Biological Report 80(40.19)
June 1985
Air Pollution and Acid Rain
Report No. 19
VULNERABILITY OF SELECTED
LAKES AND STREAMS IN THE
MIDDLE ATLANTIC REGION
TO ACIDIFICATION:
A REGIONAL SURVEY
Office of Research and Development
U.S. Environmental Protection Agency JKMP
Fish and Wildlife Service
U.S. Department of the Interior
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REPORTS ISSUED
FWS/0BS-80/40.1
FWS/0BS-80/40.2
FWS/0BS-80/40.3
FWS/OBS
FWS/OBS
FWS/OBS'
FWS/OBS'
FWS/OBS
FWS/OBS
FWS/OBS
FWS/OBS
-80/40.4
¦80/40.5
¦80/40.6
•80/40.7
-80/40.8
¦80/40.9
-80/40.10
-80/40.11
FWS/0BS-80/40.12
FWS/0BS-80/40.13
FWS/0BS-80/40.14
FWS/0BS-80/40.15
FWS/0BS-80/40.16
FWS/0BS-80/40.17
FWS/0BS-80/40.18
FWS/0BS-80/40.19
Effects of Air Emissions on Wildlife Resources
Potential Impacts of Low pH on Fish and Fish Populations
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats
Introduction
Lakes
Rivers and Streams
Forests
Grasslands
Tundra and Alpine Meadows
Deserts and Steppes
Urban Ecosystems
Critical Habitats of
Threatened and Endangered Species
Effects of Acid Precipitation on Aquatic Resources:
Results of Modeling Workshops
Liming of Acidified Waters: A Review of Methods and
Effects on Aquatic Ecosystems
The Lining of Acidified Waters: Issues and Research -
A Report of the International Liming Workshop
A Regional Survey of Chemistry of Headwater Lakes and
Streams in New England: Vulnerabi1ity to Acidification
Comparative Analyses of Fish Populations in Naturally
Acidic and Circumneutral Lakes in Northern Wisconsin
Rocky Mountain Acidification Study
Effects of Acidic Precipitation on Atlantic Salmon
Rivers in New England
Vulnerability of Selected Lakes and Streams in the
Middle Atlantic Region to Acidification: A Regional
Survey
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UNITED STATES
DEPARTMENT OF THE INTERIOR
FISH AND WILDLIFE SERVICE
EASTERN ENERGY AND LAND USE TEAM
Box 705
Kearneysville, West Virginia 25430
Dear Colleague:
The Eastern Energy and Land Use Team (EELUT) is pleased to provide you this
report on the results of a regional survey of the chemistry of lakes and
streams in the Middle Atlantic States. This study was conducted to assess the
status of these waters with respect to their sensitivity to acidification.
The report is the nineteenth in our series on air pollution and acid rain.
Other reports previously issued in this series are listed on the inside front
cover.
The extent and location of surface waters that are acidified or vulnerable to
acidification were determined from a survey of 278 lakes and streams in nine
Middle Atlantic States. These data were used to evaluate available models of
acidification and to identify potential indicators of acidification. A
companion study (FWS/0BS-80/40.15) provides similar information for the New
England States.
Your suggestions or comments on this report are welcomed.
Sincerely
R. Kent Schreiber
Acting Team Leader, EELUT
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Biological Report 80(40.19)
June 1985
Air Pollution and Acid Rain
Report No. 19
VULNERABILITY OP SELECTED LAKES AND STREAMS IN
THE MIDDLE ATLANTIC REGION TO ACIDIFICATION:
A REGIONAL SURVEY
by
Dean E. Arnold, Robert W. Light, and Eric A. Paul
Pennsylvania Cooperative Fish and Wildlife Research Unit
Ferguson Building
University Park, PA 16802
Project Officers
Paul J. Rago / R. Kent Schreiber
Eastern Energy and Land Use Team
Performed for:
Eastern Energy and Land Use Team
Division of Biological Services
Fish and Wildlife Service
U. S. Department of the Interior
Washington, D. C. 20240
Fish and Wildlife Service
U. S. Department of the Interior
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DISCLAIMER
Although the research described in this report has been
funded in large part by the U. S. Environmental Protection Agency
through Interagency Agreement No. EPA-82-D-X0581 with the U. S.
Fish and Wildlife Service, it has not been subjected to the
Agency's required peer and policy review and therefore does not
necessarily reflect the views of the Agency. Mention of trade
names or commercial products does not constitute endorsement or
recommendation for use by the Federal Government. This research
has been funded as part of the National Acid Precipitation
Assessment Program by the U. S. Fish and Wildlife Service.
This report should be cited as: Arnold, D. E., R. W. Light,
and E. A. Paul. 1985. Vulnerability of Selected Lakes and
Streams in the Middle Atlantic States to Acidification: A
Regional Survey. U. S. Fish and Wildlife Service, Eastern Energy
and Land Use Team, Biol. Rep. 80(40.19), 133 pp.
ii
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EXECUTIVE SUMMARY
In conjunction with a similar study in the six New England
States, 278 lakes and streams were sampled in the nine Middle
Atlantic States (New York, New Jersey, Pennsylvania, Maryland,
Delaware, Virginia, West Virginia, North Carolina, and
Tennessee). Primary objectives of the study were to document the
sensitivity of waters in the study area to acidification, and to
test the validity of available models and classifications for
predicting acidification of these waters. Waters were chosen to
be relatively free of direct disturbance by human activity in
their watersheds, and to be at as high an elevation as possible
while maintaining representation of all geographic areas and
bedrock and soil sensitivity classes. Lakes comprised 60% of the
sites and streams 40%. Historical data for the same waters were
gathered wherever available.
About 49% of the unpolluted, relatively undeveloped waters
sampled in the nine states were sensitive to acidification on the
basis of having alkalinity less than 200 ^e q/1 or of having
underlying bedrock of low acid neutralizing capacity. Alkalinity
is probably the most useful and relatively accurate predictor of
sensitivity to acidification. Calcite saturation index (CSI) was
highly correlated with alkalinity, and appeared to have no
significant advantage as a predictor. Of the waters sampled, 32%
were susceptible to acidification on the basis of having CSI
greater than 3. Neither bedrock nor soil class is a particularly
strong predictor of sensitivity to acidification, but bedrock
class is somewhat correlated with alkalinity of Middle Atlantic
States waters, while soil type is not, at least when determined
from rock and soil maps presently available.
Comparison with available historical data indicated that
many of the sampled waters have decreased in alkalinity in recent
years. A similar decrease in pH was not noted, but this is to be
expected so long as the acid neutralizing capacity (as
represented by alkalinity) of most waters has not been completely
depleted. However, the accuracy of the historical data could not
be adequately evaluated to enable confident conclusions about
temporal trends in acidification.
Few of the waters showed high concentrations of aluminum,
and only one stream had aluminum above 200 |ieq/1 at a pH below
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5.5, which is believed to be the range of toxicity to fish.
However, we noted a doubling of average aluminum concentrations
in waters with pH below 5, compared to waters with higher pH.
Sulfate levels (mean 227 (j.eq/1) found in the sampled waters
were higher than those reported from some other areas where
precipitation is acidic, and much higher than those from areas
with nonacidic precipitation. Sulfate was not correlated with
acidity, perhaps due to the relatively uniform deposition of
sulfate by precipitation in the study area.
pH was correlated strongly with alkalinity, conductivity,
and (negative) calcite saturation index, and weakly with calcium,
magnesium, and disturbance. Alkalinity was strongly correlated
with calcium, pH, conductivity, and magnesium, and weakly with
sulfate, chloride, disturbance, rain pH, and (negative)
elevation. Neither pH nor alkalinity was strongly correlated
with aluminum, manganese, color, total organic carbon (as
predicted from color), or size of waters.
Differences in water chemistry between lakes and streams
were small, with streams in general being less acidic. Higher
order streams had higher conductivity and pH, as expected due to
their larger, more varied watersheds. Waters most affected by
human disturbance had higher pH, conductivity, and chloride.
The calcium-pH model of Henriksen predicted the pH of waters
in the study area reasonably well. It indicated that about 28%
of the waters were acidified. Other published models and
classifications (Thompson Cation Denudation Model, Altshuller and
McBean, Sulfate-pH) that were tried predicted water pH poorly.
The study results indicate that a large number of relatively
undisturbed waters in the Middle Atlantic States are either
already acidified or are susceptible to acidification. Thus,
parts of the Middle Atlantic States should be included on maps
showing regions sensitive to acid precipitation. A large portion
of the waters we sampled in the region have decreased in pH,
alkalinity, or both in recent years.
iv
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TABLE OF CONTENTS
Page
DISCLAIMER ii
EXECUTIVE SUMMARY iii
LIST OF FIGURES vii
LIST OF TABLES ix
ACKNOWLEDGEMENTS xi
INTRODUCTION 1
MATERIALS AND METHODS 3
Sampling Methods 3
Selection of Sampling Sites 3
Sample and Field Data Collection 7
Analytical Methods 8
Field Analyses 8
Laboratory Procedures 9
Quality Assurance 12
Data Analysis 12
RESULTS AND DISCUSSION 14
Quality Assurance 14
Site Distribution 17
Chemical Factors 20
Organic Acids and Color 20
pH and Alkalinity 23
Calcite Saturation Index 29
Conductivity and Ionic Composition 34
Chloride 36
Sulfate 38
Calcium and Magnesium 40
Aluminum and Manganese 40
Precipitation Chemistry 46
Physical Factors 46
Elevation 46
Size 47
Hydrology Type 47
Stream Order 50
Bedrock Classes 51
Soil Classes 56
Human Disturbance 62
Multivariate Analyses 62
Principal Components Analysis 62
Canonical Correlation Analysis 64
Historical Comparisons 65
pH Comparisons 66
Alkalinity Comparisons 68
Application of Proposed Models of Acidification 71
v
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Sensitivity Classification 72
Calcium-pH Model 7 3
Sulfate-pH Model 75
Cation Denudation Model 77
CONCLUSIONS 80
RECOMMENDATIONS 81
LITERATURE CITED 82
APPENDICES 89
A. Data From this Study 89
B. Historical Data 124
C. Stations Dropped from Chemical Analysis Discussion 131
vi
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LIST OF FIGURES
Number Page
1 Map of the Middle Atlantic States showing
sampling sites and type of water body
for each 5
2 Sum of cations versus sum of anions for
207 sites 15
3 Theoretical versus measured conductivity
for 211 sites 16
4 Map showing distribution of sampling sites by
bedrock classification 21
5 Map showing distribution of sampling sites by
soil classification 22
6 Map showing distribution of sampling sites by
water pH . 24
7 Map showing distribution of sampling sites by
alkalinity 26
8 Map showing mean annual acid deposition from
precipitation for period 1976-1979 30
9 Map showing distribution of sampling sites by
calcite saturation index 31
10 Relation of log of alkalinity to calcite
saturation index for 218 sampled waters 33
11 Relation of alkalinity to conductivity
for 218 sampled waters 35
12 Mean ionic composition of sampled waters
classified by pH range 37
13 Map showing distribution of sampling sites by
sulfate concentration 41
14 Relation of alkalinity to caleium+magnesium
for 218 sampled waters 42
vi i
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15 Map showing distribution of sampling sites by
aluminum concentration 45
16 Frequency distribution of waters by bedrock
geology in pH intervals 52
17 Frequency distribution of waters by bedrock
geology in alkalinity intervals 53
18 Frequency distribution of pH of waters by
soil classes 59
19 Frequency distribution of alkalinity of waters
by soil classes 60
20 Recent pH value versus oldest value available
for 36 locations 67
21 Recent fixed endpoint alkalinity value versus
oldest available for 19 locations 70
22 Calcium-pH model (Henriksen plot) applied to 75
Middle Atlantic States lakes having calcium
concentrations less than 300 \ieq/l 74
23 Sulfate-pH model (Henriksen nomograph) applied to
82 Middle Atlantic States lakes having calcium
plus magnesium concentrations less than 500 iieq/1 76
24 Thompson cation denudation model applied to 74
Middle Atlantic States waters with sums of
cations and sulfate concentrations less than
600 neq/1 78
viii
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LIST OF TABLES
Number Page
1 Bedrock and Soil Sensitivity Classes 6
2 Criteria Used to Estimate Relative Amount of
Human Disturbance 8
3 Comparison of Frozen and Unfrozen Sample Analyses
and of Alkalinity Methods for 16 Waters 11
4 Comparison of Duplicate Chemical Analyses for
all Samples 17
5 Number and Classification of Lakes and Streams
Sampled in Each State 18
6 Distribution of Sampling Sites Among States by
Bedrock and Soil Types 19
7 Distribution of Average pH Values for Sampled
Waters by State 25
8 Distribution of Average Alkalinity Values for
Sampled Waters by State 25
9 pH of Surface Waters Reported by Regional Surveys 27
10 Alkalinity of Surface Waters Reported by Regional
Surveys 28
11 Percent Distribution of Calcite Saturation
Index by State 32
12 Percent Relative Composition of Major Ions in
Sampled Waters Compared With New England and
World Averages 34
13 Concentrations of Sulfate Reported from Surface
Waters by Regional Surveys 39
14 Concentrations of Calcium Reported from Surface
Waters by Regional Surveys 43
ix
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15 Chemical and Physical Factors for Lakes
Classified by Hydrology Type 48
16 Chemical and Physical Factors for Lakes
and Streams 49
17 Chemical and Physical Factors for
Streams by Stream Order 50
18 Chemical and Physical Factors for Waters
Classified by Bedrock Geology 54
19 Relations Between Bedrock Sensitivity and Water
Sensitivity Based on pH 56
20 Relations Between Bedrock Sensitivity and Water
Sensitivity Based on Alkalinity 56
21 Chemical and Physical Factors for Waters
Classified by Soil Sensitivity 57
22 Relations Between Soil Sensitivity and Water
Sensitivity Based on pH 61
23 Relations Between Soil Sensitivity and Water
Sensitivity Based on Alkalinity 61
24 Chemical and Physical Factors for Waters by
Degree of Human Disturbance 63
25 Principal Components Analysis of Chemical Factors 64
26 Canonical Correlation Analysis of Standardized
Chemical and Physical Factors 66
27 Sensitivity Classes of Altshuller and McBean 72
28 Prediction of pH of 82 Middle Atlantic Lakes
Having Sums of Nonmarine Calcium Plus Magnesium
Less than 500 |±eq/l by Henriksen's Sulfate-
pH Model 75
29 Prediction of pH of 74 Middle Atlantic Waters
Having Sums of Nonmarine Cations Less Than 600
neq/1 and Nonmarine Sulfate Less Than 500 neq/1
by Thompson's Cation Denudation Model 77
x
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ACKNOWLEDGEMENTS
This study and a concurrent survey in New England were
designed to be as similar as possible with respect to objectives
and methods. In preparing this report we have benefited from
ideas presented by the New England investigators (Terry A. Haines
and John Akielaszek) in their report (Haines and Akielaszek
1983). For the sake of consistency we present our report in a
format and order similar to theirs. Mr. Paul J. Rago of the
Eastern Energy and Land Use Team, U. S. Fish and Wildlife
Service, provided considerable data analysis, maps, and
statistical advice. Funds for the study were derived in part from
an interagency agreement with the U. S. Environmental Protection
Agency. The U. S. Fish and Wildlife Service, Pennsylvania Fish
Commission, and The Pennsylvania State University all provided
financial and material support through the Pennsylvania
Cooperative Fish and Wildlife Research Unit .
The study would not have been possible without the many
hours of field and laboratory work done by our assistants, most
of whom were paid by the College Work-Study Program. They
included: Juliet Arnold, Robert Bachman, Suzanne Ban, Daniel
Brauning, Jennifer Bryson, William Conklin, John DePasquale,
Laurie Finn, Thomas Gerlach, Margaret Harkins, Cheryl Hertzog,
Joseph Jellock, Amy Kahn, Geroyed Krebs, Judy Light, Paul
McKenna, James Miller, Robert Sewak, Timothy Shoales, Jean
Szeles, Beth Tucker, Beth Wildman, Karen Young, Lee Young, and
Margaret Zak. We also gratefully acknowledge the assistance of
our Unit Secretary, Kay Christine, and the many landowners and
colleagues who provided us with access to waters or with
historical data.
¦*- The Pennsylvania Cooperative Fish and Wildlife Research Unit is
jointly sponsored by The Pennsylvania State University, the
Pennsylvania Fish Commission, the Pennsylvania Game Commission,
the Wildlife Management Institute, and the CJ. S. Fish and
Wildlife Service. Unit Contribution Number 260.
xi
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INTRODUCTION
The precipitation that falls on the northeastern United
States is highly acidic. Its pH averages about 4.2 but is often
much lower (Likens et al. 1979; National Atmospheric Deposition
Program 1984). Research has indicated that acidic precipitation
has had profound effects on aquatic ecosystems, particularly
fish, in Scandinavia (Leivestad et al. 1976), Ontario, Canada
{Beamish 1976), and the Adirondack Mountains of New York
(Schofield 1977). Evidence of serious effects on both aquatic
and terrestrial ecosystems in the Northeast and other parts of
the United States is accumulating rapidly (see Haines 1981 and
Glass et al. 1982 for reviews).
The impact of acidic precipitation on aquatic ecosystems
depends on a variety of climatic, geologic, topographic,
morphometric, biotic, and anthropogenic factors. Even in areas
where effects of acidification on lakes have been pronounced (La
Cloche Lakes, Ontario, and Adirondack Mountains, New York) there
are many lakes that seem unaffected, interspersed with acidified
lakes. This phenomenon illustrates the need for indices of
vulnerablity that can be applied to individual lakes and streams
and that are sensitive to local conditions of rock, soil, and
hydrology.
If regulations or legislation controlling emissions of
sulfur and/or nitrogen oxides are to be sought as a means of
reducing the adverse effects of acidic precipitation on
ecosystems, detailed information will be required to substantiate
the need for such reductions. Such information would need to
include projections of the quantity of aquatic habitat that is
susceptible to acidic precipitation inputs, and dose-response
estimates for the effects of such inputs. Dose-response
estimates will require detailed, long-term study in a variety of
habitats subject to varying inputs of acidic precipitation.
The present study is an attempt to improve available
estimates of the extent of vulnerable habitat by documenting the
sensitivity of waters in the study area to acidification, and to
determine which, if any, of the available predictors of
vulnerability is most appropriate for use on these waters. The
terms "sensitivity" and "vulnerability" as used in this report
(and most acid rain literature) refer to no or low acid
neutralizing capacity, thus the quality of being easily
1
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acidified. Existing surface water chemistry data were not
satisfactory for validating these predictors. Existing data were
predominantly collected in high order watersheds where soils are
thick and geological complexity makes the presence of buffering
materials likely, while acid precipitation is most likely to
affect low order, headwater lakes and streams. Also, many
existing water chemistry data sites were selected to determine
the effects of human activities on water quality. Human
activity makes natural chemistry difficult to ascertain, and
there is almost always significant alkalinity contained in
discharges from human activities (with the exception of acid mine
drainage and a few other wastes). In general, human activity is
concentrated in areas that were originally selected for
agricultural productivity, and are thus likely to be well
buffered. The most pervasive anthropogenic impact on alkalinity
may be from agricultural liming (Johnson 1984). Finally, many
existing water chemistry data were collected with methodology
(e.g., colorimetric pH, fixed endpoint alkalinity titration) that
produces inaccurate results when used in low ionic strength
waters. Thus the present study was planned to collect valid
present-day data on waters as unmodified as possible, located on
low order watersheds, and to use best available methods for low
ionic strength waters. The data collected, in addition to
indicating current degree of acidification, if any, were used to
evaluate several proposed indices of vulnerability.
The study was divided into two parts. The six New England
States (Maine, New Hampshire, Vermont, Massachusetts,
Connecticut, and Rhode Island) were sampled and the results
prepared by Dr. Terry A. Haines and associates at the U. S. Fish
and Wildlife Service field station at the University of Maine in
Orono, Maine (Haines and Akielaszek 1983; hereafter referred to
as "the New England study"). We sampled sites in the nine Middle
Atlantic States (New York, New Jersey, Pennsylvania, Maryland,
Delaware, Virginia, West Virginia, and the Appalachian parts of
North Carolina and Tennessee).
Virtually identical field and laboratory methods were used
by the two groups, and statistical analyses of the two sets of
data were similar. In general, the two reports follow a common
outline.
2
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MATERIALS AND METHODS
The following description refers to the Middle Atlantic part
of the study but is generally true of the New England part as
well. Methods differing significantly between the study parts
are footnoted.
SAMPLING METHODS
Selection of Sampling Sites
Sites included in this study were selected from all
available surface waters with respect to several criteria:
- broad geographic coverage
- bedrock acid neutralizing capacity
- soil cation exchange capacity
- location in upper portion of watershed
(i.e., altitude, stream order)
- absence of direct human disturbance in watershed
- availability of historical data
- difficulty of access
The waters sampled were primarily lakes (168 of 278; 60%)
(for both studies, 362 of 504 or 72%). Streams were usually
sampled when a suitable lake could not be located in a desired
area, or was not accessible. In general, the lowest order stream
feasible was chosen. Similarly, impoundments (defined as any
water body whose level is controlled or fixed by a man-made
structure) were sampled only when a natural lake was not
available in the same area. This was a somewhat arbitrary
distinction in some cases, since many impoundments where the dam
had no provision for water level adjustment and the level had
apparently been stable for several years were equivalent to
"natural" lakes. Impoundments tended to be more predominant in
the southern part of the study area, probably reflecting the lack
of glaciation there. Impoundments which showed evidence of water
level fluctuation or adjustment were avoided. Lakes sampled were
classified as either "seepage" lakes with no visible inlet (n=17)
or "drainage" lakes with well-defined inlets (n=151).
3
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Geographic distribution of sampling sites over the region
was as uniform as possible (Figure 1), but a few areas were
inadequately represented. For example, there are few undisturbed
lakes or streams in central New York.
Sampling sites were selected to represent all classes of
bedrock acid neutralizing capacity and soil cation exchange
capacity present in the region. Bedrock acid neutralization
capacity classes were those described by Hendrey et al. (1980),
and soil cation exchange capacity classes were those of McFee
(1980) (Table 1).
The original maps depicting the distribution of these
classes were obtained from the authors (Stephen Norton,
University of Maine, Orono, and William McFee, Purdue University,
Lafayette, IN, personal communications). These maps were
generally of 1:500,000 scale, the same as the U. S. Geological
Survey (USGS) state base map.
Waters were selected to represent the various bedrock and
soil classes in approximate proportion to the area of the classes
in each state. Bedrock classes were generally given preference
in cases where a choice had to be made. Some waters may have
been raisclassified as to bedrock or soil class due to difficulty
in precisely locating the water on the state base map (many small
waters are not shown on these maps, and locations were
transferred from 7.5 or 15 minute USGS quadrangle maps). Also,
"smoothing" and other removal of complexity were performed in the
creation of the bedrock and soil maps. In general, sites were
selected at the highest elevations possible within chosen
bedrock/soil type areas, since higher elevation, lower order
waters are usually more susceptible to acidification and less
likely to be affected by other forms of pollution. Headwater
sites were generally low in direct human disturbance.
For each state, appropriate agencies were asked to supply
existing historical water chemistry data on the chosen waters.
Particular attention was given to selecting sampling sites for
which there were historical data readily available; nearly half
(123 of 278, 44%) of the final sites had such data (for both
studies, 218 of 504 or 43%).
Finally, accessibility of the sites was considered in making
a final selection. Preferred sites were at least one-half mile
from the nearest vehicle access point, but not more than 2 miles
from that point. Most sites had at least a marked foot trail
leading from the access point to the site.
4
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Site Type
Lake
Stream
A
Impoundment <^>
~ Qv
~a fl Pa
~
ft
~
~
~ ~ %
~
>
a
&
~
Figure 1.
Map of the Middle Atlantic States showing sampling
sites and type of water body for each.
5
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Table 1. Bedrock and Soil Sensitivity (to acidification)
Classes {See Hendrey et al. 1980 and McFee 1980
for further details)
Code Class Description
(Rock)
II No to low acid neutralizing capacity
2 II Low to medium acid neutralizing capacity
3 III Medium to high acid neutralizing capacity
4 IV "Infinite" acid neutralizing capacity
5 V Unknown but probably moderate to high
acid neutralizing capacity
(Soil)
1 SI Sensitive soils dominate the area
2 S2 Sensitive soils are significant, but
cover less than 50% of the area
3 SSI Slightly sensitive soils dominate
the area
4 SS2 Slightly sensitive soils are
significant, but cover less
than 50% of the area.
5 NS The area contains mostly nonsensitive
soils
6
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Sample and Field Data Collection
Due to the large size of the study area, the sampling period
extended over an entire year, July 1980 to August 1981, with most
samples collected in spring and summer 1981. Some values for a
particular water may thus reflect seasonal limnological
conditions.
Standard field procedures were similar to those used in New
England. Most water bodies selected for survey were visited by a
two person survey team. All were reached by driving as close as
possible with a four-wheel drive vehicle, then carrying equipment
the remaining distance. The major vegetation, exposed bedrock,
inflow and outflow streams, aquatic vegetation, and evidence of
human activity (roads, campsites, seasonal dwellings, permanent
dwellings, logging, agriculture) visible from the water and
observed on the trip into and out of the area were noted.
Although waters with human disturbance in the watershed were
avoided, a higher proportion of slightly disturbed waters had to
be included in the Middle Atlantic States than in New England due
to the scarcity of completely undisturbed sites. An arbitrary
code was devised to represent differing levels of disturbance;
waters were assigned a code number based on a visual survey of
the shoreline and watershed (Table 2). A canoe was used to reach
the deepest area of lakes and ponds except in a few cases when
thin ice prevented access by canoe or foot beyond shore
structures (e.g., ends of docks). In such cases only
near-surface samples were collected, as far from shore as
feasible. Adequately frozen lakes were sampled by spudding a
hole through the ice near the deepest area. "Surface" samples in
these cases were collected by first gently removing the water-ice
mixture from the hole and allowing it to refill, then placing the
bottle about 0.5 m below the water surface. Water samples were
collected in acid-washed, distilled-water-rinsed linear
polyethylene bottles.
In streams, water samples were collected from mid-depth and
midstream directly into the bottles. In lakes, one set of
samples was collected at 0.1 m below the surface directly into
the bottles and one set near the bottom (without disturbing
sediment) in a plastic Van Dorn-type water sampler. Temperatures
at sample depths were measured directly with a thermistor, and a
Secchi disc reading was recorded in lakes. Width of streams was
estimated visually at the site. Three 500 ml and two 125 ml
bottles of sample were collected at each depth. All bottles were
rinsed three times with the water to be sampled before filling.
Bottles were filled to the brim to exclude air, thus minimizing
gas exchange and consequent chemical changes. Immediately on
7
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Table 2.
Criteria Used to Estimate Relative Amount of
Human Disturbance
Code
Disturbance
1
No visible disturbance: access by foot trail
or aircraft only
2
Slight disturbance: access by 4-wheel
drive vehicle; one or two seasonal
dwellings in the watershed; evidence of
logging activity but not presently active
3
Moderate disturbance: access by 2-wheel
drive vehicle; more than two seasonal
dwellings; evidence of active logging
but not immediately adjacent to water
4
Severe disturbance: access by paved road;
permanent homes around lake; agriculture
in watershed; active logging adjacent to
water
return to shore, 2.5 ml of 20% nitric acid was added to each 125
ml sample to preserve it for cation analysis, and all samples
were placed on ice.
ANALYTICAL METHODS
Field Analyses
Initial measurements of pH and conductivity were performed
in the field from 1-5 hours after collection. One 500 ml bottle
from each sample depth was warmed to near 20 °C while still
closed. An Orion model 399A meter equipped with an Orion model
91-62-00 glass combination electrode (specially designed for low
ionic strength solutions) was used for pH determinations in the
field2. Before each determination, the meter was standardized
against pH 4 and 7 buffers. After being rinsed with distilled
water and sample water, the electrode was inserted directly into
8
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the bottle and moved gently
stabilized (about 5 minutes),
between sample and atmosphere,
poorly buffered waters. The
readings were recorded.
until the reading of the meter
This procedure minimized contact
a potential source of error with
procedure was repeated and both
Conductivity of the sample
bottle using a Presto-Tek model
meter. Two successive readings
associated sample temperature;
temperature compensator which sta
was measured directly in the
DP-32 conductivity/temperature
were recorded along with the
the meter had a built-in
iardized readings to 25 °C.
Laboratory Procedures
The remaining water samples were keot on ice, returned to
the laboratory, and frozen until analyzed . The acidified 125 ml
bottles were given to a subcontractor (Dr. William J. Myers,
Micro-Technic Systems Laboratory, Pleasant Gap, PA 16823) for
analysis of aluminum, calcium, manganese, sodium, magnesium, and
potassium. Total aluminum was measured by the colorimetric
method of Dougan and Wilson (1974); the other elements were
measured by the standard methods recommended by .the U. S.
Environmental Protection Agency (Kopp and McKee 1979) .
Samples for metal analysis were not filtered, in order to
maintain compatibility with the New England study (Haines and
Akielaszek 1983) . Some workers believe that unfiltered samples
may be preferable for aluminum analyses of poorly buffered waters
low in dissolved solids (Charles Driscoll, Syracuse University,
personal communication). All analyses were duplicated, using one
sample from each bottle.
2 Haines and Akielaszek (1983) analyzed alkalinity, pH,
conductivity, and color in the field, and used a plastic-body,
gel-filled combination electrode on their portable pH meter.
The New England samples were refrigerated, not frozen.
4 Haines and Akielaszek (1983) analyzed sodium and potassium by
air-acetylene flame atomic absorption spectrophotometry,
calcium and magnesium by nitrous oxide-acetylene flame AAS, and
manganese by graphite furnace AAS. They measured aluminum
initially by the colorimetric method of Dougan and Wilson
(1974) and later by graphite furnace AAS. The two methods gave
"comparable results".
9
-------
The remaining two 500 ml bottles were brought to room
temperature and immediately analyzed for alkalinity, sulfate,
chloride, and color. As a check on the field measurements, pH
and conductivity of the thawed sample were recorded.
Conductivity was re-measured using the same instrument and
technique as in the field. pH was re-measured using a Corning
model 125 meter equipped with the same electrode used in the
field, and the same technique was employed* (As fewer problems
and greater precision were noted with the lab pH measurements,
they were used for the data comparisons which follow).
Alkalinity was determined by the double endpoint,
potentiometric titration method as described in American Public
Health Association et al. (1981). Duplicate 100 ml samples were
titrated with sulfuric acid (standardized to 0.0200 N) to pH 4.5
and then to pH 4.2, recording the amount of acid used to reach
each endpoint. This method has proven to be nearly as accurate
as the Gran plot method (Stumm and Morgan 1970) and considerably
faster. It is also more accurate than the fixed (single)
endpoint method often used in the past by many investigators
(Kramer and Tessier 1982). However, the values obtained were
also used to calculate the fixed endpoint alkalinity for use in
historical data comparisons.
To verify the comparability of the double endpoint method
with the Gran plot method, and the analysis of frozen samples for
alkalinity with unfrozen samples, we collected a set of
replicated samples from 16 waters in central Pennsylvania. Nine
of these waters also appeared in the project samples; the
remainder were chosen to represent a broader range of trophic and
chemical conditions. From each water, four samples were
collected just below the surface. Two of the four were frozen
for 2 weeks and thawed just before analysis (similar to the
samples of the main survey); the other two were refrigerated for
2 days before analysis but not frozen. Samples were analyzed for
alkalinity by both the double endpoint and Gran plot methods, and
for conductivity, pH, sulfate, and color. A paired-comparison
t-test (SAS Institute 1982) was used to test for differences
between means of frozen and unfrozen samples for each analysis,
and between double endpoint and Gran plot methods for alkalinity.
