Browsing by Author "Pierce, Jeffrey R., advisor"
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Item Open Access Climate and health impacts of particulate matter from residential combustion sources in developing countries(Colorado State University. Libraries, 2018) Kodros, John K., author; Pierce, Jeffrey R., advisor; Volckens, John, committee member; Kreidenweis, Sonia, committee member; Ravishankara, A. R., committee memberGlobally, close to 2.8 billion people lack access to clean cooking technology, while 1.8 billion people lack access to electricity altogether. As a means to generate energy for residential tasks, it is common in many developing countries to rely on combustion of solid fuels (wood, dung, charcoal, trash, etc.). Solid fuel use (SFU) can emit substantial amounts of fine particulate matter (PM2.5), often in or in close proximity to residences, creating concerns for human health and climate; however, large uncertainties exist in indoor and outdoor concentrations and properties, limiting our ability to estimate these climate and health impacts. This work explores the uncertainty space in estimates of premature mortality attributed to exposure to PM2.5 from residential SFU (e.g., cooking, heating, lighting) and makes the first estimates of health and radiative effects from combustion of domestic waste (i.e., trash burning). Next, we investigate key uncertain parameters (emission size distribution, black carbon mixing state, and size-resolved respiratory deposition) that drive uncertainties in health and radiative impacts from SFU, in order to improve model estimates of aerosol impacts from all sources. In many developing regions, combustion of solid fuels for cooking and heating is not the only aerosol source impacting air quality and climate. While uncontrolled combustion of domestic waste has been observed in many countries, this aerosol source is not generally included in many global emissions inventories. Using a global chemical-transport model, we estimate exposure to ambient PM2.5 from domestic-waste combustion to cause 270,000 (5th-95th percentile: 213,000 to 328,000) adult mortalities per year, most of which occur in developing countries. Regarding aerosol radiative effects, we estimate the globally averaged direct radiative effect (DRE) to range from -40 mW m-2 to +4 mW m-2 and the aerosol indirect effect (AIE) to range from -4 mW m-2 to -49 mW m-2. In some regions with significant waste combustion, such as India and China, the aerosol radiative effects exceed −0.4 W m−2.The sign and magnitude of the global-mean DRE is strongly sensitive to assumptions on how black carbon (BC) is mixed with scattering particles, while the AIE is strongly sensitive to the emission size distribution. To determine what factors dominate the uncertainty space in mortality estimates from SFU, we perform a variance-based sensitivity analysis on premature mortality attributed to the combined exposure to ambient and household PM2.5 from SFU. We find that uncertainty in the percent of the population using solid fuels for energy contributes the most to the uncertainty in mortality (53-56% of uncertainty across Asia and South America) with the concentration-response function the next largest contributor (40-50%). In the second half of this dissertation, we explore several key uncertainties in climate and health estimates of aerosol from residential sources in order to reduce overall model uncertainty of aerosol impacts from any source. To test the sensitivity of the AIE to treatment of aerosol size distributions in global models, we estimate the AIE due to anthropogenic emissions with prognostic sectional aerosol microphysics and compare this to the AIE calculated when the simulated aerosol mass of each species is remapped onto a prescribed size distribution. Simulations using the prognostic scheme yield a global mean anthropogenic AIE of −0.87 W m−2, while the simulations with the prescribed scheme predict −0.66 W m−2. These differences suggest that simulations with prescribed size‐distribution mapping are unable to capture regional and temporal variability in size‐resolved aerosol number and thus may lead to biases in estimates of the AIE.Item Open Access Earth, humans, and metals: investigating the role of iron and other metals in the atmospheric, oceanic, and energy systems(Colorado State University. Libraries, 2022) Rathod, Sagar D., author; Pierce, Jeffrey R., advisor; Bond, Tami C., advisor; Denning, A. Scott, committee member; Fischer, Emily V., committee member; Scott, Ryan P., committee memberMetals such as iron and copper have been an integral component of the Earth system since its beginnings and have formed the basis for modern human civilization growth since the Bronze and Iron Ages. Human activities include metals at various levels, from burning coal in power plants and mining ores lead to emissions of particulate and gaseous metallic products into the atmosphere. While suspended in the air, metal oxides such as hematite and magnetite absorb solar radiation, thus warming the atmosphere. After falling into the oceans, metals such as iron and magnesium act as important nutrients for oceanic biota, and thus affect the marine nutrient and carbon