What insights can a spatiotemporal and multivariate analysis provide about the water quality? The report from Iraq

Good quality of water in the Shatt Al-Hillah River is very important for supporting agriculture, supplying safe drinking water, and maintaining the health of aquatic ecosystem in the semi-arid region of Iraq. Nevertheless, anthropogenic pressure and climate variability are causing rapid degradation of water resources. This study presents a novel, long-term (2020?2024) spatiotemporal assessment of water quality using multivariate analysis in a data-limited, semi-arid environment. Monthly water samples were collected from five sites along an upstream?downstream gradient and analyzed for nitrate ( \mathrm{NO_3?} ), phosphate ( \mathrm{PO_4?} ), ammonia ( \mathrm{NH_3} ), dissolved oxygen (DO), electrical conductivity (EC), and chlorophyll-a. Sophisticated statistical approaches Principal Component Analysis (PCA), heatmaps, and Hierarchical Cluster Analysis (HCA) showed significant seasonal and spatial variations. The nutrient and EC concentrations were higher in the downstream stations, and DO decreased to less than 5 mg/L in summer. PCA indicated that EC and nutrient levels were the key pollution drivers, whilst clustering and heatmaps demonstrated pollution peaks during the dry period. Results emphasize seasonal nature of eutrophication and salinization in rivers, especially during dry summer. This comprehensive analysis yields policy-relevant insights and calls for interventions specifically targeted at seasonal variations, including wastewater treatment and management of agricultural runoff. Statistical ecological methods used here offer a cost-effective approach to monitoring rivers in a resource-poor region. The findings contribute directly to sustainable water governance aligned with Sustainable Development Goal SDG 6 (Clean Water and Sanitation) in Iraq and similar arid regions.