Differences were standardized by means of the formula:
value before freezing - value after
difference =
(value before + value after) / 2
Replicates were treated as separate samples. Results are
presented in Table 3, and show relatively small differences
between the procedures under consideration.
10
-------
Table 3. Comparison of Frozen and Unfrozen Sample Analyses
and of Alkalinity Methods for 16 Waters (values
for conductivity in ^S/cm, for color in Pt-Co
units and others in p.eq/1)
Unfrozen Mean Standardized
Variable Difference t p >t
mean s.d. (= precision)
Conductivi ty
39
5.0
0.04
1.79
0.09
Gran plot alk.
-6
39. 2
0.06
1.24
0.23
Double ept. alk
-7
3 8.7
0.15
1.69
0.11
Sulfate
260
18.7
0.01
0.95
0.36
Color
13
3.3
0.01
0.27
0.79
Hydrogen ion
12
11.6
0.06
0.66
0.51
Double endpoint
alkalinity vs.
Gran plot alkalinity
(unfrozen):
{above)
0. Q7
0.90
0.38
Sulfate was determined by the barium sulfate turbidimetric
method (American Public Health Association et al. 1981) using a
Hach model 2100A turbidimeter. Nine standards from 0-800 neq/1
SO4"" were used to develop a calibration curve from which sample
values could be determined.
Chloride was determined using the mercuric nitrate
titrimetric method recommended by USEPA (Kopp and McKee 1979) .
Color was measured as an index of possible influence of
organic acids, using the platinum-cobalt scale as represented by
standard color discs in a Hellige Aqua Tester.
5 Haines and Akielaszek (1983) measured sulfate by the
microthorin method (Fritz and Yamamura 1955) and chloride by
the ferricyanide method (American Public Health Association et
al. 1981).
11
-------
On the advice of the author (James Kramer, McMaster
University, personal communication), calcite saturation index
(CSI) was calculated from the modified equation given by Kramer
(1981) rather than the general equation given by Kramer (1976).
Mean amount and annual pH of precipitation at each site were
determined from official state climatology maps and from the
latest National Atmospheric Deposition Program (NADP) map
summary, respectively.
QUALITY ASSURANCE
Analytical instruments were calibrated frequently against
known standards and blanks. All analyses were performed in
duplicate and statistical comparisons and evaluations were
performed between the duplicates for each analysis.
As a further check on analytical accuracy and data coding
errors, we calculated total ionic balance and theoretical
conductivity. All ion concentrations were converted to
microequivalents per liter (iieq/l), anions and cations were
summed, and the sum of anions was compared to the sum of cations
for each sample. Stations with large ionic concentration
imbalance (± >100%) were removed from the data set before data
were summarized. Theoretical conductivity was calculated by
multiplying equivalent conductance of each ion from tables in
Weast (1980) by the concentration of that ion for each sample and
summing the results. The result was compared to measured
conductivity for each sample as a check on the ionic balance;
i.e., good conductivity agreement implies that all important
ionic concentrations have been measured (Golterman et al. 1978).
DATA ANALYSIS
Results obtained from the above analyses were coded on a
standard form used by both the New England and Middle Atlantic
studies. Punched cards were produced and sent to Mr. Paul Rago
at the U. S. Fish and Wildlife Service Great Lakes Fishery
Laboratory in Ann Arbor, MI. Mr. Rago used the University of
Michigan's MIDAS (Michigan Interactive Data Analysis System) to
perform some of the more advanced statistical analyses and to
produce the maps used in this report. Many of the basic
correlations, regressions, and comparisons for this report were
performed using a duplicate data set and the Statistical Analysis
12
-------
System (SAS)^ as installed on the IBM computer complex at The
Pennsylvania State University. Further error checking was done
when the data were transformed for entry into the ACID
(Acidification Chemistry Information Database) system at
Brookhaven National Laboratory, as a contribution to the USEPA
effort to determine sensitivity to acidification of waters
throughout the United States (Omernik and Powers 1982).
For lake and impoundment sites, results from the duplicate
surface samples were averaged together with results from the
duplicate samples taken near bottom, to produce a single value
for each measurement before further statistical analysis. For
streams, the duplicate mid-depth sample results were similarly
averaged.
In order to avoid undetected effects of unusual chemistry or
possible contamination, all stations with color 30 units or
greater, with double endpoint alkalinity greater than 1000 |ieq/l,
with chloride greater than 500 (j.eq/1, or with conductivity
greater than 200 p.S/cm were eliminated from the data summaries
which follow. These were in addition to those removed for ionic
imbalance as described above. Many of the 60 stations so removed
from the data set failed more than one of the above criteria.
This process left 218 stations for further analysis. A list of
the removed stations will be found in Appendix C.
6 SAS Institute, Inc., Cary, NC.
-------
RESULTS AND DISCUSSION
QUALITY ASSURANCE
Comparison of the sums of anions and cations for each sample
from the final data set yielded reasonable agreement (Figure 2).
These calculations would not be expected to show perfect balance
because not all the ions present were measured; anions such as
nitrate in particular may have been present in significant
amounts in some waters. The regression line had an intercept of
86.54 and a slope of 0.817 (i?2 = 0.621, p = 0.0001). The
intercept was significantly different from zero (p = 0.008) and,
being positive, supports the hypothesis of "missing" anions. The
slope was not significantly different from 1.0 (p = 0.0001). The
correlation was highly significant (r = 0.788, p = 0.0001).
A further check was made by calculating the theoretical
conductivity of each sample based on content of each major ion,
and comparing these to the conductivities actually measured. The
regression line (Figure 3) had an intercept of 3.260, slightly
different from zero (p = 0.023), and a slope of 0.605, which was
significantly different from 1.0 (p = 0.0001} (R2 = 0.847, p =
0.0001). The correlation, however, was highly significant (r =
0.920, p = 0.0001). Figure 3 also supports the hypothesis that
not all ions present were measured, since in nearly every case
the measured conductivity was greater than the theoretical based
on those ions actually measured.
We also performed an analysis of correlation and percent
difference between the duplicate analyses for each sample. The
results are presented in Table 4.
These comparisons indicate that the data set is free of
major analytical or transcription errors.
14
-------
2000 i
45° line
. x ,•••*
1600
1200 -
C7
d>
3
CO
c
o
Is
£ 800
E
3
CO
400
• ^
A
.• • *
• •
• #» «• •»
V • • • ••
ft* • « •
• 19 • • • •
• • • •
• • • w •
•^4* m • / • • • •
• / •• ••• •» •« • •
• J • • • • nI • •
r m>» • • •
• v • •
• •
I I I I
400 800 1200 1600
Sum of anions Ijjeq-I"1!
Figure 2. Sum of cations versus sum of anions for 211 sites,
all samples averaged for each site. Least squares
regression equation: sum of cations = 0.817 sum of
anions + 86.54 (£?J = 0.621, p = 0.0001).
Correlation coefficient (r) = 0.788, p = 0.001.
(seven sites omitted due to missing values).
15
-------
200 -i
200
Conductivity IjiS-crrr1)
Figure 3: Theoretical versus measured conductivity for 211
sites, all samples averaged for each site. Least
squares regression equation: theoretical conductivity
= 3.260 + 0.605 measured conductivity (R2 = 0.847,
p = 0.0001). Correlation coefficient (r) =0.920,
p = 0.0001. (Seven sites omitted due to missing
values}.
16
-------
Table 4. Comparison of Duplicate Chemical Analyses for
all Samples
Sample 1 vs Sample 2
Ion or Number r mean standardized
Measurement of pairs (p = 0.0001) % difference std. dev.
( = precision)
Hydrogen ion
325
0.974
1.4
57.6
Alkalini ty
318
0.967
3.2
65.1
Conductivity
322
0.974
0.3
12.9
Aluminum
333
0.845
17.8
55.0
Calcium
333
0.999
1.2
2.8
Chloride
317
0.834
1.4
48. 5
Potassium
333
0. 999
1.4
2.7
Magnesium
333
0.999
1.2
2.6
Manganese
333
0.999
42.9
81.2
Sodium
333
0.999
1.6
19.2
Sulfate
324
0.803
0.1
24.2
Color
324
0.948
4.6
59.5
SITE DISTRIBUTION
We sampled 278 lakes and streams in the nine states.
(Figure 1). The distribution of sites among states (Table 5) was
roughly proportional to the areas of the states, but additional
stations were included where convenient (e.g., Delaware) or where
waters were being sampled for other projects (e.g.,
Pennsylvania). Lakes and impoundments comprised 61% of the
sites, and streams comprised 39%. These percentages are somewhat
different from those of the New England study (85% and 15%
respectively) due to the scarcity of suitable lakes in most of
Pennsylvania and some other areas. Stream order ranged from 1 to
4 (N: 1=26, 2=44, 3=26, 4=14). Site elevations ranged from 2 m
(MSL) to 1181 m, with a mean of 330 m.
The distribution of sites by bedrock geology and soil
classes present in each state is shown in Table 6. The
distributions are not exactly proportional to each other or to
the total area of each type in each state because suitable waters
17
-------
Table 5. Nurrber and Classification of Lakes and Streams Sairpled in Each State
No. (7,)
Lakes Area of State removed from
State Streams Total as % of total chemical
Natural Impoundment ® 9"state 3163 analysis
* curtTTwri at
sunmarxes
Delaware
2
0
19
21(7.6)
1.0
2(10)
Maryland
17
0
7
24(8.6)
5.2
9(38)
New Jersey
3
16
12
31(11.2)
3.9
8(26)
New York
8
28
10
46(16.5)
24.4
11(24)
North Carolina
3
1
6
10(3.6)
6.5a
1(10)
Pennsylvania
59
9
20
88(31.7)
22.3
14(16)
Tennessee
3
0
8
11(3.9)
5.2a
3(27)
Virginia
9
2
13
24(8.6)
19.6
4(17)
West Virginia
6
1
16
23(8.3)
11.9
8(35)
Total
110
57
111
278(100)
100.0
60(22)
0
Areas for Tennessee and North Carolina were divided by four since only the Appalachian parts of those
states were sanpled.
-------
Table 6. Distribution of Sampling Sites Among States by Bedrock and Soil Types
Number and (7c) of Numbers and (%) of
Number of Stations on Bedrock Type Stations on Soil Type
State
Stations
I
II
III
VI
NS
SSI
SS2
S2
Delaware
21
0
4
(19)
17
(81)
0
1
(5)
6
(28)
14
(67)
0
Maryland
24
0
15
(62)
5
(21)
4
(17)
8
(33)
10
(42)
5
(21)
1
(4)
New Jersey
31
5
(16)
24
(76)
0
2
(8)
12
(39)
9
(29)
10
(32)
0
New York
46
7
(15)
32
(70)
2
(4)
5
(11)
31
(68)
11
(24)
2
(4)
2
(4)
North Carolina
10
1
(10)
7
(70)
2
(20)
0
1
(10)
8
(80)
1
(10)
0
Pennsylvania
88
0
51
(58)
25
(28)
12
(14)
46
(52)
21
(24)
15
(17)
6
(7)
Tennessee
11
0
3
(27)
0
8
(73)
0
8
(73)
3
(27)
0
Virginia
24
4
(17)
9
(37)
5
(21)
6
(25)
0
21
(88)
3
(12)
0
West Virginia
23
0
10
(44)
12
(52)
1
(4)
6
(26)
16
(70)
1
(4)
0
TOTAL
278
17
(6)
155
(56)
68
(24)
38
(14)
105
(38)
110
(40)
54
(19)
19
(3)
-------
were seldom ideally located with respect to either factor. Sites
were chosen primarily in proportion to bedrock and secondarily in
proportion to soils. Most sites were on nonsensitive or slightly
sensitive soils and/or on bedrock of low to medium acid
neutralizing capacity.
Distribution of sites by bedrock class is shown in Figure 4.
As might be expected, sites in mountainous areas (generally in
the northwestern half of the study area) are usually on bedrock
classes I and II (most sensitive). On the other hand, there are
frequent occurrences of sites on less-sensitive bedrock in
mountainous areas. This emphasizes the point that generalized
maps showing large sensitive (or nonsensitive) areas, e.g. Likens
et al. (1979) and Comptroller General (1981), can be misleading.
Distribution of sites by soil class is shown in Figure 5.
No suitable waters on soil type SI ("sensitive soils dominate the
area") were available.
CHEMICAL FACTORS
Organic Acids and Color
We measured color as an index of possible presence of
organic acids or other contaminants. Only eight of our 278
waters had average color values of 30 or greater, and these were
omitted from our data analysis. (Another 52 were omitted for
other reasons.) For the remaining 218 stations, the mean color
value was 6.04 units, and only six were above 20 units.
Haines and Akielaszek (1983) derived the relationship TOC
(mg/1) = 1.32 + 0.0613 color (Pt-Co units) from analysis of total
organic carbon (TOC) on a 31-sample subset of their data. We
used this equation for our samples and found no significant
relationship between logmTOC (JR2 = 0.00003, p = 0.980) or color
(R2 = 0.0001, p = 0.866) and pH. Similarly, there was no
significant relationship between log TOC [R2 = 0.0052, p = 0.276)
or color (R2 = 0.0028, p = 0.422) and alkalinity.
We also examined the relationship between aluminum and
organic carbon, since it has been reported that aluminum forms
strong organic complexes which could affect aluminum
concentrations (Driscoll et al. 1980). The regression of
aluminum on TOC for our samples was not significant (R2 = 0.0103,
20
-------
Bedrock Class
Figure 4. Map showing distribution of sampling sites by
bedrock classification (see Table 1 for description
of classes).
21
-------
Soil Class
S 2 ~
Figure 5. Map showing distribution of sampling sites by soil
classification (see Table 1 for description
of classes).
22
-------
p = 0.126), nor was there a relationship between aluminum and
color itself (R2 = 0.0078, p = 0.184).
pH and Alkalinity
The distribution of waters by pH (surface values) is shown
in Figure 6. Only in five of the nine states did we sample
waters whose pH's were less than 5.5 at the time of sampling.
Nearly half (13 of 31) of the waters with pH less than 5.5 were
in the sandy, low-lying areas of New Jersey. Seven states had
some waters with pH between 5.5 and 6.5. In all but one state,
the largest group of waters was in the range pH 6.5-7.5. The
exception was New Jersey, in which slightly more were below 5.5.
Table 7 is a summary of the distribution of pH values by state.
The distribution of alkalinity values (Figure 7) was similar
to that of pH, although a somewhat larger portion fell in the
more sensitive (lower alkalinity) class. Only Delaware had no
waters with alkalinity below 100 (jteq/l. There were more waters
which appeared to be sensitive, or already becoming acidified, on
the basis of alkalinity than on the basis of pH. This is not
surprising considering the temporal variation common in pH
readings. Table 8 is a summary of alkalinity values by state.
pH was correlated strongly (r >0.3) with alkalinity,
conductivity and (negative) calcite saturation index, and weakly
(r <0.3) with calcium, magnesium, and disturbance. Alkalinity
was strongly correlated with calcium, pH, conductivity, and
magnesium, and weakly with sulfate, chloride, disturbance, rain
pH, and (negative) elevation. Neither pH nor alkalinity was
correlated with aluminum, manganese, color, total organic carbon
(as predicted from color), elevation, or size of waters.
Haines and Akielaszek (1983) compared a number of regional
surface water chemistry studies with respect to pH and
alkalinity. Tables 9 and 10 are summaries of those studies
compared with the present study; they suggest that waters with
low pH and alkalinity are more likely to be found in areas
receiving pronounced acidic precipitation.
The overall pH of precipitation averaged 4.3 over our study
area, but many locations within it averaged near 4.0. Our study
waters appear generally to be less sensitive than most sites in
other surveys of areas receiving acidic deposition, but are
clearly more sensitive or, in some cases, more acidified, than
23
-------
Figure 6. Map showing distribution of sampling sites by water
pH. Data are for surface samples only and are means
of duplicate analyses.
24
-------
Table 7. Distribution of Average pH Values for Sampled
Waters by State
Percent in pH Range
State n
<5.5
5.5-6.5
6.5-7.5
>7.5
{n=20)
(n=3 3)
(n=ll2)
(n=53)
Delaware
19
0
11
89
0
Maryland
15
7
20
60
13
New Jersey
23
40
30
30
0
New York
35
3
11
51
35
North Carolina
9
0
0
67
33
Pennsylvania
74
11
15
42
32
Tennessee
8
0
0
75
25
Virginia
20
0
10
50
40
West Virginia
15
7
27
53
13
All waters
218
9
15
51
25
Table 8. Distribution of Average Alkalinity (neq/1)
Values for Sampled Waters by State
Percent in Alkalinity Range
State
n
<100
100-200
200-400
>400
(n=68)
(n=36)
(n=61)
(n=53)
Delaware
19
0
10
74
16
Maryland
15
13
13
40
34
New Jersey
23
57
17
9
17
New York
35
26
20
23
31
North Carolina
9
11
11
45
33
Pennsylvania
74
43
16
15
26
Tennessee
8
38
0
25
37
Virginia
20
10
20
50
20
West Virginia
15
40
27
27
6
All waters
218
31
17
28
24
25
-------
Alkalinity
<100
100-200
200-400
>400
Figure 7. Map showing distribution of sampling sites by
alkalinity (txeq/1). Data are for surface
samples only and are means of duplicate analyses.
26
-------
Table 9. pH of Surface Waters Reported by Regional Surveys
(Modified from Haines and Akielaszek 1983).
Percent
in
pH Range
Location
Number
Reference
<5
5-6
>6
Areas
Where
Precipitation
Averages
< pH 4.6
Middle Atlantic
218
4
4
92
This study
New England
226
8
21
71
Haines &
Akielaszek 1983
West Sweden
314
36
21
43
Aimer et al. 1974
West Sweden
15
27
47
27
Dickson 1975
South Sweden
51
2
20
78
Maimer 1975
South Norway
155
18
38
44
Wright et al. 1977
South Norway
719
64
33
3
Wright and
Snekvik 1978
Denmark
14
29
57
14
Rebsdorf 1980
Scotland
72
26
36
38
Wright et al. 1980
Nova Scotia
21
52
24
24
Watt et al. 1979
Quebec
25
12
40
48
Jones et al. 1980
Central Ontario
26
8
58
34
Scheider et al.
1979
LaCloche Mts.
152
28
34
38
Beamish and
Sudbury, Ont.
Harvey 1972
150
13
15
72
Conroy et al. 1976
Adirondack Mts.
849
25
30
45
Pfeiffer and
Festa 1980
Areas
Where
Precipitation
Averages
> pH 4.6
North Norway
77
0
13
87
Wright and
Gjessing 1976
N. W. Wisconsin
265
0
6
94
Lillie and Mason
1980
North Minnesota
85
0
0
100
Glass and Loucks
1980
27
-------
Table 10. Alkalinity of Surface Waters Reported by Regional
Surveys. (Alkalinity Values are in |xeq/l)
(Modified from Haines and Akielaszek 1983).
Location Number
Percent in alkalinity range
Reference
<20 20-100 101-200 >200
Areas Where Precipitation Averages < pH 4.6
Middle
218
15
17
17
51
This study
Atlantic
New England
226
23
18
12
47
Haines and
Akielaszek
(1983)
South Norway
62
3
11
2
84
Wright et al.
1977
Denmark
14
86
14
0
0
Rebsdorf 1980
Nova Scotia
21
71
24
0
5
Watt et al.
1979
Central
26
12
73
15
0
Scheider et
Ontar io
L.
al, 1979
Ontario
600
—
16a
3 2
52
Zimmerman and
Harvey 1979
La Cloche
4
100
0
0
0
Beamish 1976
Mountains
Adirondack
692
41
25
18
16
Pfeiffer and
Mountains
Festa 1980
Areas Where
Precipitation
Averages >
pH 4.6
Northwest
265
—
15c
17
68
Lillie and
Wisconsin
Mason 1980
North
85
0
22
26
52
Glass and
Minnesota
Loucks 1980
<50 neq/1
b 51-200 neq/1
c <100 fxeq/1
28
-------
those receiving less acidic deposition. It is clear that many
waters sensitive to acidification exist within the Middle
Atlantic States.
A map of acid deposition {Figure 8) shows relatively uniform
loadings over the study area except for higher loadings near the
common border of New York, New Jersey, and Pennsylvania. This
border area, however, contained many waters with higher pH
(Figure 6) and alkalinity (Figure 7) and low calcite saturation
index (Figure 9). These waters appear to be relatively resistant
to acidification despite being located on relatively sensitive
bedrock types (Figure 2), perhaps because of nonsensitive and
slightly sensitive soils present in their watersheds (Appendix A)
as noted by Gmur (Nicholas Gmur, Brookhaven National Laboratory,
personal communication).
Calcite Saturation Index
The calcite saturation index (CSI) has been proposed as an
index of vulnerability to acidification by Conroy et al. (1974)
and Kramer (1976). It is calculated as:
CSI = p(Ca++) + p(alkalinity) - p(H+) + pK
where:
alkalinity
p(X) = log10(X)
p(K) = 1.98 at 25 °C
Ca = moles/1
and H+ = equivalents/1
Generally, higher values of CSI reflect lower acid
neutralizing capacity. We grouped our samples into categories of
CSI less than 1, between 1 and 3, and greater than 3 (modified
from Kramer 1976 and Glass and Loucks 1980).
The distribution of stations by CSI is shown in Figure 9.
It can be readily seen by comparison with Figures 7 and 8 that
the areas where most waters had CSI less than 1 were in general
those with the highest pH and/or alkalinity waters. Values of
CSI less than 1 represent waters that are saturated or nearly
saturated with respect to calcite and would not be susceptible to
acidification from acidic precipitation. Values of 1-3 represent
waters potentially susceptible, and values greater than 3
represent definite susceptibility. Of the waters in our study,
11% were not susceptible, 57% were potentially susceptible, and
32% were susceptible, on the basis of CSI. CSI was correlated (r
>0.3) negatively with pH, alkalinity, calcium, conductivity, and
magnesium, as expected. There were weaker correlations with
29
-------
Figure 8. Map showing mean annual acid deposition from
precipitation for period 1976-1979. (Units are
mg/m2/yr hydrogen ion). From February 1981
Interim Reports by work groups under U.S. - Canada
Memorandum of Intent on Transboundary Air Pollution.
Reprinted in Comptroller General (1981).
30
-------
A -Sv
~
/A A
Calclte Saturation
Index
~ «•
~ <•
°rfDa.-AQ *
Figure 9. Map showing distribution of sampling sites by
calcite saturation index. Data are for surface
samples only and are means of duplicate analyses.
Higher values of CSI indicate lower acid
neutralizing capacity.
31
-------
elevation (positive), disturbance, sulfate, and amount of annual
precipitation (all negative). Table 11 is a summary of CSI
distribution by state.
Table 11. Percent Distribution of Calcite Saturation
Index by State
Calcite Saturation Index
State n
<1 1-3 >3
Delaware
19
0
89
11
Maryland
15
13
67
20
New Jersey
23
4
65
31
New York
35
23
49
28
North Carolina
9
12
44
44
Pennsylvania
74
12
54
34
Tennessee
8
0
75
25
Virginia
20
5
60
35
West Virginia
15
7
27
66
Whole Study 218 11 57 32
Haines and Akielaszek (1983) determined that 59% of the
waters they surveyed in New England could be classified as
susceptible on the basis of CSI. They reviewed several other
studies (Kramer 1981, Glass and Loucks 1980, and Zimmerman and
Harvey 1979) and concluded that CSI does not provide a predictive
ability any better than that of pH and alkalinity measurements
alone. In our study, 49% of the waters sampled were susceptible
on the basis of alkalinity less than 200 jieq/1, and 32% were
susceptible on the basis of CSI greater than 3. Furthermore, CSI
showed a strong negative correlation with alkalinity (r = -0.657,
p = 0.0001) (Figure 10). Therefore, we see no advantage in the
use of CSI as a predictor of vulnerability of Middle Atlantic
States waters over the use of alkalinity, except that it does
relate alkalinity and vulnerability to calcium concentration.
32
-------
3.50
3.00-
• •
v":%:
rAt
2.50 -
cr
Q)
~'2.00
3 1.50 i
cn
o
.j
i.oo H
• ••
••
0.50 -
r
0
I
1
1
2
T
3
¦
4
i
5
T
6
Calcite Saturation Index
Figure 10. Relation of log of alkalinity to calcite saturation
index for 218 sampled waters (one point hidden).
Data are means of duplicate analyses and means of
surface and bottom samples where taken. Least
squares regression equation: log alkalinity =
2.367 - 0.154 CSI (i?* = 0.038, p = 0.004).
33
-------
Conductivity and Ionic Composition
Conductivity (= specific conductance) is an easily measured,
widely used parameter which is directly related to the total
ionic content of a water sample. It thus has potential as a
simple index of sensitivity to acidification. However, in some
cases, factors such as proximity to the seacoast (a source of
airborne salts) and differences in ion activities may limit its
usefulness as a predictive device.
The 218 waters included in this analysis had a range of
conductivity from 17 to 199 |iS/cm, and averaged 70. Conductivity
was positively correlated (r >0.03) with alkalinity, calcium,
chloride, magnesium, sodium, and sulfate, and negatively
correlated with elevation, pH, CSI, Secchi disc transparency, and
disturbance. The relationship between conductivity and
alkalinity (Figure 11) was statistically significant (R2 = 0.502,
p = 0.0001) but a few waters with relatively high conductivity
were low in alkalinity. This may be due to conductivity of
elevated hydrogen ion (Weast 1980) or unusual mineralogy. There
was no correlation between conductivity and distance from the
seacoast. Table 12 is a comparison of the relative concentration
of major ions in our waters with those of the New England study
(Haines and Akielaszek 1983) and with world averages.
Table 12. Percent Relative Composition of Major Ions in Sampled
Waters Compared with New England (Haines and Akielaszek
1983) and World Averages (three world averages from
other authors combined from Henriksen 1980)
New England This study
Ions World average
pH<5 pH5-6 pH>6 pH<5 pH5-5 pH>6
Cations
Ca+Mg
Na+K
Anions
HCO3
SO4
88.2
79.5
79.9
91. 0
62.8
75.6
80.9
11.8
20.5
20.1
9.0
37.2
24.4
19.1
82.7
0
15.4
74.8
0
12.5
56.9
17.3
100
84.6
25.2
100
87 .5
43.1
34
-------
200
- 150-
E
o
-------
As noted in the New England investigation, ionic composition
of the surface waters varied markedly with pH (Figure 12).
However, all pH classes of our waters had higher mean ionic
content than the corresponding New England class, and the
differences between total means were not as great. Total ionic
content was highest at high pH and declined at successively lower
pH.
Other similarities between our data and that from New
England are evident: bicarbonate disappeared below pH 5, as
expected, and sulfate remained at approximately the same
concentration while making up a greater proportion of total
anions as pH decreased. The cations calcium and magnesium
declined markedly with pH, but sodium and potassium showed no
such trend. This is probably related to the sources of these
ions; calcium and magnesium are assumed to originate primarily
from the watershed and are related to acid neutralizing capacity,
but sodium and potassium are assumed to originate primarily from
other sources and are not involved with buffering reactions.
Aluminum was approximately equal in the pH >6 and 5-6 classes,
but more than doubled below pH 5. "This is consistent with the
fact that aluminum is more soluble at low pH and thus is
frequently mobilized from watersheds under acidic conditions.
Chloride
Chloride was measured as a potential indicator of
disturbance by human activity and of influence of sea salts.
Haines and Akielaszek (1983) found that chloride concentrations
were a poor indicator of human disturbance for New England
waters. They also found a strong inverse relationship between
chloride and distance to the seacoast, and hence used chloride
concentration as a correction for the ionic contribution of
marine aerosols. This correction technique was suggested by
Wright and Gjessing (1976), who estimated seawater sulfate by
assuming that all the chloride comes from seawater and that the
amount of sulfate attributable to sea salts is proportional to
the sulfate/chloride ratio in seawater. The seawater
contribution of the other ions can be estimated in similar
fashion. Wright and Gjessing (1976) suggested that the
correction was important for areas near the coast but relatively
minor for inland lakes. Also, this technique involves the
assumption that no sulfate is retained or released by soils.
We believe that the seaspray contribution to lakes in
eastern North America is less than the contribution to lakes in
Scandinavia (where Wright and Gjessing worked) due to our
36
-------
700
n: 9
n:190
600
AI
Al
500
HCO3
Na
HCO,
o" 400
3k
300
n:15
CI
Na
Mg
CI
m9
CI
200-
Na
100-
Mg
SO,
Ca
S0A
Ca
SO.
Ca
<5
5-6
pH Range
>6
Figure 12.
Mean ionic composition of sampled waters classified
by pH range. For each water, data are means of
duplicate analyses and of all depths sampled.
(N = number of waters in each range).
37
-------
prevailing westerly winds. Our data do not show a strong
correlation between chloride or conductivity and distance to the
seacoast. We used the ratio of 55% CI to 7.7% SO4, giving a 14%
correction to be subtracted for sulfate from marine aerosols;
i.e., nonmarine SO4 = total SO4 - 0.14 CI. Also, nonmarine Ca =
total Ca - 0.021 CI and nonmarine Mg = total Mg - 0.067 CI
(Sverdrup et al. 1942). In the following sections we have
selectively included, and so labeled, both corrected
("nonmarine") and uncorrected data.
Chloride concentrations in our final set of waters ranged
from 1 to 473 |i.eq/l, with a mean of 124. Chloride was positively
(p = 0.0001) correlated with disturbance, alkalinity, calcium,
magnesium, conductivity/ sodium, and sulfate, and negatively
correlated with elevation. The latter is to be expected if
higher elevation, headwater streams tend to be both less
disturbed and more sensitive to acidification. Of the ten waters
from our final set that were highest in chloride, none were near
the seacoast and all but two had high disturbance codes. One
case with low disturbance was the largest lake sampled (684 ha),
which is a limited-access water supply but lies near an area of
commercial salt production (with possible underwater exposure to
salt beds); the other was a stream.
Sulfate
Sulfate values ranged from 18 to 932 i^eq/l (averages of
duplicate analyses at all depths). Mean sulfate concentration
for the entire study was 227 neq/1. Sulfate was positively (p =
0.001) correlated with alkalinity, conductivity, calcium,
chloride, magnesium, manganese, and sodium; but was less strongly
correlated with disturbance (p = 0.07). It had a weak inverse
correlation with calcite saturation index. All these relations
are as expected, and there is no evidence of any significant
contribution from marine aerosols. When corrected for possible
marine aerosol contribution in the manner of the New England
study (Haines and Akielaszek 1983), our sulfate values were
independent of chloride.
The concentrations of sulfate found in the Middle Atlantic
States are higher than those reported for some other areas where
precipitation is acidic, and much higher than those for areas
with nonacidic precipitation (Table 13). As in the New England
study, neither total nor nonmarine sulfate was significantly
correlated with hydrogen ion concentration (r = 0.026, 0.029
respectively, p = 0.5). Like the New England results, our
calcium and calcium+magnesium values were weakly correlated with
sulfate (r = 0.384, 0.464 respectively, p = 0.0001).-
38
-------
Table 13. Concentrations of Sulfate (|j.eq/l) Reported
from Surface Waters by Regional Surveys
Total Sulfate Nonmarine Sulfate
Region
n
mean
s.d. mean
s.d.
