Article from London School of Hygiene and Tropical Medicine (LSHTM) about a new technology that uses satellite data to map British air quality published in Remote Sensing
An article entitled “A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain”, co-authored by Guest Editor Dr Itai Kloog, was published in the Remote Sensing Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies on November 20, 2020, and has already been shared by numerous media outlets.
Dr Rochelle Schneider and her team aimed to develop a multi-stage satellite-based technique that uses artificial intelligence to estimate daily fine particulate matter (PM2.5) levels across Great Britain during 2008–2018.
In their press release entitled “AI-driven map could link UK air quality to health”, published on the same day as the article, The Engineer declared that “the output reveals the shifting patterns of air pollution across Great Britain and in time with extraordinary detail. We now hope to use this information to better understand how pollution is affecting the nation’s health, so we can take steps to minimize the risk. The vast amount of data produced will provide a vital tool for public health researchers investigating the effects of air pollution”.
As first author of the article, Dr Rochelle Schneider said in the interview, “this research uses the power of artificial intelligence to advance environmental modelling and address public health challenges”.
“This study demonstrates how cutting-edge techniques based on artificial intelligence and satellite technologies can benefit public health research,” according to Antonio Gasparrini, Professor of Biostatistics and Epidemiology at LSHTM and senior author of the study.
Mirage News announced it as “a novel method that combines artificial intelligence with remote sensing satellite technologies has produced the most detailed coverage of air pollution in Britain to date.” (https://www.miragenews.com/artificial-intelligence-and-satellite-technologies-reveal-detailed-map-of-air-pollution-across-great-britain/)
The report included an interview with Dr Vincent-Henri Peuch, Director of Copernicus Atmosphere Monitoring Service (CAMS) at European Centre for Medium-Range Weather Forecasts (ECMWF), who declared “this innovative method has combined the strengths of different data sources to give accurate and comprehensive estimates of air pollution exposure, including ground-based sensors, satellite data, and model reanalyses developed by ECMWF as part of the EU Copernicus programme. Dr Schneider and co-authors convincingly demonstrate its performance over Great Britain, paving the way for many future studies into the health effects of air pollution”.
Dr Pierre-Philippe Mathieu, Head of the Phi-lab Explore Office at the European Space Agency (ESA), said “it’s exciting to see data from Earth observation satellites being used in public health research to advance our understanding of the intricate relationship between health and air quality, improving lives in Great Britain, Europe and the rest of the world”.
Remote Sensing (ISSN 2072-4292, IF 4.509) is a peer-reviewed open access journal on the science and application of remote sensing technology, and is published semi-monthly online by MDPI. Remote Sensing publishes regular research papers, reviews, letters, and communications covering all aspects of remote sensing science, from sensor design and validation/calibration to its application in geosciences, environmental sciences, ecology, and civil engineering. Its aim is to publish novel/improved methods/approaches and/or algorithms of remote sensing to benefit the community, open to anyone in need of them.