Uncertainty in the emission estimates is one the main reasons for shortcomings in the Chemistry Transport Models (CTMs) which can reduce the confidence level of impact assessment of anthropogenic activities on air quality and climate. This dissertation focuses on understating the uncertainties within the CTMs and reducing these uncertainties by improving emission estimates The first part of this dissertation focuses on reducing the uncertainties around the emission estimates from oil and Natural Gas (NG) operations by using various observations and high-resolution CTMs. To achieve this goal, we used Weather Research and Forecasting with Chemistry (WRF-Chem) model in conjunction with extensive measurements from two major field campaigns in Colorado. Ethane was used as the indicator of oil and NG emissions to explore the sensitivity of ethane to different physical parametrizations and simulation set-ups in the WRF-Chem model using the U.S. EPA National Emission Inventory (NEI-2011). The sensitivity analysis shows up to 57.3% variability in the modeled ethane normalized mean bias (NMB) across the simulations, which highlights the important role of model configurations on the model performance. Comparison between airborne measurements and the sensitivity simulations shows a model-measurement bias of ethane up to -15ppb (NMB of -80%) in regions close to oil and NG activities. Under-prediction of ethane concentration in all sensitivity runs suggests an actual under-estimation of the oil and NG emissions in the NEI-2011 in Colorado. To reduce the error in the emission inventory, we developed a three-dimensional variational inversion technique. Through this method, optimal scaling factors up to 6 for ethane emission rates were calculated. Overall, the inversion method estimated between 11% to 15% higher ethane emission rates in the Denver-Julesburg basin compared to the NEI-201. This method can be extended to constrain oil and NG emissions in other regions in the US using the available measurement datasets. The second part of the dissertation discusses the University of Iowa high-resolution chemical weather forecast framework using WRF-Chem designed for the Lake Michigan Ozone Study (LMOS-2017). LMOS field campaign took place during summer 2017 to address high ozone episodes in coastal communities surrounding Lake Michigan. The model performance for clouds, on-shore flows, and surface and aircraft sampled ozone and NOx concentrations found that the model successfully captured much of the observed synoptic variability of onshore flows. Selection of High-Resolution Rapid Refresh (HRRR) model as initial and boundary condition, and the Noah land surface model, significantly improved comparison of meteorology variables to both ground-based and aircraft data. Model consistently underestimated the daily maximum concentration of ozone. Emission sensitivity analysis suggests that increase in Hydrocarbon (HC). Variational inversion method and measurements by GeoTAS and TROPOMI instruments and airborne and ground-based measurements can be used to constrain NOx emissions in the region.

Orientador: Luiz Fernando Costa Nascimento / Resumo: O ecossistema amazônico é impactado fortemente pelas queimadas no período da estação seca com as emissões de poluentes na atmosfera. Os efeitos sobre a saúde das populações, especialmente na região do arco do desmatamento, tem sido objeto de recentes estudos. O objetivo do presente estudo é avaliar a distribuição espacial dos focos de queimadas, da morbidade por doenças cardiovasculares, e das concentrações dos poluentes PM2,5 e CO, no estado do Tocantins. Foi desenvolvido estudo ecológico com ferramentas da análise espacial. A análise utilizou o estimador de Kernel e índice de Moran (Im), além de mapas temáticos e correlação de Spearman (rs) entre as variáveis. O geoprocessamento utilizou o programa TerraView 4.2.2. Foram encontradas altas taxas de queimadas no estado e forte associação entre as queimadas e o coeficiente de Gini (rs = 0,30 e p-valor < 0,01). Foi encontrada correlação significativa entre concentrações de monóxido de carbono e internações por doenças do aparelho circulatório (rs = 0,18 e p-valor < 0,05). O índice de Moran para focos de queimadas foi Im = 0,28 com p-valor = 0,01. Foram identificadas cidades que necessitam de prioridade de intervenções. Conclui-se que é necessária uma maior fiscalização ambiental quanto ao controle de queimadas, inclusive nas áreas de preservação ambiental. / Abstract: The Amazonian ecosystem is strongly impacted by the forest fires in the dry season with emissions of pollutants into the atmosphere. The effects on population health, especially in the deforestation arc region, have been the subject of recent studies. The goal of the present study is to evaluate the spatial distribution of forest fires, cardiovascular disease morbidity, and PM2,5and CO pollutant concentrations in the state of Tocantins. An ecological study was developed with spatial analysis tools. The Kernel estimator and the Moran index (Im) were used as spatial analysis techniques, as well as thematic maps and Spearman correlation (rs) between the variables for the analysis of the results. The geoprocessing was development through the program TerraView 4.2.2. High forest fires rates in the state and strong association with the Gini coefficient (rs = 0.30 and p-value <0.01) were found. A significant correlation was found between carbon monoxide concentrations and hospitalizations for circulatory diseases (rs = 0.18 and p-value <0.05). The Moran index for forest fires was Im = 0.28 with p-value = 0.01. Cities that need priority interventions have been identified. It is concluded that a greater environmental inspection is necessary regarding the control of forest fires, including in the areas of environmental preservation. / Mestre