Reference
Areas
where precipitation
averages <
pH
4.6
Middle
Atlantic
218
227
161
211
158
This study
S€W
Haines and
England
226
138
51
127
4S
AkielaszeJc
1983
Sou til
Gjessing et
Norway
26
99
33
92
-
al. 1976
West
Grahr. et al.
Sweden
6
200
70
180
-
1974
Scotland
72
178
91
148
76
Wright and
Gjessing 1976
Nova Scotia
16
152
28
117
25
Watt et al.
1979
Central
Dillon et al.
Ontario
15
198
97
-
-
1978
Sudbury,
Scheider et
Ontario
4
804
292
800
290
al. 1975
La Cloche
Mountains
4
287
42
285
41
Beamish 1976
Areas
where precipitation
averages >
pH
4.6
Central
Gjessing et
Norway
52
65
34
63
-
al. 1976
Western
Armstrong and
Ontario
7
63
-
58
-
Schindler
1971
39
-------
Although all stations in the coastal areas of our study (New
Jersey, Delaware, and eastern Maryland) showed sulfate levels
above 100 (j.eq/1 (Figure 13), this was also the case in several
noncoastal areas (western New York, western Pennsylvania, western
West Virginia; in general the Allegheny Plateau). A map of acid
deposition over the region (Figure 8) shows that except for a
small area of high deposition in the New York-New
Jersey-Pennsylvania boundary area, deposition over the study area
is relatively uniform. The variations in sulfate are probably
more dependent on geological factors such as gypsum or pyrite
deposits than on acidic deposition.
Calcium and Magnesium
We discuss calcium and magnesium together due to their
similar origin and behavior. The sum of calcium and magnesium
was correlated with alkalinity (r = 0.640, p = 0.0001), but not
as strongly as in the New England study (r = 0.95) (Haines and
Akielaszek 1983) nor as in Henriksen (1980) (r = 0.98). This may
be due to the influence of some higher alkalinity waters on our
data. The regression equation of our data (Figure 14) is
alkalinity = 0.42 (Ca+Mg) + 75.9 (both in neq/1) (£J = 0.409, p =
0.0001). This intercept (+75.9) is considerably higher than
those reported in the above-mentioned studies, reflecting the
generally higher level of alkalinity in the Middle Atlantic area.
When the streams and impoundments are removed from our data,
leaving only "natural" lakes, the intercept drops to 23, still
not as low as those of the New England study (-55) (Haines and
Akielaszek 1983) and Henriksen1s (1980) study of data from areas
relatively unaffected by acid precipitation. The slope of our
data is lower than for either of the above studies, suggesting a
lesser role of calcium+magnesium in the higher alkalinity waters
of our area. This is similar to the observations of Aimer et al.
(1978), that the alkalinity:(Ca+Mg) relationship was near 1:1
where sulfur deposition was low, but less than 1:1 where
deposition was high. Calcium concentrations for Middle Atlantic
States waters are compared with those found in studies of other
areas in Table 14.
Aluminum and Manganese
Aluminum was not significantly correlated with pH or
hydrogen ion concentration (r = -0.166, p >0.01 and r = 0.143, p
>0.03, respectively). As expected, high aluminum concentrations
were generally found in low pH waters, but there were enough
40
-------
Sulfate
<100 A
100-200 | |
>200
Figure 13.
Map showing distribution of sites by sulfate
concentration. Values in |ieq/l.
41
-------
1000
800
• • •
• •
600
>*
c
£ 400
<
200
• • •
• •
9 m w w
t ••• • •xTt
f
I I I I | 1
200 400 600 800 1000
Calcium + Magnesium (peg-I"1)
gure 14. Relation of alkalinity to calcium + magnesium for
218 sampled waters (one point hidden). Regression
equation is alkalinity = 0.418(Ca+Mg) + 75.99
(R1 = 0.409, p = 0.0001).
42
-------
Table 14. Concentrations of Calcium Reported from Surface
Waters by Regional Surveys (Modified from Haines
and Akielaszek (1983))
Calcium content, |xeq/l
Region n Reference
Mean S.D. Range
Areas where precipitation averages < pH 4.6)
Middle Atlantic
218
279
242
21-1279
This study
- lakes only
45
321
255
43-1086
- streams only
85
269
248
25-1279
- impoundments
88
266
229
21-1031
only
New England
226
270
395
16-2300
Haines and
Akielasze
1983
South Norway
26
57
33
*"*
Gjessing et
al. 1976
Nova Scotia
16
124
98
42-453
Watt et al.
1979
Scotland
72
239
371
14-1815
Wright and
Henriksen
1980
Areas where precipitation averages > pH 4.6)
World average - 239 - - Henriksen
1980
Central Norway 52 139 110 - Gjessing et
al. 1976
Western Ontario 40 80 - 30-225 Armstrong and
Schindler
1971
43
-------
exceptions to prevent a showing of statistical significance. The
regression equation for our waters is log Al (neq/1) = 2.83-0.23
pH (R2 = 0 . 048 , p = 0.0011). Both this intercept and slope are
lower than those reported by other regional surveys (see Table 12
of Haines and Akielaszek 1983). In general, however, our
aluminum values were higher than those found in New England,
ranging from 0 to 395 fieq/1, with a mean of 46.8. When the
"natural" lakes or the streams are separated from the rest of our
data, the relationship for each alone is only slightly stronger
(i?2 = 0.123 and 0.114 respectively, p = 0.001). Waters with high
aluminum concentrations were widely distributed throughout our
study area (Figure 15). Only six of 218 stations (2.8%) had
aluminum concentrations over 200 (j.eq/1, and only one of these was
at a pH below 5.5.' This is the pH level below which elevated
aluminum concentrations are considered toxic to some species of
fish (Schofield and Trojnar 1980).
Although Haines and Akielaszek (1983) felt that the
similarity of relations between pH and aluminum concentration in
studies reviewed by them would enable the prediction of aluminum
concentration from pH in any region, this would apparently not be
the case in the Middle Atlantic States. This may be due to the
generally higher ionic content, leading to more interferences,
and/or to the generally greater complexity of bedrock and soil
types in our area.
Manganese and aluminum are chemically similar, and our
results for the manganese-pH relation were similar to those for
aluminum. The regression equation is logioMn = 0.11-0.045 pH,
(R2 = 0.0017, p = 0.03), a weaker relationship than that for
aluminum. Haines and Akielaszek (1983) found a similar lack of
correlation between manganese and pH in New England waters.
Manganese values in our study ranged from 0 to 52 jieq/1, with a
mean of only 3.2, much lower than the aluminum concentrations.
Neither aluminum nor manganese was strongly correlated with
alkalinity (r = -0.166, 0.181 and p = 0.0140, 0.0076
respectively), nor were aluminum and manganese significantly
correlated with each other (r = -0.055, p = 0.416) despite their
chemical similarity.
7 Note that in their discussion in the corresponding section,
Haines and Akielaszek (1983) used different units of
concentration.
44
-------
Aluminum
;100
100-200
>200
~ <• 9 £ *
Figure 15. Map showing distribution of sampling sites by
aluminum concentration in p.eq/1. Data are
means of duplicate analyses and of surface
and bottom samples where taken.
45
-------
Precipitation Chemistry
As shown in Figure 8, our study area had some variation in
acid loading, but differences in bedrock, soils, and other
influences masked any relationship which might exist between acid
loading and water chemistry, A loading index was derived from
the product of mean annual weighted precipitation pH (National
Atmospheric Deposition Program 1984) and annual rainfall (from
state climatological summaries for the year of sampling) (values
listed in Appendix A) . Both this index and its logarithm were
tested against pH, alkalinity, elevation, conductivity, aluminum,
calcium, chloride, color, and sulfate. In all cases the
correlation was insignificant (r <0.02).
PHYSICAL FACTORS
Elevation
Neither pH nor alkalinity was significantly correlated with
elevation, although 24 of the 32 stations at elevations greater
than 590 m had alkalinities below 200 |ieq/l, the normal "cutoff
point" for sensitivity. Neither was there any significant
relationship between elevation and bedrock sensitivity, soil
sensitivity or disturbance. It was noted, however, that all 11
stations on bedrock class I (very low acid neutralizing capacity)
occurred in the lower half of the elevation range (< 590 M).
This was somewhat contrary to our expectations, and appeared to
be due to their location on the lower slopes of the poorly
buffered rock areas. Most such areas in the Middle Atlantic
States occur on hill and mountain tops where permanent water
bodies are rare. There were strong negative correlations between
elevation and chloride and conductivity (r = -0.512, -0.451
respectively, p = 0.0001).
As did Haines and Akielaszek (1983) in New England, we found
acidic waters at low elevations, and waters with pH less than 5.5
(some below 5.0) at elevations from near sea level to over 1,000
m. We also found 21 waters with pH greater than 6.5 at
elevations above 590 m; such waters seldom occurred in the New
England study. Although it is certainly true that higher
elevation areas usually have thin soil and sensitive bedrock,
other factors (perhaps disturbance) apparently prevent these from
being dominant influences on the chemistry of Middle Atlantic
waters. This is in contrast to the situation in Norway (Wright
46
-------
and Snekvik 1978) where acidic lakes are rare at lower
elevations, apparently due to the presence of easily weathered
sedimentary bedrock.
Size
Neither pH nor alkalinity was significantly correlated with
lake or pond area or stream width. Most of the natural lakes
sampled were less than 50 ha in area (mean 47) and had pH below
7. Most of the impoundments were also less than 50 ha in area
(mean 36), and their mean pH's tended to be lower than those of
the "natural" lakes. Most small lakes had low alkalinity, but
there were numerous small, higher alkalinity lakes and a few
large, low alkalinity lakes.
Stream width is related only imprecisely to discharge or
other measures which might be expected to reflect acid
neutralizing capacity. Only six of our 85 stream stations had
widths greater than 10 m; five of these six had pH greater than
7.5, and their alkalinities ranged from 227 to 842 [ieq/1. It is
generally true that wider streams have larger drainage areas, so
that greater buffering by natural processes in the watershed and,
possibly, human influence are expected. A high negative
correlation has been found between stream width and acidification
in Pennsylvania streams (Fred Johnson, Pennsylvania Fish
Commission, Harrisburg; unpublished).
Hydrology Type
Both natural lakes and impoundments were separated into two
types - seepage lakes (with no inlet) and drainage lakes (with
inlet). In general, seepage lakes occurred at higher elevations,
were smaller, and were less disturbed, but most chemical factors
were not significantly different between the two types (Table
15).
The chemical factors measured, except alkalinity and
calcium, were higher in the drainage lakes, probably reflecting
leaching from the upstream watershed as well as the immediate
lake basin. The differences, however, were not statistically
significant. Drainage lakes were generally lower in elevation,
larger, and had higher disturbance values, as might be expected.
These differences are probably responsible for the higher ionic
concentrations observed in the drainage lakes. Eilers et al.
(1983) found that in northern Wisconsin, lake hydrology was a
47
-------
Table 15. Chemical and Physical Factors for Lakes Classified by
Hydrology Type, "p" is the probability that means
are drawn from the same population (t-test of one-
way analysis of variance). (No significant
differences found)
Hydrology type
Factor Drainage Seepage
(n=119) (n=14)
Mean S.D. Mean S.D. p
pH, units (from H+ conc.)
5.6
0.9
6.7
0.6
0.61
alkalinity, \ieq/l
256
227
360
303
0.70
conductivity, (iS/cm
71
40
65
38
0.22
calcite sat. index, units
2.7
1.3
2.5
1.2
0.65
calcium, (ieq/1
281
242
317
215
0.49
magnesium, (ieq/1
182
151
152
124
0.28
sodium, |ieq/l
67
53
48
64
0.49
potassium, |ieq/l
47
27
31
27
0.25
aluminum, fieq/1
48
56
14
12
0.04
manganese, n.eq/1
4
8
5
5
0.69
chloride, p.eq/1
141
131
50
55
0.27
sulfate, (Jieq/l
233
173
208
185
0.21
color, Pt-Co units
7.0
5.6
5.3
2.8
0.50
Secchi disc, m
1.8
1.2
1.9
1.3
0.56
disturbance, units
3.0
0.9
2.6
0.9
0.06
elevation, m MSL
285
238
428
209
0.12
area, ha
42
84
24
31
0.22
significant factor in water chemistry; but Haines and Akielaszek
(1983) did not find any significant effect of hydrology in New
England. Differences between the chemistry of lakes and streams
(Table 16) were significant only for calcite saturation index,
manganese, color, and disturbance.
Haines and Akielaszek (1983) also found that streams were
higher in pH than were lakes, but the only other ionic
48
-------
Table 16. Chemical and Physical Factors for Lakes and Streams,
"p" is the probability that means are drawn from
the same population (t-test of one-way analysis of
variance)
Lakes Streams
(n=133) (n=85)
Factor
Mean
S.D.
Mean
S.D.
P
pH, units (based on
H+ conc.
) 5.6
0.9
5.7
1.0
0.06
alkalinity, |i.eq/l
267
237
263
240
0.73
conductivity, |iS/cm
70
39
69
38
0.42
calcite sat. index,
units
2.6
1.3
2.3
1.3
0.09
calcium, ixeq/1
284
238
269
248
0.58
magnesium, |ieq/l
179
148
166
130
0.72
sodium, (j.eq/1
66
54
68
68
0.83
potassium, |ieq/l
46
27
47
28
0.64
aluminum, neq/1
44
54
51
61
0.53
manganese, |xeq/l
4
8
1
3
0.00
chloride, neq/1
134
128
112
110
0.90
sulfate, (ieq/1
230
174
222
140
0.44
color, Pt-Co units
6.B
5.4
4.2
3.8
0.00
Secchi disc, m
1.8
1.2
NA
NA
NA
disturbance, units
3.0
0.9
2.6
0.9
0.01
elevation, m MSL
300
238
343
211
0.33
area, ha; or width,
m
40
80
5.7
5.1
NA
concentration differences between streams and lakes in their
study were in sodium and potassium rather than in manganese. It
seems unlikely that any of these differences are of importance in
controlling vulnerability to acidification, thus we have pooled
data from all waters for many of our analyses.
Sharpe et al. (1984) found that pH, alkalinity, and
dissolved aluminum were functions of stream discharge. If true
for our streams, this would indicate that the conditions we
observed would be influenced by the flow existing at the time we
sampled. This problem probably exists for lakes as well but to a
lesser extent. Unfortunately, neither discharge data nor the
resources to do periodic sampling were available to us.
49
-------
Stream Order
Average width increased with stream order, but no
significant (at the 0.05 level) differences were found among
streams of different orders for any of the characteristics
measured, except disturbance. There was, however, a general
increasing trend in content of calcium and chloride, and a
decrease in calcite saturation index and elevation with
increasing stream order (Table 17). This is the expected
pattern, due to the larger, more varied watersheds of higher
order streams. Johnson (unpublished) examined 1,812 coldwater
stream sections in Pennsylvania and found a high negative
correlation between stream order and three measures of
acidification (pH < 6, alkalinity < 5 ppm, alkalinity < 10 ppm).
Table 17. Chemical and Physical Factors for Streams by
Stream Order. Means with same following letter
are significantly different (one-way analysis of
variance with Scheffe's comparison, p <0.05)
Stream order
Factor
1
(n=18)
2
(n=35)
3
(n=21)
4
(n=ll)
pH, units
5.5
5.6
6.1
5.9
alkalinity, neq/1
279
215
280
356
conductivity, |iS/cm
66
61
69
95
calcite sat. index, units
2.2
2.5
2.3
1.5
calcium, p.eq/1
213
261
261
407
magnesium, |ieq/l
144
175
153
197
sodium, |Jieq/l
65
82
53
54
potassium, neq/1
49
59
43
34
aluminum, (xeq/1
53
64
38
26
manganese, (J.eq/1
1
1
2
1
chloride, neq/1
70
104
107
207
sulfate, |ieq/l
236
223
213
213
color, Pt-Co units
2.6
4.6
4.4
5.2
disturbance, units
2.4
2.3a
2.9
3.5a
elevation, m MSL
405
374
335
164
width, m
2.9
4.3
6.1
14.1
50
-------
Bedrock Classes
Analysis of chemical results compared among bedrock
sensitivity classes showed significant differences only in
alkalinity and potassium (Table 18). This is similar to the
results from the New England study except that they found
significant differences in pH, calcium, conductivity, and calcite
saturation index as well. There was no steady change in any
factor from bedrock class I (very low acid neutralizing capacity)
to IV (very high acid neutralizing capacity) as might have been
expected. This may be related to the fact noted above (under
Elevation) that the class I bedrock stations were not at the
highest elevations (all were less than 550 m). Due to the
relatively small number of sampling sites on class I bedrock, it
is not advisable to draw conclusions from these data concerning
the influence of interactions between bedrock and elevation on
water chemistry. The pH of waters located over bedrock of class
II or III varied over the whole range 4-8 (Figure 16). Only
waters with pH above 6.25 were found over bedrock of class IV,
and, unexpectedly, over bedrock of class I.
Examination of the relationship between bedrock and
alkalinity levels (Figure 17) yields a similar impression.
Although class I bedrock has the lowest acid neutralizing
capacity, no waters with very low alkalinity (< 75 ^eq/1)
occurred in class I bedrock areas. Most waters in class II
bedrock areas had low alkalinities, and waters in classes III and
IV bedrock areas had alkalinities over nearly the full range
considered. Thus even though many waters did reflect the
relative acid neutralizing capacity of their underlying bedrock,
the relationship was not consistent. Haines and Akielaszek
(1983) obtained similar results in New England. They pointed out
that the bedrock classes proposed by Hendrey et al. (1980) are
related to chemical factors associated with acid neutralizing
capacity and acidity but not to other factors which might
influence water chemistry.
Because of the lack of significant differences between
waters on bedrock classes I and II and similarly between III and
IV, classes I and II were pooled and classes III and IV were
pooled for further statistical analysis. Class I^II was termed
"sensitive" and class III-IV was termed "nonsensitive." Also,
waters with alkalinity of 200 neq/1 or less were termed
"sensitive" and those higher in alkalinity were termed
"nonsensi tive." A similar grouping was done for pH, with the
division placed at pH 6.75. We tested the effect of bedrock on
51
-------
70 n
60
»50
o
§
§¦40
0
LL
30
20
10-
Bedrock
Class
I
IV
III
II
ruin
SOB
4.5 5.0 5.5 6.0 6.5 7.0 7.5 BO
PH
Figure 16. Frequency distribution of waters by bedrock
geology in pH intervals. pH values listed
are midpoints of ranges of 0.5 unit.
52
-------
70 n
60 -
£ 50
c
CD
3
O"
2? 40
u.
Bedrock
Class
30
20
10
0
50 100 150 200 250 300 350 400
Alkalinity ljjeq-l_11
Figure 17. Frequency distribution of waters by bedrock
geology in alkalinity intervals. Alkalinity
values listed are midpoints of ranges of
50 jieq/1.
53
-------
Table 18. Chemical and Physical Factors for Waters Classified
by Bedrock Geology. Bedrock classes are: I = very
low acid neutralizing capacity; II = moderately low
acid neutralizing capacity; III = moderately high
acid neutralizing capacity; IV = very high acid
neutralizing capacity (Hendrey et al. 1980). Means
with the same following letter are significantly
different (one-way analysis of variance with Scheffe's
comparison, p = 0.05) (Three waters in class V omitted)
Bedrock class
Factor I II III IV
(n=ll) (n=127) (n=54) (n=23)
pH, units (from H+ conc.)
6.9
5.5
6.1
7.
alkalinity, neq/1
424
229a
263
415a
conductivity, (iS/cm
75
65
78
78
calcite sat. index, units
2.2
2.5
2.6
2.
calcium, |ieq/l
304
122
231
370
magnesium, neq/1
206
175
165
165
sodium, (i.eq/1
109
70
62
43
potassium, iieq/1
69a
49
45
34a
aluminum, p.eq/1
81
49
43
30
manganese, |i.eq/l
6
3
3
2
chloride, jxeq/1
127
122
154
75
sulfate, jieq/1
210
228
233
238
color, Pt-Co units
5.4
5.6
7.3
4.
disturbance, units
3.5
2.7
3.0
3.
elevation, m
260
342
241
346
Secchi disc (lakes only), m
1.4
1.7
1.5
2.
pH and alkalinity using several statistical measures based on
contingency tables. The most useful of these were the phi
measure (Sokal and Rohlf 1973) and the odds ratio (Bliss 1967;
Fleiss 1973). The contingency table format allows us to ask the
following questions:
54
-------
1. Are the numbers of observations within categories any
different than what would be expected due to chance
alone?
2. Is there any significant association between the bedrock
or soil class and the pH and/or alkalinity of the
overlying waters?
3. What are the odds of observing a sensitive water, given
that the bedrock or soil type is sensitive?
The general format of such tables is:
Water category
Predictor Sensitive Non-sensitive Sum
Sensitive nn n]_2 nl'
Non-sensitive n2i T122 n2'
Sum n. ]_ n. 2 n..
If the variables are fully independent, then the observed cell
frequencies will be the products of their respective row and
column marginal probabilities. Chi-square (X2) is the usual
statistic for testing the null hypothesis of independence in such
tables. The association measure {phi) is also used to evaluate
predictive usefulness; in general associations less than 0.35 are
not considered particularly useful as predictors (Sokal and Rohlf
1973). Another, more appropriate measure is known as the odds
ratio (Fleiss 1973). If A represents a sensitive bedrock or soil
class and B represents a sensitive lake type, then the odds ratio
(w) gives the odds, or chance, of observing B when A is present
relative to the chance of observing B when A is absent.
These analyses for bedrock are summarized in Tables 19 and
20. The results agree with the earlier indications (Table 17)
that bedrock is only a marginally useful predictor of the
sensitivity of a water to acidification (based on pH and
alkalinity). The odds of observing a sensitive lake in a
sensitive bedrock area are only 1.1 to 1 and 1.9 to 1 for pH and
alkalinity, respectively. Only in the case of alkalinity does
the chi-square value indicate that the ratio is significantly
different from 1.
55
-------
Table 19. Relations Between Bedrock Sensitivity and Water
Sensitivity Based on pH
PH
Bedrock Sum
Classes
<6.75 >6.75
X2 = 0.057
(p = 0.812)
I & II 54 84 138
III & IV 30 50 80 phi = 0.016
w = 1.071
Sum 84 134 218 s.e.(w) = 0.
Table 20. Relations Between Bedrock Sensitivity and Water
Sensitivity Based on Alkalinity
Alkalinity
Bedrock Sum
Classes
<200 >200
X2 = 5.277
(p = 0.0216)
I & II 74 64 138
III & IV 30 50 80 phi = 0.156
w = 1.927
Sum 104 114 218 s.e.(w) = 0.553
Soil Classes
Results of chemical analyses of sampled waters grouped by
soil class (i.e., soil cation exchange capacity of McFee (1980)
as defined in Table 1) are presented in Table 21. Most factors
exhibited a significant difference among soil classes, whereas in
the New England study only calcium, chloride, sulfate, sodium,
and alkalinity were significantly different. Although we have
56
-------
placed class SSI second in rank in Table 21, waters there exhibit
the lowest mean calcium concentration and alkalinity; they also
have the highest mean calcite saturation index and lowest mean
conductivity (along with those on class S2) and the lowest mean
pH. On the other hand, our means for physical factors suggest
that the order of sensitivity might be S2>SS1>SS2>NS, as we have
presented it.
Table 21. Chemical and Physical Factors for Waters Classified
by Soil Sensitivity. Soil Classes are:
Sl= sensitive; S2= sensitive <50%; SSl=slightly
sensitive; SS2=slightly sensitive <50%, NS= non-
sensitive. Means with the same following letter
are significantly different (one-way analysis of
variance with Scheffe's comparison, p = 0.05)
(No waters were sampled on class SI soil)
Soil
class
Factor
S2
SSI
SS2
NS
(n=39)
(n=86)
(n=46)
(n=47)
pH, units (from H+conc.)
6.2
5.5
5.6
5.7
alkalinity, n.eq/1
242
228
324
297
conductivity, nS/cm
57a
57ab
96a
78b
calcite sat. index, units
2.9
2.7
2.2
2.2
calcium, (ieq/1
240
193ab
368b
379a
magnesium, |ieq/l
106ab
142cd
241ad
223bc
sodium, neq/1
34ac
58b
90ab
86c
potassium, |ieq/l
34ab
50a
51b
48
aluminum, neq/1
31a
63ab
44
32b
manganese, neq/1
3
3
5
3
chloride, tieq/1
78ab
83cd
199bd
166ac
sulfate, |ieq/l
183a
194b
325abc
229c
color, Pt-Co units
4.8
5.1
6.9
7.0
disturbance, units
2.5a
2.8
3.1a
2.9
elevation, m MSL
434ab
331c
217bc
293a
Secchi disc, m (lakes)
2.7a
1.8
1.5a
1.7
McFee's (1980) class definitions (Table 1) present problems
of interpretation when we attempt to assign individual waters to
a class on the map. For example, a lake may clearly be located
57
-------
in an area labeled "S2" on the McFee map, but the full definition
of "S2n is: "sensitive soils are significant, but cover less than
50% of the area." Is our lake thus on sensitive soil or not?
Only a local, detailed survey of the soils around a water body
can provide such information; even then the picture may be
clouded by the presence of several adjacent soil types. Unknown
local soil effects may mask relationships between soil class and
water chemistry. Also, the maps at the scale available to us
were not sufficiently precise to associate a given water with an
exact soil class with certainty in every case. To a lesser
extent, the same problem may exist for the bedrock classes. It
should be noted that the original intent of McFee's (1980) study
was not that the classes be used as we have, but to assess the
proportions of total area in the eastern U.S. likely to be
sensitive on the basis of soil cation exchange capacity.
The frequency distribution of pH of waters by soil classes
(Figure 18) is quite similar to that for bedrock. The greatest
numbers were in the interval pH 6.76-7.25, and classes SSI, SS2,
and NS occurred in nearly all pH intervals. Class S2 occurred
only above pH 4.75. Most, but not all, waters on soil class NS
had pH of 6.25 or greater. This distribution was similar to that
found by Haines and Akielaszek (1983) except that they had no
waters on soil class S2. The relationship beteen soil classes
and alkalinity (Figure 19) was not as skewed as it was for New
England, except that the largest number of waters were in the
highest and lowest alkalinity intervals (>375 and <25 ^eq/l)„
All four soil types were represented in nearly every alkalinity
interval.
Contingency cables were also developed for soil and water
sensitivity on the bases of pH and alkalinity. As before, pH was
divided above and below pH 6.75, and alkalinity above and below
200 ^eq/1. For this analysis, soil classes S2, SSI, and SS2 were
combined and termed "sensitive," and class NS alone was termed
"norisensitive." Results are given for pH and alkalinity in
Tables 22 and 23, respectively. The contingency tables for soil
class are not significantly different from what would be expected
by chance alone. The odds ratios (1.2:1 for pH and 1.1:1 for
alkalinity) both indicate that lake sensitivity is not strongly
related to soil type.
Haines and Akielaszek (1983) found that the odds ratios for
both bedrock and soil class provided good predictions of
sensitivity, although bedrock was much better than soil. Kaplan
et al. (1981) concluded that bedrock influenced water chemistry
not directly but rather through soils* However, they used
different classifications of bedrock and soil from ours, and
58
-------
60-
50-
>*
o
c
-------
Soil
Class
50 100 150 200 250 300 350 400
Alkalinity (jjeq-T1)
Figure 19. Frequency distribution of alkalinity of waters
by soil classes. Alkalinities are midpoints of
intervals of 50 fieq/1.
60
-------
Table 22. Relations Between Soil Sensitivity and Water
Sensitivity Based on pH
pH
Soil
Classes
<6.75 >6.75 Sum
S2 + SSI
NS
+ SS2
20
64
27
107
171
47
Sum
84
134
218
X2 = 0.409
(p = 0.5225}
phi = 0.0 43
w = 1.238
s.e.(w) = 0.414
Table 23. Relations Between Soil Sensitivity and Water
Sensitivity Based on Alkalinity
Alkalinity, p.eq/1
Soil
Classes
<200 >200 Sum
S2 + SSI
NS
+ SS2
23
81
24
90
47
171
Sum
104
114
218
X2 = 0.036
-------
Human Disturbance
The degree of human disturbance in the watershed was
significantly related to elevation, pH, conductivity, and
chloride at the 95% confidence level (Table 24). This was
similar to the New England study results.
The least disturbed waters were at the highest elevations,
and the most disturbed were at the lowest. For lakes, the Secchi
disc transparency was higher in the least disturbed lakes and
lower in those most disturbed, as expected. Most of the chemical
factors closely related to acidification - alkalinity, calcite
saturation index, calcium, magnesium, and sulfate - showed
consistent trends, and pH was higher in classes 3 and 4 than in
classes 1 and 2. Aluminum, potassium, sodium, color, and
manganese were not related to disturbance in any consistent way.
Unlike the New England results, color in our study was not a
potential index of disturbance. Chloride, however, may have some
use in this regard.
Human activities such as construction, waste disposal,
farming, logging, and road travel result in exposure of soils and
rock to increased leaching of chemical constituents, and also may
increase the amount of surface runoff. This is, no doubt, the
cause of these observed relations. Unlike Haines and Akielaszek
(1983), we found no statistical evidence of a relationship
between bedrock class and disturbance (Table 18), but soil
classes S2 and SS2 were significantly related to disturbance
(Table 21). The overall correlation between soil class and
disturbance, however, was low (r = 0.134, p = 0.048).
MULTIVARIATE ANALYSES
In any study with as many variables as the present one, it
is desirable to explore multivariate analysis techniques in order
to clarify the importance of particular variables. We performed
several such techniques of which the results of two are
presented.
Principal Components Analysis
Principal components analysis is a multivariate technique
for examining relationships among several quantitative variables.
We used it to identify those chemical factors which seem to have
the most influence on the overall water chemistry, and thus might
62
-------
Table 24. Chemical and Physical Factors £or Waters by
Degree of Human Disturbance (for definitions of
classes 1-4, see Table 2), Means with the same
following letter are significantly different (One-
way analysis of variance with Scheffe's comparison,
p = 0.05)
Factor
Disturbance class
12 3 4
(n=21) (n=51) (n=88) (n=58)
pH, units (from H+ conc.)
5.2a
5.6
5.9a
5.8
alkalinity, neq/1
144
222
273
334
conductivity, (iS/cm
46a
61b
71
84ab
calcite sat. index, units
2 . 9
2.6
2.6
2.1
calcium, iieq/1
261
248
291
311
magnesium, (ieq/1
122
151
172
215
sodium, fjieq/1
56
52
70
77
potassium, kieq/l
45
46
47
49
aluminum, ^eq/l
40
52
38
58
ir.anganese, |ieq/L
3
2
4
3
chloride, y.eq/1
5 7a
78b
131
174ab
sulfate, neq/1
174
209
240
244
color, Pt-Co units
4.9
5.4
6.2
6.1
Secchi disc (lakes only),
m 1.8
2.3
1.8
1.5
elevation, m MSL
535a
399b
315
214ab
have value as predictors of vulnerability to acidification.
Results of this analysis for the major chemical measurements of
our study are presented in Table 25.
