A Review of Research in Estimation and Dispersion of Particulate Matter Using Remote Sensing Data in Southeast Asia

Abstract

Air pollution is a major challenge for the global population nowadays, while the most deleterious effects in terms of human health (cardiovascular diseases, asthma) are detected in the densely populated and highly polluted regions of south and east Asia. This study provides a comprehensive review of scientific articles dealing with particulate matter (PM) estimations from space in the climate sensitive and polluted southeast Asian (SEA) region. PM monitoring from space over this region is a challenging task due to high aerosol burden, mostly attributed to biomass-burning smoke from extensive forest, agricultural and peat fires, outside cooking and increasing urban/industrial emissions due to growing population, urbanization and industrialization. Several satellite sensors onboard polar-orbiting and geostationary satellites are reviewed, as well as influencing factors (meteorological variables, gaseous pollutants, topographic characteristics), algorithms and statistical techniques that are synergistically implemented for assessment of surface pollution (i.e. PM concentrations) from space-borne remote-sensing applications. Furthermore, current review highlights the potential of implementing Machine Learning (ML) and Deep Learning (DL) models and advanced computational techniques for developing air pollution prediction in hotspot regions of Asia (mainly), opening a new era in PM simulations and providing support to policymakers and stakeholders to design new effective pollution control strategies for attaining sustainable development goals under the challenge of climate change. International collaboration in the fields of remote sensing applications, maintenance of ground-based pollution networks and development of new ML models between researchers from various countries is especially important for future perspectives and innovations in PM estimations from space.