Measuring and modeling aviation-related air pollution in near-airport communities
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Citation
Abstract
The aviation industry drives substantial economic growth and is the fastest-growing mode of transportation worldwide, but consequently contributes to adverse environmental and public health concerns. Of interest is the impact of airport and aircraft activities on particulate matter (PM) air pollution exposure to communities living near airports. The type of fuel used in aircraft affects the size and composition of PM. Jet fuel, with its relatively high sulfur content, contributes to substantial ultrafine particle (UFP; particles with aerodynamic diameter <100 nanometers) formation, whereas piston-engine aircraft burning leaded aviation gasoline emit lead along with other combustion by-products into the environment. Isolating the contributions of in-flight aircraft emissions from ground-based airport activities and other location sources (e.g. roadway traffic) presents a significant methodological challenge. Aviation source attribution is complicated by multiple complex interactions between aircraft activity, exhaust plume dynamics, and meteorology. Because aviation emissions carry profound implications for human health and climate, there is a pressing need for research to improve source apportionment techniques and to inform effective mitigation strategies. The goal of my dissertation was to enhance our understanding of the spatial, temporal, and compositional heterogeneity of aviation-related air pollution in communities near airports. This work collectively provides insight on the breadth of potential air pollution exposures across two distinct airport types: Commercial Service Airports (CSA), which have high operation volumes but are geographically limited, and General Aviation Airports (GAA), which have lower operation volumes but are widely distributed.
In Chapter Two, we leveraged a natural experiment during the COVID-19 pandemic to disentangle source-specific UFP contributions at a long-term monitoring site impacted by multiple UFP sources in Chelsea, MA. Results show that mean UFP concentrations closely tracked road traffic activity patterns, whereas peak UFP levels occurred when the site was downwind of the airport, implicating aviation emissions as the driver of episodic high-UFP events. This analysis lays foundational evidence that aviation emissions can be distinguished from roadway emissions, underscoring the importance of considering aviation as a distinct source in exposure assessments.
In Chapter Three, we built on Chapter Two findings by applying an interpretable machine learning (ML) model to the long-term Chelsea UFP dataset to quantify aircraft contributions to ambient UFP. In summary, we were able to reliably model UFP concentrations and disentangle nonlinear interactions between meteorology and flight activity to quantify aviation contributions to community exposures. These overarching results are further supported by a detailed apportionment of aviation contributions to ambient UFP at an hourly resolution, distinguishing between arrivals, departures, and runway-specific impacts over the study period (2014-–2022, Boston, MA). These results provide a novel framework for retrospective exposure assessment to aviation emissions, offering opportunities for epidemiological studies to examine health impacts of aviation air pollution.
In Chapter Four, we expanded our focus beyond large commercial airports to investigate air pollution exposures around GAAs. We conducted ambient sampling of fine particulate-bound elements at two contrasting GAAs –— one dominated by piston-engine activity and one by jet activity –— and compared these measurements to data from U.S. Environmental Protection Agency (EPA) monitors. Results showed PM enriched in bromine, arsenic, lead, sulfur, and zinc –— contaminants indicative of aviation fuel combustion –— at levels substantially above background. These findings confirm that distance from the airport is a key determinant of pollutant concentration gradients, and importantly, reveal that communities near GAAs may be exposed to hazardous air pollutants beyond lead.
Recent developments magnify the significance of these research gaps. In 2021, the World Health Organization published guidance for recommended maximum hourly and daily exposure to UFP; the recorded measurements from Chapter Two and Three exceeded these guideline values. In 2023, the EPA issued a final endangerment determination of aircraft lead emissions and is required to set regulatory standards protective of public health; our measurements in GAA communities indicated ambient lead and other hazardous air pollutant concentrations above background levels. Air pollution exposure in communities near airports, both from CSAs and GAAs, will be a key area of interest for future research and health assessments.
Description
2025
License
Attribution 4.0 International