Next Article in Journal
A Tiny-Fault Detection Strategy Based on Phase Congruency—An Example of Carbonate Reservoir in Ordos Basin, China
Next Article in Special Issue
A Machine Learning Approach for Prediction of the Quantity of Mine Waste Rock Drainage in Areas with Spring Freshet
Previous Article in Journal
Study on the Application of a Reflux Classifier in the Classification of Ultrafine Ilmenite
Previous Article in Special Issue
Source Apportionment and Probabilistic Ecological Risk of Heavy Metal(loid)s in Sediments in the Mianyang Section of the Fujiang River, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Minerals
Volume 13
Issue 3
10.3390/min13030305
minerals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Estimation of Probabilistic Environmental Risk of Heavy Metal(loid)s in Resuspended Megacity Street Dust with Monte Carlo Simulation

by
Zhenze Wang
1,
Xinwei Lu
1,*,
Yufan Yang
1,
Bo Yu
1,
Kai Lei
2,
Huiyun Pan
3,
Peng Fan
1 and
Ling Zuo
1
1
Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
2
School of Biological and Environmental Engineering, Xi’an University, Xi’an 710065, China
3
Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, China
*
Author to whom correspondence should be addressed.
Minerals 2023, 13(3), 305; https://doi.org/10.3390/min13030305
Submission received: 28 January 2023 / Revised: 19 February 2023 / Accepted: 20 February 2023 / Published: 22 February 2023
(This article belongs to the Special Issue Mobility of Potentially Toxic Elements: Environmental Hazards)
Browse Figures
Versions Notes

Abstract

:
To improve the ecological environment quality of industrial cities and protect the health of residents, we determined the priority control factors of heavy metal(loid)s (HMs) pollution and risk in the resuspended street dust (RSD) of Shijiazhuang, an emblematic heavy-industrial city in North China, according to the probabilistic risk assessment method. The results showed that the HMs studied in Shijiazhuang RSD exhibited different pollution levels, that is, Hg showed moderate-to-severe pollution and above; Zn showed moderate-and-above pollution; Co, Cu and Pb showed non-pollution to moderate pollution; while As, Cr, Mn and Ni showed no pollution. The overall contamination of HMs in the RSD presented moderate-to-above contamination levels in >94% of samples. Mercury exhibited considerable-to-very-high ecological risk. The synthetic ecological risks of the HMs were considerable-to-above. The comprehensive pollution and synthetic ecological risk of HMs in Shijiazhuang RSD were mainly caused by Hg. The carcinogenic risk of HMs in RSD to local inhabitants and their non-carcinogenic risk to children should not be ignored. Coal-related industrial sources are a priority source to control. Hg and As are priority HMs to control. We suggest that local governments should strengthen the management of coal-related industrial sources and As and Hg emissions.

1. Introduction

Environmental pollution in heavy industrial metropolises is generally serious [1,2,3]. Nowadays, the water environment [4,5], soil [6,7,8,9] and surface dust [9,10] of many industrial cities in the world are polluted by heavy metal(loid)s (HMs) to varying degrees. As an important link between various environmental media in cities, a large number of HMs are often accumulated in urban surface dust due to the impact of high-intensity industrial production, transportation, urban construction and residents’ lives [10,11,12,13]. Due to their non-degradability, persistence and biological toxicity, HMs accumulated in urban surface dust pose a potential threat to urban ecological safety and residents’ health [12,13,14,15]. Therefore, the study of HMs pollution in urban surface dust is extremely important for the sustainable development of urban environments and protection of the health of residents.
At present, numerous scientific studies have been carried out around the world on the concentration, spatial variation, pollution, ecological health risks and sources of HMs in urban road dust [15,16,17,18]. The concentration-oriented assessment method with fixed exposure parameters was used in most existing studies on pollution and the risk assessment of HMs in urban road dust [17,18,19,20,21,22,23,24]. This assessment method cannot determine the main contributors of pollution and risk, and cannot identify the key pollution sources and key pollutants for pollution control [12,14]. There is also the uncertainty of overestimating or underestimating HMs pollution and risk [25,26,27]. The combination of source-specific HMs pollution and risk assessment with Monte Carlo simulation (MCS) can overcome the above shortcomings and improve the reliability of assessment results [26,27,28,29,30,31]. In addition, the existing research on HMs pollution of urban surface dust primarily focused on bulk dust samples [32,33,34]. It was found that resuspended street dust (RSD) (i.e., dust with a particle size less than 100 μm) poses a higher environmental risk than coarse particle dust [19,35,36]. However, the research on source-oriented pollution and the risk assessment of HMs in RSD based on MCS is lacking. Therefore, it is necessary to carry out the source-oriented probabilistic pollution and risk assessment of HMs in urban RSD for urban ecological-environment protection, residents’ health defense and environmental management.
Shijiazhuang is a typical heavy industrial metropolis in North China [19]. In the process of rapid urbanization and industrialization in the past few decades, Shijiazhuang’s environmental pollution has been extremely prominent [37], and urban road dust has been polluted by HMs in different degrees [10]. We previously estimated the pollution, and eco-health risk of HMs in Shijiazhuang RSD applying a concentration-oriented assessment method, and identified three main sources of HMs in RSD, namely, traffic sources, coal-related industrial sources, and natural sources, with the PMF method [19]. We detected that HMs in Shijiazhuang RSD were contaminated to different degrees, and the overall ecological risk was remarkably serious [19]. Is there any divergence between the above conclusions obtained from the traditional contamination and ecological-health risk estimation methods and the actual situation? Which contamination source causes the contamination and risk of HMs in the RSD? In order to better protect the ecological environment security and the health of residents, to limiting which contamination sources should the government give priority? To solve the above problems, we plan to conduct the following work on the basis of previous research: (1) use the MCS method to assess the probabilistic contamination degree and eco-health risk of HMs in the RSD of Shijiazhuang; and (2) use the previous PMF results to conduct a source-oriented probabilistic ecological-health risk estimation, and determine the priority contamination sources and target HMs. This study can provide an accurate scientific basis for formulating effective policies to control HMs contamination and reduce eco-health risks.

