DOI: 10.5281/zenodo.14259955
title: Global gridded anthropogenic emissions of air pollutants and methane for the period 1990-2050

description: 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), and

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)


VERSION 2 includes two scenarios (Baseline and MTFR), including a major update of the underlying data (including future drivers), included policies, and some revision of spatial proxies compared to Version 1. Brief explanation of assumptions used in these scenarios are provided below:

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. Following development of Version 1, CIAM has undertaken further review resulting in updates of historical data (up to 2020) and projections driving emissions of all species in the GAINS model. Most of the updates for historcial data are for Europe and Central Asia while projections are updated globally. CIAM team has held consultation meetings with all EU27 and West Balkan countries as well as Switzerland, Norway, United Kingdom, and Republic of Moldova discussing information, data, assumptions in GAINS with national experts and comparing (and updating where found necessary) GAINS model results to reported data and emissions to the Center on Emission Inventories and Projections (CEIP) [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 fourth Clean Air Outlook [4] study. Note that compared to the CAO3 (used in Version 1), the energy projections for the EU include results of the discussion (and respective modelling work) on energy market measures to speed up the clean energy transition and end Europe's dependence on gas, oil, and coal imports from Russia (REPowerEU). 

For West Balkan revision of historical data and projections were done within the EU4Green projected funded by the EU; the new projections assume a more ambitious transformation of these economies allowing to reduce CO2 emissions faster.

A dedicated consultation process with Republic of Moldova (funded by the UNECE) resulted in important updates and revisions of the data and outlook, although the backbone of the projection remained the same as in Version 1; the 9EAST project funded by the EU.

The 9EAST project projections are also used for Georgia and Ukraine, as in Version 1.

Consultation meetings and exchange with Canada, Norway, Switzerland, and UK within work on the revision of the Air Convention Gothenburg Protocol, resulted in updated historical data as well as revised projections achieving much better consistency with the data countries report to CEIP [3].

Global updates of NMVOC and CH4 emissions from fossil fuel extraction and distribution (oil, gas, coal) were developed reviewing global datasets on activity data and emission factors.   

For the rest of the world, the projections in GAINS were updated by downscaling more recent IEA and FAO outlooks (FAO, 2018; IEA, 2023).


The maximum technically feasible reduction (LRTAP MTFR) scenario uses the same activity data (energy scenario, agriculture scenario) as the Baseline case, described above. Compared to Version 1, the results differ mostly driven by the changes in the Baseline activity but also due to updates to the model structure (for CH4).

The general idea of the MTFR scenario (as described in the documentation to Version 1) is to explore 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; Gomez Sanabria et al., 2022; Höglund-Isaksson et al., 2020). 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.

VERSION 2 does not include an update of the LRTAP LOW scenario. At the same time this scenario from Version 1 is not compatible with the Baseline of the VERSION 2 and so shall not be used in conjuctions with it.

Format of the datasets:

The datasets include gridded sectoral emissions provided as netcdf files with monthly resolution for the period 1990-2050 for the Baseline scenario and the years 2030, 2040, 2050 for the MTFR scenario. 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


 Changes in spatial distribution of emissions in VERSION 2 (compared to Version 1):

VERSION 2 includes improved spatial patterns mostly specific to methane, including information on gas compressor stations, gas pipelines, ports and rice cultivation. Other, non-pollutant specific improvements include:



Spatial resolution changed to 0.1° × 0.1°

Spatiotemporal pattern for agricultural waste burning improved for years 2005 - 2020

Distribution of emissions incorporating population uses more recent population data and urban/rural classification

Road traffic allocation harmonised globally

Spatial distribution for domestic heating takes into account different fuel types for the majority of regions in the EMEP domain.


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/)

EYE-CLIMA: Improving emission estimates of climate forcers project funded by the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101081395 (https://eyeclima.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

 

References:

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., 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 

FAO, 2018. The future of food and agriculture. Alternative pathways to 2050. Food and Agriculture Organization of the United Nations (FAO), Rome; https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en/ 

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

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, 2023. World Energy Outlook 2023. International Energy Agency (IEA), Paris, France.