ORCID

0009-0005-3205-0330

Keywords

Air pollution, Big data, Low-cost sensors, Remote sensing, Urban air quality

Abstract

Air pollution remains one of the most pressing environmental and public health challenges, especially in urban areas with dense populations and concentrated emission sources. Effective air quality management requires accurate information on emissions to guide control strategies and reliable exposure estimates to evaluate health impacts. This dissertation examines multiple sources of uncertainty in both emission and exposure assessments. Two major emission sectors were investigated: on-road motor vehicles and landfills. For on-road sources, detailed traffic information inferred from crowdsourced big data were leveraged to improve the estimation of traffic-related emissions. For landfills, the application of low-cost sensor and aerial remote sensing was evaluated for methane monitoring, highlighting their potential to enhance spatiotemporal coverage while also assessing the uncertainties associated with real-world deployment. On the exposure side, two critical factors were investigated: the spatial performance variability of low-cost sensors and the role of individual mobility in exposure estimates. Dense sensor networks can provide fine-scale concentration data but require robust calibration methods. Here, spatial heterogeneity in sensor calibration performance was evaluated, and predictors of sensor accuracy were identified. Additionally, crowdsourced GPS mobility data were integrated into exposure assessments to account for time–activity patterns, bridging the gap between static, home-based exposure estimates and dynamic, mobility-based exposures to traffic-related pollutants. Together, these contributions advance new approaches for improving urban air pollution management by addressing uncertainties in both emissions and exposures.

Completion Date

2025

Semester

Fall

Committee Chair

Yu Haofei

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Environmental Engineering

Format

PDF

Identifier

DP0029824

Document Type

Thesis

Campus Location

Orlando (Main) Campus

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