Eastern Research Group, Inc. evaluated the current state of personal vehicle telematics data with respect to emission inventory development, identifying relative strengths and weaknesses, and how these data could align better with the needs of emission modelers. A market survey of telematics firms provided an overview of available data, and identified several candidate sources for location-based and engine-based telematics data on personal vehicles. Data were then purchased from three different vendors: StreetLight Data, Moonshadow Mobile, and Otonomo. These data were applied in case studies conducted in the Denver metro area, U.S., to assess strengths and weaknesses of telematics for developing emission inventories. Case studies included using telematics to estimate regional vehicle miles traveled (VMT) for annual emission inventories; tracking the VMT impacts of COVID shutdown; generating location- and time-specific vehicle activity inputs for project scale “hot spot” air quality analysis; and estimating the distribution of fuel fill level from real-world data, which is important for evaporative emissions. These case studies confirmed that telematics can serve a growing range of emission inventory use cases, and use of these data may help improve emission inventory accuracy. However, there are also several limitations of the data to consider in preparing emission inventories; for example, it can be difficult to assess the representativeness of telematics data because of a lack of vehicle information. The authors encourage telematics firms to cater data products more directly to the needs of emission inventory modelers, to better harness the enormous potential of these data for refining vehicle emission inventory estimates.

No abstract

Emissions from vehicular sources are a major contribution to air pollution in every country. Air quality models are being used to study the impact of such emissions. The CAL3QHC emissions model was developed by the U.S. Environmental Protection Agency based on California’s CAL3Q model. One area of concern with modeling results is uncertainty in input data, in model calculations, and in natural atmospheric processes. The focus of the study is uncertainty in input data for the CAL3QHC roadway model. Two practical methods—the ASTM approach and the least rigid approach—are used to perform the sensitivity analysis on the input parameters. The two important areas in which CAL3QHC requires sensitivity discussions are the emissions source strength of the vehicles in the queue and the link length (which represents the number of vehicles in a queue). The variability of these two parameters results in a nonlinear relationship between the source strengths and the predicted concentrations. Sensitivity analysis of the CAL3QHC model is carried out for a simple roadway intersection with two traffic lanes and two receptor locations (at the corner of the intersection and midblock). With results from the ASTM method, sensitivity indices are calculated for signal timing, traffic volume, number of traffic lanes, and wind speed. Among all indices, wind speed shows the maximum sensitivity on predicted carbon monoxide concentrations. The model needs no calibration because none of the parameters studied show Type IV sensitivity using ASTM results.

No abstract