Browsing by Author "Fischer, Emily, committee member"
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Item Open Access Blending model output with satellite-based and in-situ observations to produce high-resolution estimates of population exposure to wildfire smoke(Colorado State University. Libraries, 2016) Lassman, William, author; Pierce, Jeffrey, advisor; Fischer, Emily, committee member; Schumacher, Russ, committee member; Magzamen, Sheryl, committee member; Pfister, Gabriele, committee memberIn the western US, emissions from wildfires and prescribed fire have been associated with degradation of regional air quality. Whereas atmospheric aerosol particles with aerodynamic diameters less than 2.5 μm (PM 2.5 ) have known impacts on human health, there is uncertainty in how particle composition, concentrations, and exposure duration impact the associated health response. Due to changes in climate and land-management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of PM 2.5 in the western US. While composition and source of the aerosol is thought to be an important factor in the resulting human health-effects, this is currently not well-understood; therefore, there is a need to develop a quantitative understanding of wildfire-smoke-specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools are commonly used to assess exposure to wildfire smoke: in-situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations, and each of these exposure-estimation tools have associated strengths and weakness. In this thesis, we investigate the utility of blending these tools together to produce highly accurate estimates of smoke exposure during the 2012 fire season in Washington for use in an epidemiological case study. For blending, we use a ridge regression model, as well as a geographically weighted ridge regression model. We evaluate the performance of the three individual exposure-estimate techniques and the two blended techniques using Leave-One-Out Cross-Validation. Due to the number of in-situ monitors present during this time period, we find that predictions based on in-situ monitors were more accurate for this particular fire season than the CTM simulations and satellite-based observations, so blending provided only marginal improvements above the in-situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.Item Open Access Characterizing ammonia concentrations and deposition in the United States(Colorado State University. Libraries, 2015) Li, Yi, author; Collett, Jeffrey L., advisor; Kreidenweis, Sonia M., committee member; Fischer, Emily, committee member; Ham, Jay, committee memberRapid development of agricultural activities and fossil fuel combustion in the United States led to a great increase of reactive nitrogen (Nr) emissions in the second half of the twentieth century. These emissions have been linked to excess nitrogen (N) deposition in natural ecosystems through dry and wet deposition pathways that can lead to adverse environmental impacts. Furthermore, as precursors of ozone and fine particles, reactive nitrogen species impact regional air quality with resulting effects on human health, visibility, and climate forcing. In this dissertation, ambient concentrations of reactive nitrogen species and their deposition are examined in the Rocky Mountain region and across the country. Particular emphasis is placed on ammonia, a currently unregulated pollutant, despite its important contributions both to nitrogen deposition and fine particle formation. Continuous measurements of the atmospheric trace gases ammonia (NH3) and nitric acid (HNO3) and of fine particle (PM2.5) ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-) were conducted using a denuder/filter system from December 2006 to December 2011 at Boulder, Wyoming, a region of active gas production. The average five year concentrations of NH3, HNO3, NH4+, NO3- and SO42- were 0.17, 0.19, 0.26, 0.32, and 0.48 µg/m3, respectively. Significant seasonal patterns were observed. The concentration of NH3 was higher in the summer than in other seasons, consistent with increased NH3 emissions and a shift in the ammonium nitrate (NH4NO3) equilibrium toward the gas phase at higher temperatures. High HNO3 concentrations were observed both in the summer and the winter. Elevated wintertime HNO3 production appeared to be due to active local photochemistry in a shallow boundary layer over a reflective, snow-covered surface. PM2.5 NH4+ and SO42- concentrations peaked in summer while NO3- concentrations peaked in winter. Cold winter temperatures drove the NH3-HNO3-NH4NO3 equilibrium toward particulate NH4NO3. A lack of NH3, however, frequently resulted in substantial residual gas phase HNO3 even under cold winter conditions. Concentrated agricultural activities and animal feeding operations in the northeastern plains of Colorado represent an important source of atmospheric NH3 that contributes to regional fine particle formation and to nitrogen deposition to sensitive ecosystems in Rocky Mountain National Park (RMNP) located ~80 km to the west. In order to better understand temporal and spatial differences in NH3 concentrations in this source region, weekly concentrations of NH3 were measured at 14 locations during the summers of 2010 to 2014 using Radiello passive NH3 samplers. Weekly average NH3 concentrations ranged from 2.8 