2. Materials and Methods

2.1. Sampling and Determing

Sixty-four street dust samples were sampled within 2nd Ring Road of Shijiazhuang City (Figure S1). The background information of the survey area is displayed in Text S1 (Supplementary materials). At each sampling site, 5–6 sub-samples were collected from 6 to 10 points of the street surface by brush and completely mixed to form an approximately 500 g composite sample. All collected samples were transported back to the lab with plastic bags, dried naturally, and sieved with a 1.0 mm nylon sieve to get rid of foreign matter such as stones, garbage and leaves. Then, approximately 100 g of each sample were weighted and sieved through a 100 μm nylon mesh to obtain RSD [19,36,38]. Before measuring HMs content, the RSD samples were further ground to <75 μm, so that the sample could be thoroughly digested [39].
To measure the concentration of Pb, Cr, Co, Cu, Zn, Mn, and Ni in RSD with ICP-AES (Arcso, SPECTRO Analytical Instruments, Kleve, Germany), a certain weight of milled RSD sample was weighed, and it was digested with HNO3:HCl:HF mixed acid solution in a microwave instrument (MAR 6, CEM, Matthews, NC, USA), and then the solution volume was fixed. To determine the concentration of Hg and As in RSD by AFS (AFS-9700, Beijing Haiguang Instrument Co., LTD, Beijing, China), a certain weight of milled RSD sample was weighted, and it was digested with aqua region in a microwave instrument (MAR 6, CEM, Matthews, NC, USA), and then fixed the solution volume. Standard soil samples (GSS-8 and GSS-6), duplicate samples and blank samples were used for experimental quality control. The detailed sample pretreatment methods and determination process can be found in our previous papers [19,24].

2.2. Contamination Assessment Methods

The single pollution and comprehensive pollution of HMs in the RSD of Shijiazhuang were assessed by geo-accumulation index (Igeo) and Nemerow integrated geo-accumulation index (NIGI). Igeo, proposed by Müller [40] and widely used in the assessment of HMs pollution in soil [41,42,43,44], sediment [45,46,47,48] and urban surface dust [19,49,50,51,52], was calculated using Equation (1):
I g e o = log 2 C i 1.5 × B i
where Bi is the background level of HM i in Shijiazhuang soil [53], and Ci denotes the level of HM i in the RSD. NIGI is a modified Nemerow integrated pollution index based on the Igeo which may be utilized to estimate the comprehensive pollution caused by all HMs [27,43,44]. It was estimated using Equation (2):
N I G I = I g e o ( avg ) 2 + I g e o ( max ) 2 2
where Igeo (avg) and Igeo (max) are the average and maximum values of Igeo for all HMs determined in the RSD, respectively. Table S1 shows the pollution grades based on Igeo and NIGI values.

2.3. Ecological-Health Risk Estimation Methods

The ecological risk index suggested by Håkanson [54] is widely used to assess the ecological risk of toxic pollutants in sediment [55,56,57,58,59], soil [60,61,62,63] and urban dust [22,52,63]. Since the quantity, type and toxicity response coefficient of pollutants are different from Håkanson’ research [53], an improved ecological risk index, i.e., Nemerow comprehensive ecological risk index (NCRI) [12,14,24,64,65], was used to assess the ecological risks of HMs in Shijiazhuang RSD. It was estimated using the following equations:
N C R I = E i ( avg ) 2 + E i ( max ) 2 2
E i = T i × C i B i  
where Ei is the eco-risk factor of HM i, and Ei (avg) and Ei (max) are the average and maximum values of Ei for all HMs measured in the RSD, respectively. Ti is the toxicity response coefficient of HMs [19,24]. The definitions of Ci and Bi are the same as those in Equation (1). The eco-risk grades based on Ei and NCRI values are shown in Table S1 [24,27].
The health risks of HMs in the RSD to local inhabitants, including cancer risk and non-cancer risk, were assessed for three populations (i.e., children, adult women and adult men). The cancer risk of the HMs was estimated using the total cancer risk (TCR), which is equal to the sum of the product of the average daily intake dose (ADD) of all carcinogenic HMs through various exposure routes and the corresponding slope factor (SF) [66,67,68,69]. The non-carcinogenic risk of the HMs was estimated using a hazard index (HI), which is equal to the sum of the quotient of ADD and the corresponding reference dose (RfD) of all HMs through various exposure routes [49]. The detailed calculation equations of ADD are shown in Text S2 (supplementary materials), and TCR and HI were calculated according to Equations (5) and (6) [19,24].
T C R = ∑ j = 1 n ∑ i = 1 m A D D i j × SF i j
H I = ∑ j = 1 n ∑ i = 1 m A D D i j RfD i j  
where m is the number of exposure routes and n is the number of the concerned carcinogenic HMs.
MCS was used to analyze the probabilistic pollution and eco-health risks of HMs in the RSD and the Oracle Crystal Ball v11.1.24 software was run for 10,000 iterations to determine the possibility. In the probabilistic contamination and eco-risk assessment of HMs, the concentrations of HMs in the RSD were taken as uncertainty parameters, while in the probabilistic health risk evaluation, HMs content, body weight, exposure frequency, exposure area of skin, ingestion rate, inhalation rate, exposure duration, and skin adherence factor were taken as uncertainty parameters. The relative exposure parameter values used in this study are listed in Table S2 [24,27,66,69], and the slope factor and reference dose values of all HMs are listed in Table S3 [26,70,71].

