The sources-specific health risk assessment combined with APCS/MLR model for heavy metals in tea garden soils from south Fujian Province, China

Zhang, Weihua; Yu, Yongtae; Yu, Ruilian; Hu, Gongren

doi:10.1016/j.catena.2021.105306

Cited by 51 publications

(33 citation statements)

References 45 publications

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“…The APCS/MLR model was used to obtain the absolute principal component factor scores based on factor analysis after standardizing the raw data and combining with the multiple linear regression model to calculate the contribution rate (%) of various sources of each HM [34]. The multiple linear regression calculation is shown in Equation (6) [27],…”

Section: The Apcs/mlr Modelmentioning

confidence: 99%

“…In general, the contribution rate of soil parent material is higher than other sources. For example, HMs in soil are relatively high due to higher toxicity coefficients and higher Cr from soil material [27]. HMs in soil penetrate into groundwater reserves through rainfall and irrigation [36], leading to increased health risks of HMs in the groundwater.…”

Section: Source-oriented Health Risk Assessmentmentioning

confidence: 99%

“…So far, the contribution of pollution sources to the health risk by HMs has been largely ignored [27]. To further investigate the contribution of different pollution sources to the health risk, this study quantifies the health risk of HMs from different pollution sources in groundwater based on the coupling of the APCS/MLR model and the HRA model.…”

Section: Introductionmentioning

confidence: 99%

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Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Shi

Zeng

Peng

et al. 2022

IJERPH

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Groundwater is a significant component of water resources, but drinking groundwater with excessive heavy metals (HMs) is harmful to human health. Currently, quantitative source apportionment and probabilistic health risk assessment of HMs in groundwater are relatively limited. In this study, 60 groundwater samples containing seven HMs were collected from Hainan Island and analyzed by the coupled absolute principal component scores/multiple linear regression (APCS/MLR), the health risk assessment (HRA) and the Monte Carlo simulation (MCS) to quantify the pollution sources of HMs and the health risks. The results show that the high-pollution-value areas of HMs are mainly located in the industry-oriented western region, but the pollution level by HMs in the groundwater in the study area is generally low. The main sources of HMs in the groundwater are found to be the mixed sources of agricultural activities and traffic emissions (39.16%), industrial activities (25.57%) and natural sources (35.27%). Although the non-carcinogenic risks for adults and children are negligible, the carcinogenic risks are at a high level. Through analyzing the relationship between HMs, pollution sources, and health risks, natural sources contribute the most to the health risks, and Cr is determined as the priority control HM. This study emphasizes the importance of quantitative evaluation of the HM pollution sources and probabilistic health risk assessment, which provides an essential basis for water pollution prevention and control in Hainan Island.

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Section: The Apcs/mlr Modelmentioning

confidence: 99%

Section: Source-oriented Health Risk Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Shi

Zeng

Peng

et al. 2022

IJERPH

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“…Quantitative analysis of pollution sources is an important basis for the environmental management of watershed soils, and the APCS-MLR model was used to obtain the degree of contribution from different material sources to potentially toxic elements in the soil. The APCS-MLR method was based on previously published articles [56][57][58][59][60]. The measured content, C, for PTEs was used as the dependent variable, and the absolute principal component (APCS) was used as the independent variable to perform the multiple linear regression analysis (for the calculation of APCS, refer to references [36][37][38][39][40]).…”

Section: The Model Of Absolute Principal Component Score-multiple Linear Regression (Apcs-mlr)mentioning

confidence: 99%

Potentially Toxic Elements in Oasis Agricultural Soils Caused by High-Intensity Exploitation in the Piedmont Zone of the Tianshan Mountains, China

Liu

Abuduwaili

2021

Agriculture

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Considering the pollution of potentially toxic elements (PTEs) in the soils of China, the present study analyzed the current state and influencing factors of PTEs in oasis soils using the model of absolute principal component score–multiple linear regression in the piedmont zone of the Tianshan Mountains. The possible non-carcinogenic and carcinogenic risks of PTEs at current concentrations were also explored using a human-health risk-assessment model. The results suggested that the extent to which potentially toxic elements in the soils of different geographical units in the study area is affected by human activities varies considerably. The PTEs Cd and As in the soils of the Yili River Watershed were the most strongly influenced by human activities, reaching levels of 40% and 59%, respectively. However, in the Bortala River Watershed, Cu, Cd, and As were the most strongly influenced by human activities, reaching levels of 33%, 64%, and 76%, respectively. Geographical units with a high degree of economic development (e.g., the Yili River Watershed) had, in contrast, low levels of PTE pollution caused by human activities, which may be related to the regional economic development structure. The human health risk assessment showed that the non-carcinogenic and carcinogenic risks of PTEs are currently below the threshold. However, increasing the arsenic content to 1.78 times the current level in the Bortala River Watershed would lead to carcinogenic risk. For the Yili River Watershed, a 3.33-fold increase in the arsenic content above its current level would lead to a carcinogenic risk. This risk should be addressed, and targeted environmental-protection measures should be formulated. The present research results will provide important decision support for regional environmental protection.

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“…Quantitative receptor models, positive matrix factorization (PMF), UNMIX, and absolute principal component analysis‐multiple linear regression (APCS‐MLR) can identify and quantify pollutant sources in the atmosphere, sediments, waters, and urban soils (Mehr et al, 2017; Mohammad et al, 2016; Sakizadeh & Zhang, 2021; Shen et al, 2021). Recently, receptor models have been successfully applied in quantifying pollutant sources in farmland soils (Zhang, Yan, et al, 2021). As different methods have their distinct characteristics, multivariate statistical analysis, geostatistical analysis, and receptor models were integrated to identify the sources of soil HM, providing more convincing results.…”

Section: Introductionmentioning

confidence: 99%

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

et al. 2022

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The Karst Plateau is characterized by elevated heavy metals (HM), the farmland soils in the Karst Plateau area is especially vulnerable to HM pollution. To cope with soil HM pollution and conduct precaution in Karst Plateau, the key bottleneck is to understand the pollution levels, sources, and priority‐control of HM. Hence, geochemical baselines of HM in farmland soils were established to accurately evaluate the pollution characteristics. Pollution sources were identified with multivariate statistics, geostatistical methods, and receptor models. Priority‐control of HM were distinguished via health assessments with a Monte Carlo simulation. A remarkable accumulation of Pb, Sb, Zn, As, and Cd was observed. Hotspots of As, Cd, Pb, Sb, and Zn clustered in the southwestern region of Hezhang. Pb–Zn related activities, cement product activities, coal mining, and coal combustion were dominant sources. Both noncarcinogenic risk and carcinogenic risk followed the order: children>adult females>adult males. As, Cd, and Pb were found to be priority contaminants in farmland soils in Hezhang.

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The sources-specific health risk assessment combined with APCS/MLR model for heavy metals in tea garden soils from south Fujian Province, China

Cited by 51 publications

References 45 publications

Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Potentially Toxic Elements in Oasis Agricultural Soils Caused by High-Intensity Exploitation in the Piedmont Zone of the Tianshan Mountains, China

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

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