Articles | Volume 23, issue 17
https://doi.org/10.5194/acp-23-10267-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-10267-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Sep 2023
Research article |
| 14 Sep 2023
| 14 Sep 2023
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning
Vigneshkumar Balamurugan
CORRESPONDING AUTHOR
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Adrian Wenzel
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Frank N. Keutsch
School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
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Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
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Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, https://doi.org/10.5194/bg-19-1777-2022, 2022
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We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Gerrit Kuhlmann, Ka Lok Chan, Sebastian Donner, Ying Zhu, Marc Schwaerzel, Steffen Dörner, Jia Chen, Andreas Hueni, Duc Hai Nguyen, Alexander Damm, Annette Schütt, Florian Dietrich, Dominik Brunner, Cheng Liu, Brigitte Buchmann, Thomas Wagner, and Mark Wenig
Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, https://doi.org/10.5194/amt-15-1609-2022, 2022
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Dandan Wei, Hariprasad D. Alwe, Dylan B. Millet, Brandon Bottorff, Michelle Lew, Philip S. Stevens, Joshua D. Shutter, Joshua L. Cox, Frank N. Keutsch, Qianwen Shi, Sarah C. Kavassalis, Jennifer G. Murphy, Krystal T. Vasquez, Hannah M. Allen, Eric Praske, John D. Crounse, Paul O. Wennberg, Paul B. Shepson, Alexander A. T. Bui, Henry W. Wallace, Robert J. Griffin, Nathaniel W. May, Megan Connor, Jonathan H. Slade, Kerri A. Pratt, Ezra C. Wood, Mathew Rollings, Benjamin L. Deming, Daniel C. Anderson, and Allison L. Steiner
Geosci. Model Dev., 14, 6309–6329, https://doi.org/10.5194/gmd-14-6309-2021, https://doi.org/10.5194/gmd-14-6309-2021, 2021
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Over the past decade, understanding of isoprene oxidation has improved, and proper representation of isoprene oxidation and isoprene-derived SOA (iSOA) formation in canopy–chemistry models is now recognized to be important for an accurate understanding of forest–atmosphere exchange. The updated FORCAsT version 2.0 improves the estimation of some isoprene oxidation products and is one of the few canopy models currently capable of simulating SOA formation from monoterpenes and isoprene.
Taylor S. Jones, Jonathan E. Franklin, Jia Chen, Florian Dietrich, Kristian D. Hajny, Johannes C. Paetzold, Adrian Wenzel, Conor Gately, Elaine Gottlieb, Harrison Parker, Manvendra Dubey, Frank Hase, Paul B. Shepson, Levi H. Mielke, and Steven C. Wofsy
Atmos. Chem. Phys., 21, 13131–13147, https://doi.org/10.5194/acp-21-13131-2021, https://doi.org/10.5194/acp-21-13131-2021, 2021
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Methane emissions from leaks in natural gas pipes are often a large source in urban areas, but they are difficult to measure on a city-wide scale. Here we use an array of innovative methane sensors distributed around the city of Indianapolis and a new method of combining their data with an atmospheric model to accurately determine the magnitude of these emissions, which are about 70 % larger than predicted. This method can serve as a framework for cities trying to account for their emissions.
Eleni Dovrou, Kelvin H. Bates, Jean C. Rivera-Rios, Joshua L. Cox, Joshua D. Shutter, and Frank N. Keutsch
Atmos. Chem. Phys., 21, 8999–9008, https://doi.org/10.5194/acp-21-8999-2021, https://doi.org/10.5194/acp-21-8999-2021, 2021
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We examined the mechanism and products of oxidation of dissolved sulfur dioxide with the main isomers of isoprene hydroxyl hydroperoxides, via laboratory and model analysis. Two chemical mechanism pathways are proposed and the results provide an improved understanding of the broader atmospheric chemistry and role of multifunctional organic hydroperoxides, which should be the dominant VOC oxidation products under low-NO conditions, highlighting their significant contribution to sulfate formation.
