MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio INTRODUCTION Section IV of this EEBA focuses on the state of Ohio as a case study of the MP & M regulation s expected benefits and costs. Ohio has a diverse water resource base, a relatively large number of MP& M industry facilities, and a more extensive water quality ecological database than many other states. EPA gathered extensive data on MP& M facilities and on Ohio s baseline water quality conditions and water based recreation activities to support the case study analysis.

These data characterize current water quality conditions,

water quality changes expected from the regulation, and the expected welfare changes from water quality improvements at water bodies affected by MP& M discharges.

The case study analysis supplements the national level analysis performed for the MP& M regulation in two important ways. First, the case study used improved data and methods to determine MP& M pollutant discharges from both MP& M facilities and other sources. In particular, EPA administered 1,600 screener questionnaires in the state of Ohio to augment information on Ohio MP& M facilities. The Agency also used information from the sampled MP& M facilities to assign discharge characteristics to non sampled MP& M facilities1. Second, the analysis used an original travel cost study to value four recreational uses of water resources affected by the regulation: swimming, fishing,

boating, and near water activities. The added detail provides a more complete and reliable analysis of water quality changes from reduced MP& M discharges. The case study analysis therefore provides more complete estimates of changes in human welfare resulting from reduced health risk,

enhanced recreational opportunities, and improved economic productivity. Chapter 20: Baseline Conditions in Ohio CHAPTER CONTENTS 20.1 iew of Ohio s Geography, Population, and Economy ................. ........ 20 2 20.2 MP& M Facilities in Ohio ........... 20 3 20.3 ................. ... 20 6 20.3.1 ................. .... 20 8 20.3.2 r Recreation in Ohio ............. 20 11 20.3.3 ercial Fishing in Ohio .......... 20 12 20.3.4 e Water Withdrawals ........... 20 12 20.4 e Water Quality in Ohio .............. 20 12 20.4.1 in Ohio ................. ............ 20 13 20.4.2 Attainment ................. ......... 20 13 20.4.3 Non Attainment in Ohio ................ 20 14 20.5 Impairments on Water Resource Services ................. .... 20 15 20.5.1 ity Impairment on Life Support for Animals and Plants .......... 20 15 20.5.2 ect of Water Quality Impairment on Recreational Services ................. . 20 17 20.6 and Distribution of Endangered and Threatened Species in Ohio ............. 20 18 20.6.1 ................. ........ 20 19 20.6.2 sks ................. .... 20 19 20.6.3 ........... 20 20 Glossary ................. ................. .. 20 24 Acronyms ................. ................. . 20 27 References ................. ................. 20 28 Overv Profile of Ohio s Water Resources Aquatic Life Use Wate Comm Surfac Surfac Use Attainment in Streams and Rivers Lake Erie and Other Lakes Use Causes and Sources of Use Effects of Water Quality Effect of Water Qual Eff Presence E& T Fish E& T Mollu Other Aquatic E& T Species The statewide case study of recreational benefits from the MP& M regulation combines water quality modeling with a random utility model ( RUM) to assess how changes in water quality from the regulation will affect consumer valuation of water resources. The study addresses a wide range of pollutant types and effects, including water quality measures not often addressed in past recreational benefits studies. The estimated model supports a more complete analysis of recreational benefits from reductions in nutrients and   toxic  pollutants. 2 1 Appendix H provides a detailed discussion on the approach used to estimate discharge characteristics for non sampled MP& M facilities.

2 The term   toxic  used here refers to the 126 priority or toxic pollutants specifically defined as such by EPA, as well as nonconventional pollutants that have a toxic effect on human health or aquatic organisms.

20 1 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio This and the next two chapters present the Ohio case study. This chapter provides background information on the state of Ohio, the following chapter presents the results from the recreational benefits analysis, and the last chapter summarizes social costs and benefits of the final regulation for the state of Ohio.

20.1 OVERVIEW OF OHIO S GEOGRAPHY, POPULATION, AND ECONOMY Table 20.1 summarizes general information on Ohio. Ohio is large, heavily industrialized, and densely populated. The state covers a total surface area of 44,828 sq. mi. ( 106,607 sq. km.), of which water represents 3,875 sq. mi. ( 10,036 sq. km.).

About 90 percent of the water surface area consists of Lake Erie; the remainder includes inland waters, such as lakes,

reservoirs, and rivers ( including the Ohio River). The state housed 11,353,140 people in 2000. The three largest metropolitan areas are located on Lake Erie ( Toledo and Cleveland) and the Ohio River ( Cincinnati).

20 2 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Table 20.1: Facts about the State of Ohio Geography Location Midwest United States, northeast part: south of Lake Erie east of Indiana north of the Ohio River Total land area 40,953 sq. ( 106,607 sq. .)

Of the 26,451,000 acres of terrestrial surface area in Ohio: 97 percent is non federal land ( National Resources Inventory ( NRI))

3,558,000 acres, representing 13.5 percent of the total area of Ohio, are developed The remaining non federal lands are rural land, classified mostly as crop land, forest, and pasture lands.

Total water surface area 3,875 sq. ( 10,036 sq. .)

Approximately 90 percent is represented by Lake Erie, and 10 percent are inland waters including rivers, lakes, and reservoirs. a Total area 44,828 sq. ( 116,104 sq. .)

Demographics Population 11,353,140 in 2000, approximately 4 percent of total U. S. population ( U. S. Census Bureau) Population increase: 4.7 percent from 1990 to 2000, compared to a 13.1 percent increase in the U. S. population overall.

Most densely populated part of the state: northeastern Ohio, both urban and rural areas.

Largest cities: Cleveland, Cincinnati, and Toledo.

Economics Ohio Midwest U. S.

Per capita income ( 1996$) $ 23,537 Rank in per capital income in the U. S.: 21 $ 24,166 $ 24,231 Percent of population below the poverty level ( 1995 Current Population Survey data, DOC 1996) 11.5% N/ A 13.8%

Ohio per capita income increased by 16 percent from 1986 to 1996.

Income growth is consistent with other midwestern states and is 2 percent greater than overall U. S. per capita income growth.

Gross State Product ( GSP) $ 303,569,000,000 ( 1996$), representing 4 percent of Gross Domestic Product ( GDP) for the U. S. in 1996.

Percent increase in GSP/ GDP from 1986 to 1996 ( in adjusted 1996$) Ohio GSP U. S. GDP 25% 29% mi. km ( USDA, 1992a)

mi. km mi. km a Total water surface areas are estimated by the USDA s National Resources Inventory ( NRI) ( USDA 1992b).

( http:// www. ftw. nrcs. usda. gov/ nri_ data. html)

Source: U. S. EPA analysis.

20.2 PROFILE OF MP& M FACILITIES IN OHIO EPA selected Ohio as the case study state because MP& M industries account for a large share of the state's economy ( see Table 20.2). Data from the 1997 Economic Censuses show that industries containing MP& M facilities employ 19.8 percent of Ohio's total industrial workers and produce 21.2 percent of industrial worker output by value. MP& M industries also account for 22.1 percent of payroll payments, indicating that jobs in MP& M industries are more highly paid than industrial 20 3 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio jobs on average in Ohio. The discussion below explains the sources and methodology EPA used, and then presents detailed results and caveats.

Table 20.2: & M Share of Industrial Output and Employment in Ohio, 1997 Total Employment Payroll Value of Output MP& M 827,507 $ 23,233,857,000 $ 132,117,226,000 Total 4,087,393 $ 112,777,104,000 $ 677,978,137,000 MP& M Share 19.8% 22.1% 21.2% MP Source: Department of Commerce 1992 Economic Censuses.

EPA obtained employment, payroll, and output data from the 1997 Economic Census CD ROM, drawing from the eight economic censuses in Table 20.3. Employment and payroll numbers include all employees ( i. e., production plus non

production workers). The measure of output differs according to the source, but in each case the output measures shown in Table 20.2 correspond conceptually to total revenue. EPA extracted the EMPLOYEE, PAYROLL, and VALUE fields for each 4 digit SIC industry in the MP& M category and for the entire state of Ohio. Industries include both in scope and out of

scope facilities.

Table 20.3: e Economic Censuses Source Measure of Output Census of Retail Trade Value of sales Census of Wholesale Trade Value of sales Census of Service Industriesa Value of receipts Census of Transportation, Communications, and Utilities Value of revenue Financial, Insurance, and Real Estate Industries Value of receipts Census of Manufacturers Value of shipments Census of Mineral Industries Value of shipments Census of Construction Industries Value of construction work Th a Includes both taxable and non taxable establishments.

Source: Department of Commerce 1997 Economic Censuses.

The MP& M industries include facilities to which the MP& M rule may not apply. For example, MP& M industries include non dischargers, but census data do not distinguish between in scope and out of scope facilities. In addition, EPA substantially revised the scope of the final regulation by excluding from the final regulation all indirect dischargers and direct dischargers in all subcategories except for Oily Wastes. Definition of MP& M subcategories is provided in Section 4.1 of this report. The final rule applies to an estimated 172 direct discharging facilities in Ohio.

Also, the analysis examines only the industrial sectors for which the Department of Commerce compiles statistics in the Econom ic Censuses. Published industrial employment and output measures often exclude military and other government personnel and farm output and employment, whether those exclusions are noted or not. The analysis excludes $ 4.7 billion in value of agricultural products sold in 1997 by farms in Ohio, according to the U. S. Department of Agriculture's 1997 Census of Agriculture. The Ohio analysis also excludes the government sector, which employed approximately 760,000 people in 20 4 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Ohio in 1997, according to the U. S. Bureau of Labor Statistics. 3 These exclusions are normal when economists compare the size of industrial groups.

If total employment in Ohio includes the government sector, then MP& M industries account for only 16.7 percent, rather than 19.8, percent of employment. If total industrial manufacturing and non manufacturing output in Ohio includes the agricultural sector, then MP& M industries account for only 21.0, rather than 21.1, percent of output. This said, data from the Bureau of Labor Statistics and USDA are not completely consistent with the Economic Census data.

EPA augmented information on MP& M facilities available from published data sources and the Section 308 survey by oversampling the state of Ohio with 1,600 screeners. The Agency used information from the Section 308 survey and the 1,600 screeners to characterize discharges from MP& M facilities in Ohio and to assess the economic impact of the final regulation at the state level. Figure 20.1 depicts locations of the Ohio facilities included in the case study analysis.

The map of facility locations shows that the additional information from 1,600 screeners enabled EPA to perform the benefits assessment with a greater level of detail than is possible at the national level. The added detail results in a more complete and reliable analysis of changes in human welfare resulting from reduced health risk and improved recreational opportunities.

3 U. S. Bureau of the Census, Statistical Abstract of the United States, 1993, Washington, D. C., 1993.

20 5 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Figure 20.1: Location of Sample MP& M Facilities in Ohio Source: U. S. EPA analysis.

20.3 OHIO S WATER RESOURCES The benefits of enhanced water quality stem directly from enhancing water quality and/ or quantity of services provided by water resources. To aid in understanding the analysis of benefits from the final rule in Ohio, this section summarizes environmental services provided by Ohio s water resources.

Ohio is a water rich state:

  24,000+ miles of named and designated rivers and streams;

  451 mile border on the Ohio River;

  200,000 acres among 450 lakes, ponds, rivers, and reservoirs; and   230+ miles of Lake Erie shoreline.

20 6 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio These water resources provide three broad categories of services: in stream, withdrawal, and existence services.

Water resources provide in stream services prior to the withdrawal of water from the water body. Major in stream services include life support for animals and plants, water based recreation, commercial fishing and navigation, water storage, and aesthetics. Withdrawal services include uses of water resources after the water is withdrawn from the water body. These uses include drinking water supply, irrigation, production and processing services, and sanitary services. Existence services are not linked to current uses of water bodies, and arise from knowing that species diversity or the natural beauty of a given water body is preserved.

The Ohio Environmental Protection Agency ( Ohio EPA) assesses surface waters in their Ohio Water Resource Inventory ( OWRI) report based on water resource services provided by the assessed water body. The main focus of this assessment is on beneficial uses associated with Ohio s water resources, including aquatic life use, recreation, and public water supply.

Table 20.4 shows how Ohio surface waters fall into these use designations.

Table 20.4: Summary of Designated Life Uses for Ohio Surface Waters ( 1996)

Use Designation Stream/ River ( Miles) a Lakes /

Reservoir ( Acres) a Lake Erie ( Shore Miles) a Total 43,917 200,000 236 Aquatic Life Usea Exceptional Warmwater Habitat ( EWH) Warmwater Habitat ( WWH)

Other 24,067 3,217 18,318 2,532 193,903 193,903 236 236 Recreation Primary Contact ( PCR) b Secondary Contact ( SCR) 224,96 1,188 200,000 236 Public Water Supply 118,801 a Total river/ stream miles are based on Ohio EPA estimates. U. S. EPA estimates 61,532 total river miles and 29,113 total perennial miles based on RF3, which includes many smaller undesignated streams.

b Note that some water bodies have more than one designated use ( e. g., aquatic life and primary recreation).

Source: Ohio EPA, OWRI, 1996.

