Groundwater health risk assessment of North China Plain based on Monte Carlo model sensitivity analysis and morphological analysis: A case study of Shijiazhuang City

被引:1
|
作者
You, Di [1 ,2 ,3 ]
Zhou, Yahong [1 ,2 ,3 ]
Wang, Bin [1 ,3 ]
Li, Kunyuan [1 ,2 ,3 ]
Chang, Ji Xuan [1 ,2 ,3 ]
Lu, Changyu [1 ,2 ,3 ]
机构
[1] Hebei GEO Univ, Sch Water Resources & Environm, Shijiazhuang 050031, Peoples R China
[2] Hebei GEO Univ, Hebei Prov Key Lab Sustained Utilizat & Dev Water, Shijiazhuang, Peoples R China
[3] Hebei GEO Univ, Hebei Prov Collaborat Innovat Ctr Sustainable Util, Shijiazhuang, Peoples R China
关键词
groundwater; health risk assessment; HRWM; Monte Carlo model; visual MINTEQ; QUALITY;
D O I
10.1002/wer.11063
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The rapid development of the social economy and the influence of human activities can lead to aggravated groundwater pollution. Groundwater safety is the premise of residents' health. Therefore, studying the sustainable utilization and health risks of groundwater quality is important. The groundwater quality and potential health risks were evaluated in the Shijiazhuang area, which is located in the North China Plain in this paper. Based on 159 groundwater samples collected in the study area, the potential health risks of As, Cr6+, Ni, Pb, F-, and NO3- to humans were evaluated from oral and skin contact. Results of the human health risk assessment showed that the average carcinogenic risk and non-carcinogenic risk of children are higher than those of adults. According to the spatial distribution of the total risk value, adults and children in the southwest of the study area face higher risks. Because of the uncertainty of USEPA, Monte Carlo simulation was used to calculate the probability of health risk assessment and prioritization of contaminant treatment. The results of the Monte Carlo simulation showed that the acceptable range for children is 6.82%, and the acceptable range for adults is 18.07%. According to the HRWM model, carcinogenic pollutants mainly include As, Cr6+, and Ni. The most important chemical species of As is HAsO42-, followed by H2AsO4-. Similarly, CrO42- and Ni2+ are the main forms of Cr6+ and Ni. The results of this study can provide data support for the protection and management of groundwater quality in the North China Plain.Practitioner Points Children are more susceptible to carcinogenic risk than adults. After calculation, the main influencing elements are Ni and Cr. Metal morphology analysis was carried out, and the results showed that HAsO42-, CrO42-, and Ni2+ were the main types. Chemicals in groundwater come from industrial wastewater, agricultural wastewater, and domestic sewage. In this paper, the health risk assessment for adults and children was conducted through both drinking and dermal contact routes, and it was calculated that children are more susceptible to cancer risk and Ni and Cr are the main contaminants. image
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Groundwater pollution and human health risk based on Monte Carlo simulation in a typical mining area in Northern Anhui Province, China
    Huili Qiu
    Herong Gui
    Pei Fang
    Guangping Li
    International Journal of Coal Science & Technology, 2021, 8 : 1118 - 1129
  • [42] Groundwater health risk assessment and its temporal and spatial evolution based on trapezoidal fuzzy number-Monte Carlo stochastic simulation: A case study in western Jilin province
    Li, Tao
    Bian, Jianmin
    Ruan, Dongmei
    Xu, Liwen
    Zhang, Siting
    ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY, 2024, 282
  • [43] Groundwater pollution and human health risk based on Monte Carlo simulation in a typical mining area in Northern Anhui Province, China
    Qiu, Huili
    Gui, Herong
    Fang, Pei
    Li, Guangping
    INTERNATIONAL JOURNAL OF COAL SCIENCE & TECHNOLOGY, 2021, 8 (05) : 1118 - 1129
  • [44] Health Risk Assessment of Heavy Metals in Soils of a City in Guangdong Province Based on Source Oriented and Monte Carlo Models
    Chen L.
    Zou Z.-H.
    Zhang P.-Z.
    Wang Y.-H.
    Wang Z.-J.
    Lin S.
    Tang C.-M.
    Luo G.-Q.
    Zhong J.-W.
    Li Z.-Y.
    Wang Y.
    Huanjing Kexue/Environmental Science, 2024, 45 (05): : 2983 - 2994
  • [45] Groundwater pollution early warning based on QTR model for regional risk management: A case study in Luoyang city, China
    Huan, Huan
    Li, Xiang
    Zhou, Jun
    Liu, Weijiang
    Li, Juan
    Liu, Bing
    Xi, Beidou
    Jiang, Yonghai
    ENVIRONMENTAL POLLUTION, 2020, 259
  • [46] Dietary intake and health risk assessment of nitrate, nitrite, and nitrosamines: a Bayesian analysis and Monte Carlo simulation
    Malihe Moazeni
    Zahra Heidari
    Sahar Golipour
    Leila Ghaisari
    Mika Sillanpää
    Afshin Ebrahimi
    Environmental Science and Pollution Research, 2020, 27 : 45568 - 45580
  • [47] Dietary intake and health risk assessment of nitrate, nitrite, and nitrosamines: a Bayesian analysis and Monte Carlo simulation
    Moazeni, Malihe
    Heidari, Zahra
    Golipour, Sahar
    Ghaisari, Leila
    Sillanpaa, Mika
    Ebrahimi, Afshin
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 27 (36) : 45568 - 45580
  • [48] Seasonal variations of potentially toxic elements (PTEs) in drinking water and health risk assessment via Monte Carlo simulation and Sobol sensitivity analysis in southern Iran's largest city
    Mohammadpour, Amin
    Rajabi, Saeed
    Bell, Michelle
    Baghapour, Mohammad Ali
    Aliyeva, Aynura
    Khaneghah, Amin Mousavi
    APPLIED WATER SCIENCE, 2023, 13 (12)
  • [49] Seasonal variations of potentially toxic elements (PTEs) in drinking water and health risk assessment via Monte Carlo simulation and Sobol sensitivity analysis in southern Iran's largest city
    Amin Mohammadpour
    Saeed Rajabi
    Michelle Bell
    Mohammad Ali Baghapour
    Aynura Aliyeva
    Amin Mousavi Khaneghah
    Applied Water Science, 2023, 13
  • [50] Per- and polyfluoroalkyl substances (PFASs) in groundwater from a contaminated site in the North China Plain: Occurrence, source apportionment, and health risk assessment
    Li, Jie
    Peng, Guyu
    Xu, Xuming
    Liang, Enhang
    Sun, Weiling
    Chen, Qian
    Yao, Lei
    CHEMOSPHERE, 2022, 302