cycles. Human activities have increased many-fold since the beginning of the Industrial Era, and as the world moves from fossil fuel to renewable energy to reduce carbon emissions, the demand for metals is also projected to increase many folds. Yet, the past, present, and future impacts of anthropogenic activities on the atmospheric and marine metal cycles, particularly iron, remain poorly understood.In Chapter 2, I estimate the atmospheric radiative and oceanic biological impacts of anthropogenic iron emissions over the Industrial Era. I perform simulations using a mineralogy-based inventory and an Earth System Model and estimate the 1850-to-2010 global mean direct radiative forcing by anthropogenic iron to be +0.02 to +0.10 W/m2. I estimate that the enhanced phytoplankton primary production due to anthropogenic soluble iron deposition over the last 150 years caused carbon dioxide (CO2) sequestration of 0.2-13 ppmv. This sequestered CO2 also led to an 'avoided' CO2 forcing of -0.002 to -0.16 W/m2. While globally small, these impacts can be higher in specific regions; the anthropogenic iron oxide direct radiative forcing is +0.5 W/m2 over areas such as East Asia and India with more coal combustion and metal smelting. Anthropogenic soluble iron sustains >10% of marine net primary productivity in the high-latitude North Pacific Ocean, a region vulnerable to thermal stratification due to climate change. In Chapter 3, I focus on evaluating anthropogenic total iron emissions using observations and models. Performing the model-observation comparison only at sites where the modeled anthropogenic contribution is the highest, I find that the current emission inventory underestimates anthropogenic total iron emissions from North America and Europe by a factor of 3-5. Further isolating anthropogenic sectoral emissions over North America using Positive Matrix Factorization, I find that smelting and coal combustion emissions are overestimated by a factor of 3-10 in the current emission inventory, whereas heavy fuel oil emissions from ships and industrial boilers are underestimated by a factor of 2-5. By comparing modeled concentrations of iron oxides with observations from Japan, I find that the current smelting and coal combustion emissions from East Asia are only slightly overestimated in the inventory, by a factor of 1.2-1.5. Finally, in Chapter 4, I explore the regionality and magnitude of PM2.5 emissions from metal mining and smelting to meet projected global renewable energy demand. I estimate future PM2.5 (particulate matter smaller than 2.5 μm diameter) emissions from mining and smelting to meet the metal demand of renewable energy technologies in two climate pathways to be 0.3-0.6 Tg/yr in the 2020-2050 period, which is projected to contribute 10-30% of total anthropogenic primary PM2.5 combustion emissions in many countries. The concentration of mineral reserves in a few regions means the impacts are also regionally concentrated. Rapid decarbonization could lead to a faster reduction of overall anthropogenic PM2.5 emissions but also could create more unevenness in the distributions of emissions relative to where demand occurs.Item Open Access Estimating emission rates of volatile organic compounds from oil and natural gas operations in the Piceance Basin(Colorado State University. Libraries, 2015) MacDonald, Landan Patrick, author; Pierce, Jeffrey R., advisor; Collett, Jeffrey L., advisor; Ham, Jay M., committee memberOil and natural gas production has been steadily increasing in Colorado for the past 10 years. Garfield County is partially located above the natural gas rich Piceance Basin. Horizontal drilling techniques provide increased access to subsurface gas deposits while hydraulic fracturing is employed to increase the permeability of the tight gas formations by pumping pressurized fluids into the ground to allow more cost-effective oil and gas extraction. Once fractured, the fluid is allowed to flow back to the surface to be captured before the well is considered producing. Our team conducted field measurements from 2013 to 2015 in Garfield County to determine emission rates of methane, hazardous air pollutants, and ozone precursors at 18 oil and gas operations. Drilling and well completion operations were targeted because they are understudied relative to production. We estimate the emission rates of methane and 58 additional VOCs (focusing on benzene, toluene, and ethane) for three common operations. We found benzene had mean emission rates of 0.72, 0.23, and 0.055 g/s for drilling, hydraulic fracturing, and flowback operations respectively. We calculated mean methane emission rates of 6.2, 29, and 64 g/s for drilling, hydraulic fracturing, and flowback operations respectively. We use the estimated methane emission rates from drilling and well completion operations to compare to typical well lifetime emissions during a 30 year production phase and find that drilling and well completion operations may be contributing from 0.1 to 10% of total well pad emissions. These results are based on a limited sampling size (18 sites) and limited overall measurement time (4.25 hours of total measurement time included in results). It is possible we did not perform measurements