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

Home , Airshed

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

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

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

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.

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.

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 ------140

B: Computer Programs for Statistical Modeling ------150

C: Source Data Dictionaries and Samples ------173

D: Computer Programs and Outputs for Optimization Modeling ------182

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LISTS OF TABLES

Table Page 1.1 National Ambient Air Quality Standards ------3

4.1 Sulfur Dioxide Monitoring Stations by States in 1999 ------51

4.2 Size and Number of Airsheds ------52

4.3 USGS Land Use / Land Cover System Categories ------67

5.1 Summary of Annual SO 2 Concentrations (in ppm) by States in 1999 ------71

5.2 Summary of Annual SO 2 Emissions (in short tons) by States in 1999 ------74

5.3 Summary of Selected Meteorological Conditions by States in 1999 ------80

5.4 Summary of Land Use / Land Cover for the Coterminous U.S. ------82

5.5 State Summary of Concentrations, Emissions, and Meteorological Variables within the Selected 345 Airsheds ------85

5.6 Summary Statistics for Land Use / Land Cover within the Selected 345 Airsheds ------86

5.7 Descriptive Statistics for the Selected Variables over the 345 Airsheds ------89

5.8 Pearson Correlations between the Selected Independent Variables and SO 2 Concentrations ------90

6.1 Definition and Description of the Independent Variables ------95

6.2 Elasticity Functions ------105

6.3 Descriptive Statistics for the Total Elasticities (N = 345) ------106

7.1 Basic Information for the Selected Monitoring Stations ------112

ix 7.2 Emission Facilities Characteristics ------113

7.3 Summary of Emission by Types within the Selected Airsheds ------113

7.4 Summary of Meteorological Variables within the Selected Airsheds ------114

7.5 Summary of Land Use Variables within the Selected Airsheds ------114

7.6 Matrix A ------117

7.7 Matrix B ------117

7.8 Optimal Concentration Patterns for Various Air Quality Standards ------121

C.1 Variables Provided by the NET Facility Emission Report ------174

C.2 Sample of the NET Facility Emission Report ------175

C.3 EPA Tier1 and Tier 2 Categories ------176

C.4 Sample of the NET Tier Report ------177

C.5 Variables Provided by the Monitor All Columns Report œ Sulfur Dioxide ------178

C.6 Sample of the Monitor All Columns Report ------179

C.7 Variables Provided by the NCDC Climatic Database ------180

C.8 Sample of the NCDC Climatic Database ------181

D.3 Emission Facility Information ------191

D.4 Optimal Emission (in 10 3 short tons) for Each Facility and Various Air Quality Standards ------193

LISTS OF FIGURES

Figure Page 2.1 Conceptual Structure of the MLP ------20

3.1 Pollutant Mass in the Three-Dimensional Space ------33

3.2 Pollution Diffusion Elementary Box ------38

3.3 Illustration of Pollution Transfer over Linear Space ------40

3.4 Conceptual Air Pollution Airshed ------43

4.1 Example of Placement of Dummy Stations ------53

4.2 Spatial Structure of An Airshed ------54

4.3 Overlaying Point Layer with Polygon Layer ------58

4.4 Overlaying Polygon Layer with Polygon Layer ------58

4.5 Clipping Input Layer with Clipped Layer ------58

4.6 Hypothetical Overlaying of County and Airshed Coverages ------60

5.1 Average Annual SO 2 Concentrations by States in 1999 ------72

5.2 Total Annual Point Source SO 2 Emissions by States in 1999 ------77

5.3 Total Annual Area Source SO 2 Emissions by States in 1999 ------78

5.4 Share of Point and Area SO 2 Emissions by States in 1999 ------79

5.5 Thematic Map of the USGS Land Use Land Cover System for the U.S. ------83

7.1 Locations of the 10 Monitoring Stations and the 91 Emission Facilities ------116

7.2 Air Quality Standard versus Total Emissions ------122

xi A.1 Map of SO 2 Monitoring Stations in 1999 ------141

A.2 Map of Major SO 2 Emission Facilities in 1999 ------142

A.3 Map of Weather Monitoring Stations in 1999 ------143

A.4 Thematic Raster Map of Land Uses for North America from the NALC Database ------144

A.5 Airshed of Monitoring Station 345 œ Optimization Application ------145

A.6 Airshed of Monitoring Station 361 œ Optimization Application ------145

A.7 Airshed of Monitoring Station 364 œ Optimization Application ------146

A.8 Airshed of Monitoring Station 387 œ Optimization Application ------146

A.9 Airshed of Monitoring Station 487 œ Optimization Application ------147

A.10 Airshed of Monitoring Station 490 œ Optimization Application ------147

A.11 Airshed of Monitoring Station 493 œ Optimization Application ------148

A.12 Airshed of Monitoring Station 497 œ Optimization Application ------148

A.13 Airshed of Monitoring Station 506 œ Optimization Application ------149

A.14 Airshed of Monitoring Station 512 œ Optimization Application ------149

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CHAPTER 1

INTRODUCTION

Air pollution may be defined as the accumulation in the air of any natural or artificially composed materials -- solid particles, liquid droplets, and/or gases -- that are either directly or indirectly harmful or dangerous to humans in particular, or to the natural environment at large (Griffith and Layne, 1999). It has been demonstrated that air pollution causes serious health problems (i.e. respiratory illness) and negative environmental impacts (i.e. acid rain). A by-product of air pollution, acid deposition, including acid rain as one of its forms, has attracted special attention because of concerns about not only human health and atmospheric visibility impairment, but also ecological risks, including acidification of lakes and streams, negative effects on aquatic life, interference with forest and crop productivity, and corrosion of buildings. The damages and problems associated with acid rain were first scientifically identified in the 1960s. In contrast to Europe, and particularly Scandinavia, acid rain had received little attention in the United States until the mid-1970s, when researchers at Cornell University published maps of rainfall acidity in the Northeast region, showing that acidity had increased substantially between the mid-1950s and mid-1970s. Although these results generated

1 considerable doubts because of the reliability of the data, scientists agreed that acid deposition would be one of the most serious environmental problems in the future, as a result of population growth and increase in fossil fuels consumption.

With the goal of protecting the environment and public health, the Clean Air Act was enacted in the early 1970s to protect and improve ambient air quality. Under the

Clean Air Act, which was last amended in 1990 (The Clean Air Act Amendments of

1990), two types of National Ambient Air Quality Standards (NAAQS) were established œ primary and secondary standards (see Table 1-1 for details). Primary standards set limits to protect public health, including the health of —sensitive“ populations such as children and the elderly, while secondary standards set limits to protect public welfare, including protection against decreased visibility and damage to animals, crops, vegetation, and buildings. The Clean Air Act Amendments of 1990 identifies 189 toxic air pollutants for regulation, and includes requirements for reducing toxic emissions from industrial factories and other sources.

Since the Clean Air Act enactment in 1970, the U.S. aggregate emissions of the six principal pollutants (sometimes referred to as criteria pollutants) -- carbon monoxide

1 (CO), lead (Pb), nitrogen dioxide (NO 2), ozone (O 3) , particulate matter (PM-10), and sulfur dioxide (SO 2) -- have been reduced by 48%. Over the same period, the gross domestic product increased by 164%, the energy consumption increased by 42%, and the total vehicle-miles traveled increased by 155%. Among these pollutants, sulfur dioxide is recognized as a major precursor to the formation of atmospheric sulfates and acidic

1 The pollutant ozone is not emitted directly into the air, but is formed when sunlight interacts with emissions of nitrogen oxides (NOx) and volatile organic compounds (VOC). 2 deposition. Sulfate aerosols, an SO 2 derivative, have recently become the largest single component of fine particulate matter in most locations in the eastern U.S.

Pollutant Standard Value Standard Type Carbon Monoxide (CO) 8-hour Average 9 ppm (10 mg/m 3) Primary 1-hour Average 35 ppm (40 mg/m 3) Primary

Nitrogen Dioxide (NO 2) Annual Arithmetic Mean 0.053 ppm (100 µg/m 3) Primary & Secondary

Ozone (O 3) 1-hour Average 0.12 ppm (235 µg/m 3) Primary & Secondary 8-hour Average 0.08 ppm (157 µg/m 3) Primary & Secondary

Lead (Pb) Quarterly Average 1.5 µg/m 3 Primary & Secondary

Particulate (PM 10) Annual Arithmetic Mean 50 µg/m 3 Primary & Secondary 24-hour Average 150 µg/m 3 Primary & Secondary

Particulate (PM 2.5) Annual Arithmetic Mean 15 µg/m 3 Primary & Secondary 24-hour Average 65 µg/m 3 Primary & Secondary

Sulfur Dioxide (SO 2) Annual Arithmetic Mean 0.03 ppm (80 µg/m 3) Primary 24-hour Average 0.14 ppm (365 µg/m 3) Primary 3-hour Average 0.50 ppm (1300 µg/m 3) Secondary Note: 1. Source: http://www.epa.gov/air/criteria.html . 2. The parenthetical value is an approximately equivalent concentration. 3. PM 10 means particles with diameters of 10 micrometers or less, while PM 2.5 means particles with diameters of 2.5 micrometers or less. 4. ppm = parts per million, mg/m 3 = milligrams per cubic meter, µg/m 3 = micrograms per cubic meter.

Table 1.1 National Ambient Air Quality Standards

3 The major public health issues associated with exposures to high concentrations of SO 2 and sulfates are related to effects on breathing, respiratory illnesses and symptoms, alterations in lungs‘ defenses, aggravation of existing respiratory and cardiovascular disease, and mortality. According to the U.S. Environmental Protection Agency

(USEPA), children and the elderly may be particularly sensitive to exposure to SO2- related chemicals.

Title IV (Acid Deposition Control) of the CAAA of 1990 is the main driving force leading to the decrease of SO 2 emissions from fuel combustion sources, and is also responsible for the observed decline of NO x emissions. Title IV requires annual SO 2 emissions to be reduced by 10 million tons from 1985-benchmark emissions levels, and suggests that annual NO x emissions be reduced by 2 million tons from 1985 levels. Title

IV defines two stages for the reduction of SO 2: Phase I, affecting 263 mostly coal-fired units, started on January 1, 1995, and Phase II, affecting all other Title IV units, started on January 1, 2000. The affected Title IV facilities, mainly coal-fired power plants, have various options for achieving the required reductions in a cost effective manner, including participating in a market-based allowance trading system.

2 A recent report indicates that SO 2 emissions decreased by approximately 33% from 1985 to 2002. Nationally, average SO 2 ambient concentrations have been reduced by approximately 54% over the same period. However, despite aggressive law enforcement and intensive pollution monitoring, the USEPA indicates that about 160 million tons of pollutants are still emitted into the air each year in the U.S., and that there

2 The USEPA 2002 Air Trends Report: http://www.epa.gov/airtrends/

4 are approximately 146 million people living in counties where monitored air is unhealthy at times because of high levels of at least one of the six principal air pollutants. As a result, air quality planning and management are still required to ensure that future air quality is, at least, maintained at present levels, despite expected future population growth and urban development, increasing energy consumption, and a greater number of gasoline-burning vehicles. Quantitative air quality models are therefore still needed to establish suitable procedures for air quality planning and management.

Air pollution, especially SO 2, is characterized by transboundary properties, dynamic processes, and long-range transport. There is no clear boundary at which a given pollutant will stop once it is emitted from its sources. Transboundary properties combined with long-range transport make SO 2 and acid deposition control difficult. For example, the SO 2 emissions from the Midwest are usually deposited in the northeastern parts of the U.S. and Canada. Local authorities in the Northeast and Canada have absolutely no control over whatever Midwestern polluters do. The problem can only be solved by a higher-level government or a cross-country organization. The interactions between chemicals and meteorological conditions constitute dynamic processes, which make the modeling of SO 2 and acid deposition complicated. The objective of this research is to develop an innovative air quality management tool, focusing on SO 2 and combining concepts derived from both statistical and physico-chemical air quality models, based on a new airshed spatial structure. The empirical estimation of this airshed model is based on local concentrations, emissions, meteorological, and land-use data. However, the structure of this airshed-based model is such that it allows the linkage of multiple

5 airsheds, providing a completely new approach to analyze long-distance pollution transfers within the framework of an integrated, multiple-airshed model. The achievement of this research goal involves three phases: (1) building a large database that can spatially connect monitoring stations, emission sources, and various explanatory variables; (2) developing a spatial framework (i.e., airshed) to build an SO 2 air quality statistical model, using Geographic Information Systems (GIS), and accounting for pollution decay, meteorological conditions, emission uptakes, and land use / land cover characteristics; and (3) devising and applying an air quality optimization model, based on inter-linked estimated airshed statistical models and using linear programming.

This remainder of this dissertation is organized as follows. Chapter 2 presents a literature review of the different aspects of air pollution modeling and management. A conceptual air quality model is developed in Chapter 3. Data sources and processing are discussed in Chapter 4. An overview of the data at the state and airshed levels is presented in Chapter 5, including preliminary explorations of the relationships between dependent and independent variables. The air quality model estimation procedure and results are presented in Chapter 6. Chapter 7 outlines the applicability of the model for air quality management, using linear programming, as well as dynamic extensions of the approach. Conclusions and areas for future research are presented in Chapter 8.

CHAPTER 2

LITERATURE REVIEW

Several air quality-modeling techniques have been developed to tackle different aspects of air pollution, ranging from meteorological conditions to the economic costs of abatement. This chapter reviews various modeling techniques and compares their applications, including (1) diffusion-based, (2) economic optimization, (3) spatio- temporal, (4) neural networks, and (5) acid rain models. The need for a more integrated modeling approach is outlined, based on a critical synthesis of the advantages and disadvantages of these modeling techniques.

2.1 Diffusion-Based Models

Among the various techniques used to study and model air quality, two of the most common approaches are (1) atmospheric diffusion modeling and (2) statistical regression modeling. Using mathematical representations, atmospheric diffusion models attempt to represent the underlying processes that physically transport and chemically transform a given pollutant from its emission sources to the receptors where its concentration is measured. Atmospheric diffusion models are often used to calculate 7 ambient air pollution concentrations originating from point sources. Their main advantage is that they can be used to develop better abatement strategies. Several large- scale projects, such as RAINS (Hordijk, 1991) and ATMOS (Arndt et al., 1997), have taken advantage of this modeling technique. However, these models tend to be complicated in terms of computation and data gathering, because they must deal directly with the mechanisms of transportation and transformation of air pollutants. Moreover, atmospheric diffusion models are generally derived from the fundamental Fickian system of partial differential equations under restrictive assumptions, such as the invariance of wind speed and turbulent dispersion with height (Guldmann, 1983). Because of these assumptions, it is difficult for these models to deal with the influence of complex atmospheric motions on the transport of pollutants, removal mechanisms by green space uptake, and atmospheric chemical reactions.

Statistical regression modeling, on the other hand, is more commonly used, because it provides more flexibility to be incorporated into air quality management strategies. The main advantage of these models is that they need not deal with the mechanisms of pollution transportation and transformation. These are basically empirical models which, in many cases, do not require a priori information about the functional form of the effect of emissions (Milionis and Davies, 1994a). Although major point source emissions are usually known, there is a random component in emissions that can only be characterized statistically (Gleit, 1987). Because pollution emissions processes generally tend to be stochastic in nature, linear regression models are widely used. The general form of such a model can be expressed as follows (Gujarati, 1995, Milionis and

Davies, 1994a):

8 k Qj = β1 + ∑ β j X ij + µj (j=1, 2, ….., N), (2.1) i=2

where Q j is the j-th observation of the concentration of a given pollutant at time t, X 2 through X k the explanatory variables, β1 the intercept, β2 to βk the estimated coefficients, and µj the stochastic disturbance.

The explanatory variables (X i) are generally geographical characteristics, socio- economic attributes, and meteorological conditions. Milionis and Davies (1994a) also point out that some of the X i‘s may be lags of the dependent or independent variables, or even dummy variables. To account for autocorrelation 3, autoregressive (AR) time-series- based regression models have been widely used in recent studies because of the failure of the basic ordinary least square (OLS) assumptions (Roy and Pellerin, 1982, Shi and

Harrison, 1997). In general, under conditions of both autocorrelation and heteroscedasticity, the usual OLS estimators are no longer the ones with minimum variance among all linear unbiased estimators, although the models themselves may not be biased. Several factors may cause autocorrelation. Since most meteorological conditions and geographical characteristics tend to be correlated with one another, the independence assumptions of traditional regression models are violated and the OLS coefficient estimates are not efficient. Milionis and Davies (1994b) indicate that other possible reasons, which may be more serious, are model specification error, measurement errors in the dependent variable, and the case where the dependent variable is serially correlated but the explanatory variables are non-stochastic.

3 According to Gujarati (1995), autocorrelation may be defined as correlation between members of series of observation over time, as in time series data, or over space, as in cross-sectional data.

9 In earlier studies, standard regression models were generally used. Cleroux et al.

(1980) successfully relate sulfur dioxide concentrations to wind direction, wind speed, temperature, and the previous period sulfur dioxide concentrations, with impressive R 2”s around 0.7 for Montreal, Canada. Although Cleroux et al. cannot clearly conclude whether the hourly or bi-hourly or daily average concentrations are the optimal SO 2 variables to use, they show that meteorological factors have a delayed effect on the SO 2 concentration, suggesting that the choice of the data period plays an important role in air pollution statistical models. Annand and Hudson (1980) obtain similar results for

Manchester, England, and suggest, more importantly, that non-linear models generally better measure the correlation between meteorological conditions and pollutant concentrations, pointing to the non-linear nature of air pollution processes. Because they do not account for autocorrelation, the previous models may not be best linear unbiased estimations (BLUE).

AR modeling is well illustrated by Shi and Harrison (1997), who relate NO x and

NO 2 concentrations over 1989-1992 at 6 stations in the Greater London area, to several meteorological variables, such as wind speed, boundary layer depth, and relative humidity. They find that AR models perform better, although not by much, than their

OLS counterparts, and yield higher correlation coefficients. Roy and Pellerin (1982) and

Millionis and Davis (1994b) obtain similar results at other sites. Although most studies emphasize the relationships between meteorological conditions and air pollutions, Kim

(1999) successfully links ozone and carbon monoxide concentrations to the transportation patterns and socio-economics characteristics of urban areas.

10 Statistical regression models based on hourly, daily, or even monthly measurements of air pollutants generally produce very good empirical results. However, as indicated earlier, because these regression models do not require a priori information about the functional form of the effect of emissions, it is not very easy to interpret their results. Statistical regression models are generally site dependent, so precautions should be taken when comparing models estimated at different locations, or when incorporating models derived from other sites into local air pollution management processes. Moreover, they lack the ability to explicitly address the spatial dimension of the air pollution process.

Finally, like most statistical regression analyses, there is no guarantee as to the reliability of the model once it is extrapolated beyond the range of the input data used to estimate it.

2.2 Economic Optimization Models

Another stream of air pollution research focuses on its economic aspects, relying mainly on optimization techniques, such as linear programming (LP). This type of models has been widely used for environmental resources planning problems, especially water resources management. The main attractiveness of this technique is its ability to incorporate a considerable diversity of information related to complex environmental systems into a single summary model, and to process this information with a formal analytical procedure that generates explicit optimal management strategies (Fortin and

McBean, 1983). Because of their mathematical structure, these models offer solid scientific grounds as decision-making tools.

The basic goal of linear programming models is to determine an optimal pollutant abatement strategy that satisfies environmental and regulatory requirements while

11 minimizing total pollution reduction costs. The important assumptions made by these models are that (1) the source-receptor relationships are linear, and (2) the abatement cost functions are linear or piecewise-linear. A linear source-receptor relationship means that the amount of the pollutant coming from a certain source and being deposited at any given receptor is proportional to the emission of that source. Piecewise-linear cost functions arise if there are several available abatement techniques with different marginal costs and different maximum feasible emission reductions (Lehmann, 1991). The general inputs required by these models are (1) data about the transport of air pollutants from their sources to a set of receptors, (2) data about the costs of pollutant abatement at each source, and (3) data about the environmental targets that must be satisfied at the receptors, averaged over a suitable time period (Fortin and McBean, 1983; Ellis et al., 1985;

Lehmann, 1991).

There are generally two broad categories of linear programming models for air pollution abatement -- Emission-Least-Cost (ELC) and Ambient-Least-Cost (ALC) (Ellis et al., 1985). ELC models for one pollutant can be written as:

n = Minimize: TC ∑CjXj (2.2) j=1

Subject to:

n * ⋅ ≥ * a ∑ Xj b (2.3) j=1

≥ ∀ = → X j ,0j 1 n (2.4)

where:

12 Cj = marginal cost of control per time period for source j,

Xj = mass of pollutant removed per time period at source j,

a* = scalar transfer coefficient that relates total regional emissions to air

quality,

b* = scalar equal to the reduction in pollutant concentration required to

satisfy a stipulated pollution standard.

The Clean Air Act and related State Implementation Plans are based on ELC-type criteria.

The ALC model for one pollutant can be written as:

n = Minimize: TC ∑CjXj (2.5) j=1

Subject to:

n ji ⋅ j ≥ = → ∑ a X bi (i 1 m ) (2.6) j=1

≥ ∀ = → X j ,0j 1 n (2.7)

where:

Cj = marginal cost of control per time period for source j,

Xj = mass of pollutant removed per time period at source j,

aji = transfer coefficient that relates the emission at source j to the air

pollution concentration at receptor i,

bi = reduction in pollutant concentration or deposition required to meet the

standard at receptor i.

13 ALC models are more common in the literature because they produce better results (lesser cost) than their ELC counterparts, when applied to an equivalent problem.

ALC models are illustrated by Ellis et al. (1985) and Ellis (1987), who develop an acid- rain model that finds the least-cost SO 2 removal levels at the 235 largest point sources in eastern North America, given maximum wet sulfate deposition rates at 20 predetermined sensitive receptor locations. Amann and Klaassen (1995) use a similar model to derive cost-effective strategies for nitrogen oxide (NO X) and ammonia (NH 3) in Europe.

The information required by LP models on transfer coefficients, cost functions, and source emissions, can be reasonably characterized as random (Ellis et al., 1985). In particular, transfer coefficients rely heavily on long-range pollutant transport models, e.g.

RAINS, that use different, but plausible, assumptions and procedures, and therefore provide a range of solutions. As a result, the accuracy and reliability of the transport models determine the performance of the LP models. To solve this problem, Ellis et al.

(1985) and Ellis (1990) propose a probabilistic form of the models to incorporate the effects of meteorological variability on long-range transport processes, by using transfer coefficient probability density functions. Comparing the basic LP model to the probabilistic LP model while using the same data, the authors conclude that a probabilistic model is better suited for acid-rain abatement applications.

Although air quality management is multi-objective in nature, most LP models have a single objective. Ellis (1988) develops models demonstrating the usefulness of a multi-objective framework for acid-rain control, and shows that multi-objective programming yields three improvements over the single objective approach: (1) establishing more appropriate roles for the participants in the planning and decision-

14 making process; (2) identification of a wider range of alternatives; and (3) more realistic problem perception. Ellis (1988) finds that probabilistic models tend to yield substantial cost reductions, because they allow deposition limit violations at selected receptors, and concludes that the multi-objective approach can prove to be "that needed bridge between hard science and the decision-making process".

2.3 Spatio-Temporal Modeling

Spatio-temporal models have received much attention in recent years. By taking advantage of recently increasing computing resources and power, these models attempt to map air pollution transport paths and patterns, incorporating both spatial and temporal variability, such as air trajectory and air pollution emissions surface or distribution analysis in a three-dimensional space. Most spatio-temporal models are regression models in nature, and some incorporate dispersion models into the regression models.

Using GIS technologies, large-scale projects at the regional, national, or even continental levels have become feasible. Ollinger et al. (1993), for example, implicitly estimate acid deposition by using precipitation ion concentrations from the National Atmospheric

Deposition Program/National Trends Network (NADP/NTN) data set. They relate precipitation to latitude, longitude, elevation, and an intercept dummy variable. Based on estimated precipitations, ion concentrations are calculated. Then, wet deposition fields are generated by processing each pixel according to the appropriate equations for ion concentrations and precipitation amounts.

The basic idea of spatio-temporal models is to estimate pollution concentrations

(or sometimes emissions), at a given location over a given time period, mainly by using 15 regression techniques. The location could be a point or an area, while the temporal dimension could be daily, weekly, or seasonal. The general mathematical representation

(Vyas and Christakos, 1997) is:

Y(s,t) = Q[X(s,t)] (s,t) ∈ R n x T (n = 2 or 3), (2.8)

where:

Y = pollution concentrations/emissions at a given location s over a given

time period t;

X = a collection of stochastic explanatory variables corresponding to the

value of Y at a given location s over a given time period t;

Q = a mathematical transformation function.

Space in these models is usually structured by a grid system, so that each cell contains only one sample data. Arndt et al. (1997) and Chang et al. (1998) demonstrate the usefulness of spatio-temporal modeling at large scales. Arndt et al. estimate and map sectoral anthropogenic and volcanic emissions of sulfur dioxide in Asia for 1987-1988 on a 1 o x 1 o grid system, based on diffusion models, with the help of statistical techniques.

The sulfur deposition patterns resulting from the estimated emissions are calculated using a regional-scale trajectory model (ATMOS model). According to the results of transport estimation, two forms of sulfur, gaseous SO 2 and aerosol sulfate, are calculated, and deposition is separated into dry and wet SO 2 and dry and wet sulfate. The dry and wet deposition amounts for SO 2 and sulfate are calculated for each latitude and longitude point, and a GIS is used to plot the deposition surface. A total of 31.6 million tons of sulfur dioxide per year is estimated by the model as generated by anthropogenic activities,

16 mainly fossil fuel use in the industrial and power sectors. As part of the data preparation process, several sulfur deposition maps are produced.

Chang et al. utilize two long-range models (the RAINS-Asia model and the

Urban-Branching Atmospheric Trajectory model) to estimate both airborne concentrations and depositions of sulfur dioxide and sulfate for the period 1990-2010 on a 1 o x 1 o grid system, based on available data on emissions originating from area and major point sources. They apply methods similar to those used by Arndt et al. (1997) to produce air quality impact maps for two provinces in China, and evaluate the —no further control“ scenario, based on estimated ambient concentrations and depositions of sulfur and sulfate. If no further control management is enacted, sulfur dioxide emissions are projected to double by the year 2010 from the base year of 1990.

Spatio-temporal models can also be constructed by using traditional dispersion modeling techniques. Seika, Harrison, and Metz (1998) develop the Ambient

Background Model (ABM), based on the Gaussian diffusion model, for the urban area of

London, and this model can be easily integrated into an urban air quality management model. Hourly meteorological data for the whole year of 1993 are used to develop the model, combined with a detailed grid emission inventory that covers traffic and non- traffic point and area sources in the Greater London area. Comparing predicted values to historical data shows that the ABM model produces impressive predictions of long-term, but not short-term, urban background air quality. The study suggests that the ABM model cannot properly capture short-term phenomena, because the Gaussian approach relies heavily on the wind speed factor, and wind speed tends to change very little in a short period of time.

17 Most studies confirm that spatio-temporal models produce good predictions when compared to historical data, and are able to uncover clear emission paths and the corresponding spatial distributions of concentrations. Several issues arise, however, when this type of models is applied to air pollution control management. Due to the complexity of the transport process, these models are generally derived from other similar —prototype“ models. The RAINS-Asia model, for instance, is derived from the

RAINS model developed in Europe. All the assumptions used in the RAINS-Asia model come from the original RAINS model. However, those prototype models are developed under unique site characteristics or circumstances. Unless the model is fully tested for the new study area, using results from the models should be done cautiously. Another potential problem is the parameterization representation of the meteorological data and other physical processes, which has not been given proper attention in the field of atmospheric science (Pielke and Uliasz, 1998). Finally, how to select a proper resolution scale in creating a grid system is also an important issue, which should be further investigated. For example, what is the shape of the grid system that can provide a better match with other variables, e.g. precipitation, so that optimal results can be obtained?

2.4 Neural-Networks-Based Models

Neural networks applications to air pollution modeling have attracted considerable attention in recent years, because they have been shown to be effective alternatives to traditional statistical techniques (Gardner and Dorling, 1998). Neural networks, or more precisely artificial neural networks, are a branch of artificial intelligence. Essentially, a neural network is a combination of non-linear regression and

18 discriminant models, data reduction models, and non-linear dynamic systems. A neural network consists of a set of "neurons" (i.e. simple linear or non-linear computing elements), interconnected in quite complex ways and often organized into layers (Sarle,

1994). Although there are different types of neural networks, most of the literature on air pollution modeling focuses on the multiple layer perceptron (MLP).

The literature suggests that when a strong theoretical understanding of the problem, adequate data, and computing resources are available, a full numerical model representing the process physics and chemistry may be the most desirable way to model the phenomena (Gardner and Dorling, 1998). However, as the complexity of a problem increases and the theoretical understanding decreases, statistical approaches, in general, are preferable. The performance of statistical regression models gradually decreases as the dimension of the model increases because of the complexity of the underlying distributions. Unlike its regression counterpart, the MLP makes no prior assumptions about data distributions. It can model highly —non-linear functions and can be trained to accurately generalize when presented with new, unseen data“ (Gardner and Dorling,

1998). Because of the non-linear nature of air pollution processes, these features make the MLP an attractive alternative to develop numerical models. The following diagram shows the conceptual structure of the MLP.

Hidden Layers Output Layer Input Layer

I1 O1

I 2

O2 I3

I = [I 1, I 2, I 3] Input Vector

O = [O 1, O 2] Output Vector

Figure 2.1 Conceptual Structure of the MLP

The MLP consists of a system of simple interconnected neurons or nodes representing a nonlinear mapping between an input vector and an output vector. The nodes are connected by weights and output signals, which are a function of the sum of the inputs to the node modified by a simple nonlinear transfer function. Those transfer functions can be linear or non-linear. Non-linear functions, usually logistic, are more commonly used, because they produce better results than linear functions, in terms of learning process and accuracy of outputs. The input layer generally plays no computational role but merely serves to pass the input vector to the networks. A multiple layer perceptron may have one or more hidden layers. As Gardner and Dorling (1998)

20 point out, by selecting a suitable set of connection weights and transfer functions, the

MLP can approximate any smooth, measurable functions between the input and output vectors. There is theoretical and experimental evidence that a three-layer (one hidden layer) perceptron is sufficient to define arbitrary linear decision regions in the Rn mathematical space (Boznar, Lesjak, and Mlakar, 1993). Moreover, the MLP has the ability to learn through training in a supervised manner 4.

Comire (1997) compares ozone forecasts made by a multiple layer perceptron and regression models. The forecasts of summertime daily maximum (one hour) ozone concentration for various U.S. urban areas are made using average daily meteorological input data. Regression models and neural networks are quite different in nature, and both can be adjusted or made more sophisticated to suit an individual application. For the sake of comparison, Comire avoids such complications by implementing the models in their most straightforward, typical fashion. By using the same basic weather-ozone relationships and the same daily dataset, intrinsic differences can be observed. In order to compare the R 2, MLP networks model with incorporation of error backpropagation are selected, because of their regression-like structure. Also, the index of agreement5 is used in the comparison of two different models to circumvent the correlation problem associated with the R 2. Comire concludes that the neural network approach does not provide dramatic improvements over linear regression, with only small to moderate gains

4 —Supervised manner“ simply means that the results of the MLP cannot be self-obtained. The whole process should be supervised by the researchers. An error signal is defined as the difference between the desired and actual output. Training uses the magnitude of this error signal to determine to what degree the weights in the network should be adjusted so that the overall error of the multiple layer perceptron is reduced. The determination is made by the researchers. 5 The index of agreement (d a) is proposed by C. J. Willmott, 1981, —On the Validation of Models.“ Physical Geography. Vol. 2, pp. 184-194. 21 in model performance. Gardner and Dorling (1998) suggest that Comire‘s results may be misleading, because of an inappropriate dataset 6. If sub-daily (hourly or bi-hourly) data, which display more apparent non-linear relationships, were used, neural networks would significantly outperform traditional regressions.

A neural networks model developed by Boznar, Lesjak, and Mlkar (1993) shows promising results. The model attempts to predict SO 2 concentrations around the biggest

Slovenian thermal power plant (TTP) at Sostanj, with the following input parameters: wind data, air temperature, previous SO 2 concentrations, relative humidity, solar radiation, emission from the TPP, and time of day. All input variables are normalized so that they have the same influence on the neural net. The data is taken from the on-site monitoring system of TPP. The results are then compared to the actual measurements obtained by the automated measuring stations in the TPP surroundings. Boznar et al. (1993) find that a neural networks model can be a reliable air pollution prediction method in the short term, and conclude that classical statistical methods for air pollution modeling are not reliable enough in complex topography. Gardner and Dorling (2000) use U.K. data to compare the performances of linear regression, regression tree, and MLP models, and find that MLP neural networks models more accurately capture the underlying relationship between meteorological and other temporal predictor variables and the hourly surface ozone concentrations.

Although neural networks appear to be a promising alternative to regression, several issues must be considered. Since neural networks produce "purely" empirical and

6 According to Gardner and Dorling, daily data does not represent the real non-linear ozone-meteorology relationship. 22 numerical models, it may not be easy to interpret their results, because of the lack of supporting theory on the cause-effect relationships. Moreover, these models are difficult to implement in terms of the choice of suitable network architecture and proper parameters. Benediktsson et al. (1990) indicate that —the multiple layer perceptron is no panacea, and so these models should be used with care“.

2.5 Acid-Rain-Related Studies

Acidic deposition, usually known as acid rain, occurs when emissions of sulfur dioxide (SO 2) and nitrogen oxides (NO x) in the atmosphere react with water, oxygen, and oxidants to form acidic compounds, which float in the environment in either dry form

(gas and particles) or wet form (rain, snow, and fog), and are generally carried by the wind before falling to the ground. Acidic compounds can remain in the atmosphere for several days (Wojcik and Change, 1997), and therefore their transport takes place over several thousand kilometers. In the U.S., about 70 percent of annual SO 2 emissions and

30 percent of NO x emissions are produced by power plants burning fossil fuels (USEPA,

1996).

In the U.S., anthropogenic SO 2 emission sources are monitored by the U.S.

Environmental Protection Agency (USEPA). The highest density of emission sources is located along the Ohio River Valley and the lower Great Lakes. Emissions from Ohio and Indiana rank first and second among the states, and the combined emissions from most of the Midwestern states and the Western part of Pennsylvania contributed about

38% of total national emissions in 1995 (USEPA, 1996). The Clean Air Act

Amendments of 1990 (CAAA) was designed to reduce SO 2 emissions and limit increases

23 in NO x emissions, and Phase I of the CAAA was implemented in 1995. According to the

USEPA, data shows a significant reduction in SO 2 emissions for most states in general, with a 21% - 36% decrease in the emissions from the Midwestern states.

SO 2 generally originates from anthropogenic sources, and is well known to be a

2- major contributor to acid depositions. SO 2 and especially its secondary product, SO 4

(simply expressed as SO 4 in the remainder of this research), can be transported in the atmosphere over long distances, reaching areas far remote from the main emission sources. Atmospheric SO 4, which is one of the major ingredients of acid rain, is primarily produced from the oxidation of SO 2. Both gaseous-phase and aqueous-phase reactions oxidize SO 2 to SO 4. Aqueous-phase oxidation, though more sporadic than gaseous-phase oxidation, is much more rapid. While gaseous-phase oxidation occurs only in daytime, with a rate around 1% per hour, aqueous oxidation in a typical cloud can occur at 100% per hour (Dutkiewicz, et al., 2000), suggesting that the amount of SO 2 deposition depends primarily on the distance from the source.

In general, after air pollutants are emitted from their sources, they are transformed and transported by atmospheric processes until they reach their final deposition locations.

The ground-level concentration of an air pollutant, therefore, will depend on the proximity to the sources, the prevailing meteorological conditions, and the nature and extend of atmospheric chemical reactions between the sources and receptor (Holland, el al. 1999). Also, there is more of a direction relationship between source strength and downwind ambient concentration for primary air pollutants (i.e. SO 2) than for secondary pollutants (i.e. SO 4). Most importantly, Husain et al. (1998) find that there is a linear

24 relationship between SO 2 emissions in the Midwestern states and SO 4 concentrations in the northeastern states, especially New York.

In order to design control strategies for acid rain, it is necessary to know which emission sources are responsible for acid deposition in a given area, so that one can estimate how acid deposition would be changed by the reduction of anthropogenic emissions. Thus, a key issue in the acidification problem is the clear identification of the source-receptor relationships. Backward trajectories are considered as one of the most efficient tools to search for the possible sources responsible for the occurrence of atmospheric pollutants. Several methodologies using trajectory analyses have been applied to study source-receptor relationships for acidic ions collected in precipitations

(Dutkiewicz et al., 2000; Butler et al., 2001; Lynch et al., 2000; Holland et al., 1999;

Charron et al., 2000 and 2001; Hirst, 2000; and Change, 2000).

Dutkiewicz et al. (2000) find a linear relationship between aerosol SO 4 and total sulfur concentration at two sites and SO 2 emissions upwind in the Midwest, using aerosol sulfate data continuously collected at Whiteface Mountain and Mayville, New York, from 1979 to 1997. A air trajectory model, called the Hybrid Single-Particle Lagrangian

Integrated Trajectory model (HY-SPLIT 3), developed by the National Oceanic and

Atmospheric Administration (NOAA), is used to compute the position of an air mass of pollutants at 2-hour intervals, with a starting height set at 1.5 kilometers. Because of the downwind locations of the study sites and the linear relationship between SO 2 and SO 4, the study concludes that it provides the —most direct evidence“ that deposition of sulfur in the Northeast is linearly related to upwind SO2 emissions in the Midwest. It also

25 suggests the possibility of using the ratio of SO 4 concentration / SO 2 emissions to develop an empirical source-receptor relationship for the region.

