401	98.572
9	0.128	1.428	100.000

Extraction method: principal component analysis; rotation method: maximum variance rotation.

), which can better represent the information contained in the data. Cd (0.707), Cu (0.866), Pb (0.909), and Zn (0.802) were strong positive loadings in the PC1, and the variance contribution rate was 34.658% (

Table 5. Factor loads associated with heavy metals in surface dust in Yangxin County.

Elements	After Rotation
	PC1	PC2	PC3
Cd	0.707	−0.111	0.292
Co	−0.046	0.747	0.468
Cr	0.133	0.236	0.883
Cu	0.866	0.283	0.133
Mn	0.000	0.803	0.295
Ni	0.602	0.074	0.635
V	0.133	0.937	−0.148
Pb	0.909	0.027	−0.014
Zn	0.802	−0.018	0.060

Extraction method: principal component analysis; rotation method: maximum variance rotation.

). Cd and Cu were the most commonly used materials in cables and electrical and electronic components, which can be used to form many kinds of alloys and coatings [56,57]. Pb and Zn were mainly affected by traffic activities. Tire wear and corrosion may cause substances containing Pb or Zn to accumulate in road dust [58,59]. Co (0.747), Mn (0.803), and V (0.937) were higher in the PC2, and the variance contribution rate was 24.843%. Co, Mn, and V had low mean contents and were close to their background values. They were less affected by human activities. It was inferred that these elements were from the natural environment. Therefore, PC2 might be the natural source. Cr (0.883) and Ni (0.635) were higher in the PC3, and the variance contribution rate was 17.977%. Cr and Ni were the main elements added to stainless steel [60,61]. They were also used in electroplating, metallurgy, and other industrial activities. Therefore, PC3 might be the industrial source.

3.3.3. Cluster Analysis

Z-score was used to standardize the heavy metal content data, and Ward’s clustering algorithm was used to cluster analysis the nine kinds of heavy metals (Figure 4). The nine heavy metals were divided into three clusters: cluster 1 was Cd-Cu-Pb-Zn, cluster 2 was Cr-Ni, and cluster 3 was Co-V-Mn. However, there was still a moderate correlation between the clusters. Cd-Cu-Pb-Zn cluster and Cr-Ni cluster were connected, indicating that their sources may be the same. The maximum content of Cd-Cu-Zn and Cr-Ni appeared in CND, while CND was in the stage of infrastructure construction with higher traffic flow, construction, and demolition activities [62,63,64].

3.4. Health Risk Assessment

It can be seen from

Table 6. Health risks of heavy metals in road dust in Yangxin County.

Population Risk		Cd	Co	Cr	Cu	Mn	Ni	V	Pb	Zn
Child	ADDing	4.88 × 10−7	4.19 × 10−6	6.11 × 10−5	4.72 × 10−4	2.25 × 10−3	1.14 × 10−5	3.37 × 10−4	1.85 × 10−4	7.87 × 10−4
	ADDinh	1.20 × 10−10	1.03 × 10−9	1.50 × 10−8	1.16 × 10−7	5.53 × 10−7	2.80 × 10−9	8.25 × 10−8	4.54 × 10−8	1.93 × 10−7
	ADDderm	4.56 × 10−9	3.91 × 10−8	5.70 × 10−7	4.41 × 10−6	2.10 × 10−5	1.07 × 10−7	3.14 × 10−6	1.73 × 10−6	7.35 × 10−6
	HQing	4.88 × 10−4	1.40 × 10−2	2.04 × 10−2	1.18 × 10−2	4.90 × 10−2	5.71 × 10−4	4.81 × 10−2	5.29 × 10−2	2.62 × 10−3
	HQinh	1.20 × 10−7	1.80 × 10−4	5.24 × 10−4	2.88 × 10−6	1.11 × 10−2	1.36 × 10−7	1.18 × 10−5	1.29 × 10−5	6.43 × 10−7
	HQderm	4.56 × 10−4	2.44 × 10−6	9.51 × 10−3	3.67 × 10−4	1.14 × 10−2	1.97 × 10−5	4.49 × 10−2	3.29 × 10−3	1.22 × 10−4
	HI	9.44 × 10−4	1.42 × 10−2	3.04 × 10−2	1.22 × 10−2	7.15 × 10−2	5.91 × 10−4	9.30 × 10−2	5.62 × 10−2	2.75 × 10−3
Adult	ADDing	2.96 × 10−7	2.54 × 10−6	3.70 × 10−5	2.86 × 10−4	1.37 × 10−3	6.92 × 10−6	2.04 × 10−4	1.12 × 10−4	4.77 × 10−4
	ADDinh	5.51 × 10−11	4.73 × 10−10	6.90 × 10−9	5.33 × 10−8	2.54 × 10−7	1.29 × 10−9	3.80 × 10−8	2.09 × 10−8	8.88 × 10−8
	ADDderm	3.93 × 10−9	3.38 × 10−8	4.92 × 10−7	3.80 × 10−6	1.82 × 10−5	9.21 × 10−8	2.71 × 10−6	1.49 × 10−6	6.34 × 10−6
	HQing	2.96 × 10−4	8.46 × 10−3	1.23 × 10−2	7.15 × 10−3	2.97 × 10−2	3.46 × 10−4	2.91 × 10−2	3.21 × 10−2	1.59 × 10−3
	HQinh	5.51 × 10−8	8.28 × 10−5	2.41 × 10−4	1.33 × 10−6	5.09 × 10−3	6.26 × 10−8	5.43 × 10−6	5.94 × 10−6	2.96 × 10−7
	HQderm	3.93 × 10−4	2.11 × 10−6	8.21 × 10−3	3.17 × 10−4	9.87 × 10−3	1.70 × 10−5	3.88 × 10−2	2.84 × 10−3	1.06 × 10−4
	HI	6.89 × 10−4	8.55 × 10−3	2.08 × 10−2	7.47 × 10−3	4.47 × 10−2	3.63 × 10−4	6.79 × 10−2	3.49 × 10−2	1.70 × 10−3
R		1.10 × 10−9	1.47 × 10−8	9.19 × 10−7			3.44 × 10−9