for long enough periods of time at enough sites. This study is beginning to fill the information gap by focusing on drilling and well completion operations. AERMOD is an atmospheric dispersion model used for new source apportionment. We compared our measured concentration fields to AERMOD predicted concentration fields by replicating fieldwork locations and conditions. Meteorological conditions were taken from an on-site meteorological station for use in the dispersion model. Comparing to the measurements, we found there was a low log-mean bias (-0.007) with a large amount of scatter (r = 0.0007). Additionally, we use AERMOD and data from the NCEP North American Regional Reanalysis database to predict the distribution of concentrations experienced throughout for various meteorological conditions in Garfield County at various distances surrounding oil and gas wells. We normalized these predicted concentration fields by emission rate and created cumulative distribution functions.Item Open Access Estimating spatiotemporal trends in wildfire smoke concentrations in the western United States(Colorado State University. Libraries, 2018) O'Dell, Katelyn, author; Pierce, Jeffrey R., advisor; Fischer, Emily V., advisor; Ford, Bonne, committee member; Magzamen, Sheryl, committee memberThe United States (US) has seen significant improvements in seasonal air quality over the past several decades. However, particulate air quality in summer over the majority of the western US has seen little improvement in recent decades. Particulate matter with diameters < 2.5 microns (PM2.5) is a large component of ambient air quality that is associated with negative health effects and visibility degradation. Wildfires are a major summer source of PM2.5 in the western US. While anthropogenic-related sources of PM2.5 have decreased across the US, wildfires have increased in both frequency and burn area since the 1980s. It is currently uncertain how this increase in wildfires has impacted seasonal air quality trends and how the health effects of wildfire-emitted PM2.5 may differ from anthropogenic-sourced PM2.5. We do not directly address the latter uncertainty, but rather focus on improving smoke-exposure estimates, which are a critical, yet challenging, component to understanding the health effects of wildfire-emitted PM2.5. In this thesis, we use a combination of satellite estimates, surface observations, and chemical transport models to distinguish wildfire smoke PM2.5 from non-wildfire-smoke PM2.5 during the summer in the US. We update the record of seasonal trends in PM2.5 observed at surface monitors and provide the first estimates of trends in wildfire smoke-specific PM2.5. We find continued decreases in total-PM2.5 in most seasons and regions of the US. In the summer in heavily fire-impacted regions of the western US, we find non-decreasing total-PM2.5 while wildfire smoke-specific PM2.5 has increased and non-wildfire-smoke PM2.5 has decreased. We test the application of blended smoke exposure models, which use multiple data sources as input variables (e.g. satellite-derived aerosol optical depth, chemical transport models, etc.), across the full western US. We incorporate a novel dataset into the model, Facebook posts, which have been shown to correlate well with surface PM2.5 concentrations during the western US wildfire season. We find the blended smoke exposure model performs well across the western US (R2 = 0.66). However, the Facebook dataset is well correlated with interpolated surface monitors (another input variable) and thus does not significantly improve blended smoke-exposure estimates in the western US.Item Open Access From forests to the remote ocean to smoke plumes: aerosol microphysics in diverse environments(Colorado State University. Libraries, 2020) Hodshire, Anna Lily, author; Pierce, Jeffrey R., advisor; Jathar, Shantanu H., advisor; Collett, Jeffrey L., committee member; Farmer, Delphine K., committee member; Kreidenweis, Sonia M., committee memberTo view the abstract, please see the full text of the document.Item Open Access Health-relevant pollutants in US landscape fire smoke: abundance, health impacts, and influence on indoor and outdoor air quality(Colorado State University. Libraries, 2021) O'Dell, Katelyn, author; Pierce, Jeffrey R., advisor; Fischer, Emily V., advisor; Collett, Jeffrey L., Jr., committee member; Ford, Bonne, committee member; Magzamen, Sheryl, committee memberLandscape (wild, prescribed, and agricultural) fires have a significant impact on air quality in the United States (US). As anthropogenic emissions decline and emissions from landscape fires increase across the coming century, the relative importance of landscape fire smoke on US air quality and health will increase. Landscape fire smoke is a complex mixture of multiple gas- and particle-phase pollutants, which are harmful to human health. The health impacts of landscape fire smoke may differ from urban pollution as the seasonal and spatial distribution, particle size distribution and composition, and relative abundance of gas-phase species in landscape fire smoke are different from urban pollution sources. Epidemiology studies of smoke events, which often rely on particulate matter (PM) concentrations as a smoke exposure tracer, show smoke negatively impacts respiratory health. The contribution of gas-phase hazardous air pollutants (HAPs) to the health impacts of smoke has yet to be directly quantified. In addition, the implications of episodic landscape fire emissions on the sub-national temporal and spatial distribution of health events are not well characterized. Finally, a majority of the work on the health and air quality impacts of landscape fire smoke has focused on outdoor air. Recent works have shown that landscape fire smoke can impact indoor air quality, but there is large heterogeneity in both smoke events and the indoor environments impacted by smoke events. To date, no study of US wildfire smoke influence on indoor air quality has analyzed indoor fine particulate matter (PM2.5) concentrations across multiple western US cities during multiple extreme smoke events. In the first chapter of this dissertation, we combine aircraft-based in-situ smoke plume observations with interpolated regulatory surface monitor observations to quantify the health risk of HAPs in US smoke. Using observations from the Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN), a 2018 aircraft-based field campaign that measured HAPs and PM in western US wildfire smoke plumes, we identify the relationships be- tween HAPs and associated health risks, PM, and smoke age. We find the ratios between acute, chronic noncancer, and chronic cancer HAPs health risk and PM in smoke decrease as a function of smoke age by up to 72% from fresh (<1 day of aging) to old (>3 days of aging) smoke. We show that acrolein, formaldehyde, benzene, and hydrogen cyanide are the dominant contributors to gas-phase HAPs risk in smoke plumes. We use ratios of HAPs to PM along with annual average smoke-specific PM to estimate current and potential future smoke HAPs risks. Next, we use a health impact assessment with observation-based smoke PM2.5 to determine the sub-national distribution of mortality and the sub-national and sub-annual distribution of morbidity attributable to US smoke PM2.5 from 2006-2018. We estimate disability-adjusted life years (DALYs) for PM2.5 and 18 gas-phase HAPs in smoke using the HAPs to PM ratios developed in Chapter 2. Although the majority of large landscape fires occur in the western US, we find the majority of mortality (74%) and morbidity (on average 75% across 2006-2018) attributable to smoke PM2.5 occurs outside the West due to a higher population density in the East. Across the US, smoke-attributable morbidity predominantly occurs in spring and summer. The number of DALYs associated with smoke PM2.5 are approximately three orders of magnitude higher than DALYs associated with gas-phase smoke HAPs. These results indicate that awareness and mitigation of landscape fire smoke exposure is important across the US, not just in regions in proximity to large wildfires. Finally, we use a large low-cost sensor network of indoor and outdoor PM2.5 monitors to characterize the relationship between indoor and outdoor air quality during smoke events. We identify smoke-impacted regions of the western US with a high density of co-located (distance < 1000 m) indoor and outdoor PurpleAir monitors. In these regions, we calculate indoor PM2.5/outdoor PM2.5 ratios on smoke-impacted and smoke-free days and find this ratio is < 1 (indoor PM2.5 less than outdoor PM2.5) at 98% of the monitor pairs for smoke-impacted days, compared to 54% on smoke- free days. On smoke-impacted days, indoor PM2.5 concentrations increase as outdoor PM2.5 Air Quality Index (AQI) increases by 25% per AQI bin, on average. However, the ratio of indoor PM2.5 to outdoor PM2.5 decreases by 28% per AQI bin. These results show that landscape fire smoke influences indoor air quality across many indoor environments in multiple cities, and this impact increases with smoke event intensity. In addition, this work highlights the utility of low-cost monitoring in quantifying indoor air quality during smoke events. However, we show that the present distribution of these indoor monitors suggests a bias towards census tracts of lower social vulnerability.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 The influence of prescribed burning on springtime PM2.5 concentrations in eastern Kansas(Colorado State University. Libraries, 2023) Sablan, Olivia, author; Fischer, Emily V., advisor; Pierce, Jeffrey R., advisor; Magzamen, Sheryl, committee member; Ford, Bonne, committee memberAnnual springtime (March - May) prescribed burning is practiced in the Flint Hills of eastern Kansas to mitigate wildfire risk, improve nutritional value of vegetation for cattle grazing, limit woody encroachment, and maintain the health of the tall grass prairie ecosystem. Smoke from these prescribed fires produces fine particulate matter (PM2.5), degrading air quality. Smoke from prescribed fires is understudied due to their short duration and a lack of monitoring in the rural regions where prescribed burning occurs. To quantify the contribution of springtime prescribed burning to PM2.5 concentrations in the Flint Hills and downwind regions, we deployed 38 PurpleAir PM2.5 sensors for the 2022 burning season. We used observations from