Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely s...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 14; číslo 3; s. 623
Hlavní autoři:	Zeng, Qiaolin, Xie, Tianshou, Zhu, Songyan, Fan, Meng, Chen, Liangfu, Tian, Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.02.2022
Témata:	Accuracy aerosol optical depth Aerosols air pollutants Air pollution Air quality Algorithms Artificial neural networks Atmospheric models CapsNet China Coastal zone Cognition & reasoning Cold season Contaminants data collection Deep learning Depth indicators DNN dynamic routing algorithm Estimation human health Latitude Longitude Machine learning Mathematical models Meteorological parameters Model accuracy Natural environment Neural networks Neurons Normalized difference vegetative index Optical analysis Optical thickness Particulate matter particulates PM2.5 concentration Remote sensing Root-mean-square errors Seasons Statistical analysis China
ISSN:	2072-4292, 2072-4292
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Abstract	Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R2) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m3, 8 ± 3%, and 1.40 ± 0.2 μg/m3, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R2 values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m3, ~5%, and 0.60 ± 0.2 μg/m3, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants.
AbstractList	Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R2) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m3, 8 ± 3%, and 1.40 ± 0.2 μg/m3, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R2 values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m3, ~5%, and 0.60 ± 0.2 μg/m3, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants. Fine particulate matter (PM₂.₅) threatens human health and the natural environment. Estimating the near-ground PM₂.₅ concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM₂.₅ concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM₂.₅. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM₂.₅ concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM₂.₅ measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R²) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m³, 8 ± 3%, and 1.40 ± 0.2 μg/m³, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R² values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m³, ~5%, and 0.60 ± 0.2 μg/m³, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R² values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM₂.₅ concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants.
Author	Zeng, Qiaolin Zhu, Songyan Chen, Liangfu Fan, Meng Tian, Yu Xie, Tianshou
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Snippet	Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great... Fine particulate matter (PM₂.₅) threatens human health and the natural environment. Estimating the near-ground PM₂.₅ concentrations accurately is of great...
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SubjectTerms	Accuracy aerosol optical depth Aerosols air pollutants Air pollution Air quality Algorithms Artificial neural networks Atmospheric models CapsNet China Coastal zone Cognition & reasoning Cold season Contaminants data collection Deep learning Depth indicators DNN dynamic routing algorithm Estimation human health Latitude Longitude Machine learning Mathematical models Meteorological parameters Model accuracy Natural environment Neural networks Neurons Normalized difference vegetative index Optical analysis Optical thickness Particulate matter particulates PM2.5 concentration Remote sensing Root-mean-square errors Seasons Statistical analysis
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Title	Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020
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