Matching Items (107)
Description
Dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) are crucial nutrients for autotrophic and heterotrophic microbial life, respectively, in hydrothermal systems. Biogeochemical processes that control amounts of DIC and DOC in Yellowstone hot springs can be investigated by measuring carbon abundances and respective isotopic values. A decade and a half of field work in 10 regions within Yellowstone National Park and subsequent geochemical lab analyses reveal that sulfate-dominant acidic regions have high DOC (Up to 57 ppm C) and lower DIC (up to 50 ppm C) compared to neutral-chloride regions with low DOC (< 2 ppm C) and higher DIC (up to 100 ppm C). Abundances and isotopic data suggest that sedimentary rock erosion by acidic hydrothermal fluids, fresh snow-derived meteoric water, and exogenous carbon input allowed by local topography may affect DOC levels. Evaluating the isotopic compositions of DIC and DOC in hydrothermal fluids gives insight on the geology and microbial life in the subsurface between different regions. DIC δ13C values range from -4‰ to +5‰ at pH 5-9 and from -10‰ to +3‰ at pH 2-5 with several springs lower than -10‰. DOC δ13C values parkwide range from -10‰ to -30‰. Within this range, neutral-chloride regions in the Lower Geyser Basin have lighter isotopes than sulfate-dominant acidic regions. In hot springs with elevated levels of DOC, the range only varies between -20‰ and -26‰ which may be caused by local exogenous organic matter runoff. Combining other geochemical measurements, such as differences in chloride and sulfate concentrations, demonstrates that some regions contain mixtures of multiple fluids moving through the complex hydrological system in the subsurface. The mixing of these fluids may account for increased levels of DOC in meteoric sulfate-dominant acidic regions. Ultimately, the foundational values of dissolved carbon and their isotopic composition is provided in a parkwide study, so results can be combined with future studies that apply different sequencing analyses to understand specific biogeochemical cycling and microbial communities that occur in individual hot springs.
ContributorsBarnes, Tanner (Author) / Shock, Everett (Thesis advisor) / Meyer-Dombard, D'Arcy (Committee member) / Hartnett, Hilairy (Committee member) / Arizona State University (Publisher)
Created2023
Description
This work presents a thorough analysis of reconstruction of global wave fields (governed by the inhomogeneous wave equation and the Maxwell vector wave equation) from sensor time series data of the wave field. Three major problems are considered. First, an analysis of circumstances under which wave fields can be fully reconstructed from a network of fixed-location sensors is presented. It is proven that, in many cases, wave fields can be fully reconstructed from a single sensor, but that such reconstructions can be sensitive to small perturbations in sensor placement. Generally, multiple sensors are necessary. The next problem considered is how to obtain a global approximation of an electromagnetic wave field in the presence of an amplifying noisy current density from sensor time series data. This type of noise, described in terms of a cylindrical Wiener process, creates a nonequilibrium system, derived from Maxwell’s equations, where variance increases with time. In this noisy system, longer observation times do not generally provide more accurate estimates of the field coefficients. The mean squared error of the estimates can be decomposed into a sum of the squared bias and the variance. As the observation time $\tau$ increases, the bias decreases as $\mathcal{O}(1/\tau)$ but the variance increases as $\mathcal{O}(\tau)$. The contrasting time scales imply the existence of an ``optimal'' observing time (the bias-variance tradeoff). An iterative algorithm is developed to construct global approximations of the electric field using the optimal observing times. Lastly, the effect of sensor acceleration is considered. When the sensor location is fixed, measurements of wave fields composed of plane waves are almost periodic and so can be written in terms of a standard Fourier basis. When the sensor is accelerating, the resulting time series is no longer almost periodic. This phenomenon is related to the Doppler effect, where a time transformation must be performed to obtain the frequency and amplitude information from the time series data. To obtain frequency and amplitude information from accelerating sensor time series data in a general inhomogeneous medium, a randomized algorithm is presented. The algorithm is analyzed and example wave fields are reconstructed.
