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Shaun D. Fitzgerald

Publications and source records attributed to Shaun D. Fitzgerald.

3 recordsLinked to original sources

Modelling laminar flow in V-shaped filters integrated with catalyst technologies for atmospheric pollutant removal

Atmospheric pollution from particulate matter, volatile organic compounds and greenhouse gases is a critical environmental and public health issue, leading to respiratory diseases and climate change. A potential mitigation strategy involves utilising ventilation systems, which process large volumes of indoor and outdoor air and remove particulate pollutants through filtration. However, the integration of catalytic technologies with filters in ventilation systems remains underexplored, despite their potential to simultaneously remove particulate matter and gases, as seen in flue gas treatment and automotive exhaust systems. In this study, we develop a predictive, long-wave model for V-shaped filters, with and without separators. The model, validated against experimental and numerical data, provides a framework for enhancing flow rates by increasing fibre diameter and porosity while reducing aspect ratio and filter thickness. These changes lead to increased permeability, which lowers energy requirements. However, they also reduce the pollutant removal efficiency, highlighting the trade-off between flow, filtration performance and operational costs. Leveraging the long-wave model alongside experimental results, we estimate the maximum potential removal rate ($4.5\times10^{-3}$ GtPM$_{2.5}$, $6.4\times10^{-3}$ GtNO$_{\text{x}}$, $2.0\times10^{-2}$ GtCH$_{4}$ per year; $1.6\times10^{0}$ GtCO$_{2}$e per year, 20-year GWP for CH$_4$) and minimum cost (\$$3.4\times10^{3}$ per tNO$_{\text{x}}$, \$$1.1\times10^{3}$ per tCH$_{4}$; \$$1.3\times10^{1}$ per tCO$_{2}$e) if a billion V-shaped filters integrated with catalytic enhancements were deployed in operation. These findings highlight the feasibility of catalytic filters as a scalable, high-efficiency solution for improving air quality and mitigating atmospheric pollution.

physics.flu-dyn

Harnessing natural and mechanical airflows for surface-based atmospheric pollutant removal

Removal strategies for atmospheric pollutants are increasingly being considered to mitigate global warming and improve public health. However, the global potential of surface-based removal techniques has not yet been quantified based on limits of pollutant transport and removal rates. We evaluate the atmospheric pollutant transport to surfaces and assess the potential of surface-based removal technologies for global-scale deployment across a variety of configurations, including air interaction with the built environment, mechanical ventilation and convection systems, and over the global transportation fleet. Cities provide the highest transport-limited removal potential, with median annual atmospheric flow rates of 30 GtCO$_2$, 0.06 GtCH$_4$, 0.007 GtNO$_\text{x}$ and 0.0001 GtPM$_{2.5}$ to their total surface area. Cities, solar farms and HVAC systems have flow rates large enough to potentially remove more than 1 GtCO$_2$/y (1 GtCO$_2$e/y for CH$_4$, 20-year GWP), if laboratory-scale removal efficiencies from the literature are applied to their total surface area, however, achieving this would require technological advances. Based on their transport-limited upper bounds, HVAC filters have the potential to achieve costs as low as \$600 per tCO$_2$ removed (\$2000 per tCO$_2$e) if CO$_2$-sorption (CH$_4$-catalyst) technologies are incorporated into their surfaces and performance is maintained through routine replacement, compared with \$3000 per tCO$_2$ (\$10000 per tCO$_2$e) for city surfaces, using literature values for these technologies' material and application costs. These findings demonstrate that integrating surface-based pollutant removal technologies into infrastructure may offer a pathway to advance climate objectives, though further studies are needed to assess their feasibility in application, and application-implementation rates and cost.

physics.flu-dyn

Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis

As the conversation around using geoengineering to combat climate change intensifies, it is imperative to engage the public and deeply understand their perspectives on geoengineering research, development, and potential deployment. Through a comprehensive data-driven investigation, this paper explores the types of news that captivate public interest in geoengineering. We delved into 30,773 English-language news articles from the BBC and the New York Times, combined with Google Trends data spanning 2018 to 2022, to explore how public interest in geoengineering fluctuates in response to news coverage of broader climate issues. Using BERT-based topic modeling, sentiment analysis, and time-series regression models, we found that positive sentiment in energy-related news serves as a good predictor of heightened public interest in geoengineering, a trend that persists over time. Our findings suggest that public engagement with geoengineering and climate action is not uniform, with some topics being more potent in shaping interest over time, such as climate news related to energy, disasters, and politics. Understanding these patterns is crucial for scientists, policymakers, and educators aiming to craft effective strategies for engaging with the public and fostering dialogue around emerging climate technologies.

cs.CY