“1 b 4' - 't. x ‘ J5; , 4333'” 9'1"”. ‘ "‘ 5M ‘3‘” 1M - . i \ .‘w r ‘ .3” 3t 4. 1" «I ,5 ”MM mm‘mfi- UNIVERSITY LIBRARIES Ilill’iliili" Ill ll ll Ill ‘mesrs 3 1293 01399 a VRARY .Mlchlgan State University This is to certify that the thesis entitled THE SPATIAL DISTRIBUTION OF ENVIRONMENTAL HAZARDS IN THE SAGINAW BAY WATERSHED: A METHODOLOGY ADDRESSING ENVIRONMENTAL EQUITY ISSUES USING A GEOGRAPHIC INFORMATION SYSTEM presented by Mark Charles Rousseau has been accepted towards fulfillment of the requirements for MS degree in flesaumejevelopment eff Major professor AAAQQ Date 2/21/95 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution PLACE ll RETURN BOX to mavothb checkout from your "card. TO AVOID FINES mum on or Mon dd. duo. DATE DUE DATE DUE DATE DUE l l | | “SD IOMWWM Opportunity lm WM! THE SPATIAL DISTRIBUTION OF ENVIRONMENTAL HAZARDS IN THE SAGINAW BAY WATERSHED: A METHODOLOGY ADDRESSING ENVIRONMENTAL EQUITY ISSUES USING A GEOGRAPHIC INFORMATION SYSTEM By Mark Charles Rousseau A THESIS Submitted to Michigan State University in partial firlfillment of the requirements for the degree of MASTER OF SCIENCE Department of Resource Deve10pment 1995 ABSTRACT THE SPATIAL DISTRIBUTION OF ENVIRONMENTAL HAZARDS IN THE SAGINAw BAY WATERSHED: A METHODOLOGY ADDRESSING ENVIRONMENTAL EQUITY ISSUES USING A GEOGRAPHIC INFORMATION SYSTEM By Mark Charles Rousseau This study presents a systematic approach that documents the spatial distribution of pollution sources, referred to as environmental hazards, across environmental media (air, land, and water) in the Saginaw Bay watershed. Previous research has focused on single pollution sources such as hazardous waste incinerators or toxic waste sites. This study aggregates eleven environmental hazards to generate a “hotspot” index using a Geographic Information System. The index incorporates state and regional comparative risk rankings of environmental problems in order to account for difi‘erences between hazards. Statistical analyses are performed using the index and soda-demographic census data to address environmental equity issues. Results from the analysis indicate that areas of high minority populations bear a disproportionate burden of environmental hazards compared to non-minority areas. The methodology can be used as a tool to identify these inequities by environmental and natural resource managers and planners. Policy issues pertaining to the analysis and recommendations for further study are discussed. Copyright by Mark Charles Rousseau 1995 ACKNOWLEDGMENTS I would like to thank Cynthia Fridgen, Stuart Gage, Tom Edens, and Bryan Pijanowski, my thesis committee, for their guidance, support and constructive criticism. I benefited greatly from their experience. This thesis would not have been possible without the research environment provided by the Entomology Spatial Analysis Lab. Thanks for your patience Amos, and making me answer some of my own questions. Lastly, I dedicate this thesis to Leslie Wells, and thank her for the continued support with both the thesis and as my better half. TABLE OF CONTENTS Abstract ......................................................... ii List of Tables .................................................... vii List of Figures .................................................... viii Chapter One Introduction ........................................ 1 Overview Of Study .......................................... 1 Natural Resource Concepts .................................... l Approaches To Environmental Management .................. 1 Ecosystem Management Approach ......................... 4 Watershed Management ................................. 6 Comparative Risk ...................................... 7 Social Science Concepts ...................................... 8 Environmental Equity ................................... 8 Background .................................... 8 Major Studies ................................... 10 Chapter Two Study Purpose: Problem Statement, Hypothesis and Methodology .......................................... 16 Problem Statement ........................................... 16 Hypothesis For Case Study .................................... 17 Research Questions .......................................... 17 Data Description ............................................ 18 Criteria Air Pollutants ................................... 19 Act 307 Sites ......................................... 20 Hazardous Waste Facilities ............................... 23 Toxic Release Inventory ................................. 24 Leaking Underground Storage Tanks ....................... 27 Landfills (Active and Inactive) ............................ 30 Water Discharge Data .................................. 32 Methods of Analysis ......................................... 35 Aggregation of Hazards ................................. 3 5 Weighting Method ..................................... 37 Visual Analysis ........................................ 39 Statistical Analysis ..................................... 37 Summary Statistics ............................... 4O Correlations .................................... 40 Odds Ratios .................................... 40 Limitations of This Study ..................................... 42 Scale ............................................... 42 TABLE OF CONTENTS Proximity ............................................ 43 Time ............................................... 43 Chapter Three Case Study Description .............................. 44 Physical Description ......................................... 44 SociO-Economic Descriptions .................................. 46 Population ........................................... 46 Minority Population .................................... 46 Income .............................................. 50 Poverty ............................................. 50 Industrial - Economic Description ............................... 53 Importance of Case Study ..................................... 57 Chapter Four Results From Analysis ............................... 59 Visual Analysis ............................................. 59 Statistical Analysis .......................................... 77 Correlations .......................................... 77 Odds Ratios .......................................... 79 Cross-tabulations ................................ 81 Chapter Five Summary and Conclusions ............................. 84 Objectives ................................................. 84 Policy Issues Related To The Case Study ......................... 87 Further Research ............................................ 88 Appendices A Data Summary Tables ................................ 90 B Tables of Scales, Raking, Percent, and Corrected Percent ..... 92 C Table Of Zipcodes and Area in Sq. Miles and Kilometers ..... 99 D MERR and EPA Ranking Tables ....................... 103 E Summary Statistics For All Variables ..................... 104 F Raw Data For All Variables ............................ 105 G Other Cross-Tabulations of Interest and Logit Tables For Population and Poverty ............................ 112 H Principals Of Environmental Justice ..................... 116 BIBLIOGRAPHY ................................................. 117 Table LIST OF TABLES Title Page Historical Environmental Management of the Great Lakes Region ....... 3 Ethnicity of People Living in Waste Site Areas (UCC, 1987) ........... 13 Ranldng Method for Act 307 Sites .............................. 22 LUST Conversion Table ..................................... 28 EPA / Michigan Relative Risk Weighting Factors ................... 38 Correlation Matrix of Variables ................................. 77 Dichotomous Variables for Logit Analysis ......................... 79 Logit Table of Income ....................................... 79 Logit Table of Minority ..................................... . 79 Logit Table of Income and Minority .............................. 8O Logit Table of Income, Minority, and Population .................... 81 Cross-tabulations of Minority by Hotspot .......................... 82 Cross-tabulations of Income by Hotspot ........................... 82 Summary Table of Data Sets Used in Study ........................ 90 SO; Distribution ............................................. 92 N02 Distribution ............................................ 92 VOC Distribution ........................................... 93 CO Distribution ............................................. 93 Act 307 Site Distribution ...................................... 94 TRI Distribution ............................................ 94 LUST Distribution ........................................... 95 Landfill Distribution .......................................... 95 Inactive Landfill Distribution ................................... 96 Hazardous Waste Site Distribution ............................... 96 Water Discharge Permit Distribution .............................. 97 Hotspot Score Per Unit Area Distribution .......................... 97 Weighted Hotspot Data per Unit Area Distribution ................... 98 Table of Zipcode Areas (Square Miles and Kilometers) ............... 99 MERR and EPA Ranking Tables ................................ 103 Summary Statistics Tables in Appendix E .......................... 104 Raw Data For All Variables .................................... 105 Logit Table of Poverty ........................................ 112 Logit Table Of Population ...................................... 112 Cross-tabulation of Hotspot by Population ......................... 112 Cross-tabulation for Non-Minority Zipcodes of Hotspot by Population . . . 113 Cross-tabulation for Minority Zipcodes of Hotspot by Population ....... 113 Cross-tabulation for High Income Zipcodes of Hotspot by Minority ..... 113 Cross-tabulation for Low Income Zipcodes of Hotspot by IVfinority ..... 114 vii LIST OF TABLES Table Title Page G.8 Cross-tabulation of Minority by Income ........................... 114 G9 Cross-tabulation of Poverty by Hotspot ........................... 114 G. 10 Cross-tabulation of Population by Hotspot (With Population Subdivided into Five Groups) ............................. 115 viii F 7.. 6... 5.... 7.. a... «J a) 3 a) «J a: at. .. En Figure 2.1 2.2 2.3 2.4 2.5 3.1 3.2 3.3 3.4 3.5 3.6 3.7 4.1A 4.1B 4.1C 4.1D 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.11 LIST OF FIGURES Title Page Distribution of S02 ........................................... 20 Histogram of Act 307 Site Data ................................. 22 Histogram of LUST Data ...................................... 29 Histogram of Water Discharge Data .............................. 34 Flow Chart of Study ......................................... 36 Map of Saginaw Bay Watershed in Michigan ....................... 45 Population Per Zipcode ........................................ 47 Population Trend For Difi‘erent Sectors of the Watershed .............. 48 Percent of Minority Per Zipcode ................................ 49 Average Per Capita Income Per Zipcode .......................... 51 Percent of Households in Poverty ............................... 52 Decline of Percent Employed by Manufacturing for F our Cities in Watershed .................................... 56 Distribution of the Permitted S02 (Tons Per Year) .................. 60 Distribution of the Permitted N02 (Tons Per Year) .................. 61 Distribution of the Permitted CO (Tons Per Year) .................. .62 Distribution of the Permitted VOC (Tons Per Year) .................. 63 Distribution of Inactive Landfills ................................. 65 Distribution of Type II Landfills ................................. 67 Distribution of LUST Sites ..................................... 68 Distribution Of Hazardous Waste Facilities ......................... 69 Distribution of Act 307 Sites .................................... 71 Distribution of Toxic Release Inventory Sites ....................... 72 Distribution of Permitted Water Dischargers ....................... 74 Hotspot Map ............................................... 75 Difference Map (Between Hotspot and Weighted Hotspot) ............ 76 ix V0 1“ CHAPTER ONE INTRODUCTION OVERVIEW OF STUDY This study combines four topics: ecosystem management, watershed management, comparative risk and the distribution of environmental hazards. Individual environmental hazards are identified and overlaid using a Geographic Information System and areas of potential concern are identified within the watershed boundaries. The overlaying process is further expanded to include sociO-economic data, and a case study is used to address environmental equity issues. The environmental and socio-economic data is analyzed using a statistical package to address the hypothesis stated in chapter two. The methodology is described in order to be usefirl for other watersheds or regions and relevant policy issues are discussed. The concepts addressed in this study focus on natural resources as well as social science. The methodology attempts to bridge the gap between these separate disciplines. Relevant concepts and previous research related to each discipline are discussed below. NATURAL RESOURCE CONCEPTS Approaches To Environmental Management Human ”progress” has not proceeded without cost. Library shelves are filled with volumes documenting the ill-effects of pollution as a by-product of progress. These efi‘ects range from destroyed ecosystems such as Nouth American old growth forests to the tragic conditions of pollution in Eastern Europe. In response to the variety of environmental problems, laws and regulations have been passed in the US. to protect the environment and its human inhabitants. The National Environmental Protection Act (NEPA) was passed in 1969 by US senators who maintained that ”each person has a firndamental and inalienable right to a healthful environment” (W entz, 1988). Other major federal environmental legislation are: The Clean Water Act (CWA ,1948, 1972), Clean Air Act (CAA, 1963, 1970), Resource Conservation and Recovery Act (RCRA, 1976), Toxic Substance Control Act (TSCA, 1976), Comprehensive Environmental Response, Compensation and Liability Act (CERCLA, 1980), Superfund Amendments and Reauthorization Act (SARA, 1986), and the Oil Pollution Act (1990). States and local governments have also implemented their own environmental regulations as problems of waste and pollution have arisen. Tremendous strides have been made combating the most egregious forms Of pollution. The air is now cleaner in Los Angeles compared to the late 1960's (California Air Resources Board, 1993). Rivers and lakes once considered "dead", such as Lake Erie, have been remediated primarily by decreasing phosphorous loads (Michigan Department of Natural Resources (MDNR), 1993). These successes are laudable, especially in light of fi'equent constraints on budgets to implement and enforce environmental regulations. However, 25 years after NEPA was passed many environmental problems still exist. As environmental problems have been studied and researched, the interconnectedness between land, water and air has become more apparent, further complicating the ability to control pollution sources. For example, mercury emitted fiom an incinerator can deposit in a lake, contaminating fish tissue. The command and control approach of the 1970’s and 1980’s focuses on a single source, not accounting for other environmental media, and does not adequately address existing pollution. As a result, new policies are being developed that take a broader view of contaminants as they can afi‘ect surface water, groundwater, land and air. Environmental management that includes all environmental media on a regional level is referred to as ecosystem management (IJC, 1978). Another new approach to environmental regulation is the inclusion of all stakeholders affected by management including industry, local, state and federal government, and the general public. These approaches will be discussed firrther in the next section. The following chart describes the history of the main environmental management practices for the Great Lakes basin. A Table 1.1 Historic Management of the Great Lakes Ecosystem (Hartig and Zarull, 1992). Era Explanation and/or Examples Approximate Dates Settlement The fur trade was the principal industry to bring about settlement 1600’s and and development and colonization of coastal areas by western Europeans 1700’s Exploitation Clearing of forest land for agricultural purposes and timber 1800’s caused increased soil loss, which resulted in siltation and loss of spawning and nursery ground habitat for fishes. Introduction of exotic species which continues to afi‘ect the Great lakes ecosystem Reactive management Typhoid fever and cholera epidemics led municipalities to build Early 1900’s purification facilities. Cultural eutrophication of Lake Erie led governments to limit the 1960’s and phosphorus content of household laundry detergents and to limit 1970’s phowhorus in municipal and industrial effluents. Contamination of the fishery led to the banning of DDT, dieldrin, etc. er be me reg was to tl erm’s Table 1.1 (con’d). Proactive Management Waste reduction/minimization and risk/hazard assessment to 1980’s and prevent toxic substance problems 1990’s Development and Institutional structure is established, broadly representative of implementation of social, economic and environmental interests; agreement is Present Remedial Action Plans reached on problems and use impairments, goals are identified using an ecosystem and specific actions are taken to achieve these goals; work is to approach continue in perpetuity to achieve continuity of purpose and ensure accountability. Ecosystem Management Approach Environmental management sufi‘ers when the environment is broken down to individual units and studied, monitored and regulated separately. However, this is exactly how many environmental systems are managed by regulatory agencies. In Michigan there are 16 divisions within the Department of Natural Resources charged with the role of managing Michigan’s environment. Each division focuses on one aspect of the environment such as surface water, or solid waste and there is little communication between divisions. This leads to a fi'agrnented approach to environmental decision making. An example of this fiagrnented approach is seen by examining the problem of regulating dioxin. Dow Chemical of Midland, Michigan, incinerates many chemical wastes. One of the by-products of the incineration process was dioxin which was released to the atmosphere. After passage of the Clean Air Act, Dow added scrubbers to the emission stacks which captured the dioxin and discharged it with wastewater into the no sl. disposed of in landfills (I-Iofiinan, 1993). In the landfills dioxin threatened to contaminate groundwater, drinking water supplies, and other aspects of the hydrologic cycle. This example emphasizes a number of important concepts: 0 “You can never do just one thing in an ecosystem” (Odum, 1953); o a fi'agmented approach merely shifts the problem to another arena; 0 environmental problems within an ecosystem, such as a watershed, are everyone’s concern including producers, consumers, and government officials. An ecosystem approach addresses each of these concepts and poses a solution to the shortfalls of traditional approaches to environmental management. Ecosystem management has become increasingly popular with natural resource policy