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

Abstract

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 changepoints. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (COD Mn ) 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.

Risk analysis of heavy metal concentration in surface waters across the rural-urban interface of the Wen-Rui Tang River, China

“…The relative abundance of heavy metals in the water column was consistent with results obtain from sediments collected within the study area (Li et al, 2017). An estimation of background concentrations for the heavy metals from Shanxi Reservoir e a drinking water source in the mountains 50 km west of Wenzhou e showed no detectable heavy metal concentrations (Huang et al, 2017). This indicates that heavy metals in the Wen-Rui Tang River watershed are derived primarily from anthropogenic activities rather than natural sources (Bednarova et al, 2013 ) are likely sourced from wastewater and surface runoff receiving metals from electroplating and galvanizing facilities (Zn, Cu, Cr), leather tanning facilities (Cr), vehicle-related emissions (Zn, Pb, Cu), livestock and poultry farms (Cu, Zn), phosphate fertilizers (Cd, Hg, Pb, Zn), and atmospheric deposition (Cd, Zn) (Shomar, 2009;Th evenot et al, 2007;Meybeck et al, 2007;Sutherland, 2000;Kabas et al, 2014).…”

Multivariate adaptive regression splines for estimating riverine constituent concentrations

“…For riverine constituent concentration estimation, model interpretability mainly depends on the capability of the model to capture long‐term trends and seasonal patterns, as well as the important influence of discharge. The trend pattern of water quality is very important in environmental water management and remediation (Chang, ; Huang et al, ). MARS‐EC demonstrated a strong capacity for trend and change point analysis for water quality constituents.…”

Influence of land use on the persistence effect of riverine phosphorus

“…The watershed is dominated by highly weathered, iron oxide‐rich, red soils with land use dominated by forest (75%) and cultivated (15%) lands. Population density within the watershed is 236 persons/km 2 , and the remaining major nutrient inputs are from non‐point sources (Huang et al, ).…”