Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-573-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-573-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The AirGAM 2022r1 air quality trend and prediction model
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Sverre Solberg
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Philipp Schneider
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Cristina Guerreiro
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Related authors
No articles found.
Wanmin Gong, Stephen R. Beagley, Kenjiro Toyota, Henrik Skov, Jesper Heile Christensen, Alex Lupu, Diane Pendlebury, Junhua Zhang, Ulas Im, Yugo Kanaya, Alfonso Saiz-Lopez, Roberto Sommariva, Peter Effertz, John W. Halfacre, Nis Jepsen, Rigel Kivi, Theodore K. Koenig, Katrin Müller, Claus Nordstrøm, Irina Petropavlovskikh, Paul B. Shepson, William R. Simpson, Sverre Solberg, Ralf M. Staebler, David W. Tarasick, Roeland Van Malderen, and Mika Vestenius
Atmos. Chem. Phys., 25, 8355–8405, https://doi.org/10.5194/acp-25-8355-2025, https://doi.org/10.5194/acp-25-8355-2025, 2025
Short summary
Short summary
This study showed that the springtime O3 depletion plays a critical role in driving the surface O3 seasonal cycle in the central Arctic. The O3 depletion events, while occurring most notably within the lowest few hundred metres above the Arctic Ocean, can induce a 5–7 % loss in the pan-Arctic tropospheric O3 burden during springtime. The study also found enhancements in O3 and NOy (mostly peroxyacetyl nitrate) concentrations in the Arctic due to northern boreal wildfires, particularly at higher altitudes.
Rona Louise Thompson, Nalini Krishnankutty, Ignacio Pisso, Philipp Schneider, Kerstin Stebel, Motoki Sasakawa, Andreas Stohl, and Stephen Platt
EGUsphere, https://doi.org/10.5194/egusphere-2025-147, https://doi.org/10.5194/egusphere-2025-147, 2025
Short summary
Short summary
Satellite remote sensing of atmospheric mixing ratios of greenhouse gases (GHGs) can provide information on the emissions of these GHGs. This study presents a novel method to use atmospheric column mixing ratios with a Lagrangian model of atmospheric transport to estimate GHG emissions. This method can reduce model errors resulting from how an observation is represented by an atmospheric model potentially reducing the errors in the GHG emissions derived.
Yao Ge, Sverre Solberg, Mathew R. Heal, Stefan Reimann, Willem van Caspel, Bryan Hellack, Thérèse Salameh, and David Simpson
Atmos. Chem. Phys., 24, 7699–7729, https://doi.org/10.5194/acp-24-7699-2024, https://doi.org/10.5194/acp-24-7699-2024, 2024
Short summary
Short summary
Atmospheric volatile organic compounds (VOCs) constitute many species, acting as precursors to ozone and aerosol. Given the uncertainties in VOC emissions, lack of evaluation studies, and recent changes in emissions, this work adapts the EMEP MSC-W to evaluate emission inventories in Europe. We focus on the varying agreement between modelled and measured VOCs across different species and underscore potential inaccuracies in total and sector-specific emission estimates.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
Short summary
Short summary
This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Stephen M. Platt, Øystein Hov, Torunn Berg, Knut Breivik, Sabine Eckhardt, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Markus Fiebig, Rebecca Fisher, Georg Hansen, Hans-Christen Hansson, Jost Heintzenberg, Ove Hermansen, Dominic Heslin-Rees, Kim Holmén, Stephen Hudson, Roland Kallenborn, Radovan Krejci, Terje Krognes, Steinar Larssen, David Lowry, Cathrine Lund Myhre, Chris Lunder, Euan Nisbet, Pernilla B. Nizzetto, Ki-Tae Park, Christina A. Pedersen, Katrine Aspmo Pfaffhuber, Thomas Röckmann, Norbert Schmidbauer, Sverre Solberg, Andreas Stohl, Johan Ström, Tove Svendby, Peter Tunved, Kjersti Tørnkvist, Carina van der Veen, Stergios Vratolis, Young Jun Yoon, Karl Espen Yttri, Paul Zieger, Wenche Aas, and Kjetil Tørseth
Atmos. Chem. Phys., 22, 3321–3369, https://doi.org/10.5194/acp-22-3321-2022, https://doi.org/10.5194/acp-22-3321-2022, 2022
Short summary
Short summary
Here we detail the history of the Zeppelin Observatory, a unique global background site and one of only a few in the high Arctic. We present long-term time series of up to 30 years of atmospheric components and atmospheric transport phenomena. Many of these time series are important to our understanding of Arctic and global atmospheric composition change. Finally, we discuss the future of the Zeppelin Observatory and emerging areas of future research on the Arctic atmosphere.
