Coordinated by IDAEA | 32 partners across Europe | Total budget € 10 000 000
Air pollution remains a key health risk in Europe, which motivated the EU to revise the ambient air quality directive. AQ-RISE aims to significantly progress on fundamental challenges concerning monitoring and modelling of air pollutants facing stricter limit values and of air pollutants of emerging concern enabling to better answer to the needs of key stakeholders involved in the implementation of the AAQD at local, Member State or EU Level. AQ-RISE addresses the optimal and cost-effective design of monitoring networks by exploiting super sites and low-cost sampling networks, with special attention to automated quality control and data handling and to the assessment of spatial representativeness. NRT particulate matter source apportionment will be enhanced through multiinstrument configurations extending the application to UFP and OP. To support model application AQRISE will provide novel information on emissions of emerging pollutants and PM emissions from non-inventoried sources. To improve the modelling of daily limit values specific attention will be given to spatio-temporal emission variability. The functionality of multiscale air quality models for planning and source apportionment purposes will be improved for PM, UFP, BC, NH3, OP, VOCs, NOx, and O3. Novel assimilation and AI fusion techniques will be applied combining modelled distributions with sensor networks and (geostationary) satellite data.
The combination of solutions will be demonstrated in 10 pilot regions representing different pollution and climate regimes in Europe. An example is the application of EU wide high resolution AQ maps to address social equity. The project delivers a roadmap for upscaling and exploitation of the developed solutions. The AQ-RISE consortium in extremely well connected to policy support activities under FAIRMODE, CAMS, AQUILA, EMEP and ACTRIS and very active in CEN working groups ensuring the integration of AQRISE solutions into European air quality practices.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101290383.
