Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui

Dai, Xinyue; Liang, Jiahui; Shi, Huading; Yan, Tiezhu; He, Zexin; Li, Li; Hu, Hualing

doi:10.1016/j.envres.2023.117975

Cited by 19 publications

(2 citation statements)

References 39 publications

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“…Sources of heavy metal pollution in groundwater were identified using the positive matrix factorization (PMF) methodology, as proposed by the United States Environmental Protection Agency [45]. The method involves decomposing sample concentration data using both least squares and iterative methodologies to identify the optimal contribution value and component spectrum data by minimizing the objective function Q [46]. The method is based on the following equations [2]:…”

Section: Positive Matrix Factorizationmentioning

confidence: 99%

Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots

Vesković,

Onjia

2024

Metals

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Groundwater contamination was studied at several hotspot sites in the Majdanpek copper mining area (Serbia). These sites include a milling facility, a metallurgical wastewater treatment plant, a heavy vehicle service area, and a waste disposal site. In addition to Cu, high concentrations of As and heavy metals (Cd and Pb) were detected in groundwater and soil at the same sampling points. Mining operations and heavy vehicle transport activities have been identified as the main sources of pollution. The migration of metals from soil to groundwater, expressed as a concentration ratio, were the highest for Co and the lowest for Mn. The environmental implications of groundwater pollution were studied using the heavy metal pollution index (HPI), Nemerov pollution index (NPI), hazard index (HI), and incremental lifetime cancer risk (ILCR). HPI and NPI show the high potential of groundwater to have adverse environmental effects. HPI ranges in the following descending order of metals: Cd > Pb > As > Mn > Ni > Cr > Hg > Cu > Zn. NPI exceeds the threshold of 0.7 in 66.7% of the samples. Potential human exposure to the studied groundwater may cause severe health problems in adults, with HI ranging from 0.61 to 5.45 and ILCR from 1.72 × 10−4 to 1.27 × 10−3. Children were more susceptible to non-carcinogenic risk than adults, with HI ranging from 0.95 to 8.27. However, the results indicated that children were less prone to carcinogenic risks, with ILCR ranging from 5.35 × 10−5 to 3.98 × 10−4. Arsenic is the most contributing element to both risks. This research imposes the need for enhanced groundwater monitoring at hotspots in the mining area and the adoption of remediation plans and measures.

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Section: Positive Matrix Factorizationmentioning

confidence: 99%

Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots

Vesković,

Onjia

2024

Metals

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“…With the rapid development of urbanization and industry, large amounts of pollutants, including organic matter pollutants, nutrients, and particulate matter, are being released into the water system through various pathways [1,2]. Heavy metals have gained attention for their characteristics of strong toxicity, bioaccumulation, and nonbiodegradability, thereby posing a threat to ecology and the food chain, and consequently to human health [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China

He,

Chen,

Wang

et al. 2024

Toxics

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To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of the heavy metals were analyzed using correlation analysis (CA), principal component analysis (PCA), the heavy metal contamination factor (Cf), the pollution load index (PLI), and the potential ecological risk index (PERI). Additionally, the chemical fractions were analyzed for mobility characteristics. The results indicate that the average concentration of the heavy metals in the sediment ranked in the descending order of Zn > Cr > Cu > Pb > As > Ni > Cd > Hg, and most of the elements existed in less-mobile forms. The Cfwas in the order of Hg > Zn > Cd > As > Pb > Cr > Ni; the accumulation of Hg, Zn, Cd, and As was obvious. Although the spatial variability of the heavy metal contents was pronounced, the synthetical evaluation index of the PLI and PERI both reached a high pollution level. The PCA and CA results indicate that industrial, transportation, and agricultural emissions were the dominant factors causing heavy metal pollution. These results provide important data for improving water resource management efficiency and heavy metal pollution prevention in Dianchi Lake.

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Spatial pattern, source apportionment, and source-oriented health risk quantifying of heavy metals in farmland soils of southern China

Hu,

He,

Zhou

et al. 2024

Environ Monit Assess

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Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui

Cited by 19 publications

References 39 publications

Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots

Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots

Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China

Spatial pattern, source apportionment, and source-oriented health risk quantifying of heavy metals in farmland soils of southern China

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