makers, regulators and legislators. Numerous books and scientific articles demand that the only way to address the increasingly difiicult environmental problems facing society is to use an ecosystem management approach (IJC, 1978, 1992; Hartig and Zarull, 1992; EPA, 1992). The ecosystem approach attempts to manage natural resources by accounting for the complex interrelationships among water, air, land and all living things, including humans. It is an integrative and holistic process that recognizes interrelationships and interdependence among all parts of the system (Hartig and Zarull, 1992). “The whole is greater than the sum of the parts.” This adage and keystone of ecosystem management evolved in the early twentieth century. British ecologist Arthur Tansley coined the word “ecosystem” in 1935. Although scientists have used and advocated the ecosystem approach to perform research for years (1] C, 1978, Francis, 1979), regulatory and management agencies have made little progress implementing the approach (Harris, 1987). This changed in 1985 when the International Joint Commission began managing the Great Lakes basin as an ecosystem (Hartig and Zarull, 1992). Watershed Management Watershed management is a scale which natural resource managers can apply an ecosystem approach. A watershed is the geographic area in which water, sediments, and dissolved materials drain into a common outlet (Hoffman, 1994). The outlet can be a stream, lake, underground aquifer, estuary, bay or ocean. Each waterway has its own watershed area and all land is located within a watershed. Watershed management is defined as an integrated strategy for more efl‘ectively restoring and protecting aquatic ecosystems and protecting human health (EPA, 1992). The watershed approach focuses on hydrologically defined drainage basins, rather than using areas arbitrarily defined by political boundaries. EPA has used this approach for studying watersheds including the Chesapeake Bay, San Francisco Bay, and more recently, the Saginaw Bay watershed of Michigan. Ecosystem management at a watershed scale has been initiated by the Remedial Action Plans (RAPs) implemented by the eight states bordering the Great Lakes, in conjunction with efi‘orts of the International Joint Commission (MDNR, 1992). The RAP process views the environment holistically, specifically the Great Lakes watershed, and is a step away from the traditional “command-and-control” approach. Because the RAP process is a new and unique approach to manage ecosystems, many tools and techniques have not been developed or proven which can assist the process (MDNR, 1992). This study explores the use of a tool that can be helpful to it natural resource decision makers by providing a visual analysis of the distribution of environmental hazards in a watershed. It will also aggregate the hazards to highlight environmental “hotspots” or areas of potential concern in the watershed. The ecosystem approach calls for interaction and cooperation between decision makers and stakeholders including scientists, government officials, industry, and the public. The tool described in this study can facilitate this interaction by providing a means to illustrate and communicate the distribution of environmental hazards efi‘ectively and quickly to all the stakeholders involved. Comparative Risk As environmental problems become more complex and budgets are constrained, the concept of relative environmental risk has been promoted. EPA has initiated environmental risk ranking and analysis as a means for prioritizing environmental concerns at the federal and state levels. The process includes assessing environmental risks, measuring the characteristics and magnitude of risks and ranking them relative to the risks they pose to human and/or environmental health. A number of states and regions have attempted this complex task. Michigan produced its relative risk report in 1992 entitled Michigan ’s Environment and Relative Risk (MERR). This report ranked environmental problems in Michigan in broad categories. The lack of land-use policy or management was considered to pose the greatest risk (MERR 1992). EPA Region V (Michigan, Indiana, Ohio, Illinois, Msconsin, Minnesota) also ranked environmental risks in the six state region. Results from both of these reports will be analyzed and used as guidelines to determine weights for the difl‘erent res SOt Env with issue pollu equitj rapidl 0f em SF’SIerr Backs» Wi‘ numbe, emirom Commur Lee. 198 5610“,). . proteCIIOr environmental data sets used in this study. The weighting method will account for the difi‘erent relative risks posed by each environmental hazard. Tables which describe the results of both reports are included in Appendix D. SOCIAL SCIENCE CONCEPTS Environmental Equity Environmental equity questions the fairness of the distribution of enviromnetal hazards with respect to the population (EPA, 1992). This section provides a background on the issue of environmental equity and the major research conducted to date concerning pollution distribution and its relationship to race and income. The topic of environmental equity was first explained in the early 1970’s, but the amount of research has increased rapidly since the late 1980’s. Non-academic literature and anecdotal evidence on the topic of environmental equity has increased dramatically in the last five years, yet methods of systematically addressing these issues in a comprehensive manner are lacking. Background \Vrth limited funding, the largest environmental problems, those that efi‘ect the greatest number of people, have been addressed first (EPA, 1990a). This created some environmental protection ”gaps". There is increasing evidence that poor and minority communities bear a disproportionate burden of pollution (MacCaull, 1976; GAO, 1983; Lee, 1987; Bullard 1990; Mohai and Bryant, 1992; major research will be described below). These communities, it would seem, have fallen through these environmental protection gaps. Yet, natural resource managers, planners and decision makers do not have the tools necessary to address these issues. This research proposes a methodology that may bridge the gap between natural resources and social concerns. Environmental equity has it's roots in the issues of social justice and environmental protection. These two movements have combined as a result of the perceived disproportionate distribution of pollution in poor and minority areas. As knowledge of these conditions has spread, minority communities have organized and protested against existing polluting facilities and resisted the siting of facilities that may pose environmental hazards (Kay, 1992; Taylor, 1992; Bullard, 1990; Lee, 1992). Many of these organized groups used tactics and strategies learned in the civil rights struggles of the 1960's and 1970's (Chavis, 1991). The growing movement has begun to address the distribution of environmental risks across population groups under the label of environmental equity. Grassroots organizations facing environmental inequities proposed the following items to enhance their communities’ participation in the risk allocation decisions that accompany industrial sites: W: Wehavetherighttocleanindustryzindustrythatwill contribute to the economic development of our communities and that will enhance the environment and beauty of our landscape. We have the right to say “NO” to industries that we feel will be polluters and disrupt our lifestyles and traditions. We have the right to choose which industries we feel will benefit our communities most, and we have the right to public notice and public hearings to allow us to make these decisions. mg to Mnfion: We have the right to participate in the formulation of public policy that prevents toxic pollution from entering our communities. We support technologies that will provide jobs, business opportunities and conservation of valuable resources. As residents and workers, we have the right to safe equipment and safety measures to prevent our exposure in the community and the workplace (Austin and Schill, 1991). These items are similar to the Principals of Environmental Justice which were first proposed in 1991 at the First National People of Color Environmental Leadership Summit and are presented in Appendix H. I? in fc ex the Wt. Cle aHal 10 Recent policy and legislative responses to the issue of environmental equity on the federal level include: 0 EPA opened the Ofice of Environmental Equity in 1992. o The "Environmental Justice Act of 1993' (HR 2105) provides the federal government with the statistical documentation and ranking oftOp the 100 "environmental high impact areas” that warrant attention 0 The ”Environmental Equal Rights Act of 1993' (HR 1924) asks to amend the Solid Waste Act and would prevent waste facilities from being sited in "environmentally disadvantaged communities". 0 The "Environmental Health Equity Information Act 1993' (HR 1925) seeks to amend CERCLA to require the Agency for Toxic Substances and Disease Registry to collect and maintain information on the race, age, gender, ethnic origin, income level, and educational level of persons living in communities adjacent to toxic substance contamination. o The ”Waste Export and Import Prohibition Act” (HR) would ban waste exports as of July 1, 1994 to countries that are not members of the Organization for Economic Cooperation and Development (OECD); the bill would also ban waste exports to and imports from OECD countries as of January 1, 1999 (Bullard, 1994). Major Stum'es 7728 Distribution of Environmental Quality (Freeman, 1972) compared the air quality index between three urban centers, Kansas City, St. Louis and Washington, DC, and found that areas of lower income were exposed to higher levels of particulates and sulfates. When Freeman analyzed minority areas in each city, these areas were found to be exposed to higher levels of air pollution than even the lowest income group examined (less than 3,000 dollars per year). This was one of the first studies to address the question of who is paying for the extemalities of environmental pollution. Asch and Seneca examined urban air quality at the census tract level in Chicago, Cleveland and Nashville (Asch and Seneca, 197 8). In addition, at the city level, they analyzed air quality in urban areas in 23 states. The results fiom these two analyses were 11 similar and concluded that the correlation between median family income levels and air pollution is stronger than the relationship between the minority population percentage and air pollution. In contrast to Asch and Seneca, Gianessi published a national study that found that minorities were much more likely to sufi‘er greater damage from air pollution than whites at all income levels (Gianessi et al., 1979). Another national study which examined air pollution was conducted by Gelobter in 1987 and expanded in 1992. His initial analysis focused on urban areas throughout the country, and the expanded analysis included rural and urban areas in the US. Using EPA air quality data fi'om 1970-1984, Gelobter concluded that minority areas were consistently exposed to more air pollution than whites (Mohai and Bryant, 1992). On the urban level, income tended to be less significant in its relationship with exposure to air pollution; low income urban areas were only slightly more exposed to air pollution than high income areas. Including both urban and rural areas, higher income areas were exposed to more air pollution, as would be expected since urban areas tend to be wealthier and have more industry than rural areas. In response to a request fi'om Representative Walter Fauntroy in 1983, the U. S. General Accounting Ofice (GAO) conducted a study on the Siting of Hazw'dous Waste Landfills and Their Correlation With Racial and Economic Status of Surrounding Communities. This study examined the racial and economic characteristics of communities surrounding four hazardous waste landfills in three southeastern States (GAO, 1983). It found that the majority of the population in three of the four communities with hazardous waste landfills was African-American. Also that at least 26 12 percent of the population in all four communities had income below the poverty level and most of this population was also Afiican-American. The GAO study used 1980 census data within four miles of the hazardous waste landfills studied. The United Church of Christ (U CC) published Toxic Wastes and Race in the US in 1987, which analyzed the racial and socio-economic characteristics of communities with hazardous waste facilities on a national level. Communities in this report were defined by zipcode areas, and percentage of minority population was used as the measure of “race” (U CC, 1987). The major finding of this report was that race proved to be the best predicting factor of the location of hazardous waste facilities. Other variables in the study included mean household income, mean value of owner-occupied homes, number of uncontrolled toxic waste sites per thousand persons and pounds of hazardous waste generated per person (UCC, 1987). The report also found that communities with one commercial hazardous waste facility have double the minority population percentage compared to communities without a facility. Ifthere were two or more facilities, the minority percentage more than tripled (38 percent versus 12 percent) (U CC, 1987). This report documents empirical evidence of the inequities related to the distribution of hazardous waste facilities and attributes it to race. The report also contains a descriptive study which analyzed the communities surrounding over 18,000 sites contaminated with toxic waste. Data used in the study was supplied by EPA’s Comprehensive Environmental Response, Compensation and Liability Act Information System (CERCLIS), and is national in scope. The report found that three 13 Of five (60 percent) Afiican-American and Hispanic-Americans live in communities with sites contaminated with toxic waste. However, the following table from an appendix in the report ofi‘ers comparative figures worth noting: Table 1.2 Ethnicity of Population Living in Toxic Waste Site Areas (UCC, Table C-l, 1987). Population Group % of Group Which Lives in Toxic Waste Site Areas Total Pgrulation 54.2 Afiican American 57.1 Hispanic American 56.6 European (White) 53.6 Asian/Pacific Is. 52.8 Native American 47.3 This table shows that the percentage of whites living in toxic waste contaminated areas is also quite high at 53.6 percent. Given this background, the initial findings are put into better perspective. As a result of the UCC report and mounting interest in the field, University of Michigan Professors Mohai and Bryant organized a conference on “Race and the Incidence of Environmental Hazards” at the University of Michigan in 1990 (Mohai and Bryant, 1992). One result of the conference was a book of the same title edited by the organizers. In the book, Mohai and Bryant review 15 studies which examine the distribution of environmental hazards by income and race. They summarize: “All but one of the 11 studies which have examined the distribution of environmental hazards by race have found a significant bias. In addition, in five out of eight studies where it was possrble to assess the relative importance of race with income, racial biases have been found to be more significant. Taken together, these findings thus appear to support the assertion of those who have argued that race has an additional efl‘ect on the distribution of environmental hazards that is independent of c1ass”(Mohai and Bryant, 1992). l4 Mohai and Bryant summarize some of the major factors that play a role in the siting of polluting facilities: 0 the availability of cheap land located in minority communities (Asch and Seneca, 1978), o the lack of local opposition to the facility (Bullard and Wright, 1987), o thelackofpoliticalresourcesaswellasthcneedforjobsflJCC,1987), o thelackofmobility ofminorities resulting fi'ompovertyandhousing discrimination thattraps them in neighborhoods where waste facilities are located (Bullard and Wright, 1987; UCC, 1987; Knox, 1994), o industries tend to take the path of least resistance, which often leads to minority communities (Bullard and Wright, 1987). Other factors include the Not In My Back Yard (NIMBY) syndrome which reinforces the pattern of siting unwanted land uses in areas less likely to speak out or already accomodating similar facilities. Minorities are also under-represented on governing bodies when location decisions are made which contributes to the inequitable distribution of environmental hazards. Given Mohai and Bryant’s excellent summary, it is important to look at the distribution of environmental hazards compared with race independent of income. As a result of the conference in Michigan and increasing empirical evidence, EPA published the report, Environmental Equity - Reducing Risk for All Communities (EPA, 1992). This report does not analyze any data but reviews existing research. Its major finding is that “racial minority and low income populations experience higher than average exposures to select air pollutants, hazardous waste facilities, contaminated fish and agricultural pesticides in the work place. Exposure does not always result in 15 immediate or acute health effects. High exposure and the possibility of chronic effects are nevertheless a clear cause for health concerns” (EPA, 1992). EPA created the Ofice of Environmental Equity in 1992. Its directive is to deal with environmental impacts afi‘ecting people of color and low-income communities, with the goal of providing a clean and safe environment for every resident, in every community, in the US. (EPA, 1993). The National Wildlife Federation published a report entitled, Not Just Prosperity: Achieving Sustainability with Environmental Justice (Goldman, 1994). This report reviews 64 studies from 1987 - 1993 concerning air pollution, waste facility siting, regulatory costs and benefits, toxic and radioactive releases, occupational health, pesticide exposure, toxic fish consumption and lead poisoning. All but one of these studies found either race or income to be correlated with the environmental hazard studied. When race and income were compared in 30 of the studies, 22 concluded that race was a more significant predicting variable of environmental hazard (Bullard, 1994a). The studies described here provide evidence that environmental hazards are distributed inequitably throughout the landscape. Most of these studies review only one or two environmental hazards. Do the same trends hold true for ten or more aggregated environmental data sets? Can the severity of environmental hazards posed by each site be included and aggregated? How can the aggregate of environmental hazards be assessed on a local or regional level by planners or natural resource managers? This study describes a methodology, in the following chapters, that can be used to address some of these questions. 