The aquatic life use category is further subdivided into seven categories. The most widely applied aquatic use designation in Ohio is Warmwater Habitat ( WWH), accounting for 18,318 ( 76 percent) stream and river miles ( Ohio EPA, OWRI, 1996).

The second most widely applied designation is Exceptional Warmwater Habitat ( EWH) , accounting for 3, 217 stream and river miles ( 13 percent), 236 Lake Erie shore miles ( 100 percent), and 193,903 acres of inland lakes ( 100 percent). Other aquatic life categories include:

  Modified Warmwater Habitat ( MWH),

  Limited Resource Waters ( LRW) ,

  Limited Warmwater Habitat ( LWH),

  Seasonal Salmonid Habitat ( SSH) , and   Coldwater Habitat ( CWH) .

20 7 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Recreational uses are subdivided into Primary Contact Recreation ( PCR) and Secondary Contact Recreation ( SCR):

  Primary Contact Recreation ( PCR) rivers and streams deep enough for full human body immersion activities,

such as swimming.

  Secondary Contact Recreation ( SCR) only deep enough to permit wading and incidental contact, such as boating.

Approximately half of the designated stream miles, all inland lakes, and all of the Lake Erie shore miles are designated for PCR ( see Table 20.4). In addition, three percent of the designated stream miles ( 1,188 miles) are suitable for SCR.

The following sections detail each category of water resource use.

20.3.1 Aquatic Life Use The Ohio water resources support hundreds of aquatic species and plants. Ohio water resources are also home to a number of endangered and threatened species. Suitable stream and lake habitat are essential for both resident and transient animal populations, including imperiled aquatic species. Habitats include specific biotic components ( e. g., assemblages of plant and animal species) and physical ( e. g., dissolved oxygen ( DO ) content and temperature range) components. Water quality impairments associated with siltation, excess nutrients, or low DO can adversely affect habitats that support important activities, such as reproduction, foraging, migration, and overwintering.

The following sections briefly introduce water dependent biological resources in Ohio. Water quality effects on life support for animals and plants are discussed in Section 20.5 a. Ohio fish species Fish are found throughout Ohio in almost every inland surface water body and Lake Erie. Many fish species serve important recreational or commercial functions, while others are important forage for birds, other fish, and land based species.

Ecosystem well being therefore depends on the health of fish and other aquatic species populations. The Ohio EPA monitors biological data, especially those on sensitive aquatic species, to determine the aquatic life use attainment of surface waters.

The state gives high priority to healthy aquatic ecosystem maintenance.

Ohio s rivers and lakes offer a variety of man made and natural habitats that offer excellent fishing opportunities for numerous gamefish species. The state of Ohio spends significant resources on fishery management, trout stocking, and recreational area maintenance to enhance these fish populations. Table 20.5 below provides brief summaries of the habitat and diet of major recreational and commercial fish species in Ohio ( Ohio DNR , 1999).

Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio Fish Native or introduced? Habitat Spawning season Diet Bass Most native bass ( e. g., largemouth, smallmouth,

spotted, and sock) Ponds, lakes, rivers, and streams in every county; Lake Erie Mid April to mid June Frogs, crayfish, insects, and other fish Bullhead Native Throughout Ohio; concentrations in northern and west central Ohio Mid May to June Insect larvae, crayfish, snails, dead animals Burbot Native Lakes and rivers; prefer deep waters, but move inshore to spawn Winter Minnows and the young of other fish species Carp Introduced Warm lakes, streams, and ponds with abundant organic matter, in every county Late April to June Insect larvae, mollusks, fish,

crustaceans Catfish ( channel, flathead) Native Throughout Ohio's rivers and lakes;

tolerate a wide range of conditions When waters reach 70 ° F in temperature Bottom feeders with a diet of insect larvae, mollusks, and fish both dead and alive 20 8 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio Fish Native or introduced? Habitat Spawning season Diet Crappie,

white Larger ponds, reservoirs, and rivers,

including near shore habitats of Lake Erie, in most areas of Ohio May and June Insects and small fish Crappie, black Same general habitat as white crappie, slightly less widely distributed May and June Insects and small fish Drum Native Lake Erie; drums support a commercial fishery Spring into late summer Mollusks, crayfish, fish, insects Lamprey Lake Erie and tributaries; Ohio River and larger tributaries Some species parasitize other fish by attaching themselves to a larger host's flank and feeding on its flesh Muskellunge ( Muskie) Native Historically found in Lake Erie bays and tributaries and streams of Ohio River drainage; now also found in several impoundments April and early May, when temperatures reach low to mid 50s Suckers, gizzard shard, and other soft rayed fish Perch, white Introduced Lake Erie and tributaries April and May Insects, crustaceans, other fishPerch, yellow Native Lakes, impoundments, ponds, slow moving rivers April and May Pike Native Historically abundant in Lake Erie and tributaries; today distributed in a small portion of Lake Erie, Sandusky Bay, Maumee Bay, and their tributary streams in marshes, bays, and pools with abundant vegetation As ice breaks in late February and early March Pike is a popular ice fishing species Mostly fish, but are opportunistic feeders; will occasionally eat frogs, muskrats, small ducks Salmon ( chinook and coho) Introduced Stocked in Lake Erie for both recreational and commercial fishing purposes Sauger Native Lake Erie and its tributaries; Ohio River Spring, when water temperatures reach high 40s Insects, crayfish, other small fish during low light ( dawn and dusk)

Saugeye ( cross between sauger and walleye) Introduced Stocked into many Ohio impoundments Sucker, white Native Every county; Lake Erie April to May Bottom feeders, consuming various plant and animal species Sunfish Bluegill,

pumpkinseed,

green, warmouth,

and longear sunfish are native;

redear sunfish are introduced Rivers, streams, and lakes throughout Ohio, and Lake Erie Between May and August Adults feed mostly on smaller fish, insects, crustaceans 20 9 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio Fish Native or introduced? Habitat Spawning season Diet Trout Lake and brook trout are native; rainbow and brown trout are introduced and maintained by stocking Lake trout populations are stocked in Pennsylvania and New York and are not highly prevalent in Ohio and Lake Erie waters; Brook trout are stocked in several locations throughout Ohio Walleye Native Historically found in Lake Erie, but has been stocked in the Ohio River and reservoirs throughout the state April Shiners, gizzard shad, alewives,

rainbow smelt Whitefish Native Shallow bays of Lake Erie's western basin Bottom feeders with a diet of mollusks and insect larvae Source: U. S. EPA analysis.

b. Other species dependent on aquatic resources Resident and migratory bird species make extensive use of Ohio waters. Areas along the banks or shorelines of rivers,

streams, lakes, ponds, and reservoirs provide high quality nesting areas; the waters themselves are an abundant source of food.

Ohio waters also serve as important staging areas for birds migrating to or from points north or south. Wading or aquatic birds are generally unaffected by water quality impairments directly. They are affected indirectly, however, through feeding on fish or invertebrates whose populations may be affected by point and non point pollution sources. The regulations aimed at protecting aquatic species will therefore benefit wading and aquatic bird species indirectly. More than 130 aquatic bird species rely on Lake Erie and its tributaries. Many species are also found near inland surface waters. Major classifications of birds in Ohio include ( Ohio DNR , 1999):

  Waterfowl, residing year round in Ohio waters, especially Lake Erie. Large groups of migrating and breeding birds are also found elsewhere in the state. More than 30 species are associated with the Great Lakes area alone. All species depend on fish and crustaceans or aquatic plants for feeding. Waterfowl include loons, grebes, swans, ducks and geese. The trumpeter swan is of particular interest to Ohio, which became one of several states involved in efforts to restore these birds to the Midwest beginning in 1996 ( Ohio DNR, 1999).

  Wading birds, including bitterns, herons, and egrets. These species both reside in Ohio waters and use them as breeding grounds. They use   stand and wait  methods to catch fish or other aquatic organisms in shallow waters.

Many wading birds, such as the great egret, black crowned night heron, and American bittern, frequent Lake Erie and surrounding areas.

  Marsh birds, including rails, moorhens, coots, and gallinules. They may feed on insects, crustaceans, mollusks,

frogs, invertebrates, and small fish. These bird populations suffer from excessive development and habitat destruction. Ohio surface waters, especially those around Lake Erie, can serve as important breeding grounds for these and other bird species.

  Shore birds, including 42 species of plovers, sandpipers, gulls, and terns, in the Lake Erie and other Ohio areas.

Many of them feed on aquatic organisms from Lake Erie.

  Raptors, including the bald eagle and osprey. These birds of prey rely on fishing for a large part of their diet. Bald eagles are also a nationally listed threatened species.

  The belted kingfisher, which relies on fish in Ohio waters as a main source of food.

Ohio s biological resources also includes reptiles. Several species of lizards, snakes, and turtles depend on aquatic habitats for food and breeding. These reptiles include:

  Lizards  The five fined skink, reported in areas along Lake Erie, can be found throughout Ohio.

20 10 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio   Snakes  The eastern fox snake, Eastern massasasuga, eastern ribbon snake, copperbelly water snake, Lake Erie water snake, and northern water snake feed within aquatic habitats.

  Turtles  The midland smooth softshell turtle and eastern spiny softshell turtle, found in the Ohio River and tributaries, are among Ohio turtles requiring aquatic habitats.

20.3.2 Water Recreation in Ohio EPA used the 1994 Survey of National Demand for Water based Recreation ( NDS) ( U. S. EPA, 1994) to characterize recreational uses of Ohio s water resources. The 1994 survey collected data on demographic characteristics and water based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over.

Respondents reported on water based recreation trips taken within the previous 12 months, including the primary purpose of their trips ( e. g., fishing, boating, swimming, and viewing), total number of trips, trip length, distance to the recreation site( s),

and number of participants. EPA estimated recreational water use in Ohio by taking the following steps:

  estimate the percentage of survey respondents that visited Ohio, by state;

  apply this percentage to the total number of state residents aged 16 and over, to yield the total number of participants from each state;

  estimate the total number of recreation trips during the 12 month period for in state and out of state participants;

  estimate the total number of recreation trips for out of state participants by multiplying an average number of trips per Ohio water body visitor by the total number of participants from each state;

  estimate the average number of annual trips per out of state visitor based on the number of times the respondents visited the site of their last recreational trip ( i. e., Ohio water body). 4 EPA assumed that Ohio residents whose last recreation trip was in state used Ohio water bodies for all of their recreation trips during the 12 month period; and   estimate the total number of in state trips, summing the weighted number of recreation trips over all Ohio respondents.

EPA found that:

  An estimated one million individuals made about 6.3 million boating trips to Ohio waters in 1993. In state residents made 90 percent of the boating trips.

  Approximately one million people visited Ohio water bodies for recreational fishing. 5 These visitors accounted for about 15.6 million fishing trips to the area. Recreational fishermen from Ohio were the most frequent users of the state water resources, representing approximately 97 percent of all visitors.

  Approximately 972,000 and 896,000 visitors used the Ohio water bodies for near water viewing and swimming,

respectively, in 1993. These visitors account for approximately 9.4 and 7.8 million viewing and swimming trips to the area. Ohio residents account for 89 percent of viewers and 93 percent of swimmers.

  Most out of state recreational users came from the states surrounding Ohio, such as Indiana, Michigan, and Pennsylvania.

4 NDS collected information only on the last site visited. Its numbers do not reflect people whose last visit was to a different area, but who may have also visited an Ohio water body on a previous trip during the year. See Section 21.3 for detail on the NDS data.

5 EPA compared the estimated number of participants with total fishing licenses issued by Ohio in 1996. Ohio issued a total of 895,770 licenses for resident and nonresident fishing. The NDS data therefore provide relatively accurate information on participation rates.

20 11 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio 20.3.3 Commercial Fishing in Ohio Commercial fishing is a minor activity in Lake Erie: 12 license holders share a total of 19 licenses ( LECBA 2003).

Commercial catch data compiled by the Great Lakes Fishery Commission are summarized in Table 20.6 ( Baldwin et al.

2002).

Table 20.6: Commercial Catches for Ohio Lake Erie Waters ( 1990)

Fish Catch ( 1990 lbs)

Yellow perch 1,559,000 Carp 1,190,000 White perch 786,000 Sheepshead 640,000 White bass 392,000 ChannelcCatfish 365,000 Quillback 134,000 Buffalo 132,000 Bullheads 59,000 Suckers 41,000 Goldfish 31,000 Gizzard shad 19,000 Lake whitefish 10,000 Rock bass 1000 Source: Baldwin et al. ( 2002)

Yellow perch represents about half of the dockside value for the entire commercial fishery in the Ohio waters of Lake Erie.

The value of this fishery ranged from $ 1.3 million to $ 2.5 million between 1993 and 1998. Overfishing and pollution have decreased the yellow perch population throughout Lake Erie dramatically over the past 30+ years. Annual catches averaged around 20 million pounds during the 1960s and 70s. The Lake Erie Committee set the 1998 lakewide total allowable catch ( TAC) quota for this species at 7.44 million pounds. The yellow perch fishery rebounded somewhat over the past couple of years, due to strong annual recruitment, strict commercial catch restrictions, and a strict creel limit of 30 fish per day for the sport angler ( LECB A 2003 ).

