The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Large-scale assessment of PFAS compounds in drinking water sources using machine learning
Abstract
The monitoring of Per and Polyfluoroalkyl substances (PFAS) in drinking water sources has significantly increased due to their recognition as a major public health concern. This information has been utilized to assess the importance of potential explanatory variables in determining the presence and concentration of PFAS in different regions. Nevertheless, the significance of these variables and the reliability of the methods in regions beyond where they were initially tested is still uncertain. Hence, our research pursues two main objectives: 1) to evaluate the validity of the aforementioned variables and methods for several PFAS species in a different area and 2) to build on existing modeling work; a new PFAS predictive model is introduced which is more reliable in determining the presence and concentration of PFAS at a regional level. To achieve these goals, we reconstructed four state-of-the-art models using a statewide dataset available for Michigan. These models involve spatial regression techniques, classification and regression random forest algorithms, and boosted regression trees. They also include numerous explanatory variables, such as features of local soil and hydrology and the number of nearby contamination sources. Then, we use a Bayesian selection approach to find the most relevant among these variables. Finally, we employ the most relevant covariates to assess PFAS occurrence and estimate their concentration using a novel combination of machine learning algor…
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Hu, M., & Scott, C. (2024) · Applied and Environmental Microbiology · doi:10.1128/aem.00157-24 · Cited by 63
Metadata
- Authors
- Fernandez, N., Nejadhashemi, A. P., & Loveall, C.
- Venue
- Water Research
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 243
- Pages
- 120307
- ISSN
- 0043-1354
- Cited by
- 32
- References
- 11
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 323 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Funders
- National Institute of Food and Agriculture (1019654)
- Michigan Department of Natural Resources (200000000672)
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