16 CHAPTER II STUDY PURPOSE: PROBLEM STATEMENT, HYPOTHESIS AND METHODOLOGY This chapter will discuss the purpose of this study and the methods used to analyze the hypothesis and research questions. The problem statement is described first, followed by the researchable questions. The data sets and sources used in this study are described as well as limitations of the data. Lastly, statistical methods used to analyze the data are described. PROBLEM STATEMENT Pollution distribution is not a new topic of study. In the 1970's there were a number of studies documenting the spatial effects of pollution on society (Freeman, 1972; McCaull, 1976; Berry, 1977; Kruvant, 1978). Likewise, the projected environmental impacts of any major project have been documented in Environmental Impact Statements (EIS) as mandated by NEPA in 1969. However, environmental protection has been carried out in a piecemeal fashion with each department focusing on a single environmental problem. This study provides a systematic approach that focuses on the spatial analysis of environmental hazards and sources of pollution which occur across environmental media within an ecosystem. It combines three concepts: by using existing comparative risk analysis, the goal would be to 1) create a hazard index by aggregating hazards to 2) 14“ l7 generate single and comprehensive environmental hazard or hotspot maps and 3) provide a methodology for exploring environmental equity issues. HYPOTHESIS FOR CASE STUDY By combining environmental and socio-economic data, the issue of environmental equity will provide a test of the applicability of the methodology presented in this study. The hypothesis that will be analyzed and tested follows: l. Minority areas are more likely to contain environmental hazards compared to non-minority areas. 2. Low income areas are more likely to contain environmental hazards compared to high income areas. RESEARCH QUESTIONS This study seeks to ultimately develop a digital a map of pollution potential in a naturally defined area, in this case a watershed. In addition, the following research questions will be addressed. 1. What is the spatial distribution of point source pollution in the Saginaw Bay watershed? This would include: water discharges, air emissions, leaking underground storage tanks (LUST), Act 307 sites (environmental contamination sites which pose a risk to environmental or human health), EPA's Toxic Release Inventory (TRI) sites, landfills (municipal, industrial), and hazardous waste sites (incinerators, transfer station, or disposal site). 18 2. Because pollution comes in many forms via multiple media (air, land, and water), can overlapping sources of pollution or pollution potential be aggregated? This result would show a compounded pollution potential, or more formally, an increasing external diseconomy. 3. Because sites of environmental contamination or hazard differ in severity, can the varying degree of severity be accounted for using state environmental data? Also, because state and federal agencies have ranked difl‘erent environmental problems, can these rankings be incorporated in the final aggregation of hazards? 4. What are the soda-demographics of areas afi‘ected by environmental hazards? Socio- demographic issues include: . race (percent white, percent minority), . income (average household income per year), . poverty (percent of households in poverty), and . population. 5. Are there policy options that can be developed if environmental inequities are found? If so, what are they, and how could they best be implemented? DATA DESCRIPTION Ten environmental data sets were collected from the MDNR for this study. The following section contains a description of the data sets, where they were obtained, why they were included in this study, problems with the data, and assumptions and limitations of the data sets. Each data set will be ranked on a scale of one to five according to severity, with five being areas with the most severe hazards and one the least severe. This section also describes the method used to aggregate each data set per zipcode as well as an overall aggregation of the data sets to produce a variable called “hotspot”. Appendix A 19 includes a summary table of the data used in this study as well as their distribution and summary statistics. 1) Criteria Air Pollutants Criteria air pollutants include sulfirr dioxide (80;), nitrogen dioxide (N 02), carbon monoxide (CO), volatile organic compounds (VOC), and particulate matter less than ten microns in diameter (PM 10). These pollutants are regulated federally by the Clean Air Act (CAA) and in Michigan under the Michigan Air Pollution Act of 1965, Public Act 348, and amended most recently in 1987. The Michigan law requires a permit to be Obtained fi'om the state before any new pollutant is emitted. The owner/operator is required to file a petition describing the process, source, site, and emissions to the Air Permitting Division of the MDNR This division either grants or rejects the petition and manages the database for all permits granted. The database includes permits for all industrial, municipal, or commercial sources. It should be noted that these are the permitted amounts of air pollution. Actual amounts emitted may be less or could possibly be more because the MDNR stafi‘ monitors only the largest emitters. All emission data are reported in tons emitted per year. Each permit maintains an address, including zipcode, for the source. The zipcode was used as the spatial attribute in this study. All permits with the same zipcode were grouped together, and the amounts emitted were summed. This gave a sum total per zipcode in tons per year. The summed amounts were given a ranking depending upon the amount emitted. Figure 2.1 shows an example of the data distribution for all zipcodes of S02 emissions. 20 100 -- ‘0 75 J- as .l IOCOOQ—N o c : 4f : T1==-I No <1 10 too rose >1sso ‘l’ons Per Year ofSO2 Figure 2.1 Permitted S02 Emissions Per Zipcode Since 90 percent of the zipcodes had summed S02 emissions less than ten tons per year and because the range of the summed data was large (0 - 33,815 tons per year), the breakdown and resulting ranking followed a logarithmic increase. Therefore, it is important to note that when reading the criteria air pollutant maps, the zipcodes with a Map Ranking of “4” or “5” are many times greater than the lower rankings. Tables showing scale, ranking, number of zipcodes in each range, and percent are included in Appendix B. 2) Act 307 Sites The Michigan Environmental Response Act (MERA), Public Act 307, was passed in 1982. It provides rules for identification, risk assessment, evaluation, and cleanup of environmentally contaminated sites (MERA, 1982). Environmental contamination is defined by the IVflDNR as the release of a hazardous substance, or the potential release of a discarded hazardous substance, in a quantity, which is or may become injurious to the environment, or health, safety or welfare of the public (MDNR, 1993). This dataset is an 21 excellent source of present and potential hazards facing the watershed and should therefore be included in this study. Upon discovery of an environmentally contaminated site, the Act requires the Emergency Response Division of the MDNR to inspect the site and assign it a site “score” using the Site Assessment Methodology (SAM). The numerical SAM score incorporates the present and potential hazard posed to public health or the environment for each identified site. The SAM scores, as assigned by the WNR range fiom 0 to 48 points depending upon the severity of the site. Also reported and stored on a database are the site id, name, address, pollutants, location code, cleanup measures, remediation stage, dates of relevant action and over 170 other variables about the site. This study uses the SAM scores prepared by the MDNR as a measure Of the severity of each site. SAM score data received fi'om the MDNR was very complete, however, zipcode data were incomplete for many sites. The zipcodes for these sites could be determined by using the location code, street address, and/or city and township information. The location code provides county, township, range, section, quarter section and quarter—quarter section information which could be cross referenced with a zipcode map of each county to determine the site zipcode if necessary. The SAM scores within each zipcode were summed giving a range of 0 - 600. This range was then ranked at increasing intervals. Fifty-three percent of the zipcode areas had either no 307 sites or had a summed SAM score of less than 40, which is relatively low because the average summed score is 81.2. A histogram of the 307 site score per zipcode 22 is shown in Figure 2.2. To account for the increased hazard presented by the increasing scores per zipcode, the rankings were categorized according to the following breakdown: Table 2.1. Ranking Method for Act 307 Sites Percentage Rank (Beginning with the highest scores) Top 5 % 5 10% 4 15 °/o 3 30% 2 40% 1 No Data 0 0°“ - -100 3 °" ’9“ E M. ~60 §°°‘ 3 ~40 E ”‘ 1 0.1 « ”2° Figure 2.2 Histogram of Act 307 Site Data. 23 3) Hazardous Waste Facilities Michigan’s Hazardous Waste Management Act (HWMA) of 1979, Public Act 64, governs the regulation of hazardous waste fiom “cradle to grave”. In other words, it requires that any hazardous substance that is produced (cradle), handled, transported, stored, or disposed of (grave) be regulated by the HWMA. A hazardous waste is defined as any substance that causes or contributes to an increase in mortality or serious illness, or that poses a hazard to human health or the environment if improperly treated, stored, transported, disposed of or otherwise managed (HWMA, 1979). The goal of the Act was to eliminate hazardous substances from the “normal” waste stream process by separately landfilling, incinerating, storing or treating them as a means of reducing the risk to human health and the environment. Data on hazardous waste facilities was obtained fiom the MDNR Waste Management Division. The data includes facility names, addresses, type of management, city, and relevant regulatory status of all facilities which incinerate, store, treat or dispose of hazardous waste materials. The data received fi‘om the MDNR was complete, however it did not include zipcode information. Because the number of hazardous management facilities was small, the zipcode information was obtained by using the street address and city information and referencing the US Postal Service F ive-Digit Zipcode Reference Manual. This manual indexes street addresses and the corresponding zipcode by city or village and proved extremely useful throughout this study. The four types of hazardous waste management facilities include: 24 0 Treatment (chemical, biological, or physical treatment used to neutralize the waste), 0 Storage (in tanks or containers), 0 Disposal (surface irnpoundment, landfill, or waste-pile), and o Incineration (burning). Data fiom the MDNR did not include quantity figures for any of the facilities, therefore each type of facility was treated equally in this study. For example, disposal sites were not given added weight compared to storage sites in the calculation of the rankings. \Vrthout quantity information it is not possible to determine a relative weighting factor between facilities. The rating therefore depends on the number of sites per zipcode. Since the range is low (0 - S), a simple linear scale is used to rank the data per zipcode. 4) Toxic Release Inventory (TRI) The Toxic Release Inventory contains information on specific toxic chemical releases and transfers. The Emergency Planning and Community Right To Know Act (EPCRA) of 1986 requires manufacturing facilities to report any accident, release or transfer of toxic materials to the EPA The EPA has collected this information since 1987 and stores the data on the TRI database. TRI data is distributed to the public in Michigan through the MDNR Environmental Response Division. Releases are categorized by air-stack, total air, water, underground injection, land, publicly owned treatment work (POTW), and other off-site transfers. All releases are reported in estimated pounds released. 25 Manufacturing facilities (those in the Standard Industrial Classification [SIC]codes 20-39) that have ten or more fill] time employees and use 25,000 pounds or more for each listed chemical for manufacturing and processing, or 10,000 pounds for other nonprocess uses, must report all releases or transfers of such chemicals. The TRI list currently contains over 300 chemicals that require reporting (EPA, 1993). The list is updated annually under the auspices of the EPA The criteria for listing the chemicals which are considered toxic and thus must be reported under section 313 ofEPCRA must cause one of the following: 0 significant adverse acute health efiects at concentration levels that are reasonably likely to exist beyond facility boundaries as a result of continuous, or frequently recurring, releases. 0 in humans - eancer, teratogenic efl‘ccts; or serious or irreversrble reproductive dysfunction, neurological disorders, heritable genetic mutations, or other chronic health eflccts. o becauseofitstordcityandpersistenceintheenvimnmentoritstordcityandtendencyto bioaccumulate in the environment, a signifieant adverse efi‘ect on the environment of suficient seriousness to warrant release reporting under EPCRA section 313 (EPA, 1993). TRI data is an excellent beginning for the federal and state government and communities to keep track of toxics which pose a threat to human and environmental health. However, there are some severe limitations with the TRI data. 1. TRI data is based upon self-reporting forms. EPA does not verify the reported amounts of released or transferred chemicals. It has been estimated that about 66 percent of the facilities required to report information to the EPA did in fact file at least one report form (EPA, 1993). 26 2. Facilities with fewer than ten employees and facilities below the thresholds are not 3. required to report. Non-manufacturing facilities, such as mining and electric utilities, are not required to report although these industries are major sources of toxic releases. Also, government-owned and government-operated facilities were not required to report TRI data used in this study, which includes Department of Energy and Defense facilities that are not managed by subcontractors. However, in August 1993, President Clinton signed an Executive Order which will require these facilities to comply (EPA, 1993). The toxic chemical list includes many but certainly not all toxic chemicals produced. Thousands of new chemicals are synthesized each year, many of which are extremely toxic. . Estimation techniques for determining the amount of toxics released or transferred are not standardized. This can cause a wide variance in the amounts estimated by difl‘erent facilities. VVrth all these limitations of the TRI data, one might assume that it is not worth using. However, without the first step of collecting this information, regulators, policy makers and the general public would not be aware of the toxics in their communities, regions or states. This study uses TRI data to identify areas of potential concern. Combined with the other data sets, the TRI data will show patterns of environmental hazards which may need to be further analyzed. 27 Previous research using TRI data focused primarily on the facility location and did not include amounts released (Burke, 1993). Amounts released were included in this study to determine the severity of each release. All reported releases were summed per zipcode to give a total toxic release per zipcode. The range of release was from one pound to almost three million pounds per zipcode. Since the magnitude of this range was so large, a logarithmic scale was used to rank the data in order to gain insight when viewing and analyzing the final map of the distribution of TRI data. Although difi‘erent toxic chemicals were reported, this study did not weight chemicals difl‘erently. A pound of dioxin was treated similarly to a pound of ammonia even though the effect upon the environment and human health may be dramatically different. Given the fact that there are over 300 chemicals on the database, and that there is significant controversy over the efl‘ects of each chemical, the chemicals were not weighted difi‘erently. Releases are reported via difi‘erent pathways such as air, land, underground, and water. The totals used in the distribution maps and statistical analysis include all pathways combined even though different pathways undoubtedly effect humans or the environment difieremly. 5) Leaking Underground Storage Tank Data (LUST) The Leaking Underground Storage Tank Act, PA 478, was passed in Michigan in 1988. The act covers all tanks that are ten percent or more underground and store a hazardous substance. A regulated substance includes petroleum products and/or “hazardous substances” (a hazardous substance was defined earlier under the hazardous waste facilities section). The act requires tank owners to adhere to all of the reporting, 28 investigation, and corrective action requirements of federal regulations. LUST data is maintained by the MDNR Environmental Response Division. The database tracks information on LUST sites by address, city, corrective action status, funding information, and remediation status. The EPA has estimated that 25 percent of the underground gasoline storage tanks in the US are leaking (MDNR, 1987). Much higher estimates are assumed by the MDNR as more sites are investigated (MDNR, 1994). The petroleum products that are stored in these tanks include motor, diesel and jet fire], heating oils, solvents and automotive and industrial lubricants. Leakage of these substances can threaten drinking water supplies, as well as contaminate streams, groundwater, wetlands and other aquatic environments as the contaminant, once released, moves with the hydrologic cycle. LUST sites were determined to constitute a real and potential hazard to the environment and human health and are thus included in this study. LUST sites are inspected by the MDNR and given a score depending on the severity of the site. The scores range fi'om low (L4) to high (HI) and over 97 percent of the sites had score data. In order to rank the LUST sites by zipcode and include severity of contamination, the following conversion table was used in this study to give a numerical value to the alphanumeric data: Table 2.2 LUST Data Conversion Table. LUST Data Value Conversion Number Data, but no score 1 L4 2 L3 3 4 L2 29 Table 2.2 (con’t). Ll H4 HB H2 H1 Using the conversion table, the scored data could be summed per zipcode and ranked accordingly. The range of the summed LUST data was between one and 310. Because almost half of the summed scores were less than ten, a scale similar to the Act 307 site data was used to rank the LUST site data on the final distribution map (see Table 2.1 on page 21). A histogram of LUST data is shown in Figure 2.3. ~15O PER 3 1 mo " 03‘ #50 5021 L at- at)‘—t” . 100210350400 um Figure 2.3 Histogram of LUST Data. LUST data obtained from the MDNR was fairly complete. Some zipcode data was missing but easily determined fiom other information given. All LUST information is kept ill: ll‘. it III'I ll: ll lilllllllt l tlltll 111:...