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Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research. [ABSTRACT FROM AUTHOR]

Abstract: Overgrowing algae have brought negative impacts on daily production of water plants which are rarely eliminated by common methods. To efficiently remove algae from drinking water, a strengthening process or combined process of coagulant and chlorine, and with the dosage of potassium permanganate 0.3mgL−1 is investigated on three selected water supply and purification plants during the algae outbreak period. The results show that the algae density increases with the increase of water temperature. When the algae density in raw water is less than 1.0×106 cells L−1, more than 98% of algae can be removed with a coagulant dosage of 13mg L−1. When the algae density is increased to more than 10×106 cells L−1, i.e. during algae outbreak period, 96% or more algae can be removed using the coagulant and chlorine of dosages of approximately 20mg L−1 and 4.0mg L−1, respectively. [Copyright &y& Elsevier]