Claire Lamotte, Jonathan Guth, Virginie Marécal, Martin Cussac, Paul David Hamer, Nicolas Theys, and Philipp Schneider
Atmos. Chem. Phys., 21, 11379–11404, https://doi.org/10.5194/acp-21-11379-2021, https://doi.org/10.5194/acp-21-11379-2021, 2021
Short summary
Short summary
Improvements are made in a global chemical transfer model by considering a new volcanic SO2 emissions inventory, with more volcanoes referenced and more information on the altitude of injection. Better constraining volcanic emissions with this inventory improves the global, but mostly local, tropospheric sulfur composition. The tropospheric sulfur budget shows a nonlinearity to the volcanic contribution, especially to the sulfate aerosol burden and sulfur wet deposition.
Karl Espen Yttri, Francesco Canonaco, Sabine Eckhardt, Nikolaos Evangeliou, Markus Fiebig, Hans Gundersen, Anne-Gunn Hjellbrekke, Cathrine Lund Myhre, Stephen Matthew Platt, André S. H. Prévôt, David Simpson, Sverre Solberg, Jason Surratt, Kjetil Tørseth, Hilde Uggerud, Marit Vadset, Xin Wan, and Wenche Aas
Atmos. Chem. Phys., 21, 7149–7170, https://doi.org/10.5194/acp-21-7149-2021, https://doi.org/10.5194/acp-21-7149-2021, 2021
Short summary
Short summary
Carbonaceous aerosol sources and trends were studied at the Birkenes Observatory. A large decrease in elemental carbon (EC; 2001–2018) and a smaller decline in levoglucosan (2008–2018) suggest that organic carbon (OC)/EC from traffic/industry is decreasing, whereas the abatement of OC/EC from biomass burning has been less successful. Positive matrix factorization apportioned 72 % of EC to fossil fuel sources and 53 % (PM2.5) and 78 % (PM10–2.5) of OC to biogenic sources.
Cited articles
Barré, J., Petetin, H., Colette, A., Guevara, M., Peuch, V.-H., Rouil, L., Engelen, R., Inness, A., Flemming, J., Pérez García-Pando, C., Bowdalo, D., Meleux, F., Geels, C., Christensen, J. H., Gauss, M., Benedictow, A., Tsyro, S., Friese, E., Struzewska, J., Kaminski, J. W., Douros, J., Timmermans, R., Robertson, L., Adani, M., Jorba, O., Joly, M., and Kouznetsov, R.: Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models, Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021, 2021.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
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.
Carslaw, D. C.: The openair manual – open-source tools for analysing air
pollution data, Manual for version 2.6–6, University of York, https://github.com/davidcarslaw/openair (last access: 21 January 2022), 2019.
Carslaw, D. C.: deweather: Remove the influence of weather on air quality
data, R package version 0.7, https://github.com/davidcarslaw/deweather (last access: 21 January 2022), 2021.
Carslaw, D. C. and Ropkins K.: openair – an R package for air quality data
analysis, Environ. Model. Softw., 27–28, 52–61, https://doi.org/10.1016/j.envsoft.2011.09.008, 2012.
Chan, E. and Vet, R. J.: Baseline levels and trends of ground level ozone in Canada and the United States, Atmos. Chem. Phys., 10, 8629–8647, https://doi.org/10.5194/acp-10-8629-2010, 2010.
Chang, K. L., Schultz, M. G., Lan, X., McClure-Begley, A., Petropavlovskikh,
I., Xu, X., and Ziemke, J. R.: Trend detection of atmospheric time series:
Incorporating appropriate uncertainty estimates and handling extreme events,
Elem. Sci. Anth., 9, 1–28, https://doi.org/10.1525/elementa.2021.00035, 2021.
