气候变化领域集成服务门户(CCPortal): Estimating PM2.5in Southern California using satellite data: Factors that affect model performance

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DOI	10.1088/1748-9326/ab9334
	Estimating PM2.5in Southern California using satellite data: Factors that affect model performance
	Stowell J.D.; Bi J.; Al-Hamdan M.Z.; Lee H.J.; Lee S.-M.; Freedman F.; Kinney P.L.; Liu Y.
发表日期	2020
ISSN	17489318
卷号	15 期号:9
英文摘要	Background: Studies of PM2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM2.5/AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results: Mean predicted PM2.5 concentration for the study domain was 8.84 µg m-3. Linear regression between CV predicted PM2.5 concentrations and observations had an R 2 of 0.80 and RMSE 2.25 µg m-3. The ratio of PM2.5 to PM10 proved an important variable in modifying the AOD/PM2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R 2 and a 0.56 µg m-3 decrease in CV RMSE). Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	air quality; AOD; pm10; pm2.5; remote sensing; satellite
语种	英语
scopus关键词	Satellites; Aerosol optical depths; Geographically weighted regression; Linear mixed-effects model; PM2.5 concentration; Remote sensing data; Satellite remote sensing data; Southern California; Statistical modeling; Remote sensing; aerosol composition; aerosol formation; air quality; particle size; particulate matter; performance assessment; satellite altimetry; South Carolina; 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/153827
作者单位	Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States; Nasa, George C. Marshall Space Flight Center, Universities Space Research Association, Huntsville, AL, United States; California Air Resources Board, Sacramento, CA, United States; South Coast Air Quality Management District, Diamond Bar, CA, United States; Department of Meteorology and Climate Science, San Jose State University, San Jose, CA, United States; Department of Environmental Health, Boston University School of Public Health, Boston, MA, United States
推荐引用方式 GB/T 7714	Stowell J.D.,Bi J.,Al-Hamdan M.Z.,et al. Estimating PM2.5in Southern California using satellite data: Factors that affect model performance[J],2020,15(9).
APA	Stowell J.D..,Bi J..,Al-Hamdan M.Z..,Lee H.J..,Lee S.-M..,...&Liu Y..(2020).Estimating PM2.5in Southern California using satellite data: Factors that affect model performance.Environmental Research Letters,15(9).
MLA	Stowell J.D.,et al."Estimating PM2.5in Southern California using satellite data: Factors that affect model performance".Environmental Research Letters 15.9(2020).