AQ-WATCH (Air Quality: Worldwide Analysis and Forecasting of Atmospheric Composition for Health) is an international consortium, which co-develops and co-produces tailored products and services derived from space and in situ observational data for improving air quality forecasts and attribution. For this purpose, AQ-WATCH develops a supply chain leading to innovative downstream products and services for providing air quality information. These prototypes are based on existing space and in situ observations of air quality and tailored to the identified needs of international users. The project allows small and medium enterprises to integrate datasets associated with earth’s observations with advanced predictive models. These innovative products and services are aimed at improving public health and optimizing service provided by the energy sector in different regions of the world. The project also creates opportunities for new environmental technologies and enhances the interaction between research institutions, stakeholders and enterprises in support of the world-wide effort to improve air quality.
The AQ-WATCH products and services are organized into 5 modules allowing users to access historical and air pollution data as well as air quality forecasts at a global and regional scale, to compare different air pollution reduction scenarios, to assess the effect of wildfires or fracking activities on air quality, and to derive the changes in solar irradiance due to the presence of dust in the atmosphere. These 5 modules include: (1) Global and regional air quality atlas, (2) Air quality attribution & mitigation, (3) Dust and fire forecasting system, (4) Fracking analysis, and (5) Air quality forecast. In this presentation, apart from an overview of the AQ-WATCH project and its products, extra focus will be put on the last product module, Air quality forecast, in particular on the effort of the consortium to provide air quality forecasts from their hosting regional models and the execution of the AQ-WATCH multi-model air quality forecast system.
How to cite: Li, C. W. Y., Brasseur, G., Sofiev, M., Timmermans, R., Kumar, R., Pfister, G., Mo, D., Granier, C., Doumbia, T., Basart, S., Salvi, O., Caillard, B., and Boose, Y.: Introduction to the AQ-WATCH project and its multi-model air quality forecast system, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-164, https://doi.org/10.5194/ems2022-164, 2022.
