Machine-Learning Predictions of High Arsenic and High Manganese at Drinking Water Depths of the Glacial Aquifer System, Northern Continental United States-Reference-Cited by-同舟云学术

Machine-Learning Predictions of High Arsenic and High Manganese at Drinking Water Depths of the Glacial Aquifer System, Northern Continental United States

Published:2021-04-06 Issue:9 Volume:55 Page:5791-5805
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Erickson Melinda L.¹^ORCID,Elliott Sarah M.¹,Brown Craig J.²,Stackelberg Paul E.³,Ransom Katherine M.⁴,Reddy James E.⁵,Cravotta Charles A.⁶^ORCID

Affiliation:

1. U.S. Geological Survey, 2280 Woodale Drive, Mounds View, Minnesota 55112, United States

2. U.S. Geological Survey, 101 Pitkin Street, East Hartford, Connecticut 06108, United States

3. U.S. Geological Survey, 425 Jordan Road, Troy, New York 12180, United States

4. U.S. Geological Survey, 6000 J Street, Sacramento, California 95819, United States

5. U.S. Geological Survey, 30 Brown Road, Ithaca, New York 14850, United States

6. U.S. Geological Survey, 215 Limekiln Road, New Cumberland, Pennsylvania 17070, United States

Funder

U.S. Geological Survey

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.0c06740

Reference147 articles.

1. Spatial and Temporal Variations of Groundwater Arsenic in South and Southeast Asia

2. A review of the source, behaviour and distribution of arsenic in natural waters

3. Depth Stratification Leads to Distinct Zones of Manganese and Arsenic Contaminated Groundwater

4. Water Manganese Exposure and Children’s Intellectual Function in Araihazar, Bangladesh

5. Arsenic and Manganese Contamination of Drinking Water Resources in Cambodia: Coincidence of Risk Areas with Low Relief Topography

Cited by 69 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning;Journal of Hazardous Materials;2024-09

2. Artificial intelligence for geoscience: Progress, challenges, and perspectives;The Innovation;2024-09

3. Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning;Water Research;2024-08

4. Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling;Environmental Science and Pollution Research;2024-07-09

5. Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan;Science of The Total Environment;2024-07