High resolution inventory of GHG emissions of the road transport sector in Argentina

- A high resolution inventory of road transport emission is presented.
- A better spatial distribution of urban areas is obtained using DMSP-OLS satellite.
- Fuel consumption at district level is spatially reassigned to street segment.
- The proposed GHG emission inventory is compared to international databases.
- The information is organized under GIS environment for air quality models.

Air quality models require the use of extensive background information, such as land use and topography maps, meteorological data and emission inventories of pollutant sources. This challenge increases when considering the vehicular sources. The available international databases have uneven resolution for all countries including some areas with low spatial resolution associated with large districts (several hundred km). A simple procedure is proposed in order to develop an inventory of emissions with high resolution (9 km) for the transport sector based on a geographic information system using readily available information applied to Argentina. The basic variable used is the vehicle activity (vehicle - km transported) estimated from fuel consumption and fuel efficiency. This information is distributed to a spatial grid according to a road hierarchy and segment length assigned to each street within the cell. Information on fuel is obtained from district consumption, but weighted using the DMSP-OLS satellite “Earth at night” image. The uncertainty of vehicle estimation and emission calculations was tested using sensitivity Montecarlo analysis. The resulting inventory is calibrated using annual average daily traffic counts in around 850 measuring points all over the country leading to an uncertainty of 20%. Uncertainties in the emissions calculation at pixel level can be estimated to be less than 12%. Comparison with international databases showed a better spatial distribution of greenhouse gases (GHG) emissions in the transport sector, but similar total national values.