Browsing by Subject "PM2.5"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Sensor systems for the characterization of vent products from thermal runaway of lithium-ion batteries(2023-12) Pinkerton, Katherine Ann; Ezekoye, Ofodike A.With the increasing prevalence of lithium-ion batteries in residential and commercial applications, their failure, via thermal runaway, is also becoming increasingly common. Four primary hazards result from this self-heating phenomenon: flammability hazards, explosion hazards, toxicity hazards, and inhalation hazards. This thesis aims to explore sensor systems, both handheld and benchtop, that can be used to characterize these vented gases and particulates. The first chapter investigates the accuracy and time response of commercial multi-gas sensors commonly utilized by firefighters. Recommendations for interpretations of readings are provided, along with equations and correction factors to improve sensor accuracy. The second chapter describes the use of benchtop and commercially available PM2.5 sensors. Additionally, validation of these sensors via comparison with calibrated equipment is provided, along with recommendations for interpretations and adjustments that should be considered during use. The following chapter describes the use and results of these particle sizing sensors and handheld multi-gas sensors in near source and dilute battery thermal runaway environments. This data provides preliminary guidelines on the composition and concentrations of battery thermal runaway particulates released for varying cell chemistries and states of charge. The final chapter details an acquired structure test involving lithium-ion batteries of varying capacity and chemistry. The sensor systems described previously were utilized to characterize the resulting gaseous products, alongside additional sensors measuring temperature and heat fluxes. The summation of this work provides guidelines for the use of particulate and gas analysis sensor systems to characterize lithium-ion battery thermal runaway vent products for the improved safety of firefighters and the general public.Item Setting the agenda on air pollution : examining the traditional and social media agendas and their relationships 2011-2015(2017-08) Zheng, Pei; Chen, Gina Masullo; McCombs, Maxwell; Chen, Wenhong; Chyi, Hsiang; Johnson, ThomasUnder the theoretical frameworks of agenda-setting and authoritarian environmentalism , this dissertation examined traditional and social media agendas on the air pollution issue in China from 2011 to 2015. It adopted Granger’s causality analysis to test the causal relationships among four traditional media outlets (N = 1,147), six types of actors on the Chinese social media platform called Weibo (N = 4,045), and between agendas of traditional media outlets and social media actors. The results showed most of news stories were framed under “publicity and government trust” frame between 2011 and 2012, and under “war on pollution” and “science” frames after 2013. Government officials, environmental scientists and researchers dominated media sources. The state-owned media, People’s Daily, set the agenda for other local and commercial media outlets. Agendas on social media were fragmented with media setting the agenda for NGOs and verified individual’s accounts. Agenda-setting effects existed only between traditional media and media’s Weibo accounts, and between traditional media and verified individuals’ Weibo accounts. The agendas of ordinary people on Weibo were independent of the agendas other social media actors and of traditional media. The opinion leaders on Weibo were mostly business leaders and celebrities. This dissertation is the first study to provide a holistic view and clear trajectory of agendas on air pollution over five years. It explained authoritarian environmentalism from a media perspective and contributed to agenda-setting theory by capturing the fragmented nature of the social media agenda. Methodologically, this dissertation advanced existing study by applying a computer-assisted social media data collection method and conducting a more rigorous causality analysis called Granger’s causality.