Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks
File(s)
Published version
Author(s)
Bruni Zani, N
Lonati, G
Mead, M
Latif, MT
Crippa, P
Type
Journal Article
Abstract
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.
Date Issued
2020-10-08
Date Acceptance
2020-09-10
Citation
Environmental Research Letters, 2020, 15 (10)
ISSN
1748-9326
Publisher
IOP Publishing
Journal / Book Title
Environmental Research Letters
Volume
15
Issue
10
Copyright Statement
© 2020 The Author(s). Published by IOP Publishing Ltd Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000577018800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
AEROSOL OPTICAL DEPTH
air quality
AOD
CHINA
EMISSIONS
Environmental Sciences
Environmental Sciences & Ecology
Equatorial Asia
FIRE
FOREST
health impact assessment
Life Sciences & Biomedicine
machine learning
Meteorology & Atmospheric Sciences
particulate matter
Physical Sciences
PM10
PM2.5
POLLUTANTS
POLLUTION
Science & Technology
ULTRAFINE PARTICLE CONCENTRATIONS
Publication Status
Published
Article Number
104088
Date Publish Online
2020-10-08