Naomi Zimmerman

Roadside vehicle emission measurements often rely on expensive and complex reference-grade equipment. Monitoring stations are often limited in deployment, with individual sites covering large geographic areas. Reference-grade equipment is therefore, ill-suited when attempting to understand the spatiotemporal behaviour of traffic-related air pollution; one solution is low-cost sensor technologies. This thesis aims to validate whether several, calibrated low-cost sensors are able to measure roadside vehicle emissions across a large geographic area. Additionally, traffic counts are used to understand relationships between air quality and traffic trends. This work uses low-cost sensors as a solution to measure vehicle emission factors within a large vehicle fleet. The thesis makes use of the Remote Air Quality Monitoring Platform (RAMP) developed by Sensit, measuring CO, CO₂, NO, NO₂, O₃ and PM2.5. RAMPs were calibrated based on a collocation with a near-road regulatory site. Eight sensors were deployed across the UBC campus from June-December 2021. Two Numina traffic sensors were deployed on campus to provide mode-specific traffic count data. At each RAMP location, QR code signboards were also installed, initiating conversations to promote community engagement regarding air quality. Post RAMP calibration, background-subtracted air quality data was fused with multi-modal traffic data to undertake air quality-traffic analysis. Results showed links between pollutants and vehicle modes due to fuel types. The impact of meteorological effects on detection and relationships was observed. Community interaction increased when pollution was visible. Furthermore, background-subtracted pollutant and CO₂ signals were converted to fuel-based emission factors using a plume identification algorithm. Using mode-specific traffic count data, mode-weighted emission factors were calculated, estimating each modes contribution to emission factors. Calculated emission factors fell within the range of previous studies. A disproportional contribution of high emitters was found; the top 25% of plumes contributed approximately 60% of total emissions. Emission factor counts were found to be linked with traffic count data i.e., peak during rush hour. Mode-weighted emission factors highlighted the effect of cars on CO emission factors and buses on NOx emission factors. Findings from this thesis indicate that low-cost sensors are a promising technology for measuring real-world roadside emissions.

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Traditionally, vehicle emissions measurements have relied on reference-grade instruments whose high cost and complexity have limited their deployment in real-world environments. New simple-to-operate, low-cost sensing technologies are a potential solution to this problem. This work aims to validate whether low-cost sensors, with proper calibration, could measure vehicle emissions and could support analysis of emission trends. Under that umbrella, this work provides a comprehensive low-cost solution to the measurement of vehicle emissions factors within the vehicle fleet. The Sensit Real-time, Affordable, Multi-Pollutant (RAMP) monitors measuring PM₂.₅, NO, NO₂, CO₂, O₃, and CO were the low-cost sensor used. The RAMPs were first calibrated based on a collocation with a near-road regulatory site. To assess their suitability of measuring vehicle emissions, six RAMPs were deployed in three parking garages on the UBC Vancouver campus from April–August 2019. UBC Parking Services provided real-time vehicle counts to help validate our method. After sensor calibration, integrated pollutant and CO₂ signals were converted to fuel-based emission factors (EFs) by developing a background subtraction and plume identification algorithm. The calculated EFs fell within the range of previous studies. Evening-vehicle leaving EFs when vehicles were cold were 10-50% higher than in the morning. We also observed a disproportional contribution of high emitters; the top 25% of plumes contributed 45-65% of total emissions. The findings indicate that low-cost sensors are a promising technology for real-world vehicle emissions measurement.

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