The impact of road grade on carbon dioxide (CO2) emission of a passenger vehicle in real-world driving

To accurately estimate real-world vehicle emission at 1 Hz the road grade for each second of data must be quantified. Failure to incorporate road grade can result in over or underestimation of a vehicle's power output and hence cause inaccuracy in the instantaneous emission estimate. This study proposes a simple LiDAR (Light Detection And Ranging) - GIS (Geographic Information System) road grade estimation methodology, using GIS software to interpolate the elevation for each second of data from a Digital Terrain Map (DTM). On-road carbon dioxide (CO2) emissions from a passenger car were recorded by Portable Emission Measurement System (PEMS) over 48 test laps through an urban-traffic network. The test lap was divided into 8 sections for micro-scale analysis. The PHEM instantaneous emission model (Hausberger, 2003) was employed to estimate the total CO2 emission through each lap and section. The addition of the LiDAR-GIS road grade to the PHEM modelling improved the accuracy of the CO2 emission predictions. The average PHEM estimate (with road grade) of the PEMS measured section total CO2 emission (n = 288) was 93%, with 90% of the PHEM estimates between 80% and 110% of the PEMS recorded value. The research suggests that instantaneous emission modelling with LiDAR-GIS calculated road grade is a viable method for generating accurate real-world micro-scale CO2 emission estimates. The sensitivity of the CO2 emission predictions to road grade was also tested by lessening and exaggerating the gradient profiles, and demonstrates that assuming a flat profile could cause considerable error in real-world CO2 emission estimation.