Browsing by Subject "PM2.5"

Now showing 1 - 4 of 4
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    Air quality human health impact assessment: modeling and applications for environmental policy
    (2020-05) Thakrar, Sumil
    Exposure to fine particulate matter air pollution (PM2.5) is the largest environmental risk factor for death in the United States and globally. Reducing these deaths is facilitated by better understanding how specific emissions sources affect PM2.5 concentrations, but traditional methods for doing so are computationally demanding and resource intensive. In this presentation, I describe my dissertation research into air quality-related human health impacts through the development and use of reduced complexity models (RCMs) that rapidly estimate changes in PM2.5 concentrations and associated deaths. For my first chapter, I use an RCM (InMAP) to estimate the potential air quality-related human health impacts of growing switchgrass, an important bioenergy feedstock. I find that life cycle air quality-related health impacts of switchgrass production vary greatly by location and fertilizer type, and are driven primarily by ammonia emissions from fertilizer application. For my second chapter, I use InMAP and two other RCMs to estimate the air quality impacts of all domestic, human-caused emissions in the United States to identify promising targets for reducing air quality-related deaths. I find that half of the deaths are attributable to 5 human activities, all in different sectors. Promising policy decisions for reducing the deaths include targets of historical focus, such as coal-powered electricity generation, and emerging targets, such as agricultural emissions and residential solvent use. For my third chapter, I describe the development of an open source RCM (Global InMAP) for use on a global spatial domain. I generate global chemical and meteorological inputs to parametrize Global InMAP, configure its computational grid, and run InMAP on a global emissions inventory to demonstrate its use. Overall, its performance against ground observations is comparable to current global models, but at greatly reduced computational intensity. Global InMAP can be used to further inform policy decisions for reducing air quality-related deaths worldwide.
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    Bringing About Pro-Environment Behavior Through Policies and Social Norms
    (2019-07) SANKAR, ASHWINI
    As human population and consumption have risen, waste generation and air pollution have also increased leading to steady environmental degradation. To stem overflowing landfills and combat air pollution, policies and social norms have often been used as tools to bring about change. My dissertation analyzes the impact of these tools to achieve pro-environment behavior. The first chapter tries to understand two such behaviors, i.e., how to increase recycling and reduce waste. They are key to protecting natural resources, but households probably do not derive any benefit from recycling other than social approval. Based on a theoretical model I built for households, I show that when the social norm of recycling increases, the recycling rate of the household rises and waste per capita falls. My paper is one of the first to test these propositions empirically for Minnesota data using an instrumental variable setup. I show that while waste per capita declines significantly with an increase in social capital, recycling rate does not seem to be influenced by social capital. My second chapter studies the impact of environmental regulations in India on mortality that includes all causes and all ages (or mortality). We know that chronic exposure to air pollution is more harmful to adults than babies and hence focus on mortality as the outcome, for the first time for India. Using a difference-in-differences framework, in the first part of the paper, I show that environmental regulations in India have led to a significant drop in mortality. The second part analyzes the effect of different pollution types on mortality, where I show that PM2:5 exposure is more harmful to mortality (but not infant mortality) than TSP. This further strengthens the claim that policies should focus on adults and shift its focus from TSP to PM2:5 to get greater gains in health. The last chapter studies the functional form of the relationship between PM2:5 concentrations and mortality for the first time for India. The shape of this concentration-response curve will determine if the air in India affects public health at a different or the same rate as the U.S. baseline rate. My paper is one of the first studies to analyze this relationship using panel data for India, without simply extrapolating coefficients from U.S. or European data, following a rigorous identification strategy. I then arrive at the relative risk of mortality estimates at higher pollution concentrations as well as the estimated lives saved due to the reduction in pollution exposure.
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    Predicting Influence of Relative Humidity (RH) on Low-Cost Particulate Matter Sensors (LCPMSs) with Empirically Derived Single-Parameter for Hygroscopicity based on K-Kohler Theory
    (2022-12) Tejada, Rayan
    Low-cost particulate matter sensors (LCPMSs) could provide significant insight into air quality data with their ability to be placed virtually anywhere, short sampling time, and cost to build. However, LCPMSs are also known to significantly overestimate particle counts when the relative humidity (RH) is above 65%. It is widely considered that the hygroscopic growth of aerosols is the cause. Hygroscopicity of PM can be described by a single parameter, symbolized as K, and was used in a previous study (Di Antonio et al., 2018) to correct LCPMS data with promising results. However, the study assumed ambient PM to be a pure substance, however, it is often found to be a complex mixture of organic and inorganic chemical species. This study tested if a statistically derived empirical value of K, referred to as “ambient K”, could improve representing the RH influence on LCPMSs. Ambient K is defined as the statistically best-fitting value for several experimental observations of hygroscopy and makes no assumptions on the number of species in ambient PM. Ambient K was graphically demonstrated to be more representative of the experimentally observed RH error compared to assuming K, while having the same statistical performance as conventionally assuming K. Varying observations of hygroscopic behavior among multiple sensors provided strong evidence of multiple chemical species in the observed ambient PM.
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    Soft X-Ray-Assisted Detection Method for Airborne Molecular Contamination (AMC) and its Applications to AMC Filtration Issues
    (2016-07) Kim, Chang Hyuk
    Airborne molecular contamination (AMC) represents a wide range of gas-phase chemical contaminants in cleanrooms. Because AMC can make defects of semiconductor chips by forming undesired nanoparticles and haze under ultra violet lights in the photolithography and change properties of semiconductor chips as dopants, developing methods for monitoring and controlling AMC is highly required in the semiconductor industry. This dissertation focuses on 1) the development of a detection method for AMC and 2) its applications to AMC related issues in the semiconductor industry. The detection method for AMC was developed by converting AMC into nanoparticles under soft X-ray irradiation and measuring them through the aerosol detection instrument, the scanning mobility particle sizer (SMPS). The soft X-ray-assisted detection method for AMC showed high sensitivity, e.g. down to ppt-level SO2. This soft X-ray-assisted detection method was firstly applied to evaluate the filtration efficiency of two AMC filters by measuring the concentration of SO2 downstream of the filters. This AMC detection method was also employed to develop materials, which emit VOCs lower than the conventional materials used in cleanrooms. The process for finding low-VOC replacements can be accelerated by screening candidate materials through this method. In addition, this AMC detection method was applied to measure outgassing from particulate air pollutants (PM2.5), which is a source of AMC. Using this method, a linear relationship was observed between the outgassing and PM2.5 mass loading on the filters. Subsequently, the soft X-ray-assisted detection method was used to study the removal of AMC and nanoparticles simultaneously using a single gas filter, granular activated carbon (GAC). The filtration efficiency of the GAC for 1.5-30 nm particles was investigated at different compositions and face velocities. In the present work, the GAC showed 90% filtration efficiency for sub-3 nm particles, in addition to its original gas adsorption efficiency. Furthermore, the penetration of toluene molecules through the GAC measured by the soft X-ray method was not changed when the GAC was challenged with or without nanoparticles. The results implied that the GACs can be used to remove both AMC and nanoparticles simultaneously.