Biological Services Program
FWS/OBS-80/40.1 5
April 1983
Air Pollution and Acid Rain
Report No. 1 5
A Regional Survey of
Chemistry of Headwater Lakes
and Streams in New England:
Vulnerability to Acidification
Office of Research and Development 4BKW
U.S. Environmental Protection Agency JKSK&
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-80/4Q.3
FWS/0BS-80/40.4
FWS/0BS-80/40.5
FWS/0BS-80/40.6
FWS/0BS-80/40.7
FWS/0BS-80/40.8
FWS/0BS-80/40.9
FWS/0BS-80/40.10
FWS/0BS-80/40.11
FWS/0BS-80/40.12
FWS/0BS-80/40.13
FWS/0BS-80/40.14
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.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Lakes.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Rivers and Streams.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Forests.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Grasslands.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Tundra and Alpine Meadows.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Deserts and Steppes.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: Urban Ecosystems.
The Effects of Air Pollution and Acid Rain on Fish,
Wildlife, and Their Habitats: 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 Liming of Acidified Waters: Issues and Research-
A Report of the International Liming Workshop.
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QCl.Z 0 726VO
UNITED STATES
DEPARTMENT OF THE INTERIOR
FISH AND WILDLIFE SERVICE
EASTERN ENERGY AND LAND USE TEAM
Route 3, Box 44
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
headwater lakes and streams in the New England states. This study was
conducted to assess the status of these waters with respect to acidifi-
cation. The report is a part of the EELUT series on air pollution and
acid rain. Other reports in this series are listed on the inside front
cover.
The extent and location of surface waters that are acidified or vulner-
able to acidification was determined from a survey of 226 headwater
lakes and low order streams in six New England states. This data was
compared to historical temporal trends, and were used to evaluate pro-
posed models of acidification and to identify potential indicators of
acidification. A companion study (FWS/0BS-80/40.17) provides similar
information for the middle Atlantic states.
Please feel free to send suggestions or comments to EELUT so that we
may continually strive to improve our future products.
Sincerely
R. Kent Schreiber
Acting Team Leader, EELUT
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FWS/OBS-80/40.15
April 1983
Air Pollution and Acid Rain
Report No. 15
A REGIONAL SURVEY OF THE CHEMISTRY OF HEADWATER
LAKES AND STREAMS IN NEW ENGLAND:
VULNERABILITY TO ACIDIFICATION
by
Terry A. Haines and John Akielaszek
U.S. Fish and Wildlife Service
Columbia National Fisheries Research Laboratory
Field Research Station
Orono, Maine 04469
with assistance from
Paul Rago
U.S. Fish and Wildlife Service
Eastern Energy and Land Use Team
Route 3, Box 44
Kearneysville, WV 25430
Project Officers
R. Kent Schreiber/Paul Rago
Eastern Energy and Land Use Team
Performed for:
Eastern Energy and Land Use Team
Division of Biological Services
Research and Development
Fish and Wildlife Service
U.S. Department of the Interior
Washington, DC 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 wholly
or in part by the U.S. Environmental Protection Agency through Interagency
Agreement No. EPA-82-D-X0581 to 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.
This report should be cited as:
Haines, T.A. and J. Akielaszek. 1983. A regional survey of chemistry of
headwater lakes and streams in New England: Vulnerability to acidification.
U.S. Fish and Wildlife Service, Eastern Energy and Land Use Team, FWS/OBS-
80/40.15, 141 pp.
i i
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EXECUTIVE SUMMARY
A survey was conducted of 226 headwater lakes and low order streams
in the six New England states. The waters selected were relatively
undisturbed by direct human disturbance and were low in color. The sites
were stratified by geographic area, and by bedrock geology and soil
cation exchange capacity.
Acidic (pH <5) surface waters occurred in every state.
Approximately 8% of the waters surveyed were pH <5, and 29% were pH <6.
The low pH waters tended to occur in clusters, although high pH waters
frequently occurred in the same area. Waters with low alkalinity content
were more common than low pH waters, and tended to occur in the same
areas as the acidic waters. Approximately 24% of the waters had
alkalinity concentrations of 20 yeq 1" or less, 41% had concentrations
of 100 yeq 1 or less, and 53% were 200 yeq 1" or less. The
proportion of acidic surface waters in New England is similar to that in
other areas where bedrock is low in buffering capacity and precipitation
is similarly acidic. Hydrogen ion content was not correlated with color,
an index of organic acids. Therefore the acidic lakes are not primarily
the result of natural organic acids. Highly colored, acidic lakes were
excluded from the survey.
Calcite saturation index (CSI) results were similar to alkalinity,
and CSI and alkalinity were highly correlated. Approximately 59% of the
waters surveyed had CSI values greater than 3, and were classed as
susceptible to acidification. This is the same conclusion reached based
on alkalinity data. Specific conductance, an index of total ionic
concentration, was correlated with alkalinity and could be used as a
simple index of vulnerability to acidification. However, the coefficient
of correlation was low.
The lakes and streams surveyed contained appreciable sulfate
concentrations (80-120 yeq 1" ) that could not be attributed to marine
aerosols. The concentrations were similar to those found in other areas
where precipitation is similarly acidic but were relatively uniform, and
sulfate was not positively correlated with hydrogen ion as observed in
other surveys. The lack of correlation may result because precipitation,
and thus sulfate deposition, is chemically uniform over the region
surveyed.
i i i
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Aluminum and manganese concentrations of the lakes and streams
surveyed were correlated with hydrogen ion content, although the
correlation was weak for manganese. The regression for aluminum was very
similar to that obtained from other regional surveys and apparently
represents the thermodynamic equilibrium between hydrogen ion and
aluminum.-J Of the 226 waters surveyed, 13 had aluminum concentrations of
200 tig l" or more and pH of 5.5 or lower, conditions that may be toxic
to sensitive fish species.
Of the physical factors measured, the ones most highly related to
buffering capacity and acidity were bedrock geology and, to a lesser
extent, soil cation exchange capacity. A sensitive lake or stream, as
determined by alkalinity, was nine times more likely to be found in an
area where bedrock was low in buffering capacity. A sensitive lake or
stream was three times more likely to be found in an area where soils
were low in cation exchange capacity. Soil class was correlated with
bedrock class, with sensitive soil classes largely found in areas where
bedrock was also sensitive. In headwater areas soils tend to be thin and
therefore may be less important than bedrock in these areas in
determining water chemistry.
The only other physical factors of importance were lake area and
stream order. Lakes of all sizes were low in alkalinity but only lakes
of 20 ha area or smaller were acidic. Higher order streams were higher
in pH and alkalinity. This suggests that some factor related to
watershed size may be an important factor in neutralizing acid
deposition, and watershed size was correlated with lake pH for lakes in
Maine. The waters surveyed were selected to be low in human disturbance.
Disturbance was also correlated with bedrock geology, probably as a
result of topography, confounding the effects of disturbance on water
chemi stry.
Historical pH and alkalinity data indicate that waters located in
areas where buffering capacity is low have been acidified. Of the 95
lakes with usable historical pH data, 61 (64%) were lower in pH in this
survey. Of 56 lakes for which historical alkalinity data were located,
39 (70$) were lower in the present survey. The average lake for which
historical data were available increased in hydrogen ion content
five-fold and decreased in alkalinity by 60%.
The relationship between pH and calcium content of surface waters
(Henriksen model) was tested as an indicator of acidification. This
model predicted that 57% of the waters surveyed had been acidified, a
value in good agreement with that obtained from lakes for which
historical water chemistry data wep found. The model further predicted
an average loss of about 50 yeq 1" of alkalinity. Historical data,
all from low alkalinity lakes, indicated a decline of about 100
yeq 1" . Given the large range of chemical conditions encountered,
this was considered to be reasonably good agreement. The Kramer model
gave good agreement with the data, 75% of the samples were within 0.5 pH
i v
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unit of the predicted pH based ori carbonic acid weathering alone. The
remaining ?5% of the samples were more than 0.5 pH unit lower than
predicted and are presumed to be acidified. The Thompson model also gave
good agreement with the data and a similar prediction of acidification.
The Kramer and Thompson models yield similar predictions of acidification
that are about half that of the Henriksen model.
We conclude that a substantial portion of the headwater lakes and
low orde^ streams in New England are vulnerable to acidification, and
that alkalinity is the best measure of vulnerability. Bedrock geology
was the best physical factor that could be used to predict surface water
alkalinity, and thus vulnerability to acidification, but a substantial
portion of the waters that were predicted to be vulnerable were not.
Both historical water chemistry comparisons and acidification models
indicate that the vulnerable lakes have declined in alkalinity and
increased in hydrogen icn content, presumably as the result of
atmospheric deposition of acid.
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TABLE OF CONTENTS
Page
EXECUTIVE SUMMARY iii
LIST OF FIGURES ix
LIST OF TABLES xiii
ACKNOWLEDGEMENTS XV
INTRODUCTION 1
METHODS 2
Selection of Sampling Sites 2
Sample Collection Procedure 3
Analytical Methods 5
Field Procedures 5
Laboratory Procedures 6
Quality Assurance 6
Data Analysis 7
Historical Data 7
RESULTS AND DISCUSSIONS 8
Qua! ity Assurance 8
Site Distribution 13
Chemical Factors 13
PH and Alkalinity 13
Calcite Saturation Index 20
Ionic Composition 24
Sulfate 28
Calcium and Magnesium 34
Aluminum and Manganese 38
Organic Acids 38
Precipitation Chemistry 43
Physical Factors that Affect Water Chemistry 43
Elevation 43
Size 49
Drainage Type 53
Stream Order 53
Bedrock and Soil Class 53
Human Disturbance 67
Historical Comparisons 67
PH 67
Alkalinity 74
vi i
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TABLE OF CONTENTS CONTINUED
Page
Multivariate Analyses 77
Principal Components 77
Canonical Analysis.... 79
Cluster Analysis and Discriminate Functions 33
Models of Acidification 83
Henri ksen Model 83
Kramer Model 92
Thompson Model 94
CONCLUSIONS 99
REFERENCES 101
APPENDICES 109
A. Listing of Data 109
B. Listing of Historical Data 139
vi i i
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LIST OF FIGURES
Number Page
1. Sum of total cations versus sum of total anions 11
2. Theoretical versus measured specific conductance 12
3. Map of location of 226 sample sites 14
4. Map showing distribution of surface water samples
by pH 17
5. Map showing distribution of sample sites by
alkalinity 18
6. Map showing distribution of sample sites by Calcite
Saturation Index 22
7. Relationship of alkalinity to Calcite Saturation
Index 23
8. Relationship of alkalinity to specific conductance 25
9. Relationship of chloride concentration to distance
from seacoast 26
10. Distribution of major ions in surface waters of
different pH range 27
11. Sulfate: chloride ratio versus distance from
seacoast 30
1?. Relationship of sulfate to chloride 31
13. Relationship of hydrogen ion to non-marine sulfate
concentration 33
14. Relationship of background sulfate to total cations 35
ix
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LIST OF FIGURES CONTINUED
Number Page
15. Relationship of hydrogen iori to net sulfate
concentration 36
16. Alkalinity versus non-marine calcium plus magnesium 37
17. Relationship of aluminum concentration to pH 39
18. Map showing location of sample sites grouped by
aluminum concentration 40
19. Relationship of manganese to pH 42
20. Relationship of color to pH 44
21. Relationship of total organic carbon to pH 45
22. Relationship of alkalinity to color 46
23. Relationship of aluminum to total organic carbon 47
24. Relationship of elevation to pH 48
25. Relationship of elevation to alkalinity 50
26. Relationship of lake area to pH 51
27. Relationship of lake area to alkalinity 52
28. Relationship of drainage area to lake pH 54
29. Frequency distribution of waters by bedrock class
in pH intervals 59
30. Frequency distribution of waters by bedrock class in
alkalinity intervals 60
31. Frequency distribution of waters by soil class in
pH intervals 64
32. Frequency distribution of waters by soil class in
alkalinity intervals 65
33. Current versus historical pH for all available data 70
34. Current versus historical pH for lakes with
alkalinity <^100 yeq 1" 72
x
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LIST OF FIGURES CONTINUED
Number Page
35. Current versus historical pH for lakes located in
bedrock class 1 or 2 73
36. Inflection point versus fixed endpoint alkalinity 75
37. Current versus historical alkalinity 76
38. Henriksen nomograph for pH and calcium 86
39. Relationship of pH to log alkalinity 87
40. Henriksen nomograph for calcium plus magnesium and
sulfate 88
41. Henriksen nomograph applied to adjusted sulfate
values 90
42. Percent distribution of lakes grouped by calcium
plus magnesium content under present and various
projected sulfate concentrations 93
43. Relationship of log ^cations to pH for 191
New England lakes and streams 95
44. Relationship of Ecations, sulfate, and pH for 142
New England lakes and streams 96
xi
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LIST OF TABLES
Number Page
1. Criteria used to estimate relative amount of human
disturbance 4
2. Results of analysis of EPA Water Pollution Quality
Control Samples for Minerals 9
3. Results of analysis of EPA Water Pollution Quality
Control Samples for Trace Metals 10
4. Number of lakes and streams sampled in each state 15
5. Distribution of sample sites among bedrock geology
and soil class 15
6. Regional water chemistry survey results for surface
water pH distribution 19
7. Regional water chemistry survey results for surface
water alkalinity distribution 21
8. Percent relative distribution of major ions in
New England headwater lakes and streams
compared with world averages 29
9. Amount of sulfate found in surface waters in regional
water chemistry surveys 32
10. Values for intercept and slope of aluminum versus
pH regressions 41
11. Means and standard deviations of chemical factors
classed by lake hydrology type 55
12. Means and standard deviations of chemical factors
for lakes and streams 56
xiii
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LIST OF TABLES CONTINUED
Number Page
13. Relationship of stream order to chemical factors 57
14. Mean values of chemical factors for waters classed by
bedrock geology for pH and alkalinity 58
15. Predictive ability of bedrock geology for alkalinity.... 62
16. Mean values of chemical factors for waters classed
by soil cation exchange capacity 63
17. Predictive ability of soil class for pH and
alkalinity 66
18. Mean values for chemical factors of waters classed
by disturbance code 68
19. Number of surface waters in each bedrock and
disturbance class 69
20. Principal component analysis of standardized
major water chemistry factors 73
21. Principal component values for bedrock and soil
classes 80
22. Principal component analysis of combined
chemical factors 81
23. Canonical analysis of physical and chemical factors 82
24. Correlations between canonical variates and
individual variables 84
25. The prediction of pH of New England lakes by
Henriksen's pH/sulfate nomograph 89
26. The prediction of pH in New England lakes by
Thompson's cation denudation model 97
xi v
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ACKNOWLEDGEMENTS
S.A. Norton kindly allowed the use of some water chemistry data for
Maine lakes. R.E. Blake, C.H. Jagoe, and R.G. Mines assisted in data
collection and analysis. R. Wright and A. Henriksen provided numerous
suggestions for improvement of data analysis. Financial support was
provided by U.S. Fish and Wildlife Service, and U.S. Environmental
Protection Agency through an Interagency Cooperative Agreement.
xv
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INTRODUCTION
The precipitation that falls on the northeastern United States is
highly acidic compared to that in much.of western North America. The
annual weighted mean pH of this precipitation ranges from 4.2 to 4.4
(National Atmospheric Deposition Program 1981). Acidic precipitation has
been identified as the cause of acidification of surface waters, and
consequently adverse effects on aquatic organisms, in Scandinavia (Aimer
£t ah 1978; Muniz and Leivestad 1980), Ontario, Canada (Harvey 1980),
and New York (Pfeiffer and Festa 1980).
The effects of acidic precipitation on aquatic ecosystems depends on
a variety of climatic, geologic, topographic, morphometric, biotic, and
anthropogenic factors (Galloway and Cowling 1978). The relative
importance of these factors is presently poorly understood, and the
identification and quantification of aquatic resources at risk from
acidic precipitation has been inadequate. In the United States the
effects of acidic precipitation on surface water chemistry and aquatic
biota has been documented only for the Adirondack mountain region of New
York. Several studies have predicted that substantial portions of the
northeastern United States are vulnerable to acidification based on
indirect evidence (Galloway and Cowling 1978; Hendrey et ah 1980), but
these predictions have not been widely tested.
A number of models of acidification have been proposed (Henriksen
1980; Thompson 1982). These models predict vulnerability of surface
waters to acidification based on chemical interrelationships, and
estimate the amount of acidification that has already occurred. These
models have not been tested in the northeastern United States. Existing
water chemistry data bases are not satisfactory for this purpose as most
sites are located in productive lowland areas, consist of large lakes or
high order streams, and have been affected by direct anthropogenic
activltity (e.g., agriculture, waste disposal).
This study was conducted to collect appropriate water chemistry data
to assess the chemical status of headwater lakes and streams in the
northeastern United States with respect to acidification. From these
data the extent and location of surface waters that are acidified or
vulnerable to acidification was determined. The data were compared to
historical temporal trends, and were used to evaluate proposed models of
acidification and to identify potential indicators of acidification.
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METHODS
Selection of Sampling Sites
Surface waters suitable for use in this study were selected from all
available waters according to the following:
o geographic coverage
o bedrock buffering capacity
o soil cation exchange capacity
o location in watershed
o direct human disturbance in watershed
o availability of historical data
o access
The waters sampled were primarily lakes (193 of 226; 853i}. Streams were
sampled only when a suitable lake coif}d not be located in a desired area,
or was not accessible either physically or legally.
Geographic distribution of lakes over the region was as uniform as
possible. A few areas were inadequately represented. For example, it
was very difficult to locate undisturbed lakes with public access in
Connecticut,
Sampling sites were selected to represent all classes of bedrock
buffering capacity and soil cation exchange capacity present in the
region. Bedrock buffering capacity classes were those described in
Hendrey et al. (1980), and soil cation exchange capacity groups were
those oflfcTee (1980). The original maps depicting the distribution of
these factors were obtained from the authors (S. Norton, Dept. of
Geology, University of Maine, Orono, Maine 04469; VI. McRee, Dept. of
Agronomy, Purdue University, West Lafayette, Indiana 47907). These maps
were generally of 1:500,00(3 scale, the same as the U.S. Geological Survey
state base map. lakes were selected to represent the various categories
in rough proportion to the area of the categories in each state. Lakes
may have been misclassified because of errors in precisely locating the
lake on the state base map (many small lakes are not shown on these maps
and locations were transferred from 7i or 15 minute quadrangle maps), or
because of loss of resolution on the geology and soil maps as a result of
scale or smoothing.
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Headwater sites were generally low in direct human disturbance of
watershed. Many otherwise suitable lakes were rejected because of
disturbance in the watershed. In some cases, if no alternate lakes or
streams better fit the criteria, disturbed waters were sampled. An
arbitrary code was devised that represented differing levels of
disturbance encountered, and waters were assigned a code based on a
visual survey of the lake shoreline and visible portions of the watershed
(Table 1).
In each state contact was made with the appropriate state agency
responsible for water chemistry data records (e.g., fish and game
department, water quality department), and any federal entity managing
aquatic resources in the state (e.g., U.S. Forest Service). Agencies
were asked to supply existing historical water chemistry data as well as
to suggest potential sampling locations. Particular attention was given
to selecting sampling sites for which there were historical data
available; about half (95 of 226) of the final sites had such data.
Finally, accessibility cf 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 two miles from that point. Most
sites had a marked foottrail leading from the access point to the lake.
Although size was not a selection criterion lakes fitting the above
criteria were generally small. Lakes larger than 75 ha were not sampled.
Data on watershed area were available for lakes located in the state of
Maine (M. Hutchins, Environmental Studies Center, University of Maine,
Orono, Maine). Watershed area was included as a variable in this subset
of the data.
Sample Collection Procedure
Each water body selected for survey was visited by a two person
survey team. Most waters were reached on foot, but a few were reached
by fixed-wing aircraft or pontoon-equipped helicopter. A standardized
sampling procedure was followed:
o Survey watershed: one person walked as much of the shoreline
as feasible, taking note of the major vegetation, exposed
bedrock, inflow and outflow streams, aquatic vegetation, and
evidence of human activity (roads, campsites, seasonal
dwellings, permanent dwellings, logging, agriculture).
Such observations were made on the trip into and out of
the area as well.
o Collect water samples: one person waded to the center of a
stream or rowed to the deepest area of a lake in an
inflatable raft. In streams, water samples were collected
from mid-depth directly into the sample bottles. In lakes,
one set of samples was collected at 0.1 m below the surface
directly into the sample bottles and one set near the
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Table 1. Criteria Used to Estimate Relative Amount
of Human Disturbance
CodeDisturbance
No visible disturbance: access
by foot trail or aircraft only.
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
iirmectiately adjacent to water.
Severe disturbance: access by
paved road; permanent homes around
lake; agriculture in watershed;
active logging adjacent to water.
4
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bottom (without disturbing sediment) in a plastic Van Dorn-type
water sampler. Water temperature was measured with a pocket
thermometer at the time of collection. Water samples were
placed in acid-washed, distilled water rinsed linear
polyethylene bottles. Each set of samples consisted of three
bottles -- one 500 ml for pH, alkalinity, specific conductance,
and color; one 250 ml for sulfate and chloride; and one 125 ml
for cations. The 125 ml bottle contained 6.25 ml of 4N
nitric acid. Each bottle (except the cation sample bottle)
was rinsed with sample water twice before filling. Samples
were placed on ice within 1 hour after collection.
The samples were generally collected during the open water period,
but a few were collected through the ice. Previous experience indicated
that, except for the spring run-off period, water chemistry in these
waters is relatively stable throughout the year. In any event,
geographic variability far exceeds temporal variation in individual
waters. The lakes sampled were not euthropic or dystrophic. Inasmuch as
the surface and deep samples were chemically similar, there was no
evidence that algal production or hypolimnion decomposition significantly
affected water chemistry in these lakes.
ANALYTICAL METHODS
Field Procedures
Analyses of pH, alkalinity, specific conductance, and color were
performed at field locations. Within 8 hours after sampling, and as soon
as possible, the 500 ml bottle was removed from ice and warmed to room
temperature. Two 100 ml aliquots were removed for determination of pH
and alkalinity. The pH was measured with a portable meter (Fisher model
107 or Cole Parmer DiaiSense) equipped with a plastic-body, gel filled
combination electrode. The meter was standardized with pH 7 and 4
buffers, and electrode response was verified by measuring the pH of
dilute sulfuric acid solutions of theoretical pH 4. If measured values
deviated from expected values by more than 0.1 pH units the electrode
was discarded. The electrode was rinsed thoroughly with distilled
water, blotted dry, and soaked in the sample for 15 minutes or longer —
until three successive readings at 1 minute intervals were identical ~
and pH was recorded.
Alkalinity was determined by titrating each of the 100 ml sample
aliquots with 0.0200 N sulfuric acid to pH-^4. Acid was added in
0.10 ml portions using a micro syringe until pH 5 was reached, then in
0.05 ml portions to pH <4. The pH was recorded after equilibration
following each addition of acid. Alkalinity was calculated by two
methods. Inflection point alkalinity was determined by the method of
Gran (Stumm and Morgan 1970), and fixed endpoint (pH 4.5) alkalinity was
determined as described in American Public Health Association et al_.
(1975). Inflection point results were used for all analyses ancf
5
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comparisons except for those using historical data, where fixed endpoint
data were used.
Two 50 ml aliquots of sample were measured and used for
determination of specific conductance and color. Specific conductance
was measured with a calibrated meter (Markson Scientific Company model
10), and apparent color was determined by comparison of unfiltered
samples with platinum cobalt standard solution (LaMotte Chemical Company,
Cfiestertowrij Maryland).
Laboratory Procedures
The remaining water samples were kept on ice, returned to the
laboratory, and kept refrigerated until analyzed. The unacidified
250 ml water sample was used for measurement of sulfate by the
microthorin method (Fritz and Yamamura 1955), and chloride by the
ferricyanide method (American Public Health Association et al_. 1975).
All analyses were performed in replicate. The acidified 125 ml sample
was used for measurement of cations. Sodium and potassium were measured
with air-acetylene flame atomic absorption spectrophotometry (AAS; Perkin
Elm=r model 703}, calcium end rraanesiuTi by nitrous oxide-acety'ere flane
AAS, and rargcnese by graphite -"umace AAS. Alwnrur was measured
iTrtially by ttie color me-tri c nettiod cf Doucan and fc'ilscr [1974), and
!at=r by graphite furnace AflS. The t^o iretFods gave conparab'e resuf ts.
As samples were jr-iHsred, this results obtained represer- total
concentrations of the various ions.
Calcite saturation index (CSI) was calculated from the equation
given by Kramer (1976). For samples where alkalinity was zero or
negative, a modified equation (Kramer 1981) that approximates CSI was
used.
A subset, of 31 samples ranging in color from 0 to 260 units were
analyzed for total organic carbon (T0C) by G. Glass, U.S. Environmental
Protection Agency, Duluth, MN. A linear regression analysis was
performed to determine the relationship between color and T0C.
Quality Assurance
The functioning of analytical instruments was assured by frequent
analysis of known standards. Response of pH, specific conductance,
spectrophotometers and atomic absorption spectrophotometers was checked
daily. Most analyses were performed in replicate to enable calculation
of variance. U.S. Environmental Protection Agency Water Pollution
Control Samples for Trace Metals, and Minerals (EPA Environmental
Monitoring and Support Laboratory, Cincinnati, Ohio), were analyzed
following the same procedures used for water samples. An interlaboratory
exchange of samples was conducted with the Norwegian Institute for Water
Research.
6
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As a further check ori analytical accuracy and data coding errors, we
calculated total ionic balance and theoretical specific conductance. All
ion concentrations were converted to units of yeq 1" , anions and
cations were summed, and the sum of anions was compared to the sum of
cations for each sample. Theoretical specific conductance was
calculated by multiplying ion concentrations by equivalent conductance
values for 25°C obtained from the Handbook of Chemistry and Physics
(Weast 1978). Theoretical conductance was compared to measured
conductance for each sample.
Data Analysis
Results obtained from the above analyses were entered into a SAS
(Statistical Analysis System, Raleigh, NC) data set operating on an IBM
model 4341 computer located at the University of Maine, Orono, Maine.
SAS statistical analysis and graphic routines were used for data
analysis. Punch cards produced from this data set were sent to
Paul Rago, U.S. Fish and Wildlife Service, Ann Arbor, MI, where they
were entered into a MIDAS (Michigan Interactive Data Analysis System)
data set using the University of Michigan computer system. This system
was employed for analysis of variance and multivariate data analysis.
Historical Data
Historical pH and alkalinity data were obtained through
correspondence with water chemistry and fisheries offices in the six
states. All such data were for lakes. Data were screened for obvious
errors and only data for which the method of collection was documented
and reasonable were incorporated in this study.
7
-------
RESULTS AND DISCUSSION
Quality Assurance
The laboratory analyses of EPA Quality Assurance Samples for
Minerals {Table 2) and Trace Metals (Table 3) ciave acceptable accuracy.