µg/m3 to 41.3 µg/m3 with the highest concentrations near large concentrated animal feeding operations (CAFOs). The annual summertime mean NH3 concentrations were stable in this region from 2010 to 2014, providing a baseline against which concentration changes associated with future changes in regional NH3 emissions can be assessed. Vertical profiles of NH3 were also measured on the 300 m Boulder Atmospheric Observatory (BAO) tower throughout 2012. The highest NH3 concentration along the vertical profile was always observed at the 10 m height (annual average concentration is 4.63 µg/m3), decreasing toward the surface (4.35 µg/m3 at 1 m) and toward higher altitudes (1.93 µg/m3 at 300 m). Seasonal changes in the steepness of the vertical concentration gradient were observed, with the sharpest gradients in cooler seasons when thermal inversions restricted vertical mixing of surface-based emissions. The NH3 spatial distributions measured using the passive samplers are compared with NH3 columns retrieved by the Infrared Atmospheric Sounding Interferometer (IASI) satellite and concentrations simulated by the Comprehensive Air quality Model with extensions (CAMx), providing insight into the regional performance of each. U.S. efforts to reduce NOx emissions since the 1970s have substantially reduced nitrate deposition, as evidenced by strongly decreasing trends in long-term wet deposition data. These decreases in nitrate deposition along with increases in wet ammonium deposition have altered the balance between oxidized and reduced nitrogen deposition. Across most of the U.S., wet deposition has evolved from a nitrate dominated situation in the 1980s to an ammonium dominated situation in recent years. Recent measurements of gaseous NH3 concentrations across several regions of the U.S., along with longer-established measurements of gas phase nitric acid, fine particle ammonium and nitrate, and wet deposition of ammonium and nitrate, permit new insight into the balance of oxidized and reduced nitrogen in the total (wet + dry) U.S. reactive nitrogen deposition budget. Utilizing observations from 37 monitoring sites across the U.S., we estimate that reduced nitrogen contributes, on average, approximately 65 percent of the total inorganic N deposition budget. Dry NH3 deposition plays an especially key role in N deposition compared with other N deposition pathways, contributing from 19% to 65% in different regions. With reduced N species now dominating the wet and dry reactive N deposition budgets in much of the country and future estimates suggesting growing ammonia emissions, the U.S. will need to consider ways to actively reduce NH3 emissions if it is to continue progress toward reducing N deposition to sustainable levels defined by ecosystem critical loads.Item Open Access Comparing crown fire predictions in ponderosa pine stands among four fire behavior models(Colorado State University. Libraries, 2024) Ney, Jacob, author; Hoffman, Chad, advisor; Linn, Rodman, committee member; Fischer, Emily, committee memberFire and land managers commonly use fire behavior modeling systems to support their planning and decision-making process. Fire modeling systems have been increasingly used across the western United States to plan fuel treatments that reduce hazard fuels, especially as a drier climate has resulted in more frequent high severity wildfire. Given differences in model types, approaches, assumptions, and sensitivity to various input parameters, modeling systems can produce different predictions and lead to different management decisions. Variability arising from model selection results in increased uncertainty within the decision-making framework. Multi-model comparisons help identify areas of model agreement and disagreement, reduce uncertainty associated with management decisions, and identify directions for future experimentation. Here, I compare predictions of fire type and crown fire rate of spread (ROS) among four modeling systems that represent a range of model types and complexities—Wildland-urban interface Fire Dynamics Simulator (WFDS), QUIC-Fire, a Rothermel-based modeling framework, and Crown Fire Initiation and Spread (CFIS). Comparisons (n = 297) were made based on a range of forest structure and environmental conditions representative of treated and untreated ponderosa pine forest stands in the southern Rocky Mountains. All four models predicted crown fire occurrence for 71% of simulations in total. WFDS, QUIC-Fire, and CFIS agreed on fire type more than 65% of the time. Rothermel predicted crown fire for 41% of simulations with ROS predictions 45% lower than the other models. Models tended to agree on crown fire occurrence in scenarios with a low canopy base height and greater surface and canopy fuel loading, indicating lower uncertainty in predicted fire behavior among models when fuel hazard is greatest. Differences among model predictions were more evident in scenarios with greater canopy base heights, moderate surface and canopy fuel levels, and at lower windspeeds. These results suggest that uncertainty introduced by model selection is likely greatest for designing and evaluation of fuel treatments, and that further research on fire behavior in treated forests stands is needed.Item Open Access Contribution of biomass burning to carbonaceous aerosols in Mexico City during