Davis, J., Cox, W., Reff, A., and Dolwick, P.: A comparison of CMAQ-based
and observation-based statistical models relating ozone to meteorological
parameters, Atmos. Environ., 45, 3481–3487, https://doi.org/10.1016/j.atmosenv.2010.12.060, 2011.
Diaz, F. M. R., Khan, M. A. H., Shallcross, B. M. A., Shallcross, E. D. G.,
Vogt, U., and Shallcross, D. E.: Ozone Trends in the United Kingdom over the
Last 30 Years, Atmosphere, 11, 534, https://doi.org/10.3390/atmos11050534, 2020.
EEA (European Environment Agency), Leeuw, F., Guerreiro, C., and Foltescu, V.:
Air quality in Europe: 2013 report, Publications Office, 2013, https://doi.org/10.2800/92843, 2013.
EEA (European Environment Agency), González Ortiz, A., Guerreiro, C., and
Soares, J.: Air quality in Europe: 2020 report, Publications Office, 2020,
https://data.europa.eu/doi/10.2800/602793 (last access: 25 November 2022), 2020.
Fix, M. J., Cooley, D., Hodzic, A., Gilleland, E., Russell, B. T., Porter,
W. C., and Pfister, G. G.: Observed and predicted sensitivities of extreme
surface ozone to meteorological drivers in three US cities, Atmos.
Environ., 176, 292–300, https://doi.org/10.1016/j.atmosenv.2017.12.036, 2018.
Grange, S. K. and Carslaw, D. C.: Using meteorological normalisation to
detect interventions in air quality time series, Sci. Total Environ., 653,
578–588, https://doi.org/10.1016/j.scitotenv.2018.10.344,
2019.
Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., and Hueglin, C.: Random forest meteorological normalisation models for Swiss PM10 trend analysis, Atmos. Chem. Phys., 18, 6223–6239, https://doi.org/10.5194/acp-18-6223-2018, 2018.
Hastie, T. J. and Tibshirani, R. J.: Generalized Additive Models, CRC Press,
Boca Raton, FL, https://doi.org/10.1201/9780203753781, 1990.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5
hourly data on single levels from 1979 to present, Copernicus Climate Change
Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, 146, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Ho, T. K.: Random Decision Forests, Proceedings of the 3rd International
Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August
1995, 278–282, Archived from the original (PDF) on 17 April 2016, https://web.archive.org/web/20160417030218/http://ect.bell-labs.com/who/tkh/publications/papers/odt.pdf (last access: 21 January 2022), 1995.
Johansson, J. M., Watne, A. K., Karlsson, P. E., Karlsson, G. P.,
Danielsson, H., Andersson, C., and Pleijel, H.: The European heat wave of
2018 and its promotion of the ozone climate penalty in southwest Sweden,
Boreal Environ. Res., 25, 39–50, 2020.
Keller, C. A., Evans, M. J., Knowland, K. E., Hasenkopf, C. A., Modekurty, S., Lucchesi, R. A., Oda, T., Franca, B. B., Mandarino, F. C., Díaz Suárez, M. V., Ryan, R. G., Fakes, L. H., and Pawson, S.: Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone, Atmos. Chem. Phys., 21, 3555–3592, https://doi.org/10.5194/acp-21-3555-2021, 2021.
Logan, J. A., Staehelin, J., Megretskaia, I. A., Cammas, J.-P., Thouret, V.,
Claude, H., De Backer, H., Steinbacher, M., Scheel, H.-E., Stübi, R.,
Fröhlich, M., and Derwent, R.: Changes in ozone over Europe: Analysis of
ozone measurements from sondes, regular aircraft (MOZAIC) and alpine surface
sites, J. Geophys. Res.-Atmos., 117, D09301, https://doi.org/10.1029/2011JD016952, 2012.
McFadden, D.: Statistical Tools, https://eml.berkeley.edu/~mcfadden/e240a_sp01/ch4.pdf (last access: 21 January 2022), 2000.
Nychka, D.: Bayesian Confidence Intervals for Smoothing Splines, J. Am.
Stat. Assoc., 83, 1134–1143, 1988.
