Improving spatial disaggregation of vehicular emission inventories
Creators: name: Meotti, Bianca
name: Ibarra-Espinosa, Sergio
name: Hoinaski, Leonardo
Publisher: name: Taylor & Francis
Year: 2025
Subjects: subject: Space Science
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified

description: Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories. This gap poses significant challenges for accurately quantify emissions in regions that often experience rapid urbanisation and traffic growth. Here, we propose a novel method to enhance the spatial disaggregation of large-scale vehicular emission inventories. By analysing road-level emissions data from 63 Brazilian municipalities, we developed a model that predicts weighting factors to disaggregate vehicular emissions into a gridded format, based on the proportion of primary road lengths. Our findings indicate that the predicted weighting factors significantly improve the spatial disaggregation of vehicular emissions compared to the traditional road density method by reasonably increasing the emissions in high vehicular activity areas. This approach not only provides more accurate representations of vehicular emissions for urban planning in Brazil but also offers a solution that can be adapted to enhance top-down vehicular emissions inventories globally. Our study offers a valuable tool that can be tailored to various regions, enabling more precise urban planning and policy-making for air quality management worldwide. We propose a novel method to spatial disaggregate vehicular emissions that accounts for the road type and vehicle category.We developed a model to generate weighting factors based on primary roads length by municipality to disaggregate vehicular emissions.We obtained more consistent disaggregation of vehicular emissions with the proposed method compared to road density disaggregation methodology.The proposed method improves PM₁₀ concentration estimation accuracy, making it valuable for air quality planning in urban areas.The method is well-suited for regions with limited data availability. We propose a novel method to spatial disaggregate vehicular emissions that accounts for the road type and vehicle category. We developed a model to generate weighting factors based on primary roads length by municipality to disaggregate vehicular emissions. We obtained more consistent disaggregation of vehicular emissions with the proposed method compared to road density disaggregation methodology. The proposed method improves PM₁₀ concentration estimation accuracy, making it valuable for air quality planning in urban areas. The method is well-suited for regions with limited data availability.