AQ-Watch's Air Quality Source Attribution and Mitigation Service

Outdoor air pollution was estimated by the World Health Organization to be responsible for 4.2 million premature deaths in 2019. Improving the air quality will bring major health benefits. However, air pollution can only be tackled cost effectively if authorities have accurate information on its origin and on the poten tial impact of envisaged emission mitigation policies. Within the European Union Horizon 2020 AQ-WATCH project, a user driven operational source apportionment and mitigation service has been developed. It provides information on the main sources (both sectors and source regions) contributing to concentrations of NO2, SO2, PM and CO, and it enables policy makers to estimate the efficiency of planned mitigation measures in different anthropogenic source sectors on the air pollutants levels (Timmermans et al. in Report with description of mitigation service, deliver able from Horizon 2020 AQ-Watch project, 2022). The services are demonstrated for three different regions of the world (the Northern Colorado Front Range, Santiago de Chile and the Chinese city of Cangzhou) and are set-up in a generalised way that allows easy transfer to other regions in the world beyond the lifetime of the project. The services are based on a set of different air quality models, source attribution and mitigation methods. For source attribution these include the LOTOS-EUROS model for NO2 and PM, including its tagging method, and the WRF-CHEM model including CO specific tracers for source attribution. The mitigation service is based on sets of model runs (with the LOTOS-EUROS model and CHIMERE-SIRANE model chains) with different emission reduction scenarios, and the establishment of relationships between emission changes and concentrations. Evaluation of modelled concentrations showed that the performance of the model depends on the quality of the available input information, e.g., emissions and their temporal and spatial distribution, meteorological data, and land use information. The use of regional emission inventories showed an improved performance. For areas with complex topography, high resolution meteorological input is desired to correctly represent the meteorological conditions.