Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning

This study presents a methodological framework for estimating surface nitrogen dioxide (NO2) concentrations in Mexico City during 2024. Sentinel-5P satellite observations, ERA5 meteorological variables, and ground measurements from the RAMA were integrated to generate high-resolution estimates through a Random Forest model combined with statistical downscaling. After data cleaning, 3246 aligned records were retained. The model achieved robust performance (R2 = 0.9196; RMSE = 6.80 µg/m3; MAE = 4.55 µg/m3), demonstrating its ability to reproduce both spatial and temporal variations in NO2 across the metropolitan area. These results confirm that machine-learning-based downscaling effectively enhances satellite-derived pollution estimates and provides a reliable tool for urban air quality assessment.