Editorial: special issue on “air quality monitoring, assessment, & forecasting using GIScience and remote sensing”

Air quality has been a serious societal issue over the globe due to continued urbanization and ongoing climate change. Air quality affects human health and various ecosystems, such as farmlands, forests, livestock, and wildlife. Millions of people die worldwide every year due to exposure to air pollution. Climate change tends to increase the risk of wildfire leading to severe air pollution. Global collaborations are required to appropriately mitigate the air pollution problem along with local efforts. Geographical information system (GIS) and remote sensing are essential techniques for monitoring and characterizing regional and global air quality, which provide important information for policymakers, researchers, and the public.

This special issue on “Air Quality Monitoring, Assessment, & Forecasting Using GIScience and Remote Sensing” consists of six research papers and one review paper. Researchers from East Asia, South Asia, and North America contribute a wide range of topics, including mapping regional air quality, characterizing pollution patterns, deriving emission inventories, and assessing impacts of aerosols on the ecosystems. On the basis of ground observations and satellite remote sensing, the researchers employ parametric statistical methods, machine learning algorithms, and chemical transport models to investigate regional air quality in order to facilitate mitigating air pollution. GIS and remote sensing consistently play important roles in all of these studies.

Two papers in this special issue focus on the research topic of mapping ground-level concentrations of fine particulate matters (PM_2.5). Unnithan and Gnanappazham (2020) proposed a mixed effect model integrated with spatial interpolation to derive the full-coverage daily PM_2.5 across the Indian subcontinent at a spatial resolution of 10 km. Aerosol optical depth data retrieved by the MODerate resolution Imaging Spectrometer were fused with ground observations. Compared to the limited coverage of ground monitoring, the results of this study provide comprehensive information on the spatiotemporal distribution of PM_2.5 in the Indian subcontinent. Shin et al. (2020) reviewed papers published during the last 10 years that used satellite-derived aerosol products and other types of data to estimate ground-level PM concentrations. Research trends for ground-level PM monitoring were discussed regarding modeling techniques with their recent advances, followed by the limitations and challenges that arise from estimating PM concentrations. They finally suggest five aspects that should be the core elements of future research in satellite-based PM research.

Studies on air pollution characterization and assessment are presented in three papers of this special issue. Yarragunta et al. (2020) investigated the distributions of gaseous air pollutants (O₃, CO, and NO_x) during forest-fire episodes over the foothills of the northwestern Himalaya. The simulation results of the Weather Research and Forecasting model coupled with Chemistry suggested that biomass burning caused rapid production of ozone. Althuwaynee et al. (2020) assessed inter-correlation clusters of respirable particulate matter (PM₁₀) and other air pollutants (including CO, NO_x, SO₂, and O₃) in Malaysia by using decision-tree algorithms and a K-means polar cluster function. The information on the main emission sources identified was valuable for mitigating the air pollution in the study area. Filonchyk et al. (2020) performed the investigation of spatiotemporal characteristics of five main atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and CO) over the seven cities located in immediate proximity to the South Gobi deserts. The concentrations of pollutants had seasonal variations, and especially the highest concentrations were found in the period of dust storm activities. They also showed that dust air masses moved from North to Northwest China affected large deserts such as the Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.

Moreover, two papers focus on the topics of estimating emission inventories and assessing the impact of aerosols on the ecosystem. By performing classification with the Google Earth Engine, Fuentes et al. (2020) mapped mine areas across Canada based on a huge amount of remote sensing images of Landsat and Sentinel. The authors then estimated particulate matter emissions from these exposed mine disturbance areas during 1990–2018, which provided valuable information for the Canadian Air Pollutant Emissions Inventory. Feng et al. (2020) investigated the impact of aerosols on gross primary productivity (GPP). They modeled GPP using the Boreal Ecosystem Productivity Simulator (BEPS) under two aerosol scenarios over cropland and grassland ecosystems in North China, which is known to be highly polluted. The results showed that aerosols can reduce GPP of the sunlit leaves but increase GPP of the shaded leaves. They also found that the impact of aerosols on GPP is more significant over the cropland with a more complex canopy structure than the grassland. The study improved the accuracy of GPP modeling by considering the aerosol-effect on GPP via solar radiation.

From this special issue, we believe that GIS and remote sensing will continue promoting diverse research on regional or global air quality. Integrative use of remote sensing, ground observation, and numerical simulation is an effective approach to resolving complex problems of air quality. Rapid development in machine learning is expected to gain more interest in this research field. Finally, we would like to thank the authors and anonymous reviewers for their valuable contribution to this special issue. We hope the readers enjoy this special issue on air quality.

Disclosure statement

No potential conflict of interest was reported by the authors.