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Despite efforts to control atmospheric pollutant emissions, ambient air quality remains a major concern in many parts of the world. Air pollution has significant negative impacts on human health. More than 80% of the world’s population is exposed to pollutant concentrations exceeding the World Health Organization (WHO) recommended levels and around 3.6 million deaths can be attributed to ambient air pollution with another 4 million from household related sources. Moreover, air pollution can alter ecosystems, damage buildings and monuments, as well as influence earth’s energy balance and therefore climate change.  
The different Shared Socioeconomic Pathways (SSPs) have varying impacts on air pollution emissions. SSP1 and SSP5 show the most rapid emissions reductions than the other SSPs due to more effective pollution control and lower intensity for fossil fuels. SSP3 shows a consistent decline throughout the century, which is however less sharp than the reduction presented by SSP1 and SSP5. SSP3, due to larger projected population growth and relatively more slow and heterogeneous economic growth, results in an increase in emissions until 2030, and through a slight post-2030 decline end in only slightly lower emissions levels than the current ones by 2100.  


Policies to control the adverse impacts of air pollution are numerous and regionally diverse. They are generally aimed at avoiding exceeding specified targets for concentration levels (for example, sulfur-di-oxide, ozone, and particulate matter) but goals for ecosystem protection (e.g., from acidification and eutrophication) have also been pursued in several regions. Pollution targets are periodically revised at both the global level (e.g. WHO) and by national and regional bodies. Levels of pollution control are also often different across sectors.
Mitigation scenarios bring co-benefits in terms of air pollutant emission reductions. The largest emissions reductions can be seen for the SSP3 scenario, which has the highest baseline emissions, and the lowest for SSP1/SSP5. In terms of pollutants, SO2 and NOx emissions result in the largest reductions, whereas BC emissions do not decline as much - this can mainly be attributed to assumptions on fuel-substitution in the residential sector. (Rao et al, 2016[[CiteRef::MSG-GLB_rao_future_2016]])


All these complexities within current integrated scenarios cannot be captured and therefore the approach is simplified by identifying three characteristics for air pollution narratives:
<xr id="fig:MESSAGE-GLOBIOM_AP_SSP"/> presents the differences of emissions reductions between the different SSPs for both a reference case as well as for mitigation scenarios.


1. Pollution control targets (e.g. concentration standards), which we specify relative to those in current OECD countries.
<div style=" overflow: auto;">
2. The speed at which developing countries ‘catch up’ with these levels and effectiveness of policies in current OECD countries.
<figure id="fig:MESSAGE-GLOBIOM_AP_SSP">
3. The pathways for pollution control technologies, including the technological frontier that represents best practice values at a given time.
[[File:Rao et al SSP air pollution.jpg|left|678px|thumb|<caption>Emissions of SO2, NOX and BC in SSP marker baselines (Ref) and 4.5 (labeled as 45) and 2.6 (labeled as 26) W/m2 climate mitigation cases. Shaded area indicates range of total emissions from RCP scenario range from (van Vuuren et al., 2011a). Assessment Report (AR5) range refers to the full range of scenarios reviewed in the [https://tntcat.iiasa.ac.at/AR5DB/ Fifth Assessment Report (AR5)] of Working Group III of the Intergovernmental Panel on Climate Change (IPCC); Historical values are derived from (Lamarque et al., 2010); Colored bars indicate the range of all models (markers and non-markers) in 2100. (Rao et al, 2016)</caption>]] [[CiteRef::MSG-GLB_rao_future_2016]]
</figure>
</div>


Based on these characteristics, three alternative assumptions for future pollution controls (strong, medium and weak) were developed, which are further mapped to specific SSP scenarios.
In terms of regional air pollution impacts of the different SSPs, the strong air pollution control scenarios (SSP1/SSP5) show significantly lower concentrations across all regions than the less stringent air pollution control scenarios (SSP3/SSP4). OECD countries are expected to enhance their air pollution situation by 2050 under all SSP scenarios. For Middle East and Africa, mineral dust is responsible for most of the higher concentration levels, and therefore, in this region, mitigation measures will not be as effective as elsewhere. For Asia the low air pollution control scenarios (SSP3/SSP4) would increase the amount of people exposed to high levels of air pollutants - however, mitigation measures have the potential for significant co-benefits in terms of air pollutants for the region. <xr id="fig:MESSAGE-GLOBIOM_AP_SSPreg"/> illustrates these regional impacts across the SSPs. (Rao et al, 2016[[CiteRef::MSG-GLB_rao_future_2016]])


In order to quantify the levels of AP control stringency, a global dataset of emission factors derived from the GAINS model is provided. This dataset reflects recent developments in the air pollution legislation across the world and draws on data collection, model evaluation, and discussion with air quality policy, measurement and modeling communities; in particular work on the revision of the European Union National Emission Ceiling Directive, the UNECE LRTAP Task Force on Hemispheric Transport of Air Pollution (TF HTAP), UNEP Black Carbon and Tropospheric Ozone assessment, as well as various ongoing EU funded initiatives.
<div style=" overflow: auto;">
 