The first principal component accounts for the largest
portion of the variance and is dominated by magnesium, calcium,
chloride, sodium, and alkalinity. Sodium and chloride are not
involved with acid neutralizing capacity or acidity, but may
reflect disturbance or the presence of salt deposits in some
watersheds. Alkalinity, calcium, and magnesium are closely
related to acid neutralizing capacity. The second component is
dominated by alkalinity and calcium, with aluminum and potassium
showing an inverse influence. The third component is dominated
by aluminum and potassium, and inversely by hydrogen ion, the
significance of which is unclear. Results for the New England
63
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Table 25. Principal Components Analysis of Chemical Factors
Principal Components
Variable
First
Second
Thi rd
Cumulative percent
of variance explained
by component
25
40
48
Hydrogen ion
Alkalinity
Aluminum
Calcium
Chloride
Potassium
Magnesium
Sodium
Sulfate
-0. 07
0. 29
-0.01
0.35
0.32
0.12
0 . 39
0 . 30
0.26
-0.31
0.30
-0.33
0.17
-0.05
-0.43
0.03
-0.32
-0.02
-0.40
0.15
0.38
-0.20
-0.09
0.35
-0.08
0 .12
-0.15
data were similar except that sodium and chloride were important
only in the second component, and hydrogen ion (as pH) was
important in the first component.
From these results, it appears again that alkalinity is
potentially useful as a predictor, while calcium, magnesium, and
sulfate are also strongly influential on the chemistry of the
sampled waters. The principal components technique is relatively
weak for this data since the first three components account for
only 48 per cent of the variance.
Canonical Correlation Analysis
Canonical correlation is a technique for examining the
relationship between two sets of variables by testing the
hypothesis that each canonical correlation and all those smaller
are actually equal to zero in the population. Given two sets of
variables, a linear combination is found from each set, such that
the correlation between the two combinations, called canonical
variables, is maximized. This is called the first canonical
correlation. The coefficients of the combinations are called
64
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canonical weights, and can be used to determine which variables
have the most influence on the variance of the data set. Next a
second set of canonical variables, uncorrelated with the first
pair, is found to produce the second highest correlation
coefficient. This process continues until the number of canonical
variables equals the number of variables in the smaller group,
but usually not all the pairs are used (SAS Institute 1982}.
We compared chemical measurements to physical factors from
our data using canonical correlation analysis. Only the first
three canonical correlations showed reasonably high probability
of being different from zero, and are presented in Table 26. The
results show good correlations (r^ = 0.70, r2 = 0.35, r3 = 0.31)
between physical and chemical factors, all significant at the
0.05 level. Soil class and disturbance carry the highest weights
among the physical factors, while chloride, sodium, and
alkalinity dominate the chemical factors. This is the first case
in our data analyses wherein soil appeared to be a stronger
influence than bedrock.
We performed a canonical redundancy analysis (van den
Wollenberg 1977) which examines how well the original variables
can be predicted from the canonical variables. This analysis
shows that neither of the first pair of canonical variables is a
good overall predictor of the opposite set of variables, the
proportions of variance explained being only 0.103 (chemical) and
0.135 (physical). The first three canonical variables together
explain only 0.134 and 0.17 5 of the chemical and physical factor
variances, respectively. The squared multiple correlations
calculated as part of the redundancy analysis indicate that the
first canonical variable of the physical factors has some
predictive ability for chloride (0.313) and sodium (0.271) but
little or none for the other ions. The first canonical variable
of the chemical factors has some predictive power for elevation
(0.392), but little else.
We conclude that canonical correlation analysis is not a
strong predictive tool for use with our data, but in general does
not contradict the conclusions drawn from our other methods.
HISTORICAL COMPARISONS
At least one set of historical data was found for 160 of the
278 waters in our sampling program. For the following analysis,
we used unscreened data from our original set of 278 waters, but
only a few comparisons involved waters not in the reduced
65
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Table 26. Canonical Correlation Analysis of Standardized
Chemical and Physical Factors
Canonical correlation
Factor
First
Second
Third
Canonical correlations
0.70
0.35
0 .
31
(p (corr = 0))
Physical factor weights:
size
0.09
0. 40
0.
10
elevation
-0.77
0. 23
0.
24
bedrock class
-0.19
0.74
0.
14
soil class
0. 27
0.17
0.
94
disturbance
0.22
0,55
-0.
50
Chemical measurement weights;
hydrogen ion
0.22
-0.46
0.
13
alkalinity
0.30
D. 49
-0.
73
aluminum
0.17
0.09
-0.
45
calcium
-0.25
0.11
0.
60
chloride
0.65
0.23
-0.
36
potassium
-0.20
-0. 52
-0.
06
magnesium
0.07
-0.29
-0.
64
manganese
0 .17
-0.40
-0.
23
sodium
0.64
-0. 00
-0.
09
sulfate
0.19
0. 33
-0.
04
(screened) set. In several cases multiple historical data points
exist, bringing the total to about 350 sets of historical values.
Most of these contained pH data, but many were lacking alkalinity
data, and a few had alkalinity but not pH. The oldest data found
were from 1930. A listing of all historical data obtained for
each station is given in Appendix B.
pH Comparisons
We compared our pH data with the oldest available pH data
for each station {Figure 20). The low coefficient of
determination (i?2 = 0. 288) indicates that the changes in pH over
66
-------
8 H
45 line
Oldest Historical pH
Figure 20. Recent (this study) pH value versus oldest value
available for 36 locations. Diagonal line
indicates equal pH. Waters with pH > 8, alkalinity
> 250 neq/1, or time span not greater than 2
years have been excluded. Numbers represent time
between observations grouped as follows: 1 = 2-5
years; 2 = 6-10 years; 3 = 11-15 years; 4 = 16-20
years; 5 = >20 years.
67
-------
time are not consistent among sites. Of the 160 waters, 62 (39%)
decreased in pH and 98 (61%) remained the same or increased.
These are nearly the opposite of the comparable figures for New
England waters as reported by Haines and Akielaszek (1983). As
pointed out earlier by Arnold et al. (1980), there are a number
of factors, mostly related to man's activity, that can be
expected to cause increases in the pH of waters over time, but
few that cause a decrease in pH other than inputs of acid from
precipitation or obvious pollution sources such as new coal mines
in the watershed. In general, the greater incidence of human
activity in the Middle Atlantic States compared with New England
is probably an important reason for the higher number of cases in
which pH or alkalinity increased.
The length of time between the oldest "historical" pH
measurement and the measurement done by us at the same location
varied from 1-50 years, with a mean of 17 years. We plotted
change in pH against elapsed time in years for each site and
found no significant trend {R2 = 0.0003, p = 0.73). This
analysis included the entire set of waters for which historical
data were available, and indicates that there is no consistency
in rate of change, if any, among the sampled waters. Many of
these lakes have high acid neutralizing capacity and would not be
expected to show much change in pH, even with significant inputs
of acidity.
As any experienced field worker knows, measurement of
accurate pH in the field, especially on poorly-buffered waters,
is a task having limited and inconsistent success regardless of
the method used. The historical data available to us sometimes
specified the general method (electrometric or colorimetric) used
and sometimes did not. Such information is not a sufficient
basis for judging either the accuracy or the precision of the
data. Furthermore, changing limnological conditions over a few
hours or days, or between seasons, can cause significant changes
in pH and thus render historical pH comparisons meaningless
unless they involve numerous data points on the same water or
large, consistent changes. Our experience leads us to conclude
that the above analyses of changes and trends in the available
data cannot be used to draw conclusions about overall trends
because the accuracy of the historical data is unknown.
Alkalinity Comparisons
A more instructive approach is to look at changes in acid
neutralizing capacity as represented by alkalinity. For these
analyses, fixed endpoint alkalinity {by titration to pH 4.5) was
68
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used for our "recent" measurement, since considerable
investigation by ourselves and other workers has indicated that
almost all alkalinity data before approximately 1975 were
produced by fixed endpoint methods. Kramer and Tessier (1982)
and Haines and Akielaszek (1983) presented reviews of the
literature and considerations involved in historical alkalinity
comparisons. The fixed endpoint method often overestimates true
alkalinity (Kramer and Tessier 1982). We compared the fixed and
double endpoint alkalinities of our samples and found that they
were highly correlated (r = 0.9984, p <0.01), with the regression
equation being double endpoint = 0.977 fixed endpoint - 25.3 (i?2 =
0.997, p = 0.0001). Thus the fixed endpoint historical
alkalinity could be corrected for the overestimation by
subtracting 25.3 iieq/1. This correction was virtually identical
when the data were separated as natural lakes, streams, or
impoundments. Haines and Akielaszek (1983), using a factor of 32
p.eq/1, compared corrected historical data to their double
endpoint data for New England waters; whereas we measured the
fixed endpoint alkalinity and compared it directly with the
historical data. Kramer and Tessier (1982) suggested that the
true correction factor to be subtracted from alkalinity data
obtained by methyl orange titration is 81 p.eq/1. We were unable
to determine exactly which of the historical data we obtained
were done by methyl orange and which by electrometric titration,
nor the exact endpoints used in most cases. Thus the results of
these comparisons should be considered approximate.
The mean historical alkalinity for all 95 available
comparisons was 697 neq/1, and our mean "recent" alkalinity for
the same waters was 489 |ieq/l, or a decrease of 208 neq/1 (30%).
As was done for pH, we plotted the change in alkalinity against
the time in years between observations for each site and found no
significant relationship {R2 = 0.0001, p = 0.88). This indicates
that the rate of decrease in alkalinity varies considerably among
the sampled waters. Only 26 (23%) waters showed an increase or
no change in alkalinity, while 87 (77%) decreased. Haines and
Akielaszek (1983) found corresponding values of 30% and 70%,
respectively, in New England. They reviewed several other
studies which all showed decreases in mean alkalinity of waters
in Sweden and the United States over the same time period as our
data. Johnson (1984) examined data for 41 Pennsylvania streams
surveyed by Pennsylvania Fish Commission personnel using the same
techniques over a time span of about 15 years and showed that 92%
decreased in alkalinity, from an average of 400 neq/1 to an
average of 180 iieq/1.
In a number of articles on the "acid rain problem" (e. g.,
see papers by Hodanbosi, Frohliger, Smith and others in Kostik
1980; also Curtis 1980 and Hunton and Williams 1983) the
69
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Oldest Historical Alkalinity (jjeq-r1)
Figure 21. Recent (this study) fixed endpoint alkalinity value
versus oldest available for 19 locations. Diagonal
line indicates equal alkalinity (no change). Waters
with time span not greater than 2 years have been
excluded. Numbers represent time between
observations grouped as follows: 1 = 2-5 years,
2 = 6-10 years, 3 = 11-15 years, 4 = 16-20 years,
5 = > 20 years.
70
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assertion has been made that the problem is not real because few
waters have shown a major change in pH. However, because of the
bicarbonate buffering system, little change in pH is expected
over a wide range of hydrogen ion loading. Thus alkalinity
changes are a better index of acidification than are pH changes.
Also, alkalinity is a better index of sensitivity to
acidification at a given site, or to compare sensitivity between
sites. Schofield (1982) determined the change of pH between
historic and recent data for lakes in the Adirondack Mountains of
New York, and compared it with recent calcium concentrations. He
calculated that at a level of 100-125 neq/1 calcium, the
bicarbonate buffering capacity would be lost and pH would
decrease. This agreed well with the changes which he observed.
We did a similar comparison and observed some similar results,
but we also observed some increases in pH below 125 p.eq/1
calcium, and a few decreases at higher calcium levels. These
exceptions were all cases where the time span of the historical
comparison was quite long (10-20 or more years). The exceptions
may also support a hypothesis that has been suggested by Coombs
(Marjorie Coombs, Florida Department of Environmental Regulation,
personal communication) and others - that as acid neutralizing
capacity declines to critical levels, pH and other water
chemistry measurements become highly variable among successive
observations at a site. Only long-term periodic monitoring of
such sites can test this hypothesis.
Although alkalinity is, in general, a more stable
characteristic of a natural water than is pH, it can vary
somewhat in time and space within a water body, especially a
poorly buffered one. Thus, as in the case of pH, we feel that
only large, consistent changes based on several data points for
the same water are useful to suggest acidification based on
historical data. Even data showing these characteristics can
only be evaluated if the exact methodology and titration
endpoints used are known so that appropriate correction factors
can be applied as discussed above. These cautions and the small
number of alkalinity comparisons available to us suggest that the
data shown in Figure 21 should be considered only a suggestion
that a decrease in alkalinity may have occurred in many waters of
the Middle Atlantic States.
APPLICATION OF PROPOSED MODELS OF ACIDIFICATION
Several models and classification systems based on water
chemistry have been proposed to determine and/or predict the
degree of acidification of waters or their potential
susceptibility to acidification. We applied several of these to
our data as discussed below.
71
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Sensitivity Classification
Altshuller and McBean (1979) proposed a classification by
groupings under four common water chemistry measurements:
alkalinity, bicarbonate concentration, calcium concentration, and
conductivity. Since in our study bicarbonate was not determined
separately from alkalinity we have not considered it here. We
have also converted mg/1 to (ieq/1 for consistency (Table 27).
Table 27. Sensitivity Classes of Altshuller and McBean (1979)
(Percent of Waters in the Present Study Occurring
in Each Shown in Parentheses)
Alkalinity Calcium Conductivity All three
Sensitivity factors
(% of total)
(lieq/1) (ji-S/cm)
High
0-200
Q-200
0-30
(48%)
(51%)
(9%)
(9%)
Moderate
200-400
200-400
31-70
( 28%)
(22%)
(47%)
(3%)
Low
>400
>400
>70
(24%)
( 26%)
( 44%)
(16%)
Although the agreement between distributions of sensitive
waters based on alkalinity and on calcium is fairly good, there
is poor agreement between these distributions and that based on
conductivity. A larger proportion of waters fall into the higher
conductivity classes, probably due to the presence in many waters
of elements such as chloride, sodium, and potassium that
contribute to conductivity but not to acid neutralizing capacity.
When the data are sorted in this way, only 9% of all waters meet
all three criteria for high sensitivity, and only 3% meet all
three criteria for moderate sensitivity. These percentages are
much smaller than those of sensitivity determined by other
criteria discussed earlier. The classes based on alkalinity and
calcium, however, agree well with our earlier conclusions
regarding sensitivity on the basis of alkalinity and calcite
saturation index. As shown earlier, the calcite saturation index
72
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is highly correlated with alkalinity and also may serve as a
predictor of sensitivity, but it is more complex and has no clear
advantage other than to link pH and calcium with alkalinity.
Calcium-pH Model
Henriksen (1979) proposed a model involving the relationship
between pH and calcium content. He derived an empirical curve
for this relation: waters falling above the curve are classified
as acidified (i.e., they have a lower pH than would be expected
on the basis of their calcium content, due to depletion of their
acid neutralizing capacity). When this curve is applied to our
data (Figure 22) for waters with nonmarine calcium of 300 neq/1
or less, we find that 21 of 75 waters (28%) fall above the curve.
As pointed out by Haines and Akielaszek (1983), the
relationship used by Henriksen presumes that nonmarine calcium
content of surface waters is not affected by acidic deposition.
Their linear regression of alkalinity against the sum of
nonmarine calcium and magnesium was alkalinity = -53 +
0.93(Ca+Mg) (R* = 0.948), which agreed well with Henriksen's
value of alkalinity = -14 + 0.93(Ca+Mg), except for a lower
intercept. The corresponding regression equation for our data
(Figure 14) is alkalinity = 76 + 0.42(Ca+Mg) (R2 = 0 . 409 , p =
0.0001), a higher intercept but a lower slope. The positive
intercept for our data indicates that the calcium and magnesium
ions were generally balanced by bicarbonate, and its magnitude is
probably due to the larger proportion of other anions such as
chloride in our waters.
In the New England study anions other than sulfate and
bicarbonate appeared to be negligible as shown by ionic balance
data which did not include chloride. Our ionic balance (Figure
12) indicates a substantial chloride component, but chloride
probably does not enter importantly into buffering reactions.
Haines and Akielaszek (1983) presented a review of recent
studies concerning the effect of acidic deposition on calcium and
magnesium, or total cation, content of waters. Their evidence
suggests that sulfate increases are balanced by equivalent
bicarbonate decreases so long as bicarbonate is available, and
that when bicarbonate is absent, sulfate increases are balanced
by increases in cations such as aluminum, magnesium, and calcium.
The lack of historical data on concentrations of these ions
prevents us from testing this hypothesis directly. Figure 12
shows that of these ions, only aluminum seemed to increase in
73
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4
5-
7 1
8
••
l I I
100 200 300
Nonmarine Calcium Ijieq.f1]
Figure 22.
Calcium-pH model (Henriksen plot) applied to 75
Middle Atlantic States lakes having calcium
concentrations less than 300 jieq/1. Waters
falling above the curve are considered acidified,
74
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concentration at low pH values where bicarbonate was absent.
However, the data of Figure 12 are means, and trends in
individual waters over time are obscured, especially considering
the generally higher total ionic content of the higher pH waters.
Sulfate-pH Model
Henriksen (1979, 1980} also proposed that acidification
could be related to the nonmarine sulfate content of surface
waters. He constructed a nomograph relating pH to calcium plus
magnesium and sulfate content of waters. We applied the
regressions of this nomograph to our lake data and determined its
success in predicting lake pH (Table 28, Figure 23). The
predictive ability of the model was generally poor on data from
the Middle Atlantic States, except for those lakes in the range
of pH 4.7-5.3. The small number of our lakes in the "acidic"
range makes it difficult to evaluate that part of the model.
Table 28. Prediction of pH of 82 Middle Atlantic Lakes Having
Sums of Nonmarine Calcium Plus Magnesium Less than
500 |ieq/l by Henriksen's Sulfate-pH Model
pH range
<4.7
4.7-5.3
>5.3
Number actually in pH range
6
3
73
Number predicted to be in pH range
8
22
50
Number predicted correctly *
1
3
48
Percent predicted correctly *
17%
100%
66%
* i.e., portion of those actually in
within the predicted isopleths on
the pH
the plot
range that
fall
Haines and Akielaszek (1983) performed a similar analysis
and also reported poor predictive ability although the
distribution of their results was somewhat different. They
suggested that the poor predictive ability might be due to
correlation of sulfate with pH in their data, or to an extraneous
source of sulfate. They improved the model's predictive success
somewhat by applying an arbitrary reduction factor to the sulfate
75
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* 500
1
E
.3 400
CO
~#
o>
§
+ 300
E
3
¦¦M
Q 200
• •
• •
• _ •
• ••
• • •.
100
2
o
E
3
<0
100 200 300 400 500
Nonmarine Sulfate (peq-f1)
Figure 23. Sulfate-pH model (Henriksen nomograph) applied to
82 Middle Atlantic States lakes having calcium
plus magnesium concentrations less than 500 (ieq/1.
Diagonal lines indicate Henriksen's (1980)
regressions for waters of pH 4.6-4.8 ("4.7") and
5.2-5.4 ("5.3"). Symbols indicate pH of waters
sampled in this study: ~ = pH < 4.7, ~= pH 4.7-
5.3, •= pH > 5.3.
76
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values. We did not experiment with the model in this way because
our sulfate data were not correlated with pH and because, as
stated earlier, we found no evidence for a marine source of
sulfate in our data. The use of the "nonmarine" correction for
sulfate has already reduced our values below the actual
measurements.
Cation Denudation Model
Thompson (1982) proposed a model of acidification based on
the idea of "cation denudation" or the export of cations from a
watershed in runoff. It is similar to the sulfate-pH model
(Henriksen nomograph) except that the sum of cations is used
rather than only calcium plus magnesium. With this model pH can
be predicted from the carbonate buffering system of natural
waters.
Table 29. Prediction of pH of 74 Middle Atlantic Waters Having
Sums of Nonmarine Cations Less Than 600 |ieq/l and
Nonmarine Sulfate Less Than 500 \ieq/1 by Thompson's
Cation Denudation Model
pH range
4.0—5.0 5.1-6.2 6.3-6.5 6.6-6.8 >6.8
Number actually in
pH range
Number predicted to be
in pH range
Number predicted
correctly *
Percent predicted
correctly *
7
10
9
11
37
2
14
30
15
11
0
2
5
4
8
0
20
56
36
57
* i.e., portion of those actually in the pH range that
fall in it on the plot
We examined our data using this model (Table 29, Figure 24)
and compared predicted values with those observed for the actual
77
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600
T_ 500
¦
or
.E 300
CO
E
c
o
5 200
o
E
3
(/)
100
¦I I
100 200 300 400
Nonmarine Sulfate (jjeq-l~1i
500
Figure 24. Thompson cation denudation model applied to 74
Middle Atlantic States waters with sums of
cations and sulfate concentrations less than
600 (jteq/1. Numbers indicate observed pH
ranges (sample values averaged over depths and
replicates): 1 = 4.0-5.0, 2 = 5.1-6.2, 3 =
6.3-6.5, 4 = 6.6-6.8, 5 = > 6.8.
78
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samples. The predictive success was poor, ranging from 0-57%.
In the New England study the fit to this model was good at lower
pH values but deteriorated as pH of samples increased. This was
not true of our data; in fact the agreement was better at higher
pH values. There was no other obvious pattern to the agreement,
and little improvement would be derived from reducing the sulfate
values.
As pointed out by Haines and Akielaszek (1983) this model
depends on the relationship between excess sulfate and
acidification, as does the Henriksen pH-sulfate model. It can
thus be used to predict pH changes for given sulfate loads if the
relationship between sulfate in surface waters and sulfate in
atmospheric deposition is known. Unfortunately this is not yet
the case.
The cation denudation model can also be related to
acidification which has already occurred. All points falling
below the pH 5.1 line have other nonmarine cations in excess of
bicarbonate; they may thus be classified as acidified. Of the 74
waters in our survey which are included in Figure 24, only 2 (3%)
fall in the "acidified" category on this basis. This is
considerably less than the 28% found by the calcium-pH model, and
the 39% which decreased in pH based on historical data; or, of
course, the 77% which decreased in alkalinity. Thus it appears
that this model is not particularly useful for studying
acidification in waters of the Middle Atlantic states.
79
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CONCLUSIONS
1. About 45% of the unpolluted, relatively undeveloped waters
sampled in the nine Middle Atlantic States are vulnerable
to acidification on the basis of alkalinity being less
than 200 fxeq/l, or calcium content less than 200 (j.eq/1.
Differences in water chemistry between lakes and streams,
as groups, were small.
2. Alkalinity is probably the most useful and relatively
accurate predictor of sensitivity to acidification.
Calcite saturation index is also a valid predictor, but
has no advantage over alkalinity.
3. Comparisons with historical data on the same waters indicated
no significant overall change in pH but some decrease in
the alkalinity of many waters. The accuracy of the
historical data cannot be evaluated, however, so no firm
conclusions regarding change can be drawn from it.
4. The calcium-pH plot of Henriksen is a fairly good predictor
of lake pH values as related to calcium content. It
predicted that about 28% of the sampled lakes were
acidified.
5. Neither bedrock nor soil class is a particularly strong
predictor of sensitivity to acidification. Bedrock class
is somewhat related to alkalinity of Middle Atlantic
States waters, but soil type is not, at least when
determined from rock and soil maps presently available.
Thus some published maps showing geographic areas which
are sensitive to acid precipitation, but which are based
on bedrock alone, may be misleading.
6. There was only one case of a potentially toxic aluminum/pH
combination among the sampled waters.
80
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RECOMMENDATIONS
The principal unanswered questions raised in this report
could best be addressed by the collection of water chemistry data
on a regularly repeated basis, at least once per season, from a
selected group of the waters sampled for this study. Waters with
low levels of calcium, alkalinity, and total ionic content should
be emphasized. At least a majority of the waters should have
data on their past water chemistry available, in addition to that
collected for this study. Also, concurrent data on the status of
fish populations and their food chains in the same waters should
be collected in order to document the effect, or lack thereof, of
observed water chemistry trends on the aquatic ecosystem.
81
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88
-------
APPENDIX A. DATA OF THIS STUDY
Data in the head block for each station are arranged as
follows by line (see text for details):
1. station number - water body name - county - state
2. latitude (degrees/ minutes, seconds) - longitude - CJSGS
7.5-minute quadrangle name - elevation (m)
3. date of sample (year, month, day) - Secchi disc
transparency (m) (lakes only, streams = ***)
- approximate area (ha) of pond/lake, or width (m) of
stream - bedrock class (see Table 1) - soil class
(see Table 1}
4. disturbance code (see Table 2) - annual mean pH of
precipitation at site - mean annual amount of
precipitation at site (mm)
The labeled columns are as follows:
D
depth of sample (m)
T
temperature at sample depth (°C)
FLD
PH
pH measured in the field
LAB
PH
pH measured in the laboratory
FEP
ALK
fixed endpoint alkalinity as
calcium carbonate (neq/1)
DEP
ALK
double endpoint alkalinity as
calcium carbonate (neq/1)
COND
conductivity in |iS/cm
A1
aluminum (total) in neq/1
Ca
calcium in jieq/1
CI
chloride in ^teq/l
CLR
color in Pt-Co units
K
potassium in |xeq/l
Mg
magnesium in neq/1
Mn
manganese in neq/1
Na
sodium in neq/1
S04
sulfate in |ieq/l
in most cases where there are samples listed from two
depths, the second depth is the near-bottom sample taken near the
deepest part of the lake or pond. (In a few cases our sampling
equipment would not reach bottom; these were generally over 30 m
deep and the second sample was taken at the maximum depth
possible but not necessarily near the bottom).