3. Results and Discussion

3.1. Probabilistic Contamination Levels of HMs

The results of the contamination assessment of HMs in the RSD using MCS are shown in Figure 1. It can be found that the calculated mean Igeo value of each HM was highly consistent with the simulated mean Igeo value of MCS. In terms of the contamination grade (Table S1), As, Cr, Ni and Mn exhibited non-contamination in the RSD of Shijiazhuang; Cu, Co and Pb presented non-contamination to moderate contamination; while Zn exhibited moderate pollution and above. Figure 1a shows that 36.0% of the Igeo values for Hg were between 1 and 2, showing moderate contamination. In addition, 62.2% of the Igeo values for Hg were 2–7, indicating moderate-to-strong contamination and above. The above results indicate that human activities had a significant impact on Co, Cu, Pb, Zn, and Hg, which is consistent with the Igeo value calculated directly based on the determined concentration of HMs in RSD (Figure S2a).
As for the results of the comprehensive contamination assessment of HMs in the RSD, 5.9% of the NIGI values were in the range of 0.5–1, demonstrating non-pollution to moderate pollution, and 94.1% of the NIGI values were 2–5, showing moderate-to-extreme contamination (Figure S2b). Sensitivity analysis was used to determine the effect of different HMs on NIGI [26,55]. The sensitivity analysis results of NIGI displayed that the sensitivity of Hg and Zn was 91.7% and 2.6%, respectively, showing that the comprehensive contamination of the determined HMs in Shijiazhuang RSD was mainly caused by Hg pollution. Our previous study revealed that Hg in Shijiazhuang RSD primarily originated from coal-related industrial sources [19]. Therefore, it is suggested that local government departments should strengthen the supervision and control of Hg emissions from coal-related industries.

3.2. Probabilistic Ecological Risks of HMs

The results of the eco-risk estimation of HMs in the RSD of Shijiazhuang are shown in Figure 2 and Figure 3. The simulated average Ei values for all HMs were in general agreement with the detected average Ei values calculated from the determined content in the samples. Mn, Cr, Pb, Ni, Co and Zn in all samples, as well as As in 98.7% of the samples and Cu in 99.3% of the samples, presented low ecological risk (Figure 2b–i). Figure 2a showed that the Ei values of Hg were in the range of 96.0–4933.1, with a simulated average of 385.7. The 12.7% of Ei values for Hg were in the range of 80–160, presenting considerable ecological risk; while the 44.0% and 43.3% of Ei values for Hg were between 160 and 320, and ≥ 320, separately, presenting high and very high eco-risk.
The assessment results of concentration-oriented Nemerow comprehensive ecological risk showed that 32.9% of NCRI values were between 80 and 160, presenting considerable ecological risk; 48.6% of NCRI values were between 160 and 320, presenting high ecological risk; and 18.5% of NCRI values were more than 320, representing very high ecological risk (Figure 3a). The sensitivity-analysis results indicated that the synthetically ecological risk of HMs in Shijiazhuang RSD was mainly caused by Hg. According to the previous PMF source apportionment results [19], we evaluated the source-specific ecological risk using the MCS method and the results are illustrated in Figure 3b. The results demonstrated that the contribution of coal-related industrial sources (Factor 3) to the synthetically ecological risk of HMs in Shijiazhuang RSD was the largest (>75%), while the contribution of natural sources (Factor 2) was very small (approximately 1%).

3.3. Probabilistic Health Risks of HMs

3.3.1. Concentration-Based Probabilistic Health Risk

The concentration-based probabilistic health risk evaluation results based on MCS are shown in Figure 4a,b. A total of 95.4% of TCR values for children, 92.8% of TCR values for adult males and 93.3% of TCR values for adult females were between 1.0 × 10−6 (the acceptable threshold) and 1.0 × 10−4 (the severe level), and about 0.4% of TCR values for children, 0.6% of TCR values for adult males and 1.0% of TCR values for adult females were >1.0 × 10−4, indicating that the cancer risk of carcinogenic HMs in Shijiazhuang RSD was not negligible for the three groups. As for non-carcinogenic risk, Figure 4b shows that the mean HI value of adult females and males was significantly less than that of children, and the 95th percentile HI value was <1 for both adult males and females. While, for children, the 95th percentile HI value and the 16.5% of HI values were >1. According to previous studies [65], the non-carcinogenic risk of target HMs is acceptable when the 95th percentile HQ value is <1. The above results indicate that the non-carcinogenic risk of the HMs in Shijiazhuang RSD to local adults can be ignored, while the non-carcinogenic risk to children cannot be ignored.
We used sensitivity analysis to determine the impact of changes in HMs content and exposure parameters on health risks. The results showed that exposure duration (ED) was the largest contributor to the TCR values of the three groups of people, with a sensitivity contribution of 56.0% to 76.6% (Figure 4c). Of all the exposure parameters affecting the non-carcinogenic risk of children, skin adhesion factor (SL) contributed the most, followed by As, with a sensitivity of 45.0% and 27.7%, respectively (Figure 4d). For this reason, ED, SL and As are parameters that need to be studied. It was recommended that these three groups of people wash their hands, bathe and change their clothes regularly. In particular, children should develop good behavior and hygiene habits to reduce the unconscious intake of dust.

3.3.2. Source-Specific Probabilistic Health Risk

To analyze the health risk contribution of different sources of HMs in Shijiazhuang RSD, we conducted a source-specific probabilistic health-risk estimation using the previous PMF source apportionment results [19]. For children, the contributions of the three identified sources to TCR (Figure 5a) and HI (Figure 6a) were Factor 3 (coal-related industrial source) > Factor 2 (natural source) > Factor 1 (traffic source), and As was the chief contributor to cancer risk (Figure 5b) and non-cancer risk (Figure 6b) of HMs in the two anthropogenic sources (i.e., coal-related industrial sources and traffic sources). For adult females and males, the contributions of the three identified sources to TCR (Figures S3 and S4) and HI (Figures S5 and S6) are similar to those of children.
In view of the above source-specific health-risk estimation results, we naturally conclude that coal-related industrial sources are the priority contamination source of HMs pollution to control in Shijiazhuang RSD, with As as the priority HMs.

