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Water eutrophication has become one of the prominent problems of environmental protection in inland watersheds. Turbidity, total phosphorus (TP) and total nitrogen (TN) concentrations are key water quality parameters (WQPs) that reflect the level of water eutrophication in inland waters. Due to the complex interaction effects between different water quality in urban rivers, the water quality retrieval models still have the problem of single input features and poor applicability. This paper proposed a robust feature selection method based on machine learning and utilized Sentinel-2 remote sensing images for water quality retrieval of Chan and Ba rivers in Xi'an City. The ReliefF and global sensitivity analysis (GSA) methods (ReliefF-GSA) were used to select the optimal feature combination from the potential feature dataset. Based on the optimal feature combination, Random Forest regression (RFR), LightGBM and XGboost models were constructed for the three WQPs retrieval, respectively. The optimal models were then used to invert the three WQPs and the spatial-temporal variation of WQPs from January 2021 to January 2022 was analysed. The results show that (1) The RelieF-GSA method is suitable for high-dimensional feature filtration and enables optimal feature selection for specific WQPs retrieval. It is revealed that the BOI index (black odour water index) is the key feature for the retrieval of turbidity and TN concentration. (2) The RFR model was found to be better than other models and more appropriate for Chan and Ba rivers, with coefficients of determination (R2) of 0.90, 0.89 and 0.81, respectively. (3) It was found that the water qualities in the Chan and Ba rivers have prominent seasonal characteristics. Turbidity and TP concentrations showed higher, while TN concentration showed relatively low in autumn. The method and conclusions of this paper can further provide a reference for WPQs retrieval in urban rivers. [ABSTRACT FROM AUTHOR]

Atmospheric pollution affects air quality and can pose a serious risk to public health. Traditional PM2.5 monitoring is limited by the uneven distribution of ground stations, which makes it difficult to obtain spatially continuous and accurate PM2.5 concentrations information. Using aerosol optical depth (AOD) retrieved from satellite remote sensing to study the temporal and spatial variation characteristics of PM2.5 can accurately predict the PM2.5 concentrations in a wide range and provide a basis for atmospheric pollution prevention and control. Considering the spatio-temporal correlations among the data, this study uses AOD data, introduces geographic location data, temporal data, meteorological data, and elevation data to construct an Optimized XGBoost (O-XGBoost) model with spatial and temporal characteristics to estimate PM2.5 concentrations in the Guanzhong region from 2019 to 2021 and analyse its spatial and temporal distribution characteristics. The results show that compared with the random forest (RF) and XGBoost models, the O-XGBoost model has higher estimation accuracy, with $$ {R^2}$$ R 2 , $$ RMSE$$ RMSE , and $$MAE$$ MAE of 0.873, 11.460 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , and 8.061 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , respectively. The estimation of PM2.5 concentrations based on the O-XGBoost model makes up for the uneven distribution of ground monitoring stations and improves the estimation accuracy. [ABSTRACT FROM AUTHOR]