Result description
AQ-WATCH’s Module 3 offers a powerful tool for managing Sand and Dust Storms. Leveraging advanced modeling and real-time data, this service predicts dust concentrations accurately, aiding proactive measures to mitigate SDS impacts on air quality, health, and ecosystems. The 72-hour forecast covers variables like aerosol optical depth, PM2.5, PM10, dust load, and solar irradiance. A MULTIMODEL ensemble approach for Chile, China, and Colorado regions enhances accuracy using models such as CAMS, SILAM, and MONARCH.
Module 3 also focuses on the Fire Smoke Forecast Service, addressing escalating wildfire threats. With satellite imagery and advanced modeling, this service predicts fire behavior, smoke dispersion, and air quality impacts. Alerts, real-time monitoring, and prediction mapping enable resource allocation, evacuation planning, and minimizing air quality impacts. Operational air quality forecasts, focused on fire persistence, promote effective fire management and community protection.
Adaptation to climate change
Climate-neutral and smart citiesAddressing target audiences and expressing needs
- To raise awareness and possibly influence policy
- Grants and Subsidies
- Other blended financing
The level of expertise required to operate and maintained the system is very high. Thus the transfer of technology, even accompanied by capacity building actions, is not suitable. The consortium is now in contact with third-parties that could potentially act as “exploiter”. The partners would provide expertise through service contracts with the exploiter(s).
Depending on its available resources and level of expertise, the future exploiters may decide to take over the activities at different stages of the value-chains.
- Public or private funding institutions
- EU and Member State Policy-makers
- International Organisations (ex. OECD, FAO, UN, etc.)
R&D, Technology and Innovation aspects
The system is up and running. Adaptations may be needed to meet the requirements of the co-exploiter.
The dust forecast component’s replicability lies in its reliance on established models like CAMS, SILAM, and MONARCH. These models are well-documented, open-source, and extensively validated, enabling easy adoption by air quality management entities worldwide. The data products, including AOD, surface concentration, deposition, and solar irradiance, can be integrated into existing air quality monitoring systems, enhancing predictive capabilities. The multi-model ensemble approach ensures robustness across different regions, facilitating adaptation to various geographical contexts.
The replicability of the fire smoke forecast component is grounded in the use of globally recognized models such as IS4FIRES-SILAM and WRF-Chem. These models incorporate proven tracking methods and are well-documented, making them accessible to air quality professionals globally. The focus on CO and PM2.5, key indicators of fire impact, enables straightforward integration into existing air quality management frameworks. This component’s use of operational approaches ensures familiarity for adoption, while its provision of localized fire-induced CO and PM2.5 concentration information enhances the ability to assess and respond to wildfire effects.
- Africa
- Asia
- South America
