Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Abstract

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

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;. The main reasons include the stronger data mining ability of our model, and the consideration of the key spatial and temporal information of air pollution that ignored in previous studies, and the introduction of more comprehensive factors (e.g., emission inventory) that affect PM2.5 pollution.…”

“…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.…”

Reply on RC2

“…The comparison results show that the local hourly PM2.5 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;. The main reasons include the stronger data mining ability of our model, and the consideration of the key spatial and temporal information of air pollution that ignored in previous studies, and the introduction of more comprehensive factors (e.g., emission inventory) that affect PM2.5 pollution.…”

Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

“…T. developed an improved LME model, and Xue et al (2020) proposed an improved geographically and temporally weighted regression (IGTWR) model to derive hourly PM 2.5 maps based on the Himawari-8 AOD product over central and eastern China. In addition to traditional statistical regression models, several artificial intelligence models, including the random forest (RF), the gradient boosting decision tree (GBDT), the eXtreme Gradient Boosting (XGBoost), and the deep neural network (DNN), have been recently successfully adopted to obtain ground-level PM 2.5 concentrations in local regions and in the whole of China (Chen et al, 2019;Gui et al, 2020;Liu et al, 2019;Zhang et al, 2020). Nevertheless, due to their poor data-mining ability, traditional statistical regression methods usually suffer from large uncertainties.…”