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A platform for research: civil engineering, architecture and urbanism
Mapping nighttime PM2.5 concentrations in Nanjing, China based on NPP/VIIRS nighttime light data
https://orcid.org/0000-0003-4032-8759
Abstract Fine inhalable particulate matter (PM2.5) is one of the major air pollutants that affect human health and the environment. Detailed knowledge of the spatial distribution of PM2.5 is meaningful for the prevention and control of air pollution. Satellite remote sensing has become an effective way to observe PM2.5 concentrations. However, most studies have focused on mapping PM2.5 concentrations from satellite-derived daytime aerosol optical depth (AOD), which cannot effectively depict the nighttime atmospheric environment. This paper aims to develop a method to derive nighttime PM2.5 concentrations in Nanjing, China, using the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light remote sensing data during September–December 2020. The relationship between the satellite at-sensor radiance, PM2.5 concentrations and other environmental factors was first explored based on the nighttime radiative transfer equation. Taking into account the pixel direct radiation and the background scattered radiation, the spatial independent variables for estimating nighttime PM2.5 were determined. Five machine learning algorithms and multiple linear regression (MLR) were employed to develop models to estimate nighttime PM2.5 concentrations. The results showed that the MLR model had obviously lower accuracy than the machine learning models, and the RF model outperformed the other models, with a coefficient of determination (R2) of 0.81 and a mean absolute error (MAE) of 7.85 μg·m−3. Then, the developed model was applied to map the nighttime PM2.5 concentrations over Nanjing, which well characterized the nighttime atmospheric environment at a fine resolution. This paper proposes a method to map nighttime PM2.5 concentrations from nighttime light remote sensing data and provides references for monitoring nighttime atmospheric environments in other regions.
Highlights The relationship between nighttime PM2.5 and at-sensor radiance was analyzed based on nighttime radiative transfer process. Both the direct radiation and the background scattered radiation were included to estimate PM2.5 concentrations. Models that estimate nighttime PM2.5 concentrations from NPP/VIIRS nighttime light remote sensing data were developed. The performances of different models for estimating nighttime PM2.5 concentrations were tested. Random forest outperforms other machine learning algorithms.
Mapping nighttime PM2.5 concentrations in Nanjing, China based on NPP/VIIRS nighttime light data
https://orcid.org/0000-0003-4032-8759
Abstract Fine inhalable particulate matter (PM2.5) is one of the major air pollutants that affect human health and the environment. Detailed knowledge of the spatial distribution of PM2.5 is meaningful for the prevention and control of air pollution. Satellite remote sensing has become an effective way to observe PM2.5 concentrations. However, most studies have focused on mapping PM2.5 concentrations from satellite-derived daytime aerosol optical depth (AOD), which cannot effectively depict the nighttime atmospheric environment. This paper aims to develop a method to derive nighttime PM2.5 concentrations in Nanjing, China, using the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light remote sensing data during September–December 2020. The relationship between the satellite at-sensor radiance, PM2.5 concentrations and other environmental factors was first explored based on the nighttime radiative transfer equation. Taking into account the pixel direct radiation and the background scattered radiation, the spatial independent variables for estimating nighttime PM2.5 were determined. Five machine learning algorithms and multiple linear regression (MLR) were employed to develop models to estimate nighttime PM2.5 concentrations. The results showed that the MLR model had obviously lower accuracy than the machine learning models, and the RF model outperformed the other models, with a coefficient of determination (R2) of 0.81 and a mean absolute error (MAE) of 7.85 μg·m−3. Then, the developed model was applied to map the nighttime PM2.5 concentrations over Nanjing, which well characterized the nighttime atmospheric environment at a fine resolution. This paper proposes a method to map nighttime PM2.5 concentrations from nighttime light remote sensing data and provides references for monitoring nighttime atmospheric environments in other regions.
Highlights The relationship between nighttime PM2.5 and at-sensor radiance was analyzed based on nighttime radiative transfer process. Both the direct radiation and the background scattered radiation were included to estimate PM2.5 concentrations. Models that estimate nighttime PM2.5 concentrations from NPP/VIIRS nighttime light remote sensing data were developed. The performances of different models for estimating nighttime PM2.5 concentrations were tested. Random forest outperforms other machine learning algorithms.
Mapping nighttime PM2.5 concentrations in Nanjing, China based on NPP/VIIRS nighttime light data
https://orcid.org/0000-0003-4032-8759
Abstract Fine inhalable particulate matter (PM2.5) is one of the major air pollutants that affect human health and the environment. Detailed knowledge of the spatial distribution of PM2.5 is meaningful for the prevention and control of air pollution. Satellite remote sensing has become an effective way to observe PM2.5 concentrations. However, most studies have focused on mapping PM2.5 concentrations from satellite-derived daytime aerosol optical depth (AOD), which cannot effectively depict the nighttime atmospheric environment. This paper aims to develop a method to derive nighttime PM2.5 concentrations in Nanjing, China, using the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light remote sensing data during September–December 2020. The relationship between the satellite at-sensor radiance, PM2.5 concentrations and other environmental factors was first explored based on the nighttime radiative transfer equation. Taking into account the pixel direct radiation and the background scattered radiation, the spatial independent variables for estimating nighttime PM2.5 were determined. Five machine learning algorithms and multiple linear regression (MLR) were employed to develop models to estimate nighttime PM2.5 concentrations. The results showed that the MLR model had obviously lower accuracy than the machine learning models, and the RF model outperformed the other models, with a coefficient of determination (R2) of 0.81 and a mean absolute error (MAE) of 7.85 μg·m−3. Then, the developed model was applied to map the nighttime PM2.5 concentrations over Nanjing, which well characterized the nighttime atmospheric environment at a fine resolution. This paper proposes a method to map nighttime PM2.5 concentrations from nighttime light remote sensing data and provides references for monitoring nighttime atmospheric environments in other regions.
Highlights The relationship between nighttime PM2.5 and at-sensor radiance was analyzed based on nighttime radiative transfer process. Both the direct radiation and the background scattered radiation were included to estimate PM2.5 concentrations. Models that estimate nighttime PM2.5 concentrations from NPP/VIIRS nighttime light remote sensing data were developed. The performances of different models for estimating nighttime PM2.5 concentrations were tested. Random forest outperforms other machine learning algorithms.
Mapping nighttime PM2.5 concentrations in Nanjing, China based on NPP/VIIRS nighttime light data
Chen, Huijuan (author) / Xu, Yongming (author) / Zhong, Sheng (author) / Mo, Yaping (author) / Zhu, Shanyou (author)
Atmospheric Environment ; 303
2023-04-03
Article (Journal)
Electronic Resource
English
A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas
| Online Contents | 2017