Similar techniques are used in Asia and Europe to identify source-receptor relationships qualitatively and quantitatively. In Europe, Charron et al. (2000) identify emission sources for a remote site, Morvan, France, by using precipitation data collected from 1992 to 1995. A backward trajectory model, based on the specification of the

European Monitoring and Evaluation Program (EMEP), is built to establish concentration field maps of likely contributing sources. The study finds that the areas of high SO 2 emissions located in Eastern Europe are primarily responsible for the acid precipitations recorded at Morvan. Also, substantial seasonal variations, with the highest concentrations of acidic compounds during the warm season, are observed at the study site. This variation in the acidity of precipitations is consistent with seasonal meteorological conditions and atmospheric processes. Chang et al. (2000) uses the

Taiwan Air Quality Model, based on backward trajectory modeling, to analyze six different episodes in 1993, covering five types of weather conditions. They estimate that about 39 % of wet sulfate depositions and 37% of wet nitrate depositions are contributed by long-range transports, and are able to trace the sources of SO 2 emissions back to the

Shanghai area of Mainland China.

Several studies are presented in the literature to evaluate the effects of the CAAA of 1990, using backward trajectory models. Most of them compare data collected in the

1990‘s and divided into two periods œ before (1991-1994) and after (1995-1998) the implementation of Phase I of the CAAA of 1990. They provide similar findings: there is strong evidence of statistically significant declining trends in sulfur dioxide and sulfate

26 concentrations after the enactment of Phase I of the CAAA of 1990. Butler et al. (2001) find a pattern of large declines in SO 2 emissions after the CAAA implementation, and large declines in precipitation SO 4 and H +, as well as air concentrations of SO 2 and SO 4, for most regions in the U.S.. They also find that the emission/concentration relations are close to 1:1 when the source region, based on a 15-hour back trajectory, is used for the

New England region. Lynch et al. (2000) examine the emissions from 110 coal-fired power plants in 21 states, and find that SO 2 emissions at those plants dropped by 2.86 million tons (38%) from 1993-1994 to 1995-1997. The largest decrease in emissions occurred across the Mid-Appalachian and Northeast regions of the country and these two regions are specifically targeted by Phase I of the CAAA of 1990 for reducing acidic depositions.

2.6 Summary

In this chapter, various modeling techniques and their applications have been reviewed and compared. Atmospheric diffusion models use mathematical representations, derived from fundamental equations, to characterize the underlying processes that physically transport and chemically transform a given pollutant, while regression modeling utilizes statistical methods to link a dependent variable (i.e., concentrations) to a set of explanatory variables (i.e., meteorological conditions) empirically. In general, regression modeling is more convenient, because it does not require dealing with the mechanisms of transportation and transformation. Economic optimization models incorporate information related to the environmental system into a formal analytical procedure that generates explicit optimal management strategies. The basic goal of this

27 type of models is to determine an optimal pollutant abatement strategy that satisfies environmental and regulatory requirements while minimizing total pollution reduction costs. Spatio-temporal models are regression models in nature, with the assistance of

GIS technologies, and some incorporate dispersion models. The basic idea of spatio- temporal models is to estimate pollution concentrations at a given location over a given time period. The location could be a point or an area, while the temporal dimension could be daily, weekly, or seasonal. Neural network techniques applied to air pollution modeling, especially SO 2, have been demonstrated to be an effective alternative to traditional statistical techniques, particularly in the case of non-linear behaviors. Finally,

2- most acid-rain related research show that SO 2 and its secondary product, SO 4 , are transported in the atmosphere over long distances, reaching areas far remote from the main emission sources.

Several conclusions can be drawn from the above review: (1) SO 2 pollution processes are governed by physical transportation and chemical transformation; (2) SO 2 is transboundary; (3) SO 2 is a long-range transport pollutant; (4) SO 2 pollution is characterized by non-linear behavior; and (5) the SO 2 pollution process is modified by decay and uptake effects, meteorological conditions, and land cover patterns. While diffusion models are explicit in their treatment of the physical diffusion process over space, they are characterized by much uncertainty in the values of their parameters, and cannot be verified statistically. In contrast, statistical models deal with empirical observations, producing cause-effect relationships between pollutant concentrations and various explanatory variables. However, these models generally lack an explicit spatial dimension. A modeling approach, which would integrate spatial diffusion concepts into a

28 statistical regression framework and use empirical observations, would combine the advantages of both approaches. The purpose of the following chapter is to develop such a modeling approach.

CHAPTER 3

MODELING APPROACH

This chapter presents the proposed modeling approach, based on statistical techniques and on the grid approximation of the Fickian equations of diffusion, with sulfur dioxide as a case study. The concept of airshed is incorporated into the model, using Geographical Information Systems (GIS) to associate spatial features to air quality data. General, statistically-estimable relationships are developed, linking concentrations to background concentrations and emissions within this spatial framework.

3.1 Theoretical Background

Earlier air quality modeling studies were generally single dimensional, most of them trying to relate pollution concentrations to either weather conditions or historical pollution data. With knowledge advances, the physical and chemical processes of air pollution were introduced into modeling techniques. With increasing computational power, combining weather conditions and spatial factors has become the dominant research approach.

30 Air pollution is governed by the physical diffusion of pollutants and the chemical reactions between pollutants and other materials in the atmosphere. The characteristics of physical diffusion determine how a pollutant is transported, while chemical reactions determine its transformation. The physical diffusion is a function of the vertical variation of the temperature and the ventilation rate. Chemical reactions can add pollutants during the transportation process. For example, solar radiation produces ozone in the presence of nitrogen oxides (NO x) and volatile organic compounds (VOCs).

Given these fundamental physical and chemical processes, the general air pollution theoretical model is based on a system of partial differential equations that apply the principle of mass conservation. This system is known as the Fickian system.

The distribution of pollutants in the atmosphere can be obtained by solving the Fickian system, given appropriate initial and boundary conditions. Suppose that the space is structured by an Eulerian coordinate system; the X-axis is oriented along the wind vector u; the Y-axis is crosswind oriented; and the Z-axis is oriented vertically upward. Then, the Fickian partial differential equation for a steady turbulent flow can be expressed by:

∂C ∂  ∂C  ∂  ∂C    u =  K y  +  K z  + q − s (3.1) ∂x ∂y  ∂y  ∂z  ∂z 

where:

C = C(x, y, z) : the ambient concentration at point (x, y, z)

q = q(x, y, z) : pollution generation (emission) at point (x, y, z)

s = s(x, y, z) : pollution removal at point (x, y, z)

u = u(z) : wind speed at height z

31 Ky = K y(z) : crosswind eddy diffusivity at height z

Kz = K z(z) : vertical eddy diffusivity at height z

There are two approaches to solving Equation (3.1) œ the Gaussian model and grid approximation.

3.1.1 The Gaussian Model

The Gaussian model is one of the Fickian system‘s derivations. It is based on the statistical theory of wind fluctuations, and is constrained by the law of conservation of matter. Suppose that an emission source is located at the origin of a three dimensional coordinate system (x, y, z), and emits an amount Q of a pollutant at time t = 0. Suppose that wind is blowing along the x-axis, so the x-axis is the plume centerline. If υ is the mean wind speed, then the pollutant cloud center will be located at point x = υ t after travel time t. Also, assume that diffusion takes place independently in the three coordinate directions, with normal distributions of standard deviations σx, σy, and σz. At time t, any given pollutant mass can be depicted in the three-dimensional space as illustrated in Figure 3.1.

Pollutant Mass

σz σy

σx υ t Center of Pollutant Mass X O Emission Center Wind Direction Y

Figure 3.1: Pollutant Mass in the Three-Dimensional Space

33 The concentration of the pollutant at point (x, y, z) at time t is mathematically given by:

Q (x −υt) 2 y 2 z 2 C(x, y, z,t) = exp(− − − ) (3.2) 2/3 σ σ σ σ 2 σ 2 σ 2 2( π ) x y z 2 x 2 y 2 z

Equation (3.2) describes a general non-steady state situation, because this equation is capable of dealing with time-dependent emissions Q(t) and meteorological parameters σ(t) and υ (t). Although this model is flexible and powerful, an extensive predictive knowledge of the trajectories in space and the diffusion of the individual puffs is necessary.

To avoid these complexities, the model can be simplified by assuming a steady- state situation for a given period of time. The steady-state situation is characterized by constant source strength, constant wind speed and direction, constant diffusion characteristics, and non-reactive pollutants (Guldmann and Shefer, 1980). Given a steady-state situation, integrating Equation (3.2) with respect to time from zero to infinity yields:

Q y 2 z 2 C(x, y, z) = exp(− − ) (3.3) σ 2 σ 2 2( π )υσyσz 2 y 2 z

In Equation (3.3), both σy and σz are assumed to be functions of the distance x from the center of emission, because t = x / υ . Moreover, σy(x) and σz(x) are empirically determined, and the accuracy of the model is heavily dependent on the accuracy of the estimations of these two functions. Consider now a pollutant mass emitted from a pollution source with an effective height, h. The pollutant plume will spread into the

34 atmosphere and reach the ground eventually. Assuming that the ground does not absorb any of the gaseous pollutant, Equation (3.3) becomes:

Q y 2 (z − h) 2 (z + h) 2 C(x, y, z) = exp(− )[exp(− ) + exp(− )] (3.4) σ 2 σ 2 σ 2 2( π )υσyσz 2 y 2 z 2 z

The focus is usually set on ground-level concentrations. By setting z = 0, Equation (3.4) becomes:

Q y 2 h 2 C(x, y )0, = exp(− − ) (3.5) σ 2 σ 2 πυσyσz 2 y 2 z

Equation (3.5) is the model most often used in practice. In general, the emission rate Q, the wind speed υ , and the effective stack height h are known in advance, and the diffusion coefficients σy and σz need to be estimated. The usual approach is to superpose

Gaussian plumes emanating from many sources to find out the total concentration at any point within a geographically fixed system of coordinates. In line with conventional meteorological data, wind directions are assigned in terms of a discrete number of compass points. Suppose that, for any average period, all wind directions within a given sector θ occur with equal probability. Equation (3.5) can be integrated with respect to y from -∞ to ∞. Dividing the integral by the sector‘s width at distance x from the emission source ( θx) yields the crosswind-integrated formulation of the Gaussian model:

2Q h 2 C(x) = exp(− ) (3.6) π 2/1 σ υθ σ 2 2( ) z x 2 z

In the case of sulfur dioxide, natural cleansing processes must also be taken into account. For example, SO 2 usually reacts with humidity to form SO 4 during the transport process, so that the amount of SO 2 deposited will decrease as the distance from the source

35 increases. According to empirical analyses, sulfur dioxide has an exponential decay with a half-life of 4 hours. Such a cleansing process can be represented by a pollutant decay function D with half-life τ:

t D = exp(− .0 693 ) (3.7) τ

Combining Equations (3.6) and (3.7) yields the final equation:

2Q h 2 t C(x) = exp(− )exp(− .0 693 ) (3.8) π 2/1 σ υθ σ 2 τ 2( ) z x 2 z

Using the Gaussian diffusion model, it is possible to compute an air pollution transfer coefficient relating any existing or potential pollution source i to any existing or potential receptor j, with appropriate weighting to account for the frequency of occurrence of meteorological conditions. The resulting model is then:

= C j ∑ aij *qi (3.9) i

where:

Cj = ambient pollution concentration at j,

qi = amount of pollution emitted at i,

aij = transfer coefficient from i to j.

It is impossible, however, to statistically estimate the a ij ‘s values, using information on the C j and q i observations, because obviously there are not enough degrees of freedom.

Therefore, the individual coefficients a ij are not statistically identifiable by using

Equation (3.9) directly.

36 3.1.2 Grid Approximation

Grid approximation is the other approach to solving the Fickian system of equations. It can be used to establish a spatial structure for the statistical computation of the transfer coefficients in Equation (3.9). Assume that (1) only one pollutant is considered, (2) the pollutant does not deposit itself on the ground, (3) the pollutant cannot penetrate into the upper stable layer, and (4) there is no chemical reaction between the targeted pollutant and other chemicals in the atmosphere. Suppose also that the wind is blowing along the x-axis, with an average wind speed u. The three-dimensional airspace can be divided into an array of elementary boxes (or volumes of fluid), and a box is indexed by (i, j, k), with sides (∆x, ∆y, ∆z). The indices i, j, and k represent the downwind, crosswind, and vertical directions, respectively. Figure 3.2 illustrates the concept of pollution diffusion elementary box.

K+1 J+1

Box(i+1, j, k)

(i, j, k) z(k)

u y(j) J-1 K-1

X(i)

Figure 3.2 Pollution Diffusion Elementary Box

38 Under the principle of mass conservation, the grid discretization of Equation (3.1) for box (i+1, j, k), illustrated in Figure 3.2, can be expressed as:

− ∆ ∆ = ∆ ∆ − ∆ uk (Ci+ ,1 j,k Cijk ) y z (k y ) k x z(Ci+ ,1 j+ ,1 k Ci+ ,1 j,k /) y

+ ∆ ∆ − ∆ (k y ) k x z(Ci+ ,1 j− ,1 k Ci+ ,1 j,k /) y

+ ∆ ∆ − ∆ (kz )k + )2/1( y x(Ci+ ,1 j,k+1 Ci+ ,1 j,k /) z

+ ∆ ∆ − ∆ (k z )k − )2/1( y x(Ci+ ,1 j,k−1 Ci+ ,1 j,k /) z

+ − qijk sijk , (3.10)

where:

Cijk = concentration in box(i, j, k),

qijk = emission in box(i, j, k),

sijk = removal in box(i, j, k),

(k y)k = crosswind diffusivity coefficient estimated at the center of any box

with height index k,

(k z)k±1/2 = vertical diffusivity coefficient estimated at the upper (+) or

lower (-) sides of any box with height index k.

Equation (3.10) simply states that the net pollution inflow into box (i+1, j, k), resulting from convention (u k C ijk ∆y ∆z) and net emission (q ijk œ s ijk ), is equal to the net pollution outflow from box (i+1, j, k), through convection (u k C i+1,j,k ∆y ∆z) and turbulence (the remainder of the terms in Equation 3.10). Equation (3.10) can be adjusted for boxes located at ground level and at the upper boundary of the airspace, by simply dropping one or more terms on the right-hand side of equation. In the following,

39 Equation (3.10) is used to develop a general spatial relationship between concentrations and emissions. For the sake of clarity, and at no conceptual loss, consider the array of boxes along the x-axis in the airspace, as illustrated in Figure 3.3, and assume that there is no vertical or crosswind diffusion. Equation (3.10) must be verified in all the elementary boxes (i, j, k) that make up this linear airspace.

i+1 i i-1 i-2 i-3 i-k

Wind Direction (X-Axis)

Figure 3.3: Illustration of Pollution Transfer over Linear Space

Let C i be the concentration and Q i the emission from anthropogenic sources in box i. Assume that the pollution uptake S i in box i is proportional to the concentration C i, depending upon the specific characteristics of the land cover in i. Equation (3.10) can be rewritten as follows for box i+1:

Ci+1 = A i C i + B Q i (3.11)

In the case of box i, it follows that:

Ci = A i-1 C i-1 + B Q i-1 (3.12)

40 After combining Equations (3.11) and (3.12), the concentration in box (i+1) can be expressed as a linear function of the background concentration in box (i-1) and the emissions in boxes i and (i-1), with:

Ci+1 = A i A i-1 C i-1 + A i B Q i-1 + B Q i (3.13)

Equation (3.13) can be generalized as follows:

Ci+1 = f (C i-k, Q i-k, Q i-k+1 , Q i-k+2 , …….., Q i), (3.14) where f is a linear function of the background concentration in box (i-k) and the emissions in all the boxes stretching between i and (i-k). This function provides the conceptual framework for the empirical / statistical approach presented in Section 3.2.

This approach expands the linear airspace illustrated in Figure 3.3 into a two-dimensional airshed to be used as the basic geographical analytical unit.

3.2 Empirical Approach

The airshed-based modeling approach presented in this section is based on the framework of Equations (3.9) and (3.14) presented in the previous section. Airsheds are regions on the surface of earth that, owing to local topographical and climatic conditions, gather airflows from other areas. The location of the outlet-point of the airshed and its meteorological profile generally define an airshed. Because of the inherent complexities in modeling natural processes, it is not, in general, possible to exactly determine the area that encompasses all the significant relationships between concentration at the outlet point and the corresponding explanatory variables. Airsheds are important units of analysis because they have the potential to define the boundary of the contributing effects from surrounding areas on the concentrations measured at a receptor point. The structure

41 of an airshed is helpful to establish spatial relationships between (1) receptor and (2) emitters and background concentrations. One of the most important features in the airshed-based approach is that the model uses data captured spatially at the local (or airshed) level. Because of its spatial structure, the model can account for land-use effects.

Consider a monitoring station j viewed as the outlet of its airshed in terms of airflows. Since the pollutants measured at station j are moved by incoming airflows, station j should be considered as the center of its own airshed. Taking wind directions into consideration, the airshed is divided into a number of sectors, with j as the center.

Suppose that i is the closest monitoring station to j, with a distance d within sector Z. The contribution to the concentrations at j, due to an emission source located beyond i in Z, will be captured by the concentration measured at station i in sector θ. Thus, only the closest monitoring station in each sector needs to be considered. Pollutants are gradually transported toward station j, as the wind blows toward j. All emissions, between j and i within Z, contribute to the concentration measured at j, and natural pollution removal processes do occur during this transportation process. Figure 3.4 illustrates the conceptual geographical structure of an airshed.

Ring r Sector θ

Monitoring Station

Figure 3.4. Conceptual Air Pollution Airshed

43 Consider monitoring station j, at the center of a circle made of θ ( θ = 1 Θ) sectors and R (r = 1 R) rings. The ring structure is introduced into the model to take into account the locations of natural removal processes and emission sources. This ring system is built for each monitoring station, under the assumption that the number and size of these rings are such that at least one monitoring station is located within each sector in some ring. As discussed earlier, the concentration C j measured at the central monitoring station j is the sum of the contributions from the background concentrations within the airshed j, BCON j, and from emission, EMIS j. It follows that:

Cj = f(BCON j) + g(EMIS j) (3.15)

Let C θ be the annual average concentration at the station closest to station j within sector θ, and r j(θ) be the ring number where this station is located. Let F θ be the frequency of the wind blowing toward j within sector θ, and E θr the total annual emission in cell ( θ, r). Then, a relationship consistent with equation (3.15) can be written as:

= + C j ∑ ∑ar * Fθ * Eθr ∑b(rj *) Fθ *Cθ (3.16) θ r θ

In Equation (3.16), C θ accounts for the emissions in cells ( θ, r) beyond ring r j, hence emissions in these rings are not included in the model. In this case:

Eθr = 0 for r ≥ r j (3.17)

The next step is to outline the way to estimate the coefficients a r and b in Equation

(3.16). Let d r be the distance of the centroid of ring r to station j (circle center), and υ θ the average speed of the wind blowing toward j within sector θ. In line with the Gaussian diffusion model depicted by Equation (3.8), a r can be expressed as:

44 1 1 a = α * * (3.18) r r υ d r θ

Because of natural removal processes, the amount of pollutants carried from a given source to its receptors generally decreases with distance. Natural removal processes are generally performed by green space (i.e. forest) and precipitation. The more green space, the higher the removal rate. It is reasonable to assume an inverse linear relationship between removal rates and green spaces. Let L θr be the area of green space within sector θ between station j (circle center) and ring r. To account for absorption effects, Equation (3.18) is rewritten as:

1 1 1 a = α * * * (3.19) r r υ d r θ Lθr

If all distance effects are accounted for in Equation (3.19), then αr is equal to α. It should be noted that αr may also be a function of precipitation, explaining the decay of SO 2 (or the increase in SO 4). A similar relationship may be considered for b(r j), with:

= 1 1 1 b(rj ) β * * * (3.20) d r υθ Lθr

Combining Equations (3.16), (3.19), and (3.20) yields:

= 1 1 1 + 1 1 1 C j ∑ ∑ar [* * * *] Fθ * Eθr ∑ β [* * * *] Fθ *Cθ (3.21) θ r d r υθ Lθr θ d r υθ Lθr

Equation (3.21) represents a linear relationship, and OLS can be used to estimate the parameters αr and β, with an intercept constrained to be equal to zero. Alternatively, non-linear regression can be used to estimate the parameters, γ, δ, and ε, when the transfer coefficient a r is expressed as:

45 = α −γ υ −δ −ε ar r (* d r ) (* θ ) (* Lθr ) (3.22)

The data to be used to empirically implement the above approach are discussed in

Chapter 4 and 5, while the actual statistical analyses are presented in Chapter 6.

CHAPTER 4

DATA SOURCES AND PROCESSING

The estimation and testing of the model proposed in the previous chapter requires various data sets, which can be divided into four categories: (1) concentrations; (2) emissions; (3) meteorological conditions; and (4) land characteristics. The main source of data for air pollution concentrations and emissions is the EPA‘s AirData website 7, which provides both raw and processed data, organized into three databases: (1) Air

Quality System (AQS); (2) National Emission Trends (NET), and (3) National Toxics

Inventory (NTI). The selection of air pollutants included in the AirData database is based on the requirements of the Clean Air Act, which defines two classes of air pollutants: criteria and hazardous. Criteria air pollutants were defined in the original 1970 Clean Air

Act, and EPA has tracked the emissions and ambient concentrations of these six pollutants since 1970: carbon monoxide (CO), nitrogen dioxide (NO 2), ozone (O 3), sulfur dioxide (SO 2), lead (Pb), and particulate matters (PM 10 and PM 2.5 ). Hazardous air pollutants are not the focus of this research.

7 EPA‘s AirData website: http://www.epa.org/air/data/index.html 47 Meteorological data is acquired from the National Climatic Data Center (NCDC), which is operated by the National Oceanic and Atmospheric Administration (NOAA) and the National Environmental Satellite Data and Information Service (NESDIS). NCDC has the most extensive archive of weather data. Land characteristics data is acquired from the Land Processes Distributed Active Archive Center (LPDAAC), which is operated by the National Aeronautics and Space Administration (NASA) and the U.S.

Geological Survey (USGS). LPDAAC is the main distribution center for the images obtained from the Landsat 7 and the Earth Observing System (EOS) Terra satellites. All datasets collected in this research are processed by either SAS or C++ programs. GIS analyses and the preparations of digital maps are performed with either Arc/Info or

ArcMap 8.

4.1 Background Mapping

The first step in this research is to create several supporting digital maps, such as county and state boundary maps, focusing on the coterminous United States, and excluding

Alaska and Hawaii. These maps are either downloaded from the U.S. Bureau of the

Census (BC) website 9 or extracted from the BC TIGER/LINE 10 data file, and are then processed in Arc/Info with the assistance of Arc/Info Macro Language (AML). (See

Appendix B for examples of AML).

8 Arc/Info and ArcMap software are from Environmental System Research Institute, Inc (ESRI). 9 U.S. Bureau of the Census: http://www.census.gov 10 Most digital maps in this research were extracted from the 1995 TIGER/LINE database. TIGER/LINE refers to the Topologically Integrated Geographic Encoding and Referencing data format, developed by the U.S. Bureau of the Census. 48 The map projection used in this study is the Albers Equal Area Conic projection.

Because the earth surface is truly three-dimensional, a map projection tries to relate spherical coordinates on the globe surface to flat and planar coordinates by using a set of mathematical formulas. Different projections cause different types of distortions, but, at the same time, preserve certain types of spatial properties (i.e, shapes or distances).

Although the Albers projection does not preserve either shape or linear scale perfectly, it keeps distortions to a minimum, and balances both area and shape features well. It has been suggested that this projection should be used for land masses extending in an east- west direction, rather than for those in a north-south direction. As a result, the Albers projection is one of the most common choices for mapping the coterminous United States.

4.2 Airshed Construction

In the theoretical model proposed in the previous chapter, each airshed consists of a central monitoring station representing the focus of the airshed, and several concentric rings representing the catchment area of the airshed. As discussed earlier, the main advantage of using an airshed as a unit of analysis is that it defines the boundary of the contributing effects from surrounding areas on the concentrations measured at the central monitoring station. The selected airshed structure is helpful in gathering spatial data depicting local conditions. The central monitoring station of an airshed can be viewed as representing local SO 2 pollution conditions. The AirData Monitoring All Columns

Report maintains station data for the six criteria air pollutants. The number and geographical distribution of the monitoring stations vary from state to state. In 1999, there were 642 active sulfur dioxide monitoring stations throughout the U.S., including

49 Puerto Rico and the Virgin Islands (see Table 4.1 and Appendix A for the corresponding map). Most monitoring stations are located in the midwestern (34.6%) and northeastern

(12.9%) parts of the U.S., as these two regions have the most serious acid rain problems in the country.

The first step in constructing the airshed data set is to determine the size of the airshed and to select the proper sampling stations. The location information on SO 2 monitoring stations is extracted from the AirData Monitoring Address Report. Using

Arc/Info, the coordinates of each station site (in degree) are converted and transferred to a digital map. In the case of stations without coordinates, the site address, if available, is used to geocode 11 its location on the digital map.

After processing this location information, several C++ programs are developed to determine the monitoring station in each sector nearest to the selected central monitoring station. These C++ programs can be found in Appendix B. SAS programs are then written to analyze the outputs of these C++ programs, in order to determine a limit on airshed size. It is commonly accepted that sulfur dioxide is a pollutant with long-range transport characteristics. Moreover, each sector must have at least one monitoring station within the limit of the airshed. This limit was set at a radius of 320 kilometers (200 miles), as there was not much gain in the number of possible airsheds beyond that limit (See Table 4.2).

11 According to Arc/Info documentation, geocoding is the process of creating geometric representations for descriptions of locations. A geocoding service defines a process for converting non-spatial descriptions of places into spatial descriptions. 50

Total Number of Total Number of Number of Selected Number of Selected State Stations Stations State Stations Stations Alabama 5 1 Nebraska 3 0 Alaska 0 0 Nevada 0 0 Arizona 5 0 New Hampshire 11 9 Arkansas 8 2 New Jersey 14 12 California 39 8 New Mexico 9 0 Colorado 6 0 New York 31 19 Connecticut 10 10 North Carolina 11 11 Delaware 3 1 North Dakota 19 0 D.C. 1 1 Ohio 37 33 Florida 30 19 Oklahoma 7 0 Georgia 9 2 Oregon 0 0 Hawaii 5 0 Pennsylvania 52 48 Idaho 3 0 Rhode Island 2 2 Illinois 29 22 South Carolina 12 7 Indiana 38 36 South Dakota 0 0 Iowa 15 1 Tennessee 27 23 Kansas 5 0 Texas 23 0 Kentucky 15 11 Utah 4 0 Louisiana 6 0 Vermont 2 1 Maine 3 0 Virginia 11 10 Maryland 1 1 Washington 8 0 Massachusetts 16 16 West Virginia 24 24 Michigan 11 5 Wisconsin 7 0 Minnesota 9 0 Wyoming 0 0 Mississippi 4 1 Puerto Rico 4 0 Missouri 26 9 Virgin Island 5 0 Montana 17 0 TOTAL 642 345 Note: Number of selected stations = number of stations finally used in this research.

Table 4.1: Sulfur Dioxide Monitoring Stations by States in 1999

Airshed Number of Coverage Radius (km) Stations (%) < 240 163 25.4% < 260 187 29.1% < 280 215 33.5% < 300 236 36.8% < 320 270 42.1% < 340 287 44.7% < 360 298 46.4% < 380 311 48.4% < 400 322 50.2%

Table 4.2: Size and Number of Airsheds

With a radius of 320 kilometers, 270 stations have a complete system 12 . However, monitoring stations located along the seaboard must be treated differently. These stations have necessarily parts of their airshed extending over the sea. It is very unlikely that background pollutants from other continents are transported over the ocean, and it is safe to assume that emissions within such sectors can be set equal to zero. Further analyses, through visual inspection, were performed to place dummy monitoring stations into the first cell located over the sea (see Figure 4.1 for details). Combining complete airshed systems and oceanic airsheds with dummy monitoring stations, 345 monitoring stations / airsheds are obtained, or about 54% of all monitoring stations in the U.S. in 1999.

Once the size of the airshed is selected, several C++ programs are developed to generate basic geometrical features (i.e. coordinates and quadrant) for the ring-sector

12 A complete system is such that there is at least one surrounding monitoring station in each sector within the radius limit of the airshed.

52 structure of the airshed, and the outputs are inputs into Arc/Info to generate final digital data, thus creating 345 coverages, each representing an airshed with a central monitoring station. Each coverage consists of 16 rings and 8 sectors, or 128 cells. An example of an airshed structure is presented in Figure 4.2.

Regular Stations

Dummy Stations

Coastline

Figure 4.1: Example of Placement of Dummy Stations

53 FigureSpatialAirshed Structure 4.2: an of

54 4.3 Emission Data

Two types of emission data are used in this study: facility and area. Both data sets are downloaded from the NET database of the AirData website. The National

Emission Trends (NET) database is the national inventory of air pollution sources, and contains estimates of the annual emissions of criteria air pollutants from three classes of sources: points, areas, and mobile. Point sources, also known as facilities, are usually industrial or commercial entities, such as power plants or factories. Area sources include point sources that cannot be tracked individually, such as homes and small office buildings, and diffuse types of stationary sources, such as wildfires or agricultural tilling.

The emission estimates of area sources are aggregated for each pollutant 13 by county.

Mobile sources include most types of on-road and off-road vehicles or equipment with either gasoline or diesel engines, such as cars, trucks, railroad locomotives, water vessels, and airplanes. Mobile source data is also compiled into county aggregate emission estimates. The USEPA conducts an emissions inventory every three years (1996, 1999, etc.), and completes a major update of the NET database using the inventory results.

There are 18,305 records in the NET Facility Emissions Report for sulfur dioxide and for 1999. Emissions are reported in short tons per year. Each record represents a major SO 2 emission point, and has such information as facility name, location, and type of industry or business. A table listing the data included in the NET Facility Emission

Report can be found in Appendix C. The facility location information is extracted from the report, processed by SAS and C++ programs, and converted into a point coverage

13 State and local environmental agencies supply most of the point source data. Pollutants monitored by the NET are carbon monoxide (CO), nitrogen dioxide (NO 2), sulfur dioxide (SO 2), volatile organic compounds (VOCs), ammonia (NH 3), and particulate matter (PM 10 and PM 2.5 ). 55 with Arc/Info. Informations other than location are analyzed and sometimes corrected separately, and the results are then attached back to the facility point coverage.

The second step is to associate facility points with the ring-sector structure of an airshed. As the NET facility map is a point coverage, and the airshed is a polygon coverage, it is important to preserve all features in the area common to both coverages.

The overlaying operation is performed to combine the two coverages. Figure 4.3 provides a conceptual demonstration of intersecting a point coverage with a polygon coverage. The results are exported for further analyses, and the dataset includes not only emission estimates but spatial features as well. The same procedure is applied to all sampled airsheds, producing 345 geodata sets.

For each cell of the facility emission geodata set, two measures of facility emission values are calculated œ aggregated and distance-weighted emissions. Let θ(r,s) be the cell located in ring r and sector s of an airshed with central monitoring station C.

Suppose that, in cell θ(r,s), there are n point sources, P i , emitting E i tons of SO 2 per year, and the distance between C and P i is D i (i = 1 [ n). For cell θ(r,s), the aggregated facility emission estimate (TEMIS r,s ) is computed as follows:

n = TEMISr,s ∑ Ei (4.1) i=1

It is conceivable that the farther a facility is from the central monitoring station, the lesser its emissions will reach the central station. In other words, a closer facility contributes more to the concentration measured at the central monitoring station than a distant one. Considering these distance effects, the distanced-weighted facility emissions estimate is calculated as follows for any cell θ(r,s):

56 n Di n ∑ = D = i=1 WEMISr,s ∑ Ei , D (4.2) i Di n

These two emissions measures are computed with SAS programs, taking output files processed by Arc/Info as inputs. (See Appendix B.)

Area emissions data is extracted from the NET Tier Report, which summarizes annual SO 2 emissions, in short tons, from area sources in each county by source categories (also known as Tier). The USEPA has defined 14 Tier-1 categories (major types of sources) and 84 Tier-2 categories (sub-types of sources). Appendix C lists both

Tier-1 and Tier-2 categories. There are 63,192 records in the NET Tier Report for sulfur dioxide in 1999, and the data provides all possible combination of state, county, Tier-1, and Tier-2 SO 2 area emissions.

The first step in processing the Tier dataset is to aggregate area emissions by county, since the type of industry or pollution-producing activity is not the focus of this research. Using GIS, this aggregated data is then attached to a county coverage, and county area and county centroid coordinates are computed. Associating area emissions with the spatial structure of an airshed is the second step. The overlaying operation combines two polygon coverages. Figure 4.4 provides the conceptual illustration of this operation. The resulting maps are exported for further processing. The same procedure is applied to all sampled airsheds, producing 345 geodata sets.

Point Coverage Polygon Coverage Overlay Result

Figure 4.3: Overlaying Point Layer with Polygon Layer

Polygon Coverage Polygon Coverage Overlay Result

1 1 1 2 3

2 3 2 4 5 3 6 7 4 5 8 9

Figure 4.4: Overlaying Polygon Layer with Polygon Layer

Input Coverage Clipped Coverage Overlay Result

1 1 1 2 3 2 3 2 4 5 4 5

Figure 4.5: Clipping Input Layer with Clipped Layer

58 As illustrated in Figure 4.6, it is possible for a given cell to have polygons belonging to different counties. Since area emissions are aggregated by county, and the basic unit in this research is the ring-sector cell, the area emissions must be apportioned and summarized at the cell level. Because of the nature of area emissions, it is reasonable to assume that these emissions are homogeneously spread across a county. Therefore, for any cell θ(r,s), the cell area emission estimate is calculated with the following formula:

n = AEMISr, s ∑ Ri *CNTYEi (4.3) i=1

where:

AEMIS r,s = total area emissions within cell θ(r,s),

Ri = ratio of the area of county i located within cell θ(r,s) to the total area

of county i,

CNTYE i = total area emissions for county i,

n = total number of counties within cell θ(r,s).

Cell θ(r,s)

CNTY 2 CNTY 1

CNTY 3

Figure 4.6: Hypothetical Overlaying of County and Airshed Coverages

Two variables are needed to compute the ratio R i in the above formula œ the polygon area, which is obtained by the overlaying operation, and the county area, which is extracted from the county coverage. Both geodata sets are merged, and the computation is executed with a SAS program. Finally, weighted area emissions estimates are also computed for each cell, by taking the distance factor into consideration. The same method used to calculate distance-weighted facility emissions, is applied here, using the distance from the central monitoring station of the airshed to the centroid of the polygon.

60 4.4 Concentration Data

The concentration dataset is downloaded from the AirData website. These data are extracted from the Monitor All Columns Report, which includes the ambient concentrations of the criteria pollutants in outdoor air, as measured by state environmental agencies. The report is based on the Air Quality System (AQS) of the

AIRS database. Each monitor measures the concentration of a particular pollutant in the air, and the reported data are usually average concentrations for one or more time periods

(e.g. 1 hour, 24 hours, or 1 year). The USEPA compiles these summary measures to gauge compliance with the air quality standards established by the Clean Air Act, and those standards are framed in terms of summary average measures, that vary from one pollutant to another. Therefore, the information provided by the report varies with the pollutant selected. Appendix C lists all the data categories about sulfur dioxide provided by the Monitor All Columns Report.

There are 642 records for sulfur dioxide in 1999. The concentration is measured in parts per million (ppm). The longitudes and latitudes of the monitoring stations are first converted into a point coverage by using Arc/Info. Data other than location are analyzed separately, and only those necessary data, such as concentrations, are joined back to the monitor point coverage. The second step is to associate monitor points with the ring-sector structure of the airshed. A procedure similar to the one creating the NET emission facility geodata set is applied (see Section 4.3 for more details). The final result is the production of 345 geodata sets.

As discussed in the previous chapter, only the surrounding monitoring stations that are closest to the central monitoring station in each sector are to be used. Any effect

61 of emissions beyond the location of these closest monitoring stations is captured by the concentrations measured at those stations. Of course, those monitoring stations may also serve as the outlets (i.e. centers) of their own airsheds. For each sector of a monitor concentration geodata set, two types of concentration measures are computed œ average and inverse-distance weighted concentrations. Let θ(r,s) be the cell located in ring r and sector s of an airshed with central monitoring station O. Assume that θ(r,s) contains the closest neighboring monitoring station K in sector s. Suppose further that, in cell θ(r,s), there are n monitoring stations, including K, denoted as P i , with annual SO 2 mean concentration C i (ppm), and that the distance between O and P i is D i (i = 1 [ n). For any sector s, the average SO 2 concentrations value (AVGC s) is computed as the follows:

n ∑Ci i=1 AVGCs = (4.4) n

However, using the average concentration ignores the fact that the effects of a pollutant on a receptor decrease as the distance between emitter and receptor increases.

To take this effect into consideration, inverse-distance-weighted SO 2 concentrations

(IDWC s) are calculated as follows:

n ∑ Ci = i=1 Di IDWCs n (4.5) ∑ 1 i=1 Di

To compute AVGC s and IDWC s, it is first necessary to determine the closest monitoring station in each sector and the cell where it is located (see the C++ program in

Appendix B). Once the information about these closest stations is obtained, the data is

62 merged with the geodata sets created earlier with Arc/Info. Then, the calculation of the two concentration measures is done using SAS programs.

4.5 Meteorological Data

Meteorological data, such as wind speed, wind direction and precipitations, are downloaded from the website 14 of the National Climatic Data Center (NCDC). NCDC offers various weather data sets. The Local Climatological Data (LCD) database is used in this research, because it provides the most comprehensive and detailed information.

Each LCD set contains five data files: (1) hourly, (2) daily, (3) daily average, (4) hourly precipitation observations, and (5) weather station location information, organized by year and month. Wind speed and wind direction data is extracted from the hourly observations file, whereas precipitation data is extracted from the hourly precipitation observations file. In 1999, 821 weather stations were listed as active stations in the U.S.

There are 8,061,326 records in the LCD data set. Each record contains several meteorological variables. (See Appendix C for a detailed list). The first step is to create a point coverage (GIS map) of weather stations. The coordinates of the stations are processed by both SAS and C++ programs (see Appendix B), and the outputs are transferred to Arc/Info to generate a point coverage. Other location informations (i.e. elevation or time zone) are analyzed, corrected when necessary, and attached back to the weather station point coverage. A map of the stations is available in Appendix A.