89
-------
FLD LAB FEP
D T J>H_ £H_ ALK
i 10 1 CAYUTA LAKE SCHUYLER HI
422127 764434 ALPINE 402a
81/3/3 1. 5* lUBha BS2 S5
DIST4 BAIMpH: 4.1 364aa
0 a 7.2 7.33 745
2 0 7. 2 7. 59 1155
2 10 2 GPE2N V AL LET LAKE SCHCTLE8 HI
422440 770323 MAINE 3B1a
8 1/ 3/ 3 1.5k 3Qha BR2 S5
DIST4 RATNpH: 4.1 3 13an
0 0 7. 3 8. 26 4 16
2 0 7.2 B.44 457
2103 CIlNNINSHAfl CREEK STEOBEN NT
4 21926 773550 CANI57E9 470a
f) 1 / 3/ 3 ***a 4a BH2 S5
DI5T3 i! A IMpil: 4. 1 864aa
0 0 7.5 6. 8 a. 6 74
2101 C.ASE LAKE CATTARA0GU HT
421357 7d2358 FEAKKLISTILLS 534a
SI / 3/ 3 ***¦ 25ha Bfl2 54
DIST3 RAIKpH: 4.1 }067*a
0 0 7.7 7.39 313
2135 B22HTJSTE& CBESK CATTAEA03A 8Y
420553 7o4d2a LIKESTOHE 432a
01/ 3/ 4 **«¦ 7a BB2 S5
DIST3 RAISpH: 4. 1 13 67aa
0 0 6.9 7.40 167
2136 0 K NAMED POND STEUBEN NY
421156 771634 RATHBONE 4 11a
81/ 6/29 1.7a aha BH2 S5
0IST3 HAItfpH: 4. 1 364aa
C 22 6.6 9.29 b 23
2 21 9.2 9. 41 633
2137 FOSTEB LAKE ALLEGANI NT
4213:.5 774943 ANDOVER 689b
31/ b/29 1.3a 12ha BB2 S5
DIST3 RAINpH: 4. 1 914aa
0 22 7.0 7.71 181
2 21 6.7 7.44 176
213B FAULKHER ?OS3 A LL EGA NT HI
420746 73J73J BOLIVAK 701a
o 1 / 6/29 O.tia 1n a BR2 S5
31S72 E.A I Noli: 4.1 914aa
a 23 7. 2 7. 11 63
3 21 7.3 7. 26 72
696 200
140 1 215
24 764
30
688
417
108
3 4
279 3.i> 235
499 0.0 160
391
426
380
42 1
1 17
118
29
43
521
70 9
311
296
39
61
223
453
3.0 165 2d0
0.3 11 5 273
629 173 36 1250 292 4 77 674 0.0 133 5 19
29 1 76
26
445
123
93
177 0.0 185
3 25
1 40
52
38
322
39
59
176 0.0 131
182
587 125
574 125
3
3
654
66 3
313
298
12
13
214
223
1. 3 18
1.9 19
163
163
149
145
49
50
271
27 3
34
32
17
17
122
124
2.3 15
2.0 14
i a u
133
31 34 5 154 31 3 16 65 4.4 7 111
49 36 6 162 2 3 16 67 7. 2 7 106
-------
FLD LAB FEP
D T pH pB ALK
2 10 9 K5TSEE PES'EH VOIP CA.TTAR AUGO HI
<•20640 78573d STE AHBUHG 503a
81/ 6/29 1.5* Hha B32 S5
DIST3 HAIHpH: 4.1 IIIBaa
0 24 6.6 7.81 172
7 10 6.3 6.68 343
2110 SA3SDERS POSD ALLEGAHI 8/
421333 775815 SELLSVILLE HOE 533*
tf1/ 6/29 0. 5m 61ta BB2 S5
DIST3 RAIHpH: 4. 1 914aa
0 23 9.0 9.24 851
1 22 9.0 8.56 878
2111 CLEAB LAKE EBIE HI
423312 785108 LANGFORD 369a
6 1/ 3/ 4 0.9« 19ba BB2 S5
DIST2 EAI HpE: 1. 1 1316m
0 0 7.1 7.37 407
2 0 7.2 7.21 556
2117 HEHLOCK LAKE LI?IMG5TOH HI
424600 773703 H0NE3IE FALLS 276*
t»1'/ 6/14 2.5a bd4ha BB2 55
DIST2 EAIKpH: U.I 613aa
0 21 8.5 3. 17 954
15 12 7.5 7.63 1090
2120 DUCK LAKE CArUIA SfT
430855 76"412«) VIC TO B T 121a
61/ f>/l4 1.3m 86 h a BB4 S5
DIST4 PAISpH: 4. 1 914 nm
0 23 8.5 3.33 2022
2 21 6.5 7.72 2023
212 1 SILVER LAKE OSHEGO HI
432240 76330) OSWEGO 0EST 106a
81/ 6/14 »*»a 47ha BR2 S5
DIST3 RAI Npli: 4. 1 914aa
0 21 7.5 8.J1 729
2123 CHASE POND 0SSE3O MI
422112 755912 PAH THE fi LAKE 172a
81/ 3/ 4 **»» 23tia 3E3 S5
DIST3 HAINpH: 4.2 1270ma
0 0 6.0 5.87 108
;124 ECHO LAK2 ONEIDA KY
432509 75155t> BCONEVILLE 397a
81/ 3/ 5 2.7b 37ha BR4 S3
DIST4 BAIHpH: 4.2 1321am
0 0 6.6 7.22 383
3 2 6. 5 7. 19 1216
DEP
ALK COHD A1 Ca CI CLR K Hg Wn Ni SO^
145 45 23 79 29 4 33 67 1.7 15 127
321 62 50 120 39 4 46 145 40.3 46 127
801 135 11 617 39 10 22 356 9. 5 57 UB
846 139 12 590 47 10 23 367 10.8 57 153
375 131 48 755 234 11 60 535 0.0 133 303
525 141 37 612 279 12 72 331 0.0 134 248
918 19b 7 1123 414 3 17 52a 0.4 61 361
1050 203 & 1050 422 3 23 525 0.5 7 2 390
199 1 219 10 1886 345 4 2 1 J065 0.9 159 204
1968 246 9 2163 335 7 20 1 145 1. 4 158 208
656 85 9 242 39 9 20 127 0.9 18 72
69 31 31 546 13 18 6b 2bS 0.D 144 148
351 52 30 445 3 1 93 195 0.0 163 72
1170 126 31 477 11 1 81 226 0.0 156 203
-------
FLD LAB FEP
D T pH pH ALK
2125 CATSPA8 LAKE LEWIS HI
434217 751730 BRANTINSHAH 361*
81/ 3/ 5 1.5* 13ha Bfi1 S3
DIST3 HA XNpH: 14.2 1321**
0 0 4.8 4.79 4
2 3 *.8 5.33 37
1126 SEARS POND LEWIS NY
434425 75*259 SEAKS POKD 529*
81/3/5 «*«* 34ha Bfi2 S5
DIST3 RAINpH: <1.2 1219**
0 0 6.3 7.32 239
2128 ASHCBAFT POND OUTLET LEWIS ST
440156 752808 NATOBAL BRIDGE 262*
81/ 3/ 5 16* BB1 S5
DIST1 KAINpH: 4.2 13 16**
D 1 5.6 6.55 136
2129 LAKE OF THE WOODS JEFFERSON NY
44 1900 754333 BUS KELL0 Ht5E LA 100*
81/ 6/11 5.5* 72ha BR4 S5
t>IST2 UIDdH: A. 7 914**
0 2 6.6 9.13 679
22 10 7. 1 7. 5} 554
2131 TRODT BROOK ST LABRENC NT
443956 744750 PARISHVILLE 2 15*
81/ 3/ 5 3b BR2 S4
DIST3 HAINpH: 4.2 ?14bb
0 0 7.3 8.53 518
2132 LAKE DOANE 3UTLST FRANKLIN HY
444128 741854 LAKE IirUS 457m
8 1/3/ 6 ***« 2* BR 1 S3
DIST3 RAINpH: 4. 3 9 Han
0 0 5.3 6.61 126
1134 SILVER LAKE CLINTON HY
443045 735150 REDFORD 025a
81/ 3/ 6 2. 1* 3 25h a BK1 S3
DIST4 RAINpH: 4.3 9 14m*
0 0 6.0 7.23 189
2 0 6.0 7. 17 153
2 135 NEWPORT POND ESSEX HY
4 4 34JI8 733545 WITHERBEE 118a
8 1/ 3/ 6 1.5* 9ha BR2 S5
DIST2 RAINpH: 4.3 914aa
0 0 5. 2 6. 41 95
4 2 5.5 6. 49 217
-10
9
14
15
31
31
406
496
82
3 1
189 0.0 157 46
236 0. 0 152 57
221
37
38
633
7 25
70
357 0.0 132
74
104
32
50
981
13 25
45
733 0.0 98
208
634 85
519 133
493
487
53
60
1 4
13
177
178
0.0 17
0.5 17
127
132
481
84
28
395
55
95
161 0.0 175
288
99 54
44
811
113
54
533 0. 0 112
101
161
125
35
39
27
29
368
427
10
13
101
79
146 3.0 182
182 0. 0 168
216
216
76 39 31 467 97 20 72 218 0.0 157 84
190 53 37 659 134 15 66 363 O.O 129 78
-------
FLD LAB FEP
D T pH pH ALK
2137 CRYSTAL LAKE (f ABB EH HY
433918 734435 BRANT LAKE 291a
81/ 3/ 6 1. 5a 23ha BB1 S3
DIST4 BAINpH: 4.3 914aa
0 0 6.0 6.SI 165
9 2 5.7 7.26 317
2138 BOTLEB PORD tfARHEB #r
432152 734357 GLENS FALLS 317a
81/3/6 »«~» 36ha BE2 S2
DIST1 BAIHpH: 4.3 1067aa
0 2 5.5 6.73 137
2140 DOCK POHD FBANKLXH HI
443603 740840 DeBAB HOOHTAI8 502a
81/ 3/ 6 0.9a 26ha BB 1 S3
DIST2 BAIHpH: 4.3 914aa
0 0 4.9 5. 15 28
2 2 4.7 5. 17 32
214 1 KING PHILLIPS SPRIHG ESSEI NY
440435 733949 BIT HERBEE 305a
81/3/6 ***m 1m . BS2 S5
DISTO BAIMpH; 4.3 914aa
0 4 5. 2 6. 31 149
2143 BALPOOR LAKE ESSEX KY
445132 743033 NEWCONB 15' 545a
8 1/3/6 2.1a 38ba 022 S3
DIST3 RAINpR: 4.3 914aa
0 0 5.4 5.B2 63
2 1 5.5 6.28 81
2144 TBIB TO SPBUCE CREEK REBKIHEB HY
431322 744842 SALIS30HT 476a
81/ 3/ 7 **»a 5a BH2 S5
DIST.2 BAIHdU; 4,2 1219aa
0 1 5.5 4. 33 0
214 6 CATAMOUNT POND FBANKLIN NY
44 1548 74 3 800 CHILDHOLD 463a
81/ 6/12 1.5a 42ha BH2 S3
DIST2 RAINpH: 4.2 1016bb
0 20 7.3 6.73 112
3 18 7. 3 6.67 80
2147 ROCK LAKK HASILTON HY
434958 742033 BLUE SOU ST AIN 520a
81/ fa/13 2.3a 75h a BB1 55
DIST1 RAINpH: 4.3 914aa
0 19 7.0 7.03 127
4 18 6. 9 7.20 122
149 47 38 645 81 9 12 355 0.0 131 94
276 58 34 558 73 5 82 277 0.0 145 89
93
36
25 326
39
89
122 9.0 191
265
0
5
13
20
31
31
491
473
79
65
230
221
0.0 153
0.0 157
35
127
136 31
156
32 3.0
62
36
49
45
72
40
40
683
698
229
464
9
11
65
56
398
408
0.0 126
0.0 123
67
72
-9 30 89 2035 5 9 29 2650 9.3 54 72
63
45
41
35
134
129
123
84
25
25
18
18
63 0.6
63 0.9
100
89
86 38 8 31 87 15 18 16 0.7 2 72
100 41 15 92 63 20 25 62 0.5 11 62
-------
FLD LAB FEP
D T pH pH ALK
2148 «G' LAKE HAMILTON HI
432500 743833 PISECO LAK 15' 619a
81/ 6/13 2.5* 36ha B82 S3
DIST1 RAINpH: 4.3 1321aa
0 20 5.5 5.59 36
6 13 6.2 5. 19 31
2149 KECKS CENTER CREEK FULTON SI
433240 742707 PECK LAKE 325a
81/ 6/13 1a BR4 S2
DIST1 RAINpH: 4.3 13 16aa
0 18 7.4 7.65 1327
2151 BOND LAKE HIAGABA NT
431048 785500 BANSOH7ILLH 167a
81/ 6/15 3.0a 13ha BE2 S5
DIST3 RAINpH: 4. 1 864aa
0 25 8.7 9.39 124)
2 22 8.8 9.27 1509
2152 DABIEN LAKE GENESEE HI
425435 782700 CORFU 271a
bl/ 6/15 3. Oa 23ha BR4 S5
DIST4 HAIWpH: 4.1 864aa
0 22 7.9 7. 53 16 14
3 22 7.9 7.55 1512
2190 SKI L AK B BHOOHE HI
420452 753253 GOLF StJSfllT 524a
81/ 7/23 1.5a 12ha BB2 S5
DIST3 RAINpH: 4.2 1367aa
0 25 6.4 7.28 203
3 23 6.3 7.23 189
2191 CRTSTAL LAKE SULLIVAN NI
413646 745442 EL0RED 387a
81/ 7/23 2.3a 61ha Bfi 2 S5
DIST3 RAINpH: 4.3 1118aa
0 25 5.9 6.63 69
6 12 5.6 6.11 157
2192 GOYHARD LAKE ORANGE NY
412555 743555 OTISVILLE 217a
81/ 7/23 1.7a 13ha BR2 S5
DIST3 RAINpH: 4.3 1118aa
3 26 6.3 7.47 183
3 25 6.3 7.24 204
DEP
ALK COND AJ. Ca Cl_ CLR K Mg Mn Na SO^
9
13
22
€4
339
33
5
5
3
19
31
16
1.8
1. 3
62
67
991 118
811
31
10
217 0.0 14
78
1204 187 45 832 443 6 44 901 0.7 287 120
1339 187 39 861 517 4 4 1 913 1.3 258 137
1475 553 0 780 4249 4 10 216 0.4 16 295
1388 578 0 1731 4072 15 b 417 0.9 26 321
139
130
34
39
14
13
13b
142
34
36
7
10
25
24
90
85
3. 2
3. 7
15
14
67
72
23
10 2
41
43
9
10
132
125
103
79
10
1 1
23
22
69 0. 9
70 13.8
9
ID
148
101
125 79 19 233 219 5 28 148 3.3 30 273
143 61 22 184 142 7 32 150 3.6 32 249
-------
FLD LAB FEP
D I pH pH ALK
2193 KLBINE KILL LAICK OLSTEB it
<> 1 <»502 740631 HOHONR LIKE 171*
SI/ 7/23 I.J* 2ia BB2 S3
DIST1 HAlNpH: 4.3 111811
0 23 7.1 7.77 759
3 IB 6.8 7.76 920
2194 NOKTfi LAKE SSSEHB SI
421102 740223 KAATEiSKILL 650b
ol/ 7/22 1. j>* 16h a BB2 S5
DIST4 HAINpH: 4.3 11 18*1
0 23 6. 5 6.45 120
2 23 6.6 6.99 1W
219 5 ALDEB LUKE BLSTEB HT
420259 744051 ABSNA 674*
81/ 7/22 1.5* 16ha BB2 S5
DIST2 HAISpH: 4.3 IHBll
0 23 7.6 7,57 25}
4 17 6.4 6.47 287
^ 2196 SO HFJILL PO»D DELAHABE ST
<-n 422730 745350 DAVSSPOfiT 503*
81/ 7/22 2.3* lha BH2 S5
DIST4 EAINpH: 4.3 1067**
0 23 6.6 7.35 282
2197 TOMS LAKE DELA8 AEE HI
421356 744649 ASDES 608*
dl/ 7/22 1.3* 7ha Bfi2 55
DIST3 RAIMpHl 4.3 1092**
0 24 6. 2 6. 36 294
2 23 8.6 9.03 296
i198 SEXSfllTH LAKE DELAKABE NT
422754 74 u 9*1 DAVENPOfiT 564*
61/ 7/22 2.0* 13ha BH2 S5
DIST3 RAIUpH; if. 3 U 67»a
0 24 6.6 7.24 129
3 1<* b. 3 7. 13 106
2199 rillB TO ELH CK53K ST LA8
442030 751256 5D«ART>S
fa1/ 6/12 **«« 2n BB3
PIST2 EAIMoH: 4.2 1016«b
ENC NI
225®
3
0 14 3.1 3.62 606
2201 l-AKE GEHEVISVE MASSES KJ
U15937 745802 BLAIHSTOBH 125*
01/ 1/29 1.5a 8ha BB4 S3
DI3T4 RAINpfl: 4.3 1219m
0 0 7.3 7.24 2349
1 4 7.4 7.43 2571
6S9 103
860 122
29
30
428
433
36
6B
35
37
403 2.1 98
425 20. 3 107
327
244
69
88
29
35
110
108
31 18
73 19
19
20
55 2. 4
54 2.5
106
223
213
245
40
67
253
242
7
26
9
10
65 1. 2
66 4.9
89
111
236
42
11
192
10
22
110 2.9 17
122
251
231
45
53
15
14
19 1
200
29
113
7
6
26
25
129
129
1.5 24
1.7 23
494
528
87
55
37
31
12
13
130
131
113
21
0
7
24
23
81
61
1. 7 13
1.8 13
284
340
606 79
2 3 30 5
26 25
31
251 0.0 5 i
67
2261 230 31 922 514 3 37 432 0.8 154 577
2520 338 37 846 543 4 77 45u 0.9 135 564
-------
FLD LAB FEP
D T pH pH ALK
2202 FAIRVI3W LAKE SDSSEX aj
410437 745445 FLATB3 00K7ILLE 278m
81/ 1/29 2.1m 43ha BH2 S4
DIST3 RAIHpH: a. 3 1168mm
0 0 6.0 6.95 259
3 <1 6.2 6. 55 19a
220 3 ASHH05 LUKE SOSSEX SJ
411050 744855 caLVSBS GAP 241m
«1/ 1/29 2.1m 13ha BR2 S5
DIST3 RAIHpH: 4. 3 1143am
0 0 6.4 6.93 352
2 5 6.5 6.50 402
2204 STEENTKIL L LIKE SUSSEX BJ
411907 744034 POBT JERVIS SO 406a
81/ 1/29 2.4i 4ha BB2 S4
DIST4 RAINpH: 4.3 1118mm
0 0 5. 9 6.63 194
3 4 6.1 6.17 222
2205 LAKE LOOKOUT SOSSEX MJ
410930 742335 BAtUTANDA 363m
vo 61/ 1/30 1.2m 5ha PS2 S4
0-1 DIST0 RAIK^H; 4.3 U6a*m
0 0 5.6 5. 99 125
1 4 5. 6 6. 03 171
2206 STEPHENS LAKE PASSAIC NJ
410315 741605 WANAJUE 125a
01/ 1/30 1.2a 3b a Bfi1 S3
DIST4 KAINpH: 4.3 1168am
0 0 6. 9 6. 20 162
1 6 6. 3 6. 27 152
2237 SUBPfilSE LAKE HORBIS NJ
405714 74 2 11B POflPTON PLAINS 198m
81/ 1/30 1. 2a 7ha BK1 S3
DIST3 SAISpH: 4.3 1194ma
0 0 7. 1 7. t*3 493
2 5 6.8 7. 23 537
2208 HOT1 NT HOP*; POND AOFRIS MJ
405547 743255 DOVER 259m
81/ 1/30 2. 1m 3h a BR 1 S 4
DXST4 KAIRpH: 4.3 12 19mm
0 0 6.3 6.^6 241
3 5 6. 8 6. (17 157
120 9 SUNSET LAKE SOSSEX NJ
411150 743640 FRANKLIN 326a
81/ 1/30 1.2a 4 ha BP 1 S4
niST3 RAXKpK: 4.3 1194am
0 0 6.6 6.95 823
2 U 6.6 6. 76 d 27
245 30
175 85
31 427 298 5
36 436 237 4
90 201 1.7 158 377
74 228 1. 7 136 349
321
365
69
27
34
36
583
687
57
29
73
59
294 3.5 142
375 3. 2 132
292
292
162
176
154
174
33
34
36 6
44 5
831
831
7 1
75
178 2. 5 149
222 2.5 144
387
390
97
134
87
88
53
48
76 2
103 8
55
73
5
7
49
53
587 25.6 93
731 28.6 132
662
662
125 222 54 100 1 89 10 50 788 1 1. 1 93 2126
106 236 55 936 174 10 52 766 10.3 89 2275
472
703
99
102
3 1
33
47 1
51 3
176
285
73
69
218
249
1.7 157
1.2 150
361
364
213
144
115
85
34
43
226
36 1
433
393
t 4
53
115
22<»
2.6 140
2.6 115
373
398
787 145
791 140
24 616 55 1
41 969 4U 5
90 218 8.6 2?2 516
69 599 18. 1 117 523
-------
FLD LAB FEP
D T £H_ pH ALK
2210 nOOHTkIN LAKE HASHES NJ
405130 745913 BASHIKGroK 125a
8 1/ 1/10 1.5b 42ha BH4 S5
DIST4 HAIKpH: 4.3 1219aa
0 0 7.6 7.36 1341
1 4 7.4 8.59 1757
2211 UK? SOLITUDE H 0HTERDON BJ
40 4023 745315 HIGH BBIDGE 87a
61/ 1/30 2.0a 7h a B81 S5
01ST4 BAIMpH: 4.3 1219aa
0 0 7.5 8.39 1452
2 1 7.3 3.37 1507
2212 ALEXAUKEH CBEEK DAH HUBTEEDON NJ
402422 745430 STOCKTON 50a
81/ 1/31 1.2b 6ba BB2 SS
DIST4 BXINpH: 4.3 1219aa
0 0 6.8 6.43 670
3 3 6.3 6.90 629
^ 2213 HABIHOKAKE CBEEK HOSTERDO* NJ
40324 4 750407 FEEHCffTOHK 35a
81/ 1/31 4a BB2 S5
DIST4 FAIKprt: 4.3 1219bb
0 0 7.2 6.92 814
2210 SIK >111.5 RON SOMERSET
4 0 2812 743242 HONBOOTH JUHCT
31/ 2/12 ***a 6a B82 SJ
DIST4 KAIKpft: 4.3 1219bb
KJ
17a
0 0 6.9 6.92 347
2215 ROCK BON POND SOBERSET NJ
402440 744153 HOCKT HILL 27a
81/ 2/12 0.3b 4ha BB2 S4
DIST4 RATNpH: 4.3 1219aa
0 0 6.6 7. 13 217
2 0 6.4 6.94 231
2216 PE8BINEVILLE LAKE HONBODTH BJ
401333 742611) EOOSEVELT 50a
81/ 2/12 0.6a 6ha 3H2 S5
DIST4 RAINpH: 4.3 116Baa
0 3 6.3 6.26 46
2217 BSD VM.L2I LAKE HONBCUTH BJ
400946 7428S5 ROOSEVELT «la
81/ 2/12 #**n 7ha BR2 SS
DIST4 HAINpH: 4.3 1168aa
1 0 6.3 6.36 171
1217 209 21 819 155 6 134 256 1.7 23 1 490
1716 223 38 518 161 4 66 289 3.4 127 509
1383 223 16 625 380 4 169 153 0.3 300 34*
1480 212 30 492 385 8 d5 223 0.3 160 326
650
589
131
99
34
45
916
1 147
263
192
65
55
469
765
13. 6
13. 6
143
108
402
415
749 1bB
30
538 435
78
236 0. 8 165
476
3 29 230
52
24 4 630 10
48
190 7.7 93
654
185 120
199 116
69
79
252
383
365
369
HO
40
33
33
25V 6.0
449 6.8
71
61
528
511
111
63
532
39)
45
506 4.3 77
236
1u5
•J2
86
162
414
30
229 1 2.fi, 5 7
2d6
-------
2216 J ACKSON 'S MILLS LA.KS OCEAK NJ
400859 741932 ADELPHIA 29a
81/ 2/12 0.6a 8ha BB2 S3
DIST4 BlIKpK: 4.3 1168aa
FLD LAB FEP DEP
pH pH ALK ALK COND A1 Ca CI CLR K Mn N_a SO^
4.7 4.28 -2D -14 74 128 134 153 12 17 253 5.2 38 415
4.7 4.31 -19 -18 75 171 88 158 15 16 227 6.0 28 424
2219 BRISBANE LAKE HONflOOTH NJ
400940 740725 ASBORI PARK 23a
61/ 2/13 0.4a 5ha BE2 S3
DIST4 RAIHpHx 4.3 1168aa
0 0 5. 1 5.64 32 0 49 51 155 205 6 54 116 3.4 97 182
2 4 5.8 5. 15 32 5 71 98 138 351 6 26 231 6.0 50 225
2220 SUCCESS LAKE OCEAN HJ
400330 742342 CASSTILLE 29a
81/ 2/13 1.2a 15ha BH2 S5
DIST2 RAIHpH: 4.3 1118aa
0 1 4.1 4.34 -11 -11 55 *** **** 110 20 »»•» **** *** »»» 297
1 2 4.4 4.38 -12 -12 55 49 153 103 19 48 110 1.7 10 1 295
2221 BAHB2B LAKE OCEAN NJ
395336 741909 KESWICK GBOVE 23a
8 1/ 2/13 ***• 24ha BB2 S4
DIST4 RAINpH: 4.3 1118aa
0 2 4. 1 5.39 23 0 25 31 62 113 9 87 28 1. 7 156 89
2222 CRANB2RBY B3S BESERV OCEAN NJ
395510 742726 HHITING 38a
81/ 2/13 ~~~a 16ha BB2 S3
DJST1 BAIHpH: 4.3 1118aa
0 1 4.2 4.30 3 -15 43 46 268 113 8 50 184 1.7 10 5 265
2223 OTSTEH CREEK OCEAN NJ
394755 741502 BROOKVILLE 17b
81/ 2/13 ***m 5a BB2 S3
DIST3 BAINpH: 4.3 1118aa
0 « 4.5 4.42 -6 -29 47 32 440 174 6 70 213 0.0 152 132
2224 DECOY POND BOaLINGXOB NJ
394855 742715 HOODMANSI3 35b
81/ 2/13 ***a 2ha BB2 S4
DISTO RAIHpH: 4.3 1118bb
0 4 4.9 6. 47 13 0 12 33 412 33) 15 77 204 1. 7 149 1 17
2225 OS#EGO LAX 5 B OBLINGTON NJ
394355 742923 OSBEGO LAKE 16a
81/ 2/13 •~•a 36ha BB2 SU
DIST3 RATNpH: 4.3 1118aa
0 2 5. 0 4.35 3 0 4b 34 123 102 3 65 62 0.0 14 1 148
-------
FLD LAB FEP
D T pH pH ALK
2226 ATSION LAKE B OBLINGTON HJ
39 4429 744411 ATSIOH 15a
81/2/13 2.2a 4Dha BB2 S5
DIST4 BAINpH: 4.3 1118aa
0 2 H. 5 4. 57 23
2 3 4.3 4.54 9
2227 PARVIN LAKE SALEH HJ
393103 753803 ELHBR 20a
81/ 2/14 0.9a 37ha BR2 S5
DIST4 RAINpH: 4.3 1118aa
1 3 6.8 6.75 167
2228 ELK LAKE COHBEBLAND HJ
392445 751823 SHILOH 9a
81/ 2/14 **** 3ha BB2 S3
DIST3 BAINpH*. 4.3 1118aa
0 2 6.1 5.87 69
2229 HASKELL SILLPOND SALES HJ
39 2926 752403 CANTO!) 2a
81/ 2/14 2. Da 15Ha BB2 S5
DIST4 RAINpH: 4.3 1118aa
vo 0 0 5.0 5.40 29
^ 2 3 4.9 5.96 19
2230 TUCKAHOE LAKE CAPE HAT HJ
391630 744402 NARHOBA 2a
81/ 2/14 ***» 7ha BE2 S5
DIST3 RAINpH: 4.3 1118aa
0 4 5.1 5.58 41
2231 RESEB? OK OTSTEfi CB OCEAN HJ
394733 741642 BBOOKTILLE 14a
31/ 2/13 9ha B82 Stt
DIST1 RAINpH: «. 3 1116aa
0 3 4.8 4. 57 -5
2301 JONES HILL BUN SOHEBSET PA
400119 79155* SEVEN SPRINGS 676a
80/ 7/21 •~~a 3a BB2 S2
DIST2 BAINpH: 4.2 1118aa
0 18 6.7 6.98 371
2302 VEIKEBT RUN UNIOK PA
405022 771820 KEIKERT 268a
80/ 7/17 »**¦ 5b Bfl3 S5
DIST3 BAINpH: 4. 2 1041 aa
0 17 6.2 7.47 211
2303 CLEAR SHADE CBSEK SOHEBSET PA
400744 7U44-4U OGLETOWH 691a
60/ 7/21 6b BB2 S3
DIST2 RAINpH: 4.2 1168a«
0 15 6.5 7.09 143
DEP
ALK COND A_1 Ca £1 CLR K Mg >tn N_a SO
10 45 54 111 95 4 50 88 0.0 89 309
-19 50 40 64 75 3 67 37 0.0 123 282
138
90
42
328 329
63
201 0.0 116
325
51
87
43
357
445
64
228 0.0 11 1
321
19
5
59
31
40
44
47
91
219
403
63
60
28 0.0 120
59 0.0 112
257
395
14 95 35 451 333 20 62 239 0.0 138 464
-20 39 41 55 153 4 58 34 0.0 118 106
323 54 15 587 **•* 3 152 133 0.0 313 112
178 30 26 407 **** b 13 0 162 0.0 186 83
105 3 ft 33 408 ~*+* *+ 7U 199 0. 0 147 190
-------
FLD LAB FEP
D T pH pB ALK
2305 LITTLE PISHING CREEK CEHTRE PA
*~05322 773828 BIBGOVILLE 402*
61/ 3/31. 4a BB2 S5
DIST2 P.AINpH: 4.2 1016aa
0 9 6.9 6.90 ***
2306 LITTLE SfcBDI CHEEK VENASGO PA
2258 795543 POLK 332a
80/ 8/21 4a BH2 S4
DIST3 BAINpH: 4.1 1041aa
0 18 7.4 7.91 741
2307 LITTLE ALLEGHEMI RES BLAIB PA
403110 782620 ALTOONA 397a
81/ 6/30 4ha BB3 S3
DIST3 BAINpH: 4.2 1016m
0 24 7,6 8.04 429
5 10 6.6 6.90 556
2308 FO0LEB HOLLOS BOH PEBBY PA
401558 773515 BLA1V 290a
80/ 8/26 ***» 3a BB2 S5
DIST1 BAINpH: 4.3 1067a>
0 1U 6.3 6.50 41
2309 N BB BOBHAN'S CSEEK. LUZEEN2 PA
a 412123 761405 SVEET VhLLEY 641«
80/ 9/23 ~•*» 5a BB2 S5
DI5T1 BAINpH: 4. 3 965an
0 19 5.7 6.11 57
2310 STRAIGHT HUB TIOGA PA
414726 772417 ASAPH 384a
80/ 7/24 •*»a 3a BB2 S5
DIST2 BAINpH: 4. 2 965aa
0 22 7.2 8.04 513
2311 UPPER THE EE BOSS 3LEARPIELD PA
411230 780745 DEVIL'S ELBOW 519a
61/ 4/ 7 a 4a BB2 S3
DtST2 BAIWp.H: 4.2 1016aa
0 ** **+ 4.99 13
2312 HYCKOPP BOH CAMEflON PA
411656 780800 DRIFTWOOD 323a
81/ 7/16 »*»a 9a BR2 S3
OIST2 BAIHpH: 4.1 1316*8
0 15 7.2 7.67 724
2313 BED SON SLK PA
411709 781439 DEIFTWOOD 370a
81/ 8/17 ***¦ 8a BB 2 S3
DTST2 RAIVpH: 4. 1 1316aa
0 13 5.6 6.43 87
88 37 160
51 20 9 32 113 0.0 66 258
713 108 37 650 ***» 8 66 360 14.6 130 367
411 88 15 205 176 3 26 138 0.0 25 169
520 110 8 366 2J5 3 19 182 30.7 24 203
*** 37 29 169 »**~ ** 07 73 0.0 170 148
19 30 31 115 ***» 3 31 53 0.0 157 209
475 71 31 519 *~** 3 3 2 235 2 5. 6 158 234
-29 4 1 133 34 19 1 75 67 2.9 36 249
93 34 50 75 47 2 us 95 0. 0 29 221
42 36 51 54 27 1 4 7 6H 3.5 22 225
-------
FLD LAB FEP
D I pH pH ALK
5314 GIPFOfiD SOW (LOBEB) CLEABFII1D PA
411024 781430 DEVIL'S ELBOI 433a
81/ 7/14 ***¦ 8a BR2 S3
DIST2 FAIHpH: 4.1 1316aa
0 24 6.0 7.57 167
2315 BOSQniTO CREEK CLEARFIELD Pi
11 1254 781442 DEVIL'S ELBOR 464a
81/ 7/14 **»¦ 9b BB2 S3
DTST2 RAIWpH: 4.1 1016aa
0 19 5.3 4.79 37
2316 PEBBLE BUN 5LK PA
411535 781820 DEHTS BOS 634a
81/ 4/ 7 1b BR2 S3
DIST1 BAINpH: 4.1 1016ma
0 9 4.8 4.54 0
2317 DEEB CHEEK CLEARFIELD PA
