United States Environmental Research EPA/600/R-93/074
Environmental Protection Laboratory February 1993
Agency Corvallis OR 97333
Research and Development
&tzPA COMPARISON OF SELECTED
CRITICAL LOADS ESTIMATION
APPROACHES FOR ASSESSING
THE EFFECTS OF SULFATE
DEPOSITION ON LAKES
IN THE NORTHEASTERN
UNITED STATES
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Comparison of Selected Critical Loads Estimation Approaches
for Assessing the Effects of Sulfate Deposition on Lakes
in the Northeastern United States
George R. Holdren1, Jr., Timothy C. Strickland1, Barbara Rosenbautn1,
Robert S. Turner2, Patrick F. Ryan2, Michael K. McDowell1, Gary D. Bishop1,
Glenn E. Griffith1, Katie Smythe3, and Paul L. Ringold4
Project Officer
Parker J. Wiginglon, Jr.
U.S. EPA Environmental Research Laboratory
200 SW 35th Street
Corvallis, Oregon 97333
February 1993
The research described in this report has been funded by the U.S. Environmental Protection Agency.
This document has been prepared at the EPA Environmental Research Laboratory in Corvallis,
Oregon, through Contract No. 68-C8-0006 to ManTech Environmental Technology, Inc., and through
Interagency Agreement No. DW89934711-01-2 (through DOE) with Oak Ridge National Laboratory.
It has been subjected to the Agency's peer and administrative review and approved for publication.
Mention of trade names or commercial products does not constitute endorsement or recommendation
for use.
ENVIRONMENTAL RESEARCH LABORATORY - CORVALLIS
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CORVALLIS, OREGON 97333
1 ManTech Environmental Technology, Inc., U S EPA Environmental Research Laboratory, 200 SW 35th Street,
Corvallis, OR 97333
2 Oak Ridge National Laboratory, Environmental Sciences Division, PO Box 2008, Oak Ridge, TN 37831-6038
3 Science & Policy Associates, Inc , The Landmark Bldg, Suite 400, 1333 H Street, NW, Washington, DC 20005
4 United States Environmental Protection Agency, OfTice of Environmental and Process Effects Research, RD 682, 401
M Slreel SW, Washington, D C 20012.
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EXECUTIVE SUMMARY
Recent international agreements for controlling atmospheric pollutant emissions have been
based on the use of target loads that, in turn, require countries to develop national estimates of
critical loads—the highest deposition of acidifying compounds that will not cause chemical
changes leading to long-term harmful effects on ecosystem structure and function according to
present knowledge (Nilsson and Grennfelt, 1988). Critical loads data are most commonly pre-
sented as maps that can be readily compared between nations. The United States has proposed a
uniform generic framework (Strickland el al., in press) that allows critical loads to be developed
using a variety of methods for different geographic scales (e.g., region, subregion, or ecoregion).
That framework is used here to qualitatively compare the critical loads estimates obtained by
different modeling and mapping methodologies designed to predict the effect of acidic sulfate
deposition on lake chemistry. The objective of this report is not to provide recommendations of
critical or target loads that should be adopted, but to show the relative effects of using different
methodologies to estimate critical sulfate loads.
This document examines some of the options currently available for estimating critical loads
for acidic sulfate deposition to lakes and for mapping atmospheric deposition and ecosystem
status and response. Examples of approaches available for representing deposition, ecosystem
response, and critical loads include: point maps, grid maps, functional subregion maps, contour
maps, and isopleth maps. Point maps show the locations of systems exhibiting specific character-
istics. Grid maps show the relative spatial distribution of systems exhibiting some specific
characteristic. Functional subregion maps focus on the population characteristics for the region
specified. Information on geographic context can be conveyed in these maps by dividing the
region into functional subregions that capture spatial differences in population sensitivity Each
mapping option provides information at a different level of detail, and is more or less useful
under alternate applications and data base status.
A qualitative comparison of results from the different analysis and presentation approaches
used indicates-
• There are systematic differences in critical load estimates from different models or
from the inclusion of different operating assumptions within individual models,
therefore, it is important that ranges of values from multiple approaches be reported
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along with critical loads estimates and that these be considered when risk managers
develop target loading approaches.
o Even though the absolute value of estimated critical loads changes with the approach
used, the relative pattern of estimates appears constant.
Thus, while all of the approaches examined suggest that sulfate deposition in the Northeast must
be reduced to minimize adverse effects on sensitive lakes, there is currently no "best" technical
basis from which to select the magnitude of reductions necessary.
in
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ACKNOWLEDGEMENTS
The authors thank Vicki Alwell, Jack Cosby, David Crigal, Ann Hairston, Donald King, and
William McFee for review comments on an earlier draft of this report. We also thank Carol
Roberts for report assembly and production. Support for this project was provided to ManTech
Environmental Technology, Inc., by the U.S. Environmental Protection Agency under contract 68-
C8-0006. Support at the Oak Ridge National Laboratory was provided by the U.S. EPA through
the U.S. DOE under Interagency Agreement No. DW89934711-01-2 (EPA) and 1824 C020 A1
(DOE). This is ORNL Environmental Sciences Division Publication No. 4028. Oak Ridge
National Laboratory is managed by Martin Marietta Energy Systems, Inc. for the U.S. DOE under
Contract No. DE-AC05-840R21400.
iv
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TABLE OF CONTENTS
Section Page
Executive Summary ii
Acknowledgements iv
List of Figures vi
Abbreviations and Acronyms vii
Symbols vii
1. INTRODUCTION 1
1.1 Critical Loads Concept 1
1.2 The N0X Protocol 2
1.3 Purpose of This Demonstration 4
1.4 The United States Approach 5
2. METHODS AND APPROACHES 9
2.1 Region of Study "and Resource Identification 9
2.1.1 Methods of Subregionalization 10
2.1.2 Cridded Areas 11
2.1.3 Bedrock Sensitivity 13
2.1.4 Major Land Resource Areas (MLRAs) 15
2.1.5 Acid Sensitivity Subregions 15
2.1.6 Aggregated Acid Sensitivity Class Subregions 15
2.2 Deposition Characterization 20
2.3 Selection of Standards 21
2.4 Selection of Indicators and Endpoints 24
2.5 Resource Characterization 25
2.6 Description of Models 26
2.6.1 Steady-State Water Chemistry Model 31
2.6.1.1 Data Requirements 31
2.6.1.2 Correcting Cation Data for Marine Contnbutions 32
2.6 1.3 F-Factor Computation 33
2.6.1.4 Estimating Critical Loads 34
2.6.2 MAGIC Model 34
2.7 Results Comparison 38
3. RESULTS SUMMARY 41
BIBLIOGRAPHY 43
APPENDIX A - GENERAL DESCRIPTIONS OF ACID SENSITIVITY SUBREGIONS . . 47
APPENDIX B - CURRENT STATUS MAPS BASED ON NSWS-ELS DATA 53
APPENDIX C - CRITICAL LOADS MAPS IN THE NORTHEASTERN UNITED STATES 83
V
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LIST OF FIGURES
Figure Page
1-1 Critical loads development framework 6
2-1 Distribution of 1 x 0.5° grid cells assigned to the northeastern United States .... 12
2-2 Distribution of bedrock sensitivity subregions 14
2-3 Major land Resource Areas of the northeastern United States 16
2-4 Designation of acid sensitivity subregions 17
2-5 Distribution of relative acid sensitivities in the northeastern United States 18
2-6 Isopleth map of sulfate deposition (kg/ha/yr) 22
2-7 Distribution of lake ANC by acid sensitivity class 26
2-8 Distribution of lake pH by acid sensitivity class 27
2-9 Distribution of lake sulfate by acid sensitivity class 28
2-10 Distribution of lake nitrate by acid sensitivity class 29
2-11 Point map indicating individual NSWS-ELS lake locations 30
2-12 Distribution of sulfate critical loads (calculated with the SSWC model using the
Norwegian F-factor) in the northeastern United States by acid sensitivity class ... 35
2-13 Distribution of sulfate critical loads (calculated with the SSWC model using the
Paleo F-factor) in the northeastern United States by acid sensitivity class 36
2-14 Distribution of sulfate critical loads (calculated using the MACIC model) in the
northeastern United States by acid sensitivity class 39
VI
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LIST OF ABBREVIATIONS AND ACRONYMS
ANC — acid neutralizing capacity
DDRP — (U S. EPA) Direct/Delayed Response Project
ELS — (U.S. EPA) Eastern Lake Survey
EPA — Environmental Protection Agency
ha — hectare
ILWAS — Integrated Lake/Watershed Acidification Study
kg — kilogram
m — meter
LRTAP — Long Range Transboundary Air Pollution
MAGIC — Model for Acidification of Groundwater in Catchments
MLRA — major land resource area
SSR — Soviet Socialist Republic
SSWC — steady-state water chemistry model
TFM — Task Force on Mapping
UN-ECE — United Nations Economic Commission for Europe
USDA — U.S. Department of Agriculture
yr — year
SYMBOLS
N0X — nitrogen oxide
NO3 — nitrate
r2 — correlation coefficient
S0X — sulfur oxide
SO4 — sulfate
/Lteq/L — microequivalenls per liter
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SECTION 1
INTRODUCTION
In 1970, the Clean Air Act was enacted to protect and enhance the quality of U.S. air
resources and thus to promote the public health, welfare, and productive capacity of the U.S.
population (U.S. EPA, 1971). Substantial revisions to the Act have recently been adopted (Clean
Air Act Amendments, 1990). As the Act has been implemented, the control of sulfur and nitro-
gen oxides (SOx and NOx) has been one of its important elements. The Act requires the
Administrator of the U.S. Environmental Protection Agency (EPA) to establish emission standards
for existing coal-fired electric generating units that would reduce NOx emissions by 2 million tons
below the emissions levels projected to occur in the year 2000. The sulfur control program is
centered around a market-based approach to the trading of sulfur emissions The Act also directs
the Administrator to evaluate the potential benefits of allowing emissions trading between N0X
and SOx. In addition, the United States is participating in discussions within the Convention on
Long Range Transboundary Air Pollution (LRTAP) to evaluate the use of the critical loads
approach to regulating air quality emissions.
1.1 CRITICAL LOADS CONCEPT
The critical loads concept first appeared in the Swedish Ministry of Agriculture's 1982
document entitled Acidification Today and Tomorrow. It was also discussed in the 1983 United
States-Canada Memorandum of Intent on Transboundary Air Pollution and has been embraced by
the United Nations Economic Commission for Europe (UN-ECE) under the LRTAP convention
(Sverdrup et ai, 1990). The philosophy proposes critical loads of individual and interacting
pollutants as those below which significant harmful effects do not occur. Specifically, a critical
acid deposition load has been defined by Nilsson and Crennfelt (1988) as, "the highest
deposition of acidifying compounds that will not cause chemical changes leading to long-lcnn
harmful effects on ecosystem structure and function according to present knowledge " In
adopling the critical loads philosophy, the UN-ECE iccogniics that national policy offices may
set emission regulations at higher exposure levels lo accommodate economic and social factors, as
well as ecological factors This higher level of exposuie, termed the "target load," provides a
1
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pragmatic means for regulating emissions based on the scientifically determined critical load
(Nilsson and Grennfelt, 1988). Target loads may be higher, equal to, or lower than critical loads
In 1986, the UN-ECE Working Group on Effects proposed a workshop to address critical
loads for long-term deposition of nitrogen and sulfur with regard to their effects on soils and
groundwater. Other recent international meetings have addressed excess nitrogen deposition
(Lealherhead, U.K., 1987), critical levels of nitrogen (Bad Harzburg, FRG, 1988), critical loads
for sulfur and nitrogen, (Skokloster, Sweden, 1988), and the role of nitrogen in the acidification
of soils and surface waters (Copenhagen, Denmark, 1988). In the proceedings from the workshop
on critical loads held in Skokloster, Sweden, Nilsson and Grennfelt (1988) described both the
state of the art of critical loads research and the aims of the Skokloster Workshop:
The concept of critical loads is only in us infancy. The underlying scientific knowledge
is in many cases weak, and the importance of different processes involved is not always
quantified. This is especially the case for nitrogen, where long-term, effects by nitrogen
itself and in combination with acidification need to be studied in much more detail.
Thus, the aim of the report is not to give absolute and indisputable values on critical
loads, but to give the best estimates with present knowledge and to inspire further
research. We also believe that the values presented will be useful in a first estimate of
areas at risk in different regions in Europe.
1.2 THE ISOx PROTOCOL
In 1981, the United Slates became a party lo the LRTAP Convention; in 1985, negotiations
began towards a protocol to apply the critical loads approach for regulating emissions of N0X. By
May 1985, agreement had been reached on most elements of the Protocol and it was opened for
signature on October 31, 1988, in Sophia, Bulgaria. By December 31, 1988, the following
Parties to the LRTAP Convention had signed the N0X Protocol: Austria, Belgium, Byelorussian
SSR, Canada, Czechoslovakia, Denmark, the Federal Republic of Germany, Finland, France, the
German Democratic Republic, Greece, Italy, Liechtenstein, Luxembourg, The Netherlands,
Norway, Poland, Spain, Sweden, Switzerland, Ukrainian SSR, the Union of Soviet Socialist
Republics, the United Kingdom, and the United States of America. This agreement is embodied
in the N0X Protocol (UN-ECE, 1988).
The key provisions of the N0X Protocol are found in Articles 2, 3, 4, and 6 and include the
following-
2
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• A freeze, or cap, on NOx emissions.
• Technology-based standards for new stationary and mobile sources and controls on
existing sources
• Annual reporting and information exchange requirements.
• The availability of unleaded gasoline on international routes.
• Research and cooperation to attempt to establish control strategies based on critical
loads, as defined in the Protocol.