{1‘ 30 6) Landfill Data The Solid Waste Acts of 1978, PA 641, and 1987, PA 207, regulate the disposal of solid waste in Michigan. Landfill owners and operators are required by the MDNR Waste Management Division to obtain a license for landfilling waste. Landfills are separated according to type. Type I landfills handle hazardous waste and require strict permitting and compliance records. Type II landfills are considered sanitary landfills and are designed to accommodate general types of solid waste including municipal garbage and excluding hazardous waste as defined by CERCLA. Type III landfills pose little threat or contamination to groundwater or the environment because these landfills accept trees, stumps, and demolition material. Solid waste disposal sites can pose a threat to ecological and human health primarily fi'om surface water runofi‘ of contaminants to streams, lakes and other surface water bodies, or discharge of groundwater contaminated by leachate to wetlands or surface water (EPA, 1992a). The contaminants in ground and surface waters can affect drinking water supplies. Landfills in the Saginaw Bay watershed have been identified as the source for at least 47 Act 307 sites through 1988 (MDNR, 1988a). Since municipal landfills receive hazardous waste either as components of solid waste (metals in batteries), or by other means (illegal dumping), it is reasonable to assume that additional sites will be identified as landfills are closed (EPA, 1991a). Therefore, landfills can be considered a present and/or potential hazard and were determined to be usefirl in this study. 31 Data obtained from the MDNR includes all active and inactive Type II sanitary landfills. The database contains information on the facility name, owner, location, liner data, type, size, closure status and permit dates. However, of the 77 active landfills located in the watershed, fewer than 12 percent had information on landfill size and much of the zipcode information was missing. Zipcodes were obtained using addresses and zipcode maps of Michigan counties (MI Information, 1994). Because data on landfill size was incomplete, landfills were ranked according to the number of landfills per zipcode area. A limitation of this method is that it fails to incorporate landfill severity per zipcode. For example, a zipcode region may have only one landfill with a capacity of 200 acres, while another zipcode area may have three small landfills each with a capacity of 20 acres. The latter area will receive a higher ranking than the more severe, or larger, former site. Likewise, information on landfill content was not included in the data fi'om the MDNR However, any landfill that is known to be causing environmental contamination would be listed as an Act 307 site. Inactive landfills were mapped separately because they may pose more of a threat to human health and/or the environment since older sites were not regulated by the Solid Waste Acts. Toxic inactive landfills are more prevalent than active ones because of the lack of strict environmental laws before the 1970’s. Inactive landfill data did not contain any acreage or landfill content information and were not audited by the MDNR Waste Management Division. 32 7) Water Discharge Data Federal and state water discharge or wastewater regulations date back to the 1920’s with the Water Resources Commission Act, PA 245, of 1929. Problems stemming from industrial and municipal wastewater became more prevalent in the 1960’s culminating in “dead lakes and rivers” which were so polluted that few fish or other non-algae organisms could survive mainly due to high phosphorous loads. These problems led to the passage of the federal Clean Water Act (CWA) in 1972. The CWA established the goal of making all waters in the nation “fishable and swimmable” and created the National Pollutant Discharge Elimination System (NPDES) (MDNR 1990). The NPDES requires a permit to be obtained for any discharge to surface waters. Permits contain effluent limitations on the types and amounts of waste material that can be discharged for the protection of human health and the environment. The following hazards are associated with point source discharge to surface waters: toxic chemicals (metals, pesticides, originate chemicals), oxygen-demanding pollutants and other physical and chemical parameters. These hazards pose a threat to the receiving waters and to organisms, including humans, that utilize these waters. Point source water discharge data was gathered from the MDNR Surface Water Quality Division. The data include site information, address, location coordinates, and a host of pollutant parameters such as: o Turbidity 0 Color 0 Oil Films 0 Taste and Odor o Foams 0 Deposits 0 Total Dissolved Solids 0 pH and Temperature 0 Chlorides o Solids o Toxic Substances o Radioactive Substances 0 Phosphorus o Nutrients o Fecal Coliforrn o Dissolved Oxygen 33 Permits are categorized as major or minor depending upon the amount and content of discharged material. Major dischargers are distinguished fiom minor discharges by virtue of their waste volume, the concentration of the contaminant, and/or their location with respect to sensitive water uses (EPA, 1991a). As a general rule, major municipal systems are those that treat over one million gallons of wastewater per day or more (MDNR, 198 8). Major industrial dischargers include any facility that scores over 80 points on EPA’s facility rating system which includes factors such as: potential for the pollutants to be toxic, the size and type of the waste stream, potential public health impacts, and whether the effluent limits are water quality or technology based (MDNR 198 8). Minor industrial dischargers are those that receive a score of less than 80 points and generally treat less than one million gallons of wastewater per day. Although every NPDES permit issued by the MDNR should receive a score, after two days of reading hundreds of permit files at the ofices of the MDNR Surface Water Quality Division, only 12 complete EPA facility rating system score sheets had been located for minor dischargers. As a result of this lack of data, minor discharges received an equivalent rating of one and major dischargers a rating of three in order to incorporate severity into the ranking of discharges. This was based on the ratio of the average score of the major emitters to the known average score of the minor emitters which was roughly three to one. The equivalent ratings were summed per zipcode. The summed amounts were then ranked and mapped. The range of summed dischargers for the watershed is between one and 14, and the rankings follow a linear increase. The histogram of water discharges per zipcode is shown in Figure 2.4. 34 Figure 2.4 Histogram of Water Data A limitation of the discharge data used in this study is that the major and minor classification primarily focuses on the volume of discharge and ignores many chemical differences that afi‘ect the severity of the hazard to humans and the environment. A similar scheme was used by the Risk Ranking Project of EPA’s Region II (New York, New Jersey, Puerto Rico, and Virgin Islands) for discharges to water (EPA, 1991a). This scheme, though limited, is better than no breakdown between major and minor dischargers but could be much more useful if the “scoring” sheets were completed by the MDNR Other hazards that afi‘ect water quality which are not included: urban runofi‘ (no data), agricultural non-point source pollution including soil, pesticides, manure (all lack data), and underground injection wells. 35 METHODS OF ANALYSIS A variety of tools and techniques were used in this study. A flow chart documenting the major aspects of the methodology is provided in Figure 2.5. Figure 2.5 may be a useful reference as the methodology is described in the text below. Aggregation of Hazards The data described above were mapped, each zipcode was designated a rank score of one through five for each data set. The rank scores per zipcode were summed, and then divided by the zipcode area (in square miles) to generate a “hotspot” variable. Since the size of the zipcode was factored into the final rating, large zipcodes with an equal score to a smaller zipcode receive a lower “score”. Conversely, small zipcodes with an equal score are amplified and receive a higher final score. This is appropriate for two reasons. First, smaller zipcodes represent areas with higher population densities and therefore more people are potentially exposed to a given hazard. Secondly, pe0ple living in smaller zipcodes will inevitably live closer to the hazards in that zipcode compared to larger zipcodes. For these reasons it is appropriate to factor zipcode size into the final hotspot rating. A table of zipcodes and corresponding unit area in square meters, kilometers and miles is included in Appendix C. The hotspot rating will be used to address the environmental equity issue later in this study. 36 Census Data Socio-economic Enviromeutal Pollution ““3”“ Geographic Data Datasets (Boundary Files) 0 MDNR ' o Watershed 0 EPA o Zipcode (ASCII Format) Import into Database Management System 0 Paradox l 0 Oracle Import into 618 . . (ARC/INFO) Data Mm” Clean and Build 0 Clean Mlssrng or Geographic Covera Incomplete Data fi 0 Organize to Scale Spatial Analysis (Link DBMS and 618 Systems) . Environmental Aggregate Pollution Data . . Sum totals of each hazard ' ““4“” within zipcode. 0 Overlald (Hotspot Index) Census Data . Individually 0 Combined with Rank Summed Data Environmental Data Rank on seale of 1-5 depending on the range and distribution of the data Spatial Output (Maps) . . Statistical Anal sis Individual or 1‘1me “@811th Import Data info Combined Variables Fmigge and.“ ._. Statistical Package 0 Environmental ”‘9 Pm.mRa“k’ng (SYSTAT) - Social 1 . Summary Statistics 0 Correlations ‘0 Odds Ratios Results and Observations Figure 2.5 Flow Chart of Study 37 Weighting Method In order to analyze the difi‘erence in severity between the various environmental hazards, the hotspot map will be compared to a weighted hotspot map. The Region V EPA office ranked different environmental risks with respect to ecological risk and human health risk. Using their breakdown as a guideline, risk factors were assigned to each environmental data set in this study. Table 2.3 shows the resulting weighting factors to be used to incorporate the difi‘erences in severity between environmental hazards. Each environmental hazard score was multiplied by the corresponding weighting factor and the weighted scores were summed to give a final “weighted hotspot” map of the watershed. Zipcode size was factored into this variable as well by dividing the weighted hotspot by the square miles of the zipcode. This map will be analyzed with respect to the nonweighted map to observe the difi‘erences between the two. This can be a usefill method for environmental decision makers to observe the efi‘ects of a risk ranking project. In addition, as environmental problems are ranked, the location of the environmental hazards can be analyzed using this method. 38 Table 2.3 Weighting Factors fiom MERR and EPA Risk Ranking Reports. M EPA Region V Michigan Environmental Risk Factor Environment and Hazard/Pollutant Ecological Human Relative Risk Health Health &' NO; 3 3 1 SO; 3 3 1 CO 3 3 l VOC 4 3 1 m Act 307 3 2 2 LUST 2 2 2 Landfills 1 1 2 Inactive Landfills 1 l 2 Hazardous Waste Facilities 2 l 2 Water; Point Source Discharges 3 2 2 (Industrial/Municipal) Combingg TRI Sites 4 4 2 High = 4 High High = 4 ScoreEquivalentsinEPA Medium High=3 High=3 (l99l)andMERR(l992) Medium=2 Medium High = 2 reports. Report summariesin Low=l Medium=1 AppendixD. 39 Visual Analysis Each environmental and social variable is presented spatially using ARC/INFO (version 6.1.2) Geographic Information System (GIS). GIS is a powerful tool which allows researchers to store, retrieve, plot, and analyze any data that has a spatial context, and is an ideal tool for this type of study. Visual analysis of each variable is presented and noteworthy attributes are discussed. The hotspot map, aggregating the individual variables, will be compared to the EPA weighted hotspot map and difi‘erences discussed. Statistical Analysis In order to test the hypothesis presented in this study, various statistical methods are employed using the statistical package SYSTAT. The hypothesis states that low income and minority status zipcodes are more likely to contain environmental hazards than non- minority and higher income areas with a significant level of confidence. The independent variables include: percent minority, income level (mean income per capita), percent poverty, and population per zipcode. The dependent variable, termed “hotspot”, is an aggregation of eleven environmental variables: 0 SO; 0 CO 0 Water Discharges 0 Toxic Release Inventory 0 N02 0 LUST Sites 0 Act 307 Sites 0 Hazardous Waste Facilities 0 VOC o Landfills 0 Old Landfills S tistic Summary statistics for all variables included in the study are presented including minimum, maximum, mean and standard deviation (Appendix E). Correlations This statistical method will establish the relationship between variables of interest. It uses a scale of -1 to 1 with the negative sign describing inversely related variables (change in the opposite direction) and the positive sign describing variables that change in the same direction. Correlation is often confused with causation and it should be noted that this test does not measure causation. Another limitation is that a high correlation may in fact be a spurious correlation, which occurs when other factors that are not included in the analysis are contributing to the “high” correlation. Correlation is a good initial method used to determine general relationships among a number of variables to be used in subsequent analysis. dd Rati Odds ratio is a statistical procedure which uses logit analysis to determine an estimated or beta coeficient for each of the independent variables. The coefficient is used to determine the odds that an independent variable will occur given its relationship with the dependent variable and other independent variables. For this study, the odds ratio can determine if a minority zipcode is more or less likely to occur in a hotspot area compared to nonminority zipcodes. Income, poverty and population are also included as independent variables. 41 First, the odds ratio requires the variables to be dichotomously grouped and recoded using the dummy variables one and zero. This breaks the variable of interest into an “either - or” set. For example, zipcodes with minority populations above five percent (watershed average) are assigned a “1” and the rest of the zipcodes a “0”, thus the zipcodes become m minority 9_r; not minority. The procedure is repeated for the remaining variables. These dichotomous variables are used in the logit analysis to generate beta coeficients. Next, the beta coeficient for the independent variables will be calculated separately and then together. The beta coeficient should not change dramatically when other variables are added. Once the beta coefi'rcient is determined for each variable the inverse log, e" , is calculated (where x is the corresponding beta coefi'rcient) to give the final odds ratios. This test will also Show which independent variable is more likely to occur in areas of hotspots. Odds ratios provide a higher degree of sophistication for describing the present situation in the watershed. The analysis conducted on the given data will attempt to describe the present relationships among variables in the watershed, but it is impossible to establish causality given the limited scope of this thesis. Other factors that are not included in this study that may influence the incidence of an environmental hazard are: 0 closeness to transportation lines (highways, railroads, airports, rivers, Great LaRCS), favorable zoning areas, land values, tax incentives to entice heavy industry and manufacturing facilities, and proximity to natural resource base (oil, gas, timber, salt, copper). Other limitations and assumptions of this study are described in the next section. 42 LMTATIONS OF THIS STUDY Scale Issues of appropriate scale for measuring difi‘erent variables are, and will remain, a hotly contested topic. From the literature reviewed, studies covering large geographic areas (regions or the entire country) used zipcode level or greater spatial scales. If a city or single county was studied, census tracks seem to be the scale of choice. Given the size and scope of this research, a zipcode level of analysis was the logical choice for the following reasons: 1. The Saginaw Bay watershed covers 22 counties measuring 8,709 square miles. At this size, working on a census track level would have exceeded the resources available for this project. All of the MDNR data had zipcode information (roughly 85 percent of which was complete). Only two data sets have latitude and longitude measurements and these were incomplete, thus a zipcode scale was the only usable scale available. A major impetus for this research was to determine if the process was viable. Could over ten environmental data sets be collected, analyzed, condensed and aggregated to produce useful information? Could this research be conducted by an interested community member, extension agent, or MDNR stafi‘ person without access to census track information? Is this a portable process? In other words, can it be useful on a broad basis to gain insight to environmental problems (and environmental equity issues) occurring outside the Saginaw Bay watershed. 43 4. Adequate sample size is important to ensure statistical significance(Wilkinson, 1990.) There are 168 zipcodes in the watershed which is an adequate sample size for the given analysis. Using the zipcode level of analysis has, however, been criticized because it is considered too large to reflect true neighborhoods, thus it might not be detailed enough to capture the relationship between the environmental hazard and affected population (Burke, 1993, Openshaw, 1984). Proximity Proximity is not a surrogate for risk. Even though this study includes severity of environmental hazards wherever possible, a limitation exists because any of the hazards may occur on the border of the host zipcode and afi‘ect a neighboring zipcode as much or more than the host zipcode. This is a possibility whenever proximity-based assessments are conducted using census tract, minor civil division, zipcode, or county data. A way to avoid this limitation is to analyze a bufi‘er area around each site or hazard and then analyze the variables of interest within the buffer area (Glickrnan, 1994). Time The study was conducted using the most recent data available for the watershed, however, it must be stressed that it is only a static view of the watershed. Environmental data fi'om the MDNR was not available for past years, therefore longitudinal change and trend analysis is not possible using this analysis. However, this study could be used as a baseline for yearly comparisons and serve as an environmental “report car ”. CHAPTER THREE CASE STUDY DESCRIPTION This section will describe the physical, social, and industrial aspects of the Saginaw Bay watershed. A brief history of the area is included to provide perspective on the temporal dynamics which influence the environmental hazards. It will also describe why this area is an excellent case study, although the method can be applied to any watershed or ecosystem where data is available. Hopefully it can be used and improved upon by natural resource managers, community development practitioners, or planning officials. The Saginaw Bay watershed is by no means unique in its environmental contamination, but it does provide a good example. PHYSICAL DESCRIPTION The Saginaw Bay watershed is located in the east central portion of the lower peninsula of Michigan, as outlined in Figure 3.1. The land area drains into the Saginaw Bay and covers 22 counties. The watershed is the largest in Michigan comprising approximately 15 percent of the state’s land area totaling 8,709 square miles (MDNR, 1988) 45 Saginaw Bay Watershed Figure 3.1 Map of Saginaw Bay Watershed in Michigan SOCIO-ECONOMIC DESCRIPTION Population The population of each zipcode is shown in Figure 3.2 using data fi'om the 1990 US Bureau of the Census. The major urban centers of the watershed include: Flint, Saginaw, Bay City, Mt. Pleasant and Midland. The thumb area of the watershed (Huron county) is the least populated and the northwest region is also less densely populated. However, the northwest area is growing more rapidly due to increasing numbers of retired people settling there as well as increasing numbers of those attracted by recreational aspects of this area (MUCC, 1993). A summary of the statistics for population per zipcode, as well as the other variables analyzed in this study, are provided in Appendix E. The present average number of people per: zipcode is 8,637 for the watershed. Figure 3 .3 shows population growth trends over 50 years for the 22 county area as designated by agricultural, urban, and rural regions (MUCC, 1993). These trends are important for natural resource managers as well as city managers and planners because the majority of the population of the watershed reside in the urban areas. Minority Population Minority in this study refers to African-American, Hispanic, Asian, and Native American people. It is used in terms of percent of the population that is included in one of these categories as opposed to Caucasian or White. Figure 3 .4 shows the percent of the minority population per zipcode. The average percent minority per zipcode for the watershed is 4.5 percent, while the state average is 12.9 percent and the national average is close to 20 percent. Only six zipcodes in the 47 wsmcmo 9: mo :mmSm m3 80.8 h I 009% - 50.9 D one? - Eon I oooa - Sod a. econ v D 20.001 *0 .03 EDZ OZmOmZ a»... {Will-all \Ix \wtx r l t\ Dmmw oucoEN com sown—sac...