Ordóñez, C., Mathis, H., Furger, M., Henne, S., Hüglin, C., Staehelin, J., and Prévôt, A. S. H.: Changes of daily surface ozone maxima in Switzerland in all seasons from 1992 to 2002 and discussion of summer 2003, Atmos. Chem. Phys., 5, 1187–1203, https://doi.org/10.5194/acp-5-1187-2005, 2005.
Ordóñez, C., Garrido-Perez, J. M., and Garcia-Herrera, R.: Early
spring near-surface ozone in Europe during the COVID-19 shutdown:
Meteorological effects outweigh emission changes, Sci. Total Environ., 747,
141322, https://doi.org/10.1016/j.scitotenv.2020.141322, 2020.
Otero, N., Sillmann, J., Mar, K. A., Rust, H. W., Solberg, S., Andersson, C., Engardt, M., Bergström, R., Bessagnet, B., Colette, A., Couvidat, F., Cuvelier, C., Tsyro, S., Fagerli, H., Schaap, M., Manders, A., Mircea, M., Briganti, G., Cappelletti, A., Adani, M., D'Isidoro, M., Pay, M.-T., Theobald, M., Vivanco, M. G., Wind, P., Ojha, N., Raffort, V., and Butler, T.: A multi-model comparison of meteorological drivers of surface ozone over Europe, Atmos. Chem. Phys., 18, 12269–12288, https://doi.org/10.5194/acp-18-12269-2018, 2018.
Pernak, R., Alvarado, M., Lonsdale, C., Mountain, M., Hegarty, J., and
Nehrkorn, T.: Forecasting Surface O3 in Texas Urban Areas Using Random
Forest and Generalized Additive Models, Aerosol Air Qual. Res., 9,
2815–2826, https://doi.org/10.4209/aaqr.2018.12.0464, 2019.
Petetin, H., Bowdalo, D., Soret, A., Guevara, M., Jorba, O., Serradell, K., and Pérez García-Pando, C.: Meteorology-normalized impact of the COVID-19 lockdown upon NO2 pollution in Spain, Atmos. Chem. Phys., 20, 11119–11141, https://doi.org/10.5194/acp-20-11119-2020, 2020.
R Core Team: R: A language and environment for statistical computing, R
Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/, last access: 25 November 2022.
Sicard, P., De Marco, A., Troussier, F., Renou, C., Vas, N., and Paoletti,
E.: Decrease in surface ozone concentrations at Mediterranean remote sites
and increase in the cities, Atmos. Environ., 79, 705–715, https://doi.org/10.1016/j.atmosenv.2013.07.042, 2013.
Simpson, D., Arneth, A., Mills, G., Solberg, S., and Uddling, J.: Ozone —
the persistent menace: interactions with the N cycle and climate change,
Curr. Opin. Env. Sust., 9–10, 9–19, https://doi.org/10.1016/j.cosust.2014.07.008, 2014.
Solberg, S., Walker, S.-E., Schneider, P., Guerreiro, C. and Colette, A.:
Discounting the effect of meteorology on trends in surface ozone:
Development of statistical tools, ETC/ACM Technical paper 15/2017, European
Topic Centre on Air Pollution and Climate Change Mitigation,
https://www.eionet.europa.eu/etcs/etc-atni/products/etc-atni-reports/etcacm_tp_2017_15_discount_meteo_on_o3_trends (last access: 25 November 2022), 2018a.
Solberg, S., Walker, S.-E., and Schneider, P.: Trend in measured NO2 and
PM: Discounting the effect of meteorology, ETC/ACM Eionet Report 9/2018,
European Topic Centre on Air Pollution and Climate Change Mitigation,
https://www.eionet.europa.eu/etcs/etc-atni/products/etc-atni-reports/eionet_rep_etcacm_2018_9_no2_pm_trends (last access: 25 November 2022), 2018b.
Solberg, S., Walker, S.-E., Guerreiro, C., and Colette, A.: Statistical
modelling for long-term trends of pollutants – Use of a GAM model for the
assessment of measurements of O3, NO2 and PM, ETC/ATNI Report
14/2019, European Topic Centre on Air Pollution and Climate Change
Mitigation, https://www.eionet.europa.eu/etcs/etc-atni/products/etc-atni-reports/etc-atni-report-14-2019-statistical-modelling-for-long-term-trends-of-pollutants-use-of-a-gam-model-for-the-assessment-of-measurements-of-o3-no2-and-pm-1 (last access: 25 November 2022),
2019.
