Earth Observation–Based Geospatial Analysis of Population–Air Quality Interaction

Abstract. Against the backdrop of rapid urbanisation and environmental governance in megacities, understanding the spatial interactions between population distribution and the Air Quality Index (AQI) is crucial for achieving sustainable urban development. Taking Beijing as the study area, this research integrates high-resolution air quality monitoring data from June 2025 with geographic information from the Seventh National Population Census to construct a geospatial analysis framework based on an Earth Observation (EO) perspective. By comparing three typical spatial interpolation algorithms—Ordinary Kriging, Simple Kriging and Spline interpolation—this paper quantitatively characterises the spatial heterogeneity of Beijing’s AQI and provides an in-depth analysis of the overlapping effects of population density and environmental exposure risk. The study found that: (1) when handling AQI data, which exhibits strong spatial autocorrelation, the Ordinary Kriging method yields the smallest root mean square error (RMSE) and demonstrates the best robustness; (2) the distribution of AQI in Beijing exhibits a distinct ‘higher in the south, lower in the north’ stepped pattern, with complex quadratic trends along the east-west axis; (3) The city’s core area (the six urban districts) exhibits distinct characteristics of dual exposure to both high population density and high pollution levels. This study provides crucial data support and scientific evidence for the optimised allocation of environmental resources and the implementation of differentiated emission reduction strategies in megacities.