20.3.4 Surface Water Withdrawals Water resources provide a wide range of services upon being withdrawn ( removed) from the water body. Once used, water can be returned to its original sources, returned to another water body, or consumed ( e. g., for human drinking water). Water withdrawals from surface water averaged 9,615 mgd in 1995 ( USGS 1995). The majority of this water is used in power generation, accounting for 85 percent of all surface water withdrawals. Public water supply accounts for ten percent of all withdrawals. Industrial and commercial water use account for one and four percent of the total, respectively. Water quality and quantity impairments can have substantial impacts on the key withdrawal services that water provides to a wide range of economic entities.

20.4 SURFACE WATER QUALITY IN OHIO This section describes current water quality conditions in Ohio and the effects of water quality impairments on beneficial uses of Ohio s water resources. Ohio EPA assessed designated use attainment in approximately 42 percent of Ohio streams and 20 12 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio rivers; approximately 64 percent of lakes, ponds, and reservoirs; and all of the Lake Erie shoreline ( Ohio EPA, OWRI, 1996).

The OWRI report summarizes the results of this assessment. This report provides information on designated use support by water type and use designation, identifies major pollutant/ stressors that affect the quality of surface water bodies and prevent designated use attainment, and lists major sources of impairment. The following three sections summarize findings from the 1996 OWRI report.

20.4.1 Use Attainment in Streams and Rivers in Ohio Most water bodies are designated for several uses and more than one use can be impaired at a time. The most commonly occurring sole impairment in fresh water bodies is to aquatic life support. The Ohio EPA used an ecosystem approach that relies on various tools to determine aquatic life use attainment. Water chemistry, physical and habitat assessment, and direct sampling of biota all contribute to determine whether a water body meets an attainment status. Field data yield biological indices that eventually determine a final attainment score.

Ohio EPA assessed 6,560 perennial river miles for aquatic life use attainment. Of the 6,560 river miles assessed for aquatic life use:

  38.5 percent ( 2,536 miles) are in full attainment ( i. e., all water quality indicators meet criteria for specific water bodies);

  10.8 percent ( 708 miles) are in full attainment, but are threatened by pollution and other sources;

  23.3 percent ( 1,528 miles ) are in partial attainment ( i. e., one of two, or two water quality indicators do not meet criteria); and   27.4 percent ( 1,797 miles) are in non attainment ( i. e., no criteria are met or the river experiences a severe toxic impact).

Fecal coliform bacteria counts determine recreational use attainment. Such counts are less stringent for Secondary Contact Recreation than for Primary Contact Recreation. Ohio EPA has assessed 2,402 river miles for recreation use since 1988 ( Ohio EPA, OW RI, 1996). Of the 2,402 river miles assessed for recreation use:

  57 percent ( 1,370.3 miles) of the sampled rivers and streams are in full attainment ( i. e., a water body meets all chemical criteria for recreational use and human contact);

  19.7 percent ( 474.1 miles) are in partial attainment ( i. e., a water body only partially meets human contact criteria);

and   23.2 percent ( 557.4 miles) are in non attainment ( i. e., a water body fails to meet human contact criteria).

20.4.2 Lake Erie and Other Lakes Use Attainment Lake Erie, which has a history of pollution problems, currently has fish consumption advisories for carp and channel catfish ( Ohio DN R, 1999). Ohio E PA assesses Lake Erie as having partial use attainment for aquatic life and fish consumption, and full attainment for recreation. 6 Ohio EPA used parameters specified by the Ohio EPA Lake Condition Index ( LCI) to develop use attainment for other lakes. Only approximately two percent of all lakes are in full use attainment for aquatic life,

recreation, and fish consumption. Approximately 82, 50, and 53 percent are in full attainment for aquatic life, recreation, and fish consumption, respectively, but are threatened by pollution for these categories. High percentages of lake acres are in partial attainment for recreation ( 38.8 percent) and public supply ( 43.8 percent) use designations. Table 20.7 shows use attainment for Lake Erie and other lakes, ponds, and reservoirs.

6 Further methodologies to better assess use attainment in Lake Erie are still under development by the Ohio EPA.

20 13 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Table 20.7: Use Attainment Summary for Lake Erie ane Other Lakes Use Category % of Total Units Assessed Full Attainment Full Attainment,

Threatened Partial Attainment Non Attainment % Units % Units % Units % Units %

Lake Erie ( Unit: Shore Miles) a Aquatic Life ( EWH) 100 236 100 Recreation 100 231 98 5 2 Fish Consumption 100 236 100 Lakes, Ponds, & Reservoirs ( Unit: Acres)

Aquatic Life ( EWH) 64.7 1,651 2.2 63,174 82.2 10,686 13.9 1,302 1.7 Recreation ( PCR) 64.4 1,392 1.8 38,499 50.3 29,793 38.9 6,582 9.0 Public Water Supply 64.1 1,301 1.7 40,846 53.6 33,365 43.8 673 0.9 a Assessments are based on unit of measure presented in parentheses.

Source: Ohio EPA, OWRI 1996.

20.4.3 Causes and Sources of Use Non Attainment in Ohio Ohio EPA assessed the causes and sources of impairment to Ohio surface waters and examined trends in major causes and sources from previous assessment cycles. The following discussion summarizes findings from the 1996 OWRI report ( Ohio EPA, 1996).

a. Causes Causes are the agents responsible for damage and threats to aquatic life. The major causes of impairment in Ohio surface waters include:

  organic enrichment/ low DO,

  habitat modifications,

  siltation,

  flow alteration,

  nutrients, and   metals.

Ohio EPA examined trends in these major causes from previous assessment cycles through 1996. They found that point source related causes declined, while non point sources became major contributors. Ohio EPA concluded that this trend   reflects the relative effectiveness of the programs to control point sources compared to general lack of measures to control many [ non point sources]  ( Ohio EPA, OWRI, 1996).

Organic enrichment, which alters DO levels and affects aquatic communities, is the main cause of impairment in Ohio s rivers and streams. Inadequate wastewater treatment from municipal and industrial point sources account for most of this impairment. Metals are a major cause of impairment to approximately 226 river miles, a moderate cause of impairment to 179 river miles, and a minor cause of impairment or threat to 165 river miles.

Nutrients, resulting mostly from agricultural non point sources, are the main cause of impairment in lakes. Metals are a major cause for impairment in approximately 250 acres of Ohio s lakes, ponds, and reservoirs, and form the main cause of impairment in Lake Erie, the major water resource in Ohio ( 90 percent of the surface water volume). Highly developed areas 20 14 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio bordering the lake contribute urban runoff, along with discharges from industrial and municipal sources. Other causes of impairment in Lake Erie include priority organics, DO, and nutrients. 7 b. Sources Sources are the origins of the agents responsible for damage and threats to water resources. The major sources of impairment to Ohio surface waters include:

  municipal and industrial discharges,

  hydromodification,

  agricultural runoff,

  urban runoff, and   mining.

Point source caused impairment has declined over time, while that from non point sources, such as agricultural and urban runoff, has increased. Point sources remain a major source of impairment in almost 900 miles, or 25 percent, of Ohio s affected rivers and streams. Point sources are the major source of impairment for Lake Erie. They form a major source of impairment for 24 shore miles, and a moderate source of impairment for an additional 281 shore miles of Lake Erie. In addition, point sources adversely affect 1,678 lake acres.

Non point sources related to agricultural and urban runoff form the major source of impairment for some 9,000 acres, or two

thirds of Ohio s lakes, ponds, and reservoirs. In addition, 46 Lake Erie shore miles list non point sources as their major impairment source.

20.5 EFFECTS OF WATER QUALITY IMPAIRMENTS ON WATER RESOURCE SERVICES Water resource services are negatively affected by pollutants that impair the aquatic ecosystems. Certain pollutants can adversely affect aquatic species directly by increasing species morbidity and/ or impairing reproductive success, or indirectly by adversely altering food chain interactions. These direct and indirect impacts can change quantity and type of fish and other species in the aquatic ecosystem. In the worst case scenario, an impaired ecosystem no longer supports any aquatic life. High pathogen counts or excessive eutrophication in water bodies that are suitable for swimming may force swimmers to go elsewhere or forego swimming altogether. Any aesthetic degradation decreases the value of each individual s recreational experience. In severe cases, the affected water bodies become unsuitable for recreation. Water quality impairments also increase the cost of treating water to meet drinking water standards.

This section details the effects of water quality impairments on in stream services provided by Ohio s water resources.

20.5.1 Effect of Water Quality Impairment on Life Support for Animals and Plants Deficiencies in water quantity and quality can impair the health of aquatic ecosystems. In worst case scenarios, the ecosystem may no longer support aquatic life at all. The major causes of water quality impairment in Ohio include high biological oxygen demand ( BOD) from organic enrichment, habitat and flow alterations, nutrients, siltation and turbidity,

metals, pH, ammonia, and priority organics. Habitat, flow alterations, and thermal discharges are unrelated to MP& M effluents and are not discussed here. MP& M effluents contribute to the remaining major causes of water quality impairment,

with the ecological effects outlined below.

a. BOD/ COD BOD and chemical oxygen demand ( COD) are two methods to determine the oxygen requirements of pollutants in wastewater. Low oxygen level is the primary cause of impairment in Ohio s rivers and streams and a major source of 7 Major, moderate, and minor impacts refer to the high, moderate, and slight magnitude codes specified by the U. S. EPA for the 301( b) report.

20 15 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio impairments in Ohio s lakes. When bacteria decompose excess organic matter, they consume DO in surface waters. Oxygen is needed to chemically ( abiotically) oxidize the pollutants present in wastewater. When too much oxygen is needed to oxidize pollutants, hypoxic ( oxygen deficient) or anoxic ( oxygen depleted) conditions result. Sources of high oxygen demand include effluents from municipal treatment plants and certain industries, and runoff from feedlots or farms. Another source is eutrophication caused by excessive nutrient input. The nutrients stimulate algal blooms. Bacteria consume the algae when they die, decreasing DO in the water column. DO is a critical variable for fish and invertebrate survival. If oxygen concentrations drop below a minimum level, organisms suffocate and either move out or die ( EPA, 1986). This effect can drastically reduce the amount of useable aquatic habitat.

b. Nutrients Nutrients are the leading causes of impairment in Ohio lakes and comprise one of the major causes of impairment in rivers,

streams, and Lake Erie. The overabundance of nitrogen and phosphorus is one of the most documented forms of aquatic ecosystem pollution. Although both compounds are essential nutrients for phytoplankton ( free floating algae) and periphyton ( attached algae), which form the base of the aquatic food web, too much nutrient input overstimulates primary productivity and results in eutrophication. The impact of these compounds has contributed significantly to water quality decline in the United States ( EPA, 1992). Phosphorus is a limiting nutrient in most freshwater systems ( Wetzel, 1983), whereas nitrogen is typically limited in estuarine and marine systems.

In freshwater, excess phosphate ( PO4 ) has been linked to eutrophication and nuisance growth of algae and aquatic weeds ( Wetzel, 1983), even though direct toxicity to fish and other aquatic species is not a major concern. DO in the water column decreases, however, when algae and other aquatic plants die off, and certain toxins may be produced, both of which can contribute to fish kills.

c. Siltation and turbidity Siltation and turbidity are the third leading causes of impairments in Ohio rivers and lakes, except Lake Erie. Siltation is the most important factor in surface water degradation in the U. S. ( EPA, 1992). Major sources include urban and stormwater runoff, mining and logging activities, and runoff from plowed fields ( EPA, 1992). All these inputs create cloudy water with increased turbidity and decreased visibility and light penetration. High primary productivity by phytoplankton following excessive nutrient input can also increase turbidity. Excess suspended matter decreases the amount of light penetrating the water column, which can reduce primary productivity. This turbidity can eliminate or displace fish species requiring clear water to live, feed, or reproduce.

d. Metals Metals are the leading cause of impairment in Lake Erie and comprise one of the major causes of impairment in inland lakes and rivers. Metals are naturally occurring inorganic constituents of the earth s crust. Priority pollutant metals commonly found in the aquatic environment include antimony, arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium,

silver, thallium and zinc ( EPA, 1998a). These compounds enter the aquatic environment via urban stormwater runoff,

industrial and municipal effluents, and atmospheric deposition. As a group, metals can be highly toxic: water quality criteria ( WQC) for acute toxicity range from around 1,100   g/ l ( chromium VI in saltwater) to around 1   g/ l ( mercury in freshwater); WQC for chronic toxicity range from 120   g/ l ( zinc in freshwater) to < 1.0   g/ l ( mercury in salt and freshwater)

and are therefore an order of magnitude lower ( EPA, 1998a).

Once metals reach the aquatic environment, they tend to associate with organic and inorganic particulates in the water column. Sediments become long term sinks for metals, which accumulate in the bottom. Metals can enter the food chain when ingested by benthic invertebrates or other burrowing organisms. Most metals have bioconcentration factors ( BCFs) ranging from 100 to 10,000 and can therefore bioaccumulate in aquatic organisms. A few, including selenium, lead,

and mercury, may reach hazardous levels in fish or wildlife receptors and result in avian developmental or neurological abnormalities.

e. Organic chemicals Priority organics are the second most frequent cause of impairment in Lake Erie and comprise one of the major causes of impairment in rivers and streams. Thousands of different compounds exist as organic chemicals, including petroleum hydrocarbons and myriad industrial chemicals. They enter the aquatic environments via municipal and industrial effluents,

stormwater runoff, contaminated groundwater, atmospheric deposition, illegal dumping, or accidental releases. Aquatic toxicities vary by orders of magnitude depending on the compound. Factors influencing toxicity and long term ecological effects include water solubility, volatility, biodegradation potential, and bioaccumulation potential.

