Date of Award
5-14-2024
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Air quality monitoring across local and hyperlocal scales has attracted increased attention from both the public and research community. There are several approaches such as passive samplers and low-cost air quality sensors, depending on the pollutant and measurement objectives. Local and hyperlocal air pollutant measurements help to characterize local emission sources such as traffic and industry, including separating these signals from large scale influences (e.g., urban and regional scales), and better understand how land use types and meteorology influence pollutant spatiotemporal patterns. Low-cost sensors (LCS) are now a popular approach to monitor several criteria pollutants (e.g., particulate matter (PM), ozone, carbon monoxide), complementing the use of relatively expensive regulatory-grade reference monitors. Utilizing LCS networks for local air quality monitoring helps people understand more about air pollutants in their immediate surroundings, raising awareness of the exposures they may encounter. LCS have several advantages compared to the reference grade monitors including cost (typically ~$300 compared to ~$10,000), compactness and ease to deploy, and largely “plug-and-play” data management with many vendors providing cloud-based services and automated mapping. However, LCS data accuracy and precision typically do not match the reference monitors and protocols are lacking to efficiently detect changes in sensor performance over time. Work is needed to assess LCS utility in harsh environments and determine whether small variations can be accurately detected. With the fast development of utilization of LCS for different air quality related projects, a general guideline for LCS deployment is needed to make sure results generated by LCS can be comparable. This dissertation includes four major chapters to advance air quality measurement strategies and/or to conduct monitoring studies at local and hyperlocal scales. It begins by meticulously characterizing five different types of low-cost sensors (LCS) under the challenging winter conditions of Mongolia, facilitating the establishment of long-term PM2.5 monitoring networks within kindergartens at hyperlocal scales. Utilizing indoor network measurements, a novel land use regression model was developed to predict indoor PM2.5 concentrations within kindergartens in the absence of physical sensors. The subsequent study focused on the characterization of selected LCS based on their sensor siting locations and varying meteorological conditions, offering valuable insights for the deployment of citizen science-based LCS networks. The final investigation centered on characterizing the impact of traffic-related ultrafine particle (UFP) concentrations by employing an engineered vegetation buffer within Green Heart Louisville. Stationary monitoring campaigns were conducted to measure UFP number concentrations in the study area both with and without the presence of the vegetation buffer, providing careful estimations of resident exposure to UFPs near highways. These projects collectively yield significant findings that enhance the accuracy of exposure estimates and inform the public about their exposure to particulate matter.
Language
English (en)
Chair
Jay Turner