4. Conclusions

The probabilistic contamination levels and ecological-health risks of nine widely considered HMs in Shijiazhuang RSD, an emblematic heavy industrial metropolis in North China, were estimated using the Monte Carlo simulation method. The probability of Hg presenting moderate-to-above contamination levels was >94% in Shijiazhuang RSD. Hg in Shijiazhuang RSD exhibited considerable-to-very-high ecological risk. The overall contamination of HMs in the RSD were moderate and moderate to heavy, and the overall ecological risk were considerable to above. The non-carcinogenic risks of the investigated heavy metal(loid)s to children and the corresponding carcinogenic risks to adult females, adult males and children cannot be ignored. Compared with the previous study of our research team, we obtained some new findings in this study, namely, first, the serious ecological risk of HMs in Shijiazhuang RSD was mainly caused by Hg; second, coal-related industrial sources contributed the most to the overall ecological risk of HMs in Shijiazhuang RSD; third, the health hazards of HMs, in the RSD, to local residents were mainly caused by coal-related industrial sources, and As was the main contributor to the cancer risk and non-cancer risk of HMs in Shijiazhuang RSD; fourth, in terms of ecological environment protection and residents’ health protection, coal-related industrial sources are a priority contamination source to control, and Hg and As are priority HMs to control. Therefore, the control of industrial-waste emissions in heavy industrial metropolises, especially the treatment of coal-fired industrial waste, should be the focus of pollution control.
Based on the results of this study, in order to protect the ecological environment and the health of residents in heavy industrial metropolises, we suggest that relevant government departments strengthen the supervision of mercury and arsenic emissions in coal-related industries, and coal-related enterprises should increase environmental-protection investment and technological innovation to improve the level of pollution control and reduce mercury and arsenic emissions. For residents, we recommend washing hands, bathing and changing clothes frequently, and, especially, children should develop good behavior and hygiene habits to reduce the unconscious intake of dust.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/min13030305/s1, Text S1: Overview of the study area; Text S2: Calculation methods of ADD; Figure S1: Study area and sampling locations of the RSD in Shijiazhuang; Figure S2: The assessment results of geo-accumulation index (Igeo) and Nemerow integrated geo-accumulation index (NIGI). (a) The Igeo values calculated based on the concentration of HMs in the RSD; (b) The NIGI of HMs in the RSD with MCS; Figure S3: Probabilistic cancer risk of specific sources for adult females. (a) Cumulative probability of TCR of specific sources; (b) The cancer risk of each carcinogenic HM from various sources; Figure S4: Probabilistic cancer risk of specific source for adult males. (a) Cumulative probability of TCR of specific sources; (b) The cancer risk of each carcinogenic HM from various sources; Figure S5: Probabilistic non-cancer risk of specific sources for adult females. (a) Cumulative probability of HI of specific sources; (b) The HQ of each HM from various sources; Figure S6: Probabilistic non-cancer risk of specific sources for adult males. (a) Cumulative probability of HI of specific sources; (b) The HQ of each HM from various sources; Table S1: Grades on the basis of Igeo, NIGI, Ei, and NCRI values; Table S2: Probability density functions of parameters and point value in health-risk-assessment (HRA) model with MCS; Table S3: Reference dose (RfD, mg (kg d)−1) and slope factor (SF, (kg d) mg−1) of HMs.

Author Contributions

Conceptualization, Z.W. and X.L.; methodology, Z.W., X.L. and Y.Y.; software, Z.W., Y.Y. and B.Y.; validation, Z.W., K.L., H.P., Y.Y. and B.Y.; formal analysis, Z.W., X.L., K.L. and H.P.; investigation, Z.W., P.F. and L.Z.; resources, Z.W., P.F. and L.Z.; data curation, Z.W., X.L. and K.L.; writing—original draft preparation, Z.W.; writing—review and editing, X.L., K.L. and H.P.; visualization, Z.W., Y.Y. and B.Y.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant number 42277487, and the Research and Development Key Project of Shaanxi Province, grant number 2020SF-433.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