20 16 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Excessive amounts of organic chemicals degrade surface water quality by causing acute or ( more typically) chronic toxicity.

This toxicity impairs growth, development, and/ or reproductive success in fish and aquatic invertebrates. Persistent and low water soluble organic chemicals accumulate in sediments and are taken up into local aquatic food chains. They can reach dangerous concentrations in fish and avian receptors, resulting in reproductive failures or other avian health effects.

f. pH Approximately 180 river miles are pH impaired in Ohio. pH is a measure of acidity. Acid reaches surface waters via atmospheric deposition (  acid rain ), industrial effluents, and leachates from mine overburdens or spoils. Acidity by itself is a key variable shaping aquatic communities: it is a toxicant in its own right but also controls metal solubility, and the toxicity of several metals and ammonia.

Aquatic species vary widely in their sensitivity to pH: the most sensitive vertebrate and invertebrate species die off when average pH ranges between 6.0 and 6.5. Most fish species are eliminated when pH reaches 5.0. Only a few can survive at pH 4.5 ( U. S. EPA, 1999). Macro invertebrates exhibit the same pattern, except that hardy species can survive down to a pH of about 3.5.

g. Ammonia Large amounts of ammonia enter lakes and rivers via wastewater treatment plants and industrial effluents, atmospheric deposition, and non point source surface runoff. Approximately 150 river miles in Ohio are ammonia impaired. This compound, unique among regulated pollutants, is also produced naturally inside fish as a metabolic waste product. Excess ammonia usually diffuses rapidly out of the blood stream and into the surrounding water via the gills. High concentrations of external un ionized ammonia ( NH3) reduce or reverse this diffusive gradient and allow ammonia to build up to toxic levels inside the organism ( EPA, 1998c).

Ammonia in surface water exists in two major forms: un ionized ammonia ( NH3), which is highly toxic to fish or invertebrates, and ammonium ion ( NH4 + ), which is much less toxic. Which form prevails depends mainly upon the pH level;

temperature and ionic composition play a smaller role. EPA calculated a WQ C that becomes more severe with decreasing acidity. For example, the acute criteria for surface waters containing salmonids equals 36.7 mg/ l at pH= 6.0 but only 2.14 mg/ l at pH= 8.5. For surface waters without salmon, the acute criteria for the same pH equal 55.0 mg/ l and 3.2 mg/ l,

respectively ( EPA, 1998c).

20.5.2 Effect of Water Quality Impairment on Recreational Services Healthy surface waters are essential to support a diversity of recreational uses, including viewing and other near water activities. Industrial or other human activities impair surface water quality. Certain metals and chlorinated compounds can bioaccumulate in aquatic food chains and reach unhealthy levels in carnivorous fish or shellfish. Health advisories to limit or avoid their consumption may result. High concentrations of toxic compounds can also lead to human contact advisories. The release of untreated or poorly treated sewage can cause high levels of pathogenic bacteria in water and result in swimming advisories or beach closures. All of these actions limit the full use of surface waters and can have significant local economic impacts.

a. Fish consumption advisories In 1997, the Ohio Department of Health ( ODH) issued a statewide fish consumption advisory to protect women of childbearing age and children six years or younger against mercury s neurological and developmental effects. The advisory,

which applies only to these two population groups, recommended that these women and children eat no more than one meal per week of any fish caught in Ohio waters. The advisory covers all state waters because most of the mercury measured in fish tissues originates from region wide fossil fuel combustion processes. The mercury reaches surface waters via atmospheric deposition on the surrounding landscape ( Ohio DNR, 1999).

Since 1983, the ODH has developed numerous water body specific fish consumption advisories for approximately 174 water body segments ( rivers and lakes) and Lake Erie. These water bodies represent a relatively small fraction of Ohio s 5,000 discrete water body segments, as determined by Ohio EPA. The contaminants of greatest concern include polychlorinated biphenyls ( PCBs) , mercury, polycyclic aromatic hydrocarbons ( PAHs) , lead, organometallics, Mirex, phthalate esters, Chlordane, and hexachlorobenzene. Of these, four mercury, PAHs, lead, and phthalates are included on the MP& M list of pollutants of concern ( POCs) . As a group, these contaminants are generally characterized as lipophilic ( i. e., fat loving), resistant to biological degradation or cellular metabolism, and toxic. Once they reach surface water, they 20 17 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio concentrate in sediments and bioaccumulate or biomagnify through aquatic food chains. These compounds can linger for decades in aquatic systems.

The kind of sports or recreational fish species affected by the consumption advisories varies by water body segment. More than 23 different species are covered by advisories, including walleye, common carp, sauger, saugeye, white crappie,

freshwater drum, and various species of bass, perch, catfish, salmon, trout, suckers, and sunfish. Restrictions vary depending on the pollutant, the fish species concerned, and the concentrations measured in edible tissues. The ODH developed maximum recommended rates of fish consumption that include outright consumption bans, one meal every two months, one meal a month, or one meal a week. The same water body segments can commonly have different advisories for different fish species ( Ohio DNR, 1999).

b. Contact advisories The ODH also issued human contact advisories for nine water body segments in Ohio located on the Black River, Little Scioto River, Mahoning River, the middle fork of the Little Beaver Creek, and the Ottawa River. Swimming or wading is prohibited due to the presence of high levels of PAHs, PCB s, Mirex, phthalate esters, and/ or Chlordane. Of these, PAHs and phthalates are included on the list of MP& M POCs. Fish consumption advisories also cover all of these segments ( Ohio DNR, 1999).

c. Beach closures Beach closures typically occur during the summer months when high levels of fecal coliform bacteria or other disease causing organisms ( e. g., Escherichia coli) proliferate in surface waters. Such waters can become contaminated from several sources,

including: agricultural runoff, sewer overflows, boating wastes, and poor hygienic practices by some bathers. Excessive levels of indicator pathogens in surface waters can indicate a serious threat to human health and may cause health departments to post warnings, restrict access, or forbid swimming altogether. The MP& M regulation is not expected to reduce beach closures during summer months.

Numerous public bathing beaches dot Ohio s 262 mile shoreline along Lake Erie. The ODH has developed a composite metric based on E. coli counts in surface waters at 11 selected beaches along Ohio s north coast. The metric tracks the average number of days that swimming advisories are posted at the 11 beaches for a 15 week period beginning around Memorial Day and continuing through Labor Day. The most recent data available show that the 11 beaches were under advisement an average of 21 days during the summer months ( minimum of 0 days and maximum of 49 days) in 1996.

The ODH developed a 4 tiered scale to score and track the average number of days that the 11 public beaches are under advisement from one year to the next. Between 1990 and 1996, the average ( based on a five year running average) number of beach advisories scored in the   fair  category consistently, meaning that the beaches were under advisement between 20 and 30 days in the summer ( State of Ohio, 1998).

Ohio s lakes, ponds, and reservoirs ( excluding Lake Erie) yielded no quantitative data on beach closures. The 1996 Ohio Water Resource Inventory of Public Lakes, Ponds and Reservoirs provides a breakdown of the portion of Ohio s 446 public lakes that are threatened or impaired as a result of high levels of fecal coliform bacteria.

20.6 PRESENCE AND DISTRIBUTION OF ENDANGERED AND THREATENED SPECIES IN OHIO Many factors can affect the survival of endangered and threatened ( E& T) species. Some factors are species specific;

others result from one or more anthropogenic stressors. Inherent vulnerability factors include narrow geographic distribution,

slow reproductive rates, or requirements for large areas. Major anthropogenic stressors include intentional taking ( e. g.,

fishing), incidental taking, physically altering habitat ( e. g., converting wetlands into agricultural land), water pollution, and introducing alien species. A single stressor or a set of stressors can contribute to a species' decline or extinction. Previous studies reported that more than 40 percent of endangered aquatic species were affected by five or more environmental stressors, and only seven percent of federally listed species had a single threat to their survival. Although stressors seldom act alone, water pollution is one of the major hazards to E& T aquatic species, cited as responsible for the decline of 19 ( 54 percent) out of 35 E& T fish species in Ohio ( Ohio DN R, 1998).

20 18 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio The following sections provide an overview of E& T species found in Ohio, their distribution, and the major hazards threatening their survival. Species discussed below include those listed under both the federal Endangered Species Act ( 50 CFR Part 17) and the Ohio Department of Natural Resources  ( DNR) Division of Natural Areas and Preserves. The MP& M regulation concentrates on water related benefits; these sections therefore describe only those species associated with aquatic environments. 8 The DNR list includes 90 E& T species with a total of 1,227 observations throughout Ohio.

  Observations  refers to locations where species were observed; most species have multiple observations. This analysis includes observations spanning the years 1980 to 1988.

20.6.1 E& T Fish E& T fish inhabit almost every major water body in Ohio, including Lake Erie and the Ohio River and its tributaries. The Ohio DNR lists 35 total state listed E& T fish species, of which 13 are threatened and 22 endangered. The list includes only one federally listed species, the scioto madtom.

Of the total E& T fish, approximately 12 species use Lake Erie as a possible habitat and nine use the Ohio River. Most of the species listed live in riverine habitats. Approximately 28 species were identified in a river system in Ohio, including the Ohio, Scioto, Muskingham, Miami, Walhondig, and Maumee River systems. MP& M facilities are found on all these major river systems.

The DNR lists 384 observations of E& T fish in Ohio, of which 240 observations of 30 different species have been reported since 1980. Figure 20.2 maps the observations of E& T fish in Ohio and shows the extent to which these observations were reported in the state. Multiple observations can occur for a single species. In southern Ohio, most observations come from the Muskingham and Scioto River systems and the Ohio River. Most observations in northern Ohio came from Lake Erie tributaries or the lake itself.

In addition to water pollution, cited above as major hazard to E& T aquatic species, other major hazards to E& T fish include siltation and impoundments. Approximately two thirds of E& T fish species are threatened by siltation, and 17 percent are threatened by impoundments or dams. MP & M regulations can improve affected ecosystems or habitats by reducing discharges from MP& M facilities. These improvements can then help reduce siltation and restore some of the E& T fish populations.

Many obscure E& T fish species have a pure existence value. Some E& T species, like brook trout and lake sturgeon, have high potential for consumptive uses. Restoring their populations and those of other commercial and recreational fish species may enhance recreational fishing opportunities. Table 20.8 lists E& T fish in Ohio, their habitat locations, and the cause for their E& T listing. The table lists species alphabetically by scientific name.

20.6.2 E& T Mollusks Mollusks yield the largest number of reported observations of aquatic E& T species in Ohio, representing 48 percent of the total 1,227 observations. The Ohio DNR lists 29 E& T mollusk species, four threatened and 25 endangered. Of these, five mollusk species are on the federal endangered species list: catspaw, clubshell, fanshell, white catspaw, and pink mucket.

Ohio s E& T mollusks concentrate in five major areas: Lake Erie and the Grand River tributary, Scioto River and Big Arby tributary, Muskingham River, Little Miami River, and the Ohio River. E& T mollusk populations reside mostly along the mainstems of large rivers and in Lake Erie, but are also found in the St. Joseph, Sandusky, and Cuyahoga Rivers.

8   Aquatic species  were identified by the Ohio Department of Natural Resources, Division of Natural Areas and Preserves. These species include any species that are   closely associated with aquatic habitats through their breeding or feeding requirements.

20 19 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Figure 20.2: E& T Fish Observances in Ohioa ( 1980 1997)

a Each $ represents an observance.

Source: U. S. EPA analysis.

20.6.3 Other Aquatic E& T Species Improved water quality resulting from the MP & M regulation may also benefit other aquatic E& T species. Unlike fish and mollusks, whose primary habitat is a surface water body at all times, these species may use surface water related habitats only for breeding or feeding. Improved water quality may benefit these populations indirectly by enhancing the quality and quantity of aquatic biological resources.

Other aquatic associated E& T species of Ohio include:

  Birds ten state listed species, one threatened and nine endangered, include one federally listed threatened species,

the bald eagle. The state listed species include: American and least bitterns, common and black terns, yellow and black Crowned night herons, king rail, osprey, and snowy egret. These species are observed mostly along the Lake Erie coast. The bald eagle is observed mostly in Ohio s northeast corner.

20 20 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio   Amphibians three state listed endangered species: blue spotted salamander, observed in the very northwest section of the state along small streams and near the Maumee River; eastern spadefoot, found near the Ohio and Muskingham Rivers; and eastern hellbender, observed along the Muskingham and Scioto River systems and tributaries of the Ohio River.

  Reptiles two species: the copperbelly water snake, a state listed endangered and federally listed threatened species found in lakes and ponds in the northwest corner of Ohio; and the Lake Erie water snake, state listed as threatened and a proposed threatened species for the federal list, found only along the edges of the Lake Erie islands.

