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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]

Abstract: Pd/Fe bimetallic nanoparticles were synthesized for treatment of 2,4-dichlorophenol (2,4-DCP) in the presence of humic acid (HA), in order to understand their applicability for in situ remediation of groundwater. In this case, 2,4-DCP was catalytically dechlorinated to form the final products – phenol (P) via two intermediates, namely o-chlorophenol (o-CP) and p-chlorophenol (p-CP). We demonstrated that the carbon mass balances during the dechlorination were in the range of 82–91%, and other carbons were absorbed on the surface of Pd/Fe bimetallic nanoparticles. Our results suggest the dechlorination reaction of 2,4-DCP by Pd/Fe bimetallic nanoparticles in the presence of HA followed pseudo-first-order kinetics. HA competed for reaction sites on the Pd/Fe bimetallic nanoparticles with 2,4-DCP, and thus reduced the efficiency and rate of the dechlorination of 2,4-DCP. Efficiencies of dechlorination and phenol formations increased significantly as the Pd content increased from 0.10wt.%, 0.15wt.% to 0.20wt.%, the removal percentage of 2,4-DCP increased from 70.4%, 98.4% to 99.4% within 300min, respectively, the nitrate (NO3 −) content in water also has a significant impact on 2,4-DCP dechlorination efficiency. Our results show that no other intermediates were generated besides Cl−, o-CP, p-CP and phenol during the catalytic dechlorination of 2,4-DCP. [ABSTRACT FROM AUTHOR]

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]

Surface ozone (O 3), a well-recognized air pollutant, exists in the atmosphere, which has a detrimental effect on public health and the ecological environment. It is reported that surface O 3 has seen a significant increase in many cities from 2019 to 2021 (COVID-19 pandemic). In this study, we applied an innovative machine learning model (Deep Forest) coupled with satellites, the Troposphere Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI), and meteorological datasets to estimate monthly surface O 3 of 1 km spatial resolution across China during this pandemic period. Our model achieved an overall R2 of 0.974, 0.963, and root mean square error (RMSE) of 6.016 μg/m3, 7.214 μg/m3 on TROPOMI-based datasets and OMI-based datasets, respectively. Also, we found the higher ozone concentration regions were in Eastern China. Simultaneously, the surface O 3 concentration was high in summer(average = 110.57 ± 15.01 μg/m3). And the ozone concentration in summer 2020 (average = 107.78 ± 13.90 μg/m3) declined unprecedently than in summer 2019 (average = 110.54 ± 16.58 μg/m3). Our results indicated that TROPOMI data could provide robust data support for surface ozone concentration estimation. Furthermore, this study could enhance our comprehension of the formation mechanisms of surface O 3 in China and assist air environment management decision-making. • Deep Forest model is applied to predict the surface ozone concentration in China. • TROPOMI can mitigate the spatial resolution deficiency of OMI satellite data in surface ozone concentration estimation. • High spatial resolution (1 km) monthly surface ozone concentration products from 2019 to 2021 are generated. • Our results provide a theoretical supplementary for air environment management. [ABSTRACT FROM AUTHOR]