气候变化领域集成服务门户(CCPortal): An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data

CCPortal

DOI	10.1016/j.atmosenv.2021.118827
	An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data
	Chen B.; Song Z.; Shi B.; Li M.
发表日期	2022
ISSN	1352-2310
卷号	268
英文摘要	Rapid urbanization and industrialization in China had led to increased pollutants emission. PM10 is one of the main components of air pollutants, which significantly impacts human health, environment, and regional or global climate. In this study, a new machine learning deep forest (DF) model was used to construct the aerosol optical depth (AOD) and near-ground PM10 concentration (AOD-PM10) model. The DF model combines the advantages of deep neural networks and tree models, which can provide model interpretability. Combined with the Himawari-8 AOD, meteorological, and auxiliary factors, the hourly PM10 concentration in China (spatial resolution: 0.05 × 0.05°) was obtained. The results show that AOD has the highest contribution to the importance of features in the AOD-PM10 model, accounting for approximately 13.5%, and the contributions of boundary layer height, temperature, and relative humidity to the importance of features were 11%, 8.6%, and 7%, respectively. A 10-fold cross-validation was used to evaluate the performance of the model. The hourly cross validation results from 09:00 to 16:00 (Beijing time) show that the R2 range was 0.82–0.88, and the root mean square error and absolute mean error were 18.55–23.12 μg/m³ and 11.54–16.82 μg/m³, respectively. The R2 values of daily, monthly, seasonal, and annual average PM10 estimated by the model were 0.87, 0.91, 0.94, and 0.94, respectively. The areas with high PM10 concentrations are mainly in northern China, especially in the North China Plain, and the peak value of daily average PM10 can reach 91 μg/m³; the Intraday variation of PM10 in southern China ranges from 67 μg/m³ to 72 μg/m³. A large-scale dust weather process was analyzed. Based on the AOD-PM10 model, the contribution of long-range transport dust to PM10 in China and Northern China were 25.6% and 38.1%, respectively. The PM10 measured by the station and estimated by the DF model indicated good consistency. © 2021 The Authors
关键词	AOD Dust transport Himawari-8 Machine learning PM10
语种	英语
scopus关键词	Aerosols; Air pollution; Boundary layer flow; Boundary layers; Deep neural networks; Forestry; Learning algorithms; Mean square error; Aerosol optical depths; Air pollutants; Dust transport; Forest modelling; Himawari-8; Industrialisation; Northern China; PM 10; Pollutants emissions; Rapid urbanizations; Dust; aerosol; boundary layer; emission; industrialization; machine learning; optical depth; particulate matter; relative humidity; urbanization; aerosol; article; boundary layer; China; cross validation; deep neural network; forest; human; optical depth; particulate matter 10; relative humidity; weather; China; North China Plain
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/248140
作者单位	Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou, 730000, China
推荐引用方式 GB/T 7714	Chen B.,Song Z.,Shi B.,et al. An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data[J],2022,268.
APA	Chen B.,Song Z.,Shi B.,&Li M..(2022).An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data.ATMOSPHERIC ENVIRONMENT,268.
MLA	Chen B.,et al."An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data".ATMOSPHERIC ENVIRONMENT 268(2022).