  Mammals the river otter is state listed as endangered. Sparse observations of the animal come from various small creeks and lakes in the eastern part of Ohio.

  Crustaceans the state listed endangered Sloan s crayfish has been observed in several small tributaries of the Great Miami River system.

  Insects nine state listed species, one threatened and eight endangered, are reported throughout the state.

20 21 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Table 20.8: Endangered and Threatened Fish Species of Ohio Common Name Scientific Name Number of Observations Last Observed Federal Status State Status Habitat Causes for Listing Lake sturgeon Acipenser fulvescens 3 1979 E Lake Erie, spawning in larger rivers such as Maumee and Auglaize Pollution and dams Longnose sucker Catostomus catostomus 1 1950 E Lake Erie Pollution creating low oxygen levels Rosyside dace Clinostomus funduloides 53 1997 T Small, upland streams of Teays and Little Scioto River systems Runoff and siltation Cisco Coregonus artedi 1 1976 E Lake Erie Pollution and overfishing Blue sucker Cycleptus elongatus 2 1985 E Ohio River and lower reaches of large tributaries Pollution, dams, increased turbidity and siltation Lake chubsucker Erimyzon sucetta 28 1994 T Lakes ( not Erie) and larger streams Increased turbidity and siltation Bluebreast darter Etheostoma camurum 19 1995 T Scioto and Muskingham River systems,

large streams Pollution and siltation Spotted darter Etheostoma maculatum 8 1992 E Large streams of Muskingham and Scioto systems Pollution and siltation Tippecanoe darter Etheostoma tippecanoe 11 1994 T Muskingham and Scioto River systems Tonguetied minnow Exoglossum laurae 16 1996 T Great Miami River system Undetermined, likely pollution and siltation Western banded killifish Fundulus diaphanus menona 9 1994 E Lake Erie and larger tributaries Siltation Goldeye Hiodon alosoides 16 1989 E Ohio River and lower reaches of large tributaries Pollution Mississippi silvery minnow Hybognathus nuchalis 1 1983 E Ohio River and tributaries Siltation Ohio lamprey Ichthyomyzon bdellium 4 1992 E Ohio River and lower reaches of large tributaries Pollution and siltation Northern brook lamprey Ichthyomyzon fossor 25 1992 E Small streams, tributaries of Grand and Scioto rivers Pollution, siltation, and dams Mountain brook lamprey Ichthyomyzon greeleyi 6 1993 E Mahoning River and tributaries Pollution, siltation, and dams Silver lamprey Ichthyomyzon unicuspis 40 1993 T Lake Erie and larger tributaries Pollution, siltation, and dams Blue catfish Ictalurus furcatus 1 1987 E Scioto River Spotted gar Lepisosteus oculatus 1 1978 E Lake Erie Siltation and dredging 20 22 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Table 20.8: Endangered and Threatened Fish Species of Ohio Common Name Scientific Name Number of Observations Last Observed Federal Status State Status Habitat Causes for Listing Shortnose gar Lepisosteus platostomus 9 1981 E Scioto River and tributaries Pollution and siltation Speckled chub Macrhybopsis aestivalis 1 1990 E Ohio and Muskingham rivers, large rivers Pollution and siltation Greater redhorse Moxostoma valenciennesi 12 1989 T Maumee river system, large streams Pollution and siltation Popeye shiner Notropis ariommus 4 1993 E Extirpated from Ohio, creeks and small rivers of Maumee system Siltation Bigeye shiner Notropis boops 22 1995 T Great Miami River and Ohio River systems, upland streams Siltation and impoundments Bigmouth shiner Notropis dorsalis 16 1994 T Black and Rocky River systems, brooks and small streams Competition with silver minnow Blackchin shiner Notropis heterodon 2 1983 E Lake Erie and other lakes Increased turbidity and siltation Blacknose shiner Notropis heterolepis 7 1983 E Lake Erie and other lakes Siltation Mountain madtom Noturus eleutherus 11 1991 E Ohio River tributaries, larger streams and rivers Pollution and siltation Northern madtom Noturus stigmosus 10 1989 E Muskingham, Little Miami, Walhondig Rivers Scioto madtom Noturus trautmani 1 1957 E E Big Darby Creek, tributary of Scioto Pollution and siltation Pugnose minnow Opsopoeodus emiliae 6 1982 E Lakes, canals, streams, and Lake Erie Increased turbidity and siltation Channel darter Percina copelandi 18 1991 T Lake Erie and Ohio River Siltation River darter Percina shumardi 8 1989 T Lake Erie and larger tributaries of Ohio River Pollution and siltation Paddlefish Polyodon spathula 11 1996 T Ohio River tributaries, larger streams and rivers Pollution and siltation Brook trout Salvelinus fontinalis 1 1997 T Tributaries of Lake Erie Habitat destruction

timbering and non native species Source: Division of Natural Areas and Preserves, Ohio Department of Natural Resources, Natural Heritage Program 1998. 20 23 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio GLOSSARY ammonia: a compound of nitrogen and hydrogen ( NH3 ). It is a colorless, pungent gas.

biological oxygen demand ( BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose organic material in polluted water.

bioconcentration factors ( BCFs): indicators of the potential for chemicals dissolved in the water column to be taken up by aquatic biota across external surface membranes, usually gills.

biotic: pertaining to the characteristics of a naturally occurring assemblage of plants and animals that live in the same environment and are mutually sustaining and interdependent.

chemical oxygen demand ( COD): the amount of oxygen consumed in the complete chemical oxidation of matter, both organic and inorganic, present in polluted water.

Coldwater Habitat ( CWH): a designation assigned to a water body based on the potential aquatic assemblage.

dissolved oxygen ( DO): oxygen freely available in water, vital to fish and other aquatic life and for the prevention of odors. DO levels are considered a most important indicator of a water body's ability to support desirable aquatic life.

Secondary and advanced waste treatment are generally designed to ensure adequate DO in waste receiving waters.

( http:// www. epa. gov/ OCEPAterms/ dterms. html)

endangered and threatened ( E& T): animals, birds, fish, plants, or other living organisms threatened with extinction by anthropogenic ( i. e., man caused) or other natural changes in their environment. The Endangered Species Act contains requirements for declaring a species endangered.

Endangered Species Act: federal legislation enacted in 1973 that protects animals, birds, fish, plants, or other living organisms threatened with extinction by anthropogenic or other natural changes in their environment. For a species to be protected under this act it must be " listed" as either an " endangered" or " threatened" species.

eutrophication: process by which bodies of water receive increased amounts of dissolved nutrients, such as nitrogen and phosphorus, that encourage excessive plant growth and result in oxygen depletion.

Exceptional Warmwater Habitat ( EWH): the aquatic life use designed to protect aquatic communities of exceptional diversity and biotic integrity. Such communities typically have a high species richness; often include strong populations of rare, endangered, threatened, and declining species; and/ or are exceptional sport fisheries.

existence services: services that are not linked to current uses of water bodies. They arise from the knowledge that species diversity or the natural beauty of a given water body is being preserved.

in stream services: water use taking place within the stream channel for purposes such as life support for animals and plants, water based recreation, hydroelectric power generation, navigation, commercial fishing, water storage, and aesthetics.

Limited Resource Waters ( LRW): an aquatic life use assigned to streams with very limited aquatic life potential, usually restricted to highly acidic mine drainage streams, or highly modified small streams (< 3 sq. mi. drainage area) in urban or agricultural areas with little or no water during the summer months.

Limited Warmwater Habitat ( LWH): see limited resource waters.

metals: inorganic compounds, generally non volatile ( with the notable exception of mercury), that cannot be broken down by biodegradation processes. They are of particular concern due to their prevalence in MP& M effluents. Metals can accumulate in biological tissues, sequester into sewage sludge in POTW s, and contaminate soils and sediments when released into the environment. Some metals are quite toxic even when present at relatively low levels.

  g/ l: micrograms per liter.

20 24 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio Modified Warmwater Habitat ( MWH): aquatic life use assigned to streams that have irretrievable, extensive, man

induced modifications that preclude attainment of the Warmwater Habitat use, but which harbor the semblance of an aquatic community. Such waters are characterized by poor chemical quality ( low and fluctuating dissolved oxygen), degraded habitat conditions ( siltation, habitat simplification), and species that are tolerant of these effects.

nonconventional pollutants: a catch all category that includes everything not classified as either a priority or conventional pollutant.

nutrients: any substance, assimilated by living things, that promotes growth. The term is generally applied to nitrogen and phosphorus in wastewater, but is also applied to other essential and trace elements.

( http:// www. epa. gov/ OCEPAterms/ nterms. html)

Ohio EPA Lake Condition Index ( LCI): an ecologically based index that aggregates results across ten ecological metrics.

Ohio Water Resource Inventory ( OWRI): a biennial report to U. S. EPA and Congress required by Section 305( b) of the Clean Water Act. The report is composed of four major sections: ( 1) inland rivers and streams, wetlands, Lake Erie, and water program description; ( 2) fish tissue contaminants; ( 3) inland lakes, ponds, and reservoirs; and ( 4) groundwater.

overburdens: rock and soil cleared away before mining.

( http:// www. epa. gov/ OCEPAterms/ oterms. html)

pH: an expression of the intensity of the basic or acid condition of a liquid. Natural waters usually have a pH between 6.5 and 8.5. ( http:// www. epa. gov/ OCEPAterms/ pterms. html)

pollutants of concern ( POCs): the 131 contaminants identified by EPA as being of potential concern for this rule and that are currently being discharged by MP& M facilities. EPA used fate and toxicity data, in conjunction with various modeling techniques, to identify these pollutants and assess their potential environmental impacts on receiving water bodies and POTWs. MP& M pollutants of concern include 43 priority pollutants, 3 conventional pollutants, and 86 nonconventional pollutants.

polychlorinated biphenyls ( PCBs): a group of toxic, persistent chemicals that are mixtures of chlorinated biphenyl compounds having various percentages of chlorine. PCBs are industrial chemicals formerly used in electrical transformers and capacitors for insulating purposes, and in gas pipeline systems as a lubricant.

polycyclic aromatic hydrocarbons ( PAHs): a class of organic compounds with a fused ring aromatic structure. PAHs result from incomplete combustion of organic carbon ( including wood), municipal solid waste, and fossil fuels, as well as from natural or anthropogenic introduction of uncombusted coal and oil. PAHs include benzo( a) pyrene, fluoranthene, and pyrene.

Primary Contact Recreation ( PCR): water recreation activities requiring full human body immersion, such as swimming, diving, water skiing, and surfing.

priority organics: prority pollutants that are organic chemicals.

priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,

sediment, groundwater, or soil samples.

random utility model ( RUM): a model of consumer behavior. The model contains observable determinants of consumer behavior and a random element.

Secondary Contact Recreation ( SCR): water recreation activities requiring some direct contact with water but where swallowing of water is unlikely, such as paddling, wading, and boating.

siltation: deposition of finely divided soil and rock particles on the bottom of stream and river beds and in reservoirs.

Survey of National Demand for Water based Recreation ( NDS): a U. S. EPA survey of recreational behavior. The 1993 survey collected data on socioeconomic characteristics and water based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. ( http:// www. epa. gov/ opei)

20 25 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio total allowable catch ( TAC): amount of fish permitted to be removed under a fishery management regime in which the total catch allowed of a certain species for a fishing season has been fixed in advance.

  toxic  pollutants: refers to the 126 priority or toxic pollutants specifically defined as such by EPA, as well as nonconventional pollutants that have a toxic effect on human health or aquatic organisms.

turbidity: cloudy condition in water that interferes with the passage of light through the water column. It is caused by the presence of suspended silt or organic matter in the water body.

un ionized: neutral form of an ionizable compound. With reference to ammonia, it is the neutral form of ammonia nitrogen in water, usually occurring as NH4 OH. Un ionized ammonia is the principal form of ammonia that is toxic to aquatic life. The relative proportion of un ionized to ionized ammonia ( NH4+ ) is controlled by water temperature and pH.

Warmwater Habitat ( WWH): a designation assigned to a water body based on the potential aquatic assemblage.

water quality criteria ( WQC): specific levels of water quality that, if reached, are expected to render a body of water suitable for certain designated uses.

withdrawal services: services associate with water removed from the ground or diverted from a surface water source for uses such as drinking water supply, irrigation, production and processing services, and sanitary services.

20 26 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio ACRONYMS BCFs: bioconcentration factors BOD: biological oxygen demand COD: chemical oxygen demand CWH: Coldwater Habitat DO: dissolved oxygen E& T: endangered and threatened EWH: Exceptional Warmwater Habitat LRW: Limited Resource Waters LWH: Limited Warmwater Habitat MWH: Modified Warmwater Habitat ODH: Ohio Department of Health DNR: Ohio Department of Natural Resources LCI: Ohio EPA Lake Condition Index OWRI: Ohio Water Resource Inventory POCs: pollutants of concern PCBs: polychlorinated biphenyls PAHs: polycyclic aromatic hydrocarbons PCR: Primary Contact Recreation RUM: random utility model SSH: Seasonal Salmonid Habitat SCR: Secondary Contact Recreation NDS: Survey of National Demand for W ater based Recreation TAC: total allowable catch WWH: Warmwater Habitat WQC: water quality criteria 20 27 MP& M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio REFERENCES Baldwin, N. A., R. W. Saalfeld, M. R. Dochoda, H. J. Buettner, and R. L. Eshenroder. August 2002. Commercial Fish Production in the Great Lakes 1867 2000. http:// www. glfc. org/ databases/ commercial/ commerc. asp Lake Erie Charter Boat Association ( LECB A). 2003. www. lecba. org.