Jack C. Hensley, Adam W. Birdsall, Gregory Valtierra, Joshua L. Cox, and Frank N. Keutsch
Atmos. Chem. Phys., 21, 8809–8821, https://doi.org/10.5194/acp-21-8809-2021, https://doi.org/10.5194/acp-21-8809-2021, 2021
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We measured reactions of butenedial, an atmospheric dicarbonyl, in aqueous mixtures that mimic the conditions of aerosol particles. Major reaction products and rates were determined to assess their atmospheric relevance and to compare against other well-studied dicarbonyls. We suggest that the structure of the carbon backbone, not just the dominant functional group, plays a major role in dicarbonyl reactivity, influencing the fate and ability of dicarbonyls to produce brown carbon.
Alexander Zaytsev, Martin Breitenlechner, Anna Novelli, Hendrik Fuchs, Daniel A. Knopf, Jesse H. Kroll, and Frank N. Keutsch
Atmos. Meas. Tech., 14, 2501–2513, https://doi.org/10.5194/amt-14-2501-2021, https://doi.org/10.5194/amt-14-2501-2021, 2021
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We have developed an online method for speciated measurements of organic peroxy radicals and stabilized Criegee intermediates using chemical derivatization combined with chemical ionization mass spectrometry. Chemical derivatization prevents secondary radical reactions and eliminates potential interferences. Comparison between our measurements and results from numeric modeling shows that the method can be used for the quantification of a wide range of atmospheric radicals and intermediates.
Florian Dietrich, Jia Chen, Benno Voggenreiter, Patrick Aigner, Nico Nachtigall, and Björn Reger
Atmos. Meas. Tech., 14, 1111–1126, https://doi.org/10.5194/amt-14-1111-2021, https://doi.org/10.5194/amt-14-1111-2021, 2021
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Climate change is one of the defining issues of our time. However, most of the current emission estimates are based on calculations, not on actual measurements as it is difficult to quantify the emissions of large sources such as cities. This study shows how to use the relatively new approach of column measurements to quantify urban greenhouse gas emissions in an exact way using only a few compact measurement systems. The approach can be used to evaluate the effectiveness of mitigation policies.
Ying Zhu, Jia Chen, Xiao Bi, Gerrit Kuhlmann, Ka Lok Chan, Florian Dietrich, Dominik Brunner, Sheng Ye, and Mark Wenig
Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, https://doi.org/10.5194/acp-20-13241-2020, 2020
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Average NO2 concentration of on-street mobile measurements (MMs) near the monitoring stations (MSs) was found to be considerably higher than the MSs data. The common measurement height (H) and distance (D) of the MSs result in 27 % lower average concentrations in total than the concentration of our MMs. Another 21 % difference remained after correcting the influence of the measuring H and D. This result makes our city-wide measurements for capturing the full range of concentrations necessary.
Lei Zhu, Gonzalo González Abad, Caroline R. Nowlan, Christopher Chan Miller, Kelly Chance, Eric C. Apel, Joshua P. DiGangi, Alan Fried, Thomas F. Hanisco, Rebecca S. Hornbrook, Lu Hu, Jennifer Kaiser, Frank N. Keutsch, Wade Permar, Jason M. St. Clair, and Glenn M. Wolfe
Atmos. Chem. Phys., 20, 12329–12345, https://doi.org/10.5194/acp-20-12329-2020, https://doi.org/10.5194/acp-20-12329-2020, 2020
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We develop a validation platform for satellite HCHO retrievals using in situ observations from 12 aircraft campaigns. The platform offers an alternative way to quickly assess systematic biases in HCHO satellite products over large domains and long periods, facilitating optimization of retrieval settings and the minimization of retrieval biases. Application to the NASA operational HCHO product indicates that relative biases range from −44.5 % to +112.1 % depending on locations and seasons.
Qiansi Tu, Frank Hase, Thomas Blumenstock, Rigel Kivi, Pauli Heikkinen, Mahesh Kumar Sha, Uwe Raffalski, Jochen Landgraf, Alba Lorente, Tobias Borsdorff, Huilin Chen, Florian Dietrich, and Jia Chen
Atmos. Meas. Tech., 13, 4751–4771, https://doi.org/10.5194/amt-13-4751-2020, https://doi.org/10.5194/amt-13-4751-2020, 2020
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Two COCCON instruments are used to observe multiyear greenhouse gases in boreal areas and are compared with the CAMS analysis and S5P satellite data. These three datasets predict greenhouse gas gradients with reasonable agreement. The results indicate that the COCCON instrument has the capability of measuring gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gases.
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
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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.
In this study, machine learning models are employed to model NO2 and O3 concentrations. We...
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