May 2013(Colorado State University. Libraries, 2014) Tzompa Sosa, Zitely Asafay, author; Kreidenweis, Sonia M., advisor; Fischer, Emily, committee member; Sullivan, Amy, committee member; Volckens, John, committee memberThe Mexico City Metropolitan Area (MCMA) is one of the largest megacities in the world with a population of 20 million people. Anthropogenic emissions have been controlled in past decades; however, emissions transported from outside the basin, such as wildfires and agricultural burning, represent a potentially large contribution to air quality degradation. This study analyzed PM10 filter samples from six different stations located across the MCMA from May, 2013, which represented the month with the most reported fire counts in the region over the last 11 years (2002-2013). Two meteorological regimes were established considering the number of satellite derived fire counts, changes in predominant wind direction, ambient concentrations of CO, PM10 and PM2.5, and precipitation patterns inside MCMA. The filter samples were analyzed for biomass burning tracers including levoglucosan (LEV), water-soluble potassium (WSK+); and water-soluble organic carbon (WSOC). Results of these analyses show that LEV concentrations correlated positively with ambient concentrations of PM2.5 and PM10 (R2=0.61 and R2=0.46, respectively). Strong correlations were also found between WSOC and LEV (R2=0.94) and between WSK+ and LEV (R2=0.75). An average LEV/WSOC ratio of 0.0147 was estimated for Regime 1 and 0.0062 for Regime 2. Our LEV concentrations and LEV/WSOC ratios are consistent with results found during the MILAGRO campaign (March, 2006). To the best of our knowledge, only total potassium concentrations have been measured in aerosol samples from MCMA. Therefore, this is the first study in MCMA to measure ambient concentrations of WSK+. Analysis of gravimetric mass concentrations showed that PM2.5 accounted for 60% of the PM10 mass concentration with an estimated PM10/PM2.5 ratio of 1.68. Estimates from our laboratory filter sample characterization indicated that we measured 37% of the total PM10 mass concentration. The missing mass is most likely crustal material (soil or dust) and carbonaceous aerosols that were not segregated into WSOC fraction. Assuming that LEV is inert in the atmosphere, the estimated biomass burning contributions to WSOC ranged from 7-23%. When assuming a LEV lifetime of 1.1 to 5 days, the estimated contributions increased on average 80%. Thus, we conclude that biomass burning sources had a large impact on WSOC and PM2.5 during May 2013, potentially explaining up to half of the measured WSOC. Our results indicate that primary emissions from biomass burning sources represent significant contributions to ambient PM. Future studies are needed to improve the emission inventories that are commonly used by decision makers in the MCMA to design air quality policies and emission source controls.Item Open Access How women's calling for science careers relates to psychological predictors of persistence in science(Colorado State University. Libraries, 2018) Reed, Kaitlyn A., author; Dik, Bryan J., advisor; Henry, Kim, committee member; Bloodhart, Brittany, committee member; Chavez, Ernest, committee member; Fischer, Emily, committee memberSociety is lacking numbers and diversity of trained scientists to address important key problems. Undergraduate women have been identified as a group that leaves the science-career pipeline at high rates (NSF, 2015), though researchers have highlighted science self-efficacy, identity, values, and intentions, as critical predictors of their persistence (Estrada et al., 2011). The current study proposes and investigates a new predictor of women's persistence in science: perceiving a calling as a scientist. Perceiving a calling predicts career development tasks and outcomes that are similar to known predictors of women's persistence in science (Hirschi, 2012). The present study explores if and how calling as a scientist relates to undergraduate women's science self-efficacy, identity as a scientist, interest in science, scientific community values, and intentions to pursue science. Bivariate correlations suggest perceiving a calling as a scientist is positively related to undergraduate women's science self-efficacy, identity as a scientist, prosocial values of the scientific community, and intentions to pursue science. Using Social Cognitive Career Theory (SCCT) as a framework, the hypothesis that the relationship between perceiving a calling as a scientist and intentions to pursue science is mediated by science self-efficacy and science identity (respectively) was supported. Explanations and implications of all investigated relationships are discussed. This study establishes calling as a new predictor, and SCCT as useful framework, for continued investigation of women's persistence in science.Item Open Access Ice nucleating particles in the Arctic: measurement and source tracking(Colorado State University. Libraries, 2024) Barry, Kevin Robert, author; Kreidenweis, Sonia, advisor; DeMott, Paul, advisor; van den Heever, Susan, committee member; Fischer, Emily, committee member; Trivedi, Pankaj, committee memberThe Arctic landscape is rapidly changing in a warming climate, with sea ice melting and permafrost thawing. Its near-surface air