Articles | Volume 21, issue 14
https://doi.org/10.5194/acp-21-10881-2021
© Author(s) 2021. 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-21-10881-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jul 2021
Research article |
| 20 Jul 2021
| 20 Jul 2021
Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Paul I. Palmer
School of GeoSciences, University of Edinburgh, Edinburgh, UK
National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
Christoph Knote
Model-Based Environmental Exposure Science (MBEES), Faculty of Medicine, University of Augsburg, Augsburg, Germany
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Timothy J. Wallington
Research & Advanced Engineering, Ford Motor Company, Dearborn, MI 48121-2053, USA
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Shihan Sun, Paul I. Palmer, Richard Siddans, Brian J. Kerridge, Lucy Ventress, Achim Edtbauer, Akima Ringsdorf, Eva Y. Pfannerstill, and Jonathan Williams
Atmos. Chem. Phys., 25, 15801–15818, https://doi.org/10.5194/acp-25-15801-2025, https://doi.org/10.5194/acp-25-15801-2025, 2025
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Isoprene released by plants can impact atmospheric chemistry and climate. The Amazon rainforest is a major source of isoprene. We derived isoprene emissions using satellite retrievals of isoprene columns and a chemical transport model. We evaluated our isoprene emission estimates using ground-based isoprene observations and satellite retrievals of formaldehyde. We found that using satellite retrievals of isoprene can help us better understand isoprene emissions over the Amazon.
Chlöe N. Schooling, Paul I. Palmer, Auke Visser, and Nicolas Bousserez
Atmos. Chem. Phys., 25, 15631–15652, https://doi.org/10.5194/acp-25-15631-2025, https://doi.org/10.5194/acp-25-15631-2025, 2025
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This study presents a new method to estimate fossil fuel CO2 (ffCO2) emissions by modelling NOx chemistry. Our regression models predict NOx chemical rates and NO2 : NO ratios with R² values above 0.95 using meteorological inputs. Incorporating these regressions reduces computational time compared to traditional methods and enables integration into model inversion frameworks. This scalable approach supports global emissions monitoring and climate change mitigation efforts.
Liang Feng, Paul I. Palmer, Luke Smallman, Jingfeng Xiao, Paolo Cristofanelli, Ove Hermansen, John Lee, Casper Labuschagne, Simonetta Montaguti, Steffen M. Noe, Stephen M. Platt, Xinrong Ren, Martin Steinbacher, and Irène Xueref-Remy
Atmos. Chem. Phys., 25, 13053–13076, https://doi.org/10.5194/acp-25-13053-2025, https://doi.org/10.5194/acp-25-13053-2025, 2025
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The year 2023 saw unexpectedly large global atmospheric CO2 growth. Satellite data reveal a role for increased tropical emissions. Larger emissions over eastern Brazil can be explained by warmer temperatures, which has led to exceptional drought, while hydrological changes play more of a role in emission increases elsewhere in the tropics. Broadly, we find that this situation continues into 2024.
Haolin Wang, William Maslanka, Paul I. Palmer, Martin J. Wooster, Haofan Wang, Fei Yao, Liang Feng, Kai Wu, Xiao Lu, and Shaojia Fan
EGUsphere, https://doi.org/10.5194/egusphere-2025-2594, https://doi.org/10.5194/egusphere-2025-2594, 2025
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We examine the impact of diurnally varying African biomass burning (BB) emissions on tropospheric ozone using GEOS-Chem simulations with a high-resolution satellite-derived emission inventory. Compared to coarser temporal resolutions, incorporating diurnal variations leads to significant changes in surface ozone and atmospheric oxidation capacity. Our findings highlight the importance of accurately representing BB emission timing in chemical transport models to improve ozone predictions.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Samantha Petch, Liang Feng, Paul Palmer, Robert P. King, Tristan Quaife, and Keith Haines
EGUsphere, https://doi.org/10.22541/essoar.173343481.12875858/v1, https://doi.org/10.22541/essoar.173343481.12875858/v1, 2025
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The growth rate of atmospheric CO2 varies year to year, mainly due to land ecosystems. Understanding factors controlling the land carbon uptake is crucial. Our study examines the link between terrestrial water storage and the CO2 growth rate from 2002–2023, revealing a strong negative correlation. We highlight the key role of tropical forests, especially in tropical America, and assess how regional contributions shift over time.
Zhu Deng, Philippe Ciais, Liting Hu, Adrien Martinez, Marielle Saunois, Rona L. Thompson, Kushal Tibrewal, Wouter Peters, Brendan Byrne, Giacomo Grassi, Paul I. Palmer, Ingrid T. Luijkx, Zhu Liu, Junjie Liu, Xuekun Fang, Tengjiao Wang, Hanqin Tian, Katsumasa Tanaka, Ana Bastos, Stephen Sitch, Benjamin Poulter, Clément Albergel, Aki Tsuruta, Shamil Maksyutov, Rajesh Janardanan, Yosuke Niwa, Bo Zheng, Joël Thanwerdas, Dmitry Belikov, Arjo Segers, and Frédéric Chevallier
Earth Syst. Sci. Data, 17, 1121–1152, https://doi.org/10.5194/essd-17-1121-2025, https://doi.org/10.5194/essd-17-1121-2025, 2025
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This study reconciles national greenhouse gas (GHG) inventories with updated atmospheric inversion results to evaluate discrepancies for three principal GHG fluxes at the national level. Compared to our previous study, new satellite-based CO2 inversions were included and an updated mask of managed lands was used, improving agreement for Brazil and Canada. The proposed methodology can be regularly applied as a check to assess the gap between top-down inversions and bottom-up inventories.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Alexander Kurganskiy, Liang Feng, Neil Humpage, Paul I. Palmer, A. Jerome P. Woodwark, Stamatia Doniki, and Damien Weidmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-94, https://doi.org/10.5194/egusphere-2025-94, 2025
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This study introduces GEMINI-UK, the first UK-wide network using ground-based instruments to monitor net fluxes of CO2 and methane. By simulating its performance, we show that GEMINI-UK will significantly reduce uncertainties in these flux estimates, complementing data from existing tall towers and future satellite missions. The network will strengthen the UK's ability to track greenhouse gases, evaluate climate policies, and meet net-zero goals.
Petri Clusius, Metin Baykara, Carlton Xavier, Putian Zhou, Juniper Tyree, Benjamin Foreback, Mikko Äijälä, Frans Graeffe, Tuukka Petäjä, Markku Kulmala, Pauli Paasonen, Paul I. Palmer, and Michael Boy
EGUsphere, https://doi.org/10.5194/egusphere-2025-39, https://doi.org/10.5194/egusphere-2025-39, 2025
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Cloud condensation nuclei are necessary to form clouds, and their size distribution affects cloud properties and therefore Earth’s energy budget. This study modelled the origins of cloud condensation nuclei at SMEAR II, Hyytiälä, Finland, and found that primary emissions and new particle formation separately contribute to more than half of the condensation nuclei, but they suppress each other, leading to current concentrations. Largest condensation nuclei originated mostly from emissions.
Barbara Ervens, Andrew Rickard, Bernard Aumont, William P. L. Carter, Max McGillen, Abdelwahid Mellouki, John Orlando, Bénédicte Picquet-Varrault, Paul Seakins, William R. Stockwell, Luc Vereecken, and Timothy J. Wallington
Atmos. Chem. Phys., 24, 13317–13339, https://doi.org/10.5194/acp-24-13317-2024, https://doi.org/10.5194/acp-24-13317-2024, 2024
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Chemical mechanisms describe the chemical processes in atmospheric models that are used to describe the changes in the atmospheric composition. Therefore, accurate chemical mechanisms are necessary to predict the evolution of air pollution and climate change. The article describes all steps that are needed to build chemical mechanisms and discusses the advances and needs of experimental and theoretical research activities needed to build reliable chemical mechanisms.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
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
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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.
