Hydro-chemical profiling and contaminant source identification in agricultural canals using data driven clustering approaches

Abstract

Canal networks are vital for irrigated agriculture in semi-arid regions, yet their water quality is increasingly endangered by diffuse agro-chemical runoff and unregulated effluent discharges. Despite this growing risk, long-term, high-resolution assessments that simultaneously capture spatial patterns and seasonal dynamics remain scarce—leaving practitioners with limited evidence for targeted interventions. Addressing this gap, the study sampled ten canal sites monthly for 11 months across Charkhi Dadri District (Haryana, India) and analysed sixteen physicochemical parameters, including heavy metals and irrigation-relevant ions. A suite of multivariate techniques—R- and Q-mode hierarchical clustering, principal-component analysis (PCA), correlation matrices and one-way ANOVA—was employed to disentangle pollution drivers, while the Irrigation Water Quality Index (IWQI) translated complex chemistry into management-ready scores. Two principal components explained 72.6% of variance, with aluminium, iron and copper emerging as dominant contributors; ANOVA revealed significant seasonal shifts (p < 0.05) in these metals. Cluster analysis pinpointed contamination hotspots, and IWQI values of 67.3–85.5 classified canal water as “good” to “very good” for irrigation. By integrating granular spatiotemporal monitoring with advanced multivariate statistics, the study delivers a scalable framework for managing irrigation canals in data-limited, semi-arid landscapes.