Articles | Volume 22, issue 16
https://doi.org/10.5194/acp-22-10551-2022
https://doi.org/10.5194/acp-22-10551-2022
Research article
 | 
19 Aug 2022
Research article |  | 19 Aug 2022

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Minghao Qiu, Corwin Zigler, and Noelle E. Selin

Related authors

The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025,https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
A tool for air pollution scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies
William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
Geosci. Model Dev., 15, 7767–7789, https://doi.org/10.5194/gmd-15-7767-2022,https://doi.org/10.5194/gmd-15-7767-2022, 2022
Short summary
Understanding mercury oxidation and air–snow exchange on the East Antarctic Plateau: a modeling study
Shaojie Song, Hélène Angot, Noelle E. Selin, Hubert Gallée, Francesca Sprovieri, Nicola Pirrone, Detlev Helmig, Joël Savarino, Olivier Magand, and Aurélien Dommergue
Atmos. Chem. Phys., 18, 15825–15840, https://doi.org/10.5194/acp-18-15825-2018,https://doi.org/10.5194/acp-18-15825-2018, 2018
Short summary
Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth System Model version 1.2 with CAM4 (CESM1.2 CAM-chem): MOZART-4 vs. Reduced Hydrocarbon vs. Super-Fast chemistry
Benjamin Brown-Steiner, Noelle E. Selin, Ronald Prinn, Simone Tilmes, Louisa Emmons, Jean-François Lamarque, and Philip Cameron-Smith
Geosci. Model Dev., 11, 4155–4174, https://doi.org/10.5194/gmd-11-4155-2018,https://doi.org/10.5194/gmd-11-4155-2018, 2018
Short summary
Maximizing ozone signals among chemical, meteorological, and climatological variability
Benjamin Brown-Steiner, Noelle E. Selin, Ronald G. Prinn, Erwan Monier, Simone Tilmes, Louisa Emmons, and Fernando Garcia-Menendez
Atmos. Chem. Phys., 18, 8373–8388, https://doi.org/10.5194/acp-18-8373-2018,https://doi.org/10.5194/acp-18-8373-2018, 2018
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Modelling of atmospheric variability in gas and aerosols during the ACROSS campaign 2022 of the greater Paris area: evaluation of the meteorology, dynamics and chemistry
Ludovico Di Antonio, Matthias Beekmann, Guillaume Siour, Vincent Michoud, Christopher Cantrell, Astrid Bauville, Antonin Bergé, Mathieu Cazaunau, Servanne Chevaillier, Manuela Cirtog, Joel F. de Brito, Paola Formenti, Cecile Gaimoz, Olivier Garret, Aline Gratien, Valérie Gros, Martial Haeffelin, Lelia N. Hawkins, Simone Kotthaus, Gael Noyalet, Diana L. Pereira, Jean-Eudes Petit, Eva Drew Pronovost, Véronique Riffault, Chenjie Yu, Gilles Foret, Jean-François Doussin, and Claudia Di Biagio
Atmos. Chem. Phys., 25, 4803–4831, https://doi.org/10.5194/acp-25-4803-2025,https://doi.org/10.5194/acp-25-4803-2025, 2025
Short summary
Spatial–temporal patterns in anthropogenic and biomass burning emission contributions to air pollution and mortality burden changes in India from 1995 to 2014
Bin Luo, Yuqiang Zhang, Tao Tang, Hongliang Zhang, Jianlin Hu, Jiangshan Mu, Wenxing Wang, and Likun Xue
Atmos. Chem. Phys., 25, 4767–4783, https://doi.org/10.5194/acp-25-4767-2025,https://doi.org/10.5194/acp-25-4767-2025, 2025
Short summary
A comprehensive global modeling assessment of nitrate heterogeneous formation on desert dust
Rubén Soussé Villa, Oriol Jorba, María Gonçalves Ageitos, Dene Bowdalo, Marc Guevara, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 25, 4719–4753, https://doi.org/10.5194/acp-25-4719-2025,https://doi.org/10.5194/acp-25-4719-2025, 2025
Short summary
AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, P. Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankararaman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Johann Engelbrecht, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbigniew Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gómez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal L. Weagle, and Xi Zhao
Atmos. Chem. Phys., 25, 4665–4702, https://doi.org/10.5194/acp-25-4665-2025,https://doi.org/10.5194/acp-25-4665-2025, 2025
Short summary
Long-term trends in aerosol properties derived from AERONET measurements
Zhenyu Zhang, Jing Li, Huizheng Che, Yueming Dong, Oleg Dubovik, Thomas Eck, Pawan Gupta, Brent Holben, Jhoon Kim, Elena Lind, Trailokya Saud, Sachchida Nand Tripathi, and Tong Ying
Atmos. Chem. Phys., 25, 4617–4637, https://doi.org/10.5194/acp-25-4617-2025,https://doi.org/10.5194/acp-25-4617-2025, 2025
Short summary

Cited articles

Abatzoglou, J. T. and Williams, A. P.: Impact of anthropogenic climate change on wildfire across western US forests, P. Natl. Acad. Sci. USA, 113, 11770–11775, 2016. a
Beijing Municipal Ecology and Environment Bureau: Beijing Clean Air Action Plan (2013–2017), http://sthjj.beijing.gov.cn/bjhrb/index/xxgk69/sthjlyzwg/wrygl/603133/index.html (last access: March 2022), 2013. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a
Burke, M., Driscoll, A., Heft-Neal, S., Xue, J., Burney, J., and Wara, M.: The changing risk and burden of wildfire in the United States, P. Natl. Acad. Sci. USA, 118, e2011048118, https://doi.org/10.1073/pnas.2011048118, 2021. a
Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone in urban areas and their use in assessing ozone trends, Atmos. Environ., 41, 7127–7137, https://doi.org/10.1016/j.atmosenv.2007.04.061, 2007. a
Download
Short summary
Evaluating impacts of emission changes on air quality requires accounting for meteorological variability. Many studies use simple regression methods to correct for meteorology, but little is known about their performance. Using cases in the US and China, we show that widely used regression models do not perform well and can lead to biased estimates of emission-driven trends. We propose a novel machine learning method with lower bias and provide recommendations to policymakers and researchers.
Share
Altmetrics
Final-revised paper
Preprint