High-resolution spatial-distribution maps of road transport exhaust emissions in Chile, 1990–2020 - M. Osses; M. Osses; N. Rojas; C. Ibarra; C. Ibarra; V. Valdebenito; I. Laengle; N. Pantoja; N. Pantoja; D. Osses; K. Basoa; S. Tolvett; et al. (Earth System Science Data, 2022)
This description paper presents a detailed and consistent estimate and analysis of exhaust pollutant emissions generated by Chile's road transport activity for the period 1990–2020. The complete database for the period 1990–2020 is available at the following DOI: https://doi.org/10.17632/z69m8xm843.2 (Osses et al., 2021). Emissions are provided at a high spatial resolution (0.01∘ × 0.01∘) over continental Chile from 18.5 to 53.2∘ S, including local pollutants (CO; volatile organic compounds, VOCs; NOx; PM2.5), black carbon (BC) and greenhouse gases (CO2, CH4). The methodology considers 70 vehicle types, based on 10 vehicle categories, subdivided into 2 fuel types and 7 emission standards. Vehicle activity was calculated based on official databases of vehicle records and vehicle flow counts. Fuel consumption was calculated based on vehicle activity and contrasted with fuel sales to calibrate the initial dataset. Emission factors come mainly from the Computer programme to calculate emissions from road transport version 5 (COPERT 5), adapted to local conditions in the 15 political regions of Chile, based on emission standards and fuel quality. While vehicle fleet grew 5-fold between 1990 and 2020, CO2 emissions have followed this trend at a lower rate, and emissions of air local pollutants have decreased due to stricter abatement technologies, better fuel quality and enforcement of emission standards. In other words, there has been decoupling between fleet growth and emissions' rate of change. Results were contrasted with global datasets (EDGAR, CAMS, CEDS), showing similarities in CO2 estimations and striking differences in PM, BC and CO; in the case of NOx and CH4 there is coincidence only until 2008. In all cases of divergent results, global datasets estimate higher emissions.