ContributorsBarclay, Bryce Matthew (Author) / Mahalov, Alex (Thesis advisor) / Kostelich, Eric J (Thesis advisor) / Moustaoui, Mohamed (Committee member) / Motsch, Sebastien (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2023
Description
As air quality standards become more stringent to combat poor air quality, there is a greater need for more effective pollutant control measures and increased air monitoring network coverage. Polluted air, in the form of aerosols and gases, can impact respiratory and cardiovascular health, visibility, the climate, and material weathering. This work demonstrates how traditional networks can be used to study generational events, how these networks can be supplemented with low-cost sensors, and the effectiveness of several control measures. First, an existing network was used to study the effect of COVID-19 travel restrictions on air quality in Maricopa County, Arizona, which would not have been possible without the historical record that a traditional network provides. Although this study determined that decreases in CO and NO2 were not unique to the travel restrictions, it was limited to only three locations due to network sparseness. The second part of this work expanded the traditional NO2 monitoring network using low-cost sensors, that were first collocated with a reference monitor to evaluate their performance and establish a robust calibration. The sensors were then deployed to the field to varying results; their calibration was further improved by cycling the sensors between deployment and reference locations throughout the summer. This calibrated NO2 data, along with volatile organic compound data, were combined to enhance the understanding of ozone formation in Maricopa County, especially during wildfire season. In addition to being in non-attainment for ozone standards, Maricopa County fails to meet particulate matter under 10 μm (PM10) standards. A large portion of PM10 emissions is attributed to fugitive dust that is either windblown or kicked up by vehicles. The third part of this work demonstrated that Enzyme Induced Carbonate Precipitation (EICP) treatments aggregate soil particles and prevent fugitive dust emissions. The final part of the work examined tire wear PM10 emissions, as vehicles are another significant contributor to PM10. Observations showed a decrease in tire wear PM10 during winter with little change when varying the highway surface type.
ContributorsMiech, Jason Andrew (Author) / Herckes, Pierre (Thesis advisor) / Fraser, Matthew P (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2023
Description
The prevalence and unique properties of airborne nanoparticles have raised concerns regarding their potential adverse health effects. Despite their significance, the understanding of nanoparticle generation, transport, and exposure remains incomplete. This study first aimed to assess nanoparticle exposure in indoor workplace environments, in the semiconductor manufacturing industry. On-site observations during tool preventive maintenance revealed a significant release of particles smaller than 30 nm, which subsequent instrumental analysis confirmed as predominantly composed of transition metals. Although the measured mass concentration levels did not exceed current federal limits, it prompted concerns regarding how well filter-based air sampling methods would capture the particles for exposure assessment and how well common personal protective equipment would protect from exposure. To address these concerns, this study evaluated the capture efficiency of filters and masks. When challenged by aerosolized engineered nanomaterials, common filters used in industrial hygiene sampling exhibited capture efficiencies of over 60%. Filtering Facepiece Respirators, such as the N95 mask, exhibited a capture efficiency of over 98%. In contrast, simple surgical masks showed a capture efficiency of approximately 70%. The experiments showed that face velocity and ambient humidity influence capture performance and mostly identified the critical role of mask and particle surface charge in capturing nanoparticles. Masks with higher surface potential exhibited higher capture efficiency towards nanoparticles. Eliminating their surface charge resulted in a significantly diminished capture efficiency, up to 43%. Finally, this study characterized outdoor nanoparticle concentrations in the Phoenix metropolitan area, revealing typical concentrations on the order of 10^4 #/cm3 consistent with other urban environments. During the North American monsoon season, in dust storms, with elevated number concentrations of large particles, particularly in the size range of 1-10 μm, the number concentration of nanoparticles in the size range of 30-100 nm was substantially lower by approximately 55%. These findings provide valuable insights for future assessments of nanoparticle exposure risks and filter capture mechanisms associated with airborne nanoparticles.
ContributorsZhang, Zhaobo (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Shock, Everett (Committee member) / Fraser, Matthew (Committee member) / Arizona State University (Publisher)
Created2023
Description
Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
ContributorsSwarup, Shray (Author) / Eikenberry, Steffen (Thesis director) / Mahalov, Alex (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
Lithium (Li) is a trace element in kerogen, but the content and isotopic distribution (δ7Li) in kerogen has not previously been quantified. Furthermore, kerogen has been overlooked as a potential source of Li to sedimentary porefluids and buried sediments. Thus, knowing the content and isotopic composition of Li derived from kerogen may have implications for research focused on the Li-isotopes of buried sediments (e.g., evaluating paleoclimate variations using marine carbonates).The objective of this work is to better understand the role of kerogen in the Li geochemical cycle. The research approach consisted of 1) developing reference materials and methodologies to measure the Li-contents and δ7Li of kerogen in-situ by Secondary Ion Mass Spectrometry, 2) surveying the Li-contents and δ7Li of kerogen bearing rocks from different depositional and diagenetic environments and 3) quantifying the Li-content and δ7Li variations in kerogen empirically in a field study and 4) experimentally through hydrous pyrolysis.
