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Hindson, James

University of British Columbia

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.

Text

2022-12-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83444

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/