We appreciate Run Luo, Yi Song, Xin Li and Kai Zhang for their help with sample preparation and experiments.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Krupnova, T.G.; Rakova, O.V.; Gavrilkina, S.V.; Antoshkina, E.G.; Baranov, E.O.; Yakimova, O.N. Road dust trace elements contamination, sources, dispersed composition, and human health risk in Chelyabinsk, Russia. Chemosphere 2020, 261, 127799. [Google Scholar] [CrossRef]
  2. Proshad, R.; Kormoker, T.; Abdullah Al, M.; Islam, M.S.; Khadka, S.; Idris, A.M. Receptor model-based source apportionment and ecological risk of metals in sediments of an urban river in Bangladesh. J. Hazard. Mater. 2022, 423, 127030. [Google Scholar] [CrossRef]
  3. Wiseman, C.L.S.; Levesque, C.; Rasmussen, P.E. Characterizing the sources, concentrations and resuspension potential of metals and metalloids in the thoracic fraction of urban road dust. Sci. Total Environ. 2021, 786, 147467. [Google Scholar] [CrossRef]
  4. Fang, T.; Wang, H.; Liang, Y.; Cui, K.; Yang, K.; Lu, W.; Li, J.; Zhao, X.; Gao, N.; Yu, Q.; et al. Source tracing with cadmium isotope and risk assessment of heavy metals in sediment of an urban river, China. Environ. Pollut. 2022, 305, 119325. [Google Scholar] [CrossRef]
  5. Rahman, M.S.; Ahmed, Z.; Seefat, S.M.; Alam, R.; Islam, A.R.M.T.; Choudhury, T.R.; Begum, B.A.; Idris, A.M. Assessment of heavy metal contamination in sediment at the newly established tannery industrial Estate in Bangladesh: A case study. Environ. Chem. Ecotoxicol. 2022, 4, 1–12. [Google Scholar] [CrossRef]
  6. Abbaszade, G.; Tserendorj, D.; Salazar-Yanez, N.; Zacháry, D.; Völgyesi, P.; Tóth, E.; Szabó, C. Lead and stable lead isotopes as tracers of soil pollution and human health risk assessment in former industrial cities of Hungary. Appl. Geochem. 2022, 145, 105397. [Google Scholar] [CrossRef]
  7. Khan, J.; Singh, R.; Upreti, P.; Yadav, R.K. Geo-statistical assessment of soil quality and identification of heavy metal contamination using integrated GIS and multivariate statistical analysis in industrial region of Western India. Environ. Technol. Innov. 2022, 28, 102646. [Google Scholar] [CrossRef]
  8. Shi, X.; Liu, S.; Song, L.; Wu, C.; Yang, B.; Lu, H.; Wang, X.; Zakari, S. Contamination and source-specific risk analysis of soil heavy metals in a typical coal industrial city, central China. Sci. Total Environ. 2022, 836, 155694. [Google Scholar] [CrossRef]
  9. Long, Z.; Zhu, H.; Bing, H.; Tian, X.; Wang, Z.; Wang, X.; Wu, Y. Contamination, sources and health risk of heavy metals in soil and dust from different functional areas in an industrial city of Panzhihua City, Southwest China. J. Hazard. Mater. 2021, 420, 126638. [Google Scholar] [CrossRef]
  10. Yang, Y.; Lu, X.; Fan, P.; Yu, B.; Wang, L.; Lei, K.; Zuo, L. Multi-element features and trace metal sources of road sediment from a mega heavy industrial city in North China. Chemosphere 2023, 311, 137093. [Google Scholar] [CrossRef]
  11. Khademi, H.; Gabarrón, M.; Abbaspour, A.; Martínez-Martínez, S. Environmental impact assessment of industrial activities on heavy metals distribution in street dust and soil. Chemosphere 2019, 217, 695–705. [Google Scholar] [CrossRef]
  12. Heidari, M.; Darijani, T.; Alipour, V. Heavy metal pollution of road dust in a city and its highly polluted suburb; quantitative source apportionment and source-specific ecological and health risk assessment. Chemosphere 2021, 273, 129656. [Google Scholar] [CrossRef]
  13. Han, Q.; Liu, Y.; Feng, X.; Mao, P.; Sun, A.; Wang, M.; Wang, M. Pollution effect assessment of industrial activities on potentially toxic metal distribution in windowsill dust and surface soil in central China. Sci. Total Environ. 2021, 759, 144023. [Google Scholar] [CrossRef]
  14. Men, C.; Liu, R.; Xu, L.; Wang, Q.; Guo, L.; Miao, Y.; Shen, Z. Source-specific ecological risk analysis and critical source identification of heavy metals in road dust in Beijing, China. J. Hazard. Mater. 2020, 388, 121763. [Google Scholar] [CrossRef]
  15. Deng, W.; Hao, G.; Liu, W. Source-specific risks apportionment and critical sources identification of potentially harmful elements in urban road dust combining positive matrix factorization model with multiple attribute decision making method. Ecol. Indic. 2022, 144, 109449. [Google Scholar] [CrossRef]
  16. Ghanavati, N.; Nazarpour, A.; Vivo, B.D. Ecological and human health risk assessment of toxic metals in street dusts and surface soils in Ahvaz, Iran. Environ. Geochem. Health 2019, 41, 875–891. [Google Scholar] [CrossRef]
  17. Ali, M.U.; Liu, G.; Yousaf, B.; Abbas, Q.; Ullah, H.; Munir, M.A.M.; Fu, B. Pollution characteristics and human health risks of potentially (eco) toxic elements (PTEs) in road dust from metropolitan area of Hefei, China. Chemosphere 2017, 181, 111–121. [Google Scholar] [CrossRef]
  18. Gope, M.; Masto, R.E.; George, J.; Hoque, R.R.; Balachandran, S. Bioavailability and health risk of some potentially toxic elements (Cd, Cu, Pb and Zn) in street dust of Asansol, India. Ecotoxic. Environ. Saf. 2017, 138, 231–241. [Google Scholar] [CrossRef]
  19. Fan, P.; Lu, X.; Yu, B.; Fan, X.; Wang, L.; Lei, K.; Yang, Y.; Zuo, L.; Rinklebe, J. Spatial distribution, risk estimation and source apportionment of potentially toxic metal(loid)s in resuspended megacity street dust. Environ. Int. 2022, 160, 107073. [Google Scholar] [CrossRef]
  20. Men, C.; Liu, R.; Wang, Q.; Guo, L.; Shen, Z. The impact of seasonal varied human activity on characteristics and sources of heavy metals in metropolitan road dusts. Sci. Total Environ. 2018, 637–638, 844–854. [Google Scholar] [CrossRef]
