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Toward Computational Accuracy in Realistic Systems to Aid Understanding of Field-Level Water Quality Issues

Physchem 2021 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
William A. Alexander

Summary

This perspective paper discusses the potential of computational chemistry to improve understanding of how pollutants behave in real-world water systems. Better computational models could help predict how microplastics and chemical contaminants interact and move through waterways. The paper is primarily a methodological discussion rather than a primary research study.

Contemplating what will unfold in this new decade and those after, it is not difficult to imagine the increasing importance of conservation and protection of clean water supplies. A worrying but predictable offshoot of humanity’s technological advances is the seemingly ever-increasing chemical load burdening our waterways. In this perspective are presented a few modest areas where computational chemistry modelling could provide benefit to these efforts by harnessing the continually improving computational power available to the field. In the acute event of a chemical spill incident, true quantum-chemistry-based predictions of physicochemical properties and surface-binding behaviors can be used to help decision making in remediating the spill threat. The chronic burdens of microplastics and perfluorinated “forever chemicals” can also be addressed with computational modelling to fill the gap between feasible laboratory experiment timescales and the much-longer material lifetimes. For all of these systems, field-level accuracy models will avail themselves as the model computational systems are able to incorporate more realistic features that are relevant to water quality issues.

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