Reflection, refraction, and rejection : copper smelting heritage and the execution of environmental policy

This dissertation examines the global technological and environmental history of copper smelting and the conflict that developed between historic preservation and environmental remediation at major copper smelting sites in the United States after their productive periods ended. Part I of the dissertation is a synthetic overview of the history of copper smelting and its environmental impact. After reviewing the basic metallurgy of copper ores, the dissertation contains successive chapters on the history of copper smelting to 1640, culminating in the so-called German, or Continental, processing system; on the emergence of the rival Welsh system during the British industrial revolution; and on the growth of American dominance in copper production the late 19th and early 20th centuries. The latter chapter focuses, in particular, on three of the most important early American copper districts: Michigan’s Keweenaw Peninsula, Tennessee’s Copper Basin, and Butte-Anaconda, Montana. As these three districts went into decline and ultimately out of production, they left a rich industrial heritage and significant waste and pollution problems generated by increasingly more sophisticated technologies capable of commercially processing steadily growing volumes of decreasingly rich ores. Part II of the dissertation looks at the conflict between historic preservation and environmental remediation that emerged locally and nationally in copper districts as they went into decline and eventually ceased production. Locally, former copper mining communities often split between those who wished to commemorate a region’s past importance and develop heritage tourism, and local developers who wished to clear up and clean out old industrial sites for other purposes. Nationally, Congress passed laws in the 1960s and 1970s mandating the preservation of historical resources (National Historic Preservation Act) and laws mandating the cleanup of contaminated landscapes (CERCLA, or Superfund), objectives sometimes in conflict – especially in the case of copper smelting sites. The dissertation devotes individual chapters to the conflicts that developed between environmental remediation, particularly involving the Environmental Protection Agency and the heritage movement in the Tennessee, Montana, and Michigan copper districts. A concluding chapter provides a broad model to illustrate the relationship between industrial decline, federal environmental remediation activities, and the growth of heritage consciousness in former copper mining and smelting areas, analyzes why the outcome varied in the three areas, and suggests methods for dealing with heritage-remediation issues to minimize conflict and maximize heritage preservation.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.