— ”850m ed 2:9”. 48 Population Growth by Region in Saginaw Bay Watershed 1940 - 1990 Thousands 'u w. ill. 40 50 60 70 80 90 5 Recreation (7) MAgricultural (8) Urban/Industrial (7) ITotal (22) (#) = Number of Counties in Region Source: Institute for Public Policy and Social Research, Michigan State University Figure 3.3 49 mzmcmo 9: ac ammuzm m3 "monsom .m:mo:mE< m>:m2 ncm moEwaf .w_._m_w< .m_._m.o:oE<-:moE< means. 3:522. 5052150,“. 2.53.1..62 some.“ I $3.55 $8.0m- a... mm-m_e a. fix. m...» - O D .0 2.690.... 2 Eco 07$qu .......-..|\\ 82 otoonfi can. 5:252 5020.. dd 2:3 titers alien < apcod “3161 ml, lncc rf’ rr' 50 watershed have a minority population greater than the state average, 12.9 percent, but when compared to the watershed average (4. 5 percent) the number increases to 19 zipcodes out of 168 zipcodes in the watershed. Minority areas are primarily located in two regions around Flint and Saginaw. Each city contains one zipcode with a minority population greater than 75 percent, and another zipcode with a minority population between 50 and 75 percent. No other area in the watershed has this high of a concentration of a minority population and these areas will be analyzed filrther in the environmental equity case study. Income Average income per capita per zipcode in the watershed is shown in Figure 3.5. The overall average for the watershed is $13,178 per year with a range of $7,515 - $22,895. For comparison purposes, the state average is $15,698, while the national average is $16, 1 73 . Zipcode areas in the watershed with the highest income are around Midland, Lapeer, Grand Blanc, and the northern tip of the Brighton area that reaches into the southern part of the watershed. Most of the low income areas are located in rural areas with the exception of two zipcodes in Saginaw and one in Flint. Poverty Poverty is obviously related to income, however useful information is still obtained fi'om a map of the distribution of poverty. Figure 3.6 shows the percent of the p0pulation in poverty per zipcode. Any household of four persons earning less than $12,674 per year in 1990 was considered to be in poverty (for households of two and one person(s), the 51 n70? H .m>< .mj F H .021 Cwafoéc OOOdN A D 000....um - $56.. Cccd; - www.mr Ham. (‘1 03:96:. . 000;. we“ I P . PDQOF D I mnmzoo mmmr _.: 5mm D 2 m U m ._ 2.... // \ m . tar- .3/ ./ “xxx \\ //tlllk\ 96ch or: .0 33:6 m3 "mousom 8a.. accoEN no...— 05005 $0553“ can 9:0; 52 23:00 9.: mo :mmSm m3 "wouzom “an u.9<.®: mr H .021 £09522 s_oe A -- an‘s on . 5 D fit Om . K I e_ou- acne $ mr- 0 mm _-_o:u_jnom 1.50;. *0 Emoumn. _.: 2mm. OZmOmj .. a __ Kit: ..rrrl .., trivial/ta. .11.. k 0...... \. ommr evocEN com >to>om E cows—sach— +o Eocene— ©.m 059“. povef’t The 21'] aims rem sport: the 51 haul four. POD less the (1" 53 poverty thresholds are $8,076 and $6,310, respectively) (US Bureau of the Census, 1990). The zipcodes with the highest percentage of households in poverty are in Flint and Saginaw. The area with the next highest percentage of households in poverty is in the northwest region. This area is primarily rural with only seasonal income fiom hunting, sport fishing, and tourism. The thumb area is a heavily agricultural area and also has over ten zipcodes with 14 - 20 percent of the households considered in poverty. Figure 3.6 shows that while there are many zipcodes in the watershed that are above the state (13.0) and national (12.7) averages for percent poverty, the severe poverty is limited to the cities of Flint and Saginaw and more widespread but less severe poverty is found in the rural areas. In addition, the areas of severe poverty are also the most populated zipcodes in the watershed, although the areas of the northwest and thumb are less densely populated as described above in the section on population. The severe poverty found in areas of Flint and Saginaw (> 30 percent) is partly due to the manufacturing decline of the auto industry and its satellite companies in all of Michigan. The issue of manufacturing loss is well documented and its afi'ects on communities is devastating (Knox, 1994). The industrial-economic landscape of the watershed will be described next. INDUSTRIAL-ECONOMIC DESCRIPTION Historically, the Saginaw Bay watershed has shared the experience of a boom and bust cycle in economic sectors such as timber, fishing, mining, and more recently, in the auto industry. The vast potential of the area was described by De Tocqueville as early as 1831 when he visited the watershed and commented on the “richness” of the area which “'35 the NE the 54 was still covered with trees and wetlands. Schoolcrafl noted that “the Saginaw bay and the land of it’s tributaries is ripe for agricultural and other economic enterprises” (Schoolcraft, 1825) The logging and timber industry clear-cut the watershed, as well as the rest of Michigan, in the second halfof the nineteenth century. Bay City was first established in the early 1850’s as a logging town exporting logs and lumber throughout the midwest via the Great Lakes. The city of Saginaw also grew rich from the logging boom which ended around the early 1900’s. The last virgin stand of white pine in Michigan is located in the watershed at Hartwick Pines State Park. Having been cleared, the land was open for agricultural opportunities. The watershed had been underwater in recent geologic years, leaving sediments that make some of the richest top soil in the US. European-style farming started in the area even before the timber boom and continues on a large scale today. The watershed is the largest producer of white and navy beans in the country, and produces some of the highest corn yields in the US. (US Agricultural Census/Statistics, 1994). Mth the advent of industrial farming, agriculture’s effects on the land and water is receiving increasing attention. High fertilizer runoff and leaching, and the increased use of pesticides, herbicides and filngicides are causing alarm. However, there remains very little data on this type of nonpoint source pollution and these environmental hazards are not included in this study. The fishing industry also afi‘ected the watershed. Bay City and Saginaw were home to many fish processing plants during the period of 1900-1950. As the fishing industry in the Git me am; P6. people {Knot hfchl'g General oppom hlch'lg (than €01:an the ef ClOSu more 16,0i l0a (an ire.- 55 the Great Lakes declined in the 1950’s, other industries were quickly moving in, namely the auto industry. Perhaps more than any other industry, the automotive industry has brought more people to Michigan and the Saginaw Bay watershed in search of economic opportunity (Knox, 1994). Since Henry Ford chose Detroit as the site for his first auto plant, the Michigan economy has revolved around this fast growing industry. Afier World War II, General Motors, Ford, and Chrysler grew at astonishing rates creating jobs and opportunity. People living in economically depressed areas of the country soon flocked to Michigan, especially to the cities of Detroit, Flint, and Saginaw. The combination of increased energy prices, increased competition fiom abroad, (mainly Japan), and slow economic growth led to urban distress in many parts of the country. High manufacturing areas such as Detroit, Flint and Saginaw were hardest hit by the efl‘ects of “deindustrialization”. Symptoms included falling rates of profit, plant closures, rising unemployment, and intensified poverty. During the 1970’s Detroit lost more than 166,000 jobs or 30 percent of its 1970 employment base, and Flint lost nearly 16,000 jobs or 23 percent of its job base (Knox, 1994). The process of deindustrialization can cause a downward trend: unemployment leads to a shrinking local tax base and tax yield, which causes the deterioration of infrastructure (roads, buildings, homes), and results in a deteriorated environment and lower quality of life (Knox, 1994). Figure 3 .7 shows the relative decline of the population employed by the manufacturing industries for the major industrial areas of the watershed. The loss of 56 No 0.52”. 89.89 US$89.20 259.com w .508 .385 mango w: Hmenace 3258...... 2922...... 2E--- 26 >aml ommw ommw one 08.. 00mm or ON on 0v om 8 Stow 92303355. 5 mnemnod uo>anm do Econon. is heir Water Kath bring enhz Cor. cre; issu mat 57 manufacturing jobs was most likely replaced by service sector jobs or not replaced, in the case of Flint. Areas in the watershed that have escaped this decline are Midland, and Mt. Pleasant/Alma. These cities have maintained higher employment rates due to more diversified economies including chemical companies, universities and the petroleum industry. IMPORTANCE OF CASE STUDY This brief background of the watershed shows it is an area of heavy industry, manufacturing, agriculture, as well as recreation, sport fishing and hunting. In order to accommodate this variety of resource uses, comprehensive management of the watershed is necessary. EPA has targeted the watershed as a prime study area for pilot environmental management projects which address issues on land-use and resource sustainability. The watershed was selected as the nation’s first area to be designated under the EPA’S National Watershed Initiative Program (MDNR, 1992). The Initiative was developed to bring together local, state, and federal resources, with citizen input, to protect and enhance selected watersheds. In addition, the Saginaw Bay and River are an Area of Concern (AOC) as defined by the International Joint Commission. This Commission was created by a treaty between the US. and Canada in 1909 to better manage water quality issues within the Great Lakes (Michigan Legislative Service Bureau, 1992). It also mandated the individual states and provinces that surround the Great Lakes to implement pros: AOC mdus Thes hope: 58 programs to contfol trans-boundary pollution. Since the Saginaw Basin was designated an AOC, a wealth of information has been collected, primarily by the MDNR. This watershed that is biologically diverse yet maintains significant manufacturing, industrial, and recreational uses, has been well studied and is thus a prime study area. These local, state, national and international efi‘orts underscore the need for proper management of human use of natural resources. This study provides a tool which can hopefirlly aid in that efi‘ort. 59 CHAPTER FOUR RESULTS FROM ANALYSIS This chapter will present visual maps, descriptive statistics, correlations, and odds ratios for environmental hazards in the Saginaw Bay watershed and their relationship with socio-economic variables of the corresponding zipcodes. First, maps showing the distribution of the individual hazards such as hazardous waste management facilities or Act 307 sites will be presented followed by an aggregated hazard map. The aggregated hazards are termed “hotspots” and the correlation between these hotspots and the socio- economic data on the areas will be discussed. A comparison of the hotspot map and weighted hotspot map will also be presented. Raw data for each variable used in the statistical analysis are provided in Appendix F. VISUAL ANALYSIS The following pages show the distribution of environmental hazards throughout the watershed. Figures 4.1 A- D, show the criteria air pollutants (802, N02, CO, and VOC) per zipcode in permitted tons emitted per year. A Bay City zipcode stands out on SO; and NO; maps (A and B, respectively) due to the large coal burning electric power plant located near the mouth of the Saginaw River (MDNR Air Quality Division, 1994). High levels of permitted emissions generally are found in urban areas as would be expected because emitting facilities are generally located in urban industrial zones. Thus the urban 59220 3:35 :6. EEO—.2 ”conjom 009 A I can; . 2: I R: OF - Sea 02 D ENCEEF E Eco QZmOmj dam _. oucoEN acn— mcofiflfim Now 49% 2:9”. 6l 000? A I 89 - for I 0.9 - I U or - F I F v H memo 02 D Guarantee. c. Eco OZmOmfi C0635 3.630 :5. $292 "conjom Vamp swoon—N can. «CORE—em. N02 m}. use: 62 50655 >590 En $292 ”8.30m 009 A I com; . :H; I DO— - n; . Sea 02 D .m..w..r...m_._o.r _.__ Eco __ _ _. ’- r... w? a... a q DZmOmJ It'll: .t.t.. i W/K I...l\at\nn\.. .ll at.....mw.tl.ti.. Hark am... a- 5-.-... . gar evocafi non. mcfimfiEm Oo n14» Mimi 63 52220 >590 :< $292 "850m 009 A I QQOF - For I on: - 3 D 0.. - F I P v E Eng 02 D .wm>._m_._0H E 300 OZmomJ was: cvocnfi non. «223:5. 50>“ nuance—zoo 3.390 c==c> O _..V 050E areas of Saginaw, Bay City, and Midland stand out as areas emitting high levels of criteria air pollutants. An exception to this trend include large manufacturing facilities located in rural areas. Note the high level of permitted VOC emissions in Figure 4.1D in the northern section. This rural zipcode in Iosco county is the home of Bowlsby Oil which emits high levels of VOC’s (MDNR Air Quality Division, 1994). The source for the highest permitted CO emissions is found in map C. This zipcode in Vassar, Tuscola county, is home to Grede Foundries, the highest permitted emitter of carbon monoxide with over 3,000 tons of CO per year (MDNR Air Quality Division, 1994). Hazards originating on land include landfills. The distribution of inactive landfills per zipcode are shown in Figure 4.2. The highest concentration of inactive landfills are located in the northwest portion of the watershed. This rural area has abundant space I available and may be landfilling urban refilse as an additional source of income. Another factor could be that since population density in this area is low, small villages may have had their own landfills due to abundant space, thus contributing to the higher number of total landfills. Another factor may be the extent of rural industrial sites (mining, oil and gas) in these areas which may have had “private” landfills which are now in the inactive landfill database. The MDNR Waste Management Division does not record inactive landfill size and they do not verify each inactive landfill reported. The distribution of active Type H landfills is shown in Figure 4.3. As with inactive landfills, active landfills are predominately located in the northwest region of the 65 2063.0 :..m_..._oom:m_2 9mm}... $292 "8.30m (9 L0 DIIUII ___..o_._3 2.. 2.2.2.6.. m>_.uw_.._ .o 59.9.32 QZmOmfi 1--.- he... 39 ovconfi can. 2.5.5.. 95an no 52:52 We can: 66 watershed. This is probably due to the reasons mentioned above regarding inactive landfills. The distribution of LUST sites are Shown in Figure 4.4. These sites are mainly found in urban areas due to the higher number of gasoline stations located in cities. Therefore, Saginaw, Bay City, Midland, and Mt. Pleasant have higher concentrations of LUST sites. One interesting note is the absence of the high number of sites around Flint. Since Flint is the most populous area of the watershed and has many gas stations, the lack of LUST sites is noteworthy. One possible explanation for this phenomenon is that once a LUST site has been reported, the owner/operator of the site is required to initiate remedial activities within a specific timefi'ame. Ifan owner/operator is unable or unwilling to pay a non-refilndable deductible the leak will probably not be reported. Hazardous Waste Facilities in the watershed include incineration, storage, treatment, and disposal sites. The distribution of these sites in the watershed are shown in Figure 4.5. Saginaw, Flint and Midland areas account for about 60 percent of these facilities, while Flint alone maintains ll of 34 facilities or 32 percent. Act 307 sites are distributed in a higher concentration in the northwest portion of the watershed as shown in Figure 4.6. Cities in these areas include Mt. Pleasant, St. Louis, and Gladwin, however most of the Act 307 sites in these areas are due to oil and gas extraction and occur in unpopulated areas. Other more populated areas with a high concentration of Act 307 sites include Bay City, Saginaw, Midland and Howell. The sources for these sites vary and include chemical manufacturing plants, automobile 67 506.20 :._mc..mmm:m.>. 9mm} £205. “8.30m e A I a i a n. a I a a 2:35 o... D 22:36.. .0 .mnE:Z OZmOm... tr ..../ .\ a...“ l- a. a}. . o . a. . ., jthsit} .1...» .x.---lt.\ 39 season .9. 2.52.3 _. 2.3 2.84 no .8532 me use; 68 205.20 32039:. GOP A OO— - Fm Om - mm OF-.. «0.5 Hmfil 02 L0 C4 UIIDII norm .0 9.2mm OZmOmm tama— cwooafi 3.. 9.5... 06035 tcsonmhoaccb 9530.. _o.:oE_._0..>:m mZOS. ”00509 .mmzm HmDJ .0 69:5... 96 >Eo>mm mm.m.oa.oor: on:.m. .wr:u Véuufiai 69 50.220 EoEmmmcwE 9mm}; £20.). "mo.:om .mw:m .mwOQwi ncm .:..0E.mm: .mmmBB 60.5.05. 3:63 3.2%; oz _l].. mutm Emma... .o 59:52 OZmOm... - x... 3m r oucoEN .5... «Qt—Ea”. 3am; «savanna... a... use. 70 manufacturing and related industries, and gas stations (MDNR Environmental Response Division, 1994). Toxic Release Inventory (TRI) sites are shown in Figure 4.7. Areas with the highest amounts of reported toxic releases include Midland, Saginaw, Flint, Alma, Grand Blanc, and Cass areas. Individual companies can be responsible for the high rating which is the case for Midland (Dow Chemical), Grand Blanc (GMC Truck Plant), Cass (Copper Cable Company) and Alma (Total Petroleum Refinery) (EPA Toxic Release Inventory, 1992). The urban areas of Saginaw and Flint have a number of companies that contribute to their high rating. Water dischargers throughout the watershed are shown in Figure 4.8. Of the 250 permitted dischargers, only 20 are considered major, and most of these are publicly owned treatment work facilities. The total number of dischargers includes 127 wastewater treatment facilities and 87 industries that discharge directly to surface water in the Saginaw Bay watershed. Major industrial dischargers in the Saginaw Bay watershed include: 0 primary metals industries 0 petroleum refining 0 electronic manufacturing 0 sugar beet processing 0 chemical manufacturing 0 transportation equipment 0 power utility manufacturing 0 battery manufacturing (NIDNR 198 8) More populated areas require a greater number of municipal waste treatment facilities and thus the urban areas of Saginaw, Flint, Bay City and Mt. Pleasant have a higher water 71 20.2.20 mmcoammm 55995. £205. 