this ground-based network alongside a suite of satellite products to determine the PM2.5 attributable to smoke. In 2022, the Flint Hills were also impacted by dust and transported smoke from high winds, drought, and wildfires in New Mexico. We separated the local and transported smoke effects for our exposure estimates. Across the low-cost sensor network, 24-hour median PM2.5 increased by 5.2 µg m-3 on days impacted by smoke from fires in the eastern Kansas region versus smoke-free days. We compared our findings to two existing PM2.5 estimates derived from satellites and ground-based measurements. Satellite-based products show a similar daily smoke-driven median increase in PM2.5 concentration and a consistent increase in seasonal average PM2.5 concentrations in the Flint Hills region as our estimates based on in situ monitors.Item Open Access Understanding the photochemical evolution of organic aerosol from mobile sources and wildfires(Colorado State University. Libraries, 2021) Akherati, Ali, author; Jathar, Shantanu H., advisor; Pierce, Jeffrey R., advisor; Bond, Tami C., committee member; Volckens, John, committee member; Farmer, Delphine K., committee memberTo view the abstract, please see the full text of the document.Item Open Access Using modelling tools to advance the understanding of ammonia dry-deposition and bidirectional flux processes next to large animal feeding operations(Colorado State University. Libraries, 2020) Lassman, William, author; Pierce, Jeffrey R., advisor; Collett, Jeffrey L., Jr., advisor; Fischer, Emily V., committee member; Ham, Jay M., committee memberAmmonia in the atmosphere is a trace gas that can play a big role in the Earth's climate, as well as human and ecological health. Due to its stickiness and solubility, ammonia can enter the biosphere via wet and dry deposition, where excess ammonia input often results in soil acidification, disruption of natural ecological equilibria, and loss of biodiversity. Additionally, ammonia is the most abundant alkaline species in the atmosphere and can react with atmospheric acids to form aerosols, which can affect the earth's radiative balance as well as human health. Ammonia emissions tend to be associated with agricultural sources, such as fertilized fields or animal waste at concentrated Animal Feeding Operations (CAFOs). Consequently, ammonia emissions tend to be dynamic and highly heterogeneous, and ammonia surface-fluxes are difficult to measure. However, in regions with many large CAFOs, ammonia can be an important regional pollutant, especially if there are sensitive ecosystems or other regional sources of atmospheric acids present. In this dissertation, I study ammonia dry-deposition fluxes immediately downwind of CAFOs using a variety of modelling tools. First, I discuss original research where I use a coupled a K-epsilon model with a Lagrangian-Stochastic ammonia bidirectional exchange surface model to simulate the dispersion and deposition of ammonia downwind of an idealized CAFO. Based on these simulations, the amount of ammonia that undergoes dry deposition depends greatly on the land surface downwind of the CAFO; replacing bare soil or unmanaged grassland with leafier surfaces such as cropland or forests can increase the fraction of total ammonia emissions that deposits from 2 - 10% to 30 - 50%, though this is sensitive to the ammonia emission potential in the model plant canopy. Next, I describe a separate study where I use a 3-D Large-Eddy Simulation model to simulate the dispersion of ammonia and methane from a CAFO with a time-resolved modelling tool. I use this modelling system to produce synthetic observations, which are used to develop an inversion approach to quantify the ammonia dry deposition near a CAFO with colocated mobile measurements of ammonia and methane. While I demonstrate that such an inversion technique is feasible with surface-based measurements, considerable value is added, in terms of minimizing method bias and increasing method precision, by mounting measurements on a small Unmanned Aerial System (sUAS). Finally, I present measurements of PM2.5 concentration and composition that were made in Palapye, Botswana. Botswana is a developing country with a hot and arid climate. Beef and livestock production are important economic activities in Botswana; however, the agricultural practices differ considerably from the CAFOs discussed in the rest of the dissertation. Furthermore, these livestock activities occur against a backdrop of emissions and air pollutants that differ considerably from the United States and Europe. The measurements show that PM2.5 concentrations were on average 9 μg m-3 during the 5-week measurement period. While below levels that are typically considered hazardous, there was considerable variability in the measured concentrations, and the measurement period is too short to conclusively determine that air pollution is not a public health concern in this region. The aerosol composition is dominated by carbonaceous species, probably from biomass burning, though inorganic sulfate also is abundant in the aerosol phase. As Botswana continues to undergo economic development, the types of emissions and pollution present will continue to change.