Household air pollution (HAP) is responsible for almost 4 million premature deaths every year, a burden that is primarily carried by women and children in developing countries. The mortality and morbidity impact of HAP can be significantly alleviated through clean cookstove interventions. However, for these interventions to be effective, the new intervention stove must be a substantially cleaner technology and adoption should be high and sustained over time. Woody biomass is the fuel of choice in many developing communities, and contributes substantially to HAP. Several organizations have launched clean cooking interventions to address this issue. However, the majority of those interventions do not address adoption related challenges, that they often face. This thesis explores previous studies on Human-Centered Design (HCD) and the impact of feedback and data access on behavior change. It details a HCD process and methodology applied during the design process of an air quality feedback system, to improve adoption of liquefied petroleum gas (LPG) cookstoves in Rwanda. The feedback system is intended to provide real-time air quality information to stove users and potentially encourage them to abandon traditional biomass cookstoves in favor of the cleaner LPG stoves.

Environmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM), which typically seeks to estimate the average difference in study units' potential outcomes. If the exposure Z is binary, then we may express this as E[Y(Z=1)-Y(Z=0)]. An important assumption under RCM is no interference; that is, the potential outcomes of one unit are not affected by the exposure status of other units. The no interference assumption is violated if we expect spillover or diffusion of exposure effects based on units' proximity to other units and several other causal estimands arise. For example, if we consider the effect of other study units on a unit in an adjacency matrix A, then we may estimate a direct effect, E[Y(Z=1,A)-Y(Z=0,A)], and a spillover effect, E[Y(Z,A)=Y(Z,A`)]. This thesis presents novel methods for estimating causal effects under interference. We begin by outlining the potential outcomes framework and introducing the assumptions necessary for causal inference with no interference. We present an association study that assesses the relationship of animal feeding operations (AFOs) on groundwater nitrate in private wells in Iowa, USA. We then place the relationship in a causal framework where we estimate the causal effects of AFO placement on groundwater nitrate using propensity score-based methods. We proceed to causal inference with interference, which we motivate with examples from air pollution epidemiology where upwind events may affect downwind locations. We adapt assumptions for causal inference in social networks to causal inference with spatially structured interference. We then use propensity score-based methods to estimate both direct and spillover causal effects. We apply these methods to estimate the causal effects of the Environmental Protection Agency’s nonattainment regulation for particulate matter on lung cancer incidence in California, Georgia, and Kentucky using data from the Surveillance, Epidemiology, and End Results Program. As an alternative causal method, we motivate use of wind speed as an instrumental variable to define principal strata based on which study units are experiencing interference. We apply these methods to estimate the causal effects of air pollution on asthma incidence in the San Diego, California, USA region using data from the 500 Cities Project. All our methods are proposed in a Bayesian setting. We conclude by discussing the contributions of this thesis and the future of causal analysis in environmental epidemiology.