14 NCDC website: http://www.ncdc.noaa.gov/oa/ncdc.html

63 The second step is to associate a weather station to the ring-sector structure of an airshed. A procedure similar to the creation of the NET emissions facility geodata set is used to overlay two coverages (see Figure 4.3). The results are exported and merged with the hourly observations data for later use. Each central monitoring station must be associated to a weather station, so that the frequencies of wind directions can be computed for its airshed. The distances between the central SO 2 monitoring station and all the weather stations located within the airshed are calculated and ranked, using several

C++ and SAS programs (see Appendix B). In most cases, the closest weather station is used to represent the weather conditions at the central monitoring station. When a selected weather station does not have data for the full year, the second closest weather station is used instead. The same procedure is applied to all sampled airsheds, producing

345 geodata sets.

In order to introduce the wind direction variable into the proposed model, wind directions at the central monitoring station are assigned to 45-degree sectors, and each direction is measured by its relative frequency. However, wind directions in the LCD are measured in tens of degree, from true North moving clockwise. To deal with the wind directions of 90 o, 180 o, 270 o, and 360 o, frequencies for those directions are evenly divided and re-distributed to the appropriate sectors. Precipitation data is processed differently. Instead of using measures for the central stations, precipitation data are averaged over all the weather stations within the airshed, because rain will wash out a certain amount of air pollution, no matter how far this occurs or which direction the wind blows in.

64 The meteorological variables require further processing to account for climatic and topographical variations. As indicated previously, each point of interest, either an emission source or a monitoring station, is assigned to the closest weather station available, and the weather conditions measured at that weather station are assumed to represent those of the corresponding monitoring station or emission point. Since the area covered by an airshed (radius of 320 kilometers) is fairly large, it is quite possible that the weather conditions at the central receptor are different from those at a surrounding emitter or monitoring station. These possible meteorological differences may have an effect on the transport of pollutants between emitter and receptor. Ideally, a detailed backward trajectory analysis would be the optimal way to deal with this issue. However, such an approach would not be consistent with the —simplifying“ rationale of the statistical approach used in this research. As a compromise to deal with this potential problem, meteorological conditions are averaged out as follows:

WEA ij = (WEA i + WEA j) / 2; (4.6)

where:

WEA i = meteorological variable at receptor i ,

WEA j = meteorological variable at emitter or monitoring station j,

WEA ij = average meteorological variable on the path from j to i.

65 4.6 Land Cover Data

Land cover data is downloaded form the website 15 of the Land Processes

Distributed Active Archive Center (LPDAAC), and extracted from its North America

Land Cover (NALC) database. The NALC database, which covers Canada, Mexico, and the U.S., is part of the Global Land Cover Characteristics database (GLCC), which was developed on a continent-by-continent basis. All data are based on 1-km Advanced Very

High Resolution Radiometer (AVHRR) satellite images collected between April 1992 and March 1993, with a 1-km nominal spatial resolution. The database also provides a set of derived thematic maps generated by the aggregation of seasonal land covers in the region. These maps are: (1) global ecosystems, (2) IGBP land cover classification, (3)

USGS land use / land cover system, (4) simple biosphere model, (5) simple biosphere model II, (6) biosphere-atmosphere transfer scheme, and (7) vegetation life forms. The thematic map of the USGS land-use / land-cover system is used in this research, with 24 categories (see Table 4.3).

15 LPDACC website: http://edcdacc.usgs.gov/main.html

Code Land Use / Land Cover Code Land Use / Land Cover 01 Urban and Built-up Land 13 Evergreen Broadleaf Forest 02 Dryland Cropland and Pasture 14 Evergreen Needleleaf Forest 03 Irrigated Cropland and Pasture 15 Mixed Forest 04 Mixed Dryland/Irrigated Cropland and Pasture 16 Water Bodies 05 Cropland/Grassland Mosaic 17 Herbaceous Wetland 06 Cropland/Woodland Mosaic 18 Wooded Wetland 07 Grassland 19 Barren or Sparsely Vegetated 08 Shrubland 20 Herbaceous Tundra 09 Mixed Shrubland/Grassland 21 Wooded Tundra 10 Savanna 22 Mixed Tundra 11 Deciduous Broadleaf Forest 23 Bare Ground Tundra 12 Deciduous Needleleaf Forest 24 Snow or Ice

Table 4.3: USGS Land Use / Land Cover System Categories

Because the NALC database is made of raster image data, it cannot be overlaid with airshed coverages, which are in vector format. The overlaying operation requires that the two digital geodata sets be in the same format, so format conversion is necessary.

The raster image map for the U.S. can be found in Appendix A. In order to perform further analyses, the information from both the land cover and the airshed coverages must be retained. An Arc/Info operation, UNION, is used to keep identifiers, such airshed ID, sector number, ring number, and land-use code, so that polygon attribute table (PAT) files, which store all vital spatial data, can be produced to be processed in SAS. There are two main reasons for choosing the vector format for the land cover coverage, instead of the raster format. First, because all other data sets are in vector format, it is more practical for land-cover data to be converted to the vector format in order to facilitate

67 further GIS operations. Second, each land-use cell must carry such identification information as airshed ID, sector number, and ring number, so that the information can be used in the statistical analyses to be performed with SAS. It is not possible for raster data to include such information.

Due to computer storage constraints and software limitations, it is impossible to directly convert the NALC data from raster format to vector format. The approach selected is then to clip the areas located within airsheds out of the NALC image data, allowing this much smaller portion of the NALC data to be converted into vector format.

In order to perform clip operations (see Figure 4.5), a boundary coverage (serving as clipper) for every sampled airshed is first generated, and then used to clip out the desired portion of the NALC coverage. The clipped image data are then converted into vector coverages. To associate land cover data with the ring-sector structure of the airshed, a procedure similar to the creation of the NET emissions facility geodata set is used (see

Figure 4.4). The results are retrieved for later analyses by SAS programs. In any given cell, there are up to 24 different types of land cover. The same procedure is performed on all sampled airsheds, producing 345 geodata sets.

CHAPTER 5

VARIABLE DEFINITIONS AND OVERVIEW

As discussed in the previous chapter, the data used in this research can be grouped into four categories: (1) concentrations, (2) emissions, (3) meteorological conditions, and

(4) land use / land cover. These data sets are analyzed and cross-checked, and then linked to each other through their association with a central monitoring station. Each data set consists of both original observations and spatial features. In this chapter, both descriptive statistics and simple correlations between sulfur dioxide concentrations and the potential explanatory variables are first examined to lay down the foundations for the specification and estimation of the air quality model outlined in Chapter 3. Most of the variables are described and analyzed at both the national and airshed levels.

5.1 National Overview

5.1.1 SO 2 Concentrations

Table 5.1 presents various summary data for SO 2 concentrations by states in 1999.

The average annual mean concentration for the whole U.S. is 0.0041 ppm, which is far below the National Ambient Air Quality Standard (see Table 1.1) for sulfur dioxide

69 established by the USEPA. In most states, the maximum measured concentrations for periods of either 24 hours or 3 hours do not exceed the corresponding standards (0.14 ppm for 24 hours and 0.5 ppm for 3 hours). Hawaii and Illinois had maximum observed concentrations exceeding the standard for the 3-hour period, while Hawaii, Iowa, and

Pennsylvania had maximum observed concentrations exceeding the standard for the 24- hour period. Although Hawaii is not part of this research sample, it is interesting to note that the state has relatively high SO 2 concentrations, although it is not heavily industrialized. A plausible explanation is that Hawaii‘s high SO 2 concentrations are not due to anthropogenic sources, but to the intensive volcano activities in this state, over a relatively small amount of land mass.

Figure 5.1 illustrates the geographic distribution of average annual concentration measures across states in 1999. Midwestern states have the highest average concentrations in the country, followed by New England states. It is commonly believed that power plant emissions in the Midwest are responsible for the high concentrations found in New England and in Southeastern Canada. West Virginia, Pennsylvania, and

Ohio are the top three highest SO 2 concentration states, with average measures of 0.0108,

0.0074, and 0.0064 ppm, respectively. On the West coast, California has very low SO 2 concentrations (0.0018 ppm), because of its strict environmental laws and small number of coal-powered electricity generators. Vermont experiences a relatively low average annual SO 2 concentration (0.0025 ppm), while the average annual concentrations in New

York and the other New England states are around 0.005 ppm, possibly because of topographic conditions and low population density.

70 Number of Max 1-hour Max 3-hour Max. 24-hour Max. Annual Average State Stations Value Values Value Mean Annual Mean 3 Alabama 5 0.241 0.136 0.044 0.007 0.0046 Arizona 5 0.159 0.084 0.025 0.008 0.0032 Arkansas 8 0.127 0.098 0.024 0.005 0.0035 California 39 0.157 0.103 0.034 0.005 0.0018 Colorado 6 0.121 0.064 0.021 0.004 0.0027 Connecticut 10 0.080 0.056 0.029 0.007 0.0051 Delaware 3 0.180 0.146 0.056 0.007 0.0057 D.C. 1 0.135 0.065 0.020 0.007 0.0070 Florida 30 0.420 0.265 0.071 0.008 0.0033 Georgia 9 0.190 0.122 0.024 0.004 0.0026 Hawaii 5 0.954 0.618 0.197 0.008 0.0024 Idaho 3 0.327 0.160 0.061 0.007 0.0040 Illinois 29 0.725 0.567 0.107 0.009 0.0056 Indiana 38 0.476 0.328 0.115 0.015 0.0058 Iowa 15 0.320 0.259 0.148 0.010 0.0033 Kansas 5 0.278 0.149 0.062 0.006 0.0032 Kentucky 15 0.262 0.172 0.064 0.008 0.0059 Louisiana 6 0.152 0.098 0.029 0.005 0.0033 Maine 3 0.129 0.044 0.017 0.004 0.0033 Maryland 1 0.111 0.065 0.026 0.006 0.0060 Massachusetts 16 0.114 0.086 0.042 0.007 0.0049 Michigan 11 0.229 0.146 0.061 0.009 0.0050 Minnesota 9 0.091 0.071 0.031 0.003 0.0014 Mississippi 4 0.126 0.111 0.042 0.002 0.0018 Missouri 26 0.968 0.390 0.092 0.009 0.0048 Montana 17 0.366 0.151 0.047 0.006 0.0036 Nebraska 3 0.099 0.049 0.011 0.001 0.0010 New Hampshire 11 0.194 0.148 0.037 0.007 0.0040 New Jersey 14 0.128 0.081 0.035 0.008 0.0046 New Mexico 9 0.279 0.198 0.052 0.009 0.0027 New York 31 0.319 0.206 0.067 0.014 0.0056 North Carolina 11 0.347 0.125 0.031 0.007 0.0049 North Dakota 19 0.401 0.346 0.097 0.006 0.0021 Ohio 37 0.347 0.269 0.104 0.011 0.0064 Oklahoma 7 0.274 0.234 0.088 0.011 0.0049 Pennsylvania 52 0.472 0.359 0.146 0.015 0.0074 Rhode Island 2 0.090 0.069 0.029 0.007 0.0060 South Carolina 12 0.219 0.099 0.029 0.003 0.0018 Tennessee 27 0.417 0.289 0.074 0.010 0.0049 Texas 23 0.380 0.230 0.062 0.007 0.0027 Utah 4 0.048 0.033 0.012 0.003 0.0020 Vermont 2 0.055 0.044 0.027 0.004 0.0025 Virginia 11 0.185 0.132 0.046 0.009 0.0049 Washington 8 0.124 0.112 0.036 0.007 0.0046 West Virginia 24 0.549 0.289 0.093 0.015 0.0108 Wisconsin 7 0.593 0.244 0.072 0.006 0.0031 Note: 1. Stations in both Puerto Rico and the Virgin Islands are not listed in this table. 2. Data is compiled from the SO 2 Monitor All Columns Report for the whole U.S. in 1999. 3. Average Annual Mean (in a State) = Total Concentrations / Total Number of Stations.

Table 5.1: Summary of Annual SO 2 Concentrations (in ppm) by States in 1999

0.005 ppm Concentrations by States in 1999 by Statesin Concentrations 2 FigureAverage 5.1 : Annual SO

72 The spatial distribution of monitoring stations in the country is related to the spatial pattern of SO 2 concentration: the higher the state average annual concentration, the more stations are located in this state (See Table 5.1 and Figure A.1). About 30% of all SO 2 monitoring stations (191 out of 642) are located in Midwestern states.

Pennsylvania has the highest monitoring rate, with 52 stations, and, as indicated earlier, this state has the second highest average annual SO2 concentration. Although its SO 2 concentration is low, California has the second highest monitoring rate, with 39 stations.

Figure A.1 shows that most of the stations in California are located in the Los Angeles and San Francisco regions, two of the most populated metropolitan areas in the country.

There were no active SO 2 monitoring stations in 1999 in Alaska, Nevada, Oregon, South

Dakota, and Wyoming.

5.1.2 SO 2 Emissions

Total annual SO 2 emissions, from both point and area sources, are summarized by state in Table 5.2. Area sources include point sources that cannot be tracked individually, such as homes and small office buildings, and diffuse stationary sources, such as wildfires or agricultural tilling. In 1999, about 18.9 million short tons of SO 2 were emitted into the atmosphere in the U.S., including 16.3 million (86.2%) from major point sources and 2.6 million (13.8%) from area sources. There were 18,305 point sources listed in the NET Facility Emissions Report. Area emissions are generally much smaller than point emissions, and more than 77% of point emissions (12.7 million short tons) are from the electricity service industry, especially coal-burning power plants.

73 Number of Major State Point Sources Total Point Emissions Total Area Emissions Alabama 449 665674.16 64264.12 Alaska 19 3127.62 8137.68 Arizona 79 175801.49 23482.36 Arkansas 117 134320.13 30737.01 California 1279 41697.08 92977.48 Colorado 748 103929.98 23456.21 Connecticut 296 48461.76 9594.61 Delaware 51 70091.00 13087.95 D. C. 5 2371.00 6725.19 Florida 431 820490.73 103244.99 Georgia 151 594915.26 41027.79 Hawaii 71 58252.80 3246.52 Idaho 18 23670.00 14907.99 Illinois 1898 974849.81 79825.55 Indiana 440 1097194.99 36075.88 Iowa 52 242702.00 34317.47 Kansas 221 132155.01 27358.08 Kentucky 413 698853.58 86642.09 Louisiana 208 339242.08 127390.98 Maine 78 39177.90 17737.32 Maryland 129 305476.54 25564.82 Massachusetts 379 144186.77 81781.47 Michigan 771 504929.25 66637.75 Minnesota 321 134209.90 31266.13 Mississippi 131 164070.69 85807.11 Missouri 363 380347.49 55870.22 Montana 138 49361.51 9779.20 Nebraska 288 67751.23 26453.57 Nevada 31 51469.95 13353.61 New Hampshire 232 63261.96 87592.98 New Jersey 372 134818.19 131377.26 New Mexico 103 169822.48 13496.94 New York 674 448647.28 181682.00 North Carolina 919 544459.53 63470.30 North Dakota 51 252901.61 67123.79 Ohio 490 1700165.23 108412.31 Oklahoma 789 139430.66 21042.05 Oregon 235 26195.90 34798.54 Pennsylvania 340 1117674.63 124073.32 Rhode Island 43 2302.08 10230.28 South Carolina 199 272586.20 30054.94 South Dakota 7 27595.78 28039.67 Tennessee 142 609059.00 76972.14 Texas 895 977412.10 121556.30 Utah 133 67311.78 19567.08 Vermont 79 1902.90 14427.44 Virginia 1718 335023.09 40173.29 Washington 104 129167.62 35639.54 West Virginia 224 754697.06 55158.64 Wisconsin 870 297544.78 63567.03 Wyoming 111 154909.68 19481.75 TOTAL 18305 16295671.25 2588688.74 Note: 1. Data compiled from the NET Facility Emissions Report and the NET Tier Report for the whole U.S. in 1999. Table 5.2: Summary of Annual SO 2 Emissions (in short tons) by States in 1999

74 The top three states for point source emissions are Ohio (1.70 million short tons,

490 facilities), Pennsylvania (1.12 million short tons, 340 facilities), and Indiana (1.10 million short tons, 440 facilities), while the top three states for area source emissions are

New York (181,682 short tons), New Jersey (131,377 short tons), and Louisiana (137,390 short tons). Figures 5.2 and 5.3 illustrate the magnitude of SO 2 emissions across states for point and area sources, respectively. The sharing among these two emission sources is illustrated in Figure 5.4. Hawaii had total emissions of 61,499 short tons, including

58,252 from point sources and 3,246 from area source, with 71 reported point facilities

(see Table 5.2). This would support the hypothesis that Hawaii‘s high SO 2 concentrations are not due to anthropogenic sources.

A large number of facilities does not necessarily imply a high level of emissions.

California has 1,279 facilities producing only 41,697 short tons of SO 2, whereas about

1.7 million short tons of SO 2 are emitted by 490 facilities in Ohio. In fact, much of SO 2 emissions is generated by relatively few sources. In the 1999 NET Facility Emissions

Report, the top ten emitting facilities produced about 10% of the national point source emissions. The geographic distribution of point source emissions follows that of concentrations. The eastern part of the U.S. has much higher point source emissions than its western part, and the Midwest, of course, has the largest point source emissions (see

Figure 5.2). However, area source emissions are characterized by a different geographical pattern (see Figure 5.3). As area source emissions are, by definition, related to population and buildings, their highest levels of emissions occur in the most populated states, such as California, New York, and Texas. Note that point source emissions are

75 larger than area source emissions in most states, except California, D.C., New Hampshire,

Oregon, Rhode Island, South Dakota, and Vermont.

5.1.3 Meteorological Conditions

As discussed in the previous chapter, each SO 2 monitoring station at the center or in a cell of a ring-sector system is assigned to the closest weather station with complete meteorological data, representing the climatic conditions at the center of the airshed or at the centroid of a cell. There were 821 active weather monitoring station in the U.S. in

1999. Table 5.3 summarizes selected weather data by states, excluding Alaska and

Hawaii. According to NOAA 16 , the country experienced below average precipitation in

1999, with nationally averaged precipitation of 29.93 inches. The data compiled in this research indicates that the national average wind speed was 6.78 knots (1 knot = 1.15155 miles per hour) in 1999.

The variation of precipitation across states is very significant. The top three rainiest states in 1999 were Alabama (36.56“), Tennessee (35.05“), and Massachusetts

(33.84“), whereas the top three driest states were Nevada (2.13“), Arizona (5.07“), and

New Mexico (5.71“). The top three windiest states were North Dakota, South Dakota, and Nebraska, with average wind speeds of 9.33, 9.26, and 9.16 knots, respectively. On the other hand, New Hampshire, Vermont, and Maryland were the least windy states, with average wind speeds of 4.48, 4.52, 4.76 knots, respectively.

16 U.S. National Precipitation (http://www.ncdc.noaa.gov/oa/climate/research/1999/ann/us_national.html) 76

320,000 short tons short 320,000 Emissions by States in Emissions 1999 by States 2

FigureTotal Point SO 5.2 : Source Annual

50,000 short tons 50,000 short

Emissions by States in Emissions 1999 by States 2

FigureTotal Area SO 5.3 : Source Annual

PointSource Emissions Area Area SourceEmissions Emissions by Statesin 1999 2 FigureShareSO 5.4 : of and Area Point

Avg. Avg. State Precip 1 Wsp 2 WD1 WD2 WD3 WD4 WD5 WD6 WD7 WD8 Alabama 36.56 4.97 10.98 12.54 12.69 11.15 11.50 13.07 14.57 13.50 Arizona 5.07 6.12 10.32 9.33 8.85 11.33 16.14 17.73 12.63 13.68 Arkansas 30.11 5.52 13.69 11.37 10.02 10.55 11.09 17.22 15.03 11.04 California 8.05 5.61 8.73 8.18 14.23 18.99 15.69 11.87 12.05 10.26 Colorado 10.83 7.48 11.19 12.11 13.52 11.61 11.97 14.64 13.82 11.14 Connecticut 24.03 6.22 9.45 16.38 16.42 12.27 12.30 14.81 11.69 6.68 Delaware 29.15 6.92 13.49 11.51 12.86 16.05 13.08 13.98 11.32 7.71 Florida 29.59 6.14 16.79 13.99 11.94 10.10 10.43 9.57 11.06 16.12 Georgia 30.69 4.98 14.09 10.50 12.75 15.08 13.63 10.66 10.75 12.53 Idaho 11.23 6.29 9.06 10.89 10.79 11.61 15.06 20.01 13.24 9.34 Illinois 24.42 7.72 11.23 11.10 12.21 12.81 12.22 16.61 14.53 9.29 Indiana 25.35 7.43 10.58 9.66 11.19 12.32 15.52 17.63 12.50 10.60 Iowa 17.71 9.00 9.21 10.05 15.43 13.83 8.26 13.65 17.71 11.85 Kansas 24.70 8.99 9.84 14.59 13.47 8.38 5.90 15.25 21.28 11.29 Kentucky 33.23 5.92 9.96 12.45 10.72 12.14 14.54 18.23 12.72 9.24 Louisiana 31.14 5.69 11.80 12.97 10.27 9.64 9.29 14.58 18.36 13.09 Maine 28.82 6.36 7.13 11.04 18.73 16.40 10.31 12.78 14.92 8.69 Maryland 26.55 4.76 11.70 10.13 15.15 17.33 12.29 11.38 12.30 9.72 Massachusetts 33.84 7.14 9.42 12.09 13.89 15.39 14.81 16.33 9.83 8.25 Michigan 21.99 7.56 10.42 9.32 12.51 15.62 15.41 16.14 11.02 9.56 Minnesota 22.48 7.81 9.89 8.52 15.07 14.28 10.44 12.75 16.86 12.20 Mississippi 30.82 5.16 9.62 14.21 13.44 9.49 8.72 14.23 18.96 11.32 Missouri 26.87 7.45 10.73 12.14 12.34 11.47 9.43 15.82 16.17 11.90 Montana 8.52 7.64 11.10 8.42 10.27 16.91 17.19 14.35 10.30 11.48 Nebraska 15.96 9.16 7.40 10.96 17.80 13.69 7.38 11.20 19.63 11.94 Nevada 2.13 6.87 9.13 11.83 12.45 12.06 12.20 19.35 14.64 8.34 New Hampshire 29.82 4.48 7.78 10.99 17.90 15.31 11.37 14.74 13.56 8.36 New Jersey 29.83 5.85 11.96 13.39 15.47 14.25 13.13 13.09 10.52 8.18 New Mexico 5.71 8.39 9.63 12.50 12.37 15.18 15.56 12.93 11.52 10.31 New York 22.91 7.31 9.69 11.06 12.67 15.43 18.37 15.34 10.05 7.40 North Carolina 30.48 5.88 12.22 19.00 12.39 10.07 13.52 14.91 9.72 8.17 North Dakota 12.63 9.33 8.65 9.25 16.02 17.34 9.13 10.19 16.87 12.56 Ohio 22.48 7.29 9.08 11.19 11.46 13.36 16.46 17.65 12.21 8.61 Oklahoma 21.92 9.03 8.27 14.92 12.81 6.15 5.73 16.90 24.83 10.40 Oregon 26.92 5.67 8.30 9.91 13.36 12.49 13.65 16.43 15.90 9.96 Pennsylvania 27.03 6.21 10.15 10.91 13.85 17.07 15.54 13.36 10.21 8.91 Rhode Island 29.13 7.63 6.98 14.42 15.22 13.49 11.12 18.29 13.22 7.25 South Carolina 25.10 5.40 15.89 17.70 8.69 9.60 15.92 14.52 9.11 8.57 South Dakota 9.53 9.26 8.75 9.24 17.51 15.04 9.10 9.49 18.49 12.39 Tennessee 35.05 5.06 11.16 14.93 11.46 10.36 14.48 17.05 12.90 7.65 Texas 16.18 7.90 8.90 11.55 10.02 6.53 6.19 15.03 27.23 14.54 Utah 6.94 6.37 7.37 12.08 13.76 13.52 10.45 16.59 17.51 8.72 Vermont 26.85 4.52 8.17 10.63 17.01 15.24 11.44 15.03 13.91 8.58 Virginia 27.31 5.83 11.00 13.50 13.76 12.20 12.55 16.98 11.29 8.73 Washington 27.52 6.16 7.90 9.77 10.04 9.97 16.53 18.99 17.32 9.48 West Virginia 30.87 4.86 8.81 10.18 11.52 13.99 16.63 17.04 11.93 9.91 Wisconsin 19.89 7.24 10.84 10.40 11.45 14.76 14.21 16.63 12.64 9.09 Wyoming 5.84 8.07 7.24 8.74 15.97 18.55 16.99 11.60 10.65 10.26 Note: 1. Precipitation (Avg. Precip) is measured in inch. 2. Wind speed (Avg. WSP) is measured in knot. 3. Wind direction (WD) for each sector is measured in percentage (WD i/Total*100). 4. Data is compiled from the NCDC database for the whole U.S. in 1999.

Table 5.3: Summary of Selected Meteorological Conditions by State in 1999 80 Table 5.3 also indicates wind frequencies among the different directions.

Southwestern winds have a higher share than other directions nationally. WD i (i = 1 to 8) represents the sector i from which the wind blows towards the weather monitoring station.

New England states have two main wind directions - northwestern (sector 4) and southwestern (sector 6) -, while Midwestern states have a dominant southern wind direction (sectors 6 and 7). Western winds (sectors 4 and 5) dominate the West coast states (California and Washington), while southeastern winds are dominant in southern states like Texas and Louisiana (sectors 7 and 8). There is a tendency for the most prevailing wind direction to have the highest wind speed.

5.1.4 Land Use and Land Cover

Table 5.4 summarizes the share of each land classification in the coterminous

U.S., and the corresponding thematic map is presented in Figure 5.5. Table 5.4 presents land areas measured in terms of numbers of cells, because the land-use data is converted from a raster source. Since the AVHRR satellite images have a 1-km nominal spatial resolution, the area of each cell is 1 square kilometer. The top three land classifications are Shrubland (17.54%), Grassland (15.9%), and Evergreen Needleleaf Forest (14.55%).

Only about 0.99% of the total land is covered by urban areas, while water bodies make up around 1.28% of the total land mass. Note that water bodies do not include the Great

Lakes, the Gulf of Mexico, and the Atlantic and Pacific Oceans.

Number Code Classifications of Cells 1 % of Total LULC1 Urban and Built-up Land 77022 0.99 LULC2 Dryland Cropland and Pasture 1040103 13.40 LULC3 Irrigated Cropland and Pasture 105937 1.36 LULC4 Mixed Dryland/Irrigated Cropland and Pasture 0 0.00 LULC5 Cropland/Grassland Mosaic 786101 10.12 LULC6 Cropland/Woodland Mosaic 451962 5.82 LULC7 Grassland 1234242 15.90 LULC8 Shrubland 1361480 17.54 LULC9 Mixed Shrubland/Grassland 13923 0.18 LULC10 Savanna 232499 2.99 LULC11 Deciduous Broadleaf Forest 771070 9.93 LULC12 Deciduous Needleleaf Forest 0 0.00 LULC13 Evergreen Broadleaf Forest 303 0.00 LULC14 Evergreen Needleleaf Forest 1129430 14.55 LULC15 Mixed Forest 363493 4.68 LULC16 Water Bodies 99436 1.28 LULC17 Herbaceous Wetland 0 0.00 LULC18 Wooded Wetland 5879 0.08 LULC19 Barren or Sparsely Vegetated 85439 1.10 LULC20 Herbaceous Tundra 0 0.00 LULC21 Wooded Tundra 5419 0.07 LULC22 Mixed Tundra 77 0.00 LULC23 Bare Ground Tundra 0 0.00 LULC24 Snow or Ice 363 0.00 TOTAL 7764178 100.00 Note: 1. Each cell is 1 square kilometer .

Table 5.4: Summary Land Use / Land Cover for the Coterminous U.S.

Land UseFigure LandThematic Cover the of Systemfor 5.5: the U.S. Map USGS

83 5.2 Airshed Overview

This section focuses on the data characterizing the selected 345 airsheds 17 . Each airshed has a radius of 320 kilometers (200 miles). Table 5.5 presents an overview of the selected concentration, emission, and meteorological airshed variables used in this research, while Table 5.6 presents descriptive statistics for land use / land cover within the selected airsheds. The selected airsheds are located in 28 states and the District of

Columbia. Pennsylvania, Indiana, and Ohio are the top three states, with 48, 36, and 33 airsheds, respectively.

The average annual mean concentration for the selected airsheds is 0.0051 ppm, which is slightly higher than the national average annual mean (0.0041 ppm), but still far below the Nation Ambient Air Quality Standard for sulfur dioxide (0.03 ppm). West

Virginia, Pennsylvania, and Michigan have the highest annual SO 2 concentrations, with average measures of 0.0108, 0.0074, and 0.0074 ppm, respectively. Note that, in contrast to its neighboring states, D.C. experiences a relatively high average annual SO 2 concentration (0.007 ppm).

17 As an airshed may spread across more than one state, the central monitoring stations are used to represent airsheds in terms of location. 84

Number Concen- Number of tration of Point 3 Area 3 AVG 4 State Name Airsheds 2 Mean 1 Facilities Emissions Emissions WSP WD1 5 WD2 WD3 WD4 WD5 WD6 WD7 WD8

Alabama 1 0.0040 449 665674 10632 4.87 10.98 12.54 12.69 11.16 11.50 13.07 14.57 13.50

Arkansas 2 0.0030 93 131510 4615 5.19 12.88 11.42 9.76 11.15 11.53 16.96 15.19 11.10

California 8 0.0021 1061 24568 4131 4.52 8.92 8.36 13.66 19.77 17.57 10.78 10.55 10.38

Connecticut 10 0.0051 296 48462 1460 6.14 9.45 16.38 16.42 12.27 12.30 14.81 11.69 6.68

Delaware 1 0.0070 51 70091 2391 6.77 13.49 11.51 12.86 16.05 13.08 13.98 11.32 7.71

D. C. 1 0.0070 4 2209 2861 N/A N/A N/A N/A N/A N/A N/A N/A N/A

Florida 19 0.0033 431 820491 13401 6.08 17.06 13.97 11.87 10.09 10.37 9.45 10.98 16.19

Georgia 2 0.0035 151 594915 9447 4.75 14.09 10.50 12.75 15.08 13.63 10.66 10.75 12.53

Illinois 22 0.0061 1898 974850 14066 7.59 11.23 11.10 12.22 12.80 12.22 16.60 14.53 9.29

Indiana 36 0.0058 440 1097195 8441 7.22 10.58 9.66 11.19 12.32 15.52 17.62 12.50 10.60

Iowa 1 0.0010 49 201384 6189 8.53 9.63 10.23 14.69 13.76 8.71 14.17 17.32 11.49

Kentucky 11 0.0063 413 698854 20490 5.79 9.96 12.45 10.72 12.14 14.54 18.23 12.72 9.25

Maryland 1 0.0060 129 305477 6067 6.40 11.70 10.13 15.15 17.33 12.29 11.38 12.29 9.72

Massachusetts 16 0.0049 379 144187 10974 6.83 9.42 12.09 13.89 15.39 14.81 16.33 9.83 8.25

Michigan 5 0.0074 743 477172 12206 7.38 10.53 9.42 11.47 15.34 16.24 17.19 11.00 8.82

Mississippi 1 0.0020 130 164070 15148 5.06 9.62 14.21 13.44 9.49 8.72 14.23 18.96 11.33

Missouri 9 0.0054 363 380347 13218 7.16 10.75 12.13 12.32 11.55 9.64 15.69 16.02 11.90 New Hampshire 9 0.0040 232 63262 12925 4.14 7.78 10.99 17.89 15.31 11.37 14.74 13.56 8.36

New Jersey 12 0.0048 372 134818 29506 5.47 11.96 13.39 15.47 14.25 13.13 13.09 10.52 8.18

New York 19 0.0064 674 448647 42286 6.84 9.69 11.06 12.67 15.43 18.37 15.34 10.05 7.40

North Carolina 11 0.0049 919 544460 13237 5.62 12.22 19.00 12.39 10.08 13.52 14.91 9.72 8.17

Ohio 33 0.0065 490 1700165 23609 6.98 9.08 11.19 11.46 13.36 16.46 17.65 12.20 8.61

Pennsylvania 48 0.0074 340 1117675 21974 5.83 10.15 10.91 13.85 17.07 15.54 13.36 10.21 8.91

Rhode Island 2 0.0060 43 2302 2047 7.07 6.98 14.42 15.22 13.49 11.13 18.29 13.22 7.25

South Carolina 7 0.0017 199 272586 4549 5.18 15.89 17.70 8.69 9.60 15.92 14.52 9.11 8.58

Tennessee 23 0.0051 142 609059 14634 4.83 11.16 14.93 11.46 10.37 14.48 17.05 12.90 7.65

Vermont 1 0.0040 79 1903 2528 4.01 8.17 10.63 17.01 15.24 11.44 15.03 13.91 8.58

Virginia 10 0.0048 1718 335023 13437 6.09 11.03 13.43 13.48 12.12 12.70 17.20 11.33 8.72

West Virginia 24 0.0108 196 754603 11386 4.56 8.81 10.18 11.52 13.99 16.63 17.03 11.93 9.91

TOTAL 345 14211 14232794 395261 Note: 1. Concentration mean (ppm) = Total Concentrations within the State / Total Number of Stations in that State. 2. Number of Airsheds = Selected airsheds with the central monitoring station within the State. 3. Emissions are measured in short tons. 4. Wind speed (Avg. WSP) is measured in knots. 5. Wind direction (WD) for each sector is in percentage (WD i/Total*100). 6. Weather data is compiled from the NCDC database for the whole U.S. in 1999.

Table 5.5: State Summary of Concentrations, Emissions, and Meteorological 6 Variables within the Selected 345 Airsheds

Land Classification Mean Sum % of Total Minimum Maximum Urban and Built-up Land 1161.000 400625.000 2.401 77.043 3466.000 Dryland Cropland and Pasture 9008.000 3107904.000 18.626 2.000 85147.000 Irrigated Cropland and Pasture 43.809 15114.000 0.091 0.000 2662.000 Mixed Dryland/Irrigated Cropland and Pasture 0.000 0.000 0.000 0.000 0.000 Cropland/Grassland Mosaic 2266.000 781669.000 4.685 4.000 36480.000 Cropland/Woodland Mosaic 8484.000 2927017.000 17.542 0.000 48242.000 Grassland 201.549 69534.000 0.417 0.000 5309.000 Shrubland 307.201 105984.000 0.635 0.000 18470.000 Mixed Shrubland/Grassland 63.297 21838.000 0.131 0.000 7321.000 Savanna 380.641 131321.000 0.787 0.000 11575.000 Deciduous Broadleaf Forest 15737.000 5429269.000 32.539 0.000 79335.000 Deciduous Needleleaf Forest 0.000 0.000 0.000 0.000 0.000 Evergreen Broadleaf Forest 3.464 1195.000 0.007 0.000 64.594 Evergreen Needleleaf Forest 2432.000 839197.000 5.030 0.000 53715.000 Mixed Forest 5000.000 1725034.000 10.339 0.000 62274.000 Water Bodies 3167.000 1092599.000 6.548 0.000 38166.000 Herbaceous Wetland 0.000 0.000 0.000 0.000 0.000 Wooded Wetland 63.246 21820.000 0.131 0.000 4393.000 Barren or Sparsely Vegetated 40.255 13888.000 0.083 0.000 5307.000 Herbaceous Tundra 0.000 0.000 0.000 0.000 0.000 Wooded Tundra 3.917 1351.000 0.008 0.000 395.635 Mixed Tundra 0.318 109.832 0.001 0.000 5.696 Bare Ground Tundra 0.000 0.000 0.000 0.000 0.000 Snow or Ice 0.061 21.000 0.000 0.000 7.000

Selected Grouped Land Urban and Built-up Land 1161.000 400625.000 2.401 77.043 3466.000 Pasture Land 9052.000 3123018.000 18.717 2.000 85147.000 Grass Land 952.688 328677.000 1.970 0.053 30119.000 Deciduous Forest Land 15737.000 5429269.000 32.539 0.000 79335.000 Evergreen Forest Land 2436.000 840392.000 5.037 0.000 53723.000 Water Area 3167.000 1092620.000 6.548 0.000 38166.000 Note: 1. All variables, except % of Total, are in square kilometers. 2. Grouped pasture land includes dryland cropland and pasture, irrigated cropland and pasture, and mixed dryland / irrigated cropland and pasture. 3. Grouped grassland includes grassland, shrubland, mixed shrubland / grassland, and savanna. 4. Grouped deciduous forest land includes deciduous broadleaf forest and deciduous needleleaf forest. 5. Grouped evergreen forest land includes evergreen broadleaf forest and evergreen needleleaf forest. 6. Water area includes water bodies and snow or ice.

Table 5.6: Summary Statistics for Land Use / Cover within the Selected 345 Airsheds

86 Table 5.5 shows that about 14.6 million short tons of SO 2, or more than 77.5% of the total national emissions, came from sources located inside the selected 345 airsheds, including 14.2 million short tons (97.3%) of point emissions and 0.4 million short tons

(2.7%) of area emissions. There were 14,211 reported facilities (77.63% of all reported facilities in the U.S.) located within the selected airsheds. The top three SO 2 emissions states are Ohio (1.7 million short tons), Pennsylvania (1.13 million short tons), and

Indiana (1.10 million short tons), and most of these emissions are from point sources.

New York experiences the highest area SO 2 emissions with 42,286 short tons, as a result of the high population densities in this state.

There were 821 active weather monitoring stations in the U.S. in 1999, and 426 of them were associated to either the monitoring stations or the airshed cells used in this research. The average precipitations for the selected airsheds across states are very similar to the ones obtained with all stations across the U.S. (See Table 5.3). The average wind speeds for the selected airsheds by state are lower than the ones obtained with all stations for the whole U.S., except for Maryland. The dominant wind direction for the selected airsheds is southwestern (mainly sector 6), which is consistent with the national trend and corresponds to the movement of SO 2 in North America.

