1990年から2050年における大気汚染物質とメタンの全球グリッド人為排出量

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🔬研究者:Provides a consistent, scenario-based emission dataset for integrated assessment modeling of air quality and climate.

🏢実務担当者:Useful for understanding future emission trajectories and potential mitigation options in supply chains.

🏛政策担当者:Offers scenario analysis to support policy design for air quality and climate co-benefits.

二酸化硫黄（SO2）、窒素酸化物（NOx）、アンモニア（NH3）、粒子状物質（PM2.5、PM10、BC、OC、OMを区別）、非メタン揮発性有機化合物（VOC）、一酸化炭素（CO）、メタン（CH4）を含む世界の人為起源排出量は、CIAM/IIASA[2]において作成された。これらの排出データセットは、UNECE長距離越境大気汚染条約（LRTAP）の下での作業においてGAINSモデル[1]（Amann et al., 2011）を用いて作成され、1990年から2050年までの期間を対象としている。各排出データセットは、5年ごとの世界の年間および月間排出量から構成され、データセットのバージョンに応じて2～3のシナリオを含む：現行法制度ケース（LRTAP Baseline）と呼ばれるベースライン、全汚染物質種の技術的削減可能性を探るシナリオ（LRTAP MTFR）、および気候政策、行動変容、全汚染物質の強力な技術的削減を組み合わせたシナリオ（LRTAP LOW）である。これら3つのシナリオで用いられた仮定の簡単な説明。現行法制度シナリオ（LRTAP Baseline）は、大気汚染物質および温室効果ガスの排出に影響を与えるすべての確定済みのエネルギー・環境政策の実施と効果的な執行を仮定している。CIAMは、EUROSTAT、国際エネルギー機関（IEA）、国連食糧農業機関（FAO）からの統計情報、ならびに排出インベントリ・予測センター[3]へのデータおよび排出量の報告を活用し、GAINSモデルにおける全物質の排出量を駆動する歴史データ（2020年まで）のレビューと更新を実施した。EU27については、エネルギーおよび農業予測は、欧州グリーンディールおよびFit for 55パッケージの目標（2050年までのEUのカーボンニュートラル化）と整合的であり、これらはEU第3次クリーンエア・アウトルック[4]で使用された予測と整合的である。西バルカン、モルドバ共和国、ジョージア、ウクライナについては、EUと同様の一連のモデリングツールを用いて、新たに整合的な予測セットを作成した。その他の地域については、GAINSモデルはIEAおよびFAOからの予測をダウンスケーリングし（Alexandratos and Bruinsma, 2012; IEA, 2018）、国内外の情報源から大気汚染法制度を更新している（例：DieselNet, n.d.; He et al., 2021; Zhang, 2016; Zheng et al., 2018; Klimont et al., 2017; Rao et al., 2017）。なお、本研究で使用したベースラインには最近のショック事象は含まれていない、すなわちこれらのシナリオはウクライナ戦争以前に作成されたものである。最大技術的削減可能量（LRTAP MTFR）シナリオは、CLEケースと同じ活動データ（エネルギーシナリオ、農業シナリオ）を使用し、経験が存在する削減技術（Amann et al., 2013）で達成可能な、GAINSモデルデータベースで定義された最低排出係数を特徴とする技術的対策を適用したさらなる排出削減の可能性を探るものである。これには、産業（フィルター、スクラバー、一次対策）、運輸部門、住宅燃焼（クリーン燃焼ストーブ、ペレットストーブおよびボイラー）における高効率の排ガス処理技術、農業における対策（適宜換気空気の浄化を含む新規低排出畜舎、堆肥の被覆貯蔵、農地への堆肥の即時または効率的施用、インヒビターを使用した尿素施用）が含まれる。溶剤使用部門および化石燃料生産・流通については、漏洩管理、改善された維持管理、焼却、ならびに代替または低溶剤製品が適用される。削減シナリオ（LRTAP LOW）は、いくつかの追加政策を含み、大気汚染物質（Amann et al., 2020, 2013; Rafaj et al., 2018）およびメタン（Gomez Sanabria et al., 2022; Höglund-Isaksson, 2012; Höglund-Isaksson et al., 2020）についてGAINSモデルに組み込まれた実証済みの技術的削減可能性を活用したさらなる排出削減オプションの実施を仮定する。EU27については、LOWシナリオはCLE（グリーンディール）と同じエネルギー予測を持つが、その他の地域では、パリ協定の目標と整合的であり、調理・暖房用のクリーンエネルギーへのアクセスなどのいくつかのSDGsに対処する気候政策を含む。さらに、農業部門における大幅な変革に関する追加的な仮定があり、家畜頭数、特に牛と豚の大幅な削減につながるものである。これは、メタンのさらなる大幅な削減をもたらす。後者は、'Growing Better report 2019' [5]（The Food and Land Use Coalition, 2019）のシナリオと、窒素利用効率の野心的な改善と健康的な食事要件への対応を考慮した他の研究（Kanter et al., 2020; The EAT-Lancet Commission, 2019）に基づいており、これは以前に世界の大気汚染研究のシナリオで使用されたものである（Amann et al., 2020）。データセットの形式：データセットには、グリッド化されたセクター別排出量が含まれ、1990年から2050年までの期間（ベースラインシナリオ）および2025年から2050年までの期間（MTFRおよびLOWシナリオ）について、月間解像度のnetcdfファイルとして提供される。排出量には国際海運が含まれるが、国際航空は含まれない。バイオマスの野焼きには、農業残渣の野焼きによる排出量のみが含まれ、森林、泥炭地、サバンナ火災は含まれない。グリッドデータが提供されるセクター（汚染物質によって異なる場合があり、一部のレイヤーは欠落する可能性がある）には以下が含まれる：エネルギー部門、住宅燃焼（調理および暖房）、運輸、産業（燃焼およびプロセス）、溶剤使用、廃棄物管理、農業（家畜および肥料施用）、農業残渣の野焼き、国際海運。謝辞：排出シナリオの開発およびそれぞれの空間明示的な排出量配分は、以下によって支援された：UNECE長距離越境大気汚染条約からIIASAにホストされているEMEP統合評価モデリングセンター（CIAM）への資金提供、欧州連合のHorizon 2020研究・イノベーションプログラムからグラント契約番号821205のもとで資金提供されたFORCeS（改善された気候予測のための制約付きエアロゾル強制力）プロジェクト（https://forces-project.eu/）[1] https://gains.iiasa.ac.at/models/gains_models4.html [2] 国際応用システム分析研究所（IIASA）、ラクセンブルク、オーストリアにホストされている統合評価モデリングセンター（CIAM）（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/ 参考文献：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

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

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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