Computational model integration for high-resolution spatial emission simulation: a tool for urban transport management in resilient cities

Le résumé fourni par la source

Air pollution represents a critical environmental risk to public health, particularly that derived from the combustion of fossil fuels, which may account for millions of premature deaths annually worldwide. In intermediate Latin American cities, the sustained growth of the vehicle fleet and fragmented urban planning exacerbate this situation. High-spatial-resolution diagnostics of vehicle emissions could facilitate sustainable mobility policies. This research proposes an integrated methodological model to optimize the spatial disaggregation of bottom-up vehicle inventories in contexts of limited information for small and medium-sized cities. The proposal links three sequential computational models using the intermediate city of Tandil, Argentina, as a case study: 1) base emission estimation using the International Vehicle Emissions (IVE) model; 2) road network characterization through GIS based on demographic, commercial, and educational densities; and 3) adaptation of the open-source DROVE algorithm in R. en road categories were identified to integrate the functional heterogeneity of the urban system. A high-resolution spatial disaggregation for criteria pollutants —specifically PM10 —was analyzed. By incorporating urban variables, simulation precision in DROVE was significantly improved, with results further enhanced as the spatial resolution of the grid increased. This integration of sociodemographic factors allowed for the classification of the road network according to urban behavior patterns, successfully identifying critical points and corridors of pollutant loads that would otherwise remain attenuated. The proposed methodological sequence accounts for the heterogeneity of the data reflecting the urban system, disaggregating emissions from inventories by leveraging local spatial databases. This reduces dependence on vehicle counts and traffic models, which are often the most significant constraints in resource-constrained contexts. The contribution to mobility management and SDG 11 (Sustainable Cities and Communities) is achieved by focusing on less-studied urban scales with high global representativeness, providing an accessible and efficient tool for studying and understanding vehicle emissions.

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