Improving spatial disaggregation of vehicular emission inventories.

Abstract

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.