that the order of daily average exposure doses of nine kinds of heavy metals, for children and adults, was Mn > Zn > Cu > V > Pb > Cr > Ni > Co > Cd. The order of non-carcinogenic exposure doses of children and adults was ADD_ing > ADD_derm > ADD_inh. The exposure doses of the hand–oral intake route accounted for a relatively high proportion of the total exposure dose, reaching 99.05% and 98.67%. Obviously, the hand–oral intake route was the most important route of road dust exposure [65,66,67]. The exposure dose of children to road dust was higher than that of adults, indicating that children were more threatened by road dust [68,69,70]. The results were consistent with those reported in other studies [71,72].

The non-carcinogenic risks of heavy metals in road dust, through three exposure routes, was V > Mn > Pb > Cr > Co > Cu > Zn > Cd > Ni. The order of non-carcinogenic risk was HQ_ing > HQ_derm > HQ_inh. The results showed that no significant non-carcinogenic risk was found for all measured heavy metals in the study areas since the HQi and HI values were <1 [36,73]. The non-carcinogenic risk of children was higher than that of adults but lower than the allowable limit. Adults’ health risks were relatively low [74,75,76]. The maximum single non-carcinogenic risk of the three exposure routes was 0.068, indicating that road dust did not pose a non-carcinogenic risk to adults. However, the possibility that these metals may cause serious health effects through their accumulation in body tissues still exists.

In terms of carcinogenic risk, the order of carcinogenic risk of the four carcinogenic elements was Cr > Co > Ni > Cd. The range of carcinogenic risk in the study area was 1.1 × 10⁻⁹–9.19 × 10⁻⁷, while lower than 1 × 10⁻⁶ was the accepted. However, there were still some deficiencies in the current risk assessment research. For example, the soil background value was used to replace the lack of surface dust background value; there were other potentially toxic metals (e.g., Sb, Fe, As, etc.) and organic pollutants (e.g., polycyclic aromatic hydrocarbons, microplastics, etc.) in the dust, which have not been evaluated [77,78]. Therefore, more research on other pollutants is needed in the future.

4. Conclusions

The average concentrations of Cd, Cr, Cu, Pb, and Zn in the road dust in Yangxin County were higher than the soil environmental background values in Hubei Province and were obviously enriched. Cd pollution in dust was the most serious, showing moderate to severe pollution, Cu was moderate pollution, Cr, Pb, and Zn were not polluted to moderate pollution, and Co, Mn, Ni, and V were not polluted. The heavy metal pollution levels were consistent in EDZ, OT, and CND (except Pb). Traffic activities, industrial production activities, building pollution, and the natural environment were the main sources of heavy metals in urban road dust. Hand–oral ingestion was the main exposure route of non-carcinogenic risk. The non-carcinogenic risks and carcinogenic effects of heavy metals in urban road dust were acceptable to children and adults. Therefore, we should pay attention to the impact of heavy metals on the ecological environment and human health. Plantation, using native plants and green belts, has contributed to the reduction in the annual rates of mobile sand and dust [79,80]. The use of native cultivation was efficient in controlling pollution [81,82,83,84]; therefore, dense cultivations with native vegetation should take place to reduce the pressure on the ecological environment.