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Yang S, Zhou Q, Sun L, Qin Q, Sun Y, Wang J, Liu X, Xue Y. Source to risk receptor transport and spatial hotspots of heavy metals pollution in peri-urban agricultural soils of the largest megacity in China. JOURNAL OF HAZARDOUS MATERIALS 2024; 480:135877. [PMID: 39353271 DOI: 10.1016/j.jhazmat.2024.135877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 09/14/2024] [Accepted: 09/16/2024] [Indexed: 10/04/2024]
Abstract
The traditional concentration-based health risk assessment of heavy metal (HMs) pollution in soil has often overlooked the initial loading and toxicity differences of HMs from various sources. This oversight hinders effective identification of the risky source, complicating precise risk management of soil HMs pollution. This study applied a source-oriented health risk assessment framework that integrates source profiling, exposure risk assessment, and spatial cluster analysis. Taking the Shanghai City, the largest megacity in China as a case, the findings revealed that overall environmental quality of peri-urban agricultural soil in Shanghai remains good, though 3.03 % of Cd concentrations exceeded the national reference standards. Industrial & traffic activities, primarily contributing Hg, Cd, and Pb, accounted for the highest proportion (44.3 %) of total metal concentrations and posed the greatest non-cancer risk (54.6 % for children and 53.1 % for adults). Notably, natural activities, mainly contributing Cr, ranked only third in concentration contribution (26.55 %) but induced the highest cancer risk (58.55 % for children and 57.08 % for adults). These findings suggest that sources with lower concentration contributions may still pose significant health risk. Integrating source apportionment with health risk assessment can more precisely identify the risky source and target areas for mitigating the human health hazards.
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Affiliation(s)
- Shiyan Yang
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, 201403, China; Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, 201403, China
| | - Qianhang Zhou
- School of Chemical and Environmental Engineering, Shanghai Institute of Technology, 201418, China
| | - Lijuan Sun
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, 201403, China; Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, 201403, China
| | - Qin Qin
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, 201403, China; Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, 201403, China
| | - Yafei Sun
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, 201403, China; Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, 201403, China
| | - Jun Wang
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, 201403, China; Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, 201403, China
| | - Xingmei Liu
- College of Environmental and Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou 310058, China.
| | - Yong Xue
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, 201403, China; Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, 201403, China.
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Fei X, Lou Z, Sheng M, Lv X, Ren Z, Xiao R. Different "nongrain" activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands. ENVIRONMENTAL RESEARCH 2024; 251:118642. [PMID: 38485078 DOI: 10.1016/j.envres.2024.118642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
"Nongrain" production on cultivated land is one of the primary environmental issues in China. Different "nongrain" activities may introduce different pollution sources to the local environment, leading to variations in heavy metal contents in soil, which can profoundly impact national food security. In this study, three typical "nongrain" regions (Nanxun (NX), Xiaoshan (XS) and Lin'an (LA)) with intensive aquaculture, tea planting and flower (seedling) growth on cultivated land around the Hangzhou metropolitan area were selected to address the spatial heterogeneity of accumulation levels, sources and source-oriented health risks of heavy metals in soil. The results showed that Hg was the main pollutant in NX and XS, while Cd and As were the major contaminants in LA. Aquiculture and sericultural industries (37.43%), natural sources (23.59%) and industrial activities (38.99%) were the major sources in NX; atmospheric deposition (37.73%), flower and seedling planting (23.49%) and metal-related industries (35.16%) were the major sources in XS; and atmospheric deposition (28.06%), excessive application of fertilizers and pesticides during tea planting (43.47%) and natural sources (28.47%) were the major sources in LA. The major risk population, area, exposure route and hazardous elements were children, LA, ingestion and As and Cr, respectively. From the perspective of source-based health risk assessment, in addition to natural sources that are difficult to intervene in, industrial activities, especially leather and wood process industries, metal-related industries and excessive fertilizer and pesticide application during tea planting contributed the most to the total health risk, which explained 67%, 41% and 42%, respectively, of the total risk in NX, XS and LA. High health risks are present in sources with heavy loadings of hazardous heavy metals (As and Cr); thus, to protect human health, the corresponding high-risk anthropogenic pollution sources in different "nongrain" areas need to be controlled.