411006 783053 THE KNOBS 580b
81/ 4/ 7 ***« 2a BB2 S3
DIST1 BAINpH: 4.1 19 16aa
0 6 5.0 4.75 15
2318 SIX HILB BOS CSJITRB PA
40 5510 780701 BLACK HOSHAJflfO 459a
81/ 4/ 7 5a BB2 S3
DIST3 BAINpH: 4.2 965aa
0 9 7.3 7. 16 109
2319 GIFFORD RUN [OPPEHJ CLEARFIELD PA
411058 781723 THE KIIOBS 537a
81/ 4/ 7 «»*¦ 3a DB2 S3
DIST2 RAINpH: 4.1 1016BB
0 8 4.7 4.91 25
232 5 STOKE VALLEY LAKE HUNTIHGTON PA
40 3934 775500 PIHE SKOVE flit 253a
8 1/ 8/ 7 5.0« 27ha BB4 S4
DIST3 RAINpH: 4.2 965aa
0 24 7.5 7.22 434
8 9 6.9 6.91 80}
2330 H P08K TAHGASCOOrACX CLIHTOJ) PA
410945 773443 FA2RA8DSVILLE 204a
8 1/ 7/29 **»a 10a BB2 S2
DIST2 BAINpH: 4. 2 965aa
0 14 6.2 6.87 12)
2331 LITTLE 3EAB C8EEK LTCOflING PA
412112 765331 HOHTEBSVILIE 292a
81/ 7/29 ***¦ 5a 3R2 S2
DIST2 RAINpH: 4.2 1016aa
0 15 6.3 6.65 102
130 34
85 44 28 5 61 71 0.7 29 370
-56 33 62 41 15 5 53 55 3.0 23 269
-37 37 133 34 26 1 75 67 2.9 36 2b5
-15 39 141 30 34 2 77 62 2.0 34 249
54 48 19 91 58 0 28 66 0. 0 1 3 193
-10 28 395 29 18 0 57 41 1.2 16 241
389 76 1 494 bl 3 11 14 1 0.5 11 94
751 115 0 720 97 4 10 192 43.9 13 111
6S 34 17 97 23 3 2B 70 0,7 13 161
41 22 18 67 53 3 27 46 0.5 9 57
-------
FLD LAB FEP
D T pH pH ALU
2332 TROUT RON LTCOHIKG P*
412342 770349 THOOT BUN 219«
81/ 7/29 »»*¦ 6a BR3 S3
DIST2 RAIBpH: 4.2 13 16»»
0 16 6.7 6.75 238
2333 5HTE BBS LTCOB11IG PA
413130 773151 LEE FIHE TOHEB 308*
81/ 7/29 10b B83 S3
DIST2 RAINpH: 4. 2 965««
0 16 6.9 8. 43 333
233 4 LEFT BB HTNEE BUB CLINTOH PA
412430 773602 SLATE BOH 375«
61/ 7/29 **»¦ 4« BB3 S2
DIST2 ItAINpH: 4.2 965«»
0 15 6.5 7. 16 208
2335 COOK'S RUB CLIKTOH PA
411819 775557 KEATUG 3»5«
81/ 7/29 6k B82 S2
DIST2 RlISpH: 4.2 965««
0 17 7.0 7.12 199
O 2336 LONG PIKE RUN EES EH A DABS PA
to 395618 772615 CALEDONIA PABK 415«
81/ 7/31 6.3a 55ha BB3 S2
DIST2 RfclHpH: 4.3 1016a»
0 25 5. 9 7. 21 63
16 23 5. 3 5. 83 18
2337 HED LION BESEBV0I3 ¥0RK PA
395633 763455 BED LION 183*
81/ 7/31 3.5a 4ha BB3 S2
DIST2 RAIHpH: 4.3 13 16aa
0 26 7.3 7.25 532
7 15 6.8 S. 89 1837
2330 OPOSSUM LAKE CUMBERLAND PA
401432 771637 PLAINFIELD 137»
81/ 7/31 4.,3a 24ha BB2 S2
DIST3 BAINpH: 4.2 1016m»
0 28 8. 2 8.58 610
7 16 6.3 6.69 726
2339 LETTEBKENNT RESERVOI FRANKLIN PA
400651 774126 BOXBUHY 267«
81/ 7/31 4.0a 27ha BE2 S3
DIST2 DAINpH: 4.3 13 16ii*
0 24 6.9 7.53 545
4 14 6.9 7.29 609
DEP
ALK COND A1 Ca CI CLR K Mn Na SO
A
195
44
166
16
71 0. 5
195
259
54
13 201
34
24
123 0.6 2 1
143
342 37
31
64
23
35
60 0.6 15
84
162 53
23
117
32
99 0.5 22
14 j
40
-9
23
41
56
65
37
37
34
36
49
53
47
51
1.9
2. 2
16
19
94
96
461 137
1805 191
13
15
340
745
185
205
3
5
32
26
288
482
1.0 66
*** 88
661
5
569
6 4 7
101
120
45
33
257
32 3
114
114
45
37
299
326
2.9
**«
94
85
733
890
534 67 3 489 4 5 12 159 0.6 13 96
572 78 3 499 33 6 12 157 3.0 13 143
-------
FLD LAB FEp
D T pH pH ALK
2340 HILL'S CHEEK LAKE TIOGk Pi
414815 771142 CROOKED CREEK 453a
81/ 8/ 3 1.5a 51ha BB2 S2
DIST4 BAIHpH: 1.2 914aa
0 24 7.7 8.3t» 681
5 20 6.5 7.39 744
234 1 FOORBILE BDif TIOGA Pi
411134 772932 TIADAGHTON 494a
81/ 8/ 3 la BE 3 S3
OLSTZ e*c*pffi 4. 2 965mm
0 14 6.9 8.46 527
2342 L7JIAH SON RESERVOIR POTT2B PA
4K324 774544 CBEBBT SPSINGS 494a
31/ 8/ 3 3.3a 18ha BB2 S2
3IST3 BAIBpH; 4.1 965a*
0 22 6.3 7.SI 402
6 14 6.2 7.26 232
2343 ti DIHGHAI'S BUS POTTES PA
41*1641 780308 CO0DERSPORT 525b
81/ 8/ 3 *»*¦ 2a BB2 S2
DIST2 BAIVpH: 4.1 1016aa
0 16 6.4 6.91 176
g 234U TROUT BROOK POTTEH PA
4111702 780649 COODERSPOfiT 506a
81/ 8/ 3 *•* a 3a BB2 32
DIST2 BAItipH: 4.1 1316aa
0 17 6.8 6.95 192
2345 SKtHHEB CREEK HcKEA* PA
41
-------
FLD LAB FEP DEP
D T pH pH ALK ALK COND AI Ca CI CLR K Hn Na SO^
23U9 BcLMJGHLIH CUEEK CBAHFOBD Pi
414018 794283 TIXOSYILLE NOB 406a
81/ 8/ 4 6a BB4 S2
DIST3 BAINpH: 4.1 1392BB
0 18 6.8 8.56 13 13 918 225 4 928 126 9 13 343 0.5 32 215
2350 HOODCOCK CBEEK CBAHFOBD Pi
413953 795754 TOSNVILLE 403a
81/ a/ 4 **~» 4a BB2 52
DIST3 BAINpH: 4. 1 1D67aa
0 22 6.5 7.39 1165 1119 155 3 1075 102 9 11 330 2 2.1 27 288
2351 TAHABKCK LAKE CBA8F0RD PA
413450 800442 COCHRAMrON 371a
81/ 8/ 4 1.0a 231ha BR2 S2
DIST3 BAINpH: 4.1 13 41aa
0 26 7.6 8.93 4 25 369 97 4 482 1S3 11 14 177 1.7 17 174
4 24 6.3 7.01 503 448 89 4 464 784 7 14 182 8.8 17 148
2 352 FRENCH CBEEK CBAHFOBD PA
414250 800643 HEfcDYILLE 334a
81/ 3/ 4 20a BR2 S2
DIST4 RAIBpHi 4.1 1041m
0 25 7.6 8.02 1429 1369 232 3 169ti 314 10 11 520 0.9 42 484
h-"
O 2353 PINE BON HBBCEB Pi
411125 S01155 S5BCER 361a
81/ 8/ 4 »»»a 3a BB4 S2
DIST3 RAINpB: 4.2 1316aa
0 20 7.5 8.11 2642 2577 77 8 3879 632 5 18 1792 1.8 235 2953
2354 GLADE RON RESERVOIR BDTLER PA
404257 795412 VALENCIA 336a
31/8/4 0. 5m 19ha BB3 S4
DIST2 HAIHpH: 4. 1 965aa
3 26 8.5 8.75 9 2? 865 173 5 1203 189 7 14 452 2.8 48 547
4 20 6.3 7.19 1606 1533 211 5 1281 191 9 15 483 »** 50 281
2355 PENH BOOSEYELT LAKE CENTBE PA
404337 774205 BAB BTILLS 496a
81/ 8/ 5 + *«» 2a BS2 S2
DIST1 RklHpll: 4.2 1016am
0 17 5.7 6.23 54 18 15 103 25 5 3 68 44 0.5 21 50
2 13 5.9 6.16 59 13 23 101 28 52 4 66 48 0.6 22 41
2357 8 BB STANDING STONE HONTIMGDON PA
403855 774535 HcALEYX'S FOBT 256a
81/ 7/ 8 6a BB3 S2
OIST3 BAIHpH: 4.2 965aa
0 14 6.3 7.18 95 63 35 54 61 34 3 47 78 0.5 26 153
2358 GLOBE StJN BONTINGDON PA
40 3853 775953 PINE GBOVS MIL 278a
81/ 7/ 8 »»*¦ 6i BR4 S3
DIST1 HAINpH: 4. 2 965an
0 18 6. 3 6. 95 86 58 34 66 70 21 5 54 9B 0.9 37 127
-------
FLD LAB FEP DEP
D T pH pH ALK ALK CONP A1 Ca CI CLR K iLg
2359 HIGH POINT LAKE SOHEFSET PA
394656 791356 HAHKLETON 756a
81/ 7/15 1.Ja 111ha BR2 S2
DIST3 RAINpH: 4.3 1168aa
0 25 6.9 7.79 749 707 86 0 559 57 5 7 102 1.8 5 778
5 24 7.0 7.60 564 513 71 0 558 33 5 7 102 3. 5 5 473
2360 JORDON CHEEK LEHIGH PA
403717 753314 ALLSHTOim BEST 98m
81/ 7/28 **~* 20« SB4 S4
DIST4 RAINpH: 4.3 1118aa
0 23 8.1 3.36 846 804 188 5 1279 332 7 15 492 0.8 51 494
2361 ANTIETAH RESERVOIR BERKS PA
402124 755217 BTHDSBORO T59«
81/ 7/28 1.0a 7ha BR4 S4
DIST3 RAIBpH: 7. 2 1392aa
0 24 9.0 7.59 9 66 925 145 31 411 204 10 36 389 0.0 99 1080
10 19 6.4 7.64 962 920 156 38 344 207 1 4 42 381 7.3 195 292
2362 CRABBT CBEEK CHESTER PA
400259 752803 VALLEY POSSE 98m
81/ 7/29 3a BR4 S2
DIST3 BAINpH: 4.3 IMBaa
0 1 8 7. 3 8. 27 1 4 67 1 40 4 3 6 8 30 1 40 5 866 5 3 6 1 265 0 . 0 32 4 6 1 0
2363 VALLEY CREEK CHESTER PA
400301 753330 MALVERN 87a
81/ 7/29 •**a 3a BB4 S4
DIST4 RAINpH: 7.3 1118aa
0 17 7.5 7. 15 1802 1748 48 23 2357 1329 2 31 2011 0.0 422 741
2364 PICKERING CREEK CHESTER Pi
400605 753213 HALTERN 44b
81/ 7/29 »*»a 35a BR2 S5
DIST3 RAINpH: 4.3 1118aa
0 22 7.5 8.43 879 842 180 18 702 423 7 28 502 0.5 190 351
2365 HARSH CR32K LAKE CHESTER PA
400343 75 431J DOWNINGTOUN 110a
b1/ 7/29 3.3> 217ha BB3 S3
DIST3 RAINpH: 4.3 1092aa
0 26 8. 9 8.72 610 569 1.60 12 552 680 5 23 333 0.6 53 249
9 20 6.7 7.37 574 518 94 13 574 442 5 24 345 11.1 58 796
2366 YHCK CMIP POSD LEBANON PH
40 1443 762337 BABiHEIfl 226a
al/ 7/29 1.5a lha BB3 S2
DIST3 RAINpH: 4.2 1067ao
0 24 7.3 3.04 582 545 86 92 125 109 11 65 211 0.8 96 111
2 22 6. 9 d.00 635 638 97 98 102 115 13 b7 176 1. 4 83 101
-------
D
2367 HOME B0« PERHY PI
433020 773522 BEHABD 192*
81/ 7/29 4* 3B2 S2
DIST4 BAIKpH: a.2 1067«*
0
2368 BELL HOLLOW SDN CENTRE PA
404812 783B39 SAUDI BIDGE U61d
81/ 7/31 *»** 3* BB2 S2
DIST3 RAIHpH: 4. 2 9 65**
0
2369 LA (J BEL RUN CLEAHFIELD PA
405346 734333 BAHAFFEY 387*
81/ 7/31 **~* 6* BB3 SO
DIST2 RAIHpH: 4.2 1118**
0
2370 BUDL1CK BOH IBDIANA PA
435208 790249 HABION CEHTER 397*
81/ 7/31 5* BR3 S4
DIST4 HAINpB: 4.2 1967**
2371 BROSH CHEEK IHDIANA PA
403135 790152 BBDSH VALLEY u36*
81/ 7/31 **** 6a B82 S4
DIST3 RAINpH: 4.3 1118m*
0
2372 LITTLE SALTLICK BUS CAHBRIA PA
432412 784843 SANTI GLO 543*
81/ 7/31 • 3* BE3 S4
DIST2 BAIHpH: 4.2 1168**
0
2373 STEHABT BOS CAH3BIA PA
402616 784751 SANTT GLO 601a
81/ 7/31 **** »¦ BB4 S4
DXST2 RAINpH: 4.2 1143**
0
2374 ARCH SPHING BLAIB PA
403620 781214 SPR'JCH C8EEK 273*
81/ 7/31 *~»* 2a BR 4 52
DIST2 BAIHpH: 4.2 131bBl!
0
2375 BLUE HOLE CFS2K SOMERSET ?A
395807 791731 KING HOOD 591*
81/ 7/15 ~ »»* 5* BB2 S4
DIST2 RAIHpli: 4.3 1118a*
0
FLD LAB FEP
T JH_ pB ALK
17 6.0 7.86 619
14 6.9 7.25 109
14 6.8 6.77 136
15 7. 1 8. 53 317
16 6.9 7.88 194
14 4.4 4.55 10
16 6.8 6.63 19)
16 7.1 7.82 1181
13 6. 5 6. 15 208
DEP
ALU COUP A1 Ca CI CLR K Mn Na SO^
583 47 179 44 88 2 90 98 0.7 62 327
72 34 40 73 5 2 42 77 0.0 22 160
90 94 55 207 63 3 49 271 0.0 90 507
271 114 21 332 166 2 30 259 1.2 54 296
125 73 36 180 245 2 38 133 1.1 50 303
-79 bO 86 70 63 7 60 110 7.8 3 343
58 84 30 154 192 3 36 150 1.4 37 288
1120 164 5 1083 97 3 14 407 0.0 4 2 294
157 37 39 88 21 5 42 95 O.t) 28 413
-------
FLD LAB FEP
D T pH pR ALK
2376 SUM CREEK PIKE PI
410758 753133 TUELfEfllLE PON 308b
81/ 6/25 »*»¦ 5* BH3 S2
DIST2 B&IHpH: 4.3 1168a*
0 22 6.2 6.03 113
2377 LITTLE BOSH KILL PIKE PA
411023 750037 TMELT36ILE PO* 3U5a
81/ 6/25 **** 7a BR2 S2
DISTj fihlHpU; 4.3 1143ia
0 211 6.6 6.42 49
2378 BRQOgEAD CREEK BOSBOE PK
410056 751155 3 STBOQDSBUBG 133a
61/ 6/25 »»*¦ 6b BR3 53
DIST9 AMNpH: U. 3 1219bb
0 19 6.6 7.tO 20S
2379 UNITI K'SEHTOII HESTHORELA Pk
40 1650 79 3003 LATROBE 3 37a
81/ 7/15 3. 3b 19ha BR4 S4
DIST4 RAIMpH: 4,3 1016aa
0 25 6. 9 8.^4 462
14 11 6.6 7.08 462
2380 REDSTONE CBEEK FATETT2 PA
395035 794222 BROHKFIELD 436b
81/ 7/15 2a 883 S2
DIS?1 SAIKpH: 4.3 1092aa
0 16 6.6 6.71 221
2381 DEES VALLEY LAKE SOB2BSET PA
394745 791135 HARKLETON 809a
81/ 7/15 3.5a 45ha BB2 S2
DIST3 BAINpH: 4.3 1143bb
0 25 6.4 6.H2 10S
5 23 6.3 6.91 13J
2382 HOBtH FOBK B35EKVOIB SOHEESET PA
43160^ 790023 BACBELBOOD 466a
81/ 7/15 4.3a 39ha BH2 S4
DIST2 PMWpH: 4.2 1143ia
O 25 7.5 3.) 3 5 24
5 16 6.4 6.73 164
23B3 COB AN'3 GAP LAKE POLTOS PA
4i>0005 775530 BURST CABINS 371a
81/ 7/11 2.8a 20ha Bfi3 52
DIST3 RAINpH: 4. 3 965na
0 27 6.2 5.77 133
4 17 6.0 6.26 10J
2394 SEAVEBDALE R2SESVGIP CAfiBFIA PA
*01620 7B 1. 5a 2iha 6b5 52
!USn KATNpri: 4.2 I118aa
0 22 5.7 6.3d 31
8 15 5.5 6.i5 45
91
26
113
63 100
15
45 0.0
67
40
27
22
98
39 60
32
79 7.1 16
94
185
53
20
139
155
2 S
111 0.5 22
122
4 11
407
126
131
551
588
154
145
21
20
299
299
0.0
15. 6
4 1
« 1
900
965
166
58
29
143
42
34
130 0.4 32
169
51
78
43
42
188
197
75
5)
13
12
62
60
2. 6
2. 4
175
163
lid 3
125
7H
63
21
28
17 0
149
*•**
57
3 3
35
13d
135
0. 4
0.9
23
33
292
314
67
67
33
31
42
45
7 1
67
58
59
44
4tt
80
79
1.6 2 4
6.4 25
96
96
4 27 29 54 21 3 35 51 2.4 12 57
13 27 32 5a 23 3 37 53 2.5 14 <16
-------
FLD LAB FEP
D T pH pB ALK
2385 SPOBTSSAH'S LAKE BEDFORD PA
395754 782957 CLEABYILLE 437a
81/ 7/ 8 2.5b 6ha BR4 S4
DIST4 RAI¥pH: 4. 3 965»»
0 27 6.9 7. 59 154
6 16 6.9 7.98 268
2386 LA0B2L BOH HONTINGDON PA
402015 78063S CA3STILLE 024a
81/ 7/ 8 ***m 3b Bfi2 S2
DIST2 BAIMpH: <1.2 1316KB.
0 14 5.6 6.42 53
2387 SD (IFISH POHD BRADFORD PA
413840 764148 LBROT 632b
81/ 7/ 7 2.3b 19ha BB2 S2
DIST2 BAXHpH: 4.2 914aa
0 25 7.4 6.16 40
3 23 7.D 5.95 43
2388 OiTHPIC LAKE SOSQUEHAB* PA
415559 760618 FHIEHDSVILLE 503b
81/ 7/ 7 2.5b 19ha BB3 S3
DIST3 HlIHpH: 4.2 914bb
i-4 0 25 7. 1 7.41 172
g 5 22 7.2 6.93 149
2389 TOSCABORA USE SOSQUEHANN PA
414420 76052i) AUBOas :ESTER 354b
81/ 7/ 7 0.5a 25h a B83 S3
DIST2 EAINpH: 4.2 9 14nip
0 25 10.3 3.93 299
5 13 6.5 6.36 502
2390 LAKE JEAN LOZERSE PA
412009 761800 R2DR0CK 678b
31/ 7/ 7 3.71 100ha BR2 S2
DIST3 RAIHpH: 4.2 1016bb
0 22 5.5 6.21 27
4 23 5.5 6.6b 31
2391 SIL7EB LAKE PIKE PA
411523 745845 EDGERERE 399a
81/ 6/25 5.5a 57ha Bfi4 S2
DIST3 EM Up H: 4.3 ingil
0 23 6.1 6.42 40
15 23 6.4 6.57 45
2392 DEEP LAK3 SONiOS PA
410310 752350 POCOBO PIHE5 557»
81/ 6/25 3.5a 4ha B85 S2
DIST1 RAINpH: 4.3 1341aa
0 22 5.2 4.86 22
7 21 5.2 4.36 19
DEP
ALK COWD A1 Ca CI CLR K Mg Mn Na SO
4
122 49 33 156 39 3 37 154 0.0 42 163
226 70 27 187 65 3 35 172 3. 2 43 304
34 266 27 33 2 136 7b 0.5 61 184
9 25 4 118 3S 3 14 46 2. 3 4 84
22 24 4 126 31 3 13 44 2.5 4 78
145 39 9 142 42 4 21 76 4.7 10 101
113 36 1 1 166 35 5 21 93 1. 7 14 101
262 55 1 1 120 63 17 22 71 1. 8 11 *4
471 70 9 296 44 8 20 153 16.0 22 35
0 33 0 61 29 3 b 13 1.8 0 89
2 29 0 145 52 3 5 ly 2.3 0 122
13 26 8 111 26 4 19 55 1. 0 7 1 1 1
18 26 9 105 39 3 20 54 1.0 a 101
0 23 6 64 34 1 16 28 3. 9 3 7U
0 23 6 6 1 34 3 1 7 27 3.9 3 62
-------
2393 SH ADIGEE CB5EK BATHE
<*15353 7527148 STASBOCCA
61/ 6/22 10a Bfi3
DIST4 EAINpH: 4. 2 1067aa
FLD LAB FEF
D T pH pH ALK
PA
409b
S2
0 20 7.8 1.J5 434
2394 LONG POND BATflE PA
414000 752037 ALDBHTILLS 414b
81/ 6/22 3.5b 33ha BS2 S2
DI314 BAIHpH: 4.3 1118»B
0 24 7.9 7.44 199
0 24 7.9 6.98 294
2395 V BR DrBSBBV CRE2K BAyifE Pi
413937 751725 ALDEHV1LLE 317»
81/ 6/22 •*»a 10b BH2 S2
HIST2 RAIHpH: 4.3 1118*»
3 21 7.9 3. 56 489
M 2396 DTBE8BT CfiEEK BATHE PA
9 413543 751632 HOMBSPALE 296b
61/ 6/22 15» BB2 S2
DIST4 BAIHpH: 4.3 11 18aa
0 21 7.9 7. 93 443
2397 1ACKAUAXEN BITER UAIDE PA
413435 751615 HON3SDILE 316a
81/ 6/22 *»*a 25a BB2 S2
DIST4 PAIUpHl 4.3 1118BB
0 23 7.8 8.32 348
2398 tAKE LACAHAC WITME PA
412236 751731 HAHI.ST 439a
81/ 6/22 3.5a 21ha BB2 S2
DIST1 EAINpH: 4. 3 1168aa
0 24 6.0 6.64 95
11 12 5.8 5.95 100
2399 W ALLSNP AUPACK C33EK PIKE PA
41 1355 75105b NEW FOlf NDLAH D 393a
81/ 6/22 »*«¦ 15i BB2 S2
0IST4 RAISpH: 4. 3 1168bb
0 19 7.6 7.93 265
2401 CUB3AG2 POND SOSSEX DS
385156 752339 ELLENDALE 4b
80/11/ 7 1.6a 1lha BR3 S4
DIST3 RAINpH: 4.5 11 18b«
0 11 6.5 6. (J2 337
2 10 6.2 7.05 342
398 84 0 445 182 10 8 95 1.3 5 89
172 49
267 60
0 27 4 52 10
0 121 79 10
64 0.9 4 132
24 5.8 1 101
453 89 0 360 134 10 0 77 0. 7 4 117
411 81 0 492 121 10 8 99 1.1 5 137
312 77 0 397 142 9 9 98 2.6 6 153
77 35
67 35
2 168 34 10
0 179 29 10
11
9
49 1.6 3 106
44 4. 5 2 84
249 67 0 281) 13+ 5 13 79 0. 5 6 101
309 93
314 95
^6 230 271 13
3 3 2-10 253 18
94
5d
86 0.8 188
115 0.8 155
57
52
-------
2402 SILVEB LAKE 2 NEH CASTLE DE
393726 754332 SAINT GEORGES 15b
80/11/ 4 0.5b 18ha B32 S4
DIST3 RAINpH: 4. 3 1118aa
2403 LOHS POND HEg CASTLE DE
393345 754357 SAINT GEORGES 13a
80/11/ 4 0.6a 77ha BB2 S4
DIST3 KAINpH: 4.3 11 18aa
13
14
10
1 1
FLD
pH
7.0
7.0
6.0
6. 2
LAB
pR
FEP
ALK
7.07 432
6.34 408
7.26 4 80
7.47 480
DEP
ALK
COND A1
394 132 68
380 128 76
442 130 41
447 128 35
Ca
285
302
632
661
CI CLR
343 5
337 12
385 2
343 3
K Mn Na SO
— U
42
33
62
83
28 5
338
5. 2
5. 1
7 3
64
389 1.7 117
345 1. 2 141
309
314
452
432
240 4 SHAUCROSS LAKE
NEWCASTLE DE
393054 754026 HIDDLETOiN 12a
80/11/ 4 0.5a 19ha BR 2 S3
DIST4 RAINpH: 4.3 1118aa
O
3
10 7.0 7. 19 722
10 6.6 7.00 632
637 176
579 195
55 839
61 1073
343 8
353 5
42
39
661 4.3 90
973 3. 4 82
361
364
240 5 HOBBIS flILLPOHD SUSSEX
363844 751854 HARBESON
80/11/ 7 0.9a 18ha BB3 S3
DIS13 HMHpH: 4.5 1118ma
DE
0 9 6.0 6.59 332 308 91 45 121 311 2 58
1 9 6.2 6.78 361 328 89 41 165 306 25 62
78 2.6 109 182
101 2.6 120 140
2406 SILfER LAKE 1 SEW CASTLE DE
392616 754222 HIDDLETOSB 12a
80/11/ 4 0.5a llha BB2 S3
DIST3 RAINpH: 4.4 1118aa
10 6.7 7.23 608
10 6.5 7. 08 613
579 211
566 211
49
53
58 5
593
538 6
551 4
45
49
424 10.3 99
468 12. 1 9 3
614
611
2407 HAVEN LAKE KEMT
385446 752703 3ILFOBD
80/11/ 6 0.9a 33ha B£3 S4
DIST4 RAINpH: 4.5 1 1 18bb
DE
11 6.3 6.97 366
12 6.7 6.91 3 28
337 99 36
295 102 54
407
349
306 20
311 20
58
41
215
275
3.4 136
3.4 92
202
216
2408 TRAP POND SUSSEX
383126 752823 TBAP POND 130
80/11/ 6 1. 1a 43ha BF3 S3
DIST3 RAINpH: 4.5 1118aa
DE
240 9 LAKE COtlO KENT
391730 753613 SHYASA
80/11/ 5 0, 3a 17ha BR3 S4
DISTt RAINpH: 4.4 1118la
DE
6a
10
10
9
If)
6.4
6.2
5. 9
6. 4
6.83
6.62
6.51
6.96
19J
19)
3 13
342
162
157
57
57
45
41
285 14J 97
309 134 88
34
73
353
369
193 14
213 10
335 8
343 1
50
54
28
27
23
45
1.7 139
1.7 117
506 10.4 51
488 1 1. 0 55
119
145
439
473
-------
FLD LAB FE t DEP
D T pH pB ALK ALK COND A1 Ca CI CLR K Mn Na SO
2410 GARRISONS LAKE REST DE
391523 753522 DOTES 3k
80/11/ 5 0.5k 35ha BB3 S4
DIST4 SAIMpH: 4.4 1118**
2*11 BASSET'S 1ILLPOHD KENT DE
391»21 753732 DOVER 5»
80/11/ 5 0.5a 15ha Bi3 S4
DIST4 RAINpH: 4.4 1118aa
2412 CRIPtlAN POBD SOSSEX DE
383345 753220 L&l'BEL 6a
80/11/ 6 1.la 13ha BB3 S3
DIST3 HAIHpB: 4.5 1118aa
2413 FLEETWOOD POND SOSSEX DE
383838 753032 SEAFOBD EAST 9a
80/11/ 6 ~~•a 12a BB3 S3
DIST3 BAIMpH: 4.5 1118ll
4
0 9 7.0 7.19 731 660 174 90 587 377 13 30 793 6.1 54 450
1 9 7.2 7.19 674 641 167 71 586 417 18 43 601 6.8 69 458
3 10 7.0 7.17 75J 718 224 5b 666 443 10 40 540 5.1 86 523
1 10 7.0 7.11 741 703 211 58 773 427 6 42 671 6.0 84 523
0 10 6.4 6. 95 280 252 94 31 418 274 15 74 193 0.8 158 202
1 11 6.7 7.10 285 257 96 29 382 277 18 98 163 1.7 168 223
10 6.1 6.33 276 248 81 32 115 213 5 87 55 1.7 148 93
2414 HOD HILLPOND KENT DE
390413 754456 HA«?TD2L 11a
80/11/ 5 ~~~a 5a BB3 S5
DIST3 BAIHpH: 4.5 1118aa
0 12 6. 3 6. 38 319 295 124 49 312 340 3 48 225 1.7 132 4 11
2415 HTOKIHS LAKE KENT DE
390740 753320 DOVER 9a
d0/11/ 5 0.5a 21ha BE3 S4
DIST4 RAINpH: 4.4 1118aa
0 10 6.9 6.45 379 347 141 42 314 361 10 62 193 2.6 118 335
1 10 7.2 6.94 370 342 152 42 399 377 15 54 250 3.4 115 348
2416 VOSH2LL SILLPOttD KENT DS
390535 753148 MIOBING 6a
80/11/ 5 0.5a 15ha B83 S4
DIST3 fiAISpff: 4.4 I118aa
0 11 6.9 6.53 333 314 135 44 402 427 9 50 257 4.2 112 434
2 10 7.2 6.55 323 290 169 49 360 448 9 58 259 4.3 100 416
2417 A N DBS DS LAKE KENT DE
390130 753032 VTOBING 3a
80/11/ 6 0. 6a 7h a 983 S4
DIST4 RATNpH: 4.4 1118fim
0 9 6.1 6.47 276 257 143 41 246 366 10 53 149 6.0 117 369
3 9 6.1 6.77 237 205 131 4 4 283 39S 14 52 185 6.0 110 369
-------
21418 (1ARSHY HOPS CHEEK KENT DE
385054 751025 BICKSAM 12a
80/11/ 6 **»¦ 7b BH3 S4
DISTJ RAINpH: 4.5 1118aa
FLD LAB FEP DEP
T pH pH ALK ALK COND A1 Ca CI CLR K Hg Mn Na so
8 6.4 6.92 261 220 88 49 365 279 3 56 266 5.2 99 346
2419 KILLENS POND KENT DE
385753 753154 HARRINGTON 5a
80/11/ 6 0.6b 3Dha BR3 S4
DIST3 RAINpH: 4.4 1118aa
0 9 6.5 6.80 285 257 153 45 479 372 25 55 324 5.2 107 593
2 9 6.6 6. 50 291 266 150 42 492 377 22 62 310 4. 3 11 3 588
2420 COLLINS POtlD SDSSEI DE
334236 753 120 SEAFORD EAST 9a
80/11/ 6 0.3« 38ha BB3 S4
DIST3 RAINpH: 4.5 111Bbb
0 10 5.9 6.42 181 147 51 44 77 168 8 55 50 1.7 110 140
2 9 6.0 6.48 193 162 54 50 113 145 8 44 82 1.7 99 166
2421 HEAHNS POND SOSSEt DE
384041 753544 SEAFORD EAST 8a
80/11/ 6 0.8a 23ha BB3 S4
DIST4 RAINpH: 4.5 1118aa
0 10 6.3 5.66 37J 338 114 35 307 333 8 68 206 2.6 126 299
1 10 6.2 6.71 366 324 111 39 369 290 8 81 157 3.4 139 333
2502 BLAIR VALLEY LAKE WASHINGTON HD
394156 775633 CLEAR SPRING 210a
81/ 2/17 1.5a 11 ha B82 S5
DIST3 RAISdH: 4. 3 965*a
0 0 6.7 7.53 444 407 89 34 557 63 21 67 283 0.0 144 390
8 4 6.9 7.19 707 680 106 37 779 50 5 76 420 0.0 131 432
2503 ROCKY TAP EON ALLEGANY HD
394257 783836 ETITTS CREEK 357a
80/12/27 **»i ' 2a BR4 S3
DIST2 EAINpil: 4.3 914BM
O 0 6.7 6.61 1369 1406 183 34 184 147 3 74 94 0.0 141 385
2504 PINEY CRE3K 3 AH RETT (ID
394218 790045 AVILION 711a
80/12/27 «**a 4a BR3 S4
DIST2 RAINpH: 4.3 1168am
0 1 6.2 5.83 185 148 107 55 437 427 4 44 350 1.7 89 231
2505 CRABTREE CREEK GARRETT HD
393016 790923 BITTINGER 460a
60/12/27 ***a 6a BB2 S2
