Assessment of the impact of urbanization on air pollution in Hanoi using satellite data and in-situ measurements during the 2021‒2025 period
Abstract
Rapid urbanization in large cities has posed many environmental challenges, especially air pollution. This study used multi-source remote sensing data to analyze the impact of urbanization on air pollution in Hanoi during 2021–2025. Landsat 8/9 Collection 2 Level 2 Surface Reflectance imagery was used to map urbanization through the Index-based Built-up Index (IBI), combined with land-use classification based on machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), calibrated with 331 field-collected samples. A total of 99 Landsat scenes (single-date summer images for RF/SVM classification and multi-temporal dry-season composites for IBI analysis) were processed across the study period. We characterized air pollution using daily MODIS MCD19A2 AOD products (1 km resolution), validated against 55 Aerosol Robotic Network (AERONET) Level 2.0 measurements at Nghia Do station (R² = 0.62) and correlated with hourly PM₂.₅ concentrations from 11 ground monitoring stations (AQMIS and OpenAQ networks). We developed pollution distribution maps for each year and used statistical tests (Root Mean Square Error (RMSE), R²) to assess the reliability of the satellite data. The study spatially overlaid urbanization and air pollution maps, ran correlation analyses using Moran's I, and built univariate and multivariate regression models incorporating ERA5 meteorological covariates to quantify the relationship between urbanization and pollutant concentrations. The results showed that built-up area (IBI > 0) expanded from 417.77 km² (12.4%) in 2021 to 701.36 km² (20.9%) in 2025. The linear regression between RF-classified urban area and mean AOD yielded R² = 0.82 (p < 0.001), while AOD correlated with ground-level PM₂.₅ at R² = 0.34–0.65 across 11 stations (improved to R² = 0.48–0.80 with meteorological covariates). Spatial autocorrelation analysis confirmed significant clustering of both urbanization (Moran's I = 0.62) and pollution (Moran's I = 0.78). The study highlighted the role of satellite data in urban environmental monitoring and provided a scientific basis for sustainable urban development planning.