temperature is warming 3.8 times faster than other regions around the world. This rapid warming is known as Arctic amplification. Clouds contribute to this amplification, with their presence and phase is important for determining the surface energy budget. Arctic mixed-phase clouds can last for several days but are not represented well in climate models. Special aerosols, called ice nucleating particles (INPs) trigger ice formation in the atmosphere at temperatures warmer than -38 °C, and thus are important for determining the initiation, lifetime, and radiative properties of these clouds. Observations of INPs, especially over the central Arctic, are limited, and many sources are unknown. This dissertation has the overarching goal of increasing understanding of Arctic INPs. This is achieved through first presenting a full year of INP measurements in the central Arctic, as well as a full year of their composition, using coincident sampling of bacteria and fungi to gain insight into airmass origin. Next, some of the potentially most active Arctic INP sources are explored. Permafrost, which was known previously to contain high levels of INPs, was tested for its activity and persistence in water, and ability to be aerosolized through bubble bursting over several weeks. Then, sources of INPs were surveyed in a region that is controlled by permafrost (a thermokarst landscape). This included field measurements of permafrost, vegetation, sediment, active layer soil, water, and aerosol samples. A high temperature heat test was developed as a diagnostic tool to differentiate sources. Coincidentally, clean working methods to measure INPs were optimized, as efforts to reduce contamination are needed to accurately sample in this region. The main findings from this work suggest a regionally relatively homogenous population of Arctic INPs at most times of year, which is encouraging for efforts to represent them in numerical models across scales and understand their changes in the future. Permafrost-sourced INPs showed high activity and were enhanced near the coast. Unexpectedly, other components of the thermokarst landscape were found to be rich, organic INP reservoirs, emphasizing that the Arctic tundra is a diverse collection of potential contributors to the aerosol.Item Open Access Impacts of oil and natural gas development and other sources on volatile organic compound concentrations in Broomfield, Colorado(Colorado State University. Libraries, 2022) Lachenmayer, Emily, author; Collett, Jeffrey, advisor; Fischer, Emily, committee member; Marchese, Anthony, committee memberIn 2017 substantial new oil and natural gas (ONG) extraction was approved by the City and County of Broomfield (CCOB). A monitoring program was established by CCOB to determine how new ONG extraction impacted local air quality. Multiple instruments were utilized to monitor air quality in the county including weekly volatile organic carbon (VOC) sampling canisters deployed across CCOB by Colorado State University and Ajax Analytics and hourly VOC, methane, and criteria pollutant measurements taken by the Colorado Air Monitoring Mobile Lab (CAMML) deployed near an ONG well-pad by the Colorado Department of Public Health and Environment (CDPHE). Weekly samples, collected from October 2018 through December 2020 were analyzed for 52 VOCs using a 5-channel gas chromatograph. The CAMML reported 20 VOCs, methane, PM2.5, PM10, nitrogen oxides (NOx), and ozone. Positive Matrix Factorization (PMF) was applied to both datasets to characterize key air pollution sources and their impacts in space and time. Six factors were found to describe the weekly data best: Background (biogenic), Combustion, Light Alkane, Complex Alkane, a Drilling factor, and an Ethyne factor. Contributions of the ONG-related PMF factors increased most strongly near well-pads during particular ONG pre-production activities. The Light Alkane factor was most active during production and coiled tubing operations, and flowback at one or more of the new well-pads. The Complex Alkane factor iii was strongly associated with drilling and coiled tubing operations and flowback at one of two well-pads. The Drilling factor contained a VOC profile that closely matched volatiles released from a drilling mud (lubricant for the drill bit) used at two of the three sites. The Ethyne profile represents an unknown and previously undocumented source composition originating from a well-pad. This ethyne and benzene-rich emission was independently observed in other CCOB air monitoring efforts. Five factors best explained the hourly CAMML data; these factors resembled those derived from PMF analysis of the weekly data set. Three factors, Combustion, Ozone background, and Particulate Matter, were not found to be related to local ONG extraction while the profiles containing many of the alkane species (Light Alkane factor and Complex Alkane factor) showed correlation with pad activities. Wind direction analysis suggests emissions associated with these factors were transported from the pad. Benzene was a particular focus of the study given its potential health effects at modest concentration levels. On average, the source factors contributing most to benzene were combustion (38%), longer-lived alkanes from ONG production (22%), and shorter-lived alkanes from ONG production (16%). ONG activities