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Atmosphere
Volume 17
Issue 1
10.3390/atmos17010037
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Yolanda Rosenda Monzón Herrera
1,
Mayrén Polanco Gaytán
2,
Raúl Teodoro Aquino Santos
3,*,
Lakshmi Babu Saheer
4,
Oliver Mendoza-Cano
1 and
Rafael Julio Macedo-Barragán
5
1
Facultad de Ingeniería Civil, Universidad de Colima, Coquimatlán 28400, Colima, Mexico
2
Facultad de Economía, Universidad de Colima, Villa de Álvarez 28970, Colima, Mexico
3
Coordinación General de Investigación Científica, Universidad de Colima, Colima 28040, Colima, Mexico
4
Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK
5
Facultad de Ciencias Biológicas y Agropecuarias, Universidad de Colima, Tecomán 28930, Colima, Mexico
*
Author to whom correspondence should be addressed.
Atmosphere 2026, 17(1), 37; https://doi.org/10.3390/atmos17010037 (registering DOI)
Submission received: 26 November 2025 / Revised: 18 December 2025 / Accepted: 22 December 2025 / Published: 26 December 2025
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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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.
Keywords: nitrogen dioxide; Sentinel-5P; RAMA; Random Forest; statistical downscaling; bilinear interpolation; air pollution; Mexico City nitrogen dioxide; Sentinel-5P; RAMA; Random Forest; statistical downscaling; bilinear interpolation; air pollution; Mexico City
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MDPI and ACS Style

Monzón Herrera, Y.R.; Polanco Gaytán, M.; Aquino Santos, R.T.; Babu Saheer, L.; Mendoza-Cano, O.; Macedo-Barragán, R.J. Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning. Atmosphere 2026, 17, 37. https://doi.org/10.3390/atmos17010037

AMA Style

Monzón Herrera YR, Polanco Gaytán M, Aquino Santos RT, Babu Saheer L, Mendoza-Cano O, Macedo-Barragán RJ. Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning. Atmosphere. 2026; 17(1):37. https://doi.org/10.3390/atmos17010037

Chicago/Turabian Style

Monzón Herrera, Yolanda Rosenda, Mayrén Polanco Gaytán, Raúl Teodoro Aquino Santos, Lakshmi Babu Saheer, Oliver Mendoza-Cano, and Rafael Julio Macedo-Barragán. 2026. "Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning" Atmosphere 17, no. 1: 37. https://doi.org/10.3390/atmos17010037

APA Style

Monzón Herrera, Y. R., Polanco Gaytán, M., Aquino Santos, R. T., Babu Saheer, L., Mendoza-Cano, O., & Macedo-Barragán, R. J. (2026). Estimating Surface NO2 in Mexico City Using Sentinel-5P and Machine Learning. Atmosphere, 17(1), 37. https://doi.org/10.3390/atmos17010037

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