气候变化领域集成服务门户(CCPortal): A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD

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DOI	10.1016/j.atmosenv.2021.118217
	A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD
	Yao F.; Palmer P.I.
发表日期	2021
ISSN	1352-2310
卷号	248
英文摘要	We present a new method to infer ground-level fine particulate matter (PM2.5) from satellite remote sensing observations of aerosol optical depth (AOD). The conventional method generally uses a range of modelling approaches to determine PM2.5:AOD relationships that are subsequently used to infer ground-level PM2.5 concentrations from satellite-retrieved AOD. Here, we use a high-resolution atmospheric chemistry simulation to explore how changes in the vertical distribution of aerosol extinction coefficients affects the PM2.5:AOD relationship and how we can use that information to improve the robustness of inferred estimates of ground-level PM2.5 over eastern China. We define a metric, ΓPBLAOD, that describes the fraction of AOD that resides in the planetary boundary layer compared with the total columnar AOD. We determine physically-meaningful PM2.5:AOD relationships using data for which ΓPBLAOD≥50%, a criterion based on sensitivity analyses on data clusters that we identify using a hierarchical clustering method. We use statistical and machine learning methods to develop independent models that describe these PM2.5:AOD relationships, and use a Monte Carlo approach to quantify the improvement after our selection of more physically relevant data records. Benefiting from the improved representativeness of AOD for ground-level PM2.5, our method effectively reduces bias in inferred estimates of ground-level PM2.5 by 10–15% (9–12%) for space-borne sensors passing over in the morning (afternoon). It also captures more variations in ground-level PM2.5 by up to 8% (5%) for space-borne sensors passing over in the morning (afternoon), particularly over areas dominated by natural aerosols such as dust. Accordingly, our method improves the seasonal ground-level PM2.5 maps, e.g. the bias of the autumn (winter) mean of ground-level PM2.5 estimates over Qinghai and Gansu (Shaaxi, Shanxi, and Henan) provinces reduces from −8% to −5% (11%–6%). © 2021 Elsevier Ltd
关键词	AOD Machine learning Model of atmospheric chemistry and transport PM2.5 Statistical regression
语种	英语
scopus关键词	Aerosols; Atmospheric chemistry; Boundary layers; Hierarchical clustering; Monte Carlo methods; Remote sensing; Satellites; Sensitivity analysis; Aerosol extinction coefficient; Aerosol optical depths; Fine particulate matter (PM2.5); Hierarchical clustering methods; Machine learning methods; Planetary boundary layers; Satellite remote sensing; Vertical distributions; Learning systems; aerosol composition; atmospheric chemistry; boundary layer; concentration (composition); machine learning; optical depth; satellite altimetry; satellite sensor; vertical distribution; aerosol optical depth; Article; atmosphere; autumn; boundary layer; China; cross validation; data clustering; dust; ground level; hierarchical clustering; machine learning; Monte Carlo method; optical depth; particulate matter 2.5; priority journal; quantitative analysis; regression analysis; remote sensing; season; sensitivity analysis; simulation; statistical analysis; winter; China; Gansu; Henan; Qinghai; Shanxi
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/248580
作者单位	School of GeoSciences, University of Edinburgh, United Kingdom; National Centre for Earth Observation, University of Edinburgh, United Kingdom
推荐引用方式 GB/T 7714	Yao F.,Palmer P.I.. A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD[J],2021,248.
APA	Yao F.,&Palmer P.I..(2021).A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD.ATMOSPHERIC ENVIRONMENT,248.
MLA	Yao F.,et al."A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD".ATMOSPHERIC ENVIRONMENT 248(2021).