The coefficient of variation was always less than 5% for minerals and
less than 10% for trace elements. The element determined with poorest
precision was aluminum*, however, the recoveries we obtained were
comparable to those reported by EPA, and our standard deviations were
smaller. An interlaboratory comparison was conducted with the Norwegian
Institute for Water Research for calcium, magnesium, sodium, and
potassium determinations on 21 water samples collected in Norway, and
sulfate and chloride determinations on IB water samples collected in
Maine. The following linear regression equations were obtained:
Calcium (ME) = 1.02 Calcium {NIVA) -0.03
Haanesium (ME) = 0.97 Magnesium (NIVA) + 0.003
Sodium (ME) = 1.06 Sodium (NIVA) + 0.01
Potassium (ME) = 0.97 Potassium (NIVA) = 0.03
Sulfate (ME) = 0.94 Sulfate (NIVA) + 0.33
Chloride (ME) = 1.09 Chloride (NIVA) -0.19
In all cases intercepts were not different from zero and slopes were not
different from one (t test, £ <0.05).
Recovery of known additions of trace metals and minerals was always
within 5% of that expected. The pH of dilute sulfuric acid solutions was
always within 0.1 units of theoretical. In those instances where results
deviated from theoretical by more than this amount, the electrode was
replaced and the meter recalibrated. Acceptable results were then
obtained.
A further check on the accuracy of water chemistry determinations
was made by comparison of the sum of anions and the sum of cations
(Figure 1), and of theoretical and measured conductance (Figure 2). The
sums of ions were nearly equal, with only two obvious outliers. The
line of best fit had an intercept of 14 ueq 1"' and a slope of 1.00,
not significantly different from 0 and 1.0 respectively {j> <0.0001).
The linear correlation was highly significant (jr = 0.97, £ <0.0001).
The line of best fit for theoretical and measured conductances had an
intercept of -1.22 ijS cm" , and a slope of 1.12, both not
3
-------
Table 2. Results of Analysis of EPA Water Pollution Quality Control Samples for Minerals.
Hean Values for PH were Computed from Hydrogen Ion Concentrations
EPA sample True Laboratory results
number Factor value 5! S.D. C. V. 95? C.I.
-------
Table 3. Results of Analysis of EPA Water Quality Control Samples for Trace Metals
EPA sample True Laboratory results EPA Recovery
number Element Value X S.17: C7V. 95* C.L X S.D. C.V. 95? C.I.
(N=3)
1
A1
350
424.3
29.9
7.1
350.1-498.5
369
41.7
11.3
286-45?
Mn
55
62.3
2.6
4.2
58.2-66.4
54.8
5.7
10.4
43.4-66.2
2
A1
50
68.3
5.5
8.1
54.5-82.0
74.9
24.3
32.4
26.1-123
Mn
11
13.7
0.6
4.4
12.2-15.2
11.0
3.8
34.5
3.2-18.6
3
A1
700
726.7
46.2
6.4
612.0-841.4
712
62.1
8.7
588-835
Nn
350
387.5
6.5
1.7
377.2-308.8
348
18.6
5.3
311-385
-------
2500-1
-h
O c
^ 3
O -*• "H
ft J3 3 -»•
<-+¦ c 1Q
-** Q> -c CZ
O -s -O
(/) u>
H» I—»
-~*-S I*
-S (D »-*
fXD
-5
ii re
c/>
o l/i
• j—» o
o vo -h
>43 W
' ft
• fD O
O —»• c+
C c+fl»
n> n> —J
C+ CO
—<•« cu
O =5
3 -»•
• •CO
-S-3
-+i V>
Or
o <
3 fl> fD
-«• -5
O C/> VI
3 O* C
w 3 tfl
-o
II —»
re c
3
O O
*-»<< c+
~ • O
O c+
O
r- —»
re
o» o
cn o>
r+ c+
-i.
O
3
(A
2000-
S
u
M
0
F
fl
N
1
0
N
S
1500-
1000-
~
500-
0-
0
~
a
~
~ r\ D
~
~
~
~
I I I I | 1 I I I I I I I I | I I I—I I I I I I | i 1 I I I I 1 I I j
1000 1500 2000 2500
SUM OF CATIONS
-------
Figure 2. Theoretical versus measured specific conductance
for 195 sites, surface samples only. Least squares
regression equation: Theor. Cond. = -1.2? + 1.12 Meas. Cond.
(r* = 0.97).
12
-------
significantly different from 0 and 1.0, respectively {£<0.0001). The
linear regression was highly significant (r = 0.97; £ = <0.0001).
These two comparisons indicate that there are no major analytical or
coding errors in the water chemistry data set.
Site Distribution
We sampled 226 lakes and streams in the six New England states
(Figure 3). Many other sites were visited and rejected as unsuitable for
inclusion in this study, because of high color, disturbance in the
watershed, and other reasons. The distribution of sites among states
(Table 4) was roughly proportional to the state area. Lakes comprised
85% of the sites and streams 15% (Table 4). Streams were sampled only
when a suitable lake could not be located in the desired area. The lakes
sampled were either seepage lakes with no visible inlet (N=122) or
drainage lakes with permanent inlets and outlets (N=71). Stream order
ranged from 1 to 4 (N: 1=15, 2=5, 3=2, 4=11).
The sample sites were distributed among bedrock geology classes in
proportion to the area of each state that was underlain by each bedrock
class (Table 5). Sites were selected in areas within each soil class
present ir each state (Table 5], but the proportional distribution was
not determined. Most bedrock types were represented in rough proportion
to the proportion of land area in each state, especially classes 1 and
2. All soil classes present were represented, but the proportional
distribution by land area is not known.
CHEMICAL FACTORS
pH and Alkalinity
Surface waters of pH <5 were found in all six states (Figure 4).
Nineteen sites (8%) were in this category. Sixty-five sites (29%) had
pH <6. The low pH waters tend to occur in clusters, i.e. southeastern
Maine, northern and southern Vermont, etc. A similar distribution was
found for low alkalinity waters (Figure 5). There were 63 sites (2456)
with alkalinity <20 ueq 1" , S3 (41%) with alkalinity £100 neq 1~ ,
and 120 (53%) with alkalinity ^200 yeq I" .
A number of other regional surveys of surface water chemistry have
been conducted. Results from the distribution of lakes by pH range for
areas where precipitation is acidic (pH<4.6 weighted annual average) and
non-acidic (pH>4.6) are summarized in Table 6. There is a pronounced
difference in the proportion of acidic lakes (pH<5) between areas that
do and do not receive acidic precipitation. Within the acidic
precipitation group, however, there is considerable variation in the
proportion of lakes in each pH range. This is not surprising
considering the number of factors that may affect lake pH (e.g., organic
acids, bedrock geology, soil chemistry, wastershed size, land use,
etc.), and the variability in the procedures followed in the different
13
-------
A
A
Figure 3. Map of New England showing the location of the
??6 sample sites and type of water body sampled.
14
-------
Table 4. Number of Lakes and Streams Sampled in Each State
State Number of lakes Number of streams
Connecticut
16
7
Maine
62
0
Massachusetts
34
0
New Hampshire
38
11
Rhode Island
a
0
Vermont
35
15
Total Number
193
33
Grand Total 226
15
-------
Table 5. Distribution of Sample Sites Among Bedrock Geology and Soil Class. Bedrock Geology
Classes are those Described by Hendrey et jjl_. (1980), and Soil Classes are those
of McFee (1980). A Dash Indicates that that Category was not Present
Number of Percent of state area Number (and %} of sample sites Number {and %} of sample
sites in bedrock class in bedrock class sites in soil class
T 2 3 4 I 2 3 4 5^ SS^ NS
Connecticut
23
8
85
5
2
1
(4)
21
(92)
1
(4)
0
13
(57)
4
(17)
6
(26)
Maine
62
17
55
20
8
18
(29)
38
(61)
6
(10)
0
7
(11)
33
(53)
22
(36)
Massachusetts
34
32
31
27
10
18
(56)
6
(19)
6
(2)
2
(6)
7
(21)
18
(53)
9
(26)
New Hampshire
49
30
68
1
1
11
(22)
38
(78)
0
0
17
(35)
34
(65)
Rhode Island
8
63
35
2
0
7
(88)
1
(1?)
0
-
3
(38)
2
(24)
3
(38)
Vermont
50
3
51
30
16
5
(10)
33
(66)
8
(16)
4
(8)
2
(4)
48
(96)
-------
Figure 4. Map of New England showing the distribution of
surface water samples bypH. Data are for surface samples
only and are means of two replicates.
17
-------
'p
Figure 5. Map of Nev/ England showing the distribution of
sample sites by alkalinity. Data are for surface samples
only and are means of two replicates.
18
-------
Table 6. Regional Water Chemistry Survey Results for
Surface Water PH Distribution
Location
Number
Percent
in pH
range
Reference
<5
5-6
>6
Areas Where Precipitation Averages
pH 4.6
North Norway
77
0
13
87
Wright and Gjessing 197(
Northwest Wisconsin
265
0
6
94
Li 1 lie and Mason 1980
North Minnesota
85
0
0
100
Glass and Loucks 1980
19
-------
surveys. For example, Wright et al_. (1977) followed a randomization
procedure to select an unbiasedTset of lakes in south Norway, whereas
Wright and Snekvik (1978) working in the same area surveyed only lakes
for which fish population data were available, although they avoided
very large or highly disturbed lakes. New England falls within the low
end of the range of the proportion of acidic lakes, being similar to
south Sweden and central Ontario. Although we selected small,
headwater, undisturbed waters for our survey, we attempted to obtain a
representative sample from a large (168,000 km ) area with wide
chemical diversity. Many other surveys were restricted to areas where
buffering capacity was known to be low.
Regional surveys that included alkalinity measurements are less
common (Table 7). Again, there is a pronounced difference between
regions where precipitation is acidic and regions where it is non-acidic
with respect to the proportion of low alkalinity,waters found. Waters
with total alkal inity _<20 peq 1" or <100 yeq 1 are much more
common in areas that receive acidic precipitation. New England is
intermediate in alkalinity in the group of regions that receive acidic
precipitation. As the regions are all known to be low in buffering
capacity, the increased proportion of very low alkalinity waters in acid
rain districts may be the result of titration of alkalinity by deposition
of excess hydrogen ion.
Calcite Saturation Index
CSI has been proposed as an index of vulnerability to acidification
that is superior to a simple measure of buffering capacity (Kramer
1976). Generally, larger values of CSI reflect lower buffering
capacity. We grouped our samples into categories of CSI <1, 1-3, and >3
(Figure 6.) These categories were modified from Kramer (1976) and Glass
and Loucks (1980). CSI <1 represents waters that are saturated or
nearly saturated with respect to calcite and would not be susceptible to
reduction in pH from acidic precipitation. Values of 1-3 represent
waters that are potentially susceptible to acidification, and values of
>3 are waters that are susceptible to acidification. Of the waters
surveyed, 15% are not susceptible, 26$ are potentially susceptible, and
59% are susceptible. The distribution of susceptible waters is very
similar to that obtained from pH and alkalinity results. This is not
surprising because CSI is calculated from both pH and alkalinity, as
well as calcium. CSI was highly correlated with alkalinity (Figure 7).
Tha regression equation was log alkalinity (weq 1" ) = 3.42-0.39 CSI
(r = 0.929, £ <0.0001). The majority (59$) of the waters surveyed 1n
New England were classed as sensitive to acidification, CSI >3. This
proportion is very similar to that classed as sensitive on the basis of
alkalinity alone (53$ were alkalinity £200 ueq l" ).
Regional surveys that included measurement of Calcite Saturation
Index are rare. Kramer (1981) calculated CSI with water chemistry data
collected at road crossings over streams by a variety of workers in
20
-------
Table 7. Regional Water Chemistry Survey Results for Surface Water
Alkalinity Distribution. Alkalinity values are in neq 1
Location Number Percent in alkalinity range Reference
<20 21-100 101-200 >201
Areas Where Precipitation Averages pH 4.6
Northwest Wisconsin 265 - 15c 17 68 Li Hie and Mason 1980
North Minnesota 85 0 22 26 52 Glass and Loucks 1980
a £50 peq 1"^
b 51 - 200 peq 1"
c £100 veq l"x
21
-------
Figure 6. Map of New England shewing the distribution of
sample sites by Calcite Saturation Index (CSI). Data are
for surface samples only, and are means of two replicates.
22
-------
3.5-1
3.0-
L
0
G
A
L
K
fl 2.0-1
L
1
N
I 1.5-1
T
2.5-
S
~
~
0
~
U 1.0-
E
Q
/
L 0.5-J
~ ~
~ D ~
~
0.0-
| i i i i i i i i i | i i i i i i i f i | r i i i i i i i i |
-2
111
1111111111111111111111 p
2 4 6
CflLCITE SflTURflTI ON INDEX
8
10
Figure 7. Relationship of alkalinity to Calcite Saturation
Index for 195 New England lakes and streams. Data are means
of two replicates and means of surface and deep samples
where taken. Least squares regression equation:
log Alk - 3.42 - 0.39 CSI (rf = 0.929).
23
-------
eastern Canada. The data were presented as a contour map, ana showed
that portions of central and western Ontario had CSI values of 3 or
more, and that most of Quebec and the Maritime Provinces had water with
CSI greater than 3. Glass and Loucks (1980) calculated CSI for 85 lakes
in northern Minnesota, an area that does not receive acidic
precipitation. They found 11% had CSI<1, 53% had CSI of 1-3, and 36%
were CSI>3. The distribution of New England headwater lakes and
streams with respect to CSI indicates that many more are sensitive to
acidification than in Minnesota (59% vs. 36%), and that the distribution
may be similar to that in Ontario. However, CSI does not seem to provide
any superior predictive ability to pH and alkalinity measurements, and
represents an additional mathematical manipulation. Zimmerman and Harvey
(1979) reached a similar conclusion for surface waters in Ontario.
Ionic Composition
Specific conductance is an index of total ionic content of water,
and thus has potential for use as a simple index of buffering capacity.
However other factors, such as distance from seacoast and differences in
ion activities, also affect specific conductance, limiting its
usefulness for this purpose. As a result, the relationship between
specific conductance and alkalinity (Figure 8), although statistically
significant, is not especially strong (r. = 0.71). Some waters that
are high in specific conductance are low in alkalinity. In some cases
this represents increased hydrogen ion content, which has very high
equivalent conductance (Weast 1978). In other cases it represents
marine aerosol input, or unusual minerology such as gypsum.
Chloride was measured for use in correction of other ions for marine
aerosol contribution, and is a possible indicator of human disturbance.
The relation of chloride to distance from the sea coast (shortest
straight line) is given in Figure 9. The influence of marine aerosols
close to the coast is evident, with high chloride concentrations close to
the coast falling off very sharply at,75 to 100 km to background
concentrations of less than 50 ueq 1" . This is almost identical to
the situation in south Norway (Wright and Henriksen 1978), and Scotland
(Wright and Henriksen 1980). There are four waters with anomalously high
chloride concentrations. These four waters also were ranked high in
human disturbance. However, there were many other sites that were also
ranked high in human disturbance that could not be identified on the
basis of high chloride concentration. As chloride concentration was
almost entirely related to marine aerosols, it was used to correct other
ions for marine aerosols contribution. Chloride was not used as an index
of human disturbance.
Ionic composition of the surface waters, corrected for marine
aerosols, varies markedly with pH (Figure 10). Total ionic content is
highest at high pH and declines with successively lower pH. Of the
anions, bicarbonate decreases markedly below pH 6 and is absent below
pH 5, as expected. However, although sulfate composes a larger
24
-------
R
L
K
fl
L
I
N
I
T
T
U
E
Q
/
L
2500-
2000-
1500-
1000-
500-
0-
-500-
-1000-
' | » i < r > i i i i | i i i t i i i i i | i i i i i i i i i | i i i i i i i r i | i it i f i i i t | i i i i i i i i i |
0 50 100 150 200 250 300
SPECIFIC CONDUCTANCE US
Figure 8. Relationship between alkalinity and specific
conductance for 195 New England lakes and streams. Data
are means of two replicates and means of surface and deep
samples where taken. Least squares regression equation:
Alk = -185 + 8.06 Sp. Cond. (rf = 0.711}.
25
-------
550
500-
450
100-
350-
300-
250-
200-
150-
100
50-
0
Fig
dis
Chi
of
+
+
+ +
+
+
+ +
~
+ ¦*
++ +
+ *
++
4
+
++
+
+ T i+ +++ +
+
/+ +
+ + +
+ +
+ + + +
+ + \4-
. +. ¦*+*+
+
T % *+\ + +
~*+ ^ I ~ +$V! +t ' *
+ ++ ++ ^ ~+ * * ++ ++
I 25 50 75 100 125 150 175 200 225 250 275 300
DISTANCE FROM SERHflTEfi (KM)
re 9. Relationship of chloride concentration to
snee from seawater for 184 New England lakes,
"ide data are means of two replicates and means
irface and deep samples where taken. Distance
seacoast is the shortest straight lino to open
"l ^
26
-------
ro
1 ~n
Qi
?rzn
a>
e
CA
-s
n>
Oi
"3
i—•
Q- O
<-+
"5
a
a>
c 300
o
c
pH Range
6
-------
proportion of total anions as pH declines, the concentration of sulfate
does not change appreciably. Of the cations, hydrogen ion increases as
pH declines, as expected, calcium and magnesium decline markedly, and
sodium and potassium decline moderately. The present composition of
cations and anions is compared to world averages in Table 8. High pH
New England lakes have anion distribution similar to the world averages,
but a somewhat larger proportion of ca7cium and magnesium.
The mean total ionic content of the surface waters surveyed was
about 1,000 peq 1 at pH>6, 300 yeq 1 at pH 5-6, and 200 yeq 1"
at pH<5 (Figure 10). Henriksen (1980) summarized ion composition data
for 13 surveys of areas unaffected by acidic precipitation. Mean total
ionic content ranged from 200 to 2,300 yea 1" , but in 11 of the 13
surveys the mean was less than 800 yeq 1" . There was no apparent
difference in ionic strength of acidic New England lakes and non-acidic
softwater lakes elsewhere in the world.
Sulfate
Virtually all of the sulfate in the New England lakes and streams
surveyed is from non-marine sources. One lake, Holts Pond,
Massachusetts, had an extremely high sulfate concentration (mean of
surface and deep samples, replicated = 44 mg 1" 915 yea 1" ). The
sulfate was nearly balanced by calcium (mean 16.3 mg 1" , 815
yeq 1" ), indicating that this lake was most likely influenced by a
local deposit of gypsum, and it was not included in sulfate analysis.
For the remaining waters, the SO,: CI ratio (Figure 11) shows that most
samples exceeded the ratio of these ions in seawater (0.10), especially
those located more than 75 km from the seacoast where marine aerosols are
relatively unimportant. This indicates a non-marine source of sulfate.
As further evidence of this, total sulfate concentration is independent
of chloride concentration (Figure 12). The S0-: CI ratios are higher
than those reported for south Norway. Wright and Henriksen (1978) report
SO.: CI ratios 3-20 times higher than in seawater. In the lakes we
surveyed the ratios were as much as 70 times higher than seawater, and
40% of the lakes had ratios exceeding 20 times that in seawater.
The concentrations of total and non-marine sulfate found in
Mew England lakes are comparable to those found in other regions where
precipitation is acidic, and are much higher than in areas where
precipitation is not acidic (Table 9). However, contrary to the
situation in south Norway, non-marine sulfate content was not positively
correlated with hydrogen ion content in waters with low (<100ueq 1 )
calcium content (Figure 13). Brown and Sadler (1981), usTng the data of
Wright and Snekvik {19781, found a highly significant correlation in 471
lakes in south Norway (r = 0.354, £ <0.0001), even though they did
not stratify the lakes Fy calcium concentration. Brown et a]_. (1980),
using the same data, also found significant positive correlations
between non-marine sulfate and calcium, and calcium plus magnesium, in
addition to hydrogen ion. These correlations were low in New England,
28
-------
Table 8. Percent Relative Distribution of Major Ions in New England
Headwater Lakes and Streams as Compared with World
Averages, Corrected for Marine Aerosols. Ion
Concentrations are ueq 1"
Ions
World averages3
New England
Cations
Clark
1924
Livingstone
1963
Rodbe
1949
pH<5
pH 5-6
pH>6
Ca + Mg
88.2
87.9
88.6
79.5
79.9
91.0
Na + K
11.8
12.1
11.4
20.5
20.1
9.0
Anions
HC03
83.0
82.0
83.0
0
15.4
74.8
S04
17.0
18.0
17.0
100
84.6
25.2
aAfter Henriksen (1980)
29
-------
50-
45-j
40-
35-
S
0
U 30-
/
C
L 25-
R
fl 20
T
1
a is
10-^
5-
0-
+
+
+
+ + +
+
+
+ +
+±+^+ ++ + +
+ . + * + .+
V + >"+ 4. ++4. ** ^
j i i i i i i i i I | i i I i i i i r > | i i i i i I i i i | f f I i > i p i i | I i i » i i i i i | i i i i i i i » t |
0 50 100 150 200 250 300
DISTANCE FROM SEAWATER (KM)
Figure 11. Sulfate: chloride ratio versus distance
from seacoast. for 184 New England lakes. Sulfate
and chloride are means of two replicates and means
of surface and deep samples where taken. Distance
from seacoast is shortest straight line to open ocean.
30
-------
400-1
350-
T 300-
0
T
fl 250-
L
S 200-
0
150-
U
e
Q 100-
/
L
50-
•H-
+ +
¦*• *
+ f + ++ + +
L.+ ++ ++ .
+ +> + + + * + +
+ +
+ +
o-
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
100
200 300
CL (UEQ/L)
>400
500
600
Figure 12. Relationship of sulfate concentration to
chloride concentration. Sulfate is total concentration,
not corrected for marine aerosols. Data are for 184 New
England lakes, are means of two replicates, and are
means of surface and deep samples where taken.
31
-------
Table 9. Amount of Sulfate (yeq 1~^) Found in Surface
Waters in Regional Water Chemistry Surveys
Region
N
Total
sulfate
Non-marine sulfate
Reference
X
S.D.
X
S.D.
Areas Where
t Precipitation Averages
pH 4.6
Central Norway
52
65
34
63
-
Gjessing et al_. 1976
Western Ontario
7
63
-
58
-
Armstrong and
5chindler 1571
32
-------
120-1
100-
~
H 80-
I
0
N
60-
U
E
Q
/ 140-
L
~
~
20-
0-
~
n ~ IP D j
a ~ ~ on gggunl t&P
~
~
~
IIp I I 1 I I 11 l|
HO 60
I I I I I I I I I I II I I I I 11 I I I I I I I I H I I 11 I I I I I 11 I I I I
80
I | I I I II
mo i6o
111111111111 ii111
20
100 120
NON-MflRINE SULFATE (UEQ/L)
Figure 13. Relationship of hydrogen ion to non-marine
sulfate concentration for 72 New England waters with
calcium <100 yeq 1" . Data are means of two replicates
and means of surface and deep samples where taken.
The linear regression was not significant (r * 0.047,
£ = 0.12).
33
-------
?
r_ = 0.096 and 0.070, respectively. In our samples, high calcium
waters had significant bicarbonate ion whereas low calcium waters were
lacking in bicarbonate and sulfate was the major anion. The lack of
correlation between surface water pH and non-marine sulfate content may
result from the fact that precipitation throughout the region is
relatively uniform in chemistry (National Atmospheric Deposition Program
1981) unlike Scandinavia, except for the relationship between marine
aerosols and distance from the seacoast. Waters receive relatively
uniform amounts of non-marine sulfate deposition, but only some lakes
become acidic as the result of low buffering capacity.
Another possible explanation is that there is a regional
"background" level of sulfate, in addition to marine aerosols, that masks
the sulfate associated with acid. Background sulfate could originate
from terrestrial dusts or anthropogenic sulfate that is not acidic.
Wright (Richard Wright, Norwegian Institute of Water Research, personal
communication) has proposed that a proportion of non-marine sulfate
equivalent to the sum of non-marine sodium plus potassium be subtracted
as a correction for background,sulfate. Background sulfate calculated by
this method averages 50 yeq/l~ for New England. Background sulfate
content of a lake should be independent of ionic strength if it is truly
of regional character. However because of the principal of ionic balance
there will probably be some relationship between background sulfate and
total ionic strength. Background sulfate calculated from our data is
correlated with total cations (Figure 14), but the slope is very close to
zero. Using 50 yeq 1" as the best estimate of regional background
sulfate, net non-marine sulfate was calculated and compared to hydrogen
ion (Figure 15). The relationship is no better than that obtained with
non-marine sulfate.
Calcium and Magnesium
2 Calcium and magnesium were highly correlated with alkalinity
(r = 0.951, £ <0.0001; Figure 16). The regression equation is
Alkalinity (yeq 1" ) = -55 + 0.95 (non-marine Ca + Mg, yeq 17 ).
This compares favorably with the,equation Alkalinity (yeq 1" ) = -14 +
0.93 (non-marine Ca + Mg, yeq 1" ) jr =0.98 obtained by Henriksen
(1980) based on 13 sets of data from areas that are relatively
unaffected by acidic precipitation. The slopes are very similar but the
intercept is lower for our data. This suggests that acidic
precipitation has not affected the proportional relationship of calcium
plus magnesium to alkalinity in New England, although alkalinity may be
reduced generally. In contrast to this, Aimer et al_. (1978) reported
that the relationship between these factors was nearly 1:1 in an area of
Sweden that received a small sulfur load, but much less than 1:1 in an
area that received a higher sulfur loading. Sulfur loading is
relatively uniform over New England (National Atmospheric Deposition
Program 1981).
Acidic lakes tend to be lower in calcium than comparable non-acidic
lakes. Aimer et al_. (1974) found that calcium plus magnesium content
34
-------
CflK+MGK+NflK+K*» UEQ/L
Figure 14. Relation of calculated background sulfate
concentration to the sum of cations for 168 lakes. Lakes
with non-marine sulfate or calcium plus magnesium concentration
exceeding 400 neq 1 were eliminated. The regression equation
is BSU ¦ 0.26 cations - 3.49, r' a 0.52, p ¦ 0.0001, computed
from means of two replicates and means orsurface and deep
samples where taken.
35
-------
150-1
H
Y
D
R
0 100-
G
E
N
1
0
N 50-
U
E
Q
/
L
0-
~
1111»1111111
50
t i i i | i i i i i i i i i | r i i i i i i i i | i i i i i i i i i |
100 150
NET SOU* UEQ/L
200 250
Figure 15. Relation of hydrogen ion to net non-marine sulfate
concentration^ The regression equation is H ion ¦ 3.24 + 0.02
net sulfate, r = 0.006, not significant..
36
-------
NON-MflRINE Cfl + MG (UEQ/L)
Figure 16. Relationship of alkalinity to non-marine
calcium plus magnesium for 226, surface waters in
New England. Data are means of two replicates and
mea^s of surface and deep samples where taken.
Least squares regression equation:
Alk - -53 + 0.93 (Ca+Mg) (r* = 0.948).
