Biased Ozone Precursors in the Upper Troposphere: Evaluation, Recommendations, and Implications

Ozone is a significant air quality pollutant and the third largest anthropogenic source of climate forcing. Despite its importance, ozone can only be sparsely sampled in the atmosphere. As a result, model simulations are required to fully understand ozone's effects as an air quality pollutant and as a short-lived climate forcer. Simulations predict ozone by modeling the interaction between the environment and chemical precursors. Current simulations have known biases for precursors in the upper troposphere, the altitude at which ozone is most efficient at climate forcing. In present global and regional chemical transport models (CTMs), the limiting ozone precursors-nitrogen oxides-are biased low compared to observations in the upper troposphere. Identifying the source(s) of error in a CTM can be difficult if compensating errors in one or several model processes (e.g., emission, transport, deposition, or chemistry) mask symptoms of a model deficiency. Each process, therefore, must be evaluated with minimal influence from other processes. This study develops a framework for isolating the chemistry process in the upper troposphere by combining aircraft observations with statistical physics models. First, this dissertation evaluates the chemistry process in the upper troposphere and quantifies biases that are specific to chemistry. Then, chemical reactions important to tropospheric ozone are evaluated for potential to cause the nitrogen oxides low-bias, and each reaction rate's uncertainty is constrained using Bayesian techniques. This study identifies a revision to a critical reaction that removes nitrogen oxides and radicals that drive ozone production (NO2 + HO.→HNO3). Finally, the downward revision of the reaction rate is implemented and evaluated in a full global CTM. Evaluation in the global CTM improves the partitioning of nitrogen precursors to ozone, while increasing model sensitivity to emissions of nitrogen oxides. The three phases of this dissertation identify chemistry process bias, constrain uncertain reactions, and demonstrate the importance of findings. Improving simulated chemistry in the upper troposphere contributes to the scientific understanding of processes that produce ozone. Improving the simulated processes helps to decrease the uncertainty in simulated future scenarios and the emission reduction tests that form the scientific basis for policy development

The global nonmethane reactive organic carbon budget: A modeling perspective

The cycling of reactive organic carbon (ROC) is central to tropospheric chemistry. We characterize the global tropospheric ROC budget as simulated with the GEOS-Chem model. We expand the standard simulation by including new emissions and gas-phase chemistry, an expansion of dry and wet removal, and a mass tracking of all ROC species to achieve carbon closure. The resulting global annual mean ROC burden is 16 Tg C, with sources from methane oxidation and direct emissions contributing 415 and 935 Tg C yr⁻¹. ROC is lost from the atmosphere via physical deposition (460 Tg C yr⁻¹), and oxidation to CO/CO2 (875 Tg C yr⁻¹). Ketones, alkanes, alkenes, and aromatic hydrocarbons dominate the ROC burden, whereas aldehydes and isoprene dominate the ROC global mean surface OH reactivity. Simulated OH reactivities are between 0.8–1 s⁻¹, 3–14 s⁻¹, and 12–34 s⁻¹ over selected regions in the remote ocean, continental midlatitudes, and the tropics, respectively, and are consistent with observational constraints.United States. National Oceanic and Atmospheric Administration (NA14OAR4310132

Assessing Public Health Burden Associated with Exposure to Ambient Black Carbon in the United States

Black carbon (BC) is a significant component of fine particulate matter (PM2.5) air pollution, which has been linked to a series of adverse health effects, in particular premature mortality. Recent scientific research indicates that BC also plays an important role in climate change. Therefore, controlling black carbon emissions provides an opportunity for a double dividend. This study quantifies the national burden of mortality and morbidity attributable to exposure to ambient BC in the United States (US). We use GEOS–Chem, a global 3-D model of atmospheric composition to estimate the 2010 annual average BC levels at 0.5 x 0.667° resolution, and then re-grid to 12-km grid resolution across the continental US. Using PM2.5 mortality risk coefficient drawn from the American Cancer Society cohort study, the numbers of deaths due to BC exposure were estimated for each 12-km grid, and then aggregated to the county, state and national level. Given evidence that BC particles may pose a greater risk on human health than other components of PM2.5, we also conducted sensitivity analysis using BC-specific risk coefficients drawn from recent literature. We estimated approximately 14,000 deaths to result from the 2010 BC levels, and hundreds of thousands of illness cases, ranging from hospitalizations and emergency department visits to minor respiratory symptoms. Sensitivity analysis indicates that the total BC-related mortality could be even significantly larger than the above mortality estimate. Our findings indicate that controlling BC emissions would have substantial benefits for public health in the US

Agricultural, socioeconomic and environmental variables as risks for human verotoxigenic Escherichia coli (VTEC) infection in Finland

<p>Abstract</p> <p>Background</p> <p>Verotoxigenic <it>E. coli </it>(VTEC) is the cause of severe gastrointestinal infection especially among infants. Between 10 and 20 cases are reported annually to the National Infectious Disease Register (NIDR) in Finland. The aim of this study was to identify explanatory variables for VTEC infections reported to the NIDR in Finland between 1997 and 2006. We applied a hurdle model, applicable for a dataset with an excess of zeros.</p> <p>Methods</p> <p>We enrolled 131 domestically acquired primary cases of VTEC between 1997 and 2006 from routine surveillance data. The isolated strains were characterized by virulence type, serogroup, phage type and pulsed-field gel electrophoresis. By applying a two-part Bayesian hurdle model to infectious disease surveillance data, we were able to create a model in which the covariates were associated with the probability for occurrence of the cases in the logistic regression part and the magnitude of covariate changes in the Poisson regression part if cases do occur. The model also included spatial correlations between neighbouring municipalities.</p> <p>Results</p> <p>The average annual incidence rate was 4.8 cases per million inhabitants based on the cases as reported to the NIDR. Of the 131 cases, 74 VTEC O157 and 58 non-O157 strains were isolated (one person had dual infections). The number of bulls per human population and the proportion of the population with a higher education were associated with an increased occurrence and incidence of human VTEC infections in 70 (17%) of 416 of Finnish municipalities. In addition, the proportion of fresh water per area, the proportion of cultivated land per area and the proportion of low income households with children were associated with increased incidence of VTEC infections.</p> <p>Conclusions</p> <p>With hurdle models we were able to distinguish between risk factors for the occurrence of the disease and the incidence of the disease for data characterised by an excess of zeros. The density of bulls and the proportion of the population with higher education were significant both for occurrence and incidence, while the proportion of fresh water, cultivated land, and the proportion of low income households with children were significant for the incidence of the disease.</p