Deep Learning Architecture for Estimating Hourly Ground-Level PM_2.5 Using Satellite Remote Sensing

Comment on acp-2020-1277

“…concentrations retrieved from our national-scale model are more accurate than those derived from the models developed separately in local areas, e.g., the LME model (Wang et al, 2017), and the GWR, SVR, RF, and DNN models in the BTH region (Sun et al, 2019); the two-stage RF and DNN models in the YRD region (Fan et al, 2020;Tang et al, 2019). In addition, our model outperforms most of the statistical regression models, machine learning models focusing on entire China, e.g., the I-LME, and IGTWR, RF, Adaboost, XGBoost, and their stacked models in China (Chen et al, 2019;Liu et al, 2019;Xue et al, 2020;.…”

“…Over the years, Wang et al (2017) used the linear mixed-effect (LME) model, and Sun et al (2019) applied the geographically weighted regression (GWR) and support vector regression (SVR) models to estimate hourly PM2.5 data from the Himawari-8 aerosol optical depth (AOD) products in the Beijing-Tianjin-Hebei (BTH) region. developed an improved LME model, and Xue et al (2020) proposed an improved geographically and temporally weighted regression (IGTWR) model to derive the hourly PM2.5 maps based on Himawari-8 AOD products over central and eastern China.…”

“…developed an improved LME model, and Xue et al (2020) proposed an improved geographically and temporally weighted regression (IGTWR) model to derive the hourly PM2.5 maps based on Himawari-8 AOD products over central and eastern China. In addition to the traditional statistical regression models, several artificial intelligence models, including the random forest (RF), eXtreme Gradient Boosting (XGBoost), and deep neural network (DNN), have been recently successfully adopted to Himawari-8 data to obtain hourly PM2.5 concentrations from local regions to the whole of China (Chen et al, 2019;Liu et al, 2019;Sun et al, 2019). Nevertheless, due to its poor data mining ability, the traditional statistical regression methods usually suffer from large uncertainties.…”

“…While artificial intelligence methods can achieve higher accuracy, they are often highly demanding on computational power and are thus often slow. In addition, the spatiotemporal variations of PM2.5 were often neglected in previous studies (Chen et al, 2019;Liu et al, 2019;Sun et al, 2019;Wang et al, 2017;, resulting in relatively low accuracy.…”

A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation

“…To estimate ground atmospheric PM 2.5 from satellite observations, the popular approach is to establish a statistical relationship between the satellite observations (e.g., aerosol optical depth (AOD) [12], top-of-atmosphere reflectance [14]) and ground PM 2.5 measurements. There have been numerous satellite-based PM 2.5 estimation models developed for the estimation of PM 2.5 , primarily including the early statistical models, such as multiple linear regression [15], semiempirical model [16], and so on; and the more advanced statistical models, for instance, the linear mixed effects model [17], geographically weighted regression [18], [19], and neural networks [20]- [22], etc. With the use of these models, high-resolution ground PM 2.5 data can be effectively generated from satellite observations.…”