Table 5.6 presents descriptive statistics for land use / land cover within the selected airsheds. The dominant land classification is the deciduous broadleaf forest, covering about 32% of the airsheds, followed by dryland / cropland / pasture, occupying about 19% of the airsheds. Only about 2.4% of the airsheds is made of urban and built- up land. Water bodies, excluding the Great Lakes, the Atlantic and Pacific Ocean, and the Gulf of Mexico, cover about 6.5% of the airsheds. Similar land covers are grouped

87 together, to simplify correlation analyses, with: Urban Land (2.4%), Pasture Land

(18.7%), Grass Land (2.0%), Deciduous Forest (32.5%), Evergreen Forest (5.0%), and

Water Area (6.5%).

5.3 Correlation Analyses

Table 5.7 provides basic descriptive statistics for the selected variables. The

-3 mean value for SO 2 concentration is 5.91*10 ppm, and the average distance between a central monitoring station and its neighboring monitoring stations is 84.5 kilometers. The

-3 mean value for background SO 2 concentration is 5.26*10 ppm, while the mean value for

SO 2 emission is 2,188,000 short tons, including both point and area sources. To examine the relationships between the selected variables and SO 2 concentrations, correlation analyses are performed, and the results are presented in Table 5.8. The results of these correlation analyses are to be used to formulate the specification of the statistical models presented in Chapter 6.

Both background SO 2 concentrations and SO 2 emissions are expected to have positive correlations with central station SO 2 concentrations (MEAN0). Wind speeds are expected to be inversely correlated with SO 2 concentrations. A higher wind speed may keep SO 2 particulate matters in the atmosphere longer, with smaller SO 2 depositions.

Precipitations are expected to have a negative correlation with SO 2 concentrations, because rain is viewed as a natural cleansing process, washing out SO 2 particulate matters.

Urban and built-up land is expected to be positively correlated with SO 2 concentrations, because urban areas have larger populations, generating more SO 2 emissions. Green spaces, such as forests, and water bodies are expected to be negatively correlated with

88 SO 2 concentrations, as they may absorb some of the airborne pollutants and reduce the amounts deposited on the ground. As there is no background research available, it is difficult to predict the effects of land covers such as pasture and tundra.

Standard Variable Description Mean Deviation Sum Minimum Maximum MEAN0 SO2 concentration 6 3 2038 1 15 Distance between central and DT_CON neighboring stations 84 41 29145 8 210 Distance-weighted background WT_CON concentration 5 2 1814 1 11 WT_EMS Distance-weighted SO2 emissions 2188 1079 754738 74 3959 AVGWSP Average wind speed 6 2 2120 2 10 Wind-frequency-weighted wind WTWSP speed 6.41 2 2211 2 10 AVGPRECP Average precipitations 27 4 9213 4 37 Inverse-distance-weighted IDWPRECP precipitations 28 5 9490 4 46 URBAN Urban land use 1161 685 400625 77 3466 PASTURE Pasture land use 9052 14784 3123018 2 85147 GRASS Grass land use 953 3756 328677 0 30119 FOR_DE Deciduous forest 15737 17618 5429269 0 79335 FOR_EG Evergreen forest 2436 6548 840392 0 53723 WATER Water bodies 3167 6036 1092620 0 38166 Frequency of wind blowing from WDSEC1 sector 1 1144 366 394678 39 2223 Frequency of wind blowing from WDSEC2 sector 2 1267 416 437048 121 2537 Frequency of wind blowing from WDSEC3 sector 3 1349 386 465466 101 2825 Frequency of wind blowing from WDSEC4 sector 4 1430 488 493221 30 4514 Frequency of wind blowing from WDSEC5 sector 5 1512 490 521732 39 2841 Frequency of wind blowing from WDSEC6 sector 6 1582 456 545926 48 2658 Frequency of wind blowing from WDSEC7 sector 7 1181 387 407457 24 2726 Frequency of wind blowing from WDSEC8 sector 8 1000 330 344886 25 2378 Note: 1. Concentrations are measured in 10 -3 ppm. 2. Emissions are measured in 10 3 short tons. 3. Wind speeds are measured in knots. 4. Precipitations are measured in inches. 5. Land uses are measured in square kilometers.

Table 5.7: Descriptive Statistics for the Selected Variables over the 345 Airsheds

Pearson Correlation Prob > |r| under H0: Variables Coefficients Rho=0 DT_CON -0.24347 <.0001 WT_CON 0.7168 <.0001 WT_EMS 0.5481 <.0001 AVGWSP 0.18123 0.0007 WTWSP 0.17135 0.0014 AVGPRECP -0.05594 0.3002 IDWPRECP -0.04021 0.4566 WDSEC1 -0.15544 0.0038 WDSEC2 -0.18433 0.0006 WDSEC3 -0.06186 0.2518 WDSEC4 -0.01762 0.7443 WDSEC5 0.39472 <.0001 WDSEC6 0.22985 <.0001 WDSEC7 -0.13034 0.0154 WDSEC8 -0.04145 0.4429 LAND USE Urban and Built-up Land -0.15685 0.0035 Dryland Cropland and Pasture -0.20788 0.0001 Irrigated Cropland and Pasture -0.18023 0.0008 Mixed Dryland/Irrigated Cropland and Pasture N/A Cropland/Grassland Mosaic -0.13911 0.0097 Cropland/Woodland Mosaic -0.10208 0.0582 Grassland -0.25398 <.0001 Shrubland -0.18285 0.0006 Mixed Shrubland/Grassland -0.12395 0.0213 Savanna -0.20647 0.0001 Deciduous Broadleaf Forest 0.09770 0.0699 Deciduous Needleleaf Forest N/A Evergreen Broadleaf Forest -0.07592 0.1594 Evergreen Needleleaf Forest -0.28897 <.0001 Mixed Forest -0.05939 0.2713 Water Bodies -0.21439 <.0001 Herbaceous Wetland N/A Wooded Wetland -0.15373 0.0042 Barren or Sparsely Vegetated -0.13806 0.0102 Herbaceous Tundra N/A Wooded Tundra -0.09484 0.0786 Mixed Tundra 0.12156 0.0239 Bare Ground Tundra N/A Snow or Ice -0.14829 0.0058 Selected Grouped Land Use Urban and Built-up Land -0.15685 0.0035 Pasture Land -0.21166 <.0001 Grass Land -0.24138 <.0001 Deciduous Forest Land 0.09770 0.0699 Evergreen Forest Land -0.28898 <.0001 Water Bodies -0.21440 <.0001 Note: 1. Refer to Table 5.6 for the grouping information about Selected Grouped Land. 2. See Table 5.7 for variable definitions. 3. This table only presents the variables captured within the selected 345 airsheds.

Table 5.8: Pearson Correlations between the Selected Independent Variables and SO 2 Concentrations

90 Distances between receptors and emitters should have a negative correlation with concentrations, because distance is a decay factor during pollutant transport. The correlation for distance has the expected sign, and is very significant. Both background

SO 2 concentrations and SO 2 emissions have positive correlations, as expected, and are very significant. The average wind speed and frequency-weighted wind speed do not have the expected sign. There may be some unaccountable micro-interactions that create a positive relation, and this issue will be discussed in greater details in Chapter 6. The correlations for the precipitation variables have the expected sign, but they are insignificant. The significant negative correlation for urban and built-up land does not match expectations. One possible explanation is that, in addition to being an emission center, urban and built-up land may absorb pollutants, because of the heat island effect, which involves the trapping of heat and pollutants in heavily built-up areas. The correlation for deciduous broadleaf forest does not have the expected sign, but is insignificant. Interestingly, although the amount of both broadleaf and needleleaf forests is inversely correlated with the average SO 2 concentrations, needleleaf forests have a much stronger correlation than broadleaf forests. Water bodies have the expected and significant negative correlation. When individual land use types are aggregated into groups, all the correlation coefficients are consistent with the initial expectations, with very significant coefficients, except for deciduous forest land. Note that grouped pasture land is negatively and significantly related to SO 2 concentrations.

Based on the results of the above correlation analyses, some important findings can be summarized as follows. Most variables have the expected correlations with SO 2 concentrations. Variables such as distance, precipitation, evergreen forest land, grass

91 land, and water bodies have negative correlations with SO 2 concentrations, while the variables with positive correlations are the background concentrations and emissions.

Because of the heat island effect, urban land use has a negative correlation with SO 2 concentrations, trapping pollutants and reducing their flows to downwind locations.

Wind speed is expected to have a negative correlation with SO 2 concentrations. However, because of the time and distance components in the decay function, wind speed may have an unpredictable effect in the model. As presented in Chapter 6, the final estimated model displays a negative relationship between wind speed and emissions and a positive one for concentrations.

CHAPTER 6

MODEL ESTIMATION AND ANALYSIS

Based on the theoretical framework outlined in Chapter 3 and on the preliminary statistical analyses presented in Chapter 5, air quality models for SO 2 are estimated, and their implications are examined. The multi-stage procedure of model estimation is first discussed, and the results of each stage are presented. The coefficient of determination

(R 2) is the primary model selection criterion, while the selection of explanatory variables depends on their significance, sign, and theoretical importance. To obtain better explanatory models, variables are transformed or weighted. In order to assess the impacts of the independent variables in the final model, elasticity functions are formulated and elasticities are calculated.

6.1 Statistical Model

6.1.1 Overview

Based on the literature review and the results of the correlation analyses, the hypothesized relationships between SO2 concentrations and various explanatory variables are first briefly reviewed. Both background SO 2 concentrations and SO 2 emissions are

93 expected to have positive correlations with central station SO 2 concentrations. Wind speeds are expected to be positively correlated with SO 2 concentrations, due to the impact of pollution decay. Precipitation is expected to have a negative correlation with SO 2 concentrations, because of the cleansing process. The heat island effect leads urban and built-up land to have a negative relationship with SO 2 concentrations. Green spaces, such as forests, and water bodies are expected to be negatively correlated with SO 2 concentrations, because of their absorption capabilities. As discussed in Chapter 3, the concentration measured at the central monitoring station is the result of two contributions:

(1) background concentrations and (2) air pollution emissions within the airshed. The definition and description of the independent variables used in the model estimation process are presented in Table 6.1. Mathematically, the proposed model has the following general form:

C 0 = f (CON) + g(EMIS) (6.1)

Before reaching the central monitoring station, both background concentrations and emissions are continuously modified by meteorological and land-cover conditions.

In addition, decay effects take place during the transportation process. These factors are represented by the generalized relationship:

α1 α 2 α 3 C 0 = a + a ⋅CON ⋅WEA ⋅ LU ⋅ DIST + 0 1 (6.2) ⋅ ⋅ β1 ⋅ β 2 ⋅ β 3 a2 EMISS WEA LU DIST

NAME DESCRIPTION Category Sub-Category Dependent Variable

C0 Concentration measured at the central monitoring station

CON SO 2 concentrations

CONW i Inverse-distance-weighted concentration measured in sector i (i = 1 G 8)

CONA i Average concentration measured in sector i (i = 1 G 8)

EMIS SO 2 emissions

TOTEMS j Sum of point and area source emissions in cell j (j = 1 G 128)

TOTWEMS j Sum of distance-weighted point and area source emission in cell j (j = 1 G 128)

EMA j Total Area source emissions in cell j (j =1 G 128)

EMP j Total Point source emissions in cell j (j = 1 G 128)

EMWP j Total Distance-weighted point source emissions in cell j (j = 1 G 128) NFAC Number of facilities WEA Meteorological conditions

CWSP i Average wind speed computed in sector i (i = 1 G 8) for concentrations

EWSP i Average wind speed computed in sector i (i = 1 G 8) for emissions

WTWSP i Wind-frequency-weighted wind speed computed in sector i (i = 1 G 8)

WDSEC i Frequency of wind blowing from sector i (i = 1 G 8)

FD i Relative frequency (%) of wind direction in sector i (i = 1 G 8) AVGPRECP Average precipitations in an airshed IDTPRECP Inverse-distance-weighted precipitations in an airshed LU Land uses CURB Urban and built-up land for concentrations EURB Urban and built-up land for emissions CPAS Pasture land for concentrations EPAS Pasture land for emissions CGRS Grass land for concentrations EGRS Grass land for emissions CFORDE Deciduous forest land for concentrations EFORDE Deciduous forest land for emissions CFOREG Evergreen forest land for concentrations EFOREG Evergreen forest land for emissions CWAT Water, snow, and ice area for concentrations EWAT Water, snow, and ice area for emissions DIST (or d) Distance between two points d_con Distance factor for concentrations d_ems Distance factor for emissions Note: 1. The formulas used to compute the concentration, emission, and meteorological variables are presented in Chapter 4.

Table 6.1: Definition and Description of the Independent Variables 1

95 In Equation (6.2), a 1 and a 2 represent the transfer coefficients for background concentrations and pollution emissions, respectively, while a 0 denotes any residual or unaccountable pollutant deposition. This air quality model establishes a statistical relationship between the ambient pollution concentration at the receptor and the amount of pollution emitted from the sources, using a set of transfer coefficients (see Chapter 3).

The exponents, \ 1, \ 2, \ 3, ]1, ] 2, and ] 3, measure the magnitudes of the impacts of the variables WEA, LU, and DIST. Equation (6.2) must, of course, be reformulated to take into consideration wind direction frequencies, with:

8  α 3 α1 α 2  0 = + ⋅ ⋅ ⋅ ⋅ ⋅ + C a0 a1 ∑ FDi DISTi WEAi LU i CONWi   i=1  (6.3)  8  16  ⋅ ⋅ β 3 ⋅ β1 ⋅ β 2 ⋅  a2 ∑ FDi ∑ DISTij WEAij LU ij TOTWEMSij   i=1  j=1 

where:

LU i and LU ij represent combinations of land uses,

WEA i and WEA ij represent combinations of meteorological variables,

DIST i and DIST ij represent distance variables,

FD i represents wind frequency,

i represents an airshed sector (i = 1 G 8), and

j represents an airshed ring (j = 1 G 16).

Distances calculated between the central monitoring station and the surrounding monitoring stations, emission points, or centroids of cells, are expected to have negative effects on the deposition of pollutants, because the farther the emitter, the smaller the amount of pollutant reaching a receptor. Wind speed is expected to be inversely related 96 to SO 2 concentrations, based on the formulation of the Gaussian diffusion model. The higher the wind speed, the faster and farther away particles are moving in the atmosphere, so that fewer pollutants are deposited at a given receptor. Precipitation is also expected to have a negative relationship with SO 2 concentrations. It is commonly known that rain will —wash out“ some particles in the air, so raining is part of the natural pollution removal process. The impact expectations for different land uses were discussed in more details in the previous chapter, and, based on preliminary correlation analyses, it appears more fruitful to use the grouped land uses in the statistical analysis. In general, green spaces and water bodies should have inverse relationship with SO 2 concentration, while urban areas are expected to be positively related to them.

6.1.2 Estimation Process

Given the model framework outlined in Equation (6.3), the selection of the —best“ regression model is a difficult task, as there are more than 30 variables (including those not presented in Table 6.1) with a large number of potentials combinations, and about 18 potential parameters (a 0, a 1, a 2, and the various exponents in Equation 6.3), with different possible combinations, have to be estimated. The choice of the independent variables is based on the literature review, that indicates that the air pollution process involves decay effects, meteorological conditions, emission uptakes, and land use patterns. The selection of the final model will be based upon: (1) the overall explanatory power of the model and

(2) the significance and sign of each coefficient.

If the only variables were the concentration and emission, the model would be linear, with:

97 Co = A 0 + A 1 CON + A 2 EMIS , (6.4) where:

CON = FD ⋅CONW , (6.5) ∑i i i

EMIS = FD TOTWEMS . (6.6) ∑i i ∑ j ij

However, the introduction of the other variables into the coefficients A 1 and A 2, with unknown exponents, renders the model essentially nonlinear. While Equation (6.4) can be written as:

Co = a 0 + a 1 CON + a 2 EMIS , (6.7) the variables CON and EMIS are now expressed as:

CON = FD ⋅ DIST α 3 ⋅WEAα1 ⋅ LU α 2 ⋅CONW , (6.8) ∑i i i i i i

EMIS = FD DIST β 3 ⋅WEAβ1 ⋅ LU β 2 ⋅TOTWEMS . (6.9) ∑i i ∑ j ij ij ij ij

It is difficult to implement a nonlinear regression procedure, due to the complex structure of the model represented by Equation (6.7) œ (6.9), where the variables CON and EMIS are complex summations over the basic variables CONW i and TOTWEMS ij .

To overcome this problem, the model is estimated through ordinary least squares (OLS) regression, and the unknown exponents of the model are treated exogenously by performing grid searches over the possible values of these exponents. A linear- logarithmic model specification, in which the regressand (C o) is linear but the regressors are logarithmic, is used to obtain starting values for the exponents, which are used to construct the first exponent intervals in the grid search. This step also helps eliminate certain combinations of variables that are statistically insignificant or have incorrect signs.

98 The grid search technique is used in an interactive manner. Possible parameter search intervals, with appropriate increment (or decrement) steps, are defined, and the center of the search interval is set to be the best estimated parameter so far. Once the estimated parameter is deemed stable (i.e., it does not change after several regression runs), the increment step is reduced. For example, assume that the currently best parameter estimate is 5, within the search interval [1 œ 10], with an increment step of 1.

If this value is stable, the increment step is changed to 0.1, and the next round of grid search is started. If each parameter has 10 possible values, then obtaining the best estimates for 5 parameters requires estimating 100,000 regressions, a very time- consuming process.

The primary criterion to select the best set of parameters is the R 2 in each round of the grid search, since the estimation procedure is OLS regression. For each set of parameters, a model is estimated, and the value of the R 2 is computed. If two models have the same R 2, the t-statistics of the variables are compared to select the —better“ model.

6.1.3 Results

The cell of an airshed is the basic spatial analysis unit in this research. It is possible to have more than one emission source or monitoring station within a cell.

Variables associated to those emitters or stations are aggregated at the cell level, using various weighting schemes (see Equation (4.1) to (4.6) in Chapter 4). The first step is to determine the better weighting schemes for background concentrations and pollutant emissions. Inverse-distance-weighted concentrations and distance-weighted emissions

99 turn out to produce the best model, with the expected signs and statistically significant coefficients. In the first stage, Equation (6.4) is estimated as:

 8  + ⋅ + Co = .1 945 .6 300 ∑ FDi CONWi  (6.10) .6( 10) .4( 56) i=1 

 8  16  ⋅  .0 066 ∑ FDi ∑TOTWEMSij  (10.68) i=1  j=1 

R2 = 0.526

In the next stage, the distance effect is introduced into the model, under the assumption that distance should be inversely related to the SO 2 concentration measured at the central monitoring station. Since the distance effects of the surrounding concentrations and emissions may be different, grid searches for distinct distance exponents are carried out. Negative exponent values yield higher R 2 and more significant regression coefficients than positive values, as expected. It is interesting to note that the distance exponent is smaller (in absolute terms) for concentrations (-0.15) than for emissions (-0.8), pointing to a much stronger attenuation effect for emissions. Equation

(6.11) is the optimal result of the grid search over the distance variables, with:

 8  + ⋅ − .0 15 ⋅ + Co = .0 802 .1 517 ∑ FDi d _ coni CONWi  (6.11) .2( 95) (17.23) i=1 

 8  16  ⋅ − 8.0 ⋅  .0 202∑ FDi ∑ d _ emsij TOTWEMSij  )5.3(  i=1  j=1 

R2 = 0.548

100 In the third stage, several meteorological variables are introduced into the model.

The average wind speed appears to have the strongest effect on the model, and grid searches for distinct wind speed exponents for the concentration and emission parts of the model are conducted. The optimal wind speed exponent for emissions is negative, as expected. However, the optimal wind speed exponent for concentrations is positive. The estimated model is:

 8  + ⋅ − .0 15 ⋅ .0 15 ⋅ + Co = .0 826 .1 123 ∑ FDi d _ coni CWSPi CONWi  (6.12) .3( 05) (17.38) i=1 

 8  16  ⋅ − 8.0 ⋅ − .0 75 ⋅  .0 937 ∑ FDi ∑d _ emsij EWSPij TOTWEMSij  .6( 12) i=1  j=1 

R2 = 0.549

In the final stage, land-use variables are introduced into the model. Grid searches are carried out for various combinations of land-use variables, while considering distinct impacts on concentrations and emissions. The final model, estimated through OLS regression, with 345 observations, is:

 8  C = − .0 15 1.0 − .0 02 (6.13) o .1 08002+ .1 26796 ∑ FDi ⋅ d _ coni ⋅ CWSPi ⋅CURBi ⋅CONWi  .4( 12) (15 )7.  i=1 

 8  16 ⋅ − .0 85 ⋅ − .0 75 ⋅ − .0 08 ⋅ .1 54755∑ FDi ∑ d _ emsij EWSPij EURBij .4( 01)  i=1  j=1

− .0 12 − 2.0  EFOREGij ⋅ EWATij ⋅TOTWEMSij  

R2 = 0.556

101 The estimated model explains about 56% of the variations of SO 2 concentrations

(C 0). All the regression coefficients have the expected signs, and are statistically significant. The signs for CWSP and CURB are not as initially expected. However, if the heat island effect is taken into account, then urban land can conceivably be inversely related to SO 2 concentrations, because of the trapping of pollutants. Wind speed plays a subtle role in the model, and is initially expected to be inversely related to SO 2 concentrations. The estimated model displays a negative relationship for emissions and a positive relationship for concentrations. The positive relationship may be explained by the decay function:

D = e -^t , (6.14) where D is the decay, ^ is the rate of decay, and t is the travel time. If x is the distance and _ is the mean wind speed, Equation (6.14) can be rewritten as:

x −γ D = e υ (6.15)

When the wind speed _ increases, the term œ 1/ _ also increases, leading to an increase in the decay D. There are, therefore, two opposite effects interacting in the model. The balance of these two effects determines the sign of the wind speed variables, which may be different for concentrations and emissions.

102 6.2 Elasticity Analysis

An elasticity analysis is performed to serve as a sensitivity analysis and to provide a basis for comparing the effects of the different explanatory variables. Because explanatory variables have different units, it is difficult to have a sense of how a variable behaves in the model without a common ground. Elasticity calculation provides this common ground. To examine the interrelationships between the independent variables and the dependent variable, the elasticity, defined as the percentage change in the dependent variable (C o) resulting from a one percentage change in the independent variable, is computed, based on the general formula:

∂C C ε = o o (6.16) ∂X X where X is any of the independent variables. Equation (6.13) is differentiated with respect to each independent variable, and the resulting elasticity functions are presented in Table 6.2. The basic elasticities are related to a variable associated either to a sector,

`(X i), or to a cell, `(X ij ). In general, such basic elasticities are not very informative, and are therefore summed up over all sectors or over all cells. The total elasticities for variable X are defined as:

T ` = ∑i `(X i) (6.17) or

T ` = ∑ij `(Xij ) , (6.18) and measure the percentage change in C o resulting from a simultaneous increase (or decrease) by 1 percent of all the variables X i (or X ij ). For instance, in the case of distance d_con i, the total elasticity would measure the impact of a 1% increase in the distances 103 from all surrounding stations to the central station. The total elasticities with respect to all the independent variables are computed for the selected 345 airsheds, and descriptive statistics are presented in Table 6.3.

The elasticity of d_con varies from -0.39 to -0.02 across the selected 345 airsheds.

On average, a 1% increase in the distance between the central monitoring station and all its eight surrounding monitoring stations leads to a decrease of 0.12% in C o. Compared to the other variables (except CON), d_con has the greatest effect on SO 2 concentrations.

Although d_con cannot be used as a policy indicator, this elasticity has implications for the construction of SO 2 monitoring systems. The elasticity range for CWSP is [0.013 -

0.264]. On average, a 1% increase in the average wind speed in all directions results in an increase of about 0.08% in C o. The elasticity of CURB ranges from -0.053 to -0.003.

On average, a 1% increase in the urban and built-up land in all 8 sectors leads to a decrease of 0.015% in C o.

104

) ij ) ) ) ) ) ij ij ij ij / WEAT / / EFOREG / / EURB / / EWSP / / d_ems / o o o o o ) / (C ) / ) / (C ) / ) / (C ) / ) / (C ) / ) / (C ) / ij ij ij ij ij ) ij ‡ TOTWEMS ‡ TOTWEMS ‡ TOTWEMS ‡ TOTWEMS ‡ TOTWEMS /TOTWEMS -1.2 -0.2 -0.2 -0.2 -0.2 o

ij ij ij ij ij ) i ) i ) ) (C / i -0.2

‡ EWAT ‡ ‡ EWAT ‡ ‡ EWAT ‡ ‡ EWAT ‡ ‡ EWAT ‡ ij / CURB / / d_con / -0.12 -1.12 -0.12 -0.12 -0.12 o o

/ CWSP / ij ij ij ij ij o ‡ EWAT ) / (C / ) ) / (C / ) i i ) ) ) / (C / ) i i -0.12

ij ‡ EFOREG ‡ EFOREG ‡ EFOREG ‡ EFOREG ‡ EFOREG ‡ CONW ‡ CONW -0.08 -0.08 -1.08 -0.08 -0.08 ‡ CONW

/ CONW / ij ij ij ij ij o -1.02 -0.02

i i -0.02 ‡ EFOREG

i ) / (/ ) C -0.08

‡ ERUB ‡ ‡ ERUB ‡ ‡ ERUB ‡ ‡ ERUB ‡ ‡ ERUB ‡ ij -0.02 ‡ CRUB ‡ ‡ CRUB ‡

-0.75 -0.75 -0.75 -1.75 -0.75 ‡ CRUB ij ij ij ij ij 0.1 0.1 i i -0.9 i ‡ ERUB ‡ CRUB -0.75 ‡ EWSP ‡ EWSP ‡ EWSP ‡ EWSP ‡ EWSP ij ‡ CWSP ‡ ‡ CWSP ‡ 0.1 i ‡ CWSP -0.85 -0.85 -0.85 -0.85 -1.85 -0.15 -1.15 ij ij ij ij ij i i -0.15 i ‡ EWSP

‡ CWSP -0.85 ‡ d_ems ‡ ‡ d_ems ‡ ‡ d_ems ‡ ‡ d_ems ‡ ‡ d_con ‡ ‡ d_con ‡ ‡ d_ems ‡ -0.15 ij i i i i i i i i ‡ d_con ‡ i = 1.54755. 1.54755. = 2 ‡ d_con ‡ ‡ d_ems ‡ i i ‡ FD ‡ ‡ (-0.20) ‡ FD ‡ (-0.20) ‡ ‡ (-0.12) ‡ FD ‡ (-0.12) ‡ ‡ (-0.08) ‡ FD ‡ (-0.08) ‡ ‡ (-0.75) ‡ FD ‡ (-0.75) ‡ ‡ (-0.02) ‡ FD ‡ (-0.02) ‡ ‡ (0.1) ‡ FD (0.1) ‡ ‡ FD ‡ ‡ (-0.15) ‡ FD ‡ (-0.15) ‡ ‡ (-0.85) ‡ FD ‡ (-0.85) ‡ 1 2 2 2 2 1 1 2 1 2 (a (a (a (a (a (a (a (a (a Elasticity Functions Elasticity (a Table 6.2: Elasticity Functions

= 1.26796, a 1.26796, = 1

ij ij i i ij ij

i ij CURB D_ems CWSP EWSP Variables d_con EURB EFOREG EWAT EMS CON Note: Note: 1. a

105

Variable Mean Standard Deviation Minimum Maximum d_con -0.116 0.049 -0.396 -0.020 CWSP 0.077 0.033 0.013 0.264 CURB -0.015 0.007 -0.053 -0.003 d_ems -0.017 0.026 -0.238 0 EWSP -0.015 0.023 -0.210 0 EURB -0.002 0.002 -0.022 0 EFOREG -0.002 0.004 -0.034 0 EWAT -0.004 0.006 -0.056 0 CON 0.774 0.326 0.133 2.641 EMS 0.019 0.031 0 0.280 Note: 1. Elasticity = 0 means that there is no data for the variable in the sector or cell. 2. In the case of distance, elasticity = 0 means that there are dummy monitoring stations within the airsheds.

Table 6.3: Descriptive Statistics for the Total Elasticities (N = 345)

The elasticity of d_ems ranges from -0.24 to 0. A zero elasticity means that there is no data for this variable in at least one of the selected airsheds. Some airsheds have parts of their areas located over the ocean, so there is no emission data for those cells. On average, a 1% increase in the distance between the central monitoring station and all its surrounding emission sources leads to a decrease of about 0.015% in C o. The range of elasticity for EWSP is [-0.21 œ 0]. On average, a 1% increase in the average wind speed in all directions results in a decrease of about 0.015% in C o. The elasticity of EURB ranges from -0.022 to 0. On average, a 1% increase in the urban and built-up land in all cells leads to a decrease of 0.0016% in C o. Compared to the other variables, EURB has the least impact on SO 2 concentrations. The elasticity of EFOREG ranges from -0.034 to

0. On average, a 1% increase in evergreen forest land in all cells results in a decrease of

106 0.0023% in C o. This effect points to the important role of green spaces in the natural cleansing of air pollution. The elasticity of EWAT ranges from -0.056 to 0. On average, a 1% increase in water bodies in all cells leads to a decrease of 0.0039% in C o.

Compared to forest land, water bodies seem to more effectively absorb SO 2. Note, however, that the mixing of sulfur dioxide and atmospheric moisture produces sulfates, and therefore the reduction of SO 2 concentrations due to water bodies may be associated

- - with a significant increase in SO 4 depositions. The interaction between SO2 and SO 4 is a potential area for further research.

The elasticity of CON varies from 0.13 to 2.64 across the selected 345 airsheds, and the elasticity of EMIS ranges from 0 to 0.28. On average, a 1% increase in background concentrations in all sectors leads to an increase of 0.77% in C o, while a 1% increase in air pollution emissions in all cells results in an increase of 0.019% in C o.

Although it is inelastic, CON has the strongest effect on SO 2 concentrations. Because background concentrations, as measured at the surrounding monitoring stations, represent

SO 2 depositions from sources outside of the airshed, this result underscores the long- range transport of SO 2.

107

CHAPTER 7

APPLICABILITY AND DYNAMIC EXTENSION OF THE MODEL

In the previous chapter, a statistical model based on an airshed spatial framework has been developed and estimated. This chapter shows how this model can be used for air quality management purposes, both in forecasting and planning modes. A linear programming (LP) formulation of the planning model is developed and implemented in a case study focusing on Pennsylvania and neighboring areas. Next, dynamic/temporal extensions of the statistical model and its management applications are outlined. Finally, an overview of the modeling and policy implications of the approach is presented.

7.1 Air Quality Management

The statistical model finally selected in Chapter 6 (Equation 6.13) can be reformulated as:

8 8 16 = + ⋅ ⋅ + ⋅ ⋅ C0 a0 a1 ∑Ai Ci a2 ∑∑ Bij Eij (7.1) i=1 i =1 j =1 where the coefficients A i and B ij are functions of wind direction frequencies, wind speeds, land uses, and distances between (1) background monitoring stations and emission

108 sources, and (2) the central monitoring station. For the sake of clarity and at no conceptual loss, Equation (7.1) assumes that there is no need to combine and weight monitoring station concentrations and emissions, hence the notation C i for the concentration at monitoring station i, and E ij for the emission in sector-ring cell (i, j). The regression coefficients a 0, a 1, and a 2 are as defined in Chapter 6. By incorporating a 1 and a2 into A i and B ij , Equation (7.1) can be re-written as:

8 8 16 = + ⋅ + ⋅ C0 a0 ∑Ai Ci ∑∑ Bij Eij (7.2) i=1 i =1 j =1

Equation (7.2) characterizes a specific airshed. Consider now a larger territory

(e.g., the Northeast, the Midwest, or the whole U.S.) including m monitoring stations (i =

1[ m) and n emission sources (j = 1[ n). Let C i be the concentration at station i, and E j the emission from source j. Assume that area sources are represented by their centroids as point sources. Each monitoring station can be viewed as the center of an airshed, with its concentration linked to the concentrations of the surrounding stations and to the emissions of sources within the airshed, as defined in the statistical model. All the airsheds are necessarily inter-linked, which makes it possible to account for different meteorological patterns across airsheds. Equation (7.2) is then specified for each of the m airsheds. These m equations can be pooled together into a unique matrix equation. Let

C be the column vector of size m representing all concentrations, and E the column vector of size n representing all emissions. Equation (7.2) can be generalized as:

C = A0 + AC + BE (7.3) where A0 is the m-dimensional column vector made of the regression intercept a 0, A is a square m x m matrix, and B an m x n matrix. The generic element of matrix A, noted a ik ,

109 takes a positive value (computed according to the statistical model) if monitoring station k serves as a background station for central station i, and a zero value otherwise.

Likewise, the generic element of matrix B, noted b ij , takes a positive value (computed according to the statistical model) if emission source j is located within the airshed of central station i and not beyond the background station in the same sector, and a zero value otherwise. Using matrix inversion, Equation (7.3) is re-written as:

-1 -1 C = ( I œ A) A0 + ( I œ A) BE (7.4) or

C = C0 + HE (7.5)

In Equation (7.5), C0 is the vector of background concentrations from non-anthropogenic sources (e.g., SO 2 from volcanos), and H = [h ij ] is the matrix of transfer coefficients between emission sources (j) and receptor concentrations (i). While the transfer coefficient h ij looks deceptively similar to the transfer coefficients traditionally computed with Gaussian-like diffusion models, it results from a chaining computation involving a large number of inter-linked airsheds. The coefficients h ij thus encompass all ranges of pollution transport.

Equation (7.4) or (7.5) can be used to forecast the concentration vector CF resulting from a given forecast of the emission vector EF. This would be consistent with the traditional impact analyses using diffusion models, where different assumptions related to pollution abatement can be tested. However, the model can also be used in a

max planning/optimization mode. Assume that E j is the maximum emission from source j,

when no pollution abatement takes place. If E j is the actual emission, then the amount of

110 max pollution abated is (E j - E j). Let CA j be the cost of pollution abatement for source j. It is a function of the amount abated, and thus of the actual emission, with:

CA j = CA j (E j) (7.6)

Let C* be the vector of ambient concentration standards at the n stations (assumed to be the same), and Emax the vector of maximum emissions of the m sources. Then a simple, receptor-oriented cost minimization model can be written as:

m = Minimize TCA ∑CA j (E j ) (7.7) j=1

Subject to:

C ≤ C * : ambient standards (7.8)

E ≤ Emax : maximum emissions (7.9)

(I œ A)C = A0 + BE : emission-concentration relationships (7.10)

7.2 Optimization Application

A case study providing a numerical illustration of the optimization model outlined in Section 7.1 is presented in this section. The data used in this case study is drawn from the database developed in this research (See Chapter 4). The case study area encompasses the state of Pennsylvania, with additional adjacent areas in New York and

New Jersey. The reasons for choosing this area are as follows: (1) Pennsylvania is one of the states with the highest SO 2 concentrations; (2) it has a large number of monitoring stations (high SO 2 monitoring rate); and (3) SO 2 monitoring stations have a relatively even spatial distribution. Ten stations (1 in New York, 1 in New Jersey, and 8 in

Pennsylvania,) are selected. The 1999 concentrations measured at the 10 monitoring

111 stations range from 2 to 10, with a mean of 5.3, which is higher than the national value of

4.1 (See Section 5.1.1). Basic locational data on these stations are presented in Table 7.1.

Station ID MEAN 1 MONID 2 CITY COUNTY STATE LAND-USE 3 LOCTP 4 LAT LON

345 4 340273001 - 1 Morris NJ Agricultural Rural 40.7872 -74.6775

361 7 360130005 - 1 Jamestown Chautauqua NY Industrial Urban / Center City 42.0953 -79.2369

364 3 360150003 - 1 Elmira Chemung NY Commercial Urban / Center City 42.1094 -76.8022

387 2 361111005 - 1 Ulster NY Commercial Rural 42.1378 -74.5147

487 8 420730015 - 1 New Castle Lawrence PA Industrial Suburban 40.9958 -80.3467

490 5 420810403 - 1 Williamsport Lycoming PA Commercial Urban / Center City 41.2461 -76.9897

493 5 420910013 - 1 Norristown Montgomery PA Residential Suburban 40.1122 -75.3092

497 3 420990301 - 1 Perry PA N/A Rural 40.4569 -77.1656

506 6 421070003 - 1 Shenandoah Schuylkill PA Residential Rural 40.8206 -76.2122

512 10 421290008 - 1 Greensburg Westmoreland PA Commercial Suburban 40.3044 -79.5056 Note: 1. MEAN represents the mean concentration, measured at 10 -3 ppm, in 1999. 2. MONID is the station identification number assigned by EPA. 3. Land-use types in this table are as reported in the Monitor All Column Report of AirData, and are different from the land-use types used in this research. 4. LOCTP represents Location Type as designated by EPA.

Table 7.1: Basic Information for the Selected Monitoring Stations

To simplify calculations, only point sources with SO 2 emissions greater than

10,000 short tons per year are included, resulting in 91 emission facilities. The distribution of these emissions by Standard Industry Classification (SIC) code is presented in Table 7.2, while the total emissions by airshed, together with airshed area emissions (not used in this analysis) are presented in Table 7.3. Additional airshed meteorological and land-use characteristics are presented in Tables 7.4 and 7.5. Area source emissions are excluded in this case study, because (1) compared to point source emissions, area source emissions make up a relatively small portion of total emissions

112 (see Tables 5.2 and 5.5), and (2) area sources would have to be clustered and represented by their centroids in order to compute the coefficients in matrix B.