693m dozm .o >..._m>ww Ucw 52:5... mmchc. 95mm. now .04. .35 35559.6 H DOM A. Gem . Pm: on: - Pm me - mm mm Eng 0: DIIUII‘ mm._w New .0 GEEK OZmOmJ .hthllslllr ./I a. . _.. .. x. a ... J a mi ....L.s.\.r.r...r M f. \J).atm.h..\l\\_ ./ x . he .. ../.. t.\\ unseen 8.. anew Em and on use... 72 {mm ”850m .mmédg A. .4H_oo.oo F deed P _.H_,_H__u_._u_ 7000; I o co . com -n__.u_n..n._o P i _H_ I ,u Wm“ coo; w. Ema 02 D Eucaoa _.: Ema QZmOmfi fl _ .1 3. ....y 3. .... _,_. awki 3U. R .../ “‘k \x ..»I1\ 39 OUOOEN .0...— auao 33:99:. ammo—om o_xo._. uwtoaom 5r 12%; 73 discharge rating as shown in Figure 4.8. Other higher rated areas include Midland, Howell, Corruna/Owasso due to population size and economic activity. The aggregate of the environmental hazards from the previous eleven maps are shown per square mile in Figure 4.9. The areas of Flint, Saginaw and Bay City stand out as having the highest concentration of hazards. This hazard index can be viewed as a “hotspot” map for the watershed. It will be used in subsequent analyses and in the case study to address the environmental equity issues. A comparison can be made between the weighted (Figure 4.9) and nonweighted (Figure 4.10) hotspot maps. The weighted map incorporates the EPA risk rankings as discussed earlier. The difi‘erences between the two maps were very minor; less than ten percent (14 of 168) of the zipcodes changed rank. None of the zipcodes changed more than one rank in the given legend. A difi‘erence map, Figure 4.11, shows the zipcodes that actually changed rank when the weighting method was applied to the data. The positive or negative sign indicates whether the change was an increase or decrease in rank due to the weights. EPA rankings used in this methodology were not intended to be weighting coeflicients but were useful to compare the “relative risk” posed by difi‘erent hazards. The results indicate that mapping hazards according to varying degrees of “relative risk” is possible, and as more detailed data is available, the accuracy and utility of this methodology will improve. 74 50635 3.630 653 $29.2 ”850m n A I .szama n - m I mmamsomfi .0 $90 .35 m - v U % 59:5... $320.... game m - w I F l goo 02 D maawfiowo *0 0:23“. OZmOmfl gar ocean—N hem meouhasoma $53 $05.50.. mfi 059“. 75 man- $-me 8-2U 9-9- m._. wwmo 02 D mmtw *0 02:3... OZmOmg .m__E Sauce. .8 989% .95mEco:>cm C .o Esm m m. 9.29 .mEm 39 has. «830:. awed «ED 3m mud-«Na: Eucmficohgcm ho Oaxaca—was men 2%: 76 xCom tomwoaoE I «Emu UMmmmeooO E 005050 02 D 39208005. 9.1 3.305. {mm Emir...» 0:2 Com QmS. .onmuoI ncm occEwm VEE 0.5 Income High Income Low Income > 12,000 < 12,000 Minority Low Minority High Minority < 5.0 > 5.0 Poverty Low Poverty High Poverty < 21% > 21% Population Low Population High Population < 10,000 > 10,000 The following tables are the output for the logit estimation of the variables for income (Table 4.3) and minority (Table 4.4). Table 4.5 is a logit analysis including both variables. Table 4.3 Logit Table for Income. Independent Estimated Standard Error t-statistic Variable Coefficient (Beta) Constant -1.046 0.228 4588 Income -1.487 0.517 -2.874 Table 4.4 Logit Table for Minority. Independent Estimated Standard Error t-statistic Variable Coefficient (Beta) Constant -2.048 0.257 -7.957 Minority 3.077 0.581 5.297 Table 4.5 Logit Table for Income and Minority. Independent Estimated Standard Error t-statistic Variable Coefficient (Beta) Constant -l .575 0.279 -5.651 Income -1.790 0.627 -2.856 Minority 3.320 0.662 5.017 Compared to Tables 4.3 and 4.4, Table 4.5 shows that the estimated coefficients for income and minority did not change significantly when both variables were included together in the analysis. Since there was no significant change, the estimated coeficients fi'om Table 4.5 are used to calculate the final odds ratio. The final odds ratio is calculated by taking the inverse log (e‘) of the estimated coeficient, where x is the estimated coemcient from the logit analysis. Thus, the odds ratios for the variables of interest follow: Income = 0.167 Minority = 27.66 The odds ratio for income, 0.167, means that low income zipcodes have about two tenths the possibility of having a high hotspot score compared to high income zipcodes. Therefore, this calculation shows that zipcodes with high incomes are more likely to have high hotspot scores compared to low income zipcodes. This is most likely because there are many rural low income areas that also have a low number of hotspots. The final odds ratio calculation for minorities is even more noteworthy; minority zipcodes are over 27 times as likely to have high hotspot ratings than non-minority zipcodes. 81 Frequently, more populous areas will also have greater minority populations. To ensure that the strong relationship between minority and hotspot was not being influenced by population size, the variable population was added to the analysis. Using logit analysis, a significant change in the estimated coeflicient (beta) of the independent variables would signal a strong influence of the population size on hotspot scores. Table 4.6 shows that the estimated coeficients for income and minority did not differ significantly when the variable for population was added (-1 .790 to -2.02 for income, and 3.32 to 3.936 for minority). Table 4.6 Logit Table For Income, Minority, and Population. Independent Estimated Standard Error t-statistic Variable Coefficient (Beta) Constant -1.396 0.308 4.535 Income -2.015 0.737 -2.735 Minority 3.936 0.922 4.270 Population -0.93 5 0.820 -1. 139 This analysis, which included the afl‘ect of population (“controlled for population” in statistical terms) reinforces the finding that minorities are the most likely to bear the burden of environmental hazards in the Saginaw Bay watershed. Qrgngbulatigns The cross-tabulation of minority percentage by hotspot score per zipcode is shown in Table 4.7. The numbers in the top of each box are frequencies of each variable and the lower figures are the percent of the total. Row and column totals are presented at the end of each respective row or column. “0” and “1” represent the dichotomous groupings shown in Table 4.2. Ta“: non nor the 1101 fin I I 82 Table 4.7 Cross-tabulations of Minority by Hotspot. Hotspot Count 0 1 Row Column % (Low Hotspot) (High Hotspot) Total 0 132 17 149 Minority (Low Minority) 96.4 54.8 88.7 1 5 14 19 Qfigh Minority) 3.6 45.2 11.3 Column 137 31 168 Total 81.5 18.5 100.0 Reading Table 4.7 from left to right starting on the top row shows 132 non-minority, non-hotspot counts or observations. This number drops to only 17 observations for the non-minority, high hotspot areas. However, when observing high minority areas seen in the second row, the number of low hotspot counts is 5 and jumps to 14 for the high hotspot areas. Given these observations, which are used to calculate the odds ratios, the findings become more explicit. The cross-tabulation of income by hotspot is shown in Table 4.8. This table shows Table 4.8. Cross-tabulations of Income by Hotspot. Hotspot Count 0 1 Row Column % (Low Hotspot) @gh Hotspot) Total 0 74 26 100 Income (High Income) 54.0 83.9 59.5 1 63 5 68 (Low Income) 46.0 16.1 40.5 Column 137 31 168 Total 81.5 18.5 100.0 that of 100 high income zipcodes (shown in the top row), 74 have low hotspot scores and 26 have high hotspot scores. A similar drop in fi'equency is seen in low income zipcodes only haze low phe tob Vaf 83 watershed have a lower proportion of hotspots than higher income areas (7.3 percent [5/68] versus 26 percent [26/ 100]). The results of the odds ratios and the illustrations of the cross-tabulations can now be compared to the original hypothesis: 1. Minority areas are more likely to contain environmental hazards compared to non-minority areas. 2. Low income areas are more likely to contain environmental hazards compared to high income areas. As indicated by the statistical analysis, the observed data for the Saginaw Bay watershed , only supports a positive relationship between minority populations and high environmental hazards, the first hypothesis. The data does not support the second hypothesis that low income areas are more likely to contain environmental hazards. This phenomenon is due to the high number of rural, poor areas in the watershed that tend not to be as polluted as urban areas. Eight other cross-tabulations of the socio-economic variables and environmental variables are provided in Appendix G. WEI B\ pu Cl“ prc C0 CHAPTER FIVE SUMMARY AND CONCLUSIONS The Saginaw Bay watershed has sustained human use for over 200 years. Presently, it is home to over one million people (MUCC, 1993). The watershed also contains agriculturally productive land, major manufacturing facilities, and large recreation areas. These uses have taken a toll on the watershed, which is being stressed by numerous factors. Some of these stresses, such as point source pollution, can be documented, while data on others is still lacking. To obtain an overview of environmental hazards in the watershed using an ecosystem approach, this study attempted to provide a methodology as well as useful information for local planners, natural resource managers, and the general public. As more information is shared, and all stakeholders are educated about potential environmental hazards, communities can become empowered to solve environmental problems which they face. The three main objectives of this study are reviewed and the associated findings and conclusions are discussed below. Policy issues relating to the findings of this case study and recommendations for further study are also included. OBJECTIVES 1. Creation of an environmental hazard index by aggregating environmental hazards in the Saginaw Bay watershed. The hazard index was calculated which incorporates eleven difl‘erent environmental databases for the Saginaw Bay watershed. 85 The hazard index would be more useful if it included non-point source data (urban runofi‘ and agricultural runofi‘ to surface water), however, as mentioned earlier, non- point source data is not collected nor available at this time. As data on non-point sources and other sources of environmental hazards are collected, they could be added to the hazard index without major recalculations. The hazard index also provides a good base of environmental information, which can be used for comparative purposes in the future. The index was used to achieve the second objective. . Generate individual and comprehensive environmental hazard or hotspot maps. The hotspot maps were shown in Figures 4.1 through 4.10. A weighted hotspot map was also generated using a weighting scheme based on EPA guidelines. This map was compared to a nonweighted map to observe changes in the spatial context of environmental hazards. A difi‘erence map was generated to show which zipcodes changed rankings due to the weighting process and is shown in Figure 4.11. The hotspot and individual hazard maps provide visual analysis of the distribution of hazards in the watershed. Using GIS, this methodology allows the public as well as policy analysts and decision makers to quickly observe large amounts of environmental data. As individual states prepare comparative risk rankings, as Michigan has done, this methodology can provide visual assessment of the rankings, as well as a comparison for difl‘erent rankings that are proposed. A limitation of using this methodology for risk comparison is that for many environmental problems data has not been collected or is not in a form “$611.11 to the public. b) WE t1 tl 3. Provide a methodology for exploring environmental equity issues. The case study addressed this objective by exploring the distribution of environmental hazards and socio-demographic variables in the Saginaw Bay watershed. The hypotheses stated that low income and minority areas in the watershed would have higher hotspot scores (using the index from # l). The results from the statistical analysis show that: o minority areas are 27 times more likely to contain environmental hazards than non-minority areas, while 0 low income areas actually have a lower chance of having a high hotspot score compared to higher income areas. This latter finding is primarily due to the large number of low income and low hotspot scores of rural agricultural and recreation areas in the watershed. The results of the analysis presented in this study apply only to the Saginaw Bay watershed, however the methodology is applicable to any watershed or other area of interest. This study observed the static conditions of 1994 (1990 for the social variables), and therefore, the issues of siting industrial facilities, which generate many of the hazards, in areas of high minority populations and low income are not addressed by this study. Siting issues need to address the temporal aspects of siting facilities as well as the social-economic demographics of the area before and after development of such facilities (Glickman, 1994). 87 POLICY ISSUES RELATED TO THE CASE STUDY The results of the analysis are striking. Areas of the watershed with high minority populations are definitely bearing the burden of environmental hazards. The methodology developed in this study has highlighted an area of concern that needs further analysis. The obvious question is: How has this situation come about?. Did minorities move to areas in search of economic opportunity and thus settle near heavy industry? Did industry locate in the cheapest land available?, or in areas with the least opposition? Have these hazards been permitted by the state and thus reflect an institutional bias? These types of questions need to be addressed in order to determine the underlying reasons for the present situation, and more importantly, to remedy the inequitable distribution of environmental hazards in the watershed. The following list discusses how this methodology could be used toward that end. 1. This methodology could be used to guide future industrial siting decisions. As new hazardous facilities are developed or constructed, the siting decision should incorporate the present distribution of hazards to obtain an equitable outcome. 2. This method could be used as a guide to allocate money for environmental clean up of contaminated sites to make sure fiands are equitably distributed. It could also be applied to areas that have already received funding to ensure that an equitable allocation occurred. 3. This method could be used in developing land-use master plans by city or county planners and managers. These people are responsible for designating industrial, commercial and residential zones and could use the method to ensure that heavy dis 88 industrial zones (hazardous facilities) are not solely in areas of high minority populations. 4. The findings of this study indicate that if areas with the highest hazard scores are cleaned first, by default, areas with the highest minority populations will be largely impacted by the cleanup. This strategy requires that environmental cleanup remains separate from political interests. FURTHER RESEARCH Specific ideas for further research related to environmental equity issues have been discussed in the previous section, as well as in the section discussing the limitations of this study. Another area for further research would be to use the information fi'om this study and incorporate or overlay health data with the environmental and social data. Health statistics on morbity, mortality, life expectancy, cancer rates, and lead poisioning are some of the datasets available that could generate many researchable questions. Another more general area for further research includes tracking chemical usage in ecosystems and the lack of infrastructure presentaly available to achieve this goal. Chemical usage in all aspects of manufacturing, commerce and food production is increasing. There are over 54,000 chemicals which people are exposed to by commerce, pesticides, foods, drugs, and cosmetics, as well as between 500-2000 new chemicals entering the market each year (Cutter, 1994). The National Research Council (1984) found that only 18% of the drugs or substances in drugs 10% of pesticides 5% of the food additives virtually none of the chemical used in commerce 89 have been tested sufliciently to allow an adequate assessment of their hazard (Greenberg, 1994). Future studies, research, database design, and data collection by government agencies should address the efl‘ect of the quantity and types of chemicals being introduced into ecosystems. The methodology proposed in this study takes a small step in the direction of looking at the aggregate afi‘ect of hazards on humans and the environment, and the issue of equity as an integral part of environmental decisionmaking, the challenge remains to keep up with human “progress”. Appendix A Summary Table of Data Sets Table A Summary Table of Data Sets Used In Study. .uwa -zv..-.~-.-N-.. 57-31:“! '?:_VM . . . “‘6'- If“? _.v.\ v fee; a .. “'3‘???" .c “we- v \‘ I A ‘~ ea“ 3:“ 1; ”WM“ . - . -..... . . . .: I¢C$~W¥W$€§rmm\ flit-M23: - ... Hazardous State Facility Name, EPA ID number, F eb., Hard Copy DNR Waste Waste Mgnt facility type, location, city, permit 1994 only (8 Management Facilities status (federal and state), closure pages) Division status, off-site waste acceptor status, district Lmdfills State Facility name, applicant, owner, March, 1.36 Mb DNR Waste Type 1] Sanitary location, liner data, facility type, 1994 (2 diskets) Management (Active and size, closure status, permit dates. Division Inactive Sites) Wanda Neal (517) 355-4034 Water Pollution Saginaw NPDES number, facility name, April, 601 KB DNR Surface Bay standard industrial code, county 1994 Water Quality Water- name, receiving river basin, Division shed latitude, longitude, contact name, Patty Brant address, city, state, zip, discharge (517) 373-4710 parameter, concentration average and maximum limit, quantity average and maximum limit, quantity unit code. Air Pollution Saginaw Facility name, street, city, zip, lst 2.3 MB DNR Air Quality Bay S02, NOx, Particulates, Volatile Quarter Division Water- Organics, all in tons per year, 1994 James Stewart shed UTM segment level when (517) 373-7054 available. Act 307 Sites Saginaw Site name, SAM score, latitude, 1994 DNR Bay longitude, county, city, zip, source, Environmental Water- pollutant l-3, township, range, Response shed section quarter, status Division Toxic Release SBW TRIS ID number, facility name, 1994 1.08 MB DNR/EPA Inventory Report address, city, county, zip, reported Kent Kanagy latitude and longitude, centroid lat Environmental and long, chemical name, CAS Response number, fugitive (non-point Division source) air releases, stack air (517) 337-8481 release, total air release, water releases, tmderground injection releases, land releases, POTW transfers, other off-site transfers, SIC codes 1-3. Table A (con’t). 91 LUST sites State Name, ID #, address, city, village, 1994 294 KB DNR (Leaking township, zip, county, priority Fred Sellars Undergrotmd status, corrective action status, Environmental Storage Tanks) clean type, funding info., Response Division remediation status (soil, dissolved (517) 337-4425 product etc.) Census Data: State 200 Attributes 1990 Compressed: US Census Bureau Population and 2 MB (STF 3B - Zipcode Housing Level) Appendix B Data Tables of Ranking, Number and Percent Per Zipcode, mm a m N. N 11K. Tut 92 DATA ON MAP RANKING Table 8.1 SO; Distribution (Penman? Percent _ __:f if31;?