Type of Document Dissertation Author Pakalapati, Swathi URN etd-07092009-100152 Title Integrated Data-Driven Techniques for Environmental Pollution Monitoring Degree Doctor of Philosophy (Ph.D.) Department Chemical Engineering Advisory Committee
Advisor Name Title Jose A. Romagnoli Committee Chair Ahmet Palazoglu Committee Co-Chair Kalliat T. Valsaraj Committee Member Ralph W. Pike Committee Member Arthur Penn Dean's Representative Keywords
- Ozone exposure assessment
- Sea breeze
- k-means clustering
- Wind field analysis
Date of Defense 2009-05-01 Availability unrestricted Abstract The adverse health e ffects of tropospheric ozone around urban zones indicate a substantialrisk for many segments of the population. This necessitates the short term forecast in order
to take evasive action on days conducive to ozone formation. Therefore it is important to
study the ozone formation mechanisms and predict the ozone levels in a geographic region.
Multivariate statistical techniques provide a very e ffective framework for the classifi cation
and monitoring of systems with multiple variables. Cluster analysis, sequence analysis and
hidden Markov models (HMMs) are statistical methods which have been used in a wide
range of studies to model the data structure. In this dissertation, we propose to formulate,
implement and apply a data-driven computational framework for air quality monitoring
and forecasting with application to ozone formation. The proposed framework integrates,
in a unique way, advanced statistical data processing and analysis tools to investigate ozone
formation mechanisms and predict the ozone levels in a geographic region. This dissertation
focuses on cluster analysis for identi fication and classi fication of underlying mechanisms of
a system and HMMs for predicting the occurrence of an extreme event in a system.
The usefulness of the proposed methodology in air quality monitoring is demonstrated
by applying it to study the ozone problem in Houston, Texas and Baton Rouge, Louisiana
regions. Hierarchical clustering is used to visualize air flow patterns at two time scales
relevant for ozone buildup. First, clustering is performed at the hourly time scale to identify
surface flow patterns. Then, sequencing is performed at the daily time scale to identify
groups of days sharing similar diurnal cycles for the surface flow. Selection of appropriate
numbers of air flow patterns allowed inference of regional transport and dispersion patterns
for understanding population exposure to ozone. This dissertation proposes to build HMMs
for ozone prediction using air quality and meteorological measurements obtained from a
network of surface monitors. The case study of the Houston, Texas region for the 2004 and
2005 ozone seasons showed that the results indicate the capability of HMMs as a simpler
forecasting tool.
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