Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386003376> ?p ?o ?g. }
- W4386003376 endingPage "120503" @default.
- W4386003376 startingPage "120503" @default.
- W4386003376 abstract "Microplastics (MPs) are ubiquitously distributed in freshwater systems and they can determine the environmental fate of organic pollutants (OPs) via sorption interaction. However, the diverse physicochemical properties of MPs and the wide range of OP species make a deeper understanding of sorption mechanisms challenging. Traditional isotherm-based sorption models are limited in their universality since they normally only consider the nature and characteristics of either sorbents or sorbates individually. Therefore, only specific equilibrium concentrations or specific sorption isotherms can be used to predict sorption. To systematically evaluate and predict OP sorption under the influence of both MPs and OPs properties, we collected 475 sorption data from peer-reviewed publications and developed a poly-parameter-linear-free-energy-relationship-embedded machine learning method to analyze the collected sorption datasets. Models of different algorithms were compared, and the genetic algorithm and support vector machine hybrid model displayed the best prediction performance (R2 of 0.93 and root-mean-square-error of 0.07). Finally, comparison results of three feature importance analysis tools (forward step wise method, Shapley method, and global sensitivity analysis) showed that chemical properties of MPs, excess molar refraction, and hydrogen-bonding interaction of OPs contribute the most to sorption, reflecting the dominant sorption mechanisms of hydrophobic partitioning, hydrogen bond formation, and π-π interaction, respectively. This study presents a novel sorbate-sorbent-based ML model with a wide applicability to expand our capacity in understanding the complicated process and mechanism of OP sorption on MPs." @default.
- W4386003376 created "2023-08-20" @default.
- W4386003376 creator A5022962005 @default.
- W4386003376 creator A5024782567 @default.
- W4386003376 creator A5041513523 @default.
- W4386003376 creator A5049399261 @default.
- W4386003376 date "2023-10-01" @default.
- W4386003376 modified "2023-10-12" @default.
- W4386003376 title "Predicting aqueous sorption of organic pollutants on microplastics with machine learning" @default.
- W4386003376 cites W1504181024 @default.
- W4386003376 cites W1980735224 @default.
- W4386003376 cites W1984527326 @default.
- W4386003376 cites W1984956382 @default.
- W4386003376 cites W1991292594 @default.
- W4386003376 cites W1993066596 @default.
- W4386003376 cites W2006276740 @default.
- W4386003376 cites W2008997808 @default.
- W4386003376 cites W2014870370 @default.
- W4386003376 cites W2015058782 @default.
- W4386003376 cites W2018690424 @default.
- W4386003376 cites W2031720269 @default.
- W4386003376 cites W2065202281 @default.
- W4386003376 cites W2072960740 @default.
- W4386003376 cites W2089773144 @default.
- W4386003376 cites W2097034581 @default.
- W4386003376 cites W2097613907 @default.
- W4386003376 cites W2109553965 @default.
- W4386003376 cites W2141409967 @default.
- W4386003376 cites W2155226065 @default.
- W4386003376 cites W2157970175 @default.
- W4386003376 cites W2158180779 @default.
- W4386003376 cites W2158698691 @default.
- W4386003376 cites W2226787841 @default.
- W4386003376 cites W2232748179 @default.
- W4386003376 cites W2323969435 @default.
- W4386003376 cites W2326966964 @default.
- W4386003376 cites W2329381391 @default.
- W4386003376 cites W2581321420 @default.
- W4386003376 cites W2587824581 @default.
- W4386003376 cites W2590284869 @default.
- W4386003376 cites W2593855134 @default.
- W4386003376 cites W2605932959 @default.
- W4386003376 cites W2606460416 @default.
- W4386003376 cites W2612383933 @default.
- W4386003376 cites W2736287575 @default.
- W4386003376 cites W2741642477 @default.
- W4386003376 cites W2742477205 @default.
- W4386003376 cites W2759319582 @default.
- W4386003376 cites W2769174121 @default.
- W4386003376 cites W2772872384 @default.
- W4386003376 cites W2779282391 @default.
- W4386003376 cites W2787798588 @default.
- W4386003376 cites W2789738056 @default.
- W4386003376 cites W2792212814 @default.
- W4386003376 cites W2793717135 @default.
- W4386003376 cites W2796066578 @default.
- W4386003376 cites W2801780305 @default.
- W4386003376 cites W2807810624 @default.
- W4386003376 cites W2849998820 @default.
- W4386003376 cites W2896798207 @default.
- W4386003376 cites W2902993376 @default.
- W4386003376 cites W2910368005 @default.
- W4386003376 cites W2911043732 @default.
- W4386003376 cites W2913648448 @default.
- W4386003376 cites W2918895016 @default.
- W4386003376 cites W2944612587 @default.
- W4386003376 cites W2945102480 @default.
- W4386003376 cites W2947798350 @default.
- W4386003376 cites W2967802758 @default.
- W4386003376 cites W2975966063 @default.
- W4386003376 cites W2981703366 @default.
- W4386003376 cites W2981731882 @default.
- W4386003376 cites W2999615587 @default.
- W4386003376 cites W2999667975 @default.
- W4386003376 cites W3010437674 @default.
- W4386003376 cites W3012519883 @default.
- W4386003376 cites W3023669649 @default.
- W4386003376 cites W3044787168 @default.
- W4386003376 cites W3045160178 @default.
- W4386003376 cites W3086127638 @default.
- W4386003376 cites W3087337933 @default.
- W4386003376 cites W3088567353 @default.
- W4386003376 cites W3091835018 @default.
- W4386003376 cites W3098530784 @default.
- W4386003376 cites W3100042739 @default.
- W4386003376 cites W3120367841 @default.
- W4386003376 cites W3152644573 @default.
- W4386003376 cites W3175788707 @default.
- W4386003376 cites W3186570958 @default.
- W4386003376 cites W3193682756 @default.
- W4386003376 cites W3194730353 @default.
- W4386003376 cites W3198888544 @default.
- W4386003376 cites W3201847245 @default.
- W4386003376 cites W3207710058 @default.
- W4386003376 cites W4210538803 @default.
- W4386003376 cites W4214576460 @default.
- W4386003376 cites W4281693431 @default.
- W4386003376 cites W4285678485 @default.