Abstract
Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate theuseoftheparticulatematter(PM)emissionestimationfromtheBraziliantop-downvehicleemissioninventory(VEI)of2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year−1) from vehicular sources was distributed into the urban areas of 5557municipalities,with1-km2 gridspacing,consideringtwoapproaches:(i)populationand(ii)fleetofeachcity.Acomparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling(August2015)wasperformedwithWRF-ChemforthefourmetropolitanareasofBrazilianSoutheast:BeloHorizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM10 and PM2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.