Number of Percentage of SIC Code SIC Classification Name Facilities Emissions: (%) 1 1.10 2611 Pulp Mills 1 1.10 2621 Paper Mills Exc Building Paper 1 1.10 2631 Paperboard Mills 1 1.10 2812 Alkalies And Chlorine 2 2.20 2911 Petroleum Refining 1 1.10 3241 Cement, Hydraulic 5 5.49 3312 Blast Furnaces And Steel Mills 1 1.10 3743 Railroad Equipment 1 1.10 3861 Photograph Equipment & Supplies 74 81.32 4911 Electric Services 2 2.20 4931 Electric & Other Services Combined 1 1.10 4961 Steam Supply

Table 7.2: Emission Facilities Characteristics

Airshed Number of Point Emissions 3 Area Emissions Total Emissions ID 1 Facilities 2 (in 10 3 short tons) (in 10 3 short tons) (in 10 3 short tons) 345 30 864.8 478.1 1342.9 361 47 2354.0 177.3 2531.3 364 34 1215.9 423.7 1639.6 387 28 733.3 570.9 1304.2 487 55 3098.8 216.1 3314.9 490 49 2018.9 478.6 2497.5 493 32 921.6 433.0 1354.6 497 52 2401.1 467.1 2868.2 506 40 1502.7 483.2 1985.9 512 58 3201.1 235.0 3436.1 Note: 1. The airshed ID is simply the ID of its central monitoring station. 2. Number of facilities represents facilities located within the airsheds. 3. Because a facility may be located in more than one airshed, its emission may be counted more than once. There are 91 unique facilities selected in this optimization demonstration, and their total emission, without repetition is 4,009,826 short tons.

Table 7.3: Summary of Emissions by Types within the Selected Airsheds

113

Airshed WSP WSP WSP WSP WSP WSP WSP WSP FREQ FREQ FREQ FREQ FREQ FREQ FREQ FREQ ID 3 SEC1 SEC2 SEC3 SEC4 SEC5 SEC6 SEC7 SEC8 SEC1 SEC2 SEC3 SEC4 SEC5 SEC6 SEC7 SEC8 345 1.84 3.30 4.27 2.32 2.62 3.72 2.00 1.54 0.100 0.140 0.168 0.104 0.114 0.183 0.100 0.091 361 7.59 8.61 7.78 9.88 11.42 9.47 7.46 4.38 0.096 0.096 0.096 0.129 0.245 0.229 0.065 0.043 364 3.47 5.36 5.37 6.54 6.51 5.36 3.71 3.01 0.090 0.147 0.103 0.139 0.222 0.153 0.074 0.071 387 5.25 6.55 8.96 8.67 7.83 7.06 7.55 6.22 0.056 0.068 0.184 0.188 0.149 0.143 0.151 0.062 487 6.27 7.12 8.37 8.08 8.93 7.53 6.61 7.13 0.071 0.083 0.126 0.136 0.172 0.161 0.120 0.130 490 4.94 3.12 4.69 6.46 6.61 2.84 4.15 4.45 0.132 0.082 0.122 0.195 0.190 0.072 0.083 0.123 493 3.89 4.49 5.37 5.93 4.76 4.35 2.03 1.87 0.146 0.135 0.140 0.172 0.128 0.140 0.070 0.068 497 2.20 4.51 4.60 5.15 2.76 3.31 3.32 1.82 0.087 0.151 0.178 0.164 0.095 0.127 0.117 0.080 506 2.20 4.51 4.60 5.15 2.76 3.31 3.32 1.82 0.087 0.151 0.178 0.164 0.095 0.127 0.117 0.080 512 4.49 5.04 7.16 7.60 8.78 7.20 5.86 5.63 0.058 0.066 0.163 0.147 0.154 0.170 0.151 0.091 Note: 1. WSPSEC# represents wind speed in sector # (# = 1 → 8), and is measured in knots. 2. FEQSEC# represents the relative wind frequency in sector # (# = 1 → 8). 3. The airshed ID is simply the ID of its central monitoring station.

Table 7.4: Summary of Meteorological Variables within the Selected Airsheds

Total Area 2 Percentages 3 (%) Airshed Deciduous Evergreen Water Deciduous Evergreen Water ID 1 Urban Pasture Grass Forest Forest Bodies Urban Pasture Grass Forest Forest Bodies 345 11793 6705 931 157145 418 97785 3.689 2.097 0.291 49.160 0.131 30.590 361 7632 19403 1334 163699 703 56952 2.388 6.072 0.418 51.226 0.220 17.822 364 10710 9771 1339 213913 637 34484 3.351 3.058 0.419 66.939 0.199 10.791 387 11469 4742 718 207557 343 57114 3.589 1.484 0.225 64.951 0.107 17.873 487 9031 43447 1639 167432 662 34636 2.825 13.592 0.513 52.381 0.207 10.836 490 12972 11125 994 200006 540 41789 4.059 3.481 0.311 62.575 0.169 13.074 493 10888 9001 964 137605 537 106207 3.406 2.815 0.302 43.040 0.168 33.220 497 12454 11381 1009 191535 572 33919 3.896 3.560 0.316 59.913 0.179 10.610 506 12463 10951 968 188544 497 52758 3.899 3.426 0.303 58.983 0.155 16.505 512 8111 20293 846 189754 392 26402 2.537 6.347 0.264 59.354 0.123 8.259 Note: 1. The airshed ID is simply the ID of its central monitoring station. 2. Area is measured in square kilometers. 3. Percentage of a particular land use in an airshed.

Table 7.5: Summary of Land Use Variables within the Selected Airsheds

114 After selecting the 10 monitoring stations and 91 emission points, the coefficients making up matrices A and B in Equation 7.4 are computed, using the airshed-based statistical model estimated in Chapter 6 (see Appendix D for the corresponding SAS program). The locations of the 10 stations are presented in Figure 7.1, while detailed maps of the 10 individual airsheds are presented in Appendix A. The matrices A (10 x 10) and B (10 x 91) are presented in Tables 7.6 and 7.7. The relationships between the 10 monitoring stations and the 91 emission sources are written as:

10 91 = + ⋅ + ⋅ Ck a0 ∑ akl Cl ∑b jk E j (7.11) l=1 j=1

where:

k or l is a monitoring station (k, l = 1 → 10),

j is an emission point (j = 1 → 91),

Ck or C l is the concentration measured at station k or l,

Ej is the emission measured at emission point j.

The objective function of the optimization model is the total abatement cost.

Assume that each emission source has the same linear abatement cost function. This would, for instance, be the case if all emitters were substituting the same fuels (e.g., from a high-sulfur coal to a low-sulfur one). Minimizing total abatement cost is then equivalent to maximizing total emission. Assume also that emissions can be reduced by

80%, that is, down to 20% of their actual 1999 emissions (taken as the maximum

max min * emissions). Let E j and E j be the upper and lower bounds on emission E j, C the ambient air quality standard, and δkl the generic Kronecker coefficient.

115

Figure 7.1: Locations of the 10 Monitoring Stations and the 91 Emission Facilities 91 Emission and theFigure theLocations Stations 10 Monitoring 7.1: of

116

ST1 ST2 ST3 ST4 ST5 ST6 ST7 ST8 ST9 ST10 ST1 0.0000 0.0000 0.0000 0.1585 0.0000 0.1178 0.1200 0.0000 0.0812 0.0000 ST2 0.0000 0.0000 0.0766 0.0000 0.1670 0.0655 0.0000 0.0000 0.0000 0.0799 ST3 0.0471 0.1896 0.0000 0.0567 0.0000 0.0664 0.0000 0.0000 0.0000 0.1056 ST4 0.0960 0.0000 0.1624 0.0000 0.0000 0.0000 0.0000 0.0000 0.1118 0.0000 ST5 0.0000 0.0948 0.0000 0.0000 0.0000 0.0933 0.0000 0.0000 0.0000 0.1101 ST6 0.0000 0.1281 0.0961 0.0684 0.0000 0.0000 0.0000 0.0860 0.0852 0.1213 ST7 0.1209 0.0000 0.1046 0.0000 0.0000 0.0000 0.0000 0.0000 0.1732 0.0000 ST8 0.0000 0.1124 0.0000 0.0796 0.0000 0.1305 0.0461 0.0000 0.0708 0.0867 ST9 0.0610 0.0000 0.1117 0.0851 0.0000 0.1605 0.0497 0.0802 0.0000 0.0000 ST10 0.0000 0.1132 0.0868 0.0000 0.1284 0.0000 0.0000 0.0612 0.0000 0.0000 Note: ST# represents a monitoring station ID (# = 1 [ 10).

Table 7.6: Matrix A

ST1 ST2 ST3 ST4 ST5 ST6 ST7 ST8 ST9 ST10 FAC1 0.00000 0.00020 0.00016 0.00000 0.00041 0.00024 0.00000 0.00020 0.00000 0.00133 FAC2 0.00000 0.00022 0.00016 0.00000 0.00048 0.00023 0.00000 0.00019 0.00000 0.00083 FAC3 0.00000 0.00000 0.00000 0.00000 0.00181 0.00000 0.00000 0.00000 0.00000 0.00034 FAC4 0.00000 0.00000 0.00000 0.00000 0.00072 0.00000 0.00000 0.00000 0.00000 0.00058 FAC5 0.00000 0.00000 0.00000 0.00000 0.00067 0.00000 0.00000 0.00000 0.00000 0.00188 FAC6 0.00000 0.00000 0.00000 0.00000 0.00046 0.00000 0.00000 0.00000 0.00000 0.00035 FAC7 0.00000 0.00058 0.00000 0.00000 0.00058 0.00000 0.00000 0.00000 0.00000 0.00000 FAC8 0.00000 0.00000 0.00000 0.00000 0.00123 0.00000 0.00000 0.00000 0.00000 0.00119 FAC9 0.00000 0.00000 0.00000 0.00017 0.00000 0.00279 0.00000 0.00063 0.00219 0.00000 FAC10 0.00000 0.00000 0.00000 0.00000 0.00040 0.00000 0.00000 0.00000 0.00000 0.00000 FAC11 0.00000 0.00000 0.00000 0.00000 0.00054 0.00000 0.00000 0.00000 0.00000 0.00058 FAC12 0.00000 0.00000 0.00000 0.00000 0.00052 0.00066 0.00000 0.00077 0.00068 0.00092 FAC13 0.00000 0.00000 0.00000 0.00000 0.00085 0.00000 0.00000 0.00000 0.00000 0.00088 FAC14 0.00000 0.00000 0.00000 0.00000 0.00062 0.00000 0.00000 0.00000 0.00000 0.00058 FAC15 0.00000 0.00000 0.00000 0.00000 0.00066 0.00050 0.00000 0.00000 0.00000 0.00157 FAC16 0.00000 0.00000 0.00000 0.00000 0.00059 0.00000 0.00000 0.00000 0.00000 0.00058 FAC17 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00059 0.00056 0.00074 0.00033 FAC18 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00078 0.00051 0.00118 0.00000 FAC19 0.00000 0.00000 0.00000 0.00000 0.00048 0.00000 0.00000 0.00000 0.00000 0.00064 FAC20 0.00000 0.00125 0.00043 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC21 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00071 0.00060 0.00050 0.00033 FAC22 0.00000 0.00053 0.00000 0.00000 0.00058 0.00000 0.00000 0.00000 0.00000 0.00000 FAC23 0.00000 0.00000 0.00000 0.00000 0.00084 0.00000 0.00000 0.00000 0.00000 0.00088 FAC24 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00089 0.00080 0.00062 0.00036 FAC25 0.00000 0.00034 0.00000 0.00000 0.00041 0.00000 0.00000 0.00000 0.00000 0.00000 FAC26 0.00000 0.00047 0.00055 0.00000 0.00022 0.00078 0.00000 0.00077 0.00000 0.00030 FAC27 0.00000 0.00000 0.00000 0.00000 0.00090 0.00000 0.00000 0.00000 0.00000 0.00102 FAC28 0.00036 0.00000 0.00000 0.00025 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC29 0.00000 0.00056 0.00046 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC30 0.00000 0.00000 0.00000 0.00000 0.00048 0.00000 0.00000 0.00000 0.00000 0.00037 FAC31 0.00000 0.00000 0.00000 0.00000 0.00061 0.00000 0.00000 0.00000 0.00000 0.00062

Continued Table 7.7: Matrix B

117 Table 7.7 continued FAC32 0.00000 0.00000 0.00013 0.00000 0.00063 0.00000 0.00000 0.00000 0.00000 0.00118 FAC33 0.00000 0.00000 0.00000 0.00028 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC34 0.00000 0.00000 0.00000 0.00000 0.00057 0.00000 0.00000 0.00000 0.00000 0.00035 FAC35 0.00000 0.00054 0.00000 0.00000 0.00047 0.00000 0.00000 0.00000 0.00000 0.00000 FAC36 0.00000 0.00000 0.00000 0.00000 0.00009 0.00140 0.00000 0.00097 0.00132 0.00000 FAC37 0.00000 0.00055 0.00000 0.00000 0.00048 0.00000 0.00000 0.00000 0.00000 0.00000 FAC38 0.00000 0.00033 0.00051 0.00041 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC39 0.00309 0.00000 0.00000 0.00000 0.00000 0.00000 0.00100 0.00000 0.00000 0.00000 FAC40 0.00000 0.00000 0.00000 0.00000 0.00041 0.00000 0.00000 0.00000 0.00000 0.00000 FAC41 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00101 0.00084 0.00068 0.00035 FAC42 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00051 0.00085 0.00065 0.00043 FAC43 0.00000 0.00033 0.00000 0.00000 0.00041 0.00000 0.00000 0.00000 0.00000 0.00000 FAC44 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00157 0.00005 0.00076 0.00000 FAC45 0.00000 0.00048 0.00000 0.00000 0.00065 0.00023 0.00000 0.00025 0.00000 0.00098 FAC46 0.00000 0.00000 0.00000 0.00000 0.00213 0.00000 0.00000 0.00000 0.00000 0.00039 FAC47 0.00277 0.00000 0.00020 0.00028 0.00000 0.00000 0.00058 0.00000 0.00062 0.00000 FAC48 0.00000 0.00032 0.00049 0.00041 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC49 0.00169 0.00000 0.00000 0.00080 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC50 0.00000 0.00092 0.00000 0.00000 0.00064 0.00000 0.00000 0.00000 0.00000 0.00005 FAC51 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00088 0.00079 0.00062 0.00036 FAC52 0.00000 0.00032 0.00049 0.00041 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC53 0.00000 0.00000 0.00000 0.00027 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC54 0.00000 0.00000 0.00000 0.00000 0.00138 0.00000 0.00000 0.00000 0.00000 0.00123 FAC55 0.00000 0.00000 0.00000 0.00000 0.01089 0.00000 0.00000 0.00000 0.00000 0.00033 FAC56 0.00000 0.00043 0.00000 0.00000 0.00040 0.00000 0.00000 0.00000 0.00000 0.00000 FAC57 0.00054 0.00000 0.00000 0.00000 0.00000 0.00000 0.00063 0.00000 0.00049 0.00000 FAC58 0.00180 0.00000 0.00021 0.00027 0.00000 0.00000 0.00068 0.00000 0.00068 0.00000 FAC59 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00059 0.00060 0.00066 0.00037 FAC60 0.00000 0.00000 0.00000 0.00000 0.00197 0.00000 0.00000 0.00000 0.00000 0.00000 FAC61 0.00099 0.00000 0.00000 0.00036 0.00000 0.00000 0.00058 0.00000 0.00000 0.00000 FAC62 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00072 0.00069 0.00059 0.00037 FAC63 0.00000 0.00000 0.00000 0.00000 0.00061 0.00000 0.00000 0.00000 0.00000 0.00062 FAC64 0.00349 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC65 0.00024 0.00000 0.00073 0.00039 0.00000 0.00097 0.00014 0.00012 0.00050 0.00000 FAC66 0.00000 0.00039 0.00045 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC67 0.00000 0.00000 0.00000 0.00000 0.00057 0.00066 0.00000 0.00000 0.00000 0.00119 FAC68 0.00077 0.00000 0.00000 0.00000 0.00000 0.00000 0.00094 0.00000 0.00000 0.00000 FAC69 0.00000 0.00000 0.00000 0.00028 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC70 0.00000 0.00000 0.00000 0.00000 0.00046 0.00000 0.00000 0.00000 0.00000 0.00035 FAC71 0.00000 0.00053 0.00000 0.00000 0.00058 0.00000 0.00000 0.00000 0.00000 0.00000 FAC72 0.00000 0.00053 0.00000 0.00000 0.00086 0.00000 0.00000 0.00000 0.00000 0.00000 FAC73 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00284 0.00005 0.00000 0.00000 FAC74 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00058 FAC75 0.00000 0.00008 0.00147 0.00061 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC76 0.00000 0.00000 0.00000 0.00000 0.00079 0.00000 0.00000 0.00000 0.00000 0.00084 FAC77 0.00000 0.00000 0.00000 0.00000 0.00040 0.00000 0.00000 0.00000 0.00000 0.00000 FAC78 0.00144 0.00000 0.00000 0.00000 0.00000 0.00000 0.00213 0.00000 0.00000 0.00000 FAC79 0.00079 0.00000 0.00000 0.00103 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC80 0.00000 0.00039 0.00045 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC81 0.00058 0.00000 0.00000 0.00000 0.00000 0.00000 0.00151 0.00000 0.00156 0.00000 FAC82 0.00000 0.00053 0.00000 0.00000 0.00057 0.00000 0.00000 0.00000 0.00000 0.00000

Continued

118 Table 7.7 continued FAC83 0.00074 0.00000 0.00000 0.00036 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC84 0.00000 0.00000 0.00000 0.00000 0.00143 0.00000 0.00000 0.00000 0.00000 0.00122 FAC85 0.00024 0.00000 0.00073 0.00040 0.00000 0.00097 0.00014 0.00012 0.00050 0.00000 FAC86 0.00168 0.00000 0.00000 0.00080 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 FAC87 0.00000 0.00018 0.00016 0.00000 0.00000 0.00024 0.00052 0.00022 0.00000 0.00088 FAC88 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00056 FAC89 0.00000 0.00162 0.00000 0.00000 0.00016 0.00000 0.00000 0.00000 0.00000 0.00005 FAC90 0.00000 0.00054 0.00000 0.00000 0.00086 0.00000 0.00000 0.00000 0.00000 0.00000 FAC91 0.00087 0.00000 0.00000 0.00044 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Note: 1. ST# represents a monitoring station ID (# = 1 [ 10). 2. FAC# represents an emission facility ID (# = 1 [ 91).

The optimization model (7.6) œ (7.7) becomes a linear program, with:

91 = Mazimizing ET ∑ E j (7.12) j=1

Subject to:

max Ej ≤ E (j = 1 → 91) (7.13)

min Ej ≥ E (j = 1 → 91) (7.14)

* Ck ≤ C (k = 1 → 91) (7.15)

10 91 δ − ⋅ − ⋅ = δ δ ≠ ∑( lk alk ) Cl ∑b jk E j a0 ( kl =1 if l=k, kl =0 if l k) (7.16) l=1 j =1

Ck ≥ 0 (7.17)

Ej ≥ 0 (7.18)

119 A software package 18 , the General Algebraic Modeling System (GAMS), is used to solve the linear program (see Appendix D for the GAMS program). The model has been solved over a range of ambient standard values, and the results are presented in

Table 7.8. The basic units for concentrations and emissions are 10 -3 ppm and 10 3 short tons, respectively.

By setting the maximum emission constraints in Equation (7.13) as equalities and the standard in Equation (7.15) to a large value (say 10), the resulting maximum concentration is 4.909 (station 10), with a total emission of 4009.8, which, of course, corresponds to the 1999 actual emissions of all 91 sources. By setting the minimum emission constraints in Equation (7.14) as equalities, the resulting maximum concentration is 2.712 (station 2), with a total emission of 801.9 (20% of 4009.8). If the air quality standard is set below 2.712, there is no solution under the selected constraints. If the air quality standard is set above 4.909, the solution remains the same, with all emission sources at their maximum levels. Figure 7.2 presents the relationship between air quality standards and total SO 2 emissions.

18 A trial version of the GAMS software and its manual can be downloaded at http://www.gams.com 120

Air Quality Total Concentrations 1 Standard 1 Emission 2 Station 1 Station 2 Station 3 Station 4 Station 5 Station 6 Station 7 Station 8 Station 9 Station 10 2.712 801.965 1.815 2.700 2.563 2.053 2.307 2.571 1.594 1.754 2.224 2.681 2.750 1277.832 2.025 2.750 2.622 2.174 2.411 2.629 1.732 1.772 2.310 2.750 2.800 1503.028 2.243 2.800 2.692 2.247 2.476 2.703 1.889 1.824 2.480 2.800 2.850 1665.522 2.252 2.850 2.731 2.272 2.549 2.724 1.914 1.850 2.507 2.850 2.900 1809.053 2.270 2.900 2.783 2.303 2.571 2.756 1.976 1.911 2.589 2.900 2.950 1934.484 2.288 2.950 2.834 2.329 2.599 2.788 2.049 1.981 2.661 2.950 3.000 2054.466 2.290 3.000 2.846 2.332 2.675 2.802 2.055 1.991 2.668 3.000 3.050 2173.042 2.307 3.050 2.891 2.355 2.725 2.848 2.088 2.024 2.711 3.050 3.100 2288.290 2.308 3.100 2.915 2.359 2.789 2.861 2.090 2.029 2.714 3.100 3.150 2402.465 2.309 3.150 2.925 2.361 2.873 2.873 2.090 2.033 2.716 3.150 3.200 2509.234 2.314 3.200 2.941 2.369 3.047 2.902 2.105 2.062 2.740 3.200 3.250 2604.681 2.320 3.250 2.964 2.382 3.147 2.966 2.110 2.100 2.786 3.250 3.300 2693.836 2.321 3.300 2.985 2.385 3.203 2.977 2.111 2.104 2.788 3.300 3.350 2780.705 2.323 3.350 3.003 2.387 3.262 2.988 2.111 2.108 2.790 3.350 3.400 2852.399 2.325 3.377 3.017 2.395 3.349 3.022 2.113 2.120 2.809 3.400 3.450 2920.719 2.347 3.437 3.081 2.453 3.365 3.266 2.129 2.201 2.996 3.450 3.500 2988.434 2.356 3.495 3.133 2.476 3.396 3.370 2.135 2.260 3.049 3.500 3.550 3055.595 2.356 3.507 3.139 2.477 3.442 3.377 2.135 2.264 3.050 3.550 3.600 3111.004 2.357 3.520 3.145 2.478 3.488 3.383 2.135 2.267 3.051 3.600 3.650 3158.434 2.357 3.532 3.151 2.480 3.536 3.389 2.136 2.271 3.052 3.650 3.700 3205.754 2.358 3.545 3.158 2.481 3.585 3.396 2.136 2.275 3.053 3.700 3.750 3252.656 2.360 3.567 3.172 2.484 3.618 3.411 2.141 2.286 3.056 3.750 3.800 3299.315 2.361 3.593 3.189 2.488 3.652 3.431 2.142 2.300 3.060 3.800 3.850 3345.974 2.363 3.620 3.206 2.492 3.685 3.450 2.143 2.313 3.063 3.850 3.900 3388.590 2.364 3.635 3.215 2.494 3.727 3.459 2.144 2.319 3.065 3.900 3.950 3428.025 2.365 3.662 3.225 2.499 3.765 3.480 2.145 2.338 3.075 3.950 4.000 3466.186 2.370 3.683 3.241 2.509 3.795 3.522 2.149 2.376 3.109 4.000 4.050 3504.283 2.374 3.704 3.257 2.519 3.825 3.561 2.153 2.413 3.142 4.050 4.100 3540.869 2.374 3.716 3.267 2.520 3.859 3.567 2.153 2.417 3.143 4.100 4.150 3575.796 2.375 3.729 3.273 2.522 3.911 3.574 2.154 2.421 3.144 4.150 4.200 3610.723 2.376 3.742 3.280 2.523 3.962 3.580 2.154 2.424 3.145 4.200 4.250 3645.239 2.376 3.755 3.287 2.525 4.005 3.596 2.154 2.429 3.147 4.250 4.300 3678.069 2.377 3.771 3.296 2.527 4.051 3.605 2.155 2.435 3.149 4.300 4.350 3709.607 2.378 3.790 3.309 2.530 4.073 3.618 2.156 2.445 3.151 4.350 4.400 3741.146 2.379 3.810 3.322 2.533 4.096 3.632 2.156 2.456 3.154 4.400 4.450 3772.685 2.380 3.830 3.335 2.536 4.118 3.646 2.157 2.467 3.156 4.450 4.500 3803.531 2.381 3.848 3.347 2.539 4.141 3.660 2.158 2.477 3.159 4.500 4.550 3830.649 2.382 3.861 3.355 2.541 4.168 3.678 2.158 2.481 3.161 4.550 4.600 3857.766 2.383 3.873 3.363 2.543 4.194 3.697 2.159 2.486 3.163 4.600 4.650 3884.582 2.384 3.893 3.371 2.546 4.283 3.719 2.159 2.491 3.166 4.650 4.700 3909.431 2.385 3.916 3.380 2.548 4.430 3.736 2.160 2.496 3.168 4.700 4.750 3933.466 2.385 3.926 3.386 2.549 4.454 3.740 2.160 2.500 3.169 4.750 4.800 3957.502 2.386 3.936 3.391 2.550 4.478 3.743 2.160 2.503 3.170 4.800 4.850 3981.537 2.386 3.946 3.397 2.551 4.502 3.747 2.160 2.506 3.170 4.850 4.909 4009.826 2.387 3.958 3.404 2.553 4.531 3.752 2.161 2.511 3.171 4.909 Note: 1. Air quality standards and concentrations are measured in 10 -3 ppm. 2. Emissions are measured in 10 3 short tons.

Table 7.8: Optimal Concentration Patterns for Various Air Quality Standards

121 Figure 7.2 shows that total emissions increase strongly as the air quality standard increases from 2.7 to 3.5. The rate of increase decreases when the standard is set higher than 3.5. The optimal emission of each facility is generally set at either its upper or its lower bound of allowed emissions, under most air quality standards (See Appendix D,

Table D.4). For example, facility 1, which is the largest pollution source among the selected facilities, has its optimal emission set to its lower bound (32,692 short tons) for air quality standards between 2.712 and 4.25, but has it set to its upper bound (163,462 short tons) at air quality standards between 4.5 and 4.909. With air quality standards set at 4.30, 4.35, 4.4, and 4.45, the optimal emissions are 42,242, 73,780, 105,319, and

136,857 short tons, respectively. Interestingly, the highest percentage change in the optimal emission is 74.7%, when the air quality standard shifts from 4.3 to 4.35, suggesting that facility 1 achieves large cost savings within this standard range.

4500

4000

3500

3000

2500

2000

1500

1000 Total Emissions(10^3 short tons) 500

0 2.5 3 3.5 4 4.5 5 5.5 Air Quality Standard (10^-3 ppm)

Figure 7.2: Plot of Air Quality Standard against Total Emissions

122 7.3 Dynamic Extensions

Because the variables used in the model are either obtained (e.g., concentration data) or aggregated (e.g., meteorological data) at the annual level, the model is static in nature, implicitly assuming that all the variables are constant throughout the year. If the model is applied as an air quality management tool, it should only be used for long-term planning, where annual standards are most relevant. However, because it does not account for intra-annual time-varying variables, such as emissions, concentrations, and meteorology, the model cannot be used to assess the impact of various policies on shorter-term concentrations, and possible violations of the corresponding standards. To extend the applicability of the airshed-based statistical models, the time factor could be introduced into the model, as outlined below.

Assume that the year is divided into shorter time periods, within which meteorological and pollution emission conditions remain constant. Guldmann (1988) suggests that the shortest steady-state for these variables is probably an hour. Assume that the year is subdivided into T (t = 1 → T) periods, and that concentrations, emissions, and meteorological data are available for each period t. Equation (6.3) can then be reformulated as:

8  α 3 α1 α 2  0t = + ⋅ ⋅ ⋅ ⋅ ⋅ + C a0 a1 ∑ FDit DISTi WEAit LU i CONWti   i=1  (7.19)  8  16  ⋅ ⋅ β 3 ⋅ β1 ⋅ β 2 ⋅  a2 ∑ FDit ∑ DISTij WEAijt LU ij TOTWEMSijt   i=1  j=1 

where:

123 Cot represents the concentration measured at the central monitoring station

in time period t,

FD it represents wind frequency in time period t,

DIST i and DIST ij represent the distance variables,

WEA it and WEA ijt represent combinations of meteorological variables in

time period t,

LU i and LU ij represent combinations of land uses,

i represents an airshed sector (i = 1 G 8), and

j represents an airshed ring (j = 1 G 16).

Note that distances and land uses along the transport paths do not vary with time (t).

To estimate an hourly dynamic model, three main data sets are necessary: (1) hourly concentration data at all monitoring stations; (2) hourly emission data from pollution sources; and (3) hourly meteorological data. The USEPA‘s AIRS website makes limited hourly concentration data available through its Air Quality System (AQS) interim database. However, the AQS interim database is still undergoing a re- engineering process. Building a complete concentration database requires the completion of the AQS database and its easier Internet access. The Continuous Emission Monitoring

System (CEMS) data, set up under Title IV of the Clean Air Act Amendments of 1990, provides hourly emission data for SO 2, NO x, and CO 2, for all utilities affected by Title IV, at the individual stack level. The database 19 has been available on USEPA‘s Clean Air

Market website since 1996. However, hourly (or any intra-annual) emission data are not available for non-utility point sources and area sources. Some of these emissions may be

19 http://www.epa.gov/airmarkets/monitoring/bias/bias.pdf 124 assumed to be constant over time, or may be adjusted by using a weather factor (e.g. residential area emissions are higher in winter, due to higher fuel consumption). Hourly meteorological data, such as wind speed and wind direction, are already available from the Local Climatological Data (LCD) database compiled by the National Climatic Data

Center (NCDC).

Extending the approach presented in Section 7.1, a dynamic airshed-based statistical model accounting for both spatial and temporal factors could have broader and possibly more reliable applications in forecasting and planning (optimization). Let Ct be the vector of concentration at time t, and Et the corresponding vector of emissions.

Equation (7.3) can be rewritten as:

Ct = A0 + At C t + Bt E t (7.20) where the transfer coefficient matrices At and Bt vary over time. Let CT represent the vector of average concentrations over a sequence of T subperiods, with:

∑Ct C = t (7.21) T T

The concentration(s) CT resulting from a given temporal emissions policy may then be compared to the ambient standard for the averaging time T (e.g., month, day, 3 hours, etc.). Beyond simple forecasting/impact analysis, an optimization dynamic model may also be formulated. Let C * be the vector of ambient air quality standards for the basic

* max time period t, and CT the corresponding vector for the averaging time T. Let Et be the vector of maximum emissions in time period t, and CA jt (E jt ) the cost of pollution abatement during period t to achieve the emission level E jt . Extending the static model

125 made of Equations (7.1) - (7.10), a dynamic air quality optimization model can then be formulated as:

= Minimizing TCA ∑∑CA jt (E jt ) (7.22) t j

Subject to:

Ct = A0 + At C t + Bt E t (7.23)

* CT ≤ CT (7.24)

* Ct ≤ C (7.25)

max Et ≤ Et (7.26)

7.4 Implications of the Airshed-based Approach

7.4.1 Modeling

The airshed-based modeling approach is unique in two ways: (1) the relationship between (a) receptor and (b) emitters and background concentrations is explicitly and spatially established within the airshed, using GIS technologies and accounting for meteorological conditions; (2) land-use data, which are not included in other statistical models, are explicitly incorporated into the model. Unlike other statistical models, the airshed-based approach uses data captured spatially at the local (airshed) level. In this spatial framework, spatial effects, such as transport decay and pollution uptake by land uses, can be accounted for. The approach distinguishes two main components of air pollution œ background concentrations and emissions within the airshed, which more closely represents the real-world situation than in other statistical models.

126 The approach also incorporates land-use / land-cover variables into the model, which provides the opportunity for planners to study the interactions between air quality and land uses. Land-use patterns have been known to have an effect on air pollution transportation processes, but little research is available on the relationships between SO 2 concentrations and various land-use types.

Airsheds are important units of analysis because they have the potential to define the boundary of the contributing effects from surrounding areas on the concentrations measured at the central monitoring station. By inter-connecting different monitoring stations, a large-scale air quality model can be built at various levels (i.e. state, regional, or even national). The most important scientific / technical implication of the airshed- based modeling approach is that it provides a means to construct a scaleable air quality modeling tool.

7.4.2 Policy

From the viewpoints of policy evaluation, the airshed-based modeling approach can be used in two ways œ air quality forecasting and air quality planning. The multiple- airshed model presented in this chapter can be used to forecast the concentrations resulting from a given forecast of emissions, which is consistent with the traditional impact analyses using diffusion models. In other words, the approach can be used to analyze the impacts of different abatement policies. For example, public authorities may want to study the feasibility and impacts of an abatement policy that mandates all power plants to further reduce their emissions by x% through the allowance mechanism. The forecasting capability of the approach provides a perfect means to assess such an

127 abatement policy. The approach can also be implemented in a planning mode to evaluate an air quality policy, for example, in answering the following question: What is the most cost-efficient approach to achieve a given environmental policy expressed through given pollution standards? The optimization formulation of the model is ideally suited to answer this question.

128

CHAPTER 8

CONCLUSIONS

The ultimate goal of this research was to develop an innovative air quality management tool, based on the concept of inter-connected airsheds. The most important feature of the approach is that it provides a means to construct a scaleable air quality modeling tool, using only local data to model larger-scale air pollution processes. The research involved three phases: (1) building a large database that can spatially connect monitoring stations, emission sources, and various explanatory variables; (2) developing a spatial framework to build an SO 2 air quality statistical model, using Geographical

Information Systems (GIS), to account for pollution decay, meteorological conditions, emission uptakes, and land-use / land-cover characteristics; and (3) devising and applying an air quality optimization model, based on the estimated statistical model and using linear programming.

An airshed spatial framework, combining concepts of air-back trajectories,

Gaussian diffusion modeling, and grid approximation of the Fickian system of equations, has been proposed for air quality modeling. A regression model based on this spatial framework estimates the relationship between (1) sulfur dioxide (SO 2) background

129 concentrations and pollution emissions from point and area sources, and (2) the resulting

SO 2 concentration at a receptor site located at the center of the airshed, while accounting for pollution decay, meteorological conditions, and land-use / land-cover characteristics.

Because of the extent of the territory covered (the whole U.S.) and the locational specificity of the emission sources and the monitoring stations, a database has been constructed, with extensive use of a GIS, to spatially connect pollution emission sources, air quality monitoring stations, meteorological stations, and land-use / land-cover characteristics.

Because of the complex and non-linear structure of the model, an interactive grid search procedure has been designed to estimate the model. Ordinary least squares (OLS) regression has been used for model estimation, with the unknown parameters in the non- linear components of the model treated exogenously by performing grid searches over possible value ranges for these parameters. The R 2 is the primary criterion for model selection in each round of the grid search.

The final model explains about 56% of the variations in SO 2 concentrations, and all the regression coefficients are statistically significant, with the expected signs.

Distance has a strong pollution-attenuating effect, as expected. Wind speed appears to have two opposite effects on SO 2 concentrations -- a negative one for emissions and a positive one for concentrations, possibly due to the pollution decay process. Urban land displays an inverse relationship with SO 2 concentrations, which seems contrary to expectations. This finding suggests that built-up areas may actually trap air pollutants because of the heat island effect.

130 Elasticity analyses have been performed to measure the relationships between the independent and dependent variables. The results indicate that background concentrations have the strongest effects on SO 2 concentrations, with an average elasticity of 0.77. This result underscores the importance of long-range transport of SO 2, because background concentrations, as measured at surrounding monitoring stations, represent SO 2 depositions from sources outside of the airshed. After concentration and emissions, distance is the next most significant factor, for either background concentrations or air pollution emission within the airshed, to affect SO 2 concentrations.

An air quality optimization modeling application, based on the airshed statistical model and using linear programming (LP), has been developed, and a numerical illustration has been presented, involving 10 monitoring stations and 91 emission points located in Pennsylvania and surrounding areas, and using the database developed in this research. The following assumptions have been made: (1) the same pollution abatement cost function applies to each emission source; (2) emissions can be reduced by a maximum of 80% from current levels at each source; and (3) the maximum emissions are set at the current (1999) emission levels. The best achievable air quality standard (i.e. the lowest possible maximum ambient concentration) is 2.712*10 -3 ppm, with total emissions of 801,965 short tons from all sources. The air quality standard must be set at 4.909*10 -3 ppm to allow for the total maximum emissions of 4,009,826 short tons from all the emission sources.

Since the variables used in the statistical model are either aggregated or measured at an annual level, the model is essentially static and assumes that all the variables are constant throughout the year. Some variables, such as meteorological conditions and

131 pollution emissions, may vary considerable within the year, while other variables, such as land uses, do not. To account for the time factor, airshed-based statistical models could be formulated to account for such dynamics, by dividing the year into shorter time periods, within which emission and meteorological variables would remain constant.

Because of data availability issues, it was not possible to estimate such dynamic models within the framework of this research.

The airshed-based modeling framework could be extended in the following directions in future research:

(1) As discussed above, a dynamic extension of the model would be a natural

progression of this research. The main advantage of a dynamic extension is the

consideration of the time factor for time-sensitive variables, such as emissions and

meteorological conditions. The main issue for this extension is data availability,

as discussed in Chapter 7 (Section 7.3).

(2) Another natural extension of this research is to include area emissions into the air

quality optimization model. To implement the optimization model, receptor-

emitter relationships must be defined, so that transfer coefficient for all pairs of

receptor-emitter can be calculated. Back trajectory analysis and cluster analysis

may be necessary to define emission centers for area emissions.

(3) By introducing variables (e.g., distance) with unknown parameters into the model,

the airshed-based statistical model has essentially a nonlinear structure. To

improve the estimation performance (i.e. higher R 2), better curve fitting

techniques could be applied. Since the neural network technique is designed to

produce "purely" empirical and numerical models, it appears to be a promising

132 alternative to regression. As discussed in Chapter 2, it may not be easy to

interpret the results of neural networks, because of the lack of supporting theory.

By using a two-step approach, a higher-performance model may be obtainable: (1)

the model is estimated through regular OLS regression to establish cause-effect

relationships among variables; (2) a neural network, based on the OLS model

specification, is used to produce a more accurate model.

