Authors: Jiale He | Jingwen Chen | Jieqiong Wang | Linke Ge | Feifei Cui | Xi Chen
Source: Chinese Science Bulletin (Chinese Version) (article, 2023-07-13)
Field: Chemistry — Physical and Theoretical Chemistry

Prediction models for photochemical persistence of chemicals in aquatic environments <p indent="0mm">Chemicals can be released into aquatic environments during their lifecycles, becoming emerging pollutants that cause adverse effects on health of humans and ecosystems. Photochemical transformation is important in determining environmental persistence and exposure concentrations of chemicals. It is of importance to assess photochemical persistence of chemicals in aquatic environments for sound management of chemicals and emerging pollutants. Quantum yields of direct photolysis, phototransformation rate constants, and phototransformation half-lives are key parameters characterizing environmental photochemical persistence of chemicals. Although the parameters on photochemical persistence can be determined experimentally, the experimental determination is of low throughput, expensive, time-consuming, and restricted by availability of authentic chemical standards, due to diversity of aquatic environmental factors that have impacts on the photochemical processes. It is necessary to develop prediction models on aquatic photochemical persistence of chemicals, including quantitative structure-activity relationship (QSAR) models, for high-throughput prediction of the aquatic photochemical persistence parameters of the vast number of chemicals. In this review, environmental phototransformation pathways and influencing factors on the phototransformation of chemicals were summarized. Research frontiers on prediction models of photochemical persistence were discussed. For direct phototransformation, light absorbance parameters of chemicals can be predicted by machine learning models, and the method is more efficient than experimental determination or quantum chemical calculation. Application of quantum chemical descriptors has ensured QSAR models to predict the photochemical behavior parameters of chemicals. Although several QSAR models have been developed on direct phototransformation quantum yields, rate constants or half-lives, insufficiency in the experimental data is still a limitation of the modeling. Direct phototransformation rates of chemicals depend on both intrinsic factors that are governed by molecular structures, and environmental factors such as solar spectrum and optical path length. Therefore, incorporating environmental factors with molecular structures to construct multimodal models may improve prediction performance on direct phototransformation kinetics of chemicals. For indirect phototransformation, machine learning and satellite remote sensing can be potentially employed for prediction of quantum yields and steady-state concentrations of photochemically produced reactive intermediates (PPRIs). Molecular composition, electron donor capacity, and optical properties of dissolved organic matter (DOM) could be employed as features in constructing the prediction models. Second-order reaction rate constants of chemicals with PPRIs can be predicted with QSAR models. More efforts are needed to develop prediction models on reactivity of chemicals with excited triplet states of DOM. By incorporating environmental factors such as underwater irradiance intensities, integrated models that consider both direct and indirect phototransformation of chemicals have been developed. Further research efforts are needed to construct integrated models that suit specifically the main waterbodies in China, and to incorporate more photochemical processes and environmental factors into the modeling. Overall, to improve prediction on aquatic photochemical persistence of chemicals or emerging pollutants; phototransformation pathways of chemicals in natural water bodies should be further clarified; molecular modeling such as density functional theory calculation methods should be employed for predicting some parameters that are relevant with photochemical persistence of chemicals; data sets related with phototransformation should be collected and curated, and employed to construct machine learning models for the prediction; and more efforts should be paid to prediction models on photochemical persistence of chemicals in estuarine waters, coastal seawaters and oceanic waters. Persistence (discontinuity) ; Environmental science ; Aquatic ecosystem ; Environmental chemistry ; Biological system ; Biochemical engineering ; Chemistry ; Biology ; Geology ; Engineering ; Geotechnical engineering Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Radical Photochemical Reactions