A survey of δ7Li of coals from depositional basins across the USA showed that thermally immature coals have light δ7Li values (–20 to – 10‰) compared to typical terrestrial materials (> –10‰) and the δ7Li of coal increases with burial temperature suggesting that 6Li is preferentially released from kerogen to porefluids during hydrocarbon generation. A field study was conducted on two Cretaceous coal seams in Colorado (USA) intruded by dikes (mafic and felsic) creating a temperature gradient from the intrusives into the country rock. Results showed that δ7Li values of the unmetamorphosed vitrinite macerals were up to 37‰ lighter than vitrinite macerals and coke within the contact metamorphosed coal.
To understand the significance of Li derived from kerogen during burial diagenesis, hydrous pyrolysis experiments of three coals were conducted. Results showed that Li is released from kerogen during hydrocarbon generation and could increase sedimentary porefluid Li-contents up to ~100 mg/L. The δ7Li of coals becomes heavier with increased temperature except where authigenic silicates may compete for the released Li. These results indicate that kerogen is a significant source of isotopically light Li to diagenetic fluids and is an important contributor to the global geochemical cycle.
ContributorsTeichert, Zebadiah (Author) / Williams, Lynda B. (Thesis advisor) / Bose, Maitrayee (Thesis advisor) / Hervig, Richard (Committee member) / Semken, Steven (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Greater Obsidian Pool Area just south of the Mud Volcano area in Yellowstone National Park is an active and ever-changing hot spring region. Situated next to a lake in a meadow between several hills of glacial deposits, north of the Elephant Back rhyolite flow, a diverse group of hot springs has been developing. This study examines the geologic and geomorphic context of the hot springs, finding evidence for a previously undiscovered hydrothermal explosion crater and examining the deposits around the region that contribute to properties of the groundwater table. Hot spring geochemical measurements (Cl- and SO4-2) taken over the course of 20 years are used to determine fluid sourcing of the springs. The distribution of Cl-, an indicator of water-rock interaction, in the hot springs leads to the theory of a fissure delivering hydrothermal fluid in a line across the hot spring zone, with meteoric water from incoming groundwater diluting hot springs moving further from the fissure. A possible second dry fissure delivering mostly gas is also a possible explanation for some elevated sulfate concentrations in certain springs. The combination of geology, geomorphology, and geochemistry reveals how the surface and subsurface operate to generate different hot spring compositions.
ContributorsAlexander, Erin (Author) / Shock, Everett (Thesis director) / Whipple, Kelin (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05
Description
Microplastics, plastics smaller than 5 mm, are an emerging concern worldwide due to their potential adverse effects on the environment and human health. Microplastics have the potential to biomagnify through the food chain, and are prone to adsorbing organic pollutants and heavy metals. Therefore, there is an urgent need to assess the extent of microplastic contamination in different environments. The occurrence of microplastics in the atmosphere of Tempe, AZ was investigated and results show concentrations as high as 1.1 microplastics/m3. The most abundant identified polymer was polyvinyl chloride. However, chemical characterization is fraught with challenges, with a majority of microplastics remaining chemically unidentified. Laboratory experiments simulating weathering of microplastics revealed that Raman spectra of microplastics change over time due to weathering processes. This work also studied the spatial variation of microplastics in soil in Phoenix and the surrounding areas of the Sonoran Desert, and microplastic abundances ranged from 122 to 1299 microplastics/kg with no clear trends between different locations, and substantial total deposition of microplastics occurring in the same location with resuspension and redistribution of deposited microplastics likely contributing to unclear spatial trends. Temporal variation of soil microplastics from 2005 to 2015 show a systematic increase in the abundance of microplastics. Polyethylene was prominent in all soil samples.
