FUME 2.0 – Flexible Universal processor for Modeling Emissions

Abstract

Abstract. This manuscript introduces FUME 2.0, an open-source emission processor for air quality modeling, documenting the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify it into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, WRF-Chem, and large-eddy simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets.

Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions

“…This not only ensured the reasonable spatial distribution of the emissions in the street canyon but also decreased the gradients of the emission field and, through this, the proneness of the model to numerical inaccuracy (Ardeshiri et al, 2020). The processing of the emission sources into hourly emission flows in PIDS was done in the emission model FUME recently extended for processing of the PALM emissions (Belda et al, 2024).…”