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.
Highlights
Models withresults the same input parameters, the capsule network model (CapsNet) model developed by us achieved the better validation performance
We found that the PM2.5 concentration in autumn was beyond that in spring of under clouds
(2) According to the Table 3, the accuracy of the CapsNet model was different in 2018, 2019, and 2020, which may be affected by dataset quality
Summary
Fine particulate matter (PM2.5 ) is defined as particulate matter with an aerodynamic diameter less than or equal to 2.5 μm, and it is a main pollutant causing hazy weather. Numerous studies in relation to environmental epidemiology have shown that fine particulate matter has a variety of negative effects on human health (e.g., causing respiratory and cardiovascular diseases) [1,2]. To monitor and evaluate air quality, the China National. Environmental Monitoring Centre has established ground-based stations since 2013 to measure PM2.5 concentrations in real time at an hourly scale. Despite some PM2.5 concentration reductions measured, the PM2.5 concentration is still very worrying, especially in some metropolitan regions in winter [3]. The number of ground-based stations is still limited, Remote Sens.
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