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Nitrogen dioxide pollution in 346 Chinese cities: Spatiotemporal variations and natural drivers from multi-source remote sensing data
by
Sun, Qing
, Shen, Fapeng
, Zhang, Jiaqi
, Liu, Jiahe
, Lü, Wenwen
, Cheng, Feifei
, Yang, Heming
in
Aerosol optical depth
/ Aerosols
/ Air Pollutants - analysis
/ Air pollution
/ Air Pollution - analysis
/ Algorithms
/ Atmospheric aerosols
/ Atmospheric pressure
/ China
/ Cities
/ Data processing
/ Datasets
/ Dew point
/ Environmental aspects
/ Environmental impact
/ Environmental Monitoring - methods
/ Evapotranspiration
/ Forecasts and trends
/ Humidity
/ Leaf area
/ Leaf area index
/ Leaves
/ Machine learning
/ Nitrogen
/ Nitrogen dioxide
/ Nitrogen Dioxide - analysis
/ Nonlinear response
/ Optical analysis
/ Optical thickness
/ Outdoor air quality
/ Pollutants
/ Pollution control
/ Precipitation
/ Productivity
/ Remote sensing
/ Remote Sensing Technology
/ Seasons
/ Spatial analysis
/ Spatio-Temporal Analysis
/ Urban air
/ Urban areas
/ VOCs
/ Volatile organic compounds
2025
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Nitrogen dioxide pollution in 346 Chinese cities: Spatiotemporal variations and natural drivers from multi-source remote sensing data
by
Sun, Qing
, Shen, Fapeng
, Zhang, Jiaqi
, Liu, Jiahe
, Lü, Wenwen
, Cheng, Feifei
, Yang, Heming
in
Aerosol optical depth
/ Aerosols
/ Air Pollutants - analysis
/ Air pollution
/ Air Pollution - analysis
/ Algorithms
/ Atmospheric aerosols
/ Atmospheric pressure
/ China
/ Cities
/ Data processing
/ Datasets
/ Dew point
/ Environmental aspects
/ Environmental impact
/ Environmental Monitoring - methods
/ Evapotranspiration
/ Forecasts and trends
/ Humidity
/ Leaf area
/ Leaf area index
/ Leaves
/ Machine learning
/ Nitrogen
/ Nitrogen dioxide
/ Nitrogen Dioxide - analysis
/ Nonlinear response
/ Optical analysis
/ Optical thickness
/ Outdoor air quality
/ Pollutants
/ Pollution control
/ Precipitation
/ Productivity
/ Remote sensing
/ Remote Sensing Technology
/ Seasons
/ Spatial analysis
/ Spatio-Temporal Analysis
/ Urban air
/ Urban areas
/ VOCs
/ Volatile organic compounds
2025
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Nitrogen dioxide pollution in 346 Chinese cities: Spatiotemporal variations and natural drivers from multi-source remote sensing data
by
Sun, Qing
, Shen, Fapeng
, Zhang, Jiaqi
, Liu, Jiahe
, Lü, Wenwen
, Cheng, Feifei
, Yang, Heming
in
Aerosol optical depth
/ Aerosols
/ Air Pollutants - analysis
/ Air pollution
/ Air Pollution - analysis
/ Algorithms
/ Atmospheric aerosols
/ Atmospheric pressure
/ China
/ Cities
/ Data processing
/ Datasets
/ Dew point
/ Environmental aspects
/ Environmental impact
/ Environmental Monitoring - methods
/ Evapotranspiration
/ Forecasts and trends
/ Humidity
/ Leaf area
/ Leaf area index
/ Leaves
/ Machine learning
/ Nitrogen
/ Nitrogen dioxide
/ Nitrogen Dioxide - analysis
/ Nonlinear response
/ Optical analysis
/ Optical thickness
/ Outdoor air quality
/ Pollutants
/ Pollution control
/ Precipitation
/ Productivity
/ Remote sensing
/ Remote Sensing Technology
/ Seasons
/ Spatial analysis
/ Spatio-Temporal Analysis
/ Urban air
/ Urban areas
/ VOCs
/ Volatile organic compounds
2025
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Nitrogen dioxide pollution in 346 Chinese cities: Spatiotemporal variations and natural drivers from multi-source remote sensing data
Journal Article
Nitrogen dioxide pollution in 346 Chinese cities: Spatiotemporal variations and natural drivers from multi-source remote sensing data
2025
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Overview
In this study, tropospheric column concentration of nitrogen dioxide (TNO 2 CC) were derived from Sentinel-5P data. We employed statistical and local spatial autocorrelation analyses to investigate the spatialtemporal distribution and variation of TNO 2 CC across 346 major Chinese cities from 2019 to 2023. Using Random Forest (RF) and Shapley Additive Explanations (SHAP), we analyzed the influence of 15 natural factors on ambient TNO 2 CC levels. The high R² values (0.92 and 0.76), along with the close adherence to the 1:1 line, demonstrate the model’s robustness. The most influential natural factors identified include atmospheric pressure, aerosol optical depth, Leaf Area Index, evapotranspiration, and dew point temperature. Additionally, a non-linear response curve approach was applied to examine the independent association between natural driving factors and pollutant concentrations. TNO 2 CC varied seasonally across the 346 cities, with the highest levels in winter and the lowest in summer. From 2019 to 2023, TNO 2 CC levels exhibited fluctuating trends, with notable regional disparities: higher concentrations were observed in capital cities and in northern and northeastern part of China. TNO 2 CC were significantly influenced by temperature-related variables, aerosol optical depth, and leaf area index. The findings of this study identify key natural influencing factors and provide a scientific basis for revealing the causes of urban air pollution in China, informing pollution control strategies, identifying priority areas for remediation, and supporting the natural formulation of protection policies.
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