The details and ramifications of implementing a critical loads research program arc
somewhat ambiguous in the N0X Protocol. Article 2 specifies that as a second step (i.e., after
the implementation of the NOx emissions cap) the Parties shall commence negotiations, no later
than six months after the Protocol goes into force, on further steps lo reduce national annual
emissions. Best available scientific and technological developments should be taken into
account, as well as internationally accepted critical loads and other elements from the research
program. The work elements to be undertaken to establish critical loads are outlined in Article 6
of the Protocol and include:
• Identification and quantification of the effects of N0X emissions on humans, plant and
animal life, waters, soils, and materials.
• Determination of the spatial distribution of sensitive areas.
• Development of measurements and model predictions to calculate emissions and quantify
long-range transport of NOx and related pollutants.
• Development of methods to integrate scientific, technical and economic data in the
context of a critical loads approach to determine appropriate control strategies
• An agreement lo cooperate in estimating critical loads, developing emissions reductions
as required to achieve agreed upon objectives based on critical loads, and developing
measures and a timetable (commencing by 1996) for achieving such reductions
It should be emphasized that any additions or modifications to the Protocol that might be
suggested based on critical loads studies would have lo be negotiated as amendments lo the
Protocol or established as a new protocol Piotocol revision is currently in progress; the first
international workshop designed to reconstruct the Piotocol was conducted in April, 1992
3
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1.3 PURPOSE OF THIS DEMONSTRATION
One purpose of this report is to document the mapping consequences of some example
methods that might be used to estimate critical loads. This document is not an attempt to
recommend specific critical load values for specific components of the ecosystem, nor is it an
attempt to identify the best ways of making those estimates. Such recommendations require that
many policy-specific issues, such as selection of the appropriate target populations, be addressed
in depth with the appropriate policy-setting agencies or offices prior to undertaking any assess-
ment efforts (discussed in more detail by Holdren et al., in press*5). Rather, the purpose of this
work is to investigate (qualitatively) the consequences that certain decisions, such as model
selection, endpoint selection, or regional spatial frame selection, have on the estimation of critical
load values for selected systems, thus providing the reader with a sense of comparability between
approaches.
Another major purpose of this document is to begin the process of evaluating the methods
presented and suggested by the Task Force on Mapping in their methods manual (UN-ECE,
1990). Many of the methods presented in the generic manual were developed as part of
individual national research efforts and provide high-quality estimates of critical load values in
those regions for which they were developed. It is not clear, however, how broadly applicable
these methods may be to other regions of Europe and North America that are ecologically distinct
from the areas where the methods were developed. Given the information available to us, it
would not be appropriate to attempt to make any evaluation regarding which, if any, of the
methods investigated offers the best estimate of critical load values.
Because of the limited data and model competence for nitrogen as compared to sulfur, the
U.S. program used sulfur to develop and demonstrate a method for establishing critical loads for
nitrogen. The focus is more on procedure than on results. This report describes specific tasks
and activities conducted to test a selected approach. As such, it focuses on the execution of
tasks and a comparison of the results.
4
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1.4 THE UNITED STATES APPROACH
In 1981, the United States became a Party to the LRTAP Convention. To meet U.S.
obligations to LRTAP under the N0X Protocol (UN-ECE, 1988), the U.S. Environmental
Protection Agency, with support from the U.S Department of Energy, initiated a Critical Loads
Research Program in early 1989. The program was designed to develop and demonstrate a
generic approach for setting critical loads of nitrogen (or other pollutants) for sensitive
watersheds. The method reflects a risk assessment approach and has been described in detail
elsewhere (Strickland el al., in press; Hunsaker el al., in press; Hicks el al., in press; Holdren el
al., in press8). This approach allows for the flexibility to tailor the assessment program to meet
differing needs as might be determined by data and funding availability. The framework is
designed in the form of a decision tree (Figure 1-1). The branches of the tree guide the user
through an assessment of individual needs and acceptable uncertainty.
An explicit framework will also force scientists and policy makers to acknowledge inherent
assumptions in a formal manner. This is perceived as essential to the uniform development of
critical loads, because program design and selection of measurement criteria are often based on
the cumulative learning and/or opinions of the personnel involved In the process of setting
critical loads, a uniform framework will facilitate comparability among ecosystems, regions, and
nations where differences exist in response, population distributions, deposition loads, and
ecosystem use criteria.
Tins study adopts the stepwise approach outlined elsewhere by Strickland et al. (in press).
In this section, we delineate the decision criteria used in the development of the sample maps
presented in this document. The discrete steps at which decision criteria were considered are:
1. Regionalizalion. Rcgionahzation (or subregionalization) within a depositional grid is
important because atmospheric deposition can be altered on a regional scale but not on the
scale of individual ecosystems. In addition, environmental policy is likely to be guided
more by the responses of groups or populations of ecosystems than by the response of any
individual ecosystem Therefore, characterizing the variation in ecosystem responses within
an area will become an importanl consideration in the eventual setting of target loads
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U S Critical Loads Program
Component Aspects of Process Examined
SeIect Receptor —
I
Select PoMutant
4
Determine Critical Llmlt9
¦c
Sol Is
Lakes
— Multiple Sol is
1
Lakes —
ANC=0
— ANC=25
Select Mapping Method
Select Grids, Regions, etc
Gr I ds
Bedrock Classes
— MLRA s
_Acid Sensitivity
Co 11ect Data
-~ Stocks at
Samp 11ng IntensIty
Risk Exclude Naturally
Acldlc
PoI Iutants
Po11ution Maps
Critical Levels/Loads
Critical Levels/Loads Maps
- SSWC CBrakke-FD
— SSWC CPQleo-F)
MAG IC
Critical LeveIs/Loads Exceedance Maps
1
Target LeveIs/Loads
— Steady
State
— Dynamic
C50yO
Dy nam I c
C 100yO
Figure 1-1 Critical loads development framework
6
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2. Deposition characterization. Characterization of total deposition on multiple spatial and
temporal scales is important for correlation and pattern analysis based on historical and
current ecosystem status, for model calibration, and for development of estimates of ecosys-
tem state under alternative deposition scenarios. As with the other steps of the generic
method, this information may come from multiple sources (atmospheric models, site-specific
wet and dry deposition measurements, or interpolation techniques, or from combinations of
these approaches).
3. Resource identification. This involves both explicit articulation of the issues (e.g.,
concern about the chronic status of surface waters or about episodic response) and
technical analysis of the extent of sensitive ecosystems and/or ecosystem components (e.g.,
high-elevation forests or low ANC surface waters). The output from this step is a map of
the current status of resources.
4. Defining the regulatory endpoint. The regulatory endpoint defines the threshold of the
"deleterious" condition. The development of a regulatory endpoint requires the selection of
an indicator of the endpoint. It may also require the development of information about the
state of the ecosystem in the absence of anthropogenic deposition so limits can be set on
what a reduction in deposition can be expected to achieve.
5. Model selection and response forecasting. Models predict how changes in deposition
are reflected as changes in ecosystem status. In multiple instances, many models exist to
predict a given response. Each model has benefits and disadvantages that limit or favor its
use in any particular application The application of multiple models to any particular
issue can provide increased confidence in the predictions, if the predictions are convergent,
or a basis for identifying additional research needs if the predictions are divergent
6. Ecosystem response presentation. International agreements on abatement stralegies
include agreements that major results will be provided in the form of harmonized maps An
integrated approach to critical loads and critical loads mapping must represent complex,
discontinuous, and spatially distributed factors regulating ecosystem responses lo imposed
stresses Maps should be capable of displaying comparisons between ecosystem iespouse
and pollutant load and should provide a ready means of comparing population characteris-
tics within and between regions.
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SECTION 2
METHODS AND APPROACHES
Ecosystem response to pollutant deposition is a direct function of (1) the level of toxicity of
the pollutant on the ecosystem and (2) the ability of the system to eliminate or ameliorate result-
ing damage. It is therefore undesirable to extricate the assessment of an areal critical load value
from the ecosystem response to a pollutant. It is also unwise to develop an areal critical load
value without making an assessment of the range of ecosystem responses expected within the pre-
defined area and a decision as to the proportion of the resource in question for which loss is
politically and/or economically acceptable.
2.1 REGION OF STUDY AND RESOURCE IDENTIFICATION
This demonstration is conducted using data collected from the northeastern United Stales.
The region includes all of New England, the Adirondack and Catskill regions of New York State,
and the Pocono Mountains area of Pennsylvania. The reason for choosing this area is simply that
the wealth of readily accessible, pertinent data from this area is unmatched by the data collected
for any other part of our country. This wealth of data allows us to investigate a range of models,
and to evaluate various approaches of using particular models
One of the major policy issues not addressed in the selection of this region is that of
dealing with the resource of interest. Decisions regarding the selection of appropriate sensitive
resources to be evaluated must involve policy makers and were beyond the scope of this demon-
stration However, if the EPA were to endorse a critical loads approach, it would be reasonable
to expect that the Agency might wish to protect, for example, first- and second-order lakes with
maximum depths greater than 3 m. Thus, the resource used in this work is the population of
lakes in the Northeast region characterized in the U.S EPA's Eastern Lake Survey (ELS)
(Linthurst et al , 1986). Selection of this group was a matter of convenience for this study The
target population includes lakes in the size range of 4 to 2000 ha Lakes in the 4 to 8 ha class
are under-represented in the sample population by about a factoi of two
Soils data are used only indirectly in this study Soil physical and chemical properties arc
required input for the MAGIC model. Soils data were obtained from the U.S. EPA's Direct/
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Delayed Response Project (DDRP) (Church et al , 1989). Details of the model input require-
ments and of their structures are discussed in Section 2 6.2.
Finally, lake sediment diatom data (Sullivan et al , 1990) were used lo obtain estimates of
pre-industrial, or pre-1850 (paleo-) pH and ANC values for 35 lakes in the Adirondack region of
New York State. Data were originally collected from 45 lakes in this region, but the number was
reduced by 10 for our sample because of a lack of overlap with the DDRP sample lakes. Details
of how the paleo-ANC data were used are provided in Section 2.6.1, in the description of the
steady-state water chemistry model and the development of F-factor values.
2.1.1 Methods of Subrcpionalization
Examples of approaches available for representing deposition, ecosystem response, and
critical loads include point maps, grid maps, functional subregion maps, contour maps, and iso-
pleth maps. Point maps show the locations of systems exhibiting specific characteristics. Grid
map presentations show the relative spatial distribution of systems exhibiting some specific char-
acteristic. Functional subregion maps focus on the population characteristics for the region
specified. Information on geographic context can be conveyed in these maps by dividing the
region into functional subregions that capture spatial differences in population sensitivity The
mapping options differ in the methods used to illustrate spatial relationships Each mapping
option provides information at a different level of detail, and is more or less useful under
alternate applications and data base status.
One of the major goals of this effort has been to determine how strongly the selection of
subregions affects the interpretation of critical load estimates. This is an important issue
because, short of being able to perform a complete census of the resource of inlercst, it will be
necessary to use statistical sampling of the resource to make the desired ecosystem response
projections. Different statistical sampling schemes will allow users to addiess different
populations of resources with varying degrees of accuracy.
One advantage of using the EPA ELS data for this demonstration study is that they aic
spatially neutral, meaning that sampling sites were selected randomly without consideration of
geographic location. Therefore, one can choose various spatial aggicgations of systems (i c.,
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subregions) while maintaining the statistical integrity of the different populations within each
subregion.
The intent of subregionalization is to select continuous or noncontinuous geographic areas
within which the individual ecosystem units are, if not homogeneous, then at least distinct as a
population, from those ecosystems in adjacent areas. As an extreme example, subregionalization
could be used to separate lakes located on granitic gneissic bedrock from those that might be in
an adjacent limestone province. Clearly, such systems would respond quite differently to acidic
deposition. Subregionalization schemes can be based on any of a number of ecosystem character-
istics from elevation to physiography, from land use to bedrock geology. Many schemes have
been developed on a hierarchial system of physical, chemical, and biological characteristics,
specific combinations of which can be used to describe regions or subregions, depending on the
requirements of the users (e.g. Bailey, 1976; Kuchler, 1964; Omemik, 1987). Brief descriptions
of the criteria used to establish each regionalization scheme demonstrated here are outlined in
the appropriate sections that follow. Readers should be aware that many other approaches might
equally well have been employed in this study.
2.1.2 Gridded Areas
This is the simplest of the regionalization schemes used here. The impetus for this
approach is based on an agreement contained in the Task Force on Mapping methods manual
(TFM manual) (UN-ECE, 1990), which requires that all final critical load maps be presented by
gridded areas of not greater than 0.5° latitude by 1 0° longitude. Smaller areas are permitted
under the manual guidelines, but are not used here because, in our opinion, for the purpose of
examining airsheds and regional effects, smaller areas do not make a great deal of sense. To
implement this approach, we overlaid the 0 5° latitude x 1.0° longitude grid on a map of the
northeastern United States (Figure 2-1). The basic assumption employed was that lakes within
any grid square are representative of only those systems within the grid. Grids containing no
lakes are shown as having no data.
The primary limitation of this approach is that the relatively small number of lakes 111 any
given grid may result in a low relative certainty in the representativeness of the lakes for the
region. To apply this method generally across the United States would require that a large
11
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Critical Loads Project
Northeastern United States
U.S. Envir«nm«nt
-------
number of systems be sampled in order lo obtain a representative number of samples from each
grid.
2.1.3 Bedrock Sensitivity
One of the primary factors thought lo regulate the sensitivity of surface waters to acidic
deposition is the composition of the underlying bedrock (Norton et al., 1982) Many rock types,
such as quartzites or granitic gneisses, are effectively unreactive toward mildly acidic soil and
surface waters, whereas others (e.g., limestones and dolostones) are highly reactive. Several reac-
tivity scales have been presented over the past decade (e.g., Norton et al., 1982; Hendry et al.,
1980, Sverdrup and Warfvinge, 1989). For regional scale studies, these bedrock sensitivity rank-
ings have been effective in identifying geographic areas potentially sensitive to the effects of
acidic deposition (Norton et al., 1982). The primary use of these methods, however, has been to
distinguish areas that differ in mean ANC by hundreds of /ieq/L rather than by tens of /xeq/L. At
the resolution of tens of jueq/L, other factors, such as soil depth, vegetation type and status, and
soil structure and composition may be more important for controlling surface water ANC.