Ohio Department of Natural Resources, Ohio Division of Wildlife. 1999. Fish Consumption Advisories.

http:// www. dnr. state. oh. us/ odnr/ wildlife/ index. html.

Ohio Department of Natural Resources, Division of Natural Areas and Preserves. 1998. Database File of Aquatic and Associated Aquatic Endangered & Threatened Animals.

Ohio Environmental Protection Agency. 1998. State of the Lake Report ( www. epa. ohio. gov/ oleo/ leqi/ leqi. html)

Ohio Environmental Protection Agency. 1996. Ohio Water Resource Inventory. Volume 1: Summary, Status , and Trends and 3: Ohio s Public Lakes, Ponds, and Reservoirs. www. chagrin. epa. state. oh. us/ document_ index.

U. S. Department of Agriculture ( USDA). 1992a. Agricultural Waste Management Field Handbook. National Engineering Handbook Series, Part 651. 210 AWM FH, 4/ 92.

U. S. Department of Agriculture. 1992b. National Resources Inventory. National Resources Conservation Services.

http:// www. ftw. nrcs. usda. gov/ nri_ data. html.

U. S. Department of Commerce, Bureau of the Census. 1992. Census of Manufactures, Census of Transportation, Census of Wholesale Trade, Census of Retail Trade, Census of Service Industries.

U. S. Department of Commerce, Bureau of the Census. 1999. Ohio Population, Demographic, and Housing Statistics.

http:// www. census. gov/ cgi bin/ datamap/ state? 39.

United States Geological Survey ( USGS). 1995. Water Use in the United States. http:// water. usgs. gov/ watuse.

U. S. Environmental Protection Agency ( U. S. EPA). 1986. Ambient Water Quality Criteria for Dissolved Oxygen. EPA 440/ 5 86 003.

U. S. Environmental Protection Agency ( U. S. EPA). 1992. Managing Non point Source Pollution: Final Report to Congress. EPA 506/ 9 90.

U. S. Environmental Protection Agency ( U. S. EPA). 1994. National Demand for Water Based Recreation Survey.

Washington, D. C.: Office of Policy Evaluation and Information.

U. S. Environmental Protection Agency ( U. S. EPA). 1998a. National Recommended Water Quality Criteria; Notice;

Republication. 63( 237: 68354 68364).

U. S. Environmental Protection Agency ( U. S. EPA). 1998b. Condition of the Mid Atlantic Estuaries. EPA 600 R 98 147.

U. S. Environmental Protection Agency ( U. S. EPA). 1998c. 1988 Update of Ambient Water Quality Criteria for Ammonia.

EPA 822 R 98 008.

U. S. Environmental Protection Agency ( U. S. EPA). 1999. Progress Report on the EPA Acid Rain Program. U. S. EPA Office of Air and radiation. EPA 430 R 99 011.

Wetzel, R. G. 1983. Limnology, 2nd ed. Saunders College Publishing.

20 28 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model INTRODUCTION The recreational benefits analysis outlined in this chapter focuses on Ohio as a case study of the MP& M regulation's expected benefits. EPA combined water quality modeling and a random utility model of consumer behavior ( RUM)

to assess how changes in water quality from the MP& M regulation will affect consumer valuation of water resources for recreational uses. The RUM analysis provides a framework for estimating the effect of ambient water quality and other site characteristics on the total number of trips taken for different water based recreation activities and the allocation of these trips among particular sites.

The Agency used this case study to address limitations inherent in the benefits transfer method used in the analysis of recreational benefits at the national level ( see Chapter 15 for detail). The RUM model assesses water quality characteristics directly affected by the MP& M regulation,

such as presence of ambient water quality criteria ( AWQC) exceedances and nonconventional nutrient Total Kjeldahl Nitrogen ( TKN) concentrations and their effect on recreation behavior. The direct link between the water quality measures included in the RUM model and the water quality measures affected by the regulation, as well as the site specific nature of the analysis reduce uncertainty in benefit estimates. In general, RUM models are well regarded in the economic literature and when these models are appropriately applied, the results are thought to be quite reliable. Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model CHAPTER CONTENTS 21.1 Methodology ................. ........... 21 2 21.1.1 ................. ......... 21 2 21.1.2 he Site Choice Decision ...... 21 3 21.1.3 rip Participation ............ 21 6 21.1.4 ting Welfare Changes from Water Quality Improvements ................. . 21 9 21.1.5 the State Level . . 21 10 21.2 ................. ................. . 21 10 21.2.1 ................. .... 21 11 21.2.2 Visits to Sites . . . 21 14 21.2.3 ................. 21 14 21.3 ................ 21 17 21.3.1 l ................. .... 21 18 21.3.2 ................. .... 21 19 21.3.3 ................. .. 21 20 21.3.4 Activity) Model . . . 21 20 21.4 el ................. .. 21 20 21.5 ating Benefits from Reduced MP& M Discharges in Ohio ................. ... 21 23 21.5.1 iting Reaches in Ohio ........... 21 23 21.5.2 ating creational Benefits in Ohio 21 24 21.6 itations and Uncertainty ................ 21 25 21.6.1 ................. 21 25 21.6.2 One Day Trips Only ......... 21 26 21.6.3 nefits ................. ... 21 26 21.6.4 ial Sources of Survey Bias ....... 21 26 Glossary ................. ................. .. 21 28 Acronyms ................. ................. . 21 30 References ................. ................. 21 31 Overview Modeling t Modeling T Calcula Extrapolating Results to Data The Ohio Data Estimating the Price of Site Characteristics Site Choice Model Estimates Fishing Mode Boating Model Swimming Model Viewing ( Near water Trip Participation Mod Estim Benef EstimRe Lim One State Approach Including Nonuse Be Potent Benefits transfer results are subject to uncertainty because water quality changes evaluated in available recreation demand studies are only roughly comparable with water quality measures considered in regulatory development. This case study analysis improves upon previous recreation demand studies that focused mainly on directly observable water quality effects,

e. g., designated use support ( i. e., whether a water body supports fishing), the presence of fish advisories, an oil sheen, or eutrophication. The Ohio case study includes unobservable water quality effects as well. The MP& M regulation affects a broad range of pollutants, many of which are toxic to human and aquatic life but are not directly observable ( i. e., priority and nonconventional pollutants. These unobservable toxic pollutants degrade aquatic habitats, decrease the size and abundance of fish and other aquatic species, increase fish deformities, and change watershed species composition. Water quality changes ( i. e., changes in toxic pollutant concentrations) affect consumers  water resource valuation for recreation,

even if consumers are unaware of changes in ambient pollutant concentrations.

This study allows for a more complete estimate of recreational benefits from reduced discharges of MP& M pollutants. In addition to estimates of recreational benefits from reduced frequency of AWQC exceedances, the Ohio case study evaluated changes in the water resource values from reduced discharges of TKN . The analysis also values additional recreational uses not addressed in the national analysis, such as swimming.

21 1 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model The study used data from the National Demand Survey for Water Based Recreation ( NDS) , conducted by U. S. EPA and the National Forest Service, to examine the effects of in stream pollutant concentrations on consumer decisions to visit a particular water body ( U. S. EPA, 1994).

21.1 METHODOLOGY 21.1.1 Overview The Ohio study combines direct simulation and inferential analyses to assess how changes in water quality will affect consumers  valuation of water resources.

The direct simulation analysis component estimates baseline and post compliance water quality at recreation sites actually visited by the surveyed consumers and all other sites within the consumers  choice set, visited or not.

The inferential analysis component, a RUM analysis of consumer behavior, estimates the effect of ambient water quality and other site characteristics on the total number of trips taken for different water based recreation activities and the allocation of these trips among particular recreational sites. The RUM analysis is a travel cost model ( TCM), in which the cost to travel to a particular recreational site represents the   price  of a visit.

The main advantage of the RUM model is inclusion of the effect of substitute sites on site values. For any particular site,

assuming that it is not totally unique in nature, the availability of substitutes makes the value for that site lower than it would be without available substitutes.

EPA modeled two consumer decisions:

  how many water based recreational trips to take during the recreational season ( the trip participation model); and   conditional on the first decision, which recreation site to choose ( the site choice model).

The econometric estimation proceeded in two steps, each corresponding to the above decisions. The Agency estimated these decisions in reverse order ( i. e., EPA modeled the second decision, site choice, first).

  Modeling the Site Choice Decision. Assuming that a consumer decides to take a water based recreation trip, EPA estimated the likelihood that the consumer will choose a particular site as a function of site characteristics, the price paid per site visit, and household income. A consumer weighs the attributes for various " choice set" sites against the travel costs to each site. These travel costs include both the cost of operating a vehicle and the opportunity costs of time spent traveling. The consumer then weighs the value given to the site's attributes against the cost of getting to the site when making a site selection. The site choice model estimates how recreational users value access to specific sites, and estimates per trip economic values for changes in water quality at recreational sites in the study area.

EPA estimated the site choice model using a two level nested multinomial logit ( NMNL) model, which groups sites with similar characteristics. The nested logit model assumes that individuals first choose the group of sites and then a site within that group. This study assumes that individuals first choose a water body type ( Lake Erie, rivers, or small lakes) and then a specific site. EPA used the estimated site choice model coefficients to estimate the value to the consumer of being able to choose among Ohio recreation sites on a given day. This measure is referred to as the   inclusive value.

  Modeling Trip Frequency. The site choice models estimated in the previous step treat the total number of recreational trips taken each season as exogenous to the site selection. The Agency estimated the expected number of trips taken during the recreation season using a Negative Binomial Poisson model ( Hausman et al.,

1995; Feather et al., 1995; and Creel and Loomis, 1992), which treats trip frequency as a pre season decision regarding total participation in a given recreation activity.

EPA estimated the total number of trips during the recreation season as a function of the expected maximum utility ( inclusive value) from recreational activity participation on a trip, and socioeconomic characteristics affecting demand for recreation trips ( e. g., number of children in the household). The coefficient of the individual s expected 21 2 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model maximum utility of taking a trip) provid ed a m eans o f estimating the seas onal welfare effect of water qua lity improvements, beca use ch anges in water quality ch ange the value of av ailable recre ation sites .

Estimating the site choice and total trip p articipa tion mode ls jointly is theo retically possible, but co mpu tational requirements make an integrated utility theoretic model infeasible. EPA estimated separate site choice and trip frequency models for the four recreational activities: boating, swimming, fishing, and near water recreation ( e. g., viewing wildlife). 1 The Agency used estimated coefficients of the indirect utility function with estima ted ch anges in water quality to calcula te per trip chang es in co nsum er welfare from impro ved water q uality at rec reation sites within e ach c onsu mer c hoice set. Trip frequency per season increases if site water quality changes are substantial. A sample consumer s expected seasonal welfare gain is therefore a function of both welfare gain per trip and the estimated change in number of trips per season.

Combin ing the trip freque ncy mo del s p redic tion of trip s unde r the ba seline an d po st com plianc e and the site cho ice mo del s corresponding per trip welfare measure yields the total se aso nal w elfare measure.

EP A ca lculated each individ ual s seasona l welfare g ain for e ach re creatio n activity fro m po st com plianc e wate r qua lity changes, and then used Census population data to aggregate the estimated welfare change to the state level. The sum of estimated welfare changes over the four recreation activities yielded estimates of total welfare gain.

To analyze water quality improveme nt benefits in the RUM framewo rk, EPA used available discharge, ambient concentration,

and other relevant data to measure baseline and post compliance water quality at the impact sites. H provides detail on water q uality modeling used in this analysis.

21.1.2 EPA used the RUM framework to estimate the probability of a consumer visiting a recreation site. ramework is based on the assumption that a consumer derives utility from the recreational activity at each recreation site. Each visit decision involves cho osing one site and excluding o thers.

The consumer s decision involves comparing each site and choosing the site that produces the maximum utility. An observer cannot measure all potential determinants of consumer utility, so the indirect utility function will have a non random element ( V) and a random error term (   ) , such tha t the actua l determ inants of consumer utility V   = V +   . e pro bab ility (   jn) that site j will be visited by an individual n is defined as:

( 21.1)

where:

Vjn +   jn = utility of visiting site j, and Vsn +   sn = utility of visiting a substitute site.

Estimating the m ode l requires spe cifying the fun ctiona l form o f the indire ct utility function , V, in wh ich site ch oice is mod eled a s a functio n of site ch aracte ristics and the   pric e  to v isit particular sites. r exam ple, a se t of con ditiona l utility functions ( one for each site alternative j in the choice set) can b e determ ined as follows:

( 21.2)

where:

Vjn = the utility realized from a conventional budget constrained, utility maximization model conditional on choice of site j by consumer n;

  M = marginal utility of income;

M jn = the income of individual n availab le to visit site j; Appendix Modeling the Site Choice Decision This f Th Fo 1 The Agency also attempted a model structure that allows for interaction among the choice of recreational activities. In this model, a person first chooses a recreational activity and then chooses a site. This model did not perform very well because less than ten percent of recreational users included in the dataset participate in all four activities.

