Pollutants and non-GHG forcing agents - MESSAGE-GLOBIOM - Revision history

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	Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).		Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

Rineke Oostenrijk: Text replacement - "HasParent=(.*) " to "QQQ "

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	Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).		Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

Rineke Oostenrijk: Text replacement - "|HasSeq=2 " to ""

2020-06-23T15:01:51Z

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	Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).		Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

Rineke Oostenrijk: Text replacement - "|HasLevel=2 " to ""

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	Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).		Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

Johanna Zilliacus at 09:26, 31 August 2016

2016-08-31T09:26:16Z

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			Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model. GAINS allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030 (Amann et al., 2011 [[CiteRef::MSG-GLB_amann_cost-effective_2011]]). These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). As a stand-alone model, it also tracks emissions of six greenhouse gases of the Kyoto basket with exception of NF3. The GAINS model has global coverage and holds essential information about key sources of emissions, environmental policies, and further mitigation opportunities for about 170 country-regions. The model relies on exogenous projections of energy use, industrial production, and agricultural activity for which it distinguishes all key emission sources and several hundred control measures. GAINS can develop finely resolved mid-term air pollutant emission trajectories with different levels of mitigation ambition (Cofala et al., 2007 [[CiteRef::MSG-GLB_cofala_scenarios_2007]]; Amann et al., 2013 [[CiteRef::MSG-GLB_amann_regional_2013]]). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments (see further for example Riahi et al., 2012 [[CiteRef::MSG-GLB_riahi_chapter_2012]]; Rao et al., 2013 [[CiteRef::MSG-GLB_rao_better_2013]]; Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]]).

Rineke Oostenrijk: Edited automatically from page MESSAGE-GLOBIOM setup.

2016-08-05T19:03:51Z

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Rineke Oostenrijk: Edited automatically from page MESSAGE-GLOBIOM setup.

2016-08-05T19:03:48Z

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