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

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 > 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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2026-01-16
DOI
https://doi.org/10.1038/s41597-026-06595-8
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

T. Ma, Ze Yuan
Scientific Data
Hydrology and Watershed Management Studies
article

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

T. Ma, Ze Yuan
article en

Abstract

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 > 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.

Scientific DataVol. 13(1)
Chinese Academy of Sciences (CN), Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN)
Chinese Academy of Sciences
Clean water and sanitation
Openalex Percentile: Top 87%
Hydrology and Watershed Management Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

High-resolution gridded dataset of sectoral water pollution discharges in China from 2007 to 2022 — T. Ma, Ze Yuan · Scientific Data (2026) | TGRS Research Map | TGRS