Water quality trend and change-point analyses using integration of locally weighted polynomial regression and segmented regression

• DOI: 10.1007/s11356-017-9188-x
• Publication Date: 2017-05-22T18:48:24Z
• Authors: Hong Huang; Zhenfeng Wang; Fang Xia; Xu Shang; YuanYuan Liu; Minghua Zhang; Randy A. Dahlgren; Kun Mei
• Keywords: Change-point analysis; China; 0207 environmental engineering; Chemical; 02 engineering and technology; Locally weighted polynomial regression; Long-term trend assessment; Water Quality; Geoinformatics; Water Pollutants; 14. Life underwater; Reproducibility of Results; Biological Sciences; 6. Clean water; Water quality; Segmented regression; 13. Climate action; Chemical Sciences; Earth Sciences; Generic health relevance; Seasons; Environmental Sciences; Water Pollutants, Chemical; Environmental Monitoring

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 (LWPR) and segmented regression (SegReg). 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 (TN) and chemical oxygen demand (CODMn) 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.