Tia R. Scarpelli, Paul I. Palmer, Mark Lunt, Ingrid Super, and Arjan Droste
Atmos. Chem. Phys., 24, 10773–10791, https://doi.org/10.5194/acp-24-10773-2024, https://doi.org/10.5194/acp-24-10773-2024, 2024
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Under the Paris Agreement, countries must track their anthropogenic greenhouse gas emissions. This study describes a method to determine self-consistent estimates for combustion emissions and natural fluxes of CO2 from atmospheric data. We report consistent estimates inferred using this approach from satellite data and ground-based data over Europe, suggesting that satellite data can be used to determine national anthropogenic CO2 emissions for countries where ground-based CO2 data are absent.
Margaret R. Marvin, Paul I. Palmer, Fei Yao, Mohd Talib Latif, and Md Firoz Khan
Atmos. Chem. Phys., 24, 3699–3715, https://doi.org/10.5194/acp-24-3699-2024, https://doi.org/10.5194/acp-24-3699-2024, 2024
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We use an atmospheric chemistry model to investigate aerosols emitted from fire activity across Southeast Asia. We find that the limited nature of measurements in this region leads to large uncertainties that significantly hinder the model representation of these aerosols and their impacts on air quality. As a result, the number of monthly attributable deaths is underestimated by as many as 4500, particularly in March at the peak of the mainland burning season.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
Geosci. Model Dev., 16, 6161–6185, https://doi.org/10.5194/gmd-16-6161-2023, https://doi.org/10.5194/gmd-16-6161-2023, 2023
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To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
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Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Alice Drinkwater, Paul I. Palmer, Liang Feng, Tim Arnold, Xin Lan, Sylvia E. Michel, Robert Parker, and Hartmut Boesch
Atmos. Chem. Phys., 23, 8429–8452, https://doi.org/10.5194/acp-23-8429-2023, https://doi.org/10.5194/acp-23-8429-2023, 2023
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Changes in atmospheric methane over the last few decades are largely unexplained. Previous studies have proposed different hypotheses to explain short-term changes in atmospheric methane. We interpret observed changes in atmospheric methane and stable isotope source signatures (2004–2020). We argue that changes over this period are part of a large-scale shift from high-northern-latitude thermogenic energy emissions to tropical biogenic emissions, particularly from North Africa and South America.
Bin Zhou and Christoph Knote
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-111, https://doi.org/10.5194/nhess-2023-111, 2023
Publication in NHESS not foreseen
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This study estimates the loss of life caused by wildfires in the eastern and central Mediterranean basin in summer 2021. We used a computer model to simulate concentrations of air pollutants emitted from wildfires and estimated the resulting excess human deaths based on the most relevant evidence from literature. We found that wildfire-caused air pollution accounted for several hundred excess deaths. We estimate the effects of ozone to exceed those of particles created by wildfires.
Thomas E. Taylor, Christopher W. O'Dell, David Baker, Carol Bruegge, Albert Chang, Lars Chapsky, Abhishek Chatterjee, Cecilia Cheng, Frédéric Chevallier, David Crisp, Lan Dang, Brian Drouin, Annmarie Eldering, Liang Feng, Brendan Fisher, Dejian Fu, Michael Gunson, Vance Haemmerle, Graziela R. Keller, Matthäus Kiel, Le Kuai, Thomas Kurosu, Alyn Lambert, Joshua Laughner, Richard Lee, Junjie Liu, Lucas Mandrake, Yuliya Marchetti, Gregory McGarragh, Aronne Merrelli, Robert R. Nelson, Greg Osterman, Fabiano Oyafuso, Paul I. Palmer, Vivienne H. Payne, Robert Rosenberg, Peter Somkuti, Gary Spiers, Cathy To, Brad Weir, Paul O. Wennberg, Shanshan Yu, and Jia Zong
Atmos. Meas. Tech., 16, 3173–3209, https://doi.org/10.5194/amt-16-3173-2023, https://doi.org/10.5194/amt-16-3173-2023, 2023
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NASA's Orbiting Carbon Observatory 2 and 3 (OCO-2 and OCO-3, respectively) provide complementary spatiotemporal coverage from a sun-synchronous and precession orbit, respectively. Estimates of total column carbon dioxide (XCO2) derived from the two sensors using the same retrieval algorithm show broad consistency over a 2.5-year overlapping time record. This suggests that data from the two satellites may be used together for scientific analysis.
Gregor Köcher, Tobias Zinner, and Christoph Knote
Atmos. Chem. Phys., 23, 6255–6269, https://doi.org/10.5194/acp-23-6255-2023, https://doi.org/10.5194/acp-23-6255-2023, 2023
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Polarimetric radar observations of 30 d of convective precipitation events are used to statistically analyze 5 state-of-the-art microphysics schemes of varying complexity. The frequency and area of simulated heavy-precipitation events are in some cases significantly different from those observed, depending on the microphysics scheme. Analysis of simulated particle size distributions and reflectivities shows that some schemes have problems reproducing the correct particle size distributions.
Liang Feng, Paul I. Palmer, Robert J. Parker, Mark F. Lunt, and Hartmut Bösch
Atmos. Chem. Phys., 23, 4863–4880, https://doi.org/10.5194/acp-23-4863-2023, https://doi.org/10.5194/acp-23-4863-2023, 2023
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Our understanding of recent changes in atmospheric methane has defied explanation. Since 2007, the atmospheric growth of methane has accelerated to record-breaking values in 2020 and 2021. We use satellite observations of methane to show that (1) increasing emissions over the tropics are mostly responsible for these recent atmospheric changes, and (2) changes in the OH sink during the 2020 Covid-19 lockdown can explain up to 34% of changes in atmospheric methane for that year.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
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This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Kai Wu, Paul I. Palmer, Dien Wu, Denis Jouglet, Liang Feng, and Tom Oda
Atmos. Meas. Tech., 16, 581–602, https://doi.org/10.5194/amt-16-581-2023, https://doi.org/10.5194/amt-16-581-2023, 2023
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We evaluate the theoretical ability of the upcoming MicroCarb satellite to estimate urban CO2 emissions over Paris and London. We explore the relative performance of alternative two-sweep and three-sweep city observing modes and take into account the impacts of cloud cover and urban biological CO2 fluxes. Our results find both the two-sweep and three-sweep observing modes are able to reduce prior flux errors by 20 %–40 % depending on the prevailing wind direction and cloud coverage.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
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Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Dien Wu, Junjie Liu, Paul O. Wennberg, Paul I. Palmer, Robert R. Nelson, Matthäus Kiel, and Annmarie Eldering
Atmos. Chem. Phys., 22, 14547–14570, https://doi.org/10.5194/acp-22-14547-2022, https://doi.org/10.5194/acp-22-14547-2022, 2022
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Prior studies have derived the combustion efficiency for a region/city using observed CO2 and CO. We further zoomed into the urban domain and accounted for factors affecting the calculation of spatially resolved combustion efficiency from two satellites. The intra-city variability in combustion efficiency was linked to heavy industry within Shanghai and LA without relying on emission inventories. Such an approach can be applied when analyzing data from future geostationary satellites.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Selena Georgiou, Edward T. A. Mitchard, Bart Crezee, Paul I. Palmer, Greta C. Dargie, Sofie Sjögersten, Corneille E. N. Ewango, Ovide B. Emba, Joseph T. Kanyama, Pierre Bola, Jean-Bosco N. Ndjango, Nicholas T. Girkin, Yannick E. Bocko, Suspense A. Ifo, and Simon L. Lewis
EGUsphere, https://doi.org/10.5194/egusphere-2022-580, https://doi.org/10.5194/egusphere-2022-580, 2022
Preprint archived
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Two major vegetation types, hardwood trees and palms, overlay the Central Congo Basin peatland complex, each dominant in different locations. We investigated the influence of terrain and climatological variables on their distribution, using a regression model, and found elevation and seasonal rainfall and temperature contribute significantly. There are indications of an optimal range of net water input for palm swamp to dominate, above and below which hardwood swamp dominates.