Living in counties where manufacturers release environmental toxins, such as those tracked by the Environmental Protection Agency's (EPA) toxic release inventory (TRI), may elevate infants' health risks. Because infant mortality (IM) is a strong indicator of a population's health status, it is an important topic in public health research. The purpose of this research was to examine the potential relationships between IM, community size, and factors related to mothers' SES in counties where more than 25,000 pounds of annual toxic air releases occur. The dependent variable was IM per 1,000 live births in a given community for each of the 3 years included in this analysis (1987, 1995, and 2004). The independent variables included county size and factors related to mother's SES (education, age, ethnicity, and marital status). The theoretical framework consisted of Mosley and Chen's framework for exploring child survival. Archival, publicly available data were pulled from (a) the EPAs TRI data, and (b) linked birth and infant death files from the National Center for Health Statistics. The researcher followed a quantitative, retrospective cross-sectional design and conducted 3 linear regression models to test the research questions. Results indicated that an increase in community size was significantly associated with an increase in IM. Regarding the relationships between IM and the 4 different maternal characteristics (education, age, ethnicity, and marital status) included in the analysis, findings were mixed for the 3 years examined. Despite these unexpected findings, the overall results from this investigation, when considered alongside findings from previous research on IM, indicate that policy changes and interventions are needed to reduce socioeconomic disparities in IM, and to save the lives of more infants.

Excessive PM2.5 emissions in China threaten peoples’ health and cause massive economic burdens to society. Under pressure from the public, and the international community, China published PM2.5 standards for the first time in March 2012. Following the introduction of standards, several pilot cities began to build PM2.5 monitoring networks. This paper is designed to explore whether PM2.5 monitoring can be effectively undertaken and implemented in China and whether monitoring results can offer a technical basis to facilitate a significant reduction in actual PM2.5 emissions and protect public health. PM2.5 monitoring is essential in helping the government and public monitor pollution levels and supervise local compliance with PM2.5 standards. Key aspects to facilitate an effective monitoring process are discussed in the analysis. In addition, a case study – Lanzhou’s PM2.5 monitoring network – is provided to analyze and improve current PM2.5 monitoring practices at local levels, as well as suggest credible technical support to local authorities so as to cut PM2.5 emissions levels. Based on detailed analysis, the results suggest that PM2.5 monitoring can be successfully implemented in China by following several key principles – designing a representative PM2.5 monitoring network, applying QA/QC to ensure data quality, interpreting the data scientifically to understand real pollution levels, etc. In addition, this paper recommends three measures critical to realizing PM2.5 reduction goals: (1) emissions source control, (2) public participation to add input to the decision-making process and supervise local compliance with PM2.5 standards, and (3) non-governmental organization/international cooperation to improve local government and environmental agencies’ capacity with regards to environmental protection. Lessons derived from the case study can help improve PM2.5 monitoring performance not just in Lanzhou, but in cities that share similar monitoring issues across China. Scientific monitoring, together with the application of the above three measures, can more effectively curb PM2.5 emissions, improve air quality, and mitigate negative health effects associated with air pollution. / acase@tulane.edu

Particulate air pollution is known to exacerbate respiratory diseases, such as asthma and chronic obstructive pulmonary disease, in humans. It has been proposed that transition meta ls from inhaled particles may play a role in this exacerbation by generating radical species leading to damage in the lungs. The aim of this research was to determine the role that iron from particulate air pollution played in the generation of reactive oxygen species and subsequently the induction of inflammatory mediators in cells in culture. The production of reactive oxygen species by particulate air pollution was found to be dependent on the mobilization of iron from the particles by chelators, such as the physiologically relevant citrate. The amount of iron mobilized from the combustion particulate, coal fly ash, was dependent on the type of coal used to generate the fly ash and was greatest in the smallest size fraction collected for three different coal types. In addition, the amount of iron mobilized from coal fly ash by citrate correlated closely with the amount mobilized in human lung epithelial (A549) cells, as indicated by induction of the iron storage protein, ferritin. The amount of the proinflammatory cytokine, interleukin-8, secreted in response to coal fly ash treatment varied with the amount of iron mobilized intracellularly from the particles, with the greatest response to the smaller size fractions which released the largest amounts of iron. There was a direct relationship, above a threshold level of bioavailable iron, between the level of interleukin-8 and bioavailable iron in cells treated with coal fly ash. Tetramethyl thiourea and dimethyl sulfoxide prevented the increased production of interleukin-8 by human lung epithelial cells treated with coal fly ash, suggesting the role of a radical species in the induction of this inflammatory mediator. The mobilization of iron from coal fly ash by citrate or in human lung epihelial cells, as well as the induction of interleukin-8, did not correlate with the total amount of iron in the particles. Instead, preliminary results suggest that these measured values vary directly with the amount of iron contained in the aluminosilicate fraction of the fly ash.

