Remote sensing identification of urban water pollution source types using hyperspectral data

Xiaolan Cai; Luyao Wu; Yunmei Li; Shaohua Lei; Jie Xu; Heng Lyu; Junda Li; Huaijing Wang; Xianzhang Dong; Yuxing Zhu; Gaolun Wang

doi:10.1016/j.jhazmat.2023.132080

Remote sensing identification of urban water pollution source types using hyperspectral data

J Hazard Mater. 2023 Oct 5:459:132080. doi: 10.1016/j.jhazmat.2023.132080. Epub 2023 Jul 17.

Authors

Xiaolan Cai¹, Luyao Wu¹, Yunmei Li², Shaohua Lei³, Jie Xu⁴, Heng Lyu¹, Junda Li¹, Huaijing Wang¹, Xianzhang Dong¹, Yuxing Zhu¹, Gaolun Wang¹

Affiliations

¹ School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
² School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China. Electronic address: liyunmei@njnu.edu.cn.
³ State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China.
⁴ Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecological Environment, Wuhan 430010, China.

PMID: 37499493
DOI: 10.1016/j.jhazmat.2023.132080

Abstract

Owing to accelerated urbanisation, increased pollutants have degraded urban water quality. Timely identification and control of pollution sources enable relevant departments to effectively perform water treatment and restoration. To achieve this goal, a remote sensing identification method for urban water pollution sources applicable to unmanned aerial vehicle (UAV) hyperspectral images was established. First, seven fluorescent components were obtained through three-dimensional excitation-emission matrix fluorescence spectroscopy of dissolved organic matter (DOM) combined with parallel factor analysis. Based on the hierarchical cluster analysis of the seven fluorescence components and three spectral indices, four pollution source (PS) types were determined, namely, domestic sewage, terrestrial input, agricultural and algal, and industrial wastewater sources. Second, several water colour and optical parameters, including the absorption coefficient of chromophoric DOM at 254 nm, humification index, chlorophyll-a concentration, and hue angle, were utilised to develop an identification method with a recognition accuracy exceeding 70% for the four PSs that is suitable for UAV hyperspectral data. This study demonstrated the potential of identifying PSs by combining the fluorescence characteristics of DOM with the optical properties of water, thus expanding the application of remote sensing technologies and providing more comprehensive and reliable information for urban water quality management.

Keywords: Fluorescence of dissolved organic matter; Humification index; Hyperspectral images of unmanned aerial vehicle; Remote sensing; Source types of pollution; Urban water.