A Diverse and Comprehensive Air Quality Modeling Analysis of Houston, Texas: How Will Changing Emissions, Industries, and Legislation Impact the Air and Human Health?

This dissertation utilizes air quality models to assess drivers of pollution in Houston, Texas, where air quality poses a significant risk to human health. This research focuses on how factors like emissions from the energy sector, Houston Ship Channel (HSC) activity, and evolving legislation impact the formation of ozone (O3) precursors (such as volatile organic compounds (VOCs) and nitrogen oxides (NO + NO2 = NOx)), in addition to particulate matter (PM2.5 and PM10) and greenhouse gases. Houston’s continuous growth, which intensifies transportation and energy demands, further poor air quality. To address these challenges, this dissertation provides a three-part investigation to inform more effective air quality control. Chapter 1 compares surface O3 formation in Houston’s urban and industrial environments using a novel two-step approach that combines Positive Matrix Factorization (PMF) with Random Forest machine learning interpreted by SHapley Additive exPlanation (SHAP). VOC and NOx data from the urban Milby Park and industrial Lynchburg Ferry sites were analyzed for the O3 seasons of 2017-2021. This revealed that NOx emissions suppress O3 formation in the NOx-saturated urban core while promoting formation at the industrial sites. Chapter 2 explores the connection between Houston’s air quality legislation and emissions. A comprehensive legislative timeline was constructed from 1952 to 2023, which demonstrates landmark legislation like the Clean Air Act (CAA) and the Texas Emissions Reduction Plan (TERP). The joint PMF-AI approach was applied to air quality data from 1990-2000 and 2017-2021 for comparison at the Clinton and Deer Park #2 sites to assess the impact of legislation on model results. Analysis revealed that emissions source profiles evolved over time, with decreased NOx contribution, as well as key drivers of O3 formation like temperature. Chapter 3 provides a multidecadal analysis of Houston’s pollution drivers by applying the PMF-AI approach to a 30-year dataset (1990-2021) from Clinton and Deer Park #2. By analyzing time intervals during this period, this chapter provides long-term analysis of emissions sources and predicted ozone formation. Houston showed an evolution from industrial-dominant sources in the 1990s to a more complex mix more recently; knowing this allows for more effective legislation implementation in the future.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Atmospheric Sciences

Grantor

University of Houston

Year dc:date.issued

2025

Author dc:creator

• Nelson, Delaney Lynn

Advisor dc:contributor.advisor

• Choi, Yunsoo

Committee members dc:contributor.committeemember

• Kotsakis, Alex
• Zhang, Honghai
• Flynn, James

• Atmospheric science

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/20704

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/20704