Modeling contaminant behavior in Lake Superior : a comparison of PCBs, PBDEs, and mercury

A mass‐balance model for Lake Superior was applied to polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and mercury to determine the major routes of entry and the major mechanisms of loss from this ecosystem as well as the time required for each contaminant class to approach steady state. A two‐box model (water column, surface sediments) incorporating seasonally adjusted environmental parameters was used. Both numerical (forward Euler) and analytical solutions were employed and compared. For validation, the model was compared with current and historical concentrations and fluxes in the lake and sediments. Results for PCBs were similar to prior work showing that air‐water exchange is the most rapid input and loss process. The model indicates that mercury behaves similarly to a moderately‐chlorinated PCB, with air‐water exchange being a relatively rapid input and loss process. Modeled accumulation fluxes of PBDEs in sediments agreed with measured values reported in the literature. Wet deposition rates were about three times greater than dry particulate deposition rates for PBDEs. Gas deposition was an important process for tri‐ and tetra‐BDEs (BDEs 28 and 47), but not for higher‐brominated BDEs. Sediment burial was the dominant loss mechanism for most of the PBDE congeners while volatilization was still significant for tri‐ and tetra‐BDEs. Because volatilization is a relatively rapid loss process for both mercury and the most abundant PCBs (tri‐ through penta‐), the model predicts that similar times (from 2 ‐ 10 yr) are required for the compounds to approach steady state in the lake. The model predicts that if inputs of Hg(II) to the lake decrease in the future then concentrations of mercury in the lake will decrease at a rate similar to the historical decline in PCB concentrations following the ban on production and most uses in the U.S. In contrast, PBDEs are likely to respond more slowly if atmospheric concentrations are reduced in the future because loss by volatilization is a much slower process for PBDEs, leading to lesser overall loss rates for PBDEs in comparison to PCBs and mercury. Uncertainties in the chemical degradation rates and partitioning constants of PBDEs are the largest source of uncertainty in the modeled times to steady‐state for this class of chemicals. The modeled organic PBT loading rates are sensitive to uncertainties in scavenging efficiencies by rain and snow, dry deposition velocity, watershed runoff concentrations, and uncertainties in air‐water exchange such as the effect of atmospheric stability.

Static and dynamic contact angle measurement on rough surfaces using sessile drop profile analysis with application to water management in low temperature fuel cells

Fuel Cells are a promising alternative energy technology. One of the biggest problems that exists in fuel cell is that of water management. A better understanding of wettability characteristics in the fuel cells is needed to alleviate the problem of water management. Contact angle data on gas diffusion layers (GDL) of the fuel cells can be used to characterize the wettability of GDL in fuel cells. A contact angle measurement program has been developed to measure the contact angle of sessile drops from drop images. Digitization of drop images induces pixel errors in the contact angle measurement process. The resulting uncertainty in contact angle measurement has been analyzed. An experimental apparatus has been developed for contact angle measurements at different temperature, with the feature to measure advancing and receding contact angles on gas diffusion layers of fuel cells.

DESIGN AND FABRICATION OF RESONANT GAS SENSOR FOR HIGH SENSITIVITY IN THE PRESENCE OF AIR DAMPING

Gas sensors have been used widely in different important area including industrial control, environmental monitoring, counter-terrorism and chemical production. Micro-fabrication offers a promising way to achieve sensitive and inexpensive gas sensors. Over the years, various MEMS gas sensors have been investigated and fabricated. One significant type of MEMS gas sensors is based on mass change detection and the integration with specific polymer. This dissertation aims to make contributions to the design and fabrication of MEMS resonant mass sensors with capacitance actuation and sensing that lead to improved sensitivity. To accomplish this goal, the research has several objectives: (1) Define an effective measure for evaluating the sensitivity of resonant mass devices; (2) Model the effects of air damping on microcantilevers and validate models using laser measurement system (3) Develop design guidelines for improving sensitivity in the presence of air damping; (4) Characterize the degree of uncertainty in performance arising from fabrication variation for one or more process sequences, and establish design guidelines for improved robustness. Work has been completed toward these objectives. An evaluation measure has been developed and compared to an RMS based measure. Analytic models of air damping for parallel plate that include holes are compared with a COMSOL model. The models have been used to identify cantilever design parameters that maximize sensitivity. Additional designs have been modeled with COMSOL and the development of an analytical model for Fixed-free cantilever geometries with holes has been developed. Two process flows have been implemented and compared. A number of cantilever designs have been fabricated and the uncertainty in process has been investigated. Variability from processing have been evaluated and characterized.

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.