<figure id="fig:MESSAGE-GLOBIOM_AP_SSPreg">
The projections of emission factor trajectories up to 2030 have been derived based on the World Energy Outlook (WEO) 2011 baseline scenario [2] implemented in the GAINS model. While the documentation of these recent emission scenarios is under preparation, the data has been made available to the modeling community via [www.geiacenter.org GEIA/ECCAD] and [http://eclipse.nilu.no/ ECLIPSE] web portals. Furthermore, the similar dataset (based on the WEO 2009 ([3]) developed with GAINS has been documented in the past and subsequently applied to a number of studies.
[[File:Rao et al fig2.jpg|left|678px|thumb|<caption>Left panel: region-population weighted mean PM2.5 in μg/m3 (left axis) from marker scenario (blue color bars) and average from the 3 RCP scenarios (grey bar), contribution of natural PM2.5 (hatched area) for the year 2005 (leftmost bar) and 2050. Green, orange and red colored markers indicate the fraction of the population exposed to <10, <25 and <35 μg/m3 respectively (right axis). Right panel: mean ozone concentration (maximal 6-monthly mean of daily maximum ozone). For the grouped scenarios SSP1/5 and SSP3/4 the concentration represents the mean of the respective marker scenarios. Error bars show the concentration range (min/max) of regional averages from all models in the (set of) SSP scenarios shown, including non-marker. For the RCP bars, the error bar indicates the min/max range within the set of 3 RCP2.6, RCP4.5 and RCP8.5 scenarios. (Rao et al, 2016)</caption>]] [[CiteRef::MSG-GLB_rao_future_2016]]
 
</figure>
The quantitative guidance is based on on a dataset of regional emission factors (i.e., emissions per unit of energy) for energy-related combustion and transformation sectors until 2030 based on current policies and technological options derived from the GAINS model. This dataset includes emission factors for 26 world regions for sulfur dioxide (SO2), nitrogen oxides (NOx), organic carbon (OC), black carbon (BC), carbon monoxide (CO), non-methane volatile organic carbons (NMVOC), and ammonia (NH3) from all energy combustion and process sources.  
</div>
 
(Rao et al, 2016[[CiteRef::MSG-GLB_rao_future_2016]])

Latest revision as of 16:22, 20 October 2016

Model Documentation - MESSAGE-GLOBIOM

Corresponding documentation
Previous versions
Model information
Model link
Institution International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation

The different Shared Socioeconomic Pathways (SSPs) have varying impacts on air pollution emissions. SSP1 and SSP5 show the most rapid emissions reductions than the other SSPs due to more effective pollution control and lower intensity for fossil fuels. SSP3 shows a consistent decline throughout the century, which is however less sharp than the reduction presented by SSP1 and SSP5. SSP3, due to larger projected population growth and relatively more slow and heterogeneous economic growth, results in an increase in emissions until 2030, and through a slight post-2030 decline end in only slightly lower emissions levels than the current ones by 2100.

Mitigation scenarios bring co-benefits in terms of air pollutant emission reductions. The largest emissions reductions can be seen for the SSP3 scenario, which has the highest baseline emissions, and the lowest for SSP1/SSP5. In terms of pollutants, SO2 and NOx emissions result in the largest reductions, whereas BC emissions do not decline as much - this can mainly be attributed to assumptions on fuel-substitution in the residential sector. (Rao et al, 2016MSG-GLB_rao_future_2016)

<xr id="fig:MESSAGE-GLOBIOM_AP_SSP"/> presents the differences of emissions reductions between the different SSPs for both a reference case as well as for mitigation scenarios.

<figure id="fig:MESSAGE-GLOBIOM_AP_SSP">

Emissions of SO2, NOX and BC in SSP marker baselines (Ref) and 4.5 (labeled as 45) and 2.6 (labeled as 26) W/m2 climate mitigation cases. Shaded area indicates range of total emissions from RCP scenario range from (van Vuuren et al., 2011a). Assessment Report (AR5) range refers to the full range of scenarios reviewed in the Fifth Assessment Report (AR5) of Working Group III of the Intergovernmental Panel on Climate Change (IPCC); Historical values are derived from (Lamarque et al., 2010); Colored bars indicate the range of all models (markers and non-markers) in 2100. (Rao et al, 2016)
MSG-GLB_rao_future_2016

</figure>

In terms of regional air pollution impacts of the different SSPs, the strong air pollution control scenarios (SSP1/SSP5) show significantly lower concentrations across all regions than the less stringent air pollution control scenarios (SSP3/SSP4). OECD countries are expected to enhance their air pollution situation by 2050 under all SSP scenarios. For Middle East and Africa, mineral dust is responsible for most of the higher concentration levels, and therefore, in this region, mitigation measures will not be as effective as elsewhere. For Asia the low air pollution control scenarios (SSP3/SSP4) would increase the amount of people exposed to high levels of air pollutants - however, mitigation measures have the potential for significant co-benefits in terms of air pollutants for the region. <xr id="fig:MESSAGE-GLOBIOM_AP_SSPreg"/> illustrates these regional impacts across the SSPs. (Rao et al, 2016MSG-GLB_rao_future_2016)

<figure id="fig:MESSAGE-GLOBIOM_AP_SSPreg">

Left panel: region-population weighted mean PM2.5 in μg/m3 (left axis) from marker scenario (blue color bars) and average from the 3 RCP scenarios (grey bar), contribution of natural PM2.5 (hatched area) for the year 2005 (leftmost bar) and 2050. Green, orange and red colored markers indicate the fraction of the population exposed to <10, <25 and <35 μg/m3 respectively (right axis). Right panel: mean ozone concentration (maximal 6-monthly mean of daily maximum ozone). For the grouped scenarios SSP1/5 and SSP3/4 the concentration represents the mean of the respective marker scenarios. Error bars show the concentration range (min/max) of regional averages from all models in the (set of) SSP scenarios shown, including non-marker. For the RCP bars, the error bar indicates the min/max range within the set of 3 RCP2.6, RCP4.5 and RCP8.5 scenarios. (Rao et al, 2016)
MSG-GLB_rao_future_2016

</figure>

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