Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs and a cross-cutting component of certain United Nations Sustainable Development Goals (SDGs). Hence, reliable information on air pollution and human exposure is a crucial element in policy recommendations and decisions. At the same time, Earth Observation is steadily gaining confidence as a data input in the calculation of various SDG indicators. The current paper focuses on the usability of modern satellite remote sensing in the context of SDGs relevant to air quality. We introduce the socioeconomic importance of air quality and discuss the current uptake of geospatial information. The latest developments in Earth Observation provide measurements of finer spatial, temporal, and radiometric resolution products with increased global coverage, long-term continuation, and coherence in measurements. Leveraging on the two latest operational satellite technologies available, namely the Sentinel-5P and the Geostationary Environment Monitoring Spectrometer (GEMS) missions, we demonstrate two potential operational applications for quantifying air pollution at city and regional scales. Based on the two examples and by discussing the near-future anticipated geospatial capabilities, we showcase and advocate that the potential of satellite remote sensing as a, complementary to ground station networks, source of air pollution information is gaining confidence. As such, it can be an invaluable tool for quantifying global air pollution and deriving robust population exposure estimates.

Low-cost sensors (LCS) have been increasingly deployed to monitor PM_2.5 concentrations. More than 1500 LCS have been installed in Thailand to increase public awareness of air quality. However, performance of these sensors has not been systematically investigated. In this study, PM_2.5 LCS were co-located next to a PM_2.5 federal equivalent method (FEM) reference instrument at three Thai locations—in the north, center and northeast. We evaluated the performance of a PM_2.5 LCS (PMS7003, Plantower) to understand the key factors affecting performance, including emission sources, relative humidity, temperature and PM_2.5 concentration. Low PM concentration and high humidity levels had a significant impact on performance. Sensors in a high traffic emission area showed low correlation. The unadjusted PM_2.5 LCS performance varied with locations. Errors were mainly observed at low concentrations. They significantly underestimated concentrations in congested urban environments. After calibration, accuracy was improved with multiple regression models. The performance of sensors only at Chiang Mai (CM) during the dry season and Ubon Ratchathani (URT) during the dry and wet seasons were acceptable with coefficient of variation: 5.8 ± 4.7–6.8 ± 5.0%, slope: 0.829–0.945, intercept: 1.12–5.49 µg/m³, R²: 0.880–0.934 and RMSE: 4.3–5.1 µg/m³. In the congested area in Bangkok (BKK), they underestimated concentrations of small particles.