Air pollution and health - MESSAGE-GLOBIOM
| Corresponding documentation | |
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| 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 | |
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.
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.
All these complexities within current integrated scenarios cannot be captured and therefore the approach is simplified by identifying three characteristics for air pollution narratives:
1. Pollution control targets (e.g. concentration standards), which we specify relative to those in current OECD countries. 2. The speed at which developing countries ‘catch up’ with these levels and effectiveness of policies in current OECD countries. 3. The pathways for pollution control technologies, including the technological frontier that represents best practice values at a given time.
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 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.
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 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.
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.
(Rao et al, 2016MSG-GLB_rao_future_2016)