High-resolution Atmospheric Transfer Matrices for policy-oriented air quality assessment in Italy: the new GAINS-Italy model

D'Elia, I., Briganti, G., Kiesewetter, G. ORCID: https://orcid.org/0000-0002-9369-9812, Cappelletti, A., Sander, R. ORCID: https://orcid.org/0000-0001-6507-0630, D'Isidoro, M., Schöpp, W. ORCID: https://orcid.org/0000-0001-5990-423X, & Piersanti, A. (2026). High-resolution Atmospheric Transfer Matrices for policy-oriented air quality assessment in Italy: the new GAINS-Italy model. Atmospheric Environment 382 e122183. 10.1016/j.atmosenv.2026.122183.

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Abstract

Atmospheric Transfer Matrices (ATMs) are source-receptor relationships, widely applied in integrated assessment modelling to rapidly estimate air quality responses to emission changes, supporting policy design and evaluation. They enable fast policy-oriented analyses that would otherwise require computationally demanding Chemical Transport Model (CTM) simulations. However, the traditional linear formulation of ATMs may be insufficient for indicators influenced by non-linear atmospheric chemistry, especially at high spatial resolution and for secondary pollutants, or to manage air quality responses to large variations of input emissions.
In this study, we present an extended ATM framework for Italy at 4 km resolution incorporating selected second-order terms to account for non-linear responses. The matrices are developed using CTM simulations and implemented within the GAINS-Italy model. Their performance is evaluated against full CTM runs for multiple air quality indicators relevant to national air quality plans. Results show that first-order ATMs are adequate for quasi-linear processes, while the inclusion of second-order terms improve agreement with CTM simulations for non-linear indicators, reducing discrepancies within few percent. The proposed approach enables near-real time scenario screening, while preserving the spatial structure and magnitude of CTM responses providing a practical tool to support national and regional air quality planning.

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