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Jain, Sakshi

University of British Columbia

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.

Text

2023-08-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85524

Mechanical Engineering

University of British Columbia

UBCV

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