From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information. (October 2020)

• Record Type: Journal Article
• Authors: Sartini, L.; Antonelli, Marta; Pisoni, E.; Thunis, P.
• Is Part Of: Atmospheric environment. Volume 7(2020)
• Publication Date: 2020-10
• Subjects: Source allocation -- Air quality models -- CHIMERE -- Inventory emissions -- SHERPA methodology
• DOI: 10.1016/j.aeaoa.2020.100088
• Languages: English
• ISSNs: 2590-1621

Abstract

Abstract: This study investigates the dispersion of atmospheric pollutants over a coastal region of north-western Italy by means of modelling techniques. A series of annual air quality model simulations corresponding to different emission reduction scenarios has been performed with a three-dimensional chemical transport modelling chain running at 3 km resolution. The emission reduction scenarios were used to develop bottom-up (locally produced) source-receptor relationships to perform a local source allocation analysis of the main atmospheric pollutants in a few polluted cities within the domain of interest. Results were compared with default top-down (EU-wide) source-receptor relationships, at roughly 7 km resolution. The results show the benefit of using the two sources of information in an integrated way. The analysis of the impacts of local emission reductions on concentrations and of the source allocation results reveals that nitrogen oxides concentrations are mostly affected by local emission sources, especially road transport and harbour related activities while the contribution of non-local sources is important for particulate matter (especially from industry and agriculture sources). For PM, larger scale modelling approaches (top-down) are necessary. Ideally, both a bottom-up approach for the characterisation of the local emission sources and a top-down larger scale approach to capture the impact of non-local sources would be necessary to perform an accurate source allocation, and provide support to the design of local air quality plans. Highlights: Emission reduction scenarios are simulated with an air quality model 'Source-receptor models' are built using air quality model results Both 'local' and 'EU-wide' source-receptor models are built 'Source-receptor models' are then used to allocate sources of pollution Results show usefulness of integrating 'local' and 'EU-wide' information