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DOI	10.1088/1748-9326/ab76df
	Random forest models for PM2.5 speciation concentrations using MISR fractional AODs
	Geng G.; Meng X.; He K.; Liu Y.
发表日期	2020
ISSN	17489318
卷号	15 期号:3
英文摘要	It is increasingly recognized that various chemical components of PM2.5 might have differential toxicities to human health, although such studies are hindered by the sparse or non-existent coverage of ground PM2.5 speciation monitors. The Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite has an innovative design to provide information about aerosol shape, size and extinction that are more related to PM2.5 speciation concentrations. In this study, we developed random forest models that incorporated ground measurements of PM2.5 species, MISR fractional AODs, simulated PM2.5 speciation concentrations from a chemical transport model (CTM), land use variables and meteorological fields, to predict ground-level daily PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) concentrations in California between 2005 and 2014. Our models had out-of-bag R 2 of 0.72, 0.70, 0.68 and 0.70 for sulfate, nitrate, OC and EC, respectively. We also conducted sensitivity tests to explore the influence of variable selection on model performance. Results show that if there are sufficient ground measurements and predictor data to support the most sophisticated model structure, fractional AODs and total AOD have similar predicting power in estimating PM2.5 species. Otherwise, models using fractional AODs outperform those with total AOD. PM2.5 speciation concentrations are more sensitive to land use variables than other supporting data (e.g., CTM simulations and meteorological information). © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	exposure assessment; fine particulate matter speciation; MISR; random forest; satellite remote sensing
语种	英语
scopus关键词	Decision trees; Land use; Nitrates; Organic carbon; Random forests; Remote sensing; Sulfur compounds; Chemical transport models; Exposure assessment; Fine particulate matter; Meteorological fields; Meteorological information; MISR; Multiangle imaging spectroradiometer; Satellite remote sensing; Chemical speciation; aerosol; algorithm; concentration (composition); elemental carbon; MISR; optical depth; remote sensing; satellite data; speciation (chemistry); Terra (satellite); California; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154159
作者单位	Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; Department of Environmental Health, School of Public Health, Fudan University, Shanghai, 200032, China
推荐引用方式 GB/T 7714	Geng G.,Meng X.,He K.,et al. Random forest models for PM2.5 speciation concentrations using MISR fractional AODs[J],2020,15(3).
APA	Geng G.,Meng X.,He K.,&Liu Y..(2020).Random forest models for PM2.5 speciation concentrations using MISR fractional AODs.Environmental Research Letters,15(3).
MLA	Geng G.,et al."Random forest models for PM2.5 speciation concentrations using MISR fractional AODs".Environmental Research Letters 15.3(2020).