Articles | Volume 23, issue 17
https://doi.org/10.5194/acp-23-10267-2023
https://doi.org/10.5194/acp-23-10267-2023
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

Related authors

Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors
Ayah Abu-Hani, Jia Chen, Vigneshkumar Balamurugan, Adrian Wenzel, and Alessandro Bigi
Atmos. Meas. Tech., 17, 3917–3931, https://doi.org/10.5194/amt-17-3917-2024,https://doi.org/10.5194/amt-17-3917-2024, 2024
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, https://doi.org/10.5194/acp-22-7105-2022,https://doi.org/10.5194/acp-22-7105-2022, 2022
Short summary

Related subject area

Subject: Gases | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with machine learning
Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1846,https://doi.org/10.5194/egusphere-2024-1846, 2024
Short summary
Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
Chenliang Tao, Yanbo Peng, Qingzhu Zhang, Yuqiang Zhang, Bing Gong, Qiao Wang, and Wenxing Wang
Atmos. Chem. Phys., 24, 4177–4192, https://doi.org/10.5194/acp-24-4177-2024,https://doi.org/10.5194/acp-24-4177-2024, 2024
Short summary
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, Paul J. Young, and Massimo Vieno
Atmos. Chem. Phys., 24, 3163–3196, https://doi.org/10.5194/acp-24-3163-2024,https://doi.org/10.5194/acp-24-3163-2024, 2024
Short summary
Automated detection and monitoring of methane super-emitters using satellite data
Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben
Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023,https://doi.org/10.5194/acp-23-9071-2023, 2023
Short summary
Estimating nitrogen and sulfur deposition across China during 2005 to 2020 based on multiple statistical models
Kaiyue Zhou, Wen Xu, Lin Zhang, Mingrui Ma, Xuejun Liu, and Yu Zhao
Atmos. Chem. Phys., 23, 8531–8551, https://doi.org/10.5194/acp-23-8531-2023,https://doi.org/10.5194/acp-23-8531-2023, 2023
Short summary

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, https://doi.org/10.1029/2021JD035440, 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, https://doi.org/10.5194/acp-22-7105-2022, 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], https://doi.org/10.5281/zenodo.8330479, 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
Download
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.
Altmetrics
Final-revised paper
Preprint