Articles | Volume 17, issue 13
https://doi.org/10.5194/acp-17-8211-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-8211-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Jul 2017
Research article |
| 06 Jul 2017
Estimating daily surface NO2 concentrations from satellite data – a case study over Hong Kong using land use regression models
Atmospheric Chemistry Group, Department of Chemistry, University of Leicester, University Road, Leicester, LE1 7RH, UK
Paul S. Monks
Atmospheric Chemistry Group, Department of Chemistry, University of Leicester, University Road, Leicester, LE1 7RH, UK
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Cited
36 citations as recorded by crossref.
- Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana J. Wang et al. 10.1088/1748-9326/ad2892
- Air quality in the Galapagos Islands: A baseline view from remote sensing and in situ measurements M. Cazorla & E. Herrera 10.1002/met.1878
- Role of Meteorological Parameters in the Diurnal and Seasonal Variation of NO2 in a Romanian Urban Environment M. Voiculescu et al. 10.3390/ijerph17176228
- Long-term NO2 exposure and mortality: A comprehensive meta-analysis X. Chen et al. 10.1016/j.envpol.2023.122971
- COVID-19 mortality rates in South America related to environmental factors L. Varanda Rizzo et al. 10.1080/00207233.2022.2052536
- A Novel Hyperspectral Remote Sensing Technique with Hour-Hectometer Level Horizontal Distribution of Trace Gases: To Accurately Identify Emission Sources C. Lu et al. 10.34133/remotesensing.0098
- Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact C. Zhang et al. 10.1016/j.envpol.2022.119510
- Application of land-use regression model with regularization algorithm to assess PM2.5 and PM10 concentration and health risk in Kolkata Metropolitan K. Das et al. 10.1016/j.uclim.2023.101473
- Temporal Variation of NO2 and O3 in Rome (Italy) from Pandora and In Situ Measurements A. Di Bernardino et al. 10.3390/atmos14030594
- Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies S. Yarza et al. 10.3390/atmos11020122
- Dynamic variations of inorganic N in precipitation and its influencing factors in the Hexi Corridor, northwestern China S. Qi et al. 10.1016/j.apgeochem.2020.104678
- The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction Y. Mo et al. 10.1016/j.scitotenv.2021.146305
- Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea U. Jeong & H. Hong 10.3390/rs13101877
- Spaceborne tropospheric nitrogen dioxide (NO<sub>2</sub>) observations from 2005–2020 over the Yangtze River Delta (YRD), China: variabilities, implications, and drivers H. Yin et al. 10.5194/acp-22-4167-2022
- Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China D. Zhang et al. 10.3390/ijerph16040579
- Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study A. Harper et al. 10.1016/j.apr.2021.101096
- Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics Y. Tian et al. 10.1016/j.compenvurbsys.2019.01.003
- First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China J. Liu & W. Chen 10.1016/j.scitotenv.2022.153289
- National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India N. Singh et al. 10.1016/j.aeaoa.2024.100289
- Long-term variations of ground-level NO2 concentrations along coastal areas in China N. Zhan et al. 10.1016/j.atmosenv.2022.119158
- Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan Z. Li et al. 10.5194/acp-21-5063-2021
- Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs B. He et al. 10.3390/atmos9040134
- Re-framing the Gaussian dispersion model as a nonlinear regression scheme for retrospective air quality assessment at a high spatial and temporal resolution S. Chen et al. 10.1016/j.envsoft.2019.104620
- Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland T. Karppinen et al. 10.1016/j.rsase.2023.101120
- Comparison of S5P/TROPOMI Inferred NO2 Surface Concentrations with In Situ Measurements over Central Europe A. Pseftogkas et al. 10.3390/rs14194886
- A comparison of statistical and machine-learning approaches for spatiotemporal modeling of nitrogen dioxide across Switzerland T. Liu et al. 10.1016/j.apr.2022.101611
- TROPOMI NO2 Sentinel-5P data in the Community of Madrid: A detailed consistency analysis with in situ surface observations C. Morillas et al. 10.1016/j.rsase.2023.101083
- Spatiotemporal estimates and health risks of atmospheric trace metals across Hong Kong during 2016–2020 W. Sun et al. 10.1007/s11869-024-01663-7
- Impact of weather and emission changes on NO2 concentrations in China during 2014–2019 Y. Shen et al. 10.1016/j.envpol.2020.116163
- Spatial-temporal heterogeneity of air pollution and its relationship with meteorological factors in the Pearl River Delta, China M. Hu et al. 10.1016/j.atmosenv.2021.118415
- DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite S. Li & J. Xing 10.59717/j.xinn-geo.2024.100061
- Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data M. Ghahremanloo et al. 10.1029/2021JD034925
- Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging Q. Di et al. 10.1021/acs.est.9b03358
- A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 X. Ma et al. 10.1016/j.envint.2024.108430
- Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China P. Zhang et al. 10.1016/j.envres.2022.112759
- Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review M. Siddique et al. 10.1109/MGRS.2024.3398434
36 citations as recorded by crossref.
- Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana J. Wang et al. 10.1088/1748-9326/ad2892
- Air quality in the Galapagos Islands: A baseline view from remote sensing and in situ measurements M. Cazorla & E. Herrera 10.1002/met.1878
- Role of Meteorological Parameters in the Diurnal and Seasonal Variation of NO2 in a Romanian Urban Environment M. Voiculescu et al. 10.3390/ijerph17176228
- Long-term NO2 exposure and mortality: A comprehensive meta-analysis X. Chen et al. 10.1016/j.envpol.2023.122971
- COVID-19 mortality rates in South America related to environmental factors L. Varanda Rizzo et al. 10.1080/00207233.2022.2052536
- A Novel Hyperspectral Remote Sensing Technique with Hour-Hectometer Level Horizontal Distribution of Trace Gases: To Accurately Identify Emission Sources C. Lu et al. 10.34133/remotesensing.0098
- Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact C. Zhang et al. 10.1016/j.envpol.2022.119510
- Application of land-use regression model with regularization algorithm to assess PM2.5 and PM10 concentration and health risk in Kolkata Metropolitan K. Das et al. 10.1016/j.uclim.2023.101473
- Temporal Variation of NO2 and O3 in Rome (Italy) from Pandora and In Situ Measurements A. Di Bernardino et al. 10.3390/atmos14030594
- Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies S. Yarza et al. 10.3390/atmos11020122
- Dynamic variations of inorganic N in precipitation and its influencing factors in the Hexi Corridor, northwestern China S. Qi et al. 10.1016/j.apgeochem.2020.104678
- The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction Y. Mo et al. 10.1016/j.scitotenv.2021.146305
- Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea U. Jeong & H. Hong 10.3390/rs13101877
- Spaceborne tropospheric nitrogen dioxide (NO<sub>2</sub>) observations from 2005–2020 over the Yangtze River Delta (YRD), China: variabilities, implications, and drivers H. Yin et al. 10.5194/acp-22-4167-2022
- Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China D. Zhang et al. 10.3390/ijerph16040579
- Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study A. Harper et al. 10.1016/j.apr.2021.101096
- Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics Y. Tian et al. 10.1016/j.compenvurbsys.2019.01.003
- First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China J. Liu & W. Chen 10.1016/j.scitotenv.2022.153289
- National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India N. Singh et al. 10.1016/j.aeaoa.2024.100289
- Long-term variations of ground-level NO2 concentrations along coastal areas in China N. Zhan et al. 10.1016/j.atmosenv.2022.119158
- Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan Z. Li et al. 10.5194/acp-21-5063-2021
- Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs B. He et al. 10.3390/atmos9040134
- Re-framing the Gaussian dispersion model as a nonlinear regression scheme for retrospective air quality assessment at a high spatial and temporal resolution S. Chen et al. 10.1016/j.envsoft.2019.104620
- Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland T. Karppinen et al. 10.1016/j.rsase.2023.101120
- Comparison of S5P/TROPOMI Inferred NO2 Surface Concentrations with In Situ Measurements over Central Europe A. Pseftogkas et al. 10.3390/rs14194886
- A comparison of statistical and machine-learning approaches for spatiotemporal modeling of nitrogen dioxide across Switzerland T. Liu et al. 10.1016/j.apr.2022.101611
- TROPOMI NO2 Sentinel-5P data in the Community of Madrid: A detailed consistency analysis with in situ surface observations C. Morillas et al. 10.1016/j.rsase.2023.101083
- Spatiotemporal estimates and health risks of atmospheric trace metals across Hong Kong during 2016–2020 W. Sun et al. 10.1007/s11869-024-01663-7
- Impact of weather and emission changes on NO2 concentrations in China during 2014–2019 Y. Shen et al. 10.1016/j.envpol.2020.116163
- Spatial-temporal heterogeneity of air pollution and its relationship with meteorological factors in the Pearl River Delta, China M. Hu et al. 10.1016/j.atmosenv.2021.118415
- DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite S. Li & J. Xing 10.59717/j.xinn-geo.2024.100061
- Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data M. Ghahremanloo et al. 10.1029/2021JD034925
- Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging Q. Di et al. 10.1021/acs.est.9b03358
- A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 X. Ma et al. 10.1016/j.envint.2024.108430
- Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China P. Zhang et al. 10.1016/j.envres.2022.112759
- Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review M. Siddique et al. 10.1109/MGRS.2024.3398434
Discussed (preprint)
Latest update: 14 Dec 2024
Short summary
Previous investigations into Chinese urban air quality have been hampered by a lack of available data. In this work we present a new statistical modelling technique, in which sparse ground-based measurements of nitrogen dioxide (NO2) are combined with satellite data and other parameters (e.g. road networks) to create high-resolution maps of daily surface NO2 concentrations over Hong Kong. These maps can be used to estimate population exposure, and to identify at-risk groups.
Previous investigations into Chinese urban air quality have been hampered by a lack of available...
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