Bedrock sensitivity maps have been prepared for the northeastern United States on a
county-by-county basis (Norton et al., 1982). This atlas describes the areal percentages of
bedrock in each county that fall within each of four sensitivity classes. The classes are
based on the reactivity scale proposed by Hendry et al. (1980), commonly known as the Norton
Bedrock Scale.
To develop our bedrock sensitivity regionalization scheme (Figure 2-2), we assigned each
county in the northeastern United States a sensitivity index. Index 1 was assigned to each
county that contains an areal percentage of Norton's bedrock Class 1 that is > 5%. If the
amount of Norton bedrock Class 1 is < 5%, but that of Class 2 is > 5%, the county is given an
index value of 2 Similarly, if the areal percentage of Classes 1 and 2 is < 5% each, but the
areal coverage of Class 3 is > 5%, an index value of 3 is assigned. Virtually all counties in the
study area fall into one of these three index groups.
The cutoff value of 5% used to distinguish the different classes is somewhat arbitrary, but it
was chosen to be consistent with the spirit of protective systems standards used throughout this
study (see Section 2 3) In essence, the 5% level was chosen as one example of an cndpoint
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Figure 2-2. Distribution of bedrock sensitivity subregions (modified from Norton et al., 1982). The reporting unit is at the county
level. Sensitivities are defined as: (A) > 5% of the county area is composed of bedrock in Norton's bedrock Class 1,
(B) < 5% of the county area is composed of Norton's bedrock Class 1, but > 5% of the county area is composed of
bedrock Class 2, (C) < 5% of the county area is composed of the sum of bedrock Classes 1 and 2, but > 5% is com-
posed of bedrock Class 3.
JSiiiiuiiuiiij
Critical Loads Project
Northeastern United States
U.S. E•vIr«nm«nta I Pr«t«etion Agtncy
Env i ranmenta I Rtnoch Laboratory - Corvtllil
Norton Bidrtck
Saniitivitjr Cli
R« d • f I n • d claim
¦ A) Clan t > 5*
tsa B) Clan 1 < 5X
and C I a 11 2 > SX
CD C) CI an I . 2 < SX
and Clan 3 > SX
-------
potentially interpretable as the "minimal land surface area" that might be construed to represent
a significant ecological impact. Clearly, before setting policy-relevant critical load values, policy
and technical groups must reach a consensus regarding appropriate cutoff values
2.1.4 Maior Land Resource Arca9 (MLRAs)
The MLRA approach (Figure 2-3) is a generic, hierarchial classification based on patterns
of soils, climate, water resources, and land use. MLRAs were developed by the U.S. Department
of Agriculture to "provide a basis for making decisions about national and regional agricultural
concerns, identify needs for research and resource inventories, provide a broad base for extrapo-
lating the results of research within national boundaries, and serve as a framework for organizing
and operating resource conservation programs" (USDA, 1981). As part of its efforts to predict
the potential effects of acidic sulfate deposition on the environment, the U.S. EPA recently used
the MLRA framework as a basis for ecological classification and sampling (Linthurst et al., 1986;
Church et al., 1989). We decided to include MLRAs in this demonstration to provide a frame of
reference that will be comparable to other EPA and governmental efforts.
2.1.5 Acid Sensitivity Subrcgions
The acid sensitivity classification is a recently developed, hierarchial scheme designed
specifically to group surface waters into geographic areas that should have similar responses to
given levels of acidic deposition (Griffith et al., 1990). The scheme uses spatial patterns of
physiography, soils, land use, bedrock geology, deposition, vegetation, and water chemistry to
identify and isolate geographic areas where more similarity in the response to acidic deposition is
expected within a given subregion than between subregions (Figure 2-4). Ceneral descriptions of
the Northeast region and its subregions can be found in Appendix A.
2.1.6 Aggregated Acid Sensitivity Class Subregions
Acid sensitivity classes (Figure 2-5) were assigned to the subregions described in Appendix
A concurrently with the process of defining subregions That is, the classes were based on the
15
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Mm
S14J:^»«(tk«r»
514fA: lirlkara Caaital Pill*
JI4II: Leaf Ilia*! - C«p« Col
Caaatal latleal
T153: Htd—Atlantic Caaatal f!«ia
(Saaraa: UStA. IH1)
Critical Loads Project
Northeastern United States
U.S. Environmental Pretictlen Agency
Envi ronmnntal Rutoch Loboratory - C o r v a I I is
Major Land Resource Areas
t.i.. ..,i«
lilt: (ultra Milkui Plateae
Njf|: Central Alla|laa; Plitaaa
till |f%AN*fk«ajr til laaatalaa
11)1: Eaatari Okie Till Nil*
•111! stac|at«4 Allaaiaajp Plataea nl
CaUkllli Ho.itJlM
8141: Tufkill rutin
>111: JIT liiriiu • Cliaklili Pltla
114): Nartkaaatera tUaatelaa
•144A: Nil uiliil ill Ceetera Nat Terk
Ueleal. leelkere Part
•1111: lee Cajleel Hi lutwi Naa Yerk
tlli: !S
i«l. lerlkera Perl
.naectltat ValUj
reeeteek Araa
Figure 2-3. Major land resource areas of the northeastern United States (from USDA, 1981).
-------
Critical Loads Project
Northeastern United States
U.S. E (i»I r ( aim n t«I Protection A g • n c y
Environmontal Rimch Laboratory - C o r«a I I i i
Acid Sensitivity Subregions
used For statistics
A. Adirondack!
At. Itiliiiil Adlrtidicti
J1"
S. R»rik»ra AimImIImi
It. Cotilill ImoMIm
Iti. Eottri Cttikllli
. Pooom NoooImm
C%»o. Parlphory
C. JUf. MjjjlMdt
Spas®
C3. lirtltiitrol loiiiitaHttt U»I«H/
SlltlMll ll> Nul'l
C4. Tactile Hcifittlni
C4a. Imiilni ratm
8: EktIRaw
0. Siatkari Nil Ei|lni
II: «I*'V V.' 11 f.V*" *
04. Siittt^Ja* E*|l«*4/E*t(ori
E. brlliiitiii In Eiflaa4 Plain oi< llllll
f. A«ntlt Anai of tl«
Figure 2-4. Designation of Acid Sensitivity Subregions (from Griffith et al., 1990).
-------
CO
Critical Loads Project
Northeastern United States
U.S. tn«i ronmantal Protection Ajincy
En* i ronnontal Rtnoch Laboratory - C o r v a 11 i a
Acid-Sensitive Aquatic Areas
by 6. Griffith and J. Omernik
B-
Relative Regional Soniitwty Scale
High
Figure 2-5. Distribution of relative acid sensitivities in the northeastern U.S. (from Griffith et al., 1990).
-------
same attributes and information used m delineating subregions. Tlie number of classes was a
somewhat arbitrary decision, but seven classes seemed to provide enough separation to dis-
tinguish the range of acid sensitive areas in the Northeast, and seven appears to be an optimum
number of classes for cartographic perception for color maps (Jenks and Caspall, 1971, Miller,
1956). Although sensitivity classes were assigned in a qualitative manner, they are based on
some quantitative data, (e.g., the EPA's Eastern Lake Survey, the Adirondack Lake Survey
Corporation data, and data bases collected from federal and state agencies and university
researchers used to compile the northeastern alkalinity map (Griffith and Omemik, 1988). Many
of the same elements used to define the boundaries of the subregions (surface water ANC, pH,
S04, N03, etc.) were also used to establish the relative sensitivity ranking In addition to this
numeric data, the mapped elements such as geologic type, soils, land use, and atmospheric depo-
sition levels were also used to assign the sensitivity class. Again, this was done in a qualitative
manner; a rule-based approach or weighting factors were not used. Similar to the dasymetric
mapping technique used Jo define ecological regions (Gallant et al., 1989), many additional
factors that influence the phenomena being mapped and many types and forms of information are
considered. But the relative importance, or effect on sensitivity, of any one factor might vary
from one area to another.
The high sensitivity class (Class 1) has the lowest ANC, lowest pH, and highest ratios of
acid anions to base cations and is characteristically a forested, mountainous area that receives
high acidic deposition and is located over a sensitive geology. These areas include the southwest
Adirondacks, the eastern Catskills, the Pocono Mountains, and the southern Green Mountains, for
example The weight of evidence is quite high for the high sensitivity class in terms of spatial
congruence of many sensitive water chemistry parameters and expected land characteristics
Classes 2 through 5 each have progressively fewer acid sensitive characteristics and tend to have
more uncertainty regarding where the class breaks occur. Tins uncertainty results partially from
the use of choice or discretion rather than a rigid set of criteria or attributes for differenliating a
class 4 area from a Class 5, for example, but it is also due to more heterogeneity of acid sensitive
resources in some of these mid-range areas The aquatic systems in Classes 6 and 7 arc
generally well-buffered, low-clevalion areas and agricultural lands
19
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2.2 DEPOSITION CHARACTERIZATION
International agreements on abatement strategies include agreements that results will be
provided in the form of harmonized maps The minimum set of maps necessary to develop and
represent critical loads includes.
• Deposition maps showing current and altered scenarios.
• A map of the current state of the ecosystem(s) of concerns.
• A map of critical loads or critical loads exceedances.
In addition, some philosophies would require a map of ecosystem stale in the absence of
anthropogenic deposition (for baseline approach).
An integrated approach to critical loads and critical load mapping must incorporate
complex, discontinuous, and spatially distributed factors regulating ecosystem responses to
imposed stresses. Maps should be capable of displaying comparisons between ecosystem
response and pollutant load, and should provide a ready means of comparing population charac-
teristics within and between regions.
Characterization of total deposition on multiple time and space scales is important for
(1) correlation and pattern analysis based on historical and current ecosystem status, (2) effects
model calibration, and (3) estimation of ecosystem response to alternative deposition scenarios.
It is important to know the deposition not only on a regional basis, but also at the level of
individual ecosystems of concern. This information can come from multiple sources, including
appropriate atmospheric models, wet and dry deposition measurements, and various interpolation
techniques, and from combinations of these approaches. Maps of current and altered deposition
could illustrate aspects of any spatial patterns across an assessment region. Altered deposition
could be predicted using models that report average deposition for a grid cell. Information on
subgnd cell variability for subregions can be developed by looking at patterns of current depo-
sition The grid size should be appropriate for the spatial heterogeneity of deposition in the
assessment region.
The quantity of pollutant deposited to an area will not simply be related to the precipitation
quantity, and wet deposition composition will not be strictly proporlional to dry deposition
(composition or quantity) Dry deposition is a function of pollutant concentration fields and of
individual pollutant deposition velocities, which are dependent on forest canopy trapping
20
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efficiencies In addition, short-range spatial variability resulting from orographic effects will
further complicate the production of deposition maps in mountainous areas. Hence, slope and
aspect will significantly affect both the quantity of precipitation and the composition of
deposition.
The choice of an appropriate spatial scale for investigating the effects of deposition on
selected ecosystems or for setting regulatory standards depends on the size of the systems being
studied. For small area systems, high-resolution, site-specific deposition-data may be most
desirable. As ecosystem size increases, data requirements concerning the level of detail required
to adequately model deposition drop measurably.
The mapping of deposition can be conducted on a range of spatial scales, from kilometer-
sized grids to regional representations. Precipitation composition and quantity can vary on
similarly small spatial scales, and orographic effects (especially changes in elevation and aspect)
can lead to local deposition variation. In many cases, deposition to sensitive ecosystems (e.g.,
high-elevation forests and small headwater streams and lakes) may not be typical of the region as
a whole. For these systems, more intensive approaches may be appropriate to develop deposition
estimates accurately reflecting site conditions. High-resolution estimates (kilometer-sized grids)
of deposition would be required. This data-intensive approach would require either spatially and
temporally resolved regional deposition data, or the development and implementation of models
capable of predicting site-specific deposition. If ecosystems are of sufficient size, then the scale
used to represent deposition data may be increased without loss of accuracy or critical informa-
tion. The wet sulfate deposition information used here was developed during the DDRP (Church
el al., 1989) and is presented as an isopleth map in Figure 2-6.
2.3 SELECTION OF STANDARDS
Once the regions and resources of interest have been identified, the next major step is to
decide how environmental damage will be evaluated with respect to a given level of deposition
Within the confines of the N0X Protocol (UN-ECE, 1988), a critical load is defined as that quan-
tity of deposition at which deleterious effects to an ecosystem or population begin to be
measurable. However, before this definition can be implemented, it is necessary to determine
what constitutes a deleterious effect. There are two basic approaches by which the extent of
21
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Critical Loads Project
Northeastern United States
U.S. Envlronmcntol Protect lor A g • n c y
En* i ronmonta I Rtnoth Laboratory - Corvallis
S04 Deposition kg/ha/yr
1985 - 1987 Avoragii
Figure 2-6. Isopleth map of sulfate deposition (kg/ha/yr).
-------
damage can be evaluated, a protective, or absolute, standard and a baseline, or relative,
standard.
The simplest approach is to select an absolute or protective standard This approach is
predicated on the assumption that an optimal (or minimum acceptable) condition for an
ecosystem can and should be selected to protect a particular resource, for example, a
particular fish species. With this approach, the user determines that damage has occurred to an
ecosystem if the values of an indicator (or set of indicators) exceeds (or drops below, depending
on the indicator) a specified level. The primary advantage of this approach is that it is easy to
apply and provides a tangible goal against which one can judge the success of a regulatory
program. The disadvantage is that the method does not accommodate population variability well.
The method also does not implicitly provide a mechanism for dealing with systems that might
never have been within the desired regulatory bounds. For example, we now recognize that many
lakes within the Adirondack region of New York State probably had pH < 5.3 and ANC < 0 in
colonial times (Sullivan et al., 1990). As such, it would be unreasonable to expect these systems
to exceed the values specified by such a protective standard, regardless of the reduction attained
in acidic sulfate deposition.