Air pollution is a severe issue in many big cities due to population growth and the rapid development of the economy and industry. This leads to the proliferating need to monitor urban air quality to avoid personal exposure and to make savvy decisions on managing the environment. In the last decades, the Internet of Things (IoT) is increasingly being applied to environmental challenges, including air quality monitoring and visualization. In this thesis, we present CAVisAP, a context-aware system for outdoor air pollution visualization with IoT platforms. The system aims to provide context-aware visualization of three air pollutants such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5) in Melbourne, Australia and Skellefteå, Sweden. In addition to the primary context as location and time, CAVisAP takes into account users’ pollutant sensitivity levels and colour vision impairments to provide personalized pollution maps and pollution-based route planning. Experiments are conducted to validate the system and results are discussed.

Congenital diaphragmatic hernia (CDH) results from a defect in the diaphragm through which abdominal contents enter the thorax displacing the heart and the lungs. This causes lung hypoplasia and varying degrees of pulmonary hypertension resulting in high rates of morbidity and mortality. Though CDH has a prevalence rate of 2.61 per 10,000 live births it is an expensive birth defect with an estimated annual cost of nearly $250 million for all CDH survivors. Maternal exposure to air pollutants have not been studied as risk factors for CDH in humans. Ambient ozone has been found to be risk factors for certain birth defects including congenital heart defects, chromosomal anomalies, and limb reduction defects. Cadmium, however, has been found to be a risk factor for diaphragmatic hernia, cleft palate, renal defects, anopthalmia, microphthalmia, anal atresia, undescended testes, and dysplastic ears in animal studies only. The objectives of this study were to: 1) examine the prevalence, temporal trends, and correlates of CDH among live-born infants during 1998–2012; 2) investigate the association between sociodemographic and perinatal characteristics and neonatal and one-year survival among infants with CDH and its subtypes, isolated and complex; 3) examine the role of ambient ozone as a risk factor for CDH; and 4) determine the association between maternal exposure to ambient cadmium in air and CDH and assess if maternal smoking during pregnancy is an effect modifier of the cadmium-CDH association. To answer these questions we used a population-based, retrospective cohort study using data from the 1998–2012 Florida Birth Defects Registry. We classified CDH cases into isolated and complex. A case that was associated with other anomalies listed on the National Birth Defects Prevention Network list of major structural reportable defects was classified as complex CDH. We used Poisson and joinpoint regression models to compute prevalence ratios and assess temporal trends, respectively. Kaplan-Meier survival curves and Cox proportional hazards regression were used to describe neonatal and one-year survival and estimate hazard ratios of neonatal and one-year mortality. We then used multilevel Poisson regression models to examine the association between maternal exposure to ambient ozone and CDH as well as cadmium and CDH. We conducted stratified analyses to test for effect measure modification by maternal smoking status. The study population to answer the first two questions consisted of 3,209,775 live-born infants (including 1,025 cases). To answer the third and fourth questions, the study population consisted of 3,039,685 and 2,591,395 live-born infants (including 981 and 840 cases), respectively. We found a 4% increase in the annual prevalence of CDH among complex cases, but no trend for isolated cases. We observed higher prevalence of CDH among infants born to mothers with high school or less maternal education and for multiple births. Female sex and maternal obesity were found to be associated with decreased risk for CDH. The most important predictor of neonatal and one-year mortality was gestational age

Exposure to fine particulate matter (PM2.5) in the ambient air is associated with various health effects. There is increasing evidence which implicates the central role played by specific chemical components such as heavy metals of PM2.5. Given the fact that humans are exposed to complex mixtures of environmental pollutants such as PM2.5, research efforts are intensifying to study the mixtures composition and the emission sources of ambient PM, and the exposure-related health effects. Factor analysis as well source apportionment models are statistical tools potentially useful for characterizing mixtures in PM2.5. However, classic factor analysis is designed to analyze samples of independent data. To handle (spatio-)temporally correlated PM2.5 data, a Bayesian approach is developed and using source apportionment, a latent factor is converted to a mixture by utilizing loadings to compute mixture coefficients. Additionally there have been intensified efforts in studying the metal composition and variation in ambient PM as well as its association with health outcomes. We use non parametric smoothing methods to study the spatio-temporal patterns and variation of common PM metals and their mixtures. Lastly the risk of low birth weight following exposure to metal mixtures during pregnancy is being investigated.