DOI: 10.1002/jeq2.70212 ISSN: 0047-2425

Multi‐decadal trends and drivers of urban stream water quality in Arlington, Virginia, using the National Sanitation Foundation Water Quality Index

Naveen Joseph, R. Stockton Maxwell

Abstract

This study aims to evaluate long‐term trends in urban stream water quality in Arlington, Virginia, a highly urbanized county within the Chesapeake Bay watershed, using the National Sanitation Foundation Water Quality Index and identify relationships with potential drivers of water quality. Monthly Water Quality Index (WQI) values for stream and tributary monitoring stations were calculated from 1984 to 2019 by integrating observed and modeled datasets for nine water quality parameters, including dissolved oxygen, nutrients, turbidity, and microbial contamination. Relationships between water quality and climate, hydrology, socioeconomic, and land use variables were examined through correlation analysis and principal component analysis. Mann–Kendall test was used to detect temporal trends before and after major regulatory interventions, including the 1992 Chesapeake Bay Preservation Act and Arlington's 1997 stormwater permit. Results indicate pronounced seasonal variability, with summer months characterized by lower dissolved oxygen levels, higher turbidity, and nutrient enrichment, associated with elevated temperatures and storm‐driven runoff. Long‐term analysis revealed significantly lower WQI values prior to 1997 ( p ‐value = 0.0028), followed by stabilization, suggesting that regulatory measures may have helped mitigate further degradation. Three principal components captured 86% of the variability in explanatory factors, summarizing urbanization intensity, climate variability, and hydrologic‐land cover dynamics. Regression modeling reproduced temporal WQI patterns (Training: RMSE = 1.387, R 2  = 0.640; Testing: RMSE = 1.582, R 2  = 0.447), demonstrating the utility of dimensionality reduction. This assessment underscores the value of integrating long‐term monitoring data with statistical modeling to evaluate regulatory outcomes and identify dominant drivers of water quality in urban watersheds.

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