contributed more strongly to benzene levels during pre-production and production phases.Item Open Access Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools(Colorado State University. Libraries, 2022) Cheeseman, Michael J., author; Pierce, Jeffrey R., advisor; Barnes, Elizabeth, committee member; Fischer, Emily, committee member; Ford, Bonne, committee member; Volckens, John, committee memberAmbient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas.Item Open Access Lightning channel locations, LNOx production, and advection in anomalous and normal polarity thunderstorms(Colorado State University. Libraries, 2018) Davis, Trenton, author; Rutledge, Steven A., advisor; Barth, Mary, committee member; Fischer, Emily, committee member; Reising, Steven, committee memberTropospheric ozone is a powerful greenhouse gas and OH precursor, thus understanding its sources is important. Its production is also widely studied in atmospheric science today as global climate modelers attempt to estimate future warming within the troposphere. Nitrogen oxides (NO + NO2 = NOx), serve as a precursor to ozone production. In areas where higher concentrations of OH are present, NOx will undergo reactions to produce nitric acid, thereby shortening its lifetime and limiting the production of ozone. Due to lower concentrations of OH in the upper troposphere, NOx tends to experience a longer lifetime (on the order of days) and greater ozone production at these heights. Lightning produces an appreciable amount of NOx (a.k.a. LNOx) but the final distribution of resulting LNOx, and thus its ozone production, remains poorly understood. Therefore, it is important that this source of NOx be further investigated to improve current LNOx parameterizations. Numerical modeling methods attempt to study this issue by parameterizing the nature of lightning within thunderstorms. Often, the vertical distribution of flash channels (and LNOx) is produced according to a parameterized flash rate within a defined vertical profile and reflectivity volume threshold. The structure and intensity of thunderstorms are highly variable though, causing the location of lightning within a thunderstorm to differ from one thunderstorm to the next. Furthermore, one remaining goal of the Deep Convective Clouds and Chemistry (DC3) field campaign (May – June 2012) was to compare the lightning flash locations and contributions to upper tropospheric LNOx between storms of normal and anomalous charge polarity. To address this remaining goal, five cases with over 5600 total flashes are analyzed in detail using data from DC3, three in northern Colorado and two in northern Alabama. Lightning sources are combined into 3-dimensional (3-D) flash channels and flash channel parcels, with each parcel containing the LNOx produced by its parent flash channel. Parcels are then advected forward in time during the lifetime of each storm using 3-D wind fields produced from dual-Doppler analyses. Results reveal a greater number of flashes and flash channels within anomalous polarity thunderstorms compared to normal polarity thunderstorms at a mean initiation height around 5 km. Flashes in these storms also appear to transect areas of higher vertical velocities resulting in roughly half of flash channel parcels being advected to the upper troposphere (z > 8 km). Contrary to some assumptions, an appreciable fraction of these parcels and NOx contributions remain in the boundary layer of these storms. In the two normal polarity thunderstorm cases, flash channels tend to initiate around 8 km with roughly half of the flash channel parcels remaining near or above 8 km. While both storm types appear to transport roughly 50% of their flash channel parcels to the upper troposphere, significantly larger flash counts and total flash length in the anomalous polarity storms lead to much higher mixing ratios of LNOx in the upper troposphere. These results may help chemistry modelers in parameterizing LNOx formation in both normal and anomalous thunderstorm polarity structures, which will also improve global climate model parameterizations of tropospheric ozone production.Item Open Access Sources, sinks, and trends of ozone precursors and their impact on ozone in northern Colorado(Colorado State University. Libraries, 2017) Abeleira, Andrew Joseph, author; Farmer, Delphine K., advisor; Henry, Charles, committee member; Fischer, Emily, committee member; Crans, Debbie, committee memberOzone is a structurally simple molecule that plays immensely important roles in Earth's atmosphere. In the troposphere, ozone is vital in maintaining the oxidative capacity of the lower atmosphere. However, unlike the chemical structure, the formation and lifecycle of ozone in the troposphere is anything but simple. The role of ozone in severe air pollution episodes, and the negative human and ecosystem health impacts of ozone were first established in the United States during the "smog" pollution episodes of the early 1950s in the Los Angeles basin. Since then, understanding the formation and impacts of ozone has been an air quality research priority in the United States. The primary source of tropospheric ozone is the photochemically initiated oxidation of anthropogenic or biogenic volatile organic compounds in the presence of nitrogen oxides. The production of ozone relies on the interplay