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Affiliation(s)
- Xufeng Fei
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China.
| | - Zhaohan Lou
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Meiling Sheng
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Xiaonan Lv
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Zhouqiao Ren
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Rui Xiao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
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Fei X, Lou Z, Sheng M, Xiaonan L, Ren Z, Xiao R. Quantitative heterogeneous source apportionment of toxic metals through a hybrid method in spatial random fields. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133530. [PMID: 38232550 DOI: 10.1016/j.jhazmat.2024.133530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/19/2024]
Abstract
Toxic metals in soils pose hazards to food security and human health. Accurate source apportionment provides foundation for pollution prevention. In this study, a novel hybrid method that combines positive matrix factorization, Bayesian maximum entropy and integrative predictability criterion is proposed to provide a new perspective for exploring the heterogeneity of pollution sources in spatial random fields. The results suggest that Cd, As and Cu are the predominant pollutants, with exceedance rates of 27%, 12% and 11%, respectively. The new method demonstrates superiority in predicting toxic metals when combined major and all sources as auxiliary information., with the improvements of 44% and 46%, respectively, Although the major sources identified with the hybrid method are the primary contributors to the accumulation of toxic metals (e.g. coal combustion for Hg, traffic emission for Pb and Zn, industrial activities for As, agricultural activities for Cd and Cu and natural sources for Cr and Ni), the impact of nonmajor sources on toxic metal sin specific regions should not be ignored (e.g. industrial activities on Ni, Pb and Zn in the north and natural sources on Cd, Cu, As, Pb and Zn in the south). For better pollution control, specific local sources should be considered.
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Affiliation(s)
- Xufeng Fei
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Zhaohan Lou
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Meiling Sheng
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Lv Xiaonan
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
| | - Zhouqiao Ren
- Zhejiang Academy of Agricultural Sciences, Hangzhou, China; Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China.
| | - Rui Xiao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
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Zhao M, Wang H, Sun J, Tang R, Cai B, Song X, Huang X, Huang J, Fan Z. Spatio-temporal characteristics of soil Cd pollution and its influencing factors: A Geographically and temporally weighted regression (GTWR) method. JOURNAL OF HAZARDOUS MATERIALS 2023; 446:130613. [PMID: 36584651 DOI: 10.1016/j.jhazmat.2022.130613] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Soil Cd pollution is the result of the combined influence of various human activities over a long period of time, and then quantifying the influence is essential for the prevention and control. Based on published literature data during 2000-2020, this study investigated the pollution characteristics and influencing factors of soil Cd in the Yangtze River Delta. The results were as follows: (1) The average Cd concentration was higher than the Chinese soil criteria value (0.30 mg/kg), and the proportion of Cd concentration exceeding its background value was 87.43%. (2) The assessment results using Contamination factor (CF) and Geo-accumulation index (Igeo) indicated that the soil Cd pollution risk could not negligible in the study area. (3) The pollution center shifted significantly owing to the combined effect of human activities. (4) The main influencing factors of Cd pollution obtained by Geographically and temporally weighted regression (GTWR) model were GDP per capita, Consumption of chemical fertilizer, Output value of primary industry, and Output value of secondary industry, but there were significant differences in the dominant factors for different provinces. Our findings contribute to the current understanding of the relationship between Cd pollution and human activities, and provide a scientific basis for pollution control.
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Affiliation(s)
- Menglu Zhao
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Huijuan Wang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jiaxun Sun
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Rui Tang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Boya Cai
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xiaoyong Song
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xinmiao Huang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jian Huang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
| | - Zhengqiu Fan
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
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