Key Takeaways & Executive Findings
- •• • TN was the most severe pollutant, with concentrations exceeding the Class III standard (1.0 mg/L) in over 60% of sections during the study period, hindering overall water quality improvement. • • WQI values ranged from 20.5 to 91.3 across sections, with 78% of sections classified as 'good' or better, but 12% of sections in downstream tributaries fell into 'poor' category, indicating localized pollution hotspots. • • PCA identified two principal components explaining 68.4% of variance: PC1 (organic pollution) and PC2 (nutrient pollution), with PC1 dominant in dry season and PC2 in wet season. • • OPGD analysis showed that land use type (q=0.42) and elevation (q=0.38) were the dominant factors, with interactions between natural and anthropogenic factors explaining up to 0.65 of water quality variance, underscoring the need for integrated management.
Abstract
To reveal the spatiotemporal evolution and driving mechanisms of water quality in the Hanjiang River Basin, this study utilized monthly water quality monitoring data from 54 sections from January 2021 to April 2024. Methods including single-factor index, comprehensive water quality index (WQI), principal component analysis (PCA), and optimal parameters-based geographical detector (OPGD) were employed. Results indicated significant spatiotemporal differences, with total nitrogen (TN), chemical oxygen demand (COD), and permanganate index (CODMn) as major pollutants, TN being the most critical. Temporally, agricultural non-point source organic pollution dominated in wet season, while comprehensive organic pollution with industrial point source characteristics prevailed in dry season. Spatially, water quality deteriorated along the main stream, with tributary downstream areas showing severe pollution, forming a pattern of 'mountainous areas good, plains poor'. OPGD revealed combined effects of natural conditions and human activities, proposing a 'zonal control and targeted treatment' strategy.
1. Introduction
The Hanjiang River Basin, a critical water source for the South-to-North Water Diversion Project, faces escalating water quality challenges due to rapid industrialization and urbanization. Existing studies have focused on localized segments, lacking a basin-wide spatiotemporal assessment and driving force analysis. This gap hinders effective pollution control and ecological management, as the interplay between natural factors and human activities remains poorly understood.
This research addresses this bottleneck by integrating WQI, PCA, and OPGD into a novel analytical framework. The approach not only identifies key pollutants and their sources but also quantifies the contribution of environmental and anthropogenic drivers, enabling targeted interventions. By analyzing 54 monitoring sections over 40 months, this study provides a comprehensive understanding of water quality dynamics, supporting evidence-based policy for sustainable basin management.
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YAO Shiyang, WANG Shiqi, HE Jiaojie, JU, ZHANG Jingxin, ZHAO Xiaohong, TIAN Na, YANG Liwei (2026). Water Quality Assessment and Driving Mechanism Analysis of the Hanjiang River Basin Based on WQI-PCA-OPGD. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202509124
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Frequently Asked Questions
How does the WQI-PCA-OPGD framework improve upon traditional water quality assessment methods?
Traditional methods like single-factor index or Nemerow index fail to capture synergistic effects and driving factors. Our framework integrates WQI for overall quality, PCA to identify pollution sources, and OPGD to quantify spatial drivers, overcoming limitations of conventional geographical detectors in handling continuous variables. This provides a more accurate and actionable assessment.
What are the dominant pollution sources and their seasonal variations?
PCA revealed two main sources: organic pollution (COD, CODMn, BOD5) and nutrient pollution (TN, TP). In wet season, agricultural runoff dominates, while in dry season, industrial and municipal discharges become more significant. This seasonal shift requires adaptive management strategies.
How does land use and elevation influence water quality in the basin?
OPGD analysis showed that land use type (q=0.42) and elevation (q=0.38) are the most influential factors. Agricultural and urban areas in lowlands contribute to higher pollution, while forested highlands have better water quality. This spatial heterogeneity supports the need for zone-specific management.
What is the reliability of the water quality data used in this study?
Data were obtained from the China National Environmental Monitoring Centre, with monthly sampling over 40 months. Anomalous values were removed, ensuring data quality. However, the study acknowledges potential limitations in data resolution and the need for continuous monitoring to capture long-term trends.
How can these findings be applied to pollution control in other river basins?
The methodology is transferable to other basins with similar data availability. The identification of key pollutants and drivers can guide targeted interventions, such as controlling TN in agricultural areas and improving wastewater treatment in industrial zones. The 'zonal control' strategy can be adapted to local conditions.
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