The alternate approach for establishing a reference condition is the baseline, or relative,
standard. The baseline approach can be developed relative to the state of the ecosystems, either
under current deposition or before the onset of anthropogenic deposition. Although this approach
does not in and of itself characterize the indicator status when an individual system is receiving a
critical load, it does allow for substantial population variability in assessing the effects of
deposition.
We have assumed a protective standard in generating all critical load maps in this report
Aside from the ease of application, the protective standard is not constrained by the paucity of
paleo-environmental data necessary for application of a baseline standard; such data are available
in the United States only for the Adirondack State Park Region of the Northeast. Use of the
protective standard is also suggested in the Task Force on Mapping metliods manual (UN-ECM,
1990). This approach will make our results more readily comparable to those obtained Ijy out
European counterparts.
23
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2.4 SELECTION OF INDICATORS AND ENDPOINTS
Within the guidelines of the Task Force on Mapping methods manual, biologically rclevanl
indicators of potential ecosystem damage are to be used in determining the critical loads for
deposition. Table 2 1 of that manual contains a list of suggested indicators that might be applied
to various water-related receptors. Companion documents to the methods manual (eg., Nilsson
and Crennfelt, 1988) go further by suggesting endpoints that might be used in evaluating certain
indicators. Specification of these endpoints assumes the implementation of a protective standard
The indicator used in this study is surface water ANC, and the two endpoints investigated
,are 0 and 25 jU-eq/L. The ANC values were selected to correspond approximately to surface water
pH values of 5.3 and 6.0, respectively. The importance of these pH values has been discussed
extensively in the companion documents to the TFM manual (UN-ECE, 1990, Annexes II and III)
and elsewhere (Schindler, 1988; Baker el al., 1990; Baker el al., 1991).
Several criteria were used in the selection of the indicators and endpoints. As suggested by
the TFM manual, ANC is a biologically relevant parameter, and thus policy relevant. Excellent
regional ANC data are available for surface water resources in various parts of the United States.
We also have a moderately detailed understanding of the processes and mechanisms that regulate
surface water ANC values, and multiple models are available to predict the response of surface
waters to different loadings of acidic compounds. Finally, information amassed over the last
decade suggests that surface water ANC values from sensitive ecosystems should respond to
changes in deposition on time scales ranging from years to a few decades. Each of these criteria
is a necessary component of a meaningful indicator. However, having selected an indicator, it
remains to select defensible, and policy relevant endpoints for evaluating the magnitude of
changes imposed on ecosystems through deposition.
As with the selection of any set endpoint, certain limitations are associated with the
selected ANC values of 0 and 25 /ieq/L Individual species of organisms will respond quite
differently to each of these levels (Baker el al., 1990). Some, such as cJams and crayfish, will
begin to experience deleterious effects when chronic pH values drop below about 6 0 oi 6 2
More tolerant species, however, might not be affected until the chronic conditions are distinctly
acidic. Even then, if the level of surface water acidity is driven by natural organic acids (Krug ci
al., 1985), the direct biological effects of inorganic acid deposition might be minimal
24
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Indicators other than ANC are not pursued in this report (other than to present information
on current conditions of the sample population). We acknowledge that others might be more
useful for evaluating the biological effects of acidic deposition (e.g., dissolved A1 species
concentration and organic acid content), especially with regard to chronic acidification of surface
waters. However, it is likely that ANC will remain an important component of any attempt to
determine critical loadings for atmospheric pollutants.
2.5 RESOURCE CHARACTERIZATION
We examine the potential for lakes in the northeastern United States to undergo chronic
acidification (reduction of ANC to 25 /xeq/L or 0). The critical load for a region was assumed to
have been exceeded if the critical load for any lake in the region was exceeded. The base popu-
lation of lakes was drawn from the U.S. EPA's Eastern Lake Survey (Linthurst et al., 1986;
Kanciruk et al., 1986). Data were gathered for 762 lakes ranging from northeastern Pennsylvania
to Maine, representing a total of over 7150 lakes in the region. These lakes are a subsampling of
lakes in the size range of 4 to 2000 ha, with lakes in the size class of 4 to 8 ha being under-
represented in the population.
To place the model predictions of critical loads in context, current status chemistry maps
for ANC, air-equilibrated pH, sulfate, and nitrate were developed using all of the regionalization
schemes described in Section 2.1. For simplicity, at this point in the report, we present only
maps developed using the acid sensitivity subregion scheme (Figures 2-7 to 2-10; all other cur-
rent status maps are presented in Appendix B). For ANC and pH, maps were shaded according
to the lake having the lowest value in a subregion, for sulfate and nitrate, maps were shaded
according to the lake having the highest concentration in a subregion. A point map has also
been included to indicate the locations of lakes used in this study (Figure 2-11).
2.6 DESCRIPTION OF MODELS
The value of any model-based risk assessment hinges on the quality of the dose-response
models used to predict ecosystem responses to imposed environmental stresses. Because of the
importance that model-based predictions will have, one valuable exercise is to determine llic
25
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to
On
Critical Loads Project \
Northeastern
United States \^\
U.S. E»» 1 ronmn111 Pr»l«etlen A|inty -——\ ~"V—\
E n» r o n m • n o 1 Reieoch Lokorotory - C o r * a 1 1 It —"T \ b
National Surfoc« Volar Survey
Eoetern Loka Survay Dots
\ \ -A—-—\ V, — \
I \ \ -—\ "* \ \ h it" '' ' 1 j i&jjljtt ^\ \
Add Seneltivity Claim
V— \ \ JL-—"?r 11 JiHK \l3M \
CT \ \ ¦ \—' \
By Clan Uiinj Data From
Currant Statu of Sompled Lokoi
\ —\Z^( \
Acid Neutraliilng Capacity (m/l)
¦ <0
\ y/ \ \(*^ 1* 1 \
¦ 0 - H
— \ < >hl is \
\ IV.. . \ V ^.^l^^Bnnwiljh^i . ijwlww ¦ i - - " \
ia u- t»o
E3 > III
L I\ I \ .in 'I" ""1 ^V \
r\ \ \' \— \
-M—-^ETr .'Y . • » i'i^^^MMjpr-1 jpi—v jpn| > V^-
r" 11 ;,-
Map dioicti thi louit ANC valaoi
(rant NSWS-ELS lakn within tha
regional tromeiork. For oach
' regional unit, a value «ai onioned
bond an tha lowt ANC ii) aiy
•imilar dan. Unihadad rogiani ihs*
arias vith no NSWS-ELS lakai in
them.
ftZ V 1 I t —I i—131—* »r 1 a—-*— * i —
Figure 2-7. Distribution of lake ANC by acid sensitivity class. The NSWS-ELS lake with the lowest ANC governs the shading within
each subregion.
-------
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Envi ronm«ntoI Reseoeh Laboratory - C a r v a I I is
to
National Surface later Survey
Eastern Lake Survey Date
Acid Sensitivity Classis
By Close Using Dote From
Current Statsa el Sampled Lakes
pH (air equilibrated)
¦ < 5.J
ED 5.3 - 1.0
a >«.«
Nop depicts the lotest pH values
from NSWS-ELS Lakes within the
regional fromeeork. For each
regional unit, o velue tee assigned
based en the lotest pH In any
similar class. Unshaded regions shot
areas with no NSWS-ELS likes it
them.
Figure 2-8. Distri
each
bution of lake pH by acid sensitivity class. The NSWS-ELS lake with the lowest pH governs the shading within
subregion.
-------
Criticol Loot
Is Project
Northeastern
United States -—\
II S £nvi'r»nmen(a( Proteelfon Afleney - T \ ^ \
t n» i r o nirit n 1 a 1 Rtiiicli Laboratory - Coriallis —V \ ^
Notional Surface ffoler Survajr
Eastern Lake Survey Oato
L—-V-"—\ \ —¦—\ \
-r-^\ \ \ ^\
Acid Sensitivity Claistl
By CI da 3 U * in 3 Dot# from
Current Status of Sampled Lakes
Sulfate (uaq/l)
a < "
a !! - 100
CD 100 - Iii
BB ill - 156
~ >
Hep depicts the hiotieet sulfate
values from NSWS-uS Lakes (ithin
the regional Ifcmeeork. For each
reqionijl unit, o lotvie i«l otiijnti
based on the hlqheit sulfate In any
similar dots Unshaded regions ,sho»
areoj ffith na NSUS-CLS fekei in
them
Figure 2-9 Distribution of )ake sulfate by acid sensitivity c)ass. The JVSWS-ELS Jake with ihe highest sulfate governs the shading
within each subregion.
-------
N3
vO
Critical Loads Project
Northeastern United States
U S. environmental Protection Agency
Environmental Reieoeh Laboratory - Corvollle
Notlonol Surface Voter Survey
Ea*tern Lake Survey Oatc
Acid Seneitivity Claim
By Clou Using Data From
Current Statu* of Sampled lakee
Nitrate (ueq/l)
a < COS
~ 0 0] - 0 13
039 0 IS - 0.79
m 0 73 - 1 0
Uop depicts the hieheet nitrote
values from NSWS-ELS Lakee within
the reoional trameiork. tor each
to!
regional unit, o volue toi assigned
boted on the highest nitrate in any
umilor dan. Unihaded regiani iho»
oreos tilh no NSW5-ELS lakei In
them
Figure 2-10 Distribution of lake nitrate by acid sensitivity class. The NSWS-ELS lake with the highest nitrate governs the shading
wiilmi ciich subregion.
-------
Critical Loads Project
Northeastern United States
U.S. Envi ronmental Protection Agency
E n»i r o time n I o I Riuotb laboratory - Cofvollii
Notional Surfoce later Survey
Eoitern Lake Surny Sites
Figure 2-11. Point map indicating individual NSWS-ELS lake locations.
-------
scope and magnitude of differences in the regional critical loads estimates yielded by different
approaches. Even though individual methods might yield remarkably different results for
individual systems, it is conceivable that after regional aggregation of the results, the models
might all produce comparable estimates for critical loads.
The following sections provide summaries of the different modeling approaches usejl in this
demonstration study to generate critical loads estimates. The results from each of these
approaches are taken through the entire process to generate a regional critical load map, the
specifics of which were discussed in detail in Section 2.1.
2.6.1 Steady-Stale Water Chemistry Model (SSWQ
The SSWC model is a steady-state method used to estimate changes in surface water ANC
values as functions of changes in mobile anion (primarily sulfate) concentration. The method
bases projections on an empirical "F-factor," which is defined as the fractional change in base
cation concentration per unit change in sulfate on an equivalent basis (Henriksen, 1979).
F-factors for systems at steady state fall in the range of 0 to 1; for systems not at steady state,
apparent values may fall either above (Sullivan el al., 1990) or below (Dillon el al., 1987);
Waide and Swank, 1987) this range. The primary purpose of the F-factor is to obtain estimates
of pre-industrial (i.e , background) surface water base cation concentrations.
This method was taken from the TFM manual and the associated Annexes (UN-ECE, 1990),
with the modification that two independent methods for estimating the F-factors were investigated
(see subsection 2.6.1.3).
2.6.1.1 Data Requirements
The data needed to complete this computation include the following (Henriksen, 1979, as
subsequently modified by the UN-ECE TFM, 1990):
• [Ca2+], - present day calcium ion concentration in the surface water of interest (/xcq/L)
• [Na+], - present day sodium ion concentration in the surface water of interest (^/.cq/L)
• [Mg2+], - present day magnesium ion concentration in the surface water of interest
fyieq/L).
31
-------
• [K+], — present day potassium ion concentration in the surface water of interest (/ieq/L).
• [S042~]t - present day sulfate ion concentration in the surface water of interest (jLteq/L).
• [CI"], - present day chloride ion concentration in the surface water of interest (/J.eq/L).
• [ANC], - present day ANC in the surface water of interest (/xeq/L).
• [Ca2+]d - present day calcium ion concentration in deposition (kg/lia).
• [Na+]c| - present day sodium ion concentration in deposition (kg/ha).
• [Mg2+]d - present day magnesium ion concentration in deposition (kg/ha).
• [K+]d - present day potassium ion concentration in deposition (kg/ha)
• [Cl"]d - present day chloride ion concentration in deposition (kg/ha).
• Q - Runoff in units of m/yr.
• pptn - average annual precipitation in a watershed (m/yr).
2.6.1.2 Correcting Cation Data for Marine Contributions
The first step is to correct the data for marine contributions
[Ca2+]*t = [Ca2+], - 0.037*[Cl']t
[Mg2"1"]*, = [Mg2"1"], - 0.198*[Cl']t
[Na+]*, = [Na+], - 0.858*[Cl ]t
[K+f, = [ K+]t - 0.018*[Cl"]t
or
[BC]*, = [Ca2+], + [Mg2"1"], + [Na+], + [K+]t - [Cl"],*l.lll,
where 1.111 = 0.037 + 0.198 + 0 858 + 0.018
Similar corrections are made with the deposition values, except that the units arc converted
from kg/lia to keq/ha before the above corrections are applied, i e.,
[BC]*d = ([Na+]d/22 9898 + [K+]d/39 098 + [Ca2+]d/20 04 + [Mg2+]d/12.156)
- ([CI-Jd/35.453)*!.!! 1.
32
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2.6.1.3 F-Faclor Compulation
Computation of the F-factor is done in two different ways. The method outlined in the TFM
methods manual, referred to here as the Norwegian algorithm, uses the approach:
If current BC* < 0, then F = 0.
If currently, 0 < BC* < 400, then F = sin{(7r/2)*([BC]*/400)}
If current BC* > 400, then F = 1.00.
The TFM manual cautions that this approach has been developed for use in Norway, and that
application to other surface water systems may not be optimal. However, because there is very
little other guidance in the manual regarding the determination of F, it is possible that other
users of the TFM manual will rely on this set of algorithms to obtain F-factor estimates.
