Capturing macro trend and uncertainty in speed-related CO2 emission rates for heavy-duty diesel trucks

Accurate estimation of CO 2 emissions from heavy-duty diesel trucks (HDTs) is critical for researchers and policymakers, but remains challenging due to uncertainties in vehicle operating conditions and engine emission factors (EFs), especially when working with limited input data such as trace average speed and operating length (duration or mileage) at the macro scale. In this context, capturing the uncertainty in HDTs’ CO 2 emission estimates under various operating conditions and modeling scales becomes even more critical. This study analyzes and quantifies the macro trend and uncertainty in CO 2 emission rates, expressed as emissions per 100 km traveled, in relation to trace average speed at different trace aggregation classes. Utilizing half-yearly second-by-second trajectory and corresponding CO 2 emission data from 28 HDTs in China, the research examines trace aggregation classes, including durations ranging from 10 min to 120 min, and mileages ranging from 3 km to 100 km. Results show that CO 2 emission rates decrease rapidly at lower speed bins (< 15 km/h) and stabilize at higher speed bins across all classes. The greatest uncertainty occurs at lower speeds (around 5 km/h) in duration classes due to cumulative uncertainty over time and at intermediate speeds (around 30 km/h) in mileage classes due to variability in operating conditions and EFs. Increasing trace aggregation length significantly reduces CO 2 emission rate uncertainty. This study offers insights into the median and 95-percent confidence intervals of CO 2 emission rates from HDTs at specific speed bins and aggregation classes, providing valuable information for evaluating uncertainties in various modeling scales.