  21. Soltani, N.; Keshavarzi, B.; Moore, F.; Tavakol, T.; Lahijanzadeh, A.R.; Jaafarzadeh, N.; Kermani, M. Ecological and human health hazards of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in road dust of Isfahan metropolis, Iran. Sci. Total Environ. 2015, 505, 712–723. [Google Scholar] [CrossRef]
  22. Yesilkanat, C.M.; Kobya, Y. Spatial characteristics of ecological and health risks of toxic heavy metal pollution from road dust in the Black Sea coast of Turkey. Geoder. Reg. 2021, 25, e00388. [Google Scholar] [CrossRef]
  23. Zhou, L.; Liu, G.; Shen, M.; Liu, Y. Potential ecological and health risks of heavy metals for indoor and corresponding outdoor dust in Hefei, Central China. Chemosphere 2022, 302, 134864. [Google Scholar] [CrossRef]
  24. Zuo, L.; Lu, X.; Fan, P.; Wang, L.; Yu, B.; Lei, K.; Yang, Y.; Chen, Y. Concentrations, sources and ecological–health risks of potentially toxic elements in finer road dust from a megacity in north China. J. Clean. Prod. 2022, 358, 132036. [Google Scholar] [CrossRef]
  25. Rehman, A.; Liu, G.; Yousaf, B.; Zia-ur-Rehman, M.; Ali, M.U.; Rashid, M.S.; Farooq, M.R.; Javed, Z. Characterizing pollution indices and children health risk assessment of potentially toxic metal(oid)s in school dust of Lahore, Pakistan. Ecotoxic. Environ. Saf. 2020, 190, 110059. [Google Scholar] [CrossRef]
  26. Guan, Q.; Liu, Z.; Shao, W.; Tian, J.; Luo, H.; Ni, F.; Shan, Y. Probabilistic risk assessment of heavy metals in urban farmland soils of a typical oasis city in northwest China. Sci. Total Environ. 2022, 833, 155096. [Google Scholar] [CrossRef]
  27. Huang, J.; Wu, Y.; Sun, J.; Li, X.; Geng, X.; Zhao, M.; Sun, T.; Fan, Z. Health risk assessment of heavy metal(loid)s in park soils of the largest megacity in China by using Monte Carlo simulation coupled with Positive matrix factorization model. J. Hazard. Mater. 2021, 415, 125629. [Google Scholar] [CrossRef]
  28. Dehghani, F.; Omidi, F.; Fallahzadeh, R.A.; Pourhassan, B. Health risk assessment of occupational exposure to heavy metals in a steel casting unit of a steelmaking plant using Monte–Carlo simulation technique. Toxicol. Indus. Health 2021, 37, 431–440. [Google Scholar] [CrossRef]
  29. Gu, X.; Lin, C.; Wang, B.; Wang, J.; Ouyang, W. A comprehensive assessment of anthropogenic impacts, contamination, and ecological risks of toxic elements in sediments of urban rivers: A case study in Qingdao, East China. Environ. Adv. 2022, 7, 100143. [Google Scholar] [CrossRef]
  30. Wang, J.; Wu, H.; Wei, W.; Xu, C.; Tan, X.; Wen, Y.; Lin, A. Health risk assessment of heavy metal(loid)s in the farmland of megalopolis in China by using APCS-MLR and PMF receptor models: Taking Huairou District of Beijing as an example. Sci. Total Environ. 2022, 835, 155313. [Google Scholar] [CrossRef]
  31. Yuan, B.; Cao, H.; Du, P.; Ren, J.; Chen, J.; Zhang, H.; Zhang, Y.; Luo, H. Source-oriented probabilistic health risk assessment of soil potentially toxic elements in a typical mining city. J. Hazard. Mater. 2023, 443, 130222. [Google Scholar] [CrossRef]
  32. Kamani, H.; Ashrafi, S.D.; Isazadeh, S.; Jaafari, J.; Hoseini, M.; Mostafapour, F.K.; Bazrafshan, E.; Nazmara, S.; Mahvi, A.H. Heavy metal contamination in street dusts with various land uses in Zahedan, Iran. Bull. Environ. Contam. Toxicol. 2015, 94, 382–386. [Google Scholar] [CrossRef]
  33. Pan, H.; Lu, X.; Lei, K. A comprehensive analysis of heavy metals in urban road dust of Xi’an, China: Contamination, source apportionment and spatial distribution. Sci. Total Environ. 2017, 609, 1361–1369. [Google Scholar] [CrossRef]
  34. Wang, H.; Cai, L.; Wang, Q.; Hu, G.; Chen, L. A comprehensive exploration of risk assessment and source quantification of potentially toxic elements in road dust: A case study from a large Cu smelter in central China. Catena 2021, 196, 104930. [Google Scholar] [CrossRef]
  35. Urrutia-Goyes, R.; Hernandez, N.; Carrillo-Gamboa, O.; Nigam, K.D.P.; Ornelas-Soto, N. Street dust from a heavily-populated and industrialized city: Evaluation of spatial distribution, origins, pollution, ecological risks and human health repercussions. Ecotoxicol. Environ. Saf. 2018, 159, 198–204. [Google Scholar] [CrossRef]
  36. Wang, Q.; Lu, X.; Pan, H. Analysis of heavy metals in the re-suspended road dusts from different functional areas in Xi’an, China. Environ. Sci. Pollut. Res. 2016, 23, 19838–19846. [Google Scholar] [CrossRef]
  37. Diao, L.; Zhang, H.; Liu, B.; Dai, C.; Zhang, Y.; Dai, Q.; Bi, X.; Zhang, L.; Song, C.; Feng, Y. Health risks of inhaled selected toxic elements during the haze episodes in Shijiazhuang, China: Insight into critical risk sources. Environ. Pollut. 2021, 276, 116664. [Google Scholar] [CrossRef]
  38. Xu, X.; Lu, X.; Han, X.; Zhao, N. Ecological and health risk assessment of metal in resuspended particles of urban street dust from an industrial city in China. Curr. Sci. 2015, 108, 72–79. [Google Scholar]
  39. Adimalla, N.; Qian, H.; Wang, H. Assessment of heavy metal (HM) contamination in agricultural soil lands in northern Telangana, India: An approach of spatial distribution and multivariate statistical analysis. Environ. Monit. Assess. 2019, 191, 246. [Google Scholar] [CrossRef]
  40. Müller, G. Index of geoaccumulation in sediments of the Rhine River. Geol. J. 1969, 2, 108–118. [Google Scholar]
  41. Ackah, M. Soil elemental concentrations, geoaccumulation index, non-carcinogenic and carcinogenic risks in functional areas of an informal e-waste recycling area in Accra, Ghana. Chemosphere 2019, 235, 908–917. [Google Scholar] [CrossRef]