37
-------
increased from 290 yeq 1" in Swedish lakes of pH 4.9 or less to 820
yeq 1" in lakes with pH 7.0 or more. Gjessing et aK (1976) reported
that lakes in south Norway with median pH 4.76 haB" mean calcium content
of 57 yeq 1" whereas lakes in central Norway with median pH 6.40 had
mean calcium content of 139 yeq 1" . The explanation for this is that
areas that receive acidic precipitation contain both well buffered and
poorly buffered waters, depending on the chemical content of bedrock and
soil. Well buffered waters contain proportionally larger amounts of
calcium and bicarbonate, which is of geological origin, than poorly
buffered waters. Only the poorly buffered waters will be acidified, and
therefore acidic waters will be lower in calcium than non-acidic waters
in the same area. Mohn et al_. (1980) concluded that headwater lakes in
south Norway that were low in calcium, and therefore in buffering
capacity, had water concentrations of hydrogen and sulfate ion that were
significantly related to precipitation chemistry. Precipitation
chemistry in the New England region is relatively uniform, and this
relationship cannot be tested.
Aluminum and Manganese
Aluminum concentration was related to pH (Figure 17), with high
aluminum concentrations generally found inflow pH waters, as expected.
The regression equation is login A1 (yg 1" ) * 3-93 - 0.36 pH and
the correlation coefficient is significant (jr = 0.37, £ <0.001). 1
Eighteen of 226 samples (8%) had aluminum concentrations of 200 yg l"1
or higher, the highest being 510 yg 1" . These waters were
distributed in the same areas as low pH waters (Figure,18). Forty-seven
samples (21%) had aluminum concentrations of 100 yg 1" or more.
There.were 13 sample sites that had an aluminum concentration of 200
yg 1" or more and a pH of 5.5 or less. These conditions may be toxic
to some species of fish (Schofield and Trojnar 1980).
The relationship between aluminum and pH found in New England is
very similar to that found in lakes in Sweden (Dickson 1975, 1980),
Norway (Wright and Gjessing 1976), Scotland (Wright and Henriksen 1980),
and New York (Schofield 1982), as shown in Table 10. Schofield and
Trojnar (1980) made multiple measurements of pH and aluminum
concentration in one stream in New York and found a similar relationship
to that found in the regional surveys cited above (Table 10). This
suggests that the relationship is an expression of the thermodynamic
equilibrium of aluminum and hydrogen ion, enabling the prediction of
aluminum concentration from pH in any region.
Manganese is only slightly related to pH (Figure 19). Manganese
generally was found in lower concentrations than aluminum, and there is
more variability in the manganese: pH relationship (r = 0.015;
£>0.05). The reason for this variability is unknown.
Organic Acids
Color was measured as an index of organic acids. Color was highly
correlated with total organic carbon (T0C) determinations performed on a
38
-------
Figure 17. Relationship of total aluminum concentration
to pH. The pH data are means of two replicates, and both
pH and aluminum are means of surface deep samples where
taken. Least squares regression equation:
log A1 = 3.93 - 0.36 pH (r* * 0.37).
39
-------
Figure 18. Map of New England showing location of sample
sites grouped by aluminum concentration {pg 1" ). Data are
for surface samples only.
40
-------
Table 10. Values for the Intercept (A) and Slope (B) for the Linear
Regression of Total Aluminum and pH. Log1Q (yg 1 ) = A + B (pH),
from Regional Water Chemistry Surveys
Location
N
Intercept
Slope
Reference
New England
226
3.93
-0.36
This Study
Sweden
37
3.85
-0.35
Dickson 1975
Sweden
73
3.94
-0.38
Dickson 1980
Norway
47
3.85
-0.41
Wright and Gjessing 1976
New York
214
4.14
-0.33
Schofield 1982
New York
68 a
3.97
-0.87
Schofield and Trojnar 1980
aAcid Brook, measured 68 times from March to August.
41
-------
2.4-
2. 1-
L
0
G 1.8-
G
fl
N
E 1.2-
S
E
U 0.9-
G
/
L
0.6-]
0.3-
~
~ r-. O
~
~
~ ~
~
~ ~ ~
~
°D.
O
~
%
~
»
~ cp oD ~ 5°
rfp D D
~
~
~
~ o o „D D
~ ~
D ~~~
~ ^
D\°Jo ~ ~
~ ~ ° ~ ~
~ o
~
~ ~ ~
~ D
~
~
^ | I I I I I I I I I | I I > I I I I I I | I I I ) I > I I I | I I I I I I I I I | I I I I I I I I I f I I I I t I I I I | I I I ) | I I I I [ I I I M I I I I |
4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
PH
Figure 19. Relationship of manganese to pH for 195
New England surface waters. The pH data are means of
two replicates, and both pH and manganese are means
of surface and deep samples where taken. Least squaress
rearession equation: „
'log Mn = 1.83 - 0.05 pH (r* = 0.015).
42
-------
set of 31 samples ranging in color from 0 to 260 units. The regression
equation was TOC (mg 1" ) = 1.32 + 0.0613 color (color unit), and
_r = 0.902. This equation was used to calculate TOC values for all
samples. This relationship agrees well with the approximation TOC =
color t 10 used by the Norwegian Institute for Water Research
(R. Wright, personal communication).
A plot of color or log,Q TOC versus pH (Figures 20,21) shows no
relationship between these factors. Although we attempted to avoid
highly colored waters in the selection of sampling sites, a few were
included. On some occasions water turned out to be more colored than we
realized when the sample was collected. In other cases a colored water
was sampled if there were no obvious bog characteristics and we needed a
sample in that area. The lack of relationship between pH and color (or
TOC) suggests that the acidic lakes are not the result of organic acids.
Similarly, there was little relationship between alkalinity and color
(Figure 22). Only 16 samples had alkalinity of more than 500 yeq 1" ,
and these all had color less than 40 units. The remaining 210 waters
showed no relationship between alkalinity and color. There is no
evidence of increased buffering capacity (alkalinity) contributed by
organic acids in highly colored waters, as was reported by Johannessen
(1980) for humic lakes in Norway.
Driscoll et al_. (1980) reported that aluminum formed strong
complexes with organic ligands. It is therefore possible that changes in
total organic carbon, which are independent of pH, could account for some
of the variation in aluminum concentration in the waters surveyed. A
comparison of aluminum and total organic carbon concentrations (Figure
23) indicates that this relationship is not significant (_r = 0.17).
Precipitation Chemistry
To successfully correlate surface water and precipitation chemistry,
a large region of highly uniform geology and climate with strong
gradients in precipitation chemistry would be required. Such a situation
exists in Scandinavia (Wright and Gjessing 1976), but not in New England.
As a result, attempts to relate surface water chemistry to precipitation
chemistry were unsuccessful. Chemistry of precipitation in New England,
as reported by the National Atmospheric Deposition Program (1981), is
relatively uniform. The small differences in annual deposition of
hydrogen ion over the region are far outweighed by differences in surface
water chemistry resulting from watershed interations, bedrock type, soil
type, etc.
PHYSICAL FACTORS THAT AFFECT WATER CHEMISTRY
Elevation
Surface water pH was only weakly related to elevation (Figure 24).
Some very acidic lakes are located at elevations below 100 m, and lakes
43
-------
8.0-4
7.5-
7.0-
6.5-
P 6.0-
H
5.5-
5.0
1.5-
H.0-
+
+ +
* + +
+
. *
+ + *++
+ 4
I *
+ +
~ + + ++
, + ± $ *
k- + ++
+ +
+
*+ +
+
± 1 ++J* ++
T V+J* ++ + +
+ + +
•i
+ + +
+
+
+
+
+ + + *
+ * + + +
++
+ + +
*;~
*
+
+ +
+
+
+ +
+ +
+
rrTr
Tyry-f
"TTrT
TTT-TT
"T"
"IT.
PT-p-r
"T
1
1
i
1
1
1
2
3
u
5
8
7
8
9
0
1
2
3
4
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
COLOR
Figure 20. Relationship of pH to apparent color. Data are
means of two replicates and means of surface and deep samples
where taken.
44
-------
\.o-
0.9-
0.8
L 0.7-
0
G
0.6-^1
C 0.5-
M 0.4-
G
/
L 0.3-
0.2
0. H
0.0-
~ ~
~
On % °
~ ddd
~ DO
~
~
~
~
~
~ ~
~ ~ ~
~ a
~ m
~ D[jp ~ ~~ rr^ij
~ °
~ ~ ~ QD
if] ~ ~
m ~
~ ~ ~ a cP cfatrp ~ an ~ an ~ m ~
~ D11
~ ~~ ~ a ~~~~ an ~
~ °
m ~ ao ~ cud ~ inn am odd ~ o ~~
~ ~ co D D
n ~ ~ on ~
a
I ~ EED
~ ~
~
a o
~ ~~ ~ UD
~
~ ~~ ~~ CD CD ~
* r11riiii)iiiii> tii|iiiiiiiii|im 11iiii|»im i mi i ) i 111i mi i | ii i > 11 i i i | i 11 i i i ii 11
4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
PH
Figure 21. Relationship of total organic carbon (T0C) to pH.
T0C was calculated from color by the equation:
TOC * 1.32 + 0.0613 color.
45
-------
COLOR
Figure 22. Relationship of alkalinity to apparent color.
Data are means of two replicates and means of surface and
deep samples if taken.
46
-------
600-
500-
400-
fl
L
U
G
/
L
300-
200-
100-
0-
0
D
00
~
g ~
D
~
D
n ~ D _~
D q D
1
11 m 1111H11 m h 11 r 11111 ii n i f 11111 imp i rim i»j i >> 111111| 111 > 11i k 1111111 iti( n»11 rn n
0
8
10 12 14 16 18
T.0.C. (MG/L)
Figure 23. Comparison of aluminum concentration to total
organic carbon concentration in 196 New England lakes and
streams.
47
-------
1000-
900-
800—]
E
L
E 700-1
V
fl
T
I
0
N 500-1
600-
I
N 400-1
M
E
T
E
R
S
300-
200-
100-
0-
++
+
+ + + +
+ +
+
++
+ V 3+
+
% +
h + +
h *+ +
+
+ + +++
•H- V±
+
+
+ +
+
+ + +
+ j, >+*
+ + .
+ +
+
*++
+ +.
++ +
+ +
+
+ +
+ + +
+ +,
+
+
, ++". +++ $
+
*+Z b *
++ * +
++ +
+ + +
+
+ + V+ + + + +
+ ++ + + + % + *
* ++ + + ++++ +Hf
| ii i 11 »i 111 i 111 >' ' i 11111'i'>11[i11111i1111111'i11111i11 11 i 11 111 1111 n i | 11 i 111 11|
Ut0 >4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
PH
Figure 24. Relationship of elevation of sample site to pH.
Elevation was determined from U.S. Geological Survey topographic
maps. The pH data are means of two replicates and means of surface
and deep samples if taken.
48
-------
of pH <5 are distributed throughout the range of 10 to 1,000 m
elevation. However, waters with pH of 6.5 or more were seldom found
above 600 m. A similar situation exists with alkalinity (Figure 25).
Surface waters with negligible alkalinity were found at all elevations
from 10 to 1,000 m, but all but two,lakes located at 600 m elevation or
higher had alkalinity of 100 ueq 1~ or less.
The influence of elevation on surface water chemistry is most likely
a complex interrelationship of bedrock geology, soil type and depth,
depth of glacial till, and watershed size. Higher elevation areas would
be expected to have bedrock that is resistant to chemical weathering, and
thin soils and till. Watersheds may be smaller at higher elevation
because of the steepness of the terrain. The presence of low pH and low
alkalinity waters at low elevation indicates that marine influence does
not necessarily result in increased buffering capacity in New England, in
contrast to the situation in Norway. Some very acidic lakes are located
near the seacoast in Maine, Massachusetts, and Rhode Island. In Norway
acidic lakes were rare at elevations less than 200 m, because of the
presence of readily weathered sediments deposited during the late
Pleistocene marine submergence (Wright and Snekvik 1978). Although the
proportion of acidic lakes and streams is greater at higher elevations in
New England, there are as many acidic lakes at lower as at higher
elevations.
Size
In general, acidic lakes were also small lakes. All but two lakes
that had a pH of 5.5 or less were also less than 20 ha surface area
(Figure 26). Obviously, not all small lakes were acidic, but lakes
larger than 20 ha rarely were. But the larger lakes were low in
alkalinity (Figure 27). All but two lakes with area greater than 20 ha
also had alkalinity of 400 ueq 1" or less. Other regional surveys of
lake chemistry seem not to have considered lake area as a factor
governing chemistry. In general, larger lakes would be expected to have
larger, more complex watersheds with a greater variety of bedrock types
and thicker soils. Therefore we expected larger lakes to be better
buffered and higher in pH (lower in hydrogen ion). The largest lakes we
surveyed were relatively low in alkalinity, and therefore buffering
capacity, yet tended to be of higher pH than smaller lakes of comparable
alkalinity.
There is apparently some factor related to size that caused the
larger lakes to have lower concentrations of hydrogen ion than smaller
lakes of the same alkalinity. One possible explanation is watershed
size. Larger lakes tend to have larger watersheds, which may be capable
of neutralizing more hydrogen ion even though they are low in bufering
capacity. This could be the result of lower-gradient slopes and thicker
soils and till, which would allow more contact time. We tested this
hypothesis using lakes in Maine, for which drainage area data were
available. We compared log1Q drainage area to pH, drainage area to
49
-------
1000-
900-
800-1
I
L
£ 700-1
V
R
T
I
0
N 500-
600-
I
N UOO-q
M
E
T
E
R
S
300-
200-
100-
0-
+
+
+
t
+
+
+ +
u+
t * *
\U
+
$?+ ~ ~ ~
-it + +
x*' * *
fitV
+ +
¦1 I I I I I 'l I I | I I I I I I I I I [ I I t I < I I I I | I I I I 1 I I I I | | I I |
-250 250 750 1250 1750
RLKflLINITY UEQ/L
l » > | I > I I I l fl I | I
2250
2750
Figure 25. Relationship of sample site elevation to alkalinity.
Elevation was obtained from U.S. Geological Survey topographic
maps. Alkalinity data are means of two replicates and means of
surface and deep samples where taken.
50
-------
R
R
E
A
I
N
H
E
C
T
fl
R
E
S
10CH
75-
50-
25-
0-
+
+ +
+++++ +
-H-
++ +*
+ + + + + + ++
+ ++ ¦""++++ + *++ %¦ + -f*
+ +1+ +?• +* * % + +
+ ~ + t+4:^-#"- iLj ++++ + +
U+++ «¦ +++ \ +* ^ ++ +
|iini ini|i hi hi M|nruiirt|iiiiiiiii| uiiiini)iyrrMn t] i i i i n i i i i i i [
.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
PH
Figure 26. Relationship of lake area to pH. The pH data are
means of two replicates and means of surface and deep samples
where taken.
51
-------
100-1
75-
50-
25-
+
+
\
+ *
+ +
+
+ + +
+ +
K i +
A*
I I II I I I 1 I II I M I I M 1 I I IP M I I I I I
T
-250
I I I I » I I > I 1
| ii i > i">'t TfjT'r rt 'i i i i i ( i i r
250
750 1250 1750
RLKflLINITY UEQ/L
2250
2750
Figure 27. Relationship of lake area to alkalinity. Alkalinity
data are means of two replicate and means of surface and deep
samples where taken.
52
-------
alkalinity, and the ratio lake area/drainage area to pH and alkalinity by
use of linear regression. The only statistically significant regression
was for log.g drainage area and pH (r = 0.14, £ <0,003; Figure 28).
This supports the hypothesis that increased area of the drainage basin
can neutralize increased amounts of hydrogen ion independent of
alkalinity.
Drainage Type
Lakes were classed as drainage lakes {with outlet) and seepage lakes
(no outlet). The two types of hydrology apparently do not significantly
affect lake water chemistry (Table 11). Drainage lakes had a nearly
significant tendency to be more highly colored than seepage lakes. In
contrast to this, lake hydrology was an important factor in controlling
buffering capacity in northern Wisconsin lakes. (EfTers
et al_., in preparation).
Lakes and streams were chemically similar, differing only in pH,
sodium, and potassium (Table 12). The streams were higher in pH, which
may reflect the acid neutralizing effect of larger watershed area or the
fact that lakes can receive an appreciable amount of precipitation
dfrectly an the lake surface, without watershed contact.
Stream Order
Higher order streams tended to be higher in pH and alkalinity,
specific conductance, Calcite Saturation Index, calcium, magnesium,
sodium, potassium, and chloride (Table 13). There were no siginifcant
differences for aluminum, manganese, sulfate, or color. This pattern is
as expected, because higher order streams have larger watersheds and are
more likely to include a variety of bedrock geology types.
Bedrock and Soil Class
Bedrock geology type had a pronounced effect on chemical factors
related to acidity and buffering capacity, but little effect on other
factors (Table 14). Mean pH, alkalinity, specific conductance, calcium,
and magnesium increased consistently along the spectrum from bedrock type
1 (very low buffering capacity) to type 4 (very high buffering capacity),
and calcite saturation index consistently decreased.
Waters located 1n areas where bedrock was class 1 or 2 varied widely
in pH (Figure 29) covering nearly the entire range measured. Waters
underlain by bedrock class 3 or 4, however, were largely restricted to
higher pH. Low alkalinity waters were much more abundant in areas with
bedrock class 1 and 2, and high alkalinity waters were more abundant in
class 3 and 4 (Figure 30). However, low alkalinity waters were found 1n
all bedrock classes.
The bedrock geology classes devised by Hendrey et al. (1980) were
intended to reflect buffering capacity Inherent in tRe "Bedrock. This
53
-------
p n
Figure 28. Relationship of log.Q drainage area to lake pH.
Lake pH values are means of two replicates and means of
surface and deep samples where taken. Least squares
regression equation:
log drainage area - 0.26 + 0.31 (pH).
54
-------
Table 11. Mean Values and Standard Deviations of Chemical Factors
for Lakes Classed by Hydrology Type. P is Probability that
Means are Drawn From the Same Populations, One Way
Analysis of Variance Test
Factor Hydrology type P
Drainage Seepage
X S.D. X S.D.
PH, units
6.34
0.86
6.27
0.91
0.66
Alkalinity, yeq l"1
231
411
220
483
0.88
Specific conductance, uS cm"*
54
47
49
46
0.51
Calcite saturation index, units
3.67
1.72
3.52
1.67
0.62
Calcium, mg l"^
5.4
7.4
4.5
7.4
0.45
Magnesium, mg 1"*
0.9
0.7
1.1
1.9
0.39
Sodium, mg 1"^
2.3
2.6
2.5
3.0
0.61
Potassium, mg l"*
0.6
0.5
0.6
0.5
0.95
Aluminum, yg 1
85
111
89
113
0.83
Manganese, pg l"1
40
46
38
30
0.80
Chloride, mg l"1
3.6
5.2
3.5
4.6
0.95
Sulfate, mg 1"^
7.4
5.7
6.4
2,2
0.15
Color
30
28
23
19
0.06
55
-------
Table 12. Chemical Factor Means and Standard Deviations for Lakes and
Streams. P is Probability that Means are Drawn From the Same
Populations, One Way Analysis of Variance Test
X
Lake
$.D.
X
Stream
S.
P
D.
PH, units
6.29
0.89
6.74
0.53
i
O
• i
O I
1—» 1
Alkalinity, yeq 1"*
281
534
217
130
0.55
Specific conductance, yS cm"*
54
53
57
28
0.81
Calcium, mg l-1
5.4
*3-
00
4.0
ro
•
o
0.40
_i
Magnesium, mg 1
1.1
1.7
1.2
0.7
0.73
Aluminum, yg 1"*
84
101
71
82
0.55
Manganese, ug 1
39
35
45
45
0.44
Sodium, mg 1"*
2.2
2.6
3.5
2.5
0.02
Potassium, mg 1"*
0.6
0.5
1.2
0.5
<0.01
Chloride, mg 1"*
3.2
4.6
5.2
5.1
0.06
Sulfate, mg 1"*
6.5
2.5
6.9
2.3
0.46
Color, units
14
80
12
66
0.39
Significant for® = 0.05
56
-------
Table 13. Relationship of Stream Order to Water Chemistry Factors.
Values Reported are Arithmetic Means of all Available Samples.
Means with the Same Superscript Letter are not significantly
Different (One Way Analysis of Variance Test
with Scheffe's Comparison, p <0.05)
Factor
Stream order
1
2
3
4
PH, units
6.3a
6.2a
7.2a'c
7.0b,c
Alkalinity, yeq 1"^
174a
85a,b
302a,c'd
241a *d
Specific conductance, yS cm"*
43a
35a
54a,c
71b,c
Calcite saturation index, units
3.5a
3.8a
2.5a'c
2.6b,c
Calcium, mg 1"*
3.7a
2.4a'b
3.5a
4.5a'c
Magnesium, mg 1"*
1.0a
0.5a'b
1.7a,c
1.3a,c
Sodium, mg 1~*
2.2a
2.4a'c
3.0a,c
4.9b
Potassium, mg 1"*
0.9a
0.7a,c
1.4a'd
1.5b,d
Aluminum, yg 1"*
127a
110a
14a
89a
Manganese, yg 1"*
54a
53a
13a
39a
Chloride, mg l"1
2.5a
3.4a,c
4.8a,c
8. lb
Sulfate, mg 1"*
6.0a
5.5a
5.4a
7.6a
Color, units
35a
42a
10a
21a
57
-------
Table 14. Mean Values of Chemical Factors for Surface Waters Classed
by Bedrock Geology. Bedrock Classes: 1 = Very Low Buffering
Capacity, 2 = Moderately Low Buffering Capacity, 3 =
Moderately High Buffering Capacity, 4 = Very High
Buffering Capacity (Hendrey et jil_. 1980). Means
with the Same Superscript Letter are not
Significantly Different (One-Way
Analysis of Variance with
Scheffe's Comparison
p <0.05)
Factor Bedrock class
"T 2 5 T
PH, units
6,02a
6.25a
6.86b
7.21b
Alkalinity, ueq l"1
8a
172a
575b
l,025b
Specific conductance, yS cm~^
38a
43a
84b
149c
Calcium, mg l"1
2.1a
3.9a
11.5b
18.7°
Magnesium, mg 1~*
0.7a
0.8a
1.5b
4.8C
Aluminum, yg l"1
90a
80a
110a
114a
Manganese, yg 1"*
41a
34a
38 a
102b
Sodium, mg l"1
2.8a
2.0a
3. la
2.5a
Potassium, mg l"1
0.5a
0.6a
0.8a
0.8a
Chloride, mg 1~*
4.2a
3.0a
3.2a
2.8a
Sulfate, mg l"1
5.5a
6.5a,b
7.6b
15.9C
Color, units
26a
26a
25a
28a
Calcite saturation index, units
4.08a
3.87a
2.03b
1.68c
58
-------
U1
to
r+ a- -n
3- Q-tO
-J C
3 O -s
O (T»
Q. 7• C
J 3 re
tU 3
T3 p
3
C+ ~Q.
fD 3 ->•
~i c+ m
< fl> c+
W -I -s
• oi cr
3 c
| + «/> C+
¦ mmim
O O
3
o m
tn o> o
o -h
C 3-
3 trt
-••"O C
r+ 31 "5
• -+>
< tt
o» o
—¦(0
c
n> s
Oi
«£J r+
->• n>
< -i
fl> w>
3
to
FREQUENCY
60 -
50 -
HO -
30
20 -
10 -
v*v
¦
06005
¦
¦
|
>Kv
HHi
¦BHI
U.O 1.5 5.0
BEDROCK CLASS
fS/SA
-------
FREQUENCY
60 -
0 50 100 150 200 250 300 350 400
ALKALINITY (UEQ/L)
BEDROCK CLASS I I 1 65533 2 WTTZK 3 ¦¦ 1
Figure 30, Frequency distribution of surface waters by bedrock
geology in alkalinity intervals. Each alkalinity value given is
the midpoint of the interval, + 50 yeq 1" .
60
-------
geological factor does affect surface water chemistry for those chemical
factors that are related to acidity and buffering capacity but not other
factors, as intended. The relationships are consistent but lack
precision because of high variability within a bedrock class. Waters
located in bedrock classes 1 and 2 were not significantly different from
each other for any chemical factor measured, and classes 3 and 4 were
different for only a few factors. Based on the above analysis bedrock
types 1 and 2 were pooled and types 3 and 4 were pooled. The ability of
these groupings to predict surface water sensitivity to acidification
was tested by preparing a contingency table for bedrock geology and
alkalinity. Bedrock types 1 and 2 were termed sensitive and types,3 and
4 were termed non-sensitive. Waters with alkalinity of 200 yeq 1 or
less were termed sensitive and those exceeding 200 yeq 1 were termed
non-sensitive. The hypothesis that bedrock class has no effect on
alkalinity was tested by use of a contingency table (Table 15). From
this table we calculated x » the log-likelihood ratio (G-statistic),
and the odds ratio {Bliss 1967, Fleiss 1981). The x and G statistics
were highly significant (£ <0.001). The odds ratio indicated that a
sensitive lake or stream is nine times more likely to be found in bedrock
class 1 or 2 as in class 3 or 4.
There were only three of the five possible types of soil groups
(McFee 1980) in New England: SS., SS2, and NS. The highly sensitive
groups (SI, S2) were not present. Soil type was much less important than
bedrock type in affecting surface water chemistry (Table 16). Classes
SSI and SS2 were not significantly different for any factor measured.
SSI and SS2 were both significantly different from NS for alkalinity,
calcium, sodium, and chloride. Class SS2 alone was different from NS for
pH and sulfate.
Soil classes in our sample were distributed nearly normally with
respect to pH (Figure 31), with intermediate pHs being most common in all
soil classes and waters of all pHs being found in nearly every soil type.
The relationship of soil classes to alkalinity (Figure 32) is highly
skewed with an abundance low alkalinity waters. But the proportion of
soil classes within each alkalinity interval is nearly equal, and waters
of all alkalinities are found in each soil class.
The predictive ability of soil class for alkalinity, calculated in
the same manner as for bedrock geology, is shown in Table 17. Soil
classes SSI and SS2 were combined as sensitive for this comparison. The
and G statistics were highly significant ( x P<0.001, G £ <0.005).
The odds ratio indicates that sensitive lakes anastreams are three times
more likely to be found in areas with sensitive soil class than in areas
with non-sensitive soils. Thus soil class is not as good a predictor of
alkalinity as is bedrock class. Kaplan et al. (1981), working with
different bedrock and soil classes, concludes? that there was no direct
influence of bedrock on surface water chemistry but there was for soil
type, and bedrock indirectly influenced water chemistry through soils.
However, the data set used in KapTan's study consisted largely of
61
-------
Table 15. Contingency Table Tests of the Predictive Ability
of Bedrock Geology for Surface Water Alkalinity.
Table Entries are Numbers of Lakes Sampled.
