Date of Award

2016-01-01

Degree Name

Doctor of Philosophy

Department

Environmental Science and Engineering

Advisor(s)

Peter Golding

Abstract

Secondary analysis on quantitative data sets is the in-depth analysis of relationships, trends, patterns or behaviors that are not obvious from a superficial examination of data but that can be very germane in the application of that data. The present work presents a framework for investigators to use in applying secondary analysis on big data that correlates to the research topic. The framework can facilitate the illumination of possible data behaviors or patterns that could be useful in arriving at an answer to a question. Behavior of monitored equipment (analyzers, meters, etc.) can easily be depicted and can be used to indicate graphically how patterns in the data support or reject possible outcomes to a question.

This present work illustrates the value of secondary analyses in three different case studies, where this approach is demonstrably used to discover behaviors in operational data of a large gas transmission pipeline, and in sanctioning air quality permit actions for an electric generating facility. The analyses performed provide great insight as to how decisions and responses to regulatory-related actions are being improved upon using big data sets. The tangible results of the application of secondary analysis in environmental science and engineering decision making, exemplified in these case studies, is presented as evidence of its intrinsic value. Moreover, the inherent value of this study is derived by the fact that the tools used to perform these analyses did not require expensive, complex resources.

It is shown that there is substantial, quantifiable value in applying the methods presented here for secondary analysis. Benefits can be quantified not only monetarily but also in improving operations by offering operational flexibility. Querying of available data stores through secondary analysis offers substantial opportunities for industry to gain insights and understanding into previously concealed databased relationships.

Language

en

Provenance

Received from ProQuest

File Size

123 pages

File Format

application/pdf

Rights Holder

Luis G. Perez

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