River pollution monitoring over an industrial catchment in urban ecosystem: Challenges and proposed geospatial framework

• Published in: Environmental challenges (Amsterdam, Netherlands) Netherlands), 2022-04, Vol.7, p.100496, Article 100496
• Main Authors: Sekharan, Sheeba, Samal, Dipak R., Phuleria, Harish C., Chandel, Munish K., Gedam, Shirish, Kumar, Rakesh, Sethi, Virendra, Supate, Amar R., Karmakar, Subhankar
• Format: Article
• Language: English
• Subjects: GIS; Industrial catchment; Multivariate statistics; River pollution; Urban ecosystem; Urban river
• ISSN: 2667-0100; 2667-0100
• DOI: 10.1016/j.envc.2022.100496

•Identification of challenges associated with environmental monitoring over urban industrial catchments.•A geospatial framework developed to transparentize urban river pollution monitoring.•Framework is generalizable as a decision support tool to identify polluted stretches.•Suggested recommendations are extendable to similar urban river networks. Urban river pollution is considered a ‘necessary evil’ consequence of disproportionate developmental expansion in metropolises. Unprecedented expansion and anthropic activities amongst other reasons lead to choking of urban rivers with municipal and industrial sewage. Urban rivers are dying while awaiting rescue, despite the hard efforts by civic authorities, largely due to lack of coordination amongst river authorities and stakeholders, underlying conflicts spanning across all levels, balkanisation, pluralization, scarcity of reliable technical data, and financial constraints. Challenges faced by stakeholders in river pollution management as revealed in informational interviews are foregrounded. In an attempt to reduce some of the overwhelm faced by officials on ground, a geospatial framework is proposed which if functionally implemented can transparentize river water quality (WQ) monitoring, and facilitate pollution control. The pollution situation in a persistently polluted urban river near Mumbai city, India was explored and assessed in the middle of a restoration battle waged by environmental activists. Following secondary river water quality data acquisition, primary sample collection campaign and WQ testing, multivariate statistical data analysis was performed under data- and resource-constraint situation. Spatiotemporal monitoring and visualization of river water quality data holds great promise for effectively controlling anthropic river pollution. A subsequent geospatial analysis of the study area was performed using digital elevation model (DEM) based watershed delineation, land use land cover (LULC) classification, mapping of WQ monitoring locations, mapping of industrial clusters, integration of spatial data, and identifying polluter industries. This steered us to formulate and propose the geospatial framework and supplementary recommendations on how better to save this dying river. The framework displays near real-time information on river water quality at different impaired river stretches in urban industrial ecosystem. Applicable universally for monitoring any river in urban industrial catchment,