Alkalinity class
Sensitive-, Non-sensitive
Bedrock class 1200 veq 1" >200 yeq 1" Total
Sensi tive
(Class 1 & 2) 135 29 164
Non-sensitive
{Class 3 & 4) 9 18 27
Total 144 47 191
Test statistics
= 29.98 (p <.001)
* 25.75 (p <.001)
= 9.31 (p <.01)
«= 4.26
2
X
G
oj
62
-------
Table 16. Mean Values for Chemical Factors Classed by Soil Cation
Exchange Capacity. Soil Class: SS, = Slightly Sensitive Soils
Dominate Area, SS« = Slightly Sensitive Soils are Significant
but Less than 50% of the Area, NS = Mostly Non-Sensitive
Soils (McFee 1980). Means with the Same Superscript
Letter are not Significantly Different (One-Way
Analysis of Variance with Scheffe's Comparison
p <0.05)
Factor Soi 1 type
r—1
IS)
IS)
ss2
NS
PH, units
6.22a'b
6.06a
6.461
A1kalinity, yeq 1~*
157a
83a
331b
Specific conductance, yS cnf*
49a
44a
55a
Calcium, mg l"1
3.2a
2.5a
7.0b
Magnesium, mg 1"*
l.la
0.8a
l.la
Aluminum, yg l"1
833
77a
95a
Manganese, yg 1"*
35a
40a
40 a
Sodium, mg 1"*
3.3a
3.1a
1.7b
Potassium, mg l"1
0.7a
0.6a
0.5a
Chloride, mg 1"*
5.2a
5.6a
1.7b
Sulfate, mg I"1
6.1a,b
5.8a
7.5b
Color, units
27a
26a
25a
63
-------
FREQUENCY
CTv
r+ o -ti
zr —1 —
fD Oj to
cn c
-»• in -j
rs n>
c+ -<•
fD 3 CO
-J «-•
< "D •
a rc
« -n
1 + r+ n>
fD -O
o -J cr
• < n>
U1D) 3
—' O
E V> «<
3 •
-4. Q_
r+ -»•
• rn tn
OJ f+
O -S
C~> 3"
—• KT
BJ X3 C
Wirt
«/>
< O
WOi 3
n cr o
fD —+)
CO
Crt
-pi 3 O)
O
it —fc. rt»
in
l/i s:
C/)f+0>
ro 3" r+
<• t9 (9
OT3 w
-J.¦
it a. cr
-a «<
z o
CO —t/1
• 3 O
O
-h
50 -
U0 -
30 -
20 -
10 -
LAAAJ
4.0 14.5 5.0 5.5
SOIL CLASS
3
6.0 6.5 7.0 7.5 8.0
PH
E223 *
5
-------
FREQUENCY
80 -
0 50 100 150 200 250 300 350 400
ALKALINITY (UEQ/L)
SOIL CLASS I 13 S8ga
-------
Table 17. Contingency Table Tests of the Predictive Ability of
Soil Cation Exchange Capacity for Surface Water Alkalinity.
Table Entries are Numbers of Lakes Sampled.
Alkalinity class
Sensitive, Non-sensitive
Soil class <200 ueq 1" >200 yeq 1 Total
Sensitive
(SSj + SS2) 88 14 102
Non-sensitive
(NS) 58 33 91
Total 146 47 193
Test statistics
x2 = 13.26
G = 8.84
a) =3.58
S.E. = 1.29
0)
(p <.001)
(p <.0i)
(p <.05)
66
-------
lowland sites, where soils would be thicker and more likely to influence
water chemistry. In headwater areas soils are likely to be thin and may
be absent in large areas, resulting in a greater influence of bedrock on
water chemistry. There may also be an interaction between bedrock class
and soil class, with sensitive soils being associated with sensitive
bedrock. Also, Kaplan e£ al. (1981) classified bedrock by formation
(igneous, metamorphic, etcTT rather than by chemical content. Thus,
marble, for example, would be classed as metamorphic and relatively low
in buffering capacity.
Human Disturbance
Human disturbance had an important effect on many water chemistry
factors (Table 18). Generally, increasing human disturbance results in
increased pH, alkalinity, calcium, magnesium, Calcite Saturation Index,
and specific conductance. There was no effect on aluminum, manganese, or
color, and minimal effect on sodium and potassium. Chloride, often used
as an index of human disturbance, did not increase consistently with
disturbance code but mean chloride values were higher in codes 3
and 4 than in codes 1 and 2.
Human activities such as logging, road and building construction,
and agriculture disturb natural vegetation and expose soil to increased
leaching of chemical constituents. Runoff may also be increased.
Disposal of human wastes further adds ions to the receiving waters.
This may explain the increase in chemical factors, especially those
associated with the buffering complex, with increasing human disturbance.
Alternatively, this relationship may result from an interaction between
human disturbance and bedrock geology. The waters surveyed in bedrock
classes 1 and 2 tended to be low in disturbance whereas those In classes
3 and 4 tended to be high (Table 19). This may reflect the tendency for
human activity to be concentrated in areas where soil fertility is high
and topography is gentle, which are generally associated with bedrock
types high in carbonate minerals.
The role of disturbance in the relationship among chemical factors
was evaluated by performing statistical analyses on the entire data set
and on waters with disturbance code 1 or 2 only. As there were no major
discrepancies that could be attributed to disturbance alone, we used all
sample sites in the analyses reported here.
HISTORICAL COMPARISONS
jlH
Of the 95 locations with usable historical pH data, 34 (36%) either
were the same or higher in the recent analysis and 61 (6435) were lower
(Figure 33). If there were only random variation, 1t would be expected
that the same proportion of locations would increase as decrease. The
recent pH values are significantly lower than historical values (paired t
test, t = 4.17, £ =<0.0001).
67
-------
Table 18. Mean Values for Chemical Factors Classed by Disturbance Code.
Disturbance Codes: 1 = No Disturbance, 2 = Slight Disturbance
3 = Moderate Disturbance, 4 = Severe Disturbance. See Text
for Description of Disturbance Assessment and Assignment
of Classes. Means with the Same Superscript Letters
are not Significantly Different (One-Way Analysis
of Variance with Scheffe's Comparison, p <0.05)
Factor Disturbance class
_ ^ g 4
PH, units
6.10a
6.20a
6.88b
7.1 lb
Alkalinity, yeq 1~*
138a
179a
412b
1 ,043b
Specific conductance, yS cm"*
33a
49b
81c
131d
Calcium, mg 1"*
3.2a
4.5a
7.7b
14.6C
Magnesium, mg 1"*
0.6a
0.9a'b
1.5b
5.0C
Aluminum, yg 1"*
82a
90a
79a
164a
Manganese, vg 1"*
32a
41a
46a
52a
Sodium, mg 1~*
1.3a
2.6b
4.5C
2.9a'b'c
Potassium, mg 1"*
0.4a
0.5a,c
1.0b
1.0b,c
Chloride, mg l"1
1.9a
3.6a
6.0b
5.0a,b
Sulfate, mg 1"*
5.7a
7.3b,c
6.9a,c
8.8a,c
Color, units
23a
26a
31a
16 a
Calcite saturation index, units
4.0a
3.7a
2.7b
2. lb
68
-------
Table 19. Number of Surface Waters Sampled in Each Bedrock
Class and Disturbance Code. See Text for Explanation
of Bedrock and Disturbance Code
Disturbance Bedrock class
Code ~T 2 3 T
1
32
71
10
0
2
47
98
16
8
3
12
27
12
2
4
2
0
4
2
69
-------
00
- r-
1
Q_
cn
o
- CD •—<
CO
CD
CO
- IT)
h =f
UDCCtClxJZI— O. H
Figure 33. Current versus historical pH for 95 New England
lakes. Solid line indicates equivalent pH.
70
-------
If the comparison is restricted to those waters that are sensitive
to acidification, the proportion that have declined in pH is increased.
For waters that presently have alkalinity of 100 yeq l" or less, 13
of 53 locations (253!) either did not change or increased and 40 of 53
(75%) decreased in pH (Figure 34). This difference is also significant
(paired t test, t = 4.38, £ =<0.0001). A similar comparison for waters
located in bedrock classes 1 or 2 only (the lowest in buffering capacity)
gave comparable results (Figure 35).
The mean pH of stations for which historical data were located was
5.37 (calculated from hydrogen ion) versus 6.07 historically. Or, stated
another way, mean hydrogen ion increased from 0.8 yeq 1" historically
to 4.3 yeq 1 recently, a five-fold increase. The historical data
were determined as early as 1937 and as recently as 1978. Data more
recent than 1978 were located but not used. There was no obvious trend
of change in pH with elapsed time between measurements. However, the
historical data set is rather small and includes lakes with a wide range
in buffering capacity. Lakes with high buffering capacity would not be
expected to change significantly in pH. If only low buffering capacity
lakes are considered (alkalinity <100 ueq 1" ) the data set becomes too
small to be meaningful.
The historical pH data comparisons, although not numerous,
nevertheless indicate a substantial increase in hydrogen ion content of
the lakes that are low in buffering capacity. The historical pH data
were largely determined colorimetrically. Only one historical
measurement was located that was determined electrometrically.
Colorimetric pH indicators may give erroneous results, especially in
water with low buffering capacity (Boyd 1976, 1977, 1980; Pfeiffer and
Festa 1980; Wright 1977). However, Schofield (1982) showed that the
error was not large in Adirondack Mountain lakes, being 0.2 unit or
less. We have done extensive testing of the colorimetric indicator
methods commonly used in New England and concluded that the limit of
accuracy of the method was ±0.2 unit, and the error was unbiased (Haines
et al_., in press). Norton et aK (1981) compared the two methods for a
set of New England lakes ancTconcluded that the two methods were
equivalent within 0,1 unit. Aimer et al. (1974) measured pH in a number
of lakes using both methods and foundT tWat the results were comparable,
seldom differing by more than 0.2 unit. Lewis (1982) compared
electrometric pH measurements with those made with the same type of
indicators used 40 years previously and found "good agreement".
Many other comparisons of historical and recent pH data have been
made. A number of the most significant ones were reviewed by Haines
(1981). Most comparisons that were made in areas where precipitation is
acidic (pH<4,6 annual weighted mean) and where acid neutralizing
capacity of bedrock is low have demonstrated similar declines in pH.
Many studies in Scandinavia, reviewed by Wright and Gjessing (1976),
reported that lakes declined from pH 6-7 during the period 1940-1950 to
pH 5-6 fn the early 1970's. Wright (1977) compared historical and
71
-------
+ + +•
+ + +
¦*\+
+ +
+ 4-
VH- * +
+ +
+ \ +
-H-
+ +
+
+ + +
| I I I I I I M I [ M M M II I | I I M M M I |'M ) M 1 I M
00 r- CD ID 3.
UDCCUJZh- Q_ X
Figure 34. Current versus historical pH for 53 New England
lakes with alkalinity of 100 yeq l"1 or less. Solid line
indicates equivalent pH.
72
-------
+
+
+
+
++ + -H-
4f
+f+ +
+#+*+++
-H- +
+
+ 4f + -K +
+ \++ +
+ +
+ +»- +
+
+
+
+
+ +
+ +
+
+
I i i i i i i i i i ) i i i i i i i i i | i i i
00 r- to
i i i i i i | i i i i
in
UrDCCfELUZt- Q-X
Figure 35. Current versus historical pH for 85 New England
lakes located in bedrock class 1 or 2. Solid line indicates
equivalent pH.
73
-------
recent pH data for 128 lakes in southern Norway and found that 63% had
become more acidic. Recently, Morling (1981) reported annual pH
measurements from 1960-1966 to 1979 for 10 Swedish lakes, with similar
results.
McColl (1981) found that the hydrogen ion activity of two reservoirs
in the,Sierra Nevada mountains of California increased about 0.06
yeq 1" during the period 1954 to 1979. The mean monthly variation in
hydrogen ion was correlated with mean monthly variation in air pollution
in the San Francisco Ray Area. Lewis (1982) resurveyed 104 lakes in
Colorado that had been surveyed previously. He found that pH declined
from a mean value of 7.09 during 1938-1960 to 6.87 recently. Hendrey et
al. (1980) resurveyed 42 New Hampshire streams for which historical pH
"ciata were available and found that pH had declined in 37 (88%). In a
previous survey of Maine lakes, Davis et aJL (1978) reported that the pH
declined from 6.18 in 1937-1943 to 6.09 in 1969-1974. This small decline
most likely results from the fact that the lakes surveyed were large,
lowland lakes that were not highly vulnerable to acidification. Pecently
Norton j3t (1981) reported historical versus recent pH comparisons for
69 lakes in Maine, New Hampshire, and Vermont and found that 51 (74%) had
decreased pH. This finding agrees well with our results, and there is
some overlap in the data sets.
Alkalinity
Historical alkalinity data were less common than pH. Only 56 lakes
had such data. The historical alkalinity data were all obtained by
fixed endpoint titrations to pH 4.5 (methyl orange endpoint). This
method overestimates true alkalinity because the actual endpoint is not
fixed but increased with decreasing alkalinity. We measured both fixed
endpoint and inflection point alkalinity for our samples. A comparison
of these two values (Figure 36) shows that they are highly linearly
correlated (Inflection Point = -3? + 1.00 fixed endpoint; jr = 0.999).
Therefore fixed endpoint alkalinity data can be simply corrected for the
overestimation by subtracting 32 yeq 1" , which was done for all
historical data. Conversely, we could have compared our fixed endpoint
alkalinity values to unadjusted historical values. The results are the
same in either case, but presentation of fixed endpoint alkalinity values
indicates erroneously high alkalinity levels.
A comparison of adjusted historical to recent alkalinity results
(Figure 37) shows that 17 of 56 results (30%) increased or did not change
and 39,(70i) decreased. Historical alkalinity data averaged 166
yeq 1 and recent data averaged 68 yeq 1" , a 60% decrease. This
decrease was significant (paired t test, t = 4.03, £ = 0.002).
Generally, waters with the highest historical alkalinity exhibited the
largest declines. This may be because these waters had alkalinity that
could be lost whereas waters that were low in alkalinity historically had
little.to lose. The highest historical alkalinity value located was 600
yeq 1 , which is not very high on a world-wide basis. As for pH, there
-------
150
400
350
300'
250-
200-
150-
100-
50-
0-
-50-
100-
~
T^-
' ¦ I
T
T
"I ' ' ' ' ' 1
I ' '
eo
120 180 2U0 300 380
FIXEO ENOPOXNT ALKALINITY
120
480
jre 36- Inflection point (Gran plot) versus fixed endpolnt
4.5) alkalinity for 184 sample sites. Least squares
"ession equation: IP = -32 + 1.00 FP (jr = 0.999).
75
-------
Figure 37, Current versus historical alkalinity for 56
New England lakes. Solid line indicates equivalent alkalinity.
76
-------
was no discernable relationship between time interval between
measurements and magnitude of the change 1n alkalinity. The change in
alkalinity agrees well with the change in pH and indicates comparable
levels of acidification.
Because historical alkalinity data are scarce, other comparisons of
historical and recent alkalinity data are rare. Morling (1981) reported
annual alkalinity measurements for four Swedish lakes from 1967-1968 to
1979. All declined by 10 to 20 ueq 1 . Lewis (1982) reported that
alkalinity averaged 440,peq 1" in 64 Colorado lakes during 1938-1960,
and averaged 360 peq 1" in 1979. McColl (1981) analyzed weekly
aTkalinity data over the period 1944-2979 for one reservoir in the
Sierra Nevada mountains of California. Alkalinity decreased from about
350 ueq 1 to about 230 yeq 1 . During the period 1954 to 1979,
for which hydrogen ion measurements were also available, alkalinity
declined about 90 yeq 1" .
Multivariate Analyses
The large number of factors measured in this study makes the
identification of key variables difficult. A number of multivariate
analytical methods were explored 1n an attempt to reduce the complexity
of the data set and identify the factors that contribute most to the
variance.
Principal Components
Principal Components Analysis (PCA) is a procedure that reduces a
large set of intercorrelated variables to a smaller set of hypothetical
variables, each of which is Independent of the others. PCA forms
successive linear combinations of original variables that "account" for
decreasing amounts of variation in the original data set. The problem
of different units for different variables can be eliminated by
standardizing the variables with respect to the mean. We applied PCA to
the standardized water chemistry data in an attempt to identify the
chemical factors that could best be used to categorize surface waters
with respect to vulnerability to acidification.
The results of the PCA for standardized major water chemistry
factors (Table 20) define three axes that account for 11% of the
population variance. The variable weights are defined such that the sum
of the weights squared equals one. Thus the weight is an Index of the
proportion of the variance accounted for by the principal component that
is attributed to the Individual variable. PCI 1s dominated by the
factors pH, alkalinity, calcium, and sulfate. These factors are related
to acidity and buffering capacity. PC2 is dominated by chloride,
sodium, and potassium 1n an inverse relationship, as these weights are
negative. These are neutral ions that contribute to 1on1c strength but
not to acidity or buffering capacity. PC3 is dominated by the metals
aluminum and manganese.
77
-------
Table 20. Principal Component Analysis of Standardized Major Water
Chemistry Factors of New England Lakes. Analysis Included
Only Lakes with Ion Balance of 0 _+ 75 yeq 1"
Variable weights for
principal components
Variables I 2 3
PH 0.46 0.12 -0.16
Alkalinity 0.49 0.25 0.14
Calcium 0.51 0.25 0.08
Chloride 0.16 -0.57 -0.02
Sodium 0.17 -0.56 0.04
Potassium 0.27 -0.45 0.22
Sulfate 0.40 <0.01 -0.22
Aluminum -0.05 0.08 0.55
Manganese 0.03 0.05 0.74
% Variance explained 34 58 71
by each PC
78
-------
Next, the PCA scores for each lake were considered as the observed
variables and compared to bedrock geology and soil class for each sample
(Table 21). The mean values for PCI increase with increased bedrock
buffering capacity, but PC2 and PC3 do not show consistent trends. The
relationship of bedrock class to principal component variables was
tested with analysis of variance. Bedrock classes 1 and 2 were not
significantly different from each other (j) = 0.10), but all other
possible contrasts were different (js <0.01). For soil class only PC2
consistently increases with increasing cation exchange capacity (Table
21). Analysis of variance shows that soil classes SS. and SS? were
not significantly different from each other (jd = 0,617, but both were
different from class NS (jd<0.C01).
Based upon the groupings of chemical factors suggested by the above
PCA, we conducted another analysis on a reduced data set. We defined
four variables: alkalinity factor (sum of H ion, alkalinity, and Ca in
yeq 1" ), salts (sum of CI, Na, and K in yeq 1" ), sulfate (SCL
concentration, yeq 1 ), and metals (sum of A1 and Mn, yeq 1" j. We
used only lakes where the difference of anions and cations was 0±75
yeq 1" . The results of this analysis (Table 23) show that the first
two PC factors account for nearly all (>99%) of the variance. PCI is
dominated by alkalinity and PC2 by salts. These are the factors that
also dominated the first two PC factors in the previous PCA. Note that
PCI (alkalinity) accounts for 88% of the variance. PC factors 3 and 4,
although they contribute very little to the variance, are dominated by
sulfate and metals respectively. These results indicate that the
alkalinity factors ~ pH, alklinity, and calcium -- are the most
important chemical factors in lake classification.
Canonical Analysis
In canonical analysis two sets of variables are transformed such
that the correlation of the two sets is maximized. This is done by
applying a weighting factor to each variable in each set. By examining
the weights applied to each variable one can ascertain which variables
are most important in describing the variations in the data. Unlike
principal components, however, the magnitude of the coefficients is not
constrained. Thus, they cannot be used to directly estimate the
proportion of canonical correlation attributable to each variable.
Canonical analysis was used to relate physical factors such as
bedrock and soil class, elevation, etc. to lake chemistry factors. The
results (Table 23) show that reasonably high correlation (r, = 0.70,
jr„ = 0.56) exists between physical and chemical factors. The
correlation coefficients were statistically significant for variables 1
and 2 (Bartletts test, 2 <0.001). The physical factors with highest
weights in canonical variable 1 are bedrock and soil class; the highest
weighted chemical factors were calcium and alkalinity in the chemical
set. Variable 2 assigned highest weights to elevation in the physical
set and to calcium and alkalinity in the chemical set.
79
-------
Table 21. Mean Principal Component Values for
Bedrock and Soil Classes
Principal component
Bedrock class I 2 J
1 -0.89 -0.23 0.10
2 -0.16 0.17 -0.03
3 1.47 <0.01 0.09
4 4.68 1.56 0.52
Soil class
SSj 0.04 -0.88 0.09
SS2 -0.57 -0.64 -0.07
NS 0.25 0.67 -0.01
80
-------
Table 22. Principal Components Analysis of Combined Chemical Factors.
All Units were yeq 1 . The Factors were Defined: Alkalinity
= Sum of H, Alkalinity, Ca; Salt = Sum of CI, Na, K;
Sulfate = SO^; Metal = Sum of A1, Mn
Principal component
I 1 3 *
Variable weight
Alkalinity
0.999
0.016
-0.041
0.003
Salt
-0.016
0.999
-0.022
0.003
Sulfate
0.040
0.023
0.999
-0.006
Metal
-0.002
-0.003
0.006
0.999
Variance
88.36
99.67
99.98
100.00
81
-------
Table 23. Canonical Analysis of Physical and Chemical
Factors for New England Lakes
Canonical
variable
1
2
3
4
Physical factor weights
Area
-0.14
-0.03
0.14
1.03
Hydrology type
-0.22
-0.16
-0.27
0.52
Elevation
-0.38
0.63
-0.95
0.03
Bedrock class
0.68
0.23
0.09
0.20
Soil class
0.50
0.27
0.49
-0.20
Disturbance code
0.35
-0.33
-0.83
0.02
Chemical factor weights
PH
0.22
0.26
-0.33
1.01
Alkalinity
-0.63
-1.80
-0.96
-0.61
Specific conductance
-0.11
-0.49
0.75
0.74
A1umi num
0.17
0.30
-0.51
0.04
Calcium
1.12
2.14
0.54
-0.71
Chloride
-0.31
-0.25
0.12
0.47
Color
0.15
-0.03
0.06
-0.62
Potassium
0.15
-0.02
-0.61
-0.41
Magnesium
0.24
0.17
-0.23
0.10
Manganese
-0.03
-0.06
-0.40
-0.10
Sodium
0.34
-0.50
0.02
-0.49
Sulfate
0.31
-0.21
0.39
-0.01
Cannonical correlations
0.70
0.56
0.42
0.31
82
-------
Relationships between the hypothetical canonical variates and the
variables in the set that comprise it were investigated by computing the
correlation of each variable in the set with each canonical variate,
following the method of Timm (1975). These results (Table 24) show
bedrock class and disturbance code to be the most important physical
factors in canonical variable (CV) 1. Among chemical factors, specific
conductance, calcium, alkalinity, sulfate, magnesium, and pH were most
important. These are the same chemical factors that dominate principal
component 1.
For CV2, elevation and soil type dominated physical factors and
chloride, sodium, and potassium dominated chemical factors. The chemical
factors are the same as those dominating principal component 2. The
importance of bedrock and soil class in relation to lake chemistry is
confirmed. The importance of elevation in these analyses may be the
result of an interaction between elevation and bedrock and soil classes.
Cluster Analysis and Discriminate Functions
Application of cluster analysis and distriminate function analysis
did not produce useful results. Discriminate functions could not
classify surface waters to clusters with better than 50% accuracy. No
rational basis for formation of clusters could be elucidated, and
consequently these approaches were abandoned.
MODELS OF ACIDIFICATION
Henriksen Models
Henriksen (1979, 1980) proposed that ionic composition of surface
waters, corrected for marine aerosols, could be used to determine whether
acidification had occurred. In unacidified waters calcium and magnesium
are the major cations and bicarbonate is the major anion. The regression
of calcium plus magnesium on alkalinity, forced through the origin,
produced the relationship alkalinity = 0.91 (Ca + Mg). As the ratio of
calcium to magnesium is relatively constant over a wide range of
concentrations, calcium can be substituted for the sum of calcium and
magnesium.
Henriksen (1979, 1980) hypothesized that the acidification of lakes
was analogous to titration of a bicarbonate solution with a strong acid.
The first stage of titration he termed bicarbonate lakes, with pH above
5.5. The second stage was termed transition lakes, characterized by
depletion of bicarbonate and pH fluctuations in the range of pH 5.0-5.5.
The third stage was termed acid lakes, with no bicarbonate, excess
hydrogen ion and ionic aluminum, and pH below 5.0. Working with surface
water chemistry data sets from regions experiencing a gradient in sulfate
deposition, two empirical relationships were derived that relate lake
acidification to ionic composition. The first relates non-marine calcium
concentration to lake pH. An empirical curve was derived based on the
relationships of calcium and pH to alkalinity, separating lakes into two
83
-------
Table 24. Correlations Between Canonical Variates and Individual
Variables, Derived from the Standard Coefficients in
Table 21 and the Simple Correlation Matrix
Physical factors
Area
Hydrology
Elevation
Bedrock class
Soil class
Disturbance
% Variance explained
Chemical factors
PH
Alkalinity
Specific conductance
Aluminum
Calcium
Chloride
Color
Potassium
Magnesium
Manganese
Sodium
Sulfate
% Variance explained
Canonical variable
1 2 3
-0.02 0.13 0.20
-0.18 -0.13 -0.24
-0.19 0.90 -0.38
0.81 0.27 -0.11
0.29 0.67 0.19
0.54 -0.55 -0.55
18.3% 27.8% 9.9%
0.59 0.03 -0.23
0.74 0.08 -0.23
0.88 -0.28 -0.04
0.05 0.17 -0.40
0.84 0.16 -0.04
0.09 -0.80 0.06
0.02 -0.15 0.02
0.35 -0.54 -0.39
0.64 -0.24 -0.28
0.27 -0.04 -0.23
0.26 -0.87 -0.10
0.71 0.04 0.37
29.6% 16.0% 5.9%
84
-------
groups. The groups of lakes that plot below the empirical line have pHs
in the range expected based on the calcium concentration and associated
alkalinity. Lakes that plot above the line have pHs lower than predicted
from the calcium - alkalinity - pH relationships for undisturbed lakes.
This would result if addition of acid reduced the alkalinity and pH below
that which would normally be associated with the calcium content, and
calcium content itself was not affected.
Application of this pH-calcium nomogram to data for New England
lakes with non-marine calcium of 500 ueq 1" or less (Figure 38)
indicates that slightly more than half of the lakes (80 of 140; 57%) fall
above the empirical line and are presumably acidified to some degree.
This is similar to the percentage of lakes in the historical pH data set
that are now lower in pH (see Figure 33). In our data set, pH is
significantly related to alkalinity (Figure 39). The relationship
differs from-the theoretical relationship. At atmospheric C0? (partial
pressure 10 atm), the theoretical relation is pH = 11.3 + log
(HCO '). The difference in our data may be the result of higher
partial pressure of CO^ in these waters or of organic acids.