As an alternate approach, we developed a separate set of equations to obtain estimates of F
for lakes in the northeastern United States. The estimates are based on the work of Sullivan el
al. (1990), who collected diatom data from the sediments of a number of lakes in the Adiron-
dacks. The diatom data were used to obtain estimates of current and pre-industrial ANC in each
of the study lakes. Then, using these results, a plot was prepared in which the diatom-based
estimates for changes in ANC were plotted against the current observed ANC. Data were then
broken into two groups, and these were used to obtain the estimates for the F-factor (we use the
term "Paleo-F") (Shaffer el al., 1991) as:
If current ANC < 100, then F = (0.004613 * ANC) + 0.8731
If current ANC > 100, then F = 1.33
While this formulation yields F-factors higher than the steady-state limit of one, we have
retained the computed values because (I) the field data support them and (2) evidence from
modeling suggests that lakes in the region have not attained steady state with respect to their
base cation systems Although this choice for using nonsteady-stale values of F may seem at
odds with the application in a steady-state model, the primary purpose of F, as used here, is to
obtain an accurate estimate of the pre-industnal base cation concentration in surface waters
33
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Thus, the use of a nonsteady-slate F-factor that presumably reflects actual changes in base cation
concentrations since that time is most directly related to this goal.
2.6.1.4 Estimating Critical Loads
Once F-factors are determined, they can be used in conjunction with the current lake
chemistry and watershed runoff values to obtain estimates for the critical load of sulfate
deposition associated with a particular system. The two primary equations used in implementing
the SSWC method are:
[BC]*0 = [BC]*t - F([S04f, - [SO/J
and
CL = Q • ([BC]*0 - [ANC]linut) - [BC]*d
where CL is the critical load of sulfate deposition (in keq/ha/yr); Q is the yearly runoff value
(m/yr); [BC]*0 is the marine-corrected, pre-industrial base cation concentration in the surface
water (keq/m3); [BC]*, is the marine-corrected, current day base cation concentration in the
surface water; [BC]*^ is the marine-corrected, base cation deposition at the site; [S04]*0 and
[SO4]*t are the marine-corrected pre-industrial and current day sulfate concentrations in lake
water, respectively. An example of a critical load map prepared using the Norwegian F is
presented in Figure 2-12 and can be directly compared with an example map prepared using the
Paleo-F (Figure 2-13). Maps developed from all combinations tested in this comparison study
can be found in Appendix C.
2.6.2 MAGIC Model
The methods manual prepared by the Task Force on Mapping also allows for the applica-
tion of integrated watershed models as a means for projecting watershed responses to diffcnng
levels of deposition Several different models are mentioned, but, because of the widely varying
input data requirements of the different models and because of the range of technical complexity
34
-------
LO
On
Critical Loads Project
Northeastern United States
U.S. Environmantol Protection Agoncy
En*i ronioontal Roieock Laboratory - Corvollii
Acid Sonoitivity Claim
Using Sulfate Dopooition required
to ochleve 0 ANC
Stood/ Stoto lotor Chomiotry Model
with Norwofion 'F* Velioo
SULFATE kg/ho/yr
m t - u
BED 11 - 24
CD > 24
~ I) lit!
iittrlked I» leetlei 2.1.1), ru for ill
ras.i£]r'
Ittleill Mill1 ikeOlaa rtflttli Ike lalfiU
(lililtlii utiiiifi It ri4aci Ik* ANC tf
Ik# mil imiIIIh lik* li itek mil te 0.
Figure 2-12. Distribution of sulfate critical loads (calculated with the SSWC model using the Norwegian F-factor) in the northeastern
United States hy acid sensitivity class. Subregions are shaded to represent the sulfate deposition (kg/ha/yr) required to
reduce the AINC of the most sensitive FLS lake found in a given class to zero.
-------
US
ON
Critical Loads Project / ¦ ¦ \
Northeastern United States /\
Protection Ag«ncy A
Loborotory - v
Add Sonsitivity Claim
Ulinf Sulfito Dopooilion roquirod
to actiiovo 0 ANC
Steady Stoti fotor Chomiotry Modi!
•ith Polio *F" Voluit
SULFATE kfl/ho/yr
m • - m
US 11-14
~ > !4
~ I) Out.
~r~r~"' jf . r*,
» s\. ii'' *
ckiiitt lint Hit ii |i(irri< from S lake found in a given class to zero.
-------
involved in using the different models, it was not deemed appropriate to recommend the use of
specific models.
Within the United Slates, several integrated watershed models have been employed to pro-
ject the responses of watersheds to varying levels of acidic deposition. The three models used in
the Direct/Delayed Response Project (Church et al., 1989) include the MAGIC model (Cosby et
al., 1985), the 1LWAS model (Chen et al., 1984), and the Extended Trickle Down model
(Schnoor et al., 1985). Each of these models incorporates multiple chemical, biological, and
hydrological processes. As a result, they are not prima facie models, but rather require careful
calibration as the initial step in their application. Calibration routines for each of the models
vary. A detailed comparison of the models' performance is contained within the DDRP report
(Church et al., 1989).
For the MACIC model, calibration data were available for 129 watersheds in the northeast-
ern United States. These data formed the foundation from which the MAGIC model projections
were completed. For each watershed in the study, the model was run using each of six different
deposition scenarios. The modeled deposition levels in all cases are based on the current levels
in each watershed (Church et al., 1989). Final deposition levels for the model runs were at 50%,
70%, 80%, 100%, 120%, and 130% of the current values. The simulations were conducted for
50 years each, with the input deposition following the same pattern in each case. For each
simulation, current deposition was used for the 0-5-year period. Then, in the 5-15-year interval,
the deposition was ramped linearly to the new value. Finally, from year 15 through the end of
the simulation (year 50), the deposition was maintained at the new level.
Output from the model includes pH and ANC values of surface waters at 10-year intervals.
For our purposes, we extracted the ANC values obtained for year 50 (ANC^) from each of the
model runs, and used these results for our subsequent analyses. For the watersheds in the
northeastern United States, most of the systems had essentially attained steady state with regard
to sulfate retention (i.e , Soutpul = S|npul ± 5%), although substantial changes were still
occurring with the base cation pools. Therefore, depending on the deposilion scenario, wc would
expect some additional changes to occur in the predicted ANC values for each system beyond the
50-year window. However, given the uncertainty associated with each of these estimates, wc
believe this to be a reasonable approximation of the system responses.
37
-------
To obtain estimates of critical loads for each watershed, the ANCjq for each of the six
deposition scenarios was plotted against deposition Regressions were obtained for each system.
For the deposition ranges covered, the regressions were essentially linear, with correlation coef-
ficients (r2) in the range of 0.986 to 0.999. Using this regression curve, estimates were made for
the deposition levels that would be required to yield ANC^ values of 0 and 25 /xeq/L (see for
example, Figure 2-14; the balance of maps prepared can be found in Appendix C). In many
cases, these estimates were substantially higher than current levels of deposition. Although
values extrapolated beyond the ranges of deposition for which the model has been evaluated are
obviously highly suspect in terms of absolute values, it is reasonable to interpret these results as
indicating that the critical loads for these systems are higher than any reasonable deposition
levels that might be expected to occur in the northeastern United States.
2.7 RESULTS COMPARISON
This report is intended only as a qualitative comparison of the three critical sulfate load
estimation approaches tested. As such, only gross patterns and results are discussed here. A
more detailed comparison is presented by Holdren el al. (in pressb). As a general pattern that
was consistent across regionalization schemes, the SSWC model predicts lower sulfate critical
loads than does the MAGIC model; the SSWC model is even more restrictive using the Paleo-F-
factor than using the Norwegian-F (e.g , compare Figures 2-12, 2-13, and 2-14). A qualitative
examination of these maps also indicates that each of the three approaches identifies the same
subregions as being relatively more sensitive. For example, the southwest Adirondacks and high
peaks areas are consistently predicted to be in the most sensitive category, while the White
Mountain and northernmost parts of Maine are predicted by all three approaches to be the least
sensitive.
Although the absolute values of the critical sulfate load predicted by the three approaches
do vary, and thus complicate assessments as to what specific protective standard might be feasi-
ble, it is interesting to note that all three approaches do predict that in extensive pails of the
Northeast, critical loads aie being exceeded at current levels of deposition Using the most
sensitive system approach to regional critical load assignment, all models examined pi edict that
large parts of the Northeast have critical sulfate loads that are at or below 18 kg/ha/yr Since
38
-------
OS
vO
Critical Loads Project
Northeastern United States
U.S. En¥ Ironm««ta I Protect ion Agoncy
En* i ronmtn to I Ronocti Laboritory - Cor»ollli
Iiwr L,.v«,li.Mi\r.,i"M<
will' BkaOlna nfWcti tka tillitt
(ibilllu Mtmtri It [Win III ARC il
(li «ni Mailtlft l«k• li tick lilt to 0.
Acid Sonoitivity Clouoi
Uoing Sulfoto Dopolition roquirod
to ockiovo 0 ANC
UACIC Mod.I
SULFATE kg/ho/yr
¦ 1-11
Em H - 14
ED > !«
~ Ho lit!
Figure 2-14. Distribution of sulfate critical loads (calculated using the MAC1C model) in the northeastern United States by acid sensi-
tivity class. Subrcgions are shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most
sensitive ELS lake found in a given class to zero. (See text for description of MAGIC model.)
-------
total estimated sulfate deposition (wet + dry; Church et al., 1989) in the Northcasl ranges from
18 to 56 kg/ha/yr, the acceptance of this particular combination of critical loads approaches
would indicate that sulfate deposition should be reduced substantially to protect sensitive lake
systems.
40
-------
SECTION 3
RESULTS SUMMARY
The critical loads approach is an attractive option for developing ecologically meaningful
assessments of the impacts of acidic deposition on sensitive ecosystems. Although the modeling
used to support these assessments is not yet to the point where we can confidently state that
deposition needs to be reduced to a specific value to bring the ecological costs to an acceptable
level, the approach offers a foundation upon which an ecologically defensible decision-making
framework can be built.
Our objective has been to compare the outputs from various modeling approaches that have
been used to estimate critical loads and to determine whether these approaches produce ecologic-
ally similar estimates of critical loads. Clearly, there are real differences among models.
Decisions regarding model selection should be guided by factors such as data availability and the
degree of accuracy required by the risk managers.
Comparison of the results from the different models shows that their absolute projections
vary from an ecological perspective. However, the direction of ecosystem response predicted by
the models tested is consistent. For example, if one model predicts a low critical load for a lake,
then all of the models predicted a relatively lower critical load for that system.
Receptors within a small geographic area can have widely different critical load values.
For a population of receptors, critical loads will span values from low (or negative) for the most
sensitive systems through relatively high for the more robust systems. Development of regional
critical load values, therefore, requires some implicit or explicit decisions about the value of
certain sensitive resources. Responsibility for making and communicating these decisions to
scientists and the public rests with risk managers.
The modeling approaches used to extrapolate critical load estimates from individual systems
to whole regions arc sensitive to factors such as the percentage of systems in the population thai
are sampled, the grouping of systems into ecologically similar regions and the definition of
significant damage. While there is a technical basis for addressing these issues, the much
broader economic and social perspectives must be included in resource management decisions <¦.<>
that the benefits can be explicitly delineated.
41
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APPENDIX A
GENERAL DESCRIPTIONS OF ACID SENSITIVITY SUBREGIONS
(after Griffith et ai, 1990)
47
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GENERAL DESCRIPTIONS OF ACID SENSITIVITY SUBREGIONS
(after Griffith el al., 1990)
Northeast: Glaciated, relatively hilly or mountainous, with high levels of acidic deposition.
Sensitive surface waters on several different bedrock types. For lakes > 4 ha, 71% are drainage
lakes, 17% are reservoirs, 7% are seepage lakes, and 5% are closed.
A. Adirondacks: Distinct group of hills and mountains of a nearly circular structural dome
separated by lowlands from the other sensitive areas of the Northeast. Primarily gneiss,
schist, syenite, gabbro, and anorthosite rocks. Well-drained, sandy-loam Spodosol soils,
usually highly acidic with thick organic horizon at surface. Mixed deciduous-coniferous
forest. Mostly drainage lakes of mixed sizes with different basin types (e.g., fault-
controlled valleys, rock basins from glacial scouring, or behind morainal dams). Lakes
with ANC < 50 Jieq/L generally located above 450 m elevation. ANC and pH
negatively correlated with elevation.
1. Southwest Adirondacks: Area of lowest ANC/highest sensitivity in Adirondacks; high
levels of extractable aluminum; orographic effect produces high precipitation
amounts, maximum in the southwest.
2. Adirondack High Peaks: Elevations 600—1600 m; mostly anorthosite and gneiss;
fewer and smaller lakes.
3. Eastern and Northern Periphery: Relatively lower elevations and less precipitation;
sedimentary rocks at the margins. Thicker, more calcium-rich soils and generally
higher ANC surface waters; mix of high and low sensitivity in the north; a lake belt
north of the High Peaks region extends southwest from Loon Lake. The Tug Hill
cuesta, capped by sandstone, is included in this region due to proximity and lesser
degree of sensitivity.
B. Northern Appalachians: Low mountains, open low mountains and high hills, and plateau
areas; some areas with considerable local relief and steep hillsides. Original plateau
areas to the east are so greatly dissected that the topography is commonly designated as
mountains.
1. Catskill Mountains: Low mountains, elevations 450-1300 m. Receives highest load-
ings of acidic deposition in the Northeast. Sandstones, shales, conglomerates,
moderately to poorly reactive; calcium and magnesium content of bedrock may
increase westward. Surficial deposits more varied than bedrock; some carbonate
material in northern watersheds; till thickness may be major determinant of ANC.
Excessively to well-drained Inceptisols, moderately to extremely acidic. Deciduous
forest of beech, maple, birch, and hemlock along stream banks; many watersheds
contain first-growth forests above 730 m; some agricultural land use at low eleva-
tions. Mostly stream resources, as well as New York City reservoirs.
4$
-------
a. Eastern Catskills: Highest elevations, steepest slopes, thin till and soils; some
alpine type glaciation. Lowest ANC; headwater streams incised to bedrock.
