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




Professor of Water & Resource Processing


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.


Garduño-Jiménez, A., Durán-Álvarez, J., & 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.

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
Keywords Pollution; Waste Management and Disposal; Environmental Chemistry; Environmental Engineering; Machine learning
Public URL
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