The second empirical relationship derived by Henriksen (1982) uses
the relationship of calcium or calcium plus magnesium concentration to
sulfate concentration to predict lake pH. Lakes are separated into three
groups. Bicarbonate lakes have low sulfate content, significant acid
neutralizing capacity, and pHs generally above 5.5. Transitional lakes
have intermediate sulfate content, have lost most acid neutralizing
capacity, and have varying pH, but generally between 5.0 and 5.5. Acid
lakes have no acid neutralizing capacity, high sulfate and ionic aluminum
content, and pHs generally below 5.0. Henriksen (1980) used empirically
derived equations equivalent to pH 4.7 and 5.3 to divide lakes into these
three groups.
Application of the calcium - sulfate relationships to data from New
England lakes with calcium plus magnesium and sulfate concentrations
below 400 ueq 1" (Figure 40) did not agree well with measured data
(Table 25). The model predicts pH to be much lower than was actually the
case. One reason for this may be high background sulfate concentrations
in New England as compared to Scandinavia. Accordingly, non-marine
sulfate was adjusted by subtracting 50 yeq 1" as the best estimate of
background sulfate (see Figure 14) to yield net sulfate. Further, the
lakes were divided into three pH categories pH >5.5, pH = 5.0-5.5, and
pH <5.0, and a linear regression was run on calcium plus magnesium
against net sulfate for lakes in the pH range 5.0-5.5. This produced the
regression: Ca + Mg » (1.18 net sulfate) -9.9. The regression 1s
significant (r = 0.67, £ = 0.0001, N = 23). This regression line was
used to divide the lakes into two groups: pH >5.5 and pH <5.0. The
transitional lakes (pH 5.0-5.5) are plotted but are not included in the
analysis. Application of this model to our data (Figure 41) produces
excellent results (Table 25). Acid and bicarbonate lakes are separated
nearly completely. The transitional lakes are highly variable In pH.
85
-------
N0N-MRRINE Cfl CUEQ/L)
Figure 38. Henriksen calcium-pH model applied to 140 New England
surface waters having calcium concentrations 500 yeq 1~ .
86
-------
LOG ALKALINITY (UEQ/L)
Figure 39. Relation of pH to log,Q alkalinity in 182 New England
lakes. The regression equation is pH = 4.75 + 0.87 log alkalinity,
£ = 0.78, £ = 0.0001, computed from the mean of two replicates
and averaging surface and deep samples where taken.
87
-------
SULFATE, UEQ/L
LEGEND: PH • • • <4,7
+ + + 4.7 TO 5.3
~~~>5.3
Figure 40. Henriksen sulfate-pH model applied to 120 New England
lakes having calcium plus magnesium concentrations 400 yea 1 .
Solid lines indicate regressions for waters of pH 4.6-4.8 (4.7)
and 5.2-5.4 (5.3), after Henriksen (1980).
88
-------
Table 25. The Prediction of PH of New England Lakes by Henriksen's
Nomograph and the Actual PH of these Lakes for Original
and Adjusted Sulfate Values
Original Nomograph
pH Range
<4.7
4.7-5.3
>5.3
Number in pH range
7
14
101
Predicted number in pH range
25
37
60
Predicted correctly
6
5
60
Percent predicted correctly
24%
14%
100%
Modified Nomograph
pH Category
TO— >5.5
Number in pH category
16
94
Predicted number in pH
16
94
category
Predicted correctly
14
92
Percent predicted correctly
88%
98%
89
-------
0 50 100 150 200
NET SOU*, UEQ/L
LEGENOs PH • • • <5.0
+ + + 5.0 TO 5.5
~ ~ d >5.5
Figure 41. Henriksen sulfate - pH model as in Figure 40, except
sulfate is corrected by subtracting 50 veq 1 background, resulting
in net non-marine sulfate. The equation of the regression line is
given in the text.
90
-------
This is reasonable considering that the lakes were sampled only once and
lakes in this category will vary greatly in pH depending on recent
climatological and hydrological events.
Henriksen (1980) further proposed that the calcium - sulfate model
could be used to predict pH of lakes if sulfate deposition were to
change. This approach is based on two assumptions: 1) lake
acidification is related to atmospheric deposition of sulfate, and 2) the
concentrations of calcium and magnesium will not change if sulfate
deposition changes. In southern Norway there is a steep gradient in
sulfate deposition rate moving from south to north, and lake
acidification is highly correlated with sulfate in precipitation (Wright
et a]_. 1977; Henriksen 1979). In New England, however, sulfate .
deposition is relatively uniform, ranging only from 40 to 60 kg ha
yr (National Atmospheric Deposition Program 1981). Attempts to
relate sulfate deposition to lake sulfate concentration or hydrogen 1on
concentration were not successful. However, Wright (R. Wright, Norwegian
Institute of Water Research, personal communication) has compared
atmospheric sulfate deposition to lake sulfate concentration for a number
of areas in North America and found a significant correlation. These
data sets covered a larger gradient in sulfate deposition than exists in
New England.
If lake calcium and magnesium content change with changes in acid
loading, estimates of preacidification alkalinity will be in error.
Given that ionic balance must be preserved, the addition of sulfuric acid
to a lake may result in a decrease in bicarbonate concentration, an
increase in cation concentration (primarily calcium and magnesium), or
both . Henriksen (1982) examined a number of data sets; some showed no
change in calcium or magnesium concentration with increased acid loading
but some showed an increase. The maximum increase in calcium plus
magnesium concentration was equivalent to 0.4 times net non-marine
sulfate concentration.
Wright (personal conminication) has extended the approach of
Henriksep to a^more generalized acidification equation:
0.91 (Ca + Mg ) - HC03 + H + A1 = net SO. where the asterisk
denotes that concentrations have been corrected for marine aerosols. The
left side of the equation represents loss of alkalinity, or
acidification, which is balanced by input of sulfate. The relation
between the various components of the loss of alkalinity term will vary.
H and A1 will only be important bejow pH 5.0, and HCO- will only be
important above this pH. Ca +*Mg may change, probably within the
range of 0 to 0.4 times net SO. , but the actual change cannot be
predicted at present.
For New England lakes with Ca + Mg and net SO. below
400 ueq 1" the average calculated loss of alkalinity is 50.3
yeq 1" , and the average calculated net SO. is 61.6 yeq 1 .
These values are not different statistically (paired to test,
91
-------
t = -1.46, £ = 0.15, N = 168), as expected. However, the regression of
loss of alkalinity on net sulfate for individual lake$ is?likewise not
significant (loss of alkalinity = 27.6 + 0.36 net SO. , r = 0.05).
This is probably the result of variability in lake cRemistry, hydrology,
climatology, etc. The data we have are not precise enough to adequately
apply the acidification equation to an individual lake. However, the
equation does appear to apply to the average condition in lakes in the
region, and may be applied to predict average changes on a regional, but
not individual lake, basis.
To apply the acidification equation as a predictor equation the
following relationships are employed £R. Wright, personal communication).
The fjfactiojj of the change in net SO. that is compensated by change
in Ca = }0g is*aiven by*the factor F, which varies between 0 and
0.4: (Ca + Mg ) = (Ca + Mg ). x F (change in net SO. ) where
the subscripts p and t denote predicted and today, respectively, Lakes
above a threshold level of (Ca + Mg ) will still have bicarbonate
and pH will be above 5.5. Lakes*below*this threshold will generally be
belgw pH 5.0. The threshold (Ca + Mg ) wijl be related to net
S04 : threshold (Ca + Mg ) = 1.1 (net SO. ) where the*
factor 1.1 is derived f^om 1/Q.91. A change in net SO. loading will
change the threshold Ca + Mg .
The predicted portion of New England lakes that wilj be acidic based
on these relationships and for various levels of net SO. are shown
in Figure 42. In Figure 42a the factor F = 0 is used and in Figure 42b
the factor F = 0.4. Sulfate = 60 represents present conditions in New
England where calculated average net SO, is 60 yeq 1" . Sulfate *
= 30 and Sulfate = 120 represent a halving and doubling of net SO. ,
respectively. For F = 0, the model predicts that 16.23S of the lakes will
be acidic (pH <5) at the present net SO* of 60 peq 1 . The
actual portion of lakes that were pH <5 was B%. Thus the model
overestimates the proportion of acidic lakes. However, for F = 0.4 the
model predicts that 7.5% of lakes would be acidic under present
conditions, a value in excellent agreement with that observed. If net
SO. is increased to an average of 120 yeq 1" , the model predicts
31.6% of New England lakes will be,acidic. Conversely, if the average
net SO. were to drop to 30 yeq 1" , the percent of lakes that are
predicted to be acidic declines to 3.6%.
Kramer Model
Kramer and Tessier (1982) proposed that all cations should be
considered in chemical acidification models, not just calcium or calcium
plus magnesium. The theoretical relation between pH and the sum of
cations can be calculated from equations representing carbonic acid
weathering reactions, holding Pco2 constant. Lakes that deviate from
the theoretical line would then represent either an input of hydrogen ion
or an additional neutralizing substance.
92
-------
SULFATE=30
SULFATE=60
SULFATE = 120
PERCENTHGE
16
12
8 -
U -
J
PEflCENTfiGE
1G
nUnnnllnfllln
1 1 1 [ 2 2 2 ? 3 3 3 3 II H I S
25702570257025702570
5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0
12
8 -
ll
nlinflnllnfin
XL
2 2 2
2 5 7
5 0 5
PERCENTBGE
16
12
8 A
nUnflnllnnrin
11 1 1 2 2 2 2 3 3 3 3 II I| U 5
25702570257025702570
50505050505050505050
SULFATE=30
SULFATE=60
SULFATE=120
co
PEflCENTfiGE
16
12
8
U -
J
nnllnnn
PERCENTAGE
16
12
a -
¥ -
M
n n
1 1 1 1 2 2 2 2 3 3 3 3 U U 5
25702570257025702570
050S05050S05050S05050
mlnllnllnllnn.
PERCENTAGE
16
12
U -
1 1 1 1 2 2 2 2 3 3 3 3 U U 5
2570257025702570257G
050505050505050505050
B
n
finnlinflnn
1 1 1 1 2 2 2 2 3 3 3 3 i| 1 il 1 5
25702570257025702570
50505050505050505050
Figure 42. Frequency distribution of Takes in intervals of
25 yeq T" calcium plus magnesium. In section A a correction factor
F = 0 is used, and in section B F = D.4 is used. The calcium plus
magnesium threshold between acid and bicarbonate lakes is indicated
by the vertical line for present net sulfate concentration
{60 ueq 1~ ) and the projected,threshold is indicated for net sulfate
concentrations half (30 peq 1" } and double (120 ueq 1 ) present
levels. Shaded bars indicate lakes that are predicted to be pH<5.
-------
The application of this model to the data for New England (Figure
43) shows that samples with high cation content and high pH plot very
close to the line. As cation concentration decreases, the points
increasingly deviate from the line. The deviation above the line
indicates an excess of hydrogen ion over that predicted from carbonic
acid weathering. Acidification was arbitrarily defined as a deviation of
more than 0.5 pH unit from the predicted pH. In our data, 48 of 191
(25%) lakes and streams were classified as acidified by this
criterion. Many of the acidic lakes fall in this category and are
presumably acidified as the result of atmospheric deposition. However,
some of the most acidic lakes in our samples do not fit this model very
well. Actual Pco2 in the sample was calculated from the equation
pPcOp = pH measured - pK + pHCO," where p signifies the negative
log and pK * measures pH was then corrected by normalizing
Pco2 to 10" *° atm., using the equation pH corrected = pH measured +
2.5 pPco2 where 2.5 is the negative log of 10" ' , and pPco2 is
the value calculated as described above. Some acidic samples had a very
low calculated Pco?, resulting in a very large pH correction.
Inasmuch as Pco2 was not measured directly, it is not known whether
this correction is realistic.
Thompson Model
Thompson (1982} has proposed a cation denudation model of
acidification, which can be expressed as a rate model or a concentration
model. The rate model depicts the export of cations from a watershed in
runoff. This model is very similar to the Henriksen model, except that
the sum of cations replaces the sum of calcium and magnesium, and pH is
predicted theoretically rather than empirically. The model allows
prediction of pH based upon the relationship between bicarbonate ion and
hydrogen ion in the carbonic acid system: pH = pK + pPco., - pHC03".
In this model, pK is assumed to be 7.75 and pPco? is set at 2.5, which
is reasonable for our samples based on the calculated values of Pco2
obtained above. The model assumes that the cations are balanced by
sulfate and bicarbonate. Sulfate is assumed to originate from
atmospheric deposition (all ions are corrected for marine aerosols), and
no correction for background sulfate is applied.
The application of this model to our data (Figure 44) gives fair
results. Lower pH lakes generally fit the model better than higher pH
lakes (Table 26). Subtraction of background sulfate does not improve the
fit of the model significantly. This model depends on the relation of
excess sulfate to acidification, as does the Henriksen model. As such,
it could be used to predict pH changes for given sulfate loads if the
relation between surface water sulfate content and atmospheric deposition
of sulfate were known. This model assumes that cations will be mobilized
by sulfate and therefore does not depend on stable cation concentrations.
The cation denudation model can also be used to predict the amount of
acidification that has already occurred- All points falling below the
5.1 line have cations in excess of bicarbonate and are presumed to be
94
-------
c
0
R
R
E
C
T
E
D
P
H
4. 5^
5.0-
5.5-
6.0-
6.5-
7.0-
7.5-
8.0-
~ •
~ D
~ •
£ Sa «~
~ D° * *
* % 6 *
skpl ^
i i \ t i i i i \ i i \ i \ i i t t
I I t I I j J l I S » 1 t 1 S j t t 1 I I I I I I | I I I I 1 I 1 t l | I I
-2.5 -3.0 -3.5 -4.0 -4.5 -5.0
LEGEND: PH
LOG SUM CATIONS
• • • <5.0 * * * >6.0
~ ~ ~ 5.0-6.0
Figure 43. Application of tfie Kramer model to 191
New England lakes and streams. The diagonal line 1s the
computed rela£1gn for carbonic acid weathering assuming
a Pcog of 10 ' atm.
95
-------
SULFATE, UEQ/L
LEGEND: PH
•
•
•
>6.8
+
+
+
4.0-5.0
o
O
O
5.1-6.2
A
A
A
6.3-6.5
~
~
~
6.6-6.8
Figure 44. Application of the Thompson model for 142 New England lakes
and streams. Samples with £ cations greater than 400 yeq 1 were
omitted. Measured mean pH is shown by the symbols.
96
-------
Table 26. The Prediction of PH in New England Lakes and
Streams 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
Measured sulfate
Actual number 16 60 27 20 19
in pH range
Number predicted 36 57 33 14 2
in pH range
Number predicted 14 34 6 2 1
correctly
Percent predicted 39% 60% 6% 14% 50%
correctly
97
-------
acidified. The deviation below the line is an indicator of the degree of
acidification. On this basis, 36 of 142 (25%) of the lakes and streams
we surveyed would be classed as acidified. This is a smaller proportion
than that predicted by the Henriksen model, but agrees well with the
Kramer model.
98
-------
CONCLUSIONS
A substantial portion of the surface waters in New England
that were not affected by direct human disturbance were found to
be very low in acid neutralizing capacity and therefore
vulnerable to acidification. Approximately half of the waters
surveyed were classed as vulnerable by several indicators,
including alkalinity, calcium, and Calcite Saturation Index. The
vulnerable waters were located in clusters. The locations of the
clusters generally coincided with areas underlain by bedrock that
was low in acid neutralizing capacity. Both comparisons with
historical water chemistry data and application of chemical
models of acidification indicated that approximately 60% of the
waters surveyed had been acidified, as indicated by a reduction
in pH, alkalinity, or both. Presently about 8% of the waters
surveyed were acidic (pH_<5.0), had elevated aluminum
concentration (>200 wg l" ), or both, conditions that may be
toxic to sensitive fish species.
99
-------
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Fleiss, J. 1981. Statistical methods for rates and proportions.
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Fritz, J., and S. Yamamura. 1955. Rapid microtitration of sulfate.
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Grahn, 0., K. Hultberg, and L. Landner. 1974. 01igotrophication a
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103
-------
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104
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105
-------
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106
-------
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107
-------
APPENDIX A
The data collected are presented in tabular form, subdivided
by state. For each state the following information is presented:
water code
name:
date:
latitude and
longitude:
elevation:
area:
type:
hydrology:
bedrock:
soil type:
disturbance:
a unique code number assigned to
each water sampled
the name of the water
date the water was sampled
the location of the water in
degrees, minutes anc± seconds of
latitude and longitude
elevation of water in meters
the area of lakes, in hectares,
or width, in meters, for streams
1 = lake, 2 = stream, 3 = impoundment
1 = first order stream, 2 = second
order stream, 3 = third order stream,
4 s fourth order stream, 5 = drainage
lake, 6 = seepage lake
1 = very low in buffering capacity
2 = moderately low, 3 = moderately high,
4 = very high
1 - SI, sensitive; 2 c 52, sensitive
50%; 3 = SSI, slightly sensitive;
4 = SS2, slightly sensitive 50%;
5 = MS, mostly non-sensitive
1 = more, 2 = slight, 3 = moderate,
4 = high
109
-------
precipitation pH:
annual
precipitation:
depth:
temperature:
pH:
fixed endpoint
alkalinity:
inflection point
alkalinity:
specific
conductance:
color:
chloride:
sulfate
aluminum:
calcium:
mean weighted annual pH of
precipitation as obtained from maps
produced by the National Atmospheric
Deposition Program
amount, in mm, for the most recent
year available as recorded at the
nearest U.S. Weather Bureau station
depth at which the sample was
collected, in meters
temperature of water at the time
of collection, in degrees celcius
pH of water sample, measured in
replicate
alkalinity of water sample determined
by titration of pH 4.5, ueq 1 ,
in replicate
alkalinity of water sample determined
by Gran titration, yeq 1" , in
replicate
specific conductance of water sample,
uSiemens cm" at 25°C, in replicate
apparent color of water sample
(unfiltered) by comparison to
platinum-cobalt standards, color
units, in replicate
chloride concentration of water
sample, mg 1" , in replicate
sulfate concentration of water
sample, mg 1" , in replicate
aluminum concentration of water
sample, yg 1" , average of five
measurements
calcium concentration of water
sample, mg 1" , average of five
measurements
110
-------
potassium:
magnesium:
manganese:
sodium:
potassium concentration of water
sample, mg 1" , average of five
measurements
magnesium concentration of water
sample, mg 1" , average of five
measurements
manganese concentration of water
sample, mg 1" , average of five
measurements
sodium concentration of water
sample, mg 1" , average of five
measurements
111
-------
USFWS AfIT) RMN SENSITIVITY TRHJECT
STATE IF CONNECTICUT
WATER
HJ4HF
DATE
L AT miof
inMR.
EL FV
ARM
TY«F
HVORO.
Rpn
cflU
nr stu.
"RFfip.
4NMIM
CODE
OAY-MON-YR
nfG-MIN-SEC
(1*1
CHS f
RCCK
rvfp
"H
PR PC ! >'
1039
CLUBHOUSE Pfl
INLEI
aioao
414159
722428
1 71
2
2
1
2
5
2
4.3
1^34
1096
BLftCKienGF R.
hohs.
81080
414536
722659
178
1
2
I
2
5
2
4.3
15*4
1098
01CK1N SON CRK
. HOWS.
81080
413916
722P58
1?3
1
2
1
2
~K
1
4.3
I 5 ? 4
1099
SALMON RIVFf
81080
411057
722A57
6
13
2
4
2
4.3
I 121
1100
S"fSTRUM RRK.
HhKS.
810BO
413221
722R32
55
T
2
1
?
3
2
'«.3
15 **4
1101
MOHAWK PO
91080
414838
73171f
360.
6
1
6
2
2
4. '
t 5 ? 4
1102
strmts pd
91080
414*20
732230
360
6
1
5
2
3
r
4.2
1574
1147
RTfiELnw PO
1112B0
415112
720M5
195
7
1
5
2
-i
\
4.3
1 574
1140
6LUE PO
<31280
413024
715 35 1
ns
2?
1
6
*
4
4
4.">
I 1 ? 1
1149
flURR Pf>
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415230
730527
301
34
1
5
2
4
?
4 .3
1574
1150
DARLING PO
111280
414118
720735
1 10
1
1
6
2
5
2
4.^
15>4
1151
€"MONS PO
1<31280
420055
725411
3 36
3
1
5
2
->
4.1
! 5 2 4
1152
GRIGGS PO
1112B0
4 2000 2
720502
222
16
1
5
2
¦K
->
4.3
15 2 4
1151
HfHlFLLS PO
101/W
4?0 J ??
725934
341
7
1
5
2
¦}
2
4.3
1 S? 4
1154
HOLBROOK PD
111280
414033
722230
1 5")
29
1
5
2
5
2
4.3
1524
1155
millers PO
101280
412B57
713602
171
13
1
5
?
5
2
4.3
13U
1156
NORWICH PO
91280
412311
721813
27
11
I
6
2
3
3
4.3
1321
1157
POTTERS PO
111280
420000
720304
219
7
I
6
2
5
?
4. 3
1 524
1158
SPRING PO
101280
415529
724851
61
3
1
6
2
4
?
4.3
1524
1159
(INCAS po
91280
412226
721852
25
28
1
5
1
4
3
4."*
1321
lt60
WHITMAN'S PO
1212 BO
415B49
714811
159
10
1
6
2
3
?
4. 3
1524
1189
BLACKLEOGE R.
MCBITH
81080
413610
722516
134
3
2
4
2
1
>
4.3
I 524
1190
DICKINSON CRK
. molth
81080
413337
722652
55
5
2
4
2
3
?
4. 3
I5'4
-------
MAT ER
DEPTH
TfMP
PH
PH
AlK
-------
HATER
CHLORIDE
CHLORIDE
¦SULFATE
SULFATE
AU^IMJN
CIOF
IPP»I
lePH!
»PP«I
1059
*.0
4.5
9.6
10.8
14
1096
7.9
1.9
7.4
1.2
15
1098
2.4
2.6
6.4
6.1
67
1099
14. T
14.9
13.4
13.2
19
1100
9.0
9.0
6.4
5.9
14
1101
0.9
1.2
7.4
7.2
10
1101
1.5
1.6
7.2
6.8
12
1102
1.5
3.6
6.7
6.2
146
110?
4. 1
3.6
6.2
5.8
131
1147
7.1
7.1
4.8
4.8
59
1148
7.4
7.4
8.5
8.4
70
1148
5.2
7.7
6.5
6.7
81
1149
IB. 8
18.8
6.7
6.7
47
11*9
18.9
18.4
6.9
7.0
33
1150
3.6
3.7
7.0
6.7
79
UM
8.1
8.6
7.0
7.2
334
lis?
1.7
3.7
6.1
5.7
74
11ST
1.1
1.5
11.0
12.0
109
115*
2.3
2.2
10.9
10.2
131
115*
7.5
7.7
15.1
15.5
21
115*
7.9
8.2
12.9
12.6
161
1155
7.6
2.5
8.4
8. 3
56
1155
3.2
1.4
6.9
7.9
79
1156
4.3
4.2
A. 8
8.4
62
1156
4.2
4.0
8.6
8.6
93
1157
3.1
3.0
10.5
10.3
55
1158
7.8
7.9
11.2
10.7
12
1159
3.6
3.6
8.6
8.7
0
1159
3.8
4.0
8.8
8.5
7
1160
5.8
5.8
8. 2
7.9
367
1189
14.4
14.6
11.8
11.8
38
1190
15.2
15.2
10.9
10.6
33
CALCIUM
PHTASSTUM
HARMFStUM
M/H'r, A\'r
smi iim
Trr.
( PPM)
(PPH)
fpov |
f P|*«l
( OPM I
5.99
1.56
1.24
0.071
1.74
2.5
3.16
1. 34
1. 96
0. 015
2.95
1.05
0. 55
0.083
?.10
1.«
9.44
0.66
1.40
0.01 T
3. 71
1.9
2.94
0.85
1. 10
O.017
'.98
1.9
2.91
0.85
1.08
O.OH
2.9?
1.6
1 .70
0.43
0. 49
0. 08 1
1. 84
l.P
6.37
2.33
1.71
0.014
8.16
1.9
4. 85
1.9?
0.12
0.03?
8.F17
1.9
-------
USFHS ACID RAIN SENSITIVITY PROJECT
STATE OF MAINE
MATER
MAKE
DATE
LATITUDE
LONG.
ELEV
AREA TYPE
HYDRO.
BED
SOIL
0!STB.
PRFCIP.
ANNUAL
CODE
as.r-*cN~rR
f*f
tHAt
ROC*
rrpe
"H
PKECtP
1012
LITTLE SHIFT STVEfl P
3O07SB
4A5152
1035*2
735
6 1
6
7
4
1
4.1
ilia
1013
50U1H OB
3 107 BO
4+5252
103251
662
+ 1
6
2
4
I
4.3
1119
1025
LITTLE LOW PD
1208 BO
443806
63
22 1
5
1
4
I
4.4
1?2T
1026
TILOE*
120-0 BO
443150
eB0430
63
15 1
b
1
4
1
4.4
1727
J.027
HUD PO
14 98 BO
443750
6B0538
110
I 1
6
I
4
1
4.4
1727
10 2B
SILX1N PO
1409 BO
443150
680441
94
4 1
6
1
4
1
4.4
1727
L021
CM "CONTAIN ®D
19D0BO
444633
701500
4flT
5 1
6
2
4
2
4.3
IUB
1030
SPENCER 00
lacflBo
444911
704047
663
6 1
6
4
2
4.3
1118
1067
ANDERSON PO
I709R0
443842
680351
66
5 1
6
1
3
1
4.4
1524
1068
UNNAHEO PD
1709B0
443842
680621
76
7 1
5
1
4
3
4.4
1524
1108
PFFP PD
211080
445436
675339
78
13 I
6
1
4
2
4.4
1524
1109
PRETTY PO
2 HOBO
444941
675425
82
11 1
6
3
3
2
4.4
1524
1110
SAL "ON Pt>
211080
445502
675144
16
4 1
6
1
3
2
4.4
1524
1162
IRONBOUND PO
40381
454604
700523
413
16 1
6
5
1
4.3
914
1163
FOLEY PO
40381
455605
70Q316
450
50 I
5
2
5
2
4.3
914
1164
CRANBERRY PD
40381
455133
69*808
323
18 1
6
3
5
2
4.2
914
1165
UPPER FIRST ST. JOHN
40381
460208
695907
566
12 1
6
2
5
1
4.2
914
1166
ALLAGASH po
40381
462527
694011
409
42 1
6
2
5
2
4.2
914
1167
MOUNTAIN PO
403B1
461845
695609
346
8 1
6
2
5
1
4.2
914
1168
JONES PO
40381
454837
701241
412
53 1
5
2
5
2
4.2
914
1169
CORNER PO
40381
462350
694350
487
40 1
6
2
5
2
4.2
914
1170
TURNER PO
40381
461636
694730
409
42 1
5
2
5
2
4.2
914
1171
FERGUSON PD
403B1
465158
684155
252
21 1
5
3
5
2
4.3
914
1172
AUSTIN Pt>
40381
460313
695655
366
17 1
6
2
5
1
4.2
914
1173
UGH Pt>
40391
4#«4il
A9371+
JJ6
« 1
A
2
5
I
4.2
914
1174
4TH P ELLETIER BRK. L
40381
4 70144
685328
398
20 1
5
2
5
2
4.2
914
1175
fl LAKE
40381
461203
68ono
220
27 1
6
3
4
2
4.3
lllfl
1176
•KPHERsnm pd
40381
463302
685907
252
31 I
5
2
5
2
4.2
914
1177
BLUFFER PD
40381
462230
690558
347
16 1
5
3
5
1
4.2
1118
1178
2ND CURRIER PO
40381
462502
690532
386
11 I
6
3
5
t
4.2
914
1179
UPPER HUDSON PD
40381
463433
690144
427
13 1
6
2
5
1
4.2
914
1180
CUNLIFFE PO
40381
464644
69*804
346
25 1
6
2
5
2
4.2
914
1181
WEST LAKE
40381
462414
680718
2 83
9 1
6
2
4
1
4.3
1118
IttT
CAf# PO
19681
4^930
61
tJ I
6
2
4
2
4.4
1524
1198
COLBY PD
10681
442128
69*254
129
11 1
6
2
4
2
4.4
1321
1199 •
LITTLE PO
10681
443425
681019
74
12 1
6
1
4
2
4.4
IT27
1200
NEM8ERT Of)
10681
442005
691630
86
2a i
6
2
4
2
4.4
1524
1201
HALFmLE TO
170581
445350
681500
176
4* 1
6
2
4
2
4.4
1524
1202
ROUND PD
2B04B1
433056
703526
70
2 1
6
1
3
4
4.3
1321
1203
SUNSET PO
40581
452254
69?508
406,
3 1
6
2
4
2
4.3
1321
1204
NT (MAY PO
40581
45Z253
69?507
401
5 1
5
2
4
2
4.3
1321
1212
ANDERSON PD
240779
442116
693837
76
4 1
6
I
5
1
4.3
1321
1213
BEAVER PO
2 309 78
444828
704115
602
8 1
5
2
4
4
4.3
1118
1214
BUeBLF PO
100978
441957
681415
137
13 1
6
1
3
1
4.5
1524
1215
OFBEC PO
120978
444739
68? 719
125
13 1
5
1
4
3
4.4
1524
1216
DENNY PO
210879
465623
6852m
366
10 1
6
2
5
1
4.3
1016
1217
F11TS PO
190978
444547
683326
98
43 1
5
1
4
1
4.3
1321
1218
HEAL!) PD
2 2097 8
454332
701425
457
75 I
5
2
5
1
4.2
111B
1219
HORSESHOE PO
220978
453022
69?446
448
65 1
5
2
4
1
4.3
1118
1220
LINCOLN PO
230978
450419
705655
518
138 1
5
1
4
I
4.3
1016
1221
IITTLF TUN* PO
260779
443508
680707
71
57 1
5
1
4
1
4.5
1524
1222
SANDY PO
220978
445419
695523
127
43 1
5
2
3
1
4.3
1321
1223
SIDE PISTOL LAKE
30779
451111
681009
130
60 1
5
1
4
1
4.4
1524
1224
TRAFTON PD
80879
435049
70513 7
152
23 1
6
2
3
3
4.3
1219
-------
1225
tbcut po
60879
4*1*19
1226
IPDOE PO
200779
4*5450
1227
fAST CHAIRSACK PD
1607B0
452747
1220
MOUNTAIN PO-COBUR*
290580
452840
1229
SPECK Pn
609 79
443346
1230
TUMBLEDOWN PO
270778
444458
1231
KLONDIKE PO
141078
455539
1232
The hohns po
120779
450840
1233
MOUNTAIN PO-RANGEtEr
160879
445340
70*908
242
26 1
5
I
4
4.3
1118
703200
911
3 1
6
1
4
4-3
1118
691643
466
18 I
6
2
4
4.3
1321
700651
8 71
2 I
6
2
4
4.2
HIS
705821
999
4 1
6
2
4
4.3
1016
701240
613
4 1
5
2
4
4.3
1118
605710
999
5 I
5
1
5
4.3
1321
702014
948
5 I
6
2
4
4.3
1118
70^6*9
733
17 I
5
2
4
4.3
1118
-------
WATER
OEPTH
TENP
PH
PM
ALK IF.E.P1
AlK (F.E.
r.OOE
Ml
t CI
1 UEO/LI
(UEQ/LI
1012
0
21
5.55
•
38
•
101?
4
18
5.55
•
40
•
1013
0
20
5.60
•
36
1013
2
19
5.60
»
38
1025
0
•
6.05
5.95
43
47
1025
15
•
5.70
5.75
46
43
1026
0
23
6.55
6.60
86
72
1026
9
15
5.T5
5.80
91
86
1027
0
23
4. 10
4.65
16
14
1027
11
13
4.10
4.05
0
0
1028
0
6.T5
6,75
91
91
1028
8
15
5.85
5.80
111
111
1029
0
«
7.20
7.20
383
366
1029
6
•
6.40
6.45
409
398
1030
0
21
6.C5
6.05
50
54
1030
7
21
6.10
6.10
54
51
1067
0
•
6.15
6.13
46
53
1067
5
•
6.C8
6.10
50
43
1068
0
•
5. e9
5.80
51
40
1068
3
*
5.80
5.75
46
52
1108
0
9
5.15
5.73
34
41
1108
5
9
5.S0
5.70
37
37
1109
0
9
4.80
4.92
12
29
1109
3
9
4.60
4.92
17
28
1110
0
10
4.18
4.70
22
13
1110
4
9
4.75
4.75
14
14
1162
0
6.65
6.62
460
487
116?
2
4
6.55
6.50
516
547
1163
0
I
6.55
6.50
186
199
1163
10
4
6.70
6.75
505
513
1164
0
1
6.C5
6.62
360
360
1164
1
3
6.SO
6.96
1160
1163
1165
0
1
6.C5
6.33
113
126
1165
?
2
6.15
6.16
132
131
1166
0
5.65
5.78
145
69
1166
2
1
5.95
6.22
157
151
1167
0
6.60
5.95
221
196
1168
0
1
6.55
6.42
226
225
1168
4
4
6.65
6.6 3
441
452
1169
0
6.45
6.42
276
264
1169
2
1
6.45
6.48
519
519
1170
0
1
6.15
6.37
125
137
1170
9
3
6.30
6.46
186
212
1171
0
1
7.26
7.10
977
1007
1171
2
3
7.45
7.15
1601
1537
1172
0
1
6.45
6.40
307
278
1172
2
4
6.88
6.70
1019
10T5
1173
0
1
6.25
6.43
267
254
1173
1
1
6.45
6.50
418
394
1174
0
6.60
7.22
TT7
T66
1174
1
7.05
7.20
146T
1461
1175
0
1
6.55
6.35
215
201
1175
2
3
6.59
6.55
476
470
1176
0
1
6.30
6.52
170
162
ALK ( I.PI ALK (I.PI COND
(UEO/LI (UEQSLI IUS/CMI
6 . IS
10 . 15
4.17
* . 15
7 1T 23
15 10 24
44 3T 24
51 48 26
-22 -22 31
-SB -66 41
47 45 21
80 74 20
342 391 44
3T4 36T 48
13 15 16
16 19 16
19 20 18
22 18 16
20 15 21
18 IT 20
11 12 16
12 12 13
-16 -15 23
-13 -14 18
-18 -15 14
-13 -16 16
433 452 5T
489 519 61
162 16T 3T
474 478 62
329 337 47
1133 1130 120
84 91 33
108 105 33
115 34 22
118 181 34
154 168 34
195 202 35
425 430 52
234 230 40
475 4T7 66
9T 97 33
160 168 36
992 944 110
1563 1524 165
271 320 43
987 1036 100
224 230 38
393 376 55
740 741 85
1441 1430 140
176 172 32
454 428 52
133 128 36
CCNO COLOR COLOR
tUS/CNIIUNIT SHUNIT5I
.
20
.
.
30
.
.
10
.
.
10
.
22
0
0
23
0
0
22
10
10
26
30
30
33
0
0
46
20
20
23
20
20
21
10
10
43
40
40
48
40
40
16
0
0
L6
0
0
18
10
10
16
10
10
21
120
120
20
110
110
16
10
10
13
0
0
15
0
10
16
0
0
14
20
20
16
20
20
62
10
10
70
10
10
35
30
20
60
10
10
47
40
30
120
30
30
36
20
30
36
20
30
23
40
40
35
40
40
33
20
20
35
20
20
51
20
20
45
30
40
T4
50
70
36
40
60
41
50
70
125
20
30
180
20
30
42
30
20
100
20
20
35
40
30
51
30
30
84
20
20
140
20
20
33
20
20
52
20
20
36
20
20
-------
1176
5
1
6.35
6
1177
0
0
6.55
6
1177
1
I
6.(0
6
1178
0
I
6.15
6
1178
2
1
6.59
6
1179
0
0
6.45
6
1179
8
1
6.80
6
1180
0
0
6.75
6
1180
3
1
6.75
6
1181
0
1
6.35
6
1181
1
1
7.12
7
1197
3
8
6.65
6
1198
2
9
5.40
5
1199
0
18
5.75
5
1200
2
7
4.55
4
1201
0
11
6.50
6
1201
17
10
•
1202
0
12
6.25
1202
4
11
5. 55
1201
0
8
5.15
1204
0
5.85
1212
1
6.62
1213
1
6.30
1214
1
6.40
1215
1
6. e5
1216
1
6.93
1217
1
5.97
1218
1
6.65
1219
1
6. 70
1220
1
6.55
1221
1
6.13
1222
1
6.20
1223
1
6.41
1224
1
•
6.88
1225
1
•
6.40
1226
0
21
4.50
1226
6
6
4.50
1227
1
22
5.15
1227
9
20
5.20
1228
0
11
6.60
1228
3
11
6.65
1229
0
18
5.10
1229
8
13
5.C5
1230
0
•
4.80
1231
1
•
5. SO
1231
8
•
5.eo
1232
6
•
5.60
1233
0
14
5.75
174
173
197
208
263
242
209
197
615
580
232
220
267
255
267
267
395
406
197
197
777
777
188
45
50
47
5
•
111
•
•
55
42
32
43
84
56
66
316
360
54
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116
•
120
•
50
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•
192
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27
29
75
75
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55
73
75
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65
9?
91
70
80
10
15
60
5*
00
55
55
141
138
37
36
20
20
170
173
46
52
20
30
235
207
50
54
20
30
181
172
34
40
30
40
587
560
58
68
70
100
192
190
38
46
10
20
241
221
39
46
10
20
244
246
41
50
40
40
375
377
56
62
30
40
179
172
28
32
30
50
748
742
85
97
20
30
154
38
40
10
10
17
26
28
60
60
24
24
29
27
10
10
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32
34
100
100
82
24
24
0
0
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•
.
33
18
10
20
17
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0
24
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22
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.
15
13
.
.
-------
WATER
CHLORIDE
CHLftPIOE
SULFATE
SULFATE
ALUMINUM
CHOP
(PPMI
(PPMt
(PPMI
IPPMI
(PPftl
1012
1.1
1.1
5.3
4.8
117
1012
0.6
0.8
4.6
4.5
194
101)
0.1
0.0
4.0
4.1
12
1013
0.2
0.2
4.1
4.1
136
1025
2.7
2.8
3.0
3.2
66
1025
3.4
3.3
2.8
2.6
12
1026
2.1
2.2
2.8
2.5
0
1026
3.3
3.2
2.4
2.2
0
102T
3.7
3.8
3.a
3.6
240
1921
4.2
4.4
3.5
3.8
406
102S
3.?
2.9
2.0
2.3
2
1028
2.8
2.6
l.a
2.0
51
1029
1.1
1.4
3.0
2.9
43
1029
0.5
0.4
3.4
3.2
55
103O
0.7
0.7
3.5
3.6
54
1030
0.0
0.1
3.7
3.5
48
1067
2.1
1.9
4.5
4.1
0
106?
2.2
2.0
4.1
3.8
13
1068
3.1
3.0
3.B
3.4
89
1068
2.4
2.2
3.3
3.7
137
1108
2.1
1.7
3.4
3.5
23
HOB
1.6
1.7
3.1
2.6
21
1109
1.2
1.1
3.5
3.7
108
1109
1.2
1.2
4.1
3.5
127
1110
3.7
3.6
3.5
3.6
40
1110
1.2
1.1
3.4
3.4
76
1162
0.5
0.7
8.6
7.2
28
1162
0.6
0.6
6.4
7.0
12
1161
0.1
0.2
6.9
6.5
71
1163
0.0
0.0
6.7
6.7
39
1164
1.0
1.3
6.5
6.2
122
1164
0.6
0.6
8.1
7.8
50
1165
1.3
1.3
8. 1
7.7
65
1165
1.2
1.3
6.0
6.2
58
1166
0.4
0.7
6.7
6.7
176
1166
0.3
0.4
6. 7
7.2
121
1167
0.0
0.0
7.7
7.2
174
1168
0.0
o.t
6.6
6.?
25
1168
0.1
0.2
6.1
5.9
16
1169
0.3
1.4
7.3
7.3
43
1169
0.0
0.0
1.2
6.7
15
1170
0.2
0.3
a. 3
8.6
110
1170
0.7
0.6
7.4
7.2
105
1171
0.7
0.8
11.4
11.0
24
1171
1.3
1.2
16. 2
16.0
7
1172
0.9
1.0
6.4
6.5
87
1172
0.9
0.8
5.9
5.9
20
1173
0.8
1.0
6.2
6.0
5
1173
0.9
0.9
6.3
7.0
17
1174
1.0
1.1
6.1
5.9
20
1174
0.6
1.1
5.9
6.7
25
1175
1.3
1.4
5.7
5.3
53
1175
0.7
0.6
4.8
4.8
3
1176
1.0
1.1
9.0
9.2
60
CALCIUM
POTASS SUM
MAGNESIUM
MANGANESE
SODIUM
TOC
IPPMI
( PPM)
IPPM1
IPPMI
IPPMI
IPPMI
1.40
0.35
0.28
0. 025
0.74
2.5
1.43
0.30
0.27
0. 029
0.61
3.2
1.20
0.15
0.26
0.031
0.55
1.9
1.23
0.14
0. 26
0.033
0.52
1.9
0.81
0.38
0.33
0.076
2.17
1.3
0. 86
0.35
0.34
0.056
2.09
1.3
1.03
0.33
0. 34
0.013
2.17
1.9
1.3B
0.34
0.40
0.124
2. 14
3.2
0.75
0.34
0.33
0.026
2.38
1.3
O. 66
0.40
0.32
0.029
2.19
2.5
1.23
0.61
0.38
0.110
2.23
2.5
1.54
0.52
0.43
0.071
1.96
1 .9
6. 74
0.44
0.46
0.023
1.07
3.8
7.00
0.71
0.41
0. 310
0.93
3.8
1.15
0.46
0.42
0.053
0.67
1.3
1.13
0.45
0.43
0.054
0.70
1.3
0.70
0.36
0. 30
0.020
1.83
1.9
0.77
0.42
0.29
0.024
1.91
1.9
0.88
0.26
0.41
0.039
2.22
8.7
0.92
0.22
0.41
0. 016
2.24
8.1
0.50
0.39
0.20
0.012
1.47
1.9
0.49
0.36
0.20
0.012
1.44
1.3
0.33
0.20
0.18
0.020
1.08
1.6
0.32
0.20
0.18
0.013
1.07
1.3
0.36
0.15
0.16
0.017
1.02
2.5
0.38
0.14
0. 16
0.041
1.02
2.5
8.23
0.31
1.04
0.032
1.04
1.9
8.65
0.31
1.04
0.039
1.15
1.9
4.58
0.21
1.15
0.010
0.63
2.9
8.54
0.21
1.88
0.010
1.04
1.9
6.56
0.52
1.04
0.006
0.63
3.5
26.15
0.31
1.35
0.005
0.83
3.2
3.54
0.31
1.04
0.027
0.63
2.9
6.46
0.31
1.77
0.100
0.83
2.9
2.08
0.42
0.63
0.018
0.63
3.8
3.65
0.42
0.83
0.015
0.73
3.8
4.48
0.21
0.83
0.007
0.73
2.0
4.58
0.31
0.94
0.018
0.73
2.5
7.81
0.31
1.46
0.19B
1.77
2.5
5.63
0.31
1.04
0.034
0.73
3.5
6.04
0.31
1.04
a.044
0.73
5.0
3.65
0.31
1.04
0.020
0.83
4.4
4.58
0.31
1.25
0.029
0.94
5.0
21.47
0.21
1.25
0.010
1.25
2.9
31.26
0.21
1.46
0.022
1.46
2.9
5.31
0.31
1.56
0.021
0.73
2.9
13.13
0.31
3.33
0.089
1.15
2.5
6.9S
0.42
0.63
0.042
0.73
3.5
5.94
0.42
0.73
0.050
D.B3
3.2
15.21
0.21
1.25
0.010
9.83
2.5
25.84
0.21
2.08
0.057
1.15
2.5
3.44
0.42
1.04
0.012
0.94
2.5
7,19
0.42
1.56
0.083
0.94
2.5
4.69
0.31
0.73
0.006
0.94
2.5
-------
1176
0
9
1177
0
6
1177
1
0
1178
0
7
1179
0
9
1179
0
6
1179
0
0
1180
1
0
11 BO
0
7
1181
0
4
net
0
5
1197
3
4
1198
1
3
llH
1200
3
*
1201
1201
1202
1
<3
1202
1
a
1201
1204
1212
1213
121*
1215
1216
121T
1218
1219
1270
1221
1222
1223
122*
1225
1226
1226
1227
1727
1228
1220
1229
1229
1230
1231
1231
1232
1233
l
9
6
9
6
8
11
5
11
6
8
12
0
12
5
8
8
3
7
7
0
4
8
5
3
6
7
2
7
2
0
6
0
7
0
B
7
7
7
9
5
8
5
8
5
7
7
2
7
2
6
7
4
8
*
8
o
9
7
1
0
0
0
1
0
0
0
0
0
0
3
3
1
I
•55
5.21
0.31
0.73
0.006
0.9*
2.5
85
7.09
0.21
0.83
0.001
0.73
2.9
85
9.79
0.21
0.83
0.007
0.83
2.9
76
4.90
0.31
0. 83
0. 02*
0.63
1.5
107
9.90
0.*2
1.0*
0.138
0.73
6.5
0
5. 31
0.31
0.73
0.002
0.73
2.2
18
7.92
0.21
1.0*
0.004
0.73
2.2
58
6.25
0.42
0.9*
0.005
0.94
3.8
32
8. S6
0.31
1.25
0.008
1.25
3.5
38
*.69
0.21
0. *2
0.008
0.73
3.8
29
16.15
0.31
0.83
0.003
1.04
2.9
23
3.**
0**2
0.73
0.005
2.29
1.9
124
1.25
0.21
0. 42
0.018
1.67
5.0
5*
2.60
0.31
0.42
0.004
1.35
1.9
98
I. *6
0.31
0.52
0.016
1.35
7.4
10
1.98
0. *2
0.42
0.005
1.56
1.3
28
1.98
0.4?
0.42
0.006
1.56
.
3
0.9*
0.42
0.31
0.033
0.83
1 .9
9
0.9*
0. 31
0.31
0.033
0.73
t .9
1*8
1.25
0.63
0.52
0.072
0.73
1.3
61
1.0*
0.21
0.52
0.029
0.42
1.3
1.83
0.52
0.52
0.006
1.24
4.1
•
1.58
0.2*
0.39
0.037
0.89
1-3
•
2.20
0.23
0.63
0.016
3.68
1.3
•
2.95
0.41
0. 73
0.03T
5.60
2.2
•
7.*3
0.52
0. 75
0.008
1.00
1.9
•
1.62
0.33
0.40
0.006
1.4*
1.3
•
3. 31
0. 23
0.71
0.011
0.57
1.8
•
2.60
0.54
0.71
0.009
0.73
1.3
•
2.28
0.29
0.64
0.004
0.73
1.3
•
1.3*
0. 36
0.41
0.025
1.91
1.7
•
2.65
0.46
0.59
0.006
4.44
1.7
•
1.63
0.56
0.35
0.006
l.*2
1.6
•
3.13
0.42
0.52
0.013
1.82
1.9
1.83
0.26
0.30
0.012
0.97
286
1.2*
0.14
0.21
0.030
0.38
,
286
1.25
0. 19
0.21
0.031
0.35
•
103
0.59
0.09
0.35
0. C61
0.48
1.3
78
0.61
0.08
0.33
0.070
0.48
1.3
128
1.77
0.22
0.38
0.020
0.48
2.5
362
1.6?
0.23
0. 39
0. 020
0.49
2.5
»
0.99
0.18
0. 21
0.070
0.40
.
1.01
0.21
0.22
0.070
0.41
.
68
0.88
0.10
0.25
0.033
0.39
1.3
61
1.08
0.21
0.07
0.008
0.76
1.8
66
1.07
0.21
0.06
0.007
0.75
1.7
m
1 .60
0.27
0.34
0.022
0.53
2.2
81
1.09
0.17
0.35
0.030
0.57
.
-------
USFWS ACID RAIN SENSITIVITY PROJECT
STATE OF MASSACHUSETTS
WATER
NAME
OAT E
LATITUDE
LONG.
ELEV
AREA
TYPE HYDRO.
BED
SOIL 01 STB# PRFCIP.
ANNUAL
CODE
OAY-MCN-YR
OEG-HIN-SEC
(Ml
(HA I
ROCK
TYPE PH
PREC IP
1050
ADAMS i»n
1109 SO
422113
720444
290
9
1 5
2
4 3 4.3
1524
1051
BOG PD
90980
423826
730228
566
16
1 5
2
5 1 4.3
1321
1052
CHARNCCK ROAD PD
110980
422342
715758
257
4
1 6
4 3 4.3
1524
1051
CROW HILL PO
110980
423136
715118
251
4
1 6
1
4 3 4.3
1524
1054
FOLEY PO
110980
421925
720239
222
2
1 6
2
4 3 4.3
1524
1055
GOOnNOU ROAD PO
90980
421513
724500
283
4
1 6
4
5 2 4.1
»32l
1056
HALLS PI
1C0980
42061T
725917
385
4
1 6
1
4 2 4.3
1524
105T
HnhE PO
110980
421251
720000
203
4
1 6
3
4 3 4.3
1524
1058
NORTH POND
90980
423906
730317
586
7
1 6
1
5 2 4.3
1321
1059
SHEOMET LAKE
90980
424043
721806
210
13
1 5
4 3 4.1
1321
1060
SOUTH PO
90980
423842
730306
585
10
1 6
1
5 2 4.1
1321
1061
STRICTER PD
110980
422008
715406
317
8
3 5
1
4 2 4.3
1524
1062
UPPER SPECTACLE PD
100960
421041
730719
437
29
1 5
1
5 2 4.3
1321
1061
HICKETT PD
90980
423305
722938
325
13
1 6
1
4 2 4.3
1321
1064
WOLF SROOK PO
120980
424155
714037
98
2
1 6
1
4 3 4.3
1524
1065
LONER MR I GMT PO
120980
424016
714906
299
9
1 6
1
4 2 4.3
1524
1066
YORK PD
100980
420629
731104
471
15
1 6
1
5 2 4.3
1321
1127
SAflOACOOK PO
llllSQ
423706
713149
71
31
I 6
3
3 3 4.3
1574
ll?<
BALOPATE PO
101180
424147
710000
28
22
1 6
3
5 3 4.3
1321
1129
flIC REAR PO
121180
415627
705938
17
15
1 5
2
3 2 4.3
1524
1130
ECHO LAKE
121180
420949
711720
82
1
1 6
1
3 2 4.3
1321
1131
HOLTS P»
121180
420946
711901
52
I
I 5
4
5 2 4.3
1321
1132
JOHN" S PO
131180
41550/
705035
34
8
1 6
1
4 3 4.3
1524
1133
LEACH PO
121180
420148
710905
63
41
1 6
1
5 2 4.3
1524
1134
little cliff PO
131180
414625
700000
16
12
1 6
3 3 4.3
1118
1135
LITTLE COLLEGE PO
131180
415418
704105
30
1
I 6
1
4 1 4.3
1524
1136
LITTLE FA** PO
111180
421500
712241
41
9
I 6
1
4 3 4.3
1321
113T
HILLHAN STREET PO
111180
42191T
713613
84
3
1 6
4 3 4.3
1321
1138
NEW LONG PO
131180
415256
704149
30
9
1 6
1
4 2 4.3
1524
1139
ROUND PD
141180
423553
704901
14
15
1 5
I
4 2 4.3
1321
1140
RUTH PO
111180
41453T
700211
16
3
1 6
1 2 4.3
1118
1141
STEARNS PD
101180
423T05
710404
34
17
1 6
1
4 2 4.3
1321
1142
MALHEN PO
111180
422615
712018
48
24
1 6
3 2 4.3
1321
1143
WHEELER PD
111180
422508
713117
79
8
1 5
3
3 2 4.3
1321
-------
water
DEPTH
TE*P
PH
PH
ALU IF.E
cnoE
«"l
( CI
IUEO/1
1050
I
21
5.69
5.60
57
1050
0
21
5.BO
5.TO
55
1051
0
20
5. 05
5.85
67
1051
3
20
5.95
5. 85
63
1052
0
20
6.85
6.96
229
1052
3
19
6.85
6.98
220
1053
0
21
7.12
6.*5
72
1053
3
21
6.«4
6.3 5
72
105*
0
22
6.25
6.3*
70
1055
0
22
7. *0
7.59
*16
1055
1
21
7.35
7.36
398
1056
0
21
6.*8
6.*5
111
1056
*
18
6.C?
6.10
327
1057
0
19
6.50
6.59
168
1057
2
19
6.50
6.65
168
1058
0
22
6.65
6.56
93
1058
8
17
6.10
6.29
208
1059
0
20
6.70
6.82
1*8
1059
3
20
6.65
6.68
1*8
1060
0
21
5.33
*.95
*3
1060
3
21
5.23
*.95
39
1061
0
20
6.58
6.*5
11*
1061
2
20
6.58
6.45
111
1062
0
21
6. 72
6.65
196
1062
3
21
6.75
6.70
193
1063
0
21
*.00
*.70
22
1063
1
21
*.75
*.70
20
106*
0
19
6.55
7.06
125
106*
2
19
6.15
6.30
168
1065
0
21
5.50
5.35
*5
1065
5
20
5. *3
5.25
5*
1066
0
21
7.*5
7.87
3*8
1066
3
22
7. *5
7.78
355
1127
0
5
6.S5
7.06
3*9
1127
7
5
6.95
7.02
351
1128
0
9
7. 15
7.06
*02
1128
*
8
7. 15
7.10
391
1129
0
7
7.25
7.30
288
1129
1
7
7.25
7.30
286
1130
0
*
6.50
6.50
103
1130
1
*
6.50
6.*5
102
1131
0
5
5.3*
5.30
*5
1131
1
*
5.35
5.30
*2
1132
0
6
5.15
*.95
28
1132
*
7
5.C6
*.95
25
1133
0
5
6.10
6.07
50
1133
1
5
6.10
6.03
50
113*
0
7
7.00
6.82
133
113*
*
7
6.95
6.B3
125
1135
0
6
*.30
*.*6
0
1135
I
6
*.30
*.*7
0
1134
a
6
7. *6
7.25
505
1136
3
5
7.3*
7.25
500
1137
0
5
7.C5
7.06
309
ALK IF.E.
