Global gridded anthropogenic emissions of air pollutants and methane for the period 1990-2050

The global anthropogenic emissions of air pollutants including sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), particulate matter (distinguishing PM2.5, PM10, BC, OC, OM), non-methane volatile organic compounds (VOC), carbon monoxide (CO), and methane (CH4) were developed at CIAM/IIASA[2]. These emission datasets have been produced with the GAINS model[1] (Amann et al., 2011) within work under UNECE Convention on Long-Range Transboundary Air Pollution (LRTAP) and cover the 1990-2050 period. The respective emission datasets consist of 5-yearly global annual and monthly emissions and include two to three scenarios, depending on the version of the dataset: a baseline that is referred to as current legislation case (LRTAP Baseline), a scenarios exploring technical mitigation potential for all pollutant species (LRTAP MTFR), and a scenario that combines climate policy, behavioral changes, and strong technical mitigation of all pollutants (LRTAP LOW) Brief explanation of assumptions used in these three scenarios. The current legislation scenario (LRTAP Baseline) assumes implementation and effective enforcement of all committed energy and environmental policies affecting emissions of air pollutants and greenhouse gases. CIAM has undertaken a review and update of historical data (up to 2020) driving emissions of all species in the GAINS model, drawing on the statistical information from EUROSTAT, International Energy Agency (IEA), UN Food and Agriculture Organization (FAO), as well as reporting of data and emissions to the Center on Emission Inventories and Projections[3]. For the EU27, the energy and agriculture projections are consistent with the objectives of the European Green Deal and Fit for 55 package making EU carbon neutral by 2050; these are consistent with the projections used in the EU third Clean Air Outlook [4]. For West Balkan, Republic of Moldova, Georgia, and Ukraine, a similar set of modelling tools was used as for the EU developing a new consistent set of projections. For the rest of the world, GAINS model downscales projections from IEA and FAO (Alexandratos and Bruinsma, 2012; IEA, 2018) and updating the air pollution legislation from national and international sources (e.g., DieselNet, n.d.; He et al., 2021; Zhang, 2016; Zheng et al., 2018; Klimont et al., 2017; Rao et al., 2017). Note that the baseline used in this work does not include any recent shock events, i.e., these scenarios were developed before the Ukraine war The maximum technically feasible reduction (LRTAP MTFR) scenario uses the same activity data (energy scenario, agriculture scenario) as the CLE case and explores the potential for further emission mitigation applying technical measures which are characterized with lowest emission factors (as defined in the GAINS model databases), attainable with reduction technologies for which experience exists (Amann et al., 2013). These include highly efficient end of pipe technologies in industry (filters, scrubbers, primary measures), transport sector, residential combustion (clean burning stoves, pellet stoves and boilers), measures in agriculture including: new low emission houses (including cleaning of ventilation air where applicable), covered storage of manures, immediate or efficient application of manures on land, urea use with inhibitors. For the solvent use sector and fossil fuel production and distribution, control of leaks, improved maintenance, incineration as well as substitution or low solvent products are applied. The mitigation scenario (LRTAP LOW) includes several additional policies and assumes implementation of further emission reduction options, exploiting the proven technical mitigation potential as embedded in the GAINS model for air pollutants (Amann et al., 2020, 2013; Rafaj et al., 2018) and methane (Gomez Sanabria et al., 2022; Höglund-Isaksson, 2012; Höglund-Isaksson et al., 2020). While for the EU27 the LOW scenario has the same energy projections as for the CLE (the Green Deal), the rest of the world includes climate policies compatible with Paris Agreement goals and addressing several SDGs, e.g., access to clean energy for cooking and heating. Furthermore, additional assumptions about significant transformation in the agricultural sector leading to strong reduction of livestock numbers, especially cattle and pigs; this brings significant additional reductions of methane. The latter is based on the scenarios from the 'Growing Better report 2019' [5] (The Food and Land Use Coalition, 2019) and other studies considering ambitious improvements in nitrogen use efficiency and addressing healthy dietary requirements (Kanter et al., 2020; The EAT-Lancet Commission, 2019) as used earlier in scenarios for the global air pollution study (Amann et al., 2020). Format of the datasets: The