Using multi-threshold regression techniques to assess river fecal pollution in the highly urbanized Tamsui River watershed

Abstract

Rivers are an important urban water resource. This study adopted multivariate linear regression (MLR) and logistic regression (LR) with multiple thresholds to assess river fecal pollution in the Tamsui River watershed using auxiliary environmental data. First, environmental data between 2015 and 2017 on land use, antecedent precipitation, population density, sewerage infrastructure, and river water quality were obtained using geographic information systems and served as explanatory variables. River fecal coliforms (FC), the dependent variable, were also collected for the same period. Then, MLR was used to establish an overall prediction model after validation, and to determine significant factors influencing the level of river fecal pollution. Finally, after stratifying the fecal pollution as low, medium, and high levels, LR with multiple thresholds was employed to explore key factors affecting different FC pollution levels. The study results revealed that land use type and river water quality (other than FC) strongly affected river FC pollution. The discharge of household sewage and wastewater from urban areas was a major source of river FC pollution, particularly for low and medium pollution levels, while farmland land use was negatively correlated with the medium and high levels of river FC pollution in the highly urbanized watershed. Biochemical oxygen demand and suspended solids were highly correlated with medium and high pollution levels in river water.

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Acknowledgements

The author would like to thank the Taiwan Environmental Protection Administration generously supporting the fecal coliform data in the Tamsui River watershed, and the Taiwan Ministry of Science and Technology for financially supporting this research under Contract No. MOST 107-2410-H-424-008.

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Correspondence to Cheng-Shin Jang.

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Jang, CS. Using multi-threshold regression techniques to assess river fecal pollution in the highly urbanized Tamsui River watershed. Environ Monit Assess 193, 113 (2021). https://doi.org/10.1007/s10661-021-08893-7

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Keywords

  • Multivariate linear regression
  • Logistic regression
  • Geographic information system
  • Fecal coliforms
  • Urban