U.S. Department of the Interior

U.S. Geological Survey

Relations of Biological Indicators to Nutrient Data

for Lakes and Streams in Pennsylvania

and West Virginia, 1990-98

by Robin A. Brightbill and Edward H. Koerkle

Water-Resources Investigations Report 03-4033

In cooperation with the

PENNSYLVANIA DEPARTMENT OF ENVIRONMENTAL PROTECTION and the

U.S. ENVIRONMENTAL PROTECTION AGENCY

New Cumberland, Pennsylvania

2003
ii

U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary

U.S. GEOLOGICAL SURVEY

Charles G. Groat, Director

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S.

Government.

For additional information Copies of this report may be

write to: purchased from:

District Chief U.S. Geological Survey

U.S. Geological Survey Branch of Information Services

215 Limekiln Road Box 25286

New Cumberland, Pennsylvania 17070-2424 Denver, Colorado 80225-0286

Email: dc_pa@usgs.gov Telephone: 1-888-ASK-USGS

Internet Address: http://pa.water.usgs.gov
iii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Purpose and scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Data sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Algae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Macroinvertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Relations among chlorophyll a, nutrients, other water-quality characteristics, and

habitat and macroinvertebrate indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Comparison among nutrient ecoregions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Predictive relations between chlorophyll a and nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Predictive relations between chlorophyll a and nutrients for nutrient ecoregions

VII, VIII, IX, and XI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Nutrient ecoregion VII . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Nutrient ecoregion VIII . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Nutrient ecoregion IX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Nutrient ecoregion XI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Ecological indicators of nutrient enrichment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Considerations for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

References cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Appendix—Laboratory methods used by different governmental agencies . . . . . . . . . . . . . . . . . . . . . . . . . 45

CONTENTS

Page
iv

Figures 1-2. Maps showing:

1. Lake sites for which chlorophyll a and nutrient data were available within

the nutrient ecoregions of Pennsylvania and West Virginia,

1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2. Stream sites for which chlorophyll a and nutrient data were available within

the nutrient ecoregions of Pennsylvania and West Virginia, 1990-98 . . . . . . . 4

3-28. Graphs showing the:

3. Distribution of pH, specific conductance, alkalinity/acid neutralizing

capacity, total nitrogen concentrations, total phosphorus

concentrations, phytoplankton chlorophyll a, and Secchi depth in lakes

by nutrient ecoregions of Pennsylvania and West Virginia, 1990-98 . . . . . . 10

4. Distribution of pH, specific conductance, alkalinity/acid neutralizing

capacity, total nitrogen concentrations, total phosphorus concentrations,

periphyton chlorophyll a, and turbidity in streams by nutrient

ecoregions of Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . 14

5. Relation between log of phytoplankton chlorophyll a concentrations and the

log of concentrations of total nitrogen (A) and total phosphorus

(B) in lakes within nutrient ecoregion VII, Pennsylvania, 1990-98 . . . . . . . . 16

6. Relation between observed and predicted logs of phytoplankton

chlorophyll a concentrations in lakes within nutrient ecoregion VII,

Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7. Relation between log of periphyton chlorophyll a concentrations and the

log of concentrations of total nitrogen and total phosphorus in streams

within nutrient ecoregion VII, Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . 18

8. Relation between observed and predicted logs of periphyton chlorophyll a

concentrations in streams within nutrient ecoregion VII,

Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

9. Relation between logs of phytoplankton chlorophyll a concentrations and

logs of concentrations of total nitrogen and total phosphorus

in lakes within nutrient ecoregion VIII, Pennsylvania, 1990-98. . . . . . . . . . . 21

10. Relation between log of periphyton chlorophyll a concentrations and logs

of concentrations of total nitrogen and total phosphorus in streams

within nutrient ecoregion VIII, Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . 21

11. Relation between log of phytoplankton chlorophyll a concentrations and

logs of concentrations of total nitrogen and total phosphorus in lakes

within nutrient ecoregion IX, Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . . 22

12. Relation between observed and predicted logs of phytoplankton

chlorophyll a concentrations in lakes within nutrient ecoregion IX,

Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

13. Relation between logs of periphyton chlorophyll a concentrations and logs

of concentrations of total nitrogen and total phosphorus in streams

within nutrient ecoregion IX, Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . . 24

ILLUSTRATIONS

Page
v

Figure 3-28. Graphs showing the:

14. Relation between observed and predicted logs of periphyton chlorophyll a

concentrations in streams within nutrient ecoregion IX, Pennsylvania,

1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

15. Relation between log of phytoplankton chlorophyll a concentrations and

logs of concentrations of total nitrogen and total phosphorus

in lakes during summer months within nutrient ecoregion XI,

Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

16. Relation between log of phytoplankton chlorophyll a concentrations and

logs of concentrations of total nitrogen and total phosphorus in lakes

during winter months within nutrient ecoregion XI, Pennsylvania

and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

17. Relation between observed and predicted logs of phytoplankton chlorophyll a

concentrations in lakes during summer months within ecoregion XI,

Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

18. Relation between observed and predicted phytoplankton chlorophyll a

concentrations in lakes during winter months within nutrient

ecoregion XI, Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . 27

19. Relation between log of periphyton chlorophyll a concentrations and logs of

concentrations of total nitrogen and total phosphorus in lakes

during summer months within nutrient ecoregion XI, West Virginia,

1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

20. Relation between log of periphyton chlorophyll a concentrations and logs of

concentrations of total nitrogen and total phosphorus in lakes

during winter months within nutrient ecoregion XI, West Virginia,

1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

21. Relation between observed and predicted logs of periphyton chlorophyll a

concentrations in lakes during summer months within nutrient

ecoregion XI, West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

22. Relation between observed and predicted logs of periphyton chlorophyll a

concentrations in lakes during winter months within nutrient

ecoregion XI, West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

23. Relation between log of periphyton chlorophyll a concentrations

and the logs of concentrations of total nitrogen and total phosphorus

in streams during summer months within nutrient ecoregion XI,

Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

24. Relation between observed and predicted logs of periphyton chlorophyll a

concentrations in streams during summer months within nutrient

ecoregion XI, Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . 32

25. Relation between log of phytoplankton chlorophyll a concentrations and

logs of concentrations of total nitrogen and total phosphorus in

streams during summer months within nutrient ecoregion XI,

West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

ILLUSTRATIONS—Continued

Page
vi

Figure 3-28. Graphs showing the:

26. Relation between log of phytoplankton chlorophyll a concentrations and logs

of concentrations of total nitrogen and total phosphorus in

streams during winter months within nutrient ecoregion XI,

West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

27. Relation between observed and predicted logs of phytoplankton chlorophyll a

concentrations in streams during summer months within nutrient

ecoregion XI, West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

28. Relation between observed and predicted logs of phytoplankton chlorophyll a

concentrations in streams during winter months within nutrient

ecoregion XI, West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

ILLUSTRATIONS—Continued

Page
vii

Table 1. Explanatory variables examined for significance in regressions of the log of

chlorophyll a concentrations for nutrient ecoregions VII, VIII, IX, and

XI lakes, Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2. Explanatory variables examined for significance in regressions of the log of

chlorophyll a concentrations for nutrient ecoregions VII, VIII, IX, and

XI streams, Pennsylvania and West Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3. Results of multiple-stage Kruskal-Wallis test for significant differences in

median values of water-quality constituents data from lakes in nutrient

ecoregions VII, VIII, IX, and XI, Pennsylvania and West Virginia, 1990-98 . . . . . . . . . 9

4. Results of multiple-stage Kruskal-Wallis test for significant differences in

median values of water-quality constituent data from streams in nutrient

ecoregions VII, VIII, IX, and XI, Pennsylvania and West Virginia, 1990-98 . . . . . . . . 13

5. Regression equation statistics for phytoplankton chlorophyll a concentrations in

nutrient ecoregion VII lakes, Pennsylvania, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

6. Regression equation statistics for periphyton chlorophyll a concentrations in

nutrient ecoregion VII streams, Pennsylvania, 1990-98. . . . . . . . . . . . . . . . . . . . . . . . . 19

7. Regression equation statistics for phytoplankton chlorophyll a concentrations in

nutrient ecoregion IX lakes, Pennsylvania, 1990-98. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

8. Regression equation statistics for summer-season periphyton chlorophyll a

concentrations in nutrient ecoregion IX streams, Pennsylvania, 1990-98. . . . . . . . . . 24

9. Regression equation statistics for summer-season phytoplankton chlorophyll a

concentrations in nutrient ecoregion XI lakes, Pennsylvania and West

Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

10. Regression equation statistics for winter-season phytoplankton chlorophyll a

concentrations in nutrient ecoregion XI lakes, Pennsylvania and West

Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

11. Regression equation statistics for summer-season periphyton chlorophyll a

concentrations in nutrient ecoregion XI lakes, West Virginia, 1990-98 . . . . . . . . . . . . 29

12. Regression equation statistics for winter-season periphyton chlorophyll a

concentrations in nutrient ecoregion XI lakes, West Virginia, 1990-98 . . . . . . . . . . . . 30

13. Regression equation statistics for summer-season periphyton chlorophyll a

concentrations in nutrient ecoregion XI streams, Pennsylvania and West

Virginia, 1990-98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

14. Regression equation statistics for summer-season phytoplankton chlorophyll a

concentrations in nutrient ecoregion XI streams, West Virginia, 1990-98 . . . . . . . . . . 34

15. Regression equation statistics for winter-season phytoplankton chlorophyll a

concentrations in nutrient ecoregion XI streams, West Virginia, 1990-98 . . . . . . . . . . 35

16. Summary of influential lake explanatory variables related to lake chlorophyll a

through regression analysis for each nutrient ecoregion and season . . . . . . . . . . . . . 37

17. Summary of influential stream explanatory variables related to stream chlorophyll a

through regression analysis for each nutrient ecoregion and season . . . . . . . . . . . . . 38

TABLES

Page
viii

Multiply By To obtain

Abbreviated water-quality units used in report:

ft/mi, feet per mile

μg/L, micrograms per liter

μg/m2, micrograms per square meter

μS/cm, microsiemens per centimeter at 25 degrees Celsius

mg/L, milligrams per liter

mg/m2, milligrams per square meter

Length

foot (ft) 0.3048 meter

mile (mi) 1.609 kilometer

Area

square mile (mi2) 2.590 square kilometer

Temperature

degree Fahrenheit (°F) °C=5/9.(°F-32) degree Celsius

CONVERSION FACTORS AND ABBREVIATIONS
1

ABSTRACT

The Clean Water Action Plan of 1998 provides a blueprint for federal agencies to work with

states, tribes, and other stakeholders to protect and

restore the Nation’s water resources. The plan

includes an initiative that addresses the nutrientenrichment problem of lakes and streams across

the United States. The U.S. Environmental Protection Agency (USEPA) is working to set nutrient criteria by nationwide nutrient ecoregions that are an

aggregation of the Omernik level III ecoregions.

Because low levels of nutrients are necessary for

healthy streams and elevated concentrations can

cause algal blooms that deplete available oxygen

and kill off aquatic organisms, criteria levels are to

be set, in part, using the relation between chlorophyll a and concentrations of total nitrogen and

total phosphorus.

Data from Pennsylvania and West Virginia,

collected between 1990 and 1998, were analyzed

for relations between chlorophyll a, nutrients, and

other explanatory variables. Both phytoplankton

and periphyton chlorophyll a concentrations from

lakes and streams were analyzed separately within

each of the USEPA nutrient ecoregions located

within the boundaries of the two states. These four

nutrient ecoregions are VII (Mostly Glaciated

Dairy), VIII (Nutrient Poor, Largely Glaciated

Upper Midwest and Northeast), IX (Southeastern

Temperate Forested Plains and Hills), and XI (Central and Eastern Forested Uplands).

Phytoplankton chlorophyll a concentrations

in lakes were related to total nitrogen, total phosphorus, Secchi depth, concentration of dissolved

oxygen, pH, water temperature, and specific conductivity. In nutrient ecoregion VII, nutrients were

not significant predictors of chlorophyll a concentrations. Total nitrogen, Secchi depth, and pH were

significantly related to phytoplankton chlorophyll a concentrations in nutrient ecoregion IX.

Lake periphyton chlorophyll a concentrations from

nutrient ecoregion XI were related to total phosphorus rather than total nitrogen, Secchi depth,

and pH. In all cases, Secchi depth was inversely

related to the chlorophyll a concentrations in a

lake. Nutrient ecoregion VIII had too few samples

for any type of analysis.

Streams within the different nutrient ecoregions had many variables that were significantly

related to periphyton chlorophyll a concentrations.

These variables consisted of total nitrogen, total

phosphorus, drainage area, percent forest cover,

several macroinvertebrate indices, pH, basin slope,

total residue, total suspended solids, and water

temperature. Nutrients were not significantly

related to periphyton chlorophyll a in streams

within nutrient ecoregions VII or IX but were in

nutrient ecoregion XI. Drainage area, percent forest

cover, and several invertebrate indices were significant variables in nutrient ecoregion VII. Percent

forest cover and several invertebrate indices had a

negative relation with chlorophyll a concentrations

in these streams. Percent forest cover and basin

slope had a negative effect on periphyton in nutrient ecoregion IX streams. Light availability was

more critical to periphyton growth in streams than

nutrients.

Ecoregion XI had enough samples to do seasonal analyses. Summer-season periphyton chlorophyll a concentrations in nutrient ecoregion XI

streams were positively related to total phosphorus and drainage area but negatively related to

percent forest cover. Summer-season phytoplankton in streams was related to different variables

within the same nutrient ecoregion. Both total

nitrogen and total phosphorus were positively

related with chlorophyll a concentrations as well as

basin slope, total residue, and total suspended solids but negatively related to pH. The winter stream

phytoplankton chlorophyll a concentrations were

related to water temperature only.

RELATIONS OF BIOLOGICAL INDICATORS TO NUTRIENT DATA

FOR LAKES AND STREAMS IN PENNSYLVANIA AND WEST VIRGINIA,

1990-98

by Robin A. Brightbill and Edward H. Koerkle
2

INTRODUCTION

A presidential initiative called the Clean

Water Action Plan, released in 1998, provides a

blueprint for federal agencies to work with states,

tribes, and other stakeholders to protect and

restore the Nation’s water resources. The plan

includes an initiative to address the nutrientenrichment problem of lakes and streams across

the United States. Emphasis is on the development

of nutrient water-quality criteria for every geographic region in the country (U.S. Environmental

Protection Agency, 1998). The premise was to compile water-quality, physical, biological, and chlorophyll a data in each state, create a working

database, analyze the data, and report on the findings. Lakes and streams were to be analyzed separately. The U.S. Geological Survey (USGS), in

cooperation with the Pennsylvania Department of

Environmental Protection (PaDEP) and the U.S.

Environmental Protection Agency (USEPA), conducted an analysis of data gathered from state and

federal studies in both Pennsylvania and West Virginia in support of the USEPA nutrient-criteria

program.

Nitrogen and phosphorus are nutrients

needed for algal growth. These algae then become

a food source for other organisms. Excess nutrients

(nitrogen and phosphorus) can cause algal blooms

that affect the taste and esthetic quality of water,

and can deplete oxygen in the water, causing fish

kills in streams, lakes, or ponds (Stevenson and

others, 1996; Stevenson and Lowe, 1986; Sze, 1986).

Point-source discharges of nutrients, which are

fairly constant in load, are controlled by USEPA

regulations (Buck and others, 2000). Nonpointsource nutrients from fertilization of crops and

grazing cattle have become a problem in some

agricultural areas (Manahan, 1984). Some of these

nutrients are released to the atmosphere and are

returned to waterways in the form of rainfall (Buck

and others, 2000).

Nutrient and algal criteria are to serve as

benchmarks for the evaluation of the relative success of any nutrient-management effort (Buck and

others, 2000). USEPA recommends using chlorophyll a as an indicator of the amount of nutrients in

a stream or lake. Chlorophyll a is used as the algalbiomass indicator because it is a principal photosynthetic pigment in all algae (Sze, 1986; Collins

and Weber, 1978). In cases in which nutrients are

the limiting factor (essential for algal growth),

chlorophyll a should increase with an increase of

nutrients to the system. The trend should be similar for each nutrient ecoregion (designated areas of

similar characteristics); however, these thresholds

may be different depending on the underlying

geology. Other factors also may complicate this

relation, such as light attenuation (Secchi depth

and turbidity), temperature, alkalinity, pH, specific

conductance, and aquatic invertebrates. A correlation between chlorophyll a and nutrients must be

established and statistically valid if chlorophyll a is

to be used as an indicator of nutrient enrichment.

Laboratory stream studies have shown that

concentrations of chlorophyll a greater than 0.10 to

0.15 μg/m2, or a cover greater than 20 percent by

filamentous algae, are unacceptable because they

are harmful to stream biota (Horner and others,

1983). Other studies tend to suggest similar limits

on the maximum level of biomass to maintain

healthy streams. The Water Management Branch in

British Columbia, Canada, suggests less than

0.05 μg/m2 of chlorophyll a for recreational waters

and less than 0.10 μg/m2 to protect aquatic life

(Nordin, 1985). New Zealand’s Ministry for the

Environment has recommended that biomass

should not exceed 0.10 μg/m2 of chlorophyll a for

recreational waters (Zuur, 1992). However, a more

recent study in New Zealand suggests that the

maximum algal biomass should be 0.20 μg/m2

(Biggs, 2000), which is higher than the original

standard.

Purpose and Scope

This report describes the relations among

chlorophyll a, total nitrogen, total phosphorus,

other water-quality constituents (turbidity, Secchi

depth, specific conductance, pH, temperature, and

alkalinity), habitat, topographic and land-use characteristics, and macroinvertebrate indices in the

lakes and streams of four nutrient ecoregions of

Pennsylvania and West Virginia. Data from Pennsylvania and West Virginia collected between 1990

and 1998 were used to show the influence of the

explanatory variables on chlorophyll a concentrations. The concentrations of chlorophyll a and

water-quality constituents, including nutrients,

were determined from grab samples, and the

macroinvertebrate indices used were generated

using the USEPA Rapid Bioassessment Protocols

(RBP). Lake and stream site maps were generated

to indicate where more data are needed to fully

describe the relations within a given nutrient

ecoregion.
3

Study Area

The study area includes the states of Pennsylvania and West Virginia. The states are divided

into four nutrient ecoregions that are an aggregation of the Omernik (1987) level III ecoregions. The

nutrient ecoregions are regions VII - Mostly Glaciated Dairy, VIII - Nutrient Poor, Largely Glaciated

Upper Midwest and Northeast, IX - Southeastern

Temperate Forested Plains and Hills, and XI - Central and Eastern Forested Uplands. All of West Virginia and a large part of Pennsylvania are in

nutrient ecoregion XI (U.S. Environmental Protection Agency, 2000). Nutrient ecoregions VII, VIII,

and IX are not located in West Virginia.

Nutrient ecoregion VII is a combination of

the Omernik level III Northern Appalachian Plateau and Uplands, and the Erie Drift Plains Ecoregions. Nutrient ecoregion VIII is the Northern

Central Appalachians Ecoregion, and nutrient

ecoregion IX is the Northern Piedmont Ecoregion

(U.S. Environmental Protection Agency, 2001).

Nutrient ecoregion XI contains all of West Virginia

and part of Pennsylvania and is a combination of

the Blue Ridge, the Ridge and Valley, the Central

Appalachian, and the Western Allegheny Plateau

ecoregions (U.S. Environmental Protection Agency,

2001).

Chlorophyll a and nutrient data were available for 18 lakes and 20 streams in nutrient ecoregion VII, 6 lakes and 27 streams in nutrient

ecoregion VIII, 11 lakes and 12 streams in nutrient

ecoregion IX, and 67 lakes and 170 streams in

nutrient ecoregion XI. The locations of lake and

stream sites are shown in figures 1 and 2, respectively.

Figure 1. Lake sites for which chlorophyll a and nutrient data were available within the nutrient ecoregions of

Pennsylvania and West Virginia, 1990-98. (From U.S. Environmental Protection Agency, 2001.)
4

Acknowledgments

Many colleagues have contributed to the

data aggregation and presentation of this report.

Data aggregation was provided by Rodney Kime

and Barbara Lathrop (Pennsylvania Department of

Environmental Protection (PaDEP)), Michael

Arcuri (West Virginia Department of Environmental Protection), Donna Beatsch (Ohio River Valley

Water Sanitation Commission), Robert Edwards

(Susquehanna River Basin Commission), Brian Hill

(U.S. Environmental Protection Agency (USEPA)),

and Gregory Wacik (U.S. Army Corps of Engineers). Many others provided information about

laboratory methods. Colleague reviewers were

Ifeyinwa Davis, who also coordinated this effort,

and others from USEPA, Charles McGarrell

(PaDEP) and Kimberly Miller (U.S. Geological Survey (USGS)). Technical assistance was provided by

Michael Bilger, James Bubb, Scott Hoffman, Leona

McLanahan, Katherine Paybins, Terriann Preston,

Jennifer Rasberry, Marla Stuckey, and Naomi

Langland, all of the USGS. Technical expertise was

provided by J. Kent Crawford (water-quality specialist) and Kim Otto (reports specialist) (USGS).

Figure 2. Stream sites for which chlorophyll a and nutrient data were available within the nutrient ecoregions of

Pennsylvania and West Virginia, 1990-98. (From U.S. Environmental Protection Agency, 2001.)
5

APPROACH

Data for this effort were compiled from

sources in Pennsylvania and West Virginia. Several

agencies aided the effort to have as complete a data

set of water-quality data from 1990 to 1998 as possible, including nutrients, chlorophyll a, and macroinvertebrates. These data were then assembled

into a single database, which was provided to

USEPA, PaDEP, and West Virginia Department of

Environmental Protection (WVDEP) for their use

in setting nutrient criteria. The USGS used the

information to examine relations between chlorophyll a, water-quality constituents including nutrients, and macroinvertebrates.

Data Sources

Chlorophyll a, nutrients and other waterquality constituents, and habitat and macroinvertebrate indices were compiled for lakes and

streams in Pennsylvania and West Virginia for the

years 1990 through 1998. Major sources of information were the USGS National Water Quality Information System (NWIS), PaDEP, WVDEP,

Susquehanna River Basin Commission (SRBC),

Delaware River Basin Commission (DRBC), U.S.

Army Corps of Engineers (COE), the USEPA Environmental Monitoring and Assessment Program

(EMAP), the USEPA Storage and Retrieval

(STORET) database, the National Park Service

(NPS), and Ohio River Valley Sanitation Commission (ORSANCO). A large part of the macroinvertebrate data were collected according to the USEPA

Rapid Bioassessment Protocol technique (Plafkin

and others, 1989) with some variations on the level

of identification. Land use, drainage-basin area,

and slope were calculated from Geographical

Information System (GIS) data. Land use was

derived from USGS Multi-Resolution Land Characteristics 2000 (MRLC2000) data (U.S. Geological

Survey, 2001). MRLC2000 land-use classes were

simplified into three classes: forested (MRLC

classes 8, 9, 10, 11), urban (MRLC classes 2, 3, 4),

and agriculture (all remaining MRLC classes).

Laboratory methods are listed in the appendix.

Data Analysis

Data were assembled into a Microsoft

ACCESS database as requested by the USEPA.

Data were analyzed using S-Plus. The GIS data

that were created were added to the ACCESS database and were used as explanatory variables.

Algae

Data from lakes and streams were analyzed

separately. Lake-water samples were characterized

by use of phytoplankton chlorophyll a except in

some West Virginia lakes where periphyton chlorophyll a data were collected in some streams.

Stream-water samples were characterized using

periphyton chlorophyll a except in West Virginia

where phytoplankton chlorophyll a data were collected in some streams. The West Virginia stream

phytoplankton chlorophyll a data were analyzed

separately using the same explanatory variables as

used for the periphyton chlorophyll a data. Lake

data from Pennsylvania, West Virginia, and the

EMAP program were combined, where appropriate, to form a more complete data set. The same

approach was used in the compilation of the

stream data. Differences in data collection and laboratory methods among these groups were not

great enough to prevent grouping the data. Data

were removed from the final data set when the

methods were not identified clearly or sampling

was completed by some unknown method. Phytoplankton and periphyton chlorophyll a lake data

were examined separately because they represent

different environments, sampling techniques, and

concentration reporting units.

The final stream data set used for this report

includes chlorophyll a, water quality, basin characteristics, habitat characteristics, and aquatic macroinvertebrate indices. The lake data set included

chlorophyll a and other water-quality characteristics. Not all types of data were available for all

sampling points. At a minimum, data for chlorophyll a, nutrients, and basin characteristics were

available. Following Gibson and others (2000), lake

data were considered independent across sampling locations and years. The stream data, however, were considered independent across sampling locations but not years. This was done to prevent biasing the data set because of an unbalanced

number of years of data for the various sampling

locations. Where sites were in close proximity

along a stream and no significant tributaries were

present that could potentially alter water quality

among these sites, the data were combined using a

mean for the stream length. This provided one data

point and reduced the potential for bias in the data

set. A GIS procedure was used to determine the

proximity of sites to each other.
6

Seasonal means of the data were used in the

analysis of lakes and streams. The seasons consist

of summer (May through September) and winter

(October through April). If multiple samples for a

season were not available, single-sample data were

substituted for seasonal means. Winter-season lake

data were limited to six or less points in nutrient

ecoregions VII, VIII, and IX; therefore, no winterseason analysis was done for these nutrient ecoregions. Nutrient ecoregion XI had sufficient data for

winter-season lake data analysis. Summer-season

data for streams included both periphyton and

phytoplankton chlorophyll a. Winter-season

stream data were limited or nonexistent for nutrient ecoregions VII, VIII, and IX, so no analysis was

completed in these nutrient ecoregions. Analysis of

winter-season stream data for nutrient ecoregion

XI was completed for both periphyton and phytoplankton chlorophyll a.

Data reduction was used to minimize the

number of potential explanatory variables and

make the largest possible data set for analysis.

Individual explanatory variables were, in many

cases, populated with data from multiple parameter codes. Field and laboratory parameter codes

were compiled under one parameter for specific

conductance (00094, 00095) and pH (00400, 00403).

Secchi depth data were compiled from meters

(00078), feet (48701), or inches (00077) parameter

codes and converted to meters, if required. A mean

value was used per lake per year using a similar

approach as the other potential explanatory variables. Instantaneous and mean streamflow (00061,

00060) were compiled into one parameter. Alkalinity and acid neutralizing capacity (ANC) data were

reduced into one parameter compiled from four

parameter codes (00410, 29801, 39086, 39087).

Because very little total nitrogen data (parameter

code 00600) were available, various component

species of nitrogen were combined to produce estimates of total nitrogen. The various combinations

used were: total Kjeldahl nitrogen (00625) + total

nitrite plus nitrate (00630); total Kjeldahl nitrogen

(00625) + total nitrite (00615) + total nitrate (00620);

total Kjeldahl nitrogen (00625) + dissolved nitrite

plus nitrate (00631); total organic nitrogen (00605)

+ total ammonia (00610) + total nitrite plus nitrate

(00630). Combining of phosphorus species was not

required. The term nutrients throughout the text

will refer to total nitrogen and total phosphorus.

Phytoplankton chlorophyll a values were combined to show the mean for a lake regardless of the

sampling depth. Invertebrate indices were reduced

using the same approach. The indices used in the

calculations were generated from data collected

using the RBP method or a variation of that

method. A mean of the indices was calculated

where appropriate, but this was infrequent

because there were rarely overlapping studies at

the same site using different methods of sampling.

USEPA is interested in several key constituents for both lakes and streams. If data for those

constituents were available, they were included in

the final regression analysis. Some of the constituents are response variables, but for this analysis, all

were used as causative variables. For lakes, USEPA

was most interested in total nitrogen, total phosphorus, total organic carbon, suspended solids,

transparency, turbidity, direct algal counts, and

biochemical oxygen demand (Gibson and others,

2000). For streams, total nitrogen, total phosphorus, total suspended solids, transparency, turbidity,

flow, and velocity were key characteristics (Buck

and others, 2000). Information on these constituents was included in the multiple-regression analysis.

Across nutrient ecoregions, statistical comparisons were made using a nonparametric multiple-stage Kruskal-Wallis test (Helsel and Hirsch,

1995). Chlorophyll a, total nitrogen, total phosphorus, specific conductance, alkalinity or acidneutralizing capacity (ANC), pH, Secchi depth,

and turbidity were examined for significant differences among the four nutrient ecoregions.

Within each nutrient ecoregion, multiple

linear regression was used to select water-quality

constituents and environmental indicators that

were significant in explaining chlorophyll a concentrations. Prior to regression analysis, correlation coefficients among the explanatory variables

were examined. For highly correlated (r >= 0.9)

pairs, the variable less commonly cited as an indicator variable was deleted from the list of potential

explanatory variables. Highly correlated variables

in a multiple linear regression will exhibit multicollinearity. The existence of multicollinearity can

result in incorrect sign, incorrect magnitude, and

inflated variance in the coefficient estimates

(Freund and Littell, 1986). The explanatory variables available for chlorophyll a regressions within

each nutrient ecoregion are listed in table 1 for

lakes and in table 2 for streams. In nutrient ecoregions VII, VIII, and IX, an explanatory variable that

was limited in number of data to less than 75 percent of the total number of data for that nutrient
7

Table 1. Explanatory variables examined for significance in regressions of the log of chlorophyll a concentrations for

nutrient ecoregions VII, VIII, IX, and XI lakes, Pennsylvania and West Virginia, 1990-98

[ANC, acid neutralizing capacity]

Nutrient ecoregion VII Nutrient ecoregion VIII Nutrient ecoregion IX Nutrient ecoregion XI

Explanatory

variables

alkalinity, ANC

pH

Secchi depth

specific conductivity

total nitrogen

total phosphorus

water temperature

alkalinity, ANC

dissolved oxygen

pH

Secchi depth

specific conductivity

total nitrogen

total phosphorus

water temperature

alkalinity, ANC

dissolved oxygen

pH

Secchi depth

specific conductivity

total nitrogen

total phosphorus

water temperature

alkalinity, ANC

dissolved oxygen

pH

Secchi depth

specific conductivity

total nitrogen

total phosphorus

water temperature

Table 2. Explanatory variables examined for significance in regressions of the log of chlorophyll a concentrations for

nutrient ecoregions VII, VIII, IX, and XI streams, Pennsylvania and West Virginia, 1990-98

[pct., percent; ANC, acid neutralizing capacity; HBI, Hilsenhoff’s biotic index; EPT, Ephemeroptera-PlecopteraTrichoptera]

Nutrient ecoregion VII Nutrient ecoregion VIII Nutrient ecoregion IX Nutrient ecoregion XI

Explanatory

variables

alkalinity, ANC

basin slope

drainage area

Ephemeroptera

Ephemeroptera taxa

Ephemeroptera taxa richness

EPT: Chironomidae ratio

EPT index

EPT taxa

HBI

pct. agriculture land use

pct. collector gatherers

pct. dominant taxa

pct. filterer collectors

pct. forest land use

pct. Plecoptera individuals

pct. scrapers

pct scraper filterers

pct. Trichoptera individuals

pct. tolerant individuals

pct. urban land use

pH

Plecoptera taxa richness

specific conductivity

taxa richness

tolerant taxa richness

total residue

total nitrogen

total phosphorus

Trichoptera taxa richness

turbidity

alkalinity, ANC

basin slope

drainage area

Ephemeroptera

Ephemeroptera taxa

Ephemeroptera taxa richness

EPT: Chironomidae ratio

EPT index

EPT taxa

HBI

pct. agriculture land use

pct. collector gatherers

pct. dominant taxa

pct. filterer collectors

pct. forest land use

pct. Plecoptera individuals

pct. scrapers

pct scraper filterers

pct. Trichoptera individuals

pct. tolerant individuals

pct. urban land use

pH

Plecoptera taxa richness

specific conductivity

taxa richness

tolerant taxa richness

total residue

total nitrogen

total phosphorus

Trichoptera taxa richness

turbidity

basin slope

dissolved oxygen

drainage area

pct. agriculture land use

pct. forest land use

pct. tolerant individuals

pct. urban land use

pH

Plecoptera taxa richness

specific conductivity

stream discharge

suspended sediment

total nitrogen

total phosphorus

water temperature

alkalinity, ANC1

basin slope1

drainage area1

Ephemeroptera

Ephemeroptera taxa

Ephemeroptera taxa richness

EPT: Chironomidae ratio

EPT index

EPT taxa

HBI

pct. agriculture land use1

pct. collector gatherers

pct. dominant taxa

pct. filterer collectors

pct. forest land use1

pct. Plecoptera individuals

pct. scrapers

pct. Trichoptera individuals

pct. tolerant individuals

pct. urban land use1

pH1

Plecoptera taxa richness

specific conductivity1

taxa richness

tolerant taxa richness

total residue1

total nitrogen1

total phosphorus1

total suspended solids2

Trichoptera taxa richness

turbidity

water temperature2

1 Explanatory variables examined for periphyton and phytoplankton regression relations.

2 Explanatory variables examined for phytoplankton regression relations only.
8

ecoregion was not considered for model inclusion.

The regression procedure trims the available data

for all variables to that of the variable with the

smallest number, including variables with less

than 75 percent of the total available number of

data. This would result in a loss of acceptable data.

Because the data set for streams in nutrient ecoregion XI is much larger than for streams in the other

nutrient ecoregions, subsets as small as 24 samples

were used to allow inclusion of as many explanatory variables as possible. Chlorophyll a concentrations and explanatory variables were log transformed, if required, to satisfy any statistical assumptions. Statistical significance was set at α = 0.05.

The final regression equations were selected

on the basis of minimizing the Mallow CP statistic,

the PRESS statistic and the absence of pathologies

such as multicollinearity, high influence, and coefficient signs and magnitudes contrary to known

system behaviors. Multicollinearity was diagnosed

with eigenvalues and the variance inflation factor.

Multicollinearity was resolved by deleting the

explanatory variable as described above. Outliers

and high-influence points were diagnosed with

partial leverage plots, standardized residuals,

Cook’s D, and Studentized residuals. Outliers and

high-influence points were deleted from regression

data sets if, as a result of an F test comparison, their

inclusion was shown to significantly alter regression coefficients. The decision to delete outliers

with high influence was made considering the

unknown quality of much of the available data. In

most cases, only one and at most two data points

were deleted from any one data set. In this manner,

individual data points of unknown quality were

prevented from significantly altering regressions

that, in their absence, better represented the bulk of

the data.

Macroinvertebrates

Macroinvertebrate data were available for

streams but not for lakes. Correlations among macroinvertebrate indices were examined. As with

other explanatory variables, correlation coefficients

equal to or greater than 0.90 resulted in eliminating

one of the correlated indices from the data analysis. Other reasons for selecting an index for deletion included indicators not typically reported and

small numbers of available data. The remaining

indices were entered as potential explanatory variables in the regression procedure.
9

RELATIONS AMONG CHLOROPHYLL a,

NUTRIENTS, OTHER WATER-QUALITY

CHARACTERISTICS, AND HABITAT AND

MACROINVERTEBRATE INDICES

Algae are primary producers in lakes and

streams. They are a food source and shelter for animals, and chemical modulators transforming

many inorganic chemicals into their organic forms

(Stevenson and others, 1996). Algae show active

uptake of nitrate, ammonia, and other nutrients

during the day (Triska and others, 1989), are an

important sink for nutrients (Wetzel, 1996), and

help to stabilize the substrata by decreasing the

effects of scour (Biggs, 1996). Too much algae, however, especially filamentous green algae, in a lake

or stream can have a detrimental affect on watersupply usability, aesthetic appeal, and in-water

recreational use (Stevenson and others, 1996;

Wharfe and others, 1984).

Nutrients—nitrogen and phosphorus—are

required in moderate quantities in aquatic and terrestrial ecosystems (Stevenson and others, 1996).

Major inputs of nutrients to streams are from rainfall and runoff in the summer and heavy rains or

snowmelt in the winter (Goldman and Horne,

1983). Streams provide the largest input of nitrogen to lakes, even though the lake surface is in constant contact with the air, which is an inexhaustible

nitrogen reservoir (Goldman and Horne, 1983).

Large influxes of nutrients into a stream usually

will cause the biomass of plants and animals to

increase downstream of the inflow. Even though

uptake by plants may buffer the amount of nitrate

in the water, it is commonly so abundant that only

a small fraction is removed (Casey, 1977). Nitrogen

gas is the most common form of nitrogen in lakes

and typically shows no seasonal or depth variations except those resulting from temperature

changes (Goldman and Horne, 1983).

In the northern half of the United States,

phosphorus is typically more limiting than nitrogen (Stevenson and others, 1996; Sze, 1986; Smith,

1980). Phosphorus has a high affinity for soils and

little phosphorus is released into the water column

unless the soil is eroded; nitrogen in the form of

nitrate moves freely through soils (Goldman and

Horne, 1983). Nitrogen may be limiting during

periods of low streamflow when the denitirification process may reduce the nitrogen supply

(Lohman and others, 1991).

Comparison Among Nutrient Ecoregions

Lakes.—Median concentrations of phytoplankton chlorophyll a and water-quality constituents except for specific conductance and

alkalinity show significant differences among the

nutrient ecoregions (fig. 3) (table 3). Lake sites in

West Virginia, where periphyton data were collected, were not used in figure 3 and table 3.

Table 3. Results of multiple-stage Kruskal-Wallis test for significant differences in median values of waterquality constituents data from lakes in nutrient ecoregions VII, VIII, IX, and XI, Pennsylvania and

West Virginia, 1990-98

[Median values contained in shaded blocks are not significantly different. Median values in unshaded

blocks are significantly different from all other values. μS/cm, microsiemens per centimeter; mg/L,

milligrams per liter; μg/L, micrograms per liter; m, meters]

Water-quality constituent

Median values

Nutrient

ecoregion VII

Nutrient

ecoregion VIII

Nutrient

ecoregion IX

Nutrient

ecoregion XI

pH (units) 7.6 6.8 8.1 7.2

Specific conductance (μS/cm) 171 76 180 143

Alkalinity (acid neutralizing capacity) (mg/L) 51 27 48 30

Phytoplankton chlorophyll a (μg/L) 14.5 6.00 28.0 6.76

Total nitrogen (mg/L) 1.00 .53 1.24 .63

Total phosphorus (mg/L) .110 .023 .078 .032

Secchi depth (m) 2.20 2.35 1.04 2.00
10

Figure 3. Distribution of pH, specific conductance,

alkalinity/acid neutralizing capacity, total nitrogen

concentrations, total phosphorus concentrations,

phytoplankton chlorophyll a, and Secchi depth in

lakes by nutrient ecoregions of Pennsylvania and

West Virginia, 1990-98.
11

Figure 3. Distribution of pH, specific conductance,

alkalinity/acid neutralizing capacity, total nitrogen

concentrations, total phosphorus concentrations,

phytoplankton chlorophyll a, and Secchi depth in

lakes by nutrient ecoregions of Pennsylvania and

West Virginia, 1990-98—Continued.
12

Median concentrations of phytoplankton chlorophyll a (28 μg/L), total nitrogen (1.24 mg/L), and

pH (8.1) are greatest in nutrient ecoregion IX and

decrease in order from nutrient ecoregion VII, XI,

to VIII. Nutrient ecoregion IX also had the lowest

median value for Secchi depth (1.04), which was

significantly lower than the other three nutrient

ecoregions. Nutrient ecoregion VII had the greatest

median concentration of total phosphorus

(0.110 mg/L) and alkalinity/acid-neutralizing

capacity (51 mg/L as CaCO3) although neither

were significantly greater than those in nutrient

ecoregion IX. Nutrient ecoregion VIII had the lowest median values for phytoplankton chlorophyll a

(6.0 μg/L), concentration of total nitrogen

(0.53 mg/L), concentration of total phosphorus

(0.023 mg/L), specific conductance (76 μS/cm),

alkalinity/acid-neutralizing capacity (27 mg/L as

CaCO3), and pH (6.8) of all nutrient ecoregions but

had the highest median value for Secchi depth

(2.35). However, phytoplankton chlorophyll a, total

nitrogen, Secchi depth, alkalinity, and specific conductance were not significantly lower than in

nutrient ecoregion XI. Median values of specific

conductance were not significantly different

among any of the nutrient ecoregions, nor was

water temperature with a maximum difference in

median water temperature of less than 2°C.

In general, concentrations of chlorophyll a

and water quality tend to separate the nutrient

ecoregions into two groups, nutrient ecoregions

VII and IX and nutrient ecoregions VIII and XI.

Nutrient ecoregions VII and IX have the highest

median values and nutrient ecoregions VIII and XI

have the lowest. Where median concentrations of

chlorophyll a are highest, Secchi depths are lowest,

and where median concentrations of chlorophyll a

are lowest, Secchi depths are highest. Notably,

nutrient ecoregion XI had the largest sample concentrations for phytoplankton chlorophyll a

(160 μg/L), total nitrogen (5.66 mg/L), and alkalinity/ANC (168 mg/L as CaCO3) but some of the

lowest median concentrations. The substantially

larger data set and broader areal coverage of nutrient ecoregion XI likely contributed to these maximum individual sample values.

Streams.—Median values for the waterquality constituents tended to be greatest in nutrient ecoregion IX streams and least in nutrient

ecoregion VIII streams (fig. 4 and table 4). Significantly higher concentrations of periphyton chlorophyll a (24 mg/m2), total nitrogen (6.22 mg/L),

total phosphorus (0.143 mg/L) and specific conductance (579 μS/cm) were measured in nutrient

ecoregion IX. Significantly higher alkalinity/acidneutralizing capacity (876 mg/L as CaCO3) and

pH (8.2) were measured in nutrient ecoregion VII.

Nutrient ecoregion VIII had the lowest median

values for all variables presented. Chlorophyll a

was not significantly lower than nutrient ecoregions VII and XI, while alkalinity was not significantly different from nutrient ecoregions IX and XI.

The median turbidity was significantly lower in

nutrient ecoregion VIII and highest in XI. Data

from sites where phytoplankton data were collected in West Virginia streams are not shown in

figure 4 and table 4.

The distribution of periphyton chlorophyll a

among the nutrient ecoregions tends to increase

positively with the distribution of instream nutrients (fig. 4). The substantially greater median concentration of periphyton chlorophyll a measured in

nutrient ecoregion IX (fig. 4) coincides with higher

concentrations of total nitrogen and total phosphorus. A similar relation can be observed in nutrient

ecoregion VIII for the lowest median concentrations of chlorophyll a and nutrients.
13

Predictive Relations Between

Chlorophyll a and Nutrients

Predictive relations between concentrations

of chlorophyll a and nutrients have been reported

previously. For benthic algae (periphyton), Dodds

and others (1997) suggested the following general

regression equations if more locale specific relations are not available:

log (mean chlorophyll a) =

0.01173 + 0.5949 (log total nitrogen) (1)

log (mean chlorophyll a) =

1.091 + 0.2786 (log total phosphorus) (2)

These regression equations lack strong predictive power, particularly if total phosphorus is

the explanatory variable. The r2 for the total nitrogen regression equation (eqn. 1) is 0.35 and for the

total phosphorus regression equation (eqn. 2) is

0.089. Dodds and others (1997) combined total

nitrogen and total phosphorus for an increase in

predictive power (eqn. 3) (r2 = 0.43).

log (mean chlorophyll a) = -3.233

+ 2.826 (log total nitrogen)

- 0.43 (log total nitrogen)2

+ 0.255 (log total phosphorus) (3)

where (eqns. 1, 2, and 3)

Chlorophyll a is concentration, in milligrams

per square meter;

Total nitrogen is concentration, in micrograms

per liter; and

Total phosphorus is concentration, in

micrograms per liter.

Table 4. Results of multiple-stage Kruskal-Wallis test for significant differences in median values of

water-quality constituent data from streams in nutrient ecoregions VII, VIII, IX, and XI, Pennsylvania

and West Virginia, 1990-98

[Median values contained in shaded blocks are not significantly different. Median values in unshaded

blocks are significantly different from all other values; ND, no data; μS/cm, microsiemens per centimeter;

mg/L, milligrams per liter; mg/m2, milligrams per meter squared; NTU, turbidity units]

Water-quality constituent

Median values

Nutrient

ecoregion VII

Nutrient

ecoregion VIII

Nutrient

ecoregion IX

Nutrient

ecoregion XI

pH (units) 8.2 7.1 7.8 7.8

Specific conductance (μS/cm) 134 46 579 116

Alkalinity (acid neutralizing capacity) (mg/L) 876 62 109 247

Periphyton chlorophyll a (mg/m2) .13 .01 24.0 .18

Total nitrogen (mg/L) .81 .35 6.22 .57

Total phosphorus (mg/L) .036 .008 .143 .015

Turbidity (NTU) 2.2 1.3 ND 2.7
14

Figure 4. Distribution of pH, specific

conductance, alkalinity/acid neutralizing capacity,

total nitrogen concentrations, total phosphorus

concentrations, periphyton chlorophyll a, and

turbidity in streams by nutrient ecoregions of

Pennsylvania and West Virginia, 1990-98.
15

Figure 4. Distribution of pH, specific conductance,

alkalinity/acid neutralizing capacity, total nitrogen

concentrations, total phosphorus concentrations,

and periphyton chlorophyll a, and turbidity in

streams by nutrient ecoregions of Pennsylvania and

West Virginia, 1990-98—Continued.
16

For suspended algae (phytoplankton) in

streams, Van Nieuwenhuyse and Jones (1996)

reported the following equation:

log (chlorophyll) =

-1.65 + 1.99 (log total phosphorus)

- 0.28 (log total phosphorus)2 (4)

where

Chlorophyll is concentration, in milligrams per

cubic meter; and

Total phosphorus is concentration,

in milligrams per liter.

The predictive power of equation 4

(r2 = 0.67) is considerably better than those suggested by Dodds and others (1997) for benthic

algae. By incorporating drainage-basin area into

the Van Nieuwenhuyse and Jones equation (1996)

(eqn. 4), the resulting equation 5 has a slightly

increased predictive power (r2 = 0.73).

log (chlorophyll) =

-1.92 + 1.96 (log total phosphorus)

- 0.30 (log total phosphorus)2

+ 0.12 (log drainage-basin area) (5)

where

Chlorophyll is concentration, in milligrams per

cubic meter;

Total phosphorus is concentration, in

milligrams per liter; and

Drainage-basin area is area, in square

kilometers.

All five equations are general in scope and

stronger predictive relations should be expected

from nutrient ecoregion specific data (U.S. Environmental Protection Agency, 2000). Several studies have indicated that in multiple regression

equations, constituents such as water velocity and

discharge may explain as much or more of the variation in benthic algal biomass as nutrients (Welch

and others, 1988; Biggs and Close, 1989; Duncan

and Blinn, 1989), or that phosphorus input into

streams is positively related to the average slope of

the drainage basin (Kirchner, 1975).

Predictive Relations Between

Chlorophyll a and Nutrients

for Nutrient Ecoregions VII, VIII, IX, and XI

The previous section discussed general predictive equations that could be applied to all nutrient ecoregions as a whole. By looking at each

nutrient ecoregion separately, better and more precise predictive equations relating chlorophyll a to

nutrients should be attainable.

Nutrient Ecoregion VII

Lakes.—The data set consisted of 47 samples

from 18 lakes. The relation between concentrations

of phytoplankton chlorophyll a and concentrations

of total nitrogen and total phosphorus (fig. 5) show

considerable scatter. The data for total nitrogen

suggest a positive correlation (fig. 5A), but no relation is discernible in the data for total phosphorus

(fig. 5B).

Figure 5. Relation between log of phytoplankton chlorophyll a concentrations and the log of concentrations of

total nitrogen (A) and total phosphorus (B) in lakes within nutrient ecoregion VII, Pennsylvania, 1990-98.

A B
17

In the absence of other explanatory variables, concentrations of total nitrogen had a low

but significant predictive power (r2 = 0.16) (eqn. 6)

(table 5) for phytoplankton chlorophyll a. Concentrations of total phosphorus had no significant predictive power. When other water-quality data were

entered into the regression analysis, a substantial

increase in the predictive power of the resulting

equation (r2 = 0.75) was noted (eqn. 7) (table 5),

although the data set was smaller. Secchi depth

and the concentration of dissolved oxygen combined to explain about three-quarters of the variation in the log of concentrations of phytoplankton

chlorophyll a (fig. 6). Concentrations of total nitrogen were not significant in this regression equation.

Secchi depth and chlorophyll a concentrations are negatively related. Secchi depth, which is

measured by lowering a black and white colored

disk into the water and determining how far down

in the water column the disk can be seen, is used to

determine the water clarity in a lake. Secchi depth

can be used as a causative or an explanatory variable. As turbidity increases in the water column

from sediment, chlorophyll a concentrations can

decrease. However, in this relation, Secchi depth is

a response variable, and as chlorophyll a concentrations increased, meaning the bloom is greater,

the Secchi depth decreased and the water became

more turbid.

Concentrations of dissolved oxygen also can

be a response variable to the amount of algae in a

lake. Algae produces oxygen during the day and

can raise the amount of dissolved oxygen in the

lakes. At night, the algae respire and use the oxygen. Large algal blooms can deplete oxygen supplies in the water and cause fish kills.

log (Chlorophyll a) = I + A log (Total nitrogen) (6)

log (Chlorophyll a) = I + B (Secchi depth)

+ C (Dissolved oxygen)] (7)

where

Chlorophyll a is concentration, in micrograms

per liter;

Total nitrogen is concentration, in milligrams

per liter;

Secchi depth is depth, in meters; and

Dissolved oxygen is concentration, in

milligrams per liter.

Table 5. Regression equation statistics for phytoplankton chlorophyll a concentrations in nutrient ecoregion VII lakes,

Pennsylvania, 1990-98

[—, not used in equation; <, less than; A, B, and C are regression coefficients]

Equation

Intercept Total nitrogen Secchi depth Dissolved oxygen

n r2

Residual

standard

error I p-value A p-value B p-value C p-value

6 1.143 <0.001 1.191 0.007 — — — — 45 0.16 0.403

7 1.551 <.001 — — -0.345 <0.001 0.078 0.003 35 .75 .223

Figure 6. Relation between observed and

predicted logs of phytoplankton chlorophyll

a concentrations in lakes within nutrient

ecoregion VII, Pennsylvania, 1990-98.

(Predicted concentrations were computed

using equation 7.)
18

Streams.—No significant regression equations for periphyton chlorophyll a and nutrients

were noted when modeled in the form suggested

by Dodds and others (1997) (eqns. 1 and 2). The

data set for streams in nutrient ecoregion VII consisted of 20 samples. Basin areas above the sampling sites ranged from 0.32 to 26.9 mi2. Median

basin slope was 488 ft/mi. The predominant land

use was forest; the median cover was 67 percent.

The relations between chlorophyll a and concentrations of total nitrogen and total phosphorus show

scatter with no discernible linearity (fig. 7). Of the

potential explanatory variables evaluated (table 2),

water temperature, stream discharge, dissolved

oxygen, suspended sediment, and all habitat indicators were not included. These variables had

either no or limited (<10 points) available data.

Variables describing drainage-basin characteristics

and water quality (including nutrients) were

entered in the regression analysis, but resulted in

no significant relation. In the absence of other

explanatory variables, the benthic-invertebrate

indices were not significantly related to concentrations of chlorophyll a. However, when combined

with the site characteristics of drainage-basin area

and percent forest cover, the indices Ephemeroptera, Plecoptera, Trichoptera index (EPT index),

percent collector/gatherer, percent filterer/collector, percent Plecoptera individuals, and percent

Trichoptera individuals showed a strong relation

to chlorophyll a (r2 = 0.85) (eqn. 8) (table 6 and

fig. 8). Percent forest cover, EPT index, percent collector/gatherer, and percent filterer/collector are

negatively related to concentrations of periphyton

chlorophyll a in this nutrient ecoregion VII.

log (Chlorophyll a) = I + A (drainage area)

+ B (percent forest cover) + C (EPT Index)

+ D (percent collector-gatherer)

+ E (percent filterer-collector)

+ F (percent Plecoptera individuals)

+ G (percent Trichoptera individuals) (8)

where

Chlorophyll a is concentration, in milligrams

per square meter;

Drainage area is area, in square miles;

Percent forest cover is in percent;

EPT index is the total number of distinct taxa

within the orders Ephemeroptera,

Plecoptera, and Trichoptera compared to

the total number of taxa present;

Percent collector-gatherer is in percent;

Percent filterer-collector is in percent;

Percent Plecoptera individuals is in percent;

and

Percent Trichoptera individuals is in percent.

Figure 7. Relation between log of periphyton chlorophyll a concentration and the log of concentrations of total nitrogen

(A) and total phosphorus (B) in streams within nutrient ecoregion VII, Pennsylvania, 1990-98.

A B
19

Table 6. Regression equation statistics for periphyton chlorophyll a concentrations in nutrient ecoregion VII streams,

Pennsylvania, 1990-98

[A, B, C, D, E, F, and G are regression coefficients]

Equation

Intercept Drainage area Percent

forest cover EPT index Percentcollector/gatherer

I p-value A p-value B p-value C p-value D p-value

8 1.3734 0.0035 0.0405 0.003 -0.0281 0.0001 -0.045 0.0298 -0.0251 0.001

Percent

filterer/collector

Percent

Plecoptera

individuals

Percent

Trichoptera

individuals n r2 Standard error of

estimate

E p-value F p-value G p-value

-0.016 0.0054 0.0405 0.0025 0.101 0.0002 20 0.847 0.173

Figure 8. Relation between observed and

predicted logs of periphyton chlorophyll a

concentrations in streams within nutrient

ecoregion VII, Pennsylvania, 1990-98.

(Predicted concentrations were computed

using equation 8.)
20

Examination of the coefficients for the

benthic-invertebrate indices (table 6) coupled with

the small size of the data set suggests that the relation may be a chance occurrence rather than a

functional relation between concentration of

periphyton chlorophyll a and these explanatory

variables. With a data set of less than 30 samples, it

is easy to draw the incorrect conclusions because

the sample size is not large enough to truly reflect

the total population. To satisfy the Central Limit

Theorem, a sample size of at least 30 is needed to

approximate a normal distribution with little variation from the population mean (Ott, 1993) and to

achieve adequate information in sampling the

binomial populations (Helsel and Hirsch, 1995).

Grazing of algae by benthic macroinvertebrates can offset or lessen the increase in biomass

caused by nutrient enrichment, and the effects will

not be noted until the invertebrate population

begins to increase in response to the increase in

algal growth (Mundie and others, 1991; Hill and

others, 1992; Rosemond and others, 1993; Peterson

and others, 1993). Grazing can cause either a

decrease or an increase in algal growth. Where

nutrient concentrations are low, grazers actually

cause nutrient regeneration from within the periphyton community, allowing more algal cells to

grow (McCormick and Stevenson, 1991). Temperature and light also can affect nutrient uptake by

algae (Stevenson and others, 1996; Borchardt and

others, 1994; Dortch and others, 1991; Winterbourn, 1990; Hill and Knight, 1988), suggesting

other factors sometimes may be more important in

determining algal composition than nutrients

alone (Stevenson and others, 1996).

Nutrient Ecoregion VIII

Lakes.—The data set consisted of 28 samples

from 6 lakes. No significant regression equations

for phytoplankton chlorophyll a resulted from

analysis of the available data. The relation between

logs of phytoplankton chlorophyll a concentrations

and logs of concentrations of total nitrogen and

total phosphorus showed no recognizable linear

relation (fig. 9).

Streams.—No significant regression equations for periphyton chlorophyll a resulted from

analysis of the available data. The data set for

streams in nutrient ecoregion VIII consisted of

27 samples. Basin areas above the sampling sites

ranged from 0.12 to 590 mi2. Median basin slope

was 418 ft/mi. The predominant land use was forest; the median cover was 96 percent. The logs of

periphyton chlorophyll a concentrations show little

or no change with changes in the logs of concentrations of total nitrogen or total phosphorus (fig. 10)

with the exception of one outlier point (periphyton

chlorophyll a <0.001 mg/m2 [log -3.22]). Because

this outlier could strongly influence any regression

equation, data were analyzed with and without the

outlier. No significant regression equations

resulted either with or without the outlier. Regression analysis of potential explanatory variables

other than nutrients (table 2) also resulted in no

significant equations. Water temperature, stream

discharge, dissolved oxygen, suspended sediment, and all habitat indicators were not included

as explanatory variables. These indicators had

either no or limited (< 10 points) available data.
21

Figure 9. Relation between logs of phytoplankton chlorophyll a concentrations and logs of concentrations of total

nitrogen (A) and total phosphorus (B) in lakes within nutrient ecoregion VIII, Pennsylvania, 1990-98.

A B

Figure 10. Relation between log of periphyton chlorophyll a concentrations and logs of concentrations of total nitrogen

(A) and total phosphorus (B) in streams within nutrient ecoregion VIII, Pennsylvania, 1990-98.

A B
22

Nutrient Ecoregion IX

Lakes.—The data set consisted of 29 samples

from 11 lakes. The relation between the logs of

phytoplankton chlorophyll a concentrations and

logs of total nitrogen concentrations shows a possible positive linear correlation (fig. 11A); a similar

plot for concentrations of chlorophyll a and total

phosphorus (fig. 11B) does not.

When nutrients alone were evaluated, concentrations of total nitrogen were determined to

have significant although low (r2 = 0.16) predictive

power (eqn. 9) (table 7). The addition of other

water-quality constituents to the total nitrogen

equation resulted in no significant improvement

but showed Secchi-depth data from one of the

lakes (Lake Marburg) had a significant influence

on the regression coefficients. Although the Lake

Marburg data were not suspect, deleting these data

resulted in a significant and much improved

regression equation (r2 = 0.78) (eqn. 10) (table 7

and fig. 12). A comparison with equation 7 shows

the Secchi-depth coefficients, which are the most

significant, are nearly identical and lends support

to the validity of the equation 10 coefficients. The

relation between predicted and observed logs of

phytoplankton chlorophyll a concentrations

(fig. 12) shows good agreement. In this equation,

both Secchi depth and pH were inversely related to

chlorophyll a concentrations.

log (Chlorophyll a) = I + A log (Total nitrogen) (9)

log (Chlorophyll a) = I + A log (Total nitrogen)

+ B (Secchi depth) + C (pH) (10)

where

Chlorophyll a is concentration, in micrograms

per liter;

Total nitrogen is concentration, in milligrams

per liter;

Secchi depth is depth, in meters; and

pH is in standard units.

Table 7. Regression equation statistics for phytoplankton chlorophyll a concentrations in nutrient ecoregion IX lakes,

Pennsylvania, 1990-98

[—, not used in equation; <, less than; A, B, and C are regression coefficients]

Equation

Intercept Total nitrogen Secchi depth pH

n r2

Standard

error of

I p-value A p-value B p-value C p-value estimate

9 1.338 <0.001 0.575 0.044 — — — — 25 0.16 0.278

10 3.181 <.001 .480 .025 -0.332 0.001 -0.179 0.010 15 .78 .144

Figure 11. Relation between log of phytoplankton chlorophyll a concentrations and logs of concentrations of total

nitrogen (A) and total phosphorus (B) in lakes within nutrient ecoregion IX, Pennsylvania, 1990-98.

A B
23

Streams.—Regression analysis of summerseason stream data from nutrient ecoregion IX

resulted in one significant regression equation for

the log of periphyton chlorophyll a concentrations.

The data set for nutrient ecoregion IX (12 samples)

was the smallest of the stream data sets. Basin

areas above the sampling sites ranged from 1.10 to

261 mi2. Median basin slope was 271 ft/mi. The

predominant land use was agriculture; the median

cover was 76 percent. Comparisons of chlorophyll a and concentrations of total nitrogen and

total phosphorus (fig. 13) show no discernible

relation and also reveal an outlier point similar to

nutrient ecoregion VII. Data were analyzed with

and without the outlier. The presence of the outlier

did affect the significance of the regression results;

deleting the outlier resulted in a significant

regression equation with good predictive power

(r2= 0.74) (eqn. 11) (table 8). The relation between

the observed and predicted logs of periphyton

chlorophyll a concentrations is shown in figure 14.

In this equation, both percent forest cover and pH

have a negative effect on periphyton chlorophyll a

concentrations.

log (Chlorophyll a) =

I + A (Percent forest cover) + B (pH) (11)

where

Chlorophyll a is concentration, in milligrams

per square meter;

Percent forest cover is in percent; and

pH is in standard units.

Figure 12. Relation between observed and

predicted logs of phytoplankton chlorophyll a

concentrations in lakes within nutrient

ecoregion IX, Pennsylvania, 1990-98.

(Predicted concentrations were computed

using equation 10.)
24

Figure 13. Relation between logs of periphyton chlorophyll a concentrations and logs of concentrations of total

nitrogen (A) and total phosphorus (B) in streams within nutrient ecoregion IX, Pennsylvania, 1990-98.

A B

Table 8. Regression equation statistics for summer-season periphyton chlorophyll a concentrations in

nutrient ecoregion IX streams, Pennsylvania, 1990-98

[A and B are regression coefficients]

Equation

Intercept Forest cover pH

n r2

Standard

error of

I p-value A p-value B p-value estimate

11 6.109 0.002 -0.016 0.012 -0.570 0.014 11 0.74 0.181

Figure 14. Relation between observed and

predicted logs of periphyton chlorophyll a

concentrations in streams within nutrient

ecoregion IX, Pennsylvania, 1990-98.

(Predicted concentrations were computed

using equation 11.)
25

Nutrient Ecoregion XI

Lakes.—The data set of phytoplankton

chlorophyll a concentrations consisted of 78 summer-season samples from 43 lakes and 25 winterseason samples from 19 lakes. The relations

between logs of summer-season phytoplankton

chlorophyll a concentrations and logs of concentrations of total nitrogen and total phosphorus

(fig. 15) visually suggest positive correlations but

contain large amounts of scatter. The relations

between winter-season phytoplankton chlorophyll a concentrations and logs of concentrations

of total nitrogen and total phosphorus (fig. 16) suggest a positive correlation for concentrations of

total nitrogen but no discernible correlation for

total phosphorus.

Figure 15. Relation between log of phytoplankton chlorophyll a concentrations and logs of concentrations of total nitrogen

(A) and total phosphorus (B) in lakes during summer months within nutrient ecoregion XI, Pennsylvania and West Virginia,

1990-98.

A B

Figure 16. Relation between log of phytoplankton chlorophyll a concentrations and logs of concentrations of total nitrogen

(A) and total phosphorus (B) in lakes during winter months within nutrient ecoregion XI, Pennsylvania and West Virginia,

1990-98.

A B
26

Nutrient concentrations alone had minimal

explanatory power for summer-season concentrations of phytoplankton chlorophyll a in nutrient

ecoregion XI lakes. In the absence of other explanatory variables, concentrations of total nitrogen

alone explained about 18 percent of the variation in

chlorophyll a concentrations (r2 = 0.18) (eqn. 12)

(table 9) and concentrations of total phosphorus

alone explained 12 percent of the variation

(r2 = 0.12) (eqn. 13) (table 9). Concentrations of

total phosphorus were not significant in combination with total nitrogen. When all available explanatory variables were included in the regression

analysis, total nitrogen and Secchi depth (negative

relation) provided the best equation but explained

less than half of the variation in logs of phytoplankton chlorophyll a concentrations (r2 = 0.44)

(eqn. 14) (table 9). Although this two-variable

equation was statistically significant, it exhibited

poor linearity (fig. 17). In particular, the equation

underestimates chlorophyll a concentrations at

greater concentrations of total nitrogen, possibly

because of under specification of the equation or,

equally likely, limiting behavior from a variable

other than total nitrogen controlling phytoplankton production. Once an excess of nutrients are

reached, the bio-mass of algae in a lake changes

very little; however, at this point, the species composition can change significantly. Several studies

have shown biomass stays fairly constant as nutrients increase, but the community composition

changes from species that fix nitrogen to species

that are nutrient tolerant, such as Cladophora, and

the species composition may even decrease in

number (Carrick and others, 1988; Keithan and

others, 1988; Winder and Duthie, 2000).

log (Chlorophyll a) = I + A log (Total nitrogen) (12)

log (Chlorophyll a) =

I + B log (Total phosphorus) (13)

log (Chlorophyll a) =

I + A log (Total nitrogen)

+ C (Secchi depth) (14)

Table 9. Regression equation statistics for summer-season phytoplankton chlorophyll a concentrations in nutrient

ecoregion XI lakes, Pennsylvania and West Virginia, 1990-98

[—, not used in equation; <, less than; A, B, and C are regression coefficients]

Equation

Intercept Total nitrogen Total phosphorus Secchi depth

n r2

Standard

error of

I p-value A p-value B p-value C p-value estimate

12 0.901 <0.001 0.491 <0.001 — — — — 78 0.18 0.363

13 1.543 <.001 — — 0.501 0.002 — — 78 .12 .376

14 1.235 <.001 .384 .002 — — -0.156 <0.001 62 .44 .297

Figure 17. Relation between observed and

predicted logs of phytoplankton chlorophyll a

concentrations in lakes during summer

months within nutrient ecoregion XI,

Pennsylvania and West Virginia, 1990-98.
27

where

Chlorophyll a is concentration, in micrograms

per liter;

Total nitrogen is concentration, in milligrams

per liter;

Total phosphorus is concentration,

in milligrams per liter; and

Secchi depth is depth, in meters.

The best regression equation for winter-season lake data used different variables than the

summer-season model. In absence of other explanatory variables, the concentration of total nitrogen

was evaluated as a significant explanatory variable. However, the total-nitrogen equation

(eqn. 15) (table 10) had minimal explanatory

power (r2 = 0.18) and failed the normality of residuals test. The concentration of total phosphorus

was not significant. Entering all variables into the

regression analysis improved the predictive power

(r2 = 0.69) and resulted in an equation with water

temperature, specific conductance (negative relation), and pH as significant variables (eqn. 16)

(table 10). Concentration of total nitrogen was no

longer significant to algal growth when the other

variables were entered into the equation. The variables deemed important to algal growth in the

winter are not the same variables deemed important to algal growth over the summer. Algal needs

change with the season and the water temperatures.

At lower concentrations of chlorophyll a

there is more scatter in the plot (fig. 18) meaning

that at lower concentrations of phytoplankton

chlorophyll a water temperature, specific conductivity, and pH are not strong indicators of algal biomass. These variables become more critical to algal

biomass as algal biomass increases.

log (Chlorophyll a) = I + A log (Total nitrogen) (15)

log (Chlorophyll a) = I + B (Water temperature)

+ C (Specific conductance) + D (pH) (16)

where

Chlorophyll a is concentration, in micrograms

per liter;

Total nitrogen is concentration, in milligrams

per liter;

Water temperature is in degrees Celsius;

Specific conductance is in microsiemens per

centimeter; and

pH is in standard units.

Table 10. Regression equation statistics for winter-season phytoplankton chlorophyll a concentrations in nutrient

ecoregion XI lakes, Pennsylvania and West Virginia, 1990-98

[—, not used in equation; <, less than; A, B, C, and D are regression coefficients]

Equation

Intercept Total nitrogen Water

temperature

Specific

conductance pH

n R2

Standard

error of

estimate I p-value A p-value B p-value C p-value D p-value

15 1.044 <0.001 0.470 0.047 — — — — — — 23 0.18 0.430

16 -2.991 .002 — — 0.057 <0.001 -0.001 0.005 0.439 0.001 27 .69 .289

Figure 18. Relation between observed and

predicted phytoplankton chlorophyll a

concentrations in lakes during winter months

within nutrient ecoregion XI, Pennsylvania and

West Virginia, 1990-98. (Predicted concentrations were computed using equation 16.)
28

Nutrient ecoregion XI lakes in West Virginia

were sampled by WVDEP for periphyton rather

than phytoplankton. The data set of nutrient ecoregion XI periphyton chlorophyll a concentrations

consisted of 63 summer-season samples from

22 lakes and 41 winter-season samples from

18 lakes. The relation between logs of summer-season periphyton chlorophyll a concentrations and

logs of total nitrogen concentrations (fig. 19) is

mostly scattered. However, the relation between

periphyton chlorophyll a concentrations and logs

of total phosphorus concentrations has a positive

correlation up to chlorophyll a concentrations of

about 0.1 mg/L (-1.0 log units). Concentrations of

total phosphorus above 0.1 mg/L show no further

increase of chlorophyll a concentrations. The relation between winter-season periphyton chlorophyll a concentrations and logs of total nitrogen

concentrations is similar to the summer-season

relation. The relation between periphyton chlorophyll a concentrations and total phosphorus is

positive (fig. 20). If the 0.1 mg/L breakpoint

present in the summer samples exists in winter, the

limited range of measured concentrations of total

phosphorus does not show this relation.

Figure 19. Relation between log of periphyton chlorophyll a concentrations and logs of concentrations of total nitrogen

(A) and total phosphorus (B) in lakes during summer months within nutrient ecoregion XI, West Virginia, 1990-98.

A B

Figure 20. Relation between log of periphyton chlorophyll a concentrations and logs of concentrations of total nitrogen

(A) and total phosphorus (B) in lakes during winter months within nutrient ecoregion XI, West Virginia, 1990-98.

A B
29

Large or filamentous periphyton taxa can be

correlated positively with total water-column

phosphorus (Cattaneo, 1987). Typically, the

response between periphyton in lakes and nutrients in the water column is weak, but periphyton

does play a significant role in the release of sediment-associated phosphorus (Riber and Wetzel,

1987; Carlton and Wetzel, 1988). This is one possible explanation why the relation between periphyton and concentrations of total phosphorus is

stronger than the relation between phytoplankton

and total phosphorus.

Logs of total phosphorus concentrations,

alone and in combination with other explanatory

variables, and logs of total nitrogen concentrations

alone were significant predictors of logs of summer-season periphyton chlorophyll a concentrations in nutrient ecoregion XI lakes (table 9). In the

absence of other explanatory variables, the log of

total phosphorus concentrations was the strongest

predictor and explained almost 40 percent of the

variation in logs of chlorophyll a concentrations

(r2 = 0.40) (eqn. 17) (table 11). The log of total nitrogen concentrations alone was a significant but

weaker predictor of logs of periphyton chlorophyll

a concentrations (r2 = 0.15) (eqn. 18) (table 11). The

total-nitrogen equation also failed the normality of

residuals test. When water temperature and Secchi

depth (negative relation) were entered into the

total-phosphorus equation, the predictive power

increased (r2 = 0.66) (eqn. 19) (table 11). The equation using total phosphorus, water temperature,

and Secchi depth also underpredicts concentrations of periphyton chlorophyll a at greater values

(fig. 21) but less so than the model using only total

phosphorus.

log (Chlorophyll a) =

I + A log (Total phosphorus) (17)

log (Chlorophyll a) = I + B log (Total nitrogen) (18)

log (Chlorophyll a) =

I + A log (Total phosphorus)

+ C (Water temperature) + D (Secchi depth) (19)

Table 11. Regression equation statistics for summer-season periphyton chlorophyll a concentrations in nutrient

ecoregion XI lakes, West Virginia, 1990-98

[—, not used in equation; <, less than; A, B, C, and D are regression coefficients]

Equation

Intercept Total phosphorus Total nitrogen Water

temperature Secchi depth

n r2

Standard

error of

estimate I p-value A p-value B p-value C p-value D p-value

17 2.484 <0.001 1.067 <0.001 — — — — — — 61 0.40 0.560

18 1.128 <.001 — — 1.276 0.002 — — — — 61 .15 .668

19 1.598 <.001 .556 <.001 — — 0.031 0.031 -0.230 <0.001 58 .66 .386

Figure 21. Relation between observed and

predicted logs of periphyton chlorophyll a

concentrations in lakes during summer

months within nutrient ecoregion XI, West

Virginia, 1990-98. (Predicted concentrations

were computed using equation 19.)
30

where

Chlorophyll a is concentration, in milligrams

per square meter;

Total phosphorus is concentration, in milligrams per liter;

Total nitrogen is concentration, in milligrams

per liter;

Water temperature is in degrees Celsius; and

Secchi depth is depth, in meters.

Logs of winter-season periphyton chlorophyll a concentrations were predicted best by the

log of total phosphorus concentrations and Secchi

depth (negative relation). Regression analysis of

nutrients and periphyton chlorophyll a resulted in

an equation containing only concentrations of total

phosphorus (r2 = 0.46) (eqn. 20) (table 12). Alone or

in combination with total phosphorus, total nitrogen was not a significant explanatory variable.

Secchi depth was the remaining significant explanatory variable. Logs of total phosphorus concentrations and Secchi depth explained more than half

the variation in logs of periphyton chlorophyll a

concentrations (r2 = 0.65) (eqn. 21) (table 12). The

relation between observed and predicted logs of

periphyton chlorophyll a concentrations is shown

in figure 22.

log (Chlorophyll a) =

I + A log (Total phosphorus) (20)

log (Chlorophyll a) =

I + A log (Total phosphorus) + B (Secchi depth)(21)

where

Chlorophyll a is concentration, in milligrams

per square meter;

Total phosphorus is concentration, in milligrams per liter; and

Secchi depth is depth, in inches.

Streams.—Regression analysis of stream

data for nutrient ecoregion XI resulted in a number

of significant periphyton regression equations. The

stream data set of nutrient ecoregion XI periphyton

consisted of 136 summer-season samples. Basin

areas above the sampling sites ranged from 0.04 to

6,860 mi2. Median basin slope was 668 ft/mi. The

predominant land use was forest; the median cover

was 83 percent, although agricultural and urban

Table 12. Regression equation statistics for winter-season periphyton chlorophyll a

concentrations in nutrient ecoregion XI lakes, West Virginia, 1990-98

[—, not used in equation; <, less than; A and B are regression coefficients]

Equation

Intercept Total phosphorus Secchi depth

n r2

Standard

error of

I p-value A p-value B p-value estimate

20 2.692 <0.001 1.193 <0.001 — — 37 0.46 0.386

21 2.082 <.001 .541 .028 -0.242 0.001 62 .65 .292

Figure 22. Relation between observed and

predicted logs of periphyton chlorophyll a

concentrations in lakes during winter months

within nutrient ecoregion XI, West Virginia,

1990-98. (Predicted concentrations were

computed using equation 21.)
31

land use were as great as 95 and 89 percent,

respectively, in individual basins. The relations

between logs of periphyton chlorophyll a concentrations and logs of concentrations of total nitrogen

and total phosphorus (fig. 23) are scattered but

suggest a positive correlation in both cases.

Logs of concentrations of total phosphorus

alone and total nitrogen alone were significant predictors of summer-season concentrations of

periphyton chlorophyll a in nutrient ecoregion XI

streams. The log of total nitrogen concentrations

explained about 30 percent (r2 = 0.32) (eqn. 22)

(table 13) and the log of total phosphorus concentrations explained slightly less than 20 percent

(r2 = 0.18) (eqn. 23) (table 13) of the variation in

chlorophyll a. However, both regression equations

failed the residuals normality test. Including the

drainage-basin characteristics—drainage area and

percent forest cover—with total phosphorus in the

regression analysis resulted in the strongest equation (r2 = 0.54) (eqn. 24) (table 13). Phosphorus

input into streams is correlated positively to average slope of the drainage basin (Kirchner, 1975).

Percent forest cover had a negative relation with

chlorophyll a concentrations. As percent forest

cover increases, less light reaches the stream.

Because algae are primary producers and need the

sunlight to grow, less light to the stream causes

algae to grow slower. In this situation, light is more

critical to algal growth than are nutrients. A stream

can contain excess nutrients, but without light,

algae still cannot grow. No benthic-invertebrate or

habitat indices were significant explanatory variables.

Figure 23. Relation between log of periphyton chlorophyll a concentrations and the logs of concentrations of total

nitrogen (A) and total phosphorus (B) in streams during summer months within nutrient ecoregion XI, Pennsylvania

and West Virginia, 1990-98.

A B

Table 13. Regression equation statistics for summer-season periphyton chlorophyll a concentrations in nutrient

ecoregion XI streams, Pennsylvania and West Virginia, 1990-98

[—, not used in equation; <, less than; A, B, C, and D are regression coefficients]

Equation

Intercept Total nitrogen Total

phosphorus Drainage area Percent forestcover n r2

Standard

error of

estimate I p-value A p-value B p-value C p-value D p-value

22 -0.365 <0.001 1.239 <0.001 — — — — — — 135 0.32 0.796

23 1.287 <.001 — — 1.028 0.001 — — — — 135 .18 .874

24 .941 <.001 — — .531 .003 0.528 <0.001 -0.012 <0.001 134 .54 .661
32

Limestone streams within nutrient ecoregion

XI may be phosphorus limited. Excess phosphorus

can be removed from wastewaters by applying

lime (Manahan, 1984) and the same process may be

happening in limestone streams. A future step in

the process may be to analyze the limestone

streams separately. This may yield stronger relations between concentrations of total phosphorus

and chlorophyll a concentrations for limestone

streams.

Equation 24 tends to overpredict lower concentrations of periphyton chlorophyll a and underpredict higher concentrations (fig. 24). The prediction biases do not appear to arise from non-linearity in the data. Outliers or high-influence points,

which commonly cause fitting problems, were not

identified.

log (Chlorophyll a) = I + A log (Total nitrogen) (22)

log (Chlorophyll a) =

I + B log (Total phosphorus) (23)

log (Chlorophyll a) = I + B log (Total phosphorus)

+ C log (Drainage area)

+ D (Percent forest cover) (24)

where

Chlorophyll a is concentration, in milligrams

per square meter;

Total nitrogen is concentration, in milligrams

per liter;

Total phosphorus is concentration, in

milligrams per liter;

Drainage area is area, in square miles; and

Percent forest cover is in percent.

Only one winter-season periphyton chlorophyll a concentration was available for nutrient

ecoregion XI. No data analysis was done.

Several nutrient ecoregion XI streams in

West Virginia were sampled for phytoplankton,

not periphyton, chlorophyll a. The phytoplankton

data set consisted of 34 summer-season and

17 winter-season samples. Basin areas above the

sampling sites ranged from 2.80 to 910 mi2. Median

basin slope was 989 ft/mi. The predominant land

use was forest; the median cover was 92 percent.

The relations between logs of summer-season phytoplankton chlorophyll a concentrations and logs

of concentrations of total nitrogen and total phos- phorus (fig. 25) show a weak negative correlation

for total nitrogen and a weak positive correlation

for total phosphorus. The total phosphorus correlation is weak partly because a majority of the concentrations are at the 0.01 mg/L (-2.0 log units)

reporting limit for total phosphorus that restricts

the range over which a linear relation can be

developed. Logs of winter-season phytoplankton

chlorophyll a concentrations show no apparent

relation with logs of total nitrogen concentrations

but show a positive correlation with logs of total

phosphorus concentrations (fig. 26).

Figure 24. Relation between observed and

predicted logs of periphyton chlorophyll a

concentrations in streams during summer months

within nutrient ecoregion XI, Pennsylvania and

West Virginia, 1990-98. (Predicted concentrations

were computed using equation 24.)
33

Figure 25. Relation between log of phytoplankton chlorophyll a concentrations and logs of concentrations of total

nitrogen (A) and total phosphorus (B) in streams during summer months within nutrient ecoregion XI, West Virginia,

1990-98.

A B

Figure 26. Relation between log of phytoplankton chlorophyll a concentrations and logs of concentrations of total

nitrogen (A) and total phosphorus (B) in streams during winter months within nutrient ecoregion XI, West Virginia,

1990-98.

A B
34

Regression analysis of nutrient ecoregion XI

summer-season phytoplankton chlorophyll a data

resulted in significant regression equations with

total phosphorus alone and in combination with

other explanatory variables. The equation using

only the log of total phosphorus concentration

(eqn. 25) (table 14) explained approximately half

(r2 = 0.48) the variation in log of chlorophyll a concentrations. Total nitrogen by itself and in combination with total phosphorus was not a significant

explanatory variable. However, total nitrogen was

significant in the strongest equation (r2 = 0.95) that

included total phosphorus and four other explanatory variables (eqn. 26) (table 14). All relations

were positive except for pH. This is similar to the

results from nutrient ecoregion IX. The relation

between predicted and observed concentrations

for eqn. 26 are shown in figure 27.

log (Chlorophyll a) = I + B log (Total phosphorus)(25)

log (Chlorophyll a) = I + A log (Total nitrogen)

+ B log (Total phosphorus) + C (Basin slope)

+ D (pH) + E (Total residue)

+ F (Total suspended solids) (26)

where

Chlorophyll a is concentration, in milligrams

per square meter;

Total nitrogen is concentration, in milligrams

per liter;

Total phosphorus is concentration,

in milligrams per liter; and

Basin slope is slope, in feet per mile;

pH is in standard units;

Total residue is concentration, in milligrams

per liter; and

Total suspended solids is concentration,

in milligrams per liter.

Table 14. Regression equation statistics for summer-season phytoplankton chlorophyll a

concentrations in nutrient ecoregion XI streams, West Virginia, 1990-98

[—, not used in equation; <, less than; A, B, C, D, E, and F are regression coefficients]

 Equation

Intercept Total nitrogen Total phosphorus Basin slope pH

I p-value A p-value B p-value C p-value D p-value

25 2.146 <0.001 — — 1.007 <0.001 ————

26 7.531 <.001 0.542 0.005 .428 .008 0.0005 0.003 -1.016 <0.001

Equation

Total residue Total suspended

solids n r2 Residual standard

error

E p-value F p-value

25 — — — — 33 0.48 0.256

26 0.001 0.038 0.020 0.005 18 .95 .101

Figure 27. Relation between observed and

predicted logs of phytoplankton chlorophyll a

concentrations in streams during summer

months within nutrient ecoregion XI, West

Virginia, 1990-98. (Predicted concentrations

were computed using equation 26.)
35

Regression analysis of the data on winterseason phytoplankton chlorophyll a concentrations

for streams in nutrient ecoregion XI resulted in

several regression equations judged significant but

of poor form. Analysis of total nitrogen alone

(r2 = 0.41) (eqn. 27) (table 15) and total phosphorus

alone (r2 = 0.55) (eqn. 28) (table 15) resulted in significant equations but the residuals from the totalnitrogen equation exhibited curvature and nonconstant variance when plotted in relation to the

predicted data. The relation between concentrations of total nitrogen and phytoplankton chlorophyll a was negative. The total-phosphorus

regression equation failed the normality of residuals test. Concentrations of total nitrogen and total

phosphorus were not significant in combination.

Entering basin-characteristic and water-quality

constituents into the regression analysis resulted in

water temperature alone explaining the greatest

(r2 = 0.74) (eqn. 29) (table 15) proportion of variance in the logs of phytoplankton chlorophyll a

concentrations. Residuals from equation 29 also

exhibited curvature when plotted in relation to the

predicted data. The relation between predicted and

observed concentrations are shown in figure 28.

log (Chlorophyll a) = I + A log (Total nitrogen) (27)

log (Chlorophyll a) =

I + B log (Total phosphorus) (28)

log (Chlorophyll a) = I + C (Water temperature)(29)

where

Chlorophyll a is concentration, in micrograms

per liter;

Total nitrogen is concentration, in miligrams

per liter;

Total phosphorus is concentration,

in milligrams per liter; and

Water temperature is in degrees Celsius.

Table 15. Regression equation statistics for winter-season phytoplankton chlorophyll a concentrations in

nutrient ecoregion XI streams, West Virginia, 1990-98

[—, not used in equation; <, less than; A, B, and C are regression coefficients]

Equation

Intercept Total nitrogen Total phosphorus Water

temperature n r2

Standard

error of

estimate I p-value A p-value B p-value C p-value

27 -0.111 0.348 -0.947 <0.010 — — — — 15 0.41 0.202

28 1.494 <.001 — — 0.739 0.001 — — 16 .55 .173

29 -.245 .013 — — — — 0.060 <0.001 17 .74 .178

Figure 28. Relation between observed and

predicted logs of phytoplankton chlorophyll a

concentrations in streams during winter

months within nutrient ecoregion XI, West

Virginia, 1990-98. (Predicted concentrations

were computed using equation 29.)
36

ECOLOGICAL INDICATORS

OF NUTRIENT ENRICHMENT

Nutrient enrichment of lakes and streams is

difficult to describe using algal biomass. Interpreting algal responses to nutrient limitation on the

basis of chlorophyll a commonly is misleading,

because algae are sensitive to a number of different

stresses, including light, predation (Keithan and

others, 1988; Bourassa and Cattaneo, 1998), iron,

water color, and the percentage of the watershed in

urban and suburban land use (Hill and others,

2000). Yet, some generalizations about algae can be

made. Filamentous algae in lakes and streams are

correlated positively with total water-column

phosphorus (Cattaneo, 1987); filamentous chlorophytes typically become more abundant when

nitrogen and phosphorus levels are relatively high

and light is sufficient. Some diatoms and cyanobacteria dominate when there is a sufficient

amount of phosphorus and not enough nitrogen in

the system (Elwood and others, 1981; Fairchild and

others, 1985; Stockner and Shortreed, 1978; Hill

and Knight, 1988). Phytoplankton communities

typically will become dominated with cyanobacteria—nitrogen-fixing bacteria—when phosphorus

enrichment occurs (Stevenson and others, 1996).

Nutrient levels low enough to prevent

impaired stream water quality may not be possible

to attain. But even in areas where agriculture is the

dominant land use, riparian buffer strips may help

reduce nutrient input to the stream and provide

shading to reduce the algal growth (Biggs, 2000).

On the basis of results of this study, shading had a

greater effect on algal growth than did nutrient

availability.

In nutrient ecoregion VII lakes, nitrogen is a

limiting factor for algal growth. When other

explanatory variables were added to the equation,

total nitrogen was no longer dominant; rather, Secchi depth and concentrations of dissolved oxygen

had a much stronger relation with chlorophyll a

concentrations in the lake. Streams in this nutrient

ecoregion showed no significant relation between

concentrations of chlorophyll a and nutrients. The

most influential variables on periphyton chlorophyll a concentrations are drainage area, percent

forest cover, EPT index, percent collector/ gatherer, percent filterer/collector, percent Plecoptera

individuals, and percent Trichoptera individuals

(tables 16 and 17).

Neither lake nor stream chlorophyll a data in

nutrient ecoregion VIII showed a discernible relation with any environmental indices available for

this analysis. Sample sizes for both lakes and

streams were less than optimal (tables 16 and 17).

Like nutrient ecoregion VII, the lake chlorophyll a in nutrient ecoregion IX showed a weak

relation with total nitrogen concentration (tables 16

and 17). When other variables were added to the

equation, total nitrogen was still an important variable as well as Secchi depth and pH. Because this

equation is similar to nutrient ecoregion VII, combining these two nutrient ecoregions may define a

better relation for lakes. The stream data within

this nutrient ecoregion generated no significant

relations with nutrients although there was a relation between periphyton chlorophyll a, percent forest cover, and pH. The data set contained only

12 samples. More data are necessary to determine

the validity of this equation in this nutrient ecoregion. Combining data from nutrient ecoregion VII

and IX could more accurately represent what is

happening in this area.

Nutrient ecoregion XI had seven separate

analyses performed with the data. Periphyton and

phytoplankton data were available for the lakes

and streams and sufficient winter and summer

samples were available to run seasonal analyses,

except for winter-stream periphyton. A large area

of Pennsylvania and all of West Virginia is in this

nutrient ecoregion. WVDEP sampled different

algal communities than the other agencies within

Pennsylvania and West Virginia; therefore, the data

could not be combined into the larger nutrient

ecoregion XI data set. The analysis of the WVDEP

data was a separate exercise.

Summer-season lake phytoplankton chlorophyll a concentrations show a relation with concentrations of both total nitrogen and total

phosphorus; total nitrogen has the greater predictive value (table 16). When combined with the

other explanatory variables, the strongest equation

included total nitrogen concentration and Secchi

depth; concentration of total phosphorus was not

significant. Winter-season phytoplankton chlorophyll a concentrations also showed a relation with

total nitrogen concentrations, but when other variables were added to the equation, total nitrogen

was no longer significant. During winter, water

temperature, specific conductivity, and pH have a

stronger relation with algal growth than concentration of total nitrogen.
37

Table 16. Summary of influential lake explanatory variables related to lake chlorophyll a through regression analysis for each nutrient e[mg/L, milligrams per liter; m, meters; °C, degrees Celsius; μS, microsiemens; --, no relation]

Explanatory variable to

chlorophyll a

Nutrient ecoregion

VII

Summer lake

phytoplankton

chlorophyll a

Nutrient ecoregion

VIII

Summer lake

phytoplankton

chlorophyll a

Nutrient ecoregion

IX

Summer lake

phytoplankton

chlorophyll a

Nutrient ecoregion XI

Summer lake

phytoplankton

chlorophyll a

Winter lake

phytoplankton

chlorophyll a

Summer la

periphyton

chlorophyll 

Equation

value p-value Equation value p-value Equation value p-value Equation value p-value Equation value p-value Equation value p-v

Total nitrogen (mg/L)

(nutrients only)

1.191 0.007 -- -- 0.575 0.044 0.491 <0.001 0.471 0.047 1.276 0.0

Total phosphorus

(mg/L)(nutrients only)

-- -- -- -- -- -- .501 .002 -- -- 1.067 <.0

Total nitrogen (mg/L)

(with other variables)

-- -- -- -- .480 .025 .384 .002 -- -- -- 

Total phosphorus

(mg/L) (with othervariables)

-- -- -- -- -- -- -- -- -- -- .556 <.0

Secchi depth (m) -0.345 <.001 -- -- -.332 .001 -.156 <.001 -- -- -.230 <.0Dissolved oxygen

(mg/L)

.078 .003 -- -- -- -- -- -- -- -- -- 

pH -- -- -- -- -.179 .010 -- -- .439 .001 -- Water temperature ( ̊C) -- -- -- -- -- -- -- -- .057 <.001 .031 .0Specific conductivity (μS) -- -- -- -- -- -- -- -- -.001 .005 -- 
38

Table 17. Summary of influential stream explanatory variables related to stream chlorophyll a through regression analysis for each nutrie[mg/L, milligrams per liter; sq. mi, square mile; EPT, Ephemeroptera, Plecoptera, Trichoptera; ft/mi, feet per mile; °C, degrees Celsius; --, 

Explanatory variable to

chlorophyll a

Nutrient ecoregion

VII

Summer stream

periphyton

chlorophyll a

Nutrient ecoregion

VIII

Summer stream

periphyton

chlorophyll a

Nutrient ecoregion

IX

Summer stream

periphyton

chlorophyll a

Nutrient ecoregion XI

Summer stream

periphyton

chlorophyll a

Winter stream

periphyton

chlorophyll a

Summer st

phytoplank

chlorophy

Equation

value p-value Equation value p-value Equation value p-value Equation value p-value Equation value p-value Equation value p

Total nitrogen (mg/L)

(nutrients only)

-- -- -- -- -- -- 1.239 <0.001 -- -- -- 

Total phosphorus

(mg/L)(nutrients only)

-- -- -- -- -- -- 1.028 .001 -- -- 1.0070 <0

Total nitrogen (mg/L)

(with other variables)

-- -- -- -- -- -- -- -- -- -- .5420 

Total phosphorus

(mg/L)(with other variables)

-- -- -- -- -- -- .531 .003 -- -- .4280 

Drainage area (sq. mi) 0.040 0.0030 -- -- -- -- .528 <.001 -- -- -- Percent forest cover -.028 .0001 -- -- -0.016 0.012 -.012 <.001 -- -- -- EPT index -.045 .0298 -- -- -- -- -- -- -- -- -- Percent collector/

gatherer

-.025 .0010 -- -- -- -- -- -- -- -- -- 

Percent filterer/

collector

-.016 .0054 -- -- -- -- -- -- -- -- -- 

Percent Plecoptera

individuals

.040 .0025 -- -- -- -- -- -- -- -- -- 

Percent Trichoptera

individuals

.101 .0002 -- -- -- -- -- -- -- -- -- 

pH -- -- -- -- -.570 .014 -- -- -- -- -1.0160 <Basin slope (ft/mi) -- -- -- -- -- -- -- -- -- -- .0005 Total residue (mg/L) -- -- -- -- -- -- -- -- -- -- .0010 Total suspended solids

(mg/L)

-- -- -- -- -- -- -- -- -- -- .0200 

Water temperature (°C) -- -- -- -- -- -- -- -- -- -- -- 
39

Summer-season lake periphyton chlorophyll a concentrations are controlled more by concentrations of total phosphorus than total nitrogen

but both are influential to algal growth (table 16).

The most influential regression equation for predicting log of chlorophyll a concentrations

included concentration of total phosphorus, water

temperature, and Secchi depth. Winter-season

chlorophyll a concentrations were not related to

total nitrogen but were related to total phosphorus.

Concentration of total phosphorus was a significant explanatory variable both alone and in combination with Secchi depth.

Periphyton in streams from summer-season

samples show concentrations of total nitrogen and

total phosphorus are influential to chlorophyll a

concentrations when analyzed alone (table 17).

When other variables are introduced, total phosphorus concentrations, drainage area, and percent

forest cover best explained the chlorophyll a concentrations. Total nitrogen concentrations are no

longer influential to chlorophyll a concentrations.

Only one winter periphyton chlorophyll a

sample was available, and so no analysis could be

completed.

Summer-season stream phytoplankton chlorophyll a samples are influenced by total phosphorus, but not total nitrogen (table 17). When

combined with other variables, both concentrations of total nitrogen and total phosphorus, along

with basin slope, pH, total residue, and total suspended solids are part of the final equation for predicting log of chlorophyll a concentrations of

stream phytoplankton. Winter-season samples

show concentrations of total nitrogen and total

phosphorus being indicative of chlorophyll a concentrations, but the best predictor is water temperature. When water temperature is added to the

equation, concentrations of total nitrogen and total

phosphorus became insignificant.

As determined by this work, periphyton and

phytoplankton are influenced by different environmental factors. Nutrient ecoregion plays a role in

determining the most significant variables for chlorophyll a production. Sometimes, environmental

indicators are more influential than the amount of

nutrients in the water. Many variables play a role

in algal reproduction and biomass other than just

nutrients and all must be added into the total equation.

Percent forest cover was significant for

stream periphyton chlorophyll a concentrations in

all nutrient ecoregions. The higher the percent forest cover, the lower chlorophyll a concentrations no

matter what the nutrient concentrations were. In

these streams, light needed for algal growth was

more critical to the process than nutrient availability.

Secchi depth was significant in lakes in all

nutrient ecoregions. As Secchi depth increased,

phytoplankton chlorophyll a concentrations

decreased. This is more a response to the algal concentrations in the lake than a causative response.

As algal biomass increases, the light attenuation

decreases. For phytoplankton samples, total nitrogen is more significant to algal growth than total

phosphorus but is not the strongest explanatory

variable in the equation. Periphyton lake concentrations are related more to concentration of total

phosphorus and Secchi depth than total nitrogen.

CONSIDERATIONS FOR FUTURE

RESEARCH

In general, lakes tend to separate into two

groups based on water quality. The groups consist

of nutrient ecoregions VII and IX and nutrient

ecoregions VIII and XI. Given the fairly strong

grouping of the water-quality characteristics

among nutrient ecoregions, the data indicate that

no additional information is gained by specifying

four rather than two nutrient ecoregions for lakes

in Pennsylvania and West Virginia. The data set

analyzed was limited, and perhaps more data

would show that a different approach would be

better and that smaller groups of lakes should be

studied rather than putting two nutrient ecoregions together.

Nutrient ecoregion IX, although similar to

nutrient ecoregion VII, has greater concentrations

of chlorophyll a, total nitrogen, and total phosphorus and specific conductance than the other three

nutrient ecoregions. The other water-quality constituents are not different, but these four may put

nutrient ecoregion IX in its own group. Better

trends can be seen between chlorophyll a and

nutrients by combining nutrient ecoregions VII,

VIII, and XI for analysis and analyzing nutrient

ecoregion IX by itself. A clustering procedure may

shed valuable insight on the question of grouping

the nutrient ecoregions into larger areas for criteria

development.
40

If more samples were collected within nutrient ecoregions VII, VIII, and IX, different results

than presented here are possible or they may reinforce what has been seen in the analyses made

here. As always, a larger sample size would give a

more accurate representation of nutrient-biological

relations in the lakes and streams of Pennsylvania

and West Virginia.

Nutrient ecoregion XI has a variety of different lithologies. Limestone-type streams, in particular, may be phosphorus limiting. Nutrient criteria

work may be enhanced if the limestone streams

were analyzed separately from the remaining

streams in nutrient ecoregion XI. The USGS

National Water-Quality Assessment Program

study showed that wells in carbonate areas and

areas where soils and aquifers consist of sand and

gravel have higher nitrate concentrations than

other areas where soils are less permeable and that

total phosphorus concentrations in ground water

are similar in urban and agricultural settings while

nitrates are higher in agricultural than urban settings (Gilliom and others, 2002). Further research

may show these groupings are better at explaining

the role of nutrients in surface water than nutrient

ecoregions are.

Many variables affect algal growth in

streams. Commonly, the nutrient enrichment of a

stream is not noticed until the streams flow into a

small pond or lake. The quiet waters allow algal

colonies to establish and bloom. The nitrates and

nitrites in the water must be converted to ammonium for utilization by the algae (Sze, 1986); therefore, ammonia concentrations may be a better

constituent to measure than total nitrogen and a

stronger relation may be seen.

A relation also exists between concentrations

of total phosphorus and chlorophyll a. The relation

is linear until elevated concentrations of total phosphorus are reached. At this point, concentrations of

total phosphorus and algae no longer correlate,

indicating a possible threshold for total phosphorus. The same pattern was seen by Van Nieuwenhuyse and Jones (1996). Orthophosphate is the

form of phosphorus that is most readily used by

algae (Sze, 1986), and future work may lead to

sampling for orthophosphate instead of total phosphorus.

Other researchers have found that algal species data may be a better predictor of nutrient levels than chlorophyll a alone. As nutrients become

more available, the algal community shifts and the

biomass may stay the same (Stevenson and others,

1996). Filamentous chlorophytes typically become

more abundant when nitrogen and phosphorus

levels are relatively high and light is sufficient, diatoms and cyanobacteria typically dominate when

there is sufficient phosphorus but not nitrogen,

and phytoplankton communities typically become

dominated with cyanobacteria when phosphorus

enrichment occurs (Stockner and Shortreed, 1978;

Elwood and others, 1981; Fairchild and others,

1985; Cattaneo, 1987; Hill and Knight, 1988). Species data appear more closely related to nutrient

concentrations than algal biomass.

In addition to nutrients, other environmental

indices, such as canopy cover, turbidity, flow,

drainage area, and aquatic invertebrates and fish

that eat algae, can deter algal growth—thereby

preventing a bloom even when nutrients are abundant in stream waters. These factors generally are

not significant in lakes but can be quite influential

in streams. Understanding all the factors that influence algal blooms or the lack of algal blooms will

take more study.

More rigorous statistics and models can be

used to look at these relations. Future work could

include an approach described as the General

Additive Models that estimates stresser-response

relations for scaled metrics (scaled to reference

conditions) as suggested by Danielle Tillman (U.S.

Environmental Protection Agency, written commun., September 2002). Another possible model to

use with the data is the Vollenweider model, as

suggested by David Flemer (U.S. Environmental

Protection Agency, written commun., September

2002).

SUMMARY

A presidential initiative called the Clean

Water Action Plan was released in February 1998.

This provides a blueprint for federal agencies to

work with states, tribes, and other stakeholders to

protect and restore the Nation’s water resources.

The plan includes an initiative to address the nutrient enrichment problem of lakes and streams

across the United States. Developing more effective controls for polluted runoff and promoting the

protection of water quality within a watershed are

the goals. USEPA is working to develop nutrient

criteria using nutrient ecoregions to help prevent

excessive nutrients in lakes and streams. Low levels of nutrients are necessary for healthy lake and

stream communities; however, high nutrient con-
41

centrations can cause algal blooms that, in turn,

can cause oxygen depletion in the system which

leads to the death of aquatic animals.

This study involved gathering available data

within Pennsylvania and West Virginia in support

of the USEPA nutrient-criteria program. Many

agencies were asked to provide data consisting of

chlorophyll a, nutrients, water quality constituents

(Secchi depth, turbidity, pH, specific conductance,

etc.), invertebrate indices, and habitat indices.

These data were compiled into a database and

those samples where both chlorophyll a and nutrient data existed were used in the final analysis.

Other consistently sampled variables were added

into the analysis.

Each of the four nutrient ecoregions (formed

by combining level III ecoregions) of Pennsylvania

and West Virginia were analyzed for relations

between chlorophyll a, and nutrients (total nitrogen and total phosphorus) and other environmental indicators. Within each nutrient ecoregion, both

lakes and streams data were analyzed. Results varied among the four nutrient ecoregions, but nutrients alone were related to chlorophyll a, or in

combination with other explanatory variables. In

some cases, nutrients alone were still most strongly

related to chlorophyll a concentrations, and, in

some cases, other variables explained more of the

chlorophyll a variation. Each nutrient ecoregion

showed different variables influencing chlorophyll a concentrations.

Concentrations of phytoplankton chlorophyll a in nutrient ecoregion VII lakes were negatively related to Secchi depth and positively

related to concentration of dissolved oxygen;

periphyton streams were positively related to

drainage area, percent Plecoptera individuals, and

percent Trichoptera individuals, and negatively to

percent forest cover, the EPT index, percent collector/gatherer, and percent filterer/ collector. Nutrients were not statistically significant as being

influential to algal chlorophyll a within this nutrient ecoregion.

Concentrations of chlorophyll a in nutrient

ecoregion VIII lakes and streams had too few samples, and so no regressions could be determined.

Nutrient ecoregion IX lake phytoplankton

chlorophyll a concentrations were positively influenced by total nitrogen, both alone and when combined with other environmental data. Secchi depth

and pH had a negative relation with algal growth.

This equation is similar to results from nutrient

ecoregion VII, and these nutrient ecoregions possibly could be combined for future analysis. The

stream periphyton data generated no significant

relations with nutrients, but forest cover and pH

were negatively related to chlorophyll a concentrations.

Nutrient ecoregion XI had a varied lake data

set. There were winter and summer, periphyton

and phytoplankton lake data. Summer phytoplankton was positively related to total nitrogen

and negatively related to Secchi depth. Winter

phytoplankton was related to total nitrogen until

other variables were added to the equation. Phytoplankton was then positively related to pH and

water temperature and negatively related to specific conductance. Summer-lake periphyton concentrations were positively related to total

phosphorus and water temperature, and negatively to Secchi depth. Winter periphyton were

positively related to total phosphorus and negatively to Secchi depth.

The streams data for nutrient ecoregion XI

were broken into three groups for analysis—periphyton summer, phytoplankton summer, and

phytoplankton winter. Summer-season periphyton chlorophyll a concentrations were positively

related to total phosphorus and drainage area and

negatively related to percent forest cover. The summer-season phytoplankton chlorophyll a concentrations were positively related to total nitrogen,

total phosphorus, basin slope, total residue, and

total suspended solids and negatively to pH. The

winter-season phytoplankton chlorophyll a concentrations were positively related to water temperature.

Different variables influenced the concentrations of chlorophyll a in streams among the nutrient ecoregions. Sometimes nutrients were

significant variables in algal biomass. Some equations, however, showed other environmental indicators were more significant predictors of

chlorophyll a concentrations than nutrients were.

In some instances, the environmental indicators

were so influential in predicting the chlorophyll a

concentrations that nutrients were deemed insignificant. Analysis of stream samples showed percent forest cover was a significant predictive

variable. Where there is not sufficient light for primary algal growth, nutrient overloads do not matter. In these instances, nutrients can be sufficient

for algal growth but light is the controlling factor.
42

Secchi depth was a key predictor for most

phytoplankton lake samples. This can be both a

causative and response variable. In most cases, it

appears to be a response variable. As algal biomass

increases, the light penetration decreases. Nutrient

concentrations were significant in predicting phytoplankton biomass only in nutrient ecoregions IX

and XI. Secchi depth was negatively related to

chlorophyll a concentrations in all nutrient ecoregions.

More research is needed for a better determination of the response of algal blooms to environmental indicators. Nutrients do contribute to algal

blooms, but in certain nutrient ecoregions, other

variables are more controlling to algal blooms even

when nutrients are available.

REFERENCES CITED

Biggs, B.J.F., 1996, Patterns in benthic algae of

streams, in Stevenson, R.J., Bothwell, M.L.,

and Lowe, R.L. (eds.), 1996, Algal ecology—

Freshwater benthic ecosystems: San Diego,

Calif., Academic Press, p. 31-56.

_____2000, Eutrophication of streams and rivers—

Dissolved nutrient-chlorophyll relationships

for benthic algae: Journal of the North

American Benthological Society, v. 19, no. 1,

p. 17-31.

Biggs, B.J.F., and Close, M.E., 1989, Periphyton

biomass dynamics in gravel bed rivers—The

relative effects of flows and nutrients:

Freshwater Biology, v., 22, p. 209-231.

Borchardt, M.A., Hoffmann, J.P., and Cood, P.W.,

1994, Phosphorus uptake kinetics of

Spirogyra fluviatilis (Charophyceae) in

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45

APPENDIX—LABORATORY METHODS USED BY

DIFFERENT GOVERNMENTAL AGENCIES

Delaware River Basin Commission

Parameter name: Residue, total nonfilterable (00530)5

Unit of measure: milligrams per liter

Method name: Gravimetric, dried at 103-105°C (EPA 160.2)

Method summary: A well mixed sample is filtered through a standard glass fiber filter, and

the residue retained on the filter is dried to constant weight at 103-105°C

Reporting limits: 0.2, 1, 2 milligrams per liter

Years sampled: 1992-1997

Parameter name: Nitrogen, total as N (00600)2

Unit of measure: milligrams per liter

Method name:

Method summary: no method provided

Reporting limits: Not listed

Years sampled: 1997

Parameter name: Nitrogen, total organic as N (00605)2

Unit of measure: milligrams per liter

Method name:

Method summary: no method provided

Reporting limits: 0.16, 0.39, 0.44, 0.78,0.84, 0.85, 0.9, 0.97, 1.7 milligrams per liter

Years sampled: 1992-1996

Parameter name: Nitrogen, total ammonia as N (00610)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated phenate (EPA 350.1)

Method summary: Alkaline phenol and hypochlorite react with ammonia to form

indophenol blue that is proportional to the ammonia concentration. The blue color formed is

intensified with sodium nitroprusside.

Reporting limits: 0.01, 0.02 milligrams per liter

Years sampled: 1992-1997

Parameter name: Nitrite, total as N (00615)5

Unit of measure: milligrams per liter

Method name: Spectrophotometric (EPA 354.1)

Method summary: The diazonium compound formed by diazotation of sulfanilamide by

nitrite in water under acid conditions is coupled with N−(1-naphthyl)−ethylenediamine

dihydrochloride to produce a reddish-purple color which is read in a spectrophotometer.

Reporting limits: Not listed

Years sampled: 1992-1997
46

Delaware River Basin Commission—Continued

Parameter name: Nitrate, total as N (00620)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, cadmium reduction, and Spectrophotometric

(EPA 353.2 minus 354.1)

Method summary: A filtered sample is passed through a column containing granulated

copper-cadmium to reduce nitrate to nitrite. The nitrite is determined by diazotizing with

sulfanilamide and coupling with N-(1-naphthyl)-dthylenediamind dihydrochloride to form a

highly colored azo dye which is measured colorimetrically. (minus) The diazonium

compound formed by diazotation of sulfanilamide by nitrite in water under acid conditions is

coupled with N−(1-napthyl)−ethylenediamine dihydrochloride to produce a reddish-purple

color which is read in a spectrophotometer at 540 nm.

Reporting limits: Not listed

Years sampled: 1992-1997

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric, semi-automated block digester, AAII (EPA 351.2)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4, and

HgSO4 for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination. This digested sample may also be used for

phosphorus determination.

Reporting limits: 1 milligram per liter

Years sampled: 1992-1997

Parameter name: Nitrite plus nitrate, total as N (00630)5

Unit of measure: milligrams per liter

Method name:

Method summary: no method provided

Reporting limits: 0.02 milligrams per liter

Years sampled: 1991-1994

Parameter name: Phosphorus, total as P (00665)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, ascorbic acid (EPA 365.1)

Method summary: Ammonium molybdate and antimony potassium tartrate react in an acid

medium with dilute solutions of phosphorus to form an antimony-phospho-molybdate

complex. This complex is reduced to an intensely blue-colored complex by ascorbic acid. The

color is proportional to the phosphorus concentration. Only orthophosphate forms a blue

color in this test. Polyphosphates (and some organic phosphorus compounds) may be

converted to the orthophosphate form by manual sulfuric acid hydrolysis. Organic

phosphorus compounds may be converted to the orthophosphate form by manual persulfate

digestion. The developed color is measured automatically on the AutoAnalyzer.

Reporting limits: 0.01 milligrams per liter

Years sampled: 1992-1997
47

Delaware River Basin Commission—Continued

Parameter name: Phosphorus, dissolved orthophosphate as P (00671)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, ascorbic acid (EPA 365.1)

Method summary: Ammonium molybdate and antimony potassium tartrate react in an acid

medium with dilute solutions of phosphorus to form an antimony-phospho-molybdate

complex. This complex is reduced to an intensely blue-colored complex by ascorbic acid. The

color is proportional to the phosphorus concentration. This is measured automatically on the

AutoAnalyzer.

Reporting limits: Not listed

Years sampled: 1991 and 1997

Parameter name: Orthophosphorus, total as P (70507)5

Unit of measure: milligrams per liter

Method name:

Method summary: no method provided

Reporting limits: 0.01, 0.02, 0.05, 0.07 milligrams per liter

Years sampled: 1991-1994
48

National Park Service

Parameter name: Nitrogen, total ammonia as N (00610)3

Unit of measure: milligrams per liter

Method name: Automated phenate method (Standard method 4500-NH3-H)

Method summary: Alkaline phenol and hypochlorite reach with ammonia to form

indophenol blue that is proportional to the ammonia concentration. The blue color is

intensified with sodium nitroprusside.

Reporting limits: 0.02 milligrams per liter

Years sampled: 1991-1995

Parameter name: Nitrite, total as N (00615)6

Unit of measure: milligrams per liter

Method name: Determination of inorganic anions by ion chromatography (EPA 300.0)

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into an ion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Reporting limits: 0.004, 0.012 milligrams per liter

Years sampled: 1991-1995

Parameter name: Nitrate, total as N (00620)6

Unit of measure: milligrams per liter

Method name: Determination of inorganic anions by ion chromatography (EPA 300.0)

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into an ion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Reporting limits: Not listed

Years sampled: 1991-1995

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric, semi-automated block digester, AAII (EPA 351.2)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4, and

HgSO4 for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination.

Reporting limits: 0.2 milligrams per liter

Years sampled: 1991-1995

Parameter name: Phosphorus, total as P (00665)3

Unit of measure: milligrams per liter

Method name: Automated ascorbic acid reduction (Standard method 4500-P-F)

Method summary: Ammonium molybdate and potassium antimonyl tartrate react with

orthophosphate in an acid medium to form an antimony-phosphomolybdate complex, which

yields an intense blue color suitable for photometric measurement.

Reporting limits: Not listed

Years sampled: 1991-1995
49

Ohio Environmental Protection Agency

Parameter name: Residue, total nonfilterable (00530)5

Unit of measure: milligrams per liter

Method name: Gravimetric, dried at 103-105°C (EPA 160.2)

Method summary: A well mixed sample is filtered through a standard glass fiber filter, and

the residue retained on the filter is dried to constant weight at 103-105°C

Reporting limits: 2 milligrams per liter

Years sampled: 1994

Parameter name: Nitrogen, total ammonia as N (00610)3

Unit of measure: milligrams per liter

Method name: Colorimetric, automated phenate (EPA350.1)

Method summary: Alkaline phenol and hypochlorite react with ammonia to form indophenol

blue that is proportional to the ammonia concentration. The blue color formed is intensified

with sodium nitroprusside.

Reporting limits: 0.05 milligrams per liter

Years sampled: 1994

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric, semi-automated block digester, AAII (EPA 351.2)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4, and

HgSO4for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination. This digested sample may also be used for

phosphorus determination.

Reporting limits: Not listed

Years sampled: 1994

Parameter name: Nitrite plus nitrate, total as N (00630)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, cadmium reduction (EPA 353.2)

Method summary: A filtered sample is passed through a column containing granulated

copper-cadmium to reduce nitrate to nitrite. The nitrite is determined by diazotizing with

sulfanilamide and coupling with N−(1-naphthyl)−ethylenediamine dihydrochloride to form a

highly colored azo dye which is measured colorimetrically.

Reporting limits: Not listed

Years sampled: 1994

Parameter name: Phosphorus, total as P (00665)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, block digestor AA II (EPA 365.4)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4 and HgSO4

for two and one half hours. The residue is cooled, diluted to 25 mL and placed on the Auto

Analyzer for phosphorus determination.

Reporting limits: Not listed

Years sampled: 1994
50

Ohio River Valley Water

Parameter name: Nitrogen, total ammonia as N (00610)3

Unit of measure: milligrams per liter

Method name: Colorimetric, automated phenate (EPA350.1)

Method summary: Alkaline phenol and hypochlorite react with ammonia to form indophenol

blue that is proportional to the ammonia concentration The blue color formed is intensified

with sodium nitroprusside.

Reporting limits: 0.03 milligrams per liter

Years sampled: 1990-1998

Parameter name: Nitrite plus nitrate, total as N (00630)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, cadmium reduction (EPA 353.2)

Method summary: A filtered sample is passed through a column containing granulated

copper-cadmium to reduce nitrate to nitrite. The nitrite is determined by diazotizing with

sulfanilamide and coupling with N−(1-naphthyl)−ethylenediamine dihydrochloride to form a

highly colored azo dye which is measured colorimetrically.

Reporting limits: 0.02 milligrams per liter

Years sampled: 1990-1998

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric, semi-automated block digester, AAII (EPA 351.2)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4, and

HgSO4 for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination. This digested sample may also be used for

phosphorus determination.

Reporting limits: 0.05 milligrams per liter

Years sampled: 1990-1992

Parameter name: Phosphorus, total as P (00665)5

Unit of measure: milligrams per liter

Method name: Colorimetric, ascorbic acid, single reagent

Method summary: Ammonium molybdate and antimony potassium tartrate react in an acid

medium with dilute solutions of phosphorus to form an antimony-phospho-molybdate

complex. This complex is reduced to an intensely blue-colored complex by ascorbic acid. The

color is proportional to the phosphorus concentration.

Reporting limits: 0.05 milligrams per liter

Years sampled: 1990-1998
51

Pennsylvania Department of Environmental Protection

Parameter name: Residue, total nonfilterable (00530)5

Unit of measure: milligrams per liter

Method name: Gravimetric, dried at 103-105°C (EPA 160.2)

Method summary: A well mixed sample is filtered through a standard glass fiber filter, and

the residue retained on the filter is dried to constant weight at 103-105°C

Reporting limits: 2 milligrams per liter

Years sampled: 1992-1996

Parameter name: Nitrogen, total as N (00600)5

Unit of measure: milligrams per liter

Method name:

Method summary: Calculated value - typically 00625 (total kjeldahl) + 00630

(nitrite+nitrate)

Reporting limits: Not listed

Years sampled: 1995-1996

Parameter name: Nitrogen, total organic as N (00605)5

Unit of measure: milligrams per liter

Method name: Calculated value (EPA351.2 - EPA350.1)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4,

and HgSO4 for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination. This digested sample may also be used for

phosphorus determination. (minus) Alkaline phenol and hypochlorite react with ammonia

to form indophenol blue that is proportional to the ammonia concentration. The blue color

formed is intensified with sodium nitroprusside.

[nitrogen, kjeldahl, total]-[nitrogen, ammonia]

Reporting limits: 0.01 milligrams per liter

Years sampled: 1994

Parameter name: Nitrogen, total ammonia as N (00610)4

Unit of measure: milligrams per liter

Method name: Automated phenate method (Standard method 4500-NH3-H)

Method summary: Alkaline phenol and hypochlorite react with ammonia to form

indophenol blue that is proportional to the ammonia concentration. The blue color is

intensified with sodium nitroprusside.

Reporting limits: 0.02 milligrams per liter

Years sampled: 1991-1996

Parameter name: Nitrite, total as N (00615)5

Unit of measure: milligrams per liter

Method name: Determination of inorganic anions by ion chromatography (EPA 300.0)6

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into an ion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Reporting limits: 0.004, 0.04 milligrams per liter

Years sampled: 1991-1994
52

Pennsylvania Department of Environmental Protection—Continued

Parameter name: Nitrate, total as N (00620)6

Unit of measure: milligrams per liter

Method name: Determination of inorganic anions by ion chromatography (EPA 300.0)

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into an ion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Reporting limits: 0.04 milligrams per liter

Years sampled: 1991-1994

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric, semi-automated block digester, AAII (EPA 351.2)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4, and

HgSO4 for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination.

Reporting limits: 0.2 milligrams per liter

Years sampled: 1991-1994

Parameter name: Phosphorus, total as P (00665)4

Unit of measure: milligrams per liter

Method name: Automated ascorbic acid reduction (Standard method 4500-P-F)

Method summary: Ammonium molybdate and potassium antimonyl tartrate react with

orthophosphate in an acid medium to form an antimony-phosphomolybdate complex,

which yields an intense blue color suitable for photometric measurement.

Reporting limits: Not listed

Years sampled: 1991-1996

Parameter name: Phosphorus, dissolved as P (00666)4

Unit of measure: milligrams per liter

Method name: Automated ascorbic acid reduction (Standard method 4500-P-F)

Method summary: Ammonium molybdate and potassium antimonyl tartrate react with

orthophosphate in an acid medium to form an antimony-phosphomolybdate complex, which

yields an intense blue color suitable for photometric measurement.

Reporting limits: Not listed

Years sampled: 1991-1995

Parameter name: Orthophosphate, dissolved as P (00671)3

Unit of measure: milligrams per liter

Method name: Automated ascorbic acid reduction (Standard method 4500-P-F)

Method summary: Ammonium molybdate and potassium antimonyl tartrate react with

orthophosphate in an acid medium to form an antimony-phosphomolybdate complex, which

yields an intense blue color suitable for photometric measurement.

Reporting limits: 0.002 milligrams per liter

Years sampled: 1995
53

Pennsylvania Department of Environmental Protection—Continued

Parameter name: Phytoplankton, chlorophyll a, spectrophotometric, uncorrected (32230)3

Unit of measure: micrograms per liter

Method name:

Method summary:

Reporting limits: Not listed

Years sampled: 1991-1996

Parameter name: Orthophosphorus, total as P (70507)3

Unit of measure: milligrams per liter

Method name: Automated ascorbic acid reduction (Standard method 4500-P-F)

Method summary: Ammonium molybdate and potassium antimonyl tartrate react with

orthophosphate in an acid medium to form an antimony-phosphomolybdate complex, which

yields an intense blue color suitable for photometric measurement.

Reporting limits: 0.002 milligrams per liter

Years sampled: 1991-1994
54

Susquehanna River Basin Commission

PaDEP laboratory is used for all data analysis. All parameters follow the same methods

procedure as listed for PaDEP.
55

U.S. Army Corps of Engineers - Huntington

Parameter name: Residue, total (00500)3

Unit of measure: milligrams per liter

Method name: Total solids dried at 103-105°C (SM2540B)

Method summary: A well-mixed sample is evaporated in a weighed dish and dried to

constant weight in an oven at 103 - 105°C. The increase in weight over that of the empty

dish represents the total solids.

Reporting limits: Not listed

Years sampled: 1990-1998

Parameter name: Residue, total nonfilterable (00530)3

Unit of measure: milligrams per liter

Method name: Total suspended solids dried at 103-105°C (SM2540D)

Method summary: A well mixed sample is filtered through a weighed standard

glass-fiber filter and the residue retained on the filter is dried to a constant weight at 103 to

105°C. The increase in weight of the filter represents the total suspended solids. If the

suspended material clogs the filter and prolongs filtration, the difference between the total

solids and the total dissolved solids may provide an estimate of the total suspended solids.

Reporting limits: 1, 5 milligrams per liter

Years sampled: 1990-1998

Parameter name: Nitrogen, dissolved ammonia as N (00608)3

Unit of measure: milligrams per liter

Method name: Ammonia-selective electrode method using known addition

(SM4500-NH3F)

Method summary: The ammonia-selective electrode uses a hydrophobic gas-permeable

membrane to separate the sample solution from an electrode internal solution of

ammonium chloride. Dissolved ammonia is converted to NH3(aq) by raising pH to above

11 with a strong base. NH3(aq) diffuses through the membrane and changes the internal

solution pH that is sensed by a pH electrode. The fixed level of chloride in the internal

solution is sensed by a chloride ion-selective electrode that serves as the reference

electrode. Potentiometric measurements are made with a pH meter having an expanded

millivolt scale or with a specific ion meter.

Reporting limits: 0.02 milligrams per liter

Years sampled: 1996-1998

Parameter name: Nitrogen, kjeldahl dissolved as N (00623)5

Unit of measure: milligrams per liter

Method name: Colorimetric; titrimetric; potentiometric (EPA 351.3)

Method summary: The sample is heated in the presence of concentrated sulfuric acid,

K2SO4, and HgSO4, and evaporated until SO3 fumes are obtained and the solution

becomes colorless or pale yellow. The residue is cooled, diluted, and is treated and made

alkaline with a hydroxide-thiosulfate solution. The ammonia is distilled and determined

after distillation by nesslerization, titration or potentiometry.

Reporting limits: Not listed

Years sampled: 1997-1998
56

U.S. Army Corps of Engineers - Huntington—Continued

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric; titrimetric; potentiometric (EPA 351.3)

Method summary: The sample is heated in the presence of concentrated sulfuric acid,

K2SO4, and HgSO4, and evaporated until SO3 fumes are obtained and the solution

becomes colorless or pale yellow. The residue is cooled, diluted, and is treated and made

alkaline with a hydroxide-thiosulfate solution. The ammonia is distilled and determined

after distillation by nesslerization, titration or potentiometry.

Reporting limits: 0.02, 0.1 milligrams per liter

Years sampled: 1992-1998

Parameter name: Nitrite plus nitrate, total as N (00630)6

Unit of measure: milligrams per liter

Method name: Determination of inorganic anions by ion chromatography (EPA 300.0)

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into anion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Years sampled: 1991-1998

Parameter name: Nitrite plus nitrate, dissolved as N (00631)6

Unit of measure: milligrams per liter

Method name: Determination of inorganic anions by ion chromatography (EPA 300.0)

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into anion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Reporting limits: Not listed

Years sampled: 1998

Parameter name: Phosphorus, total as P (00665)3

Unit of measure: milligrams per liter

Method name: Digestion method and Ascorbic acid method (SM4500-P B and

SM4500-P E)

Method summary: Convert the phosphorus form of interest to dissolved orthophosphate

and use colorimetry to determine concentration. The separation of phosphorus into its

various forms is defined analytically but the analytical differentiations have been selected

so that they may be used for interpretive purposes. And, ammonium molybdate and

potassium antimonyl tartrate react in acid medium with orthophosphate to form a

heteropoly acid (phosphomolybdic acid) that is reduced to intensely colored

molybdenum blue by ascorbic acid.

Reporting limits: 0.01, 0.02, 0.05 milligrams per liter

Years sampled: 1992-1998
57

U.S. Army Corps of Engineers - Huntington—Continued

Parameter name: Phosphorus, orthophosphate, dissolved as P (00671)3

Unit of measure: milligrams per liter

Method name: Ascorbic acid method (SM4500-P E)

Method summary: Ammonium molybdate and potassium antimonyl tartrate react in acid

medium with orthophosphate to form a heteropoly acid (phosphomolybdic acid) that is

reduced to intensely colored molybdenum blue by ascorbic acid.

Reporting limits: 0.01, 0.02, 0.05 milligrams per liter

Years sampled: 1992-1998

Parameter name: Chlorophyll a, phytoplankton, spectrophotometric acid (32211)2

Unit of measure: microgram per liter

Method name: Spectrophotometric determination of chlorophyll (SM10200H-2)

Method summary: With a narrow band width between 0.5 and 2.0 nm the spectral band

shows the amount of chlorophyll a present in the sample

Reporting limits: 1 microgram per liter

Years sampled: 1991-1998
58

U.S. Army Corps of Engineers - Philadelphia

Parameter name: Nitrogen, total ammonia as N (00610)3

Unit of measure: milligrams per liter

Method name: Colorimetric, automated phenate (EPA350.1)

Method summary: Alkaline phenol and hypochlorite react with ammonia to form indophenol

blue that is proportional to the ammonia concentration. The blue color formed is intensified

with sodium nitroprusside.

Reporting limits: 0.01 milligrams per liter

Years sampled:

Parameter name: Nitrite, total as N (00615)5

Unit of measure: milligrams per liter

Method name: Spectrophotometric (EPA 354.1)

Method summary: The diazonium compound formed by diazotation of sulfanilamide by

nitrite in water under acid conditions is coupled with N−(1-naphthyl)−ethylenediamine

dihydrochloride to produce a reddish-purple color which is read in a spectrophotometer.

Reporting limits: 0.01 milligrams per liter

Years sampled:

Parameter name: Nitrate, total as N (00620)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, cadmium reduction (EPA 353.2)

Method summary: A filtered sample is passed through a column containing granulated

copper-cadmium to reduce nitrate to nitrite. The nitrite is determined by diazotizing with

sulfanilamide and coupling with N-(1-naphthyl)-dthylenediamind dihydrochloride to form a

highly colored azo dye which is measured colorimetrically.

Reporting limits: 0.05 milligrams per liter

Years sampled:

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric, semi-automated block digester, AAII (EPA 351.2)

Method summary: The sample is heated in the presence of sulfuric acid, K2SO4, and

HgSO4 for two and one half hours. The residue is cooled, diluted, and placed on the

AutoAnalyzer for ammonia determination. This digested sample may also be used for

phosphorus determination.

Reporting limits: 0.1 milligrams per liter

Years sampled:

Parameter name: Nitrite plus nitrate, total as N (00630)6

Unit of measure: milligrams per liter

Method name: Addition of EPA 354.1 and EPA 353.2 methods

Method summary: The diazonium compound formed by diazotation of sulfanilamide by

nitrite in water under acid conditions is coupled with N−(1-naphthyl)−ethylenediamine

dihydrochloride to produce a reddish-purple color which is read in a spectrophotometer.

(plus) A filtered sample is passed through a column containing granulated

copper-cadmium to reduce nitrate to nitrite. The nitrite is determined by diazotizing with

sulfanilamide and coupling with N-(1-naphthyl)-dthylenediamind dihydrochloride to form a

highly colored azo dye which is measured colorimetrically.

Reporting limits: 0.05 milligrams per liter

Years sampled:
59

U.S. Army Corps of Engineers - Philadelphia—Continued

Parameter name: Phosphorus, total as P (00665)3

Unit of measure: milligrams per liter

Method name: Phosphorus, colorimetric, phosphomolybdate, automated-segmented

flow

Method summary: All forms of phosphorus, including organic phosphorus, are

converted to orthophosphate by an acid-persulfate digestion.

Reporting limits: 0.0177 milligrams per liter

Years sampled:

Parameter name: Phosphorus, dissolved orthophosphate as PO4 (00666)3

Unit of measure: milligrams per liter

Method name: Colorimetric, ascorbic acid, single reagent

Method summary: Ammonium molybdate and antimony potassium tartrate react in an acid

medium with dilute solutions of phosphorus to form and antimony-phospho-molybdate

complex. Ascorbic acid is added to reduce the complex and an intense blue color is formed.

The color is proportional to the orthophosphate concentration.

Reporting limits: 0.050 milligrams per liter

Years sampled:

Parameter name: Phytoplankton, chlorophyll a 6

Unit of measure: milligrams per cubic meter

Method name: APHA 10200 H-2

Method summary: Quantitative filtration (glass fiber) in field, extraction of filter into

acetone; analysis by spectrophotometry (monochromatic)

Reporting limits: 0.0679 milligrams per cubic meter

Years sampled:
60

U.S. Environmental Protection Agency

Parameter name: Residue, solids, total evaporation (00500)6

Unit of measure: milligrams per liter

Method name: Gravimetric, dried at 103-105 ̊C (EPA 160.3)

Method summary: A well mixed aliquot of the sample is quantitatively transferred to a

pre-weighed evaporating dish and evaporated to dryness at 103-105°C

Reporting limits: 0.1 milligrams

Years sampled: 1993-1995

Parameter name: Nitrogen, total as N (00600)6

Unit of measure: milligrams per liter

Method name: Determination of total nitrogen (EPA 353.2 modified)

Method summary: Alkaline persulfate digestion with determination of nitrate by

cadmium reduction and determination of nitrite by automated colorimetry.

Reporting limits: 1 micrograms per liter

Years sampled: 1993-1995

Parameter name: Nitrogen, total ammonia as N (00610)6

Unit of measure: milligrams per liter

Method name: Determination of ammonium - photometric method (EPA 350.7)

Method summary: Alkaline phenol and hypochlorite react with ammonia to form an

amount of indophenol blue that is proportional to the ammonium concentration. The blue

color intensifies with sodium nitroprusside.

Reporting limits: 0.02 milligrams per liter

Years sampled: 1993-1995

Parameter name: Nitrite, total as N (00615)5

Unit of measure: milligrams per liter

Method name: Spectrophotometric (EPA 354.1)

Method summary: The diazonium compound formed by diazotation of sulfanilamide by

nitrite in water under acid conditions is coupled with N−(1-naphthyl)−ethylenediamine

dihydrochloride to produce a reddish-purple color which is read in a spectrophotometer.

Reporting limits: Not listed

Years sampled: 1993-1995

Parameter name: Nitrate, total as N (00620)6

Unit of measure: milligrams per liter

Method name: Determination of nitrate by ion chromatography (EPA 300.6)

Method summary: Ion chromatography combines ion exchange chromatography, eluent

suppression, and conductimetric detection. A filtered sample portion is injected into anion

chromatograph and run through columns to separate the anions. The separated anions are

measured using a conductivity cell. Anion identification is based on retention time.

Quantification is performed by comparing sample peak heights to a calibration curve

generated from known standards.

Reporting limits: 0.03 milligrams per liter

Years sampled: 1993-1995
61

U.S. Environmental Protection Agency—Continued

Parameter name: Phosphorus, total as P (00665)3

Unit of measure: milligrams per liter

Method name: Phosphorus, colorimetric, phosphomolybdate, automated-segmented

flow

Method summary: All forms of phosphorus, including organic phosphorus, are

converted to orthophosphate by an acid-persulfate digestion.

Reporting limits: 1 microgram per liter

Years sampled: 1993-1995

Parameter name: Periphyton, chlorophyll a (70955)6

Unit of measure: milligrams per square meter

Method name: APHA 10200 H-2

Method summary: Quantitative filtration (glass fiber) in field, extraction of filter into

acetone; analysis by spectrophotometry (monochromatic).

Reporting limits: 1 microgram per liter

Years sampled: 1993-1995

Reporting limits: 0.03 milligrams per liter

Years sampled:
62

U.S. Geological Survey

Parameter name: Residue, total nonfilterable (00530)1

Unit of measure: milligrams per liter

Method name: Solids, residue at 105°C, suspended, gravimetric

Method summary: The unfiltered sample is mixed thoroughly and an appropriate

volume is rapidly poured into a graduated cylinder. The suspended solids are collected on

a glass-fiber filter, and the insoluble residue is dried and weighed.

Reporting limits: Not listed

Years sampled: 1993-1995

Parameter name: Nitrogen, total as N (00600)2

Unit of measure: milligrams per liter

Method name: Calculated value - Negatives are eliminated by taking the max using zero

on the left

Method summary: Add parameter 00625 and parameter 00630.

Reporting limits: Not listed

Years sampled: 1993 and 1998

Parameter name: Nitrogen, total organic as N (00605)2

Unit of measure: milligrams per liter

Method name: Calculated value - Negatives are eliminated by taking the max using zero

on the left.

Method summary: Subtract parameter 00610 from parameter 00625.

Reporting limits: Not listed

Years sampled: 1998

Parameter name: Nitrogen, dissolved organic as N (00607)2

Unit of measure: milligrams per liter

Method name: Calculated value - Negatives are eliminated by taking the max using zero

on the left.

Method summary: Subtract parameter 00608 from parameter 00623.

Reporting limits: Not listed

Years sampled: 1998

Parameter name: Nitrogen, dissolved ammonia as N (00608)1

Unit of measure: milligrams per liter

Method name: Nitrogen, ammonia, low ionic-strength water, colorimetry,

salicylate-hypochlorite, automated-segmented flow

Method summary: Ammonia reacts with hypochlorite and salicylate ions in the presence

of ferricyanide ions to form the salicylic acid analog of indophenol.

Reporting limits: 0.002, 0.02 milligram per liter (regular line, low level line)

Years sampled: 1993-1998

Parameter name: Ammonia, total as N (00610)1

Unit of measure: milligrams per liter

Method name: Nitrogen, ammonia, colorimetric, salicylate-hypochlorite, automatedsegmented flow

Method summary: Ammonia reacts with sodium salicylate, sodium nitroprusside, and

sodium hypochlorite to form an intense color compound that is directly proportional to

the concentration of ammonia present.

Reporting limits: 0.01, 0.02 milligram per liter

Years sampled: 1993-1995
63

U.S. Geological Survey—Continued

Parameter name: Nitrite, total as N (00615)1

Unit of measure: milligrams per liter

Method name: Nitrogen, nitrite, colorimetric, diazotization, automated-segmented flow

Method summary: Nitrite ion reacts with sulfanilamide under acidic conditions to form a

red compound and the absorbance is measured colorimetrically.

Reporting limits: 0.1 milligram per liter

Years sampled: 1993-1995

Parameter name: Nitrate, dissolved as N (00618)1

Unit of measure: milligrams per liter

Method name: Nitrogen, nitrate, ion-exchange chromatographic, automated

Method summary: Nitrate is determined by ion-exchange chromatography.

Reporting limits: 0.01 milligrams per liter

Years sampled: 1993 and 1998

Parameter name: Nitrate, total as N (00620)1

Unit of measure: milligrams per liter

Method name:

Method summary: Calculated value.

Reporting limits: Not listed

Years sampled: 1993-1995

Parameter name: Nitrogen, dissolved kjeldahl as N (00623)1

Unit of measure: milligrams per liter

Method name: Nitrogen, ammonia plus organic, colorimetry, block digestor-salicylatehypochlorite, automated-segmented flow

Method summary: Organic nitrogen compounds are reduced to the ammonium ion by

digestion with sulfuric acid in the presence of mercuric sulfate and potassium sulfate. The

ammonium ion produced is determined by reaction with sodium salicylate, sodium

nitroprusside, and sodium hypochlorite in an alkaline medium. The resulting color is

directly proportional to the concentration of ammonia present.

Reporting limits: 0.10 milligrams per liter

Years sampled: 1993-1996

Parameter name: Nitrogen, total kjeldahl as N (00625)1

Unit of measure: milligrams per liter

Method name: Nitrogen, ammonia plus organic, colorimetry, block digestor-salicylatehypochlorite, automated-segmented flow

Method summary: Organic nitrogen compounds are reduced to the ammonium ion by

digestion with sulfuric acid in the presence of mercuric sulfate and potassium sulfate. The

ammonium ion produced is determined by reaction with sodium salicylate, sodium

nitroprusside, and sodium hypochlorite in an alkaline medium. The resulting color is

directly proportional to the concentration of ammonia present.

Reporting limits: 0.10 milligrams per liter

Years sampled: 1993-1998
64

U.S. Geological Survey—Continued

Parameter name: Nitrite plus nitrate, total as N (00630)1

Unit of measure: milligrams per liter

Method name: Nitrogen, nitrite plus nitrate, colorimetric,

cadmium reduction-diazotization,

automated-segmented flow

Method summary: An acidified sodium chloride extraction procedure is used to extract

nitrate and nitrite from bottom material for this determination.

Reporting limits: 0.2 milligrams per liter

Years sampled: 1993-1995

Parameter name: Nitrite plus nitrate, dissolved as N (00631)1

Unit of measure: milligrams per liter

Method name: Nitrogen, nitrite plus nitrate, colorimetric,

cadmium reduction-diazotization,

automated-segmented flow

Method summary: An acidified sodium chloride extraction procedure is used to extract

nitrate and nitrite from bottom material for this determination.

Reporting limits: 0.05 milligrams per liter

Years sampled: 1993-1998

Parameter name: Orthophosphate, dissolved as PO4 (00660)2

Unit of measure: milligrams per liter

Method name: Calculated value - Negatives are eliminated by taking the max using

zero on the left.

Method summary: Multiply parameter 00671 by 3.06618.

Reporting limits: Not listed

Years sampled: 1998

Parameter name: Phosphorus, total as P (00665)1

Unit of measure: milligrams per liter

Method name: Phosphorus, colorimetric, phosphomolybdate, automated-segmented

flow

Method summary: All forms of phosphorus, including organic phosphorus, are

converted to orthophosphate by an acid-persulfate digestion.

Reporting limits: 0.004, 0.05 milligram per liter

Years sampled: 1993-1998

Parameter name: Phosphorus, dissolved as P (00666)1

Unit of measure: milligrams per liter

Method name: Phosphorus, colorimetric, phosphomolybdate, automated-segmented

flow

Method summary: All forms of phosphorus, including organic phosphorus, are

converted to orthophosphate by an acid-persulfate digestion.

Reporting limits: 0.004, 0.05 milligrams per liter

Years sampled: 1993-1998
65

U.S. Geological Survey—Continued

Parameter name: Orthophosphate, dissolved as P (00671)1

Unit of measure: milligrams per liter

Method name: Phosphorus, orthophosphate, colorimetry, phosphomolybdate,

automated-segmented flow

Method summary: Orthophosphate ion reacts with ammonium molybdate in acidic

solution to form phosphomolybdic acid and is reduced with ascorbic acid to produce an

intensely blue color. Antimony potassium tartrate is added to increase the rate of

reduction.

Reporting limits: 0.01 milligrams per liter

Years sampled: 1993-1996

Parameter name: Periphyton chlorophyll a (70957)1

Unit of measure: milligrams per square meter

Method name: Chlorophyll in periphyton by chromatography and fluorometry

Method summary: A periphyton sample is obtained and the chlorophylls a and b

are extracted from the algal cells. The chlorophylls are separated from each other and

chlorophyll are eluted and measured using a spectrofluorometer.

Reporting limits: 0.1 milligram per meter square

Years sampled: 1993-1998
66

West Virginia Division of Environmental Protection

Parameter name: Residue, total nonfilterable (00530)3

Unit of measure: milligrams per liter

Method name: Gravimetric, dried at 103-105°C (EPA 160.2)

Method summary: A well-mixed sample is filtered through a glass fiber filer, and the

residue retained on the filter is dried to constant weight at 103-105°C.

Reporting limits: 0.5, 5 milligrams per liter

Years sampled: 1993-1997

Parameter name: Nitrogen, total ammonia as N (00610)5

Unit of measure: milligrams per liter

Method name: Colorimetric; titrimetric; potentiometric - distillation procedure (EPA 350.2)

Method summary: The sample is buffered at a pH of 9.5 with a borate buffer in order to

decrease hydrolysis of cyanates and organic nitrogen compounds and is then distilled into

a solution of boric acid. The ammonia in the distillate can be determined colorimetrically

by nesslerization, titrimetrically with standard sulfuric acid with the use of a mixed

indicator, or potentiometrically by the ammonia electrode.

Reporting limits: 0.01, 0.1, 0.5, 0.6 milligrams per liter

Years sampled: 1993-1996

Parameter name: Nitrogen, total kjeldahl as N (00625)5

Unit of measure: milligrams per liter

Method name: Colorimetric; titrimetric; potentiometric (EPA 351.3)

Method summary: The sample is heated in the presence of concentrated sulfuric acid,

K2SO4, and HgSO4, and evaporated until SO3 fumes are obtained and the solution

becomes colorless or pale yellow. The residue is cooled, diluted, and is treated and made

alkaline with a hydroxide-thiosulfate solution. The ammonia is distilled and determined

after distillation by nesslerization, titration or potentiometry.

Reporting limits: 0.01, 0.1, 0.5 milligrams per liter

Years sampled: 1993-1995

Parameter name: Nitrite plus nitrate, total as N (00630)5

Unit of measure: milligrams per liter

Method name: Colorimetric, automated, cadmium reduction (EPA 353.2)

Method summary: A filtered sample is passed through a column containing granulated

copper-cadmium to reduce nitrate to nitrite. The nitrite is determined by diazotizing with

sulfanilamide and coupling with N−(1-naphthyl)−ethylenediamine dihydrochloride to

form a highly colored azo dye which is measured colorimetrically.

Reporting limits: 0.01, 0.05, 0.1, 0.5 milligrams per liter

Years sampled: 1993-1997

Parameter name: Phosphorus, total as P (00665)5

Unit of measure: milligrams per liter

Method name: Colorimetric, ascorbic acid, two reagent (EPA 365.3)

Method summary: Ammonium molybdate and antimony potassium tartrate react in an

acid medium with dilute solutions of phosphorus to form an antimony-phospho-molybdate

complex. This complex is reduced to an intensely blue-colored complex by ascorbic

acid. The color is proportional to the phosphorus concentration.

Reporting limits: 0.02, 0.1 milligrams per liter

Years sampled: 1993-1997
67

West Virginia Division of Environmental Protection—Continued

Parameter name: Periphyton, chlorophyll a (32223)3

Unit of measure: milligrams per square meter

Method name: Spectrophotometric determination of chlorophyll (Standard method 10200)

Method summary: With a narrow band width between 0.5 and 2.0 nm the spectral band

shows the amount of chlorophyll a present in the sample.

Reporting limits: 0.5 milligrams per square meter

Years sampled: 1993 -1994

Parameter name: Periphyton, chlorophyll a (32223)3

Unit of measure: milligrams per square meter

Method name: Fluorometric determination of chlorophyll a (Standard method 10200)

Method summary: Optimum sensitivity for chlorophyll a extract measurements is

obtained at an excitation wavelength of 430 nm and an emission wavelength of 663 nm.

Reporting limits: 1, 6 milligrams per square meter

Years sampled: 1995-1997

1Fishman, M.J., and Friedman, L.C., eds., 1989, Methods for determination of inorganic substance in

water and fluvial sediments: U.S. Geological Survey Techniques of Water-Resources Investigations of the

United States Geological Survey, book 5, chap. A1, p. 545.

2Fishman, M.J., ed., 1993, Methods of analysis of by U.S. Geological Survey national water quality

laboratory—Determination of inorganic and organic constituents in water fluvial sediments:

U.S. Geological Survey Open-File Report 93-125, p. 217.

3Greenberg, A.E., Clesceri, L.S., and Eaton, A.D., 1992, Standard methods for the examination of

water and wastewater: Washington, D.C., American Public Health Association, p. 981.

4U.S. Environmental Protection Agency, 1974, Methods for chemical analysis of water and

wastes: Cincinnati, Ohio, EPA/625/6-74/003, p. 297.

5U.S. Environmental Protection Agency, 1979, Methods for chemical analysis of water and

wastes: Cincinnati, Ohio, EPA/600/4-79/020, p. 552.

6U.S. Environmental Protection Agency, 1987, Handbook of methods for acid deposition studies—

Laboratory analysis for surface water chemistry: Washington, D.C., EPA/600/4-87/026, p. 376.