Title page for ETD etd-07092009-100152


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 substantial

risk 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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