2. Pocono Mountains: Open high hills moderate relief. Noncalcareous sandstones, silt-
stones, and some shales. Oak-hickory and maple-beech-birch forests. Small lakes
and reservoirs; sensitive lakes generally < 20 ha and at elevations above 500 m;
high sulfate and calcium levels.
3. Appalachian Plateau: Distinct plateau areas; sandstones; maple-beech-birch forests.
Small headwater lakes and reservoirs; sensitive systems above 500 m.
4. Northern Appalachian Periphery: Open high hills, elevations 150-610 m. Sand-
stone, shale, conglomerate. Cropland with pasture, woodland, and forest; maple-
beech-birch forest. ANC mostly 200-400 /xeq/L or > 400 fieq/L, but small areas of
low ANC such as Shawangunk Mountains and Moosic Mountains; streams, small
ponds, and lakes.
C. New England Highlands: High hills and low mountains, moderate to high relief. Con-
tains continuous and extensive mountain masses as well as isolated monadnocks on a
plateau-like surface; mix of geologic types but mostly metamorphic and igneous intrusive
in the sensitive areas; gneiss in the Hudson Highlands, Berkshire Hills, and Green
Mountains are the New England extensions of the same rock type of the Blue Ridge in
the Southeast. Maple-beech-birch and spruce-fir forests. Relatively small lakes, except
in Maine.
1. Green Mountains: Low mountains of moderate to high relief; 300-1200+ m. Schist,
quartzite, gneiss, and graywacke. Maple-beech-birch with minor spruce-fir forest.
Few lakes, many streams.
a. Southern Green Mountains: Open low mountains, elevations 450-1200 m.
Gneiss and quartzite. Primarily maple-beech-birch with minor spruce-fir forest.
Small lakes and streams; largest area of acidic and sensitive surface waters in the
Green Mountains. May receive significant acid loadings from air masses flowing
unobstructed through the Mohawk Valley between the Catskill and Adirondack
Mountains.
2. White Mountains: Low to moderate mountains, some mountain masses, some plateau
surfaces with isolated monadnocks; moderate to high relief; elevations 300-1900 m.
Schist, quartzite, granitic plutons.
a. White/Blue Mountains: Low to moderate mountains, some high relief;
300-1900+ m; includes the more continuous and massive mountains of the
White Mountain region. Maple-beech-birch and spruce-fir forest; mostly streams
and small lakes.
b. Katahdin. Open low mountains, moderate relief, elevations 300-1600+ m
Granitic intrusions, slate, and metasandstones Spruce-fir forests with maplc-
49
-------
beech-birch at lower elevations; small treeless alpine zone at highest elevation.
Mix of large and small drainage lakes.
3. Northcentral Massachusetts Upland/Southwest new Hampshire: Open hills and low
mountains, moderate relief, elevations 90-950 m. Gneiss, quartzite, quartz monzo-
nite, mica schist. Oak-hickory, maple-beech-birch, and while-red-jack pine forests.
Primarily streams, drainage lakes, and reservoirs; low pH, low ANC, high aluminum.
4. Taconic Mountains: Discontinuous high hills and low mountains; elevation generally
300-600 m with a few peaks > 900 m; moderate relief. Metamorphosed
sedimentary rocks: phyllite, quartzite, schist, slate, metagraywacke. Maple-beech-
birch forests; mostly streams and a few small lakes.
a. Rensselaer Plateau: Small plateau area approximately 8 x 20 miles, 360-600 m
in elevation with rolling topography. Metagraywacke, gneiss-cobble conglomerate,
quartzite. Maple-beech-birch forests. At least 10 lakes with ANC < 50 /ieq/L;
lakes with ANC < 20 fieq/L tend to be more shallow and humic; systematic
pattern of excess Ca and Mg in relation to total alkalinity and evidence for
accelerated accumulation of mercury in game fish.
5. Hudson Highlands: Open high hills, 150-450 m. Cneiss bedrock similar to south-
ern Green Mountains and Adirondacks, some exposed bedrock at higher elevations.
Maple-beech-birch and oak-hickory forests. Many lakes of high recreational impor-
tance; most sensitive and acidified lakes at elevations above 240 m.
6. Lower Hills and Valleys: Open high hills to open low mountains, plains with hills;
woodland and forest with some cropland and pasture; oak-hickory, transition, and
northern hardwoods forest. Generally nonsensitive; ANC > 400 fie q/L Includes
parts of Connecticut western uplands, Berkshire/Hoosac/Housatonic Valley of Massa-
chusetts, Vermont Piedmont, Vermont Valley, and upper Connecticut Valley.
D. Southern New England: Coastal plain and plains with hills; generally low elevation and
low relief; mix of sedimentary, metamorphic, and igneous intrusive rock, with overlying
glacial deposits. Generally oak-hickory forests, with loblolly-shortleaf pine in the eastern
sandy areas. High population density. High percent of reservoirs; high sulfate, high
sodium chloride (coastal and road salt).
1. Cape Cod: Irregular plains, glacial moraines, pitted outwash plains of low relief,
elevations 0-60 m. Granitic and gneissic bedrock covered by glacial materials and
sandy soils. Oak-pine forests; numerous seepage lakes; some cranberry bogs.
2. Southeast Massachusetts: Irregular plains, low relief, 0-90 m. Granite, gneiss, and
schist, Paleozoic sandstone, graywacke, shale, and conglomerate; Inceptisol, Spodosol,
and Histosol soils. Oak-hickory and elm-ash-cottonwood forest. Mix of drainage and
seepage lakes and reservoirs; some cranberry bogs.
50
-------
3. Western Rhode Island: Plains with hills, low to slightly moderate relief, elevations
30—270 m. Gneiss and granitic gneiss; glacial till derived from granite, schist, and
gneiss; Inceptisol, Spodosol, and Histosol soils. Oak-hickory forest. Primarily
drainage lakes and reservoirs.
4. Southern New England/Eastern Long Island: Irregular plains, plains with hills, open
hills; low to slightly moderate relief; elevation 0-305 m. Granites, gneiss, schist,
conglomerates, sandstone; Spodosols, Inceptisols, Histosols. Oak-hickory forest.
Moderate ANC and sensitivity; streams, mostly small ponds, lakes, and reservoirs.
E. Northeastern New England Plains and Hills: Plains with hills and high hills, some open
high mountains in northwestern Maine; moderate relief, elevations 0-600 m. Mix of
metamorphosed siltstone, sandstone, shale, graywacke, and conglomerate; some intrusive
igneous rock, such as biotite and biotite-muscovite granite, quartz monzonite, gabbro,
and diorite, and areas of calcareous sedimentary and metamorphic rock; mostly
Spodosols and Inceptisols. Spruce-fir forest dominant in the north, while-red-jack pine
in the south; scattered areas of maple-beech-birch. Many large lakes and large extent of
area in lakes, mostly drainage; small seepage lakes are most sensitive. Although this
region is heterogeneous, with a mosaic of surface water quality and chemistry, it is
considered as one unit here because it receives relatively low levels of acidic deposition.
F. Nonsensitive areas: Irregular plains, plains with hills, tablelands of moderate relief,
open high hills; Devonian, Silurian, and Ordovician sedimentary rocks. Cropland with
pasture, woodland, and forest; maple-beech-birch forest. Primarily streams, large finger
lakes, and small reservoirs.
51
-------
52
-------
APPENDIX B
CURRENT STATUS MAPS BASED ON NSWS-ELS DATA
53
-------
CURRENT STATUS MAPS BASED ON NSWS-ELS DATA
List of Maps
Figure Pa6c
B-l Point map indicating individual lake location and ANC 55
B-2 Distribution of lake ANC by grid 56
B-3 Distribution of lake ANC by MLRA 57
B-4 Distribution of lake ANC by acid sensitivity class 58
B-5 Distribution of lake ANC by acid sensitivity subregion 59
B-6 Distribution of lake ANC by bedrock sensitivity class 60
B-7 Distribution of lake ANC by bedrock sensitivity subregion 61
B-8 Point map indicating individual lake location and pH 62
B-9 Distribution of lake pH by grid 63
B-10 Distribution of lake pH by MLRA 64
B-ll Distribution of lake pH by acid sensitivity class 65
B-12 Distribution of lake pH by acid sensitivity subregion 66
B-13 Distribution of lake pH by bedrock sensitivity class 67
B-14 Distribution of lake pH by bedrock sensitivity subregion 68
B-15 Point map indicating individual lake location and sulfate concentration 69
B-16 Distribution of lake sulfate by grid 70
B-l7 Distribution of lake sulfate by MLRA 71
B-l8 Distribution of lake sulfate by acid sensitivity class 72
B-19 Distribution of lake sulfate by acid sensitivity subregion 73
B-20 Distribution of lake sulfate by bedrock sensitivity class 74
B-21 Distribution of lake sulfate by bedrock sensitivity subregion 75
B-22 Point map indicating individual lake location and nitrate concentration 76
B-23 Distribution of lake nitrate by grid 77
B-24 Distribution of lake nitrate by MLRA 78
B-25 Distribution of lake nitrate by acid sensitivity class 79
B-26 Distribution of lake nitrate by acid sensitivity subregion 80
B-27 Distribution of lake nitrate by bedrock sensitivity class 81
B-28 Distribution of lake nitrate by bedrock sensitivity subregion 82
54
-------
PM'P"! §Ul
'P"t rl„.
-------
8
Critical Loads Project
Northeastern United States
U.S. E n»i r o nmi n t o I Protection A g o n c jr
Environmontol Rintcb Laboratory - C o r v a I I i t
Notional Surfaco Wator Sur»ey
Eoetarn Lako Survey Dot*
Criddod formot ol
Current Statu of Sampled Lahee
Acid Neutralizing Capacity (ueq/l)
< 0
o - is
li - tat
> in
Map depict* the looeet ANC veliieo
Iron NSWS-ELS Lakei within each
aria square. Unahoded qrlda aho*
greet with no NSWS-ELS lakes it
then.
Figure B-2. Distribution of lake ANC by grid. The NSWS-ELS lake with the lowest ANC governs the shading within each individual
grid cell.
-------
Critical Loads Project
Northeastern United States
U.S. Envl ronmantdl Prol«ct Ion Agency
Cnvironmanlal Reieach Laboratory - C o r v a t I la
oi
Notional Sarfoee foUr Survey
Cattorn Lako Survey Data
Major Land Reeource Aroae
By Clan Uoiag Data From
Current Statoi of Sampled Lekei
Aeid Neutralizing Capacity (ueq/l)
¦ « - IS
ess « - 100
o > 1M
Map depict* the loteit ANC valval
from NSWS-ELS Lakee within the
regional frameaork. For each
regional unit, o value »oi onioned
band on the laeeet ANC in that
eontiguon unit. Unahadad raaloni
•ho* araai «lth no NSWS-ELS lakee
in them.
Figure B-3. Distribution of lake ANC by MLRA. The NSWS-ELS lake with the lowest ANC governs the shading within each
subregion.
-------
Cn
00
Notional Surfoc* foter Survey
Eostern Lake Surrey Data
Add Sansitivfty Classes
By Clan Using Data From
Currant Statu* of Sampled Lakaa
Add Nautralizing Capacity (seg/l)
oa h -100
~ > too
Map deplete the Unit ANC values
from KSWS-ELS Lakaa sithin tha
regional tromeserk, For each
regional unit, a value tat oeeigned
based on the losest ANC in any
similar close. Unshodod regions show
oreos with no NSIS-EIS lakes in
them.
Figure B-4.
Distribution of lake ANC by acid sensitivity class. The NSWS-ELS lake with the lowest ANC governs the shading
within each subregion.
-------
Criticol Loads Project
Northeastern United States
U.S. Environmental Protect l«n A 4«n c y -A
E n » r • 11111 a 1 lintct Laboratory - C 0 r v a 1 1 1 1 —V \
Notional Surfoco Wator Survey
Eoitorn loko Survoy Dot*
-A— \ ^ \ —-iT"\ ^ \
\ \ —-V""~—" \ \ \ \ \
Acid Soneitivity Subrogee!
By Subr09ion Doing Doto From
Curroat Stotoo ot Sampled Lake*
rZA \ \ V~" \ ^T:i \ — \
-^T \ \ ^' \ ^
Acid Niulrolizinj Capacity (ooq/l)
¦ 0 - IS
tsa is - 110
~ > 100
- - i">-- \ ^ —\ \ „ v;
\ — iT. \ \ -
/ \ _^(«iiw,-!,^li.!|!r.= \ - J^fc£2u"TOrl hl»f; \ V \
/ . ,1 Or^xlsV-'— \ \ ,.-" -*
\ Jk( u ^ > j*\ JarK \ ,jr-
\\ y-— \ \ -
Hap dopieti tho ioiaat ANC voluei
Iron NSIS-ELS Lokoi oithin tho
regional frometrerk. For each
tuBregion. a value tai onioned
baud on the loooit ANC in that
lobreoion. Umhadod roaioni ihot
areot oith no NSIS-ELs lokei !•
them.
Figure B-5. Distribution of lake ANC by acid sensitivity subregion. The NSWS-ELS lake with the lowest ANC governs the shading
within each subregion.
-------
s
Critical Loads Project
Northeastern United States
U.S. Cn» i ronmcntol Protection Agency
Environmontol Roiooch Laboratory - Corvollle
Notionol Surfoce Voter Survey
Eastern Loko Survey Dote
Bedrock Seneitivity Closest
By Cloee Using Dot* From
Current Statu of Sompled Lake*
Acid Neutralizing Cepocity (eeg/l)
¦ 0 - IS
BSD M - too
~ > too
Mop depict* the loveet ANC values
from KSfS-ElS Lakes within the
regionol framework. For eoch
regional unit, o velue set assigned
based on the loteit ANC in any
similar does. Unshoded regions ihov
ereos with no NSIS-EIS lakes ii
them.
Figure B-6.
Distribution of lake ANC by bedrock sensitivity class. The NSWS-ELS lake with the lowest ANC governs the shading
within each subregion.
