Modelling reactive intermediates for predicting photodegradation of organic micropollutants in freshwater environment

50个水库样本和7个湖泊和池塘样本取自日本从冲绳到北海道的各地。从水化学和吸收光谱的角度对储层样品进行表征，表明溶解有机质（DOM）的主要变化是分子结构和组成。水库单重态氧的光产量子产率接近陆源DOM主导的河流和湖泊，但低于微生物DOM主导的水体。开发了线性（单一和多个）和非线性模型（机器学习），可用于高精度估计储层的量子产率。此外，该模型还可用于估计湖泊、池塘和参考DOM样本中的量子产率。

50 reservoir samples and 7 lake and pond samples were taken across Japan from Okinawa to Hokkaido. The characterization of reservoir samples in terms of water chemistry and absorption spectra indicated that major variations in the dissolved organic matter (DOM) were molecular structures and compositions.Quantum yield for photo-production of singlet oxygen of the reservoirs are close to river and lakes dominated by terrestrial DOM but lower than water bodies dominated by microbial DOM.Linear (single and multiple) and non-linear models (machine-learning) were developed and can be used to estimate quantum yield with a high accuracy for reservoirs.In addition, the models can be additionally used for estimating quantum yidl in lakes, ponds, and referenced DOM samples.