{91'1lat???%:“:j?ji}fif’i‘fj‘fiiiégif? ’3173523???:i‘iéTffi‘égfiféiiig Tom No Data 25.6 <1 52.9 1-10 11.6 11-100 5.2 101 - 1000 4.1 > 1000 0.45th— 0.6 * Corrected for “No Data” values. Table 8.2 NO; Distribution N02 per Zipcode (TonsBN9 Summer! Amount of .; Number of Zipcodes in Percent H,,.,,,,.,,,_: I33§f§atg2175 ' V {3}}: 3:1." Range 5‘: Corrected Percent * m: No Data A -9999 > ' w 33' 119:5 <1 56 33.1 41.2 1-10 42 24.9 30.9 11-100 24 14.2 17.6 101-1000 12 7.1 8.8 > 1000 Ut-thI-I 2 1.2 1.5 ‘ Corrected for “No Data” values. I F hirlhiLlllLllLC 3 Ta Table B.3 VOC Distribution 93 summed" ~ mm of i EEG-W1: _) “ —. * g; '1 * R 'i Map" W": 7 ' Number of A ZipcodSin. .. ’ Percent - of w I T061117 f -< :=:-:;,::.__,x_p, -~ - NoData -9999 29 17.2 <1 1 49 29.0 35 1-10 37 21.9 26.4 11 - 100 38 22.5 27 101-1000 15 8.9 10.7 > 1000 2 3 4 5 0.6 0.7 * Corrected for “No Data” values. Table 8.4 CO Distribution . . 1,5 7 2",de in .9999 e 41 ‘ 24.2 1 81 47.9 63.3 35 20.7 27.3 7 4.1 5.5 101 ~ 1000 4 2.4 3.1 > 1000 (II-#0319 l 0.6 0.8 ‘ Corrected for “No Data” values. Table B.5 Act 307 Site Distribution 94 Summed i _ Zipcode SAM Scores per Rankin: ' Zipcodesin * «Person 4 i of A, 1 Total _ ,j _. .. Corrected ' . tosses-sf '- NoData —9999 47 27.6 <40 1 25.9 35.8 41-100 39 22.9 31.7 101-200 18 10.6 14.6 201 - 350 14 8.2 11.4 > 350 2 3 4 5 4.7 6.5 * Corrected for “No Data” values. Table B.6 TRI Distribution (1b) SummedReportedl Map f ' Number of '- erpcodes in Range “#96th '1 : , . 3. i_ Total __. Corrected Percent“ No Data 123 72.3 1-1,000 6 3.5 12.8 1,001 - 10,000 5 2.9 10.6 10,001- 100,000 15 8.8 31.9 100,001 - 500,000 15 8.8 31.9 > 500,000 9999 1 2 3 4 5 6 3.5 12.7 " Corrected for “No Data” values. 95 Table 3.7 LUST Distribution Sumo oddeosMo No Data -9999 21 12.3 1 - 10 l 62 36.5 41.6 11-25 26-50 51-100 UI-kluJN N \O p—o >1 p—o .—n .‘0 LII >100 * Corrected for “No Data” values. Table 3.8 Landfill Distribution ‘0. q ‘ 0 “.126 “714.0, 14.7 56.8 10 5.9 22.7 7 4.1 15.9 0.6 2.3 1 0.6 2.3 p—I .— N M kWN 0.wa y—o > 4 * Corrected for “No Data” values. Table B.9 Inactive Landfill Distribution Nmnberoflnactrve Map . Numberof ‘i'ii-Pel'cellitfl" . A _ WW’ Ranldng 59‘0““ . - 9‘ Perm" o f o 41 f 24.1 1 1 53 31.2 41.0 2 37 21.8 28.7 3 - 4 23 13.5 17.8 2 3 5 - 6 4 13 7.6 10.0 > 6 5 3 1.8 2.3 ‘ Corrected for “No Data” values. Table B. 10 Hamdous Waste Site Distribution Numb” Mao Number of Peres-so? r Corrected???” Facihtresper Ranlnng . ercodesm of Percent‘EI-if 2|de 1' TOW 88.0 o 0 12 7.1 60.0 2.4 20.0 1 2 4 3 3 1.8 15.0 4 o MhWNv—I 5 * Corrected for “No Data” values. 97 Table B. 11 Water Discharge Permit Distribution Number of Map Number of Percent Corrected . Discharge Permits Ranking Zipcodes in of Percent * per Zipcode Range Total 0 0 67 38.9 _ 1 l 49 28.5 46.7 2 - 3 2 31 18.0 29.5 4 - 5 3 17 9.9 16.2 6 - 9 4 4 2.3 3.8 > 9 5 4 2.3 3.8 * Corrected for “No Data” values. Table B. 12 Hotspot Score Per UnitArea Distribution Summed Score per Map Number of Percent Corrected Sq. Mile Ranking Zipcodes in of Percent * per Zip code Range Total 0 0 5 3.0 _ 0.001 - 0.199 1 60 35.7 36.8 0.2 - 0.399 2 50 29.7 30.6 0.4 - 0.99 3 26 15.5 16.0 l.0-5.0 4 20 11.9 12.3 > 5.0 5 7 4.2 4.3 * Corrected for “No Data” values. 98 Table B. 13 Weighted Hotspot Data per Unit Area Distribution gr'i'Smediw—dfiht—‘td sore 1”st sozoooe ;~ Number of I 74me i‘Percent [Total 5 i : Corrected ' Percent" 0 5 3.0 < 0.409 67 35.7 36.8 0.41 - 0.89 50 29.7 30.6 0.9 - 1.76 26 15.5 16.0 1.761 - 3.99 20 11.9 12.3 > 4.0 £11me 7 4.2 4.3 * Corrected for “No Data” values. Appendix C Table of Zicode Areas in Square Miles, and Hotspot Scores Per Area 99 Table from Oracle onipcode, Hotspotseore,AreainSquaremiles and hotspot/squaremiles. Table C Table of Zipcode Areas Hotspot Areain am Score Sq. Miles nggpot/Area 48348 0 33.9028934 0 48436 0 29.1322356 0 48465 0 45.4833787 0 48807 0 23.9769582 0 48760 0 26.8591596 0 48652 3 160.792233 .018657618 48632 9 261.828778 .034373609 48747 4 109.278861 .036603603 48350 1 26.2144584 038146888 48754 3 780153629 .038453965 48889 3 72.2868494 041501325 48614 2 43.455204 . 046024407 48417 3 630518616 04757988 49665 14 293.768517 04765657 48635 3 57.8625653 051846993 48656 6 115.283875 05204544 48883 15 253.157274 059251704 48624 20 322.835361 061951082 48765 6 96.4963459 06217852 48866 5 70.3000334 071123722 48649 5 70.2953846 071128425 48662 5 676687378 073889364 48353 2 26.5819757 075238952 48414 8 100.164599 079868538 48654 12 144.959306 082781853 48623 9 106.959877 084143702 48806 8 88.4212056 090476034 48767 6 63.1096378 095072642 49342 4 41.4398774 096525382 48428 3 30.8496053 097245977 48613 3 30.320472 098943051 48756 11 109.333087 .100609983 48612 17 166903751 .10185511 48743 2 19.4622527 .102763027 48475 11 106803902 .102992491 48470 6 57.5394893 .104276212 48435 4 37.157016 .107651271 48831 5 6.4402671 .107665186 48770 8 3.981153 .108135649 48416 13 20.178681 .108172264 48610 8 2.5825381 .110219348 48733 9 1.190175 .110850851 48456 7 1.858499 .113161491 49340 13 13.628578 .114407839 49310 10 5.8599165 .116468783 48427 20 69.921552 .117701373 48847 13 09.278861 .118961708 48432 48766 48891 48625 49332 48661 48467 48659 48460 48748 48741 48421 48739 48472 48836 48726 48418 48455 48628 49305 4873 1 48618 48730 48750 48651 48727 48877 48415 48473 48453 48817 48746 48843 48657 48461 48650 48438 48720 48744 48723 48430 48642 48832 48880 48442 48413 48449 48462 48423 48446 484 12 48701 Table C (eon’t) 23 31 10 13 15 12 11 14 25 10 12 21 16 21 11 10 10 17 15 14 30 15 24 12 23 18 11 18 19 23 10 15 30 15 11 9.6817287 2.8251425 3.1643058 82.139722 2.4693826 37.064414 4.9070629 6.7765594 3.6769518 08.706478 7.1666493 2.2160617 3.4794126 6.420968 7.1280523 71.59869 7.3814931 7.0598448 6.2679976 5.271849 5.3198139 29.261837 7.7961005 28.239675 6.257281 0.0392321 1.883814 8.7598773 7.2302162 6.9998515 4.466069 7.5519992 65.77949 8.7545427 0.2108258 27.512316 5.8358395 0.639613 0.7093032 15.481034 9.4075998 3.6398824 4.3723184 5.7573122 9.0080714 06.125103 8.0978455 4.880523 6.1279382 31.748652 5.8499338 8.0017148 100 .120768744 .121857811 .123010803 .126276683 .128062735 .130766147 .13349876 .134330049 .137372224 .137986256 .139941734 .142126 .143748017 .143939553 .144139614 .145688758 .147737007 .149120536 .152276548 .154621474 .159320627 .162460943 .163605705 .163755873 .16601949 .166448995 .168296138 .170184154 .174732871 .175258 .177586103 .180524037 .180963278 .184598183 .187007176 .188217113 .195335175 .196852268 .19766328 .199166903 .201325167 .205071292 .205150775 .209894638 .213463787 .21672535 .221020784 .222813803 .226833021 .227706315 .227790662 .22915848 Table C (eon’t) 48420 48749 48858 48433 48451 48658 48622 48634 48463 48655 48878 48768 48729 48637 48818 48620 48445 48617 48763 48725 48886 48626 48116 48867 48458 48603 48615 48371 48601 48471 48426 48429 48457 48735 48734 48706 48640 48755 48611 48829 48757 48439 48631 48759 48616 48356 48532 48604 48464 48801 48703 48357 30 13 10 19 17 12 22 10 31 14 10 25 18 11 15 20 27 31 16 25 11 16 45 21 13 14 19 37 45 15 18 19 13 33 16 23 20 10 10 21 15 28 17 10 16 7.9251206 9.4551712 EBL95454 3.6147976 L0211266 130087062 ‘10074662 6.1767363 117650456 L0195859 6.7373573 12.576813 111049981 9.7928912 7.0909382 .38140764 3.239129 1.192827 7.8738035 5.0235725 7.2816531 2.319816 82.4055898 L2208752 5.6032023 9.6633038 9.9117458 2.2320111 117.445174 4.033227 7.8238143 32.5112237 3.158605 0.9083805 2.0820639 8.0651518 91.8559075 0.1349599 5.1393619 (i5863805 4L509018 9.5894915 8.8234367 39.1982885 2.2665626 5.4026319 4L9963486 9.1497045 20.739913 8.6133977 0.3655941 L8228118 101 .235553501 .237649272 .240087315 .242470374 .243776825 .253303929 .253703072 .259871116 .266547841 .27153928 .272202487 .275367538 .276166834 .281164633 .295301697 .295734826 .300850242 .307908973 .311021549 .314074186 .317247791 .320925209 .327647676 .339834494 .350852554 .358868997 .367748512 .378859533 .383157506 .388649747 .392732996 .39986191 .422213179 .430449408 .451498768 .473963082 .489897724 .497760742 .512246069 .519318931 .530417007 .553788917 .555103826 .586760313 .619836709 .649239692 .666828989 .720418967 .723243151 .725136913 .834741178 .845822482 4884 1 48722 48708 48509 485 19 48507 48504 48893 48732 48506 48505 48529 4963 1 48503 48602 48607 48502 Table C (eon’t) 5 20 27 10 8 30 19 8 30 29 26 ll 21 24 28 9 13 168 rows selected. .95681069 17.8938552 4.1 180758 .84185848 .93633252 3.4181724 4.7998042 .96373882 1.0686564 9.4844515 1.469485 .83453399 . 12980016 .59484886 . 10209712 98376759 .47967738 102 1.00871312 1.11770213 1.11949229 1.13098395 1.15334724 1.28105641 1.28380076 1.34144037 1.42391614 1.48836625 2.26688469 2.27529686 2.30015988 2.79237022 3.45589538 9.14850224 27.101549 Appendix D MERR and EPA Ranking Tables 103 Table D MERR and EPA Ranking Table. EPA Region V Michigan's RRAP Ecological Risk Human Health Risk High High High High Absence of Land Use Planning Accidental Chernlcal Releases Accidental Chemical Releases Degradation of Urban CO2 and Global Warming indoor Ar Polutants Erwlronmenis Hazardous/Toxic Air Pollutants . hdoor Radon Energy Production and Nonpoint Source Dlsdiarges to Mmicipal Wastewater Commotion: Practices and ' Sutace Waters Dischcrges Consequences Physical Degradation of Stratospheric Ozone Depletion Global Climate Change Terrestrial Ecosystems Lack of Environmental Physical D radation of Water Awareness and We nds Habitat Stratospheric Ozone Depletion Stratospheric Ozone Depletion . High .. Medium High Modum l-iigh Alteration of Surface Water and Abandoned/Supermnd Sites Hazardous/Toxic Air Polutants Groundwater Hydrology industrial Wastewater Discharges Lead Atmospheric Transport and Ozone and Carbon Monoxide - Nonpoint Sauce Discharges Deposition of Air Tones Pesticides Ozone'and Cuban Monoadde Biodiverslty/Habitat Modification Sulfur and Nitrogen Oxides Pesticides indoor Pollutants . Radiation Other than Radon Nonpoint Source Discharges to sun: and Nitrogen Oxides Sutace Water and Groundwater Trace Metals in the Ecosystem Medium High Medium Low Medium Low Contaminated Sites RCRA Hazardous Waste Abandoned/Stperlund Sites Contaminated Surface Water Storage Tanks Aggregated Drinking Water Sediments Aggregated Groundwater Generation and Disposal at A'rborne Lead Hazardous Waste lndustrid Said Waste Sites Generation and Disposal of Industrial Wastewater Discharges High-level Radioactive Waste Municipal Wastewater Generation and Disposal of Dischages Low-level Radioactive Waste Parlicdate Matter Generation and Disposal of PCB Worker Exposure—TSCA Wmsi ipal and industrial Solid Storage Tanks 0 e Photochemical Smog Point Sauce Discharges to Surtace Water and Groundwater Medium Low low Accidental Releases and industrial Solid Waste Sites Municipal Sold Waste Sites Responses Municipal Solid Waste Sites Physical Degradation of Acid Deposition . Terrestrid Ecosystems Criteria and Related Air RCRA Hazadous Waste Pollutants Electromagnetic Field Effects Appendix E Summary Statistics For All Variables 104 Table E Summary Statistics Total Number of Observations: 168 Variable Minimgm Maximum Mean Standard Deva“ tign N02 0 14972.940 120.909 1 159.670 S02 0 33815.858 218.515 2608.942 VOC 0 3278.257 55.082 270.841 CO 0 3148.52 29.540 250.451 Act 307 Sites 0 597.000 81.244 115.501 Haz Waste 0 5.000 0.202 0.662 TRI 0 2983842000 70139.429 288214.006 LUST 0 310.000 25.702 43.195 Inactive LF 0 10.000 1.792 1.791 Active LF 0 7.000 0.458 0.972 Water Discharg. 0 14.000 1.631 2.366 Population 237.000 55547.000 8637.375 10609.725 Income 7515.000 22895.000 13177.702 3035.650 Poverty 2.400 53.300 14.098 7.123 Minority 0.3 88.4 4.496 11.989 Hotspot 0 27.102 0.569 2.225 Weighted 0 77.135 1.605 6.333 Hog). Appendix F Raw Data For All Variables 105 Table F Raw Data For All Variables ZIP ACT307 N02 CO VOC $02 HAZ TRI LUST OLD_LF 48116 363 142.637 1.776 327.757 1.903 0 111286 30 1 48348 0 0.000 0.000 0.000 0.000 0 0 0 0 48350 20 0.000 0.000 0.000 0.000 0 0 0 0 48353 27 0.000 0.000 0.000 0.000 0 0 1 0 48356 124 0.547 0.109 27.277 0.003 0 0 4 0 48357 22 0.025 0.005 11.853 0.000 0 23499 9 0 48371 0 456.636 0.120 22.438 1.958 1 57955 22 0 48412 63 325.840 0.570 3.674 351.443 0 0 1 1 48413 141 3.643 0.774 89.658 4.454 0 0 33 4 48414 33 0.133 0.000 34.265 0.001 0 0 l 1 48415 19 0.000 0.000 6.218 0.000 0 0 21 2 48416 0 1.780 0.406 12.118 0.539 0 0 7 3 48417 0 0.000 0.000 0.000 0.000 0 0 ll 0 48418 27 0.842 0.209 0.038 0.903 0 0 1 0 48420 75 0.052 0.033 9.243 0.378 0 0 28 4 48421 336 0.000 0.000 0.000 0.000 0 0 1 1 48423 22 0.649 0.142 6.066 1.513 0 750 43 1 48426 0 0.235 0.114 0.036 0.002 0 0 3 2 48427 49 29.682 0.814 48.928 1.515 0 183331 13 2 48428 50 0.000 0.000 0.000 0.000 0 0 5 0 48429 69 1.362 0.127 1.028 0.381 0 0 l9 0 48430 0 15.176 2.598 87.339 0.137 0 11821 38 2 48432 0 0.004 0.037 0.028 0.005 0 0 0 2 48433 39 2.865 1.156 1.529 0.153 0 0 19 0 48435 0 0.020 0.005 0.000 0.045 0 0 0 1 48436 0 0.000 0.000 0.000 0.000 0 0 0 0 48438 0 5.977 1.215 0.332 0.040 0 0 6 0 48439 145 111.050 29.531 482.479 165.297 1 105319624 3 48442 153 42.691 0.405 33.263 0.093 0 0 50 4 48445 0 2.444 0.188 1.001 0.367 0 0 9 2 48446 189 130.038 14.706 136.147 0.813 1 170270 88 3 48449 29 0.070 0.000 0.001 0.000 0 0 0 0 48451 0 4.782 1.041 0.299 0.328 0 0 9 0 48453 14 4.669 0.644 26.026 0.142 0 15400 37 2 48455 75 0.113 0.375 0.338 0.094 0 0 3 1 48456 0 0.066 0.021 0.282 0.001 0 0 10 2 48457 62 0.439 0.797 0.530 0.703 0 0 9 3 48458 24 29.048 5.599 106.280 0.470 0 0 18 1 48460 0 0.195 0.039 0.011 0.002 0 0 3 0 48461 24 0.477 0.108 70.572 335.587 0 0 10 2 48462 43 0.007 0.006 0.129 0.010 0 0 28 1 48463 419 0.000 0.000 0.011 0.000 0 0 2 2 48464 211 2.724 0.674 9.452 76.135 0 399 4 1 48465 0 0.000 0.000 0.000 0.000 0 0 0 0 48467 31 0.370 0.055 0.543 0.023 0 0 8 2 48470 0 0.050 0.010 0.003 0.000 0 0 0 3 48471 78 9.506 3.477 30.056 4.892 0 2239 63 2 48472 0 2.150 0.008 0.022 18.385 0 0 14 1 48473 41 0.575 0.089 13.269 0.094 0 0 10 1 48475 0 29.333 0.041 27.126 0.259 0 0 10 2 48502 32 44.760 11.365 1.923 0.802 0 0 16 1 106 Table F (can’t) ZIP AC'I‘307 N02 CO VOC $02 HAZ TRI LUST OLD_LF 48503 52 19.511 4.456 267.030 1.114 1 949357 38 0 48504 207 6.825 2.755 25.780 0.250 2 0 36 1 48505 116 32.417 6.653 21.902 0.308 2 67755 39 2 48506 228 239.558 4.378 30.131 4.487 3 7091 89 6 48507 109 15.460 3.716 4.923 2.632 3 2975 88 5 48509 36 1.600 0.320 0.086 0.010 0 0 28 1 48519 0 5.965 1.193 0.316 0.037 0 0 7 0 48529 30 4.395 0.016 3.316 0.280 0 0 12 0 48532 0 7.768 1.185 0.966 0.787 0 0 ll 0 48601 551 462.592 220.016 281.243 851.445 5 1274113310 5 48602 260 64.639 23.208 172.827 86.898 1 136781 133 1 48603 75 9.043 2.380 27.039 0.062 0 97000 197 5 48604 161 1.884 0.901 74.302 1.401 0 519 89 2 48607 83 0.681 0.204 0.244 0.015 0 0 35 0 48610 0 0.001 0.013 0.005 0.000 0 0 8 5 48611 66 3.531 0.777 3.120 0.045 0 21547 28 3 48612 190 1.387 0.302 8.388 0.009 0 0 24 6 48613 0 0.000 0.000 0.000 0.000 0 0 4 1 48614 0 0.000 0.000 0.000 0.000 0 0 4 1 48615 0 7.110 0.237 1.755 0.034 0 0 35 1 48616 31 12.380 7.545 15.916 10.804 0 0 71 1 48617 278 2.413 0.651 28.342 0.239 1 0 73 4 48618 76 10.597 2.640 274.918 0.089 0 21000 38 1 48620 0 0.000 0.000 0.000 0.000 0 0 0 1 48622 97 0.023 0.043 4.492 0.045 0 105070 27 1 48623 12 0.910 0.140 4.661 0.005 0 0 22 0 48624 314 1.745 0.462 0.331 0.019 0 0 53 10 48625 101 4.266 6.631 0.635 7.023 0 0 45 8 48626 55 25.674 6.068 82.760 0.115 0 268615 23 1 48628 36 0.000 0.000 0.000 0.000 0 0 5 1 48631 128 0.042 0.000 24.188 0.000 0 0 10 5 48632 348 0.000 0.000 0.000 0.000 0 0 3 2 48634 25 0.245 0.054 4.103 0.226 0 0 22 1 48635 21 0.000 0.000 0.000 0.000 0 0 0 1 48637 44 1.789 0.313 1.007 0.011 0 0 15 3 48640 411 285.978 352.237 181.568 22.244 3 2983842240 8 48642 0 2.607 0.603 0.083 0.462 0 323500 7 0 48649 0 0.399 0.000 0.008 0.001 0 0 8 1 48650 53 2.746 0.500 68.355 0.473 0 111052 47 5 48651 12 0.580 0.143 0.082 40.953 0 0 27 1 48652 26 0.000 0.000 0.000 0.000 0 0 4 1 48654 86 0.068 1.541 0.032 0.012 0 0 4 1 48655 35 4.544 2.049 119.671 0.030 0 164792 51 4 48656 30 0.000 0.000 0.810 0.000 0 0 25 2 48657 20 0.007 0.098 3.435 0.001 0 0 20 1 48658 53 2.018 0.326 38.413 0.088 0 11314 69 2 48659 57 0.033 0.463 0.164 0.005 0 0 3 6 48661 208 64.253 6.591 110.668 3.699 1 275612 4 4 48662 14 0.010 0.140 0.230 0.002 0 0 0 0 48701 79 0.336 0.000 2.186 9.815 0 0 7 2 48703 43 0.268 0.860 0.300 0.079 0 0 18 3 107 TableF(eon’t) ZIP ACT307N02 CO VOC SOZ HAZ TRI 48706 597 27.625 6.367 958.662 4.167 2 40000 48708 170 69.187 13.362 27.423 22.505 1 35155 48720 0 2.000 0.400 0.106 0.012 0 0 48722 123 12.755 2.193 3.475 0.153 0 500 48723 0 100.386 54.976 47.396 247.8080 0 48725 26 0.204 0.079 0.040 0.007 0 0 48726 87 2.173 1.159 22.598 0.216 0 966198 48727 0 0.000 0.000 4.787 0.000 0 0 48729 0 0.001 0.013 0.010 0.002 0 0 48730 97 0.397 0.101 1.870 0.140 0 8229 48731 39 3.011 0.472 4.265 0.014 0 0 48732 47 14972.940 70494192069 33815.858 48733 0 0.028 0.000 4.748 0.000 0 0 48734 50 43.105 9.046 2.465 5.965 0 0 48735 67 0.399 0.075 0.032 0.004 0 0 48739 28 0.270 0.005 8.698 0.001 0 0 48741 0 0.003 0.027 2.205 0.003 0 0 48743 19 0.000 0.000 0.000 0.000 0 0 48744 20 4.010 0.823 2.872 0.247 0 0 48746 40 2.509 0.162 34.792 2.999 0 0 48747 0 0.854 0.068 0.007 0.002 0 0 48748 43 397.725 0.007 2.539 0.135 0 0 48749 17 0.003 0.038 0.014 0.000 0 0 48750 189 11.607 3.357 8.218 0.241 0 0 48731 0 0000 0000 0000 00G) 01 0 48755 0 7.840 1.482 2.489 1.193 0 0 48756 59 0.026 0.219 0.986 0.002 0 0 48757 0 3.347 0.658 6.826 0.112 0 19855 48759 48 109.184 63.160 59.928 259.7810 0 48W“) 0 0000 00“) (0X0 00m) (1 0 48763 91 0.918 0.638 12.465 0.193 0 35330 48765 0 0.013 0.183 0.065 0.002 0 0 48Wfii 17 0003 (HXO 00m) (HXO 0 0 48767 37 0.000 0.000 0.000 0.000 0 0 48768 165 84.351 3148.520 208.433 32.916 1 48770 0 0.000 0.000 3278.257 0.000 0 48801 273 21.401 6.532 88.770 3.598 2 522903 48806 62 0.200 0.040 0.010 0.002 0 0 48807 0 00m) (“X0 00“) 0000 l) 0 48817 26 11.223 3.570 10.792 268.8460 0 48818 113 0.005 0.005 0.005 0.000 0 0 48829 266 2.144 0.375 4.138 0.399 0 318440 48831 92 0000 00G) (HXO 00m) 0) 0 48832 0 0.343 0.000 0.007 0.001 0 0 48836 52 3.617 0.876 13.567 0.212 1 0 48841 17 0.273 0.000 0.005 0.001 0 0 48843 523 277.493 5.790 77.178 13.060 1 33578 48847 279 0.349 0.133 2.090 0.287 0 0 48858 314 18.582 5.948 47.232 0.522 0 362894 48866 48 0.000 0.000 0.000 0.000 0 89547 48867 286 31.078 7.559 140.227 2.072 1 339866 LUST 194 140 6 28 58 14 19 0 3 38 7 0 17 67 4 \ON-hxiN N - O p—s & uoxowscop—owwoo p—n N OLD_LF 74 -hO«kids-rop-HNOHOOOt—OKNNr-u—NOU—r—NWNMHNONNOHNHNwOHwONMNHN—oom i—O\ VOC 0.000 0.110 0.470 0.905 171.765 33.303 0.243 0.000 0.030 0.146 0.000 0.009 0.148 3.265 0.000 502 0.000 9.871 0.188 0.166 0.000 0.001 0.036 0.000 0.001 0.005 0.230 0.000 507.798 0.011 4.494 0.019 108 COOOOOOOOO CQOCOOOOOOOOOE ‘63... £8 LF WATER HOTSPOT POV. DICOME MIN. POP. Table F (oon’t) ZIP ACT307N02 CO 48877 21 0.000 0.000 48878 30 2.493 0.618 48880 360 6.999 1.709 48883 369 0.500 0.075 48886 0 1358.197 48889 31 0.000 0.000 48891 28 0.006 0.085 48893 48 0.131 0.469 49305 0 0.000 0.000 49310 81 0.175 0.035 49332 24 0.030 0.417 49340 24 6.023 1.658 49342 0 0.000 0.000 49631 174 2.455 0.130 49665 126 0.102 0.269 ZIP 48116 0 2 27 48348 0 0 0 48350 0 0 1 48353 0 0 2 48356 0 0 10 48357 0 0 10 48371 0 0 16 48412 0 0 15 48413 3 5 23 48414 0 0 8 48415 2 1 10 48416 1 1 13 48417 0 1 3 48418 0 l 7 48420 1 3 16 48421 0 0 6 48423 1 3 15 48426 0 0 7 48427 0 1 20 48428 0 0 3 48429 0 5 13 48430 0 l 18 48432 0 0 6 48433 0 5 13 48435 0 0 4 48436 0 0 0 48438 0 0 7 48439 3 1 33 48442 0 3 19 48445 0 1 10 48446 0 5 30 48449 1 0 4 48451 1 3 10 3.3 2.4 4.3 3.3 3.0 6.6 6.4 5.0 16.7 7.4 12.3 14.9 10.4 7.4 10.4 8.1 6.3 16.6 17.6 6.1 12.9 6.0 16.3 6.0 11.6 4.5 4.3 3.7 7.0 16.2 10.2 6.2 5.4 21382 22678 19586 20577 17726 16665 17660 15364 11068 13628 13463 11950 12067 14161 15302 13559 16693 9786 10978 16892 13396 19431 10100 18965 12918 16418 19636 22895 16323 10487 14777 16245 18172 1.8 1.5 2.4 1.0 0.9 1.4 1.6 1.1 1.3 1.3 1.5 1.3 3.6 1.3 2.6 1.4 2.7 1.0 1.8 1.0 1.3 1.7 0.9 3.1 1.7 1.5 1.2 6.0 3.2 0.6 3.0 1.9 1.1 37205 14635 5949 4584 8161 7376 13306 5779 7321 7282 7961 4707 5487 3085 21345 6499 26713 480 4572 3095 7770 25313 988 23082 2179 2931 5123 30390 15119 1082 27654 3800 10151 LUST OLD_LF 3 8 16 26 0 12 4 21 22 12 4 10 2 11 31 NhWNNHwOHONN-hv—O 109 Table F (can’t) ZIP LP WATER HOT SPOT POV. INCOME MIN. POP. 48453 0 1 17 12.7 12044 1.1 4869 48455 1 l 10 4.4 20834 1. 