气候变化领域集成服务门户(CCPortal): Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks

CCPortal

DOI	10.1088/1748-9326/abb733
	Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks
	Bruni Zani N.; Lonati G.; Mead M.I.; Latif M.T.; Crippa P.
发表日期	2020
ISSN	17489318
卷号	15 期号:10
英文摘要	Atmospheric pollution of particulate matter (PM) is a major concern for its deleterious effects on human health and climate. Over the past 50 years, Equatorial Asia has experienced significant land-use change and urbanization, which have contributed to more intense and frequent extreme PM concentrations associated with increased anthropogenic and wildfire emissions. Recent advances in remote sensing instrumentation and retrieval protocols have enabled effective monitoring of PM from space in near real time with almost global coverage. In this study, long-term satellite-based observations of key chemical and physical parameters, integrated with ground-based concentrations of PM with aerodynamic diameter <10 μm (PM10) measured at 52 stations, are used to develop a machine learning approach for continuous PM10 monitoring. As PM atmospheric pollution, like most of environmental processes, is highly non-linear and influenced by numerous variables, machine learning approaches seem very suitable. Herein, deep neural networks are developed and tested over different temporal scales and used to map PM10 over Equatorial Asia during the period 2005-2015. The proposed model captures both PM10 seasonal variability and the occurrence of extreme episodes, which are found to impact air quality on the regional scale. The modeled annual mean fine PM (PM2.5) concentrations are used to estimate long-term premature mortality. This study indicates that the region is experiencing increasing mortality rates related to long-term exposure to PM2.5, with 150 000 (108 000-193 000) premature deaths in 2005 and 204 000 (145 000-260 000) in 2015. This is mostly due to air quality worsening and high population growth in urban areas, although the contribution of years of intense wildfires results as well significant. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	air quality; AOD; Equatorial Asia; health impact assessment; machine learning; particulate matter
语种	英语
scopus关键词	Air quality; Atmospheric chemistry; Deep learning; Deep neural networks; Fires; Land use; Learning systems; Population statistics; Remote sensing; Turing machines; Urban growth; Aerodynamic diameters; Atmospheric pollution; Chemical and physical parameters; Deleterious effects; Environmental process; Machine learning approaches; Satellite based observations; Seasonal variability; Neural networks; air quality; artificial neural network; estimation method; long-term change; mortality; pollution effect; satellite altimetry; Asia
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/153578
作者单位	Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, United States; Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy; Centre for Atmospheric Informatics and Emissions Technology, Cranfield University, Cranfield, United Kingdom; Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
推荐引用方式 GB/T 7714	Bruni Zani N.,Lonati G.,Mead M.I.,et al. Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks[J],2020,15(10).
APA	Bruni Zani N.,Lonati G.,Mead M.I.,Latif M.T.,&Crippa P..(2020).Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks.Environmental Research Letters,15(10).
MLA	Bruni Zani N.,et al."Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks".Environmental Research Letters 15.10(2020).