Articles | Volume 22, issue 11
https://doi.org/10.5194/acp-22-7105-2022
© Author(s) 2022. 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-22-7105-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jun 2022
Research article |
| 02 Jun 2022
| 02 Jun 2022
Secondary PM2.5 decreases significantly less than NO2 emission reductions during COVID lockdown in Germany
Vigneshkumar Balamurugan
CORRESPONDING AUTHOR
Environmental Sensing and Modeling, Department of Electrical and Computer Engineering, Technical University of Munich (TUM), Munich, Germany
Environmental Sensing and Modeling, Department of Electrical and Computer Engineering, Technical University of Munich (TUM), Munich, Germany
School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
Environmental Sensing and Modeling, Department of Electrical and Computer Engineering, 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
Related authors
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
Short summary
This study examined the transferability of machine learning calibration models among low-cost sensor units targeting NO2 and NO. The global models were evaluated under similar and different emission conditions. To counter cross-sensitivity, the study proposed integrating O3 measurements from nearby reference stations, in Switzerland. The models show substantial improvement when O3 measurements are incorporated, which is more pronounced when in regions with elevated O3 concentrations.
Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 10267–10285, https://doi.org/10.5194/acp-23-10267-2023, https://doi.org/10.5194/acp-23-10267-2023, 2023
Short summary
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.
Dan Topa, Berthold Stoeger, Frank Norman Keutsch, and Gheorghe Ilinca
Eur. J. Mineral., 37, 971–994, https://doi.org/10.5194/ejm-37-971-2025, https://doi.org/10.5194/ejm-37-971-2025, 2025
Short summary
Short summary
We conducted this research to gain an understanding of the unknown crystal structure of mineral launayite, which contains elements like antimony, copper, and lead. Using advanced X-ray techniques, we solved its crystal structure and discovered new details, such as unit cell parameters, symmetry, and substitutions. These findings provide accurate information about its relationship with rouxelite.
Daniel Kühbacher, Jia Chen, Patrick Aigner, Mario Ilic, Ingrid Super, and Hugo Denier van der Gon
Geosci. Model Dev., 18, 9967–9990, https://doi.org/10.5194/gmd-18-9967-2025, https://doi.org/10.5194/gmd-18-9967-2025, 2025
Short summary
Short summary
We present DRIVE v1.0, a data-driven framework to estimate road transport emissions, their temporal profiles, and the associated uncertainties. The method was applied to the city of Munich, where we present bottom-up emission estimates for the years 2019 to 2022. The estimates are compared against official municipal reports as well as national and European downscaled inventories.
Yanxia Li, Hengheng Zhang, Xuefeng Shi, Yaowei Li, Sophie Abou-Rizk, Jessica Smith, Zhaojin An, Adrian Wenzel, Junwei Song, Thomas Leisner, Frank Keutsch, Jia Chen, and Harald Saathoff
EGUsphere, https://doi.org/10.5194/egusphere-2025-5191, https://doi.org/10.5194/egusphere-2025-5191, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed Munich air samples across seasons to identify pollution sources. Traffic contributes less to particles than expected, while biomass burning dominates year-round. Summer barbecuing and winter heating release significant pollution. Monoterpene emissions from plants produce particles at night. Effective air quality improvement requires year-round strategies targeting biomass burning, not just vehicles. Understanding each city's pollution patterns is essential for public health.
Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
Atmos. Chem. Phys., 25, 15969–15990, https://doi.org/10.5194/acp-25-15969-2025, https://doi.org/10.5194/acp-25-15969-2025, 2025
Short summary
Short summary
We compare six datasets of global ground-level ozone, developed using geostatistical, machine learning, or reanalysis methods. The datasets show important differences from one another in ozone magnitude, greater than 5 ppb, and trends, globally and regionally. Compared with measurements, performance varies among datasets, and most overestimate ozone, particularly at lower concentrations. These differences among datasets highlight uncertainties for applications to health and other impacts.
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
Atmos. Chem. Phys., 25, 14387–14410, https://doi.org/10.5194/acp-25-14387-2025, https://doi.org/10.5194/acp-25-14387-2025, 2025
Short summary
Short summary
To support the city of Zurich in tracking its path to net-zero greenhouse gas emissions planned to be reached by 2040, a CO2 emission monitoring system was established. The system combines a dense network of CO2 sensors with a high-resolution atmospheric transport model GRAMM/GRAL. This study presents the setup of the model together with its numerous inputs and evaluates its performance in comparison with observations from the CO2 sensor network.
Ann-Kristin Kunz, Samuel Hammer, Patrick Aigner, Laura Bignotti, Lars Borchardt, Jia Chen, Julian Della Coletta, Lukas Emmenegger, Markus Eritt, Xochilt Gutiérrez, Josh Hashemi, Rainer Hilland, Christopher Holst, Armin Jordan, Natascha Kljun, Richard Kneißl, Changxing Lan, Virgile Legendre, Ingeborg Levin, Benjamin Loubet, Matthias Mauder, Betty Molinier, Susanne Preunkert, Michel Ramonet, Stavros Stagakis, and Andreas Christen
EGUsphere, https://doi.org/10.5194/egusphere-2025-4856, https://doi.org/10.5194/egusphere-2025-4856, 2025
Short summary
Short summary
We present radiocarbon (14C)-based fossil fuel CO2 fluxes from relaxed eddy accumulation measurements on tall towers in the cities of Zurich, Paris, and Munich. By separating net CO2 fluxes into fossil and non-fossil components, these data reveal significant and variable contributions from human, plant, and soil respiration, as well as point-source emissions. These unique insights into CO2 flux composition offer crucial information for observation-based validation of urban emission estimates.
Dan Topa, Berthold Stoeger, Frank N. Keutsch, and Gheorghe Ilinca
Eur. J. Mineral., 37, 591–616, https://doi.org/10.5194/ejm-37-591-2025, https://doi.org/10.5194/ejm-37-591-2025, 2025
Short summary
Short summary
We conducted this research to gain understanding of the mineral rouxelite, which contains elements like antimony, copper, mercury, and lead. Using advanced X-ray techniques, we re-examined its crystal structure and discovered new details, such as unit-cell parameters, symmetry, and substitutions. These findings provide accurate information about how the mineral forms and behaves. This work not only updates scientific knowledge of rouxelite but also offers insights into its chemical complexity.
Patrick Aigner, Jia Chen, Felix Böhm, Mali Chariot, Lukas Emmenegger, Lars Frölich, Stuart Grange, Daniel Kühbacher, Klaus Kürzinger, Olivier Laurent, Moritz Makowski, Pascal Rubli, Adrian Schmitt, and Adrian Wenzel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4157, https://doi.org/10.5194/egusphere-2025-4157, 2025
Short summary
Short summary
Dense urban CO2 monitoring is challenging due to cost and operational constraints. We developed a mid-cost sensor network for Munich, deployed on 17 rooftops. Temperature-stabilized enclosures and automated 2-point calibration ensured reliable performance, assessed by side-by-side comparison with a Picarro reference. In the first year, the network collected 70 million measurements and resolved urban-rural gradients and seasonal diurnal patterns, capturing spatial CO2 variability at city scale.
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025, https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Short summary
The balance between CO2 uptake and emissions from urban green areas is still not well understood. This study evaluated for the first time the urban park CO2 exchange simulations with four different types of biosphere model by comparing them with observations. Even though some advantages and disadvantages of the different model types were identified, there was no strong evidence that more complex models performed better than simple ones.
Elise Penn, Daniel J. Jacob, Zichong Chen, James D. East, Melissa P. Sulprizio, Lori Bruhwiler, Joannes D. Maasakkers, Hannah Nesser, Zhen Qu, Yuzhong Zhang, and John Worden
Atmos. Chem. Phys., 25, 2947–2965, https://doi.org/10.5194/acp-25-2947-2025, https://doi.org/10.5194/acp-25-2947-2025, 2025
Short summary
Short summary
The hydroxyl radical (OH) destroys many air pollutants, including methane. Global-mean OH cannot be directly measured, and thus it is inferred with the methyl chloroform (MCF) proxy. MCF is decreasing, and a replacement is needed. We use satellite observations of methane in two spectral ranges as a proxy for OH. We find shortwave infrared observations can characterize yearly OH and its seasonality but not the latitudinal distribution. Thermal infrared observations add little information.
Takashi Sekiya, Emanuele Emili, Kazuyuki Miyazaki, Antje Inness, Zhen Qu, R. Bradley Pierce, Dylan Jones, Helen Worden, William Y. Y. Cheng, Vincent Huijnen, and Gerbrand Koren
Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025, https://doi.org/10.5194/acp-25-2243-2025, 2025
Short summary
Short summary
Five global chemical reanalysis datasets were used to assess the relative impacts of assimilating satellite ozone and its precursor measurements on tropospheric ozone analyses for 2010. The multiple reanalysis system comparison allows an evaluation of the dependency of the impacts on different reanalysis systems. The results suggested the importance of satellite ozone and its precursor measurements for improving ozone analysis in the whole troposphere, with varying magnitudes among the systems.
Dylan Jones, Lucas Prates, Zhen Qu, William Cheng, Kazuyuki Miyazaki, Takashi Sekiya, Antje Inness, Rajesh Kumar, Xiao Tang, Helen Worden, Gerbrand Koren, and Vincent Huijen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3759, https://doi.org/10.5194/egusphere-2024-3759, 2025
Short summary
Short summary
We evaluate five chemical reanalysis products to assess their potential to provide useful information on tropospheric ozone variability. We find that the reanalyses produce consistent information on ozone variations in the free troposphere, but have large discrepancies at the surface. The results suggests that improvements in the reanalyses are needed to better exploit the assimilated observations to enhance the utility of the reanalysis products at the surface.
Zhaojin An, Rujing Yin, Xinyan Zhao, Xiaoxiao Li, Yuyang Li, Yi Yuan, Junchen Guo, Yiqi Zhao, Xue Li, Dandan Li, Yaowei Li, Dongbin Wang, Chao Yan, Kebin He, Douglas R. Worsnop, Frank N. Keutsch, and Jingkun Jiang
Atmos. Chem. Phys., 24, 13793–13810, https://doi.org/10.5194/acp-24-13793-2024, https://doi.org/10.5194/acp-24-13793-2024, 2024
Short summary
Short summary
Online Vocus-PTR measurements show the compositions and seasonal variations in organic vapors in urban Beijing. With enhanced sensitivity and mass resolution, various species at a level of sub-parts per trillion (ppt) and organics with multiple oxygens (≥ 3) were observed. The fast photooxidation process in summer leads to an increase in both concentration and proportion of organics with multiple oxygens, while, in other seasons, the variations in them could be influenced by mixed sources.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Neil Humpage, Hartmut Boesch, William Okello, Jia Chen, Florian Dietrich, Mark F. Lunt, Liang Feng, Paul I. Palmer, and Frank Hase
Atmos. Meas. Tech., 17, 5679–5707, https://doi.org/10.5194/amt-17-5679-2024, https://doi.org/10.5194/amt-17-5679-2024, 2024
Short summary
Short summary
We used a Bruker EM27/SUN spectrometer within an automated weatherproof enclosure to measure greenhouse gas column concentrations over a 3-month period in Jinja, Uganda. The portability of the EM27/SUN allows us to evaluate satellite and model data in locations not covered by traditional validation networks. This is of particular value in tropical Africa, where extensive terrestrial ecosystems are a significant store of carbon and play a key role in the atmospheric budgets of CO2 and CH4.
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
Short summary
This study examined the transferability of machine learning calibration models among low-cost sensor units targeting NO2 and NO. The global models were evaluated under similar and different emission conditions. To counter cross-sensitivity, the study proposed integrating O3 measurements from nearby reference stations, in Switzerland. The models show substantial improvement when O3 measurements are incorporated, which is more pronounced when in regions with elevated O3 concentrations.
Benedikt Herkommer, Carlos Alberti, Paolo Castracane, Jia Chen, Angelika Dehn, Florian Dietrich, Nicholas M. Deutscher, Matthias Max Frey, Jochen Groß, Lawson Gillespie, Frank Hase, Isamu Morino, Nasrin Mostafavi Pak, Brittany Walker, and Debra Wunch
Atmos. Meas. Tech., 17, 3467–3494, https://doi.org/10.5194/amt-17-3467-2024, https://doi.org/10.5194/amt-17-3467-2024, 2024
Short summary
Short summary
The Total Carbon Column Observing Network is a network of ground-based Fourier transform infrared (FTIR) spectrometers used mainly for satellite validation. To ensure the highest-quality validation data, the network needs to be highly consistent. This is a major challenge, which so far is solved by site comparisons with airborne in situ measurements. In this work, we describe the use of a portable FTIR spectrometer as a travel standard for evaluating the consistency of TCCON sites.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Short summary
We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Yaowei Li, Corey Pedersen, John Dykema, Jean-Paul Vernier, Sandro Vattioni, Amit Kumar Pandit, Andrea Stenke, Elizabeth Asher, Troy Thornberry, Michael A. Todt, Thao Paul Bui, Jonathan Dean-Day, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 15351–15364, https://doi.org/10.5194/acp-23-15351-2023, https://doi.org/10.5194/acp-23-15351-2023, 2023
Short summary
Short summary
In 2021, the eruption of La Soufrière released sulfur dioxide into the stratosphere, resulting in a spread of volcanic aerosol over the Northern Hemisphere. We conducted extensive aircraft and balloon-borne measurements after that, revealing enhanced particle concentration and altered size distribution due to the eruption. The eruption's impact on ozone depletion was minimal, contributing ~0.6 %, and its global radiative forcing effect was modest, mainly affecting tropical and midlatitude areas.
Xinxu Zhao, Jia Chen, Julia Marshall, Michal Gałkowski, Stephan Hachinger, Florian Dietrich, Ankit Shekhar, Johannes Gensheimer, Adrian Wenzel, and Christoph Gerbig
Atmos. Chem. Phys., 23, 14325–14347, https://doi.org/10.5194/acp-23-14325-2023, https://doi.org/10.5194/acp-23-14325-2023, 2023
Short summary
Short summary
We develop a modeling framework using the Weather Research and Forecasting model at a high spatial resolution (up to 400 m) to simulate atmospheric transport of greenhouse gases and interpret column observations. Output is validated against weather stations and column measurements in August 2018. The differential column method is applied, aided by air-mass transport tracing with the Stochastic Time-Inverted Lagrangian Transport (STILT) model, also for an exploratory measurement interpretation.
Brandon Bottorff, Michelle M. Lew, Youngjun Woo, Pamela Rickly, Matthew D. Rollings, Benjamin Deming, Daniel C. Anderson, Ezra Wood, Hariprasad D. Alwe, Dylan B. Millet, Andrew Weinheimer, Geoff Tyndall, John Ortega, Sebastien Dusanter, Thierry Leonardis, James Flynn, Matt Erickson, Sergio Alvarez, Jean C. Rivera-Rios, Joshua D. Shutter, Frank Keutsch, Detlev Helmig, Wei Wang, Hannah M. Allen, Johnathan H. Slade, Paul B. Shepson, Steven Bertman, and Philip S. Stevens
Atmos. Chem. Phys., 23, 10287–10311, https://doi.org/10.5194/acp-23-10287-2023, https://doi.org/10.5194/acp-23-10287-2023, 2023
Short summary
Short summary
The hydroxyl (OH), hydroperoxy (HO2), and organic peroxy (RO2) radicals play important roles in atmospheric chemistry and have significant air quality implications. Here, we compare measurements of OH, HO2, and total peroxy radicals (XO2) made in a remote forest in Michigan, USA, to predictions from a series of chemical models. Lower measured radical concentrations suggest that the models may be missing an important radical sink and overestimating the rate of ozone production in this forest.
Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 10267–10285, https://doi.org/10.5194/acp-23-10267-2023, https://doi.org/10.5194/acp-23-10267-2023, 2023
Short summary
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.
Ruosi Liang, Yuzhong Zhang, Wei Chen, Peixuan Zhang, Jingran Liu, Cuihong Chen, Huiqin Mao, Guofeng Shen, Zhen Qu, Zichong Chen, Minqiang Zhou, Pucai Wang, Robert J. Parker, Hartmut Boesch, Alba Lorente, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, https://doi.org/10.5194/acp-23-8039-2023, 2023
Short summary
Short summary
We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Short summary
We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Andreas Forstmaier, Jia Chen, Florian Dietrich, Juan Bettinelli, Hossein Maazallahi, Carsten Schneider, Dominik Winkler, Xinxu Zhao, Taylor Jones, Carina van der Veen, Norman Wildmann, Moritz Makowski, Aydin Uzun, Friedrich Klappenbach, Hugo Denier van der Gon, Stefan Schwietzke, and Thomas Röckmann
Atmos. Chem. Phys., 23, 6897–6922, https://doi.org/10.5194/acp-23-6897-2023, https://doi.org/10.5194/acp-23-6897-2023, 2023
Short summary
Short summary
Large cities emit greenhouse gases which contribute to global warming. In this study, we measured the release of one important green house gas, methane, in Hamburg. Multiple sources that contribute to methane emissions were located and quantified. Methane sources were found to be mainly caused by human activity (e.g., by release from oil and gas refineries). Moreover, potential natural sources have been located, such as the Elbe River and lakes.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Short summary
We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Qing Ye, Matthew B. Goss, Jordan E. Krechmer, Francesca Majluf, Alexander Zaytsev, Yaowei Li, Joseph R. Roscioli, Manjula Canagaratna, Frank N. Keutsch, Colette L. Heald, and Jesse H. Kroll
Atmos. Chem. Phys., 22, 16003–16015, https://doi.org/10.5194/acp-22-16003-2022, https://doi.org/10.5194/acp-22-16003-2022, 2022
Short summary
Short summary
The atmospheric oxidation of dimethyl sulfide (DMS) is a major natural source of sulfate particles in the atmosphere. However, its mechanism is poorly constrained. In our work, laboratory measurements and mechanistic modeling were conducted to comprehensively investigate DMS oxidation products and key reaction rates. We find that the peroxy radical (RO2) has a controlling effect on product distribution and aerosol yield, with the isomerization of RO2 leading to the suppression of aerosol yield.
Maximilian Rißmann, Jia Chen, Gregory Osterman, Xinxu Zhao, Florian Dietrich, Moritz Makowski, Frank Hase, and Matthäus Kiel
Atmos. Meas. Tech., 15, 6605–6623, https://doi.org/10.5194/amt-15-6605-2022, https://doi.org/10.5194/amt-15-6605-2022, 2022
Short summary
Short summary
The Orbiting Carbon Observatory 2 (OCO-2) measures atmospheric concentrations of the most potent greenhouse gas, CO2, globally. By comparing its measurements to a ground-based monitoring network in Munich (MUCCnet), we find that the satellite is able to reliably detect urban CO2 concentrations. Furthermore, spatial CO2 differences captured by OCO-2 and MUCCnet are strongly correlated, which indicates that OCO-2 could be helpful in determining urban CO2 emissions from space.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
Short summary
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
Short summary
Short summary
We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
Short summary
Short summary
We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
Short summary
Short summary
This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Andreas Luther, Julian Kostinek, Ralph Kleinschek, Sara Defratyka, Mila Stanisavljević, Andreas Forstmaier, Alexandru Dandocsi, Leon Scheidweiler, Darko Dubravica, Norman Wildmann, Frank Hase, Matthias M. Frey, Jia Chen, Florian Dietrich, Jarosław Nȩcki, Justyna Swolkień, Christoph Knote, Sanam N. Vardag, Anke Roiger, and André Butz
Atmos. Chem. Phys., 22, 5859–5876, https://doi.org/10.5194/acp-22-5859-2022, https://doi.org/10.5194/acp-22-5859-2022, 2022
Short summary
Short summary
Coal mining is an extensive source of anthropogenic methane emissions. In order to reduce and mitigate methane emissions, it is important to know how much and where the methane is emitted. We estimated coal mining methane emissions in Poland based on atmospheric methane measurements and particle dispersion modeling. In general, our emission estimates suggest higher emissions than expected by previous annual emission reports.
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
Short summary
Short summary
Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
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
Short summary
Short summary
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
Short summary
Short summary
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO2 in Munich, we conducted the Munich NO2 Imaging Campaign (MuNIC), where NO2 was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO2 and its sources.
Tia R. Scarpelli, Daniel J. Jacob, Shayna Grossman, Xiao Lu, Zhen Qu, Melissa P. Sulprizio, Yuzhong Zhang, Frances Reuland, Deborah Gordon, and John R. Worden
Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, https://doi.org/10.5194/acp-22-3235-2022, 2022
Short summary
Short summary
We present a spatially explicit version of the national inventories of oil, gas, and coal methane emissions as submitted by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. We then use atmospheric modeling to compare our inventory emissions to atmospheric methane observations with the goal of identifying potential under- and overestimates of oil–gas methane emissions in the national inventories.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
Short summary
Short summary
We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
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
Short summary
Short summary
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.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
Short summary
Short summary
The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
Short summary
Short summary
We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
Short summary
Short summary
We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
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
Short summary
Short summary
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.
Cited articles
Allen, H. M., Draper, D. C., Ayres, B. R., Ault, A., Bondy, A., Takahama, S., Modini, R. L., Baumann, K., Edgerton, E., Knote, C., Laskin, A., Wang, B., and Fry, J. L.: Influence of crustal dust and sea spray supermicron particle concentrations and acidity on inorganic NO
Ansari, A. S. and Pandis, S. N.: Response of inorganic PM to precursor
concentrations, Environ. Sci. Technol., 32, 2706–2714, 1998. a
Ayres, B. R., Allen, H. M., Draper, D. C., Brown, S. S., Wild, R. J., Jimenez, J. L., Day, D. A., Campuzano-Jost, P., Hu, W., de Gouw, J., Koss, A., Cohen, R. C., Duffey, K. C., Romer, P., Baumann, K., Edgerton, E., Takahama, S., Thornton, J. A., Lee, B. H., Lopez-Hilfiker, F. D., Mohr, C., Wennberg, P. O., Nguyen, T. B., Teng, A., Goldstein, A. H., Olson, K., and Fry, J. L.: Organic nitrate aerosol formation via NO
Baker, A. K., Beyersdorf, A. J., Doezema, L. A., Katzenstein, A., Meinardi, S.,
Simpson, I. J., Blake, D. R., and Rowland, F. S.: Measurements of nonmethane
hydrocarbons in 28 United States cities, Atmos. Environ., 42,
170–182, 2008. a
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, e
Banzhaf, S., Schaap, M., Wichink Kruit, R. J., Denier van der Gon, H. A. C., Stern, R., and Builtjes, P. J. H.: Impact of emission changes on secondary inorganic aerosol episodes across Germany, Atmos. Chem. Phys., 13, 11675–11693, https://doi.org/10.5194/acp-13-11675-2013, 2013. a
Barré, J., Petetin, H., Colette, A., Guevara, M., Peuch, V.-H., Rouil, L., Engelen, R., Inness, A., Flemming, J., Pérez García-Pando, C., Bowdalo, D., Meleux, F., Geels, C., Christensen, J. H., Gauss, M., Benedictow, A., Tsyro, S., Friese, E., Struzewska, J., Kaminski, J. W., Douros, J., Timmermans, R., Robertson, L., Adani, M., Jorba, O., Joly, M., and Kouznetsov, R.: Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models, Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021, 2021. a
Bauer, S. E., Koch, D., Unger, N., Metzger, S. M., Shindell, D. T., and Streets, D. G.: Nitrate aerosols today and in 2030: a global simulation including aerosols and tropospheric ozone, Atmos. Chem. Phys., 7, 5043–5059, https://doi.org/10.5194/acp-7-5043-2007, 2007. a
Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J. F., Van Gent, J., Eskes, H., Levelt, P. F., Van Der A, R., Veefkind, J. P., Vlietinck, J., Yu, H., and Zehner, C.:
Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and
OMI observations, Geophys. Res. Lett., 47, e2020GL087978, https://doi.org/10.1029/2020GL087978, 2020. a
Behera, S. N. and Sharma, M.: Investigating the potential role of ammonia in
ion chemistry of fine particulate matter formation for an urban environment,
Sci. Total Environ., 408, 3569–3575, 2010. a
Biswal, A., Singh, T., Singh, V., Ravindra, K., and Mor, S.: COVID-19 lockdown
and its impact on tropospheric NO2 concentrations over India using
satellite-based data, Heliyon, 6, e04764, https://doi.org/10.1016/j.heliyon.2020.e04764, 2020. a
Blanchard, C. L. and Tanenbaum, S. J.: Differences between weekday and weekend
air pollutant levels in southern California, J. Air Waste
Ma., 53, 816–828, 2003. a
Campbell, P. C., Tong, D., Tang, Y., Baker, B., Lee, P., Saylor, R., Stein, A.,
Ma, S., Lamsal, L., and Qu, Z.: Impacts of the COVID-19 Economic Slowdown on
Ozone Pollution in the US, Atmos. Environ., 264, 118713, https://doi.org/10.1016/j.atmosenv.2021.118713, 2021. a
Carlton, A. G., Wiedinmyer, C., and Kroll, J. H.: A review of Secondary Organic Aerosol (SOA) formation from isoprene, Atmos. Chem. Phys., 9, 4987–5005, https://doi.org/10.5194/acp-9-4987-2009, 2009. a
Cesari, D., De Benedetto, G. E., Bonasoni, P., Busetto, M., Dinoi, A., Merico, E., Chirizzi, D., Cristofanelli, P., Donateo, A., Grasso, F. M., Marinoni, A., Pennetta, A., and Contini, D.: Seasonal
variability of PM2.5 and PM10 composition and sources in an urban background
site in Southern Italy, Sci. Total Environ., 612, 202–213,
2018. a
Chan, Y.-C., Evans, M. J., He, P., Holmes, C. D., Jaeglé, L., Kasibhatla, P., Liu, X.-Y., Sherwen, T., Thornton, J. A., Wang, X., Xie, Z., Zhai, S., and Alexander B.: Heterogeneous
nitrate production mechanisms in intense haze events in the North China
Plain, J. Geophys. Res.-Atmos., 126, e2021JD034688, https://doi.org/10.1029/2021JD034688,
2021. a
Chang, W. L., Bhave, P. V., Brown, S. S., Riemer, N., Stutz, J., and Dabdub,
D.: Heterogeneous atmospheric chemistry, ambient measurements, and model
calculations of N2O5: A review, Aerosol Sci. Tech., 45, 665–695,
2011. a
Chang, W. L., Brown, S. S., Stutz, J., Middlebrook, A. M., Bahreini, R.,
Wagner, N. L., Dubé, W. P., Pollack, I. B., Ryerson, T. B., and Riemer,
N.: Evaluating N2O5 heterogeneous hydrolysis parameterizations for CalNex
2010, J. Geophys. Res.-Atmos., 121, 5051–5070, 2016. a
Clark, H., Bennouna, Y., Tsivlidou, M., Wolff, P., Sauvage, B., Barret, B., Le Flochmoën, E., Blot, R., Boulanger, D., Cousin, J.-M., Nédélec, P., Petzold, A., and Thouret, V.: The effects of the COVID-19 lockdowns on the composition of the troposphere as seen by In-service Aircraft for a Global Observing System (IAGOS) at Frankfurt, Atmos. Chem. Phys., 21, 16237–16256, https://doi.org/10.5194/acp-21-16237-2021, 2021. a
Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009. a
Collivignarelli, M. C., Abbà, A., Bertanza, G., Pedrazzani, R., Ricciardi,
P., and Miino, M. C.: Lockdown for CoViD-2019 in Milan: What are the effects
on air quality?, Sci. Total Environ., 732, 139280, https://doi.org/10.1016/j.scitotenv.2020.139280, 2020. a, b
Copernicus Atmosphere Service: Sentinel-5P Pre-Operations Data Hub, Copernicus Services [data set], https://s5phub.copernicus.eu/dhus/#/home, last access: 15 January 2022. a
Copernicus Climate Change Service: Database of atmospheric, land and oceanic climate variables, Copernicus Climate Change Service [data set], https://doi.org/10.24381/cds.bd0915c6, 2022. a
Deroubaix, A., Brasseur, G., Gaubert, B., Labuhn, I., Menut, L., Siour, G., and
Tuccella, P.: Response of surface ozone concentration to emission reduction
and meteorology during the COVID-19 lockdown in Europe, Meteorol.
Appl., 28, e1990, https://doi.org/10.1002/met.1990, 2021. a
Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: MUCCnet: Munich Urban Carbon Column network, Atmos. Meas. Tech., 14, 1111–1126, https://doi.org/10.5194/amt-14-1111-2021, 2021. a
Doumbia, T., Granier, C., Elguindi, N., Bouarar, I., Darras, S., Brasseur, G., Gaubert, B., Liu, Y., Shi, X., Stavrakou, T., Tilmes, S., Lacey, F., Deroubaix, A., and Wang, T.: Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling, Earth Syst. Sci. Data, 13, 4191–4206, https://doi.org/10.5194/essd-13-4191-2021, 2021. a, b, c, d
European Environment Agency: The European air quality database, European Environment Agency [data set], https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm, last access: 15 January 2022. a
Erisman, J. and Schaap, M.: The need for ammonia abatement with respect to
secondary PM reductions in Europe, Environ. Pollut., 129, 159–163,
2004. a
Ernst, J. and Massey, H.: The effects of several factors on volatilization of
ammonia formed from urea in the soil, Soil Sci. Soc. Am.
J., 24, 87–90, 1960. a
ESA: Mapping Germany’s agricultural landscape,
https://www.esa.int/ESA_Multimedia/Images/2017/08/Mapping_Germany_s_agricultural_landscape (last access: 15 January 2022),
2017. a
EUROSAT: Livestock density by NUTS 2 regions, EU-28, 2013,
https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Livestock_density_by_NUTS_2_regions,_EU-28,_2013.png (last access: 15 January 2022),
2013. a
Field, R. D., Hickman, J. E., Geogdzhayev, I. V., Tsigaridis, K., and Bauer, S. E.: Changes in satellite retrievals of atmospheric composition over eastern China during the 2020 COVID-19 lockdowns, Atmos. Chem. Phys., 21, 18333–18350, https://doi.org/10.5194/acp-21-18333-2021, 2021. a
Filonchyk, M., Hurynovich, V., and Yan, H.: Impact of Covid-19 lockdown on air
quality in the Poland, Eastern Europe, Environ. Res., 198, 110454, https://doi.org/10.1016/j.envres.2020.110454,
2021. a
Fisher, J. A., Jacob, D. J., Travis, K. R., Kim, P. S., Marais, E. A., Chan Miller, C., Yu, K., Zhu, L., Yantosca, R. M., Sulprizio, M. P., Mao, J., Wennberg, P. O., Crounse, J. D., Teng, A. P., Nguyen, T. B., St. Clair, J. M., Cohen, R. C., Romer, P., Nault, B. A., Wooldridge, P. J., Jimenez, J. L., Campuzano-Jost, P., Day, D. A., Hu, W., Shepson, P. B., Xiong, F., Blake, D. R., Goldstein, A. H., Misztal, P. K., Hanisco, T. F., Wolfe, G. M., Ryerson, T. B., Wisthaler, A., and Mikoviny, T.: Organic nitrate chemistry and its implications for nitrogen budgets in an isoprene- and monoterpene-rich atmosphere: constraints from aircraft (SEAC4RS) and ground-based (SOAS) observations in the Southeast US, Atmos. Chem. Phys., 16, 5969–5991, https://doi.org/10.5194/acp-16-5969-2016, 2016. a
Fortems‐Cheiney, A., Dufour, G., Hamaoui‐Laguel, L., Foret, G., Siour, G., Van Damme, M., Meleux, F., Coheur, P. F., Clerbaux, C., Clarisse, L., Favez, O., Wallasch, M., and Beekmann, M.:
Unaccounted variability in NH3 agricultural sources detected by IASI
contributing to European spring haze episode, Geophys. Res. Lett.,
43, 5475–5482, 2016. a, b
Fujita, E. M., Campbell, D. E., Zielinska, B., Sagebiel, J. C., Bowen, J. L.,
Goliff, W. S., Stockwell, W. R., and Lawson, D. R.: Diurnal and weekday
variations in the source contributions of ozone precursors in California’s
South Coast Air Basin, J. Air Waste Ma.,
53, 844–863, 2003. a
Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier van der Gon, H., Facchini, M. C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J. G., Spracklen, D. V., Vignati, E., Wild, M., Williams, M., and Gilardoni, S.: Particulate matter, air quality and climate: lessons learned and future needs, Atmos. Chem. Phys., 15, 8217–8299, https://doi.org/10.5194/acp-15-8217-2015, 2015. a
Gaubert, B., Bouarar, I., Doumbia, T., Liu, Y., Stavrakou, T., Deroubaix, A., Darras, S., Elguindi, N., Granier, C., Lacey, F., Müller, J. F., Shi, X., Tilmes, S., Wang, T., and Brasseur G. P.: Global changes in
secondary atmospheric pollutants during the 2020 COVID-19 pandemic, J. Geophys. Res.-Atmos., 126, e2020JD034213, https://doi.org/10.1029/2020JD034213, 2021. a, b, c, d, e
Gensheimer, J., Turner, A. J., Shekhar, A., Wenzel, A., Keutsch, F. N., and
Chen, J.: What are different measures of mobility telling us about surface
transportation CO2 emissions during the COVID-19 pandemic?, J. Geophys. Res.-Atmos., 126, e2021JD034664, https://doi.org/10.1029/2021JD034664, 2021. a, b
Geyer, A., Alicke, B., Konrad, S., Schmitz, T., Stutz, J., and Platt, U.:
Chemistry and oxidation capacity of the nitrate radical in the continental
boundary layer near Berlin, J. Geophys. Res.-Atmos.,
106, 8013–8025, 2001. a
Goldberg, D. L., Anenberg, S. C., Griffin, D., McLinden, C. A., Lu, Z., and
Streets, D. G.: Disentangling the impact of the COVID-19 lockdowns on urban
NO2 from natural variability, Geophys. Res. Lett., 47,
e2020GL089269, https://doi.org/10.1029/2020GL089269, 2020. a
Grange, S. K., Lee, J. D., Drysdale, W. S., Lewis, A. C., Hueglin, C., Emmenegger, L., and Carslaw, D. C.: COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas, Atmos. Chem. Phys., 21, 4169–4185, https://doi.org/10.5194/acp-21-4169-2021, 2021. a, b
Guevara, M., Jorba, O., Soret, A., Petetin, H., Bowdalo, D., Serradell, K., Tena, C., Denier van der Gon, H., Kuenen, J., Peuch, V.-H., and Pérez García-Pando, C.: Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns, Atmos. Chem. Phys., 21, 773–797, https://doi.org/10.5194/acp-21-773-2021, 2021. a, b, c, d
Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H., Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M. E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, Th. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D., Szmigielski, R., and Wildt, J.: The formation, properties and impact of secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys., 9, 5155–5236, https://doi.org/10.5194/acp-9-5155-2009, 2009. a
Hammer, M. S., van Donkelaar, A., Martin, R. V., McDuffie, E. E., Lyapustin, A., Sayer, A. M., Hsu, N. C., Levy, R. C., Garay, M. J., Kalashnikova, O. V., and Kahn, R. A: Effects of COVID-19 lockdowns on fine particulate matter
concentrations, Science Advances, 7, eabg7670, https://doi.org/10.1126/sciadv.abg7670, 2021. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes,., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux,P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P. D., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 2020. a
Higham, J., Ramírez, C. A., Green, M., and Morse, A.: UK COVID-19
lockdown: 100 days of air pollution reduction?, Air Qual. Atmos.
Hlth., 14, 325–332, 2021. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a
Hörmann, S., Jammoul, F., Kuenzer, T., and Stadlober, E.: Separating the
impact of gradual lockdown measures on air pollutants from seasonal
variability, Atmos. Pollut. Res., 12, 84–92, 2021. a
Huang, X., Ding, A., Gao, J., Zheng, B., Zhou, D., Qi, X., Tang, R., Wang, J., Ren, C., Nie, W., Chi, X., Xu, Z., Chen, L., Li, Y., Che, F., Pang, N., Wang, H., Tong, D., Qin, W., Cheng, W., Liu, W., Fu, Q., Liu, B., Chai, F., Davis, S. J., Zhang, Q., and He, K.: Enhanced secondary pollution offset reduction of
primary emissions during COVID-19 lockdown in China, Natl. Sci. Rev.,
8, nwaa137, https://doi.org/10.1093/nsr/nwaa137, 2021. a
Hudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R., Valin, L. C., and Cohen, R. C.: Steps towards a mechanistic model of global soil nitric oxide emissions: implementation and space based-constraints, Atmos. Chem. Phys., 12, 7779–7795, https://doi.org/10.5194/acp-12-7779-2012, 2012. a
IASI: Atmospheric composition Level 2 data products retrieved from the IASI/Metop observations, IASI [data set], https://iasi.aeris-data.fr/catalog/, last access: 15 January 2022. a
Jacob, D. J.: Introduction to atmospheric chemistry, Princeton University
Press, https://doi.org/10.1515/9781400841547, 1999. a
Jacobson, M. Z.: Fundamentals of atmospheric modeling, Cambridge University
Press, ISBN 9780521548656, 1999. a
Jiménez, P., Parra, R., Gasso, S., and Baldasano, J. M.: Modeling the ozone
weekend effect in very complex terrains: a case study in the Northeastern
Iberian Peninsula, Atmos. Environ., 39, 429–444, 2005. a
Juda-Rezler, K., Reizer, M., Maciejewska, K., Błaszczak, B., and Klejnowski,
K.: Characterization of atmospheric PM2.5 sources at a Central European
urban background site, Sci. Total Environ., 713, 136729, https://doi.org/10.1016/j.scitotenv.2020.136729, 2020. a
Kang, M., Zhang, J., Zhang, H., and Ying, Q.: On the Relevancy of Observed
Ozone Increase during COVID-19 Lockdown to Summertime Ozone and PM2.5
Control Policies in China, Environ. Sci. Tech. Lett., 8,
289–294, 2021. a
Kang, Y.-H., You, S., Bae, M., Kim, E., Son, K., Bae, C., Kim, Y., Kim, B.-U.,
Kim, H. C., and Kim, S.: The impacts of COVID-19, meteorology, and emission
control policies on PM2.5 drops in Northeast Asia, Sci. Rep.-UK, 10,
1–8, 2020. a
Keller, C. A., Evans, M. J., Knowland, K. E., Hasenkopf, C. A., Modekurty, S., Lucchesi, R. A., Oda, T., Franca, B. B., Mandarino, F. C., Díaz Suárez, M. V., Ryan, R. G., Fakes, L. H., and Pawson, S.: Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone, Atmos. Chem. Phys., 21, 3555–3592, https://doi.org/10.5194/acp-21-3555-2021, 2021. a
Kim, Y. J., Spak, S. N., Carmichael, G. R., Riemer, N., and Stanier, C. O.:
Modeled aerosol nitrate formation pathways during wintertime in the Great
Lakes region of North America, J. Geophys. Res.-Atmos.,
119, 12–420, 2014. a
Koukouli, M.-E., Skoulidou, I., Karavias, A., Parcharidis, I., Balis, D., Manders, A., Segers, A., Eskes, H., and van Geffen, J.: Sudden changes in nitrogen dioxide emissions over Greece due to lockdown after the outbreak of COVID-19, Atmos. Chem. Phys., 21, 1759–1774, https://doi.org/10.5194/acp-21-1759-2021, 2021. a
Kroll, J. H., Heald, C. L., Cappa, C. D., Farmer, D. K., Fry, J. L., Murphy,
J. G., and Steiner, A. L.: The complex chemical effects of COVID-19 shutdowns
on air quality, Nat. Chem., 12, 777–779, 2020. a
Kuttippurath, J., Singh, A., Dash, S. P., Mallick, N., Clerbaux, C., Van Damme, M., Clarisse, L., Coheur, P. F., Raj, S., Abbhishek, K., and Varikoden, H.: Record high
levels of atmospheric ammonia over India: Spatial and temporal analyses,
Sci. Total Environ., 740, 139986, https://doi.org/10.1016/j.scitotenv.2020.139986, 2020. a
Lee, J. D., Drysdale, W. S., Finch, D. P., Wilde, S. E., and Palmer, P. I.: UK surface NO2 levels dropped by 42 % during the COVID-19 lockdown: impact on surface O3, Atmos. Chem. Phys., 20, 15743–15759, https://doi.org/10.5194/acp-20-15743-2020, 2020. a
Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J., Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R., Shan, Y., Canadell, J. G., Friedlingstein, P., Creutzig, F., and Peters, G. P.: Temporary reduction in daily global CO2 emissions during the
COVID-19 forced confinement, Nat. Clim. Change, 10, 1–7, 2020. a
Liu, L., Bei, N., Hu, B., Wu, J., Liu, S., Li, X., Wang, R., Liu, Z., Shen, Z.,
and Li, G.: Wintertime nitrate formation pathways in the north China plain:
Importance of N2O5 heterogeneous hydrolysis, Environ. Pollut., 266,
115287, https://doi.org/10.1016/j.envpol.2020.115287, 2020. a, b
Marais, E. A., Jacob, D. J., Jimenez, J. L., Campuzano-Jost, P., Day, D. A., Hu, W., Krechmer, J., Zhu, L., Kim, P. S., Miller, C. C., Fisher, J. A., Travis, K., Yu, K., Hanisco, T. F., Wolfe, G. M., Arkinson, H. L., Pye, H. O. T., Froyd, K. D., Liao, J., and McNeill, V. F.: Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the southeast United States and co-benefit of SO2 emission controls, Atmos. Chem. Phys., 16, 1603–1618, https://doi.org/10.5194/acp-16-1603-2016, 2016. a
Matthias, V., Quante, M., Arndt, J. A., Badeke, R., Fink, L., Petrik, R., Feldner, J., Schwarzkopf, D., Link, E.-M., Ramacher, M. O. P., and Wedemann, R.: The role of emission reductions and the meteorological situation for air quality improvements during the COVID-19 lockdown period in central Europe, Atmos. Chem. Phys., 21, 13931–13971, https://doi.org/10.5194/acp-21-13931-2021, 2021. a, b, c, d
Mendez-Espinosa, J. F., Rojas, N. Y., Vargas, J., Pachón, J. E.,
Belalcazar, L. C., and Ramírez, O.: Air quality variations in Northern
South America during the COVID-19 lockdown, Sci. Total Environ.,
749, 141621, https://doi.org/10.1016/j.scitotenv.2020.141621, 2020. a
Menut, L., Bessagnet, B., Siour, G., Mailler, S., Pennel, R., and Cholakian,
A.: Impact of lockdown measures to combat Covid-19 on air quality over
western Europe, Sci. Total Environ., 741, 140426, https://doi.org/10.1016/j.scitotenv.2020.140426, 2020. a, b
Mollner, A. K., Valluvadasan, S., Feng, L., Sprague, M. K., Okumura, M., Milligan, D. B., Bloss, W. J., Sander, S. P., Martien, P. T., Harley, R. A., McCoy, A. B., and Carter, W. P. L.: Rate of gas phase association of hydroxyl radical and nitrogen
dioxide, Science, 330, 646–649, 2010. a
Mozurkewich, M.: The dissociation constant of ammonium nitrate and its
dependence on temperature, relative humidity and particle size, Atmos.
Environ. A-Gen., 27, 261–270, 1993. a
Murray, L. T.: Lightning NOx and impacts on air quality, Current Pollution
Reports, 2, 115–133, 2016. a
Ordóñez, C., Garrido-Perez, J. M., and García-Herrera, R.: Early
spring near-surface ozone in Europe during the COVID-19 shutdown:
Meteorological effects outweigh emission changes, Sci. Total
Environ., 747, 141322, https://doi.org/10.1016/j.scitotenv.2020.141322, 2020. a
Pathakoti, M., Muppalla, A., Hazra, S., D. Venkata, M., A. Lakshmi, K., K. Sagar, V., Shekhar, R., Jella, S., M. V. Rama, S. S., and Vijayasundaram, U.: Measurement report: An assessment of the impact of a nationwide lockdown on air pollution – a remote sensing perspective over India, Atmos. Chem. Phys., 21, 9047–9064, https://doi.org/10.5194/acp-21-9047-2021, 2021. a
Pay, M. T., Jiménez-Guerrero, P., and Baldasano, J. M.: Assessing
sensitivity regimes of secondary inorganic aerosol formation in Europe with
the CALIOPE-EU modeling system, Atmos. Environ., 51, 146–164, 2012. a
Petetin, H., Sciare, J., Bressi, M., Gros, V., Rosso, A., Sanchez, O., Sarda-Estève, R., Petit, J.-E., and Beekmann, M.: Assessing the ammonium nitrate formation regime in the Paris megacity and its representation in the CHIMERE model, Atmos. Chem. Phys., 16, 10419–10440, https://doi.org/10.5194/acp-16-10419-2016, 2016. a, b
Petetin, H., Bowdalo, D., Soret, A., Guevara, M., Jorba, O., Serradell, K., and Pérez García-Pando, C.: Meteorology-normalized impact of the COVID-19 lockdown upon NO2 pollution in Spain, Atmos. Chem. Phys., 20, 11119–11141, https://doi.org/10.5194/acp-20-11119-2020, 2020. a
Putaud, J.-P., Pozzoli, L., Pisoni, E., Martins Dos Santos, S., Lagler, F., Lanzani, G., Dal Santo, U., and Colette, A.: Impacts of the COVID-19 lockdown on air pollution at regional and urban background sites in northern Italy, Atmos. Chem. Phys., 21, 7597–7609, https://doi.org/10.5194/acp-21-7597-2021, 2021. a
Qu, Z., Jacob, D. J., Silvern, R. F., Shah, V., Campbell, P. C., Valin, L. C.,
and Murray, L. T.: US COVID-19 shutdown demonstrates importance of background
NO2 in inferring NOx emissions from satellite NO2 observations, Geophys.
Res. Lett., 48, e2021GL092783, https://doi.org/10.1029/2021GL092783, 2021. a, b
Ramanantenasoa, M. M. J., Gilliot, J.-M., Mignolet, C., Bedos, C., Mathias, E.,
Eglin, T., Makowski, D., and Génermont, S.: A new framework to estimate
spatio-temporal ammonia emissions due to nitrogen fertilization in France,
Sci. Total Environ., 645, 205–219, 2018. a
Renner, E. and Wolke, R.: Modelling the formation and atmospheric transport of
secondary inorganic aerosols with special attention to regions with high
ammonia emissions, Atmos. Environ., 44, 1904–1912, 2010. a
Russell, A. G., Cass, G. R., and Seinfeld, J. H.: On some aspects of nighttime
atmospheric chemistry, Environ. Sci. Technol., 20, 1167–1172,
1986. a
Salameh, D., Detournay, A., Pey, J., Pérez, N., Liguori, F., Saraga, D., Bove, M. C., Brotto, P., Cassola, F., Massabò, D., Latella, A., Pillon, S., Formenton, G., Patti, S., Armengaud, A., Piga, D., Jaffrezo, J. L., Bartzis. J., Tolis. E., Prati., P., Querol, X., Wortham, H., and Marchand, N.: PM2.5
chemical composition in five European Mediterranean cities: a 1-year study,
Atmos. Res., 155, 102–117, 2015. a
Samek, L., Stegowski, Z., Styszko, K., Furman, L., Zimnoch, M., Skiba, A.,
Kistler, M., Kasper-Giebl, A., Rozanski, K., and Konduracka, E.: Seasonal
variations of chemical composition of PM2.5 fraction in the urban area of
Krakow, Poland: PMF source attribution, Air Qual. Atmos. Hlth.,
13, 89–96, 2020. a
Scarlat, N., Fahl, F., Dallemand, J.-F., Monforti, F., and Motola, V.: A
spatial analysis of biogas potential from manure in Europe, Renew.
Sust. Energ. Rev., 94, 915–930, 2018. a
Schaap, M., van Loon, M., ten Brink, H. M., Dentener, F. J., and Builtjes, P. J. H.: Secondary inorganic aerosol simulations for Europe with special attention to nitrate, Atmos. Chem. Phys., 4, 857–874, https://doi.org/10.5194/acp-4-857-2004, 2004. a
Schiferl, L. D., Heald, C. L., Van Damme, M., Clarisse, L., Clerbaux, C., Coheur, P.-F., Nowak, J. B., Neuman, J. A., Herndon, S. C., Roscioli, J. R., and Eilerman, S. J.: Interannual variability of ammonia concentrations over the United States: sources and implications, Atmos. Chem. Phys., 16, 12305–12328, https://doi.org/10.5194/acp-16-12305-2016, 2016. a, b, c
Schumann, U., Poll, I., Teoh, R., Koelle, R., Spinielli, E., Molloy, J., Koudis, G. S., Baumann, R., Bugliaro, L., Stettler, M., and Voigt, C.: Air traffic and contrail changes over Europe during COVID-19: a model study, Atmos. Chem. Phys., 21, 7429–7450, https://doi.org/10.5194/acp-21-7429-2021, 2021. a
Seinfeld, J. H. and Pankow, J. F.: Organic atmospheric particulate material,
Annu. Rev. Phys. Chem., 54, 121–140, 2003. a
Shah, V., Jaeglé, L., Thornton, J. A., Lopez-Hilfiker, F. D., Lee, B. H., Schroder, J. C., Campuzano-Jost, P., Jimenez, J. L., Guo, H., Sullivan, A. P., Weber, R. J., Green, J. R., Fiddler, M. N., Bililign, S., Campos, T. L., Stell, M., Weinheimer, A. J., Montzka, D. D., and Brown S. S.: Chemical feedbacks weaken the wintertime response of
particulate sulfate and nitrate to emissions reductions over the eastern
United States, P. Natl. Acad. Sci. USA, 115,
8110–8115, 2018. a
Shah, V., Jacob, D. J., Li, K., Silvern, R. F., Zhai, S., Liu, M., Lin, J., and Zhang, Q.: Effect of changing NOx lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO2 columns over China, Atmos. Chem. Phys., 20, 1483–1495, https://doi.org/10.5194/acp-20-1483-2020, 2020. a, b
Sharma, M., Kishore, S., Tripathi, S., and Behera, S.: Role of atmospheric
ammonia in the formation of inorganic secondary particulate matter: a study
at Kanpur, India, J. Atmos. Chem., 58, 1–17, 2007. a
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and
polluted rural environments, Atmos. Environ., 33, 1821–1845, 1999. a
Sillman, S., Logan, J. A., and Wofsy, S. C.: The sensitivity of ozone to
nitrogen oxides and hydrocarbons in regional ozone episodes, J. Geophys. Res.-Atmos., 95, 1837–1851, 1990. a
SO2 emission (EEA), Indicator Assessment:Sulphur dioxide (SO2) emissions.
https://www.eea.europa.eu/data-and-maps/indicators/eea-32-sulphur-dioxide-so2-emissions-1/assessment-3 (last access: 15 January 2022),
2021. a
Solberg, S., Walker, S.-E., Schneider, P., and Guerreiro, C.: Quantifying the
Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels
throughout Europe, Atmosphere, 12, 131, https://doi.org/10.3390/atmos12020131, 2021. a
Souri, A. H., Chance, K., Bak, J., Nowlan, C. R., González Abad, G., Jung, Y., Wong, D. C., Mao, J., and Liu, X.: Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI, Atmos. Chem. Phys., 21, 18227–18245, https://doi.org/10.5194/acp-21-18227-2021, 2021. a
Squizzato, S., Masiol, M., Brunelli, A., Pistollato, S., Tarabotti, E., Rampazzo, G., and Pavoni, B.: Factors determining the formation of secondary inorganic aerosol: a case study in the Po Valley (Italy), Atmos. Chem. Phys., 13, 1927–1939, https://doi.org/10.5194/acp-13-1927-2013, 2013. a
Steinfeld, J. I.: Atmospheric chemistry and physics: from air pollution to
climate change, Environment: Science and Policy for Sustainable Development,
40, 26–26, 1998. a
Steinmetz, H., Batzdorfer, V., and Bosnjak, M.: The ZPID lockdown measures
dataset for Germany, ZPID Science Information Online [data set], 20, https://doi.org/10.23668/psycharchives.6679, 2020. a
Stephens, S., Madronich, S., Wu, F., Olson, J. B., Ramos, R., Retama, A., and Muñoz, R.: Weekly patterns of México City's surface concentrations of CO, NOx, PM10 and O3 during 1986–2007, Atmos. Chem. Phys., 8, 5313–5325, https://doi.org/10.5194/acp-8-5313-2008, 2008. a
Tai, A. P. K., Mickley, L. J., and Jacob, D. J.: Impact of 2000–2050 climate change on fine particulate matter (PM2.5) air quality inferred from a multi-model analysis of meteorological modes, Atmos. Chem. Phys., 12, 11329–11337, https://doi.org/10.5194/acp-12-11329-2012, 2012. a
Theys, N., De Smedt, I., Yu, H., Danckaert, T., van Gent, J., Hörmann, C., Wagner, T., Hedelt, P., Bauer, H., Romahn, F., Pedergnana, M., Loyola, D., and Van Roozendael, M.: Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis, Atmos. Meas. Tech., 10, 119–153, https://doi.org/10.5194/amt-10-119-2017, 2017. a
Turner, A. J., Kim, J., Fitzmaurice, H., Newman, C., Worthington, K., Chan, K.,
Wooldridge, P. J., Köehler, P., Frankenberg, C., and Cohen, R. C.:
Observed Impacts of COVID-19 on Urban CO2 Emissions, Geophys. Res.
Lett., 47, e2020GL090037, https://doi.org/10.1029/2020GL090037, 2020. a
Viatte, C., Wang, T., Van Damme, M., Dammers, E., Meleux, F., Clarisse, L., Shephard, M. W., Whitburn, S., Coheur, P. F., Cady-Pereira, K. E., and Clerbaux, C.: Atmospheric ammonia variability and link with particulate matter formation: a case study over the Paris area, Atmos. Chem. Phys., 20, 577–596, https://doi.org/10.5194/acp-20-577-2020, 2020. a, b, c, d, e, f
Viatte, C., Petit, J. E., Yamanouchi, S., Van Damme, M., Doucerain, C., Germain-Piaulenne, E., Gros, V., Favez, O., Clarisse, L., Coheur, P. F., Strong, K., and Clerbaux. C.: Ammonia and PM2.5 Air Pollution in Paris during the 2020 COVID
Lockdown, Atmosphere, 12, 160, https://doi.org/10.3390/atmos12020160, 2021. a, b
Von Schneidemesser, E., Monks, P. S., and Plass-Duelmer, C.: Global comparison
of VOC and CO observations in urban areas, Atmos. Environ., 44,
5053–5064, 2010. a
Wang, L., Wang, J., and Fang, C.: Assessing the Impact of Lockdown on
Atmospheric Ozone Pollution Amid the First Half of 2020 in Shenyang, China,
Int. J. Env. Res. Pub. Hea., 17, 9004, https://doi.org/10.3390/ijerph17239004,
2020. a
Wang, S., Nan, J., Shi, C., Fu, Q., Gao, S., Wang, D., Cui, H., Saiz-Lopez, A.,
and Zhou, B.: Atmospheric ammonia and its impacts on regional air quality
over the megacity of Shanghai, China, Sci. Rep,-UK, 5, 1–13, 2015. a
Wang, W., van der A, R., Ding, J., van Weele, M., and Cheng, T.: Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations, Atmos. Chem. Phys., 21, 7253–7269, https://doi.org/10.5194/acp-21-7253-2021, 2021. a
Wang, Y., Zhang, Q. Q., He, K., Zhang, Q., and Chai, L.: Sulfate-nitrate-ammonium aerosols over China: response to 2000–2015 emission changes of sulfur dioxide, nitrogen oxides, and ammonia, Atmos. Chem. Phys., 13, 2635–2652, https://doi.org/10.5194/acp-13-2635-2013, 2013. a
Watson, J. G., Chow, J. C., Lurmann, F. W., and Musarra, S. P.: Ammonium
nitrate, nitric acid, and ammonia equilibrium in wintertime Phoenix, Arizona,
Air & Waste, 44, 405–412, 1994. a
Webb, J., Menzi, H., Pain, B., Misselbrook, T., Dämmgen, U., Hendriks, H.,
and Döhler, H.: Managing ammonia emissions from livestock production in
Europe, Environ. Pollut., 135, 399–406, 2005. a
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017. a
Womack, C. C., McDuffie, E. E., Edwards, P. M., Bares, R., de Gouw, J. A., Docherty, K. S., Dubé, W. P., Fibiger, D. L., Franchin, A., Gilman, J. B., Goldberger, L., Lee, B. H., Lin, J. C., Long, R., Middlebrook, A. M., Millet, D. B., Moravek, A., Murphy, J. G., Quinn, P. K., Riedel, T. P., Roberts, J. M., Thornton, J. A., Valin, L. C., Veres, P. R., Whitehill, A. R., Wild, R. J., Warneke, C., Yuan, B., Baasandorj, M., and Brown, S. S.: An odd oxygen
framework for wintertime ammonium nitrate aerosol pollution in urban areas:
NOx and VOC control as mitigation strategies, Geophys. Res. Lett.,
46, 4971–4979, 2019.
a
Wu, S.-Y., Hu, J.-L., Zhang, Y., and Aneja, V. P.: Modeling atmospheric
transport and fate of ammonia in North Carolina – Part II: Effect of ammonia
emissions on fine particulate matter formation, Atmos. Environ., 42,
3437–3451, 2008. a
Wu, Y., Gu, B., Erisman, J. W., Reis, S., Fang, Y., Lu, X., and Zhang, X.: PM2.5 pollution is substantially affected by ammonia emissions in China,
Environ. Pollut., 218, 86–94, 2016. a
Yan, C., Tham, Y. J., Zha, Q., Wang, X., Xue, L., Dai, J., Wang, Z., and Wang,
T.: Fast heterogeneous loss of N2O5 leads to significant nighttime NOx
removal and nitrate aerosol formation at a coastal background environment of
southern China, Sci. Total Environ., 677, 637–647, 2019. a
Yarwood, G., Stoeckenius, T. E., Heiken, J. G., and Dunker, A. M.: Modeling
weekday/weekend ozone differences in the Los Angeles region for 1997, J.
Air Waste Ma., 53, 864–875, 2003. a
Yin, H., Lu, X., Sun, Y., Li, K., Gao, M., Zheng, B., and Liu, C.:
Unprecedented decline in summertime surface ozone over eastern China in 2020
comparably attributable to anthropogenic emission reductions and meteorology,
Environ. Res. Lett., 16, 124069,
https://doi.org/10.1088/1748-9326/ac3e22,
2021. a
Zhai, S., Jacob, D. J., Wang, X., Liu, Z., Wen, T., Shah, V., Li, K., Moch, J. M., Bates, K. H., Song, S., Shen, L., Zhang, Y., Luo, G., Yu, F., Sun, Y., Wang, L., Qi, M., Tao, J., Gui, K., Xu, H., Zhang, Q., Zhao, T., Wang, Y., Lee, H. C., Choi, H., and Liao, H.: Control of particulate nitrate air
pollution in China, Nat. Geosci., 14, 1–7, 2021. a
Short summary
In this study, we investigated the response of secondary pollutants to changes in precursor emissions, focusing on the formation of secondary PM, during the COVID-19 lockdown period. We show that, due to the decrease in primary NOx emissions, atmospheric oxidizing capacity is increased. The nighttime increase in ozone, caused by less NO titration, results in higher NO3 radicals, which contribute significantly to the formation of PM nitrates. O3 should be limited in order to control PM pollution.
In this study, we investigated the response of secondary pollutants to changes in precursor...
Altmetrics
Final-revised paper
Preprint