21 3 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model Pjn = a composite measure of travel and time costs for consumer n on site alternative j;

  = a vector of coefficients representing the marginal utility of a specified site characteristic to be estimated along with   M ( e. g., size of the water body, presence of boating ramps); and Xjn = a vector of site characteristics for site alternative j as perceived by consumer n. These characteristics include the actual monitored and/ or modeled water quality parameters that are hypothesized to be determinants of consumer valuation of water based recreation resources, and that may also be affected by the MP& M regulation.

The magnitude of the coefficients in Equation 21.2 reflects the relative importance of site characteristics when consumers decide which site to visit. The coefficients (   ) of water quality characteristics of recreation sites are expected to be positive;

that is, all else being equal, consumers of water based recreation would prefer " cleaner" recreation sites. The coefficient on travel cost is expected to be negative, i. e., consumers prefer lower travel costs.

To estimate the site choice probabilities, EPA specified and estimated a nested multinomial logit model ( NM NL) for fishing,

boating, and swimming activities. The nested structure explicitly groups similar alternatives, which allows for a richer pattern of substitution among alternative sites. The NMNL is based on the assumption that an individual chooses first between groups of alternatives and then, within the chosen group, between individual alternatives. For this analysis, EPA grouped all recreational sites in Ohio by water body type based on site similarities. EPA tested various alternative site groupings, but the models presented here were most successful at explaining the probability of selecting a site. The best model used the following activity specific site groupings: 2   Fishing model:

  Group 1: Lake Erie sites;

  Group 2: river sites;

  Group 3: small lakes and reservoirs;

  Boating model:

  Group 1: Lake Erie sites;

  Group 2: inland sites, including rivers, small lakes, and reservoirs;

  Swimming model:

  Group 1: Lake Erie sites;

  Group 2: inland sites, including rivers, small lakes, and reservoirs;

  Viewing model: EPA used a non nested model in which an individual compares all sites and chooses the one offering the highest utility level for each trip occasion.

First, the Agency attempted to estimate a nested model based on the three water body types lakes, rivers, and Lake Erie for all four recreational activities included in the analysis. This structure, however, performed well only for fishing. A two nested model that included inland and Lake Erie sites seemed to perform better for the boating and swimming models.

None of the nested structures performed well for participants in near water/ wildlife viewing activities.

This finding is not surprising because sites are grouped based on their similarities within a given nest. It is reasonable to assume that inland lakes, rivers, and Lake Erie sites are dissimilar from an angler's point of view, because each of the three water body types is likely to support different fish species. Lake sites may therefore not be close substitutes for rivers sites.

For other activities, differences in fishery resources across water body types are unlikely to be important. Water body size and the presence of recreational amenities are likely to play a more important role than differences in fish species and the type of aquatic habitat. Lake and river sites may therefore be regarded as substitutes for each other by boaters and swimmers.

Lake Erie, on the other hand, is a unique water resource that differs from inland water bodies because of its physical characteristics ( e. g., size and water temperature); river and lake sites are therefore not likely to be considered substitutes for Lake Erie sites. Finally, participants in near water recreation use water resources indirectly and are therefore more likely to regard recreational sites located on different water body types as close substitutes to each other. For this reason, the viewing model is a simple logit model without a nested structure.

2 Three of the four models ( fishing, boating, and swimming) passed specification tests for appropriateness of a nested structure ( see Section 21.3 for detail). Test results showed that only two site groups are appropriate for the boating and swimming models inland sites ( rivers, small lakes, and reservoirs) and Lake Erie sites in Ohio. The fourth activity, wildlife viewing, did not pass specification tests for a nested structure and was estimated as a flat multinomial logit ( MNL) model.

21 4 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model The models assume that an individual first decides to visit a specific water body grouping ( hereafter, region), then decides which site within that group to visit. An individual probability of visiting site j, given the choice of region R, is a simple multinomial logit. If the random terms   nj for individual n at site j are independently and identically distributed and have an extreme value Weilbull distribution, then   jn takes the form ( McFadden, 1981):

( 21.3)

where:

  jn| r = probab ility of selecting site j in region r;

= the consumer s utility from visiting site j;

r = regions   Lake Erie,    rivers,  etc. as specified above for a given activity; and = the sum of the consumer s utility at each site j for all sites in the opportunity set for region r.

Estimated parameters of the indirect utility function are then used to estimate the inclusive value. n, the inclusive value measures the overall quality of recreational opportunities for each water based activity and represents the expected maximum utility of taking a trip. although EPA used a rando m draw from the oppo rtunity set for the purp ose o f estimating the mo del param eters, the Agency calculated the inclusive value ( i. e., the ex pected m aximu m utility)

using all recreation sites in the consumer's opportunity set in a given region.

The inclusive value is calculated as the log of the denominator in Equation 21.2 ( M cFadden, 198 1).

( 21.4)

where:

Ir = inclusive value for sites associated with region R;

= individual n s utility from visiting site j; and W = a vector o f baseline water qua lity characteristics.

The prob ability of choosing a p articular region is:

( 21.5)

where:

  r = probability of selecting region r;

Ir = the inclusive values for a given region;

 r = the coefficient on the inclusive value for a given region;

r = activity specific regions ( e. g.,   Lake Erie,    rivers,  and   small lakes  for fishing).

To estimate the mo del d escrib ed b y Eq uation s 21.2 and 2 1.5, E PA used a stand ard sta tistical softwa re pa ckag e, LIMDEP. For consumer Note that,

and 21 5 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 21.1.3 Modeling Trip Participation After modeling the site choice decision, the next step modeled the determinants of the number of water based recreation trips a consumer takes during a season. To link the quality of available recreation sites with consumer demand for recreation trips,

EPA modeled the number of recreation trips taken during the recreation season as a function of the inclusive value estimated in the previous step and socioeconomic characteristics affecting demand for recreation activities. The dependent variable, the number of recreation trips taken by an individual during the recreation season, is an integer value greater than or equal to zero. To account for the non negative property of the dependent variable, EPA used count data models based on probability densities that have the non negative integers as their domain.

One of the simplest count data models is a Poisson estimation process, which is commonly used with count data, such as number of recreation trips taken during the recreation season. Inherent in the model specification is the assumption that each observation of a number of trips is drawn from a Poisson distribution. Such a distribution favors a large number of observations with small values ( e. g., two trips, four trips) or zeros, resulting in its being skewed toward the lower end. Due to the nature of the observed number of trips, it is quite reasonable to assume that the underlying distribution can be characterized as a Poisson distribution. Figure 21.1 shows the number of recreation trips taken per year and the number of respondents who reported taking that number of trips.

21 6 MP& M EEBA Part V: Ohio Case StudyChapter 21: Modeling Recreational Benefits in Ohio With a RUM Model 21 7 Figure 21.1: Number of Trips Per Year By Activity Type Source: U. S. EPA analysis of NDS data ( U. S. EPA, 1994) MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model Estimating the Poisson model is similar to estimating a nonlinear regression. The single parameter of the Poisson distribution is   , which is both the mean and variance of yn . The probability that the actual number of trips taken is equal to the estimated number of trips is estimated as follows ( Green, 1993):

( 21.6)

where:

Yn = the actual number of trips taken by an individual in the sample;

yn = the estimated number of trips taken by an individual in the sample;

n = 1, 2,..., N, the number of individuals in the sample; and   n =     X, expected numb er of trips for an individual in the sample, where X is a vector of variables affecting the demand for recreational trips ( e. g., inclusive values and socioeconomic characteristics) and   is the vector of estimated co efficients.

From Equation 21.6, the expected number of water based recreation trips per recreation activity season taken by an individual is given b y:

( 21.7)

where:

E[ yn| xn] = the exp ected numb er of trips, yn, given xn; Var[ yn| xn] = the variance of the numbe r trips, yn, given xn;   = a vector of coefficients on x; and x = a matrix of socioec ono mic va riables and inclusive values.

An empirical drawback of the Poisson model is that the variance of the number of trips taken must be equal to the mean numb er of trips, and this equa lity is not always supp orted by actual data. ticular, the ND S survey data exhibit overdispersion, a condition where variance exceeds the mean. The estimated variance to mean ratios of the number of trips in the NDS d ata sample are 31, 2 7.9, 3 5.6, and 10.5 fo r fishing, swim ming, viewing, and boating trips, resp ectively.

Overdisp ersion is therefo re pre sent in the data se t.

To address the problem of overdispersion, EPA used the negative binom ial regression mo del, an extension of the Poisson regression model, which allows the variance of the number of trips to differ from the mean. In the negative binomial mod el,   is respecified so that ( Green, 1993):

( 21.8)

where the error term (   ) has a gamm a distribution, E[ exp(   i)] is equal to 1.0, and the variance of   is   .

Th e resulting probab ility distributio n is:

( 21.9)

where:

yn = 0,1,2 ... numb er of trips taken b y individual n in the sample;

n = 1,2,..., N number of individuals in the sample; and   n = expected number of trips for an individual in the sample. In par 21 8 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model Integrating   from E quation 21 .9 pro duces the un conditiona l distribution of yn. ive binomial model has an add itional p aram eter,  , which is the over dispe rsion p aram eter, suc h that:

( 21.10)

The overd ispersion rate is then given by the following equation:

( 21.11)

EPA used the negative binomial model to predict the seasonal number of recreation trips for each recreation activity based on the inclusive value, individ ual soc ioeco nom ic characteristics, and the ove rdispersion parameter,   . the inclusive value has the anticipated positive sign, then increases in the inclusive value stemming from improved ambient water quality at recreation sites will lead to an incre ase in the numb er of trips. e com bined MNL mod el site cho ice and coun t data trip participation models allowed the Agency to account for changes in per trip welfare values, and for increased trip participation in response to improve d amb ient water quality at recreation sites.

21.1.4 EPA estimated the welfare change associated with water quality improvements from the baseline to post compliance conditions as a compensating variation ( CV), which equates the expected value of realized utility under the baseline and post compliance conditions. d seasonal change in welfare attributed to the quality improvements for an individual n in the sample co nsists of two comp onents:

  per trip welfare gain, and   increased numb er of trips unde r the po st compliance water quality cond ition.

The Age ncy first calculated the welfare gain from water quality improvement for each consumer on a given day by using a CV m easure for consumer n ( Kling and Thom pson, 1996):

( 21.12)

where:

CVn = the compe nsating variation for individual n at site j on a given d ay;

r =   Lake Erie,    inland,  etc.

j = 1,... Jr represents a set of alternative sites for a given recreational activity in region r;

= the inclusive value index ( I);

W 0 = a vector of info rmatio n describing base line wate r qua lity;

W 1 = a vector of information describing post compliance water quality; and   M = the implicit coefficient on income that influences recreation behavior.

In deriving E quation 21 .12, E PA assumed that the marginal utility of inc ome ,   M , is constant across alternatives ( as well as across quality change s). ption doe s not ap ply, the derivatio n of E q. 21 .12 is m ore c omp licated ( Ha usma n et al.,

1995). The negat If Th Calculating Welfare Changes from Water Quality Improvements The expecte If this assum 21 9 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model EP A then estimated the lo w and high values of the seasonal welfare gain for individual n in the sam ple as fo llows: 3 ( 21.13)

( 21.14)

where:

W low, n = lower bound estimate o f the seaso nal welfare gain for individual n;

W high, n = upper bound estimate of the seasonal welfare gain for individual n;

I1 = the post policy inclusive value;

Y1 = the estim ated n umb er of trip s after wa ter qua lity impro vement;

I0 = the baseline inclusive value;

Y0 = the estimated number of trips in the baseline; and     = the implicit coefficient on income that influences recreation behavior.

These estimates are per individual in the population for those individuals meeting qualifications for inclusion in the NDS respo nse set ( i. e., respo ndents who se home state is Oh io and respo ndents from the neighbo ring states whos e last trip w as to Ohio s sites). 4 EPA extrapolated the estimates of value per individual to the Ohio state level based on Census data ( U. S.

Bure au of the Cen sus, 2000 ). section details the extrapo lation method used in the analysis.

21.1.5 Extrapolating Results to the State Level EPA used a simplified extrapolation technique to estimate the state level benefits. EPA first estimated the number of participants in fishing, swimming, boating, and wildlife viewing in Ohio, based on the estimated percentage of the NDS survey respo ndents residing in O hio who pa rticipate in a given activity and the state adult po pulatio n. e 20 00 C ensus d ata provide information on the number of Ohio residents aged 16 and older. EPA then multiplied the estimated average seasonal welfare gain per participant in a given recreational activity by the corresponding number of recreational users. The total welfare gain to the users of water based recreation in Ohio is the sum of fishing, swimming, boating, and wildlife viewing benefits.

