Meta-analysis and machine learning to explore soil-water partitioning of common pharmaceuticals

Garduño-Jiménez, Andrea-Lorena; Durán-Álvarez, Juan-Carlos; Gomes, Rachel Louise

Abstract

The first meta-analysis and modelling from batch-sorption literature studies of the soil/water partitioning of pharmaceuticals is presented. Analysis of the experimental conditions reported in the literature demonstrated that though batch-sorption studies have value, they are limited in evaluating partitioning under environmentally-relevant conditions. Recommendations are made to utilise environmental relevant pharmaceutical concentrations, perform batch-sorption studies at temperatures other than 4, 20 and 25 °C to better reflect climate diversity, and utilise the Guideline 106 methodology as a benchmark to enable comparison between future studies (and support modelling and prediction). The meta-dataset comprised 82 data points, which were modelled using multivariate analysis; where Kd (soil/water partitioning coefficient) was the independent variable. The dependent variables fit into three categories: 1) pharmaceutical studied (including physical-chemical properties), 2) soil characteristics and 3) experimental conditions. The pharmaceutical solubility, the soil/liquid equilibration time (prior to adding the pharmaceutical), the soil organic carbon, the soil sterilisation method and the liquid phase were found to be significantly important variables for predicting Kd.

Citation

Garduño-Jiménez, A.-L., Durán-Álvarez, J.-C., & Gomes, R. L. (2022). Meta-analysis and machine learning to explore soil-water partitioning of common pharmaceuticals. Science of the Total Environment, 837, Article 155675. https://doi.org/10.1016/j.scitotenv.2022.155675

• Journal Article Type: Article
• Acceptance Date: Apr 29, 2022
• Online Publication Date: May 6, 2022
• Publication Date: Sep 1, 2022
• Deposit Date: May 16, 2022
• Publicly Available Date: May 17, 2022
• Journal: Science of The Total Environment
• Print ISSN: 0048-9697
• Electronic ISSN: 1879-1026
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 837
• Article Number: 155675
• DOI: https://doi.org/10.1016/j.scitotenv.2022.155675
• Keywords: Pollution; Waste Management and Disposal; Environmental Chemistry; Environmental Engineering; Machine learning
• Public URL: https://nottingham-repository.worktribe.com/output/8041238
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0048969722027711?via%3Dihub