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<p>High-resolution datasets of anthropogenic water pollution discharges are essential for characterizing pollution dynamics and informing water quality management. However, China’s pollution source data remain limited to provincial scales and decadal censuses, constraining spatiotemporal analyses and policy evaluation. We present a High-resolution Sectoral Water Pollution Discharge Dataset for mainland China (2007–2022), providing annual data at 30 arc-second (approximately 1 km at the equator) resolution. By integrating pollution source statistics with geospatial data through a top-down downscaling framework, we allocated provincial discharges to grid cells. The dataset quantifies gridded anthropogenic discharge measured by chemical oxygen demand (COD) and ammonium nitrogen (NH<sub>3</sub>-N) from five sectors: urban residential, rural residential, industrial, crop farming, and livestock farming. Validation was performed by comparing city-level aggregated estimates against official census records from 73 cities, demonstrating strong agreement (R² &gt; 0.6) for both pollutants across all sectors. This dataset enables identification of fine-scale pollution hotspots within river basins that were previously obscured by provincial-scale data, thereby supporting the implementation of targeted pollution control strategies.</p>

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High-resolution gridded dataset of sectoral water pollution discharges in China from 2007 to 2022

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Ze Yuan,
  • Ting Ma

摘要

High-resolution datasets of anthropogenic water pollution discharges are essential for characterizing pollution dynamics and informing water quality management. However, China’s pollution source data remain limited to provincial scales and decadal censuses, constraining spatiotemporal analyses and policy evaluation. We present a High-resolution Sectoral Water Pollution Discharge Dataset for mainland China (2007–2022), providing annual data at 30 arc-second (approximately 1 km at the equator) resolution. By integrating pollution source statistics with geospatial data through a top-down downscaling framework, we allocated provincial discharges to grid cells. The dataset quantifies gridded anthropogenic discharge measured by chemical oxygen demand (COD) and ammonium nitrogen (NH3-N) from five sectors: urban residential, rural residential, industrial, crop farming, and livestock farming. Validation was performed by comparing city-level aggregated estimates against official census records from 73 cities, demonstrating strong agreement (R² > 0.6) for both pollutants across all sectors. This dataset enables identification of fine-scale pollution hotspots within river basins that were previously obscured by provincial-scale data, thereby supporting the implementation of targeted pollution control strategies.