FARM (Flexible Air Quality Regional Model) is a 3D Eulerian model with multiple grids for simulating photochemical processes at various spatial scales, from urban to regional, national, and continental levels. FARM is commonly used to assess ambient air concentrations of primary and secondary pollutants (e.g., ozone) and their deposition on the ground (dry and wet).
Model Development:
- Origins: Began in the 1990s within national and European research projects, particularly EUROTRAC-1 and EUROTRAC-2 (European Projects on the Transport of Atmospheric Contaminants).
- Goals: Aimed to create a model suitable for both research activities (e.g., impact of radiative processes on calculated ozone levels, use of different numerical schemes for solving complex systems of chemical equations) and practical applications (e.g., regional air quality assessment, impact assessment of works and infrastructure).
Applications:
- Research: Studying the influence of radiative processes on ozone levels, testing various numerical schemes for solving chemical equation systems.
- Practical Studies: Evaluating regional air quality, assessing the environmental impact of infrastructure projects.
A significant boost to the model’s development was the creation of the MINNI system (Integrated National Model to support International Negotiation on air pollution issues) as part of a multi-year agreement between the Ministry of the Environment and the Protection of Land and Sea (MATTM) and ENEA. Within this framework, FARM forms the core of the Atmospheric Modeling System.
Key Enhancements:
- Updated Modules: Implementation of updated modules in FARM for handling the chemical and physical processes leading to the formation of atmospheric particulate matter.
- Radiation and Pollutant Interactions: Incorporation of processes of interaction between radiation and atmospheric pollutants (radiative transfer), leading to their photodissociation and the consequent production of radical species, such as the OH radical. This radical is extremely reactive and capable of oxidizing most chemical elements present in the troposphere.
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A further boost to the model’s development has been the continuous collaboration with the regional environmental protection agencies. This collaboration enabled the introduction of data assimilation schemes into the model, integrating model-generated fields with experimental data from monitoring networks.
Optimizations and Enhancements:
- Collaboration with CINECA: Intense optimization and parallelization activities of the model, conducted in collaboration with CINECA.
- Parallel Execution: Parallel execution enables the optimal and scalable use of available computing resources in both shared memory and distributed memory systems.
- Parallelization Techniques: The introduction of MPI and hybrid parallelization provides significant advantages, including high scalability on distributed memory systems and the ability to handle computationally demanding scenarios effectively.
Nell’ambito di una nuova convenzione tra MATT ed ENEA sono stati recentemente implementati nel modello algoritmi in grado di stimare il contributo delle diverse sorgenti ai livelli di inquinamento (“source apportionment”).
As part of a new agreement between MATT and ENEA, algorithms have recently been implemented in the model to estimate the contribution of different sources to pollution levels (“source apportionment”).