  42. Liu, X.; Chen, S.; Yan, X.; Liang, T.; Yang, X.; El-Naggar, A.; Liu, J.; Chen, H. Evaluation of potential ecological risks in potential toxic elements contaminated agricultural soils: Correlations between soil contamination and polymetallic mining activity. J. Environ. Manag. 2021, 300, 113679. [Google Scholar] [CrossRef]
  43. Mazurek, R.; Kowalsha, J.B.; Gąsiorek, M.; Zadrożny, P.; Wieczorek, J. Pollution indices as comprehensive tools for evaluation of the accumulation and provenance of potentially toxic elements in soils in Ojców National Park. J. Geochem. Expl. 2019, 201, 13–30. [Google Scholar] [CrossRef]
  44. Santos-Francés, F.; Martínez-Graña, A.; Zarza, C.Á.; Sánchez, A.G.; Rojo, P.A. Spatial distribution of heavy metals and the environmental quality of soil in the Northern Plateau of Spain by geostatistical methods. Int. J. Environ. Res. Pub. Health 2017, 14, 568. [Google Scholar] [CrossRef]
  45. John, P.M.; Murali, V.; Chakraborty, K.; Lotlikar, A.; Shameem, K.; Rahmanm, K.H.; Gopinath, A. Spatial and seasonal trends of trace metals in the surficial sediments from off Kochi-Geochemistry and environmental implications. Mari. Pollut. Bull. 2022, 182, 114029. [Google Scholar] [CrossRef]
  46. Özşeker, K.; Erüz, C.; Terzi, Y. Spatial distribution and ecological risk evaluation of toxic metals in the southern Black Sea coastal sediments. Mari. Pollut. Bull. 2022, 182, 114020. [Google Scholar] [CrossRef]
  47. Williams, J.A.; Antoine, J. Evaluation of the element pollution status of Jamaican surface sediments using enrichment factor, geoaccumulation index, ecological risk and potential ecological risk index. Mari. Pollut. Bull. 2020, 157, 111288. [Google Scholar] [CrossRef]
  48. Zheng, H.; Ren, Q.; Zheng, K.; Qin, Z.; Wang, Y.; Wang, Y. Spatial distribution and risk assessment of metal(loid)s in marine sediments in the Arctic Ocean and Bering Sea. Mari. Pollut. Bull. 2022, 179, 113729. [Google Scholar] [CrossRef]
  49. Kamarehie, B.; Jafari, A.; Zarei, A.; Fakhri, Y.; Ghaderpoori, M.; Alinejad, A. Non-carcinogenic health risk assessment of nitrate in bottled drinking waters sold in Iranian markets: A Monte Carlo simulation. Accredit. Qual. Assur. 2019, 24, 417–426. [Google Scholar] [CrossRef]
  50. Luo, H.; Wang, Q.; Guan, Q.; Ma, Y.; Ni, F.; Yang, E.; Zhang, J. Heavy metal pollution levels, source apportionment and risk assessment in dust storm in key cities in Northwest China. J. Hazard. Mater. 2022, 422, 126878. [Google Scholar] [CrossRef]
  51. Ma, J.; Yan, Y.; Chen, X.; Niu, Z.; Yu, R.; Hu, G. Incorporating bioaccessibility and source apportionment into human health risk assessment of heavy metals in urban dust of Xiamen, China. Ecotoxi. Environ. Saf. 2021, 228, 112985. [Google Scholar] [CrossRef]
  52. Lima, L.H.V.; do Nascimento, C.W.A.; da Silva, F.B.V.; Araújo, P.R.M. Baseline concentrations, source apportionment, and probabilistic risk assessment of heavy metals in urban street dust in Northern Brazil. Sci. Total Environ. 2023, 858, 159750. [Google Scholar] [CrossRef]
  53. Cheng, H.; Li, K.; Li, M.; Yang, K.; Liu, F.; Cheng, X. Geochemical background and baseline value of chemical elements in urban soil in China. Earth Sci. Front. 2014, 21, 265–306. [Google Scholar]
  54. Håkanson, L. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
  55. Dan, S.F.; Udoh, E.C.; Wang, Q. Contamination and ecological risk assessment of heavy metals, and relationship with organic matter sources in surface sediments of the Cross River Estuary and nearshore areas. J. Hazard. Mater. 2022, 438, 129531. [Google Scholar] [CrossRef]
  56. Du, H.; Lu, X. Source apportionment and probabilistic ecological risk of heavy metal(loid)s in sediments in the Mianyang section of the Fujiang River, China. Minerals 2022, 12, 1513. [Google Scholar] [CrossRef]
  57. Hossain, M.B.; Rahman, M.A.; Hossain, M.K.; Nur, A.A.U.; Sultana, S.; Semme, S.; Albeshr, M.F.; Arai, T.; Yu, J. Contamination status and associated ecological risk assessment of heavy metals in different wetland sediments from an urbanized estuarine ecosystem. Mar. Pollut. Bull. 2022, 185, 114246. [Google Scholar] [CrossRef]
  58. Lyla, P.S.; Manokaran, S.; Ajmalkhan, S.; Ansari, K.G.M.T.; Raja, S.; Reshi, O. Spatial analysis, ecological risk assessment, control factors, and sources of heavy metal pollution in the shelf surface sediments of the southwest Bay of Bengal, India. Reg. Stud. Mar. Sci. 2022, 56, 102705. [Google Scholar] [CrossRef]
  59. Wei, Y.; Ding, D.; Qu, K.; Sun, J.; Cui, Z. Ecological risk assessment of heavy metal pollutants and total petroleum hydrocarbons in sediments of the Bohai Sea, China. Mar. Pollut. Bull. 2022, 184, 114218. [Google Scholar] [CrossRef]
  60. Wang, F.; Wang, F.; Yang, H.; Yu, J.; Ni, R. Ecological risk assessment based on soil adsorption capacity for heavy metals in Taihu basin, China. Environ. Pollut. 2023, 316, 120608. [Google Scholar] [CrossRef]
  61. Zhang, H.; Zhang, F.; Song, J.; Tan, M.L.; Kung, H.T.; Johnson, V.C. Pollution source, ecological and human health risk assessment of heavy metals in soils from coal mining areas in Xinjiang, China. Environ. Res. 2021, 202, 111702. [Google Scholar] [CrossRef]
  62. Zhou, Y.; Jiang, D.; Ding, D.; Wu, Y.; Wei, J.; Kong, L.; Long, T.; Fan, T.; Deng, S. Ecological-health risks assessment and source apportionment of heavy metals in agricultural soils around a super-sized lead-zinc smelter with a long production history, in China. Environ. Pollut. 2022, 307, 119487. [Google Scholar] [CrossRef]
  63. Liang, Q.; Tian, K.; Li, L.; He, Y.; Zhao, T.; Liu, B.; Wu, Q.; Huang, B.; Zhao, L.; Teng, Y. Ecological and human health risk assessment of heavy metals based on their source apportionment in cropland soils around an e-waste dismantling site, Southeast China. Ecotoxicol. Environ. Saf. 2022, 242, 113929. [Google Scholar] [CrossRef]
