Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks

Bibliographic Data

ID15547825
AuthorsNicola Bruni Zani (0000-0002-7809-8023, University of Notre Dame), Giovanni Lonati (0000-0002-9872-6314, Politecnico di Milano), Mohammed Iqbal Mead (0000-0003-0436-4074, Cranfield University), Mohd Talib Latif (0000-0003-2339-3321, National University of Malaysia), Paola Crippa (0000-0003-3248-9675, University of Notre Dame, corresponding author)
Year2020
Volume15
Issue10
Pages104088-104088
Publication date2020-09-10
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/abb733
OpenAlexW3084844439
LanguageEN
Citations received1
References cited51

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 mu m (PM10) measured at 52 stations, are used to develop a machine learning approach for continuous PM(10)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 PM(10)over Equatorial Asia during the period 2005-2015. The proposed model captures both PM(10)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

Air pollution · Air quality index · Atmospheric sciences · Climatology · Geography · Meteorology · Particulates · Satellite · Term (time · Urbanization · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Climate Change and Health Impacts · Environmental Science · Ecology · Pollution

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