21.2 DATA This section describes the data and supporting analyses required to implement the RUM analysis. ollowing general categories of data and supporting analyses are required:

  information on the consumers of water based recreation responding to the NDS in Ohio;

  recreation sites identified for the water quality and RUM analyses, including the sites visited by consumers of water

based recreational activity and sup plemen tal sites in their choice sets;

  estimated price of visiting the sites. isit price  is estimated as a function of travel distance ( and travel time)

between each consumer s hometown and each site in the choice set; and   information on site characteristics likely to be important determinants of consumer behavior. Of particular importan ce to this analysis are the water qua lity and related characteristics of sites in the choice set, and h ow those characteristics may be expected to change as a result of regulation. The following Th The f The   v 3 EPA selected this approach for calculating seasonal welfare gain per individual based on Dr. Parsons  recommendation ( G. R. Parsons, 1999).

4 Section 21.2.1 provides a detailed description of the data sample used in the analysis.

21 10 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model The following sections discuss each category of data and/ or supporting analysis below.

21.2.1 The Ohio Data EPA obtained information on survey respondent socioeconomic characteristics and recreation behavior, including last trip profile and the annual number of trips associated with each water based activity, from the NDS ( U. S. EPA, 1994). The 1994 survey collected data on demographic characteristics and water based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. Respondents reported on water based recreation trips taken within the past 12 months, including the primary purpose of their trips ( e. g., fishing, boating, swimming, and viewing), total number of trips, trip length, distance to the recreation site( s), and number of participants. Where fishing was the primary purpose of a trip, respondents were also asked to state the number of fish caught. Table 21.1 shows the number of trips taken per year by primary recreation activity, as reported in the NDS.

EPA selected case study observations for Ohio residents who took trips within or outside of the state. Trips to Ohio recreation sites by residents of neighboring states were also included in the site choice models, but not in the trip participation models. 5 All four activity models included single day trips only. EPA included only activity participants with valid hometown ZIP co des, whose destination site was uniquely identified. The Agency used data on both Ohio participants and Ohio non participants to estimate total seasonal trips, but included only Ohio participants and several residents of nearby states in the site choice models. Although they could not be used in the site choice model, participant observations from Ohio with missing location information were used to analyze the number of trips. Tables 21.1 and 21.2 list valid observations by activity, residence, and model type. Figure 21.2 illustrates the distribution of the sample observations in relation to the location of MP& M facilities affected by the rule in Ohio.

Table 21.1: lassification of Sample Observations for Estimation of the Site Choice Models Total Ohio Residents Ohio Residents with Last Trip In State Valid Ohio Residents with Last Trip In

State Valid Ohio Residents with Last Trip Outside State Valid Nonresidents with Last Trip in Ohio Valid for Site Choice Model Participants ( Total) 609 408 237 35 11 297 Fishing 122 103 66 9 0 84 Swimming 147 100 58 14 2 76 Viewing 231 126 64 2 7 73 Boating 109 79 49 10 2 64 C Source: U. S. EPA analysis.

5 These additional observations total 11 across the four activities and thus represent only a small fraction of total observations.

Including only Ohio respondents in the trip participation models underestimates the benefits associated with water quality improvements,

because the welfare gains to recreators from neighboring states are ignored.

21 11 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model Table 21.2: Classification of Sample Observations for Estimation of the Trip Participation Models Ohio Residents Total Residents with Last Trip In State Residents with Last Trip Outside State Valid for Trip Participation Model Non Participants 300 291 Participants ( Total) 609 408 34 322 Fishing 122 103 4 84 Swimming 147 100 9 78 Viewing 231 126 7 75 Boating 109 79 14 85 Total Observations 909 408 34 613 Source: U. S. EPA analysis.

21 12 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model Figure 21.2: Location of MP& M Facilities in Relation to the Visited Sites Source: U. S. EPA analysis.

21 13 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model 21.2.2 Estimating the Price of Visits to Sites EPA estimated trip   price  for each consumer of water based recreation as the sum of travel costs plus the opportunity cost of time, following the procedure described in Haab et al. ( 2000). and Kealy ( 1992), this study assumed that time sp ent   on  site  is constant ac ross sites a nd ca n be ignore d in the p rice ca lculation .

To estimate consumers  travel costs, EPA first used ZipFip software to calculate the one way distance to each site for each participant. 6 The average estimated one way distance to the site visited is 37.5 6 miles. EP A then multiplied round trip distance by average motor vehicle cost per mile ($ 0.29, 1993 do llars). 7,8 The model adds the opportunity cost of travel time,

measured in terms of wages lost, to the travel cost for those who would have lost income by taking the recreation trip. For these consumers the dummy variable LOSEINC equals one. times equal the round trip distance divided by a travel speed of 40 mph and multiplied by the individual s hourly wage as calculated below.

The travel cost variable in the mo del was calculated as follows:

( 21.15)

Individuals not losing income ( e. g., individuals taking vacation or a weekend trip or individuals whose work schedule is not flexible) do not face lost wages as a result of the trip and inclusion of the opportunity cost of time would be inappropriate.

These consumers still have an opportunity cost for their travel time, which could otherwise be spent doing something else,

like fishing. istance traveled allows for a longer time spe nt at the recreation site.

consum ers, the analysis included an ad ditional round  trip travel time variable calculated a s:

( 21.16)

The average o ne way estimated trave l time to the visited site is 56.34 minutes. 9 21.2.3 Site Characteristics EPA identified 1,954 recreation sites on 1,631 reaches in the universal opportunity set. hese, 580 observations are known recreational sites ( e. g. parks); 1,366 observations are Reach File 1 ( RF1) reaches without a known recreational site; and eight observ ations a re neithe r located in R F1 nor ide ntified as known recreation sites but w ere visited by an N DS respo ndent. Based on Parsons Travel In other words, a shorter d For these Of t 6 The program was created by Daniel Hellerstein and is available through the USDA at http:// usda. maunlib. cornell. edu/ datasets/ general/ 93014.

7 Note that all expenditures are in 1993 dollars because the NDS trip choices and the associated expenditure occurred in 1993.

8 The estimate of motor vehicle cost per mile was based on estimates compiled by the Insurance Information Institute.

9 The average travel time to the visited site was fairly uniform across the activities. Average one way time to the visited site was 51.38 minutes, 71.64 minutes, 43.76 minutes, and 58.57 minutes for fishing, boating, swimming, and viewing, respectively.

21 14 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model Each consumer choice set theoretically includes hundreds of substitutable recreation sites in Ohio and in the neighboring states. To prevent the recreation site analysis from becoming unmanageable, EPA analyzed a sample of recreation sites for each consumer observation. The Agency then created a randomly drawn reduced choice set for each recreational activity as follows: 10   Fishing. The reduced choice set consists of 20 Lake Erie sites, 20 river sites, and 20 small lakes/ reservoirs. Thus, a total individual choice set consists of 60 alternatives ( including the chosen site);

  Boating. The reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites ( including rivers and lakes/ reservoirs). A total individual choice set consists of 40 alternative sites ( including the chosen site);

  Swimming. Similar to boating, the reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites ( including rivers and lakes/ reservoirs). A total individual choice set consists of 40 alternative sites ( including the chosen site);

  Wildlife Viewing. The reduced choice set consists of 40 sites, including Lake Erie, river, and small lake/ reservoir sites.

Each participant choice set, by definition, includes the site actually visited by the respondent. For each consumer, EPA drew the additional sites from a geographic area defined by a distance constraint ( and the water body types listed above). The Agency used a 120 mile distance limit for inland recreation sites ( Ohio rivers, small lakes, or reservoirs). All Lake Erie sites are eligible for inclusion in the choice sets for all models. EPA assumed that consumers of water based recreation would be willing to travel farther to visit Lake Erie sites, because this water resource presents unique recreational opportunities. 11 EPA used the resulting aggregate choice set of sites for all individuals participating in a given recreation activity to model consumer decisions regarding trip allocation across recreation sites.

The Agency used two classes of characteristics to estimate site choice:

  those unaffected by the MP& M regulation, but likely to determine valuation of water based recreational resources;

and   those affected by the regulation and hypothesized to be significant in explaining recreation behavior and resource valuation.

Regulation independent site characteristics include water body type and size, location characteristics, and the presence of site amenities ( e. g., boat ramps, swimming beaches, picnic areas). Regulation dependent site characteristics include regulation

affected water quality variables.

a. Regulation independent site characteristics Site characteristics that are likely to be important determinants of consumer valuation of water based recreational resources but that are independent of the MP& M regulation include general site descriptors. These descriptors include the type and size of the water body and location characteristics, and the presence of site amenities. EPA obtained data on regulation

independent site characteristics from two main sources, RF1 and the Ohio Department of Natural Resources ( ODNR).

RF1 provided water body type ( i. e., lake, river, or reservoir) and physical dimension ( i. e., length, width, and depth). The dummy variables, LAKE ERIE, RIVER, and LAKE characterize water body types. If a site is located on Lake Erie, LAKE ERIE takes the value of 1; 0 otherwise. If a site is located on river, RIVER takes the value of 1; 0 otherwise. Finally, if a site is located on a small lake or reservoir, LAKE takes the value 1; 0 otherwise. Water body size was determined by the length of the reach segment in miles for rivers and Lake Erie sites. For small lakes and reservoirs, the appropriate water body size is the water body area in acres. The site choice models use the logarithm of water body size as a measure of site importance,

10 McFadden ( 1981) has shown that estimating a model using random draws can give unbiased estimates of the model with the full set of alternatives.

11 Travel distance from respondent s hometown to the Lake Erie sites did not exceed 250 miles.

21 15 MP& M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model because people are more likely to be aware of large water bodies. 12 Water body size data for sites not located in RF1 came from the ODNR.

ODN R, supplemented by the Ohio Atlas and Gazetteer, provided data on recreational amenities and site setting ( e. g.,

presence/ absence of boat ramps, swimming beaches, or picnic areas; public accessibility; and size of land available for recreation). EPA used land available for recreation, LN( LAND), ( e. g., acreage of state park, fishing, hunting, and other recreation areas) to approximate site setting and attractiveness. Dummy variables represent the presence of three recreational amenities: BEACH is a swimming beach; RAMP is a boating ramp; and PARK indicates a park.

b. Regulation dependent site characteristics Selecting regulation dependent site characteristic variables that are both policy relevant and significant in explaining recreation behavior proved challenging. MP& M facilities discharge many pollutants, most of them unlikely to have visible indicators of degraded water quality ( e. g., odor, reduced turbidity, etc). EPA hypothesized that pollutant loadings can,

nonetheless, reduce the likelihood of selecting a recreation site. Reduced pollutant discharges improve water quality and aquatic habitat, thereby increasing fish populations and enhancing the recreational fishing experience. In addition, in stream nutrient concentrations are good predictors of eutrophication, which causes aesthetic losses and may thus affect the utility of a water resource for all four recreational uses.

The connection between the policy variables ( i. e., the change in concentrations of MP& M pollutants) and the effects perceived by consumers ( e. g., increased catch rate, increased size of fish, greater diversity of species, or improved aesthetic qualities of the water body) are not modeled directly, but are captured implicitly in the differential valuation of water resources as reflected in the RUM analyses.

EPA considered two types of pollutant effects in defining water quality variables for model inclusion:

  visible or otherwise directly perceivable effects ( e. g., water turbidity); and   unobservable toxic effects likely to impact aquatic habitat and species adversely.

The Agency accounted for directly observable effects using the ambient concentrations of nutrients ( e. g., TKN ) as an explanatory variable.

Rather than include the concentrations of all toxic pollutants separately, EPA constructed a variable to reflect the adverse impact potential of toxic pollutants on aquatic habitat. EPA identified recreation sites at which estimated concentrations of one or more MP& M pollutants exceeds AWQC limits for aquatic life protection, to assess the likely adverse impacts on aquatic organisms. A dummy variable, AWQC_ EX, takes the value of 1 if in stream concentrations of at least one MP& M pollutant exceed AWQC limits for aquatic life protection, 0 otherwise. This approach accounts for the fact that adverse effects on aquatic habitat are not likely to occur below a certain threshold level.

c. Biological factors Numerous biological parameters ( e. g., abundance of sport fish) that are a function of the availability and quality of suitable habitat for breeding and feeding are also likely to affect recreation behavior. To account for biological parameters affecting the demand for water based recreation, EPA used relative fish abundance ( Biomass) obtained from the Ohio Water Resource Inventory ( OWRI) database ( OH EPA, 1996). Relative fish abundance is measured as the total fish weight ( in kg) per 300 meters. Because this variable reflects presence of both tolerant and intolerant fish species, it is less correlated with the two regulation dependent water quality variables ( i. e., TKN and AW QC) included in the analysis compared to the index of well being ( IWB2) used in the proposed rule analysis.

Chemical properties of the waters ( e. g., pollutant concentrations) are likely to affect the diversity and abundance of the fishery resources. Biological parameters may also be affected by numerous anthropogenic stressors unrelated to water quality, such as over fishing, physical alteration of habitat, invasion of exotic species, etc. Although EPA used the baseline values of relative fish abundance to estimate the site choice models, the Agency did not estimate changes in biological parameters caused by the regulation analysis due to data limitations and the challenges posed by modeling population impacts of a broad spectrum of pollutants at hundreds of recreation sites.

12 EPA uses the logarithm of water body size because it expects the effect of water body size on utility to diminish as that size increases.