  64. Li, X.; Bing, J.; Zhang, J.; Guo, L.; Deng, Z.; Wang, D.; Liu, L. Ecological risk assessment and sources identification of heavy metals in surface sediments of a river–reservoir system. Sci. Total Environ. 2022, 842, 156683. [Google Scholar] [CrossRef]
  65. Yan, Y.; Wan, R.-a.; Yu, R.-l.; Hu, G.-r.; Lin, C.-q.; Huang, H.-b. A comprehensive analysis on source-specific ecological risk of metal(loid)s in surface sediments of mangrove wetlands in Jiulong River Estuary, China. Catena 2022, 209, 105817. [Google Scholar] [CrossRef]
  66. Sun, J.; Zhao, M.; Huang, J.; Liu, Y.; Wu, Y.; Cai, B.; Han, Z.; Huang, H.; Fan, Z. Determination of priority control factors for the management of soil trace metal(loid)s based on source-oriented health risk assessment. J. Hazard. Mater. 2022, 423, 127116. [Google Scholar] [CrossRef]
  67. Huang, J.; Wu, Y.; Li, Y.; Sun, J.; Xie, Y.; Fan, Z. Do trace metal(loid)s in road soils pose health risks to tourists? A case of a highly-visited national park in China. J. Environ. Sci. 2022, 111, 61–74. [Google Scholar] [CrossRef]
  68. Tan, B.; Wang, H.; Wang, X.; Ma, C.; Zhou, J.; Dai, X. Health risks and source analysis of heavy metal pollution from dust in Tianshui, China. Minerals 2021, 11, 502. [Google Scholar] [CrossRef]
  69. Zhao, Z.; Hao, M.; Li, Y.; Li, S. Contamination, sources and health risks of toxic elements in soils of karstic urban parks based on Monte Carlo simulation combined with a receptor model. Sci. Total Environ. 2022, 839, 156223. [Google Scholar] [CrossRef]
  70. Lei, M.; Li, K.; Guo, G.; Ju, T. Source-specific health risks apportionment of soil potential toxicity elements combining multiple receptor models with Monte Carlo simulation. Sci. Total Environ. 2022, 817, 152899. [Google Scholar] [CrossRef]
  71. Yang, S.; Zhao, J.; Chang, S.; Collins, C.; Xu, J.; Liu, X. Status assessment and probabilistic health risk modeling of metals accumulation in agriculture soils across China: A synthesis. Environ. Int. 2019, 128, 165–174. [Google Scholar] [CrossRef]
Minerals 13 00305 g001 550
Figure 1. The geo-accumulation index of all HMs in the RSD based on MCS. (a)Igeo (Hg); (b) Igeo (As); (c) Igeo (Mn); (d) Igeo (Co); (e) Igeo (Pb); (f) Igeo (Ni); (g) Igeo (Cu); (h) Igeo (Cr); (i) Igeo (Zn).
Figure 1. The geo-accumulation index of all HMs in the RSD based on MCS. (a)Igeo (Hg); (b) Igeo (As); (c) Igeo (Mn); (d) Igeo (Co); (e) Igeo (Pb); (f) Igeo (Ni); (g) Igeo (Cu); (h) Igeo (Cr); (i) Igeo (Zn).
Minerals 13 00305 g001
Minerals 13 00305 g002 550
Figure 2. Cumulative probability distribution of the modified ecological risk factor (Ei) in the RSD based on MCS. (a)Ei (Hg); (b) Ei (As); (c) Ei (Mn); (d) Ei (Co); (e) Ei (Pb); (f) Ei (Ni); (g) Ei (Cu); (h) Ei (Cr); (i) Ei (Zn).
Figure 2. Cumulative probability distribution of the modified ecological risk factor (Ei) in the RSD based on MCS. (a)Ei (Hg); (b) Ei (As); (c) Ei (Mn); (d) Ei (Co); (e) Ei (Pb); (f) Ei (Ni); (g) Ei (Cu); (h) Ei (Cr); (i) Ei (Zn).
Minerals 13 00305 g002
Minerals 13 00305 g003 550
Figure 3. Cumulative probabilistic distribution of NCRI. (a) Concentration-oriented probabilistic risk assessment; (b) Source-oriented probabilistic risk assessment.
Figure 3. Cumulative probabilistic distribution of NCRI. (a) Concentration-oriented probabilistic risk assessment; (b) Source-oriented probabilistic risk assessment.
Minerals 13 00305 g003
Minerals 13 00305 g004 550
Figure 4. Concentration-based probabilistic health risk estimation results. (a) Cumulative probability of TCR; (b) cumulative probability of HI; (c) the sensitivity of TCR; (d) the sensitivity of HI.
Figure 4. Concentration-based probabilistic health risk estimation results. (a) Cumulative probability of TCR; (b) cumulative probability of HI; (c) the sensitivity of TCR; (d) the sensitivity of HI.
Minerals 13 00305 g004
Minerals 13 00305 g005 550
Figure 5. Probabilistic cancer risk of source-specific for children. (a) Cumulative probability of TCR of source-specific; (b) the cancer risk of each carcinogenic HM from various sources.
Figure 5. Probabilistic cancer risk of source-specific for children. (a) Cumulative probability of TCR of source-specific; (b) the cancer risk of each carcinogenic HM from various sources.
Minerals 13 00305 g005
Minerals 13 00305 g006 550
Figure 6. Probabilistic non-cancer risk of source-specific for children. (a) Cumulative probability of HI of source-specific; (b) the HQ of each HM from various sources.
Figure 6. Probabilistic non-cancer risk of source-specific for children. (a) Cumulative probability of HI of source-specific; (b) the HQ of each HM from various sources.
Minerals 13 00305 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Z.; Lu, X.; Yang, Y.; Yu, B.; Lei, K.; Pan, H.; Fan, P.; Zuo, L. Estimation of Probabilistic Environmental Risk of Heavy Metal(loid)s in Resuspended Megacity Street Dust with Monte Carlo Simulation. Minerals 2023, 13, 305. https://doi.org/10.3390/min13030305

AMA Style

Wang Z, Lu X, Yang Y, Yu B, Lei K, Pan H, Fan P, Zuo L. Estimation of Probabilistic Environmental Risk of Heavy Metal(loid)s in Resuspended Megacity Street Dust with Monte Carlo Simulation. Minerals. 2023; 13(3):305. https://doi.org/10.3390/min13030305

Chicago/Turabian Style

Wang, Zhenze, Xinwei Lu, Yufan Yang, Bo Yu, Kai Lei, Huiyun Pan, Peng Fan, and Ling Zuo. 2023. "Estimation of Probabilistic Environmental Risk of Heavy Metal(loid)s in Resuspended Megacity Street Dust with Monte Carlo Simulation" Minerals 13, no. 3: 305. https://doi.org/10.3390/min13030305

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop