## A LITERATURE REVIEW FOR

USE IN NUTRIENT CRITERIA DEVELOPMENT FOR

FRESHWATER STREAMS AND RIVERS

IN VIRGINIA

VIRGINIA POLYTECHNIC INST ITUTE AND STATE UNIVERSITY BLACKSBURG, VIRGINIA

2006This special report is a publication of the Virginia Water Resources Research Center. Funds for the support of this project and public ation are provided in part by the Virginia Department of Environmental Quality. The cont ents of this publication do not necessarily

reflect the views or policies of the Virginia Department of Environmental Quality or the Virginia Water Resources Research Center. The mention of commercial products, trade names, or services does not constitute an endorsement or recommendation.

Additional copies are available onli ne or may be obtained from the Virginia Water Resources Research Center for a small fee. 23 Agnew Hall Blacksburg, VA 24061 (540) 231-5624 FAX: (540) 231-6673

e-mail: water@vt.edu web address: http://www.vwrrc.vt.edu

Tamim Younos, Interim Director

Virginia Tech does not discriminate against empl oyees, students, or applicants on the basis of race, color, sex, sexual orientation, disability, ag e, veteran status, national origin, religion, or political affiliation. Anyone having questions concerning discrimination should contact the Equal Opportunity and Affirmative Action Office.

## A LITERATURE REVIEW FOR

USE IN NUTRIENT CRITERIA DEVELOPMENT FOR

FRESHWATER STREAMS AND RIVERS

IN VIRGINIA

Prepared for the Virginia Department of Environmental Quality, Division of Water Quality Programs

Prepared by

Jane Walker Virginia Water Resources Research Center Virginia Tech

Carl Zipper Department of Crop and Soil Environmental Sciences Virginia Tech

Leonard Shabman

Resources for the Future

Tamim Younos Virginia Water Resources Research Center Virginia Tech

Virginia Water Resources Research Center 23 Agnew Hall Virginia Tech Blacksburg, VA 24061

February 15, 2006

VWRRC Special Report SR28-2006

## TABLE OF CONTENTS

Page


### TABLE OF CONTENTS i



### LIST OF FIGURES ii



### LIST OF TABLES iv



### ACKNOWLEDGMENTS viii



### MEMBERS OF THE 2004 – 2005 ACADEMIC ADVISORY

COMMITTEE ix



### ACRONYMS AND ABBREVIATIONS x



### ABSTRACT xii



### SECTION I -- BACKGROUND INFORMATION 1

Introduction 1 Purpose 2 Nitrogen 3 Phosphorus 3 Primary Production 4 Problems Caused by Excess Nutrients and Excess Primary Production 11



### SECTION II -- APPROACHES TO SETTING NUMERIC NUTRIENT

CRITERIA 13

1. Reference Approach 15 2. Predictive Relationships Approach 22 A. Trophic State Classification 22

B. Models 28

C. Biocriteria 42

3. Published Nutrient Criteria and Threshold Approach 57

Established Guidelines and Criteria 58 Nutrient Thresholds or Recommended Algal Limits 68

Downstream Effects 72



### SECTION III -- ALTERNATIVE APPROACH TO SETTING

NUMERIC NUTRIENT CRITERIA 79



### SUMMARY 81



### REFERENCES 88ii 

## LIST OF FIGURES

Page Figure 1. Selecting reference values for total phosphorus concentration (μg/L) using percentiles from reference stream s and total stream populations. 16

Figure 2. Map of Virginia showing U.S. EPA Aggregate Nutrient Ecoregions. 17

Figure 3. Illustration of refere nce condition calculation. 18

Figure 4. Schematic diagram showing relations hip between production and respiration measurements and water quality. 25

Figure 5 . Growth curves fitted for stream sites classified as high-, moderate-, and low nutrient sites following a catastrophic flood in fall 1986. Points are means ± SE; n = 6 for high- and low-enrichment sites a nd n = 10 for moderate-enrichment sites. 30

Figure 6. Nomograph of mean monthly soluble nut rient concentrations that are predicted to result in maximum benthic algal biomass indicative of oligotr ophic, mesotrophic, and eutrophic conditions for varying days of accrual ( d

a ) in gravel/cobble-b ed streams. The oligotrophic-mesotrophic boundary was set at 60 mg/m 2 chlorophyll a and the mesotrophic-eutrophic boundary was set at 200 mg/m 2 chlorophyll a (after Dodds et al. 1998). These boundaries also equate to ma ximum biomass criteria adopted for the protection of benthic biodiver sity (oligo- to mesotrophic), aesthetics, and trout fishery values (meso- to eutrophic) in New Z ealand streams (Biggs 2000[b]). The lines delineating the trophic boundaries were calc ulated using soluble inorganic N (SIN) equation 4 in [Table 11]. However, they also approximate P-limited communities by reference to the right-hand scale, which has been set at 0.1 x the SIN scale, because the

mean ratio of biomass from the SIN a nd soluble reactive P (SRP) models was 10.8. 34

Figure 7. Diagrams of Matlock Periphyt ometer and photo showing Matlock Periphytometer treatment arrays. 36

Figure 8. Monod curve fit of PO 4 -P concentrations to LETSI values for P-limited and not limited results. EI = current study [McF arland et al. 2004] and Matlock = Matlock and Rodriguez (1999). X-axis truncated at 1.0 mg/L. 37

Figure 9 . Log-log relation between mean to tal phosphorus concentration and mean chlorophyll concentration among streams. Da shed curves approximate 65% confidence

interval for individua l predicted values. 39

Figure 10 . Arithmetic-scale plot of the phosphorus -chlorophyll relationship in streams

and its variation with stream catchment area (A

c ). The solid curve shows the predicted relationship for streams regardless of A

c ; the broken curves show the predicted relationship for streams of widely differing A

c (100,000 and 100 km 2 ). Values are

corrected for tran sformation bias. 40iii Figure 11. Application of the Neuse River Estu ary structural equation model (SEM) for estimating the probability of attainment of the designated use (logit transformation of the expert's response) for the entire summer range of dissolved oxygen and chlorophyll a levels in the Neuse River estuary [Figure 11a ]. The two surfaces correspond to specific

levels of the two candidate criteria, i.e., di ssolved oxygen = 5 mg/L and chlorophyll a = 20 μg/L (the latter is al so shown in [Figure 11b]). 41

Figure 12 . Characteristic appearance of bacterial growth on cauda l cerci (tail filaments)

of Ephemerella sp. immersed in 80% ethanol. Pl ate a (20X magnification) shows uninfested cerci with delicate, hair-like se tae visible. Plate b (20X magnification) illustrates the appearance of heavy bacteria l growth (>25% of body covered). Bacterial sheaths nearly fill the space between cerci a nd obscure the delicate setae. Plate c (15X magnification) shows an advanced stage of co lonization in which bacterial filaments have become matted and partially covered by silt particles. Infestation of the degree shown in plates b and c was associated with 100% mo rtality in laboratory survival studies and reduced numbers of mayflies in the field. The condition can be easily diagnosed in the field using a hand lens with 10 – 20X magnification. Scale bars = 0.5 mm. 49

Figure 13. Comparison of the phosphorus-chlorophyll relationship in temperate streams and lakes. Solid curves show predicted Chl concentrations in str eams of widely differing catchment area (100,000 and 100 km 2 ). The broken curve depicts the trajectory of expected Chl concentration in P-limited lake s (i.e., total nitrogen to TP ratio = 25, Forsberg and Ryding 1980) and is calculated fr om McCauley et al.’s (1989) model on the basis of data compiled form the literature (n = 382, R 2 = 0.80, s = 0.23 assumed for bias correction). 73

Figure 14. Nutrient concentration ranges, nutrient loads, stre amflow, and nutrient yields for the River Input Monitoring statio ns. [Concentration graph shows 90 th percentile, 75 th
percentile, 25 th percentile, and 10 th percentile. Load graphs shows mean annual streamflow (left and right), mean annual load of total nitrogen (l eft), and mean annual load of total phosphorus (right).] 76

Figure 15. Map of Virginia showing U.S. EP A Aggregate Nutrient Ecoregions 83iv 

## LIST OF TABLES

Page

Table 1. River algal species occurring in more than 50% of studies reviewed by Rojo et al. (1994), and species in this study re ported to cause algal blooms. Only Cyclotella meneghiniana and Pediastrum duplex are high occurrence and blooming species. 10

Table 2. Aggregate nutrient ecoregions and Omer nik’s (1987) level III ecoregions for streams in Virginia. 17

Table 3. U.S. EPA’s nutrient threshold reco mmendations for Aggregate Nutrient Ecoregions and Level III Ecoregions applicable to Virginia’s rivers and streams. Based on the median for all seasons’ 25th percentiles. 19

Table 4. Proposed nutrient criteria for different environmental nutri ent zones in the Upper Midwest region based on results of regression-tree analyses when land-use characteristics were excluded from the analyses (-- means no data). 22

Table 5 . Proposed eutrophy classification for river sites in Hornberger et al. (1977), the site location, potential sour ces of pollution or primary land use, and number of days dissolved oxygen, temperature, and solar radiation measurements were collected. 23

Table 6. Physical and chemical conditions, and da tes of sampling, for the rivers used in water quality analysis. 24

Table 7. Chlorophyll-a values, ash-free dry mass (AFDM), and conductivity for stream trophic states based on catchment geology a nd land use as determined from monthly monitoring for one year in 16 streams in New Zealand (data reported in Biggs 1996). 26

Table 8. Suggested boundaries for trophic clas sification of streams from cumulative frequency distributions. The boundary between oligotrophic and mesotrophic systems represents the lowest third of the distribution, and the bounda ry between mesotrophic and eutrophic marks the top third of the distribution. [Dodds et al. (1998) reported TN and TP concentrations in μg/L; for consistency within this review, these values were converted to mg/L] 27

Table 9. Regression of pe riphyton Chl-a (mg/m 2 ) as a function of TP concentration (μg/L) from the present study [Chételat et al. 1999] in comparison with other models

obtained from the literature. 31

Table 10. Regression statistics for log 10 of mean monthly benthic algal biomass (mg/m 2
chlorophyll a ) concentration as a function of mean days of accrual ( d

a ) and mean monthly

soluble nutrient concentrati ons. SIN = soluble inorganic N, SRP = soluble reactive P, mg/m 3 (n = 30). 32v Table 11. Regression statistics for log 10 of maximum monthly benthic algal biomass (mg/m 2 chlorophyll a ) concentration as a function of mean days of accrual ( d

a ) and meant monthly soluble nutrient concentrations. SIN = soluble inorganic N, SRP = soluble reactive P, mg/m 3 (n = 30). 33

Table 12 . Predictive power of diatom infere nce models for total phosphorus (TP), orthophosphate (O-P), ammonia (NH 3 -N) and nitrates (NO 3 -N), as determined using weighted averaging (WA)-regression and calibration. 38

Table 13. Eutraphentic diatom algal genera pres ent in streams of the Mid-Atlantic region as identified by Hill et al. (2000). 43

Table 14. Mean water quality and periphyton growth for a waterway in Finland impacted by a point source. Samples collected from July 20 – September 22, 1981 (adapted from Heinonen 1984). 44

Table 15. Median and seventy-fift h percentile nitrate+nitrite nitrogen concentrations [mg/L] by stream size and ecoregion for reference sites [in the following Ohio ecorgeions: HELP, Huron/Erie La ke Plain; IP, Interior Plat eau; EOLP, Erie-Ontario Lake Plane; WAP, Western Allegheny Plateau; and ECBP, Eastern Corn Belt Plains], oligomesotrophic and meso-eutrophic boundaries gi ven by Dodds et al. (1998), and proposed statewide criteria for WWH [w armwater habitat], EWH [exceptional warmwater habitat], and MWH [modified warmwater habitat] streams. Valu es corresponding to the IBI [Index of Biotic Integrity] range typical of the MWH use best attainable concentrations

for MWH streams. 54

Table 16. Median total phosphorus concentrations [mg/L] by IBI range (from the ALL data set), ANOVA results, and suggested crit eria for the protection of aquatic life. 55

Table 17. A process for determining the risk to aquatic life use atta inment based on TP

and NO 3 -N concentrations in Ohio rivers a nd streams. The risk of aquatic life impairment increases as sample statistics increasingly deviate from reference site results (REF) or observed associations between biol ogical community performance and nutrients (ALL data). 57

Table 18. Examples of freshwater numeric values (excluding lakes and reservoirs) for nitrogen criteria and guidelines se t by U.S. EPA and U.S. states. 59

Table 19. Examples of numeric criteria and gui delines for total phosphorus in the United

States. 60vi Table 20. Examples of numeric turbidity criteria for U.S. states applicable to freshwater

streams and rivers. Source of informati on came from individual state surface water quality regulations located at the following website: http://www.epa.gov/waterscience/standards/states/ . NTU = Nephelometric Turbidity

Units 61

Table 21. Default trigger values for slightly disturbed ecosystems as reported in

Australian and New Zealand Guidelines for Fresh and Marine Water Quality (2000). Values for some specific rivers, alpine stre ams, and glacial and lake-fed streams differ from those reported here. Chl-a = ch lorophyll-a, TN = total nitrogen, NO

x = oxides of nitrogen, NH 4 + = ammonium, TP = total phosphorus, FRP = filterable reactive phosphate, D.O. = dissolved oxygen, and Turb. = turbidity. 64

Table 22 . Provisional biomass and cover guideli nes for periphyton growing in gravel/

cobble bed streams for three main instream values (AFDM = ash-free dry mass). 65

Table 23. Soluble inorganic nitrogen (SIN = NO 3 -N + NO 2 -N + NH 4 -N) and soluble reactive phosphorus (SRP) concentrations [mg/L] predicted to prevent maximum periphyton biomass from exceeding the given le vels. The nutrient concentrations were determined as mean monthly concentrations ov er a year. Limits of detection are assumed to be around [0.005 mg/L] for SIN and [0.001 mg /L] if analyses are carried out using standard autoanalyser techniques. The chlorophyll a at 120 mg/m 2 refers to filamentous green algae dominated communities wh ereas the chlorophyll-a at 200 mg/m 2 refers to

diatom dominated communities. AFDM = ash- free dry weight. [Biggs (2000b) reported SIN and SRP concentrations in mg/m 3 ; for consistency within this review, these values were converted to mg/L.] 67

Table 24. Suggested criteria from various st udies for maximum benthic biomass (as Chl-a) levels to avoid problems for recreati onal and aesthetic use in streams (adapted from a table in Dodds et al. 1998). 68

Table 25. Median concentrations of total nitr ogen and total phosphorus and the range of

the concentration data for th e non-tidal, most downstream mon itoring station of the major rivers of Virginia that drain to the Ches apeake Bay (data from Sprague et al. 2000). 75

Table 26. The flow, total nitrogen, and total pho sphorus contribution to the Chesapeake Bay as determined from the non-tidal, most downstream mon itoring station of the Bay’s main feeder rivers in Virginia (dat a from Belval and Sprague 1999). 75

Table 27. Site specific nu merical chlorophyll-a criteria applicable March 1 – May 31 and July 1 – September 30 for the tidal James River segments JMSTF2, JMSTF1, JMSOH, JMSMH, JMSPH (excludes tributar ies) (proposed 9 VAC 25-260-310). 78

Table 28. U.S. EPA’s nutrient threshold reco mmendations for aggregate nutrient ecoregions applicable to Virginia’s rivers and streams. Chlorophyll-a determined by the spectrophotometric method, and periphyton Chl- a values are listed separately. 83vii Table 29. Suggested boundaries for trophic clas sification of streams from cumulative frequency distributions. The boundary between oligotrophic and mesotrophic systems represents the lowest third of the distribution, and the bounda ry between mesotrophic and eutrophic marks the top third of the distribution. [Dodds et al. (1998) reported TN and TP concentrations in μg/L; for consistency within this review, these values were converted to mg/L] 84

Table 30. Proposed statewide criteria for wa rmwater habitat (WWH), exceptional warmwater habitat (EWH), and modified warmwater habitat (MWH) streams in Ohio (data from Rankin et al. 1999). 86

Table 31. Criteria or guidelines used to protect stream uses in various provinces or

countries. 87viii 

## ACKNOWLEDGMENTS

We appreciate the members of the Academic Advisory Committee, particularly Carl Hershner, Eric Smith, Len Smock, and Gene Yagow, for recommending literature to include in this document and for reviewing this work. We also appreciate the Virginia Department of Environmental Quality for fina ncially supporting this project. Special thanks for granting permission to reprint the tables and figures used in this document to the following organizations and individuals : American Chemical Society; American Society of Agricultural and Biological Engine ers; BioOne; Elsevier; Ministry for the Environment New Zealand; National Resear ch Council of Canada; North American Benthological Society; Ohio Environmental Protection Agency; Texas Institute for Applied Environmental Research, Tarleton St ate University; The Academy of Natural Sciences; U.S. Environmental Protection Agen cy; U.S. Geological Survey; Barry Biggs; Don Charles; Jack Jones; Kirk Lohman; and Fr ancis Pick. And last, but not least, thank you to all the researchers work ing to understand the processes and impacts of nutrients in

streams and rivers.ix 

## MEMBERS OF THE 2004 – 2005

ACADEMIC ADVISORY COMMITTEE

E. Fred Benfield

Paul A. Bukaveckas Thomas J. Grizzard, Jr. Carlton H. Hershner, Jr.

Howard I. Kator

John C. Little Wu-Seng Lung Saied Mostaghimi John J. Ney

Leonard A. Shabman

Eric P. Smith

Leonard A. Smock

Kurt Stephenson Eugene R. Yagow

Tamim Younos

Carl Zipperx 

## ACRONYMS AND ABBREVIATIONS

AAC: Academic Advisory Committee

ac : acre AFDM : ash free dry mass

ag : agricultural

ANOVA : analysis of variance

BMP : best management practices

Chl-a : chlorophyll-a

CO 2 : carbon dioxide DEQ : Department of Environmental Quality

DIN : dissolved inorganic nitrogen

DNA : deoxyribonucleic acid

D.O. : dissolved oxygen

EPA : U.S. Environmental Protection Agency

EPT : Ephemeroptera (mayflies), Plecoptera (s toneflies) and Tric optera (caddisflies)

FTU : Formazin Turbidity Units (Calibration base d on formazin primary standards. Does not specify how the instrument measures the sample [angl e to the incident light]) g/m 2 : grams per square meter

ha : hectare

HPLC : high pressure liquid chromatography

HPO 4 2- : monohydrogen phosphate ion

H 2 PO 4 - : dihydrogen phosphate ion

IBI : Index of Biotic Integrity

ICI : Invertebrate Community Index

KDHE : Kansas Department of Health and Environment

KDOW : Kentucky Division of Water kg/d : kilograms per day

km 2 : square kilometers

L : liter

m : meter

μ g/L : micrograms per liter (1 μ g/L = 0.001 mg/L)

μ S/cm : microSiemens per centimeter mg/L : milligrams per liter (1 mg/L = 1,000 μ g/L or 1,000 mg/m 3 ) mg/m 2 : milligrams per square meter mg/m 3 : milligrams per cubic meter (1 mg/m 3 = 0.001 mg/L)

m/sec : meters per second

n : number of observations in a sample (sample size)

N : nitrogen

N
c : cellular nitrogen NAWQA : National Water-Quality Assessment

NH 3 : ammonia

NH 4 + : ammonium ion

NO 2 - : nitrite ion

NO 3 - : nitrate ionxi NPDES : National Pollutant Discharge Elimination System

NTU : Nephelometric Turbidity Units (Calibrati on based on formazin primary standards. Measures scattered light from the sample at a 90-degree angle from the incident light.)

O-P : ortho-phosphate

p : observed significance level

P : phosphorus

P
c : cellular phosphorus

PIBI : Periphyton Index of Biological Integrity

PO 4 3- : phosphate ion (ortho-phosphate ion) QHEI : Qualitative Habitat Evaluation Index

r : coefficient of correlation for a sample

r 2 or R 2 : coefficient of determination for a sample

RMSE : root mean square error
RTAG : Regional Technical Assistance Groups

s : sample standard deviation

SE : standard error

SIN : soluble inorganic nitrogen

SRP : soluble reactive phosphorus

TIN : total inorganic nitrogen

TKN : total Kjeldahl nitrogen

TMDL : total maximum daily load

TN : total nitrogen

TP : total phosphorous

Turb : turbidity

U.S. EPA : United States Environmental Protection Agency

USGS : United States Geological Survey

WWTF : wastewater treatment facilitiesxii 

## ABSTRACT

To protect the designated uses of streams a nd rivers, the U.S. Environmental Protection Agency (U.S. EPA) is directing states and au thorized tribes to develop numeric criteria for nutrients. As a part of the efforts in Virginia, the Virginia Department of Environmental Quality requested specifical ly that the Academic Advisory Committee conduct a comprehensive literature search on the following topics:

• Investigate methods for defi ning undesirable (nuisance) levels of periphytic algae in wadeable streams, and what such studies have concl uded as undesirable (nuisance) levels.

• Investigate a corresponding approach to planktonic algae in non-wadeable

streams.

The purpose of this review is to provide b ackground information for the interested public, the Academic Advisory Committee, and the Virginia Department of Environmental Quality to facilitate discussions concerning nutrient criteria development in Virginia. The review aims to identify approaches that c ould help Virginia in its task of developing

nutrient-related numeric criteria for non-tidal freshwater stre ams and rivers and highlights

criteria values defined in other areas through application of such methods.

Section I contains background information pert aining to nutrient cr iteria development, nitrogen, phosphorus, and primary production. Section II provides information about and examples of research or criteria proposed in other regions that pert ain to the approaches recommended by the U.S. EPA for establis hing numeric nutrient criteria. These approaches include: (1) the reference appro ach, (2) the predictive relationship approach, and (3) the published nutrient criteria and threshold approa ch. Section II also includes information about the development of numeri c criteria to protect downstream waters. Section III of the review provides an alternat ive approach to developing numeric nutrient criteria proposed by the Kansas Department of Health and Environment but not yet reviewed by the U.S. EPA. This alternat ive approach emphasizes nutrient reductions by adhering to a developed nut rient export budget.

This document does not provide information on many issues that need to be discussed before Virginia sets numeric nu trient criteria. Th e literature review, therefore, should not

be used to select numeric values for non-tidal freshwater streams and rivers in Virginia simply based on the research or criteria of ot hers. Additional work is needed to address

issues not discussed in this review.1 

## SECTION I -- BACKGR OUND INFORMATION

INTRODUCTION

The U.S. Environmental Protection Agency (U.S. EPA) has identified nutrients as a major reason for impaired water quality in the nation’s streams and rivers (U.S. EPA 2000a). The U.S. EPA is therefore directing states and aut horized tribes to deve lop numeric criteria for nutrients to protect the designated uses of streams and rivers. The purpose of this mandate by U.S. EPA to develop

nutrient criteria is to address cultural eutrophication (waters enrich ed with nutrients because of human activities).

To assist water quality managers in developing numeric criteria, U.S. EPA developed a technical guidance, Nutrient Criteria Technical Guidance Manual: Rivers and Streams (U.S. EPA 2000a). In the guidance document, the agency has outlined three main approaches:

• the use of reference reaches,

• the application of predictive relationships (e.g., models), and

• the utilization of published nutrient thresholds or recommended algal limits. The agency recommends that states and authorized tribes use these approaches or a combination of these approaches. It also directs states and authorized tribes to c onsider downstream effects when developing nutrient-related nu meric criteria. In addition, U. S. EPA has developed a set of guidance criteria for two causal variables (total nitrogen [TN] and tota l phosphorus [TP]) and two early indicator resp onse variables (chlorophyll-a [Chl-a] and turbidity) (U.S. EPA 2000a).

States and authorized tribes can use U.S. EP A’s guidance criteria in developing criteria applicable to their own streams and rivers.

U.S. EPA directs that the develope d criteria must be scientifica lly defensible and must protect the designated uses of the streams and rivers. In Virginia, numeric criteria are needed to meet water quality standards that requ ire protection of a “balanced, indigenous population of aquatic life” (which includes algal populatio ns) (9 VAC 25-260-10) and require the control of substances

that “nourish undesirable or nuisanc e aquatic plant life” (9 VAC 25-260-20).

U.S. EPA proposes the following goal for states to aim towards when developing nutrient criteria: Maintain an acceptabl e level of primary production in freshwater streams and rivers,

even when other resources besides nutrient s are not limiting (biomass accumulation not limited by light, flow, grazing invertebrate s, etc.) (U.S. EPA 2000a). Rec khow et al. (2005) suggest that states should develop criteria th at are easily measured, good pred ictors of the designated use attainment, and consider societal values that balance environmental protection and cost. Reckhow et al. (2005, p. 2918) caution: “...natura l variability and criterion-use prediction uncertainty will almost certainly result in some risk of nonattainment.... Furthermore, the selection of the acceptable probabi lity [of nonattainment] is a valu e judgment best left to policy

makers and should not be ‘hard-wired’ in to the criteria level analysis.”

Section I of this literature review covers general information about nitrogen, phosphorus, and primary production, and the effects of excess nut rients and primary production in streams and rivers. Section II of the review is organized a ccording to the three main approaches suggested by U.S. EPA and includes possible downstream effe cts. Section III provides a summary of an2 alternative approach proposed by the Kansas Department of Health and Environment. Throughout the document the term “streams” is us ed to refer collectiv ely to both non-tidal,

freshwater streams and non-tid al, freshwater rivers.



### PURPOSE

The purpose of this review is to provide bac kground information for th e interested public, the Academic Advisory Committee (AAC), and the Vi rginia Department of Environmental Quality (DEQ) as discussions begin concerning nutrient cr iteria development in Virginia. The review aims to identify approaches that could help Virg inia in its task of de veloping nutrient-related numeric criteria for non-tidal, freshwater streams and rivers and highlights criteria values defined in other areas through application of those methods.

Virginia DEQ requested that the AAC conduct a comprehensive litera ture search on the following topics:

• Investigate methods for defini ng undesirable (nuisance) leve ls of periphytic algae in wadeable streams, and what such studies have concluded as u ndesirable (nuisance)

levels.

• Investigate a corresponding approach to pl anktonic algae in non-wadeable streams.

This literature review should not be used to select numeric va lues for freshwater streams in Virginia simply based on the research or criteria of others. Furthermore, this document does not provide an exhaustive revi ew of the literature on the topic of nutrients and freshwater streams and rivers. Instead, it focuses on re search that addresses the question: “How much is too much?”

While some publications cite thresholds levels for streams and rivers that are based on data from lakes (e.g., lake data are pr ovided in Novotny and Olem 199 4, Ryding and Rast 1989, Wetzel 2001), few lake-derived values are included in this review. When included, they are identified as coming from lake water samples.

Further work is needed to address issues not di scussed in this review. For example, because

selected nutrient criteria need to relate to the us es of particular stream types, an assignment of uses for the freshwater streams in Virginia is needed prior to sett ing nutrient criteria. Furthermore, a discussion is needed on the purpose of the criteria; e.g., “Will the criteria be used simply for screening purposes to look for possibl e problems?” Also, the state needs to decide whether or not it will follow the approach taken by Ohio in categorizing streams according to their anticipated attainability leve l (e.g., streams in forested regions are expected to be able to attain higher levels of water quality than are streams in urban settings).3 

### NITROGEN

Nitrogen (N) is an essential nutrien t for living organisms. It is pr imarily used to form proteins, which are the building bloc ks of all living matter. Proteins provide structural support, act as enzymes, regulate cell activity, etc. Nitrogen is also an importan t component of chlorophyll, the green pigment that makes photosynthesis possible.

Nitrogen in streams may come fr om natural sources, such as th e decomposition of plants and animals, waste products from aqua tic life within the stre ams, urine and feces of wildlife, or (in generally small amounts) mineral dissolution of rock s. Much of the nitroge n that enters streams is often of direct human origin (such as discha rges from sewage treatment plants or leachate from septic systems) or is rela ted to human activities (such as wastes from poultry and livestock facilities, runoff of fertilizers, or nitrous oxides from fuel combustion). Nitrogen can be transported to streams through atmosphe ric deposition, runoff, or groundwater.

Most algae and other primary pr oducers (organisms able to photos ynthesize) are able to utilize inorganic forms of nitrogen: nitrates (NO 3 - ), nitrites (NO 2 - ), ammonia (NH 3 ), and ammonium ions (NH 4 + ). Various forms of organic nitrogen (nitrogen that is bound to carbon-based molecules) may also be available to algae. For example, urea ([NH 2 ] 2 CO), a soluble organic compound containing nitrogen that is excreted in urine and applied to land as fertilizer, easily degrades into inorganic forms of nitrogen. Like wise, organic nitrogen found in plant and animal tissues can become available for use by primary pr oducers if converted by b acteria into inorganic forms of nitrogen.

Nitrogen in water samples can be measured in several different forms, for example:

• nitrate-N = (NO 3 -N)

• nitrite-N = (NO 2 -N)

• total ammonia-N = ammonia-N (NH 3 -N) + ammonium-N (NH 4 -N)

• dissolved inorganic nitrogen (DIN; filtered sa mples), soluble inorganic nitrogen (SIN; filtered samples), or total inorganic nitrogen (TIN; unfiltered samples) = nitrate-N +

nitrite-N + total ammonia-N

• total Kjeldahl nitrogen (TKN) = total ammonia-N + total organic nitrogen

• total nitrogen (TN) = nitrate-N + nitrite-N + total ammonia-N + total organic nitrogen Nitrogen values are typically measured in units of μg/L, mg/L, or mg/m 3 . To make comparisons

between different studies easie r, values within this revi ew are reported as mg/L.



### PHOSPHORUS

Phosphorus (P) is an essential nut rient for living organisms. Fo r instance, phosphorus is found in DNA (the genetic material of living organisms), used to form cell membranes, and is utilized at the cell level (as ATP, adenosine tr i-phosphate) to ge nerate energy.

Phosphorus enters streams from a number of di fferent sources, e.g., point-source discharges, terrestrial runoff, feces from wa terfowl, decaying organisms, and streambed rocks containing phosphorus. Some sources of phosphor us to streams are natural, such as waste products from4 aquatic organisms and wildlife, and decaying tissu es of plants and animals. Other natural sources of phosphorus in streams include dissolved minerals containing phosphorus and atmospheric deposition of pa rticulate-bound phosphorus (e.g., phosphorus attached to wind blown soils). Sources of phosphorus in streams that result from human activities often include industrial and municipal effluents and surface ru noff from lands affected by fertilizer, poultry litter, and/or livestock waste. Human activitie s that increase soil erosi on may also contribute phosphorus to streams as particulate-bound phosphorus.

In the aquatic ecosystem, phosphorus can be found in the water column, within the bodies of aquatic organisms, or attached to particles (such as in sediment) in the water. Primary producers are able to directly incorporat e inorganic forms of phosphates. Primary producers may also be able to indirectly obtain phosphorus from various organic compounds (phosphorus bound to carbon-based molecules, as in excrement and plan t and animal matter). For example, organic phosphorus incorporated in plant and animal tissu es may be made available for use by primary producers through bacterial conversi on into soluble inorganic phospha tes. Likewise, particulatebound phosphates (phosphates bound to particles) can be used by primary producers if the phosphorus disassociates from its particle to become solubl e in the water column.

Because phosphorus tends to bind to mineral part icles, water quality la boratories often filter samples through a 0.45-micron filter. The phosphorus that passes through the filter is called filterable phosphorus . Filterable phosphorus is often refe rred to as “soluble” or “dissolved” phosphorus even though the filtrate may contain bot h dissolved and colloida l (tiny particles in suspension) forms of phosphorus. The part of the sample that cannot pass through the 0.45-

micron filter is referred to as particulate phosphorus . These particulate samples may contain mineral materials as well as organic components such as bacteria, alg ae, zooplankton, particles of plant material, etc.

The phosphorus parameters reported in the literat ure and reviewed for this document include:

• soluble reactive phosphorus (SRP), filterable reactive phosphorus (FRP), or orthophosphate (O-P) (mainly filterable dihydrogen phosphates [H 2 PO 4 -P], monohydrogen phosphates [HPO 4 -P], and ortho-phosphates [PO 4 -P]), and

• total phosphorus (TP: all the organic and i norganic, filterable and particulate phosphorus forms). Phosphorus values are generally measur ed in units of μg/L, mg/L, or mg/m 3 . In this review, phosphorus values were converted to mg/L to make comparisons between studies easier.



### PRIMARY PRODUCTION

In general terms, primary production is the am ount of organic matter made from inorganic materials through the process of photosynthesis. Primary producer s need essential nutrients — nitrogen, phosphorus, magnesium, calcium, iron, zi nc, etc. — in sufficient amounts in order to live and grow. Of the essential nutrients, nitr ogen and phosphorus are most likely to be limiting in aquatic environments. Nitrogen and phosphorus are considered to be “causal parameters” because in excessive amounts, they may cause proliferation of primary producers: periphyton, phytoplankton and/or macrophytes.5 Periphyton refers to a community of organism s usually dominated by algae but also including bacteria, fungi, protoz oa, and other microbes. The primary types of algae that make up periphyton include diatoms, green algae, red algae, chrysophytes, and xanthophytes. Periphyton assemblages, also known as benthic algae, grow on stable surfaces, such as rocks, woody debris, and vascular plants. Periphyton accumulation tends to occur along the streambed of shallow waters.

Phytoplankton are suspended in the water column and are made up of photosynthesizing organisms such as algae and cyanobacteria (formerly called bl ue-green algae). Phytoplankton may exist as single cells, fila ments, or colonies of cells. Most phytoplankton have limited mobility so are ca rried downstream with the flow of the water (or settle to the streambed). P hytoplankton tend to be the dominant algal community in deeper waters, where the amount of sunlight reaching the streambed is inadequate for the growth of periphyton.

Macrophytes are plants large enough to be seen with the naked eye. They generally have roots, stems, and leaves. However, mosse s are macrophytes that lack these tissues. Macrophytes may be rooted in the sediment or free-floating.

Algae and other primary producers serve as the basis for aquatic food chains. Some filamentous algae and macrophytes also provide habitat for ot her organisms. In nutrient-enriched streams (eutrophic conditions), however, these primary producers are often found at high levels and can

interfere with the uses of a stream . For example, nuisance levels of Cladophora (green algae) have been observed in many streams with high phosphorus conditions (e.g., Wong and Clark 1976, Wharfe et al. 1984, Watson and Gestring 1996, Biggs et al . 1998a, Chételat et al. 1999, Ponader and Charles 2003). In low-order stream s (small, headwater streams), nuisance algae are often filamentous algae (peri phyton). In large streams (4 th order or larger), blooms of phytoplankton may occur when certain types of microscopic phytoplankton grow quickly in water, forming visible patches. Nuisance phytoplankton blooms are frequently caused by diatoms or cyanobacteria.

Chlorophyll-a

The biomass of periphyton and phytoplankton are often estimated by measuring the amount of Chl-a, the predominant green pigment used in photosynthesis. Chl- a is considered an appropriate surrogate measurement for primary pr oduction because researchers have consistently found strong correlations between Chl-a values an d algal biomass. Chl-a can be determined from a sample of periphyton collected from rock s (or other surfaces) in the stream or from a sample of phytoplankton collected from the water column. To determine the amount of Chl-a, the chlorophyll is extracted from the cells using a solvent such as acetone. The Chl-a value is then measured by such means as spectrophotometry, fluorometr y, or high pressure liquid chromatography (HPLC).

U.S. EPA cautions that Chl-a values deri ved from the different methodologies are not interchangeable (U.S. EPA 2000b–d). Spectropho tometry measures the amount of light6 absorbance at specific wavelengths. One method of spectrophotomet ric analysis relies on trichromatic equations. U.S. EPA does not recommend the use of Chl-a values derived by trichromatic equations unless no other data ex ist (U.S. EPA 2000b–d). Fluorometry measures the amount of light emitted at a particular wave length when exposed to light at a different wavelength. The presence of other chlorophyll pi gments (e.g., pheophytin) can interfere with the Chl-a measurement using either the spectrophoto metric or fluorometric methods. For this reason, U.S. EPA prefers data from methods that also incorporate an acid correction treatment (U.S. EPA 2000b). Standard Methods for the Examination of Water and Wastewater (APHA 1998), however, does not recommend the acidifica tion step for freshwaters when using the fluorometric technique if pheopigments (Chl-b) are also present. Although HPLC accurately separates the pigments based on physical characteristics, it is an expensive and time-consuming method so is not used as often as the spectr ophotometric and fluoromet ric methods (NC WRRI 2001).

According to an article in the Water Resources Research Institute News of The University of

North Carolina , “No method of measuring chlorophyll a ac counts for all sources of interference or variation. Every method addresses some inte rferences but ignores ot hers. For this reason some experts support the reporting of ‘total chlorophyll pigments.’” (NC WRRI 2001, p. 10). Furthermore, Carlson and Simpson (1996) state: “It is strongly recomm ended that the total chlorophyll pigment be reporte d in addition to chlorophyll a . This value, although flawed by interferences by other chlorophyl ls, phaeo-pigments, as well as a number of other possible interferences, is the only value that remains fairly i ndependent of chlorophyll methodology. Therefore, it is the only measurement that provides historical cons istency. Chlorophyll a methodologies have changed over the past 25 ye ars, and with each change, the previous chlorophyll estimates became obsolete and non-compar able to the new methods. If everyone had reported total chlorophyll, at least there would be one c onsistent value th at would allow comparison. In a monitoring program, where histor ical data consistency is absolutely necessary, this value should be reported.”

Periphyton

Periphyton biomass is estimated by removing peri phyton from a known or measured area of a substrate. The substrate can be natural, such as rocks in the streambed, or artificial, such as a clay tile onto which periphyton growth is allowe d to accumulate for a controlled amount of time (e.g., two to four weeks). The periphyton is remo ved from its substrate by scraping with a brush, rubber spatula, knife, or other in strument. The content of Chl-a in the sample is measured as described in the above section. Chl-a periphytic data are recorded as milligrams per square meter (mg/m 2 ).

A relationship between the nutrient concentratio n in streams and the growth of periphyton has been observed in many studies but not all (see re view in ENSR 2001). In lab studies (Horner et al. 1983) and field artificial enri chment studies (Bothwell 1989, Watson et al. 1990), nutrients were found to stimulate the growth of stream periphyton. Horner et al . (1983) obtained what they considered to be nuisance leve ls of periphytic biomass, 100 – 150 mg/m 2 of Chl-a (determined fluorometricly according to Strickla nd and Parsons 1972), at SRP levels between 0.015 and 0.025 mg/L. Perrin et al. (1987) increase d the biomass of stream periphyton by more7 than ten times that of the control section (< 10 mg/m 2 ) by raising stream SRP above background levels (0.001 mg/L) to maximum SRP levels of about 0.100 mg/L (average SRP being 0.015 – 0.025 mg/L), which yielded Chl-a levels around 90 – 150 mg/m 2

.

In addition to a relationship betw een periphyton biomass and stream nutrient levels, specific taxa can be related to nutrient levels. Working in the riffle zones of 13 rivers in southern Ontario and western Quebec, Chételat et al. (1999) found th e presence of green algae to dominate at moderately eutrophic sites while red algae taxa we re most common at sites with the lowest Chl-a values. Specific taxa, Cladophora (green algae) , Melosira (diatom), and Audouinella (red algae), were positively correlated with TP c oncentrations over the range of 0.006 – 0.082 mg/L (r 2 = 0.39 – 0.64, p < 0.005) and were dominant at sites with the highest nutrient concentrations (Chételat et al. 1999).

Factors other than nutrient levels also influe nce the growth and composition of periphyton, and may even exhibit more control than nutrient leve ls. For example, many studies suggest that light availability and stream flow may be the dominan t control factors of periphyton accumulation in temperate streams (see ENSR 2001). Ponader and Charles (2003) state: “a strong correlation

between nutrient and biomass is at best difficult to establish, and that othe r factors such as light (as a function of river basin size) and substrat e must be taken into account when estimating nutrient-biomass relationships... Therefore, commonly used biomass measures (Chl a , AFDM [ash-free dry mass]) must be interpreted with cau tion, and inferences of nut rient levels in rivers based on these measures should be made only in conjunction with analyzing other variables” (p. 36). Thus, although very low nutri ents levels in a stream system can be expected to result in low levels of periphyton, it does not necessarily follow that high nutrient levels will cause high levels of periphyton biomass.

Shading from vegetation, either growing along th e stream bank or in the water, and water column turbidity caused by bac kground coloration, sediments, et c. may reduce the amount of sunlight reaching the periphyton. Thus, shading ma y limit the growth of periphyton. In contrast, increases in light availability have been associ ated with excessive algal growth in streams (as cited in Mosisch et al. 1999: Lyford and Gr egory 1975, Shortreed and Stockner 1983, Graham et al. 1995).

The depth and velocity of th e water impacts periphyton accumulation and loss. Increasing velocity to a certain level can increase biom ass growth by providing a constant supply of nutrients (Horner 1983, Welch et al. 1989), whereas higher water velocities can dislodge the periphyton community from the substrate. A num ber of studies suggest that the length of floodfree periods may be at least as important as nutr ients in determining levels of periphyton biomass in streams (Biggs and Close 1989, Lohman et al. 1992, Biggs 1995, Biggs 2000a). For example, periphyton levels decreased at all study sites (22 sites on 12 Ozark streams) following floods (Lohman et al. 1992). Furthermore, growth of periphyton to nuisance leve ls often occurs during periods of low flow. Periphyton accumulation is limited in larger rivers by water depth, which decreases light availability.

Other factors, such as temperature, appear to control periphyton growth only at extremes (e.g., low, 0 – 1 o C, and high, 35 o C, temperatures) (ENSR 2001).8 In general assemblages of periphyton grow poorly on sand, clay, and highly organic substrates, preferring rocks and other more stable substrat e (Welch et al. 1992, ENSR 2001). In a study in eastern Ontario and western Quebec, Cattaneo et al. (1997) found that co bbles had the highest biomass of periphyton, while gravels had the lowest biomass, and sand and boulders were intermediate. Also, different types of periphy ton did better on diffe rent substrates, e.g., filamentous algae prefer large ro cks while cyanobacteria grew better in sand habitats (Cattaneo et al. 1997).

Grazing by snails, caddisfly larvae, mayfly larvae, and other organisms can control the growth of periphyton even when nutrient levels are high (Hill et al. 1992, Rosemond et al. 1993, Steinman 1996). For example, when working in a lowland st ream (a stream with a low channel gradient) in New Zealand, Biggs et al. (1998b) found that grazing by snails reduced the mean periphyton biomass by 80% during periods of stable flows.

The biomass of periphyton may vary greatly from season to season, from year to year, and from one stream reach to another. For example, th e biomass levels within a stream vary between shaded and open areas and between pool, run, and riffle habitats. Different stream reaches experience peaks of algal biomass at different times (Watson and Gestring 1996). The long-term patterns of periphyton biomass within a stream reach that are often observed include:

• constant, low periphyton biomass;

• cycles of accrual and sl oughing of periphyton; and

• seasonal cycles of the growth of periphyton. Seasonal patterns may result from changes in the light availability (more sunlight in summer), temperature (warmer water temperatures in the summer), and grazer activity (more grazing pressure in the summer). Seasonality in distur bances, such as floods associated with tropical storms and hurricanes, are also evident. Differences in dominant periphyton taxa are observed

from the headwaters to the middle sections of rivers, with the upper reach es generally dominated by green algae (e.g., Cladophora) and the middle reaches generally dominated by diatoms (Watson and Gestring 1996).

Phytoplankton

The relative levels of phytoplankt on are estimated by collecting a representative water sample (e.g., composite sample), filtering the sample, extr acting the Chl-a pigment into a solvent, and measuring the Chl-a concentration in the sample . Phytoplankton Chl-a data are expressed in terms of mg/m 3 or μg/L (whereas periphyton biomass is expressed in mg/m 2 of the substrate). In this review, phytoplankton measurements are genera lly reported as micrograms per liter (μg/L).

Phytoplankton populations in streams and ri vers develop from dislodged periphyton, phytoplankton from an upstream lake, or riverine phytoplankton (that which develops within the stream). Some algal species can live either as periphyton or as phytoplan kton. For example, the

diatoms Fragilaria (formerly Synedra ) ulna and Navicula tripunctata/lanceolata are able to grow and reproduce whether attached to a surface or floating with in the water column (Reynolds and Descy 1996). Based on a review of published year-long studies of 67 rivers worldwide, Rojo et al. (1994) found that abou t half of the phytoplankton species reported in these studies are9 of benthic origin and half are truly riverine plankton. Phytopla nkton that develop within lakes

tend not to survive well under ri verine conditions (Prygiel an d Leitao 1994, Reynolds and Descy 1996, Yang et al. 1997).

The true riverine phytoplankton are thought to primarily develop in water storage zones (also referred to as dead zones or phyt oplankton nurseries) that are found in the side arms and eddies within the river system. Such areas within the river are likely to have slower water flow and warmer temperatures, which promote phytoplankt on cell growth (Reynolds 1995). Citing four studies at Leighton on the Rive r Severn (U.K.), Reynolds and Descy (1996) reported that nonflowing waters had more than 40 times the algal c oncentration as the main stem of the river. Under normal flow conditions, the ce lls in these nursery areas are slowly leached into the main

river channel.

Factors that impact the amount of phytoplankton biomass within a given stream reach include water velocity, solar radiation at the water su rface, water column turbidity, temperature, nutrients, settling of cells out of the photic zone, and consump tion by zooplankton (e.g., rotifers) and benthic suspension feeders (e.g., clams). St ream discharge, availability of light, and temperature have been reported as being the most important factors controlling phytoplankton biomass (Greenberg 1964, Gosselain et al. 1994, Kiss et al. 1994, Schmidt 1994). In general, more phytoplankton accumulate during periods of low flow, high light availability, and warm temperatures. Herbivorous consumption by organisms can negatively impact phytoplankton biomass levels. Cohen et al. ( 1984) reported that a particular re ach of the Potomac River with the highest biomass of an introduced clam, Corbicula fluminea, also had the lowest phytoplankton biomass relative to other areas of the river.

According to Reynolds and Descy (1996), “...nutr ient limitation of al gal production probably never arises in most rivers and, with the ex ception of case studies on the Solmões and Negro tributaries of the Amazon (Hammer 1965), we ar e not aware of cases where it has been demonstrated otherwise” (p. 169). Nutrient en richment from human ac tivities over the past decades has brought about significant changes of biomass and composition of phytoplankton (Gosselain et al. 1994). Excess amounts of nutrients have been linked to algal blooms. For example, eutrophication of the River Ebro in nor th-east Spain has greatly increased since the 1970’s along with increases in the orthophosphate levels (from 0.2 mg/L in the 1970’s to 0.9 mg/L in the 1990’s) and nitrate concentration (from 3.0 mg/L in the 1970’s to 9.0 mg/L in the 1990’s) (Ibañez et al. 1995).

Citing over 40 studies, Hynes (1970) concluded that phytoplankton in streams are almost always dominated by diatoms. Likewise, published studies from rivers around the world showed diatoms, primarily, and green alg ae, secondly, to dominate the stre am species (Rojo et al. 1994). Furthermore, the species most of ten recorded in rivers from various parts of the world are generally not those that cause blooms (Table 1.). Of the phytoplankton causing blooms, Stephanodiscus hantzschii (5 studies) and Cyclotella meneghiniana (4 studies) populations were reported most frequently ( S. hantzschii was reported as a group and not as a species) (Rojo et al. 1994).10 Table 1. River algal species occurring in more th an 50% of studies reviewed by Rojo et al. (1994), and species in this study report ed to cause algal blooms. Only Cyclotella meneghiniana and Pediastrum duplex are high occurrence and blooming species.

High Occurrence Species Blooming Species

Actinastrum hantzschii [green algae] Aulacoseira granulate [diatom] Ankistrodesmus falcatus [green algae] Aulacoseira italica [diatom] Asterionella formosa [diatom] Cylindrotheca closterium [diatom] Aulacoseira granulate [diatom] Cyclotella meneghiniana [diatom] Cyclotella meneghiniana [diatom] Cyclotella pseudostelligera [diatom] Fragilaria capucina [diatom] Microcystis aeruginosa [cyanobacteria] Fragilaria ulna [diatom] Pediastrum duplex [green algae]

Melosira varians [diatom] Skeletonema costatum [diatom]

Nitzschia acicularis [diatom] Skeltonema potamos [diatom] Pediastrum duplex [green algae] Stephanodiscus hantzschii [diatom]

Scenedesmus acuminatus [green algae] Stephanodiscus parvus [diatom] Scenedesmus quadricauda [green algae] Stephanodiscus tenuis [diatom]

Phytoplankton communities change from upstream to downstream. In general, diatoms replace green algae in dominance down river, but such ch ange is not abrupt. The transition zone from dominance by green algae to dominance by diatom s migrates up and down the river, possibly depending on streamflow (Reynolds 1995).

Predictable seasonal changes have also been reported for the phytoplankton community. Working on the River Meuse (Belgium), Gosselain et al. (1994) found diat oms to dominate in spring, with green algae becoming more important in the summer. Accord ing to Köhler (1994), the few studies of river phytoplankton lasting lo nger than two years in dicate a regular annual sequence of phytoplankton taxa occurring in rivers.

Phytoplankton biomass tends to be highly vari able, changing from upstream to downstream within a stream system. By following a portion of water in the Rhine River from upstream to downstream, de Ruyter van Steveninck et al. (1 992) found Chl-a concentrations to increase from 13.8 μg/L (at Maxau) to 170.5 μg/L (at Dusseldorf) in about 400 km. Further downstream, the Chl-a concentrations of phytoplankton declined sharply, possibly owing to increased grazing by zooplankton and filter-feeding organisms (Chl-a was determined photometrically, correcting for phaeopigments after acidification) . As another example related to upstream-downstream levels,

some downstream reaches in the Riddeau Rive r in Ontario, Canada increased in phytoplankton biomass, while other reaches decreased in biom ass (Basu and Pick 1995). Furthermore, Yang et al. (1997) found that the Rideau River reaches with the higher amounts of algal biomass were caused by an increase in the size of the plankton, not necessarily the abundance of phytoplankton.

Phytoplankton biomass can also differ by season a nd year. Seasonal changes in algal biomass in temperate rivers are obvious, with phytoplankton biomass levels tending to be lowest during winter. As an example of year-to-year differe nces, researchers of the lower River Rhine found11 maximum Chl-a levels to be less than 25 μg/L dur ing a cold and rainy summer; whereas in most years, the maximum Chl-a values reached betw een 50 – 100 μg/L, with values up to 170 μg/L (Chl-a was determined photometrically, correctin g for phaeopigments after acidification) (de Ruyter van Steveninck et al . 1990, 1992). Similarly, Greenberg (1964) found the peak plankton population in June 1961 to be almost four tim es that found during the peak population in September 1960.



### PROBLEMS CAUSED BY EX CESS NUTRIENTS AND EXCESS

PRIMARY PRODUCTION

The effect of nutrient enriched waters on the bi otic community depends on many factors. In some instances, nutrient inputs to streams can be utilized by the existi ng aquatic life without causing measurable changes in the community structur e. In other situations, excessive levels of nutrients can increase the amount of prim ary production, which then may support more macroinvertebrates and other aquatic life. Unfo rtunately, excess nutrients and high levels of primary production can also have negative effects on the stream sy stem. Most problems caused by excess nutrients are related dire ctly or indirectly to the excessive growth of primary producers (periphyton, phytoplankton, and macrophytes).

Potential Impacts on Stream Use

Excessive nutrient levels may allow excessive increases in algae and other primary producers, which may in turn, prevent streams from meeting their designated uses. The adverse effects of either high nutrient levels or the nuisance grow th of primary producers include, for example:

• Impairment of the aquatic life use -- Daily fluctuations in oxygen concentrati ons and pH values may negatively impact aquatic life (see explanations below). -- Toxicity may result if high am monia levels (e.g., > 1 mg/L NH 3 -N) contribute to the high nitrogen levels. -- Some algal blooms release toxic compounds (e.g., cyanotoxins) (see additional information below). -- A loss of diversity and othe r changes in the aquatic plan t, invertebrate, and fish community structure may result.

• Negative impact on the drinking wate r and industrial water supply use -- Methemoglobinemia (blue-baby syndrome) ma y affect infants if nitrate levels >10 mg/L -- Diatoms and filamentous algae can clog intake screens and filters in water treatment plants. -- Decay of algae may lead to taste and odor problems of drinking water. -- Potentially carcinogenic di sinfection by-products (trihalomethanes, THMs) may form during treatment of drinki ng water from eutrophic waters. -- Treatment costs may rise for waters drawn from eutrophic sources by requiring more backwashing, etc.

• Degradation of the aesthetic and recreational use12 -- Unsightly algal growth is unappealing to many swimmers and other stream users. -- Slippery streambeds caused by heavy grow ths of algae on rocks are difficult to

walk on. -- Fishing lures may become ta ngled in algae and macrophytes. -- Boat propellers may get ta ngled by aquatic vegetation.

Dissolved Oxygen Depletion

Excessive growth of primary producers may lead to a depletion of dissolved oxygen. During the day, primary producers provide oxygen to the wate r as a by-product of photos ynthesis. At night, however, when photosynthesis ceases but respira tion continues, dissolved oxygen concentrations decline. Furthermore, as primary producers die, they are decomposed by bacteria that consume oxygen, and large populations of decomposers ca n consume large amounts of dissolved oxygen. Many aquatic insects, fish, a nd other organisms become stressed and may even die when dissolved oxygen levels drop belo w a particular threshold level (e.g., below 5 mg/L). Typical daily oxygen fluctuations in six enriched streams at low flow were reported to range from a high of about 25 mg/L at noon to a low of about 3 mg/L at night (Wong and Clark 1976). Excessive

amounts of nutrients in streams and rivers ma y also negatively impact the dissolved oxygen levels of downstream receiving waters. For exam ple, a zone of hypoxia (< 2 mg/L) in the Gulf of Mexico has been linked to high nutrient inpu ts from the Mississippi River (U.S. EPA 2000a).

pH Alterations

The pH of water is a measure of its acid-base c ondition (range: 0 – 14, with 7 being neutral, less than 7 indicating acidic conditions, and greater th an 7 indicating basic conditions). The pH is controlled by the production of hydrogen ions (H + ) and hydroxyl ions (OH - ). Daily fluctuations of water column pH can be caused by excessive primary production.

When photosynthesis is occurring, the water column pH level tends to be more basic. During photosynthesis, carbon dioxide (CO 2 ) and water are converted by sunlight into oxygen and sugar (glucose, C 6 H 12 O 6 ). During the formation of glucose, hydroxyl ions are produced. These hydroxyl ions raise the wa ter column pH (make it more basic) . Furthermore, the removal of

dissolved CO 2 for photosynthesis results in lo wer levels of carbonic acid (H 2 CO 3 ) in the water column, which causes a shift to a less aci dic condition (more basic condition).

More acidic conditions occur at night when phot osynthesis ceases but re spiration continues. Respiration results in the release of CO 2 into the water and thus increases the production of

carbonic acid:

CO 2 + H 2 O H 2 CO 3

. Carbonic acid dissociates, pr oducing hydrogen ions that lo wer the water column pH (H 2 CO 3 HCO 3 - + H + ).

Extremes in stream pH are stressful and can even be deadly to aquatic organisms. High pH levels increase the toxicity of some substances, such as ammo nia, whereas low pH levels can make heavy metals in stream sediment more mobile. Water column pH also affects the13 availability of phosphorus for algal intake, w ith phosphorus being unavaila ble to algae at high and low pH levels. High pH levels can damage fish gills, eyes, and skin. Low pH levels can interfere with fish reproduction. Levels of pH too high (e.g., >9) or t oo low (e.g., <5) can kill aquatic life.

Toxins

Some kinds of primary producers re lease toxins, which can kill fish and other organisms. These toxins can taint drinking water supplies and recr eational waters. Livest ock that drank water contaminated with cyanobacteria reportedly di ed (Bowling and Baker 1996 as cited in Dodds and Welch 2000). Humans who drink or swim in water that contains high concentrations of toxins from cyanobacteria may experience gastroen teritis, skin irritation, allergic responses, or liver damage (CDC 2004). In 1991, one of th e largest recorded rive rine blooms of toxic cyanobacteria occurred in the Murray-Darling Rive r Basin in Australia, resulting in a state of emergency being declared to protect water suppl ies drawn from the river (Oliver et al. 1999). Here in the U.S., harmful algal bl ooms of the toxic dinoflagellate, Pfiesteria, have been tied to

fish kills in several tidal tributaries of the Chesapeake Bay (U.S. EPA 2000a).



## SECTION II -- APPROACHES TO SETTING NUTRIENT

CRITERIA

The U.S. EPA’s Technical Guidance Manual for Developi ng Nutrient Criteria for Rivers and

Streams (2000a) offers three approaches for water qua lity managers to cons ider. The approaches include the use of (1) reference systems, (2) predictive relationships , and (3) established threshold values. U.S. EPA recommends using a combination of these approaches when developing numeric nutrient and al gal criteria. Almost all examples used in this document incorporate more than one of the suggested approaches.

The steps outlined for the nutrient criteria setting process recommended by U.S. EPA (2000b)

include: (1) Develop reference conditions at a regional or watershed scale (2) Evaluate historical data, publishe d literature, and other information (3) Use models to simulate physical and ecol ogical processes and to determine empirical relationships for causal variable s (e.g., TN, TP) and response vari ables (e.g., Chl- a, turbidity) (4) Utilize expert review and cons ensus (best professional judgment) (5) Evaluate downstream effects.

Clark Fork River Example

One of the best-known examples where nutrient thre sholds were established is in the Clark Fork River in western Montana. This waterbody expe rienced heavy growths of filamentous algae and diatom algae that interfered with the recreation and irri gation uses of the river. Chl-a values commonly exceeded 100 mg/m 2 (Watson and Gestring 1996, Dodds et al. 1997). Because

Montana does not have numeric nut rient criteria, target values were set for TN and TP from14 studies that incorporated severa l of the approaches described a bove. The nutrient targets were set to keep mean Chl-a values below 100 mg/m 2 because levels above this value are generally considered undesirable (based on information by Ho rner et al. 1983; and 26 citations reported in Welch et al. 1988).

Using the reference approach, Dodds et al. (1997) sampled six sites on the Clark Fork River that typically exhibit Chl-a levels cons idered to be acceptable(< 100 mg/m 2 ) (stations 8.5, 9, 13, 15.5, 24, and 25 in Ingman 1992a, b as reported in Dodds et al. 1997). They established target TN and TP concentrations from the 1988 – 1992 summer means (21 June – 21 September) of these reference sites. The TN average summer c oncentration for the reference stations was 0.318 mg/L, and the mean TP concentratio n was 0.0205 mg/L (Dodds et al. 1997).

Watson et al. (1990) used artificial stream cha nnels and water from the Clark Fork River in controlled experiments. The water was spiked w ith various concentrations of soluble nitrogen and phosphorus, and the growth ra tes and standing crop of alg ae were measured. They found that algal biomass would be reduced if solubl e nitrogen levels were kept below 0.250 mg/L and SRP was kept below 0.030 mg/L (Watson et al. 1990).

A regression model using data from more than 200 river sites worldwid e (Dodds et al. 1997) was used to predict TN and TP targets for keep ing mean summer Chl-a values below 100 mg/m 2 in

the Clark Fork River. The results from this regression yielded mean summer TN target concentrations of less than 0.350 mg/L and mean summer TP target concentrations of less than 0.0455 mg/L (U.S. EPA 1999).

Dodds et al. (1997) used a probabilistic modeli ng approach based on the methods developed by Heiskary and Walker (1988) for lakes. This model assessed the risk of exceeding 50, 100, and 200 mg/m 2 of Chl-a. For example, using the probabil istic model, they predicted that neither Chl-a seasonal mean nor maximum values would exceed 100 mg/m 2 more than 50 percent of the time if TN was kept below 0.200 mg/L. Simila rly, if TP was kept below 0.050 mg/L, mean Chl-a levels would be less than 100 mg/m 2 and maximum Chl-a values would be below 150 μg/L 50 percent of the time (Dodds et al. 1997).

Dodds et al. (1997) also reported results from regression analyses of the Clark Fork River data set. In this study, they used iteration equations to find values of TN that would give 50, 100, and 200 mg/m 2 of Chl-a. The results of the TN values were then used to es timate corresponding TP values from the Redfield ratio of 7.23 N: 1 P by mass. Additional iterations were performed to determine TP values that would give 50, 100, and 200 mg/m 2 of Chl-a, with TN values set to the Redfield ratio. Holding the maximum Chl-a value at 100 mg/m 2 , they predicted seasonal means of around 0.275 mg/L TN and 0.035 mg/L TP.

Based on the results from the studies reported in Dodds et al. (1997), the authors recommended setting targets at 0.350 mg/L for mean TN concentrations and 0.030 mg/L for mean TP concentrations to keep mean Chl-a values below 100 mg/m 2 in the Clark Fork River (150 mg/m 2
maximum). The Tri-State Implementation C ouncil overseeing the Clar k Fork River TMDL (total maximum daily load) accepted a summer mean Chl-a value of 100 mg/m 2 and peak Chl-a value of 150 mg/m 2 as the boundary between impaired and unimpaired water quality. To allow15 for a margin of safety (as requi red in TMDL development), they set mean targets at 0.300 mg/L for TN, 0.020 mg/L for TP upstream of Missoula, and 0.039 mg/L for TP below Missoula. The Council also recommended monitoring for both total and soluble form s of nitrogen and phosphorus to continue the study of point source and nonpoint source imp acts on the river (U.S. EPA 1999).



### 1. REFERENCE APPROACH

A group of national experts working with U.S. EPA in addressing nutrien t criteria development

issues recommended that U.S. EPA “not deve lop single criteria values for phosphorus or nitrogen applicable to all wa ter bodies and regions of the country. Rather, the experts recommended that EPA put a premium on region alization, develop guidance (assessment tools and control measures) for specifi c waterbodies and ecological regi ons across the country, and use reference conditions (conditions that reflect pristine or minimally impacted waters) as a basis for developing nutrient criteria” (U.S. EPA 2000b, p. 1).

With this advice, the U.S. EPA developed the Technical Guidance M anual for Developing Nutrient Criteria for Rivers and Streams (U.S. EPA 2000a), which included methods for establishing reference conditions and means for us ing the reference condit ions to set nutrient targets. The reference conditions in the guida nce manual are based on reference reaches defined as “relatively undisturbed stream segments that can serve as examples of the natural biological integrity of a region” (U.S. EPA 2000a, p. 94). The manual notes that reference reaches are only

a section of a stream and differ from reference streams , which include the whole stream and have little or no human impacts in the entire catchment area. U.S. EPA recommends selecting at least

three reference conditions for each stream class a nd suggests that all reference systems should be “minimally disturbed and should have prim ary parameter values (i.e., TN, TP, chl a , and turbidity) that reflect this condition” (U.S. EPA 2000a, p. 94).

The U.S. EPA technical guidance manual covers th ree methods by which nutrient criteria can be based on reference reaches for each stream class within a given region: (1) Use best professional judgment to characteri ze reference reaches a nd set the criteria to equal the mean (or median) value for the wa ter quality parameters of the reference

reaches. (2) Use a frequency distribution of data co llected from reference reaches (e.g., TN, TP values) and set the criteria equal to the 75 th percentile. (3) Use a frequency distribution of data co llected from all streams (reference and nonreference) or a random sample from all st reams and set the crite ria equal to the 5 th to 25 th
percentile (Use the 5 th percentile if most reference reaches are impacted by human activity to some extent.) (U.S. EPA 2000a).

Of the three reference methods, U.S. EPA prefers the frequency distribution that represents the

reference reaches. U.S. EPA further recommends using both frequency distribution approaches, if possible, and selecting whicheve r criteria are most protective. U.S. EPA states that the use of

the 75 th , 25 th , or 5 th percentiles are only recommendations and stresses that the main reason to16 chose a particular threshold s hould be based on the actual distri bution of data for the given region (U.S. EPA 2000a).

The agency chose the 75 th percentile of reference reaches for criteria setting because it is “likely associated with minimally impacted conditions , will be protective of designated uses, and provides management flexibility” (U.S. EPA 2000b, p. 10).

The 25 th percentile of all streams was selected by U.S. EPA because studies indicate this boundary approximates the 75 th percentile of reference streams, as illustrated in Figure 1 (U.S. EPA 2000b). In this example, the 75 th percentile for the referen ce streams is at 20, and the 25 th
percentile for all streams is at 25. A line is drawn at 23, indicati ng the middle value, which could be used as the threshold point.

Figure 1. Selecting reference values for total phosphor us concentration (μg/ L) using percentiles from reference streams and total stream populations. 1

U.S. EPA recognizes that some streams, such as those that flow over bedrock with high phosphorus content, may not be able to meet the cr iteria because of natural causes. The agency

also cautions that reference condi tions could be highly variable if the data are lumped (from different stream orders, seasons, years, etc. ) over a large geographical area (U.S. EPA 2000b). When using the reference approach, regions with st reams that have high nutrient levels are likely to have higher criteria limits than those with mostly low nutrient levels. Consequently, the criteria may be too high in ecoregions with ma inly high-nutrient reference conditions and too low for ecoregions with mostly low- nutrient reference conditions.

1 Reprinted from Nutrient Criteria Technical Guidance Manual: Rivers and Streams, EPA-822-B-00-002, Office of Water and Office of Science and Technology, United Stat es Environmental Protectio n Agency, Washington, DC, 152 pp., published 2000.17 U.S. EPA’s Reference Approach Usin g Aggregate Nutrient Ecoregions

Ecoregions are a means of classifying areas base d on similarities of natu ral geographic features (e.g., geology, soils, climate, hydrol ogy, vegetation, and w ildlife) and land use patterns. U.S.

EPA divided the continental United States into 14 nutrient ecoregions (a ggregates of level III ecoregions) with similarities in characteristics expected to affect nutrient concentrations. Virginia is included in Aggregate Nutrient Ec oregions IX, XI, and XI V (Table 2, Figure 2). Although U.S. EPA promotes the use of the aggreg ate nutrient ecoregion, it also suggests that

states and authorized tribes sele ct reference conditions at smaller geographic scales, such as the level III ecoregion develope d by Omernik (1987).

Table 2. Aggregate nutrient ecoregions and Om ernik’s (1987) level III ecoregions for streams in Virginia.

Aggregate Nutrient Ecoregion Level III Ecoregion 45, Piedmont 64, Northern Piedmont IX 65, Southern Plains 66, Blue Ridge 67, Ridge and Valley XI
69, Central Appalachians
XIV 63, Middle Atlantic Coastal Plain

Figure 2. Map of Virginia showing U.S. EP A Aggregate Nutrient Ecoregions.18 To define the reference condition, which could then be utilized in setting nutrient criteria, U.S. EPA used the following general procedures. Fo ur median values, representing each season (winter, spring, summer, fall), were determined for each waterbody used. Within a given ecoregion, a frequency distribution of the median values for each season was plotted, and the 25 th
percentiles were selected (Fi gure 3). The reference condition for the ecoregion was then

established as the median value of the seasonal 25 th percentiles (U.S. EPA 2000b). Using this method, the U.S. EPA’s calculated nutrient thres hold recommendations applicable to Virginia’s

rivers and streams are listed in Table 3.

Figure 3. Illustration of refe rence condition calculation. 2

2 Reprinted from Ambient Water Quality Criteria Recomm endations: Information Supporting The Development Of

State And Tribal Nutrient Criteria For Rivers And Str eams In Nutrient Ecoregion XIV, EPA 822-B-00-022, Office of Water—Office of Science and Technology Health and Ecological Criteria Division, United States Environmental Protection Agency. Washington, D.C. 79 pp., published 2000.19Table 3.U.S. EPA’s nutrient threshold recommendationsfor Aggregate Nutrient Ecoregions and Level III Ecoregions applicable toVirginia’s rivers and streams. Based on the median for all seasons’ 25thpercentiles.IX – 45PiedmontIX – 64NorthernPiedmontIX – 65SouthernPlainsIXXI – 66BlueRidgeXI – 67Ridge &ValleyXI – 69CentralAppalach.XIXIV – 63EasternCoastalPlainXIVTKN (mg/L) 0.234 0.3 0.30.102 0.169 0.1130.51NO2+ NO3(mg/L)0.177 0.995 0.0950.058 0.23 0.1770.04TN (mg/L)Calculated0.411 1.295 0.3950.16 0.399 0.290.55TN (mg/L)reported0.615 2.225 0.6180.690.28 0.214 0.5020.310.870.71TP(mg/L)0.030 0.040 0.02250.036560.007125 0.010 0.0076250.010000.05250.03125Turb. (NTU) 5.713 2.825 6.21 2.4 2.1752.303.89Turb. (FTU) 7.488 3.15 4.3385.71.675 4.25 1.91.74.53.04Turb. (JCU) 5.95 4.4 6.550.8 3.425 2.4444.73Chl-a (μg/L)–F 3.3 - 1.4381.625zz- -0.44Chl-a (μg/L)–S 3.493 1.205 0.0490.932 1.0631.613.753.75Periphyton Chl-a(mg/m2)- 20.35 -20.35- 32.75 --Chl-a – F = Fluorometric MethodChl-a – S = Spectrophotometric Methodzz calculated medians from less than 3 seasons’ data.20 U.S. EPA recommends using its suggested referen ce conditions as a guide or “first step” in setting nutrient criteria. States and authorized tribes ar e encouraged to select reference conditions at smaller geographic sc ales (level III ecoregi ons or watershed scal e) and refine their criteria through the use of models and published literature a nd in consideration of downstream effects and expert judgment (U.S. EPA 2000b).

Ponader and Charles (2003) applied the refere nce reach approach outlined by U.S. EPA to

nutrient data from the Northern Piedmont ecoregion in New Jerse y. They calculated a reference condition using the 25 th percentile from all reaches in their study and compared their values with those reported by U.S. EPA (2000b) for the Northe rn Piedmont subecoregion (64). Ponader and Charles (2003) found fairly good agreement in th e reference conditions for TN (1.28 mg/L to U.S. EPA’s 1.30 mg/L) and TP (0.051 mg/L to U. S. EPA’s 0.040 mg/L). In contrast, their 25 th
percentile Chl-a periphy ton values (48.07 mg/m 2 , determined by fluorescen ce) were substantially higher than U.S. EPA’s Chl- a reference values (20.35 mg/m 2 ), showing that the reference reach approach can give very different results simply by using different data sets.

USGS Reference Approach Using En vironmental Nutrient Zones

The U.S. Geological Survey (USGS), working as part of a Regional Technical Assistance Group (RTAG), evaluated and refined nutrient region b oundaries in the Upper Midwest. The USGS followed the U.S. EPA reference approach in pr oposing nutrient criteria values but developed alternative nutrient regions, called environmental nutrient zones . The nutrient zones were established to address three main problems c oncerning the U.S. EPA’s nutrient ecoregions. These issues include the following: (1) The relative importance of the environmenta l characteristics used to delineate the U.S. EPA nutrient ecoregion boundaries is unknown. This lack of information makes it

difficult to attribute differences in wate r quality among ecoregions to any specific

environmental factor. (2) The most important environmental char acteristic used to delineate the nutrient ecoregion may be different than that which has the greatest impact on the water quality. If such occurs, it is possible that there will be greater variation of water quality within an ecoregion than between ecoregions. (3) Although U.S. EPA states that classifica tions should be based on natural differences,

land use was often used to delineate the nut rient ecoregions (Robertson et al. 2001).

In the USGS study, regression-tree analyses were used to determine the relative importance of various environmental characteristics (e.g., basin, climate, and soil characte ristics) that affect nutrient concentrations. They then delineated th e environmental nutrient zones based on the two or three most statistically significant characteristic s that affect nutrient concentrations. For each zone, frequency distributions of the nutrient data were then used to propose nutrient criteria.

Criteria were based on the 25 th percentile of all the data with in a given zone, and alternative criteria were proposed using the 75 th percentile of data from sites within the zone that represent

“reference” conditions.21 The USGS study was based on TN, TP, and wate rshed characteristic data from 234 sites

collected from 1961 to 1999. The data set incorporated data from 15 different studies or sources and included data from the New River and Big Sa ndy River in Virginia. The data set was first analyzed including land use as an environmental characteristic. Subsequently, as a way to represent only non-anthropogenic (n atural) differences, the data set was analyzed excluding land

use as a factor.

In the study, nutrient concentratio ns were most strongly correlated with land-use characteristics, particularly the percentages of fo rest and agriculture. The most important environmental factors for nitrogen included the amount forest cover ( ≥ 9 % and ≥ 33 %) and the amount of precipitation ( ≥ 30.1 inches per year). For to tal phosphorus, forest cover ( ≥ 30 %), percent clay ( ≥ 26 %), and amount of runoff ( ≥ 12.3 inches per year) were the most important factors.

When land-use characteristics were excluded, th e most important environmental factors for nitrogen were soil slope ( ≥ 11 %), air temperature ( ≥ 43 o F), and nonglacial sediments or exposed bedrock ( ≥ 99 %). Important non-land-use factors for total phosphorus included till deposits ( ≥ 59 %), amount of runoff ( ≥ 10.3 inches per year), and percent clay ( ≥ 26 %).

The total nitrogen concentrations were more affe cted by land use than we re the total phosphorus concentrations. The nitrogen-based nutrient zones could be divided into low, moderate, and high amounts of agriculture. Furthermore, the delineated boundaries for the nitrogen nutrient zones

that included land use as a factor were substant ially different from thos e that excluded land-use characteristics. In contrast, the phosphorus-bas ed nutrient zones were similar for those that

included and excluded the land-use f actor (Robertson et al. 2001).

Using the data set where land-us e characteristics were exclud ed, USGS proposed TN and TP

criteria based on the 25 th percentile of all the data w ithin a given zone and the 75 th percentile for

“reference conditions” in the nutrient zone. In this study, the “reference condition” was assumed

to be sites in watersheds with less than 25 percent agricultural la nd use. It was noted, however, that sites with less than 25 percent agriculture were sometimes dominated by urban land use and therefore not representative of the natural condition (Robertson et al. 2001).

Based on the 25 th percentile of all the data within a nutri ent zone, TN criteria for the different nutrient zones in the Upper Midwest ranged fr om 0.51 mg/L to 1.75 mg/L (Table 4). The proposed TN criteria using the “referen ce” condition within the zone (the 75 th percentile of the

data for watersheds with less th an 25 percent agricu ltural land use) rang ed from 0.67 mg/L to 9.00 mg/L. For the proposed TP criteria, the 25 th percentile of all the da ta within a zone ranged from 0.02 mg/L to 0.11 mg/L. The 75 th percentile of the TP data for the “reference” conditions ranged from 0.05 mg/L to 0.16 mg/L (Robertson et al. 2001).

The Virginia waters (New River and Big Sandy River) included in this study are located in Environmental Nitrogen Zone-4 (ENZ-4) and Envi ronmental Phosphorus Zone-2 (EPZ-2) (based

on data when land-use characteristics are ex cluded from the analysis). From the 25 th percentile of all the data for ENZ-4 and EPZ-2, a TN cr iterion of 0.51 mg/L and a TP criterion of 0.02 mg/L, respectively, could be expected (Robertson et al. 2001).22 Table 4. Proposed nutrient criteria for differe nt environmental nutrient zones in the Upper Midwest region based on results of regression-tree analyses when landuse characteristics were excluded from the analyses (-- means no data).

Nutrient Zone 1 (mg/L) Zone 2 (mg/L) Zone 3 (mg/L) Zone 4 (mg/L)

25 th percentile of all

data TN 0.91 1.75 1.29 0.51

75 th percentile of 0-25% ag. land use TN 1.10 9.00 -- 0.67

25 th percentile of all

data TP 0.06 0.02 0.06 0.11

75 th percentile of 0-25% ag. land use TP 0.06 0.05 0.06 0.16

As a part of the study, the USGS also compared th e variability of nutrient concentrations within U.S. EPA’s nutrient ecoregions to that within its nutrient zones (der ived including land-use characteristics). The variability among the TN da ta within nutrient ecoregions and nutrient zones was similar for the two approaches. The within region variability in TP concentrations, however, decreased by about 50 percent using the nutrien t zone approach compared to the U.S. EPA nutrient ecoregion approach (Robertson et al. 2001).



### 2. PREDICTIVE RELA TIONSHIPS APPROACH

U.S. EPA recommends evaluating nutrient-algal re lationships in streams to help predict algal responses to certain levels of nutrient input. In the technical guidance manual, U.S. EPA outlines three means by which to develop pred ictive relationships: th e use of trophic state classification, models, and bioc riteria (U.S. EPA 2000a).



### A. TROPHIC STATE CLASSIFICATION

The term trophic state refers to a classification of th e nutrient condition for a waterbody. Unfortunately, there are no standard ized ways to classify the trophi c state of a stream. There are also no standardized parameters to measure fo r determining the trophic state of the stream. Dodds (2003) suggests that because of biotic upt ake and remineralization, trophic state is more appropriately represented by TN and TP concentra tions than by inorganic nitrogen and inorganic phosphorus concentrations.

The trophic state classification met hod described in this section by Dodds et al. (1998) is similar to the frequency distribution methods described in the Reference Appro ach section. In the examples used in the previous section, the stre ams were divided into two classes: impaired streams and non-impaired streams. However, fre quency distributions can be divided into more than two classes, such as reference streams, accep table quality streams, and impaired streams.23 With the trophic state classifica tion method, the frequency distribu tion is generally divided into three states: (1) oligotrophic streams, (2) meso trophic streams, and (3) eutrophic streams. In general terms, oligotroph ic streams are those that are nutri ent poor; mesotrophic streams have an

intermediate level of available nutrients, and eutrophic streams ar e overly enriched by nutrients.

Trophic State Based on Photosynthesis/Respiration Ratios

Hornberger et al. (1977) used a s ubjective ranking of the eutrophic stat e of six river sites, five in Virginia and one in New Hampshire, based on th eir best professional j udgment and knowledge of the land use; concentrations of nitrate, phosphate, and chlorophyll-a; and other information (Tables 5 and 6). They determined the productiv ity at each site from continuous measurements of dissolved oxygen, temperature, and solar radia tion. The authors then compared their proposed eutrophic state with the results from their productivity study. They determined that productivity measurements can be used to classify the eu trophic state of rivers and developed a diagram (showing the relationship between productivity measurements and water quality) for use in classifying rivers of unknown trophic status. The authors concluded that the eutrophication potential for a river could be estimated by co mparing several weeks of productivity data for summer, low-flow conditions with the data from the six streams in their study.

Table 5 . Proposed eutrophy classification for rive r sites in Hornberger et al. (1977), the site location, potentia l sources of pollution or primary land use, and number of days that dissolved oxygen, temperature, and solar radiation measurements were collected.

Proposed Eutrophy

Rank River Location Primary

Pollution Sources

/Land Use Days of

data

collection

High Mechums River Albemarle Co., VA;

downstream of town

of Crozet PS: raw & partially treated sewage; NPS: agricultural

runoff 78

High

intermediate South Fork

Rivanna River Albemarle Co., VA; 1

km downstream of

Rivanna Reservoir Moormans and

Mechums Rivers 14

High Rivanna River Albemarle Co., VA; downstream of city of

Charlottesville PS: two secondary STPs;

NPS: urban runoff 42

High South River Augusta Co., VA; upstream of the city of Waynesboro NPS: agricultural

runoff 38

Intermediate Rappahannock

River Frederick Co., VA; upstream of the city of Fredericksburg “...lower end of one of the longest reaches of large undisturbed river

in the Eastern United

States” 28

Very Low Baker River NH; in town of Rumney Mainly forested; limited agriculture 2324 Table 6. Physical and chemical conditions, and da tes of sampling, for the rivers used in water quality analysis. 3

Location and dates NO 3 + N0 2 (N)

range mg/L PO 4 (P)

range mg/L Typical periphytonchlorophylla (mg/m 2 ) Base

flow (m 3 /sec) Mean

channel

width

(m) Mean

water depth

at base

flow (m) Mean Velocity

at
base flow

(m/sec)

Mechums R. 19 Apr.-19 Aug. 0.4-0.6 0.08-0.30 60 0.80 16 0.33 0.15

South Fork Rivanna R. 5 May-25 Jul. 0.1-0.3 <0.003-0.01 135 0.55 26 0.37 0.06

Rivanna R. 20 Jul.-2 Sept. 0.3-1.0 0.02-0.08 50 2.40 36 0.33 0.20

South R. 16 Aug.-20 Sept. 0.4-0.8 0.03-0.04 500 1.40 11 0.40 0.30

Rapphannock R. 4 May-24 June 0.3-0.7 0.003-0.05 22.00 180 2.00 0.06

Baker R. 16 Jul. -2 Aug. 0.1-0.2 <0.003 16 1.80 18 0.48 0.30

In the study, the river representing the low eu trophic status, Baker River, had the lowest

concentrations for NO 3 +NO 2 , PO 4 , and typical periphyton chlorophyl l-a (Table 6). The nutrient concentration ranges for the river representing the intermediate eutrophic status, Rapphannock River, overlapped with those of the high eutrophic rivers. It was also as high as or higher than

the nutrient concentrations fo r South Fork Rivanna River, ranked high-intermediate. The

nutrient concentrations for the three rivers ra ted high-eutrophic (Mechu ms, Rivanna, and South) ranged from 0.3 to 1.0 mg/L for NO 3 +NO 2 and 0.02 to 0.30 mg/L for PO 4 . Typical periphyton chlorophyll-a values (determined using procedur es in Strickland and Parsons 1972) had a wide range, 50 – 500 mg/m 2 , for the high eutrophic rivers.

The authors observed that the vari ability within the pr oductivity measurements could be used as an indicator of the eutrophi cation status: the sites with more variability had a higher eutrophication ranking. Their fi ndings supported the work by Ball et al. (1973), where the “cleanest sites on several Michigan rivers were most stable while disturbed sites showed the most variability” (Hornberger et al. 1977, p. 68).

The main purpose of the study by Hornberger et al. (1977) was to determine if cultural eutrophication and the resulting dissolved oxygen depletion could be evaluated from measurements of productivity a nd respiration. They calculated the mean gross production (P), net production (P - R), respiration (R), and the P:R ratio for each ri ver site. Using discriminant analysis, the authors found that respirati on and P:R ratio, used in conjunction, were good

3 Reprinted from Water Research; Vol. 11; G.M. Ho rnberger, M.G. Kelly, and B.J. Cosby; Evaluating eutrophication potential from river community productivity; pages 65-69; copyright 1977; with permission from

Elsevier.25 indicators of water quality. When log R was plotte d against the square root of P/R, the resulting graph could be divided into regi ons based on eutrophy (Figure 4). The lower left section of the graph was considered representati ve of “clean” (oligotrophic) wa ters, while the upper left area represented rivers with possibl e eutrophication eventhough the river seem s “unpolluted” from the qualitative impressions of the authors. Rive rs plotting in the upper ri ght are eutrophic, and waters in the lower right (high respiration but low production) re present waters contaminated by organic material (Hor nberger et al. 1977).

The authors stress that the regions of the graph should not be used as strict thresholds, but rather general indications of the troph ic condition. They further reco mmended using the physical and

chemical characteristics of the river as well as the biol ogical community and habitat

characteristics of the river in addition to th eir proposed method for determining trophic state.

Figure 4. Schematic diagram showing re lationship between production and respiration measurements and water quality. 4

Trophic State Based on Geology and Land Use

Based on catchment geology and land use, Biggs (1995) divided 12 New Ze aland watersheds (16 sites) into one of three classifications of poten tial enrichment: enriched, moderately enriched, or unenriched. He measured periphyton accumula tion, algal community structure, and water column characteristics at each site every four weeks for one year. Chl-a was determined spectrophotometrically and corrected for phaeopigments by acidification.

4 Reprinted from Water Research; Vol. 11; G.M. Ho rnberger, M.G. Kelly, and B.J. Cosby; Evaluating eutrophication potential from river community productivity; pages 65-69; copyright 1977; with permission from

Elsevier.26 Periphyton cellular nitrogen concentrations and conductivity correlated str ongly with the apriori designation of the site enrichment groups. I norganic N and P concentr ations of the water column, however, did not correspond to the geology/land use apriori designation. The water column inorganic N and P concentrations also di d not correlate significantly (at the 0.05 level) with cellular N and P concentrations respectivel y (Biggs 1995). Limited sampling, fluctuations

in water column nutrient concentr ations, and the ability of alg ae to store nutrients may account for these observations (Biggs 1995).

Biomass accrual appeared to be limited by the amount of nutrients in the unenriched streams; however, in the moderately-enriched and enriched categories, biomass accrual was controlled by flood frequency. Thus, Biggs (1995) concluded that the quantity of nutrients control the rate of biomass accrual during periods of stable flow. Data from this study ar e summarized in Biggs (1996) and provided in Table 7. Biggs (1996) observed that in the enriched streams, Chl-a values were higher than 100 mg/m 2 for about 40% of the year, whereas the moderately enriched streams exceeded 100 mg/m 2 less than 1% of the time during that year.

Table 7. Chlorophyll-a values, ash-free dry mass (AFDM), and conductivity for stream trophic states based on catchment geology a nd land use as determined from monthly monitoring for one year in 16 streams in New Zealand (data reported in Biggs 1996).

Trophic State

of Monitored

Sites 25 th Percentile –

75 th Percentile Chl-a (mg/m 2 ) Median

Chl-a (mg/m 2 ) Median

AFDM (g/m 2 ) Mean Conductivity (μS/cm)

Unenriched 0.5 – 3 1.7 1.5 87 Moderately

Enriched 3 – 60 21 4.8 106

Enriched 25 – 260 84 15 271

Trophic State Based on the Frequenc y Distributions of Nutrient Values

Dodds et al. (1998) proposed an initial means by which to ch aracterize the trophic state of streams based on the frequency distributions of chlorophyll and nutrients. Because of the high variance observed in stream systems, Dodds et al. (1998) recommended using their method only as “a general first approach to categorizing stream ecosystems” ( p. 1455). They used data from published studies (Omernik 1977, van Nieuwe nhuyse and Jones 1996, and Dodds et al. 1997) that represented hundreds of streams, mostly in North America and New Zealand. The data included benthic Chl-a, water column Chl-a, TN, and TP. Using the distribution frequencies of each parameter, they divided the streams into cate gories with the lowest third (lowest values) of the distributions indicating oligotrophic c onditions, the middle third suggesting mesotrophic conditions, and the top third (highe st values) representing eutrophic conditions (Table 8). Dodds et al. (1998) caution, “Sharp, na tural boundaries in trophic state and functional relations among the variables have yet to be id entified for streams, so these boundaries should be viewed as provisional” (p. 1458).27 Table 8. Suggested boundaries for trophic clas sification of streams from cumulative frequency distributions. The boundary between oligotrophic and mesotrophic systems represents the lowest third of the distribution, and the bounda ry between mesotrophic and eutrophic marks the top third of the distribution. [Dodds et al. (1998) reported TN and TP concentrations in μg/L; for consistency within this review, these values were converted to mg/L] 5

Variable (units) Oligotrophicmesotrophic

boundary Mesotrophiceutrophic boundary n

Mean benthic Chl-a (mg/m 2 ) a 20 70 286

Max. benthic Chl-a (mg/m 2 ) a 60 200 176

Sestonic Chl-a (μg/L) b 10 30 292

TN (mg/L) a,c 0.700 1.500 1070 TP (mg/L) a,b,c 0.025 0.075 1366

a Data from Dodds et al. (1997).

b Data from van Nieuwenhuyse and Jones (1996).

c Data from Omernik (1977).

Dodds et al. (1998) found their tr ophic categories to be analogous to those proposed by Biggs (1996). Approximately 90% of the benthic Chl- a values in Biggs’ une nriched category were below 20 mg/m 2 , the oligotrophic-mesotrophic boundary defined by Dodds et al. (1998). Similarly, about 90% of the benthi c Chl-a values in Biggs’ modera tely enriched category were below 100 mg/m 2 , which is comparable to the 70 mg/m 2 mesotrophic-eutrophic boundary proposed by Dodds et al. (1998).

An advantage of applying the frequency distri bution method proposed by Dodds et al. (1998) to classify streams lies in its potential use in studying ecological proce sses within individual streams. For example, Dodds et al. (1998) hypothesi zed that if a stream has TP levels in the top

10% of the distribution but benthi c or sestonic Chl-a values in the lowest 10%, other factors besides nutrients, such as bent hic grazers or scouring floods, are likely affecting algal accrual.

There are also disadvantages to using this method. Of most signi ficance, the method of classifying the trophic condition of streams as proposed by Dodds et al. (1998) rests on the assumption that the lowest third of the data represents oligot rophic conditions and the highest third signifies eutrophic conditions . Unfortunately, there is no ba sis for this assumption. It is unlikely that about 33% of streams are eutr ophic, 33% mesotrophic, and 33% oligotrophic (ENSR 2001).

5 Reprinted from Water Research; Vol. 32; W.K. Dodds, J.R. Jones, and E.B. Welch; Suggested classification of stream trophic state: Distributions of temperate stream types by chlorophyll, total nitrogen, and phosphorus; pages 1455-1462; copyright 1998; with permission from Elsevier.28 

### B. MODELS

Mathematical models can be used to express relationships between different parameters, e.g., water column TP and algal biomass accumulation. M odels are classified as empirical or process based. Empirical models identify patterns but do not explain them, whereas process-based models are explanatory. Empirical models use regression analysis, wher e the value of a known variable is used to predict the value of an unknown variable. For example, empirical models that

correlate TN and/or TP with benthic algal biomass have been developed by Lohman et al. (1992), Dodds et al. (1997), Bourassa and Cattane o (1998), Chételat et al . (1999), Biggs (2000a), and others. Empirical models have also been developed to identify relationships between nutrients and phytoplankton, as described, for ex ample, by van Nieuwenhuyse and Jones (1996). Process-based models are made from equations that contain observable parameters. AQUATOX is an example of a process-based model devel oped by U.S. EPA with potential use in developing

nutrient criteria for streams and rivers (http://www.epa.gov/waterscien ce/models/aquatox/index.html ) (accessed January 16, 2006).

Empirical (Regression) Models

Statistics are useful in show ing the relationships among sampli ng parameters and are therefore used in regression models. Simple descriptive sta tistics, such as the mean (average), median, and ranges for each measured parameter, are helpful in identifying the differences and similarities

between streams. The observed significance level , known as the “p” value, expresses the probability of no relationship between variable s (e.g., Chl-a and TP). A small p value (for example, p < 0.001) indicates that the va riables are signifi cantly related.

The coefficient of determination for a sample, r 2 , reflects the proportion of variation explained by a regression model. It illust rates the fit of the data around the predicted relationship (0 < r 2 < 1). In general, the closer the r 2 value is to 1, the less scatter in the data and the higher the confidence that the data support th e model relationship.

Empirical Models Relating Nutrients and Periphyton

--Dodds, Smith, and Zander Study

Dodds et al. (1997) used data fr om published literature repres enting approximately 200 sites in North America, New Zealand, and Europe to deve lop mathematical equations to predict benthic chlorophyll-a values. They recommend using the equations only when local relationships have not been developed. Regression equations were developed to es timate mean Chl-a or maximum Chl-a using TN, TP, or both TN and TP concentrations.

The model was used to predict benthic Chl-a values in the Clark Fork River in Montana. The r 2
values for the equations used in the Clark Fo rk River study ranged from 0.35 to 0.43. From the equations, Dodds et al. (1997) predicted that if seasonal mean TN concentrations in the Clark Fork River are reduced to 0.275 mg/L, the ma ximum chlorophyll-a values would be 100 mg/m 2

. Additionally, if TN concentrations do not ex ceed 0.252 mg/L and TP concentrations are kept29 below 0.035 mg/L, Chl-a values in the Clark Fork River would be expected to stay below 100 mg/m 2

.

--Lohman, Jones, and Perkins Study

In a two-year study of 22 sites on 12 streams in the Ozarks, Lohman et al. (1992) developed regression equations to predict be nthic Chl-a values from TN and TP concentrations. Benthic Chl-a was determined fluorometrically and corr ected for phaeopigments by acidification. Chl-a was found to be positively correlated for bot h study years with respect to log TN (R 2 = 0.58, 0.60) and log TP (R 2 = 0.47, 0.60). Covariance analysis used to test for differences between years suggested that the slopes were not different but the intercepts were for both TN and TP data. Benthic Chl-a levels tended to be higher during the second year of the study. Lohman et al. (1992) credit the strength of their regression analysis on the use of long-term averages (a March – November “annual” average) and the wide range of TN (range of site annual means: 0.148 – 9.188 mg/L) and TP concentrations (range of site annual means: 0.006 – 3.264 mg/L).

Lohman et al. (1992) log-transformed the TP da ta from a New Zealand study (Biggs and Close 1989) and regressed it against Chl-a. A comparis on of the regression equa tions in each of the two studies, suggested that neithe r the slopes nor in tercepts were significantly different for the

Ozark and New Zealand streams. From this an alysis, Lohman et al. (1992) concluded that empirical models may be useful in assessing st ream nutrient-periphyton relationships, at least

under certain circumstances.

Lohman et al. (1992) also categorized the 22 Missouri stream sites as highly enriched, moderately enriched, or unenr iched based on mean annual TP concentrations. These mean annual TP concentrations ranged from 0.212 to 3.264 mg/L for the enriched sites (n = 6), 0.014 to 0.125 mg/L for the moderately enriched site s (n = 10), and 0.006 to 0.019 mg/L for the low enrichment sites (n = 6). At one site, land use was taken into considerat ion to distinguish the category. This site was categorized as modera tely enriched because it was known to receive point-source effluents, even though its mean a nnual TP concentrations were 0.017 mg/L in 1985 and 0.014 mg/L in 1986. Excluding this site, th e mean annual TP concentrations for the moderately enriched sites ra nged from 0.028 to 0.125 mg/L.

Growth curves for each trophic state category were developed from post-flood recovery data (Figure 5). From the growth curves, Lohman et al. (1992) predicted that the moderately and highly enriched sites would ex ceed Chl-a levels of 150 mg/m 2 within three or four weeks following a scouring flood. The unenriched sites, however, were not predicted to exceed 75 mg/m 2 of benthic Chl-a. Actual measurements were somewhat higher than predicted for the unenriched streams, but five of the six streams in this trophic class had maximum Chl-a values of less than 150 mg/m 2

.30 Figure 5 . Growth curves fitted for stream sites classified as high-, moderate-, and low nutrient sites following a catastrophic flood in fall 1986. Po ints are means ± SE; n = 6 for high- and lowenrichment sites and n = 10 for moderate-enrichment sites. 6

--Chételat, Pick, Morin, and Hamilton Study

Chételat et al. ( 1999) studied the relationship of nutrient s to periphyton biomass and community composition in riffle areas from 13 rivers in southern Ontario and western Quebec during summer low flows (see Biocriteria section). Sa mples were collected once each year for three years at 33 sites. Chl-a was determined using a spectrophotometer and a trichromatic equation. The Chl-a, TP, and TN data were log transformed and regressed. Chl-a values were correlated with water conductivity (r 2 = 0.71), TP (r 2 = 0.56), and TN (r 2 = 0.50). TN and TP concentrations were also positively corr elated with conductivity (r 2 > 0.70, p < 0.001). TP, TN, and conductivity were negatively correlat ed with catchment ar ea, indicating that th e smaller rivers in the Chételat et al. (1999) study had higher nutrient concentrations . The velocity measurements, ranging from 10 – 107 cm/s, explained less than 2% of the Chl-a variation. The authors suggest that this lack of a relationship between current velocity and algal biomass may be due to the relatively stable flows of the ri vers and possibly due to the ro ugh estimates made for velocity (Chételat et al. 1999).

For comparison, Chételat et al. (1999) log transfor med and regressed the Chl-a and TP data from

New Zealand rivers studied in Biggs and Close (1989) and Ozark streams described in Lohman et al. (1992) (Table 9). The slope of the Chételat et al. (1999) model was sim ilar to that of Biggs and Close (1989) and was much greater than thos e of Lohman et al. (1992). Stream size and drainage area seemed to account for this finding, as the New Zealand rivers and catchment areas were comparable whereas the Ozark streams and dr ainage areas were smaller. Also, the impact from flooding in the Ozark streams could contribu te to the differences observed in the slopes,

whereas the two river studie s had more stable flows.

6 Reprinted from Canadian Journal of Fisheries and Aquatics Sciences; Vol. 49; K. Lohman,, J.R. Jones, and B.D. Perkins, Effects of nutrient enrichment and flood frequency on periphyton biomass in northern Ozark streams; pages 1198-1205, copyright 1992, with permission from National Research Council Canada.31 Table 9. Regression of pe riphyton Chl-a (mg/m 2 ) as a function of TP concentration (μg/L) from the present study [Chételat et al. 1999] in co mparison with other models obtained from the

literature. 7

Coefficients

Intercept (±SE) log TP (±SE) r 2 RMS p n Source

0.490 ±0.213 0.905 ±0.140 0.56 0.071 < 0.001 33 Present stud y
0.338 ±0.310 0.722 ±0.246 0.49 0.089 0.022 9 Bi gg s and Close 1989

1.207 ±0.115 0.247 ±0.056 0.46 0.038 < 0.001 22 ( i ) Lohman et al. 1992

1.383 ±0.095 0.228 ±0.047 0.52 0.025 < 0.001 22 ( ii ) Lohman et al. 1992
Note: Data sources: Biggs and Close (1989) (data are geometric annual means from nine New Zealand rivers) and Lohman et al. (1992) (data are annual means from 22 sites on 12 Nort hern Ozark streams for (i) 1985 and (ii) 1986). Data are log transformed. The coefficient of determination (r 2 ), residual mean square (RMS), p value, and number of observations (n) are presented for each model

.

--Biggs Studies

Biggs (1995) divided 16 stream s ites in New Zealand into one of three potential enrichment categories based on catchment geology and land us e (see Trophic State Classification section). Every four weeks for a one-year period, he measured flooding disturbance, periphyton accumulation, Chl-a (determined spectrophotometr ically and corrected for phaeopigments by acidification), algal cellular nutrient concentrations, water column total inorganic nitrogen, water column dissolved reactive phosphorus, and other pa rameters at each site. Cellular nitrogen (N

c ) was measured as TKN, and cellular phosphorus (P

c ) was measured as TP. The ash free dry mass of the sample was used to standardize the va lues to give percent cellular nitrogen (% N

c ) and percent cellular phosphorus (% P

c ) (Biggs 1995).

Using a stepwise multiple regression, Biggs (199 5) determined that flood frequency (estimated as the number of annual events when the hydrogra ph exceeds 1 m/s), proportion of high-intensity agricultural land use, and proportion of alkaline rock could explain 89% of the variation in the mean monthly Chl-a levels. Combining the flood ing disturbance factor (flooding frequency) and stream enrichment factor (as measured by cellula r N concentrations), he developed a means to predict the growth of periphyton (n = 15, R 2 = 0.865). From this research, it appears that only streams having a combination of a few flood events (e.g., < 10) per year and enrichment of at

least 3% N

c should yield benthic algal biomass (Chl-a) in excess of nuisan ce levels set at 100 mg/m 2 (Biggs 1995).

In 2000, Biggs (2000a) published a paper in whic h he combined and reanalyzed data from previous studies (Biggs 1995, Bi ggs et al. 1998b, 1999) and developed models for predicting mean monthly and maximum Chl-a as a function of SRP, SIN, and days of accrual. The

dissolved nutrient-biomass models were deri ved for unshaded stream s with gravel-cobble

substrate and a flood threshold leve l of greater than thre e times (>3x) the median discharge. The models did not account for grazing benthic macroinvertebrates or site-specific characteristics

7 Reprinted from the Canadian Journal of Fisheries and Aquatic Sciences; Vol. 56; J. Chételat, F.R. Pick, A. Morin, and P.B. Hamilton; Periphyton biomass and community composition in rivers of different nutrient status; pages 560- 569; copyright 1999; with permission from National Research Council of Canada

.32 (e.g., local water velocities, differences in bed sediment for the sites), and therefore did not explain a large part of the variance in Chl-a va lues. The models, however, did explain up to 49%

of the variation in mean monthl y Chl-a (Table 10) and up to 74 % of the variation in maximum Chl-a (Table 11).

Table 10. Regression statistics for log 10 of mean monthly benthic algal biomass (mg/m 2 chlorophyll a ) concentration as a function of mean days of accrual ( d

a ) and mean monthly soluble nutrient concen trations. SIN = soluble inorganic N, SRP = soluble reactive P, mg/m 3 (n = 30). 8

Effect Value /

coefficient SE p (2-tail) r 2

1. Constant -0.888 0.434 0.050
Log 10 d

a 1.355 0.315 0.001 0.397

2. Constant 0.109 0.434 0.804
Log 10 SIN 0.483 0.245 0.057 0.122

3. Constant 0.468 0.192 0.022
Log 10 SRP 0.697 0.244 0.008 0.226

4. Constant -1.229 0.494 0.019
Log 10 d

a 1.245 0.320 0.001
Log 10 SIN 0.284 0.206 0.179 0.437

5. Constant -0.926 0.408 0.031
Log 10 d

a 1.152 0.310 0.001
Log 10 SRP 0.462 0.212 0.038 0.488

8 Reprinted from Journal of the North American Benthologi cal Society, Vol. 19(1), B.J.F. Biggs, Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae, pages 17-31, copyright 2000, with permission from the North Ameri can Benthological Society.33 Table 11. Regression statistics for log 10 of maximum monthly benthic algal biomass (mg/m 2 chlorophyll a ) concentration as a func tion of mean days of accrual ( d

a ) and meant monthly soluble nutrien t concentrations. SIN = soluble inorganic N, SRP = soluble reactive P, mg/m 3 (n = 30). 9

Effect Value/

coefficient SE P (2-tail) r 2

1. Constant -2.886 1.449 0.057
Log 10 d

a 5.223 1.937 0.012
(Log 10 d

a ) 2 -1.170 0.624 0.072 0.618

2. Constant 0.711 0.317 0.031
Log 10 SIN 0.688 0.168 0.001 0.325

3. Constant 1.400 0.184 0.001
Log 10 SRP 0.797 0.233 0.002 0.295

4. Constant -2.946 1.217 0.023
Log 10 d

a 4.285 1.649 0.015
(Log 10 d

a ) 2 -0.929 0.529 0.091
Log 10 SIN 0.504 0.144 0.002 0.741

5. Constant -2.714 1.264 0.040
Log 10 d

a 4.716 1.696 0.010
(Log 10 d

a ) 2 -1.076 0.545 0.059
Log 10 SRP 0.494 0.160 0.005 0.721

Using the developed regression models for maxi mum biomass and the trophic state boundaries set by Dodds et al. (1998) – an oligo-mesotrophic boundary of 60 mg/m 2 Chl-a and a mesoeutrophic boundary of 200 mg/m 2 Chl-a – Biggs (2000a) developed a nomograph to predict oligo-, meso-, and eutrophic conditions (Figur e 6). The nomograph shows the mean monthly soluble nutrient concentrations predicted to gi ve maximum algal biomass for the different trophic states for varying days in accrual. Although Biggs warns that the models need additional testing,

he concludes that “nutrient criteri a for the prevention of benthic al gal proliferations could be set in streams in relation to regimes of local fl ood frequency and expected time available for biomass accrual. The present analysis suggests that managing nutrient supply could not only reduce the magnitude of maximum biomass, but also reduce the frequency and duration of benthic algal prolifer ations in streams” (Biggs 2000a, p. 17).

9 Reprinted from Journal of the North American Benthologi cal Society, Vol. 19(1), B.J.F. Biggs, Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae, pages 17-31, copyright 2000, with permission from the North Ameri can Benthological Society.34 Figure 6. Nomograph of mean monthly soluble nutri ent concentrations that are predicted to result in maximum benthic algal biomass indicat ive of oligotrophic, me sotrophic, and eutrophic conditions for varying days of accrual ( d

a ) in gravel/cobble-bed st reams. The oligotrophicmesotrophic boundary was set at 60 mg/m 2 chlorophyll a and the mesotrophic-eutrophic boundary was set at 200 mg/m 2 chlorophyll a (after Dodds et al. 1998) . These boundaries also equate to maximum biomass criteri a adopted for the protection of benthic biodiversity (oligo- to mesotrophic), aesthetics, and trou t fishery values (meso- to eu trophic) in New Zealand streams (Biggs 2000[b]). The lines delineating the trophic boundaries were cal culated using soluble inorganic N (SIN) equation 4 in [Table 11]. However, they also approximate P-limited communities by reference to the right-hand scale, which has been set at 0.1 x the SIN scale,

because the mean ratio of biomass from the SIN and soluble reactive P (SRP) models was 10.8. 10

--TIAER Study

The North Bosque River in central Texas has expe rienced elevated levels of Chl-a and nutrients, which have negatively impacted it s contact recreation, aquatic life , and public water supply uses. Two segments of the river have been listed as im paired by Texas in its 30 3(d) report. Since the early 1990’s, the Texas Institute for Applied Environmental Research (TIAER) has been performing water quality studies in the Bosque River watershed (Kiesling et al. 2001).

TIAER scientists modeled stream productivity – estimated by the lotic ecosystem trophic state index (LETSI) method – as a function of instream nutrient concentrations . The data fit a Monod type growth function (a model th at relates algal growth to exte rnal nutrient concentrations). Using the half-saturation constant, McFarland et al. (2004) proposed a PO 4 -P target of 0.023 mg/L for the North Bosque River. This phospho rus concentration was similar to the target

10 Reprinted from Journal of the North American Benthologi cal Society, Vol. 19(1), B.J.F. Biggs, Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae, pages 17-31, copyright 2000, with permission from the North Ameri can Benthological Society.35 recommended by the North Bosque Ri ver Advisory Committee, 0.030 mg/L PO 4 -P (McFarland et al. 2004). A description of the methodology used to reach this target is provided below.

TIAER scientists have applied LETSI, an in situ bioassay method developed at TIAER, to measure nutrient limitations and trophic status (Matlock et al. 1999, Kiesling et al. 2001, McFarland et al. 2004). The LETSI value is dete rmined by relating the ratio of baseline primary productivity to maximum productivity. In other wo rds, the researchers co mpare the Chl-a values

from a control treatment to the Chl-a values from a N+P treatment. The LETSI values range from 0 to 1, with higher values representing nutrie nt enriched conditions. For example, a stream with a LETSI of 0.75 represents conditions exhibi ting 75% of the potential growth response to

nutrients. Streams with a LETSI value of 1.0 are at their maximum potential productivity.

In McFarland et al. (2004), anal ysis of variance (ANOVA) results indicated that when the LETSI values were greater than 0.5, neither N nor P were limiting. Conversely, LETSI values below 0.5 tended to be associated with N, P, or N+ P limitation. The limiting nutrient can be identified by comparing the Chl-a values for treatments rece iving solutions enriched with only one nutrient (N or P), a solution enriched with N+P, and th e control solution. Using this method, Kiesling et al. (2001) observed that PO 4 -P tended to be the limiting nutrient in the Bosque River watershed.

To determine the LETSI values, researchers depl oy Matlock periphytometers in the study stream for a two-week period. The periphytometers work by allowing nutrients to diffuse from a bottle containing one of four treatment solutions acro ss a semi-permeable membrane and onto a glass fiber filter that covers the mouth of the bottle and serves as a substrate for algal growth (Figure 7). Each bottle contains one of four treatme nt solutions: a control (deionized water), a Ntreatment (NH 4 NO 3 ), a P-treatment (Na 2 HPO 4 ·7H 2 O), or a N+P-treatment (NH 4 NO 3 and

Na 2 HPO 4 ·7H 2 O). An aluminum screen protects the al gal growth from grazing invertebrates and fish. At the end of the deployment, the artifi cial substrate of each sample is analyzed for chlorophyll-a to estimate primar y productivity. If algal growth is limited by nutrients, the samples receiving the nutrient solution treatm ents will be much higher compared to those receiving the control treatment.

Additionally, grab samples from the str eams were collected for analyses of NH 3 -N,

NO 2 -N+NO 3 -N, TKN, PO 4 -P, TP, and Chl-a. McFarland et al. (2004) found all N-limited

LETSI values when DIN concentrations were below 0.05 mg/L and all P-limited LETSI values

at or below PO 4 -P concentrations of 0.01 mg/L.

Data plotted for nutrient concentrations vs. LETS I values followed a Monod type growth pattern (Matlock and Rodriguez 1999, Kiesling et al. 20 01, McFarland et al. 2004) (Figure 8). The Monod model is based on the following equation:

μ = (μ

max * S) / (K

s + S)

where μ = algal growth rate (represented by the LETSI value)

μ max = maximum algal growth rate (represente d by a normalized growth potential based on the general range of LETSI values) S = external nutrient concentrations (repre sented by instream nutrient concentrations)36 K
s = half-saturation constant (represented by the instream nutrient concentration when the LETSI is equal to half the maximum potential productivity).

McFarland et al. (2004) combined data from previous work (Matlock and Rodriguez 1999) with the results from their study and define d target concentra tions based on the K

s value. They

estimated a K

s value of 0.023 mg/L for PO 4 -P, 0.145 mg/L for TP, 0.117 mg/L for DIN, and 1.34 mg/L for TN. The PO 4 -P and TP data fit the Monod equation better than the DIN and TN data. Furthermore, the confidence intervals were tighter for the PO 4 -P data than the TP data, indicating a closer biotic response of productiv ity to soluble P than to TP.

Figure 7. Diagrams of Matlock Periphytometer and photo showing Matlock Periphytometer

treatment arrays. 11

11 Reprinted from Transactions of the ASAE; Vol. 42(3); M.D. Matlock, D.E. Storm, M.D. Smolen, M.E. Matlock, A.M.S. McFarland, and L.M. Hauck; Development and application of a lotic ecosystem trophic status index; pages 651-656; copyright 1999; with permission of the American Society of Agricultural and Biological Engineers.37 Figure 8. Monod curve fit of PO 4 -P concentrations to LETSI values for P-limited

and not limited results. EI = current study [McFarland et al. 2004] and Matlock = Matlock and Rodriguez (1999). X-axis truncated at 1.0 mg/L. 12

--Ponader and Charles Study

The report by Ponader and Charles (2003) covers the first two years of a study to develop algal indicators for assessing cultu ral eutrophication. They used algal biomass and species composition plus water chemistry and physical stream measurements to develop models and metrics for determining the effects of nutrients in Piedmont streams in New Jersey (see Biocriteria section). Ponader and Charles (2003) found diatom in ference models to be better predictors of nutrient concentra tions and river eutrophication than either biomass measures or

12 Reprinted from Using Periphytometers to Evaluate Impairment due to Nutrient Enrichment; TR0404; A. McFarland, R. Kiesling, and J. Back; 51 pp.; copyright 2004; with permission from Texas Institute for Applied Environmental Research, Tarleton St ate University, Stephenville, TX.38 soft-algae inference models. For example, of all the parameters considered, Chl-a was only significantly correlated with visual estimates of Cladophora sp. coverage (p = 0.01, r = 0.40). Furthermore, with one exception (NH 3 -N correlated at the 0.05 level with Audouinella sp.), there were no significant correlations between nutrien ts and soft algal species composition. In contrast, O-P and TP were found to have strong influence on diatom species distribution, and

NO 3 -N and NH 3 -N expressed moderately strong influen ce on diatom species distribution.

Thus, nutrient inference models for each of the four nutrient variables were developed based on diatom species composition. The models were calib rated using two different data sets (complete 2000 data set [n = 85], 2001 data se t [n = 54]) (Table 12). The best model, TP inference model, was tested by evaluating the distribution of obser ved TP values (from 2001 data set) versus the diatom-inferred TP values (model based on 2000 data set). The test showed that the TP model could be used to predict TP concentrations from other diatom samples collected in freshwater rivers in the New Jersey Piedmont ecoregion (r = 0.78, r 2 = 0.61, Ponader and Charles 2003). With this result, the authors recommended the use of the diatom inference model as a means to monitor eutrophication and outlined plans to improve the model to increase its predictive power.

Table 12 . Predictive power of diatom infere nce models for total phosphorus (TP), orthophosphate (O-P), ammonia (NH 3 -N) and nitrates (NO 3 -N), as determined using weighted averaging (WA)-regression and calibration. 13

n = 85 n = 54

Parameter r 2
(apparent) RMSEp (boot) (log) μg/L r 2
(apparent) RMSEp (boot) (log) μg/L

TP 0.72 0.33 0.69 0.22

O-P 0.69 0.42 0.67 0.31

NH 3 -N 0.71 0.36 0.68 0.34

N0 3 -N 0.68 0.26 0.64 0.21

Although predicting eutrophication was listed as a goal of the project, the study, as described in the report, has thus far focused on algal relati onships to nutrients a nd not specifically to eutrophication. While use of diat oms to predict TP concentrations could be beneficial, there is

still a need to relate TP concentrations to cultur al eutrophication before se tting nutrient criteria.

13 Reprinted from Understanding the Relationship between Natural Conditions and Loadings on Eutrophication: Algal Indicators of Eutrophication for New Jersey Streams, Final Report Year 2, Report No. 03-04, K. Ponader and D. Charles, New Jersey Department of Environmental Protection Division of Science, Research and Technology, copyright 2003, with permission of The Academy of Natural Sciences.39 Empirical Models Relating Nutrients and Phytoplankton

--van Nieuwenhuyse and Jones Study

van Nieuwenhuyse and Jones (1996) developed a re gression model to predict mean suspended chlorophyll values in small and la rge temperate streams. The model is based on summer (May – September) mean TP values (mg/m 3 ) and Chl concentrations (μg/L) from various literature sources for 292 temperate streams (Chl values we re uncorrected for phae ophytin). Most of the data set is from North American streams (n = 231) , particularly from tributaries of the Missouri and Mississippi rivers (n = 181). The mean TP concentrations ranged between 0.005 mg/L and 1.030 mg/L (5 mg/m 3 and 1,030 mg/m 3 ), and the mean Chl ranged between 0.4 μg/L and 170 μg/L (65% of Chl values ranged between 5 and 65 μg/L). Regression analysis showed that

summer mean Chl values had a curvilinear relati onship with summer mean TP concentrations (R 2 = 0.67, Figure 9).

Figure 9 . Log-log relation between mean total phos phorus concentration and mean chlorophyll concentration among streams. Dashed curves approximate 65% confidence interval for individual predicted values. 14

Using a bivariate model, van Nieuwenhuyse and J ones (1996) also found that stream catchment area significantly affects the Chl concentrations at all levels of TP (Figure 10). Streams with larger catchment areas (10 5 km 2 ) had much higher Chl concentrations than those with smaller catchment sizes (10 2 km 2 ) for any given mean TP concentrat ion. The authors interpret this finding as indicating that the hydraulic flushing ra te or other physical factors may coregulate (regulate along with phosphorus) th e amount of phytoplankton grow th. They explain that the average phytoplankton biomass rate of loss d ecreases with increasing catchment size (van

14 Reprinted from Canadian Journal of Fisheries and Aquatic Sciences, Vol. 53, E.E. van Nieuwenhuyse and J.R. Jones, Phosphorus-chlorophyll relationship in temperate streams and its variation with stream catchment area, pages 99-105, copyright 1996, with permission from National Research Council Canada.40 Nieuwenhuyse and Jones 1996). Thus, phytoplankton biomass increases with increasing stream

catchment area.

Figure 10 . Arithmetic-scale plot of the phosphorus-chl orophyll relationship in streams and its variation with stream catchment area (A

c ). The solid curve shows th e predicted relationship for streams regardless of A

c ; the broken curves show the predicte d relationship for streams of widely differing A

c (100,000 and 100 km 2 ). Values are corrected for transformation bias. 15

--Reckhow et al. Study

Reckhow et al. (2005) used structural equati on modeling, SEM, to identify the relationships between nutrient-related parameters and the predicti ve use attainment for four waterbodies in the United States: Neuse Estuary, San Francisco Bay, Lake Washington, and Lake Mendota. They first identified the designated uses in each waterbody that could be potentially impacted by nutrients. They then used an expert elic itation approach, a well- established method to systematically obtain subjective judgments from experts. In this st udy, state officials or university scientists familiar with the particular waterbody were interviewed. Using the responses from the experts and water quality data , Reckhow et al. (2005) developed structural equation models to propose water quality criteria and relate the probability that the criterion would protect the designated use. Based on the results of their study, the authors found that the current Chl-a criterion of 40 μg/L and D.O. le vel of 5 mg/L in the Neuse Estuary has a 60% probability of attaining the use designations. Furthermore, the model predicts that assuming D.O. concentrations are at least 5 mg/L, Chl-a co ncentrations would need to be less than 10 μg/L to achieve a high level of attainment (Figure 11) . The authors contend th at realistically, this

15 Reprinted from Canadian Journal of Fisheries and Aquatic Sciences, Vol. 53, E.E. van Nieuwenhuyse and J.R. Jones, Phosphorus-chlorophyll relationship in temperate streams and its variation with stream catchment area, pages 99-105, copyright 1996, with permission from National Research Council Canada.41 criterion may not be attainable ; however, quantifying th e risk of nonattainment provides valuable information for use in setting criteria.

Structural equation modeling pr ovides a means for studying a network of relationships among a set of correlated variables. An advantage to using the structural equation model includes its ability to test for indirect e ffects between two variables influe nced by a third variable. For example, it can be used to study the imp act that P has on z ooplankton by affecting phytoplankton. Another advantage of the model is its ability to represent variables that are not directly measured, such as phyt oplankton: often represented by Chl-a concentrations. One disadvantage of the current study is its use of only one expert for each waterbody. The authors recommend interviewing more than one expe rt in future studies and intend to make

modifications to the described method.

Figure 11. Application of the Neuse River Estu ary structural equation model (SEM) for estimating the probability of attainment of the de signated use (logit transformation of the expert's response) for the entire summer range of dissolved oxygen and chlorophyll a levels in the Neuse River estuary [Figure 11a]. The two surfaces corr espond to specific levels of the two candidate criteria, i.e., dissolved ox ygen = 5 mg/L and chlorophyll a = 20 μg/L (the latter is also shown in [Figure 11b]). 16

16 Reprinted from Environmental Science & Technology; Vol. 39(9); K.H. Reckhow, G.B. Arhonditsis, M.A. Kenny, L. Hauser, J. Tribo, C. Wu, K.J. Elcock, L.J. Steinberg, C.A. Stow, and S. J. Mc Bride; A predictive approach to nutrient criteria; pages 2913-2919, copyri ght 2005; with permission from the American Chemical Society.42 Process-Based Models

AQUATOX

AQUATOX is a process-based model because it he lps to identify and explain the cause and effect relationships between the chemical, physical, and biological components of aquatic ecosystems. The model, which was developed by U.S. EPA over a 30-year period (based on combining CLEAN, CLEANER, LAKETRAC E, MACROPHYTE, PE ST, and TOXITRACE models), predicts the fate of various environm ental stressors (includi ng nutrients) and their effects on algal, macrophyte, invertebrate, and fi sh communities. The model has been designed for streams and small rivers as well as ponds, lakes, and reservoirs. Potential applications of the model related to nutrient criteria development in clude simulations of ni trogen (total, nitrate, ionized and un-ionized ammonia) , phosphorus (total, total solubl e phosphate), chlorophyll-a, periphyton biomass, phytoplankton biomass, etc. AQUATOX has been su ccessfully calibrated and validated against independent data in Walk er Branch, Tennessee to simulate periphyton response to nutrients, light leve ls, grazing by snails, and variable flow (U.S. EPA 2001).

U.S. EPA developed a technical document that e xplains the individual co mponents of the model so that the appropriateness of its applicability can be determined (Park and Clough 2004). To run the model, users must supply site, chemical, a nd biological characteristic s. For example, to model the change in phytoplankton biomass, user s must provide inputs for loadings of the phytoplankton, rate of photosynthesis, respirat ory loss, photorespirati on, nonpredatory mortality, herbivory, the loss or gain due to sinking, loss due to being carried dow nstream, and turbulent diffusion. AQUATOX can also be used with char acteristics and loadings from BASINS data layers or from HSPF and SWAT watershed m odels (U.S. EPA models frequently used in TMDLs). The AQUATOX model uses differential equations to simulate numerous biological and ecological processes operating within the eco system. A simulation may cover a time period from several days to several years. Outputs from the model include biomass, biomass risk graphs, direct and indirect effects, etc.

The AQUATOX model may prove beneficial to stat e and tribal agencies in modeling processes to predict ecosystem effects from changes in st reams and small rivers, e.g., reducing phosphorus loadings. An up-dated version of AQUATOX (R elease 2.1) was published in 2005. U.S. EPA states that AQUATOX Release 2.1 offers improved simulation of periphyton and phytoplankton, particularly for river systems, and more realistically tracks the amount of nutrients in the waterbody. Testing of the model by U.S. EPA and improvements to the model are on-going. More information about AQUATOX can be f ound at http://www.epa.gov/ost/models/aquatox/ (accessed February 10, 2006).



### C. BIOCRITERIA

The use of biocriteria to esta blish whether or not streams are impaired because of excess

nutrients relies on the use of bi ological indicators (e.g., algal biomass, presence/absence of algal species, fish Index of Biotic Integrity [IBI]). Biocriteria can either be non-taxonomic or taxonomic. Non-taxonomic biocriteria rely on th e mass or amount of the biological parameter43 but make no distinction based on the species present or absent. Algal biomass can be expressed through measurements of periphyton Chl-a (mg/m 2 ), phytoplankton Chl-a (μg/L), AFDM (g/m 2 ), streambed coverage by filamentous algae (%), or turbidity (FTU, NTU). Taxonomic biocriteria, on the other hand, rely on species composition and knowledge of the tolerances and sensitivities of species.

Algal Bioindicators

Because in almost all instances nuisance levels of algae directly or indirectly cause the problems associated with excess nutrient input, U.S. EPA recommends using algal bi oindicators to validate stream trophic classification (U.S. EPA 2000a). The use of algal bioindicators, however, is not yet common in the U.S. As of 2000, only Kentuc ky, Montana, and Oklahoma use algal data in their stream water quality asse ssments (U.S. EPA 2000a).

Indicator Algae

Indicator algal species are thos e with known sensitivities and tolerances and are therefore beneficial in describing wa ter quality. For example, Cladophora is often associated with stream

overenrichment and can thus be described as an indicator of nutrient overenrichment. The percent visual estimates of Cladophora sp. coverage (as determined from the rapid bioassessment method) were significantly correlat ed (at the 0.01 level) w ith Chl-a measurements (r = 0.40, Ponader and Charles 2003). Similarly, certa in species of diatoms are used as indicator algae (as cited in U.S. EPA 2000a: KDOW 1993, Pan et al. 1996, Kelly et al. 1998). In a periphyton index of biological integrity develope d by Hill et al. (2000) for streams in the MidAtlantic region, the authors iden tified one eutraphentic -diatom metric of pa rticular importance for its association with nutrient impacted streams. Eutraphentic diatom genera identified in Hill et al. (2000) and included in the metric are presented in Table 13.

Table 13. Eutraphentic diatom algal genera present in streams of the Mid-Atlantic region as identified by Hill et al. (2000).

Eutraphentic Diatom Genera Amphora Melosira

Asterionella Meridion

Cocconeis Nitzschia Cymatopleura Rhoicosphenia Diatoma Stephanodiscus Fragillaria Synedra Gomphonema Thalassiosira Gyrosigma44 Algal Growth Studies

-- Heinonen Study

Heinonen (1984) used algal growth plates in a nu trient study of a waterway situated between two

lakes in southern Finland. Evidence showed that the discharge from a large fish breeding station located on the waterway was negatively affecting the aquatic life and recreational uses of the waterway by lowering the reproduct ion rate of the natural trout population, increasing the growth of periphyton on stones in some rapids, and causing slime growth on some beaches in the downstream lake. The mean loading to the waterw ay from the fish station was estimated at 93.4 kg/d N and 15.6 kg/d P during the study period. Hei nonen (1984) placed grow th plates upstream

and downstream from the fish station and meas ured the amount of periphyton (represented by Chl-a) that grew during a thre e-week period (with three incuba tion times during the study period, July 20 – September 22, 1981). Significant differe nces were observed between the upstream and downstream plates, with the downs tream growth plates having much more periphyton (Table 14). The mean nutrient concentrations in th e water column collected during the study period, however, were low and not significantly different. The author concluded that Chl-a is more sensitive in identifying the early stages of eutrophication than are N and P water column concentrations (Heinonen 1984).

Table 14. Mean water quality and periphyton growth for a waterway in Finland impacted by a point source. Samples collected from July 20 – September 22, 1981 (adapted from Heinonen 1984).

Immediately

Upstream 4 km

Downstream 15 km

Downstream* TN (mg/L) 0.355 0.360 0.385

NO 3 -N (mg/L) 0.043 0.043 0.043

NH 4 -N (mg/L) 0.010 0.014 0.013 TP (mg/L) 0.009 0.011 0.012

PO 4 -P (mg/L) 0.002 0.001 0.002 Chl-a (mg/m 2 ) 0.97 2.73 3.99

*located downstream of the second lake.

-- Tualatin River Study

About 40 miles of the Tualatin Ri ver in the Portland, Oregon region were listed impaired for not supporting fishing, contact recreation, aesthetics, and aquatic life as a result of nuisance algal growths. Additionally, the downstream lake in to which the Tualatin River flows was not supporting some of its designated uses because of excess algae. As part of the TMDL development for this river, a series of algal gr owth studies was performed. The purpose of the study was to set a TP limit for the river that would meet Oregon’s esta blished criteria of maintaining a mean suspended Chl-a level of 15 μg/L. From the results of the study, the

researchers observed a reduction in periphyton when TP levels were held below 0.100 mg/L and45 lower growth rates in periphyton at TP levels below 0.050 mg/L. Based on this study, a TMDL target limit, which included a margin of safety (i.e., a more conservative limit), was set as an average TP concentration of 0.070 mg/L during the growing season (based on monthly means from May 1 to October 31) (U.S. EPA 1999).

Biotic Indices

Biotic indices are based on the co rrelation of algal assemblages with current and past physical

and chemical characteristics of the habitat. Indi ces are usually comprised of six to ten metrics that include a measure of the abundance of organi sms, species richness, and trophic structure. The metrics are based on their sensitivity to human activities related to nut rient input to streams, their precision, and their usefulness among different regions and habitat types. Selected metrics should reflect the breadth of biological responses to varying nutrient levels (Hill et al. 2000, U.S. EPA 2000a). Indices generally rely on refere nce conditions to evaluate streams.

Periphyton Indices

--Ponader and Charles Study

Ponader and Charles (2003) tested benthic algal indices to indicate stream impairment owing to excess nutrients. They analyzed samples for diatoms and water chemistry from 1 st – 6 th order wadeable streams in the Piedmont ecoregion of New Jersey. A Spearman’s rank-order

correlation was used to determine if six different diatom metrics were correlated with different environmental variables used to signify stream impairment, e.g., land use, nutrient concentrations. The following three metrics: Siltation Index, cen tric diatoms/pennate diatoms, and number of diatom taxa in the sample showed moderately strong/strong (r > 0.3) and significant correlations (at 0.01 level) with basin size, NO 3 -N, TP, and O-P. (The Siltation Index metric represents the frequency and severity of sedimentation and is indicated by the percent abundance of species in the genera Navicula, Nitzschia, Cylindrotheca, and Surirella. )

Furthermore, Ponader and Charles (2003) used th e New Jersey Piedmont diatom data set to

calculate three different diatom indices used in Europe (Tro phic Diatom Index, Biological Diatom Index, and Specific Polluosensitivity Inde x). Because the flora in Europe differs somewhat from that in New Jersey, only about 80% of the diatom species in the Northern Piedmont samples were used in the calculatio ns. Despite the species differences, the three European diatom indices were good predictors of TP and O-P in New Jersey Piedmont rivers and indicate a potential for their use in the U.S., particularly if they are modified to include North American diatom species (Ponader and Charles 2003).

-- Ponader, Flinders, and Charles Study

Ponader et al. (2005) conducted a pi lot study in Virginia in an a ttempt to develop algal indices and determine the best metrics for identifying str eams with nutrient impairments. Thirty-seven

stream sites from across the state were random ly selected using a probabilistic monitoring approach. The streams were located in four ecoregions of Virginia: Central Appalachians, Central Appalachian Ridge and Valley, Blue Ri dge Mountains, and Piedmont. Within each46 stream, during the period from late August to ea rly November in 2004, samples were collected to determine algal biomass (estimated as Chl-a a nd AFDM) and algal species composition (diatoms and soft algae). Additionally, st ream samples were analyzed for NH 3 +NH 4 , NO 2 , NO 3 , NO 2 +NO 3 (reported as NO 3 -N), TP, and O-P. Ordination analysis (CANOCO for Windows, version 4.5) was used to study patterns in the species composition data and to determine the strength of the correlation be tween environmental variables (e.g., nutrient concentrations, Chl-a values) and species composition. A Pearson corre lation matrix was used to compare nutrient concentrations (TP and NO 3 -N) with algal metrics, trophic indices, and Chl-a levels. In the study, NO 3 -N was used to establish nitrogen categorie s because TN data were unavailable for the study period (Ponader et al. 2005).

Four hurricanes struck Virginia during the field season of this study and were believed to have greatly impacted the results of the study. For example, high fl ows forced the researchers to reduce the sampling sites from 50-60 streams to 37 st reams. Furthermore, of the 343 diatom taxa identified, scour-resistant taxa were prevalent, sugges ting that high flows may have impacted the algal species compositions. Also, the authors speculated that high flows resulted in lower levels of algal biomass than expected at the high nut rient sites. Additionally, the hurricanes likely impacted the nutrient concentra tions (Ponader et al. 2005).

A small sample size, narrow range of nutrient concentrations, and uneve n distribution of data points along the nutrient gradient limited the ability of the resear chers to model the assemblagenutrient relationships. The NO 3 -N data ranged from less than 0.1 mg/L to 1.14 mg/L, with most of the samples being below 0.3 mg/L. The TP data ranged from less than the detection limit (0.01 mg/L) for 13 of the 37 streams to 0.16 mg/L. Most of the TP concentrations were below 0.05 mg/L (Ponader et al. 2005). The narrow and uneven distribution of the nutrient concentrations may have resulted from the hurri canes and/or the use of random sample sites, which tends to lead to a clumping of less extreme values.

Despite the drawbacks of this p ilot study, the authors were able to draw several conclusions from the study:

• NO 3 -N and TP concentrations expl ained significant variation in the diatom (particularly) and soft algae species assemblages.

• Algal assemblages differed among the four ecoregions, indicating a need to develop

indicators at the ecoregion level.

• Significant (P < 0.01) and strong correlat ions were measured between (1) TP

concentrations and the Silta tion Index, (2) TP concentratio ns and the high TP Index developed from a USGS NAW QA data set, and (3) NO 3 -N concentrations and Chl-a

values.

From the observed changes in the diatom assemb lages, the authors suggest ed threshold limits of 0.5 mg/L for NO 3 -N and 0.05 mg/L for TP to protect agai nst conditions they termed as nutrient impairment. The NO 3 -N threshold was selected because be nthic chlorophyll-a levels above 100 mg/m 2 occurred at NO 3 -N levels above 0.5 mg/L, although several sites with NO 3 -N levels above 0.5 mg/L did not exceed benthi c chlorophyll-a levels of 100 mg/m 2 . The TP threshold identification was based on the finding that seve ral diatom species indices correctly assigned samples to the TP concentration categorie s 0.01-0.05 mg/L and 0.05-0.10 mg/L, indicating a change in the diatom species composition above and below 0.05 mg/L TP (Ponader et al. 2005).47 Ponader et al. (2005) suggested conducting addi tional, multi-year sampling across all ecoregions in Virginia for all nutrient conditions. The new da ta set could then be us ed to develop diatom inference models and algal-based indicators for each ecoregion (or by combining data sets for ecroregions with similar characteristics).

--Hill and Others Study

Hill et al. (2000) developed a 10-metric periphyt on index of biological integrity (PIBI) for streams in the Mid-Atlantic regi on. The PIBI is based on (1) alga l genera richness, (2) relative abundances for diatoms and cyanobacteria, (3 ) Chl-a and AFDM data, and (4) alkaline phosphatase activity (Metrics in clude: richness, diatom, cya nobacteria, dominant diatom, acidophilic diatoms, eutraphentic diatoms, motile diatoms, chlorophyll, biomass, and phosphatase). The index was based on generic-leve l identifications so th at non-experts in algal taxonomy can use it. The environmental preferen ces of the diatom genera were taken from published studies (as cited in Hill et al. 2000: Lowe 1974, Christie and Smol 1993, van Dam et al. 1994). One metric of particular importance fo r identifying nutrient imp acted streams is based on eutraphentic diatoms, which are often associated with nutrient impacted streams (as cited in Hill et al. 2000: Palmer 1969, Lange-Berlatot 1979, Hall and Smol 1992, Christie and Smol 1993, Pan et al. 1996) (Table 13).

Data from 233 stream site-visits in the Mid-At lantic region (including st reams in Virginia) had PIBI scores that ranged from 48.0 to 85.1 (out of a possible 100), w ith an overall regional score (+ 1 SE) of 66.1 + 0.5. Significant differences in the PIBI scores for lowland versus highland

streams and for the different stream orders were ob served. Hill et al. (2000) classified streams in the upper 25 th percentile (PIBI > 72) as acceptable and those in the lower 25 th percentile (PIBI < 60) as degraded.

Phytoplankton Indices

The Kentucky Division of Water (KDOW) is in the process of developi ng the environmental preferences for dominant and a bundant phytoplankton taxa in Kent ucky. For this process, the agency is analyzing samples for phytopla nkton density (cells/mL ) and/or phytoplankton community structure. If blooms are present, the KDOW field biologi sts record the bloom characteristics (e.g., odor if presen t, surface sheen if pr esent, color of surface scum, thickness of bloom, etc.). The biologists also estimate a nd record the algal bloom coverage from bank to bank: absent (0%), sparse (1 – 29%), moderate (30 – 59%), dense (60 – 99 %), and total (100%). In the laboratory, phytoplankton sa mples are allowed to settle (to concentrate the sample), identified to the lowest possibl e taxon, and quantified as algal de nsity, biovolume, taxa richness, diversity, percent similarity, or other metrics (KDOW 2002).

The metric results are compared to those from co ntrol sites, other similar sites in the watershed, reference conditions, historic data, or data published in the scientif ic literature. Impairment is determined for each use—aquatic life, drinking water supply, and recreation. To determine if streams are supporting their aquatic life use, KDOW assesses the phytoplankton community and compares their findings with published informa tion on the water quality conditions under which48 different taxa thrive (based on studies from Whitford and Schumacher 1963, Bennett 1969, Lowe 1974, Palmer 1977, Patrick 1977, Collins and Weber 1978, van Landingham 1982, and others as cited in KDOW 2002). To assess the potenti al impact of phytoplan kton on drinking water supplies, algae associated with taste and odor problems and potenti ally toxic taxa are identified and enumerated. To determine the impairment of recreational use, KDOW assesses algal blooms and bloom potential from Chl-a a nd/or cell density (KDOW 2002).

Saprobic Bioindicators

Species of algae, bacteria, prot ozoan, and fungi that obtain their energy from dissolved nutrients in the surrounding water, generally fro m decaying organic matter, are called saprozoic organisms . As a group, they form a community referred to as sewage fungus, a useful indicator of organic pollution.

The Saprobic System was developed in the earl y 1900’s by Kolkwitz and Marsson (Friedrich et al. 1996). It is based on observations in change s of stream biota imme diately downstream of highly organic point sources and recovery of th e biota further downstream. Streams are divided into saprobic zones based on their physical, chemi cal, and biological charact eristics. In Europe, the Saprobic System is used to monitor the eff ects of treated wastewater on streams (as cited in Hill et al. 2000: Lange-Bertalot 1979, Friedrich et al. 1992).

The first Saprobic Index was developed in 1955 by Pa ntle and Buck (Friedrich et al. 1996). In general, for each saprobic-indicator species at a given sampling site, the frequency of occurrence and its saprobic value are denoted numerica lly following the established methodology. The product of these two numbers yields the saprobic value for that par ticular indicator species at the given stream location. The Saprobic Index for a pa rticular sampling station is calculated as the weighted mean of all the indicator species at that site. The index has been refined several times, e.g., by Sládecek (1973) and Friedrich et al. (1990). In the most recent refinement by Friedrich et al. (1990), the metrics pert aining to photoautotrophic specie s (those that can obtain energy through photosynthesis) were rem oved to “avoid interactions between indicating saprobity and trophic status” (Friedri ch et al. 1996, p 192.).

Data obtained using the Saprobic Index, modified to only include North American species, might be helpful in the nutrient criter ia development process, but no attempts to modify and use the index in this country are know n. Alternatively, modifying the method to only consider the photoautotrophic indicator species might prove beneficial for iden tifying the trophic state of

streams.49 Bacterial Bioindicators

Lemly (2000) developed a method of identifying nutrient enrich ed streams with the use of bacterial growth on aquatic insect larvae (Figur e 12). From laboratory studies, Lemly found that mayfly ( Epeorus sp.) mid-instar larvae with more than 25% of their bodies covered by bacteria had mortality rates of about 100% while nearly all those uninfested with bacteria survived. Mayflies with 10 – 25% coverage by bacter ia appeared healthy and unaffected.

Lemly (2000) tested his method in a field study in the Appalachian Mountains of Virginia (Craig Creek, Montgomery County). He measured TN and total orthophosphate c oncentrations in the water column, aquatic insect density, and bacter ia coverage on insect larvae upstream and downstream of a cattle pasture. The nutrient concentratio ns were significantly higher downstream than upstream (p < 0.01). Downstream from the cattle pasture, the density of the insect community was lower, and the occurrence a nd amount of bacterial infestation was higher. Lemly (2000) proposed that bacter ial coverage of aquatic insects provides a practical, quick, and easy way to screen for nutrient enrichment in streams.

Figure 12 . Characteristic appearance of bacterial growth on caudal cerci (tail filaments) of Ephemerella sp. immersed in 80% ethanol. Plate a ( 20X magnification) shows uninfested cerci with delicate, hair-like setae visible. Plate b (20X magnification) illust rates the appearance of heavy bacterial growth (>25% of body covered). B acterial sheaths nearly fill the space between cerci and obscure the delicate setae. Plate c ( 15X magnification) shows an advanced stage of colonization in which bacterial filaments have become matted and partially covered by silt particles. Infestation of the degr ee shown in plates b and c was a ssociated with 100% mortality in laboratory survival studies and reduced numbers of mayflies in the field. The condition can be easily diagnosed in the field using a hand lens with 10 – 20X magnification. Scale bars = 0.5

mm. 17

Lemly and King (2000) applied the method describe d above to two third- order streams in low flowing, cypress-gum wetlands in the Cape Fear basin in North Carolina. The two watersheds were physically similar, and the land use was si milar in both watersheds except that hog farms

17 Reprinted from Wetlands, Vol. 20(1), A.D. Lemly and R.S. King, An insect-bacteria bioindicator for assessing detrimental nutrient enrichment in wetlands, pages 91-100, copyright 2000, with permission from BioOne.50 were located in the watershed of one of the stream s. Nutrient levels tend ed to be 2 to 10 times higher in the stream with hog farms located in the watershed. For instance, mean TN

concentrations in the unenriched stream ra nged from about 0.715 to 1.97 mg/L, whereas mean

TN concentrations in the nutrient-rich stream ranged from 1.927 to 3.889 mg/L. Similarly, the mean TP concentrations ranged from 0.054 to 0.198 mg/L in the unenriched stream and ranged from 0.169 to 0.620 mg/L in the enriched str eam. Although peak nutrient values in the

unenriched streams sometimes exceeded the lowest concentrations in the enriched streams, the

authors concluded that conditions must not have been favorable to cause bacterial blooms. All

insect orders in the unenriched stream were free of bacterial growth, whereas bacteria ( Sphaerotilus sp. and Leptothris sp.) colonized all orders of in sect larvae in the stream with higher nutrient levels. Additiona lly, the abundance of mayflies wa s significantly higher in the unenriched stream compared to the enriched stream. Laboratory tests using mayflies ( Ephemerella sp. and Drunella sp.) collected from the stream s yielded a threshold of 25% body coverage by bacteria for surviv al (Lemly and King 2000).

The authors note that because field-collected insect s were used in the labo ratory studies, other factors, besides bacteria, could have accounted for the differences in survivorship. Also Lemly (2000) mentions that none of the field-collect ed nymphs with heavy bacterial growth were mature, while nymphs without bacterial growth were represented by all life stages. It may be possible that the life stage at collection could have influenced survivorship, i.e., older nymphs with no coverage of bacteria may have been be tter able to handle the st ress of habitat change than younger nymphs.

Macroinvertebrate and Fish Bioindicators

Miltner and Rankin Study

Miltner and Rankin (1998) used physical, chemi cal, and biological data from streams throughout Ohio to study the relationship between nutrients a nd biotic integrity. Biot ic integrity is defined as the “extent to which a community has a species composition, dive rsity and functional organization comparable to that expected for th e natural habitat of a region.” (Miltner and Rankin 1998, p. 2, citing Karr and Dudley 1981). Fo r each study site, they assessed the fish community with the Index of Bio tic Integrity (IBI, modified for Ohio), the macroinvertebrate community utilizing the Invert ebrate Community Index (ICI), habitat using the Qualitative Habitat Evaluation Index (QHEI), and water samples for TP, TIN, and NH 3 . The metrics for all indices (IBI, ICI, and QHEI) were scored agai nst reference conditions, with higher scores denoting higher biotic in tegrity or higher quality stream habitat.

Stream size classes were divided into categories based on catchment area:

Headwaters: < 20 mi 2

Wadeable streams: 21 – 200 mi 2

Small rivers: 201 – 1,000 mi 2
Large rivers: > 1,001 mi 2

. These stream classes were selected primarily be cause of observed changes in the patterns of the biological community and nutrient concentrations and because of theoretical expectations about

nutrient concentrations for different stream classe s (Rankin et al. 1999). Using the stream size51 class and frequency distributions of the P and N concentrations (25 th , 50 th , 75 th , and 90 th
percentiles), the streams were assigned one of six codes:

Code 1: both ≤ P 25 N 25 Code 2: either ≤ P 50 N 50 Code 3: ≤ P 75 ≤ N 90 Code 4: > P 75 < > N 90 Code 5: both ≥ P 90 N 90 Code 6: NH 3 ≥ 1.0 mg/L

Miltner and Rankin’s Macroinvertebrate Results

Habitat scores (QHEI) generally explained betwee n 20% to 30% of the variance in ICI scores (and IBI scores) across all stream sizes and were si gnificant for all stream classes. In contrast, TP concentrations only explained from 2% to 16% of the variation, depending on stream size and

model used.

Regression results from nutrient-and-habitat models and water-quality mode ls indicated that ICI scores were, in general, nega tively correlated with increasing TIN and TP concentrations in

headwaters and wadeable streams. To some extent, macroinvertebrates were also shown to respond positively to intermediate levels of nut rient enrichment in wadeable streams. For example, when the TIN and TP concentrations in wadeable streams were between the 50 th and

75 th percentiles (TIN:1.65 – 3.63 mg/L; TP:0.12 – 0.32 mg/L), th e abundance of mayflies was highest. High N concentrations, TIN > 75 th percentile, had th e largest effect on macroinvertebrates in headwater (TIN > 3.61 mg/L ) and wadeable streams (TIN > 3.63 mg/L). For example, at the higher nutrient concentrations , the number of EPT taxa (mayflies, stoneflies, and caddisflies; indicators of “good” water quality) and relative abundance of Tanytarsini midges

decreased while the relative abundance of other non-Tanytarsi ni dipterans and non-insects increased (Miltner and Rankin 1998).

Differences in the ICI scores as a response to TIN and TP concen trations were not evident for small and large rivers. However, Miltner and Rankin (1998) found lower mean ICI scores for all

stream classes where the NH 3 -N was greater than or equal to 1.0 mg/L, indicating the possibility

of chronic NH 3 toxicity.

Miltner and Rankin’s Fish Results

As with the macroinvertebrate IC I indices, habitat scores generall y explained the majority of the variance in the fish IBI scores (across all stream sizes and models). Fi sh community indices, however, appeared to be more influenced by nutri ent concentrations than were macroinvertebrate community indices. The IBI scores negatively co rrelated with higher TIN and TP concentrations

in headwaters and wadeable streams. In small rivers, only high concentrations of TIN negatively affected the IBI scores, and in large rivers, no relationships for TIN and TP were apparent. As with the macroinvertebrate ICI scores, the fish IBI scores were significantly lower whenever

NH 3 -N exceeded 1.0 mg/L for all stream classes (Miltner and Rankin 1998).52 More specifically, Miltner a nd Rankin (1998) found headwater streams with either TIN below 1.37 mg/L or TP below 0.17 mg/L (50 th percentiles) to have signifi cantly higher IB I scores than headwaters with higher nutrient co ncentrations. For wadeable str eams, the mean IBI scores were significantly higher the lower th e nutrient concentration (25 th > 50 th > 75 th > 90 th percentiles), such that the highest IBI scores were for fish communities where TIN concentrations were less than 0.61 mg/L and TP was less than 0.06 mg/L (25 th percentiles).

In headwaters and wadeable streams with low or intermediate nutrient concentrations (< 50 th
percentile; headwaters: TIN < 1.37 mg/L, TP < 0.17 mg/L; wadeable: TIN < 1.65 mg/L, TP < 0.12 mg/L), the number of sensitive fish speci es was significantly higher. Similarly, in nutrient-rich headwaters, wadeable streams, and small rivers, the relativ e abundance of tolerant and omnivorous fish increased significantly. In large rivers, there were no observed relationships between the fish community and nutrie nt concentrations, except that top carnivores were positively related to higher nutrient levels. A comparison of the macroinvertebrate data with the fish data suggested that a loss in EP T taxa corresponded with a decrease in the number of sensitive fish. Also, an increase in the number of dipterans and non-insects related to a

decrease in insectivorous fish a nd an increase in omnivorous fish.

Miltner and Rankin (1998) found that TP concentr ations in Ohio were highest where habitat quality is lowest. They cautione d that the decreases observed in ICI and IBI scores along a TP gradient may also be reflecting degraded habita t conditions. In wadeable streams, however, the IBI differences behaved independently of habitat conditions (a t least up to the 50 th percentile), supporting “a cause and effect re lationship between nutrients and biotic integrity” (Miltner and Rankin 1998, p. 156).

Ohio EPA Study

In an Ohio EPA publication, Association Between Nutrients, Habitat, and the Aquatic Biota in

Ohio Rivers and Streams , Rankin et al. (1999) documented th e localized effect s of nutrients on the aquatic organisms of streams and rivers in Ohio and proposed statewide criteria for nitrate+nitrite nitrogen and TP. The authors also present a method for using the nutrient concentrations in grab samples to rank the re lative risk to aquatic life use attainment.

Water column nutrient concentrat ions, collected from June 15 th to October 15 th during 14 field seasons, were correlated with IBI, ICI, and QHE I scores. Higher IBI and ICI values signify higher biotic integrity. An approximate narr ative description of th e scores suggests: < 20 = very poor 20 – 29 = poor

30 – 39 = fair 40 – 49 = good 50 – 60 = exceptional. Similarly, higher QHEI scores reflect higher quality stream habitats, with sc ores in the range of 61 to70 reflecting the highest quality stream habitats reported in this study (Rankin et al. 1999).

The data were separated by ecoregion (HELP: Hur on/Erie Lake Plain; ECBP: Eastern Corn Belt Plains; WAP: Western Allegheny Plateau; EOLP: Erie-Ontario Lake Plane; and IP: Interior53 Plateau) as well as being divide d into stream class by catchment size (using the same stream classes as Miltner and Rankin 1998). Data from re ference sites (the REF database) were utilized in setting expected values for least impacted si tes in streams designated as warmwater habitats (WWH) and exceptional warmwater habitats (EWH), or best attainable in streams designated as modified warmwater habitats (MWH; channelized , impounded, or otherwise significantly altered waterways). Furthermore, a database that repr esents a wide range of expected anthropogenic impacts (the ALL database) was used to devel op relationships between nutrient concentrations and impacts to the biological community. Rankin et al. (199 9) theorized that measured parameters with strong effects would show a strong correlation, and parameters with weak effects or those that covary with othe r factors would yield weaker correlations.

Summary statistics of nitrate+nitrite nitrogen co ncentrations obtained from the REF database, the oligo-mesotrophic and meso-eutrophic boundaries given by Dodds et al. (1998), and suggested

statewide criteria are shown in Ta ble 15. The concentrations of NO 3 -N in the reference streams varied by ecoregion and stream size. The highest concentrati ons were generally found in the large rivers. Rankin et al ( 1999, p.2) state: “While nutrient co ncentrations are expected to increase in the larger mainstem rivers, the concentrations consider ed as ‘reference’ are

themselves indicative of enrichment that is largely the product of an thropogenic sources and

activities.”

Analysis of the ALL database indicat ed that only the highest median NO 3 -N values (>3 – 4 mg/L) had a negative relationship wi th the IBI and ICI scores, and c onsistently only in headwater streams (IBI only) and small rivers (IBI and ICI). In large rivers, median NO 3 -N seldom exceeded 4 mg/L (ALL database), and no rela tionship between nitrogen and the biological

indices were observed for this st ream class (Rankin et al. 1999).

Table 16 shows the median total phosphorus con centrations from the REF and ALL databases for different IBI ranges, analys is of variance (ANOVA) significance levels for differences in IBI scores between TP categories, a nd proposed statewide TP criteria to protect aquatic life. For most ecoregions and stream sizes, the sites with th e highest water quality, as determined from its REF classification or high IBI and ICI scores, had lower TP medians. Median TP concentrations at REF sites were frequently less than 0.10 mg/L (except in the HELP ecoregion and in large rivers). An analysis of the ALL data (excludi ng large rivers) yielded median TP concentrations less than 0.15 mg/L at sites receiving exceptional IBI and ICI scores (50 – 60), and the ALL sites with IBI and ICI ratings of “good” (40 – 49) generally had TP concentrations less than 0.20 mg/L (Table 16) (Rankin et al. 1999).

Additionally, TP concentrations were higher at locations with low quality habitats. These findings suggest that TP, or a covariate (e.g., se diment, toxins), are directly affecting the biological community. For example, Rankin et al. (1999) found that TP is frequently a covariate with other factors, part icularly where point source discharges occur. These factors are known to result in an increased incidence of fish with deformities, eroded fins, lesions, and tumors —

characteristics associated with chronic stress.54 Table 15. Median and seventy-fifth pe rcentile nitrate+nitrite nitrogen concentrations [mg/L] by stream size and ecoregion for reference site s [in the following Ohio ecorgeions: HELP, Huron/Erie Lake Plain; IP, In terior Plateau; EOLP, Erie-Ontar io Lake Plane; WAP, Western Allegheny Plateau; and ECBP, Eastern Corn Belt Plains], oligo-mesotrophic and meso-eutrophic boundaries given by Dodds et al. (1998), and propos ed statewide criteria for WWH [warmwater habitat], EWH [exceptional warmwater habitat], and MWH [modified warmwater habitat] streams. Values corresponding to the IBI [Index of Biotic Integrity] ra nge typical of the MWH

use best attainable concen trations for MWH streams. 18

Ecoregional Criteria State-wide

Criteria

HELP IP EOLP WAP ECBP Oligomesotrophic Meso-eutrophic

boundaries † WWH EWH MWH *

Headwaters (drainage area < 20 mi 2 )

median

75th %

20 - 29 0.38

2.26

1.22 0.49

1.18

3.15 0.42

1.00

0.56 0.15

0.34

0.21 0.98

2.24

0.86

Wadable (drainage area 20 mi 2 < 200 mi 2 )

median

75th %

20 - 29 0.16

0.60

0.68 0.24

0.54

1.42 0.43

1.05

1.60 0.22

0.47

0.50 0.84

2.80

1.34

Small Rivers (drainage area 200 mi 2 < 1000 mi 2 )

median

75th %

20 - 29 1.88

3.24

2.01 0.43

0.96

- 1.00

1.42

1.97 0.64

1.02

1.55 1.65

3.06

1.88

Large Rivers (drainage area > 1000 mi 2 )

median

75th %

20 - 29 1.47

2.76

1.73 2.63

2.93

- -

-

- 1.50

2.20

2.60 3.08

4.14

3.98 0.7

1.5

0.7

1.5

0.7

1.5

0.7

1.5 1.0

1.0

1.5

2.0 0.5

0.5

1.0

1.5 1.0

1.6

2.2

2.4

† Oligotrophic-mesotrophic and mesotrophic-eutrophic boundaries ar e given by Dodds et al. (1998) and were derived from

data sets covering a wide range of stream sizes.

* MWH criteria are the statewide median concentrations from the ALL database for an IBI range of 20 - 29.

18 Reprinted from Association between Nutrients, Habitat, and the Aquatic Biota in Ohio Rivers and Streams; Ohio EPA, Technical Bulletin MAS/1999-1-1; E. Rankin, B. Miltn er, C. Yoder, and D. Mishne; 70 pp; published 1999.55 Table 16. Median total phosphorus concentrations [mg/ L] by IBI range (from the ALL data set), ANOVA results, and suggested criteria for the protection of aquatic life. 19

Ecoregion Criteria Statewide Criteria

IBI Range a HELP IP EOLP WAP ECBP ALL c WWH † EWH † MWH

Headwaters (drainage area < 20 mi 2 )

20 – 29 0.42 2.88 0.19 0.05 0.58 0.34

40 – 49 - 0.13 0.05 0.05 0.07 0.06

50 – 60 - 0.05 - 0.05 0.05 0.05

ANOVA b ns ns 0.05 ns 0.05 0.05 0.08 0.05 0.34

Wadable (drainage area 20 mi 2 < 200 mi 2 )

20 – 29 0.33 0.50 0.25 0.07 0.22 0.28

40 – 49 - 0.15 0.07 0.05 0.11 0.09

50 – 60 - 0.07 0.05 0.05 0.08 0.07 ANOVA ns ns 0.10 0.10 0.10 0.05; 0.10 0.10 0.05 0.28

Small Rivers (drainage area 200 mi 2 < 1000 mi 2 )

20 – 29 0.25 - 0.20 0.25 0.25 0.25

40 – 49 - 0.33 0.12 0.08 0.16 0.18

50 – 60 - 0.15 0.08 0.05 0.17 0.14

ANOVA ns ns 0.10 0.10 ns ns 0.17 0.10 0.25

Large Rivers (drainage area > 1000 mi 2 )

20 – 29 0.22 - - 0.51 0.60 0.32

40 – 49 - 0.35 - 0.18 0.41 0.34

50 – 60 - - - 0.15 0.46 0.24

ANOVA ns ns - ns ns ns 0.30 0.15 * 0.32

a Median total phosphorus concentrations for the given IBI range are from Appendix Table 2 [in Rankin et al. (1999)].

b ANOVAs were run on three categories of total phosphorus concentrations, ≤ 0.05, 0.06 ≤ 0.10, and > 0.10, total phosphorus

concentrations listed in ANOVA rows show concentrations where di fference in IBI scores betwee n categories were significant.

c For IBI ranges, ALL is the average of all ecoregions. Data we re pooled across ecoregions for the ALL ANOVAs, otherwise

ANOVAs were stratifie d by ecoregion and drainage area.

† Values in the WWH and EWH columns represent suggested total phos phorus concentrations that are protective of aquatic life.

* TP concentration chosen to reflect N:P ratio ≥ 10.

ns ANOVAs for the stream size and ecoregi on were not signi ficant (P > 0.05)

19 Reprinted from Association between Nutrients, Habitat, and the Aquatic Biota in Ohio Rivers and Streams; Ohio EPA, Technical Bulletin MAS/1999-1-1; E. Rankin, B. Miltn er, C. Yoder, and D. Mishne; 70 pp; published 1999.56 Rankin et al. (1999) also developed a system for ranking the rela tive risk to aquatic life based on nutrient concentrations in grab samples (Table 17). The predictive model is based on the probability of departing from the nutrient concentrations of (1) the REF database or (2) the

subset of the ALL database that correlates with IBI and ICI scores suppor ting the aquatic life use for warmwater habitats or exceptional warmwate r habitats (e.g., IBI scores of 40+). To determine the risk, the frequency distribution of the sampling results is compared to the

distribution for the reference sites (REF) or to the distribution of th e subset of the ALL data that reflects minimally impacted conditions. Specifically, the 50 th (median) and 90 th percentiles of the water samples are compared to the 50 th , 75 th , or other percentiles of the REF data and ALL data that support the desired bioc riteria. The more the sample distribution deviates from these reference values, the higher the probability that th e tested stream reach w ill not attain the criteria associated with the warmwater habitat or exceptional warmwater habitat aquatic life uses.

From the results of the study, statewide criteria for nitrate+nitrite and TP were proposed (Tables 15 and 16). Because streams w ith high nutrient concentrations sometimes have exceptional biological communities, Rankin et al . (1999) warn against using the proposed criteria in the same way as criteria for toxic substances are used. Instead, they recommend: “a tiered or multicriteria approach, especially in light of the importance of habitat. In other words, a single exceedance should not necessarily trigger a violation of wa ter quality standards.... Moreover, nutrient values should not be interpreted in a vacuum of biological informa tion given that high values of both can co-occur. Instances where biological in dex scores meet the biological criteria but nutrient concentrations are high, implies that nu trients are not locally problematic” (Rankin et al. 1999, p. 3).

Problems may still occur downstrea m owing to nutrients from ups tream sources. Rankin et al. (1999) suggests sampling all along the stream reach or river system to determine whether nutrients are assimilated before causing a problem. Furthermore, they recommend a determination of the trophic state of the stream from certain measured values, such as Chl-a or the composition of the periphyton community, in re sponse to nutrient levels . Secondary effects, such as diel dissolved oxygen variations or the presence of cyanotoxins, should also be monitored according to Rankin et al. (1999).57 Table 17. A process for determining the risk to aquatic life use attainment based on TP and

NO 3 -N concentrations in Ohio rive rs and streams. The risk of aquatic life impairment increases as sample statistics increasingly deviate from reference site results (REF) or observed

associations between biol ogical community performan ce and nutrients (ALL data). 20

Risk of Impairing Aquatic Life Sample Median Sample 90th Percentile

None Less Than REF Median Less Than ALL or REF Median for Aquatic Life Use for Aquatic Life Use

Minimal Less Than REF Median Less Than ALL or REF 75 th %tile for Aquatic Life Use for Aquatic Life Use

L ow G r e at er Than REF M ed ia

n L ess Than ALL o r REF 75 th % til e for Aquatic Life Use for Aquatic Life Use

M ode rat e G r e at e

r Than RE F L ess Than ALL o r REF 75th %tile [Median + 2* Interquartile Range] for Aquatic Life Use for Aquatic Life Use

Mod./Hi gh G r e at e

r Than RE F L ess Than ALL o r REF [Median + 2* Interquartile Range] [Median + 2* 75th %tile - Median] for Aquatic Life Use for Aquatic Life Use

Hi g h Greater Than ALL or REF [Median + 2* Interquartile Range]

for A q uatic Life Use
Greater Than ALL or REF Extreme [Median + 2* Interquartile Range] for Aquatic Life Use



### 3. PUBLISHED NUTRIENT CRI TERIA AND THRE SHOLD APPROACH

In addition to using either the reference reach approach or the predictiv e relationship approach,

U.S. EPA offers that literature values may be used as criteria “if a strong rationale is presented that demonstrates the suitability of the threshold value to the stream of interest (i.e., the system

of interest should share characte ristics with the systems used to derive the threshold, published values)” (U.S. EPA 2000a, p. 100). U.S. EPA furthermore suggests that published values could

20 Reprinted from Association between Nutrients, Habitat, and the Aquatic Biota in Ohio Rivers and Streams; Ohio EPA, Technical Bulletin MAS/1999-1-1; E. Rankin, B. Miltn er, C. Yoder, and D. Mishne; 70 pp; published 1999.58 serve as temporary limits until other criteria can be established from reference reaches or other

means.



### ESTABLISHED GUIDEL INES AND CRITERIA

United States Criteria

In the United States, criteria ar e the part of the water quality standards designed to support the uses of the waterbody. Criteria ca n be expressed as numeric con centrations or levels, or as

narrative statements.

There are no national phosphorus or nitrogen criteria for fres h waters to protect against eutrophication. However, U.S. EPA warns that exceeding the nitrate crit erion for drinking water (set to protect human health) and ammonia criter ion (set to protect aqua tic life from toxicity) may cause problems of enhanced algal growth (U .S. EPA 1986 as cited in U.S. EPA 2000a). The national water quality criteria to protect public drinking wate r is 10 mg/L for nitrate-N, 1 mg/L for nitrite-N, and 10 mg/L for nitrate-N + nitrite-N (40 CFR 141.62). U.S. EPA has also established ammonia criteria for freshwaters to pr otect aquatic life. These criteria are based on toxicity data on aquatic organism s, with the toxicity level va rying depending on water pH and temperature. Virginia’s estab lished ammonia criteria can be f ound in the Virginia Water Quality Standards – 9 VAC 25-260-155 (h ttp://www.deq.virginia.gov/wqs/ ) (accessed August 1, 2005).

Presently, all states have narrativ e criteria related to nutrients (U.S. EPA 2003a). Examples of

narrative criteria include:

Indiana—All waters shall be free from substanc es “... that will cause or contribute to the growth of aquatic plants or algae to such de gree as to create a nuisa nce, be unsightly or deleterious or be harmful to human, animal, pl ant, or aquatic life or otherwise impair the designated uses” (327 IAC 2-1-6).

New Mexico—“Plant nutrients from other than natural causes shall not be present in concentrations which will produce undesirable aquatic life or result in a dominance of nuisance species in surface waters of the state” (20 NMAC 6.4.12).

Virginia—“State waters, including wetlands, shal l be free from substances attributable to sewage, industrial waste, or other waste in concentrations, amount s, or combinations

which contravene established sta ndards or interfere di rectly or indirectly with designated

uses of such water or which are inimical or harmful to human, animal, plant, or aquatic life. Specific substances to be controlled include, but are not limited to: ...substances

which nourish undesirable or nuisance aquatic plant life” (9 VAC 25-260-20).

Examples of numeric nitrogen, phosphorus, and turb idity criteria and guidelines for streams and rivers (and U.S. EPA’s drinking water standards for nitrates) ar e reported in Tables 18, 19 and 20. Although not a complete list, these summaries show the variation of criteria and guidelines

within individual states and between states.59 Table 18. Examples of freshwater numeric values (e xcluding lakes and re servoirs) for nitrogen criteria and guidelines set by U.S. EPA and U.S. states.

Form of Nitrogen State and

Waters Nitrogen Criteria Values Reference

Arizona
River specific: Annual mean: 0.3 - 1.0 mg/L

90 th percentile: 0.75 - 2.50 mg/L Single sample max.: 0.10 - 10.00 mg/L AAC R18-11-109

Total N Hawaii

Inland streams Geometric mean, not to exceed 0.25 mg/L --Wet season (Nov. 1-Apr. 30) 0.18 mg/L -- Dry season (May 1-Oct. 31) HAR 11-54-5.2

Hawaii

Inland streams Geometric mean, not to exceed 0.07 mg/L -- Wet season (Nov. 1-Apr. 30) 0.03 mg/L -- Dry season (May 1-Oct. 31) HAR 11-54-5.2

Nitrate-N +

nitrite-N Ohio

Ohio River Maximum limit: 10 mg/L (Based on toxicity effects) OAC 3745-1

U.S. EPA
Public drinking

water Maximum limit: 10 mg/L (Based on toxicity effects) 40 CFR 141.62

Nevada*
River specific Mostly maximum limit: 10 mg/L; Maximum: 90 mg/L for Virgin River (at mouth of Lake Mead to Mesquit) NAC 445A.

NAC 445A.177

New Jersey

Pineland waters Maximum: 2 mg/L, unless shown that a lower level is needed to protect existing water quality. NJAC 7:9B-1.14(b)

North Dakota

Class 1 streams Maximum limit: 1.0 mg/L (interim guideline limit) NDAC 33-16-02-09

Utah

Streams and rivers to protect aquatic wildlife; 3B, 3C waters Maximum limit: 4 mg/L (Used as pollution indicator; when exceeded, further investigations are conducted) UAC R317-2
(Table 2.14.2) Nitrate-N

Vermont

Class A1 & A2 (> 2,500 ft);

Class A1 & A2 (< 2,500 ft);

Class B Maximum limit: 0.20 mg/L at low median monthly flow

2.0 mg/L at low median monthly flow

5.0 mg/L at low median monthly flow VWQS 3-01-B3

Nevada*
River specific Mostly maximum: 0.06 mg/L or 1.0 mg/L Maximum: 5 mg/L for Virgin River (at mouth of Lake Mead to Mesquit) NAC 445A

NAC 445A.177

Nitrite-N
Ohio

Ohio River Maximum limit: 1.0 mg/L (Based on toxicity effects) OAC 3745-1

* Different requirements may exist to ma intain existing higher quality streams.60 Table 19. Examples of numeric criteria and guidelin es for total phosphorus in the United States.

State and Waters Phosphorus Criteria Values Reference

Arizona
River Specific Annual mean: 0.05 - 0.20 mg/L 90 percentile: 0.10 - 0.33 mg/L Single sample maximum: 0.20 - 1.0 mg/L AAC R18-11-109

Arkansas

All waters Maximum limit: 0.100 mg/L (guideline) 2 AAC 2.509

Hawaii

Inland streams Geometric mean, not to exceed 0.050 mg/L -- Wet season (Nov. 1-Apr. 30) 0.030 mg/L -- Dry season (May 1-Oct. 31) HAR 11-54-5.2

Illinois

Streams at entrance to

reservoir or lake with surface

area of 8.1 hectares or more Maximum limit: 0.05 mg/L 35 IAC 302.205

Nevada*
River specific Mostly, average: 0.1 mg/L NAC 445A

New Jersey

Streams Maximum limit: 0.1 mg/L, unless demonstrate TP is not a limiting nutrient

and will not render the waters unsuitable for designated uses. NJAC 7:9B-1.14(c)

New Mexico
Perennial reaches of specific waters in Rio Grande, Pecos River, and San Juan River

basins Maximum limit (single sample): 0.1 mg/L 20 NMAC 6.4.109

20 NMAC 6.4.208

20 NMAC 6.4.404

20 NMAC 6.4.407

North Dakota
Class I, IA, II and III streams Maximum limit: 0.1 mg/L (interim guideline limit) NDAC 33-16-02-09

Oregon

Yamhill River and its

tributaries Monthly median: 0.070 mg/L as measured during summer low flow OAR 340-041-0350

Utah
Streams and rivers to protect aquatic wildlife; 3B, 3C waters Maximum limit: 0.05 mg/L (used as pollution indicator; when exceeded, further investigations are conducted) UAC R317-2
(Table 2.14.2)

Vermont
Upland streams (> 2,500 ft.) Maximum limit: 0.010 mg/L at low median monthly flow VWQS 3-01-B2

Washington
Spokane River (river mile: 34-58) Average euphotic zone: 0.025 mg/L (during June 1 to October 1) WAC 173-201A-130

* Different requirements may exist to ma intain existing higher quality streams.61 Table 20. Examples of numeric turbidity criteria for U.S. states applicable to freshwater streams

and rivers. Source of informa tion came from individual state surface water quality regulations located at the following website: http:// www.epa.gov/waterscience /standards/states/

. NTU = Nephelometric Turbidity Units

State and Waters Criteria

Alabama
All use classes, except

“limited warm water fishery” Not to exceed 50 NTU above the background turbidity

Connecticut
Class AA, A, and B Not to exceed 5 NTU over ambient levels an d none exceeding level necessary to protect and maintain all designated uses

Delaware

Freshwater Not to exceed natural levels by more than 10 Nephelometric or Formazin turbidity units
Florida

All classes Mixing zones Not to exceed 29 NTU above natural background conditio ns at any time Average < 41 NTU above natural background conditions New Hampshire

Class B Not to exceed naturally occurring cond itions by more than 10 NTUs

New Jersey

Freshwater with discharge Maximum 30-day average of 15 NTU. Maximum of 50 NTU at any time

North Carolina
Streams (not trout waters) Streams designated as

trout waters Not to exceed 50 NTU; if exceeded due to natural conditions, the ex isting turbidity level

cannot be increased Not to exceed 10 NTU; if exceeded due to natural conditions, the ex isting turbidity level

cannot be increased Pennsylvania
Neshaminy Creek Basin Not to exceed 100 NTU to protect potabl e water supply, warm water fishes, and migratory fishes Not to exceed 40 NTU for May 15-Sept. and not more than 100 NTU for Sept.16-May 14 to protect cold water fishes in addition to potable water supply, warm water fishes, and migratory fishes

Rhode Island

Class A Class B, B1, and C Not to exceed 5 NTU over natural background

Not to exceed 10 NTU ov er natural background
South Carolina

Trout waters Not to exceed 10 NTU or 10% above the na tural conditions, provided existing uses are

maintained

Vermont
Class A(1) and A(2)

Class B-cold water fish

habitat

Class B-warm water

fish habitat Not to exceed 10 NTU

Not to exceed 10 NTU

Not to exceed 25 NTU

West Virginia

All waters Not to exceed 10 NTU’s over th e background turbidity for wate rs with 50 NTU or less;

Not to exceed more than a 10% increase in turbidity (plus 10 NTU minimum) when the background turbidity is more than 50 NTU’s62 Only two states, North Carolina and Oregon, were found to have numeric chlorophyll-a criteria applicable to freshwater rivers and streams. The criteria for these st ates are listed below:

North Carolina—“Chlorophyll a (corrected): not greater than 40 μg/L for lakes, reservoirs, and other waters s ubject to growths of macrosc opic or microscopic vegetation not designated as trout wate rs, and not greater than 15 μg /L for ...waters subject to growths of macroscopic or microscopic vege tation designated as trout waters...” (15A NCAC 02B.0211).

Oregon—“The following average Chlorophyll a va lues must be used to identify water bodies where phytoplankton may impair the re cognized beneficial uses: ...rivers: 0.015 mg/L [15 μg/L]” (OAR 340-041-0019).

British Columbia, Canada Guidelines

The Ministry of Water, Land and Air Protection in British Columbia set guidelines —safe levels of substances for the protection of a given wate r use—to protect some or all of the following uses: (1) drinking water, (2) aqua tic life, (3) agriculture (livesto ck watering and irrigation), (4) wildlife, (5) recreation and aesthetics, and (6) in dustry (food processing water). The guidelines, in conjunction with specific site characteristic s, are considered in developing water quality objectives , which are used to set waste discharge lim its. Neither guidelines nor objectives have legal standing, however, the discharge limits de rived from them do have legal standing (BC Guidelines 1998).

British Columbia has established maximum periphyton-biomass guidelines, measured as Chl-a, to protect aesthetic, recreational, and aquatic life uses. To prot ect the first two uses, a 50 mg/m 2

Chl-a criterion was set. This criterion was ba sed on “literature and e xperiences reported from British Columbia” (Nordin 1985). To protect aq uatic life, a maximum Chl-a level of 100 mg/m 2
was suggested: “This criterion is designed primar ily to protect fish habitat and changes in communities or organisms such as invertebrates which are important themselves or which may be important fish-food organisms” (Nordin 1985).

In explaining why algal biomass is used as an indicator of recreational and aesthetic protection instead of nutrients, the ministry offers that nutrients “...are not likel y to cause any direct problems in terms of body contac t or visual deterioration. Th e more likely problem would be eutrophication-related problems when high conc entrations of nitrogen (and accompanying phosphorus) cause heavy accumulations of algae” (Nordin and Pommen 1986). Furthermore, there are other conditions, “...water velocity, light, temperature and invertebrate grazing pressure

which must be satisfied before nutrients become the most important factor limiting stream algal growth” (Nordin 1985).

United Kingdom Nutrient Status

The United Kingdom assesses its river quality us ing its General Quality Assessment whereby four aspects of river quality—bi ology, chemistry, nutrients, and aes thetic quality—are measured at about 7,000 sites representing about 40,000 km of rivers and canals in England and Wales.63 The latest river quality assessment for nutrien ts was conducted in 2004. For this assessment, nitrates greater than 30 mg/L in rivers and phosphates greater than 0.1 mg/L in rivers were considered to be high concentrations (www.environment-agency.gov.uk ) (accessed August 1, 2005).

Australian and New Zealand Guidelines

Narrative and numeric guidelines to protect the water quality of fr eshwater streams from excessive enrichment have been published in Australian and New Zealand Guidelines for Fresh and Marine Water Quality (2000), referred to in this li terature review as the A-NZ Guidelines. The two councils that developed and pub lished the guidelines strongly caution: “ These Guidelines should not be used as mandatory standards because there is significant uncertainty associated with the derivation and application of water quality guideline s” (A-NZ Guidelines 2000, p. 1-1).

The guidelines pertaining to nuisance organisms, su ch as algae, are designed to protect primary contact (e.g., swimming), secondary contact (e.g., boating, fishing), a nd visual (aesthetics) use. According to the guidelines pertaining to nui sance organisms: “Mac rophytes, phytoplankton, scums, filamentous algal mats, sewage fungus ... should not be present in excessive amounts.

Direct contact activities shoul d be discouraged if algal le vels of 15,000 – 20,000 cells/mL are present, depending on the algal species” (A-NZ Guidelines 2000, p. 5-3). To protect secondary contact, the guidelines state: “To protect water-skiers from injury and boating vessels from damage, the water should be free from ...excessive growth of algae and other aquatic plants. The quality of the water should be maintained so that there is minimal alteration of the fish habitat” (A-NZ Guidelines 2000, p. 5-3). Furtherm ore, the narrative guidelines protecting the

visual use state that waters shoul d be free from “undesira ble aquatic life, such as algal blooms, or dense growth of attached plants ....” (A-NZ Guidelines 2000, p. 5-4).

In addition to the general statemen ts, the councils also define low -risk guideline trigger values. Below is a section from the A-NZ Guidelines (2 000, p. 3.3-5) that describes how these values are

to be used.

The guideline trigger values are the c oncentrations (or loads) of the key performance indicators, below which there is a low risk that adverse biological effects will occur. The physical and chemi cal trigger values are not designed to be used as ‘magic numbers’ or thres hold values at which an environmental problem is inferred if they are exceeded. Ra ther they are designed to be used in conjunction with professional judgment, to provide an initial assessment of the state of a water body regarding the issue in question. They are the values that trigger two possible responses. The first re sponse, to continue monitoring, occurs if the test site value is less than the trigge r value, showing that there is a ‘low risk’ that a problem exists. The alternative response, management/remedial action or further site-specific investig ations, occurs if the trigge r value is exceeded — i.e. a ‘potential risk’ exists. The aim with further site-specific investigations is to

determine whether or not there is an ac tual problem. Where, after continuous monitoring, with or without site-specific i nvestigations, indicator values at sites64 are assessed as ‘low risk’ (no potential im pact), guideline trigger values may be

refined. The guidelines have attempted as fa r as possible to make the trigger values specific for each of the different ecosystem types.

For the trigger values reported in the guidance document, the 80 th and/or 20 th percentiles of the reference data from slightly disturbed ecosystem s within geographical re gions of Australia and

New Zealand were used. The nutrient-relate d Australian and New Zealand water quality guidelines for fresh waters are summarized in Ta ble 21, however, the tri gger values associated with tropical Australia (north ern Queensland, the Northern Terri tory and north-west Western Australia) and low rainfall areas (South Australi a) are not included. The reported trigger values can be made more or less stringent dependi ng on the level of protection desired by the

stakeholders and in consultation with experts. The document r ecommends using biological and ecological effects data, reference system data , predictive modeling, and professional judgment when proposing changes to the trigge r values (A-NZ Guidelines 2000).

Table 21. Default trigger values for slightly disturbed ecosystems as reported in Australian and New Zealand Guidelines for Fresh and Marine Water Quality (2000). Values for some specific rivers, alpine streams, and glacial and lake-fed streams differ from those reported here. Chl-a = chlorophyll-a, TN = total nitrogen, NO

x = oxides of nitrogen, NH 4 + = ammonium, TP = total phosphorus, FRP = filterable reactive phosphate, D.O. = dissolved oxygen, and Turb. = turbidity.

Region Ecosystem
type Chla (μg/L) TN
(mg/L) NO

x (mg/L) NH 4 +

(mg/L) TP
(mg/L) FRP
(mg/L) D.O.

lower

limit
(% sat.) D.O.

upper limit
(% sat.) Turb.
(NTU)

S.E.

Aus. Upland

rivers na 0.250 0.015 0.013 0.020 0.015 90 110 2-25

S.E.

Aus. Lowland

rivers 5 0.500 0.040 0.020 0.050 0.020 85 110 6-50

S.W.

Aus. Upland

rivers na 0.450 0.200 0.060 0.020 0.010 90 na 10-20

S.W.

Aus. Lowland

rivers 3-5 1.200 0.150 0.080 0.065 0.040 80 120 10-20 N.Z. Upland

rivers na 0.295 0.167 0.010 0.026 0.009 99 103 4.1

N.Z. Lowland

rivers no data 0.614 0.444 0.021 0.033 0.010 98 105 5.6
--South-east Australia applies to Victoria, New South Wales, south-east Queenslan d, the Australian Capital Territory, and Tasmania. --South-west Australia applies to southern Western Australia --Upland streams are defined as those > 150 m altitude. --na = not applicable; for Chl-a, monitoring of periphyton biomass is recommended in upland rivers, and periphyton biomass (Chl-a, mg/m 2 ) levels were not developed at publication.65 Biggs (2000b) published periphyton guidelines for New Zealand stre ams and rivers (Table 22). The guidelines were developed to prevent degrad ation of aesthetic, recr eation, and aquatic life uses. The guidelines for aesthetics and r ecreation only apply duri ng the summer months (November 1 – April 30 in New Zealand) when r ecreational use is likely. The guidelines for benthic biodiversity and fi shing apply year-round.

In the New Zealand guidelines, percent periphyton coverage that protects the aesthetic and primary contact uses refers to the part of the st reambed that is generally visible from the stream bank during summer low flows (usually < 0.75 m d eep) or walked on. Biggs (2000b) notes that it is not an aesthetic problem for individual rock s to have thick slime, but when 60% of the streambed is covered by a thick slime, visual and recreational uses are negatively impacted. Biggs and Price (1987) reported th at when filamentous algae exceed 40 percent cover, it is very conspicuous. However, experience in New Zeala nd has shown that water users find this level too high (Biggs 2000b). A guideline of 30 percent filamentous coverage was therefore suggested (Biggs 2000b). Corresponding biomass, in terms of filamentous green algae, was set at 35 g/m 2
AFDM and about 120 mg/m 2 Chl-a (estimated from work by Horner et al. 1983, Nordin 1985, Welch et al. 1988, Welch et al. 1989, and Dodds et al. 1998). Bi ggs (2000b) states, “However, because these biomass criteria have only been arri ved at subjectively, it would be worthwhile to carry out a quantitative study to define human pe rception of algal prolifer ations and what might constitute ‘too much’ algae for recreational and aesthetic use of streams” (p. 93).

Table 22 . Provisional biomass and cover guidelines for periphyton growing in gravel/cobbl e bed streams for three main instream values (AFDM = ash-free dry mass). 21

Instream value/variable Diatoms/cyanobacteria Filamentous algae

Aesthetics/recreation (1 November - 30 April)

Maximum cover of visible stream

bed 60 % >0.3 cm thick 30 % >2 cm long

Maximum AFDM (g/m 2 ) N/A 35

Maximum chlorophyll-a (mg/m 2 ) N/A 120

Benthic biodiversity Mean monthly chlorophyll-a (mg/m 2 ) 15 15

Maximum chlorophyll-a (mg/m 2 ) 50 50

Trout habitat and angling

Maximum cover of whole stream

bed N/A 30 % >2 cm long

Maximum AFDM (g/m 2 ) 35 35

Maximum chlorophyll-a (mg/m 2 ) 200 120

21 Reprinted from New Zealand Periphyton Guideline: Detecting, Monitoring and Managing Enrichment of Streams, B.J.F. Biggs, 122 pp, copyright 2000, with permission from Ministry for the Environment.66 To set guidelines to protect be nthic biodiversity, Biggs (2000b) analyzed data from paired periphyton-invertebrate samples collected at about 30 sites in more than 20 New Zealand streams. From this work, he found, for exampl e, that EPT taxa (indicators of “good” water quality) decreased substan tially when periphyton biom ass exceeded about 5 g/m 2 AFDM, and

streams that exceeded this level tended to have more midges, worms, and snails (indicators of “poor” water quality). Using AFDM values and the regression equation:

Ln Chl-a (mg/m 2 ) = 0.338 + 1.396 x Ln AFDM (g/m 2 ) (r 2 = 0.790, n = 170),

Biggs (2000b) recommended Chl- a values of about 13 – 20 mg/m 2 . To determine maximum Chl-a values, Biggs (2000b) analyzed the peak bi omass of 16 oligotrophic streams with diverse

benthic invertebrate communitie s having “good” water quality indi cator taxa. The mean peak biomass, as measured from Chl-a, for these streams was 47 mg/m 2 . Higher biomasses were generally only found on a one-tim e occurrence. Based on these studies, Biggs (2000b) set provisional guidelines to protec t benthic biodiversity at 15 mg/m 2 for mean monthly Chl-a and 50 mg/m 2 for maximum Chl-a.

The guidelines for filamentous algal levels to prot ect benthic biodiversity ar e less certain. Biggs (2000b) states, “... the relationship between th e biomass of filamentous green algae and invertebrates is poorly defined and requires cons iderably more research” (p. 97). Based on one study and “in the absence of any more specific re search on the effects on benthic invertebrate communities of filamentous green algal prolifera tions, the diatom/cyanobacteria guidelines are

also recommended for use with filamentous comm unities where biodiversity is an issue” (Biggs 2000b, p. 97).

To develop guidelines to protect trout habita t and angling uses, Biggs (2000b) considered

scientific data from New Zeala nd and North America. For exam ple, he examined Chl-a data

from 10 river reaches in New Zealand known for thei r trout fisheries. To avoid low D.O. levels that could negatively impact fish, Quinn and McFarlane (1989) recommended that Cladophora biomass in the Manawatu River (New Zealand) should stay below 34 g/m 2 AFDM during summer. Based on these findings, Biggs (2000b) recommended maximum biomass of 35 g/m 2
AFDM (corresponding to about 200 mg/m 2 Chl-a for diatom-dominated communities and 120 mg/m 2 Chl-a for filamentous algal communities). The impact of filamentous algae on the fishing experience was also considered based on the appear ance and smell of the wa ter and in regard to the amount of fouling of lures and wet flies that filamentous algae could cause. Biggs also comments that periphyton proliferations can negativ ely affect the taste of fish. Biggs (2000b) concludes: “In the absence of any other inform ation, the aesthetics/contact recreation guidelines for percent cover of filamentous algae should be adopted” to pr otect the angling use (p. 101).

Using the guidelines proposed in Table 22, Bi ggs (2000b) derived mean monthly guidelines for specified average days of accrual expected to prevent maximum Chl-a levels (i.e., 50 mg/m 2 to protect benthic biodiversity; 120 mg/m 2 to protect aesthetics, trout habitat, and angling in communities dominated by filamentous green algae; and 200 mg/m 2 to protect trout habitat and angling in communities dominated by diatoms) (Table 23). The guidelines were primarily derived from a regression model that Biggs deve loped. This model combines days of accrual and mean monthly soluble nutri ent concentrations (see Models section). The regression67 equations were developed from 30 New Zealand ri vers sampled every 2 – 4 weeks for at least 13

months:

Log 10 (max. Chl-a) = 4.285 x (Log 10 d

a ) – 0.929 x (Log 10 d

a ) 2 + (0.504 x Log 10 SIN) – 2.946

(r 2 = 0.741)

Log 10 (max. Chl-a) = 4.716 x (Log 10 d

a ) – 1.076 x (Log 10 d

a ) 2 + (0.494 x Log 10 SRP) – 2.741

(r 2 = 0.721)

where Chl-a is in mg/m 2 , d

a refers to days of accrual, and SIN and SRP are in mg/m 3

.

Biggs (2000b) cautions that because the guidelines were derived from an empirical model, they will contain some error. He further warns, “It is important not to get bound up in minor breaches of the recommended nutrient levels,” acknow ledging that under natural conditions, mean monthly nutrient concentrations may exceed the guidelines without causing excess proliferations of periphyton (Biggs 2000b, p. 104). Particularly in reference to protecting benthic diversity (Chl-a = 50 mg/m 2 ), Biggs (2000b) suggests that resource managers, “focus on ‘outcomes’ rather than ‘inputs’ as measures of success” (p. 104) or in other words, determine if the benthic macroinvertebrate community is being supported under the existing water quality conditions.

Table 23. Soluble inorganic nitrogen (SIN = NO 3 -N + NO 2 -N + NH 4 -N) and soluble reactive phosphorus (SRP) concentrations [mg/L] predic ted to prevent maximum periphyton biomass from exceeding the given levels. The nutrient con centrations were determined as mean monthly concentrations over a year. Limits of detec tion are assumed to be around [0.005 mg/L] for SIN and [0.001 mg/L] if analyses are carried out us ing standard autoanalys er techniques. The chlorophyll-a at 120 mg/m 2 refers to filamentous green algae dominated communities whereas the chlorophyll-a at 200 mg/m 2 refers to diatom dominated communities. AFDM = ash-free dry weight. [Biggs (2000b) reported SIN and SRP concentrations in mg/m 3 ; for consistency within this review, these values were converted to mg/L.] 22

Study Chlorophyll-a= 50 AFDM=35

Chlorophyll-a= 120

Chlorophyll-a= 200

Days of accrual SIN SRP SIN SRP

20 < 0.020 < 0.001 < 0.295 < 0.026

30 < 0.010 < 0.001 < 0.075 < 0.006

40 < 0.010 < 0.001 < 0.034 < 0.0028

50 < 0.010 < 0.001 < 0.019 < 0.0017

75 < 0.010 < 0.001 < 0.010 < 0.001

100 < 0.010 < 0.001 < 0.010 < 0.001

22 Reprinted from New Zealand Periphyton Guideline: Detecting, Monitoring and Managing Enrichment of Streams, B.J.F. Biggs, 122 pp, copyright 2000, with permission from Ministry for the Environment.68 

### NUTRIENT THRESHOLDS OR RECOMMENDED ALGAL LIMITS

A nutrient threshold can be defined as the c oncentration at which an effect, such as eutrophication or biological impair ment, begins to occur. This s ection of the literature review

focuses on threshold values derived from studies conducted within Virginia and outside the state (mainly). Chl-a values published in the literature have been s uggested as “thresholds” for overenrichment effects. Most of the published ni trogen and phosphorus values discussed in this section, however, were not presente d in the literature as “threshol d values.” These studies are

included in the review to show similarities a nd differences between results from studies that examine the impact of nutrients on aquatic life. Threshold values derived from arbitrary distinctions within frequency di stributions (e.g., one third of str eams are oligotrophic, one third are mesotrophic, and one third ar e eutrophic) are not included.

Chlorophyll-a Thresholds

Periphyton

Suggested benthic Chl-a thresholds to identify eu trophic conditions in streams are in the range of 50 – 200 mg/m 2 (Table 24). Citing severa l studies (e.g., Horner et al. 1983; Welch et al. 1988, 1989; Biggs 1996, 2000b; and Dodds et al. 1998), Biggs (2000a) st ates, “Biomass levels > 150 – 200 mg/m 2 chlorophyll a are very conspicuous in streams, are probably unnaturally high, and can compromise the use of rivers for contact recreati on and productive sports fisheries” (p. 26).

Table 24. Suggested criteria from various studies for maximum benthic biomass (as Chl-a) levels to avoid problems for recreational and aest hetic use in streams (adapted from a table in Dodds et al. 1998).

Suggested Chl-a value or range (mg/m 2 ) Comment Reference

100-150 Based on 19 enrichment cases and

surveys Horner et al. 1983, Welch et

al. 1988

50-100 British Columbia Environment

Guideline Nordin 1985

150-200 Based on perceived impairment Welch et al. 1989

150 maximum

100 mean Guidelines for Clark Fork River, MT Tristate Implementation

Council 1996

50-200 maximum New Zealand Pe riphyton Guideline Biggs 2000b

The U.S. EPA’s technical guidance manua l (2000a) states: “While [a] 150 mg/m 2 [Chl-a] level cannot be supported as an absolute threshold ab ove which adverse effects on water quality and benthic habitat readily occur, it nonetheless is a level below whic h an aesthetic quality use will probably not be appreciably degr aded by filamentous mats or any other of the adverse effects

attributed to dense mats of filamentous al gae (e.g., objectionable ta ste and odors in water supplies and fish flesh, impediment of water move ment, clogging of water in takes, restriction of69 intro-gravel water flow and D.O. replenishm ent, D.O./pH flux in the water column, or degradation of benthic habitat) (Welch 1992). Avoidance of these prob lems in many stream systems may be achieved with a maximum of 150 mg/m 2 chl a criterion” (U.S. EPA 2000a, p. 102). Furthermore, the U.S. EPA’s technical guidance manual (2000a) cautions that studies establishing an approximate 150 mg/m 2 benthic Chl-a threshold may only be applicable for northern to mid-temperate, cobble-bottom streams since these were the stream types generally

used to establish the threshold. Streams in southern temperate zones, especially those with sandy bottoms, may not respond similarly. Also, inte rmittent streams and st reams with flow from primarily effluent sources may have diffe rent benthic Chl-a threshold levels.

Filamentous algal coverage of the streambed by about 20% (Welch et al. 1988) and 30% (Biggs 2000b) has also been proposed as thresholds for identifying nuisance conditions.

Phytoplankton

Oregon has a Chl-a criterion for phytoplankton of 15 μg/L for rivers (OAR 340-041-0019), but

information was not found to determine how this value was derived. For lakes, reservoirs, and other waters likely to have nui sance vegetation, North Carolina set a maximum planktonic Chl-a criterion of 40 μg/L for non-trout waters and a 15 μg/L criterion for trout waters (15A NCAC 02B.0211). These criteria were base d on a literature review, a study of nutrient enrichment in 69 North Carolina lakes, and professional judgmen t by government and university scientists (NC WRRI 2001). Reckhow et al. ( 2005) estimated that the 40 μg /L Chl-a criterion has a 60% probability of supporting the designated uses of the Neuse Estuary and recommended that a 10 μg/L criterion would be more suitable from a use-protection perspectiv e. Australia and New Zealand have a criterion of 15,000 – 20,000 cel ls/mL (depending on algal species) for recreational waters to protect swimmers and boaters, however, the decision-making process for setting this value was not found during th e current search of the literature.

Nitrogen and Phosphorus Thresholds

TMDL Target Limits

TMDLs developed for nutrient related problems have set target limits for TN and TP that could

be considered as thresholds. Because TMDLs mu st allow for a margin of safety, it is possible

that these concentrations are lo wer than needed to prevent nuis ance growth. The TMDL target values for TN and TP in the Clark Fork River, Montana were set to keep mean Chl-a values below 100 mg/m 2 . The Tri-State Implementation Counc il overseeing the Clark Fork River TMDL set mean targets at 0.300 mg/L for TN, 0.020 mg/L for TP upstream of Missoula, and 0.039 mg/L for TP below Missoula (see the introduction to Section II -- Approaches to Setting Nutrient Criteria for more information). For the Tualatin River in Oregon, a TMDL TP limit of 0.070 mg/L was set. This value was derived from re sults of a series of al gal growth studies to determine the TP target that would achieve Or egon’s planktonic Chl-a criterion of 15 μg/L. Algal growth was noticeably reduced at 0.100 mg/L of TP and was low at 0.050 mg/L of TP. Using this information and input from stakeholders, a TP targ et of 0.070 mg/L of TP was set (U.S. EPA 1999).70 Total Nitrogen

In their paper entitled “Establishing Nutrient Criteria in Streams,” Dodds and Welch (2000) consider numerous scientific studies conducted prim arily in the U.S. From these studies, they

conclude that water column TN concentrations should remain below 0.470 mg/L to keep benthic mean Chl-a values around 50 mg/m 2 (and thereby ensuring that Chl-a values stay below 100 mg/m 2 most of the time). They also proposed that lower criteria could be set for more pristine streams. The proposed 0.470 mg/L TN threshold was developed, in part, from earlier work using probabilistic modeling based on the methods devel oped by Heiskary and Walker (1988) to assess the risk of exceeding certain Chl-a levels (bas ed on data from 205 streams or sites throughout North America and New Zealand) (Dodds et al. 1997). The authors found that when the mean TN concentrations were kept below 0.500 mg/L , the benthic algal biomass exceeded 150 mg/m 2
only 5% of the time (Dodds et al. 1997). In la ter work using breakpoint regression and a twodimensional Kolmogorov-Smirnov statistical techni que, Dodds et al. (2002) suggested a much lower breakpoint for TN (0.040 mg/L). They primarily attributed the differences to additional data from other river systems that altered the regression equations and th erefore cautioned: “care should be taken in using any specific set of published equations for management decisions” (Dodds et al. 2002, p. 871).

In a study conducted in Missouri, Lohman et al . (1992) designated six stream sites as “low enrichment,” ten stream sites as “moderate enrich ment,” and six sites as “high enrichment” based on mean “annual” (March – November) TP concen trations and land use (s ee Models section).

The “low enrichment sites” had mean “annual” TN concentrations less than 0.330 mg/L. At these sites, the Chl-a values exceeded 150 mg/m 2 from 0% to 2.6% (average 0.4%) of the dates sampled (n = 38 for each site). The “annual” TN values for the ten “moderately enriched streams” ranged from 0.186 mg/L to 0.773 mg/L (median = 0.507 mg/L) and exceeded a Chl-a threshold of 150 mg/m 2 on average 5.3% of the time (0% – 10.5%). The mean TN concentrations at the “high enrichment sites” exceeded 0.872 mg/L, and the Chl-a threshold of 150 mg/m 2 was exceeded at these sites, on average, 18.9% (7.9% – 42.1%) of the dates sampled (n = 38 for each site) (Chl-a was fluorometrica lly determined and corrected for phaeopigments by acidification). Although the authors stated no such conclusions, their findings suggest that to keep Chl-a values below 150 mg/m 2 between 80% to 90% of the time during the summer months, the stream TN concentrations should be kept below about 0.800 mg/L.

A preliminary study in Virginia by Hill and Devlin (2003) that incorporated nutrient

concentration and benthic macroinvertebrate comm unity data suggest a TN threshold for benthic impairment somewhere between 0.35 mg/L and 0.90 mg/L. In this study, 18 reference streams that supported a viable and diverse benthic community had a mean TN concentration of 0.33 mg/L and a median TN concentr ation of 0.34 mg/L (n = 59). In contrast, the 19 streams with benthic impairments had a mean TN concentrati on of 1.82 mg/L and a median TN concentration of 0.90 mg/L (n = 69) (Hill and Devlin 2003).

Another study that also included macroinvertebrate community data suggests a higher threshold. Laboratory studies by Lemly (2000) and Lemly a nd King (2000) demonstrat ed a direct linkage between bacterial growth on benthic macroinvert ebrates and macroinvertebrate mortality (see71 Biocriteria section). In the study by Lemly and King (2000), a stre am classified as unenriched

had mean TN concentrations be tween 0.715 – 1.97 mg/L and macroinve rtebrate orders that were free of bacterial growth. The en riched stream in this study ha d mean TN concentrations in excess of 1.93 mg/L (1.93 – 3.89 mg/L) and macroinve rtebrate orders that were colonized by bacteria (Lemly and King 2000).

Inorganic Nitrogen

Based on observed changes in the diatom assembla ges found in 37 streams in four ecoregions of Virginia during the fall of 2004, P onader et al. (2005) propose a NO 3 -N threshold of 0.5 mg/L. In their pilot study, the authors found be nthic Chl-a values to exceed 100 mg/m 2 only when

NO 3 -N concentrations exceeded 0.5 mg/L. This study, however, was based on a small sample size and had a narrow range in the NO 3 -N concentration data. The study was also influenced by four hurricanes that struck the region duri ng the sampling season. Therefore, the authors recommend additional sampling to obtain multi-year data sets within each ecoregion (or aggregate ecoregion for ecoregions with similar ch aracteristics) that c over a larger range in

nutrient concentrations under normal flow conditions. These larger data sets could then be used to determine threshold limits with a higher level of confidence (P onader et al. 2005).

A review of studies considering the effects of inorganic nitrogen on macroinvertebrates and fish indicate mixed results. A preliminary study de scribed in a Virginia DEQ memorandum reports that a set of 18 non-impaired bi ological reference stations for second, third, and fourth order

streams had mean NO 3 -N monthly concentrations of 0.17 mg/L (median = 0.14 mg/L), while 19 stations with benthic impairment had monthly mean NO 3 -N concentrations of 1.35 mg/L (median = 0.40 mg/L) (Hill and Devlin 2003). Miltn er and Rankin (1998) noted that headwater

streams in Ohio with TIN concentrations belo w 1.37 mg/L had significantly higher (better) fish IBI scores than those with higher TIN concentrati ons. For wadeable streams, TIN concentrations less than 0.61 mg/L had the highest IBI scores (Miltner and Rankin 1998). Rankin et al. (1999) found that only median NO 3 -N concentrations above 3 – 4 mg/L affected the fish IBI scores in

headwater streams and the fish IBI and macroinve rtebrate ICI scores in small rivers. The NO 3 -N

concentrations seldom exceeded this concentrati on in large rivers, and no relationship between nitrogen and the ICI and IBI scores wa s observed for this stream class.

Total Phosphorus

Studies seeking to identify changes in the alga l community generally suggest a TP threshold of 0.020 – 0.060 mg/L. Dodds and Welch (2000) suggested that TP concentra tions of 0.060 mg/L would keep benthic Chl-a values below 100 mg/m 2 most of the time. From a pilot study in Virginia streams, Ponader et al. (2005) suggest a change in the diatom species composition above a TP level of 0.05 mg/L. Dodds et al. (2 002) proposed a TP breakpoint of 0.030 mg/L to keep mean benthic Chl-a values low. Chételat et al. (1999) found that the filamentous green algae, Cladophora, could dominate in streams exceeding 0.020 mg/L TP. Stevenson reported at

the March 2005 Mid-Atlantic Nutrient Criteria Development workshop that Cladophora can become a frequent nuisance when TP exceeds 0.030 mg/L (Stevenson 2005). He further reported that TP concentrations of 0.030 mg/L would likely represent stream conditions with moderate changes in the structur e of the biotic community and minimal changes in the function72 of the community. He suggested that TP con centrations of 0.010 mg/L or less should reflect minimal changes in both the structure and func tion of the biotic community (Stevenson 2005).

Studies of changes in benthic macroinvertebrate and fish communities generally suggest TP threshold levels higher than the 0.020 – 0.060 mg/L range cited above. For example, the stream

classified as unenriched in a North Carolina study by Leml y and King (2000) had mean TP concentrations less than 0.200 mg/L (range of mean TP: 0.054 – 0.198 mg/L), and all insect

orders in this stream were free of bacterial growth. Ranki n et al. (1999) reported that

macroinvertebrate ICI and fish IBI scores were typically good (40 – 49) in waters with TP concentrations between 0.10 and 0.20 mg/L and tended to be exceptional(50 – 60) when TP concentrations were below 0.10 mg/L. A set of 18 reference reaches in Virginia without macroinvertebrate impairments had a mean TP concentration of 0.06 mg/L (median = 0.07 mg/L, n = 59), whereas 19 sites with benthic impairment s had a mean TP value of 0.28 mg/L (median = 0.10 mg/L, n = 69) (Hill and Devlin 2003).

Phosphates

Studies included in this review that could be used to identif y phosphate thresholds have found that waters with less than 0.020 mg/L phosphates tend not to be nutrient impaired, whereas those with phosphate concentrations above 0.030 mg/L tend to be impaired. The highly-eutrophic rivers in the study by Hornbe rger et al. (1977) had PO 4 -P concentrations that range from 0.02 – 0.08 mg/L. An advisory committee in Texas recommended a phosphate limit of 0.030 mg/L

PO 4 -P to control excess algal grow th in the North Bosque River, and McFarland et al. (2004) proposed a similar, although somewhat stricter, p hosphate limit (0.023 mg/L) for this same river. Furthermore, McFarland et al. (2004) found P- limited conditions only when phosphate levels were less than 0.010 mg/L.



### DOWNSTREAM EFFECTS

U.S. EPA regulations require that in “desi gnating uses of a water body and the appropriate criteria for those uses, the State shall take in to consideration the water quality standards of

downstream waters and shall ensure that its wate r quality standards provide for the attainment

and maintenance of the water quality standards of downstream waters” (CFR Part 131.10[b]). Therefore, the U.S. EPA’s technical guidan ce manual (2000a) calls fo r consideration of downstream receiving waters when developing nutr ient criteria for freshwater streams.

The U.S. EPA’s technical guidance manual (2000a) specifically suggests that more stringent nutrient criteria may be required for streams that feed into lakes. For example, van Nieuwenhuyse and Jones (1996) suggest that the average abundance of sestonic algae per unit

TP tends to be lower in streams than in lakes (Figure 13). Thus, nutrient concentrations that cause no problems in streams may cause nuisance levels of algae in lakes.

The impact of nutrients in streams and rivers drai ning to estuarine waters must be considered as well. Virginia’s streams and rive rs drain into such nutrient rich waters as the Chesapeake Bay, Albemarle Sound, and the Gulf of Mexico. Mulholland et al. (in print) have also recorded73 blooms of Aureococcus anophagefferens (abundances reached 500,000 cells/mL) in Virginia’s coastal estuarine waters (Chincoteag ue Bay). As an example of the potential for nutrients carried by streams and rivers to influence downstr eam waters, nutrient-related criteria for the Chesapeake Bay and its tidal tributaries are described below.

Figure 13. Comparison of the phosphorus-chlor ophyll relationship in temperate streams and lakes. Solid curves show pr edicted Chl concentrations in streams of widely differing catchment area (100,000 and 100 km 2 ). The broken curve depicts the trajectory of expected Chl con centration in P-limited lakes (i.e., total nitrogen to TP ratio = 25, Forsberg and Ryding 1980) and is calculated from McCauley et al.’s (1989) model on the basi s of data compiled form the literature (n = 382, R 2 = 0.80, s = 0.23 assumed for bias correction). 23

Chesapeake Bay Example

The Chesapeake Bay and its tidal tributaries have been listed by U.S. EPA as impaired for not supporting balanced populations of aquatic lif e (9 VAC 25-260-10) a nd are classified as “nutrient enriched waters” (9 VAC 25-40). The signatories of Chesapeake 2000 (the governors of Maryland, Pennsylvania, and Virginia, the mayor of the District of Columbia, the chair of the Chesapeake Bay Commission, and the Administrato r of the U.S. EPA) committed to “continue efforts to achieve and maintain the 40 percent nutrient reduction goal agreed to in 1987, as well as the goals being adopted for the tributaries south of the Potomac River” (Chesapeake 2000). Furthermore, by 2010, the nutrient-related problems of the Bay and its tidal tributaries are to be

corrected so that these waters can be re moved from the list of impaired waters.

In an effort to meet these goals, Virginia desi gnated the following uses for the Bay and its tidal tributaries (9 VAC 25-260-10):

23 Reprinted from Canadian Journal of Fisheries and Aquatic Sciences, Vol. 53, E.E. van Nieuwenhuyse and J.R. Jones, Phosphorus-chlorophyll relationship in temperate streams and its variation with stream catchment area, pages 99-105, copyright 1996, with permission from National Research Council Canada.74 • The migratory fish spawning and nursery designated use

• The shallow-water bay grass designated use

• The open-water fish and shellfish designated use

• The deep-water seasonal fish and shellfish designated use

• The deep-channel seasonal refuge designated use Virginia also developed nutrient-related water quality criteria for the Chesapeake Bay and its tidal tributaries (9 VAC 25-260). These uses an d criteria went into effect June 24, 2005.

Impact of Excess Nu trients in the Bay

Excess nutrients promote blooms of phytoplankton in the Chesapeake Bay a nd its tributaries. Based on studies of light and nutrient limitati ons to phytoplankton growth in the Chesapeake Bay, Fisher and Gustafson (2003) identified a limiting threshold of DIN at 0.07 mg/L and a limiting threshold for PO 4 at 0.007 mg/L. Nutrients were found to be in excess of phytoplankton needs throughout the James River and in the tidal fresh waters of the Rappahannock River (as

cited in Butt 2004: Haas and Webb 1998, Fisher and Gustafson 2003).

U.S. EPA (2003b) suggests that blooms of Microcystis aeruginosa , a colonial species of cyanobacteria, in excess of 10,000 cells/mL adversely aff ect zooplankton communities. Levels

of M. aeruginosa have averaged over 10,000 cells/mL during the summer months in the tidal

fresh James River. The hi ghest reported levels of M. aeruginosa to the Bay exceeded 2,000,000 cells/mL (found in the Potomac River in 2000 by the Maryland Department of Natural Resources) (Butt, in print).

Nuisance levels of algae reduce the available sunl ight to the submerged grass beds and grow on the surface of the grass blades, both of which redu ce the ability of the gras ses to photosynthesize. Grasses unable to adequately pho tosynthesize will die. U.S. EP A (2003b) states: “The loss of underwater bay grasses from shallo w waters of the Chesapeake Ba y, which was first noted in the early 1960s, is a widespread, well-documented pr oblem. The primary causes of the decline of these underwater bay grasses are nutrient over-enri chment and increased suspended sediments in the water, and associated reductions in light availability” (p. xiii).

Nuisance levels of algae in the Chesapeake Bay promote excess bacterial production that leads to a decrease in the dissolved oxygen concentrations. As nuisance levels of algae die and sink to the bottom of the Bay, bacteria break down the dead algal ma terial. Large amounts of dying algae support large populations of decomposing bacteria. These bacteria use the available dissolved oxygen in the Bay, which reduces the am ount of oxygen available for other organisms. Low oxygen levels (hypoxia) and cultural eutrop hication have been linked in the Chesapeake Bay.

Non-tidal Freshwater Nutr ient Levels to the Bay

As a part of a monitoring program in major rive rs draining to the Chesapeake Bay, river stations

were established at the most downstream locatio n unaffected by tides. These sites are called river input stations and include, for example:

• Potomac River at Chain Bridge, Washington, D.C.75 • James River at Cartersville, Va.

• Rappahannock River near Fredericksburg, Va.

• Appomattox River at Matoaca, Va.

• Pamunkey River near Hanover, Va.

• Mattaponi River near Beulahville, Va.

Water samples were collected monthly or bim onthly and during storms at each station for periods that span 8 to 14 y ears (1985 – 1998). The median and data range for TN and TP concentrations are shown for the major rivers draining catchments in Virginia (Table 25) (Sprague et al. 2000). The per cent contribution of fl ow, total nitrogen, and total phosphorus to the Chesapeake Bay were calculated for each basin or sub-basin draining catchments in Virginia (Table 26) (Belval and Sprague 1999). Nutrient concentrations (mg/L), loads (lbs/year), and yields (lbs/mi 2 ) are shown for all the major river ba sins (Susquehanna, Potomac, James, Rappahannock, York, Patuxent, a nd Choptank) in Figure 14 (Bel val and Sprague 1999).

Table 25. Median concentrations of total nitrog en and total phosphorus and the range of the

concentration data for the non-tida l, most downstream monitoring st ation of the major rivers of Virginia that drain to the Chesapeak e Bay (data from Sprague et al. 2000).

Basin River Median

TN (mg/L) Range of TN (mg/L) Median

TP (mg/L) Range of TP (mg/L) Data

Collected

Potomac Potomac 1.8 0.35 - 11.4 0.06 0.01 - 3.29 1985 - 1998

James James 0.68 0.03 - 3.30 0.13 0.02 - 1.40 1988 - 1998 Rappahannock Rappahannock 0.93 0.12 - 4.21 0.06 0.008 -1.50 1988 - 1998 James Appomattox 0.56 0.10 - 1.12 0.05 0.01 - 0.20 1988 - 1998 York Pamunkey 0.67 0.16 - 2.23 0.06 0.02 - 0.50 1989 - 1998 York Mattaponi 0.54 0.03 - 1.57 0.05 0.005 - 0.26 1989 - 1998

Table 26. The flow, total nitrogen, and total phosphor us contribution to the Chesapeake Bay as determined from the non-tidal, most downstream monitoring station of th e Bay’s main feeder rivers in Virginia (data from Belval and Sprague 1999).

Basin River Flow

Contrib.

to Bay TN Load

Contrib.

to Bay TP Load

Contrib.

to Bay Data Collected

Potomac Potomac 20 % 28 % 33 % 1985 - 1998

James James 12 % 5 % 20 % 1988 - 1998 Rappahannock Rappahannock 3 % 2 % 8 % 1988 - 1998 James Appomattox 2 % <1 % 1 % 1989 - 1998 York Pamunkey 2 % <1 % 2 % 1989 - 1998 York Mattaponi <1 % <1 % <1 % 1989 - 199876 Figure 14. Nutrient concentration ranges, nutrient loads, stre amflow, and nutrient yields for the River Input Monitoring statio ns. [Concentration graph shows 90 th percentile, 75 th
percentile, 25 th percentile, and 10 th percentile. Load graphs shows mean annual streamflow (left and right), mean annual load of total nitrogen (l eft), and mean annual load of total phosphorus (right).] 24

24 Reprinted from Monitoring Nutrients in the Major Rive rs Draining to Chesapeake Ba y, U.S. Geological Survey Water-Resources Investigations Report 99-4238, D.L. Belval and L.A. Sprague, 8 pp., published 1999.77 Criteria Development for the Chesapeake Bay and Tidal Tributaries

U.S. EPA Region III developed a guidance document: Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal

Tributaries for use by the Bay states and the District of Columbia in adopting water quality standards to address nutrient po llution in the Bay and its tidal tributaries (U.S. EPA 2003b). For example, in the guidance document, U.S. EPA (2003b) proposed numeric dissolved oxygen criteria for the Bay waters designed to protect aq uatic life and to reflect the natural environments of the Bay. Following the guidance, Virginia set di ssolved oxygen criteria for the part of the Bay

and its tidal tributaries that lie within th e state’s jurisdiction (9 VAC 25-260-50).

U.S. EPA (2003b) suggested water clarity criteri a for low and high salinity habitats based on published literature, Chesapeake Bay-specific fiel d studies, and models. The agency proposed that water clarity criteria appl y only during the bay grass growi ng season: April 1 – October 31 for tidal-fresh, oligohaline, a nd mesohaline waters; and from March 1 – May 31 and September 1 – November 30 for polyhaline waters. Furtherm ore, the U.S. EPA (2003b) suggested that criteria meet minimum light pene tration levels: 13 percent for tid al-fresh and oligohaline waters; and 22 percent for mesohaline and polyhaline waters . Virginia followed this guidance in setting water clarity criteria for different segments of the Bay (9 VAC 25-260-185).

The U.S. EPA Region III guidance document offe red narrative criteria for chlorophyll-a and recommended that states “adopt numeric chlorophyll a criteria for application to tidal waters in which algal-related designated us e impairments are likely to persis t even after attainment of the applicable dissolved oxygen and water clarit y criteria” (U.S. EPA 2003b, p. xvi). Baywide numeric criteria were not proposed because of “the regional and site-specific nature of algalrelated water quality impairments” (U.S. EPA 2003b, p. 104). U.S. EPA (2003b, p. 104) did however provide Chl-a values “as a synthesis of the best available technical information” for consideration by states. The re gional guidance document suggest s using Chl-a concentrations

derived from a number of different means:

• historical data,

• literature values related to trophic status,

• characteristics of phytoplankton growth-limiting water quality conditions,

• characteristics of potentially harmful algal blooms,

• characteristics of trophic-based conditions, and

• values protective against water quality impairments.

Virginia DEQ and U.S. EPA Region III determined that Virginia needs numerical criteria for Chl-a only in the tidal James Rive r. According to Virginia DEQ, “...the tidal James has the most ‘unbalanced’ phytoplankton community compared to Vi rginia’s other tidal wa ters with numerous

observations of over-abundances of ‘undesirable’ plant life.... While the Chesapeake Bay and other major tidal tributary waters may also have algal-related impairme nts due to eutrophication, EPA and the seven watershed jurisdictions have de termined that numerical chlorophyll-a criteria are not required for these other tidal waters at this time” (Vir ginia DEQ 2005, p. 4). Instead, a narrative criterion for Chl-a, which covers th e growing period from March 1 – September 30, was adopted for the other waters (9 VAC 25-260-185).78 Mean Chl-a concentration criteria during spring and summer for specific sites within the tidal James went into effect January 12, 2006 (Table 27). The sites includ e tidal-fresh (JMSTF2, JMSTF1), oligohaline (JMSOH), mesohaline (J MSMH), and polyhaline (JMSPH) reaches. These numeric criteria were developed through an alysis of water quality data since the 1950s, U.S. EPA recommendations (presented to the Chesapeake Bay Water Quality Standards Ad Hoc Committee; March 24, 2004), refere nce conditions, models, threshol d concentrations of harmful algal blooms, and best pr ofessional judgment.

Table 27. Site specific nu merical chlorophyll-a criteria applicable March 1 – May 31 and July 1 – September 30 for the tidal James River segments JMSTF2, JMSTF1, JMSOH, JMSMH, JMSPH (excludes trib utaries) (9 VAC 25-260-310).

Designated

Use Chlorophyll a

μ g /L Chesapeake Bay Program

Segment Temporal Application

10 JMSTF2

15 JMSTF1

15 JMSOH

12 JMSMH

12 JMSPH March 1 - May 31

15 JMSTF2

23 JMSTF1

22 JMSOH

10 JMSMH Open-Water

10 JMSPH July 1 - September

30

A brief description of the reas oning behind the numeric criteria for the tidal-fresh waters is provided. The James River tidal fresh region is di vided into two segments because of different natural river bathymetric and hydrodynamic char acteristics, which likely affect the algal communities. Thus, different criteria were set for these two regions. The more upstream segment (JMSTF2) was given lower Chl-a co ncentration criteria, as supported by 1986 – 2003 monitoring data (Virginia DEQ 2005).

Because the Chesapeake Bay and its tidal tributar ies have most likely never been oligotrophic, Virginia set a goal of meeting a mesotrophic st atus (Virginia DEQ 2005). Virginia DEQ based

its tidal freshwater numeric cr iteria, in part, on Wetzel’s (2001) definition of mesotrophic

freshwater: those waters with Ch l-a concentrations in the range of 2 – 15 μg/L (however, this range is based on lake data). Based solely on phytoplankton reference communities (in the York River), average Chl-a concentrati on criteria of less than 14 μg/L in the spring (March 1 – May 31) and less than 12 μg/L in the summer (July 1 – September 30) would be set. These values lie near the mesotrophic-eutrophic boun dary proposed by Wetzel (2001) and are higher than the79 levels observed in the 1950s (mean spri ng Chl-a = 3.7 μg/L; mean summer Chl-a = 7.0 μg/L) (Virginia DEQ 2005).

To protect against dominance of undesirable, nu isance algal species, Virginia DEQ considered

studies of Microcystis aerugino sa. Zooplankton are known to avoi d ingesting both toxic and

nontoxic strains of M. aeruginosa so this species contributes little to the food web. In tidal-fresh regions, blooms of M. aeruginosa can “cover certain Bay tri butaries for miles during the summer” (Virginia DEQ 2005, p. 17). Data from the Chesapeake Bay Phytoplankton Monitoring Program showed that chlorophyll-a con centrations above 15 μg/L are associated with M. aeruginosa blooms in excess of 10,000 cells/mL (U.S. EPA 2003b). Furthermore, zooplankton communities are adversely affected by M. aeruginosa blooms exceeding 10,000 cells/mL (U.S. EPA 2003b citing Lampert 1981, Fulton and Paerl 1987, Smith and Gilbert 1995).

Because the summer conditions naturally result in higher Chl-a conditions (due to lower flows, warmer temperatures, and greater light levels), higher criteria were set fo r the summer months. Although the summer criterion for JMSTF1 and JMSOH exceeds the 15 μg/L limit often suggested, Virginia DEQ states: “...this higher criterion concen tration is justified based on naturally higher chlorophyll a con centrations observed in this sect ion of the tidal fresh reach of the James River” (Virginia DEQ 2005, p. 23).



### SECTION III -- ALTERNATIVE APPR OACH TO SETTING NUMERIC

NUTRIENT CRITERIA

The Kansas Department of Health and Envi ronment (KDHE) is proposing to U.S. EPA an alternative strategy to reducing the impact of nutrients in its waters and downstream waters (KDHE 2004). It should be st ressed, however, that the U.S. EP A has not yet commented on this approach and may or may not support it.

The KDHE strategy is to implement nutrient reductions by developing and utilizing a nutrient export budget . They propose implementation of nutri ent reductions through such means as technology-based biological nutr ient removal limits for wastewat er, voluntary best management practices in prioritized watersheds, and trad ing nutrient credits betw een sources within a watershed. They do not consider nutrient criteria to be a part of the immediate solution, but instead, propose developing criteri a in the future once more da ta have been collected and analyzed (KDHE 2004).

With regard to using the specific criteria pr oposed from the U.S. EPA aggregate nutrient ecoregions (see Reference Approach section), KDHE states: “The criteria methodology utilizes a statistical approach that attempts to predict a relatively unimpacted condition rather than relying on a more traditional approach that seeks to esta blish criteria based on pr otecting against health, aquatic life, or recreational impairments” (KDHE 2004, p.6). The USGS predicts background total phosphorus levels will ex ceed the criteria proposed through the U.S. EPA aggregate nutrient ecoregions in about half the stream r eaches throughout the nation (Smith et al. 2003 as80 cited in KDHE 2004). Furthermore, “KDHE st aff asserted the EPA-developed ecoregional

criteria were believed to be unach ievable, and would likely never be met in Kansas reservoirs or streams, even if elaborate measures were take n to control sewage trea tment plants and nonpoint sources” (KDHE 2004, p. 3). KDHE adds: “Many stat es also expressed the concern that moving forward with poorly developed nutrient criteria would cause loss of cred ibility for the state agencies and lead to prolonged legal and political conflicts that would furt her delay effecting any appreciable reduction in water borne nutrients” (KDHE 2004, p.5).

The KDHE expressed great concern about the impact that numeric nutrient criteria could have on wastewater treatment facilities (WWTF). Fe deral regulations (40 CFR 122.44[d]) require discharge permits to limit pollutant levels so that water quality standa rds are not exceeded. Therefore, because point sources must meet permit limits while nonpoint sources do not, WWTFs and other permitted facilities would be th e first impacted by numeric nutrient criteria. A question of fairness thus arises because point s ources are estimated to be responsible for 5% to 30% of the nutrients, while nonpoint sources of pollution (runoff) ar e estimated to contribute to the majority of nutrient input to surface waters (KDHE 2004). KDHE asks: “Should a minority of the problem bear the entire cost of mitigation?” (KDHE 2004, p. 5).

KDHE cites additional problems faced by WWTF if the U.S. EPA proposed numeric criteria are adopted in Kansas. (1) – Because many receiving waters will not meet the water quality criteria, the discharges from sewage treatment plants will al so need to meet the criteria limits. (2) – The tertiary treatment technologies curr ently used at many facilities may not reduce the

nutrient concentrations to the proposed criteria levels. For example, EPA’s ecoregional TN criteria range from 0.56 to 2.18 mg/L in Ka nsas, while the best expected treatment performance is estimated at 3.0 mg/L for TN (Oldham and Rabinowitz 2002 as cited in KDHE 2004). (3) – For small municipalities, the wastewater tr eatment needed to meet the criteria would be beyond their technological and financial means.

Concept of KDHE’s Alternative Approach

KDHE proposed an alternative approach to de veloping numeric nutrient criteria that includes three main components: (1) – Develop an inventory of nutrients ente ring and leaving the waters of the state; (2) – Establish a nutrient reduction target; and (3) – Establish and implement a plan to meet the nutrient reduction target.

To determine the nutrient budget of the state, Kansas estimated the TN and TP from point sources and nonpoint sources entering and leaving the state. As a result of this process, KDHE found that point sources contribute about 18% of the TN and about 25% of the TP exiting the state (KDHE 2004). KDHE then proposed nutrient re duction targets for TN and TP but stressed that while the targets provide measurable objectives, they differ from legally enforceable criteria. KDHE proposed nutrient reduction targets of 30% for both TN and TP. Based on work by Brezonik et al. (1999), KDHE estimated that a 30 % reduction in TN could substantially improve the dissolved oxygen levels in the Mississippi River and the Gulf of Mexico, and a 30%81 reduction in TP could “bring a bout a 10% increase in the numbe r of flowing waters in the Mississippi River basin that woul d meet a proposed phosphorus criteria for flowing water of 0.1 mg/L” (p. 11). Furthermore, Kansas estimates that the 30% reduction targets are attainable (KDHE 2004).

KDHE proposed specific plans for meeting the esta blished nutrient reduction targets. For point sources, the agency suggested using National Po llutant Discharge Elimination System (NPDES) permits (with best available tec hnology for municipal wastewater tr eatment facilities). Because KDHE (2004) found that about 85% of the nutrients from point sources were attributable to discharges with at least one million gallons per day (1 MGD), they suggest initially focusing on these facilities. The agency projected that biolog ical nutrient removal at these facilities could bring down annual average effluent levels to 8 mg/L for TN (a 67% reduction in the current levels) and to 1.5 mg/L for TP (a 65% reduction in the current leve ls). Implementation of this aspect of the plan alone would reduce the stat e’s export levels of TN by about 10% and the export levels of TP by 14%. For nonpoint source s, the agency suggests setting priority areas based on high potential nutrient export levels and TMDLs. KDHE suggests focusing federal grant monies on voluntary reductions through BMPs within these priority regions. Nutrient trading between point and nonpoint sources would also be promoted to more economically reach the nutrient targets (KDHE 2004).

By implementing the point source and nonpoint source strategies and focusing on priority sources, KDHE estimates that the proposed appr oach will meet or exceed the 30% reduction targets for TN and TP. The agency cites the Eu ropean Union, Mississippi River/Gulf of Mexico Watershed Nutrient Task Force, Connecticut, an d North Carolina as entities having success in addressing nutrient control by es tablishing nutrient reduction targ ets and implementing practices to meet the established targets (KDHE 2004).



## SUMMARY

The U.S. EPA has identified nutrients as a ma jor reason for impaired water quality in the nation’s streams and rivers (U.S. EPA 2000a). Excess nutrients in streams and rivers can negatively impact the use of the waterbody. Most problems with excessive nutrients are related directly or indirectly to t oo much growth of primary produc ers (periphyton, phytoplankton, and macrophytes). To protect the designated uses of st reams and rivers, U.S. EPA is directing states

and authorized tribes to devel op numeric criteria for nutrients.

The purpose of this review is to provide bac kground information for th e interested public, the Academic Advisory Committee (AAC), and the Vi rginia Department of Environmental Quality (DEQ) as discussions begin concerning nutrient crit eria development in Virginia. Virginia DEQ requested specifically th at the AAC conduct a comprehensive literature search on the following topics:

• Investigate methods for defini ng undesirable (nuisance) leve ls of periphytic algae in wadeable streams, and what such studies have concluded as u ndesirable (nuisance)

levels.82 • Investigate a corresponding approach to pl anktonic algae in non-wadeable streams.

This literature review should not be used to select numeric va lues for freshwater streams in Virginia simply based on the research or crit eria of others. Instead, much work and thorough

discussions between all stakeholders are needed before nutrient criteria can satisfactorily be set for the freshwater streams and rivers in Virginia.

The technical guidance manual developed by U.S. EPA (2000a) recommends using a combination of the (1) reference approach, (2 ) predictive relationship approach, and (3) established threshold approach to develop nutrient criteria. Thes e approaches are described in

this document.

Reference Approach

Criteria development using the reference system approach can be initia ted using a combination of reference reaches—stream segments that refl ect the water quality conditions suitable for meeting all designated uses of the stream—and best professional judgment. Criteria can also be developed from the frequency distribution of the reference reaches in a given region, and/or the frequency distribution of the gene ral population of a class of stream s in a particular area (the 25 th
percentile of all data was found to approximate the 75 th percentile of data from reference reaches).

U.S. EPA divided the continental U.S. into 14 nu trient ecoregions with similarities in geographic and land-use characteristics expected to affect nutrient concentrations. The aggregate nutrient ecoregions applicable to Virgin ia are shown in Figure 15. Usi ng the reference approach, U.S. EPA developed recommended nutrient criteria for the aggregate nutrient ecoregions (U.S. EPA 2000b–d). Within each ecoregion, U.S. EPA devel oped a frequency distribution of the median values for each season (spring, summer, fall, and winter) and selected the 25 th percentile from

these seasonal values. U.S. EPA then calcula ted the median value of the four seasonal 25 th
percentile values to set its recommended criter ia. The recommended criteria for streams and rivers in Virginia are listed in Table 28.

U.S. EPA recommends using reference conditions as a guide or “first st ep” in setting nutrient

criteria. States and authorized tribes are enco uraged to select refere nce conditions at smaller geographic scales (level III ecoregions or watershed scale) an d refine their criteria through the use of models and published lite rature and in consideration of downstream effects and expert judgment (U.S. EPA 2000b).83 Figure 15. Map of Virginia showing U.S. EP A Aggregate Nutrient Ecoregions

Table 28. U.S. EPA’s nutrient threshold recommenda tions for aggregate nutrient ecoregions applicable to Virginia’s rivers and streams. Chlorophyll-a determined by the spectrophotometric method, and periphyton Chl-a values are listed separately.

Rivers and Streams Aggregate Nutrient Ecoregions

Parameter IX XI XIV TP (mg/L) 0.037 0.010 0.031 TN (mg/L) 0.69 0.31 0.71 Chl-a (μg/L) - S 0.93 1.61 3.75 Periphyton Chl-a (mg/m 2 ) 20.35 - -

Turbidity (NTU) - 2.30 - Turbidity (FTU) 5.70 1.7 3.04

Predictive Relationship Approach

Trophic State Classification

Frequency distributions of water quality characte ristics for streams in a given region can be utilized when developing nutrient criteria with the predictive relations approach. In this application, the frequency distribut ions are applied to classify streams according to their trophic state: oligotrophic (unenriched) , mesotrophic (moderately enriched ), or eutrophic (enriched). One of the best known examples of the use of fre quency distributions is th e work by Dodds et al. (1998) (Table 29). These authors used da ta from published studies (Omernik 1977, van Nieuwenhuyse and Jones 1996, and Dodds et al. 1997) that represented hundreds of temperate streams, mostly in North America and New Zeal and. Using the distribution frequencies of each parameter, they divided the streams into categorie s with the lowest third (lowest values) of the distributions indicating oligotrophic conditi ons, the middle third suggesting mesotrophic84 conditions, and the top third (hi ghest values) representing eutr ophic conditions. The authors recommend using this method only as a first step in categorizing the trophic status of streams.

Table 29. Suggested boundaries for trophic classificat ion of streams from cumulative frequency distributions. The boundary between oligotrophic and mesotrophic sy stems represents the lowest third of the distribution, and the boundary between mesotrophic and eutrophic marks the top third of the distribution. [Dodds et al. (1998) reported TN and TP concentrations in μg/L; for consistency within this review, thes e values were converted to mg/L] 25

Variable (units) Oligotrophicmesotrophic

boundary Mesotrophiceutrophic boundary n

Mean benthic Chl-a (mg/m 2 ) a 20 70 286

Max. benthic Chl-a (mg/m 2 ) a 60 200 176

Sestonic Chl-a (μg/L) b 10 30 292

TN (mg/L) a,c 0.700 1.500 1070 TP (mg/L) a,b,c 0.025 0.075 1366

a Data from Dodds et al. (1997).

b Data from van Nieuwenhuyse and Jones (1996).

c Data from Omernik (1977).

Models

Mathematical models are often employed to identif y patterns in the water quality data, which are then used to predict numeric le vels to prevent eutr ophication. For example, Dodds et al. (1997) developed regression equations to estimate mean benthic Chl-a or maximum benthic Chl-a using either TN, TP, or both TN and TP concentrations . Applying their model to data from the Clark Fork River (Montana), Dodds et al. (1997) predicte d that if seasonal mean TN concentrations are reduced to 0.275 mg/L, the maximum ch lorophyll-a values would be 100 mg/m 2 . Additionally, if TN concentrations do not exceed 0.252 mg/L and TP concentrations are kept below 0.035 mg/L, Chl-a values would be expected to stay below 100 mg/m 2

.

In another study, scientists used a Monod model, which relates alga l growth to external nutrient concentrations, to set a nutrient target in the Nort h Bosque River in central Texas. This river has experienced elevated levels of Chl-a and nutrien ts, which have negatively impacted its contact recreation, aquatic life, and public water supply uses. B ecause phosphorus tended to be the limiting nutrient in the Bosque River watershed (Kiesling et al. 2001), Mc Farland et al. (2004) proposed using the half-sat uration constant of PO 4 -P (0.023 mg/L) as the target that would likely control algal growth in the North Bosque River.

25 Reprinted from Water Research; Vol. 32; W.K. Dodds, J.R. Jones, and E.B. Welch; Suggested classification of stream trophic state: Distributions of temperate stream types by chlorophyll, total nitrogen, and phosphorus; pages 1455-1462; copyright 1998; with permission from Elsevier.85 Many studies indicate that f actors besides nutrients grea tly influence algal biomass accumulation, e.g., flood frequency and drainage ar ea are also important. For example, Biggs (2000a) found that flooding frequency (p < 0.001) was more important than in-stream nutrient levels (p = 0.045 – 0.337) in determining algal biomass accumulation. Periphytic biomass in streams with long accrual periods (e.g., > 100 d) reacted to small in creases in dissolved nutrients (e.g., SRP > 0.005 mg/L). In fact, whenever aver age accrual periods exce eded about 50 days, the frequency of high-biomass events increase d greatly. In contrast, periphyton accrual in streams with frequent flooding was affected much less by nutrient concentrations. As an application of this finding to nutrient criteria development, Biggs (2000a) suggests using flood frequency (i.e., >3x the median flow) as a basi s for classifying streams regionally; he then recommends developing local or regi onal nutrient target concentrations.

In another study, van Nieuwenhuyse and Jones (1996) developed a regression model to predict mean suspended chlorophyll values (μg/L) for use in small and large temperate streams. These researchers found that stream catchment area signif icantly affects the Chl-a concentrations, with increases in phytoplankton biomass occurring wi th increasing stream catchment area. van Nieuwenhuyse and Jones (1996) also found that h ydraulic and other physical factors may coregulate (regulate along with phosphorus) the amount of phytoplankton growth.

Reckhow et al. (2005) used structural equation modeling to identify the relationships between nutrient-related parameters and the predictive use attainment. They identified the designated uses that could be impacted by nutrients and used an expert elicita tion approach, using the responses from the experts and water quality data . Reckhow et al. (2005) developed structural equation models to propose water quality criteria and relate the probability that the criterion would protect the designated use. Based on the results of their study, the authors found that the current Chl-a criterion of 40 μg/L and D.O. le vel of 5 mg/L in the Neuse Estuary has a 60% probability of attaining the use designations. Furthermore, the model predicts that assuming D.O. concentrations are at least 5 mg/L, Chl-a co ncentrations would need to be less than 10 μg/L to achieve a high level of attainment.

Biocriteria

A third means by which the predictive relation ap proach to setting nutrient criteria can be directed is through biocriteria, the use of biologi cal parameters to define nutrient limits. For example, several biotic indices for periphyt on or phytoplankton are being studied for possible use in the eastern United States (e.g., Hill et al. 2000, KDOW 2002, Ponader and Charles 2003), and such biotic indices are commonly used in Eu rope. Indices are usually comprised of six to

ten metrics that include a measure of the abunda nce of organisms, species richness, and trophic

structure. The metrics are based on their sensitiv ity to human activities related to nutrient input to streams, their precision, and their usefulness among different re gions and habitat types. The indices can be used to identify st reams not meeting their designated us es, particularly in regard to supporting a diverse and productive aquatic life community.

Studies by Lemly (2000) and Lemly and King (2000) illustrated the use of identifying nutrient enriched streams from bacterial growth on aquatic insect larvae. In laboratory studies, mayfly

larvae with more than 25% of their bodies cove red by bacteria had mortality rates of about 100%86 while nearly all those uninfested with bacteria survived. Mayflies with 10 – 25% coverage by bacteria appeared healthy and unaffected. Fiel d studies conducted in a mountainous stream in Virginia and in a lowland stream in North Caro lina, whereby nutrient concentrations within the

water column and bacteria infestation of in sects were measured, supported the laboratory findings. Lemly concluded that b acterial coverage of aquatic inse cts provides a practical, quick, and easy way to screen for nutrien t enrichment in streams.

Miltner and Rankin (1998) used metrics for fish (IBI), macroinvertebrates (ICI), and habitat (QHEI) to study the effects of nutrients on the aq uatic life in streams and rivers in Ohio. Building on this work, Rankin et al. (1999) developed a system fo r ranking the relative risk to aquatic life based on nutrien t concentrations in grab samples. The predictive model is based on the probability of departing from the nutrient co ncentrations determined from (1) a reference database or (2) the subset of all the data that correlates with IBI and ICI scores supporting the aquatic life use for warmwater habitats or excep tional warmwater habitats (e.g., IBI scores of 40+). The more the sample distribution deviat es from these values, the higher the probability

that the tested stream reach will not attain the criteria associated with the warmwater habitat or exceptional warmwater habitat aquatic life uses. From the results of the study, statewide criteria

in Ohio for nitrate+nitrite and TP were proposed (Table 30).

Table 30. Proposed statewide criteria for wa rmwater habitat (WWH), exceptional warmwater habitat (EWH), and modified warmwater habitat (MWH) streams in Ohio (data from Rankin et al. 1999).

Nitrate + Nitrite (mg/L) Total Phosphorus (mg/L)

Stream Type WWH EWH MWH WWH EWH MWH

Headwaters 1.0 0.5 1.0 0.08 0.05 0.34

Wadable 1.0 0.5 1.6 0.10 0.05 0.28

Small

Rivers 1.5 1.0 2.2 0.17 0.10 0.25

Large

Rivers 2.0 1.5 2.4 0.30 0.15* 0.32

* TP concentration chosen to reflect N:P ratio ≥ 10.

Published Nutrient Criteria and Threshold Approach

There appears to be a general consensus that benthic chlorophyll-a va lues between 100 – 200 mg/m 2 are associated with eutrophi c conditions. Chl-a criteria pr oposed in other countries for periphyton have generally been 200 mg/m 2 or lower. The criteria or guidelines proposed by British Columbia (Canada), Australia, and Ne w Zealand for summer conditions designed to protect various stream uses are shown in Table 31.87 Table 31. Criteria or guidelines used to protect str eam uses in various provinces or countries.

Stream Use Criteria or Guideline Province or Country

50 mg/m 2 maximum Chl-a British Columbia

100 mg/m 2 maximum Chl-a New Zealand 15,000 - 20,000 cells/mL (depending on algal species) Australia and New Zealand

60% maximum cover (> 0.3 cm thick diatom/cyanobacteria dominant) New Zealand Aesthetics, Swimming, and Boating

30% maximum cover (> 2 cm long filamentous algae dominant) New Zealand

200 mg/m 2 maximum Chl-a New Zealand Fishing

30% maximum cover (> 2 cm long filamentous algae dominant) New Zealand

100 mg/m 2 maximum Chl-a British Columbia Aquatic Life

50 mg/m 2 maximum Chl-a New Zealand

Downstream Effects

Because phytoplankton is not attached to the stream bed but is transported in the water column to downstream waters (e.g., reservoirs and estuaries) , upstream planktonic Chl- a criteria must be set to meet both local and downstream water quality needs. The U.S. EPA’s technical guidance manual (2000a) specifically suggests that more st ringent nutrient criteri a may be required for streams that feed into lakes. Likewise, of cons equence to setting nutrient criteria for streams and rivers in Virginia is the impact of nutrients to downstream estuarine waters. The nutrient criteria for such waters as the Chesapeake Bay and its ti dal tributaries may therefore drive the nutrient criteria development process in non-tidal, fr eshwater streams and rivers in Virginia.

An Alternative Approach

The Kansas Department of Health and Envi ronment (KDHE) is proposing to U.S. EPA an alternative approach to establishi ng nutrient criteria in freshwater s. At the time of publication of this literature review, the U.S. EPA has yet to comment on this alternative approach. The KDHE strategy is to implement nutrient redu ctions by developing and utilizing a nutrient export budget

. Their approach includes three main components: (1) – Develop an inventory of nutrients ente ring and leaving the waters of the state; (2) – Establish a nutrient reduction target; and (3) – Establish and implement a plan to meet the nutrient reduction target (KDHE 2004).

To determine the nutrient budget of the state, Kansas estimated the TN and TP from point sources and nonpoint sources ente ring and leaving the state. KDHE then proposed nutrient reduction targets of 30% for both TN and TP based on studies that these reductions would substantially improve the dissolv ed oxygen levels in the Missi ssippi River and the Gulf of88 Mexico and meet a proposed phosphorus criteria of 0.1 mg/L for the Mi ssissippi River (KDHE 2004). KDHE proposes implementation of technol ogy-based biological nutrient removal limits for wastewater, voluntary best management prac tices in prioritized watersheds, and trading

nutrient credits between sources within a wate rshed. By implementing the point source and nonpoint source strategies and fo cusing on priority sources, KDHE estimates that the proposed approach will meet or exceed the 30% reduction targets for TN and TP. Furthermore, they believe this approach will adequately reduce the im pact of nutrients in its waters and downstream waters without having to develop legally en forceable nutrient cr iteria (KDHE 2004).

Concluding Remarks

This review demonstrates that a number of diffe rent approaches to developing nutrient criteria are possible. Numerous studies have been condu cted to determine nutrient and/or algae levels

that demonstrate measurable effects on stream communities, and a wide range of results have been obtained. Such thresholds for benthic algae le vels vary by a factor of four, and thresholds for both nitrogen and phosphorus concentrations va ry by an order of magnitude. These results emphasize the importance of assuring that Virginia sets numeric nutrient cr iteria that are sciencebased and meet the goals of the Clean Water Act.



## REFERENCES

A-NZ Guidelines (Australian and New Zealand Guid elines for Fresh and Marine Water Quality).

2000. Australian and New Zealand Environmen t and Conservation Council; Agriculture and Resource Management Council of Australia and New Zealand. http://www.mfe.govt.nz/publica tions/water/anzecc-water-qua lity-guide-02/index.html (accessed January 16, 2006).

APHA (American Public Health Association). 1998. Standard Methods for the Examination of

Water and Wastewater. 20 th Edition.

Ball, R.C., N.R. Kevern, and T.A. Haines. 1973. An ecological evaluation of stream eutrophication. Res. Rep. 36. Inst. Wa ter Res., Michigan State Univ.

Basu, B.K. and F.R. Pick. 1995. Longitudi nal and seasonal development of planktonic chlorophyll a in the Rideau River, Ontario. Can. Fish. Aquat. Sci. 52: 804-815.

BC Guidelines (British Columbia Approved Water Quality Guidelines [Criteria]). 1998. Ministry of Water, Land and Air Protection. http://www.env.gov.bc.ca/wat/wq/ wq_guidelines.html#approved (accessed January 16, 2006).

Belval, D.L. and L.A. Sprague. 1999. Monitori ng Nutrients in the Major Rivers Draining to Chesapeake Bay: U.S. Geological Survey Water- Resources Investigations Report 99-4238. 8 pp. http://va.water.usgs.gov/ches bay/RIMP/publications.html or89 http://va.water.usgs.gov/online _pubs/WRIR/99-4238/wrir_99_4238_text.pdf (accessed January 16, 2006).

Bennett, H.D. 1969. Algae in relation to mine water. Castanea 34: 306-327.

Biggs, B.J.F. 1995. The contribution of flood di sturbance, catchment geology and land use to the habitat template of periphy ton in stream ecosystems. Freshwater Biology 33: 419-438.

Biggs, B.J.F. 1996. Patterns in benthic algae of streams. Pages 31-56 in Stevenson, R.J., M.L. Bothwell, and R.L. Lowe (eds.). Algal Ecol ogy Freshwater Benthic Ecosystems. Academic Press. San Diego, CA. 753 pp.