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
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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
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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.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
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In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Gregor Köcher, Tobias Zinner, Christoph Knote, Eleni Tetoni, Florian Ewald, and Martin Hagen
Atmos. Meas. Tech., 15, 1033–1054, https://doi.org/10.5194/amt-15-1033-2022, https://doi.org/10.5194/amt-15-1033-2022, 2022
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We present a setup for systematic characterization of differences between numerical weather models and radar observations for convective weather situations. Radar observations providing dual-wavelength and polarimetric variables to infer information about hydrometeor shapes and sizes are compared against simulations using microphysics schemes of varying complexity. Differences are found in ice and liquid phase, pointing towards issues of some schemes in reproducing particle size distributions.
Douglas P. Finch, Paul I. Palmer, and Tianran Zhang
Atmos. Meas. Tech., 15, 721–733, https://doi.org/10.5194/amt-15-721-2022, https://doi.org/10.5194/amt-15-721-2022, 2022
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We developed a machine learning model to detect plumes of nitrogen dioxide satellite observations over 2 years. We find over 310 000 plumes, mainly over cities, industrial regions, and areas of oil and gas production. Our model performs well in comparison to other datasets and in some cases finds emissions that are not included in other datasets. This method could be used to help locate and measure emission hotspots across the globe and help inform climate policies.
Thomas E. Taylor, Christopher W. O'Dell, David Crisp, Akhiko Kuze, Hannakaisa Lindqvist, Paul O. Wennberg, Abhishek Chatterjee, Michael Gunson, Annmarie Eldering, Brendan Fisher, Matthäus Kiel, Robert R. Nelson, Aronne Merrelli, Greg Osterman, Frédéric Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Martine De Mazière, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Matthias Schneider, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, and Debra Wunch
Earth Syst. Sci. Data, 14, 325–360, https://doi.org/10.5194/essd-14-325-2022, https://doi.org/10.5194/essd-14-325-2022, 2022
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We provide an analysis of an 11-year record of atmospheric carbon dioxide (CO2) concentrations derived using an optimal estimation retrieval algorithm on measurements made by the GOSAT satellite. The new product (version 9) shows improvement over the previous version (v7.3) as evaluated against independent estimates of CO2 from ground-based sensors and atmospheric inversion systems. We also compare the new GOSAT CO2 values to collocated estimates from NASA's Orbiting Carbon Observatory-2.
Dongwook Kim, Changmin Cho, Seokhan Jeong, Soojin Lee, Benjamin A. Nault, Pedro Campuzano-Jost, Douglas A. Day, Jason C. Schroder, Jose L. Jimenez, Rainer Volkamer, Donald R. Blake, Armin Wisthaler, Alan Fried, Joshua P. DiGangi, Glenn S. Diskin, Sally E. Pusede, Samuel R. Hall, Kirk Ullmann, L. Gregory Huey, David J. Tanner, Jack Dibb, Christoph J. Knote, and Kyung-Eun Min
Atmos. Chem. Phys., 22, 805–821, https://doi.org/10.5194/acp-22-805-2022, https://doi.org/10.5194/acp-22-805-2022, 2022
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CHOCHO was simulated using a 0-D box model constrained by measurements during the KORUS-AQ mission. CHOCHO concentration was high in large cities, aromatics being the most important precursors. Loss path to aerosol was the highest sink, contributing to ~ 20 % of secondary organic aerosol formation. Our work highlights that simple CHOCHO surface uptake approach is valid only for low aerosol conditions and more work is required to understand CHOCHO solubility in high-aerosol conditions.
Debora Griffin, Chris A. McLinden, Enrico Dammers, Cristen Adams, Chelsea E. Stockwell, Carsten Warneke, Ilann Bourgeois, Jeff Peischl, Thomas B. Ryerson, Kyle J. Zarzana, Jake P. Rowe, Rainer Volkamer, Christoph Knote, Natalie Kille, Theodore K. Koenig, Christopher F. Lee, Drew Rollins, Pamela S. Rickly, Jack Chen, Lukas Fehr, Adam Bourassa, Doug Degenstein, Katherine Hayden, Cristian Mihele, Sumi N. Wren, John Liggio, Ayodeji Akingunola, and Paul Makar
Atmos. Meas. Tech., 14, 7929–7957, https://doi.org/10.5194/amt-14-7929-2021, https://doi.org/10.5194/amt-14-7929-2021, 2021
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Satellite-derived NOx emissions from biomass burning are estimated with TROPOMI observations. Two common emission estimation methods are applied, and sensitivity tests with model output were performed to determine the accuracy of these methods. The effect of smoke aerosols on TROPOMI NO2 columns is estimated and compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
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The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, https://doi.org/10.5194/acp-21-16257-2021, 2021
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We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions, with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling set-up on these findings are explored through a number of sensitivity studies.
Mehliyar Sadiq, Paul I. Palmer, Mark F. Lunt, Liang Feng, Ingrid Super, Stijn N. C. Dellaert, and Hugo A. C. Denier van der Gon
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-816, https://doi.org/10.5194/acp-2021-816, 2021
Publication in ACP not foreseen
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We make use of high-resolution emission inventory of CO2 and co-emitted tracers, satellite measurements, together with nested atmospheric transport model simulation, to investigate how reactive trace gases such as nitrogen dioxide and carbon monoxide can be used as proxies to determine the combustion contribution to atmospheric CO2 over Europe. We find stronger correlation in ratios of nitrogen dioxide and carbon dioxide between emission and satellite observed and modelled column concentration.
R. Anthony Cox, Markus Ammann, John N. Crowley, Paul T. Griffiths, Hartmut Herrmann, Erik H. Hoffmann, Michael E. Jenkin, V. Faye McNeill, Abdelwahid Mellouki, Christopher J. Penkett, Andreas Tilgner, and Timothy J. Wallington
Atmos. Chem. Phys., 21, 13011–13018, https://doi.org/10.5194/acp-21-13011-2021, https://doi.org/10.5194/acp-21-13011-2021, 2021
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The term open-air factor was coined in the 1960s, establishing that rural air had powerful germicidal properties possibly resulting from immediate products of the reaction of ozone with alkenes, unsaturated compounds ubiquitously present in natural and polluted environments. We have re-evaluated those early experiments, applying the recently substantially improved knowledge, and put them into the context of the lifetime of aerosol-borne pathogens that are so important in the Covid-19 pandemic.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
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We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Julian Kostinek, Anke Roiger, Maximilian Eckl, Alina Fiehn, Andreas Luther, Norman Wildmann, Theresa Klausner, Andreas Fix, Christoph Knote, Andreas Stohl, and André Butz
Atmos. Chem. Phys., 21, 8791–8807, https://doi.org/10.5194/acp-21-8791-2021, https://doi.org/10.5194/acp-21-8791-2021, 2021
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Abundant mining and industrial activities in the Upper Silesian Coal Basin lead to large emissions of the potent greenhouse gas methane. This study quantifies these emissions with continuous, high-precision airborne measurements and dispersion modeling. Our emission estimates are in line with values reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) but significantly lower than values reported in the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2).
Abdelwahid Mellouki, Markus Ammann, R. Anthony Cox, John N. Crowley, Hartmut Herrmann, Michael E. Jenkin, V. Faye McNeill, Jürgen Troe, and Timothy J. Wallington
Atmos. Chem. Phys., 21, 4797–4808, https://doi.org/10.5194/acp-21-4797-2021, https://doi.org/10.5194/acp-21-4797-2021, 2021
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Volatile organic compounds play an important role in atmospheric chemistry. This article, the eighth in the series, presents kinetic and photochemical data sheets evaluated by the IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation. It covers the gas-phase reactions of organic species with four, or more, carbon atoms (≥ C4) including thermal reactions of closed-shell organic species with HO and NO3 radicals and their photolysis. These data are important for atmospheric models.
Thomas Thorp, Stephen R. Arnold, Richard J. Pope, Dominick V. Spracklen, Luke Conibear, Christoph Knote, Mikhail Arshinov, Boris Belan, Eija Asmi, Tuomas Laurila, Andrei I. Skorokhod, Tuomo Nieminen, and Tuukka Petäjä
Atmos. Chem. Phys., 21, 4677–4697, https://doi.org/10.5194/acp-21-4677-2021, https://doi.org/10.5194/acp-21-4677-2021, 2021
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We compare modelled near-surface pollutants with surface and satellite observations to better understand the controls on the regional concentrations of pollution in western Siberia for late spring and summer in 2011. We find two commonly used emission inventories underestimate human emissions when compared to observations. Transport emissions are the main source of pollutants within the region during this period, whilst fire emissions peak during June and are only significant south of 60° N.
Margaret R. Marvin, Paul I. Palmer, Barry G. Latter, Richard Siddans, Brian J. Kerridge, Mohd Talib Latif, and Md Firoz Khan
Atmos. Chem. Phys., 21, 1917–1935, https://doi.org/10.5194/acp-21-1917-2021, https://doi.org/10.5194/acp-21-1917-2021, 2021
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We use an atmospheric chemistry model in combination with satellite and surface observations to investigate how biomass burning affects tropospheric ozone over Southeast Asia during its fire seasons. We find that nitrogen oxides from biomass burning were responsible for about 30 % of the regional ozone formation potential, and we estimate that ozone from biomass burning caused more than 400 excess premature deaths in Southeast Asia during the peak burning months of March and September 2014.
James D. Lee, Will S. Drysdale, Doug P. Finch, Shona E. Wilde, and Paul I. Palmer
Atmos. Chem. Phys., 20, 15743–15759, https://doi.org/10.5194/acp-20-15743-2020, https://doi.org/10.5194/acp-20-15743-2020, 2020
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Efforts to prevent the COVID-19 virus spreading across the globe have included travel restrictions and the closure of workplaces, leading to a significant drop in emissions of primary air pollutants. This provides for a unique opportunity to examine how air pollutant concentrations respond to an abrupt and prolonged reduction. We examine how NO2 and O3 have been affected at several urban measurement sites in the UK. We look at the change in NO2 compared to previous years and the effect on O3.
Robert J. Parker, Alex Webb, Hartmut Boesch, Peter Somkuti, Rocio Barrio Guillo, Antonio Di Noia, Nikoleta Kalaitzi, Jasdeep S. Anand, Peter Bergamaschi, Frederic Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Coleen Roehl, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Thorsten Warneke, Paul O. Wennberg, and Debra Wunch
Earth Syst. Sci. Data, 12, 3383–3412, https://doi.org/10.5194/essd-12-3383-2020, https://doi.org/10.5194/essd-12-3383-2020, 2020
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This work presents the latest release of the University of Leicester GOSAT methane data and acts as the definitive description of this dataset. We detail the processing, validation and evaluation involved in producing these data and highlight its many applications. With now over a decade of global atmospheric methane observations, this dataset has helped, and will continue to help, us better understand the global methane budget and investigate how it may respond to a future changing climate.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Cited articles
Agarwala, M. and Chandel, A.: Temporal role of crop residue burning (CRB) in
Delhi's air pollution, Environ. Res. Lett., 15, 114020,
https://doi.org/10.1088/1748-9326/abb854, 2020. a
Ahmadov, R., McKeen, S., Robinson, A., Bahreini, R., Middlebrook, A., De Gouw,
J., Meagher, J., Hsie, E.-Y., Edgerton, E., Shaw, S., and Trainer, M: A volatility
basis set model for summertime secondary organic aerosols over the eastern
United States in 2006, J. Geophys. Res.-Atmos., 117,
https://doi.org/10.1029/2011JD016831, 2012. a, b
Ahmed, T., Ahmad, B., and Ahmad, W.: Why do farmers burn rice residue?
Examining farmers’ choices in Punjab, Pakistan, Land Use Policy, 47,
448–458, https://doi.org/10.1016/j.landusepol.2015.05.004, 2015. a
Alam, K., Mukhtar, A., Shahid, I., Blaschke, T., Majid, H., Rahman, S., Khan,
R., and Rahman, N.: Source apportionment and characterization of
particulate matter (PM10) in urban environment of Lahore, Aerosol Air
Qual. Res., 14, 1851–1861, https://doi.org/10.4209/aaqr.2014.01.0005,
2014. a
Balasubramanian, S., McFarland, D. M., Koloutsou-Vakakis, S., Fu, K., Menon,
R., Lehmann, C., and Rood, M. J.: Effect of grid resolution and spatial
representation of NH3 emissions from fertilizer application on predictions of
NH3 and PM2.5 concentrations in the United States Corn Belt, Environ. Res.
Commun., 2, 025001, https://doi.org/10.1088/2515-7620/ab6c01, 2020. a
Bangladesh Bureau of Statistics: Population and housing census 2011, Tech.
rep., Statistics Division Ministry of Planning Government of the People's
Republic of Bangladesh, Dhaka, Bangladesh, 2011. a
Begum, B. A., Hopke, P. K., and Markwitz, A.: Air pollution by fine particulate
matter in Bangladesh, Atmos. Pollut. Res., 4, 75–86,
https://doi.org/10.5094/APR.2013.008, 2013. a, b
Behera, S. N. and Sharma, M.: Spatial and seasonal variations of atmospheric
particulate carbon fractions and identification of secondary sources at urban
sites in North India, Environ. Sci. Pollut. R., 22, 13464–13476,
https://doi.org/10.1007/s11356-015-4603-7, 2015. a
Bergström, R., Denier van der Gon, H. A. C., Prévôt, A. S. H., Yttri, K. E., and Simpson, D.: Modelling of organic aerosols over Europe (2002–2007) using a volatility basis set (VBS) framework: application of different assumptions regarding the formation of secondary organic aerosol, Atmos. Chem. Phys., 12, 8499–8527, https://doi.org/10.5194/acp-12-8499-2012, 2012. a
Bhowmik, H. S., Naresh, S., Bhattu, D., Rastogi, N., Prévôt, A. S., and
Tripathi, S. N.: Temporal and spatial variability of carbonaceous species
(EC; OC; WSOC and SOA) in PM2.5 aerosol over five sites of Indo-Gangetic
Plain, Atmos. Pollut. Res., 12, 375–390, https://doi.org/10.1016/j.apr.2020.09.019,
2020. a
Bran, S. H. and Srivastava, R.: Investigation of PM2.5 mass concentration
over India using a regional climate model, Environ. Pollut., 224, 484–493,
https://doi.org/10.1016/j.envpol.2017.02.030, 2017. a
Brasseur, G. P. and Jacob, D. J.: Modeling of atmospheric chemistry, Cambridge
University Press, Cambridge, United Kingdom, 2017. a
Buchholz, R. R., Emmons, L. K., Tilmes, S., and The CESM2 Development Team, UCAR/NCAR – Atmospheric Chemistry Observations and Modeling Laboratory: CESM2.1/CAM-chem
Instantaneous Output for Boundary Conditions,
https://doi.org/10.5065/NMP7-EP60, subset: Lat: 0 to 50, Lon: 50 to 100,
October 2017–February 2018, last accessed: 29 March 2020, 2019. a, b
Chatterjee, A., Dutta, C., Jana, T., and Sen, S.: Fine mode aerosol chemistry
over a tropical urban atmosphere: characterization of ionic and carbonaceous
species, J. Atmos. Chem., 69, 83–100,
https://doi.org/10.1007/s10874-012-9231-8, 2012. a
Chauhan, B. S., Mahajan, G., Sardana, V., Timsina, J., and Jat, M. L.:
Productivity and sustainability of the rice–wheat cropping system in the
Indo-Gangetic Plains of the Indian subcontinent: problems, opportunities, and
strategies, Adv. Agron., 117, 315–369,
https://doi.org/10.1016/B978-0-12-394278-4.00006-4, 2012. a
Chowdhury, Z., Zheng, M., Schauer, J. J., Sheesley, R. J., Salmon, L. G., Cass,
G. R., and Russell, A. G.: Speciation of ambient fine organic carbon
particles and source apportionment of PM2.5 in Indian cities, J.
Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2007JD008386, 2007. a
Chuang, W. K. and Donahue, N. M.: A two-dimensional volatility basis set – Part 3: Prognostic modeling and NOx dependence, Atmos. Chem. Phys., 16, 123–134, https://doi.org/10.5194/acp-16-123-2016, 2016. a
Conibear, L. A., Butt, E. W., Knote, C., Lam, N. L., Arnold, S., Tibrewal, K.,
Venkataraman, C., Spracklen, D. V., and Bond, T. C.: A complete transition to
clean household energy can save one–quarter of the healthy life lost to
particulate matter pollution exposure in India, Environ. Res. Lett., 15, 094096, https://doi.org/10.1088/1748-9326/ab8e8a, 2020. a
CPCB: Air Quality Automatic Monitoring Data,
available at: https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing, last
access: 30 November 2020. a
David, L. M., Ravishankara, A., Kodros, J. K., Pierce, J. R., Venkataraman, C.,
and Sadavarte, P.: Premature mortality due to PM2.5 over India: Effect
of atmospheric transport and anthropogenic emissions, GeoHealth, 3, 2–10,
https://doi.org/10.1029/2018GH000169, 2019. a
DESA, U.: 2018 Revision of World Urbanization Prospects, Tech. rep., United
Nation Department of Economic and Social Affairs, United Nations, New York, 2018. a
Donahue, N., Robinson, A., Stanier, C., and Pandis, S.: Coupled partitioning,
dilution, and chemical aging of semivolatile organics, Environ. Sci.
Technol., 40, 2635–2643, https://doi.org/10.1021/es052297c, 2006. a, b
Donahue, N. M., Kroll, J. H., Pandis, S. N., and Robinson, A. L.: A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution, Atmos. Chem. Phys., 12, 615–634, https://doi.org/10.5194/acp-12-615-2012, 2012. a, b
Ek, M., Mitchell, K., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G.,
and Tarpley, J.: Implementation of Noah land surface model advances in the
National Centers for Environmental Prediction operational mesoscale Eta
model, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD003296, 2003. a
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010. a
Fountoukis, C., Koraj, D., Van Der Gon, H. D., Charalampidis, P., Pilinis, C.,
and Pandis, S.: Impact of grid resolution on the predicted fine PM by a
regional 3-D chemical transport model, Atmos. Environ., 68, 24–32,
https://doi.org/10.1016/j.atmosenv.2012.11.008, 2013. a
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, Remote Sens. Environ., 114,
168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010. a
Gani, S., Bhandari, S., Seraj, S., Wang, D. S., Patel, K., Soni, P., Arub, Z., Habib, G., Hildebrandt Ruiz, L., and Apte, J. S.: Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study, Atmos. Chem. Phys., 19, 6843–6859, https://doi.org/10.5194/acp-19-6843-2019, 2019. a, b
Gelaro, R., McCarty, W., Suárez, M. J., et al.: The
modern-era retrospective analysis for research and applications, version 2
(MERRA-2), J. Climate, 30, 5419–5454,
https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Ghude, S. D., Pfister, G. G., Jena, C., Van Der A, R., Emmons, L. K., and
Kumar, R.: Satellite constraints of nitrogen oxide (NOx) emissions from India
based on OMI observations and WRF-Chem simulations, Geophys. Res. Lett., 40,
423–428, https://doi.org/10.1002/grl.50065, 2013. a
Ghude, S. D., Chate, D., Jena, C., Beig, G., Kumar, R., Barth, M., Pfister, G.,
Fadnavis, S., and Pithani, P.: Premature mortality in India due to PM2.5
and ozone exposure, Geophys. Res. Lett., 43, 4650–4658,
https://doi.org/10.1002/2016GL068949, 2016. a
Greenstone, M. and Fan, C.: Air Quality Life Index, Annual Update, Tech.
rep., Energy Policy Institute at The University of Chicago (EPIC), Chicago, Illinois, United States, 2020. a
Grell, G. A. and Dévényi, D.: A generalized approach to parameterizing
convection combining ensemble and data assimilation techniques, Geophys. Res.
Lett., 29, 38-1–38-4, https://doi.org/10.1029/2002GL015311, 2002. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF
model, Atmos. Environ., 39, 6957–6975,
https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006. a, b, c
Gumma, M. K., Thenkabail, P. S., Teluguntla, P., and Whitbread, A. M.:
Indo-Ganges River Basin Land Use/Land Cover (LULC) and Irrigated Area
Mapping, in: Indus River Basin, 203–228, Elsevier, Amsterdam, Netherlands, https://doi.org/10.1016/B978-0-12-812782-7.00010-2, 2019. a
Gunthe, S. S., Liu, P., Panda, U., Raj, S. S., Sharma, A., Darbyshire, E.,
Reyes-Villegas, E., Allan, J., Chen, Y., Wang, X., Song, S., Pöhlker, M. L., Shi, L., Wang, Y., Kommula, S. M., Liu, T., Ravikrishna, R., McFiggans, G., Mickley, L. J., Martin, S. T., Pöschl, U., Andreae, M. O., and Coe, H.: Enhanced aerosol
particle growth sustained by high continental chlorine emission in India,
Nat. Geosci., 14, 77–84, https://doi.org/10.1038/s41561-020-00677-x,
2021. a
Guttikunda, S. K. and Gurjar, B. R.: Role of meteorology in seasonality of air
pollution in megacity Delhi, India, Environ. Monit. Assess., 184, 3199–3211,
https://doi.org/10.1007/s10661-011-2182-8, 2012. a
Guttikunda, S. K. and Jawahar, P.: Atmospheric emissions and pollution from the
coal-fired thermal power plants in India, Atmos. Environ., 92, 449–460,
https://doi.org/10.1016/j.atmosenv.2014.04.057, 2014. a
Guttikunda, S. K., Goel, R., and Pant, P.: Nature of air pollution, emission
sources, and management in the Indian cities, Atmos. Environ., 95, 501–510,
https://doi.org/10.1016/j.atmosenv.2014.07.006, 2014. a
Hardacre, C. J., Palmer, P. I., Baumanns, K., Rounsevell, M., and Murray-Rust, D.: Probabilistic estimation of future emissions of isoprene and surface oxidant chemistry associated with land-use change in response to growing food needs, Atmos. Chem. Phys., 13, 5451–5472, https://doi.org/10.5194/acp-13-5451-2013, 2013. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del
R'ıo, J. F., Wiebe, M., Peterson, P., G'erard-Marchant, P.,
Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant,
T. E.: Array programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in
Python, Journal of Open Research Software, 5, https://doi.org/10.5334/jors.148, 2017. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos.,
113, https://doi.org/10.1029/2008JD009944, 2008. a
Indian National Commission on Population: Population projections for India
and States 2011–2036, Tech. rep., Indian Ministry of Health & Family &
welfare, New Delhi, India, 2020. a
Jain, S., Sharma, S., Vijayan, N., and Mandal, T.: Seasonal characteristics of
aerosols (PM2.5 and PM10) and their source apportionment using PMF:
A four year study over Delhi, India, Environ. Pollut., 262, 114337,
https://doi.org/10.1016/j.envpol.2020.114337, 2020. a, b
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015. a, b
Jethva, H., Satheesh, S., and Srinivasan, J.: Seasonal variability of aerosols
over the Indo-Gangetic basin, J. Geophys. Res.-Atmos., 110,
https://doi.org/10.1029/2005JD005938, 2005. a
Jethva, H., Chand, D., Torres, O., Gupta, P., Lyapustin, A., and Patadia, F.:
Agricultural burning and air quality over northern India: a synergistic
analysis using NASA’s A-train satellite data and ground measurements,
Aerosol Air Qual. Res., 18, 1756–1773,
https://doi.org/10.4209/aaqr.2017.12.0583, 2018. a
Jethva, H., Torres, O., Field, R. D., Lyapustin, A., Gautam, R., and Kayetha,
V.: Connecting crop productivity, residue fires, and air quality over
northern India, Sci. Rep., 9, 1–11,
https://doi.org/10.1038/s41598-019-52799-x, 2019. a
Karambelas, A., Holloway, T., Kiesewetter, G., and Heyes, C.: Constraining the
uncertainty in emissions over India with a regional air quality model
evaluation, Atmos. Environ., 174, 194–203,
https://doi.org/10.1016/j.atmosenv.2017.11.052, 2018. a, b
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., and Zhang, Q.: Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model, Atmos. Chem. Phys., 14, 6213–6239, https://doi.org/10.5194/acp-14-6213-2014, 2014. a, b, c
Kota, S. H., Guo, H., Myllyvirta, L., Hu, J., Sahu, S. K., Garaga, R., Ying,
Q., Gao, A., Dahiya, S., Wang, Y., and Zhang, H.: Year-long simulation of gaseous
and particulate air pollutants in India, Atmos. Environ., 180, 244–255,
https://doi.org/10.1016/j.atmosenv.2018.03.003, 2018.
Kota, Sri Harsha and Guo, Hao and Myllyvirta, Lauri and Hu, Jianlin and Sahu, Shovan Kumar and Garaga, Rajyalakshmi and Ying, Qi and Gao, Aifang and Dahiya, Sunil and Wang, Yuan and Zhang,Hongliang a
Krishna, R. K., Ghude, S. D., Kumar, R., Beig, G., Kulkarni, R., Nivdange, S.,
and Chate, D.: Surface PM2.5 estimate using satellite-derived aerosol
optical depth over India, Aerosol Air Qual. Res., 19, 25–37,
https://doi.org/10.4209/aaqr.2017.12.0568, 2019. a
Kuik, F., Lauer, A., Churkina, G., Denier van der Gon, H. A. C., Fenner, D., Mar, K. A., and Butler, T. M.: Air quality modelling in the Berlin–Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data, Geosci. Model Dev., 9, 4339–4363, https://doi.org/10.5194/gmd-9-4339-2016, 2016. a
Kulkarni, S. H., Ghude, S. D., Jena, C., Karumuri, R. K., Sinha, B., Sinha, V.,
Kumar, R., Soni, V., and Khare, M.: How Much Does Large-Scale Crop Residue
Burning Affect the Air Quality in Delhi?, Environ. Sci. Technol., 54,
4790–4799, https://doi.org/10.1021/acs.est.0c00329, 2020. a
Kumar, M., Tiwari, S., Murari, V., Singh, A., and Banerjee, T.: Wintertime
characteristics of aerosols at middle Indo-Gangetic Plain: Impacts of
regional meteorology and long range transport, Atmos. Environ., 104,
162–175, https://doi.org/10.1016/j.atmosenv.2015.01.014,
2015a. a
Kumar, M., Parmar, K., Kumar, D., Mhawish, A., Broday, D., Mall, R., and
Banerjee, T.: Long-term aerosol climatology over Indo-Gangetic Plain: Trend,
prediction and potential source fields, Atmos. Environ., 180, 37–50,
https://doi.org/10.1016/j.atmosenv.2018.02.027, 2018. a
Kumar, R., Naja, M., Pfister, G. G., Barth, M. C., and Brasseur, G. P.: Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): set-up and meteorological evaluation, Geosci. Model Dev., 5, 321–343, https://doi.org/10.5194/gmd-5-321-2012, 2012a. a, b
Kumar, R., Naja, M., Pfister, G. G., Barth, M. C., Wiedinmyer, C., and Brasseur, G. P.: Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): chemistry evaluation and initial results, Geosci. Model Dev., 5, 619–648, https://doi.org/10.5194/gmd-5-619-2012, 2012b. a, b, c
Kumar, R., Barth, M., Pfister, G., Nair, V., Ghude, S. D., and Ojha, N.: What
controls the seasonal cycle of black carbon aerosols in India?, J. Geophys.
Res.-Atmos., 120, 7788–7812, https://doi.org/10.1002/2015JD023298,
2015b. a
Kusaka, H. and Kimura, F.: Coupling a single-layer urban canopy model with a
simple atmospheric model: Impact on urban heat island simulation for an
idealized case, J. Meteorol. Soc. Jpn., 82, 67–80,
https://doi.org/10.2151/jmsj.82.67, 2004. a
Kuttippurath, J., Singh, A., Dash, S., Mallick, N., Clerbaux, C., Van Damme,
M., Clarisse, L., Coheur, P.-F., Raj, S., Abbhishek, K., and Varikodenf, H.: Record high
levels of atmospheric ammonia over India: Spatial and temporal analyses, Sci.
Total Environ., 139986,
https://doi.org/10.1016/j.scitotenv.2020.139986, 2020. a
Lane, T. E., Donahue, N. M., and Pandis, S. N.: Effect of NOx on secondary
organic aerosol concentrations, Environ. Sci. Technol., 42, 6022–6027,
https://doi.org/10.1021/es703225a, 2008a. a
Lane, T. E., Donahue, N. M., and Pandis, S. N.: Simulating secondary organic
aerosol formation using the volatility basis-set approach in a chemical
transport model, Atmos. Environ., 42, 7439–7451,
https://doi.org/10.1016/j.atmosenv.2008.06.026, 2008b. a, b
Lelieveld, J., Bourtsoukidis, E., Brühl, C., Fischer, H., Fuchs, H.,
Harder, H., Hofzumahaus, A., Holland, F., Marno, D., Neumaier, M., Pozzer, A., Schlager, H., Williams, J., Zahn, A., and Ziereis, H.:
The South Asian monsoon–pollution pump and purifier, Science, 361,
270–273, https://doi.org/10.1126/science.aar2501, 2018. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013. a, b
Li, J., Georgescu, M., Hyde, P., Mahalov, A., and Moustaoui, M.: Achieving
accurate simulations of urban impacts on ozone at high resolution, Environ.
Res. Lett., 9, 114019, https://doi.org/10.1088/1748-9326/9/11/114019, 2014. a
Mallik, C. and Lal, S.: Seasonal characteristics of SO2, NO2, and CO
emissions in and around the Indo-Gangetic Plain, Environ. Monit Assess., 186,
1295–1310, https://doi.org/10.1007/s10661-013-3458-y, 2014. a
Maussion, F., Siller, M., and Rothenberg, D.: fmaussion/salem: v0.2.1,
https://doi.org/10.5281/zenodo.3509134, 2017. a
McDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., and Martin, R. V.: A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, 2020. a
Met Office: Cartopy: a cartographic python library with a matplotlib
interface, Exeter, Devon, available at: http://scitools.org.uk/cartopy (last access: 13 July 2021),
2010–2015. a
Mhawish, A., Banerjee, T., Sorek-Hamer, M., Bilal, M., Lyapustin, A. I.,
Chatfield, R., and Broday, D. M.: Estimation of High-Resolution PM2.5
over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and
Land Use Variables, Environ. Sci. Technol., 54, 7891–7900,
https://doi.org/10.1021/acs.est.0c01769, 2020. a, b
Ministry of Environment Government of Pakistan: Land Use Atlas of Pakistan,
Tech. rep., Government of Pakistan, Islamabad, Pakistan, 2009. a
Mogno, C.: catemgn/AQ-IGP: model setup files and code scripts for analysis, https://doi.org/10.5281/zenodo.5006025, 2021. a
Morrison, H., Curry, J., and Khvorostyanov, V.: A new double-moment
microphysics parameterization for application in cloud and climate models.
Part I: Description, J. Atmos. Sci., 62, 1665–1677,
https://doi.org/10.1175/JAS3446.1, 2005. a
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Lay. Meteorol., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006. a
NASA: Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center (LAADS DAAC), available at: https://ladsweb.modaps.eosdis.nasa.gov/, last access: 12 July 2021. a
National Centers for Environmental Prediction, National Weather Service, NOAA,
U.S. Department of Commerce: NCEP GDAS/FNL 0.25 Degree Global Tropospheric
Analyses and Forecast Grids, https://doi.org/10.5065/D65Q4T4Z, 2015. a, b
NCAR: FINN data: fire emissions factors and inventories, available at: https://www.acom.ucar.edu/Data/fire/, last access: 12 July 2021. a
Null, J.: El Niño and La Niña years and intensities,
available at: https://ggweather.com/enso/oni.htm, last access: 30 November 2020 a
Ojha, N., Sharma, A., Kumar, M., Girach, I., Ansari, T. U., Sharma, S. K.,
Singh, N., Pozzer, A., and Gunthe, S. S.: On the widespread enhancement in
fine particulate matter across the Indo-Gangetic Plain towards winter, Sci.
Rep., 10, 1–9, https://doi.org/10.1038/s41598-020-62710-8, 2020. a, b
Pakistan Bureau of Statistics: Provisional Summary Results of 6th Population
and Housing Census – 2017, Tech. rep., Government of Pakistan Islamabad, Islamabad, Pakistan, 2017. a
Pankow, J. F.: An absorption model of gas/particle partitioning of organic
compounds in the atmosphere, Atmos. Environ., 28, 185–188,
https://doi.org/10.1016/1352-2310(94)90093-0, 1994. a
Pant, P., Shukla, A., Kohl, S. D., Chow, J. C., Watson, J. G., and Harrison,
R. M.: Characterization of ambient PM2.5 at a pollution hotspot in New
Delhi, India and inference of sources, Atmos. Environ., 109, 178–189,
https://doi.org/10.1016/j.atmosenv.2015.02.074, 2015. a
Paolella, D. A., Tessum, C. W., Adams, P. J., Apte, J. S., Chambliss, S., Hill,
J., Muller, N. Z., and Marshall, J. D.: Effect of model spatial resolution on
estimates of fine particulate matter exposure and exposure disparities in the
United States, Environ. Sci. Technol. Lett., 5, 436–441,
https://doi.org/10.1021/acs.estlett.8b00279, 2018. a
Patel, K., Bhandari, S., Gani, S., Campmier, M. J., Kumar, P., Habib, G., Apte,
J., and Hildebrandt Ruiz, L.: Sources and Dynamics of Submicron Aerosol
during the Autumn Onset of the Air Pollution Season in Delhi, India, ACS
Earth Space Chem., 5, 118–128,
https://doi.org/10.1021/acsearthspacechem.0c00340, 2021. a
Plotly Technologies Inc.: Collaborative data science,
available at: https://plot.ly (last access: 13 July 2021), Plotly Technologies Inc., Montreal, QC, 2015. a
Rajput, P., Sarin, M., Sharma, D., and Singh, D.: Organic aerosols and
inorganic species from post-harvest agricultural-waste burning emissions over
northern India: impact on mass absorption efficiency of elemental carbon,
Environ. Sci-Proc. Imp., 16, 2371–2379,
https://doi.org/10.1039/c4em00307a, 2014. a
Rakesh, V., Singh, R., and Joshi, P. C.: Intercomparison of the performance of
MM5/WRF with and without satellite data assimilation in short-range forecast
applications over the Indian region, Meteorol. Atmos. Phys., 105, 133–155,
https://doi.org/10.1007/s00703-009-0038-3, 2009. a
Ram, K., Sarin, M., and Hegde, P.: Atmospheric abundances of primary and
secondary carbonaceous species at two high-altitude sites in India: sources
and temporal variability, Atmos. Environ., 42, 6785–6796,
https://doi.org/10.1016/j.atmosenv.2008.05.031, 2008. a
Ram, K., Sarin, M., and Tripathi, S.: Temporal trends in atmospheric
PM2.5, PM2.5, elemental carbon, organic carbon, water-soluble
organic carbon, and optical properties: impact of biomass burning emissions
in the Indo-Gangetic Plain, Environ. Sci. Technol., 46, 686–695,
https://doi.org/10.1021/es202857w, 2012. a, b
Ratnam, J. V. and Kumar, K. K.: Sensitivity of the simulated monsoons of 1987
and 1988 to convective parameterization schemes in MM5, J. Climate, 18,
2724–2743, https://doi.org/10.1175/JCLI3390.1, 2005. a
Reddy, C. S., Pasha, S. V., Jha, C., Diwakar, P., and Dadhwal, V.: Development
of national database on long-term deforestation (1930–2014) in Bangladesh,
Global Planet. Change, 139, 173–182,
https://doi.org/10.1016/j.gloplacha.2016.02.003, 2016. a
Robinson, A. L., Donahue, N. M., Shrivastava, M. K., Weitkamp, E. A., Sage,
A. M., Grieshop, A. P., Lane, T. E., Pierce, J. R., and Pandis, S. N.:
Rethinking organic aerosols: Semivolatile emissions and photochemical aging,
Science, 315, 1259–1262, https://doi.org/10.1126/science.1133061,
2007. a
Schnell, J. L., Naik, V., Horowitz, L. W., Paulot, F., Mao, J., Ginoux, P., Zhao, M., and Ram, K.: Exploring the relationship between surface PM2.5 and meteorology in Northern India, Atmos. Chem. Phys., 18, 10157–10175, https://doi.org/10.5194/acp-18-10157-2018, 2018. a
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: from air
pollution to climate change, John Wiley & Sons, Hoboken, New Jersey, United States, 2016. a
Sembhi, H., Wooster, M., Zhang, T., Sharma, S., Singh, N., Agarwal, S., Boesch,
H., Gupta, S., Misra, A., Tripathi, S. N., Mor, S., and Khaiwal, R.:
Post-monsoon air quality degradation across Northern India: assessing the
impact of policy-related shifts in timing and amount of crop residue burnt,
Environ. Res. Lett., 15, 104067, https://doi.org/10.1088/1748-9326/aba714, 2020. a
Shahid, M. Z., Liao, H., Li, J., Shahid, I., Lodhi, A., and Mansha, M.:
Seasonal variations of aerosols in Pakistan: Contributions of domestic
anthropogenic emissions and transboundary transport, Aerosol Air Qual. Res.,
15, 1580–1600, https://doi.org/10.4209/aaqr.2014.12.0332, 2015. a, b
Sharma, A., Ojha, N., Pozzer, A., Mar, K. A., Beig, G., Lelieveld, J., and Gunthe, S. S.: WRF-Chem simulated surface ozone over south Asia during the pre-monsoon: effects of emission inventories and chemical mechanisms, Atmos. Chem. Phys., 17, 14393–14413, https://doi.org/10.5194/acp-17-14393-2017, 2017. a
Sharma, S. and Khare, M.: Simulating ozone concentrations using precursor
emission inventories in Delhi – National Capital Region of India, Atmos.
Environ., 151, 117–132,
https://doi.org/10.1016/j.atmosenv.2016.12.009, 2017. a
Sharma, S., Mandal, T., Srivastava, M., Chatterjee, A., Jain, S., Saxena, M.,
Singh, B., Sharma, A., Adak, A., and Ghosh, S.: Spatio-temporal variation
in chemical characteristics of PM10 over Indo Gangetic Plain of India,
Environ. Sci. Pollut. R., 23, 18809–18822,
https://doi.org/10.1007/s11356-016-7025-2, 2016. a
Shrivastava, M., Cappa, C. D., Fan, J., et al.:
Recent advances in understanding secondary organic aerosol: Implications for
global climate forcing, Rev. Geophys., 55, 509–559,
https://doi.org/10.1002/2016RG000540, 2017. a
Singh, A. P., Singh, R., Mina, U., Singh, M. P., and Varshney, C. K.: Emissions
of monoterpene from tropical Indian plant species and assessment of VOC
emission from the forest of Haryana state, Atmos. Pollut. Res., 2, 72–79,
https://doi.org/10.5094/APR.2011.009, 2011. a
Singh, N., Banerjee, T., Raju, M. P., Deboudt, K., Sorek-Hamer, M., Singh, R. S., and Mall, R. K.: Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain, Atmos. Chem. Phys., 18, 14197–14215, https://doi.org/10.5194/acp-18-14197-2018, 2018. a, b
Sirithian, D. and Thepanondh, S.: Influence of grid resolution in modeling of
air pollution from open burning, Atmosphere, 7, 93,
https://doi.org/10.3390/atmos7070093, 2016. a
Srinivas, B. and Sarin, M.: PM2.5, EC and OC in atmospheric outflow from
the Indo-Gangetic Plain: Temporal variability and aerosol organic
carbon-to-organic mass conversion factor, Sci. Total Environ., 487, 196–205,
https://doi.org/10.1016/j.scitotenv.2014.04.002, 2014. a
Stavrakou, T., Müller, J.-F., Bauwens, M., De Smedt, I., Van Roozendael, M., Guenther, A., Wild, M., and Xia, X.: Isoprene emissions over Asia 1979–2012: impact of climate and land-use changes, Atmos. Chem. Phys., 14, 4587–4605, https://doi.org/10.5194/acp-14-4587-2014, 2014. a
Stibig, H.-J., Belward, A., Roy, P., Rosalina-Wasrin, U., Agrawal, S., Joshi,
P., Hildanus, Beuchle, R., Fritz, S., Mubareka, S., and Giri, C.: A land-cover map
for South and Southeast Asia derived from SPOT-VEGETATION data, J.
Biogeogr., 34, 625–637,
https://doi.org/10.1111/j.1365-2699.2006.01637.x, 2007. a
Stone, E., Schauer, J., Quraishi, T. A., and Mahmood, A.: Chemical
characterization and source apportionment of fine and coarse particulate
matter in Lahore, Pakistan, Atmos. Environ., 44, 1062–1070,
https://doi.org/10.1016/j.atmosenv.2009.12.015, 2010. a
Surl, L., Palmer, P. I., and González Abad, G.: Which processes drive observed variations of HCHO columns over India?, Atmos. Chem. Phys., 18, 4549–4566, https://doi.org/10.5194/acp-18-4549-2018, 2018. a
Tan, J., Zhang, Y., Ma, W., Yu, Q., Wang, J., and Chen, L.: Impact of spatial
resolution on air quality simulation: A case study in a highly industrialized
area in Shanghai, China, Atmos. Pollut. Res., 6, 322–333,
https://doi.org/10.5094/APR.2015.036, 2015. a
The pandas dev. Team: pandas-dev/pandas: Pandas,
https://doi.org/10.5281/zenodo.3509134, 2020. a
Tie, X., Madronich, S., Walters, S., Zhang, R., Rasch, P., and Collins, W.:
Effect of clouds on photolysis and oxidants in the troposphere, J. Geophys.
Res.-Atmos., 108, https://doi.org/10.1029/2003JD003659, 2003. a
University Corporation for Atmospheric Research, UCAR: WRF-Chem model code, available at: https://www2.mmm.ucar.edu/wrf/users/download/get_source.html, last access: 12 July 2021, https://doi.org/10.5065/D6MK6B4K, 2021. a
U.S. Department of State: U.S. Embassy and Consulates' air quality monitors,
available at: https://www.airnow.gov/international/us-embassies-and-consulates/, last
access: 30 November 2020. a
Vadrevu, K. P., Ellicott, E., Badarinath, K., and Vermote, E.: MODIS derived
fire characteristics and aerosol optical depth variations during the
agricultural residue burning season, north India, Environ. Pollut., 159,
1560–1569, https://doi.org/10.1016/j.envpol.2011.03.001, 2011.
a
Venkataraman, C., Brauer, M., Tibrewal, K., Sadavarte, P., Ma, Q., Cohen, A., Chaliyakunnel, S., Frostad, J., Klimont, Z., Martin, R. V., Millet, D. B., Philip, S., Walker, K., and Wang, S.: Source influence on emission pathways and ambient PM2.5 pollution over India (2015–2050), Atmos. Chem. Phys., 18, 8017–8039, https://doi.org/10.5194/acp-18-8017-2018, 2018. a
Wang, T., Song, Y., Xu, Z., Liu, M., Xu, T., Liao, W., Yin, L., Cai, X., Kang, L., Zhang, H., and Zhu, T.: Why is the Indo-Gangetic Plain the region with the largest NH3 column in the globe during pre-monsoon and monsoon seasons?, Atmos. Chem. Phys., 20, 8727–8736, https://doi.org/10.5194/acp-20-8727-2020, 2020. a
WHO: Ambient air pollution: A global assessment of exposure and burden of
disease, Tech. rep., World Health Organization, WHO, Geneva, Switzerland, 2016. a
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011. a, b
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for
simulating aerosol interactions and chemistry (MOSAIC), J. Geophys.
Res.-Atmos., 113, https://doi.org/10.1029/2007JD008782, 2008. a
Zhang, M., Uno, I., Zhang, R., Han, Z., Wang, Z., and Pu, Y.: Evaluation of the
Models-3 Community Multi-scale Air Quality (CMAQ) modeling system with
observations obtained during the TRACE-P experiment: Comparison of ozone and
its related species, Atmos. Environ., 40, 4874–4882,
https://doi.org/10.1002/2017JD027057, 2006. a
Zhang, Q. J., Beekmann, M., Drewnick, F., Freutel, F., Schneider, J., Crippa, M., Prevot, A. S. H., Baltensperger, U., Poulain, L., Wiedensohler, A., Sciare, J., Gros, V., Borbon, A., Colomb, A., Michoud, V., Doussin, J.-F., Denier van der Gon, H. A. C., Haeffelin, M., Dupont, J.-C., Siour, G., Petetin, H., Bessagnet, B., Pandis, S. N., Hodzic, A., Sanchez, O., Honoré, C., and Perrussel, O.: Formation of organic aerosol in the Paris region during the MEGAPOLI summer campaign: evaluation of the volatility-basis-set approach within the CHIMERE model, Atmos. Chem. Phys., 13, 5767–5790, https://doi.org/10.5194/acp-13-5767-2013, 2013. a
Zhang, R., Lei, W., Tie, X., and Hess, P.: Industrial emissions cause extreme
urban ozone diurnal variability, P. Natl. Acad. Sci. USA, 101, 6346–6350,
https://doi.org/10.1073/pnas.0401484101, 2004. a
Zhao, B., Wang, S., Donahue, N. M., Jathar, S. H., Huang, X., Wu, W., Hao, J.,
and Robinson, A. L.: Quantifying the effect of organic aerosol aging and
intermediate-volatility emissions on regional-scale aerosol pollution in
China, Sci. Rep., 6, 28815, https://doi.org/10.1038/srep28815, 2016. a, b
Short summary
We use a 3-D atmospheric chemistry model to investigate how seasonal emissions sources and meteorological conditions affect the surface distribution of fine particulate matter (PM2.5) and organic aerosol (OA) over the Indo-Gangetic Plain. We find that all seasonal mean values of PM2.5 still exceed safe air quality levels, with human emissions contributing to PM2.5 all year round, open fires during post- and pre-monsoon, and biogenic emissions during monsoon. OA contributes up to 30 % to PM2.5.
We use a 3-D atmospheric chemistry model to investigate how seasonal emissions sources and...
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