(4) The land-use data used in this research has been obtained from the Global Land

Cover Characteristics (GLCC) database, which is based on Advanced Very High

Resolution Radiometer (AVHRR) satellite images collected between April 1992

and March 1993, with a 1-km nominal spatial resolution. Although the

information provided by GLCC is very rich, it is relatively outdated (compared to

other data sets) and has a low resolution. If more current land-use data were used,

it might be possible to obtain a better model. Moreover, a more detailed land

classification system could provide an assessment of the interrelationships

between SO 2 concentrations and different land uses. A potential, high-resolution,

and up-to-date land-use database is the National Land Cover Data (NLCD) 20 from

the U.S. Geological Survey (USGS). The NLCD has been developed to be used

in a wide variety of national and regional applications, including watershed

management, environmental inventories, transportation modeling, fire risk

assessment, and land management, based on a 30-meter-resolution satellite data

and the modified Anderson level II classification for the coterminous U.S..

20 National Land Cover Characterization Project website: http://landcover.usgs.gov/nationallandcover.asp 133 - (5) Sulfur dioxide is known to be a major precursor of atmospheric sulfates (SO 4 )

and acidic deposition, as the mixing of sulfur dioxide and atmospheric moisture

produces sulfates. As the model indicates that water bodies have a negative

relationship with SO 2 concentrations, the reduction of SO 2 concentrations due to

- water bodies may be associated with a significant increase in SO 4 depositions.

- The interaction between SO2 and SO 4 is a potential area for model extensions.

(6) This research has used sulfur dioxide to demonstrate the validity of the modeling

concept and approach. To further extend its applicability, the airshed-based

statistical model could be used to analyze other air pollutants, such as nitrogen

oxides, particulate matters, ozone, and carbon monoxide.

134

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139

APPENDIX A

MAPS

140

StationsMonitoringin 1999 2

Figure A.1: MapFigure of SO A.1:

141

Emission in Facilities 1999 2

Figure A.2: Map SO Figure of Major A.2:

142

Figure A.3: Map of Weather Monitoring Stations in 1999 Stations MapFigure of Monitoring Weather A.3:

143

Database

ThematicFigure North Land Uses Raster the for Map A.4: America of NALC from

144

Figure A.5: Airshed of Monitoring Station 345 œ Optimization Application

Figure A.6: Airshed of Monitoring Station 361 œ Optimization Application

145

Figure A.7: Airshed of Monitoring Station 364 œ Optimization Application

Figure A.8: Airshed of Monitoring Station 387 œ Optimization Application

146

Figure A.9: Airshed of Monitoring Station 487 œ Optimization Application

Figure A.10: Airshed of Monitoring Station 490 œ Optimization Application

147

Figure A.11: Airshed of Monitoring Station 493 œ Optimization Application

Figure A.12: Airshed of Monitoring Station 497 œ Optimization Application

148

Figure A.13: Airshed of Monitoring Station 506 œ Optimization Application

Figure A.14: Airshed of Monitoring Station 512 œ Optimization Application

149

APPENDIX B

COMPUTER PROGRAMS FOR STATISTICAL MODELING

150 SAS PROGRAMS LANDUSE.SA6 FILENAME IN1 'D:\ZZZ\LANDUSE.DA0'; FILENAME IN2 'D:\ZZZ\MONCLOSE.DAT'; FILENAME IN3 'D:\ZZZ\MONSO2.DAT'; FILENAME OUT1 'D:\ZZZ\LU4CON.DAT'; FILENAME OUT2 'D:\ZZZ\LU4EMS.DAT'; *------DATA BASE LANDUSE ------FILE NAME: LANDUSE.SA6. THIS SAS PROGRAM READS LANDUSE.DA0 CREATED BY LANDUSE.SA0, AND ELIMINATES RECORDS BEYOND RING 16. IT PERFORMS CORRELATION ANALYSES, GROUPS SIMILAR LAND-USE TYPES, AND CREATES AN INDEX VARIABLE, IM, FOR EACH CELL (IM=1 IF THE CELL IS LOCATED BETWEEN THE CENTER AND THE CLOSEST SURROUNDING MONITORING STATION, IM=0 OTHERWISE). IT THEN CREATES TWO WIDTH-DELIMITED FILES, LU4CON.DAT AND LU4EMS.DAT. THE UNIT IN THE OUTPUT FILES IS SQUARE KILOMETERS. ------; DATA A; INFILE IN1 LRECL=510; INPUT ID 1-3 RING 5-7 SECTOR 8-9 @10 (LUC1-LUC25)(20.3); IF RING<17; ARRAY LUC LUC1-LUC25; DO OVER LUC; LUC=LUC/1000000; END; PROC SORT; BY ID SECTOR RING; DATA B1; INFILE IN2; INPUT ID 1-4 @5 (IDSEC1-IDSEC8) (4. +14) @9 (DISTSEC1-DISTSEC8) (11.3 +7) @20 (RINGSEC1-RINGSEC8) (3. +15); DROP IDSEC1-IDSEC8 DISTSEC1-DISTSEC8; PROC SORT; BY ID; PROC TRANSPOSE OUT=BTRANS; BY ID; DATA B; SET BTRANS; IF _NAME_='RINGSEC1' THEN SECTOR=1; IF _NAME_='RINGSEC2' THEN SECTOR=2; IF _NAME_='RINGSEC3' THEN SECTOR=3; IF _NAME_='RINGSEC4' THEN SECTOR=4; IF _NAME_='RINGSEC5' THEN SECTOR=5; IF _NAME_='RINGSEC6' THEN SECTOR=6; IF _NAME_='RINGSEC7' THEN SECTOR=7; IF _NAME_='RINGSEC8' THEN SECTOR=8; DO RING=1 TO 16; IF RING LE COL1 THEN IM=1; ELSE IM=0; OUTPUT; END; PROC SORT; BY ID SECTOR RING; DATA C; MERGE A B; BY ID SECTOR RING; URBAN=LUC1; PASTURE=LUC2+LUC3+LUC4; GRASS=LUC7+LUC8+LUC9+LUC10; FOR_DE=LUC11+LUC12; FOR_EG=LUC13+LUC14; WATER=LUC16+LUC24; PROC MEANS; VAR URBAN PASTURE GRASS FOR_DE FOR_EG WATER; DATA D1; INFILE IN3; INPUT ID 1-3 MEAN0 4-9 3 ST $ 10-11 REGION $ 12-13 @14 (WEIGHT1-WEIGHT8)(7.5) @70 (AVG1-AVG8)(7.5) @126 (RINGSEC1-RINGSEC8)(3.);

151 DROP ST REGION WEIGHT1-WEIGHT8 AVG1-AVG8 RINGSEC1-RINGSEC8; DATA D2; MERGE C D1; BY ID; IF IM=1; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25 URBAN PASTURE GRASS FOR_DE FOR_EG WATER; BY ID MEAN0; OUTPUT OUT=D2S SUM=LUCS1-LUCS25 URBANS PASTURES GRASSES FOR_DES FOR_EGS WATERS; PROC CORR DATA=D2S; VAR MEAN0 LUCS1-LUCS25; PROC CORR DATA=D2S; VAR MEAN0 URBANS PASTURES GRASSES FOR_DES FOR_EGS WATERS; DATA ET1; SET A; IF RING=1; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS1 SUM=LUCS1-LUCS25; DATA E1; SET ETS1; RING=1; DATA ET2; SET A; IF RING LE 2; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS2 SUM=LUCS1-LUCS25; DATA E2; SET ETS2; RING=2; DATA ET3; SET A; IF RING LE 3; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS3 SUM=LUCS1-LUCS25; DATA E3; SET ETS3; RING=3; DATA ET4; SET A; IF RING LE 4; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS4 SUM=LUCS1-LUCS25; DATA E4; SET ETS4; RING=4; DATA ET5; SET A; IF RING LE 5; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS5 SUM=LUCS1-LUCS25; DATA E5; SET ETS5; RING=5; DATA ET6; SET A; IF RING LE 6; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS6 SUM=LUCS1-LUCS25; DATA E6; SET ETS6; RING=6; DATA ET7; SET A; IF RING LE 7; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS7 SUM=LUCS1-LUCS25; DATA E7; SET ETS7; RING=7; DATA ET8; SET A; IF RING LE 8; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS8 SUM=LUCS1-LUCS25; DATA E8; SET ETS8; RING=8; DATA ET9; SET A; IF RING LE 9; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS9 SUM=LUCS1-LUCS25; DATA E9; SET ETS9; RING=9; DATA ET10; SET A; IF RING LE 10; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS10 SUM=LUCS1-LUCS25; DATA E10; SET ETS10; RING=10; DATA ET11; SET A; IF RING LE 11; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS11 SUM=LUCS1-LUCS25; DATA E11; SET ETS11; RING=11; DATA ET12; SET A; IF RING LE 12;

152 PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS12 SUM=LUCS1-LUCS25; DATA E12; SET ETS12; RING=12; DATA ET13; SET A; IF RING LE 13; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS13 SUM=LUCS1-LUCS25; DATA E13; SET ETS13; RING=13; DATA ET14; SET A; IF RING LE 14; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS14 SUM=LUCS1-LUCS25; DATA E14; SET ETS14; RING=14; DATA ET15; SET A; IF RING LE 15; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS15 SUM=LUCS1-LUCS25; DATA E15; SET ETS15; RING=15; DATA ET16; SET A; IF RING LE 16; PROC MEANS NOPRINT SUM; VAR LUC1-LUC25; BY ID SECTOR; OUTPUT OUT=ETS16 SUM=LUCS1-LUCS25; DATA E16; SET ETS16; RING=16; DATA E; SET E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 E16; PROC DATASETS NOLIST; DELETE E1-E16 ET1-ET16 ETS1-ETS16; PROC SORT; BY ID SECTOR RING; DATA G; MERGE E B; BY ID SECTOR RING; EURBAN=LUCS1; EPASTURE=LUCS2+LUCS3+LUCS4; EGRASS=LUCS7+LUCS8+LUCS9+LUCS10; EFOR_DE=LUCS11+LUCS12; EFOR_EG=LUCS13+LUCS14; EWATER=LUCS16+LUCS24; PROC MEANS; VAR EURBAN EPASTURE EGRASS EFOR_DE EFOR_EG EWATER; DATA _NULL_; SET C; FILE OUT1; PUT ID 1-3 RING 4-6 SECTOR 7-8 URBAN 9-20 5 PASTURE 21-32 5 GRASS 33-44 5 FOR_DE 45-56 5 FOR_EG 57-68 5 WATER 69-80 5 IM 82; DATA _NULL_; SET G; FILE OUT2; PUT ID 1-3 RING 4-6 SECTOR 7-8 EURBAN 9-20 5 EPASTURE 21-32 5 EGRASS 33-44 5 EFOR_DE 45-56 5 EFOR_EG 57-68 5 EWATER 69-80 5 IM 82; RUN;

153 MONSO2.SA2 FILENAME IN1 'D:\ZZZ\MONSO2.DA1'; FILENAME IN2 'D:\ZZZ\MONCLOSE.DAT'; FILENAME IN3 'D:\ZZZ\MONBASE.DAT'; FILENAME OUT 'D:\ZZZ\MONSO2.DAT'; *------DATA BASE EPA-AIRS-MONITOR ------FILE NAME: MONSO2.SA2. THIS PROGRAM READS THE FILES MONSO2.DA1 (CREATED BY MONSO2.SA1), MONBASE.DAT, AND MONCLOSE.DA1. MONBASE.DAT, DERIVED FROM THE EXCEL FILE AIRMON_INFO.XLS, INCLUDES BASIC DATA ABOUT SO2 MONITORING STATIONS. MONCLOSE.DAT, CREATED BY THE C++ PROGRAM ASSIGNSYS.CPP, CONTAINS DATA ON SURROUNDING STATIONS IN EACH SECTOR. SINCE SOME MONITORING STATIONS ARE CLOSE TO THE SEASHORE, IT IS POSSIBLE FOR THEM TO HAVE PARTS OF THEIR RING-SECTOR SYSTEM LOCATED OVER THE SEA. AS IT IS UNLIKELY THAT THE EMISSIONS FROM OTHER CONTINENTS CAN CROSS THE OCEAN TO REACH THE STATION, IT IS ASSUMED THAT THE EMISSION FROM THAT DIRECTION IS ZERO. A DUMMY STATION IS ASSIGNED TO THAT SECTOR WITH STATION ID=0 AND ZERO EMISSION. FINALLY, A WIDTH-DELIMITED FILE, MONSO2.DAT, IS CREATED. ------NOTE: (1) MONBASE.DAT HAS 642 RECORDS, INCLUDING ALL STATIONS IN 1999. (2) MONCLOSE.DAT HAS 345 RECORDS (ONLY CONTAINS THE SELECTED MONITORING STATION). (3) IFUSE=1 AND IFCOMP=0 MEANS THAT DUMMY MONITORING STATIONS ARE USED. DUE TO PRECISION ISSUES, WHEN TWO MONITORING STATIONS ARE VERY CLOSE, THEY MAY HAVE THE SAME COORDINATES. THERE ARE 3 PAIRS OF SUCH STATIONS - (ID=11, ID=12), (ID=248, ID=249), AND (ID=519, ID=520). ------; DATA A; INFILE IN1; INPUT ID 1-3 RING 5-6 SECTOR 8 NS 10-11 WEIGHT 13-19 5 AVG 21-27 5; PROC SORT; BY ID RING SECTOR; DATA B; INFILE IN2; INPUT ID 1-4 IDSEC1 5-8 DISTSEC1 9-19 3 RINGSEC1 20-22 IDSEC2 23-26 DISTSEC2 27-37 3 RINGSEC2 38-40 IDSEC3 41-44 DISTSEC3 45-55 3 RINGSEC3 56-58 IDSEC4 59-62 DISTSEC4 63-73 3 RINGSEC4 74-76 IDSEC5 77-80 DISTSEC5 81-91 3 RINGSEC5 92-94 IDSEC6 95-98 DISTSEC6 99-109 3 RINGSEC6 110-112 IDSEC7 113-116 DISTSEC7 117-127 3 RINGSEC7 128-130 IDSEC8 131-134 DISTSEC8 135-145 3 RINGSEC8 146-148; PROC SORT; BY ID; DATA CMER; MERGE A B; BY ID; DATA C1; SET CMER; IF RING=RINGSEC1 AND SECTOR=1; WEIGHT1=WEIGHT; AVG1=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C2; SET CMER; IF RING=RINGSEC2 AND SECTOR=2; WEIGHT2=WEIGHT; AVG2=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C3; SET CMER; IF RING=RINGSEC3 AND SECTOR=3; WEIGHT3=WEIGHT; AVG3=AVG;

154 DROP RING SECTOR NS WEIGHT AVG; DATA C4; SET CMER; IF RING=RINGSEC4 AND SECTOR=4; WEIGHT4=WEIGHT; AVG4=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C5; SET CMER; IF RING=RINGSEC5 AND SECTOR=5; WEIGHT5=WEIGHT; AVG5=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C6; SET CMER; IF RING=RINGSEC6 AND SECTOR=6; WEIGHT6=WEIGHT; AVG6=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C7; SET CMER; IF RING=RINGSEC7 AND SECTOR=7; WEIGHT7=WEIGHT; AVG7=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C8; SET CMER; IF RING=RINGSEC8 AND SECTOR=8; WEIGHT8=WEIGHT; AVG8=AVG; DROP RING SECTOR NS WEIGHT AVG; DATA C; MERGE C1 C2 C3 C4 C5 C6 C7 C8; BY ID; ARRAY WEIGHT WEIGHT1-WEIGHT8; ARRAY AVG AVG1-AVG8; DO OVER WEIGHT; IF WEIGHT=. THEN WEIGHT=0; END; DO OVER AVG; IF AVG=. THEN AVG=0; END; PROC SORT; BY ID; DATA D1; INFILE IN3; INPUT ID 1-3 MEAN0 5-9 3 X0 11-22 3 Y0 24-35 3 ST $ 37-38 REGION $ 40-41 IFUSE 43 IFCOMP 45; PROC SORT; BY ID; DATA D; MERGE C(IN=IK) D1; BY ID; IF IK; DATA _NULL_; SET D; FILE OUT; PUT ID 1-3 MEAN0 4-9 3 ST $ 10-11 REGION $ 12-13 @14 (WEIGHT1-WEIGHT8)(7.5) @70 (AVG1-AVG8)(7.5) @126 (RINGSEC1-RINGSEC8)(3.); RUN;

155 NETFAC.SA1 FILENAME IN1 'D:\ZZZ\NETPAT1.DA0'; FILENAME IN2 'D:\ZZZ\NETPAT2.DA0'; FILENAME IN3 'D:\ZZZ\NETPAT3.DA0'; FILENAME IN4 'D:\ZZZ\NETPAT4.DA0'; FILENAME IN5 'D:\ZZZ\NETPAT5.DA0'; FILENAME IN6 'D:\ZZZ\NETPAT6.DA0'; FILENAME IN7 'D:\ZZZ\NETPAT7.DA0'; FILENAME IN8 'D:\ZZZ\NETPAT8.DA0'; FILENAME IN9 'D:\ZZZ\MONBASE.DAT'; FILENAME OUT 'D:\ZZZ\NETFAC.DA1'; *------DATA BASE EPA-NET-RANKING ------FILE NAME: NETFAC.SA1. THIS SAS PROGRAM READS THE FILES NETPAT1.DA0 TO NETPAT8.DA0, CREATED BY PROGRAMS NETPAT1.SA0 TO NETPAT8.SA0, WHICH SUMMARIZE NET FACILITY DATA BY CENTER MONITORING STATION, RING AND SECTOR, FOR EACH SELECTED SYSTEM (345 RING-SECTOR SYSTEMS). IT ALSO READS MONBASE.DAT, WHICH CONTAINS BASIC INFORMATION ABOUT CENTER MONITORING STATIONS, AND THE FILE MONCLOSE.DAT, WHICH INCLUDES THE NEAREST SURROUNDING MONITORING STATION IN EACH SECTOR. IT THEN PERFORMS BASIC STATISTICAL ANALYSES, REARRANGES THE DATA STRUCTURE, AND CREATES THE WIDTH-DELIMITED DATA FILE, NETFAC.DA1. ------; DATA A1; INFILE IN1 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A2; INFILE IN2 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A3; INFILE IN3 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A4; INFILE IN4 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A5; INFILE IN5 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A6; INFILE IN6 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263;

156 DATA A7; INFILE IN7 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A8; INFILE IN8 LRECL=263; INPUT ID 1-4 NETINT_I $ 5-10 RING 11-13 SECTOR 14-15 EMIS 16-24 PERCT $ 25-33 NAME $ 34-74 ADD $ 75-130 ST $ 131-133 CNTY $ 134-151 YEAR $ 152-156 SIC $ 157-204 FACID $ 205-220 LAT $ 221-228 LON $ 229-237 REG $ 238-239 X_COORD 240-251 Y_COORD 252-263; DATA A; SET A1 A2 A3 A4 A5 A6 A7 A8; IX=1; PROC SORT; BY ID SECTOR RING; DATA B; INFILE IN9; INPUT ID 1-4 MEAN0 5-9 3 X0 11-22 3 Y0 24-35 3; IY=1; PROC SORT; BY ID; DATA C; MERGE A B; BY ID; IF IX=1; DIST=(SQRT((X_COORD-X0)**2+(Y_COORD-Y0)**2)); PROC SORT; BY ID RING SECTOR; PROC MEANS NOPRINT N SUM; VAR DIST; BY ID RING SECTOR; OUTPUT OUT=CS N=NS SUM=DISTT; DATA CS; SET CS; AVGDIST=DISTT/NS; DATA CM; MERGE C CS; BY ID RING SECTOR; DISTR=AVGDIST/DIST; WEMIS=DISTR*EMIS; PROC SORT; BY ID RING SECTOR NETINT_I; PROC MEANS NOPRINT N SUM; VAR EMIS WEMIS; BY ID RING SECTOR; OUTPUT OUT=CSS N=TOTFAC SUM=EMIST WEMIST; DATA D; SET CSS; IF RING<17; DATA _NULL_; SET D; FILE OUT; PUT ID 1-4 RING 5-7 SECTOR 8-9 TOTFAC 10-13 EMIST 14-29 3 WEMIST 30-45 3; RUN;

157 NETTIER.SA2 FILENAME IN1 'D:\ZZZ\TIERPAT1.DA0'; FILENAME IN2 'D:\ZZZ\TIERPAT2.DA0'; FILENAME IN3 'D:\ZZZ\TIERPAT3.DA0'; FILENAME IN4 'D:\ZZZ\TIERPAT4.DA0'; FILENAME IN5 'D:\ZZZ\TIERPAT5.DA0'; FILENAME IN6 'D:\ZZZ\TIERPAT6.DA0'; FILENAME IN7 'D:\ZZZ\TIERPAT7.DA0'; FILENAME IN8 'D:\ZZZ\TIERPAT8.DA0'; FILENAME IN9 'D:\ZZZ\USCNTY.DAT'; FILENAME OUT 'D:\ZZZ\NETTIER.DA2'; *------DATA BASE EPA-NET-RANKING ------FILE NAME: NETTIER.SA2. THIS PROGRAM READS THE FILES TIERPAT1.DA0 TO TIERPAT8.DA0, CREATED BY PROGRAMS TIERPAT1.SA0 TO TIERPAT8.SA0, WHICH SUMMARIZE COUNTY AREAS BY MONITORING STATION, RING AND SECTOR, FOR EACH SELECTED SYSTEM (345 RING-SECTOR SYSTEMS). IN TIERPAT#.DA0 (#=1-8), WHEN A POLYGON IS LOCATED OVER THE SEA, BOTH THE ST AND CNTY CODES ARE EMPTY IN THE RECORD, AND THAT RECORD CAN BE DELETED. THIS PROGRAM ALSO READS THE FILE USCNTY.DAT, WHICH WAS CREATED BY USCNTY.SAS. USCNTY.DAT NEEDS TO BE SUMMARIZED BY STATE AND COUNTY, BECAUSE A COUNTY MAY HAVE MORE THAN ONE RECORD. THIS HAPPENS TO A COUNTY ALONG THE SEASHORE, BECAUSE SOME OFFSHORE ISLANDS (POLYGONS) ALSO BELONG TO THAT COUNTY. THE PROGRAM THEN PERFORMS BASIC STATISTICAL ANALYSES, REARRANGES THE DATA STRUCTURE, AND CREATES THE WIDTH-DELIMITED DATA FILE, NETTIER.DA2. ------; DATA A1; INFILE IN1; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A2; INFILE IN2; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A3; INFILE IN3; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A4; INFILE IN4; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A5; INFILE IN5; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A6; INFILE IN6; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A7; INFILE IN7; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA A8; INFILE IN8; INPUT ID 1-4 TIER_I 5-9 RING 10-12 SECTOR 13-14 AREA 15-34 3 X_COORD 35-46 3 Y_COORD 47-58 3 ST $ 59-60 CNTY $ 62-64 NAME $ 66-95; DATA AC; SET A1 A2 A3 A4 A5 A6 A7 A8; IF ST='' AND CNTY='' THEN DELETE; PROC SORT; BY ST CNTY;

158 DATA B; INFILE IN9; INPUT ST $ 1-2 CNTY $4-6 STNAME $ 8-9 CNTYNAME $ 11-40 CAREA 41-64; IF STNAME='AK' OR STNAME='HI' THEN DELETE; PROC SORT; BY ST CNTY; PROC MEANS NOPRINT SUM; VAR CAREA; BY ST CNTY; OUTPUT OUT=BS SUM=CNTYAREA; DATA C; MERGE AC(IN=IK) BS; BY ST CNTY; IF IK; RATIO=AREA/CNTYAREA; PROC SORT; BY ID SECTOR RING ST CNTY; DATA D; SET C; IF RING<17; PROC PRINT DATA=D(OBS=30); DATA _NULL_; SET D; FILE OUT; PUT ID 1-4 RING 5-7 SECTOR 8-9 ST $ 10-12 CNTY $ 13-16 STNAME $ 17-19 CNTYNAME $ 20-50 AREA 51-70 3 CNTYAREA 71-94 RATIO 95-104 5; RUN;

159 NETTIER.SA3 FILENAME IN1 'D:\ZZZ\NETTIER.DA1'; FILENAME IN2 'D:\ZZZ\NETTIER.DA2'; FILENAME IN3 'D:\ZZZ\RSLIST.TXT'; FILENAME OUT 'D:\ZZZ\NETTIER.DA3'; *------DATA BASE EPA-NET-RANKING ------FILE NAME: NETTIER.SA3. THIS SAS PROGRAM READS THE FILES NETTIER.DA1, WHICH WAS CREATED BY NETTIER.SA1, AND NETTIER.DA2, WHICH WAS CREATED BY NETTIER.SA2. IT MERGES BOTH FILES AND CALCULATES TOTAL EMISSIONS BY STATE AND COUNTY. THE PROGRAM ALSO READS THE FILE RSLIST.TXT, WHICH LISTS ALL COMBINATIONS OF RING (1-16) AND SECTOR (1-8). BECAUSE SOME PARTS OF THE RING- SECTOR SYSTEM MAY BE LOCATED OVER THE SEA, THERE IS NO AREAL EMISSION DATA FOR THOSE CELLS. THIS CAUSES A PROBLEM IN CREATING THE PROPER DATA FORMAT (EACH RECORD REPRESENTS A GIVEN MONITORING STATION) FOR REGRESSION ANALYSES. RSLIST.TXT IS USED TO MERGE THE FILE WITH NETFAC.DA1 TO CORRECT THIS PROBLEM. FINALLY, A WIDTH-DELIMITED DATA FILE IS CREATED, NETTIER.DA3. ------; DATA A; INFILE IN1; INPUT ST $ 1-2 CNTY $ 4-6 STNAME $ 8-9 CNTYNAME $ 11-40 CNTYAREA 41-64 TIER1 $ 65-97 TIER2 $ 98-141 EAREA 142-149 EPT 150-157; PROC SORT; BY ST CNTY; PROC MEANS NOPRINT SUM; VAR EAREA EPT; BY ST CNTY; OUTPUT OUT=AS SUM=EAREAS EPTS; DATA B; INFILE IN2; INPUT ID 1-4 RING 5-7 SECTOR 8-9 ST $ 10-11 CNTY $ 13-15 STNAME $ 17-19 CNTYNAME $ 20-50 AREA 51-70 3 CNTYAREA 71-94 RATIO 95-104 5; PROC SORT; BY ST CNTY; DATA C; MERGE AS B(IN=IK); BY ST CNTY; IF IK; EA=EAREAS*RATIO; EP=EPTS*RATIO; PROC SORT; BY ID RING SECTOR ST CNTY; PROC MEANS NOPRINT SUM; VAR EA EP; BY ID RING SECTOR; OUTPUT OUT=CS SUM=EAS EPS; PROC PRINT DATA=CS(OBS=30); DATA D1; INFILE IN3; INPUT ID 1-4 RING 5-7 SECTOR 8-9; PROC SORT; BY ID RING SECTOR; DATA D; MERGE CS D1; BY ID RING SECTOR; IF EAS=. THEN EAS=0; IF EPS=. THEN EPS=0; PROC SORT; BY ID SECTOR RING; DATA _NULL_; SET D; FILE OUT; PUT ID 1-3 RING 4-6 SECTOR 7-8 EAS 9-20 3 EPS 21-32 3; RUN;

160 WEATHER.SA5 FILENAME IN1 'D:\ZZZ\WEABASE.DAT‘; FILENAME IN2 'D:\ZZZ\MONWEA.DAT‘; FILENAME IN3 'D:\ZZZ\MONSO2.DA0‘; FILENAME IN4 'D:\ZZZ\WEATHER.DA1‘; FILENAME OUT 'D:\ZZZ\WEATHER.DA2‘; *------DATA BASE WEATHER ------FILE NAME: WEATHER.SA5. THIS PROGRAM READS THE FILES WEABASE.DAT, SHOWING THE CORRESPONDING CONNECTION BETWEEN WEATHER MONITORING STATION ID AND WBAN, MONWEA.DAT, SHOWING THE NEAREST WEATHER MONITORING STATION TO A GIVEN SO2 AIR QUALITY MONITORING STATION, MONSO2.DA0, INDICATING IF AN SO2 AIR MONITORING STATION IS USED, AND WEATHER.DA1, SHOWING MONTHLY WIND DIRECTION FREQUENCIES BY WBAN. IT MERGES, SUMMARIZES, AND REARRANGES THE DATA. IT THEN CREATES A WIDTH-DELIMITED FILE, WEATHER.DA2. ------NOTE: (1) WIND DIRECTION='000' MEANS CALM CONDITIONS. (2) WIND DIRECTION='-' MEANS NO DATA AVAILABLE. (3) WIND DIRECTION='VRB' MEANS WIND SPEED <= 6 KNOTS. SO, WIND SPEED IS ASSUMED TO BE 3 KNOTS FOR ALL 'VRB' CASES. ------; DATA A1; INFILE IN1; INPUT WEA_ID 1-3 WBAN $ 5-9 X_COORD 11-23 3 Y_COORD 25-37 3; IV1=1; PROC SORT; BY WEA_ID; DATA A2; INFILE IN2; INPUT ID 1-3 MON_X 5-16 3 MON_Y 18-29 3 WEA_ID 31-33 DIST 35-44 3 WEA_X 46-57 3 WEA_Y 59-70 3; IV2=1; PROC SORT; BY WEA_ID; DATA A3; MERGE A1 A2; BY WEA_ID; IF IV2; PROC SORT; BY ID; DATA A4; INFILE IN3; INPUT ID 1-4 IFUSE 5-6 IFCOMP 7-8 IFDUMMY $ 9-12 DUMMY1 $ 13-17 DUMMY2 $ 18-22 DUMMY3 $ 23-27 DUMMY4 $ 28-32 DUMMY5 $ 33-37; IV3=1; IF IFUSE=1; PROC SORT; BY ID; DATA A; MERGE A3 A4; BY ID; IF IV3=1; PROC SORT; BY WBAN; DATA BBASE; INFILE IN4; INPUT WBAN $ 1-5 WDIR $ 7-9 @11 (FREQ1-FREQ12)(6.); FREQ=SUM(OF FREQ1-FREQ12); PROC SORT; BY WBAN; DATA B1T; SET BBASE; IF WDIR='050' OR WDIR='060' OR WDIR='070' OR WDIR='080'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B1TS SUM=WDSEC1T; DATA B2T; SET BBASE; IF WDIR='010' OR WDIR='020' OR WDIR='030' OR WDIR='040'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B2TS SUM=WDSEC2T; DATA B3T; SET BBASE;

161 IF WDIR='320' OR WDIR='330' OR WDIR='340' OR WDIR='350'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B3TS SUM=WDSEC3T; DATA B4T; SET BBASE; IF WDIR='280' OR WDIR='290' OR WDIR='300' OR WDIR='310'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B4TS SUM=WDSEC4T; DATA B5T; SET BBASE; IF WDIR='230' OR WDIR='240' OR WDIR='250' OR WDIR='260'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B5TS SUM=WDSEC5T; DATA B6T; SET BBASE; IF WDIR='190' OR WDIR='200' OR WDIR='210' OR WDIR='220'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B6TS SUM=WDSEC6T; DATA B7T; SET BBASE; IF WDIR='140' OR WDIR='150' OR WDIR='160' OR WDIR='170'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B7TS SUM=WDSEC7T; DATA B8T; SET BBASE; IF WDIR='100' OR WDIR='110' OR WDIR='120' OR WDIR='130'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B8TS SUM=WDSEC8T; DATA BHA; SET BBASE; IF WDIR='090'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=BHAS SUM=WDBHA; DATA BHAX; SET BHAS; WDSEC1T=WDBHA/2; WDSEC8T=WDBHA/2; DATA B1; SET B1TS BHAX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC1T; BY WBAN; OUTPUT OUT=B1S SUM=WDSEC1; DATA B8; SET B8TS BHAX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC8T; BY WBAN; OUTPUT OUT=B8S SUM=WDSEC8; DATA BHB; SET BBASE; IF WDIR='180'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=BHBS SUM=WDBHB; DATA BHBX; SET BHBS; WDSEC6T=WDBHB/2; WDSEC7T=WDBHB/2; DATA B6; SET B6TS BHBX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC6T; BY WBAN; OUTPUT OUT=B6S SUM=WDSEC6; DATA B7; SET B7TS BHBX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC7T; BY WBAN; OUTPUT OUT=B7S SUM=WDSEC7; DATA BHC; SET BBASE; IF WDIR='270'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=BHCS SUM=WDBHC; DATA BHCX; SET BHCS; WDSEC4T=WDBHC/2; WDSEC5T=WDBHC/2; DATA B4; SET B4TS BHCX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC4T; BY WBAN; OUTPUT OUT=B4S SUM=WDSEC4; DATA B5; SET B5TS BHCX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC5T; BY WBAN; OUTPUT OUT=B5S SUM=WDSEC5; DATA BHD; SET BBASE;

162 IF WDIR='360'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=BHDS SUM=WDBHD; DATA BHDX; SET BHDS; WDSEC2T=WDBHD/2; WDSEC3T=WDBHD/2; DATA B2; SET B2TS BHDX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC2T; BY WBAN; OUTPUT OUT=B2S SUM=WDSEC2; DATA B3; SET B3TS BHDX; PROC SORT; BY WBAN; PROC MEANS NOPRINT SUM; VAR WDSEC3T; BY WBAN; OUTPUT OUT=B3S SUM=WDSEC3; DATA B9; SET BBASE; IF WDIR='000' OR WDIR='VRB' OR WDIR='-'; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=B9S SUM=OTHER; DATA B; MERGE B1S B2S B3S B4S B5S B6S B7S B8S B9S; BY WBAN; IV4=1; DATA C; MERGE A B; BY WBAN; IF IV3=1; PROC SORT; BY ID; DATA C1; SET C; PROC SORT; BY WBAN; DATA C2; SET BBASE; PROC MEANS NOPRINT SUM; VAR FREQ; BY WBAN; OUTPUT OUT=C2S SUM=FREQS; DATA TEST; MERGE C2S C1(IN=A); BY WBAN; IF A; DIFF=FREQS-SUM(OF WDSEC1-WDSEC8)-OTHER; PROC MEANS; DATA TEST; SET TEST; IF DIFF NE 0; PROC PRINT; DATA _NULL_; SET C; FILE OUT; PUT ID 1-3 WEA_ID 5-7 WBAN $ 9-13 WDSEC1 15-18 WDSEC2 20-23 WDSEC3 25-28 WDSEC4 30-33 WDSEC5 35-38 WDSEC6 40-43 WDSEC7 45-48 WDSEC8 50-53 OTHER 55-58; RUN;

163 AML PROGRAMS TGRLINE.AML /* This program reads the original tiger file, and then creates line feature coverages (i.e. water coverage) /* Modified by Kang-Ping Shen

/* Get input from users &args st ct &if [null %st%] or [null %ct%] &then &call usage

/*Unzip Tiger/Line data &sv tgrpath = c:\map\Tiger1998 &type &type ************************** &type Extracting Tiger files ... &type ************************** &if [exists %tgrpath%\tgr%st%%ct%.zip] &then &sys "c:\Program Files\Winzip\wzunzip.exe" -d -o %tgrpath%\tgr%st%%ct% &else &type unable to find the file tgr%st%%ct%.zip

/* Run Arc/Info command TIGERTOOL &type &type ************************************** &type Please wait... Processing Tiger files. &type ************************************** tigertool tgr%st%%ct%.rt z%st%%ct%

/* Add item State, County, and Census Feature Class Codes (CFCC) to coverage additem z%st%%ct%1.aat z%st%%ct%1.aat state 2 2 c additem z%st%%ct%1.aat z%st%%ct%1.aat county 3 3 c additem z%st%%ct%1.aat z%st%%ct%1.aat cfcc 3 3 c

/* Go into INFO to relate necessary information &sv cov = Z%st%%ct%1 &data arc info ARC SELECT %COV%.AAT RELATE %COV%.ACODE BY %COV%-ID ORDERED MOVE $1CFCC TO CFCC MOVE [QUOTE %ST%] TO STATE MOVE [QUOTE %CT%] TO COUNTY Q STOP &end

/* Delete unnecessary by-product coverage kill z%st%%ct%3 all

/* Change projection to wanted form &type Now changing ptojection ..... project cover %cov% %cov%p projection.prj build %cov%p poly kill %cov% all

164 /* Create water coverage reselect %cov%p wa%ct% line # line reselect cfcc gt 'F82' and cfcc lt 'H50' ~ n n

/* Cleaning &type &type *********************************** &type Cleaning all intermediate files ... &type *********************************** /* Delete tiger files &do suffix &list 1 2 3 4 5 6 7 8 9 a c h i p r s z &if [delete tgr%st%%ct%.rt%suffix%] = 0 &then &type tgr%st%%ct%.rt%suffix% deleted successfully &else &type unable to delete the file tgr%st%%ct%.rt%suffix% &end /* Delete unnecessary geodata files &message &off kill %cov%p all killinfo z*.type* killinfo z*.rel &sys del /q *.rpt &message &on

&return

/* Route for the usage of this AML &routine usage &type tgrline st ct &type eg. 39 005 (st should be two digits, and ct should be three digits)

APPCOV.AML /* This program creates a file including all coverages in this workspace, and appends those coverages and /* then makes the final coverage with name of %final_cov% provided by user. However, the final coverage /* MAY need further processing by using Arc/Info commands, such as MATCHNODE, or/and CLEAN. /* Usage: appcov /* Created by Kang-Ping Shen

&args final_cov feature &severity &error &routine ERR_POP

&if [null %final_cov%] or [null %feature%] &then &call USAGE

/* SPECIFY what type of files you want to be listed and create a list &sv file = coverlist.txt &sv numobs = [filelist * %file% -cover -%feature%]

&if [exists %file%] &then &do

165 /* open and read the file &sv fileunit = [open %file% openstat -READ] &if %openstat% = 0 &then &do /* Read the file &sv record = [read %fileunit% readstat] /* append coverages to create a final coverage append %final_cov% %feature% &do &while %readstat% = 0 %record% &sv record = [read %fileunit% readstat] &end &select %readstat% &when 100,101,103,104; &do &type Complete file or record not read. &type Error code: %readstat%. &end &end &end &else &type Error opening file, error code: %openstat%

&sv closestat = [close %fileunit%] &if %closestat% = 0 &then &delvar fileunit &else &type Error closing unit %fileunit% Error code: %closestat% END /* for the APPEND command

&end &else &type File %file% doesn't exist.

