# Air pollution and health - WITCH

Model Documentation - WITCH
Corresponding documentation
Model information
Institution Fondazione Eni Enrico Mattei (FEEM), Italy, http://www.feem.it., Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy, http://www.cmcc.it.
Solution concept
Solution method
Anticipation Perfect foresight

The WITCH air pollution module relates the pollution economic activities to emission levels of the most significant air pollutants. It allows the assessment of air pollution emissions in baseline scenarios or under a climate or pollution regulation scenario.

## Implementation

The implementation originates from the LIMITS project and Its emission factors have been calculated from the GAINS model in the context of the EMF30 exercise. In the WITCH model we use information on both fuel use and the type of electricity generation technologies employed to compute the emissions E of pollutant p at time period t according to ${\displaystyle E_{p}=\sum _{j}^{}A_{j}ef_{j,p}(t)}$ where ef is the emission factor related to activity, A of sector j. We consider the air pollutants p: carbon monoxide CO, methane CH4, black carbon BC, organic carbon OC, sulfur dioxide SO2, nitrogen oxides NOx, ammonia NH3 and volatile organic compounds VOC.

The emission factors ef are calculated using the ratio of emissions(E) over activities(A) provided by GAINS, these are at a first stage aggregated over the WITCH sectors (see sector mapping on the Appendix),

${\displaystyle ef_{j,p}(t)={\frac {E_{j,p,{\text{GAINS}}}}{A_{j,{\text{GAINS}}}}},}$

where j are the WITCH sectors and p is the pollutant.

The emission factors are then aggregated into the WITCH regions, using the mean weighted by country's level of CO2 emissions.

In WITCH we do not model all the activities that generate air pollution, therefore the non-energy related pollution is accounted for exogenously. For this non-modelled sectors the emissions are taken directly from available databases and mapped into the (SNAP sectors), which are sector categories for reporting air pollutant levels. The emissions of the exo-sectors (sectors that are related to energy but are not accounted in the model directly, see the table in Appendix), from the EMF30 database. The non-energy sectors, such as Solvents, Waste (landfills, waste water, non-energy incineration), Agriculture waste burning on fields, Agriculture, Grassland burning and Forest burning and the emissions of ammonia follow the RCP8.5 emissions from the RCP) database.

## Air Pollution Policies

Air pollution emissions depend on two important factors, the activity level of the pollutant sector, and the emission factor of that given activity. Therefore the implementation of policies can be done via structural measures, such as changes in the model endogenous activities, or via air pollution controls. the latter is undertaken by controlling the emission factor ef for activity category j and for pollutant p.

Accordingly, the ef are defined per air pollution scenario/baseline/policy, which corresponds to different levels of control, also called End-of-Pipe (EOP), measures. The air pollution scenarios are CLE (current legislation), SLE (stringent legislation) and MFR (maximum feasible reduction). The CLE scenario corresponds to the implementation until 2030 of all the legislation already (in 2013) foreseen and/or enforced for that period; The SLE scenario foresees the implementation of 75% of the MFR scenario which corresponds to the maximal technological frontier of EOP. For the exogenous sectors the implementation of policies has to be carried out via emission pathways.