Satellite-Based PM2.5 Estimation in Data-Sparse Urban Environments: Comparing Machine Learning and Geostatistical Approaches in Kolkata, India

Fine particulate matter (PM_2.5) is among the foremost environmental determinants of human health, contributing to cardiovascular disease, respiratory illness, and premature mortality. In rapidly urbanizing regions of the Global South, accurate spatial characterization of PM_2.5 exposure requires spatially continuous concentration surfaces that also provide reliable uncertainty estimates, yet ground-based monitoring networks remain severely sparse. Kolkata, India’s third-largest metropolitan area (population 14.9 million), exemplifies this challenge: only seven regulatory monitoring stations cover the entire city, leaving large areas unobserved.

This study evaluates how different PM_2.5 surface generation strategies—satellite-based machine learning (ML) and spatial interpolation—differ not only in predictive accuracy but also in their ability to provide decision-relevant uncertainty under sparse monitoring conditions. Using six years of daily observations (2019–2024), we compare two complementary approaches. The first employs satellite-based ML, integrating Sentinel-5P trace gases, MODIS aerosol optical depth, ERA5 meteorological reanalysis, and static urban features (VIIRS nightlights, population density) to predict PM_2.5. The second evaluates spatial interpolation methods—ordinary kriging, inverse distance weighting (IDW), and simple averaging—using station observations alone.

For satellite-based ML (Random Forest), the station-level model achieved R² = 0.79 under leave-one-station-out (LOSO) validation, while grid-based model trained on kriging-interpolated targets reached R² = 0.70 under temporal out-of-sample validation (train: 2019–2022, test: 2023–2024). Feature importance analysis consistently identified dewpoint temperature, air temperature, and surface albedo as dominant predictors, indicating that atmospheric conditions exert stronger control on PM_2.5 variability than emission proxies or land-use variables.

For spatial interpolation evaluated under daily LOSO, all methods achieved comparable point prediction accuracy (R² ≈ 0.85). However, uncertainty calibration diverged sharply. Ordinary kriging achieved 88% empirical coverage for nominal 95% prediction intervals (90% when including observation noise)—approaching theoretical calibration—whereas IDW and simple averaging exhibited severe under-coverage (45–52%), substantially underestimating true prediction error.

These findings yield three key insights: (1) satellite-derived predictors enable spatially complete PM_2.5 estimation beyond monitoring locations, though with moderate accuracy; (2) when temporally aligned station data are available, interpolation achieves higher point accuracy than satellite-based ML; and (3) regardless of estimation strategy, only geostatistical approaches provide uncertainty estimates suitable for health-protective decision-making. We conclude that hybrid frameworks combining satellite-based spatial prediction with kriging-derived uncertainty characterization offer a principled pathway for generating spatially complete and risk-aware PM_2.5 maps in data-sparse urban environments.