Solberg, S., Colette, A., Raux, B., Walker, S.-E., and Guerreiro, C.: Long-term
trends of air pollutants at national level 2005–2019, ETC/ATNI Eionet Report
9/2021, European Topic Centre on Air Pollution and Climate Change
Mitigation, https://www.eionet.europa.eu/etcs/etc-atni/products/etc-atni-reports/etc-atni-report-9-2021-long-term-trends-of-air-pollutants-at-national-level-2005-2019 (last access: 25 November 2022),
2021a.
Solberg, S., Walker, S.-E., Schneider, P., and Guerreiro, C.: Quantifying the
impact of the Covid-19 lockdown measures on
nitrogen dioxide levels throughout Europe, Atmosphere, 12, 131, https://doi.org/10.3390/atmos12020131, 2021b.
Solberg, S., Claude, A., Reimann, S., Sauvage, S., and Walker, S.-E.: VOC measurements 2020, EMEP/CCC-Report 4/2022, https://projects.nilu.no/ccc/reports.html, last access: 12 December 2022.
Thompson, M., Reynolds, J., Cox, L., Guttorp, P., and Sampson, P.: A review
of statistical methods for the meteorological adjustment of tropospheric
ozone, Atmos. Environ., 35, 617–630, https://doi.org/10.1016/S1352-2310(00)00261-2, 2001.
Walker, S.-E.: AirGAM 2022r1 model (exact for results), Zenodo [code],
https://doi.org/10.5281/zenodo.6334104, 2022a.
Walker, S.-E.: AirGAM 2022r1 model (latest), Zenodo [code], https://doi.org/10.5281/zenodo.6334103, 2022b.
Walker, S.-E. and Solberg, S.: AirGAM 2022r1 basic data 2005–2019 and scripts,
Zenodo [data set],
https://doi.org/10.5281/zenodo.6334131, 2022a.
Walker, S.-E. and Solberg, S.: AirGAM 2022r1 input data for all stations
2005–2019, Zenodo [data set],
https://doi.org/10.5281/zenodo.6334171, 2022b.
Walker, S.-E. and Solberg, S.: AirGAM 2022r1 NO2 results for all stations
2005–2019, Zenodo [data set],
https://doi.org/10.5281/zenodo.6334195, 2022c.
Walker, S.-E. and Solberg, S.: AirGAM 2022r1 O3 results for all stations
2005–2019, Zenodo [data set],
https://doi.org/10.5281/zenodo.6334317, 2022d.
Walker, S.-E. and Solberg, S.: AirGAM 2022r1 PM10 results for all stations
2005–2019, Zenodo [data set],
https://doi.org/10.5281/zenodo.6334327, 2022e.
Walker, S.-E. and Solberg, S.: AirGAM 2022r1 PM2.5 results for all
stations 2005–2019, Zenodo [data set],
https://doi.org/10.5281/zenodo.6334334, 2022f.
Wikipedia: London Congestion Charge, https://en.wikipedia.org/wiki/London_congestion_charge, last access: 21 January 2022.
Wood, S. N.: Generalized Additive Models, An introduction with R, Chapman
and Hall/CRC Press, Boca Raton, Florida, https://doi.org/10.1201/9781315370279, 2017.
Zeileis A.: Econometric computing with HC and HAC covariance matrix
estimators, J. Stat. Software, 11, 1–17, https://doi.org/10.18637/jss.v011.i10, 2004.
Zheng, J., Swall, J. L., Cox, W. M., and Davis, J. M.: Interannual variation
in meteorologically adjusted ozone levels in the eastern United States: A
comparison of two approaches, Atmos. Environ., 41, 705–716, https://doi.org/10.1016/j.atmosenv.2006.09.010, 2007.
Short summary
We have developed a statistical model for estimating trends in the daily air quality observations of NO2, O3, PM10 and PM2.5, adjusting for trends and short-term variations in meteorology. The model is general and may also be used for prediction purposes, including forecasting. It has been applied in a recent comprehensive study in Europe. Significant declines are shown for the pollutants from 2005 to 2019, mainly due to reductions in emissions not attributable to changes in meteorology.
We have developed a statistical model for estimating trends in the daily air quality...