1 6719 48456 0 0 7 16.7 11003 0.7 1637 48457 2 3 14 9.9 13593 3.2 7181 48458 0 2 16 16.1 13451 23.5 27347 48460 0 l 6 8.8 13116 2.7 3198 48461 0 2 15 7.6 13004 1.1 6046 48462 0 0 10 4.9 17754 1.5 10315 48463 0 0 9 10.4 14262 1.6 4130 48464 0 0 15 10.7 12311 1.4 2501 48465 0 0 0 15.6 11788 1.0 606 48467 0 2 10 16.5 11169 0.8 2508 48470 0 0 6 18.4 10613 0.4 1162 48471 0 2 21 13.2 12343 1.9 4230 48472 0 l 11 16.1 10694 1.1 2308 48473 0 0 10 6.0 17942 2.8 18263 48475 0 0 11 13.3 11045 0.3 2822 48502 0 0 13 39.2 10193 41.5 1359 48503 1 1 24 26.1 13640 43.5 33451 48504 0 1 19 20.7 13094 52.8 40445 48505 0 6 26 41.1 8820 84.9 42423 48506 0 1 29 20.9 12506 5.7 35154 48507 0 10 30 13.3 16006 11.4 37864 48509 0 0 10 8.6 16183 3.7 9432 48519 0 1 8 12.4 14323 4.3 5873 48529 1 1 11 19.7 12866 5.1 11092 48532 0 3 10 10.5 18989 11.0 20367 48601 0 10 45 33.7 9564 63.8 55547 48602 0 0 28 15.7 12934 8.7 34096 48603 1 2 25 6.4 19337 6.4 37541 48604 0 4 21 13.4 12772 8.8 11937 48607 0 0 9 53.3 7515 88.4 3436 48610 0 0 8 22.4 9514 0.8 1935 48611 0 l 18 10.3 14321 1.2 6154 48612 0 3 17 21.5 10751 1.0 9654 48613 0 1 3 17.2 10357 2.0 1022 48614 0 0 2 15.4 10589 2.2 1572 48615 0 1 11 12.5 13243 3.6 2278 48616 0 5 20 13.8 13351 2.8 4567 48617 3 4 25 19.6 11374 1.6 6652 48618 0 3 21 16.4 11088 3.5 5866 48620 0 0 1 12.5 15798 0.8 237 48622 2 0 17 18.8 10453 1.4 4440 48623 0 1 9 8.5 15036 1.2 10925 48624 0 2 20 21.1 10279 0.8 11841 48625 7 0 23 23.4 9238 1.1 9234 48626 0 3 20 8.8 14238 0.9 5720 48628 1 0 4 11.3 14938 1.3 1400 48631 3 1 16 6.1 13862 1.5 3935 48632 2 0 9 21.2 10469 1.8 7748 48634 0 4 12 9.8 13382 1.4 4719 110 Table F (can’t) ZIP LF WATER HOTSPOT POV. INCOME MIN. POP. 48635 1 0 3 21.5 9581 1.2 1793 48637 0 1 14 11.5 12836 2.4 2693 48640 3 8 45 9.6 20582 3.6 26324 48642 0 1 11 8.7 18948 3.7 24643 48649 0 0 5 14.2 11486 1.9 3506 48650 2 2 24 17.4 11830 2.2 8659 48651 0 0 11 16.4 10618 0.6 4422 48652 0 0 3 15.3 12147 1.3 1981 48654 2 1 12 21.9 10210 1.0 2881 48655 0 l 22 13.5 12348 1.8 6272 48656 0 0 6 29.9 8730 1.4 3716 48657 0 0 9 12.4 14440 1.3 5154 48658 0 l 19 22.6 10805 2.6 5305 48659 0 2 13 22.0 9774 0.7 2906 48661 4 4 31 17.6 11323 0.8 7830 48662 0 0 5 14.3 11887 2.2 2132 48701 1 0 11 10.5 13388 1.5 1694 48703 3 4 17 16.5 13240 1.6 1552 48706 2 14 37 10.4 13607 2.9 41677 48708 0 5 27 19.1 12718 6.9 29920 48720 0 1 8 14.4 11381 1.1 1693 48722 0 4 20 10.5 15854 10.7 3792 48723 0 4 23 15.0 12724 3.7 11384 48725 1 1 11 13.2 12191 0.5 2937 48726 2 3 25 13.6 12777 1.7 7436 48727 0 1 5 14.1 12377 2.0 1444 48729 0 0 5 15.9 13188 1.3 531 48730 0 1 16 13.3 12230 1.9 4959 48731 1 2 12 14.8 11100 1.1 2294 48732 3 2 30 13.1 15123 3.1 12019 48733 0 1 9 11.8 13530 2.3 3188 48734 0 3 19 4.4 19961 0.6 6931 48735 0 1 9 12.7 14113 2.0 759 48739 1 1 12 19.4 9738 1.1 3266 48741 0 1 8 15.3 11863 3.2 2503 48743 0 0 2 22.0 8620 2.3 349 48744 0 1 12 13.7 11565 2.2 4271 48746 0 2 14 11.7 13349 1.3 8756 48747 0 0 4 7.0 15052 2.3 1896 48748 1 2 15 17.1 10950 1.3 1832 48749 1 0 7 17.8 11553 1.0 972 48750 1 2 21 13.4 11124 5.5 9961 48754 0 l 3 13.3 11940 0.8 1833 48755 0 2 15 14.2 11182 1.1 1891 48756 2 1 11 29.0 8701 1.2 4828 48757 0 3 13 7.3 15325 1.3 2876 48759 0 5 23 13.7 12059 1.4 3203 48760 0 0 0 11.3 12521 2.8 964 48763 0 3 18 12.6 11397 1.6 3893 48765 1 0 6 18.4 10966 1.2 2691 48766 0 1 4 20.2 10703 0.9 923 Table F (can’t) 111 ZIP LF WATER HOTSPOT POV. INCOME MIN. POP. 48767 48768 48770 48801 48806 48807 48817 48818 48829 48831 48832 48836 48841 48843 48847 48858 48866 48867 48877 48878 48880 48883 48886 48889 48891 48893 49305 49310 49332 49340 49342 49631 49665 HOOOOOOONOOOHCOHOOHHOOCOOOOOOOHOO Ob-OOOHUJOOOOh—r—rr—OOOHOQOI—OCMOHOHMOMH 6 31 8 28 8 0 15 8 19 5 5 14 5 30 13 30 5 31 2 10 18 15 15 3 9 8 7 10 8 l3 4 21 14 £18 1203 2003 15Jl 120) PLS 602 160) YL3 941 15.1 (i8 803 (32 1005 2602 991 1343 1502 130) L13 1903 1437 1431 2603 1605 2142 TLZ 1903 1437 1401 2051 1801 13156 13177 8935 11880 12362 11158 14298 11096 12087 13073 11035 15481 13729 18481 13048 10726 13156 13261 10611 12044 11293 11000 11540 11866 9491 14810 9710 11644 9741 12407 13092 9633 10241 127 3J7 037 403 L0 L5 102 3.9 241 1.1 102 1-9 1.7 1.4 302 542 1.7 1.8 1.3 241 3.2 1.9 2.1 1.3 1.5 30) 2:4 203 305 3.5 1.2 1.5 1.1 2111 9933 1765 11018 2301 1490 3892 2279 2467 2478 1006 8519 534 28078 6437 33757 5004 29985 1211 2027 7588 17334 2137 2755 3600 708 1823 2493 1843 5034 1264 4226 5765 Appendix G Other Logit Tables and Cross-Tabulations 112 Other Logit Tables and Cross-Tabulations Table 6.1 Logit Table for Poverty Independent Estimated Standard Error t-statistic Variable Coefficient (Beta) Constant -l.500 0.213 -7.047 Poverty 0.114 0.598 0.190 Table G.2 Logit Table for Population Independent Variable Estimated Standard Error t-statistic Coefficient (Beta) Constant -1.828 0.254 -7.200 Pmulation 1.174 0.426 2.756 W Table 0.3 Cross-tabulations of hotspot by population. Population Count Row Column % 0 1 Total 1 12 25 137 Hotspot 0 86.2 65.8 81.5 18 13 31. 1 13.8 34.2 18.5 Column 130 38 168 Total 77.4 22.6 100.0 Table l Table G Table G. 113 Table G.4 Cross-tabulations of population and hotspot for non-minority area (min. = 0). Population Count Row Column % 0 1 Total 11 l 21 132 Hotspot 0 88.1 91.3 88.6 15 2 17 1 11.9 8.7 1 1.4 Column 126 23 149 Total 84.6 15.4 100.0 Table 6.5 Cross-tabulations of population and hotspot for minority areas (min. = l). Population Count Row Column % 0 1 Total 1 4 5 Hotspot 0 25.0 26.7 26.3 3 l 1 14 1 75.0 73.3 73.7 Column 4 15 19 Total 21.1 78.9 100.0 Table 6.6 Cross-tabulations of hotspot and minority areas for low income zipcodes(income = 0). Minority Count Row Column % 0 1 Total 72 2 74 Hotspot 0 82.8 15.4 74.0 15 11 26 1 17.2 84.6 26.0 Column 87 13 100 Total 87.0 13.0 100.0 Table 6.7 Cross-tabulations of hotspot and minority areas for high income zipcodes(income = 1). 114 Minorij' Count Row Column % O 1 Total 60 3 63 Hotspot 0 96.8 50.0 92.6 2 3 5 l 3.2 50.0 7.4 Colurrm 62 6 68 Total 91.2 8.8 100.0 Table 6.8 Cross-tabulations of minority by income. Income Count Row Column % 0 1 Total 87 62 149 Minority 0 87.0 91.2 88.7 13 6 19 1 13.0 8.8 11.3 Column 100 68 168 Total 59.5 40.5 100.0 Table 6.9 Cross-tabulations of poverty by hotspot. Hotspot Count Row Column °/o 0 1 Total 121 27 148 Poverty 0 88.3 87.1 88.1 16 4 20 1 11.7 12.9 11.9 Column 137 31 168 Total 81.5 18.5 100.0 115 Cross—tabulation of population by hotspot, but this time population is broken into five categories depending of the number of people living in the zipcode. Table G. 10 Cross-tabulation of population by hotspot when population subdivided further. Hotspot Count Row Column % O 1 Total 0 33 4 37 0 - 2,000 24.1 12.9 22.0 1 45 9 54 2,000 -5,000 32.8 29.0 32.1 2 34 5 39 Population 5,000 - 10,000 24.8 16.1 23.2 3 20 6 26 10,000 - 30,000 14.6 19.4 15.5 4 5 7 12 > 30,000 3.6 22.6 7.1 Column 137 31 168 Total 81.5 18.5 100.0 Appendix H Principals of Environmental Justice 116 Principals of Environmental Justice 1. Environmental policy must be based on justice for all without discrimination due to race, ethnicity, nationality, religion, culture, or any other factor. 2. All life is sacred and environmental justice demands the mutual respect and protection of all life from environmental hazards, poisons, toxins, and liabilities caused directly or indirectly by industrial development, production, pollution, and waste. 3. Environmental justice afirms ecological unity and the interdependence of the diversities of all life, and the fundamental right of all life to be free from ecological destruction. 4. Human progress and development must be based upon the just sustainability of the environment and not upon the unjust exploitation, manipulation, and domination of the environment. 5. An environmental injustice anywhere is a threat to environmental justice everywhere in the global community, and thus, environmental justice movements at local, regional, and national levels must be viewed in a global context. 6. National resources must be used equitably, fi'ugally, and in the interest of environmental and social justice for all, and not in the narrow economic or political interests of any group over others. 7. Industrial producers of poisons, toxins, hazardous wastes, pollutants, radioactive substance, and other dangerous emissions must be held directly responsible for detoxification at the point of production and for other environmental injustice prevention methods at the point and site of production. 8. A government act of environmental injustice is a violation of international law and the Universal Declaration of Human Rights, as well as of the United Nations Convention on Genocide. 9. The rights to clean air, water, food, and land are fundamental rights of environmental justice for all. 10. Victims of environmental injustice have the right to demand and receive compensation for damages and discrimination. Source: Newton, C., and Ortega, F., 1991. BIBLIOGRAPHY 117 BIBLIOGRAPHY Asch, P., and J .J . Seneca, Some Evidence on the Distribution of A ir Quality, Land Economics, 54:3, pp. 278-297, 1978. Austin, R., Schill, M., Black, Brown, Poor and Poisoned: Minority Grassroots Environmentalism and the Quest for Eco-Justice, The Kansas Journal of Law and Public Policy, 1:1, pp. 69-82, 1991. Berry, J .K., Assessing Spatial Impacts of Land Use Plans, Journal of Environmental Management: 27, pp. 1-9, 1988. Berry, B.J., et al., The Social Burdens of Environmental Pollution: A Comparative Metropolitan Data Source. Cambridge, MA: Ballinger Publishing, 1977. Bishop, AB, Fullerton, H.1-L, Crawford, AB, Chambers, M.D., McKee, M., Mg Capacig in Regioml Environmpptal M_an_agement, Washington, DC, US EPA #EPA-600/5-74-021, 1974. Brundtland et al, fir Common Future, World Commission on Environment and Development, Oxford Press, 1987. Bryant, B. and Mohai, P., Race and the Incidence of Environmental Hazards, A Time for Discourse Boulder, CO: Westview Press, 1992. Bullard, B.D., Dumping in Dixie, Boulder, CO: Westview Press, 1990. Bullard, R and Wright, B., Environmentalism and the Politics of Equity: Emergent Trends in the Black Community, Mid-American Review of Sociology, 12:2, pp. 2 1 -3 7, 1987. Bullard, R, Overcoming Racism in Environmental Decisionmala'ng, Environment, 36:4, 1994. Burke, L.M., Race and Environmental Equity: AGeographic Analysis in Los Angles, National Center for Geographic Information and Anallysis, Santa Barbarba, CA Report Number 93, 1993. California Air Resources Board, Report on Air Quality for California, Sacramento, CA: State Printing Ofice, 1993. Catton, W.R, Overshpot, Chicago: University of Illinois Press, 1980. Catton, W.R., The World's Most Polymorphic Species - Carrying Capacity T ransgressed Two Ways, BioScience, 37:6, 413-419, 1987. 118 Chavis, B, Essay: Race, Justice and the Environment, Nature Conservancy, September/October, p. 38, 1991. Cutter, S.L., (ed.), Environmental Risks and Hazards, Prentice-Hall, Englewood Clifl‘s, NJ, 1994. Daly, HE, and Cobb, B.J., For the Common (md, Boston: Beacon Press, 1989. EPA, Reducing Risk: Setting Priorities and Strategies for Environmental Protection, Science Advisory Board, Washington, DC, EPA-600-6-SZ-OO3F, 1990. EPA, Risk Ranking Project, Region 2, Ecological Ranking and Problem Analysis, EPA- 212-1003, US. Government Printing Ofiice, Washington, DC, 1991. EPA, Environmental Equity, Reducing Risk For All Communities, Vol. 1, EPA-230-R- 92-008, US. Government Printing Ofice, Washington, DC, 1992. EPA, Toxic Release Inventory Data Questions and Answers, Record # 19930806, CD- ROM, 1993. EPA, Environmental Justice Initiatives, EPA-200-R-93-001, US. Government Printing Office, Washington, DC, 1994. EPA Journal, Washington, DC: Government Printing Ofice, March-April, 1992. Francis, A, in Under RAPs: prard Mrpots Elegical Demom in the m Lakes Basin, (Hartig, J .H., and Zarull, M. A. editors) Univeristy of Michigan Press, Ann Arbor, 1992. Freeman, AM, The Distribution of Environmental Quality, In AV. Kneese and RT. Bower, eds., Envirpnmental Qualig Analysis, Baltimore, MD: Johns Hopkins University Press for Resources For the Future, 1972. Gelobcr, M., The Distribution of Outdoor Air Pollution By Income and Race: 1970- 1986. Master's Thesis, Energy and Resource Group. Berkeley, CA: University of California, 1986. Gianessi, L., Peskin, HM, and Wolfl‘, E., The Distributional Effects of Uniform Air Pollution Policy in the U.S., Quarterly Journal of Economics, pp. 281-301, 1979. Glickman, T. 8., Measuring Environmental Equity With Geographic Information Systems, Renewable Resource Journal, 12:3, pp. 17-21, Autumn, 1994. Golley, F., Introducinglandscape Ecology, Landscape Ecology, 1:1, pp. 1-3, 1987. 119 Goldman, B.A, Npt Just Prosperigg: Achieving Sustainabiligr with Environmental Justice, National Wildlife Federation, Washington DC, 1994. Great Lakes Pollution Control Progress and Persistent Problems, Legislative Service Bureau, May, 1992. Greenburg, M.F., Health Effects of Environmental Chemicals, in Environmental Risks and Hazards Prentice-Hall, Englewood Cliffs, NJ, 1994. Gretchen, on, and Ehrlic, p., Population Sustainablility andEarth's Carrying Capacity, BioScience, 42:10, 761-770, 1992. Harris, C., in U_n_der RAPs: Toward gzssroots Ecological Democragr in the Great Lakes Basip, (Hartig, J .H., and Zarull, M. A editors) Univeristy of Michigan Press, Ann Arbor, 1992. Hartig, J .H., and Zarull, M.A, Under RAPs: prard Mrmts Ecological Demogracy in the (neat Lakes Basip, Univeristy of Michigan Press, Ann Arbor, 1992. Hennekens, C., and Buring, J ., Epidemiology in Medicine, Little Brown and Company, Boston, pp. 73-82, 1987. Hofinan, P., Watershed Management: Options for Michigan, A report by the Senate Select committee on Watershed Management, 1994. House, P.W., The Cm’ g Capacigr of a Nation, Lexington Books, 1975. Huang, S. and Chen, C., A System Model to Analyze Environmental Carrying Capacity for Managing Urban Growth of Taipei Metropolitan Region, Journal of Environmental Management, v.31, pp. 47-60, 1990. International Joint Commission, Environmental Management Strategy for the Great Lakes System, Windsor, Ontario, 1978. Internatioinal Joint Commission, Great Lakes Water Quality Board, Review and Evaluation of the Great Lakes Remedial Action Program, June, 1991. Kay, J ., Fighting Toxic Racism, in the San Francisco Examiner, pp. A1, A12, April 7, 1991. Kling, D., EPA 's Flagship Programs, EPA Journal, July September,l9:3, pg. 6, 1993. Knox, P., Urbanizatiop, An Introduction to Urban Geogzaphy, Prentice-Hall Press, Englewood Cfifis, NJ, 1994. 120 Kruvant, W.J., ”PeOple, Energy, and Pollution”, in D.K. Newman and D. day, eds., T_he merim Energy Consumer, Cambridge, MA: Ballinger, 1975. Kuik, O., and Verbruggen, H., In Search of Indicators of Sustainable Develppment, Dordrecht, The Netherlands: Kluwer Academic Publishers, 1991. Lee, C., Toxic Wastes and Race in the United States: A National report on the Racial and Socio-Economic Characteristics of Communities with Hazardous Waste Sites. New York: United Church of Christ, Commission for Racial Justice, 1987. Lien, J .K., Applying Expert systems Technology to Carrying Capacity Assessment: A Demonstration Prototype, Journal of Environmental Management: 37, 63-84, 1993. Lien, J .K., On the Application of Expert Systems in Environmental Performance Assessment, Journal of Environmental Systems, 21: 2, pp. 167-183, 1991-92. Malone, C., Environmental Pedorrnance Assessment, Journal of Environmental Systems, 19:2, pp. 171-184, 1990. Malthus, RT, Essay on the Principle of Population, 1798, in Pppulatipp, Evolutipn gpd Birth Cpntrpl (Ed. Garret Hardin), San Francisco: Freeman and Co., 1964. McCaull, J ., Discriminatory Air Pollution: If Poor Don't Breathe, Environment, 19:5, pp.24-29, 1975. Meadows, D.H., Meadows, D.L., Randers, J ., Beyond The Limits, Mill Post, Vermont: Chelsea Green Publishing Co., 1992. Michigan Department of Natural Resources, Air Quality Division, Air Emissions Permitting Database, 1994. Michigan Department of Natural Resources, Remedial Action Plan for the Saginaw River and Saginaw Bay, September, 1988 (Update, 1992). Michigan Department of Natural Resources, EPA and Public Sector Consultants, Michigan's Environment and Relative Risk (MERR), July 1992. Michigan Department of Natural Resources, Water Quality and Pollution Control in Michigan, Surface Water Quality Division, 1988-1993 Reports. Michigan Department of Natural Resources, Environmental Response Division, Michigan Sites of Environmental Contamination, 1992-1994 Reports. 121 MT Information, Zipcodes of Michigan, Published by Michigan Information Center, Lansing, MI, 1994. Michigan Legislative Service Bureau, The Great Lakes and Pollution Control Progress and Persistent Problems, May, 1992. Michigan United Conservation Clubs, Saginaw Bay Watershed Land Use and Zoning Study, September, 1993. Miller, T.G., Living in the Envirpnment, California: Wadsworth Publishers, 1992. National Research Council, 1984, in Envirome Risks and Hazards, (Cutter, editor), Prentice-Hall, Englewood Clifi‘s, NJ, 1994. Newton, KC, and Ortega, F ., The Workbook, Southwest Research and Information Center, Albuquerque, NM, 1991. Odum, E., Great Ideas in Ecology for the 19903, BioScience, 42:7, 542-545, 1992. Odum, E., Ecolpgy gpd Our Endangered Life Suppprt System, Massachusetts: Sinauer Assoc, 1993. Odum, E., Fudmentals pf Ecology, Philadelphia, PA: Saunders Press, 1953. Openshaw, S., Ecological Fallacies and the Analysis of A real Census Data, Environmental and Planning - A, v. 16, pp. 17-31, 1984. Santos, M.A, W New York: Bergin and Barey Publishers, 1990. Schoolcrafi, HR, Schpolcraft’s Nmtivg Journal of Travels, East Lansing: Michigan State Univeristy Press, 1992. Smith, V.K., (editor), Sgcig and gowth Reppnsidered, Washington: Resources For the Future, 1979. Taylor, D., Blacks and the Environment: Toward and Explanation of the Concern and Action Gap Between Blacks and Whites, Environment and Behavior 21 (2): 1989. Taylor, D., The Environmental Justice Movement, EPA Journal, 18:1, pp. 23-25, 1992. Tietenberg, T.A., Envirpnmental Econpmips, New York: HaperCollins Publishers, 1992. 122 U. S. General Accounting Ofice, Siting of Hazardous Waste Landfills and Their Correlation with the Racial and Socio-Economic Status of Surrounding Communities, Washington, DC: US General Accounting Ofice, 1983. US. Postal Service, Five-Digit Zipcode Reference Manual, 1994. United Church of Christ, Commission for Racial Justice, 1987. Toxic Wastes and Race in the United States: A National report on the Racial and Socio-Economic Characteristics of Communities with Hazardous Waste Sites. New York: United Church of Christ, Commission for Racial Justice, 1987. Wentz, P., Enviromnental Justice NY: State University of New York Press, 1988. Wilkinson, L., SYSTAT: The Sys_tem for Statistics, Evanston, IL., SYSTAT Inc., 1990.