Spatiotemporal scale transformation and analysis of air quality based on remote sensing big data: a case study in the Huaihai economic zone, China

Scale effects and scale transformation remains is one of the critical issues in quantitative remote sensing. To obtain the air quality indicators at 1-km resolution and city-scale, two methods for spatiotemporal scale transformation were proposed and applied on the China High Air Pollutants (CHAP) datasets, which were generated from remote sensing big data. The findings demonstrate that different sequences of spatial and temporal scale transformation have a significant impact on the identification of primary pollutants in the Huaihai Economic Zone, China from 2019 to 2023. A scientifically optimized spatiotemporal scale transformation framework can effectively capture the spatial-temporal variability of air pollutants, thereby strengthening the technical foundation for government’s pollution control strategies.