(IIEO/LI
*3
40
51
*6
52
51
85
427
*21
99
296
196
19*
110
22*
16*
167
13
16
9*
97
183
186
11
11
1*2
19*
30
39
357
365
362
377
*18
*25
302
300
99
97
35
35
I*
1*
57
56
128
131
0
0
*91
*91
323
ALK II.PI
ALK U.PI
conn
IUE0/L1
(UFO/XI
(US/CHI
1*
12
29
16
12
30
30
29
18
25
22
ia
200
203
50
191
205
*9
32
32
36
30
26
35
*2
*3
28
389
386
65
368
376
56
75
69
26
285
268
*2
1**
151
*7
1*2
152
*9
70
69
25
186
185
30
123
12B
32
117
127
32
-2
-8
*0
-2
-7
20
73
6*
50
76
66
*8
158
150
50
152
155
52
-17
-23
26
-18
-18
23
96
102
80
13*
1*9
93
6
*
32
15
11
31
31*
321
52
323
326
51
319
328
8*
319
323
84
37*
382
190
35*
392
200
262
261
UO
258
261
110
66
66
38
67
66
37
6
3
160
2
1
150
-5
-6
38
-5
-6
38
22
21
*2
18
20
*2
110
102
88
137
102
86
-38
-3*
52
-35
-35
52
*7*
462
It 0
*6*
*62
105
275
29^
135
CONR COLOR COLOR
(US/CHMUNITSI (UNITSI
29
70
70
29
60
70
17
30
30
17
30
30
50
20
20
49
20
20
35
10
20
35
10
20
28
20
30
59
40
*0
53
40
100
25
40
*0
*2
160
160
48
40
*0
47
40
*0
22
10
10
24
30
30
31
30
20
30
30
30
20
0
0
18
10
10
49
20
20
49
10
20
50
50
50
52
50
50
26
10
10
24
20
10
90
80
80
93
90
90
31
20
20
32
40
*0
51
20
20
51
20
20
86
50
50
83
50
50
190
20
20
190
20
20
115
30
20
115
20
20
38
40
*0
37
40
*0
150
40
*0
150
40
*0
38
0
0
38
0
0
4*
20
10
42
20
20
82
0
0
82
0
0
52
20
20
52
20
20
105
20
20
105
20
20
120
40
40
-------
1137
2
5
T.OO
7.05
321
323
1138
0
7
5.40
5.48
23
39
113®
1
T
5.40
5.49
26
34
1139
0
8
7.25
7.22
309
304
1139
3
8
7.25
7.75
302
307
1140
0
6
4. SB
4.85
26
14
1140
3
6
4.98
4.85
26
14
1141
0
T
6.54
6.50
104
97
1141
1
7
6.64
6.55
110
102
1142
0
8
6.85
6.92
166
171
1142
3
8
6.90
6.86
169
188
1143
0
4
7.15
7.05
350
356
1143
1
5
7.17
7.05
365
357
289
291
125
120
40
50
1
6
41
40
10
10
6
4
40
39
10
10
248
2 71
130
130
50
50
269
269
130
120
50
50
-4
-9
80
80
10
10
-2
-9
79
80
10
10
75
69
56
48
30
30
70
67
48
48
30
30
147
155
72
72
10
10
145
148
72
73
10
10
329
327
160
160
20
20
325
336
160
160
20
20
-------
WATER
COOE
1050
1050
1051
1051
1052
1052
1051
1053
105*
1055
1055
1056
1056
1057
1057
1058
1058
1059
1059
1060
1060
1061
1061
1062
1062
1061
1063
106*
1064
1065
1065
1066
1066
1127
1127
1128
1128
1129
1129
1130
1130
1131
1131
1132
1132
1133
1133
113*
113*
1135
1135
1136
U36
1137
CHLORIDE
IPPMI
2.0
2.1
0.0
0.0
1.5
7. I
*.9
6.1
*.6
1.9
*. 1
0. 3
0. 7
*.6
*.1
0.0
0.0
1.5
1.7
0.0
0.0
8. 1
8.2
*. 7
5.1
0.0
0. 0
19.6
17. 7
3.7
5.0
1.0
0.3
11.5
11.5
0.0
0.0
8.7
8.1
5.*
5. *
2. 5
*.5
7.1
9. 3
6.8
6. 7
15. 5
15.0
8.7
8.6
17.6
13.0
17.5
CHLORICE
-------
1I3T
17.7
IT.*
6. 7
6.5
23
6.13
113B
1.2
7.1
6.6
5.8
12
0.69
1138
7.2
7.1
6.3
6.1
16
0.T1
1139
20.7
20.*
7.2
T.2
0
6.10
1139
20.1
20.0
7.2
7.2
10
6.19
11*0
16.0
16.*
7.2
7.2
*0
1.03
11*0
16.2
15.9
7.9
7.9
40
1.06
11*1
6. e
6.8
6.6
6.5
56
3.5*
11*1
6. a
6.7
7.2
6.6
79
3.*5
11*2
13.2
13.2
6.1
6.1
0
*.05
11*2
13.C
12.9
6.2
6.1
31
*.00
11*3
20.7
20.2
15.8
15.8
12
12.*6
11*3
19.9
19.9
15.2
15.5
l*b
12.31
ro
tr.
1.96
1.35
0.037
13.80
*.t
0.56
0.97
0.025
*.60
1.9
0.56
0.96
0.02*
*.58
1.9
1.35
2.09
0.06*
13.1.7
*.*
1.33
2.12
0.08*
13.*1
*.*
0.61
1.52
0.026
9.62
1.9
0.61
1.53
0.02*
9.67
1 .9
0.51
0.78
0.010
3.73
3.2
0.52
0.78
0.030
3.77
3.2
1.26
0.9*
0.026
7.37
1.9
1.27
0.95
0.038
?.«*
1.9
2.8T
2. 38
0.025
13.10
2.5
2.86
2.35
0.015
13.11
2.5
-------
IJSFWS AC! 0 RAIN SENSITIVITY PROJECT
STATE OF NEW HAMPSHIRF
WATER
NAME
DATE
LATITUDE
LONG.
ELEV
AREA
TYPE
HYORO.
BED
SOIL
01 STB.
PRECIP.
ANNUAL
CORE
OAY-MON-YR
OEG-MIN-*EC
(Ml
(HAI
ROCK
TYPE
PH
PRECIP
1020
CCtO RIVER
70880
431556
721343
244
6
2
2
2
5
2
4.3
1118
10?l
CRESCENT PD OUTLET
80880
431556
721427
366
2
2
1
2
5
2
4.3
1118
1022
GR EAT BRK HONS
70880
4 30853
721544
213
2
2
2
2
5
2
4.3
1118
1021
NEWELL PO OUTLET
70880
430811
721522
426
2
2
1
2
5
7
4.3
1118
1011
BEAVER RRK
290880
432627
721236
274
2
2
4
1
5
1
4.3
1118
10??
CHAPtN PO OUTLET
290880
432522
721724
485
2
2
1
2
5
1
4.3
1118
1013
ODDGE RRK
290880
432627
721533
305
1
2
I
1
5
I
4.3
1118
1034
GOVERNOR'S PO OUTLET
2908BO
432700
721746
494
2
2
2
2
5
1
4.3
1118
1035
WEST SMMONOOSUC R.
250880
442855
711930
457
5
2
2
1
5
2
4.2
914
1036
ATUflOD PD
2 70880
435230
713334
460
1
1
6
2
5
2
4.3
1118
1037
BACON PO
2808 80
431317
720551
500
5
1
6
2
5
2
4.3
1118
1038
DURGIN PO
260880
4 35246
711055
168
6
1
5
2
5
2
4. 3
1118
1040
FLETCHER POND
280880
431227
720646
497
6
1
6
2
5
1
4.3
1118
10*1
FROG PO
280880
431219
720529
491
10
1
5
I
5
1
4.1
1118
1042
UPPER HALL PO
270880
435158
713106
484
19
1
5
2
5
1
4.3
1118
1043
MtOOLE HALL PO
270880
435134
713244
445
3
I
5
2
5
1
4.3
1118
IMS
LOWER HALL PO
290380
435124
713234
418
8
1
5
2
5
2
4.3
1118
1045
HFOGEHOG PO
280880
431219
720657
506
3
1
6
2
5
2
4.3
ills
1046
KIAH PO
z rosea
4 35117
713105
433
2
I
5
2
5
2
4.3
1118
1047
MOUNTAIN PD
260880
441011
710401
459
50
1
5
1
5
1
4.3
914
1046
NORTH pn
280880
431420
720602
504
21
1
5
2
5
2
4.3
1118
1069
ADAMS PO
250980
431741
711427
203
8
1
6
2
3
3
4.3
1118
1070
BEAR PO
220980
432912
711142
271
5
1
6
1
3
2
4.3
1118
1071
CLOUGH PO
2 30980
4 32403
712621
204
4
1
6
2
3
2
4.3
1118
1072
COLDRAIN PO
220980
432703
710847
201
11
1
6
2
3
1
4.3
1118
1073
CRANEY PO
240980
430842
714723
304
14
1
5
2
3
2
4.3
1118
1074
CROOKEO PO
250980
431741
712547
158
12
1
6
2
5
I
4.3
1118
1075
DUDLEY PD
740980
430658
714945
255
12
1
5
2
3
2
4.3
1118
1076
EATON PO
2 50980
431620
711701
227
7
1
6
2
5
1
4.3
1118
1077
FERR1N PD
240980
430232
714619
288
5
1
5
2
3
1
4.3
1321
1078
GIL*AN PO
2 20980
433040
711204
230
13
1
5
1
3
1
4.3
1118
1079
HUNKINS PO
230980
433024
713339
2 39
6
1
6
2
3
3
4.3
1118
10B0
KENISON PO
250980
4 30901
710920
93
8
1
6
I
3
2
4.3
1118
1081
KNIGHTS PD
230980
433241
711047
200
12
1
5
1
3
I
4.3
1118
1082
KNCWLES PO
230980
432543
713223
227
24
1
6
2
3
I
4.3
111B
1083
LITTIF LONG PD
250980
431612
710139
76
9
1
5
2
3
2
4.3
1321
1084
LONG PD
240980
4 30449
713613
188
13
1
5
2
3
1
4.3
1321
1085
Ml COL E PO
2409BO
431155
714806
155
1
1
5
2
3
I
4.1
11 IB
1036
POVERTY PO
2109BO
411032
714817
361
4
1
6
2
5
1
4.3
11 IB
1087
STONEHOUSE PD
2509B0
431155
710551
116
6
1
5
2
3
1
4.3
1321
1088
UPPER PO
2409 80
431211
714006
155
10
1
5
2
3
2
4.3
1118
1191
GRFAT BRK MOUTH
708 BO
430810
722420
91
5
4
2
5
3
4.3
1118
1192
WARREN BRK MOUTH
70880
430858
722146
.
5
4
2
5
3
4.3
1118
1250
CCNE MTN. PO
30879
435413
713619
469
4
1
6
2
5
1
4.3
1118
1251
UNKNOWN PO
10878
443057
712437
976
I
1
6
I
5
1
4.3
1016
1252
LOWER SAWYER PO
220879
440258
712230
551
20
1
5
1
5
1
4.3
11 IB
1253
BLACK MTN PO
51079
435258
713025
702
2
1
6
2
5
1
4.3
1118
1254
SOLITUOF PD
261079
431839
720438
729
2
1
6
2
5
1
4.3
ma
-------
MATP*
OEPTH
TEHC
PH
PH
*LK IF.E.PI
M.K (F.E.
COOE
»*l
( C1
(UEQ/L1
(UEQ/L1
1020
0
6.85
6.85
177
183
1021
0
.
6.55
6.55
100
93
1072
0
.
6.50
6.55
194
199
1023
0
.
6.55
6.55
197
197
1011
0
19
7. 10
7.15
222
220
103?
0
16
6.30
6.35
136
137
1033
0
15
6.85
6.85
348
351
1034
0
17
6.15
6.10
47
47
1035
0
16
6.65
6.65
140
140
1036
0
25
6.55
6.55
108
104
1036
4
18
6.20
6.20
597
602
103?
0
23
5.85
5.85
47
47
1037
3
22
5.70
5.75
48
48
103a
<1
26
6. <0
6.55
140
136
1018
4
25
5.? 5
5.95
145
151
1040
0
23
5.65
5.75
43
47
1040
2
23
5.70
5.80
48
48
1041
0
23
5. 50
5.55
38
39
1041
4
22
5.45
5.45
39
39
1042
0
23
6.00
5.95
52
50
1042
5
21
5.85
5.85
50
50
1043
0
23
6.45
6.45
68
72
1043
15
15
5.50
5.50
60
54
1044
0
25
6.70
6.75
59
72
1044
1
25
6.70
6.75
81
80
1045
0
21
5.20
5.25
32
32
1045
4
20
5.35
5.35
47
43
1046
0
22
6.40
6.35
81
B1
1046
5
17
6.30
6.25
306
318
1047
0
19
6.35
6.30
51
50
1047
4
21
6.25
6.20
54
51
1048
0
21
5.65
5.60
50
47
1048
4
19
5.45
5.45
48
48
1069
0
19
6.72
6.55
120
95
1069
9
13
6.12
6.05
186
159
1070
0
22
6. £5
6.73
91
100
1070
4
21
6.10
6.78
91
101
1071
0
20
6.95
6.75
135
126
1071
5
19
6.54
6.40
136
126
1072
0
21
5.79
5.65
60
47
1072
4
18
5.75
5.BO
85
75
1073
0
19
6.IS
5.95
65
60
1073
4
18
5.92
5.80
65
54
1074
0
19
6.25
6.33
93
113
1074
5
18
6.C5
6.15
111
128
1075
0
19
6.65
6.72
113
127
1075
6
18
6.35
6.46
116
132
1076
0
18
5.80
5.70
81
62
1076
4
17
5.53
5.BO
166
122
107T
0
20
6.33
6.0O
61
48
I07T
3
20
6.32
5.95
62
48
1078
0
21
6.78
6.60
128
124
1078
4
19
6.65
6.50
134
122
1079
0
21
7.35
7.45
383
398
ALU «I.P1
U.K II.P)
CONt)
CONO
COLOR
COt OR
IUE0/11
(UEQ/L1
IUS/C*I
(US/CHI IUNITSMUNITSI
144
148
64
64
60
60
70
61
34
32
10
10
164
163
45
46
10
10
165
162
37
37
30
30
185
181
35
35
60
60
105
100
22
22
50
50
319
316
52
51
10
10
17
17
20
21
10
10
110
109
27
27
10
10
58
60
23
23
50
50
546
551
63
63
130
130
10
U
20
20
20
20
16
16
20
20
20
20
95
97
26
24
30
3C
106
106
24
26
40
4C
1
7
22
22
10
10
14
12
22
22
10
10
4
9
22
22
10
10
9
9
22
22
20
20
16
16
20
20
10
10
13
16
20
22
10
10
42
41
20
21
0
0
24
24
21
21
20
20
34
30
21
21
0
0
41
41
21
21
0
0
0
1
86
86
20
20
11
12
85
88
bO
60
41
41
24
24
40
40
267
272
42
41
120
120
14
16
15
15
10
10
19
21
14
15
10
10
11
10
21
21
30
30
13
16
20
21
30
30
81
68
24
24
10
10
143
129
26
27
20
20
64
68
22
21
10
10
63
65
21
20
10
10
103
102
24
22
0
10
103
100
23
23
0
10
26
18
18
18
too
loo
50
44
20
21
100
100
25
22
22
21
10
30
25
23
22
21
30
30
66
7?
24
23
20
10
82
88
26
26
30
20
87
99
30
30
10
10
87
94
31
30
10
20
38
35
42
42
80
80
119
98
48
47
110
120
24
21
22
21.
10
10
25
21
20
20
10
10
92
91
22
22
40
40
97
95
22
22
40
30
354
360
98
96
10
10
-------
1079
5
19
6.95
7.22
392
407
10 BO
0
19
5. «
6.05
68
86
1080
7
9
5.85
6.03
263
310
1081
0
21
7.20
6.95
242
233
1091
3
20
7. 20
6.95
245
240
108?
0
20
6.35
6.39
57
70
1082
10
18
5.S5
6.10
65
80
1083
0
19
6. CI
5.90
92
75
1083
5
13
6.C9
6.05
404
333
1084
0
20
5.40
5.42
32
42
1084
5
18
5. 35
5.45
50
57
1085
0
19
6.45
6.86
147
158
1085
5
12
5.85
5.95
244
272
1086
0
21
5. 62
5.55
48
39
1086
5
18
5. 76
5.65
47
36
1087
0
19
5. JO
5.7?
*9
50
1087
15
7
6. CO
6.20
237
277
1088
0
19
6.59
6.40
169
154
1088
5
15
6.18
6.10
449
433
1191
0
•
7. 25
7.25
457
468
1192
0
•
7. 10
7.10
269
262
1250
0
•
4.48
.
0
0
1250
L
4.40
.
0
0
1251
1
•
4.35
.
0
0
1751
3
9
4.35
»
0
•
1252
I
19
6.55
.
119
•
1252
10
10
6. 10
.
154
•
1251
1
15
5.25
.
44
1253
8
.
5.60
•
47
125*
1
10
4.S5
.
21
1254
6
7
4.95
.
22
367
368
97
94
10
10
44
48
31
31
60
60
226
261
39
38
110
200
206
200
30
28
10
10
206
20^
28
29
10
10
36
35
22
22
0
0
40
45
28
22
10
0
54
53
53
51
60
70
359
306
76
76
130
160
8
3
21
20
20
30
23
18
22
21
70
80
118
118
26
26
30
40
218
227
38
38
BO
70
12
9
20
20
10
10
11
7
20
19
0
10
14
10
70
20
20
20
707
233
30
29
70
70
124
122
26
76
40
40
398
395
51
50
60
.
423
432
94
91
0
0
248
232
81
80
20
20
-23
25
•
5
-26
29
.
0
0
IB
•
30
•
•
17
.
•
•
71
19
•
m
•
117
21
•
•
0
20
.
10
10
20
.
.
-10
20
.
25
-8
20
.
40
-------
WATER
CHIOS10E
CHLORIOE
SULFATF
SUIFATF
HUHINUH
CALCIUM
CODE
IPPMt
(PPM)
tPPMl
f PPM)
IPPBI
IPPHI
1020
10.5
10.9
6.2
6.0
117
3.71
1021
3.3
3.4
5.4
5.6
15
2.24
1022
2.4
2.3
7. 3
7.2
21
4.15
1023
0.6
0.7
6.1
5.9
60
2.98
1011
1.0
1.1
5.3
4.8
70
3.70
1012
0.6
0.4
3.6
3.2
115
1.88
1031
0.6
0.5
7.5
7.6
19
4.97
1034
0.6
0.6
6.2
6.1
22
1.23
1035
2.5
2.4
4.5
4.5
63
2.39
1036
1.0
0.6
5.6
5.4
93
1.98
1036
1. 3
1.0
4. 8
4.6
348
6.19
1037
0.0
0.2
5.9
6.3
40
1.24
1037
0.0
0.2
5.1
5.3
29
1.24
103S
1.3
1.3
3.5
3.2
37
2.08
1038
1.4
1.6
4.1
3.7
45
2.16
1040
1.0
0.8
6.0
6.0
60
1.19
1040
0. 7
0.7
5.6
5.8
55
1.19
1041
0.8
1.0
6.2
6.5
67
1.57
1041
1.1
1.0
6.6
6.6
71
1.49
1042
1.5
1.4
5. 7
5.7
51
1.56
1042
0.2
0.1
5.6
5.9
70
1.57
1043
0.9
0.8
5.6
5.6
72
1.75
1043
0.6
0.5
5.1
5.4
252
1.43
1044
0.0
0.0
5.6
5.4
50
1.64
1044
0.0
0.0
5.5
5.6
30
1.64
1045
17. 1
17.4
5. 7
5.7
96
1.33
1045
17.4
17.1
5.9
5.7
138
1.41
1046
1.2
1.1
6.2
5.8
91
1.98
1046
1.4
1.3
4. 8
4.8
376
4.01
1047
0.0
0.0
4.2
3.8
25
0.89
1047
0.0
0.0
3.8
3.8
36
0.94
1048
0. 1
0.3
6. 3
6.4
67
1.35
1048
0.0
O.I
6.5
6.1
166
1.33
1069
1.6
1.7
5.5
5.1
0
1.38
1069
1.2
1.4
4.2
3.8
17
1.75
1070
0.9
0.8
4.6
4.3
12
1.64
1070
0.1
0.2
4.6
4.8
13
1.65
1071
0.3
0.5
5. 2
5.4
0
1.75
1071
1.1
1.0
5.3
5.0
1
1.75
1072
3.2
2.9
4.8
5.3
160
1.09
1072
1.6
1.9
4.3
4.2
181
1.33
1073
0.9
0.9
5.8
5.0
25
1.26
1073
1.8
1.8
4.8
4.6
63
1.25
1074
0.5
0.8
5.4
4.8
16
1.19
1074
0.9
0.8
4.8
5.3
17
1.39
1075
1.2
1.0
6.0
5.3
36
2.21
1075
3.5
3.5
5.6
5.7
23
2.27
1076
10.0
9.8
4.8
4.3
137
1.26
1076
9.0
8.7
4.1
3.9
55
1.47
1077
2.5
2.2
5.8
5.6
10
1.73
1077
0.9
1.1
5.8
5.7
23
1.23
1078
1.3
1.4
3.6
3.4
33
1.91
1078
1.0
0.9
4.1
3.7
23
1.95
1079
15.0
14.7
6.2
6.0
15
5.75
3TASSIUM
MAGNESIUM
MANGANESE
soni UN
roc
«PPM»
IPPIU
1 PPM 1
4PPNI
IPPMl
1.18
0. 83
0. 055
6.56
5.0
0.81
0.58
0.074
2.40
1.9
0.98
0.83
0.129
2.72
1.9
1.46
0.93
0.013
2.12
3.2
0.63
0.65
0.059
1.42
5.0
0.29
0.55
0.080
1.00
4.4
0.84
1.28
0.014
2.15
1.9
0.33
0.44
0.026
0.91
1.9
0.72
0.39
0.017
1.46
1.9
0. 42
0.39
0. 130
1.35
4.4
1.21
0.60
0.125
1.10
9.3
0.56
0.46
0.019
O. 86
2.5
0. 58
0. 46
0.024
0.83
2.5
0.23
0.36
0.013
1.77
3.2
0.41
0.38
0.034
1.82
3.8
0.65
0.32
0.024
1.09
1.9
0.65
0.31
0.02 7
1. 10
1.9
0.54
0.33
0.036
1.06
1.9
0. 52
0.33
0.030
1.02
2.5
0.59
0.35
0.035
0.94
1.9
0.56
0.34
0.035
0.92
1.9
0.53
0.40
0.032
1.00
1.3
0.59
0.34
0.180
0.67
2.5
0. 74
0. 30
0.015
1.13
1.3
0. 70
0. 29
0.014
1.07
1.3
0.24
0.33
0.053
10.95
2.5
0.65
0.34
0.050
10.93
5.0
0.46
0.32
0.030
1.18
3.8
0.68
0.36
0.105
0.96
8.7
0.48
0.19
0.040
0.99
1.9
0.48
0. 19
0.033
0.98
1-9
0.43
0.36
0.031
1.19
3.2
0.42
0.34
0.034
1.19
3.2
0.54
0.49
0.028
1.91
1.9
0.50
0.54
0.375
1.81
2.5
0.41
0.21
0.016
1.75
1.9
0.40
0.22
0.020
1.76
1.9
0.38
0.81
0.049
1.10
1.6
0.39
0.81
0.042
1.11
1.6
0.48
0.25
0.024
1.74
7.4
0.51
0.25
0.010
1.71
7.4
0.27
0.39
0.020
1.68
3.2
0.27
0.40
0.015
1.69
3.2
0.45
0.56
0.017
1.95
2.2
0.48
0.57
0.080
1.92
2.9
0.86
0.51
0.055
1.98
1.9
0.85
0.52
0.041
1.95
2.2
0.51
0.51
0.078
5.56
6.2
0.42
0.48
0.054
5.71
8.4
0.47
0.40
0.024
1.44
1.9
0.47
0.40
0.020
1.44
1.9
0.44
0.32
0. 017
1.92
3.8
0.44
0.31
0.020
1-92
3.5
4.99
1.58
0.028
7.95
1.9
-------
1079
14.9
14.9
6.0
5.9
21
5.85
1060
5.9
5.6
4.6
4.3
41
1.40
1080
4.4
3.8
4.3
4.3
113
1.99
1081
1. 5
1.4
3. 8
3.5
11
3.20
lost
1.0
1.0
3.6
3.5
16
3.21
1082
2. 0
2.0
6.3
5.9
2
1.16
1082
1. 1
1.1
3.6
3.5
23
1.17
io«3
11.1
11.6
4.1
4.1
18
2.00
1083
12.5
12.T
3.1
? .9
237
2.81
1084
1. 1
1.1
5.7
5.6
45
1.05
1084
1.8
2.0
5.5
5.6
87
1.25
1085
1.6
1.5
3.9
3.9
23
2.45
1085
0.8
0.7
2.7
2.1
85
4.41
1086
0. 7
0.5
6.1
6.2
19
1.60
1086
1.3
1.0
6.0
6.1
25
1.65
1087
2.0
1.8
5.0
4.8
47
0.83
1087
2.6
2.2
4.1
3.6
251
1.54
1088
0.3
0.4
4.3
4.5
4
2.47
1088
1.9
1.9
4.2
4.1
40
6.55
1191
6.9
7.2
8. 3
8.6
98
6.68
119?
10.8
10.6
5.9
5.7
63
3.84
1250
.
.
.
.
0.91
1250
.
.
.
•
0.91
1251
.
.
.
.
146
0.32
1251
.
.
•
.
132
0.28
1252
.
.
•
.
90
1.15
1252
.
.
•
.
83
1.40
1253
.
.
•
.
172
1.32
1253
.
.
.
115
1.39
1254
.
.
.
138
0.92
1254
.
.
#
.