datasets include gridded sectoral emissions provided as netcdf files with monthly resolution for the period 1990-2050 for the Baeline scenario and 2025-2050 for the MTFR and the LOW scenarios. Emissions include international shipping but not international aviation. Open burning of biomass includes only emissions from open burning of agricultural residues but not forest, peat or savannah fires. The sectors for which gridded data are provided (might vary by pollutant, i.e. some layers will be missing) include: Energy sector Residential combustion (cooking and heating) Transportation Industry (combustion and processes) Solvent use Waste management Agriculture (livestock and fertilizer application) Open burning of agricultural residues International shipping Acknowledgments: The development of the emission scenarios and respective spatially explict allocation of emissions has been supported by: UNECE Convention on Long-range Transboundary Air Pollution funding towards the EMEP Center for Integrated Asessment Modelling (CIAM) hosted at IIASA FORCeS (Constrained aerosol forcing for improved climate projections) project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821205 (https://forces-project.eu/) [1] https://gains.iiasa.ac.at/models/gains_models4.html [2] Center for Integrated Assessment Modelling (CIAM) hosted by the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria (https://iiasa.ac.at/policy/applications/centre-for-integrated-assessment-modelling-ciam) [3] www.ceip.at [4] https://environment.ec.europa.eu/topics/air/clean-air-outlook_en [5] https://www.foodandlandusecoalition.org/global-report/ References: Alexandratos, N., Bruinsma, J., 2012. World agriculture towards 2030/2050: the 2012 revision. ESA Working paper Rome, FAO. Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Höglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P., Sandler, R., Schöpp, W., Wagner, F., Winiwarter, W., 2011. Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications. Environ. Model. Softw. 26, 1489–1501. https://doi.org/10.1016/j.envsoft.2011.07.012 Amann, M., Kiesewetter, G., Schöpp, W., Klimont, Z., Winiwarter, W., Cofala, J., Rafaj, P., Höglund-Isaksson, L., Gomez-Sabriana, A., Heyes, C., Purohit, P., Borken-Kleefeld, J., Wagner, F., Sander, R., Fagerli, H., Nyiri, A., Cozzi, L., Pavarini, C., 2020. Reducing global air pollution: the scope for further policy interventions. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 378, 20190331. https://doi.org/10.1098/rsta.2019.0331 Amann, M., Klimont, Z., Wagner, F., 2013. Regional and Global Emissions of Air Pollutants: Recent Trends and Future Scenarios. Annu. Rev. Environ. Resour. 38, 31–55. https://doi.org/10.1146/annurev-environ-052912-173303 DieselNet, n.d. DieselNet: Diesel Exhaust Emission Standards [WWW Document]. URL http://www.dieselnet.com/standards/ (accessed 3.15.14). Gómez-Sanabria, A., Kiesewetter, G., Klimont, Z. et al. Potential for future reductions of global GHG and air pollutants from circular waste management systems. Nat Commun 13, 106 (2022). https://doi.org/10.1038/s41467-021-27624-7 He, X., Shen, W., Wallington, T.J., Zhang, S., Wu, X., Bao, Z., Wu, Y., 2021. Asia Pacific road transportation emissions, 1900–2050. Faraday Discuss. 226. Höglund-Isaksson, L.: Global anthropogenic methane emissions 2005–2030: technical mitigation potentials and costs, Atmos. Chem. Phys., 12, 9079–9096, https://doi.org/10.5194/acp-12-9079-2012, 2012. Höglund-Isaksson, L., Gómez-Sanabria, A., Klimont, Z., Rafaj, P., Schöpp, W., 2020. Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model. Environ. Res. Commun. 2, 025004. https://doi.org/10.1088/2515-7620/ab7457 IEA, 2018. World Energy Outlook 2018. International Energy Agency (IEA), Paris, France. Kanter, D., Winiwarter, W., Bodirsky, B., Bouwman, L., Boyer, K., 2020. Nitrogen futures in the shared socioeconomic pathways. Glob. Environ. Change 13 2003 277–29361. https://doi.org/DOI:10.1016/j.gloenvcha.2019.102029. Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P., Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of particulate matter including black carbon, Atmos. Chem. Phys., 17, 8681–8723, https://doi.org/10.5194/acp-17-8681-2017, 2017 Rafaj, P., Kiesewetter, G., Gül, T., Schöpp, W., Cofala, J., Klimont, Z., Purohit, P., Heyes, C., Amann, M., Borken-Kleefeld, J., Cozzi, L., 2018. Outlook for clean air in the context of sustainable development goals. Glob. Environ. Change 53, 1–11. https://doi.org/10.1016/j.gloenvcha.2018.08.008 Rao S, Klimont Z, Smith SJ, Van Dingenen R, Dentener F, Bouwman L, Riahi K, Amann M, et al. (2017) Future air pollution in the Shared Socio-economic Pathways. Global Environmental Change 42: 346-358. DOI:10.1016/j.gloenvcha.2016.05.012. The EAT-Lancet Commission, 2019. Healthy Diets From Sustainable Food Systems. London, UK. The Food and

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