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Pollution Source Apportionment of River Tributary Based on PMF Receptor Model and Water Quality Remote Sensing in Xinjian River, China

  • Guanhua Zhou
  • , Sizhong Chen
  • , Anqi Li
  • , Chongbin Xu*
  • , Guifei Jing
  • , Qian Chen
  • , Yinbo Hu
  • , Shunjie Tang
  • , Meile Lv
  • , Kejian Xiao
  • *Corresponding author for this work
  • Beihang University
  • China Aerospace Science and Technology Corporation
  • Beijing Engineering Technology Research Center of Aerial Intelligence Remote Sensing Equipments
  • Lishui Ecological and Environmental Monitoring Center of Zhejiang Province

Research output: Contribution to journalArticlepeer-review

Abstract

Accurately identifying the source and controlling the total amount of pollutants are the basis for achieving regulation of pollution sources, which is critical for the prevention and control of surface water pollution. For this purpose, this study used the Xinjian River in Jinyun County, Lishui City, Zhejiang Province, China, as a case study to explore whether and how the tributary inflow impacts the downstream water quality. The main pollution sources in the upstream, midstream, and downstream of the Xinjian River were apportioned using the Positive Matrix Factorization (PMF) model based on the water quality data from four sample stations from January 2018 to September 2022. According to the unmatched factor in different sections, it is plausible to infer that the TN and TP are mainly caused by the tributaries. To enhance the reliability of pollution source apportionment based on the receptor model, a series of remote sensing images with high resolution were used to derive the water quality concentrations to present the spatial distribution and reveal the long-term trend of the local water environment. It is anticipated that the apportionment results could be of great assistance to local authorities for the control and management of pollution, as well as the protection of riverine water quality.

Original languageEnglish
Article number7
JournalWater (Switzerland)
Volume15
Issue number1
DOIs
StatePublished - Jan 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Xinjian River
  • positive matrix factorization (PMF)
  • source apportionment
  • water quality remote sensing

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