Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China-Reference-Cited by-同舟云学术

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China

Published:2023-02-23 Issue:46 Volume:57 Page:17751-17761
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Zhao Wenhao¹,Ma Jin¹^ORCID,Liu Qiyuan¹,Dou Lei²,Qu Yajing¹,Shi Huading³,Sun Yi¹,Chen Haiyan¹,Tian Yuxin¹,Wu Fengchang¹

Affiliation:

1. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China

2. Guangdong Geological Survey Institute, Guangzhou 510110, P. R. China

3. Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, P. R. China

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.2c07561

Reference64 articles.

1. Contribution of Lithologic and Anthropogenic Factors to Surface Soil Heavy Metals in Western Iran Using Multivariate Geostatistical Analyses

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3. Impacts of geology and land use on magnetic susceptibility and selected heavy metals in surface soils of Mashhad plain, northeastern Iran

4. Identification of heavy metal pollution sources and its associated risk assessment in an industrial town using the K-means clustering technique

5. Assessment of soil heavy metal pollution using stochastic site indicators

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