DIST2 RAINPH: 4.3 1219aa
0 1 6.1 6.17 4 35 388 124 47 610 184 5 6 1 429 0.0 10 5 377
-------
FLD LAB FEP
D T pH pH ALU
2506 1AKE HABEEB ILLEGAHI HD
391205 783912 EVITTS CBEEK 356b
81/ 2/17 1.5b 79ha BS4 S4
DIST2 BUHpH: 4.3 9 14»«
0 0 7.3 8. 1) 6 15
15 4 7.2 8.32 553
2507 FE0ST3UBG RES3AVOIR GABHETT HD
394240 790026 MILTO* 717*
81/ 2/17 2. Ob 12ha BB3 S3
BIST2 RAIKpH: 4. 3 116Bbb
3 0 6.3 6.36 124
5 2 6.3 6. 35 146
2508 GBAVELLT BOM GARRETT HD
393211 792042 HcHEHHT 753b
80/12/27 ~•~a 2a BH2 S3
DIST3 RAINpH: 4. 3 1270bb
0 0 6.1 6.83 106
2509 GOHPO WDER PALLS BALTIHOBE HD
394126 764650 UKESBOSO 183a
80/12/29 ***b 13b BB2 S5
DIST2 RAINpH: 4.3 1118bb
0 0 6.8 6. 92 527
2510 rtEaaiSAH BEANCH BALTIHOBE flD
392738 763521 TOUHSOS 91b
80/12/29 »**b 2« BE4 S5
DIST3 RAINpH: 4.3 1168a>
0 1 6. 9 7.13 795
2511 CHIHHET BFASCH BALTIHOBE HD
392426 76513D REISIERSTOHN 91b
80/12/29 **»b 2b BB2 S5
DIST4 BAIHpH: 4.3 1118bb
0 1 7.5 7.59 2812
2512 KOBTH *ORK CBEBK FREDEHICK (13
392748 771121 LIB EFT IT OS II 131b
80/12/29 **«B 4b BB2 S4
DIST4 BAISpfl: 4. 3 1067bb
0 1 7.0 7.24 421
2513 OiEHS CRS5K FREDERICK HD
393506 772008 HOODSBOBO 88a
80/12/29 ~~~a 5b BB3 S4
DIST4 RAINpH: 4.3 1041bb
0 0 7.4 7. 34 1341
2514 HUNTING CHEEK FBEDEBICK flD
393752 772713 BLUE RIDGE SUH 283b
80/12/29 •»*« 3b BR2 S4
DIST2 BAINpH: 4.3 1316a«
0 3 7.0 7.11 541
DEP
ALU COND A1 Ca CI CLR K Mfc Mn Na SO
578
513
38
95
46
30
82 5
633
35
34
49
92
552 1. 7 106 339
28 3 0.0 16 2 341
62 69 34 164 219 4 74 94 3.0 14 t 274
138 458 55 437 **** 5 44 350 1.7 89 351
93 40 27 139 35 0 92 55 0.0 180 208
481 104 37 598 139 3 70 336 0.0 130 105
777 13i 31 954 768 3 90 449 0.0 156 230
2760 239 20 121 95 3 127 37 0.0 239 216
384 36 36 487 232 0 71 262 0.0 136 93
124« 207 30 995 345 3 93 444 0.3 160 513
454 104 39 451 216 3 57 257 0.0 124 218
-------
FLD LAB FEP
D T pH pB Al-K
2515 PATOXENT RIfEfi HOBARD «D
39 1 41 7 770323 SAHDT SPHIHG 112b
80/12/30 »*«¦ 12* BB2 S5
DIST3 RAINpH: 4. 3 1067bb
0 0 6.8 6.59 263
2516 HAlfKIHS flIVER HONTGOBEHY flD
391026 770120 SANDY SPRING 94a
60/12/30 *«*¦ 7« BR2 S5
DIST4 SAINpU: 4.3 1067bb
0 0 6.6 6.6a 370
2517 SILBERT CHEEK CHARLES BD
383045 764S28 HUGHE SVILLE 33a
BO/12/30 »**b 3a BR2 S3
DIST3 RAINpH: 1*. 4 13 16aa
0 4 6.4 6.77 194
2518 TBIKITT CHOBCH RON CHAHLES
332719 765139 CHARLOTTE HALL
81/12/30 ***¦ 3a BR 4 S3
DIST3 RAINpH: 4.4 1J16b»
HD
15b
0 5 6.4 6.61 333
2519 TflIB TO OCTOBABO CBE CECIL HD
394110 760823 COKOHINGO DAH 33>
81/ 6/ 1 **«b 3* BR2 S5
DIST4 EAISpH: 4.3 1118bb
0 16 7.3 8.00 882
2520 LAKE KOSHARE GARRETT BD
392705 792820 OAKLAND 731b
ttV/ 2/17 1.5a 4ha BE3 S3
DIST3 RAINpH: 4.3 1321na
0 0 5.2 6.57 31
2 2 5.5 6. 35 624
2521 PLU1 CRBEK CECIL HD
3 9 3 JO 5 75 531 J NORTH EAST Jb
8 1/ b/ 1 »*»a 5a BE2 S3
DIST3 RAINpH: 4.3 11 18bb
0 17 5.4 4.83 23
2522 CYPRESS BRANCH KENT HD
391525 755030 MILLIHGTON 3a
81/ 6/ 1 ***¦ 5a BB4 S3
DTST3 RAItfpH: 4.4 1118bb
0 22 6.7 7.36 353
2523 WILLISTON LAKE CAROLINE ftD
364940 755030 HOBBS 3»
81/ 6/ 1 1.2b 13ha BR2 S3
DIST4 RAINpH: 4.5 1118»a
0 23 7. 6 7. 53 304
j 20 6. 9 o.M 313
227 78 39 338 208 2 71 197 0.0 124 61
338 98 39 301 319 3 68 172 0.0 126 162
171 98 45 473 211 5 60 320 0.0 139 464
301 99 53 472 163 4 46 364 0.0 9 1 408
848 148 134 282 266 5 75 558 1.4 296 300
5
596
38
35
49
71
188
218
31
53
51
****
136
232
0.0 100
0.0 68
232
271
-5 42 99 38 100 5 65 68 7.6 30 189
313 88 115 13 8 333 70 7 2 255 5.8 122 199
279 85 29 251 277 40 35 224 1. 7 55 179
279 91 59 168 279 37 51 235 1.2 80 137
-------
2524 HIGSINS STLLPOSD DOBCHESTER BD
383110 755745 EAST NEW BASKET 2a
81/ 6/ 1 0.3a 63ha BB2 SS
DIST4 RAINpH: U.5 1168aa
FLD LAB FEP DEP
pH pR ALK ALK COND A1 Ca CI CLR K Mil fU SO^
0 24 7.2 5.99 465 416 123 78 210 n17 1D0 59 319 1.7 129 268
2 24 7.1 6.92 441 397 117 75 213 359 100 5 7 30 6 3.4 123 265
252S ADKIHS POM D tflCONICO BD
382000 752230 HAHGQ 9a
81/ 6/ 1 0.5a 9ha Bfi2 Si*
DIST4 RAIBpH: 4.5 1219aa
0 20 6.6 6.42 338 298 106 70 134 329 70 55 191 1.4 71 242
2 17 6.4 0.33 441 392 134 33 1t39 343 60 38 198 1.7 48 182
2603 BIIRCH2S BOS MARSHALL B¥
395835 803745 "10UHDSVILL2 235a
81/ 3/19 **«« 5« BH3 S4
DIST3 RAINpU: 4. 2 )067«a
0 2 7.2 7.98 1377 1338 250 0 1969 132 3 10 515 2.5 36 927
2604 CONABAY LAKE TTLEB UV
392520 805130 SHIBL5T 231a
81/ 3/19 1.8a lha BH3 S4
0IST4 RAINpH: 4.2 1118mi
(-¦
Oi
2610 EAST LYNN LAKE BAItiE
380840 822306 WAYNE
81/ 3/20 0.7a 433lia BE3 S3
DIST4 RAINpH: 4.3 1118aa
UV
196a
7.0
6.8
6. 5
6.7
7.28
ti. 96
7.27
7.00
441
4 31
142
137
40 2 13 7
39 7 03
118
103
57
60
20
18
259
261
318
352
71
71
87
63
47
73
6
5
29
28
138
135
248
260
2. 3
2. 5
5 2
5 1
194 3. 4 149
192 4.0 145
452
450
395
417
2612 hohse cheek lake ktohing hv
373425 814235 BAILETSVILLE 355a
81/ 3/20 O.Sb 5ha BH2 S3
DIST2 F.AINpH: 4.4 1118an
6.7 6.95
6.8 7.70
191
206
147
16b
76
76
182
44
7 d
43
18
25
89
4 2
178
48
2. 6 111 435
1. 3 15 439
2613 PLUM ORCHARD LAKE FAYETTE KV
375700 811435 OAK HILL 528a
81/ 3/21 U.Ob 77ha BE2 S3
OtSXi RAISpH: 4.4 1168aa
0 » 6. 8 7.00 167 138 45 134 77 23 3 77 154 1. 5 8 2 318
8 3 6.8 6.79 167 143 51 109 33 36 3 68 150 1.7 75 329
2614 f*ONCOTS LAKE BOHKOE MV
373733 802107 PAINI BANK 763a
81/ 3/21 1.51 39b a BH 4 S3
01SI3 aAINpH: 4.4 1041am
0 2 6. 7 6. 81 1 47 1 13 33 37 74 65 6 39 76 1.3 21 127
6 2 6.8 6. 54 142 108 30 37 79 35 5 3b 80 1. 3 21 127
-------
fld lab fep dep
D T £H_ £H_ ALK ALK COND A1 Ca CI CVR X Mj? Mn Na SO
2615 SHEBKOOD LUKE GREENBRIER H* 4
38D010 800033 LAKE SHEBWOOD 813b
8 1/ 3/21 1.0a 69ha BR2 S3
DIST3 BAISpH: 4.4 1316aa
0 3 6.8 6.25 73 39 23 52 36 36 3 48 46 3.1 15 67
5 3 6.8 6.72 103 73 20 0 65 29 5 10 17 1.9 1 65
2616 SPRUCE KNOB LAKE RANDOLPH UV
384210 793515 5PBOCE K NOB 1171b
81/ 7/11 1.0a 11ha BB2 S3
DIST3 BAIHpH: 4.3 13 16bb
0 25 7.5 7. 34 165 143 27 4 154 7 10 1 3 54 2. 3 5 4 1
5 16 6.5 6.44 401 356 45 9 133 5 6 20 72 29.5 10 15
2619 TETEH C8EEK LAKE BABdOOH WV
390625 795230 SONTROSE 560a
81/ 3/22 1.5a 13ha BB3 S3
DIST3 BAINpH: 4. 3 1321aa
0 3 6.6 6.73 6) U 35 78 53 14 1 58 82 2.0 32 156
5 3 6.6 6.58 65 19 31 66 48 25 1 54 69 1.9 26 182
2621 LITTLE RACCOON CHEEK PBESTON BV
392235 795010 NEH30BG »2«a
81/ 3/22 4*. BB3 S3
DIST1 BMKpH: 4.4 1270aa
0 3 7.1 7.26 255 206 59 7 274 258 3 17 125 1.3 15 333
2622 LAKE O' WOODS PBESTON WV
394205 794355 BBUCSTON HILLS 571b
81/ 3/22 ~~~a 29h a BB3 S3
DIST4 HAIKpH: 4.3 1219aa
0 2 7.1 6.37 279 250 71 0 396 41 0 9 105 1.5 7 318
2623 N BP SNOWY CREEK PRESTON WV
392715 793100 TERBA ALTA 777a
81/ 3/22 **»a 6a BR3 S3
DIST3 F.AINpH: 4.3 1321aa
0 3 7.0 6.30 83 63 53 17 80 44 0 27 59 0.9 11 2b1
2624 STONiJ* BIV EB LAKE GRANT «iV
39G312 79175a HOOHT STOBa 1035a
81/ 7/11 3.7b 165ha BB3 S5
DIST2 EAINpH: 4.3 1118aa
0 23 5.0 5.13 13 -9 22 19 47 13 3 28 35 2.6 6 67
9 18 5.1 4.84 18 -9 23 23 44 10 5 32 35 2.8 7 84
2627 CACAPON PARK LAKE NOBGAN WV
39300J 78 1805 BIDGE 311b
81/ 7/11 2.6a 2ha 3R2 S5
OIST3 RAINpH: 4. 3 918aa
0 26 7.2 6.00 977 94 1 116 3 946 34 3 12 313 3.9 27 111
5 15 6.9 7.31 1274 1140 131 0 1026 31 5 11 285 d6.6 21 72
-------
FLD LAB FEP DEP
D T pH pR ALK ALK COND A1 Ca CI CLR K Mg Mn Na SO^
2628 HARDEN LAKE HAEDI Wy
390755 783550 IELLOV SPRING 392b
&1/ 7/11 3.3b 18ha 9B3 S5
DIST3 RAIWpH: 4.3 914an
0 27 8.6 3.39 670 606 94 7 716 18 2 18 334 0.5 42 184
6 18 7.3 7.30 1688 1615 185 0 1553 15 8 11 435 48.5 33 67
2630 WIGNER RtJN RITCHIE 87
390455 810440 SHITH7ILLE 210b
91/ 3/19 4a BE3 S5
DIST3 RAIHpH: 4.2 1219a*
0 2 7.1 7.71 465 432 112 14 202 166 3 25 126 0.0 22 429
2632 ET PK BOFFALO CHEEK LOGAN 8?
375335 620940 CHAPHAHVILLE 213a
81/ 3/20 ***» 3a BE2 S3
DIST3 RAINpH: 4.4 1118*»
0 4 6.7 6.43 134 78 136 120 257 24 0 74 498 0.5 246 856
2633 STEPHENS LAKE BALEIGH UT
374640 811830 ECCLES 617a
81/ 3/20 4.0a 93ha BB2 S3
DIST3 RAIHpH: 4.4 1168aa
0 3 6.7 6.72 109 78 33 64 61 34 1 53 87 3.2 32 212
12 3 6.7 7.JD 137 107 32 36 62 39 4 39 68 2.7 18 202
2634 PANTHER CREEK NICHOLAS XT
M 381325 603940 NETTIE 677a
81/ 3/21 »»*a 3a BB2 S3
DIST3 RAIHpH: 4.4 1219aa
0 2 6.7 7.b2 274 235 230 4 439 1433 3 13 155 0.0 13 225
2635 BIG DITCH LAKE BEBSTEH WV
332410 803400 COHEN 677a
81/ 3/21 I.Pa 4a BR2 S3
DIST4 RAIHpH: 4.4 1244aa
0 1 6.8 6.53 142 108 43 12 109 125 8 23 65 1.7 11 137
5 2 6.7 7.95 166 118 111 11 111 625 9 22 64 1.3 10 94
263b PICKLES FORK RESERV BRAITOH 9V
384550 803555 ORLANDO 259m
61/ 3/22 *-*m 4ha BB3 S3
DIST3 RAINpH: 4.4 1219aa
0 1 7.1 8.31 548 515 104 9 290 47 8 20 155 1.3 21 377
2637 STONECOAL LAKE LEWIS WV
385845 802250 ROANOKE 344a
81/ 3/22 1. pa 239ha BR3 S5
DIST4 RAINpH: 4.3 1219aa
3 3 6.9 7.58 490 456 113 4 236 187 8 14 104 0.9 10 523
3 3 7.0 7.45 534 495 1ia 9 263 15B 8 19 139 0.9 19 516
2640 UPPER GLADE C3 R2SER RALEIGH W¥
374020 (113345 SHADT SPRING 823«
81/ 4/25 »»*» 33ha BR2 S3
DIST3 RAINpH: 4.4 1119aa
0 12 6.5 7.37 lid 73 95 56 70 612 0 5J 91 1.5 31 207
-------
FLD LAB FEP
T £H_ £H_
ALK
2701 PEACEFUL VALLEY LAKE FREDRICK ?A
390840 782145 HMPIELD 259b
8 1/ 4/24 1.5a 7ha BS2 S3
DIST3 RAINpH: 4. 3 914«b
0 15 7.9 3.63 975
5 15 7.7 9.36 1146
2702 1ILL CREES SHENANDOAH VA
384520 764015 COHICVILLE 281*
81/ 4/24 **«» 7b BE2 S3
DIST4 BAINpil: 4.3 914am
0 16 8. 1 7. 58 19-94
2703 DRY RON ROCKINGHAM VA
382 440 783310 ELKT05 EAST 415a
81/ 4/24 »**a 4a Bft4 S3
DIST4 BAINpH: 4.4 1316aa
0 13 7.3 8.39 387
2704 LAKE ALBEMARLE ALBSHARLE VA
3o0525 783743 C30ZJ2T 152a
81/ 4/24 1.5a 17ha BB2 S3
DIST3 RAIKpH: 4.4 1016bb
l i 0 18 7. 5 8.43 336
CO 6 10 6.7 7.08 338
2705 PLBVANNA PURITAN LAK FLUVANNA VA
375325 782225 BOYD TAVEFN 116a
31/ 4/24 3.0a 23ha BR2 S3
DIST3 RAINpH: 4.5 11 68bb
0 18 7.4 8. 63 252
7 1 1 6. 8 7. 19 299
2736 HOLIDAY LAKE APPOHATTGX VA
37 2310 783820 HOLIDAY LAK2 136a
81/ u/24 1. 3a 48ha 331 S3
DIST4 BAINpH: 4.5 1143bb
0 17 7.4 8.77 304
5 14 7.1 8.42 343
2707 UNNAMED CREEK A NHEHST VA
373235 790346 AflH^fiST 215a
81/ 4/24 **»» 3a BR 1 S3
DIST4 RAINpH: 4.5 1118an
0 13 7.3 3.1) 554
2708 COL SULPH SPR 8R SES ROCKBRIDGE VA
375830 793 11J fllLLBOBO 440a
81/ 5/13 2. 3a 4ha BB4 S3
DIST2 BAINpH: 4.5 1016an
0 19 7. 1 7.53 162
3 16 7.1 7. 39 176
DEP
ALK COND
A1
Ca
CI CL"R
M g Hn Na
936
11 17
111
147
1439
136 4
33 3
44 5
7
8
269 0.9
295 3.3
13
16
228
273
1940 242
16 2625
190 5
26 1801 0.0 326
249
358
52 57
84
25 3
48
111 0.0 38
52
313 53 16 124
289 54 25 105
108 6
_ 39 6
26
32
88
89
0.0
1. 4
1 6
20
52
57
224 33 181 43
279 32 181 45
52 6
58 5
9 1
87
10 5
106
0.0
1.1
6 3
65
62
46
269 43 143 59
318 45 192 51
47 5
51 6
79
9 1
121
123
3.0
1. 3
65
8 2
62
62
529 61 148 77
79 5
78
157 0. 5 06
40
127 34 233 43
147 34 241 48
18
29
10 1
13 3
125
122
1.9
2. 5
fi 5
85
158
188
-------
FLD LAB FEP DEP
D T pH pH ALK ALK COND A1 Ca CI CLR K Mg Hn Na SO;
2709 MOUNTAIN LAKE 3ILES VA
372135 803213 EGGLESTOB 1182»
81/ 4/25 6.0a 22ha BH4 S3
DIST4 RAINpH: 0.5 1016aa
0 8 6.6 8.33 166 157 27 8 65 13 3 19 32 0.0 4 41
9 6 6.8 6.91 109 88 21 10 88 17 3 21 49 0.0 7 89
2710 HODDI FORK CSSEK TAZEWELL VA
371530 812015 DBA 3 HELL 714m
8 1/ 4/25 ***a 3a BS4 S3
DIST4 HA I lip H : 4.5 11 18aa
0 10 6.9 a.33 333 294 51 21 130 43 0 30 101 0.9 21 195
2711 HUMGHT BOTHER LAKE SBTTH VA
365215 813125 HABIOB 671*
81/ 4/25 1.0a 40ha BH2 S3
DIST4 RAINpHi 4.5 1163aa
0 14 6.8 8.26 294 264 53 111 92 87 3 71 164 0.9 79 117
6 13 6. 9 3. 53 279 240 51 102 84 89 3 68 143 0. 8 68 106
2712 CLEAR CREEK LAtE WASHINGTON VA
363950 820720 STNDALE 641a
81/ 4/25 1.0a 20ha B84 S3
DI5T3 BAINpH: 4.5 1168aa
0 17 8.2 8.37 3003 2935 368 25 2011 25 1 33 1811 2.2 424 405
4 15 8.4 8.36 3209 3092 338 23 2094 44 1 32 1764 2.3 39 2 413
2713 CABIN CREEK GRATSOH VA
363623 813 12J PARK 1025b
B1/ 4/25 4l BE2 S3
DIST3 SAINpH: 4.5 1219aa
0 8 6.5 6.37 83 59 21 43 31 15 4 43 36 0.0 11 67
2715 Sk AN LAK2 PITTSYLVAB VA
364215 792010 BLAIRS 198a
81/4/26 0.3a 5ha B81 S4
DIS74 BAINpil: 4.5 1118aa
0 15 6.9 7.43 402 353 63 258 77 116 8 106 213 2.6 162 137
4 9 6.6 6.74 392 339 71 259 64 102 6 106 181 8.5 134 143
2716 COIINEB LAKE HALIFAX VA
365523 784810 CONNER LAKE 113a
81 / 4/26 0. 2a 41ha BR1 S3
DIST4 RAIKpii: 4.5 1118aa
0 17 7.0 3.03 463 421 69 114 87 92 3 66 155 4.6 73 252
3 15 6.9 7.33 510 1019 69 145 73 71 3 78 155 5.6 85 238
2717 SLAGLSS LAKH 3REENSVILL VA
364445 77 3 14 J ESPOaiA 36a
6 1/ 4/2t> 2. Jb 58ha B«3 S3
DISX3 PAINpH: 4.6 1118aa
0 1B 7. 2 8. 16 5 13 470 90 52 163 145 1 8 47 200 1. 3 66 333
3 18 7. 3 8. 05i 513 470 84 5d 93 124 18 50 120 0.9 43 340
-------
FLD LAB FEP
D T pH pH ALK
2718 LIKE PRINCE ISLE OP «I VA
36 4830 764105 WINDSOR 8a
81/ 4/26 #»~» 6hd Of)<4 S3
DIST2 RAIKpH: 4.6 1118aa
0 17 6.8 7.11 354
2719 TDSKEY IS LAND CREEK CHASLSS CI VA
372307 771409 20XBUHY 3a
t) 1 / 4/26 »**» Ha BB3 S4
DIST3 RAIHpH: 4.6 1118bb
0 t7 6.9 7.33 499
2720 BESEE ON LICKING CK CHESTEBFIE VA
372110 77323J BEACH 49b
u1/ 4/26 4.0b 45ha BJi3 S3
DIST3 RAISpH: 4.6 1118bb
0 19 6.B 7.76 1«2
5 17 6.7 6.96 147
2721 REED* HILLPOHD CAROLINE FA
375545 771810 P'NOLA 20b
B 1 / 4/26 O.tta 19ha BE 2 S4
O DIST4 RAIKpH: 4.5 1143aa
0 20 5.9 6.38 93
2 16 5. 9 6. 12 73
2722 GRANT LAKE SPOTSTLVAN VA
381835 774350 CHAHCELLORSVIL 103b
81/ 4/27 2. 3b 3ha BK2 S3
DI3T3 EAIHpil: 4. 5 1067aa
0 16 6.6 7.4ti 1 d 1
4 13 6.6 6.98 172
2723 NIGGER EON C0LPEPSB VA
384815 775515 JEFFER 50 HTON 110b
31/ 4/27 3a Bfi3 S3
DIST4 RAIKpH: 4. 4 1G67«a
0 14 6.9 7.03 416
2724 N FK CATOCTIN CBE 5K LOUDOUN VA
391145 774525 ROUND HILL 177b
81/ 4/27 ***a 2i BS3 S3
DI5T4 RAIKpH: 4.3 1316aa
0 17 7.3 7.6} 323
2725 GEIGG LAKE POWHATAN VA
373955 7 75 410 GOOCIILASD 48a
31/ 4/27 ~*»a 5ha BR2 S3
DIST4 EAISpH: 4.5 11101B
J 18 6.6 b.J 5 30J
325 121 5 391 321 30 15 163 1.9 17 477
442 72
37 2
161 15
ia
97 4.5
67
108
118
UQ
39
195
93
49
52
44
63
6s)
63
37
85
1.3 4 1
1.5 37
215
219
61
39
33
33
165
166
31
29
68
11 B
22
25
83
35
67
64
2.1 39
2. 2 3 9
100
68
162
145
33
30
343
335
24
25
31
42
121
122
77
81
1.9
2. 4
66
65
78
106
397 75
41
143
161
42
162 2. 3 4 6
127
309 74 162
110
71
85
.248 0.9 142
182
274
43
76
98
53
57
143 7. 1 57
148
-------
FLD LAB FEP
D T pH pH ALK
280 1 BROOKS LAKE 30ILPORD NC
361353 794333 BBOHN1S SOHHIT 228a
81/ 5/13 2. Ob 13ha BB2 S3
DIST2 RAINpH: 4.6 1143aa
0 23 8, 1 8.78 485
3 21 7.8 3. 49 475
2302 RCCK CHEEK WILKES NC
36 1220 810435 SOARING RIVER 311a
81/ 5/13 5a BR2 S3
DIST4 RAINpH: 4.5 1219aa
0 16 7.2 7.04 176
2803 PRICE LAKE WATAUGA NC
360818 314353 BOONE 1025a
61/ 5/14 1.0a 9ha BR2 S3
DIST2 HAINpH: 4.5 1321an
0 16 6.7 6.92 103
6 14 6.4 7.33 343
2804 QIJEEH'S CREEK HACOK NC
35 1635 833745 H2WITT 964a
81/ 5/16 3a B82 S3
DIST3 RAIMpH: 4.5 1626aa
M 0 17 7.2 7.78 152
NJ
1-1 2805 LAKE LOGAN HATWOOD NC
352500 8^553 5 1ATNESVILLE 837a
81/ 5/16 2. Ol 35h a BP.2 S3
DTST3 SAIHpH: 4.5 1422aa
0 16 6. 5 7.DO 83
4 16 7.0 6.34 35
2806 MORGAN LAKE MCDOWELL NC
35U330 920330 WAHION 427a
91/ 5/16 1.3* 13h a BR2 S3
DISTO BMMpH: 4.5 1321««
0 19 7.5 7.47 166
2 17 8. 5 8. 48 235
2807 SESEk ON MAIDEN CSEE CATAWBA BC
353505 811135 MAIDEN 257a
8 1/ 5/17 1.3a 9ha B82 S3
DIST3 RAINpH: 4.5 1168na
3 20 6.9 7.17 22J
5 15 6. 2 6. 39 313
2808 LAKE LYNN CABARBUS NC
352403 333129 COBCOBD 174a
81/ 5/17 2.Da 6ha UB1 S4
9IST4 SAINpH: 4.6 1219aa
3 22 7.6 7.67 65b
7 14 6.8 6.9J 975
DEP
ALK COND A1 Ca CLR K. M£ Mn Na SO^
445
44 6
56
52
53
48
10 7
120
68
63
U6
44
136
139
1.2
1. 9
44
45
89
101
21U
33
34
57
58
40
61 1.2 16
23
69
328
19
23
U
17
42
47
31
34
9
10
27
27
29
34
1. 5
1. 9
5
6
62
49
122 35
49
25
29
45
30 1.3
18
54
69
16
18
37
48
22
2 1
7
18
39
4b
23
25
1. 3
1.3
35
46
215
201
34
30
52
51
40
47
31
34
47
47
51
56
1.7 17
2.0 19
41
35
19 1
299
26
37
13
80
107
6 1
63
60
23
58
64 1. 5
96 12.5
1 0
39
46
41
622 107 16 43 1 197 5 27 306 2. 3 56 253
936 122 17 427 195 8 2d 296 6 8.8 56 106
-------
FLD LAB FEP
D T pH pH ALK
2809 D E H NTSO H15 3a EEK SOMTGOHEST JC
352320 7952D5 STIHjrsai 15'( 137m
81/ 5/17 8a BH3 S3
DIST3 RAIHpH: 4. 6 1118>a
0 20 6.9 7.31 416
2010 UHIVERSIT? LAKE ORANGE SC
355347 790539 CHAPEL HILL 105«
81/ 5/18 1. 3b 73ha BB3 S5
DIST4 KAINpH: 4.6 1118aa
0 22 7.5 7. 97 553
4 20 6.9 6.77 6 12
290 1 DAVI CBOCKETT LAKE GREENE TB
360426 825 133 DMT CROCKETT 379*
81/ 5/14 0.8i 2 55h a BR 4 S3
DIST3 HAINpil: 4.5 11 ISaa
9 19 7.6 7.33 867
j 19 7.7 7.85 982
2902 PRESSHSK'5 HOP1B LAKE HWIKIMS Tif
362705 830335 CAHELOT «112«
M 81/ 5/1ti 3.0a 7ha BB4 S4
DIST4 RAINpH: 4.5 1118»b
10 0 22 7. 9 8.58 485
6 1a 7.0 6.94 4 85
2y03 BULLRUN CBEEK ilHIOB TS
361044 835247 3IS RIU3E PARK 299a
81/ 5/14 ***a 6a B34 S4
DIST3 RAIffpH: 4.5 1168aa
3 19 8.1 6.97 1901
2904 R2S ON N F PINS Cf.F.Z SCOTT Till
363333 843225 ONEIDA NOHTH 442a
81/ 5/15 2. .3¦ 16ha BS2 S3
DIST3 RAISpH: 4. 5 1321«b
0 17 6.7 7. 22 1 08
3 15 6.7 7.03 137
2535 CITT LA.K3 OH CAHR CK OVEETOH Tti
3620*0 851953 OKALQNA 284*
81/ 5/15 3.0a 15ha BK4 S3
DIST4 HAISoH: 4.5 1346ma
0 16 8.7 7.12 1249
4 15 3.3 7.01 1327
2906 D0Bu£S3 FALLS USE PUIHAM TN
36 J 23 5 853 530 BBRSSS3 FALLS 269a
81/ 5/15 ,3. ,5a 21ha BS4 S3
i>IST3 RAINpK: 4.5 1J21na
3 16 <1. 8 7. 46 1675
4 16 8.0 7. 57 2183
387 49 17 125 63 16 27 H9 2.7 17 23
519 85 14 321 176 6 25 204 2.9 36 132
568 90 15 334 124 8 25 219 8.8 38 137
323 97 5 602 61 3 15 250 1.0 26 117
3H2 132 0 624 63 5 10 180 1.3 13 101
450 93 9 324 87 3 19 167 0.4 24 390
451 91 9 30 7 79 6 20 166 0.7 23 411
1842 245 4 2677 100 3 13 974 3.3 96 158
78 99 8 340 58 3 19 179 47.2 24 767
117 96 5 438 55 3 15 179 45.4 19 737
1205 191 0 1816 132 5 8 335 1.7 21 67
1283 183 0 1749 116 3 3 182 3.5 4 52
1.142 2 J J 3 2085 203 3 8 4«7 2.0 29 216
2121 245 3 2375 215 3 9 510 2.4 32 232
-------
FLD LAfi FEP
D T pH pH ALK
2907 FALLS CF32K LAK2 VAN BUflEH TK
353940 652 145 SAflPSOK 503a
81/ 5/15 3.0b 31ha 3R2 S3
0IST3 RAI HpH : 4. 5 1372a«
i) 16 7.6 6. db 44
7 15 7.2 6.<19 79
2903 iltJ HG3R 5 POND BSIGS TN
352020 845335 BIRCHHOOD 235a
81/ 5/15 0.5a 9ha BH4 S4
DIS13 RAIHpH: 4.5 1321»i
0 19 8.2 7. 53 113
2 19 7.1 6.76 73
2909 QIUFRT CREEK HOMBOE TH
352125 841637 TBLLICO PLAIKS 262a
81/ 5/16 3a BR2 S3
DIST2 RAIHpH: 4.5 1321b*
0 14 7.5 6.51 353
2910 LA5B28T LAKE BLOUNT TN
w 353 933 S35735 BLOCKHOUSE 326a
oj 81/ 5/16 2.5« 8ha BE4 S3
DIST2 RAIHpH: 4. 5 1321at
0 19 7.2 B.91 245
3 14 6.4 6.61 294
2911 THtB SIXMILE CCSZK BLOIJNT TN
353355 835750 BLOCKHOtJSS 296b
a1/ 5/16 ***¦ 2a BR4 S3
DIST3 EAINpK: 4.5 1321b«
0 12 7.6 7.51 789
24 23 11 71 2S 3 21 41 0.6 6 111
49 18 17 57 31 3 27 *2 1.1 7 84
38
39
27
29
137
132
63
29
12
13
44
43
1.5
1.7
67
94
313
56
83
131
6 }
60
160 0.6 65
191
215
259
29
33
22
13
90
133
5
10
31
23
74
33
0.0
4.7
16
12
57
46
764
88
61 3
154 0.5
208
-------
APPENDIX 3. HISTORICAL DATA
The historical listing can be separated by state according
to the following code, which appears in the second column of each
entry, i.e., the second digit of the station code number:
1. New ¥ork 6. West Virginia
2. New Jersey 7. Virginia
3. Pennsylvania 8. North Carolina
4. Delaware 9. Tennessee
5. Maryland
Further information on the specific waters can be found in
Appendix A.
