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Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / Permittivity, displayed when a dielectric material is exposed to an electric field, is a useful property for measuring impurities in a dielectric medium. These impurities often have a dipole moment different from the pure material, and the dipoles align through polarization and impede electric current. By measuring the resulting impedance in a known geometry, the permittivity can be determined. Four permittivity sensors were utilized to measure contaminants that are associated with biofuels, specifically glycerol, ethanol, and ammonia. These sensors were based around either stainless steel or aluminum plates to ensure durability and reliability. By connecting each of these sensors to a signal generating control box, the gain and phase can be measured at 609 frequencies, from 10 kHz up to 120 MHz. Data from each of the three contaminants were run through a method for detection. Measurements for ambient air and air with the contaminants were compared with a statistical analysis. Glycerol, ethanol, and ammonia each had significantly different measurements in the gain and phase data at a unique set of frequencies. Using a neural network analysis for detection resulted in a 95.8%, 93.9%, and 97.1% success rate for detecting glycerol, ethanol, and ammonia, respectively. For ethanol and ammonia, where multiple concentrations were measured, regression methods were used to relate the frequency response data to the contaminant concentration. Stepwise regression, wavelet transformation followed by stepwise regression, partial least squares regression, and neural network regression were the four methods used to establish these relationships. Several regressions over-fit the data, showing coefficient of determination (R[superscript]2) values of 1.000 for training data, yet very low R[superscript]2 values for validation data. However, the best R[superscript]2 values of all the regressions were 1.000 and 0.996 for the training and validation data, respectively, from measuring ammonia.

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Petroleum refineries emit a variety of airborne substances which may be harmful to human health. HEIDI II (Health Effects Indicators Decision Index II) is a computer-based decision analysis tool which assesses airborne emissions from Canada's oil refineries for reduction, based on ordinal ranking of estimated health impacts. The model was designed by a project team within NERAM (Network for Environmental Risk Assessment and Management) and assembled with significant stakeholder consultation. HEIDI II is publicly available as a deterministic Excel-based tool which ranks 31 air pollutants based on predicted disease incidence or estimated DALYS (disability adjusted life years). The model includes calculations to account for average annual emissions, ambient concentrations, stack height, meteorology/dispersion, photodegradation, and the population distribution around each refinery. Different formulations of continuous dose-response functions were applied to nonthreshold-acting air toxics, threshold-acting air toxics, and nonthreshold-acting CACs (criteria air contaminants). An updated probabilistic version of HEIDI II was developed using Matlab code to account for parameter uncertainty and identify key leverage variables. Sensitivity analyses indicate that parameter uncertainty in the model variables for annual emissions and for concentration-response/toxicological slopes have the greatest leverage on predicted health impacts. Scenario analyses suggest that the geographic distribution of population density around a refinery site is an important predictor of total health impact. Several ranking metrics (predicted case incidence, simple DALY, and complex DALY) and ordinal ranking approaches (deterministic model, average from Monte Carlo simulation, test of stochastic dominance) were used to identify priority substances for reduction; the results were similar in each case. The predicted impacts of primary and secondary particulate matter (PM) consistently outweighed those of the air toxics. Nickel, PAH (polycyclic aromatic hydrocarbons), BTEX (benzene, toluene, ethylbenzene and xylene), sulphuric acid, and vanadium were consistently identified as priority air toxics at refineries where they were reported emissions. For many substances, the difference in rank order is indeterminate when parametric uncertainty and variability are considered.

Petroleum refineries emit a variety of airborne substances which may be harmful to human health. HEIDI II (Health Effects Indicators Decision Index II) is a computer-based decision analysis tool which assesses airborne emissions from Canada's oil refineries for reduction, based on ordinal ranking of estimated health impacts. The model was designed by a project team within NERAM (Network for Environmental Risk Assessment and Management) and assembled with significant stakeholder consultation. HEIDI II is publicly available as a deterministic Excel-based tool which ranks 31 air pollutants based on predicted disease incidence or estimated DALYS (disability adjusted life years). The model includes calculations to account for average annual emissions, ambient concentrations, stack height, meteorology/dispersion, photodegradation, and the population distribution around each refinery. Different formulations of continuous dose-response functions were applied to nonthreshold-acting air toxics, threshold-acting air toxics, and nonthreshold-acting CACs (criteria air contaminants). An updated probabilistic version of HEIDI II was developed using Matlab code to account for parameter uncertainty and identify key leverage variables. Sensitivity analyses indicate that parameter uncertainty in the model variables for annual emissions and for concentration-response/toxicological slopes have the greatest leverage on predicted health impacts. Scenario analyses suggest that the geographic distribution of population density around a refinery site is an important predictor of total health impact. Several ranking metrics (predicted case incidence, simple DALY, and complex DALY) and ordinal ranking approaches (deterministic model, average from Monte Carlo simulation, test of stochastic dominance) were used to identify priority substances for reduction; the results were similar in each case. The predicted impacts of primary and secondary particulate matter (PM) consistently outweighed those of the air toxics. Nickel, PAH (polycyclic aromatic hydrocarbons), BTEX (benzene, toluene, ethylbenzene and xylene), sulphuric acid, and vanadium were consistently identified as priority air toxics at refineries where they were reported emissions. For many substances, the difference in rank order is indeterminate when parametric uncertainty and variability are considered.