-------
Ov
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Environmental Reieoch Laboratory - C o r v a II i *
Notional Surface later Survey
Eaitern Lake Survey Data
Bedrock Seneitivlty Subregions
By Subregion Using Data From
Current Statue of Sampled Lakee
Acid Neutralizing Capacity (ueg/l)
¦ ( • IS
USD 25 - 100
CD > 10»
Map depicte the lotest ANC values
from NSWS-EIS Lakee uithin the
regional frometork. For each
subregion, a valve ios assigned
based on the loteet ANC in that
eubregion. Unehoded regions shoe
areas with no NSWS-EL5 lakes in
them.
Figure B-7. Distribution of lake ANC by bedrock sensitivity subregion. The NSWS-ELS lake with the lowest ANC governs the
shading within each subregion.
-------
.Ol
f\3
-------
o
w
Critical Loads Project
Northeastern United States
U.S. En»i roniMitlol Protection Agoncy
Environmental R • > • a c h Laboratory - C o r v a I I i i
National Surface Wattr Survoy
Eaitern Loki Survoy Data
Griddad format of
Current Statu of Samplid Lake*
pH (air aqellibrotod)
m < 5.1
El S.) - 1.0
o > «.o
Map deplete tha loeoit pH (air
aquilibrotod) voluu from N5VS-ELS
lakes within each grid equare.
Unshaded grids shoe oreae with no
HSWS-ELS lakes in them.
Figure B-9.
Distribution of lake pH by grid. The NSWS-ELS lake with the lowest pH governs the shading within each individual
grid cell.
-------
Critical Loads Project
Northeastern United States
U.S. Envi renm«ntol Protettlon Agency -~""T \ ^Y. \ \
Env 1 ronM«ntal Roiaach Laboratory - C a r v a t 1 11 —V \
National Surf tea Votar Survay
Eattorn Laka Survay Data
Major land Raaourco Araoa
By Clou Using Data From
Curraat Statai of Somplad Lakai
pH (air oquilibrotad)
¦ < J.J
rdt— \ \ L-—i .\ „--V
\ ^——\ \ I \
^.jLr——\ \ \ ^ \
\ ^ \ —T iJUr-'11'1!'FflHilfflSii _\ "—*
cno s.] - i.o
~ > 1.0
-— \ V \ _\ \ ""*\
\ V, -S\ •:":: 1 il ill"1?' till I 11 " ^01 \
\ TSitl! a K HI ® yUUkili^h —\ V .
—"r —ft ^^HRhrKTsi , . •
Hap dopicti (ha lotoat pH valiai
from NSWS-ELS Lakoo •Ithln tha
regional frometork. Far each
regional unit, a valia *ai onioned
baaod an tha loiaot pH In that
contiguous unit. Unahadad rooiona
ihow araai with no NSWS-ELS lakaa
in them.
y^vT~ \ ililpli^^ Jv-—rT^
>/ \ J \ —1— " \ WHII|ll||l|HlillHt^g^'::::it-::ni;Jjijai^r^^A VX- \ 7 \ \ \
/ \ ( —T / ' llHltftfllTlWnr I L>--^ I \ —r \
Figure B-10. Distribution of lake pH by MLRA. The NSWS-ELS lake with the lowest pH governs the shading within each subregion.
-------
Critical Loads Project
Northeastern United States ^\^\
U.S. £ n»i r onme n t o 1 Pr#t#ctl#n Agency —¦—\ \ J\
Cnvironm«ntol Reseoch Loborotery - C o r v«1 1 t a V-—\
Notional Surface Voter Survey
T r r r —;ps"—\ \ Jl. !
Eastern Lake Survey Dote
Acid Seneitivity Classts
By Cloes Uiinq Dote From
Current State* of Sampled Lokes
pH (oir equilibrated)
¦ < 5.3
O l.J - i.o
~ > M
\ \ \_ —\ \ \ \ \ Ixm&m\
A-—\ \ —\—\ L
\ L--V—r\ ^X^s£\x J
\ —\ \ \ —-7m mm\
\ X——\—' \ A— \
^ —— *T~* \ \ V \ JL^-
\ \ \ \
"iT /T W ^\ J\r"^\
\ r-S \ — T Vf*^: : V . V \ V \
\ J- V \ \ \
ry ^ A--—\
1 V. ..-^W L,n, nili "1 1 I1 I1 1 MIL) 1 II—T \
%i . L:::::::::::::z::: :1:::.:;:::.:::::::::rt_J^^^^^^'l:.:,.::::^1»^BH::::::::M \ v
— "Y/ :...:: :l|! ! ,| ,,,,]ieil " \ *.*A
/-T jffilliipiilEB HHpiii^^^Mr \ t V—
/ \ ,1' | 1 i i * m \ V-" \
Mop depict* the loeest pH values
from NSWS-ELS Lakes within the
regional homework, Fer each
regional unit, a value eas assigned
based on the loeest pH in any
similar date. Unehoded regions shoe
arsas with no NSWS-ELS lakes i»
them.
\ i 11' . A^L'i"'1''" \ I'j'i JBrl V jCsjP^—\
1 l«?ry:{::I::H:::iiy ——jit y* ^jQfl
Figure B-11. Distribution of lake pH by acid sensitivity class. The NSWS-ELS lake with the lowest pH governs the shading within
each subregion.
-------
Ov
o*
—¦A—\ \--
Critical Loads Project
Northeastern United States WA
U.S. Envi ronmcntal Protect Ion Agency \ ^V—
Environmental Ra s so c h Lobor«tory - C o r»o 1 1 i i \ \1^1 ^
National Surface Water Survey
Eastern Lake Survey Date
\ A—"—\ \ 1~-—""V— \ V^—--\—jrW ""
\ \ \ - A——n \ Jl_-—-T \ \
\^V—-rT
\ \ \ T—\ W-TSikTY ™ \
Acid Sensitivity Subregions
By Subregion Using Data From
Current Statia of Sampled Lakes
•^5 \ L—-—\ \ MmwmmlBm I iii8Mii^«n \
A A--——i \ \ v--/\ \ —-
3 —r 1 1 \ ,f llliiiiffnlTnnf n niiTrlfnUli^^^^^^^r -~T \
—Tv^t \ \ ^ \
—v v^h
pH (air sqvillkrotad)
¦ < 9.)
QS3 J.J - 1.0
Q > >1
\ \\r^r \.
\ XT \ 4i!^Ki llH^s""^ \ \
\ / \ \ JT • ::lljHtf \ \
\ r \ _ T Vs* V-- :?::; ' ••liiii 'T*^ §:: 8MBV f \
' / ~~ k I ,01 'W 1 1 mil"" I . : ::*:::: . Stt^BtJu Qbt v J
/ _±r . I . __mJL\'..:. 1 rflfhi V 1 \ --— \
\ ( _/l , _L^rfillM V 1 . jrWHniM >~rT" ¦Hi \ —^6 \
\ m i, |||"" "'1 \ • ji^Ei :V ^ \ \
\ -^T"' \ 1 : ^ \
—\—1L—'—W'tL.**!^ V i u 1 \ \
Figure B-I2. Distribution of lake pH by acid sensitivity subregion. The NSWS-ELS lake with the lowest pH governs the shading
within each subregion.
-------
&
^3
Critical Loads Project
Northeastern United States
U.S. Environintntol Protection Agency
Environmental ftoiooch Laboratory - C o r » o I I I e
National Surface Vatar Survey
Eastern Lake Survey Data
Bedrock Seneitivity Cloeeet
By Clan Using Data From
Current Statue of Somp'ed Lakee
pH (air equilibrated)
¦ < 9.)
OHO 5.3 - «.o
~ > 1.0
Map depicte the lotoet pH values
from NSWS-ELS Lakee within the
regional frometork. For each
regional unit, o volue »os osaigned
bosed on the loveet pH In any
similar claee. Unshaded regions ihow
areas tith no NSWS—ELS lokes in
them.
Figure B-13. Distribution of lake pH by bedrock sensitivity class. The NSWS-ELS lake with the lowest pH governs the shading
within each subregion.
-------
Ov
CO
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Environmental Ronoch Laboratory - C o r» a I I 11
Notional Surfoce Water Survey
Eoitern Lake Survey Data
Bedrock Seneitivlty Subrogione
By Subregion Using Oata From
Current Statue of Sampled Lokei
pH (air tquillbrated)
¦ < 5.J
BSD S.J - M
ED ) 6.0
Moo deoicti the loteit pH valiee
from NSWS-EIS Lakee within the
regional frametork. For each
subregion, o value toe assigned
hand on the loeeit pH In (hot
subregion. Unshoded reaioni shot
oreae tith no NSIS-EL5 lakts in
them.
Figure R-14. Distribution of lake pH by bedrock sensitivity subregion. The NSWS-ELS lake with the lowest pH governs the shading
within each subregion.
-------
0\
*o
P,g"rc R-15
-------
o
Map depicts thi hiahaet sulfite
folioe from NSIS-tlS iekee «Tthin
each arid equore. Un*had«d arlde
skat orees with no NSfS-ELs lokae
in them.
Griddid format of
Currant Statu of Sampled Lakee
National Surface Vatar Survey
Eaitarn Lako Surrey Data
Sulfate (>eq/l)
~ < 75
EH 75 - tto
RED toa - its
m 1» - ISI
¦ > ISO
Figure B-16. Distribution of lake sulfate by grid. The NSWS-ELS lake with the highest sulfate governs the shading within each
individual grid cell.
-------
Critical Loads Project
Northeastern United States
U.S. Environmental Protect i «n Agency
Envi ronmcntaI Reeeach Laboratory - C o r * o I 11 •
Notional Surfoco Water Survey
Eastern Lake Survey Dote
Major Land Reiource Aroae
By Cloei Uiing Dote From
Current States of Sampled Lokee
Sulfate (oeq/l)
C3 < »
EH3 75 - 100
HSJ 100 - 115
m us - iso
¦ > ISO
Hep depict* the hiaheet eulfato
vofeee from NSWS-ELS Lake* titkln
the regional framieork. For eoch
realonol unit, a value eei onioned
based «n the hlfheet eelfate In that
contiaeou* unit, llnehoded region*
thof areoi with no NSWS-ELS lake*
in them.
Figure B-17. Distribution of lake sulfate by MLRA. The NSWS-ELS lake with the highest sulfate governs the shading within each
subregiou.
-------
N3
Critical Loads Project
Northeastern United States
U.S. Environmental Protect ion Agency
E n * i r o nmi n t a I Reoooch Laboratory - C o r« a I I i ¦
Notional Surface fotor Survajr
Eaitarn Laka Surrey Dote
Add Seneltieitj Claim
By Claee tiling Dete From
Current Statue of Sampled Lokei
Sulfate (ueq/l)
~
E3
< 79
75 - 100
100 - 115
1M - 1S0
> 110
Hop deplete the kigheet eulfote
valuei from NSWS-lLS Lokee within
the raalenal Irometork. Tor each
regional unit, a value tei ooiigned
bated on the klfhoot iglfate In eny
eimiler clan. Unihaded regione tho*
oreoj *lth no NSWS—ELS lakes in
thim.
Figure B-18. Distribution of lake sulfate by acid sensitivity class. The NSWS-ELS lake with the highest sulfate governs the shading
williin each subregion.
-------
Critical Loads Project
Northeastern United States
U.S. En* i ronnicntal Protection Agency
E ni i r o nut n t o I Reieoch Laboratorjr - C • r v o I I I •
National Surface Water Sur*ay
Eattorn Lake Survajr Oot«
Acid Sensitivity Subrejione
By Subregion Using Ooto From
Current Stotee of Sampled Lokee
Sulfate (ueq/l)
O < 75
EH) » - »0
BSD 100 - US
¦ 119 - ISO
¦ > ISO
Hep deplete the hiakeet eulfote
Koluei from MSWS—ELS Lokee lithin
the regional fromeeork. for each
tubroeion, o value eoe onioned
boiod on the higheit eulfate In that
oebraglon. Unihodod reeione ihoi
areos with no NSWS-EL5 lakes i«
them.
Figure B-19. Distribution of lake sulfate by acid sensitivity subregion. The NSWS-ELS lake with the highest sulfate governs the
shading within each subregion.
-------
-J
•£»
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Environmental Reseach laboratory - Corvoilis
National Surfaca Votor Survey
Eastern Lake Survey Data
Bedrock Sonsitivity Classes
By Clan Using Dota From
Current Statu of Sampled Lakee
Sulfate (tteq/l)
O < 75
ES3 75 - 100
da too - tis
m 115 - 150
¦ > 150
Mop depicts the hiaheat sulfate
values from NSVS-llS lak«s within
(he regional fromeeork. for each
regional unit, o volue ios assigned
based on the highest sulfate In any
similar class. Unshaded regions show
areas with no NSIS-ELS lakes is
them.
Figure B-20. Distribution of lake sulfate by bedrock sensitivity. The NSWS-ELS lake with the highest sulfate governs the shading
within each subregion.
-------
o
Cn
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Environmontal R e s ea c h Laboratory - C o r v a I I i •
National Surfaca Voter Survey
Eastern lake Survey Data
Bedrock Sensitivity Subregions
By Subregion Using Data From
Current Statue of Sampled Lakee
Sulfate (ueq/l)
~ < 75
ES3 75 - 100
(SO 100 - 115
¦ 12S - ISO
¦ > ISO
Hap depicts the hiahest sulfate
values from NSWS-tLS Lakes within
the regional frametork. For eoch
subregion, o value las assigned
based on the highest sulfate In that
subregion. Unshoded regions shot
areas with no NSWS-ELi lakes in
them.
Figure M-21. Distribution of lake sulfate by bedrock sensitivity subregion. The NSWS-ELS lake with the highest sulfate governs the
shadint; within eaeli subregion.