Key to column headings for Appendix B:
1-4 station numbers (see above)
6-27 water body name
29-36 date (year, month, day) of historical sample
40 depth sample taken
43-45 pH of sample
47 method code for pH: l=electrometric,
2=colorimetric
50-53 alkalinity of sample
55 method code for alkalinity: l=fixed endpoint,
2=double endpoint
58-59 temperature at sample depth (°C)
A large number of cooperators in state agencies and other sources
provided the historical data; some of these did not want themselves
or their data specifically identified. Specific queries concerning
data for particular waters will be accepted and passed on by the
authors to the appropriate source.
-------
STATN NAME
2101 C»TOTA LAKE
2100 CASS LAKE
2111 CLEAF LAKE
2117 HE*LOCK LAKE
2121 ECHO LUKE
212 5 CATSPAW LAKE
2126 SFARS PDHD
2 129 LAKE OP THE WOODS
2'3 5 KEBPORT POND
213"? CPTSTAL LAKE
2140 DUCK POJJD
YR MO DA Z PH Ml ALK M2 TEM
37/
7/19
0
«. 3
2
980
1
37/
7/1S
7
6.9
2
1332
55/
5/10
3
5.6
2
***•
*
55/
5/11
3
5.4
2
• ***
«
66/
7/2f
0
a.3
1
0
1
66/
7/26
2
4.9
2
0
1
79/
8/2 0
1
#.2
1
0
1
80/
9/17
1
a.6
1
0
1
77/
9/28
8
7.5
2
19 20
1
62/10/ 3
9
8.2
1
1200
1
66/
5/10
9
7.0
1
1000
1
75/
8/28
9
7.8
1
1106
1
80/
9/22
9
7,4
1
1080
1
81/
2/25
Q
7.2
1
#*•*
*
39/
a/**
0
8.2
2
1128
1
39/
0/*»
3
6.9
2
1300
1
49/
8/**
0
8 . 4
2
1680
1
49/
8/**
3
8.2
2
1510
1
66/
8/»*
0
6.8
2
»~**
*
66/
8/»*
3
6.8
2
208
1
76/
8/**
0
7.3
2
560
1
70/
7/**
0
7.4
2
680
1
70/
7/**
22
7.2
2
680
1
32/
8/18
8
6.2
2
6
1
32/
8/18
0
6.6
2
154
1
56/
6/25
0
6.a
2
**~*
«
56/
6/25
12
5.8
2
****
*
71/
7/ 6
0
6.9
2
160
t
71/
7/ 6
9
6.3
2
260
1
79/
8/22
1
6.5
1
163
1
32/
8/ 5
1 «
6.2
2
380
1
32/
8/ 5
0
6.8
2
360
1
30/
9/ 7
0
5.8
2
140
1
30/
9/ 7
2
5. B
2
140
1
STATN
NAME
YR MO DA
Z
PH
Ml
ALK
M2
TEM
2 1 U 3
PAT.FOn? LAK2
32/ 9/ 1
1 u
6. 0
2
430
1
»t
32/ 9/ 7
0
6.4
2
100
1
*#
56/ 8/2fi
6
fi.6
2
*
* ~
56/ 8/2P
12
6.?
2
* ~**
*
* *
64/ 6/22
0
6.8
2
130
1
*»
64/ 6/22
12
6.4
2
180
1
• *
68/ 7/30
0
6. 1
2
800
1
**
6 8/ 7/3 0
12
5.6
2
1000
1
* *
78/ 7/25
0
***
*
184
2
»~
78/ 7/31
0
7.5
1
****
*
*~
79/ 8/29
1
6.3
1
67
2
**
2146
CATA100NT POND
30/ 9/ 8
1
5.8
2
0
1
*»
3 0/ 9/ 9
0
5.8
2
100
1
* •
3 0/ 9/ 9
3
5.8
2
160
1
*»
55/ 5/26
2
5.2
2
****
*
55/ 5/26
8
6.2
2
*~~~
*
**
79/ 8/2 0
1
4.0
1
0
2
»•
2 U7
POCK LAKT
32/ 8/25
2
6.8
2
200
1
**
2148
G LAKE
34/ 8/11
8
6.0
2
228
1
»*
34/ 0/11
fl
6.7
2
168
1
• *
7 5/ 6/24
0
5.6
1
5
2
*•
78/ 7/25
0
5.2
1
****
*
**
78/ 7/25
9
4.8
1
****
*
»*
8 1/7/2
0
5.7
1
13
2
**
81/7/2
8
6.1
1
21
2
**
2151
EOND UK?
70/10/ 3
9
8.5
2
200
1
*•
21<31
CPYSTAL LAKE
35/ 7/ 5
0
7.6
2
256
1
24
3 5/7/5
7
6.0
2
408
1
15
64/ 7/21
1
6.5
2
****
*
26
6 V 7/21
6
6.5
2
****
»
12
2193
KLEIHE KILL LAKE
32/ 7/18
0
8.3
2
****
•
38/ 3/ S
0
7.0
2
• »»»
•
%*
38/ 7/30
0
7.0
2
***•
*
%*
39/ 3/ 3
0
7.2
2
* ***
*
»»
7 5/12/ 2
0
7.2
2
**•*
*
**
76/ 5/23
0
7.2
2
+ ***
*
**
76/ 6/19
0
»**
*
1360
1
**
77/ 3/30
0
6.6
2
***»
•
**
77/10/ 8
0
6.8
2
* *«*
*
*•
78/ 4/27
0
7.0
2
**•*
•
**
79/ 4/20
0
7.1
2
• ***
*
**
80/ 4/1f
0
7.2
2
*
»*
81/10/16
0
7.2
2
**»*
»
**
-------
STATN NAME
219U NORTH LAKF
2195 ALDER UKF
2197 TflNIS LAKE
220 1 LAKE1 GENEVIEVE
2202 FAIRVIES LAKE
2203 ASHBOE LAKE
2204 STEENYKILL LAKE
2207 SURPRISE LAK"
2209 SUBSET LAKE
2210 flOOHTAIN LAKE
2211 IAKE SOLITUDE
YR MO DA Z PH Ml ALK M2 TEM
36/ 6/21
0
6.2
2
150
1
**
36/ 6/2U
1
6.2
2
80
1
* +
57/ 7/15
0
6.0
2
*
**
57/ 7/15
2
6.0
2
*
71/ 7/19
0
6.8
2
684
1
**
71/ 7/19
2
6.8
2
684
1
**
80/ 7/ 9
0
7.2
2
280
1
20
80/ 7/ 9
5
6.5
2
400
1
16
73/ 4/19
0
7. 2
2
UOO
1
• *
73/ U/1 9
2
6.4
2
400
1
**
77/ 4/ 5
0
8.0
1
2800
1
9
75/12/10
0
6.2
1
260
1
4
76/ 6/ 3
0
6.8
1
300
1
IB
77/ 5/ 5
0
6.9
1
240
1
13
77/10/18
0
7. 3
1
340
1
8
76/ 3/2 4
0
6.5
1
380
1
8
52/ 8/18
0
6.7
2
182
1
24
52/ 8/18
u
6.3
2
200
1
22
75/ 7/2 4
0
6.4
1
320
1
26
76/11/ 3
0
6.7
1
400
1
9
77/ 11/ 4
0
6. 3
1
180
1
3
51/ 7/13
0
8.4
2
500
1
30
5V 7/13
3
8.3
2
560
1
19
52/ 6/26
0
8.7
2
500
1
28
52/ 6/26
3
6.6
2
480
1
19
50/*»/*»
0
8.3
2
1630
1
• *
50/**/**
**
7. 1
2
1960
1
**
75/ 8/20
0
8.6
1
2420
1
24
76/12/ 8
0
8.1
1
2180
1
1
77/ 4/ 5
0
8.0
1
1740
1
8
79/ 3/27
0
7.2
1
1590
1
1
51/ 8/ 7
3
7. 1
2
1100
1
~ »
51/ 8/ 7
0
7.4
2
1000
1
**
51/ 8/ 7
0
7.4
2
1000
1
25
51/ 8/ 7
3
7. 1
2
1100
1
24
72/ 2/ 3
0
7.7
1
1180
1
1
;tatn
NAME
YR MO DA
z
PH Ml
ALK M2
TEM
2211
LAKE SOLITUDE
72/
7/3 1
0
7. 3
1
1520
1
18
73/
V 3
0
7.4
1
10 20
1
1
73/
7/10
0
7.2
1
1300
1
21
74/
1/ «
0
7.5
1
900
1
1
74/
7/ 2
0
7.6
1
1080
1
17
75/
6/16
0
7.4
1
1160
1
18
7 5/
V 7
0
7.4
1
1120
1
2
75/
7/10
0
7.6
1
1580
1
18
76/
4/1 9
0
7.3
1
1360
1
17
7 6/
8/25
0
8.4
1
1860
1
24
76/
1/29
0
7.1
1
7I»0
1
0
76/
7/19
0
8.9
1
1600
1
22
77/
4/25
0
7.3
1
860
1
15
2213
HARIHOKAK E CREEK
78/
9/20
0
7.8
1
780
1
18
79/
2/28
0
8.1
1
420
1
U
2216
PEFRIHEVILLE LAKE
78/
6/21
0
7.0
1
240
1
26
2217
RED VALLEY LAKE
76/
3/ 9
0
6.4
1
260
1
4
2218
JACKSONS BILLS LAKE
60/
9/»*
0
4.5
2
60
1
«•
75/10/ 2
0
4.8
1
100
1
18
77/
5/ 3
0
5.7
1
120
1
15
77/
7/**
0
4.9
1
****
*
• •
BO/
6/»»
0
6.3
1
****
*
**
2219
BRISBANE LAKE
68/
7/17
0
6.8
2
60
1
**
69/
8/12
0
6.6
2
*»*~
*
• *
70/
7/23
0
5.3
2
*
• *
73/
5/21
0
5.8
1
100
1
23
81/
4/21
0
4.5
1
**»~
*
10
2220
SUCCESS LAKE
70/
5/21
0
6.4
2
20
1
»~
75/
4/30
0
H.3
1
120
1
12
76/10/ 7
0
4.8
1
80
1
18
77/
6/2 8
0
4.5
1
60
1
27
80/11/ 6
0
3.7
1
****
1
9
2221
BAHBER LAKE
76/11/ 9
0
4.7
1
120
1
5
77/
9/19
0
4.6
1
60
1
24
7 8/
6/19
0
4.6
1
120
1
24
79/79/21
0
4.0
1
1
10
-------
STATU NAME
2223 OYSTER CiFEK
2225 OSWEGO LAKE
K>
-^1
2226 HTSION LAKF
2227 PiRVIB HKf
YR MO DA
Z
PH Ml
ALK M2
TEH
67/ 8/ 9
0
1.9
2
*
**
67/10/ 3
0
6.6
2
**•*
*
**
68/ 3/11
0
7. 1
2
***«
»
**
69/ 3/10
0
7.3
2
***~
~
**
69/ 7/29
0
6.6
2
*
**
69/ 6/1 !
0
ft. 1
2
*
»*
7 3/12/16
0
7.6
2
****
*
**
71/ 3/18
0
7.0
2
«»»»
•
• *
12s 1/2 4
n
7.0
2
***«
*
**
"•7/ 1/27
0
4.M
1
0
1
5
77/ 7/22
0
(1 .7
1
100
1
IS
7ft/ 1/26
1
4.1
1
0
1
4
78/ 6/29
0
a.5
1
0
1
25
52/ 8/ 1
0
4.3
2
»***
•
*«
52/ 8/ 1
**
4.3
2
0
1
**
52/ 8/ 1
0
4.3
2
120
1
29
52/ 8/ 1
2
4.3
2
0
1
25
70/ 9/ 1
0
6. 1
2
120
1
•*
70/ 9/ 1
6.0
2
120
1
*»
75/ 8/19
0
4.7
1
0
1
**
75/ 8/18
0
a.2
1
90
1
22
76/10/13
0
4.2
1
80
1
17
77/ 5/12
0
4.3
1
ao
1
12
78/1 1/28
3
a.4
1
80
1
5
81/ 2/23
0
4.6
1
****
•
9
55/ 7/12
3
4.2
2
140
1
*»
55/ 7/12
* *
a. 2
2
**~ +
1
**
55/ 7/27
0
4.5
2
380
1
**
68/10/ 3
0
4.9
2
***•
*
**
75/ 4/24
0
4.8
1
140
1
16
76/10/ 5
0
5.2
1
80
1
17
77/ 2/ 1
3
6. 2
1
»»»»
~
3
79/ ?/ 6
a
1.7
1
20
1
0
61/ 8/19
0
3.7
1
0
1
• #
50/**/•*
d
6.7
2
240
1
**
50/**/**
**
6.6
2
360
1
*#
75/ 9/1 f
0
6.8
1
560
1
20
76/ 4/ 5
0
6.4
t
300
1
11
77/ 3/ 1
0
5.9
1
180
1
8
78/ 7/26
0
7.0
1
380
1
26
81/ 1/13
a
6.5
1
****
~
13
STATK NAME
222S rASKELL niLLPCND
2230 T^CKATfTS LAKF
2302 CLEM? rJFUQE CPERT
2305 little fishing cheek
2306 LITTLE SRNDY CREEK
2310 STP AIGHT RUN
2311 UPPER ^FIFFE BUNS
2 313 HKD SHH
2330 S FORK T H NGUS COOTACK
2 3 32 TROUT R'JN
2333 SLATE RON
2 '35 COCKS PCN
2354 GUDE TM18 RES5KVOIK
2 360 JOBDON CREEK
2362 CPUBBY CFEEK
YR MO DA
Z
PH Ml
ALK M2
TEM
7V
5/27
0
3. 1
1
****
1
2°
76/
9/27
0
5. 2
1
100
1
19
77/12/ 7
0
4.5
1
100
1
5
73/
7/20
3
5.2
2
60
1
**
7 6/
8/ 5
0
5.2
2
*
**
77/
3/ 7
0
5.1
1
120
1
1 1
8 1/
4/23
0
5.0
1
*»»»
*
**
PI/
9/16
a
4.7
1
210
1
»*
fl V
9/1£
*»
* .2
1
163
1
-*»
79/
5/10
0
6.6
1
740
1
«»
7 9/
6/1 0
0
6.0
1
160
1
72/
1/17
0
7.0
1
200
1
**
80/
9/26
0
7. 1
1
208
2
13
¦31/
3/2a
0
7.0
1
580
1
«*
75/
7/10
0
7. 6
1
UBO
1
17
8C/10/21
0
6.4
1
96
2
10
80/
8/ f
0
6. 2
1
80
1
**
81/
3/27
0
5.9
1
0
2
8
81/
7/1 6
0
7.7
1
9
2
15
R 1/
5/21
3
5. 1
1
0
2
10
C 66/
8/17
0
6.6
1
280
1
**
7 3/
5/ 3
0
7.8
1
240
*
**
8V
7/24
0
7.6
1
5*»
2
IB
78/
7/2 0
0
6.7
1
240
*
**
BO/
7/?4
3
7.0
1
337
1
16
7 3/
e/i 0
0
7.0
t
2000
1
**
66/
9/ fl
0
7. 2
2
5960
1
**
72/
1/26
0
7.5
1
800
1
**
73/
8/ ft
0
8.4
1
900
1
«*
7 1/11/ 3
a
6.6
2
T000
1
• *
-------
STATN
NAME
YR MO DA
z
PH
Ml
ALK
M2
TEM
2363
VALLEY CREEK
72/11/21
0
8.2
1
3200
1
**
72/10/ 3
0
7.8
2
1110
1
**
7«/ 8/20
0
8.6
1
3300
1
**
75/ 6/19
0
8.3
1
3520
1
**
76/ 3/ 1
0
8.1
1
3360
1
**
78/ 7/12
0
8.5
1
3560
1
**
78/ 9/27
0
7.1
2
1200
1
**
2 36#
PICKEPTNG CREEK
71/ 9/27
0
7.7
2
920
1
15
76/10/ 5
0
6.9
2
1200
1
**
77/ 3/10
0
8. 1
1
810
1
• *
2365
HARSH CREEK LAKE
75/ 7/29
0
8.0
2
***»
*
**
75/ 7/29
9
6.1
2
»***
*
**
77/ 7/21
0
8.5
1
920
1
**
77/ 7/21
9
6.9
1
1010
1
**
2369
tunBEL CREEK
75/10/21
0
6.6
1
100
1
**
2371
BRHSH CHEEK
76/ 8/21
0
6.9
1
203
1
**
237 1
ARCH SPRING
71/ 1/12
0
7.3
1
1560
1
**
2380
REDSTONE CREEK
76/11/10
0
7.1
1
610
1
**
2382
NORTH FOBK RESEBVOIR
76/ 6/16
0
6.9
1
110
1
**
210 1
COBBASE POND
55/**/**
**
6.2
*
****
*
30
2103
LTJ (IS POND
50/ 8/17
0
6.1
•
****
~
21
50/ 8/17
3
6.2
*
***»
*
23
7i/**/**
0
7.3
2
310
1
**
75/ 6/19
0
7. 3
2
310
1
30
2105
MORRIS HILL POND
55/»*/«*
**
6.5
*
****
*
28
2106
STIVER LAKE
55/**/**
*•
6.0
*
****
*
27
2107
HAVEN LAKE
50/ 8/21
2
3.2
*
****
•
26
50/ 8/21
0
6.3
*
****
*
21
50/ 8/21
0
7.2
2
*~*»
*
27
71/ 8/26
0
7.7
2
159
1
27
IS/**/**
0
7.7
2
510
1
27
3TATN
NAME
YR MO DA
z
PH Ml
ALK
>12
TEM
210 P
TFAP POND
55/**/**
* *
5.2
•
****
*
26
71/**/**
0
6.1
2
200
1
**
75/ 8/20
0
6.1
2
230
1
26
2109
lake cnfio
52/ 8/ 9
0
6.7
*
****
*
28
52/ 8/ 9
2
6.5
*
****
*
27
71/ 8/ 5
0
8.5
2
510
1
26
75/**/**
0
a.5
2
510
1
26
2110
GARRISONS lakp
71/ 5/21
0
7.0
2
820
1
18
75/**/**
0
7. 1
2
G20
1
18
211 1
HASSEYS MILLPCND
55/**/**
5.8
*
** * *
*
27
2112
chiphan pond
55/**/**
0
6. 0
2
*»**
*
29
211 f
BrOfllNT LAKE
53/ 8/ 6
2
6. 0
*
****
*
21
53/ 8/ 6
0
6. 3
*
****
*
26
2116
VOSHELL MILL POND
52/ 7/23
2
6.9
*
****
*
23
52/ 7/23
0
f. 8
*
****
*
21
2117
AKDREWS LAKE
55/**/**
• *
6. 8
*
*** *
*
30
71/ 7/22
0
Q. 2
2
180
1
21
75/**/**
0
9.2
2
520
1
25
211 P
MARSHY HOPE CREEK
71/ 9/11
0
6.5
2
~ »**
*
**
71/ 9/11
0
6.7
2
****
•
**
2119
KILLENS PCND
52/ 6/2 1
2
6.7
*
****
*
23
52/ 6/20
0
6.8
*
****
•
25
71/ 7/3 0
0
7. 1
2
820
1
27
75/**/**
0
7. 1
2
820
1
26
2 M 2 1
fl^AKNS POND
55/**/**
0
5. 2
2
****
*
29
2502
PL AIF VALLEY LAKE
70/10/ 5
0
7.5
2
1710
1
**
70/10/ 5
3
7.7
2
17 1 0
1
* *
70/ 0/21
0
8.1
2
1316
1
**
10/ 6/21
6
6 . 7
2
1368
1
**
72/ 7/17
0
9,0
2
1026
1
**
2503
FOCKY GAP RUN
66/ 7/11
0
8.6
1
» ** *
*
22
70/ 8/10
0
7.7
2
****
*
20
-------
STATN
NAME
YR
MO DA
z
PH
MI
ALK
M2
TEy
2501)
D7 VEY CREEK
69/
9/ 2
3
6.8
2
300
1
**
77/
7/ *
0
7. 0
2
***•
*
**
2535
CP ABTR E E CSEEK
7 4/
8/13
3
3.5
2
1360
1
*•
74/
1/3 0
0
7.2
1
400
1
17
79/
7/10
3
7.5
2
1720
1
**
250 6
LAKE HSBEIB
7(1/
8/23
0
7.4
1
120
1
**
71/
3/2 3
18
6.8
1
»***
*
~ *
2507
FF CSTBO H G P5SEFVOTF
68/
8/ 2
-0
7. 1
1
408
1
**
68/
8/ 2
5
6.6
1
* *»*
*
* *
77/
8/18
0
6. 3
1
****
*
**
2 50 ^
GPNPOWrHP PALLS
68/1 1/25
0
7.5
2
500
1
• *
70/
8/10
0
8.5
2
1368
1
**
7 0/
2/ 2
0
7.0
1
****
*
7V
6/ 9
0
7.8
2
1026
1
**
I i
74/
7/29
3
7. 5
1
640
1
**
K>
75/
7/ 2
0
7. 9
1
490
1
16
VO
76/
7/14
0
8.0
2
****
*
**
76/
7/12
0
7. 1
1
*•**
*
20
77/
7/ 7
0
7.5
1
****
•
23
7 8/
7/18
0
7.3
1
*»»*
*
18
80/
7/14
0
7.3
2
56 2
1
22
251 C
MEBP Y.I AN BEANCH
70/10/ 7
0
7.4
1
****
*
12
75/
9/10
0
7.7
1
600
1
15
76/10/18
0
7.5
•
****
~
9
77/
8/23
0
7.1
1
«««*
•
19
78/10/30
0
7.3
1
*
10
2511
CRTHVEY BRANCH
75/
7/28
0
8.2
1
1082
1
20
2513
CHENS CPEEK
68/
7/16
0
7.5
2
1026
1
»*
69/
7/25
0
7.0
2
855
1
»*
70/
6/15
0
***
*
1326
1
**
73/
5/29
0
7.5
2
680
1
**
80/
8/12
0
7.5
2
800
1
*•
81/
6/19
0
7.5
2
**•*
*
• *
251 4
HUNTING CREEK
66/
7/19
0
8.9
1
***•
*
26
78/
7/26
0
7.5
1
«
19
80/
8/ 6
0
7.5
2
500
1
• •
;tatn
NAME
ys ;
MO DA
7
PH
Ml
ALK
M 2
JEM
251 5
PATnXFNn" FTVJP
70/
9/1 0
6 . R
->
I
1026
* *
7 3/
1/71
1
7 . 0
2
680
1
* »
74/
7/15
¦T
7 . 2
1
343
1
23
11/
7/27
"i
7 . 5
2
'160
1
**
11/
8/ 9
0
7 . 5
1
4 1 2
1
25
78/
9/ 7
3
7. 5
2
1160
1
*•
80/
8/22
0
7.5
2
1020
1
**
81/
3/28
0
7. 5
2
2 205
1
*»
2516
HAWKINS PITEP
75/
6/10
1
6 . P
2
68 3
1
**
7R/11/ f
0
7. 1
1
* * ~ »
*
11
2517
GTI.BEET CPEEK
74/
7/16
0
c."
1
200
1
2R
2522
CYPBESS BEANCH
73/11/20
0
7.3
2
* * * «
*
10
76/
9/15
0
3. 2
1
* *» »
*
20
80/10/28
0
7.6
l
« • * »
*
13
2523
WTLLISTON LAKE
76/
5/2 4
0
7.7
1
»» « »
*
14
76/
8/16
0
7.4
t
« * * »
*
22
2 52 4
HIGGTHS HILL PCND
7 1/
6/22
3
9.0
2
***»
*
26
72/
9/27
3
ft. 4
2
* * * *
*
6
2525
ADKINS POND
71/
7/14
0
5.9
2
* * * *
*
23
2704
LAKE ALBEHARLE
75/
6/ 4
0
7. 9
2
300
1
26
7 5/
6/ 4
u
6.5
2
363
1
10
79/
R/ 6
7
7 . 3
1
30 0
1
9
79/
8/ 6
3
7.3
1
333
1
29
2705
FLI1VHNNA ROPTTAM LAKE
77/
7/13
0
6.5
1
28 0
1
3 1
2706
HOLIDAY LAKE
72/
5/2 5
0
7.0
2
* ** »
~
»*
73/
5/25
0
6.9
2
* ~ * »
*
**
74/
5/14
0
7.0
2
* ** *
*
*~
7 5/
5/20
0
6.9
2
»** «
*
**
75/
7/ 8
0
6.5
2
38 3
1
**
75/
7/ 8
0
6 . 5
1
3R0
1
28
75/
7/ 8
5
fi. 2
1
3eo
1
14
76/
6/2 4
0
7.8
2
**•*
*
**
78/
7/2B
0
7.5
2
****
*
«*
2711
HUNGRY BOTHER LAKE
74/
7/2 3
3
6.8
1
533
1
24
74/
7/2 3
5
6 . 4
1
360
1
15
74/
7/23
0
6.8
1
500
1
23
77/
7/23
5
6 . 4
1
360
1
15
-------
STATN
NAME
YR MO DA
Z
FH Ml
ALK M2
27->2
CtEAR CHEEK
6 6/
5/11
0
7.7
2
a a 20
1
77/
9/ 7
a
7.9
1
3 mo
1
79/
8/ 7
0
7.0
1
2100
1
2713
CABIN CBEEK
78/
5/10
0
7. 3
2
300
1
80/
5/29
0
7.3
2
300
1
2717
SLSGLE^ LAKE
77/
8/30
0
6.5
2
600
1
2718
LAKE PRINCE
71/
6/ 1
0
6.U
2
1000
1
71/
6/ ^
8
6.2
2
1100
1
72/
6/12
0
7.7
2
1200
1
72/
6/12
5
6.7
2
1100
1
73/
6/11
5
6.8
2
900
1
73/
6/11
5
6.8
2
900
1
73/
6/ 7
0
8.5
2
*
71/
6/1 ft
0
6.7
2
700
1
7 a/
6/1H
c
6.6
2
800
1
76/10/ 6
0
7.3
2
****
*
79/
8/ 6
0
8.1
2
****
*
2720
BESEBVOIS ON LICKIHG C
73/
5/25
3
6.3
2
****
*
72/
5/2#
0
7.3
2
• »**
*
2721
K. FORK CATOCTIN CREEK
7 5/
6/ 2
0
7.0
2
****
•
76/
6/22
0
7.7
2
****
*
77/
6/ 3
3
7.7
2
***•
*
78/
9/20
0
9.0
2
**•*
*
79/
6/21
0
7.6
2
~ ***
*
TEH
**
25
**
19
**
~ *
**
*~
**
**
~ ~
**
2B
~ ~
*~
19
28
~#
*~
**
**
*~
~*
*~
-------
APPENDIX C. STATIONS DROPPED FROM CHEMICAL ANALYSIS DISCUSSION
The stations listed below were omitted from the data used for
analysis of chemical relationships for various reasons which are
coded as follows:
a. poor ionic balance > ±100% disagreement
b. poor theoretical/observed conductivity balance
c. chloride concentration > 500 (j.eq/1
d. conductivity > 200 fiS/cm
e. double endpoint alkalinity > 1000 (ieq/1
f. color > 30 Pt-Co units
Elimination Code
Station
a b c d e £
2101 xxx
2120 x x
2121 x
2123 x
2125 x
2128 x
2132 x
2140 x
2144 x
2151 x
2152 xxx
2201 xxx
2204 x
2206 x
2210 x x
2211 x x
2214 x x
2215 x
2224 x
2334 x
2337 x
2343 x
2349 x
2350 x
2352 x x
2353 x x
131
-------
(cont inued)
Elimination Code
Station
a b c d e f
2354 x
2362 xxx
2363 x x
2365 x
2374 x
2376 x
2377 x
2406 x x
2411 x
2503 x
2507 x
2510 x
2511 x
2513 x x
2522 x
2523 x
2524 x
2525 x
2603 x x
2627 x
2628 x
2630 x
2634 x x
2635 x
2637 x
2640 x
2701 x
2702 x x
2712 x
2718 x
2803 x
2903 x x
2905 x
2906 x x
132
-------
The net result of these removals is to decrease the number
of stations used for discussion of the chemical data from 278 to
218; a 22% decrease. Removals by state were:
New York
New Jersey
Pennsylvania
Delaware
Maryland
West Virginia
Virginia
North Carolina
Tennessee
Total
of 46 (24%)
of 31 (26%)
of 88 (16%)
of 21 (10%)
of 24 (38%)
of 23 (35%)
Of 24 (17%)
of 10 (10%)
of 11 (27%)
Of 278 (22%)
11
8
14
2
9
8
4
1
3
60
133
-------
J037?-iqi
REPORT DOCUMENTATION i no. |i
page Biological Report 80(40.19)
4. Trtle and Subtitle
Vulnerability of Selected Lakes and Streams in the Middle
Atlantic Region to Acidification: A Regional Survey
7. Author(s)
Arnold, D.E., R. W. Light, and E.A. Paul
9. Performing Ofgamiat»on Name and Address
Pennsylvania Cooperative Fish and Wildlife Research Unit
Ferguson Building
University Park, PA 16802
X Recipient's Accession No
5. Report Dale
June 1985
ft. Performing Organization Rept. No
10. Proiect/Task/Work Unit No.
11. ContreCt(C) or Grant(G) No.
(C)
(G)
12. Sponsoring Organization Name and Address
U.S. Department of the Interior, Fish and Wildlife Service,
Division of Biological Services, Eastern Energy and Land Use
Team, Box 705, Kearneysville, WV 25430
13* Type of Raporl L Period Covered
Fi nal
14.
IS. Supplementary Notes
i&. Ab>tr>ei (Limit: 200word*i jjrj conjunction with a similar study in the New England States, 278
lakes and streams in the 9 Middle Atlantic States (Mew York, Mew Jersey,
Pennsylvania, Maryland, Delaware, Virginia, West Virginia, North Carolina, and
Tennessee) were surveyed. Primary objectives were to document the sensitivity of
these waters to acidification and to test the validity of available models and
classifications for predicting their acidification. The study results indicate that a
large number of relatively undisturbed waters in the Middle Atlantic States are either
already acidified or are susceptible to acidification. A large portion of the waters
sampled in the region have decreased in pH, alkalinity, or both in recent years.
Alkalinity is probably the most accurate and useful predictor of sensitivity to
acidification. About 49% of the unpolluted, comparatively undeveloped waters sampled
were sensitive to acidification on the basis of having alkalinity less than 200 ueq/1
or having underlying bedrock of low acid neutralizing capacity. The calcium-pH model
of Henriksen is a fairly accurate predictor of lake pH and indicated that about 28% of
the waters were acidified. Few of the waters showed high concentrations of
aluminum, and there was only one case of a potentially toxic aluminum/pH
combi nation.
17. Document Analysis a. Descriptors
Acidification, pH, alkalinity, aluminum, water chemistry, geology
b. !d«fttifi«r*/OD#n»€nded Terms
acid rain, acidified waters, acid deposition, water quality, stress, calcium
concentrations
c. COSATI Field/Group
3ft. Availability Statement
Unlimited
19. Security Class (This Report)
unclassi fied
21. No. of Pages
133
20. Security Cla*s JThis ,Pag«)
unclassified
22. Price
(See ANSUZ39.18) See Instructions on Reverse
& U.S. GOVERNMENT PRINTING 0FFICE:18B5- 580-331 f 25353
OPTIONAL FORM 272 <4-77)
(Formerly NTIS-35)
Department of Commerce
------- |