Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Atmosphere

DOI: 10.3390/atmos17010037

Authors: Yolanda Rosenda Monzón Herrera, Mayrén Polanco Gaytán, Raúl Teodoro Aquino Santos, Lakshmi Babu Saheer, Oliver Mendoza-Cano, Rafael Julio Macedo-Barragán

Institutions: Universidad de Colima, Anglia Ruskin University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $2,896