Articles | Volume 23, issue 17
Research article
14 Sep 2023
Research article |  | 14 Sep 2023

Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch

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Transferability of ML-based Global Calibration Models for NO2 and NO Low-Cost Sensors
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Atmos. Meas. Tech. Discuss.,,, 2024
Revised manuscript accepted for AMT
Short summary
Secondary PM2.5 decreases significantly less than NO2 emission reductions during COVID lockdown in Germany
Vigneshkumar Balamurugan, Jia Chen, Zhen Qu, Xiao Bi, and Frank N. Keutsch
Atmos. Chem. Phys., 22, 7105–7129,,, 2022
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Subject: Gases | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
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Cited articles

Balamurugan, V., Chen, J., Qu, Z., Bi, X., Gensheimer, J., Shekhar, A., Bhattacharjee, S., and Keutsch, F. N.: Tropospheric NO2 and O3 response to COVID-19 lockdown restrictions at the national and urban scales in Germany, J. Geophys. Res.-Atmos., 126, e2021JD035440,, 2021. a, b, c, d
Balamurugan, V., Balamurugan, V., and Chen, J.: Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm, Sci. Rep.-UK, 12, 1–8, 2022a. a
Balamurugan, V., Chen, J., Qu, Z., Bi, X., and Keutsch, F. N.: Secondary PM2.5 decreases significantly less than NO2 emission reductions during COVID lockdown in Germany, Atmos. Chem. Phys., 22, 7105–7129,, 2022b. a, b
Balamurgan, V., Chen, J., Wenzel, A., and Keutsch, F. N.: Spatio temporal ML model for NO2 and O3: Initial release, Version V1.0.0, Zenodo [code],, 2023. a
Bell, J., Power, S. A., Jarraud, N., Agrawal, M., and Davies, C.: The effects of air pollution on urban ecosystems and agriculture, Int. J. Sust. Dev. World, 18, 226–235, 2011. a
Short summary
In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
Final-revised paper