200
0.98
5.00
1.59
0.090
7. 86
1.9
0.52
0.55
0.030
3.14
5.0
0.57
0.64
0.077
2.38
10.B
0.49
0.35
0.009
2.08
1.9
0.48
0.34
0.011
2.05
1.9
0.40
0.55
0.034
1.76
1.3
0.36
0.55
0.117
1.69
1.6
0.47
0.45
0.024
6.37
5,3
0.97
0.60
0.054
7.89
10.2
0.17
0.35
0.024
1.42
2.9
0.25
0.38
0.026
1.44
5.9
0.46
0.49
0.026
1.70
3.5
0.84
0.57
0.133
t.82
5.9
0.23
0.33
0.012
0.74
1.9
0.24
0.33
0.014
0.74
1.6
0.32
0.40
0.032
1.71
2.5
0.40
0.40
0.080
1.69
5.6
0.41
0.48
0.027
1.62
3.8
1.14
0.58
0. 144
1.59
5.0
1.21
1.B8
0.075
3.81
1.3
1.44
1.39
0.055
8.01
2.5
0.13
0.23
0.05 8
0.49
1.6
0.14
0.22
0.058
0.50
1.3
0.19
0.07
0.031
0.24
3.2
0.18
0. 06
0.031
0.24
¦
0.70
0.22
0.006
1.16
•
0.86
0. 28
0.010
1.35
0.23
0.28
0.033
0.63
1.9
0.24
0.29
0.040
0.60
.
0.19
0. 18
0.053
0.52
2.9
0.25
0.19
0.071
0.52
3.8
-------
USFWS ACIO RAIN SENSITIVITY PROJECT
STATE OF RHODE ISLAND
WATER
NAHE
CAT E
LATITUOE
LONG.
ELEV
AREA
TYPE HYPRO.
BEtl
SfUl
11 STB.
ppfrjo.
AMNtl »L
cooe
DAY—"ON-YR
OEG-*IN-SEC
f **»
IHAI
ROCK
TYPE
PH
ORcr i o
1103
CARWINCLE Pft
71080
414158
714128
1?6
14
1 6
1
5
1
4.^
15 24
1104
IJEEP PO
71080
412*?6
713<)4f
16
8
I 6
1
¦<
2
4.3
15^4
1105
ELL P0N1
71080
413000
714628
79
3
1 6
1
5
1
T
15?4
1106
LONG PO
71080
413000
714619
79
8
1 5
I
3
¦>
4.3
15'4
HOT
SUCKER PO
61080
415613
714000
142
23
1 6
7
4
4.1
15J4
1144
CARR PO
81280
413809
713318
110
33
1 6
I
5
7
4. T
!5?4
1145
WHITE PD
91280
412453
713243
30
9
1 6
3
2
4.3
1524
1146
WILBUR PO
121280
415550
714543
189
9
1 5
1
4
7
4.-»
I5i'i
-------
MATE*
OE»TM
TF^P
"H
PH
ALK CF.f.PI
ALK »F.F."I
COOE
(HI
I CI
(UEQfLI
IIIFQ/L1
1103
0
15
6. 35
6.15
83
83
1103
6
15
6. 10
6.00
99
100
1104
0
16
5. *>5
5.TO
49
43
1104
7
14
5. 75
5. 75
99
99
1105
0 '
15
4.25
4.35
0
0
1105
1
15
4. 25
4.30
0
0
1106
0
15
4. 35
4.40
0
0
1106
6
11
5. 35
5.40
73
77
HOT
0
17
6.DO
6.HO
195
192
1107
6
16
6. 15
6.80
194
194
1144
0
5
4.70
4.78
17
17
1144
2
5
4.65
4.75
6
18
1145
0
5
4. 15
4.44
0
0
1145
e
4
4.40
4.44
0
0
1146
0
1
3. 95
4.01
0
0
aik n.p) alk u.pi r.nNO
(IIFO/ll (HFO/Lt IUS/CH
5B 58 42
73 74 42
22 18 <*0
72 70 *0
-45 -3* 3B
-47 -46 39
-35 -21 35
*5 51 23
166 160 73
163 16? 75
-16 -15 37
-15 -16 34
-33 -31 4 B
-28 -3? 48
-84 -96 12
ctm" r.ni'ip men
IIJC/C^MUNITM IUMTM
VI
20
21
11
»J
30
33
10
HI
3'.
6 a
60
37
loo
100
3 1
1 ij
100
3 '
60
r>a
70
100
no
7?
»0
2(1
f 3
20
20
3 6
0
(1
31
0
C
4
0
0
4 3
0
0
64
W)
150
-------
WATER
CHLORIDE
CHtrmiflE
SULFATE
Slfl FATE
AlllM tNUM
CALCUIH
prmssni*
ijr.uFsnj*
Slin ny
T f r
COOF
(PP«|
(ppw|
I PP H|
lPP*t
(Ppnl
( PPMI
(PPM)
(POM )
< PPM 1
[DP"I
( OPM )
1103
7.2
7.3
7.7
7.2
19
2. 04
0. 83
0.64
0.010
'i .45
2.T>
110?
7.2
7.7
7.?
6.8
47
2.1?
0. 84
0.65
O.U/fi
4 .40
1.7
1104
7.5
6.6
6.6
6.5
33
1.35
0.53
0.61
O.C.?
4.21
1 . ')
1104
6.1
6.3
5.8
5.6
156
2.00
0.69
n.f.i
O.OB?
1 .')<5
¦i.O
1105
5.1
5.6
5.0
4.8
163
0.67
0. 4 7
0.13
0.011
1
1109
5.6
5.0
4.6
4.1
187
0.71
0.46
0. 12
0.077
7.6*.
J. 4
1106
4.1
3.0
6.0
5.5
170
0.68
0.44
0.10
0.040
7.1 1
4. 7
1106
4.9
5.0
4.5
4.3
250
0.74
0. 54
0. 36
0.074
>.50
7.4
1107
13.1
13.0
6. 0
5.8
50
3.22
1.28
! .06
0.01"
7. M
7. •>
HOT
1*3. 4
17.8
5.5
5.6
60
3.26
1 .10
1 .06
o.on
r.n
7.5
1144
3.9
4.1
7.0
6.1
381
1.04
0.41
0. 14
o.ohh
?.¦;«
1.3
1144
3. a
3.8
5. 7
6.7
381
1.08
0.43
1.34
0.091
?.f.c
1.1
1145
7.4
7.6
6. 1
5.7
5 79
0.91
0.59
0.61
0.065
1.94
1.3
1145
6.8
6.9
5.9
6.1
185
0.91
0.80
o. eo
0.061
4.45
1 .1
1146
3.3
3.5
7.2
6.1
357
I .30
0.46
r>, 16
0. 041
7.1C
in. 5
-------
USEWS AC10 MI* SENSITIVITY PROJECT
STATE OF VERMONT
WATER
NS*E
CATE
LAT ITIIOE
1 TING.
ELEV
AREA
TY»F
HYURO.
BE"
SPIl nTSjr..
PPCf|P.
IHL
cnne
DAY-MON-YR
OEG-MIN-SEC
(M|
1 HA 1
rock
TYPF
OH
P9 FT. I P
1001
LYE BROOK MEADOWS ST
24OTB0
430600
730145
7 P6
1
2
1
2
5 I
4.3
1113
1002
BRANCH PD
240780
410443
73014 5
802
17
1
5
2
5 1
4. 3
1 ' 1 "
1003
W1YES po
220780
441302
771814
518
17
1
5
1
5 I
4. ¦>
914
1008
STRATTON PO
730780
430247
775830
737
71
I
6
7
5 1
1118
1009
WHEELFR PO
210780
444111
770677
477
1
1
6
I
5 I
1118
1010
KETTLE PO
7 20780
441744
721845
42 7
34
1
6
1
5 7
91
1011
OSNORF PO
220780
441816
721630
477
17
1
6
I
5 1
4.7
>314
1014
HALL «TN BROOK HOVS.
60880
430313
725431
555
7
2
1
5 1
4. 1
ma
1015
GREENOALE BROOK HEMS
70880
432019
724934
610
2
1
2
5 I
4.3
nip
1016
ROCK RIVER MOWS.
70880
425833
774673
427
7
2
1
5 1
4.3
13^3
ion
WAROSBORO BROOK HCWS
60B80
430159
725409
549
7
7
1
7
5 1
4. 3
13 71
1018
WEST RJVER MOWS.
70880
431202
724807
610
1
?
5 ¦>
4. 1
Ulfl
1019
WINHALL RIVER HOWS.
50880
430 74 7
725900
305
7
2
7
5 1
4.3
1111
1024
FORRESTER PO
60880
4 30455
775146
549
1
1
6
7
5 1
4. 3
1118
1089
8AKER PO
91080
44442 8
771415
335
1 7
1
5
3
5 4
4.7
1 U8
1090
SALO HILL PO
91080
444500
715857
518
45
I
5
3
5 3
4.'
1110
1091
COW MOUNTAIN PH
101080
443345
71423?
575
1
T
6
2
5 1
4. *»
91 4
1092
GUT PO
81080
444 056
773119
366
2
1
6
5 7
4.7
1118
1093
HAROWOOO PO
91080
442759
773000
518
17
1
6
7
5 7
4.7
914
1094
Rnnos po
71080
443946
731004
91
7
1
6
4
3 4
4. 7
914
1095
RICHMOND PO OUTLET
81080
442519
77*658
183
1
7
I
?
5 7
4.7
914
1111
BEAVFR PD
281080
440345
724340
518
1
1
6
2
5 7
4.1
91 4
1112
CHOATE PO
281080
4*4732
731608
744
10
1
6
4
5 1
4. 3
1118
1111
COITS PO
281080
442647
727019
3%
14
I
6
3
5 1
4.2
914
1114
CRANBERRY MEADOW PO
281080
447519
727755
135
10
I
5
3
5 ">
4. 7
91 4
1115
HALF«OON PO
291080
434050
731379
152
14
1
5
5 3
4. 3
1110
1116
HORN OF THE NOON PO
7810BG
441933
72,307
366
7
I
6
7
5 '
4. 3
914
HIT
HOUGH PO
291080
434 708
731101
744
7
I
6
4
5 2
4.3
1118
1118
JOHNSON PO
291080
434604
731357
177
3
1
6
4
5 7
4. 1
1118
1119
LAKE MITCHELL
311080
434500
722429
744
10
1
5
5 3
4.3
1110
1120
LAKOTA LAKE
301080
4*4107
773907
5 49
10
1
6
7
5 7
^ • 3
1118
1121
MUOOY LAKE
311080
440634
721015
7 74
1
I
6
2
5 ?
4.3
1110
1122
NORTH PO
301080
440256
7 23 7 35
457
3
1
5
3
5 1
4 . 3
914
1123
SUGAR HOLLOW PO
291080
434516
730142
7 74
14
1
6
7
5 1
4.3
1118
1124
TINY PO
301080
432719
724 157
518
1
6
7
5 1
4. 3
HIP
1125
TWIN PONDS-EAST
301080
440307
773507
396
t
6
3
5 ?
4. 3
91
1126
WALLINGFORO PO
291080
432502
775441
640
44
I
5
5 1
4.3
1118
1161
WFSTFORO PO
81080
443537
73P359
744
1
6
7
i 2
4. 7
n
-------
W»TP»
DEPTH
TFHP
p»-
PW
Al K IF.F
rone
111
( CI
1001
0
23
4.15
1002
0
23
4.65
•
8
1002
9
19
4.95
•
79
1001
0
24
1.10
435
1003
3
23
7.55
•
444
1004
0
24
5.C5
?a
1004
3
19
4.55
29
1005
0
23
5.00
.
27
1005
2
23
5.15
30
1006
0
17
5.50
36
1007
0
n
6.45
•
81
1007
5
17
5.75
*
100
1008
0
22
6. C5
•
43
1008
3
22
6.20
43
1009
0
26
7.45
301
1009
8
18
6. 30
.
290
1010
0
25
7.C5
•
136
1010
3
25
7.05
»
136
1011
0
25
7.25
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208
1011
2
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7.20
.
207
1014
0
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5.20
5.20
16
1015
0
•
6.^5
6.60
M5
1016
0
•
7.15
7.10
330
1017
0
6.50
6.55
165
1018
0
•
7. 15
7.20
487
1019
0
23
4.SO
4.90
29
1024
0
26
5.C5
5.10
38
1024
2
27
5.15
5.15
38
1089
0
10
7.56
7.32
1350
1089
5
10
7.54
7.35
1351
1090
0
12
7.68
7.25
868
1090
8
11
7.65
7.40
858
1091
0
9
6.55
6.53
149
1091
5
8
6.55
6.55
163
1092
0
13
6.86
6.47
159
1092
12
14
6.09
5.80
167
1093
0
11
5. 84
5.66
51
1093
2
10
5.64
5.57
50
1094
0
15
8.21
7.98
2821
1094
5
15
8. 15
7.72
2B78
1095
0
6.80
6.48
7 75
1111
0
5
5.79
5.00
55
till
2
5
5.85
5.55
57
1112
0
6
7.55
7.73
735
1112
3
6
7.35
7.55
905
1113
0
4
7.80
7.70
1161
1113
2
4
7.71
7.60
1184
1114
0
7
7.25
7.35
1036
1114
6
7
7.15
7.35
10 28
1115
0
7
7.75
7.65
1302
1115
2
T
7.65
7.66
1313
1116
0
6
8.00
7.95
2319
1116
2
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7. J5
7.95
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0
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170
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1782
140
145
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850
809
95
90
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781
821
90
90
10
10
160
116
117
24
21
40
40
153
120
117
7 4
24
50
50
138
124
107
24
24
10
10
146
198
119
26
26
10
10
33
18
n
71
74
10
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34
18
14
20
72
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2754
2767
2726
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100
10
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2814
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300
215
10
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254
715
226
15
16
50
50
23
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75
75
20
20
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15
19
18
70
70
8 70
701
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1121
1124
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no
110
1 1
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1118
1141
1080
110
110
10
10
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1005
905
110
100
70
70
98 2
tool
944
110
1 D5
70
20
11 T6
1268
1129
120
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10
10
1149
1233
1132
I 20
140
10
10
2281
2105
7241
200
71 C
71
->0
2248
7315
2203
220
710
70
7C
7000
2001
1943
220
?70
70
20
-------
1117
5
S
7.5<>
T.75
2035
2011
1118
0
5
7.02
6.95
307
307
it in
2
7
7.05
6.98
314
297
1119
0
6
7.E5
7.95
1871
1761
1119
3
6
7.80
7.95
1863
1810
1120
0
4
7.08
7.15
231
?54
1UO
3
4
7.05
7.05
223
247
1121
0
7.55
7.35
634
614
1121
1
6
7.60
7.55
683
614
112?
0
4
7.55
7.90
1686
1606
1122
2
7.85
7.85
1651
1584
1123
0
7
7.65
7.70
1044
925
1123
1
7
7.t5
7.70
1031
947
112*
0
7. CO
7.50
262
271
1124
3
5
6.95
6.90
269
269
1125
0
8. 10
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2898
2771
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8
7
7.6?
7.65
2307
2358
1126
0
4
6.65
6.52
1 20
117
1126
2
4
6. CO
6.49
108
127
1161
0
13
7.46
7.18
531
520
1161
6
14
6.95
6.64
532
524
1182
0
12
5.13
5.03
38
21
1187
3
13
5.35
5.16
42
32
1183
0
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6.85
6.75
97
100
1184
0
•
7.45
7.45
444
438
1185
0
•
7.C5
7.55
452
459
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0
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7.20
7.20
229
233
1187
0
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7. 15
7.15
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1188
0
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6.85
129
129
1255
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1255
5
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.
1998
1976
220
220
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20
277
273
45
41
40
40
271
259
44
44
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1827
1740
210
210
10
10
1839
1721
210
710
10
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197
219
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716
31
12
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11
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160
160
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160
160
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110
120
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1001
897
110
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226
233
15
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237
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421
426
70
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198
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90
33
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60
60
29
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22
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28
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22
.
50
.
-------
KATFR
CHLORIDE
CHLORICE
SULFATE
SULFATE
ALUMINIJI
cnoE
CP»I
IPP»1
( PPMI
IPP*»
(PPRI
1001
2.4
2.7
2.4
2.4
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1002
1.1
1.0
5.0
5.2
182
1002
0.0
0.0
5.0
5.0
245
1003
0.6
0.5
5.3
5.7
65
1003
0.1
0.0
5.0
5.0
96
100*
0.9
1.1
5.0
5.1
271
1004
2.1
2.1
5.0
4.8
358
1005
0.3
0.2
4.8
5.1
163
1005
0.6
0.7
4.8
5.0
2 54
1006
0.8
0.8
4.7
4.8
1 88
100T
0.2
0.1
4.8
4.8
144
100T
1.3
1.2
4.5
4.3
324
100#
0.9
0.7
4.6
4.4
64
looa
1.3
1.2
3.9
4.0
206
1009
0.5
0.4
5.7
5.5
108
1Q09
0.1
0.3
4.2
4.1
92
1010
0.9
0.7
6.0
6.1
60
1010
0.2
0.2
5.8
5.9
48
1011
0.4
0. 7
4.9
4.8
67
urn
1.2
1.0
4.9
4.*
77
1014
0.2
0.3
6.1
5.9
343
1015
0.6
0.6
4. 7
4.6
91
1016
0.6
0.5
7.9
7.7
21
1017
0.6
0.5
5.3
5.1
74
101(1
13.8
13.6
7.2
7.0
33
1019
O.O
1.0
3.4
3.2
327
1024
1.0
0.7
4.5
4.4
135
I'D 2*
1.1
0.9
4.3
4.3
317
1089
2.2
2.1
8.9
8.1
46
1089
1.9
2.2
5.4
5.0
59
1090
0.0
0.0
6.6
6.5
I
1090
O. 3
0.4
6.5
6.5
17
1091
0. 0
0.0
6.0
5.6
85
1091
0.0
0.0
5.8
6.0
79
1092
0.0
0.0
3.5
3.6
5
1092
0.2
0.5
5.7
5.3
5
1093
0.2
0.1
8.5
7.4
2
1093
0.4
0.3
6.1
5.9
358
109*
8.4
B.T
9.4
9.4
0
109*
9.1
9.1
9.5
9.4
2
1095
0.0
0.0
6.9
5.9
319
1111
0.6
0.5
7.0
5.5
55
ltU
0.5
0.6
4.8
5.3
50
1112
0.0
0.0
14.1
13.4
18
1112
0.0
O.O
14.1
14.2
13
1113
0.3
0.2
9.6
9.0
0
1113
0.0
0.2
9.1
6.8
33
1114
0.5
0.6
7.2
6.8
0
1114
0.5
0.2
6.5
6.5
0
1115
1.9
1.8
7.7
7.5
34
1115
2.8
2.7
7.7
7.2
0
1116
2.7
2.7
8.2
7.4
0
1116
0.8
0.7
7.8
8.3
0
HIT
1.8
1.7
16.3
17.5
7
CALCIUM
POTASSIUM
MAr.NFSI'l*
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0.93
0.54
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0.68
3.8
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0.65
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1.15
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0.94
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0. 81
1.8
0.97
0.50
0.-«6
0.0*9
0.71
1.8
0.91
0.5?
0.36
0.053
0.90
1.7
1.02
0.51
0. 18
0. 06 5
0.66
1.7
1.04
0.47
0.29
0.031
1 .08
1.3
1.66
0.31
0.88
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0.*f>
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2.05
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0. B9
0. 156
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0.57
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0. 77
0.031
1.1 7
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1.14
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0.55
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0.31
0.58
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1.9
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0.67
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0.52
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0.85
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0.54
0.79
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0. 88
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1.89
0.28
1.26
0.0?*
0.76
1 .9
2.04
0.28
1.29
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1 .9
1.72
0.26
0.51
0.058
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3.7
1.77
0.27
0.51
0.058
0.51
3.2
30.78
1.33
16.44
0.0 35
5,11
1 .9
30.69
1.30
16.26
0.0*5
*.•>5
1 .S
4.62
0.45
0.81
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0.66
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1.55
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0.12
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0 . *6
7.5
1.53
0. 16
0.32
0.030
0.'«6
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17.25
0.54
3.2B
0.009
I .08
7.2
17.33
0.55
3. 30
0.013
1.07
?.?
23.41
0.86
1.14
0.011
0. 75
1.9
23.37
0.88
1.35
0.010
0.77
1.9
18.68
0.14
2. 17
0.06*
0.75
7.5
18.61
0.34
2.14
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0. 71
2. 5
17.58
0.56
2.34
0.018
1 .27
I .9
19.30
0.53
3.17
0. 001
l.i*
1.9
38.93
0.69
4.29
0.014
1.46
2.5
39.23
0.69
4.27
0.041
1 .*7
2.5
39.19
0.4R
5.07
0. 136
1.75
i.5
-------
HIT
0*9
o.a
15.8
16.6
2
38.60
ma
!.6
l.i
4.8
3.9
r
4.55
nifl
2. B
2. 8
4.6
4.5
13
4.5a
Lm
6.1
6.1
8.5
0. 1
10
35.77
1111
.s
1.56
1.78
0.024
5.6B
1.9
0. 99
0. 79
0.024
? .06
5.0
0.4B
0. 4!
0. C?7
0.4 2
3.3
0.42
0.39
Q.02A
0.19
4.4
-------
APPENDIX B
The historical water chemistry data located for lakes surveyed
in this study are presented in tabular form:
code:
name:
year:
pH:
method:
alkalinity:
method:
water code number from Appendix A
name of lake
last two digits of the year the
historical data were obtained
historical pH as reported
1 = pH meter, 2 = colorimetric
indicator
historical alkalinity
1 = fixed endpoint titration,
2 = inflection point titration
139
-------
CODE
1025
1026
1028
1029
1030
1036
1037
1038
1040
1041
1042
1043
1044
1045
1046
1047
1048
1053
1057
1062
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1086
1087
1088
1101
1103
1104
1106
1107
1109
1109
llto
1127
1134
1142
1144
1146
1147
1149
1151
1153
COOE
NAM 6
1025
LITTLE LONG PD
1026
TILCEN PO
1028
SAL*CN PO
1029
DAY MOUNTAIN PD
1030
SPENCER PO
1036
ATWCCO "0
1037
B4CCN PO
1038
OMRGIN PO
1040
FLETCHER POND
1041
FROG PO
1042
UPPER HALL PD
1043
MIOCLE HALL PO
1044
LOVE" HALL Pn
1045
HEDGEHOG PT>
1046
KI AH PO
1047
MC!UKT Al N PD
1048
NORTH PO
1053
CRO* HILL PO
1057
HOWE PH
1062
UPPER SPECTACLE PO
1069
AO«*S PO
1070
REAR on
1071
CLOLGH PO
1072
COLCP AIN PD
1073
CRAN=Y PO
1074
CLOCKED PO
1075
CUOLEY P0
1076
EATCN PO
1077
PER RIN PO
1078
GIL"AN PO
1079
HUNK T NS PO
1080
KENISON PO
1081
KNIGHTS PD
1082
KNOVLES PO
1083
LITTLE LONG PO
1084
LONG "9
1086
POVERTY PO
1087
STPSEHOUSE PO
1088
|Jt»(>gR pij
1101
MOHAWK PO
1103
CARBUNCLE PO
1104
DEEP PO
1106
LONG PD
1107
SUCKER PO
1108
PEEP PO
1109
PRETTY PO
1110
S AL *f"N PO
1127
BAOCaCOOK PO
1134
LITTLE CLIFF PO
1142
WALCEN PO
1144
CARP PO
1146
WILBUR PO
1147
*TflELOW PD
1149
8URP PO
1151
EMMCNS PO
1153
HCWFLLS PO
PH METHOD ALK METHOD
6.4
•
143
6.7
•
143
6.8
•
82
7.0
•
245
6.8
•
•
6.5
2
•
5.8
2
122
6.1
2
184
6.2
2
163
6.2
2
612
5.4
2
143
5.9
2
82
5.7
?
61
5.2
2
41
5.7
2
102
6.1
2
•
6.4
2
143
5.7
2
•
6.7
•
•
6.5
2
327
6.1
2
61
6.9
?
122
6.5
2
163
6.2
2
245
6.2
2
61
6.9
2
143
6.6
2
245
5.2
2
143
6.2
2
102
6.6
2
204
7.2
2
367
6.1
2
306
6.4
2
184
6.8
2
184
5.9
2
204
5.2
2
204
6.4
2
41
6.4
2
265
6.6
2
102
6.6
•
82
6.5
•
0
7.8
•
0
7.8
•
0
6.2
•
•
5.9
•
•
6.2
•
41
5.5
•
•
6.8
2
551
6.1
2
347
6.5
2
82
7.0
•
•
5.5
•
•
6.4
•
•
6.0
«
424
6.0
«
•
6.8
•
82
YEAR
65
65
75
76
56
52
56
50
56
49
50
51
51
51
51
37
56
76
76
65
52
53
52
49
38
53
49
49
55
53
47
49
51
3
-------
COOE
C006
NAME
YEAR
PH
METHOD
ALK
11 54
1154
H0LBR00K PO
54
6.6
«
*
1156
1156
NOBHICH PO
73
7.1
«
«
1159
1159
UNC4J °f)
54
6.6
•
•
1162
1162
IRONBOUND PT»
78
7.5
367
1163
1163
FOLEY PO
59
7.2
•
•
1168
1168
JONES PO
61
7.1
•
265
1170
1170
TURNER PO
6*
6.6
•
184
1171
1171
FERGUSON PD
62
7.6
•
•
1173
1173
ugh PO
77
7. 1
•
•
1175
1175
B LAKE
58
7.0
•
•
1176
1176
HCPHERSON PO
57
7. 0
•
•
1177
1177
BLUFFER PO
76
7. 1
«
286
1178
1178
2N0 CURRIER PO
74
7.1
•
184
1179
1179
UPPER HUOSON PO
52
6.9
•
•
1197
1197
CAIN PO
68
6.8
•
•
1198
1198
COLBY
66
6.0
•
•
1201
1201
halfmre PO
77
6.9
•
143
1212
1212
ANDERSON PO
53
6.6
•
•
1213
1213
BEAVER "0
53
6.4
•
•
1214
1214
BUBBLE Pn
42
6.8
•
•
1215
1215
OEBEC PO
42
6.8
•
«
1216
1216
QENNY PO
54
6.9
m
•
1217
1217
FITTS PO
42
6.8
m
•
1218
1218
HEALC "0
55
6.8
•
•
1219
1219
HORSESHOE PO
39
6.8
•
•
1220
1220
LINCCLN PO
55
6.8
•
•
1221
1221
little tunk PO
52
6.7
•
•
1222
1222
SANDY PO
40
6.8
•
•
1223
1223
SITE OIST0L LAKE
54
6.4
•
•
1224
1224
TRAFTON PO
38
6.9
•
•
1225
1225
TROUT PO
53
7.3
•
•
1226
1226
LEOGE PO
64
6.4
•
•
1229
1229
SPECK PO
62
5. 5
•
82
mo
1230
TUHBLEOOWN "0
66
5.6
•
82
1233
1233
MOUNTAIN »0-RANGELEY
77
6.5
•
61
1250
1250
CONE *TN. PO
51
5.2
•
41
1251
1251
UNKNCWN PO
53
6.0
•
122
1252
1252
LOWER SAWYER PO
48
6.8
•
•
1253
1253
BLACK *TN PO
51
5.5
•
41
*ETHOO
141
------- |