Further, recreational surface waters were investigated as a potential source of microplastics in aquatic environments. The temporal variation of microplastics in the Salt River, AZ over the course of one day depicted an increase of 8 times in microplastic concentration at peak activity time of 16:00 hr compared to 8:00 hr. Concurrently, microplastic concentrations in surface water samples from apartment community swimming pools in Tempe, AZ depicted substantial variability with concentrations as high as 254,574 MPs/m3. Polyester and Polyamide fibers were prevalent in surface water samples, indicating a release from synthetic fabrics. Finally, a method for distinguishing tire wear microplastics from soot in ambient aerosol samples was developed using Programmed Thermal Analysis, that allows for the quantification of Elemental Carbon. The method was successfully applied on urban aerosol samples with results depicting substantial fractions of tire wear in urban atmospheric environments.
ContributorsChandrakanthan, Kanchana (Author) / Herckes, Pierre (Thesis advisor) / Fraser, Matthew (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2024
Description
Earth-system models describe the interacting components of the climate system and
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.
ContributorsDurazo, Juan, Ph.D (Author) / Kostelich, Eric J. (Thesis advisor) / Mahalov, Alex (Thesis advisor) / Tang, Wenbo (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018
Description
Atmospheric particulate matter (PM) has a pronounced effect on our climate, and exposure to PM causes negative health outcomes and elevated mortality rates in urban populations. Reactions that occur in fog can form new secondary organic aerosol material from gas-phase species or primary organic aerosols. It is important to understand these reactions, as well as how organic material is scavenged and deposited, so that climate and health effects can be fully assessed. Stable carbon isotopes have been used widely in studying gas- and particle-phase atmospheric chemistry. However, the processing of organic matter by fog has not yet been studied, even though stable isotopes can be used to track all aspects of atmospheric processing, from particle formation, particle scavenging, reactions that form secondary organic aerosol material, and particle deposition. Here, carbon isotope analysis is used for the first time to assess the processing of carbonaceous particles by fog.
This work first compares carbon isotope measurements (δ13C) of particulate matter and fog from locations across the globe to assess how different primary aerosol sources are reflected in the atmosphere. Three field campaigns are then discussed that highlight different aspects of PM formation, composition, and processing. In Tempe, AZ, seasonal and size-dependent differences in the δ13C of total carbon and n-alkanes in PM were studied. δ13C was influenced by seasonal trends, including inversion, transport, population density, and photochemical activity. Variations in δ13C among particle size fractions were caused by sources that generate particles in different size modes.
An analysis of PM from urban and suburban sites in northeastern France shows how both fog and rain can cause measurable changes in the δ13C of PM. The δ13C of PM was consistent over time when no weather events occurred, but particles were isotopically depleted by up to 1.1‰ in the presence of fog due to preferential scavenging of larger isotopically enriched particles. Finally, the δ13C of the dissolved organic carbon in fog collected on the coast of Southern California is discussed. Here, temporal depletion of the δ13C of fog by up to 1.2‰ demonstrates its use in observing the scavenging and deposition of organic PM.
This work first compares carbon isotope measurements (δ13C) of particulate matter and fog from locations across the globe to assess how different primary aerosol sources are reflected in the atmosphere. Three field campaigns are then discussed that highlight different aspects of PM formation, composition, and processing. In Tempe, AZ, seasonal and size-dependent differences in the δ13C of total carbon and n-alkanes in PM were studied. δ13C was influenced by seasonal trends, including inversion, transport, population density, and photochemical activity. Variations in δ13C among particle size fractions were caused by sources that generate particles in different size modes.
An analysis of PM from urban and suburban sites in northeastern France shows how both fog and rain can cause measurable changes in the δ13C of PM. The δ13C of PM was consistent over time when no weather events occurred, but particles were isotopically depleted by up to 1.1‰ in the presence of fog due to preferential scavenging of larger isotopically enriched particles. Finally, the δ13C of the dissolved organic carbon in fog collected on the coast of Southern California is discussed. Here, temporal depletion of the δ13C of fog by up to 1.2‰ demonstrates its use in observing the scavenging and deposition of organic PM.
ContributorsNapolitano, Denise (Author) / Herckes, Pierre (Thesis advisor) / Fraser, Matthew (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2018