Biggs, B.J.F. 2000a. Eutrophication of stream s and rivers: dissolved nutrient-chlorophyll relationships for benthic algae. J. N. Am. Benthol. Soc. 19(1): 17-31.

Biggs, B.J.F. 2000b. New Zeal and Periphyton Guideline: Det ecting, Monitoring and Managing Enrichment of Streams. Ministry for the Envi ronment. NIWA. Christchurch, N.Z. 122 pp. http://www.mfe.govt.nz/publications/w ater/nz-periphyton-guide-jun00.html or http://www.mfe.govt.nz/publications /water/nz-periphyt on-guide-jun00.pdf (accessed January 16, 2006).

Biggs, B.J.F. and M.E. Close. 1989. Periphyton biomass dynamics in gravel bed rivers: the

relative effects of flows and nutrien ts. Freshwater Biology 22: 209-231.

Biggs, B.J.F. and G.M. Price. 1987. A survey of filamentous algal proliferations in New

Zealand rivers. New Zealand Journal of Ma rine and Freshwater Research 21: 175-191.

Biggs, B.J.F., R.J. Stevenson, and R.L. Lowe. 1998a. A habitat matrix conceptual model for stream periphyton. Arch. Hydrobiol. 143(1): 21-56.

Biggs, B.J.F., C. Kilroy, and R.L. Lowe. 1998b. Periphyton development in three valley segments of a New Zealand grassl and river: test of a habitat matrix conceptual model within a catchment. Arch. Hydrobiol. 143(2): 147-177.

Biggs, B.J.F., R.A. Smith, and M.J. Duncan. 1999. Velocity and sediment disturbance of periphyton in headwater streams: biomass and metabolism. Journal of the North American Benthological Society 18: 222-241.

Bothwell, M.L. 1989. Phosphorus—Limited growth dynamics of lotic periphytic diatom communities: Areal biomass and cellular growth ra te responses. Can. J. Fish. Aquat. Sci. 46:

1293-1301.

Bourassa, N. and A. Cattaneo. 1998. Control of periphyton biomass in Laurentian streams (Québec). J. N. Am. Benthol . Soc. 17(4): 420-429.90 Bowling, L.C., and P.D. Baker. 1996. Major cyanobacterial bloom in the Barwon-Darling River, Australia, in 1991, and underlying limnol ogical conditions. Marine and Freshwater

Research 47: 643–657.

Brezonik, P.L. V.J. Bierman, Jr., R. Alexander, J. B. Anderson, M. Dortch, L. Hatch, et al. 1999. Effects of Reducing Nutrient Loads to Surface Wate rs within the Mississippi River basin and the Gulf of Mexico: Topic 4 Report for the Inte grated Assessment on Hypoxia in the Gulf of Mexico. Silver Spring, MD: NOAA Coastal Ocea n Program Decision Anal ysis Series No. 18. NOAA Coastal Ocean Program.

Butt, A.J. 2004. Nutrient Limitations: A Review and Assessment of Virg inia’s Tidal Waters. Interagency report. Virginia De partment of Environmental Qua lity, Chesapeake Bay Program.

Butt, A.J. In print. Status and trends of Microcystis aeruginosa in tidal fresh James River, Virginia. Proceedings of the Virginia Wa ter Research Symposium 2004, October 4-6, 2004. Blacksburg, VA.

Carlson, R.E. and J. Simpson. 1996. A Coordi nator’s Guide to Volunteer Lake Monitoring Methods. North American Lake Management Society. 96 pp.

Cattaneo, A., T. Kerimian, M. Roberge, a nd J. Marty. 1997. Peri phyton distribution and abundance on substrata of different size along a gradient of stream trophy. Hydrobiologia 354:

101-110.

CDC (Centers for Disease Contro l). 2004. About Cyanobacteria. http://www.cdc.gov/hab/cyanobacteria/about.htm or http://www.cdc.gov/hab/cyanobacteria/factsheet.pdf (accessed January 16, 2006).

Chesapeake 2000 (Chesapeake 2000 Bay Agreemen t). 2000. Water quali ty protection and restoration. Chesapeake Bay Program. h ttp://www.chesapeakebay.net/agreement.htm (accessed January 16, 2006).

Chételat, J., F.R. Pick, A. Morin, and P.B. Hamilton. 1999. Periphyton biomass and community composition in rivers of different nutrient stat us. Can. J. Fish. Aqua t. Sci. 56: 560-569.

Christie, C.E. and J.P. Smol. 1993. Diatom assemb lages as indicators of lake trophic status in

southeastern Ontario lakes. J ournal of Phycology 29: 575-586.

Cohen, R.R.H., P.V. Dresler, E.J.P. Phillips, a nd R.L. Cory. 1984. The effect of the Asiatic clam, Corbicula fluminea , on phytoplankton of the Potomac River, Maryland. Limnol. Oceanogr. 29(1): 170-180.

Collins, G.B. and C.I. Weber. 1978. Phycoperip hyton (algae) as indicators of water quality.

Trans. Amer. Micros. Soc. 97: 36-43.91 Déri, A. 1991. The role of nitrification in th e oxygen depletion of the River Danube. Verh. Int.

Ver. Limnol. 24: 1965-1968.

de Ruyter van Steveninck, E.D ., W. Admiraal, and B. van Zant en. 1990. Changes in plankton communities in regulated reaches of the lower Ri ver Rhine. Regulated Rivers: Research and Management 5: 67-75.

de Ruyter van Steveninck, E.D., W. Admiraal, L. Breebaart, G.M. J. Tubbing, and B. van Zanten.

1992. Plankton in the River Rhine: structur al and functional changes observed during downstream transport. Journal of Plankton Research 14(10): 1351-1368.

Dodds, W.K. 2003. Misuse of inorganic N and so luble reactive P concen trations to indicate

nutrient status of surface waters Journal of the North American Benthological Society 22(2):

171-181.

Dodds, W.K. and E.B. Welch. 2000. Establishing nut rient criteria in streams. Journal of the North American Benthologi cal Society 19(1): 186-196.

Dodds, W.K., V.H. Smith, and B. Zander. 1997. De veloping nutrient target s to control benthic chlorophyll levels in streams: a case study of the Clark Fork River. Water Res. 31(7): 1738-

1750.

Dodds, W.K., J.R. Jones, and E.B. Welch. 1998. Suggested classification of stream trophic state: Distributions of temperate stream t ypes by chlorophyll, total nitrogen, and phosphorus. Wat. Res. 32(5): 1455-1462.

Dodds, W.K., V.H. Smith, and K. Lohman. 2002. Nitrogen and phosphorus relationships to benthic algal biomass in temperate streams. Can. J. Fish. Aquat. Sci. 59: 865-874.

ENSR. 2001. The Relationships Between Nutrie nt Concentrations and Periphyton Levels in

Rivers and Streams – A Review of the Scientif ic Literature. New England Interstate Water

Pollution Control Commission. Document Nu mber 4933-001-400. http://www.neiwpcc.org/ (accessed January 16, 2006).

Fisher, T.R. and A.B. Gustafson. 2003. Progress Report: August 1990 – December 2002: Nutrient-addition Bioassays in Chesapeake Bay to Assess Resources Limiting Algal Growth. Maryland Department of Natural Resources, University of Maryland. Cambridge, MD. http://www.hpl.umces.edu/gis_group/Resour ce%20Limitation/2002_rep ort_27Oct03.htm#2 or ftp://fulmar.hpl.umces.edu/fi sher/REPORT2003_Fisher.pdf (accessed January 16, 2006).

Forsberg, C. and S.O. Ryding. 1980. Eutrophicati on parameters and trophi c state indices in 30 Swedish waste-receiving lakes. Arch. Hydrobiol. 89: 189-207.

Friedrich, G., P.R.P. Araujo, and A.A.S. Cr uz. 1990. Proposta de desinvolvimento de uma sistema biologico-limnologico para analise e av aliacao de aguas corren tes no estado do Rio de Janeiro, Brasil. Acta Limnol. Brasil III: 993-1000.92 Friedrich, G., D. Chapman, and A. Beim. 1992. The use of biological materials. Pages 171-238 in Chapman, D. (ed.). Water Quality Asse ssments. Chapman and Hall. London, UK.

Friedrich, G., D. Chapman, and A. Beim. 1996. The use of biological material. Pages 175-242 in Chapman, D. (ed.). Water Quality Asse ssments. E & FN SPON. London, UK. 626 pp.

Fulton R.S. III and H.W. Paerl. 1987. Toxic a nd inhibitory effects of the blue-green alga Microcystis aeruginosa on herbivorous zooplankton. Jour nal of Plankton Research 9 (5): 837-855.

Golterman, H.L. 1975. Physiological Limnology: An Approach to the Physiology of Lake Ecosystems. Elsevier Scientific Publ ishing Company. New York, NY. 489 pp.

Gosselain, V., J.-P. Descy, and E. Everbecq. 1994. The phytoplankton community of the River Meuse, Belgium: seasonal dynamics (year 1992 ) and the possible inci dence of zooplankton grazing. Hydrobiologia 289: 179-191.

Graham, J.M., C.A. Lembi, H.L. Adrain, and D. F. Spencer. 1995. Physiological responses to temperature and irradiance in Spirogyra (Zygnematales, Charophyceae). J. Phycol. 31: 531-540.

Greenberg, A. 1964. Plankton of the Sacr omento River. Ecology 45(1): 40-49.

Haas, L.W. and K.L. Webb. 1998. Resource Li mitation of Phytoplankton in the Virginia Chesapeake Bay and Tributaries Using Nutrient -Addition Bioassays. Virginia Institute of Marine Science Special Scientific Repor t # 137. January 1998. (available at http://www.deq.virginia.gov/bay/wqifdown.html ; accessed January 16, 2006).

Hall, R.I. and J.P. Smol. 1992. A weighted -average regression and calibration model for inferring total phosphorus concentration from diat oms in British Columbia (Canada) lakes.

Freshwater Biol ogy 27: 417-434.

Hammer, L. 1965. Photosynthese und Primar produktion im Rio Negro. – Int. Revue ges. Hydrobiol. Hydrogr. 50: 335-339.

Heinonen, P. 1984. Early warning of eutrophica tion in rivers by anal ysis of periphyton chlorophyll a. Pages 45-52 in Pascoe, D. and R.W. Edwards (eds.). Freshwater Biological Monitoring. Pergamon Press. Oxford, U.K. 167 pp.

Heiskary, S.A. and W.W. Walker. 1988. Devel oping phosphorus criteria for Minnesota lakes. Lake Reserv. Mngmt. 4: 1-9.

Hill, B.H., A.T. Herlihy, P.R. Kaufmann, R.J. Stevenson, F.H. McCormick, and C. Burch Johnson. 2000. Use of periphyton assemblage data as an index of biotic integrity. J. N. Am. Benthol. Soc. 19(1): 50-67.93 Hill, J. and G. Devlin. 2003. Memorandum: Asso ciations Between Biol ogical, Habitat, and Ambient Data in Upland Western Virginia Ecoreg ions. Virginia Department of Environmental Quality, West Central Regional Office. Roanoke, VA. (December 17, 2003).

Hill, W.R., H.L. Boston, and A.D. Steinman. 1992. Grazers and nutrients simultaneously limit lotic primary productivity. Can. J. Fish. Aquat. Sci. 49: 504-512.

Hornberger, G.M., M.G. Kelly, and B.J. Cosby. 1977. Evaluating eutroph ication potential from river community productivity. Wa ter Research 11(1): 65-69.

Horner, R.R., E.B. Welch, and R.B. Veenstra. 1983. Development of nuisance periphytic algae in laboratory streams in relation to enrichment and velocity. Page s 121-134 in R.G. Wetzel (ed.). Periphyton of Freshwater Ecosystems. Dr . W. Junk Publishers. The Hague.

Hynes, H.B.N. 1970. The Ecology of Running Waters . Liverpool University Press. Liverpool, U.K. 555 pp.

Ibañez, C., A. Rodrigues-Capitulo, and N. Prat. 1995. The combined impacts of river regulation and eutrophication on the dynamics of the salt wedge and the ecology of the lower Ebro River (north-east Spain). Pages 105-114 in Harper D.M. and A. J.D. Ferguson (eds.). The Ecological Basis for River Management. John Wile y and Sons. Chichester, U.K. 614 pp.

Ingman, G.L. 1992a. A rationale and alternatives for controlli ng nutrients and eutrophication problems in the Clark Fork river basin. Stat e of Montana, Department of Health and

Environmental Sciences. Section 525 of 1987 Clean Water Act amendments.

Ingman, G.L. 1992b. Assessment of phosphorus and nitrogen sources in the Clark Fork river basin. State of Montana, Department of Hea lth and Environmental Sciences. Section 525 of

1987 Clean Water Act amendments.

Karr, J.R. and D.R. Dudley. 1981. Ecol ogical perspective on water quality goals. Environmental Management 5: 55-68.

KDHE (Kansas Department of Health and E nvironment). 2004. Surface Water Nutrient Reduction Plan. KDHE, Bureau of Water. December 29, 2004. http://www.kdhe.state.ks.us/w ater/download/ks_nutrien t_reduction_plan_12_29_final.pdf (accessed January 16, 2006).

KDOW (Kentucky Division of Water). 1993. Me thods for Assessing Biological Integrity of Surface Waters. Kentucky Natural Resources and E nvironmental Protection Cabinet. Frankfort,

KY.

KDOW (Kentucky Division of Water). 2002. Me thods for Assessing Biological Integrity of Surface Waters in Kentucky. Commonwealth of Kentucky Natural Resources and

Environmental Protection Cabine t. Frankfort, KY. 182 pp. http://kywater.org/dow/news.htm or http://kywater.org/dow/pubs/SOP_Manual.pdf (accessed January 16, 2006).94 Kelly, M.G., A. Cazaubon, E. Coring, A. Dell'Uomo, L. Ector, B. Goldsmith, H. Guasch, J. Hürlimann, A. Jarlman, B. Kawecka, J. Kwandrans, R. Laugaste, E.-A. Lindstrøm, M. Litao, P. Marvan, J. Padisák, E. Pipp, J. Prygiel, E. Rott, S. Sabater, H. van Dam and J. Vizinet. 1998.

Recommendations for the routine sampling of diatoms for water qu ality assessments in Europe. J. Appl. Phycol. 10: 215-224.

Kiesling, R.L., A.M.S. McFarland, and L.M Hauck. 2001. Nutrient Targets for Lake Waco and North Bosque River: Developing Ecosystem Restor ation Criteria. TR0107. Texas Institute for Applied Environmental Research, Tarleton Stat e University. Stephenville, TX. 51 pp. http://tiaer6.tarl eton.edu/pdf/TR0107.pdf (accessed January 16, 2006).

Kiss, K.T., É. Áces, and A. Kovács. 1994. Ecological observations on Skeletonema potamos (Weber) Hasle in the River Danube, near Buda pest (1991-92, daily investigations). Hydrobiologia 289: 163-170.

Köhler, J. 1994. Origin and succession of phytoplankton in a river-lake system (Spree, Germany). Hydrobiol ogia 289: 73-83.

Lampert, W. 1981. Inhibitory and to xic effects of blue-green algae on Daphnia . Internationale Review Gesamten Hydrobiologie 66: 285-298.

Lange-Bertalot, H. 1979. Pollution tolerance of diatoms as a criterion for water quality estimation. Nova Hedwigia 64: 285-305.

Lemly, A.D. 2000. Using bacteria l growth on insects to assess nut rient impacts in streams. Environmental Monitoring and Assessment 63: 431-446.

Lemly, A.D. and R.S. King. 2000. An insect-bac teria bioindicator for assessing detrimental

nutrient enrichment in wetland s. Wetlands 20(1): 91-100.

Lohman, K., J.R. Jones, and B.D. Perkins. 1992. Effects of nutrien t enrichment and flood frequency on periphyton biomass in northern Ozar k streams. Can. J. Fish. Aquat. Sci. 49: 1198-

1205.

Lowe, R.L. 1974. Environmental requirements and pollution tolerance of fr eshwater diatoms.

EPA 670/4-74-005. U.S. Environmental Prot ection Agency. Cincinnati, OH.

Lyford Jr., J.H., and S.V. Gregory. 1975. The dynamics and structure of periphyton

communities in three Cascade M ountain streams. Verh. Int. Ver. Limnol. 19: 1610-1616.

Matlock, M.D., and A.D. Rodriguez. 1999. Pr eliminary report on findings: Periphytometer study on streams in the Lake Waco/Bosque River watershed. Te xas A&M University, Department of Agricultural Engin eering. College Station, TX.95 Matlock, M.D., D.E. Storm, M.D. Smolen, M.E. Matlock, A.M.S. McFarland, and L.M. Hauck. 1999. Development and application of a lotic ecosy stem trophic status index. Transactions of the ASAE 42(3): 651-656.

McCauley, E., J.A. Downing, and S. Watson. 1989. Sigmoid relationshi ps between nutrients and chlorophyll among lakes. Can. J. Fish Aquat. Sci. 46: 1171-1175.

McFarland, A., R. Kiesling, and J. Back. 2004. Us ing Periphytometers to Evaluate Impairment

due to Nutrient Enrichment. TR0404. Texas In stitute for Applied Environmental Research, Tarleton State University. Stephenville, TX . 51 pp. http://tiaer6.tarleton.edu/pdf/TR0404.pdf (accessed January 16, 2006).

Miltner, R.J. and E.T. Rankin. 1998. Primary nutrients and the biotic integrity of rivers and streams. Freshwater Biology 40: 145-158.

Mosisch, T.D., S.E. Bunn, P.M. Davies, and C.J. Marshall. 1999. Effect s of shade and nutrient manipulation on periphyton growth in a subtro pical stream. Aquatic Botany 64: 167-177.

Mulholland, M.R., G. Boneillo, P.W. Bernhard t, and E.C. Minor. In print. Interannual differences in brown tide ( Aureococcus anophagefferens ) bloom nutritional physiology. Proceedings of the Virginia Water Research Symposium 2004, October 4-6, 2004. Blacksburg,

VA.

NC WRRI (North Carolina Water Resources Re search Institute). 2001. The chlorophyll a standard: A primer. Water Resources Research Institute News of The University of North Carolina 328: 8-10. http://www2.nc su.edu/ncsu/CIL/WRRI/news/328.html or http://www2.ncsu.edu/ncsu/CIL /WRRI/news/marapril01news.pdf (accessed January 16, 2006).

Nordin, R.N. 1985 (updated 2001). Water Quality Crit eria for Nutrients and Algae. Ministry of Environment (now called Ministry of Water, Land and Air Protection). http://www.env.gov.bc.ca/wat/wq/BCguide lines/nutrients/nutrients.html (accessed January 16, 2006).

Nordin, R.N. and L. W. Pomm en. 1986 (updated 2001). Water Quality Criteria for Nitrogen (Nitrate, Nitrite, and Ammonia). Ministry of Environment and Parks (now called Ministry of Water, Land and Air Protection). http://www.env.gov.bc.ca/wat/wq/BCguidelines/nitrogen/nitrogen.html (accessed January 16, 2006).

Novotny, V. and H. Olem. 1994. Water Quality: Prevention, Identificati on and Management of

Diffuse Pollution. Van Nostrand Reinhold. New York, NY. 1054 pp.

Oldham, W.K, and B. Rabinowitz. 2002. Deve lopment of biological nutrient removal technology in western Canada. Journal of E nvironmental Engineering Science 1: 233-43.96 Oliver, R.L., B.T. Hart, J. Olley, M. Grace, C. Rees, and G. Caitcheon. 1999. The Darling River: Algal Growth and the Cycling and S ources of Nutrients. Murray-Darling Basin Commission Project M386. 200 pp. http://www.mdbc.gov.au/naturalresour ces/algae/algalgrow_project.html or http://www.mdbc.gov.au/naturalresources/pdf/a lgal_growth/darling_algal_growth-whole.pdf (accessed June 24, 2005).

Omernik, J.A. 1977. Nonpoint Source-Stream Nutrient Level Relationships: A Nationwide Study. EPA 600/3-77-105. U.S. Environmental Protection Agency. Corvallis, OR. 151 pp.

Omernik, J.A. 1987. Ecoregions of the conterminous United States. Ann. Assoc. Am. Geographers 77: 118-125.

Palmer, C.M. 1969. A composite rating of alga e tolerating organic po llution. Journal of Phycology 5: 78-82.

Palmer, C.M. 1977. Algae and Water Pollution: An Illustrated Manual on the Identification, Significance, and Control of Al gae in Water Supplies and Poll uted Water. EPA-600/9-77-036. U.S. EPA; Municip. Envir on, Res. Lab.; Off. Res. Devel. Cincinnati, OH.

Pan, Y., R.J. Stevenson, B.H. Hill, A. Herli hy, and G.B. Collins. 1996. Using diatoms as indicators of ecological conditions in lotic systems: a regional a ssessment. Journal of the North American Benthological Society 15: 481-495.

Park, R.A. and J.S. Clough. 2004. AQUATOX (Rel ease 2): Modeling Environmental Fate and Ecological Effects in Aquatic Ecosystems. Volume 2: Technical Documentation. EPA-823-R-04-002. January 2004. U.S. Environmen tal Protection Agency, Office of Water. Washington DC. (http://www.epa.gov/waters cience/models/aquatox/ technical/techdoc.pdf ) (accessed January 16, 2006).

Patrick, R. 1977. Ecology of freshwater diatoms and diatom communities. In: Werner, D. (ed.). The biology of diatoms. Botan. Mono. Vol. 13. Blackwell Sci. Pub.

Perrin, C.J., M.L. Bothwell, and P.A. Staney. 1987. Experimental enrichment of a coastal stream in British Columbia: Effects of organic and inorganic additions on autotrophic periphyton production. Can. J. Fish. A quat. Sci. 44: 1247-1256.

Ponader, K. and D. Charles. 2003. Understa nding the Relationship betw een Natural Conditions and Loadings on Eutrophication: Algal Indicator s of Eutrophication for New Jersey Streams. Final Report Year 2. Report No. 03-04 for the New Jersey Department of Environmental Protection Division of Science, Research and Technology. Patrick Center for Environmental Research, The Academy of Natural Sciences. Philadelphia, PA http://www.state.nj .us/dep/dsr/pub.htm (accessed January 16, 2006).

Ponader, K. C. Flinders, and D. Charles. 2005. The Development of Al gae-based Water Quality Monitoring Tools for Virginia Streams. Report No. 05-09 for the West Central Regional Office,97 Virginia Department of Environmental Quality. Patrick Center for Environmental Research, The Academy of Natural Sciences. Philadelphia, PA.

Prygiel, J. and M. Leitao. 1994. Cyanophycean bloom s in the reservoir of Val Joly (northern France) and their developmen t in downstream rivers. Hydrobiologia 289: 85-96.

Quinn, J.M. and P.N. McFarlane. 1989. Effect s of slaughterhouse and da iry factory wastewaters on epilithon: a comparison in laboratory streams. Water Research 23: 1267-1273.

Rankin, E., B. Miltner, C. Yoder, and D. Mi shne. 1999. Association Between Nutrients, Habitat, and the Aquatic Biota in Ohio Rivers and Streams. Ohio EPA Technical Bulletin MAS/1999-1-1. 70 pp. http://www.epa.stat e.oh.us/dsw/document_index/docindx.html or http://www.epa.state.oh.us/dsw/documents/assoc_load.pdf (accessed January 16, 2006).

Reckhow, K.H., G.B. Arhonditsis, M.A. Kenny, L. Ha user, J. Tribo, C. Wu, K.J. Elcock, L.J. Steinberg, C.A. Stow, and S.J. Mc Bride. 2005. A predictive approach to nutrient criteria.

Environmental Science & Technology 39(9): 2913-2919.

Reynolds, C.S. 1995. River plankton: the paradi gm regained. Pages 161-174 in Harper, D.M. and A.J.D. Ferguson (eds.). The Ecological Basi s for River Management. John Wiley & Sons. Chichester, U.K. 614 pp.

Reynolds, C.S. and J.-P. Descy. 1996. The pr oduction, biomass and structure of phytoplankton in large rivers. Arch. H ydrobiol. Suppl. 113: 161-187.

Robertson, D.M., D.A. Saad, A.M. Wieben, U.S. Environmental Protection Agency Region V, U.S. Environmental Protection Agency Region VII, and U.S. Geological Survey. 2001. An Alternative Regionalization Scheme for Defining Nutr ient Criteria for Rivers and Streams. U.S. Geological Survey Water-Resources Investigat ions Report 01-4073. Middleton, WS. 57 pp. http://wi.water.usgs.gov/pubs/wrir-01-4073/wrir-01-4073.pdf (accessed January 16, 2006).

Rojo, C., M. Alvarez Cobelas, a nd M. Arauzo. 1994. An elementar y, structural anal ysis of river phytoplankton. Hydrobiologia 289: 43-55.

Rosemond, A.D., P.J. Mulholland, and J.W. El wood. 1993. Top-down and bottom-up control of stream periphyton: effects of nutrients and herbivores. Ecology 74: 1264-1280.

Ryding, S.O. and W. Rast. 1989. The control of eutrophication of lakes and reservoirs. Man and the Biosphere Series, Volume 1. UNESCO. Parthenon Publication Group. Park Ridge, NJ. 314 pp.

Schmidt, A. 1994. Main characteristics of the phytoplankton of the Sout hern Hungarian section of the River Danube. Hydrobi ologia 289: 97-108.98 Shortreed, K.S., and J.G. Stockner. 1983. Periphyton biomass and species composition in a coastal rainforest stream in British Columbia: effects of environmental changes caused by logging. Can. J. Fish. Aquat. Sci. 40: 1887-1895.

Sládecek. V. 1973. System of water quality from the biological point of view. Arch. Hydrobiol. Beih. Ergebn. Limnol. 7 (I-IV): 1-218.

Smith, A.D. and J.J. Gilbert. 1995. Relative susceptib ilities of rotifers and cladocerans to Microcystis aeruginosa . Archives of Hydrobiology 132: 309-336.

Smith, R.A., R.B. Alexander, and G.E. Schwarz. 2003. Natural background concentrations of

nutrients in streams and rivers of the conterminous United States. Environmental Science & Technology 37: 2029-2047.

Sprague, L.A., M.J. Langland, S.E. Yochum, R.E. Edwards, J.D. Blomquist, S.W. Phillips, G.W. Shenk, and S.D. Preston. 2000. Factors Affectin g Nutrient Trends in Major Rivers of the Chesapeake Bay Watershed: U.S. Geological Surv ey Water-Resources Investigations Report 00-4218, 98 pp. http://va.water.usgs. gov/chesbay/RIMP/publications.html or http://va.water.usgs.gov/online_pubs/WRIR/00-4218text.pdf (accessed January 16, 2006).

Steinman, A.D. 1996. Effects of grazers on fr eshwater benthic algae. Pages 341-373 in Stevenson, R.J., M.L. Bothwell, and R.L. Lowe (eds.). Algal Benthic Ecosystems. Academic Press. San Diego, CA.

Stevenson, R.J. 2005. Periphyton research, statistical modeling, and nutrient criteria.

Mid-Atlantic Freshwater Nutr ient Criteria Development Workshop, March 2005. Cacapon, WV. http://www.vwrrc.vt.edu/aac/articles.asp (accessed January 16, 2006).

Strickland, J.D.H. and T.R. Parsons. 1972. A pr actical handbook on seawater analysis. Bulletin

of the Fisheries Research Board of Canada 167.

Tristate Implementation Council. 1996. Clark Fo rk River voluntary nutrient reduction program. Nutrient Target Subcommittee draft report. Mo ntana Department of Environmental Quality. Helena, MT.

U.S. EPA (U.S. Environmental Protection Agency ). 1986. Quality Criteria for Water - 1986. EPA 440/5-86-001. Office of Water, U.S. Enviro nmental Protection Agency. Washington, D.C.

U.S. EPA (U.S. Environmental Protection Agency ). 1999. Protocol for Developing Nutrient TMDLs. EPA 841-B-99-007. Office of Water (4503F ), United States Environmental Protection Agency. Washington D.C. 135 pp. www.epa.gov/owow/tmdl/techsupp.html (accessed January 16, 2006).99 U.S. EPA (U.S. Environmental Protection Agen cy). 2000a. Nutrient Criteria Technical

Guidance Manual: Rivers and Streams. EPA-8 22-B-00-002. Office of Water and Office of Science and Technology, United States Environmen tal Protection Agency. Washington, D.C. 152 pp. http://www.epa.gov/waterscience/criter ia/nutrient/guidan ce/rivers/index.html (accessed January 16, 2006).

U.S. EPA (U.S. Environmental Protection Agency). 2000b. Ambient Water Quality Criteria

Recommendations: Information S upporting The Development Of State And Tribal Nutrient

Criteria For Rivers And Streams In Nutrient Ecoregion XIV. EPA 822-B-00-022. Office of Water—Office of Science and Technology Heal th and Ecological Criteria Division, United States Environmental Protection Agency. Washington, D.C. 79 pp. http://www.epa.gov/waterscience/criteria/ nutrient/ecoregion s/rivers/index.html (accessed January 16, 2006).

U.S. EPA (U.S. Environmental Protection Agency). 2000c. Ambient Water Quality Criteria

Recommendations: Information S upporting The Development Of State And Tribal Nutrient

Criteria For Rivers And Streams In Nutrient Ecoregion IX. EPA 822-B-00-019. Office of Water—Office of Science and Technology Heal th and Ecological Criteria Division, United States Environmental Protection Agency. Washington, D.C. 103 pp. http://www.epa.gov/waterscience/criteria/ nutrient/ecoregion s/rivers/index.html (accessed January 16, 2006).

U.S. EPA (U.S. Environmental Protection Agency). 2000d. Ambient Water Quality Criteria

Recommendations: Information S upporting The Development Of State And Tribal Nutrient

Criteria For Rivers And Streams In Nutrient Ecoregion XI. EPA 822-B-00-020. Office of Water—Office of Science and Technology Heal th and Ecological Criteria Division, United States Environmental Protection Agency. Washington, D.C. 94 pp. http://www.epa.gov/waterscience/criteria/ nutrient/ecoregion s/rivers/index.html (accessed January 16, 2006).

U.S. EPA (U.S. Environmental Protection Agency). 2001. AQUATOX for Windows -- A Modular Fate and Effects Model for Aquatic Ecosystems Release 1.1. Volume 3: Model Validation Reports Addendum: Formulation, Calibration, and Validat ion of a Periphyton Submodel for AQUATOX Release 1.1. EPA-823-R-01-008. November 2001. U.S. Environmental Protection Agency, O ffice of Water. Washington D.C. (http://www.epa.gov/waterscience/models /aquatox/validation/ periphytonvalid.pdf ) (accessed January 16, 2006).

U.S. EPA (U.S. Environmental Protection Agency). 2003a. Survey of States, Tribes, and

Territories Nutrient Standards. Office of Water, United States Environmental Protection Agency. Washington, D.C.

U.S. EPA (U.S. Environmental Protection Agency). 2003b. Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries. EPA 903-R-03-002. U.S. EPA, Regi on III, Chesapeake Bay Pr ogram Office. April 2003. (available at http://www.chesapeakebay.net/baycriteria.htm ; accessed January 16, 2006).100 van Dam, H., A. Mertens, and J. Sinkeldam. 1994. A coded checklist and ecological indicator

values of freshwater diatoms from the Netherla nds. Netherlands Journal of Aquatic Ecology 28:

117-133.

van Landingham, S.L. 1982. Guide to the id entification, environmental requirements, and pollution tolerance of blue-green algae. EPA-600/3-82-073. U.S. EPA, Off. Res. Devel. Cincinnati, OH.

van Nieuwenhuyse, E.E. and J.R. Jones. 1996. Phosphorus-chlorophyll relationship in temperate streams and its variati on with stream catchment area. Can. J. Fish. Aquat. Sci. 53: 99-

105.

Varga, P., M. Ábrahám, and J. Simor. 1989. A magyar Duna-szakasz vízminösége. Vízügyi Közlemények 71: 582-598.

Virginia DEQ (Department of Environmental Quality). 2005. Virg inia Department of Environmental Quality Technical Report: Chlorophyll a Numerical Criteria for the Tidal James River, November 30, 2004 (revised 1/12/2005). 56 pp. http://www.deq.virginia.gov/wqs/rule.html or http://www.deq.virginia.gov/wqs/STD-chlarev12.pdf (accessed January 16, 2006).

Watson, V. and B. Gestring. 1996. Monitoring al gae levels in the Clark Fork River.

Intermountain Journal of Sciences 2: 17-26.

Watson, V., P. Berlind, and L. Bahls. 1990. Cont rol of algal standing crop by P and N in the

Clark Fork River. Proc. 1990 Cl ark Fork River Symposium. http://ibscore.dbs.umt.edu/clarkfork/Past _Proceedings/1990_Proceedings/1990index.htm (accessed January 16, 2006).

Welch, E.B. 1992. Ecological Effects of Wast ewater. Chapman and Hall. London, U.K.

Welch, E.B., J.M. Jacoby, R.R. Horner, and M.R. Seeley. 1988. Nuisance biomass levels of periphytic algae in streams. Hydrobiologia 157: 161-168.

Welch, E.B., R.R. Horner, and C.R. Patmont. 19 89. Predictions of nuisance periphytic biomass: a management approach. Wat. Res. 23(4): 401-405.

Welch, E.B., J.M. Quinn, and C.W. Hickey. 1992. Periphyton biomass related to point-source

nutrient enrichment in seven New Zealand st reams. Water Resources 26(5): 669-675.

Wetzel, R.G. 2001. Limnology—Lake and River Ecosystems, 3 rd Edition. Academic Press. New York, N.Y. 1006 pp.

Wharfe, J.R., K.S. Taylor, and H.A. C. Montgomery. 1984. The growth of Cladophora glomerata in a river receiving sewage e ffluent. Water Res. 18: 971-979.101 Whitford, L.A. and G.J. Schumacher. 1963. Communities of algae in North Carolina streams

and their seasonal relatio ns. Hydrobiol. 22: 133-196.

Wong, S.L. and B. Clark. 1976. Field determinati on of the critical nutrie nt concentrations for Cladophora in streams. J. Fish. Res. Board Can. 33: 85-92.

Yang, J.-R., B.K. Basu, P.B. Hamiliton, and F.R. Pick. 1997. The development of a true riverine phytoplankton assemblage along a lake-f ed lowland river. Arch. Hydrobiol. 140(2):

243-260.