Xiaozhou Zhang

Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression and segmented regression. Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen and chemical oxygen demand from Shanxi Reservoir watershed in eastern China. Results showed that this approach was straightforward and reliable for assessment of long-term trends and change-points on irregular water quality datasets. The reliability was verified by statistical tests and practical considerations for Shanxi Reservoir watershed. The newly developed integrated LWPR-SegReg approach is not only limited to the assessment of trends and change-points of water quality parameters but also has a broad application to other fields with long-term time series records.

Accurate quantification of dissolved oxygen is critically important for managing water resources and controlling pollution. Artificial intelligence models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine, to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error, determination coefficient ), and Nash-Sutcliffe model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R, and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.