HERMES_Delta: An open source, python-based, parallel software to process official emission inventories and support air quality modelling efforts in Spain

Air pollution is one of the most critical environmental threats, contributing to respiratory and cardiovascular diseases and millions of premature deaths worldwide. To support air quality assessment, forecasting and planning efforts, chemical transport models (CTMs) need to be fed with robust, temporally and spatially resolved emission input data. 

Official annual national emission inventories prepared by countries to fulfill mandatory reporting obligations provide robust and consistent data. However, for their use in CTMs, emission data needs to be spatially distributed over a grid, temporally broken down into hourly resolution and chemically mapped to the species defined in the CTMs mechanism. Bridging the gap between official inventory data and CTM model-ready emission input needs requires a scalable, transparent, and reproducible system that can process raw inventories into gridded, hourly and chemically speciated CTM-compatible datasets.

HERMES_Δ is a open-source emission model developed at the Barcelona Supercomputing Center (BSC) to address this challenge. Implemented in object-oriented Python and designed to run on High Performance Computing (HPC) infrastructures, it integrates temporal, spatial, vertical, and chemical disaggregation within a modular architecture. Configuration relies entirely on YAML or CSV files, allowing activity- and region-specific settings while maintaining traceability by preserving the connection between modeled emissions and their original reporting sources. Spatial disaggregation, which is the most computationally demanding step, is parallelized using MPI and optimized through domain decomposition. The produced output files are fully compatible with multiple state-of-the-art CTMs, including CMAQ, CHIMERE; MOCAGE, WRF-CHEM and MONARCH.

To assess the performance of HERMES_Δ, multiple benchmark experiments were performed  on the MareNostrum 5 and CIRRUS Spanish HPC facilities. All tests were performed considering a destination grid of 0.005° (~500 m) resolution covering Spain (peninsular and balearic islands), estimating hourly and speciated emissions for 24 time steps. Performance benchmarking, including time-to-solution and memory profiling, indicates good parallel scalability and resource efficiency. This enables the production of hourly gridded emissions for over 10 000 activity–region combinations, while maintaining reproducibility and strict Coordinated Universal Time (UTC) alignment.

In conclusion, HERMES_Δ provides a robust framework for processing official emission inventories to high spatial and temporal resolutions using geolocated activity proxies. By combining national emission inventories with efficient HPC methods, the system improves the representativeness of emissions in CTMs, strengthens collaboration between emission inventory compilers and air quality modellers, and enables more detailed and realistic simulations for policy development and operational forecasting.

HERMES_Δ is currently being implemented as the emission core of the official Spanish air quality forecasting system operated by the Spanish Meteorological Agency (AEMET)