SHERPA: a computational model for better air quality in urban areas

SHERPA
Good air quality is still a challenge in Europe; in 2015, 16 Member States out of 28 failed to comply with the EU air quality limit values. © EU, 2016

A novel modelling tool that can compare and provide data on air quality is now publicly available. The SHERPA (Screening for High Emission Reduction Potential on Air) tool calculates how changes in emissions – stemming from actions on traffic or residential heating for example – affect air quality. It has been designed by JRC scientists to support public authorities in selecting sound policies to improve air quality in urban areas.

SHERPA is configured to work with a predefined set of input data (including emission inventories) that cover the whole of Europe at high (roughly 7 km2) resolution. This allows for the simple and straightforward testing of new air quality policies on any given domain in Europe. At the same time, and thanks to its flexibility, SHERPA can also use locally produced high quality data.

Once downloaded to a PC, the tool will allow users to quickly and easily evaluate the scope and effect of local policies on air quality. SHERPA can help policy-makers find the maximum air quality improvement that can be achieved with locally designed measures and identify the key sectors and pollutants to be addressed in order to improve air quality in a given area. In addition, the tool can be used to calculate the contribution to local air quality coming from neighbouring regions.

Background

Good air quality is still a challenge in Europe. In 2015, 16 Member States out of 28 failed to comply with the European Union air quality limit values. The situation is more challenging in cities, where a high percentage of the population – up to 90%, according to the World Health Organization (WHO) limit values – is still exposed to pollution levels detrimental to health, both in terms of morbidity and mortality.

From European to local level, policy-makers strive to improve air quality in urban areas. Although there are many possible interventions that can be made at the city scale through measures such as congestion charges, investment in public transport, or higher share of renewables in the energy used in district heating and cooling, it is difficult for policy-makers to quickly assess the consequences of policies on local air quality. The efficacy of those policies often depends on a combination of specific factors such as meteorology or topography, only to name some. The JRC-developed tool can help fill this gap.