/* delete list file &if [delete %file%] = 0 &then &type %file% deleted successfully &else &type unable to delete the file %file%

&return

/* Route for the usage of this AML &routine USAGE &type Usage: appcov (e.g. appcov myCover poly) &return &error

&routine ERR_POP &severity &error &ignore &type ------&type ERROR: You are exiting this AML with an error.... &type ------&return &error

166 GLCCLAND.AML /* This program clips each ring-sector segment from GLCC landuse data, which is AVHRR imagery of /* North America, for each monitoring station. Note that only monitoring stations located in EPA region /* 10 are shown here. There are 10 EPA regions. /* Created by Kang-Ping Shen cd e:\1999\map\landuse w e:\1999\map\landuse

&DO I &LIST 028 029 030 031 047 051 052 054

copy e:\1999\map\reg09\rings%I% lu%I%

projectdefine cover lu%I% datum nar_c parameters 29 30 0.000 45 30 0.000 -96 0 0.000 23 0 0.000 0.000 0.000

project cover lu%I% glu%I% e:\1999\map\glccprj.prj clean glu%I% glu%I% # 0.000001 poly kill lu%I% all additem glu%I%.pat glu%I%.pat symbol 2 2 i

tables sel glu%I%.pat resel glu%I%-id > 0 cal symbol = 1 q

dissolve glu%I% golu%I% symbol poly

grid gridclip e:\1999\map\glccna_grid golu%I%_g cover golu%I% q

gridpoly golu%I%_g golu%I%_p

copy golu%I%_g e:\1999\map\landuse\grids\golu%I%_g copy golu%I% e:\1999\map\landuse\outline\golu%I% kill golu%I%_g all kill golu%I% all

&END cd e:\1999\map w e:\1999\map

&return

167 C++ AND FORTRAN PROGRAMS MAXDIST8.CPP /************************************************************************************ ** This program (maxdist8.cpp) finds the maximum distance among those nearest monitoring stations in ** each sector. ** Coded by Kang-Ping Shen *************************************************************************************/ #include #include #include #include #include #include main() { int numberPoint = 642; int numberSection = 8; ofstream outFile; ifstream inFile; int pos, strPos, centerID; int tmpID[numberPoint][numberSection+1]; double len, centerX, centerY, maxLen; double tmpX[numberPoint][numberSection+1], tmpY[numberPoint][numberSection+1]; string idString, aString, intString, xString, yString; inFile.open("near8.dat"); // check and open input file if(inFile.fail()) { cerr << "Fail to open input file..." << endl; exit (-1); } for (int i=0; i

168 cerr << "Fail to open output file..." << endl; exit (-1); } for (int i=0; i maxLen) { maxLen = len; } } } } // end of for(int j=0; j

outFile << centerID <<","<< setiosflags(ios::fixed|ios::showpoint) << setprecision(3) << maxLen << endl; } // end of for(int i=0; i

COMPLETE320.CPP /************************************************************************************* ** This program (complete320.cpp) finds the monitoring stations that are complete. The word "complete" ** means that a given monitoring station has at least one station in each sector within 320 km (200 mile). ** Coded by Kang-Ping Shen 01/15/2002 *************************************************************************************/ #include #include #include #include #include #include "tokenScanner.h" main() { int numberPoint = 642; int numberRing = 20; int numberSection = 8;

169 ofstream outFile; int sum, counter = 0; int rCount[numberPoint][numberRing+1]; string aID, aString; tokenScanner aToken("distribution.dat"); // parse input file line-by-line aToken.setDelim(':'); while (aToken.hasNext()) { aID = aToken.getNextToken(); rCount[counter][0] = atoi(aID.c_str()); for (int i=1; i

} // end of for(int i=0; i

outFile.close(); // close files } // end of main()

TOKENSCANNER.H /*************************************************************************** ** This program (tokenScanner.h) parses a text file line-by-line with provided delimiter. ** Declaration of the tokenScanner class. ** Member functions are defined in tokenScanner.cpp ** Modified by Kang-Ping Shen ***************************************************************************/ #ifndef TOKENSCANNER_H

170 #define TOKENSCANNER_H

//tokenScanner abstract data type definition class tokenScanner { public: tokenScanner(); // default constructor tokenScanner(string); // constructor ~tokenScanner(); // destructor void setDelim(char ); // set delimiter int hasNext(); // check the next available string token string getNextToken(); // get the next available string token private: tokenScanner(const tokenScanner &); // copy constructor tokenScanner & operator = (const tokenScanner &); // assign operator int isOpen; // variable indicating if file is opened char delim; // character of delimiter string fileName; // name of input text file ifstream inFile; // ifstream object list tokenQ; // an instance of class list };

#endif

LANDUSE.FPP *********************************************************************** * FILE NAME: LANDUSE.FPP * THIS FORTRAN PROGRAM READS THE ORIGINAL LAND USE DATA FILE, * MANIPULATES VARIABLE FIELDS, AND CREATES A NEW DATA FILE. * CODED BY KANG-PING SHEN

PARAMETER (NKT=55200)

INTEGER ID(626), ID1(345) REAL R(25), RF(345,16,8,25)

* ------* INITIALIZE BASIC VALUE TO ZERO FOR EACH ELEMENT OF ARRAYS * ------DO 1 IN=1,345 DO 1 IR=1,16 DO 1 IS=1,8 DO 1 IL=1,25 RF(IN,IR,IS,IL)=0 1 CONTINUE

DO 2 IK=1,626 ID(IK)=0 2 CONTINUE

* ------* READ INDEX AND MONITORING ID DATA FILE * ------

171 OPEN(UNIT=1, FILE='INDEX_ID.TXT', STATUS='OLD')

DO 3 IK=1,345 READ(1,4) IN1, IN2 4 FORMAT(I3,1X,I3) ID(IN2)=IN1 ID1(IK)=IN2 3 CONTINUE

* CLOSE DATA FILE INDEX_ID.TXT CLOSE(UNIT=1)

* ------* READ ORIGINAL LANDUSE DATA FILE * ------OPEN(UNIT=2, FILE='LANDUSE.DA1', STATUS='OLD')

DO 5 IK=1,NKT READ(2,6) IN2, IR, IS, (R(IL),IL=1,25) 6 FORMAT(I3,1X,I3,I2,25F7.3) IN1=ID(IN2)

* ------* ASSIGN PROPER VALUES TO CORRESPONDING FIELDS * ------DO 7 IL=1,25 RF(IN1,IR,IS,IL)=R(IL) 7 CONTINUE 5 CONTINUE

* CLOSE DATA FILE LANDUSE.DA1 CLOSE(UNIT=2)

* ------* CREATE NEW DATA FILE * ------OPEN(UNIT=3, FILE='LANDUSE.DAT', STATUS='NEW') DO 8 IN=1,345 WRITE(3,9) ID1(IN), (((RF(IN,IR,IS,IL),IL=1,25),IR=1,16),IS=1,8) 9 FORMAT(I3,2X,400F7.3 / 7(5X,400F7.3 /)) 8 CONTINUE

* CLOSE DATA FILE LANDUSE.DAT CLOSE(UNIT=3)

WRITE(6,100) 100 FORMAT(2X,'----- END OF PROGRAM -----')

STOP END

172

APPENDIX C

SOURCE DATA DICTIONARIES AND SAMPLES

173

Abbreviation Full Name ID Record identifier and ID used for Arc/Info EMIS Pollutant emission (in short tons) PERCT Percent of total emissions NAME Facility full name ADD Facility street address ST Facility location œ state CNTY Facility location œ county YEAR Year of emission data FACID Facility ID used by EPA SIC Industry classification type (SIC code) LAT Latitude of the facility in degree LON Longitude of the facility in degree REG EPA region code

Table C.1: Variables Provided by the NET Facility Emission Report

174

5 4 3 3 5 5 4 5 3 4 5 5 5 5 3 REG REG LON LON -89.8550 -89.8550 -86.9775 -79.1969 -79.3425 -87.7783 -80.6331 -87.9867 -81.8844 -79.9167 -84.9192 -82.1281 -87.3322 -81.4792 -80.6486 -76.6664 LAT 38.2050 37.2597 40.5142 40.6522 38.3589 40.5328 36.0278 40.1756 39.8500 34.1256 38.9161 37.9147 41.6708 40.2522 41.0700 c lectric Electric E Electric Electric Electric Electric Electric Electric Electric Electric Electric Electri Electric Electric Electric ------SIC 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services 4911 Services FACID 171570033 211770006 420630003 420050012 180510013 390815010 470850011 390315001 420590006 130150011 390535001 181730002 390855012 390815002 420930003 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 YEAR Co Co Co Co Co Co Co Co CNTY CNTY Lake Co Jefferson Jefferson Gallia Co Randolph Armstrong Bartow Co Gibson Co Coshocton Indiana Co Greene Co Warrick Co Humphreys Montour Co Muhlenberg IL IN IN IN ST KY PA PA TN PA PA GA GA OH OH OH OH OH OH

ADD State Route 154 Baldwin Il 62217 13246 State Rte 176, Ste 10 Drakesboro Ky 42337 Sr 64 W & 975 Cr Princeton In 47670 Warrick Power Plant Newburgh In 47630 - - Baldwin -

NAME Warrick Pwr Plant - Illinois Power Co Power Plant Tva Paradise - CityHomer Keystone Gibson W H Sammis Tva Johnsonville Steam Plant Columbus Southern Power Conesville Hatfield'S Ferry PowerGa Co: Bowen Kyger Creek Sigeco Alcoa Generatng Eastlake Cardinal Montour 1.50 1.11 1.00 1.00 0.98 0.93 0.92 0.89 0.87 0.86 0.83 0.73 0.71 0.71 0.70 PERCT EMIS 245243 181066 163462 162290 158901 150782 150222 144933 141872 140154 135558 119656 115619 115001 113787 1 2 3 4 5 6 7 8 9 ID ID 10 11 12 13 14 15 Facility Report Table Sample C.2: NET of Emission the

175

Tier 1 Tier 1 Tier 2 Tier 2 Tier 1 Tier 1 Tier 2 Tier 2 CODE NAME CODE NAME CODE NAME CODE NAME 01 FUEL COMBUSTION-ELECTRIC UTILITIES 09 STORAGE & TRANSPORT 01 Coal 01 Bulk Terminals & Plants 02 Oil 02 Petroleum & Petroleum Product Storage 03 Gas 03 Petroleum & Petroleum Product Transport 04 Other External Combustion 04 Service Stations: Stage I 05 Internal Combustion 05 Service Stations: Stage II 02 FUEL COMBUSTION-INDUSTRIAL 06 Service Stations: Breathing & Emptying 01 Coal 07 Organic Chemical Storage 02 Oil 08 Organic Chemical Transport 03 Gas 09 Inorganic Chemical Storage 04 Other External Combustion 10 Inorganic Chemical Transport 05 Internal Combustion 11 Bulk Materials Storage 03 FUEL COMBUSTION-OTHER 12 Bulk Materials Transport 01 Commercial / Institutional Coal 10 WASTE DISPOSAL & RECYCLING 02 Commercial / Institutional Oil 01 Incineration 03 Commercial / Institutional Gas 02 Open Burning 04 Misc. Fuel Combustion (except residential) 03 Publicly Owned Treatment Works 05 Residential Wood 04 Industrial Waste Water 06 Residential Other 05 Treatment Storage and Disposal Facility 04 CHEMICAL & ALLIED PRODUCT MFG. 06 Landfills 01 Organic Chemical Mfg. 07 Other 02 Inorganic Chemical Mfg. 11 ON-ROAD VEHICLES 03 Polymer & Resin Mfg. 01 Light-Duty Gasoline Vehicles & Motorcycles 04 Agricultural Chemical Mfg. 02 Light-Duty Gasoline Trucks 05 Paint, Varnish, Lacquer, Enamel Mfg. 03 Heavy-Duty Gasoline Vehicles 06 Pharmaceutical Mfg. 04 Diesels 07 Other Chemical Mfg. 12 NON-ROAD ENGINES AND VESSELS 05 METALS PROCESSING 01 Non-road Gasoline Engines 01 Nonferrous 02 Non-road Diesel Engines 02 Ferrous 03 Aircraft 03 Metals Processing (not elsewhere classified) 04 Marine Vessels 06 PETROLEUM & RELATED INDUSTRIES 05 Railroads 01 Oil & Gas Production 13 NATURAL SOURCES 02 Petroleum Refineries & Related Industries 01 Biogenic 03 Asphalt Manufacturing 02 Geogenic (wind erosion) Miscellaneous 07 OTHER INDUSTRIAL PROCESSES 03 (lightning/freshwater/saltwater) 01 Agriculture, Food, & Kindred Products 14 MISCELLANEOUS 02 Textiles, Leather, & Apparel Products 01 Agriculture & Forestry 03 Wood, Pulp & Paper, & Publishing Products 02 Other Combustion (wildfires) 04 Rubber & Miscellaneous Plastic Products 03 Catastrophic / Accidental Releases 05 Mineral Products 04 Repair Shops 06 Machinery Products 05 Health Services 07 Electronic Equipment 06 Cooling Towers 08 Transportation Equipment 07 Fugitive Dust 09 Construction 10 Miscellaneous Industrial Processes 08 SOLVENT UTILIZATION 01 Degreasing 02 Graphic Arts 03 Dry Cleaning 04 Surface Coating 05 Other Industrial 06 Nonindustrial 07 Solvent Utilization (not elsewhere classified) Source: EPA AirData Website ( http://www.epa.gov/air/data/index.html )

Table C.3: EPA Tier1 and Tier 2 Categories

176

TIER1 TIER2 CNTY ST Area Emission Point Emission 02-Fuel Comb. Industrial 01-Coal Abbeville Co SC 63 0 02-Fuel Comb. Industrial 01-Coal Accomack Co VA 3 0 02-Fuel Comb. Industrial 01-Coal Ada Co ID 212 0 02-Fuel Comb. Industrial 01-Coal Adair Co IA 11 0 02-Fuel Comb. Industrial 01-Coal Adair Co KY 41 0 02-Fuel Comb. Industrial 01-Coal Adair Co MO 52 0 02-Fuel Comb. Industrial 01-Coal Adams Co CO 202 0 02-Fuel Comb. Industrial 01-Coal Adams Co IA 1 0 02-Fuel Comb. Industrial 01-Coal Adams Co IL 5 5651 02-Fuel Comb. Industrial 01-Coal Adams Co IN 9 637 02-Fuel Comb. Industrial 01-Coal Adams Co ND 266 0 02-Fuel Comb. Industrial 01-Coal Adams Co NE 99 139 02-Fuel Comb. Industrial 01-Coal Adams Co OH 6 0 02-Fuel Comb. Industrial 01-Coal Adams Co WI 3 0 02-Fuel Comb. Industrial 01-Coal Addison Co VT 8 0 02-Fuel Comb. Industrial 01-Coal Aiken Co SC 526 836 02-Fuel Comb. Industrial 01-Coal Alamance Co NC 245 0 02-Fuel Comb. Industrial 01-Coal Alamosa Co CO 1 0 02-Fuel Comb. Industrial 01-Coal Albany Co NY 633 0 02-Fuel Comb. Industrial 01-Coal Albany Co WY 149 0 02-Fuel Comb. Industrial 01-Coal Albemarle Co VA 12 0 02-Fuel Comb. Industrial 01-Coal Alexander Co IL 1 453 02-Fuel Comb. Industrial 01-Coal Alexander Co NC 75 3 02-Fuel Comb. Industrial 01-Coal Alexandria city VA 5 0 02-Fuel Comb. Industrial 01-Coal Alger Co MI 0 758 02-Fuel Comb. Industrial 01-Coal Allamakee Co IA 22 0 02-Fuel Comb. Industrial 01-Coal Allegany Co NY 116 0 02-Fuel Comb. Industrial 01-Coal Alleghany Co NC 8 0 02-Fuel Comb. Industrial 01-Coal Alleghany Co VA 1686 9027 02-Fuel Comb. Industrial 01-Coal Allegheny Co PA 0 770 02-Fuel Comb. Industrial 01-Coal Allen Co IN 47 0 02-Fuel Comb. Industrial 01-Coal Allen Co KS 4 0 02-Fuel Comb. Industrial 01-Coal Allen Co KY 237 0 02-Fuel Comb. Industrial 01-Coal Allen Co OH 365 207 02-Fuel Comb. Industrial 01-Coal Allendale Co SC 6 0 02-Fuel Comb. Industrial 01-Coal Alpena Co MI 0 9246 02-Fuel Comb. Industrial 01-Coal Amherst Co VA 2 0 02-Fuel Comb. Industrial 01-Coal Anderson Co KS 1.40E-01 0 02-Fuel Comb. Industrial 01-Coal Anderson Co KY 222 0 02-Fuel Comb. Industrial 01-Coal Anderson Co SC 401 192 02-Fuel Comb. Industrial 01-Coal Anderson Co TN 168 13375 02-Fuel Comb. Industrial 01-Coal Anson Co NC 76 0 02-Fuel Comb. Industrial 01-Coal Antelope Co NE 10 0 02-Fuel Comb. Industrial 01-Coal Antrim Co MI 31 0 Note: Emission is measured in short tons.

Table C.4: Sample of the NET Tier Report 177

Abbreviation Full Name ID Record identifier and ID used in Arc/Info NO1HR Number of 1-hr values MAX1ST1H The first maximum of 1-hr values (ppm) MAX2ND1H The second maximum of 1-hr values (ppm) MAX1ST3H The first maximum of 3-hr values (ppm) MAX2ND3H The second maximum of 3-hr values (ppm) NOEX3H Number of exceedances of the maximum 3-hr values MAX1ST24 The first maximum of 24-hr values (ppm) MAX2ND24 The second maximum of 24-hr values (ppm) NOEX24H Number of exceedances of the maximum 24-hr values MEAN Annual mean of monitoring station (ppm) MONID EPA monitoring station ID ADD Address of monitoring station CITY City where monitoring station located CNTY County where monitoring station located ST State where monitoring station located REGION EPA region where monitoring station located LAND Land use type LOCTP Location type LAT Latitude of monitoring station in degree LON Longitude of monitoring station in degree NOTE: 1. LAND may be agricultural, residential, commercial, or industrial. 2. LOCTP may be rural, suburban, urban, or city center.

Table C.5: Variables Provided by the Monitor All Columns Report œ Sulfur Dioxide œ

178

LON LON -87.8214 -85.7208 -86.9147 -87.1094 -88.0283 -94.0239 -94.0239 -93.5983 -93.6000 -94.0039 -94.0039 -112.0411 -111.9167 -112.1167 -110.8722 -111.6300 LAT LAT 34.6906 34.8769 33.4858 34.5894 30.9583 33.4581 33.4794 33.4606 32.2083 32.6064 33.1875 33.1875 33.1961 33.2003 33.2058 33.2058 LOCTP Rural Rural Suburban Rural Suburban Urban / Center City Suburban Suburban Suburban Suburban Rural Rural Rural Rural Rural Rural LAND LAND Industrial Residential Residential Residential Residential Residential Residential Residential Residential Residential Residential Residential Residential Agricultural Agricultural Agricultural 04 04 04 04 04 09 09 09 09 09 06 06 06 06 06 06 REGION AL AL AL AL AL ST AZ AZ AZ AZ AZ AR AR AR AR AR AR

CNTY CNTY Colbert Co Jackson Co Jefferson Co Lawrence Co Mobile Co Maricopa Co Maricopa Co Maricopa Co Pima Co Pinal Co Co Miller Co Miller Co Miller Co Miller Co Miller Co Miller CITY Fairfield Moulton Mobile Phoenix Scottsdale Phoenix Tucson San Manuel ADD ADD Tva Colbert 14___3.98 Tva 14___3.98 Colbert SeMi Colbert Fp Tva 2.0 11 Widows Crk EseMi Widows Crk Fairfield,5229 Pfd, Court B 2799 358County Rd. Trinity, Al. 35673 U.S. 43, H'Way 5.5 N OfMiles I-65, Axis 1845 ESt- Roosevelt Central Stn Phoenix 2857Rd-S N Miller ScottsdaleStn 112827th N. Ave- GreenwoodStation 22nd & Craycroft, Tucson Griffin & 1st, San Manuel Route1, Texarkana Route1, Texarkana Route1, Texarkana Route1, Texarkana Route1, Texarkana Route1, Texarkana

MONID MONID 010330044 1- 010710020 1- 010731003 1- 010790003 1- 010970028 1- 040133002 4- 040133003 1- 040133010 1- 040191011 1- 040212001 1- 050910096 1- 050910096 2- 050910097 2- 050910098 2- 050910099 1- 050910099 2- 0.003 0.004 0.007 0.002 0.007 0.003 0.001 0.008 0.001 0.003 0.003 0.003 0.005 0.004 0.004 0.003 MEAN MEAN 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 NOEX 24 0.017 0.026 0.026 0.011 0.041 0.012 0.004 0.014 0.005 0.018 0.006 0.006 0.019 0.012 0.010 0.011 MAX2ND 24 0.044 0.026 0.036 0.012 0.044 0.013 0.005 0.016 0.005 0.025 0.008 0.008 0.019 0.015 0.015 0.016 MAX1ST 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3H NOEX 3H 0.104 0.092 0.083 0.026 0.122 0.032 0.010 0.026 0.008 0.073 0.018 0.019 0.040 0.034 0.026 0.027 MAX2ND 3H 0.136 0.126 0.088 0.051 0.125 0.037 0.018 0.033 0.012 0.084 0.020 0.022 0.098 0.035 0.053 0.053 MAX1ST 1H 0.134 0.192 0.091 0.048 0.228 0.042 0.012 0.033 0.014 0.140 0.023 0.025 0.113 0.050 0.054 0.053 MAX2ND 1H 0.221 0.194 0.092 0.070 0.241 0.050 0.050 0.037 0.016 0.159 0.024 0.026 0.127 0.051 0.094 0.092 MAX1ST 702 8592 8533 5778 8297 8671 7636 7347 8531 6121 2136 2138 2111 2125 2142 2140 NO1HR NO1HR 1 2 3 4 5 6 7 8 9 ID 10 11 12 13 14 15 16 Table Sample C.6: MonitorAll of Columns Report the

179

Abbreviation Full Name WBAN WBAN number DATE Year / Month / Day TIME Time of collecting data STYPE Station type MTIND Maintenance indicator COND Sky conditions VIS Visibility WTYPE Weather type DBTEMP Dry bulb temperature DPTEMP Dew point temperature WBTEMP Wet bulb temperature HUM Percent of relative humidity WSP Wind speed in knot (KT) WDIR Wind direction in tenth of degree WCHAR Wind characteristic gust VWCHAR Value for wind character PRESS Station pressure PTEND Pressure tendency SLPRESS Sea level pressure TYPE Record type PRECP Total precipitation

Table C.7: Variables Provided by the NCDC Climatic Database

180

------T T .03 PRECP AA AA AA AA SP AA SP AA AA AA AA AA AA SP SP AA TYPE - - - - - 346 341 334 322 320 320 317 313 306 306 298 290 SLPRESS ------6 8 8 7 PTEND - - - - - 30.55 30.54 30.52 30.48 30.48 30.47 30.47 30.46 30.44 30.43 30.41 30.39 PRESS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 VWCHAR VWCHAR ------G WCHAR WCHAR 050 040 050 040 040 050 060 050 060 060 070 090 070 070 060 040 WDIR WDIR 8 9 7 8 9 7 6 6 7 6 11 12 11 13 15 11 WSP - - - - - 70 73 73 79 86 89 89 86 86 86 92 93 HUM HUM - - - - - WB WB 34.5 34.9 34.9 34.7 35.4 35.8 35.8 36.3 37.4 39.4 41.2 42.1 TEMP - - - - - 30 30 34 34 34 37 41 DP 28.9 30.9 33.1 35.1 39.9 TEMP - - - - - 37 37 37 37 39 41 43 DB DB 37.9 37.9 37.9 37.9 42.1 TEMP

------RA -RA -RA -RA -RA WTYPE VIS 8SM 7SM 8SM 8SM 10SM 10SM 10SM 10SM 10SM 10SM 10SM 10SM 10SM 10SM 10SM 10SM COND COND SCT049 SCT035 OVC050 FEW018 BKN110 OVC080 BKN022 OVC080 BKN022 SCT022 FEW012 CLR FEW048BKN060 BKN110 FEW050 BKN095 BKN034 OVC075 BKN026 BKN080 FEW024 SCT080 BKN050 ------MTIND MTIND AO20 AO20 AO20 AO20 AO20 AO20 AO20 AO20 AO20 AO20 AO20 AO20 AO21 AO21 AO21 AO21 STYPE 0054 0154 0254 0354 0414 0454 0501 0554 0654 0754 0854 0954 1054 1107 1126 1154 TIME DATE DATE 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 19990201 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 00009 WBAN WBAN ClimaticTable Sample C.8: NCDC of the Database

181

APPENDIX D

COMPUTER PROGRAMS AND OUTPUTS FOR OPTIMIZATION MODELING

182 D.1: SAS Program to Compute Matrix Coefficients

FILENAME IN1 'D:\ZZZ\OPT_MON.DAT'; FILENAME IN2 'D:\ZZZ\WEATHER.DAT'; FILENAME IN3 'D:\ZZZ\OPT_FAC.DAT'; FILENAME IN4 'D:\ZZZ\LU4EMS.DAT'; FILENAME IN5 'D:\ZZZ\LU4CON.DAT'; FILENAME IN6 'D:\ZZZ\WFDCON.DAT'; FILENAME IN7 'D:\ZZZ\MONCLOSE.DAT'; FILENAME IN8 'D:\ZZZ\WFDEMS.DAT'; FILENAME IN9 'D:\ZZZ\WSPCON.DAT'; FILENAME IN10 'D:\ZZZ\WSPEMS.DAT'; FILENAME IN11 'D:\ZZZ\MAXCON.DAT'; FILENAME OUT1 'D:\ZZZ\OPT_MATA.DAT'; FILENAME OUT2 'D:\ZZZ\OPT_MATB.DAT'; *------DATA BASE OPTIMIZATION ------FILE NAME: OPT03.SAS. THIS SAS PROGRAM COMPUTES THE COEFFICIENTS FOR MATRICES A AND B. ------INPUT VARIABLE DEFINITIONS ------ID : CENTER MONITORING STATION ID FOR RING-SECTOR SYSTEM OPT_CID : MONITORING STATION ID USED FOR OPTIMIZATION CALCULATION OPT_EID : EMISSION SOURCE ID USED FOR OPTIMIZATION CALCULATION ------; DATA A1; INFILE IN1; INPUT ID 1-3 SECTOR 4-5 CLSRING 6-8 MEAN0 9-14 3 C_ID 15-17 ADJMONID 18-21 ADJMEAN 22-27 3 DIST 28-38 3; *------ADJUST CONCENTRATION MEASURES TO PROPER UNIT ------; MEAN0=MEAN0*1000; ADJMEAN=ADJMEAN*1000; PROC SORT; BY ID SECTOR; DATA A2; INFILE IN2; INPUT ID 1-3 WEA_ID 5-7 WBAN $ 9-13 @15 (WDSEC1-WDSEC8)(5.) @55 (WSPSEC1-WSPSEC8)(8.3); IF ID=345 OR ID=361 OR ID=364 OR ID=387 OR ID=487 OR ID=490 OR ID=493 OR ID=497 OR ID=506 OR ID=512; *------CALCULATING AVERAGE WIND FREQUENCIES FOR CONCENTRATIONS ------; DATA A2TMP1; INFILE IN6; INPUT MWCONID 1-3 WEA_ID 5-7 WBAN $ 9-13 @15 (CONWDS1-CONWDS8)(5.); PROC SORT; BY MWCONID; DATA A2TMP2; SET A1; MWCONID=ADJMONID; DROP ADJMONID; PROC SORT; BY MWCONID; DATA A2TMP3; MERGE A2TMP1 A2TMP2(IN=A); BY MWCONID; IF A; 183 ARRAY CONWDS CONWDS1-CONWDS8; DO OVER CONWDS; IF CONWDS=. THEN CONWDS=0; END; PROC SORT; BY ID SECTOR; DATA A2TMP4; MERGE A2 A2TMP3; BY ID; DROP WSPSEC1-WSPSEC8; PROC SORT; BY ID SECTOR; DATA A2WD1; SET A2TMP4; IF SECTOR=1; AVGWDS1=(WDSEC1+CONWDS1)/2; DATA A2WD2; SET A2TMP4; IF SECTOR=2; AVGWDS2=(WDSEC2+CONWDS2)/2; DATA A2WD3; SET A2TMP4; IF SECTOR=3; AVGWDS3=(WDSEC3+CONWDS3)/2; DATA A2WD4; SET A2TMP4; IF SECTOR=4; AVGWDS4=(WDSEC4+CONWDS4)/2; DATA A2WD5; SET A2TMP4; IF SECTOR=5; AVGWDS5=(WDSEC5+CONWDS5)/2; DATA A2WD6; SET A2TMP4; IF SECTOR=6; AVGWDS6=(WDSEC6+CONWDS6)/2; DATA A2WD7; SET A2TMP4; IF SECTOR=7; AVGWDS7=(WDSEC7+CONWDS7)/2; DATA A2WD8; SET A2TMP4; IF SECTOR=8; AVGWDS8=(WDSEC8+CONWDS8)/2; DATA A2AVG; MERGE A2WD1 A2WD2 A2WD3 A2WD4 A2WD5 A2WD6 A2WD7 A2WD8; BY ID; ATFEQS=SUM(OF AVGWDS1-AVGWDS8); ARRAY AVGFEQ AVGFEQ1-AVGFEQ8; ARRAY AVGWDS AVGWDS1-AVGWDS8; DO OVER AVGWDS; IF AVGWDS=. THEN AVGWDS=0; AVGFEQ=AVGWDS/ATFEQS; END; DROP MWCONID WEA_ID WBAN SECTOR WDSEC1-WDSEC8 CONWDS1-CONWDS8; PROC SORT; BY ID; PROC DATASETS NOLIST; DELETE A2TMP1-A2TMP4 A2WD1-A2WD8; *------CALCULATING AVERAGE WIND FREQUENCIES FOR EMISSIONS ------; DATA A2TMP6; INFILE IN8; INPUT ID 1-3 RING 4-6 SECTOR 7-8 WEA_ID 10-12 @13 (EMSWDS1-EMSWDS8)(5.); IF ID=345 OR ID=361 OR ID=364 OR ID=387 OR ID=487 OR ID=490 OR ID=493 OR ID=497 OR ID=506 OR ID=512; DROP WEA_ID; PROC SORT; BY ID SECTOR RING; DATA A2TMP7; MERGE A2 A2TMP6; BY ID; ARRAY WDSEC WDSEC1-WDSEC8; ARRAY EMSWDS EMSWDS1-EMSWDS8; ARRAY CELLWDS CELLWDS1-CELLWDS8; DO OVER CELLWDS; CELLWDS=(WDSEC+EMSWDS)/2; END; DROP WBAN WDSEC1-WDSEC8 WSPSEC1-WSPSEC8 EMSWDS1-EMSWDS8; DATA A2TMP8; SET A2TMP7; ETOTAL=SUM(OF CELLWDS1-CELLWDS8); ARRAY CELLWDS CELLWDS1-CELLWDS8; ARRAY EMSFEQ EMSFEQ1-EMSFEQ8; DO OVER CELLWDS; IF CELLWDS=. THEN CELLWDS=0; EMSFEQ=CELLWDS/ETOTAL; END; DATA A2EMS; SET A2TMP8; IF SECTOR=1 THEN CELLFEQ=EMSFEQ1; IF SECTOR=2 THEN CELLFEQ=EMSFEQ2;

184 IF SECTOR=3 THEN CELLFEQ=EMSFEQ3; IF SECTOR=4 THEN CELLFEQ=EMSFEQ4; IF SECTOR=5 THEN CELLFEQ=EMSFEQ5; IF SECTOR=6 THEN CELLFEQ=EMSFEQ6; IF SECTOR=7 THEN CELLFEQ=EMSFEQ7; IF SECTOR=8 THEN CELLFEQ=EMSFEQ8; PROC DATASETS NOLIST; DELETE A2TMP6-A2TMP8; DATA A3TMP1; INFILE IN3; INPUT ID 1-3 RING 5-7 SECTOR 8-9 E_ID 10-13 FAC_ID 14-19 EMIS 20-31 3 DIST 32-42 3 NAME $ 44-84; *------ADJUST EMISSION MEASURES TO PROPER UNIT ------; EMIS=EMIS/1000; PROC SORT; BY ID SECTOR RING; DATA A3TMP2; SET A1; KEEP ID SECTOR CLSRING ADJMONID; DATA A3; MERGE A3TMP1(IN=A) A3TMP2; BY ID SECTOR; IF A; IF RING>CLSRING AND ADJMONID NE 0 THEN IDX=0; ELSE IDX=1; DROP CLSRING ADJMONID; *------VARIABLE DEFINITIONS FOR LANDUSE ------#LU1: URBAN AREA #LU2: PASTURE (DRYLAND, IRRIGATED, AND MIXED CROPLAND AND PASTURE) #LU3: GRASS (GRASSLAND, SHRUBLAND, MIXED GRASSLAND, AND SAVANNA) #LU4: DECIDUOUS FOREST (BROADLEAF AND NEEDLELEAF) #LU5: EVERGREEN FOREST (BROADLEAF AND NEEDLELEAF) #LU6: WATER (WATER BODIES, SNOW, OR ICE) #: C=CONCENTRATIONS, E=EMISSIONS ------; DATA ALU4; INFILE IN4; INPUT ID 1-3 RING 4-6 SECTOR 7-8 @9 (ELU1-ELU6)(12.5) EIM 82; IF ID=345 OR ID=361 OR ID=364 OR ID=387 OR ID=487 OR ID=490 OR ID=493 OR ID=497 OR ID=506 OR ID=512; DROP EIM; PROC SORT; BY ID SECTOR RING; DATA A4ZZ; MERGE A1 ALU4; BY ID SECTOR; IF RING LE CLSRING THEN CHECK=1; ELSE CHECK=0; ELU1=ELU1*CHECK; ELU2=ELU2*CHECK; ELU3=ELU3*CHECK; ELU4=ELU4*CHECK; ELU5=ELU5*CHECK; ELU6=ELU6*CHECK; DROP DIST CHECK; DATA ALU5; INFILE IN5; INPUT ID 1-3 RING 4-6 SECTOR 7-8 @9 (CLU1-CLU6)(12.5) CIM 82; IF ID=345 OR ID=361 OR ID=364 OR ID=387 OR ID=487 OR ID=490 OR ID=493 OR ID=497 OR ID=506 OR ID=512; DROP CIM; PROC SORT; BY ID SECTOR RING; DATA A5XX; MERGE A1 ALU5; BY ID SECTOR; IF RING LE CLSRING; PROC MEANS NOPRINT SUM; VAR CLU1-CLU6; BY ID SECTOR; OUTPUT OUT=A5XXS SUM=CLUS1-CLUS6; DATA A5YY; MERGE A1 A5XXS; BY ID SECTOR; ARRAY CLUS CLUS1-CLUS6; DO OVER CLUS; IF CLUS=. THEN CLUS=0;

185 END; CURBAN=CLUS1; DROP CLUS1-CLUS6; DATA A5ZZ; SET A5YY; DFCON=-0.15; LFCON=-0.02; IF DIST=0 THEN DW=0; ELSE DW=(DIST/1000)**DFCON; IF CURBAN=0 THEN ACON=DW*1; ELSE ACON=DW*(CURBAN**LFCON); *------CALCULATING AVERAGE WIND SPEED FOR CONCENTRATIONS ------; DATA A6TMP1; INFILE IN9; INPUT MWCONID 1-3 WEA_ID 5-7 WBAN $ 9-13 @14 (CONWSP1-CONWSP8)(8.3); PROC SORT; BY MWCONID; DATA A6TMP2; SET A1; MWCONID=ADJMONID; DROP ADJMONID; PROC SORT; BY MWCONID; DATA A6TMP3; MERGE A6TMP1 A6TMP2(IN=A); BY MWCONID; IF A; ARRAY CONWSP CONWSP1-CONWSP8; DO OVER CONWSP; IF CONWSP=. THEN CONWSP=0; END; PROC SORT; BY ID SECTOR; DATA A6TMP4; MERGE A2 A6TMP3; BY ID; DROP WDSEC1-WDSEC8; PROC SORT; BY ID SECTOR; DATA A6WSP1; SET A6TMP4; IF SECTOR=1; AVGWSP1=(WSPSEC1+CONWSP1)/2; DATA A6WSP2; SET A6TMP4; IF SECTOR=2; AVGWSP2=(WSPSEC2+CONWSP2)/2; DATA A6WSP3; SET A6TMP4; IF SECTOR=3; AVGWSP3=(WSPSEC3+CONWSP3)/2; DATA A6WSP4; SET A6TMP4; IF SECTOR=4; AVGWSP4=(WSPSEC4+CONWSP4)/2; DATA A6WSP5; SET A6TMP4; IF SECTOR=5; AVGWSP5=(WSPSEC5+CONWSP5)/2; DATA A6WSP6; SET A6TMP4; IF SECTOR=6; AVGWSP6=(WSPSEC6+CONWSP6)/2; DATA A6WSP7; SET A6TMP4; IF SECTOR=7; AVGWSP7=(WSPSEC7+CONWSP7)/2; DATA A6WSP8; SET A6TMP4; IF SECTOR=8; AVGWSP8=(WSPSEC8+CONWSP8)/2; DATA A6AVG; MERGE A6WSP1 A6WSP2 A6WSP3 A6WSP4 A6WSP5 A6WSP6 A6WSP7 A6WSP8; BY ID; ARRAY AVGWSP AVGWSP1-AVGWSP8; DO OVER AVGWSP; IF AVGWSP=. THEN AVGWSP=0; END; DROP MWCONID WEA_ID WBAN SECTOR WSPSEC1-WSPSEC8 CONWSP1-CONWSP8; PROC SORT; BY ID; PROC DATASETS NOLIST; DELETE A5XX A5XXS A5YY A6TMP1-A6TMP4 A6WSP1-A6WSP8; *------CALCULATING AVERAGE WIND SPEED FOR EMISSIONS ------; DATA A6TMP6; INFILE IN10; INPUT ID 1-3 RING 4-6 SECTOR 7-8 WEA_ID 10-12 @14 (EMSWSP1-EMSWSP8)(8.3); IF ID=345 OR ID=361 OR ID=364 OR ID=387 OR ID=487 OR ID=490 OR ID=493 OR ID=497 OR ID=506 OR ID=512; DROP WEA_ID; PROC SORT; BY ID SECTOR RING;

