气候变化领域集成服务门户(CCPortal): A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei

CCPortal

DOI	10.1016/j.atmosenv.2021.118212
	A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei
	Ding Y.; Chen Z.; Lu W.; Wang X.
发表日期	2021
ISSN	1352-2310
卷号	249
英文摘要	High-resolution data of fine particulate matters (PM2.5) are of great interest for air pollution prevention and control. However, due to the uneven spatial distribution of ground stations, satellite acquisition cycle, and cloud/rain, high-resolution products cannot be provided on a complete spatio-temporal scale. To provide a full daily PM2.5 product in recent years at 1-km grid of the Beijing-Tianjin-Hebei (BTH) region, here we apply a state-of-the-art machine learning approach, CatBoost, to (1) reconstruct satellite aerosol optical depth (AOD) data; and to (2) estimate gridded PM2.5 from station measurements combining elevation, meteorological factors, and the reconstructed AOD data. Compared with existing approaches, CatBoost substantially improved the performance of AOD reconstruction by ~16%. We further show that a wavelet decomposition procedure on the station-based PM2.5 and input variables is helpful to improve the estimation accuracy. Overall, the approach has a good performance in estimating PM2.5 with a cross-validation R2 of 0.88 and root-mean-squared error of 17.79 μg/m3. From the new dataset, population-weighted PM2.5 revealed heterogeneous spatial distribution of exposure in different areas, consistently higher in the Southern and Eastern BTH and lower in Beijing and Northern BTH. In recent years, both AOD and PM2.5 in the BTH region had notable interannual decreases, which can be attributed to the emission reduction efforts and interannual natural variabilities. © 2021
关键词	AOD Beijing-Tianjin-Hebei CatBoost PM2.5 Remote sensing Wavelet decomposition
语种	英语
scopus关键词	Emission control; Mean square error; Satellites; Spatial distribution; Aerosol optical depths; Air pollution prevention; Fine particulate matter; Machine learning approaches; Meteorological factors; Root mean squared errors; Satellite acquisition; Spatio-temporal scale; Wavelet decomposition; aerosol; annual variation; decomposition analysis; optical depth; particulate matter; satellite data; spatial distribution; spatiotemporal analysis; aerosol; article; China; cross validation; decomposition; meteorological phenomena; optical depth; particulate matter 2.5; remote sensing; Beijing [China]; China; Hebei; Tianjin
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248544
作者单位	Key Lab. of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, 350108, China
推荐引用方式 GB/T 7714	Ding Y.,Chen Z.,Lu W.,et al. A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei[J],2021,249.
APA	Ding Y.,Chen Z.,Lu W.,&Wang X..(2021).A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei.ATMOSPHERIC ENVIRONMENT,249.
MLA	Ding Y.,et al."A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei".ATMOSPHERIC ENVIRONMENT 249(2021).