Through further validation, the ARIMA model of Chl-a concentrations was proved to be significantly better than the multiple linear stepwise regression model, especially when considering the key environmental factors as independent variables and optimizing their values. The established ARIMA (0,1,1) (0,1,1) model would be helpful for forecasting algae blooms in Lake Taihu and provide useful suggestions for water environmental management, such as water resources dispatch and regulation.Urban water is a significant part of the urban ecosystem. Therefore, a comprehensive evaluation method of the water environment was proposed based on domestic high-resolution images. The relationships between the spectral characteristics and water quality parameters of urban water were analyzed based on sampling in Nanjing, Wuxi, Changzhou, and Yangzhou from 2017 to 2019. An index named the U-FUI (urban Forel-Ule index) suitable for urban water based on GF-2 images was proposed to achieve the classification of urban water on the basis of the international standard chroma conversion model and the Forel-Ule index.  https://www.selleckchem.com/products/gsk1070916.html  Independent verification data showed that the recognition accuracy of the classification model could reach 72%. The results indicated that urban water can be classified into six classes from Ⅰ to Ⅵ, which represent water colors of blue, light green, dark green, yellow, yellowish brown, and dark grey, respectively, according to the U-FUI. Among them, the water quality of U-FUI Ⅰ water is good, but is rarely distributed in urban water. The concentrations of chlorophyll-a in U-FUI Ⅱ-Ⅲ water are higher than those of the other classes; the concentrations of total suspended solids, particularly inorganic suspended solids, of U-FUI Ⅳ-Ⅴ water are higher than those of the other classes; and the water quality of U-FUI Ⅵ water is poor and the water quality parameters are different from those of the other classes. Meanwhile, the method was successfully applied to the GF-2 image of Nanjing on April 9, 2018. The results showed that the urban water in Nanjing is mainly composed of U-FUI Ⅱ-Ⅳ water, whereas the distribution of U-FUI Ⅰ, Ⅴ, and Ⅵ water is lower in the city. The spatial distribution characteristics were consistent with the results of in-situ sampling in the same period.In order to explore the temporal and spatial distribution characteristics of atmospheric aerosol optical depth (AOD) in the urban agglomeration on the northern slope of the Tianshan Mountains, the temporal and spatial distribution characteristics and trends of changes in the AOD in the study area from 2000 to 2019 were analyzed by MODIS aerosol products(MCD19-A2). For 2016-2019, when the AOD was relatively stable, the parameters such as the AOD and Ångström wavelength index (α) were analyzed using multi-band sun photometer ground-based remote sensing technology. The results showed that ① the spatial distribution of AOD in the study area was consistent with the topography, and high values were mainly distributed in the low altitude area. The spatial distribution of AOD in the four seasons showed a strong seasonal change from spring (0.15±0.03) > autumn (0.14±0.03) > summer (0.14±0.02). ② In terms of time, the annual average AOD value of the study area was 0.12 from 2000 to 2019 with an annual growth rate of 1.03%, thereby showing an overall increasing trend. The annual variation in the monthly mean value of AOD was bimodal; the first and second peaks were in May and November. The main reason for the increase in AOD was the release and transmission of dust from natural sources and heating. ③ Under the influence of dust weather, the AOD changed sharply in spring, and the size and change range of aerosol particles were larger than those in summer. The high value of AOD in the study area was mainly affected by coarse mode particles. The moisture absorption growth of fine mode particles caused a fluctuation in the AOD, but it was not the cause of the high value of AOD.Based on the pollutant data provided by the environmental monitoring stations and the routine observation data of 11 national meteorological stations in Jiangxi Province from 2016 to 2019, the characteristics of ozone pollution and the relationships with meteorological factors were investigated in this study. The results showed that ozone pollution has become increasingly severe in Jiangxi Province in recent years. The annual mean concentration of ozone in Jiangxi Province (the maximum daily 8 h average) was 80.1 μg·m-3 in 2016 and reached up to 98.2 μg·m-3 in 2019, with an average annual growth rate of 6 μg·m-3. The number of over-standard days of ozone was 475 d, accounting for 72.6% of 2019 in Jiangxi Province. The average concentrations observed in summer were higher than those observed in the other seasons during 2016 to 2018, but in 2019, higher ozone concentrations were observed in autumn owing to the lower precipitation, more sufficient sunshine, and the resulting higher air temperature. Overall, the gdong and the northwest of Jiangxi Province in spring, the northwest parts of Jiangxi Province in summer, and the north of Guangdong and central Anhui Province in autumn.To study the spatiotemporal variations in fine particulate matter (PM2.5) and the impact of air quality management in autumn and winter in Zhengzhou, five sites were selected to collect PM2.5 samples from the autumn of 2017 to the winter of 2018, and the characteristics of the chemical components were analyzed. The positive matrix factorization (PMF) model was also applied to identify the sources of PM2.5, and the effect of air quality control was evaluated to provide support for air quality control in autumn and winter in the next stage. The PM2.5 concentrations in the four seasons in Zhengzhou were ranked as winter > autumn > spring > summer. The PM2.5 concentration at Zhengzhou University (ZZU) was the highest (8.7% higher than the average concentration), and the PM2.5 concentrations at the other sites were slightly lower than the average concentration. The concentration of water-soluble ions (WSIs) was low in spring and summer and high in autumn and winter. The average proportions of SO42-, NO3-, and NH4+ in the nine WSIs were as high as 22.