between two catalytic cycles that share initiation and termination reactions. The linkage of the ozone catalytic cycles, via those initiation and termination reactions, leads to the non-linear nature of the chemical production of ozone. The urbanization of the United States in the 1950s-1970s led to increased frequency of severe ozone events in urban areas from increased ozone precursor emissions – specifically emissions of NO and NO2 from automobiles and coal-fired electricity generating power plants. These high ozone events, coupled with results from ozone epidemiologic, exposure, and toxicology studies, prompted the U.S. Congress to establish the Clean Air Act of 1970. The Clean Air Act authorized the U.S. Environmental Protection Agency to establish the National Air Quality Standards for six criteria air pollutants – including ozone. The goal of this new standard was to systematically reduce ambient ozone concentrations by targeting major ozone precursor emission sources. Near 50 years later high ozone events are still occurring in densely populated urban and suburban regions in the United States. Herein, an in-depth study of the sources and sinks of ozone precursors, and the impact of precursor reductions on long-term ozone trends in Northern Colorado is presented. Chapter 1 of this dissertation provides relevant historical context regarding ozone in the United States, pertinent tropospheric ozone chemistry for urban and suburban regions, ozone precursor trends in the United States, and other important processes that affect regional and global ozone. Chapter 2 examines long-term (15 year) trends in ozone and ozone precursors in Northern Colorado with a focus on day of week ozone and NO2 trends that suggest Northern Colorado is transitioning from a NOx-saturated to peak ozone production region. Additionally, the impact of severe drought on the ozone/temperature relationship is addressed. Chapter 3 details the seasonal sources of a suite of volatile organic compounds measured during two 8-week periods in spring and summer 2015 at a ground site in Northern Colorado, and demonstrates the impact of drought on the local isoprene and reactive carbon budget. The reduction in isoprene emissions during drought is tied back to the suppression of the ozone/temperature relationship in the region. In the fourth chapter, the sources and sinks of alkyl nitrates, a key ozone precursor sink, are investigated using a simple sequential production-destruction reaction model. The final chapter highlights the need for long-term ozone and ozone precursor monitoring in Northern Colorado as population, energy demands, and ozone precursor emissions change.Item Open Access Using above-ground downwind methane and meteorological measurements to estimate the below-ground leak rate of a natural gas pipeline(Colorado State University. Libraries, 2023) Cheptonui, Fancy, author; Riddick, Stuart N., advisor; Zimmerle, Daniel J., advisor; Fischer, Emily, committee memberNatural gas (NG) leaks from below-ground pipelines present a safety, economic, and environmental hazard, and triaging the severity of leaks remains a significant issue for pipeline operators. Typically, operators conduct walking surveys using hand-held methane (CH4) detectors which output CH4 concentrations to indicate the location of a leak, but quantification often requires excavation of the pipeline. Industry-standard CH4 detectors are lower-cost and have a higher detection threshold and lower precision than optical-cavity CH4 analyzers typically used to quantify emissions. It remains unclear whether coarser CH4 concentration measurements could be used to identify the large leaks that require immediate response. To explore the utility of industry-standard detectors, above-ground downwind CH4 concentration measurements made by the detectors as input to a novel modeling framework, the ESCAPE-1 model were used to estimate the leak rates from below-ground NG pipelines. Controlled below-ground emission experiments were conducted to test this approach over a range of environmental conditions. Using 10-minute averaged CH4 mixing/meteorological data and filtering out low wind/Pasquill Gifford Stability Class (PGSC) A events, the ESCAPE-1 model estimates small distribution leaks (0.2 kg CH4 h-1) to within -31 to +75% (95% CI), and medium distribution leaks (0.8 kg CH4 h-1) to within -73 to +92%(95% CI) of the actual leak rate. When averaged over a longer period (more than 3 hours of data), the average calculated leak rate was an overestimate of 55% for the small (0.2 kg CH4 h-1) leak and an underestimate of 6% for a medium distribution leak (0.8 kg CH4 h-1). Results suggest that as the wind speed increases, or the atmosphere becomes more stable both accuracy and precision of the leak rate calculated by the ESCAPE-1 model decreases. This is likely the result of a trade-off between the high enough wind to move the gas but not high enough that the plume becomes collimated and less homogenous. Optimizing this approach for oil and gas industry applications, this study suggests that CH4 mixing ratios measured by industry-standard CH4 detectors lasting at least 3 hours could be used as a guide to prioritize NG leak repair by estimating the below-ground leak rate from a pipeline within reasonable uncertainty bounds (±55%) in favorable atmospheric conditions.