186 DATA A6TMP7; MERGE A2 A6TMP6; BY ID; ARRAY WSPSEC WSPSEC1-WSPSEC8; ARRAY EMSWSP EMSWSP1-EMSWSP8; ARRAY ACWSP ACWSP1-ACWSP8; DO OVER ACWSP; ACWSP=(WSPSEC+EMSWSP)/2; IF ACWSP=. THEN ACWSP=0; END; DROP WBAN WDSEC1-WDSEC8 WSPSEC1-WSPSEC8 EMSWSP1-EMSWSP8; DATA A6EMS; SET A6TMP7; IF SECTOR=1 THEN CELLWSP=ACWSP1; IF SECTOR=2 THEN CELLWSP=ACWSP2; IF SECTOR=3 THEN CELLWSP=ACWSP3; IF SECTOR=4 THEN CELLWSP=ACWSP4; IF SECTOR=5 THEN CELLWSP=ACWSP5; IF SECTOR=6 THEN CELLWSP=ACWSP6; IF SECTOR=7 THEN CELLWSP=ACWSP7; IF SECTOR=8 THEN CELLWSP=ACWSP8; PROC SORT; BY ID SECTOR RING; PROC DATASETS NOLIST; DELETE A6TMP6 A6TMP7; DATA A4; MERGE A3(IN=IK) A4ZZ A2EMS A6EMS; BY ID SECTOR RING; IF IK; DFEMS=-0.85; LFEMS=-0.08; WFEMS=-0.75; P_CWP=CELLWSP**WFEMS; EDTF=(DIST/1000)**DFEMS; IF ELU1=0 THEN EURB=1; ELSE EURB=ELU1**LFEMS; IF ELU5=0 THEN EFOREG=1; ELSE EFOREG=ELU5**(-0.12); IF ELU6=0 THEN EWAT=1; ELSE EWAT=ELU6**(-0.2); IF IDX=0 THEN BEMS=0; ELSE BEMS=1.54755*CELLFEQ*EDTF*P_CWP*EURB*EFOREG*EWAT; PROC SORT; BY ID SECTOR RING; PROC PRINT; VAR ID SECTOR RING CELLFEQ CELLWSP EURB EFOREG EWAT BEMS IDX; PROC DATASETS NOLIST; DELETE A3TMP1 A3TMP2; DATA A5; MERGE A5ZZ A2AVG A6AVG; BY ID; WFCON=0.1; IF SECTOR=1 THEN AVGFEQ=AVGFEQ1; IF SECTOR=2 THEN AVGFEQ=AVGFEQ2; IF SECTOR=3 THEN AVGFEQ=AVGFEQ3; IF SECTOR=4 THEN AVGFEQ=AVGFEQ4; IF SECTOR=5 THEN AVGFEQ=AVGFEQ5; IF SECTOR=6 THEN AVGFEQ=AVGFEQ6; IF SECTOR=7 THEN AVGFEQ=AVGFEQ7; IF SECTOR=8 THEN AVGFEQ=AVGFEQ8; IF SECTOR=1 THEN AVGWSP=AVGWSP1; IF SECTOR=2 THEN AVGWSP=AVGWSP2; IF SECTOR=3 THEN AVGWSP=AVGWSP3; IF SECTOR=4 THEN AVGWSP=AVGWSP4; IF SECTOR=5 THEN AVGWSP=AVGWSP5; IF SECTOR=6 THEN AVGWSP=AVGWSP6; IF SECTOR=7 THEN AVGWSP=AVGWSP7; IF SECTOR=8 THEN AVGWSP=AVGWSP8; ACON=1.26796*ACON*AVGFEQ*(AVGWSP**WFCON); PROC PRINT; VAR ID SECTOR CURBAN AVGFEQ AVGWSP DFCON LFCON WFCON ACON; *------COMPUTE CONCENTRATION MATRIX USED FOR OPTIMIZATION ------; DATA B1; INFILE IN11; INPUT ID 1-3 OPT_CID 4-6; PROC SORT; BY ID; DATA B2; SET A5; IF ADJMONID>0; PROC SORT; BY ID; DATA B3; MERGE B2(IN=A) B1; BY ID; IF A; PROC SORT; BY ADJMONID; DATA B4; SET B1; ADJMONID=ID; OPT_CID2=OPT_CID; KEEP ADJMONID OPT_CID2; DATA B5; MERGE B3(IN=A) B4; BY ADJMONID; IF A; DATA B6; SET B5; KEEP OPT_CID OPT_CID2 ACON; PROC SORT; BY OPT_CID;

187 DATA BTMP1; SET B6; IF OPT_CID2=1; ACON1=ACON; DROP ACON; DATA BTMP2; SET B6; IF OPT_CID2=2; ACON2=ACON; DROP ACON; DATA BTMP3; SET B6; IF OPT_CID2=3; ACON3=ACON; DROP ACON; DATA BTMP4; SET B6; IF OPT_CID2=4; ACON4=ACON; DROP ACON; DATA BTMP5; SET B6; IF OPT_CID2=5; ACON5=ACON; DROP ACON; DATA BTMP6; SET B6; IF OPT_CID2=6; ACON6=ACON; DROP ACON; DATA BTMP7; SET B6; IF OPT_CID2=7; ACON7=ACON; DROP ACON; DATA BTMP8; SET B6; IF OPT_CID2=8; ACON8=ACON; DROP ACON; DATA BTMP9; SET B6; IF OPT_CID2=9; ACON9=ACON; DROP ACON; DATA BTMP10; SET B6; IF OPT_CID2=10; ACON10=ACON; DROP ACON; DATA B; MERGE BTMP1 BTMP2 BTMP3 BTMP4 BTMP5 BTMP6 BTMP7 BTMP8 BTMP9 BTMP10; BY OPT_CID; ARRAY MACON ACON1-ACON10; DO OVER MACON; IF MACON=. THEN MACON=0; END; PROC PRINT; PROC DATASETS NOLIST; DELETE BTMP1-BTMP10; *------COMPUTE EMISSION MATRIX USED FOR OPTIMIZATION ------; DATA C1; MERGE A4 B1; BY ID; OPT_EID=E_ID; DROP E_ID; PROC SORT; BY OPT_EID; DATA CTMP1; SET C1; IF OPT_CID=1; BEMS1=BEMS; DROP BEMS; DATA CTMP2; SET C1; IF OPT_CID=2; BEMS2=BEMS; DROP BEMS; DATA CTMP3; SET C1; IF OPT_CID=3; BEMS3=BEMS; DROP BEMS; DATA CTMP4; SET C1; IF OPT_CID=4; BEMS4=BEMS; DROP BEMS; DATA CTMP5; SET C1; IF OPT_CID=5; BEMS5=BEMS; DROP BEMS; DATA CTMP6; SET C1; IF OPT_CID=6; BEMS6=BEMS; DROP BEMS; DATA CTMP7; SET C1; IF OPT_CID=7; BEMS7=BEMS; DROP BEMS; DATA CTMP8; SET C1; IF OPT_CID=8; BEMS8=BEMS; DROP BEMS; DATA CTMP9; SET C1; IF OPT_CID=9; BEMS9=BEMS; DROP BEMS; DATA CTMP10; SET C1; IF OPT_CID=10; BEMS10=BEMS; DROP BEMS; DATA C; MERGE CTMP1 CTMP2 CTMP3 CTMP4 CTMP5 CTMP6 CTMP7 CTMP8 CTMP9 CTMP10; BY OPT_EID; ARRAY MBEMS BEMS1-BEMS10; DO OVER MBEMS; IF MBEMS=. THEN MBEMS=0; END; PROC SORT; BY OPT_EID; PROC PRINT; VAR OPT_EID FAC_ID BEMS1-BEMS10; DATA _NULL_; SET B; FILE OUT1; PUT OPT_CID 1-2 @3 (ACON1-ACON10)(10.7); DATA _NULL_; SET C; FILE OUT2; PUT OPT_EID 1-2 @3 (BEMS1-BEMS10)(12.9);

RUN;

188 D.2: GAMS Program for Optimization Computation

$TITLE: OPTIMUM AIR POLLUTION EMISSION CONTROL *File name: EMIS_RG.GMS *This model uses the statistically-estimated air quality model. *This program runs iteratively to test different standards. *Developed by Kang-Ping Shen (09/23/2003)

************************************************************

SETS k monitoring stations /1*10/ i emission source /1*91/ alias (k,l);

TABLE A(l,k) diffusion interaction between monitoring stations *Definition of DSD $ondelim $include mxCON.csv $offdelim;

TABLE B(i,k) diffusion interaction between emission and station *Definition of DSD $ondelim $include mxEMS.csv $offdelim;

PARAMETER D(l,k) Kronecker coefficient; D(l,k)=0; D(l,l)=1;

SCALAR A0 intercept in statistical model /1.08002/; SCALAR AMBST ambient air quality standard;

PARAMETERS EMAX(i) maximum emission from source i / 1 = 163462, 2 = 162290, 3 = 150782, 4 = 144933, 5 = 141872, 6 = 135558, 7 = 115619, 8 = 115001, 9 = 113787, 10 = 111557, 11 = 108715, 12 = 104604, 13 = 104231, 14 = 100639, 15 = 99101, 16 = 80296, 17 = 75521, 18 = 71188, 19 = 67138, 20 = 57908, 21 = 57722, 22 = 55213, 23 = 55046, 24 = 54491, 25 = 52835, 26 = 51786, 27 = 49190, 28 = 48911, 29 = 48484, 30 = 44676, 31 = 44131, 32 = 41602, 33 = 34799, 34 = 34500, 35 = 34446, 36 = 33332, 37 = 32868, 38 = 31918, 39 = 31612, 40 = 31259, 41 = 31063, 42 = 30638, 43 = 28352, 44 = 27924, 45 = 27596, 46 = 27088, 47 = 26319, 48 = 25244, 49 = 25190, 50 = 25147, 51 = 24827, 52 = 24307, 53 = 23843, 54 = 22696, 55 = 22198, 56 = 20284, 57 = 19792, 58 = 19529, 59 = 19500, 60 = 19298, 61 = 18544, 62 = 17627, 63 = 17281, 64 = 17260, 65 = 17199, 66 = 16740, 67 = 16663, 68 = 16534, 69 = 16352, 70 = 15246, 71 = 14645, 72 = 14492, 73 = 14220, 74 = 13789, 75 = 13270, 76 = 12893, 77 = 12851, 78 = 12788, 79 = 12749, 80 = 12737, 81 = 12630, 82 = 12167, 83 = 11690, 84 = 11592, 85 = 11115, 86 = 10976, 87 = 10747, 88 = 10490, 89 = 10324, 90 = 10289, 189 91 = 10068/; EMAX(i)=EMAX(i)/1000;

****************************************************************

VARIABLES ET total emissions; POSITIVE VARIABLES E(i) emission from source i C(k) concentration at monitoring station k;

EQUATIONS OBJ objective function MAXEMS(i) maximum emission constraint for source i MINEMS(i) minimum emission constraint for source i CMAX(k) ambient standard for monitoring station k DIFF(k) diffusion equation for monitoring station k;

OBJ.. ET =E= sum(i,E(i)); MAXEMS(i).. E(i) =L= 1.0*EMAX(i); MINEMS(i).. E(i) =G= 0.2 *EMAX(i); CMAX(k).. C(k) =L= AMBST; DIFF(k).. sum(l,(D(l,k)-A(l,k))*C(l)) - sum(i,B(i,k)*E(i)) =E= A0;

****************************************************************

FILE EMSSOL/EMS.SOL/; PUT EMSSOL; EMSSOL.nj=1;

MODEL EMISSION /ALL/;

FOR (AMBST=2.7 to 5 by 0.05,

SOLVE EMISSION USING LP MAXIMIZING ET;

PUT AMBST:4:2, @7, ET.L:8:3, @15, LOOP(k,put C.L(k):7:3); PUT/;

);

190

ID EMISSION 1 FACULITY NAME STATE COUNTY SIC EPA_FACID 2 LAT LON 1 163462 Homer City PA Indiana 4911 420630003 40.5142 -79.1969 2 162290 Keystone PA Armstrong 4911 420050012 40.6522 -79.3425 3 150782 W H Sammis OH Jefferson 4911 390815010 40.5328 -80.6331 4 144933 Columbus Southern Power-Conesville OH Coshocton 4911 390315001 40.1756 -81.8844 5 141872 Hatfield'S Ferry PA Greene 4911 420590006 39.8500 -79.9167 6 135558 Kyger Creek OH Gallia 4911 390535001 38.9161 -82.1281 7 115619 Eastlake OH Lake 4911 390855012 41.6708 -81.4792 8 115001 Cardinal OH Jefferson 4911 390815002 40.2522 -80.6486 9 113787 Montour PA Montour 4911 420930003 41.0700 -76.6664 10 111557 Monroe MI Monroe 4911 261150020 41.8911 -83.3444 11 108715 Appalachian Power - John E Amos Plant WV Putnam 4911 540790006 38.4731 -81.8233 12 104604 Mount Storm Power Plant WV Grant 4911 540230003 39.2014 -79.2667 13 104231 Ohio Power - Kammer Plant WV Marshall 4911 540510006 39.8461 -80.8186 14 100639 Muskingum River OH Washington 4911 391675001 39.5908 -81.6797 15 99101 Monongahela Power Co.- Fort Martin Power WV Monongalia 4911 540610001 39.7000 -79.9167 16 80296 Richard Gorsuch OH Washington 4911 391677286 39.3672 -81.5208 17 75521 Potomac Electric - Morgantown MD Charles 4911 240170014 38.3583 -76.9789 18 71188 Brunner Island PA York 4911 421330020 40.1000 -76.6833 19 67138 Appalachian Power Co.-Philip Sporn Plant WV Mason 4911 540530001 38.9669 -81.9231 20 57908 Dunkirk NY Chautauqua 4911 360130325 42.4919 -79.3469 21 57722 Potomac Electric - Chalk Point MD Prince Georges 4911 240330014 38.5439 -76.6856 22 55213 Ltv Steel Company, Inc. OH Cuyahoga 3312 390351318001613 41.4675 -81.6725 23 55046 Ohio Power - Mitchell Plant WV Marshall 4911 540510005 39.8297 -80.8153 24 54491 Balto. Gas & Elec. - Brandon Shores MD Anne Arundel 4911 240030468 39.1814 -76.5328 25 52835 St Clair MI St Clair 4911 261470024 42.7617 -82.4722 26 51786 Shawville PA Clearfield 4911 420330021 41.0681 -78.3661 27 49190 Ohio Edison Company R E Burger Plant OH Belmont 4911 390135002 39.9092 -80.7603 28 48911 New England Power-Br MA Bristol 4911 250050061 41.7072 -71.1947 29 48484 C R Huntley NY Erie 4911 360291700 42.9667 -78.9167 30 44676 Appalachian Power - Mountaineer Plant WV Mason 4911 540530009 38.9794 -81.9344 Monongahela Power Co-Pleasants Power 31 44131 Station WV Pleasants 4911 540730005 39.3678 -81.2958 32 41602 Duquesne Light Company, Cheswick Station PA Allegheny 4911 420030029 40.5360 -79.7920 33 34799 Psnh/Merrimack Station NH Merrimack 4911 330130026 43.0828 -71.2809 34 34500 Mead Corporation OH Ross 2621 391415001 39.3250 -82.9744 35 34446 Uss/Kobe Steel Co. - Lorain Works OH Lorain 3312 390935004 41.4517 -82.1169 36 33332 Sunbury PA Snyder 4911 421090002 40.8667 -76.8000 37 32868 Avon Lake OH Lorain 4911 390935001 41.5042 -82.0500 38 31918 Kodak Park Division NY Monroe 3861 360558261400205 43.2019 -77.6469 39 31612 Pse & G Co. Attn Environmental NJ Union 4931 3403940011 40.6225 -74.2078 40 31259 Trenton Channel MI Wayne 4911 261630313 42.1225 -83.1811 41 31063 C P Crane MD Baltimore 4911 245100079 39.3233 -76.3667 42 30638 Potomac Electric œ Dickerson MD Montgomery 4911 240310019 39.2086 -77.4622 43 28352 Belle River MI St Clair 4911 261470029 42.7750 -82.4939 44 27924 Star Enterprise, Delaware City DE New Castle 2911 100030016 39.5881 -75.6406 45 27596 Armstrong PA Armstrong 4911 420050001 40.9292 -79.4669 46 27088 Bruce Mansfield PA Beaver 4911 420070005 40.6342 -80.4144 47 26319 Portland PA Northampton 4911 420950011 40.7550 -75.0839 48 25244 R G & E Russell Station NY Monroe 4931 360558262800068 43.2706 -77.6308 49 25190 Roseton Generating Station NY Orange 4911 360710475 41.5722 -73.9797

Continued

D.3: Emission Facility Information 191 Table D.3 continued

50 25147 Ashtabula OH Ashtabula 4911 390075001 41.9125 -80.7583 51 24827 B G E - Wagner Station MD Anne Arundel 4911 240030014 39.1778 -76.5339 52 24307 Rochester 7 NY Monroe 4911 360551752 43.2694 -77.6308 53 23843 Salem Harbor MA Essex 4911 250090194 42.5256 -70.8772 54 22696 Wheeling Pittsburgh Steel Steubenville S OH Jefferson 3312 390815006 40.3206 -80.6044 55 22198 Pa Power Co PA Lawrence 4911 420730025 40.9380 -80.3680 56 20284 River Rouge MI Wayne 4911 261630312 42.2739 -83.1119 57 19792 Delmarva Power, Indian River DE Sussex 4911 100050001 38.5800 -75.2389 58 19529 Martins Creek PA Northampton 4911 420950010 40.7500 -75.2000 59 19500 Virginia Power-Possum Point VA Prince William 4911 511530002 38.5383 -77.2808 60 19298 Ohio Edison Co - Niles Plant OH Trumbull 4911 391555007 41.1669 -80.7486 61 18544 Northport NY Suffolk 4911 361031922 40.9333 -73.3500 62 17627 Potomac River VA Alexandria 4911 515100003 38.8078 -77.0372 63 17281 Monongahela Power Co.-Willow Island WV Pleasants 4911 540730004 39.3669 -81.3003 64 17260 Hudson NJ Hudson 4911 340170021 40.7500 -74.0750 65 17199 Goudey NY Broome 4911 360070292 42.1117 -75.9747 66 16740 Aes Somerset Llc NY Niagara 4911 360639293800003 43.3547 -78.5958 67 16663 Monongahela Power Co-Albright WV Preston 4911 540770001 39.4883 -79.6367 68 16534 B L England NJ Cape May 4911 340090001 39.2900 -74.6339 69 16352 Boston Edison Mystic MA Middlesex 4911 250170128 42.3958 -71.0717 70 15246 Gen J M Gavin OH Gallia 4911 390535002 38.9347 -82.1158 71 14645 Ltv Steel Company, Inc. OH Cuyahoga 3312 390351318000078 41.4683 -81.6750 72 14492 Orrville OH Wayne 4911 391695006 40.8481 -81.7639 73 14220 Sun Company, Inc. PA Philadelphia 2911 421011501 39.9090 -75.2130 74 13789 Appalachian Power - Kanawha River Plant WV Kanawha 4911 540390006 38.2056 -81.4211 75 13270 Greenidge NY Yates 4911 361230028 42.6789 -76.9483 76 12893 Ppg Industries, Inc. WV Marshall 2812 540510002 39.7370 -80.8413 77 12851 J R Whiting MI Monroe 4911 261150022 41.7914 -83.4486 78 12788 Mercer NJ Mercer 4911 340210001 40.1750 -74.7333 79 12749 Blue Circle Cement NY Albany 3241 360014012400001 42.4994 -73.8131 80 12737 Kintigh NY Niagara 4911 360630430 43.3564 -78.5992 81 12630 Titus PA Berks 4911 420110045 40.3047 -75.9072 82 12167 Cleveland Thermal Energy Corp. OH Cuyahoga 4961 390351318000244 41.4969 -81.6900 83 11690 New Haven Harbor CT New Haven 4911 090093851 41.2836 -72.9042 84 11592 Wheeling Pitts Steel Steubenville North OH Jefferson 3312 390815008 40.3519 -80.6150 85 11115 Aes Westover NY Broome 4911 360077034600045 42.1133 -75.9736 86 10976 Danskammer NY Orange 4911 360710370 41.5719 -73.9664 87 10747 Seward PA Indiana 4911 420630002 40.4069 -79.0333 88 10490 Westvaco Corporation Bleached Board Div VA Alleghany 2611 510050003 37.7983 -79.9936 89 10324 Ge Co PA Erie 3743 420490009 42.1460 -80.0258 90 10289 Tennaco Packaging OH Wayne 2631 391695008 40.9692 -81.7761 91 10068 United Illuminating Co CT Fairfield 4911 090010195 41.1728 -73.1833 Note: 1. Emissions are measured in short tons. 2. EPA_FACID is the facility identification number assigned by EPA.

192

Standard (10 -3 ppm) 2.712 2.750 2.800 2.850 2.900 2.950 3.000 3.050 3.100 3.150 3.200 3.250 Facility 1 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 Facility 2 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 Facility 3 30.156 30.156 30.156 30.156 30.156 30.156 30.156 30.156 30.156 30.156 117.535 150.782 Facility 4 28.987 28.987 28.987 28.987 28.987 28.987 28.987 28.987 28.987 28.987 28.987 28.987 Facility 5 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 Facility 6 27.112 107.365 107.365 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 Facility 7 23.124 23.124 23.124 23.124 23.124 23.124 23.124 23.124 47.802 103.292 115.619 115.619 Facility 8 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 Facility 9 22.757 22.757 22.757 22.757 22.757 22.757 22.757 22.757 22.757 22.757 22.757 22.757 Facility 10 22.311 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 Facility 11 21.743 21.743 21.743 21.743 21.743 21.743 76.146 108.715 108.715 108.715 108.715 108.715 Facility 12 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 Facility 13 20.846 20.846 20.846 20.846 20.846 20.846 20.846 20.846 20.846 20.846 20.846 20.846 Facility 14 20.128 20.128 20.128 20.128 20.128 20.128 20.128 20.128 20.128 73.477 36.206 52.082 Facility 15 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 Facility 16 16.059 16.059 16.059 16.059 16.059 16.059 16.059 16.059 74.961 80.296 80.296 80.296 Facility 17 15.104 15.104 15.104 15.104 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 Facility 18 14.238 14.238 14.238 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 Facility 19 13.428 13.428 13.428 13.428 13.428 13.428 13.428 13.428 13.428 13.428 13.428 13.428 Facility 20 11.582 11.582 11.582 11.582 11.582 11.582 11.582 11.582 11.582 11.582 11.582 11.582 Facility 21 11.544 11.544 11.544 42.511 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 Facility 22 11.043 11.043 11.043 11.043 11.043 11.043 23.826 55.213 55.213 55.213 55.213 55.213 Facility 23 11.009 11.009 11.009 11.009 11.009 11.009 11.009 11.009 11.009 11.009 11.009 11.009 Facility 24 10.898 10.898 10.898 10.898 10.898 48.427 54.491 54.491 54.491 54.491 54.491 54.491 Facility 25 10.567 10.567 10.567 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 Facility 26 10.357 10.357 10.357 10.357 10.357 10.357 10.357 10.357 10.357 10.357 10.357 10.357 Facility 27 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 Facility 28 9.782 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 Facility 29 9.697 9.697 9.697 9.697 9.697 9.697 9.697 18.123 48.484 48.484 48.484 48.484 Facility 30 8.935 8.935 8.935 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 Facility 31 8.826 8.826 8.826 8.826 8.826 8.826 8.826 8.826 8.826 8.826 8.826 8.826 Facility 32 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 Facility 33 6.960 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 Facility 34 6.900 6.900 6.900 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 Facility 35 6.889 6.889 6.889 6.889 6.889 34.446 34.446 34.446 34.446 34.446 34.446 34.446 Facility 36 6.666 6.666 6.666 6.666 6.666 6.666 6.666 6.666 6.666 6.666 6.666 33.332 Facility 37 6.574 6.574 6.574 6.574 6.574 7.586 32.868 32.868 32.868 32.868 32.868 32.868 Facility 38 6.384 6.384 6.384 6.384 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 Facility 39 6.322 6.322 6.322 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 Facility 40 6.252 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 Facility 41 6.213 6.213 6.213 6.213 6.213 6.213 6.213 29.755 31.063 31.063 31.063 31.063 Facility 42 6.128 6.128 6.128 6.128 6.128 6.128 6.128 6.128 6.128 6.128 30.638 30.638 Facility 43 5.670 5.670 5.670 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 Facility 44 5.585 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 Facility 45 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519

Continued

D.4: Optimal Emission (in 10 3 short tons) for Each Facility and Various Air Quality Standards 193 Table D.4 continued

Standard (10 -3 ppm) 2.712 2.750 2.800 2.850 2.900 2.950 3.000 3.050 3.100 3.150 3.200 3.250 Facility 46 5.418 5.418 5.418 5.418 5.418 5.418 5.418 5.418 5.418 5.418 5.418 5.418 Facility 47 5.264 5.264 5.264 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 Facility 48 5.049 5.049 5.049 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 Facility 49 5.038 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 Facility 50 5.029 5.029 5.029 5.029 5.029 5.029 5.029 5.029 5.029 5.029 5.029 24.687 Facility 51 4.965 4.965 4.965 4.965 4.965 24.827 24.827 24.827 24.827 24.827 24.827 24.827 Facility 52 4.861 4.861 4.861 22.594 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 Facility 53 4.769 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 Facility 54 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 Facility 55 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 Facility 56 4.057 4.057 4.057 4.057 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 Facility 57 3.958 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 Facility 58 3.906 3.906 3.906 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 Facility 59 3.900 3.900 3.900 3.900 14.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 Facility 60 3.860 3.860 3.860 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 Facility 61 3.709 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 Facility 62 3.525 3.525 3.525 3.525 3.525 17.627 17.627 17.627 17.627 17.627 17.627 17.627 Facility 63 3.456 3.456 3.456 3.456 3.456 3.456 3.456 3.456 3.456 3.456 3.456 3.456 Facility 64 3.452 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 Facility 65 3.440 3.440 3.440 3.440 3.440 3.440 3.440 17.199 17.199 17.199 17.199 17.199 Facility 66 3.348 3.348 3.348 3.348 6.987 16.740 16.740 16.740 16.740 16.740 16.740 16.740 Facility 67 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 Facility 68 3.307 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 Facility 69 3.270 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 Facility 70 3.049 3.049 3.049 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 Facility 71 2.929 2.929 2.929 2.929 2.929 2.929 14.645 14.645 14.645 14.645 14.645 14.645 Facility 72 2.898 2.898 2.898 2.898 2.898 2.898 2.898 2.898 2.898 2.898 14.492 14.492 Facility 73 2.844 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 Facility 74 2.758 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 Facility 75 2.654 2.654 2.654 2.654 2.654 13.270 13.270 13.270 13.270 13.270 13.270 13.270 Facility 76 2.579 2.579 2.579 2.579 2.579 2.579 2.579 2.579 2.579 2.579 2.579 2.579 Facility 77 2.570 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 Facility 78 2.558 7.133 7.133 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 Facility 79 2.550 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 Facility 80 2.547 2.547 2.547 2.547 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 Facility 81 2.526 2.526 2.526 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 Facility 82 2.433 2.433 2.433 2.433 2.433 2.433 12.167 12.167 12.167 12.167 12.167 12.167 Facility 83 2.338 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 Facility 84 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 Facility 85 2.223 2.223 2.223 2.223 2.223 2.223 2.223 11.115 11.115 11.115 11.115 11.115 Facility 86 2.195 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 Facility 87 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 Facility 88 2.098 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 Facility 89 2.065 2.065 2.065 2.065 2.065 2.065 2.065 2.065 2.065 2.065 2.065 2.065 Facility 90 2.058 2.058 2.058 2.058 2.058 2.058 2.058 2.058 2.058 2.058 10.289 10.289 Facility 91 2.014 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068

Continued

194 Table D.4 continued

Standard (10 -3 ppm) 3.300 3.350 3.400 3.450 3.500 3.550 3.600 3.650 3.700 3.750 3.800 3.850 Facility 1 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 Facility 2 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 32.458 57.582 104.241 150.900 Facility 3 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 Facility 4 42.861 104.594 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 Facility 5 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 Facility 6 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 Facility 7 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 Facility 8 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 23.000 Facility 9 22.757 22.757 29.256 97.576 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 Facility 10 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 Facility 11 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 Facility 12 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 20.921 Facility 13 20.846 20.846 20.846 20.846 20.846 20.846 20.846 43.731 91.050 104.231 104.231 104.231 Facility 14 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 Facility 15 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 Facility 16 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 Facility 17 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 Facility 18 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 Facility 19 13.428 13.428 13.428 13.428 13.428 41.534 67.138 67.138 67.138 67.138 67.138 67.138 Facility 20 37.844 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 Facility 21 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 Facility 22 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 Facility 23 11.009 11.009 11.009 11.009 11.009 11.009 30.500 55.046 55.046 55.046 55.046 55.046 Facility 24 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 Facility 25 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 Facility 26 10.357 10.357 10.357 10.357 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 Facility 27 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 9.838 Facility 28 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 Facility 29 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 Facility 30 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 Facility 31 8.826 8.826 8.826 8.826 8.826 44.131 44.131 44.131 44.131 44.131 44.131 44.131 Facility 32 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 8.320 Facility 33 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 Facility 34 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 Facility 35 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 Facility 36 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 Facility 37 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 Facility 38 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 Facility 39 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 Facility 40 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 Facility 41 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 Facility 42 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 Facility 43 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 Facility 44 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 Facility 45 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 5.519 Facility 46 5.418 5.418 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 Facility 47 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319

Continued 195 Table D.4 continued

Standard (10 -3 ppm) 3.300 3.350 3.400 3.450 3.500 3.550 3.600 3.650 3.700 3.750 3.800 3.850 Facility 48 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 Facility 49 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 Facility 50 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 Facility 51 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 Facility 52 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 Facility 53 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 Facility 54 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 Facility 55 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 Facility 56 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 Facility 57 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 Facility 58 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 Facility 59 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 Facility 60 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 Facility 61 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 Facility 62 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 Facility 63 3.456 3.456 3.456 3.456 13.531 17.281 17.281 17.281 17.281 17.281 17.281 17.281 Facility 64 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 Facility 65 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 Facility 66 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 Facility 67 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 3.333 Facility 68 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 Facility 69 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 Facility 70 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 Facility 71 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 Facility 72 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 Facility 73 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 Facility 74 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 Facility 75 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 Facility 76 2.579 2.579 2.579 2.579 2.579 2.579 12.893 12.893 12.893 12.893 12.893 12.893 Facility 77 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 Facility 78 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 Facility 79 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 Facility 80 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 Facility 81 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 Facility 82 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 Facility 83 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 Facility 84 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 2.318 Facility 85 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 Facility 86 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 Facility 87 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 2.149 10.747 10.747 10.747 Facility 88 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 Facility 89 2.065 7.139 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 Facility 90 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 Facility 91 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068

Continued

196 Table D.4 continued

Standard (10 -3 ppm) 3.900 3.950 4.000 4.050 4.100 4.150 4.200 4.250 4.300 4.350 4.400 4.450 Facility 1 32.692 32.692 32.692 32.692 32.692 32.692 32.692 32.692 42.242 73.780 105.319 136.857 Facility 2 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 Facility 3 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 Facility 4 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 Facility 5 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 28.374 Facility 6 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 Facility 7 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 Facility 8 23.000 23.000 23.000 23.000 28.111 63.039 97.966 115.001 115.001 115.001 115.001 115.001 Facility 9 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 Facility 10 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 Facility 11 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 Facility 12 20.921 30.152 68.313 104.604 104.604 104.604 104.604 104.604 104.604 104.604 104.604 104.604 Facility 13 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 Facility 14 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 Facility 15 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 19.820 Facility 16 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 Facility 17 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 Facility 18 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 Facility 19 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 Facility 20 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 Facility 21 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 Facility 22 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 Facility 23 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 Facility 24 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 Facility 25 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 Facility 26 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 Facility 27 41.064 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 Facility 28 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 Facility 29 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 Facility 30 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 Facility 31 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 Facility 32 8.320 8.320 8.320 10.127 41.602 41.602 41.602 41.602 41.602 41.602 41.602 41.602 Facility 33 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 Facility 34 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 Facility 35 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 Facility 36 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 Facility 37 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 Facility 38 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 Facility 39 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 Facility 40 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 Facility 41 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 Facility 42 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 Facility 43 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 Facility 44 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 Facility 45 5.519 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 Facility 46 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 Facility 47 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319

Continued 197 Table D.4 continued

Standard (10 -3 ppm) 3.900 3.950 4.000 4.050 4.100 4.150 4.200 4.250 4.300 4.350 4.400 4.450 Facility 48 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 Facility 49 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 Facility 50 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 Facility 51 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 Facility 52 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 Facility 53 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 Facility 54 4.539 4.539 4.539 4.539 4.539 4.539 4.539 4.539 22.696 22.696 22.696 22.696 Facility 55 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 4.440 Facility 56 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 Facility 57 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 Facility 58 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 Facility 59 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 Facility 60 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 Facility 61 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 Facility 62 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 Facility 63 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 Facility 64 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 Facility 65 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 Facility 66 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 Facility 67 3.333 3.333 3.333 3.333 3.333 3.333 3.333 16.663 16.663 16.663 16.663 16.663 Facility 68 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 Facility 69 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 Facility 70 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 Facility 71 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 Facility 72 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 Facility 73 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 Facility 74 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 Facility 75 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 Facility 76 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 Facility 77 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 Facility 78 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 Facility 79 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 Facility 80 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 Facility 81 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 Facility 82 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 Facility 83 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 Facility 84 2.318 2.318 2.318 2.318 2.318 2.318 2.318 6.468 11.592 11.592 11.592 11.592 Facility 85 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 Facility 86 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 Facility 87 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 Facility 88 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 Facility 89 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 Facility 90 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 Facility 91 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068

Continued

198 Table D.4 continued

Standard (10 -3 ppm) 4.500 4.550 4.600 4.650 4.700 4.750 4.800 4.850 4.900 4.909 Facility 1 163.462 163.462 163.462 163.462 163.462 163.462 163.462 163.462 163.462 163.462 Facility 2 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 162.290 Facility 3 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 150.782 Facility 4 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 144.933 Facility 5 28.374 28.374 28.374 28.374 41.477 65.512 89.548 113.583 137.618 141.872 Facility 6 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 135.558 Facility 7 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 115.619 Facility 8 115.001 115.001 115.001 115.001 115.001 115.001 115.001 115.001 115.001 115.001 Facility 9 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 113.787 Facility 10 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 111.557 Facility 11 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 108.715 Facility 12 104.604 104.604 104.604 104.604 104.604 104.604 104.604 104.604 104.604 104.604 Facility 13 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 104.231 Facility 14 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 100.639 Facility 15 24.062 51.180 78.297 99.101 99.101 99.101 99.101 99.101 99.101 99.101 Facility 16 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 80.296 Facility 17 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 75.521 Facility 18 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 71.188 Facility 19 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 67.138 Facility 20 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 57.908 Facility 21 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 57.722 Facility 22 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 55.213 Facility 23 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 55.046 Facility 24 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 54.491 Facility 25 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 52.835 Facility 26 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 51.786 Facility 27 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 49.190 Facility 28 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 48.911 Facility 29 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 48.484 Facility 30 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 44.676 Facility 31 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 44.131 Facility 32 41.602 41.602 41.602 41.602 41.602 41.602 41.602 41.602 41.602 41.602 Facility 33 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 34.799 Facility 34 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 34.500 Facility 35 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 34.446 Facility 36 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 33.332 Facility 37 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 32.868 Facility 38 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 31.918 Facility 39 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 31.612 Facility 40 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 31.259 Facility 41 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 31.063 Facility 42 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 30.638 Facility 43 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 28.352 Facility 44 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 27.924 Facility 45 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 27.596 Facility 46 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 27.088 Facility 47 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319 26.319

Continued 199 Table D.4 continued

Standard (10 -3 ppm) 4.500 4.550 4.600 4.650 4.700 4.750 4.800 4.850 4.900 4.909 Facility 48 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 25.244 Facility 49 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 25.190 Facility 50 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 25.147 Facility 51 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 24.827 Facility 52 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 24.307 Facility 53 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 23.843 Facility 54 22.696 22.696 22.696 22.696 22.696 22.696 22.696 22.696 22.696 22.696 Facility 55 4.440 4.440 4.440 10.452 22.198 22.198 22.198 22.198 22.198 22.198 Facility 56 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 20.284 Facility 57 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 19.792 Facility 58 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 19.529 Facility 59 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 19.500 Facility 60 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 19.298 Facility 61 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 18.544 Facility 62 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 17.627 Facility 63 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 17.281 Facility 64 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 17.260 Facility 65 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 17.199 Facility 66 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 16.740 Facility 67 16.663 16.663 16.663 16.663 16.663 16.663 16.663 16.663 16.663 16.663 Facility 68 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 16.534 Facility 69 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 16.352 Facility 70 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 15.246 Facility 71 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 14.645 Facility 72 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 14.492 Facility 73 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 14.220 Facility 74 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 13.789 Facility 75 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 13.270 Facility 76 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 12.893 Facility 77 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 12.851 Facility 78 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 12.788 Facility 79 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 12.749 Facility 80 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 12.737 Facility 81 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 12.630 Facility 82 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 12.167 Facility 83 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 11.690 Facility 84 11.592 11.592 11.592 11.592 11.592 11.592 11.592 11.592 11.592 11.592 Facility 85 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 11.115 Facility 86 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 10.976 Facility 87 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 10.747 Facility 88 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 10.490 Facility 89 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 10.324 Facility 90 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 10.289 Facility 91 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068 10.068

200