Enhancing PM_2.5 Formation Indicator for Human Health: A Coupled Territorial LCA and Multi-Scale Atmospheric Modelling Approach

Conventional Life Cycle Impact Assessment methods rely on coarse global chemical transport models that obscure local air quality profiles, leading to distorted Human Health metrics. This study addresses this limitation by integrating territorial LCA perspective with multi-scale atmospheric modelling to refine Fine Particulate Matter Formation (FPMF) midpoint indicator, considering ReCiPe 2016 (H) as a representative baseline method. The framework evaluates commercial Smart Farming (SF) applications across three distinct Greek agricultural territories (Pieria, Kiato, Orestiada) using primary data from LIFE GAIA Sense EU project. Transitioning to a territorial LCA isolates environmental burdens into exogenous upstream (Off-Field) supply chains and direct data-driven cultivation areas (In-Field). Air quality modelling was executed using the mesoscale meteorological model MEMO and chemistry-transport model MARS-aero at a 250m×250m resolution. Total life-cycle burden on Human Health originates predominantly from the In-Field system (52.9%), making it an environmental hotspot where FPMF is the dominant stressor, driven by nitrogen fertilization losses (NH₃: 88.3%, NO_x: 11.7%). These rural environments operate under ammonia-limited regimes, where optimized nitrogen management via SF induces thermodynamic suppression of secondary ammonium salt synthesis, cutting ambient concentrations by up to 74.5%. Integrating these concentration increments with localized demographics reveals that global models artificially flatten concentrations over large cells (∼100km×100km), overestimating human inhalation impact compared to ReCiPe 2016 (H) world-average factors by 12–14.5 times in Pieria, 80–160 times in Kiato, and 1500–3500 times in Orestiada. Emission increments remain localized near fields, meaning geographical distance functionally decouples urban populations from exposure, except where cultivation zones directly adjoin urban fabrics. Consequently, refining FPMF indicator with site-specific characterization factors restructures priorities, establishing Global Warming as the main health stressor across the three cases. Ultimately, this demonstrates that mitigating PM_2.5 impacts via SF is territory-dependent rather than generalized, requiring spatially explicit management over rigid horizontal mandates.