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Airshed-Based Statistical Modeling of the Spatial Distribution of Air Pollution: the Case of Sulfur Dioxide

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Airshed-Based Statistical Modeling of the Spatial Distribution of Air Pollution: the Case of Sulfur Dioxide AIRSHED-BASED STATISTICAL MODELING OF THE SPATIAL DISTRIBUTION OF AIR POLLUTION: THE CASE OF SULFUR DIOXIDE DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Kang-Ping Shen, B.S., M.S. * * * * * The Ohio State University 2004 Dissertation Committee: Approved by Dr. Jean-Michel Guldmann, Adviser Dr. Steven I. Gordon _________________________________ Advisor Dr. Maria Manta-Conroy Department of City and Regional Planning Copyright by Kang-Ping Shen, 2004 ABSTRACT Air pollution is characterized by transboundary properties, dynamic processes, non-linear behavior, and long-range transport. Because of these characteristics, it is difficult to model air pollution behavior using the basic equations that represent the underlying physical transport and chemical transformation processes. As an alternative, a modeling approach is proposed in the particular case of sulfur dioxide (SO 2), based on a circular and sectoral spatial representation of an airshed, and combining concepts derived from physical diffusion modeling and statistical regression modeling. An important feature of this approach is that it provides a means to construct a regionally scaleable air quality model. While using data collected locally (at the airshed level) for estimation purposes, the spatial interconnection of individual airsheds provides the means to model larger-scale air pollution processes, including the national or continental scale. The basic statistical model estimates the relationship between (1) background concentrations and pollution emissions from point and area sources and (2) the resulting concentration at a receptor site located at the center of the airshed, while accounting for pollution decay and uptake, meteorological conditions, and land cover characteristics. To empirically capture this spatial relationship, a considerable database, with extensive use ii of a Geographical Information System (GIS), is developed, connecting pollution emission sources, air quality monitoring stations, meteorological stations, and land uses. Because of the complex and non-linear structure of the model, an interactive grid search procedure has been designed to estimate the model, based on Ordinary Least Squares regression. The final estimated model explains about 56% of the variations in SO 2 concentrations. An air quality optimization model, based on this statistical model and using linear programming, is introduced, and a case study, focusing on the state of Pennsylvania and larger-scale emitters (more than 10,000 tons of SO 2 per year), is presented. The solution outlines how emission patterns change with the ambient pollution standard. The applicability of this airshed-based modeling approach for policy analysis is discussed, including (1) air quality forecasting, and (2) air quality planning. Dynamic extensions of the approach and potential data sources are also discussed. Finally, areas for further research are delineated. iii Dedicated to My Parents iv ACKNOWLEDGMENTS I want to give my most sincere thanks to my adviser, Dr. Jean-Michel Guldmann, for his guidance throughout the course of this research. He was always available, with valuable and thoughtful advice. He not only taught me how to conduct research, but also gave me helpful wisdom to overcome the obstacles in my life. I have truly appreciated his encouragement, expertise, patience, and understanding. This research would not have been possible without his intellectual, mental, and financial support. I also express my gratitude to Dr. Steven Gordon, who gave me my first job as Research Assistant at The Ohio State University, for his support. I want to thank him for serving on my dissertation committee and for his thoughtful suggestions. Special thanks are due to Dr. Maria Manta-Conroy for her valuable comments. I am grateful to two of my fellow graduate students, Hag-Yeol Kim and Sanjeev Arya, for exciting research discussions. No word can express my gratitude to my mother for her unlimited trust, incessant support, and enormous encouragement. I owe a deep debt of gratitude to my wife, Chia- Yuan, for her unconditional support and trust, and keeping me focused. Finally, I want to mention my lovely daughter, Joanne. She gave me joy and the incentive to complete this long and difficult writing process. v VITA 1997 -------------------------------------------- M. S. Urban Planning, State University of New York at Buffalo, Buffalo, New York 1993 œ 1994 ----------------------------------- Assistant Engineer, Department of Urban Development, Taipei City Municipal Government, Taipei, Taiwan 2003 -------------------------------------------- Research Associate, OARnet, Ohio Supercomputer Center, Columbus, OH 1998 œ present --------------------------------- Graduate Teaching and Research Associate, City and Regional Planning, The Ohio State University Columbus, Ohio FIELDS OF STUDIES Major Field: City and Regional Planning Minor Field: Environmental Planning, Quantitative Methods, and GIS Technologies. vi TABLE OF CONTENTS Page Abstract ----------------------------------------------------------------------------------------- ii Dedication -------------------------------------------------------------------------------------- iv Acknowledgments ---------------------------------------------------------------------------- v Vita ---------------------------------------------------------------------------------------------- vi List of Tables ---------------------------------------------------------------------------------- ix List of Figures --------------------------------------------------------------------------------- xi Chapters: 1. Introduction --------------------------------------------------------------------------------- 1 2. Literature Review -------------------------------------------------------------------------- 7 2.1 Diffusion-Based Models -------------------------------------------------------- 7 2.2 Economic Optimization Models ----------------------------------------------- 11 2.3 Spatio-Temporal Modeling ---------------------------------------------------- 15 2.4 Neural-Networks-Based Models ---------------------------------------------- 18 2.5 Acid-Rain-Related Studies ----------------------------------------------------- 23 2.6 Summary -------------------------------------------------------------------------- 27 3. Modeling Approach ----------------------------------------------------------------------- 30 3.1 Theoretical Background -------------------------------------------------------- 30 3.1.1 The Gaussian Model ------------------------------------------------- 32 3.1.2 Grid Approximation ------------------------------------------------- 37 3.2 Empirical Approach ------------------------------------------------------------- 41 4. Data Sources and Processing ------------------------------------------------------------- 47 4.1 Background Mapping ----------------------------------------------------------- 48 vii 4.2 Airshed Construction ----------------------------------------------------------- 49 4.3 Emission Data -------------------------------------------------------------------- 55 4.4 Concentration Data -------------------------------------------------------------- 61 4.5 Meteorological Data ------------------------------------------------------------ 63 4.6 Land Cover Data ----------------------------------------------------------------- 66 5. Variable Definitions and Overview ----------------------------------------------------- 69 5.1 National Overview -------------------------------------------------------------- 69 5.1.1 SO 2 Concentrations -------------------------------------------------- 69 5.1.2 SO 2 Emissions -------------------------------------------------------- 73 5.1.3 Meteorological Conditions ------------------------------------------ 76 5.1.4 Land Use and Land Cover ------------------------------------------ 81 5.2 Airshed Overview --------------------------------------------------------------- 84 5.3 Correlation Analyses ------------------------------------------------------------ 88 6. Model Estimation and Analysis ---------------------------------------------------------- 93 6.1 Statistical Model ----------------------------------------------------------------- 93 6.1.1 Overview -------------------------------------------------------------- 93 6.1.2 Estimation Process --------------------------------------------------- 97 6.1.3 Results ----------------------------------------------------------------- 99 6.2 Elasticity Analysis --------------------------------------------------------------- 103 7. Applicability and Dynamic Extension of the Model ---------------------------------- 108 7.1 Air Quality Management ------------------------------------------------------- 108 7.2 Optimization Application ------------------------------------------------------ 111 7.3 Dynamic Extensions ------------------------------------------------------------ 123 7.4 Implications of the Airshed-based Approach -------------------------------- 126 7.4.1 Modeling -------------------------------------------------------------- 126 7.4.2 Policy ------------------------------------------------------------------ 127 8. Conclusions --------------------------------------------------------------------------------- 129 References ------------------------------------------------------------------------------------- 135 Appendices: A: Maps -------------------------------------------------------------------------------
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