Improving the Spatial Representation of Emission Sources for Regional Air Quality Modelling

Accurate characterization of atmospheric pollution is essential for understanding its impacts on human health, ecosystems, and climate, as well as for supporting the development of effective air quality management strategies. Numerical atmosphere–chemistry models are powerful tools for studying pollutant dynamics and assessing mitigation measures. However, the quality of their predictions strongly depends on the accuracy of the emission inventories used as input.

Most available emission inventories are provided at relatively coarse spatial resolutions, limiting their ability to represent the detailed distribution of emission sources, particularly in heterogeneous urban and regional environments. To address this limitation, we developed a methodology to generate very high-resolution emission fields from low-resolution inventories using a range of geospatial datasets. Emissions from different sectors are spatially redistributed according to their physical origin, including road networks for traffic emissions, airport infrastructure for aviation, industrial facilities for industrial sources, and land-use or population density datasets for other anthropogenic activities. The methodology also incorporates sector-specific vertical allocation and temporal profiles, allowing emissions to vary realistically at hourly, weekly, and seasonal scales.

The methodology was evaluated over the Region of Murcia (southeastern Spain) by comparison with an independently developed high-resolution emission inventory. The results showed a good agreement between both datasets. Furthermore, air quality simulations driven by the old and new emissions were compared with observational data, demonstrating an improved representation of pollutant concentrations and confirming the potential of the proposed approach to enhance air quality modelling at regional scales.