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Comparative assessment of fixed weight and PCA-based water quality indices integrating climatic, biological, and land-cover indicators
Journal article   Open access   Peer reviewed

Comparative assessment of fixed weight and PCA-based water quality indices integrating climatic, biological, and land-cover indicators

Gbenga Daniels, Kathryn McFarland, McKayla Procopio and Amanda Carneiro Marques
Journal of environmental management, v 404, 129448
27 Mar 2026
DOI: https://doi.org/10.1016/j.jenvman.2026.129448
PMID: 41904874
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1016/j.jenvman.2026.129448View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2026CC BY-NC-ND V4.0,  Open

Abstract

Surface water quality Water quality index Land use/land cover Principal component analysis
Freshwater degradation is accelerating under increasing climatic variability, urban expansion, and nutrient enrichment, underscoring the need for assessment tools that capture both chemical and ecological drivers of change. Existing fixed-weight WQI frameworks rarely incorporate biological and climatic dimensions, creating a gap in their ability to evaluate multi-stressor dynamics in changing watersheds. This study develops a data-driven WQI framework incorporating physicochemical, biological, and hydro-climatic parameters across seven sub-watersheds in southeastern Pennsylvania, USA. National Sanitation Foundation WQI (NSF-WQI) values were compared with a Principal Component Analysis-based WQI (PCA-WQI). Long-term datasets (2002-2024) from the U.S. Geological Survey and Pennsylvania Department of Environmental Protection were analyzed, including precipitation and macroinvertebrate richness to represent climatic and ecological dimensions. Key findings show that PCA retained three components explaining 86% of total variance (PC1 = 40.7%, PC2 = 30.2%, PC3 = 15.1%). Physicochemical variables accounted for most explained variance, while macroinvertebrate richness and precipitation contributed measurable secondary structure within retained components. Across sites, water quality was classified as "Good" to "Fair." The two indices were strongly correlated (Spearman ρ = 0.964, p = 0.0005); however, PCA-WQI scores were significantly lower than NSF-WQI values (Wilcoxon W = 0, p = 0.016; mean difference = 2.64 units), particularly in highly urbanized watersheds. Elevated conductance, nitrate, and phosphorus characterized urban sites, whereas higher macroinvertebrate richness was associated with less-developed watersheds. These findings demonstrate that PCA-derived weighting enhances sensitivity to nutrient enrichment and ecological impairment, and this study highlights the value of integrating biological and climatic indicators to improve the ecological interpretation of WQI assessments in urbanizing catchments.

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