Search

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]

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]