PhD, University of Cincinnati, 2020, Medicine: Epidemiology (Environmental Health)

Introduction: Manganese (Mn) is an essential trace element necessary for normal growth and development, that in excess can be neurotoxic. Excess environmental Mn can occur due to industrial emissions, but exposure pathways from environmental sources to biomarker levels, and ultimately to neurological outcomes have not been determined. Objectives: The objectives of this dissertation are to 1) determine ambient air Mn exposure levels in a population living near the longest operating ferromanganese refinery in North America, using atmospheric dispersion modeling, 2) evaluate associations between modeled ambient air, soil, and indoor dust Mn collected from residences in the exposure area, and 3) determine pathways from environmental measures of Mn to blood, hair, and toenail Mn levels in exposed children using structural equation modeling (SEM). Methods: Data are from the Communities Actively Researching Exposure Study (CARES), a cross-sectional study conducted from 2008-2013 in the Marietta, Ohio area to investigate neurological effects of Mn exposure in 7-9 year old children. Emissions from the Mn refinery were modeled using the U.S. Environmental Protection Agency (EPA) regulatory air dispersion model AERMOD. Average annual ambient air Mn concentrations were determined for census blocks within 32 km of the refinery, and for CARES participants' homes and schools. Monthly modeled ambient air concentrations for 2009-2010 were compared to concentrations from a stationary air sampler in Marietta to evaluate accuracy of the model. Exposures by census blocks were determined to estimate population sizes exposed to air Mn levels exceeding 50 ng/m3, the U.S. EPA guideline. SEM was performed to determine pathways of exposure from air, soil, and indoor dust Mn separately for blood, hair, and toenail Mn. Additional data included in the models were heating, ventilation and air conditioning in the home, average hours/week spent outside by the participant, parent education, (open full item for complete abstract)

Committee: Kelly Brunst Ph.D. (Committee Chair); Florence Fulk Ph.D. (Committee Member); Erin Haynes Dr.P.H. (Committee Member); Tiina Reponen Ph.D. (Committee Member); Heidi Sucharew Ph.D. (Committee Member) Subjects: Epidemiology

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The HVAC Applications volume of the 2019 ASHRAE Handbook: Chapter 46 gives multiple procedures for sizing building exhaust stacks and fans. The most complex of these procedures directly calculates dilution at a receptor of interest using plume theory and several empirical constants. There is suggestion in the literature that this model leads to overly conservative dilution predictions in some cases. The purpose of this work is to determine whether the ASHRAE model can be improved to give more accurate dilution predictions. First, the predictions of the existing equation are evaluated against several existing wind tunnel and full-scale studies and their shortcomings noted. The results show that the 2019 model under-predicts observed dilution mainly in two cases: near the stack and when the plume was within the assumed recirculation region. The assumptions for initial plume spread, height of recirculation zone region, plume spread and plume trajectory were varied parametrically to identify a better model. This optimized model can increase dilution predictions by factors between 2 and 500, while bounding measured data in all cases analyzed. This can reduce the required momentum ratio by 20-70%, leading to much more efficient fan sizing and operation.

Committee: Jordan Clark (Advisor); Andy May (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2015, Environmental Science

This work presents two computational tools, Firestem2D and the fire module of Regional Atmospheric Modelling System (RAMS)-based Forest Large Eddy Simulation (RAFLES), which will help to make predictions of fire effects on trees and the atmosphere. FireStem2D is a software tool for predicting tree stem heating and injury in forest fires. It is a physically-based, two-dimensional model of stem thermodynamics that results from heating at the bark surface. It builds on an earlier one-dimensional model (FireStem) and provides improved capabilities for predicting fire-induced mortality and injury before a fire occurs by resolving stem moisture loss, temperatures through the stem, degree of bark charring, and necrotic depth around the stem. The results of numerical parameterization and model evaluation experiments for FireStem2D that simulate laboratory stem-heating experiments of 52 tree sections from 25 trees are presented. A set of virtual sensitivity analysis experiments were also conducted to test the effects of unevenness of heating around the stem and with above ground height using data from two studies: a low-intensity surface fire and a more intense crown fire. The model allows for improved understanding and prediction of the effects of wildland fire on injury and mortality of trees of different species and sizes. Further, a study of the effects of particular properties of a high-resolution canopy resolving large eddy simulation (RAFLES) was conducted. RAFLES was later used to simulate the dispersion of heat and smoke inside and above forest canopies during low-intensity prescribed surface fires. RAFLES is the only large eddy simulation model that can resolve the effects of the volume of the trees in the canopy. All other models neglect the volume effects and only allow the flow to interact with the forest through a prescribed drag term. As a preliminary study for the heat dispersion simulations, the effects of resolving the tree volumes on air flow inside and (open full item for complete abstract)

Committee: Gil Bohrer Prof. (Advisor) Subjects: Civil Engineering; Environmental Engineering; Environmental Science; Fluid Dynamics

Master of Arts, University of Toledo, 2013, Geography

The deterioration of urban air quality due to industrialization or other anthropogenic reasons is of great concern because it poses serious threats to environment and human health. EPA regulates and monitors air quality to protect public health and welfare at stations located throughout USA. However, air quality observed at these sparsely located monitoring stations may not always represent the actual air quality in areas where population is mainly located. This study aims to utilize a geographic information system (GIS) approach to visualize and assess ambient air quality on smaller scale (block group level) and present it in a simplified manner using maps that are easy to understand by general public. The study uses AERMOD (US EPA recommended regulatory model) to simulate SO2 concentrations in Lima Ohio for year 2001. Model results were compared with observed values for the same year to assess the degree of representativeness of the model results. Spatial distribution of model results were shown on maps using a GIS analytical tool. Concentrations were further classified in terms of air quality index (AQI) values and color coded maps were prepared using AQI color palette. Population distribution maps were also prepared to examine potential exposure. Air quality was analyzed for 1-hour, 3-hour, 24- hour and annual average concentrations of SO2 in year 2001. The study reveals that the pollution level in City of Lima was generally above national ambient air quality standards (NAAQS) and WHO guidelines when assessed at the smaller scale (block group), although it is an attainment area. The study also reveals that the majority of the population is exposed to poor air quality and a considerable number of people even live in areas where air quality is hazardous.

Committee: Patrick Lawrence (Committee Chair); Kevin Czajkowski (Committee Member); Bhuiyan Alam (Committee Member) Subjects: Engineering; Environmental Engineering; Environmental Science; Environmental Studies; Geographic Information Science; Geography