Davi de Ferreyro Monticelli
Doctor of Philosophy in Atmospheric Science (PhD)
Cannabis cultivation facilities: Linking emissions and air quality to inform regulation
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
Recent advancements in low-cost sensor (LCS) technology have presented a new and affordable opportunity to understand and subsequently improve air quality. This thesis assessed the different stages of adoption and application of LCS technology, including calibrating the sensors, using sensors to build spatiotemporal pollutant maps, and using these maps to identify inequities in air pollution exposures.In Chapter 3, a general calibration method for commercially available low-cost PM₂.₅ sensors (PurpleAir/Plantower) was explored, such that the calibration models can be transferable to large geographical areas, especially in areas with limited monitoring. Inter-city models (e.g., trained in California and tested in India) built for regional concentrations were found to be effective in reducing errors by 30% in measurements. Chapter 4 used data from a network of 50 LCS deployed in Pittsburgh (Pennsylvania, USA) to build daily average land-use regression (LUR) and random-forests (LURF) spatiotemporal models for PM₂.₅, NO₂, and CO. The LURF models outperformed traditional regression techniques, with an increase in average externally cross-validated R² of 0.10-0.19. Models built after separating local contributions from the regional signal improved the R² by 0.14. In Chapter 5, the LURF models for PM₂.₅ were then used to build static (population spends 24 hours/day in a fixed residential area) and dynamic models (population moves between residential and commercial areas) and used to estimate variations in residents’ exposures to PM₂.₅ due to movement. The exposure estimates were consistently about 10% higher when the population spends more time in commercially-dense locations (dynamic model) vs residentially-dense locations (static model). Weekend concentrations were also 10% higher than weekday concentrations. Chapter 6 describes the deployment and analysis of data from a network of 11 LCS deployed in an environmental injustice neighborhood in Vancouver (British Columbia, Canada). PM₂.₅, NO₂, and O₃ concentrations were used to calculate cumulative hazard indices (CHIs) to identify hotspots within the neighborhood and to address the inequities in air pollution when compared to the Greater Vancouver region. Lastly, Chapter 7 summarizes the lessons learned from this thesis and provides insight into key design deployment considerations.
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
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.
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.