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)

Abstract

Abstract. Fine particulate matter with a diameter of less than 2.5 µm (PM2.5) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5. Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV-R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 µg m−3, respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT+8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

“…Since the Himawari‐8 satellite cannot cover Xinjiang, China (Song et al., 2021; Wei, Li, Pinker, et al., 2021), China’s second‐generation geostationary meteorological satellite FY‐4A successfully launched on 11 December 2016, can cover the entire territory of China. Its Advanced Geosynchronous Radiation Imager (AGRI) imager can provide multi‐band full‐disk images with a time resolution of 15 min (Y. Chen et al., 2020; Mao et al., 2021; Zhang, Zhu, et al., 2019).…”

Estimation of PM<sub>2.5</sub> concentration in China using linear hybrid machine learning model

“…Li et al (2017a) established a GRNN (generalized regression neural network) model for the whole of China to estimate PM 2.5 concentration, and the results demonstrated that the performance of the deep learning model was better than that of the traditional linear model. In addition, there are some novel algorithms such as the space-time extra-tree (STET) (Wei et al, 2021b) and space-time random forest (STRF) (Wei et al, 2019a) algorithms that are also used for PM 2.5 inversion research.…”

Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models

“…Recently, various algorithms such as extreme gradient boost (XGBoost) and light gradient boost machine (LightGBM) have been developed to overcome the shortcomings of GBM. XGBoost [18,19] and LightGBM [20][21][22] have been used in studies on PM prediction model development and demonstrated a relatively good prediction performance compared to the GBM model. However, only a few studies have applied LightGBM, a relatively new algorithm, and comparative studies between boosting algorithms are scarce.…”