-------
s
e '"<11*d
-------
<1
-a
Critical Loads Project
Northeastern United States
U.S. E n * ir onmo n t a I Protection Ajoncy
En» I ronmenta I Reeeaeh Loborotory - Corvallit
National Surf oca fotor Sarvay
Eoetern Laka Survay Data
Griddad format of
Current Statu of Sampled lokee
Nitrate (aaq/l)
O < O.IS
ED 0.0$ - 0.15
ma 0.15 - 0.75
m 0.75 - 2.0
¦ > 1.0
Mop deplete the Mokoet nitrate
voliiee from HSIS-tlS lakes within
each arid oquoro. UmNodod irldi
iho* areae with no NSfS-ELs lake*
in them.
Figure B-23. Distribution of lake nitrate by grid. Tlie NSWS-ELS lake with the highest nitrate governs the shading within eacli
individual grid troll.
-------
-0
05
Critical Loads Project
Northeastern United States
U.S. E n»I r o rim it t o I Proloetlon A 9 • n c y
Cnvirennttntol Raiooch Laboratory - C 0 r v a I I I •
National Surface Watar Sarvoy
Castorn Lokt Survoy Data
Major Loud Roiourco Aroao
By Clati Using Data from
Currant Stain* of Samplad Lakaa
Njtrato (aoq/l)
ED < Ml
ED MS - 1.1!
SQ 0.1S - 0.73
¦ 0.71 - 1.0
¦ > 1.0
Hob dopieto tho kiahaat nitrat*
yofiai from NSWS-tLS Lakoi within
the raalonal franovork. For oach
rational unit, a ralva aai oiilanod
bond on tho kifhant nitrata In that
eontiauoai unit. Unahodod roaion*
ako« araai »itk no NSfS-El$ lakaa
in thorn.
Figure B-24.
Dislribution of lake nitrate by MLRA. The NSWS-ELS lake with the highest nitrate governs the shading within each
suhrcgiun.
-------
vO
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
En»i ronmantol Rostech Laboratory - C o r v a I I i s
Notional Surface Votor Survey
Eaatorn Loko Survoy Data
Acid Sensitivity Classes
By Class Using Data Fran
Current States of Sampled Lakes
Nitrate (ieq/1)
E3 < MS
ESD MS - #15
GOD 0.15 - 0.7S
BS 0.7S - 1.0
¦ > 1.0
Map depicts the hiahest nitrate
values from NS1S-ELS Lakes within
the regional tromework. Far oaek
regional unit, o value »o» assigned
based on the highest nitrate in any
similar does. Unshoded regione shot
areos »ith no NSIS-ELS lakes is
them.
I'igure Distribution of lake nitrate by acid sensitivity class. The NSWS-ELS lake with the highest nitrate governs the shading
within each subregion.
-------
Critical Loads Project
Northeastern United States
U.S. E • t f r t urn 11 < 1 Protection Ajency — \ \
Envi rofimantal R • I«o c h Laberotory - Cortollio —V \ ^'
National Surfaca Vatar Survey
Eastern Lake Surra; Dot*
Add Soncltlelty Subragion*
By Subrtfio* Uiing Dot* From
Carraat Statu cf Sampled lokeo
Nitrota (ieq/1)
O < 0.03
ED 0.0S - 0.1J
(3D 0.25 - 0.73
\ \ \ ^Bv*». \
¦ 0.73 - 1.0
m > i.i
Mop daplcti tha hiahaat nitrota
voluee from NSWS-llS Lakes within
the regional framaterk. For tech
xkreeion, e value eas onioned
baood en the hlgbeot nitrate In thai
eikreaian. Unehodad roeiona shoo
areas vith na NSIS-El\ lakes in
them.
r \ ¦>V-—\ ^—--y
Figure B-26. Distribution of lake nitrate by acid sensitivity subregion. The NSWS-ELS lake with the highest nitrate governs the
shading within each subregion.
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00
Critical Loads Project
Northeastern United States
U.S. Environmental Protietion Agency
Environmonta I R • • • o c h Laboratory - Corvollia
National Sarfaca Vatar Survey
EotUrn lake Survey Oota
Badroek Sensitivity Claieei
By Clou Using Data Fram
Currant Statu* of Samplod Lakas
Nitrota (ueq/l)
~ < (.IS
ED MS - t.lS
113 O.J! - 0.75
n 0.75 - 2.0
¦ > !l
Mao depicts tha kiahaat nitrota
valuta from NSIS-tlS Lokai tlthin
the rational fromnork. For each
regionol unit, o value tat assigned
based on the hifhost nitrota In any
similar class. Unehodod ragions shot
oreos with no NSIS-EIS lokei it
them.
Figure B-27. Distribution of lake nitrate by bedrock sensitivity. The NSWS-ELS lake with the highest nitrate governs the shading
\\itliin each stibregion.
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CO
to
National Surface Votar Survey
Eastern Lake Survey Data
Bedrock Sensitivity Subrejions
By Subregion Using Data From
Current Status of Sampled Lakes
Nitrote (ueq/l)
~ < 0.03
ED C.OS - 0.15
OH 0.25 - 0.75
SB) (.75 - 1.0
¦ > 2.0
Map depicts the kiahest nitrote
rolues from NSWS-lLS likes within
the regional fromeeork. Far each
tubregien, a value ias assigned
based an tbe kifhest nitrote in that
sebreaion. Unshaded regions shee
areas with no NS• S-EL5 lakes is
them.
Figure B-28. Distribution of lake nitrate by bedrock sensitivity subregion. The NSWS-ELS lake with the highest nitrate governs the
shading within each subregion.
-------
APPENDIX C
CRITICAL LOAD MAPS IN THE NORTHEASTERN UNITED STATES
83
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CRITICAL LOAD MAPS IN THE NORTHEASTERN UNITED STATES
List of Maps
Figure Page
C-l Point map indicating individual lake location and sulfate deposition
(kg/ha/yr) required to reduce the lake ANC to 0 87
C-2 Distribution of sulfate critical loads in the northeastern United Slates
by grid 88
C-3 Distribution of sulfate critical loads in the northeastern United States
by MLRA subregion 89
C-4 Distribution of sulfate critical loads in the northeastern United States
by acid sensitivity class 90
C-5 Distribution of sulfate critical loads in the northeastern United States
by acid sensitivity subregion 91
C-6 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity class 92
C-7 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity subregion 93
C-8 Point map indicating individual lake location and sulfate deposition
(kg/ha/yr) required to reduce the lake ANC to 25 /ieq/L 94
C-9 Distribution of sulfate critical loads in the northeastern United Stales
by grid . . 95
C-10 Distribution of sulfate critical loads in the northeastern United States
by MLRA subregion 96
C-l 1 Distribution of sulfate critical loads in the northeastern United Slates
by acid sensitivity class 97
C-12 Distribution of sulfate critical loads in the northeastern United Slates
by acid sensitivity subregion . . 98
C-13 Distribution of sulfate critical loads in the northeastern United Slalcs
by bedrock sensitivity class .... . 9fJ
84
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C-14 Distribution of sulfate critical loads in the northeastern United Stales
by bedrock sensitivity subregion 100
C-15 Point map indicating individual lake location and sulfate deposition
(kg/ha/yr) required to reduce the lake ANC to 0 101
C-16 Distribution of sulfate critical loads in the northeastern United Slates
by grid 102
C-17 Distribution of sulfate critical loads in the northeastern United States
by MLRA subregion 103
C-18 Distribution of sulfate critical loads in the northeastern United States
by acid sensitivity class 104
C-19 Distribution of sulfate critical loads in the northeastern United States
by acid sensitivity subregion 105
C-20 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity class 106
C-21 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity subregion 107
C-22 Point map indicating individual lake location and sulfate deposition
(kg/ha/yr) required to reduce the lake ANC to 25 /ieq/L 108
C-23 Distribution of sulfate critical loads in the northeastern United States
by grid 109
C-24 Distribution of sulfate critical loads in the northeastern United Stales
by MLRA subregion 110
C-25 Distribution of sulfate critical loads in the northeastern United States
by acid sensitivity class Ill
C-26 Distribution of sulfate critical loads in the northeastern United Stales
by acid sensitivity subregion 112
C-27 Distribution of sulfate critical loads in the northeastern United Slates
by bedrock sensitivity class . . . 113
C-28 Distribution of sulfate critical loads in the northeastern United Slates
by bedrock sensitivity subregion . 1M
C-29 Point map indicating individual lake location and sulfate deposition
(kg/ha/yr) required to reduce the lake ANC to 0 . .115
85
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C-30 Distribution of sulfate critical loads in the northeastern United States
by grid 116
C-31 Distribution of sulfate critical loads in the northeastern United States
by MLRA subregion 117
C-32 Distribution of sulfate critical loads in the northeastern United Stales
by acid sensitivity class 118
C-33 Distribution of sulfate critical loads in the northeastern United Stales
by acid sensitivity subregion 119
C-34 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity class 120
C-35 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity subregion 121
C-36 Point map indicating individual lake location and sulfate deposition
(kg/ha/yr) required to reduce the lake ANC to 25 /zeq/L 122
C-37 Distribution of sulfate critical loads in the northeastern United States
by grid 123
C-38 Distribution of sulfate critical loads in the northeastern United Slates
by MLRA subregion 124
C-39 Distribution of sulfate critical loads in the northeastern United States
by acid sensitivity class 125
C-40 Distribution of sulfate critical loads in the northeaslern United States
by acid sensitivity subregion 126
C-41 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity class ¦ 127
C-42 Distribution of sulfate critical loads in the northeastern United States
by bedrock sensitivity subregion 128
86
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CO
Critical Loads Project
Northeastern United States
U.S. En« i ronmsn tal Protection Agency
Environments! Reieach Laboratory - Corvollii
Stielf flat* lilir Climlilr? (Hiarlliin'i)
ltdil litis till Nor»nla« i 'F roln (01
amrlb«< In Millet M.I). <«» '« oil
Culm Loll Simij Lit*1 '¦ "•
Norih«oat«fi US (n«7flij
lieilUft lymltl raflictt (It lulfota
dipoiitloa aaeaiiorr to radaca (la ANC of
(hi loka to 0
SULFATE kg/ho/yr
Lot* Locations, Values
Using Sulfate Deposition required
to achieve 0 ANC
Steady State Water Chemistry Model
with Norwegian "F" Valaei
r,Bi„c C-l Point map indicating individual lake location and sulfate deposition (kg/ha/yr) required to reduce the lake ANC to 0
CiHumI loads calculated witli the SSWC model using the Norwegian F-factor (see text for description).
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CO
CO
Critical Loads Project
Northeastern United States
U.S. Environmental Protect ion Agency
Environmental R o s e a c h Laboratory - Corvotlli
Griddod Values
Using Sulfate Deposition required
to ochieve 0 ANC
Steady State Voter Chemietry Model
with Norwegian "F* Values
SULFATE kg/ha/yr
¦1
< 1
¦
I - ti
littll
II - u
ED
> 24
~
lie Dete
!SteeO Stele liter Ckeeiletri (Henrttiee'e)
•itl iilee tk« Nerttelee'e "F* Tele* (««
••crlhee in eeetlee l.l.l), rie (ei ell
•¦tern Lete Svr**y letee le tie
lertkeeetere US.
Keeleeel enlti' ekeeiit reflette the eellele
d•pc•111•n Mtaiterv te red»t» tie ADC et
(lit eieit ieiefll»e lake le eeek mil te 0.
Figure C-2.
Distribution of sulfate critical loads in the northeastern United Slates by grid. Grids are shaded to represent the sulfate
deposition (kjj/ha/yr) required to reduce llie ANC of the most sensitive ELS lake within an individual grid cell to 0.
(!i it ie;iI loads calculated with the SSWC model using the Norwegian F-factor.
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cc
vC
Critical Loads Project
Northeastern United States
U.S. E»»ir o n ra•«t o I Protactlen Agency
En*ironoanta I • • • « e h Laboratory - C « r v • I I i i
Major land Raiourco Arooo
Using Sulf14
~ »• t«t«
{telly Itlti liter CkMlitrr (Haarltaaa'i
•dil Ik* ltrn)lni F villi (it
Intrlkal In •••(!•• 2.1.1), rn far ill
(libra liki Sini) lain la tka
•artkaaiiara US.
Iho ««i
aalti' ikillaa nflacti tka ailfati
a aaiaiaari la ralaai tka AlC al
nail laaaftlri laka li ink aalt t* I.
>
Figure C-3. Distribution of sulfate critical loads in the northeastern United States by MLRA subregion. Subrcgions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an
individual suhregiou In 0. Critical loads calculated with the SSWC model using the Norwegian F-factor.
-------
vo
o
Critical Loads Project
Northeastern United States
U.S. E » » 1 ronraen ta I Protictlon Agency
Environmtntal R«i««ch Laboratory - C o r v a I I it
Acid S«ilti*ity Claim
Uiing SuKata Dopotition required
to oehlovi 0 ANC
Stoady Stoti Wator Chomittry Modal
• ith Norvogion T" Voluai
SULFATE kg/ho/yr
¦ I - II
(Sffl II - 14
E3 >11
a «• Dot*
iiicrlkt! In Miflo* 2.1.1). r«i for <11
t
•(torn Lflkt Sirni Lak•• la tka
•rtkaaatara US.
laftaial •nlli' ahallaj raflatla tka aglfata
dapoolllo* laeaaaari la ratfuea tko ANC of
(hi «aat oaaaltlva laka la aaet unit ta 0.
>
Figure C-4. Distribution of sulfate critical loads in the northeastern United States by acid sensitivity class. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found in a given
class lo 0. Critical loads calculated with the SSWC model using the Norwegian F-factor.
-------
o
Critical Loads Project
Northeastern United States
U.S. Enviroom*n(ol Protection Ag• ncy
Cnvironmontol Reieoch Laboratory - Corvallii
Acid Sonaitivity. By Sobroglon
Uting Sulfate Dipoiltioit required
to achim (~ ANC
Steady State Voter Chemistry Model
with Norwegian "F* Value*
SULFATE kg/ha/yr
¦ < I
¦ 1-11
liS H - 24
a > 14
~ Ns Dote
Steely Sleti Wetar Ckeeilttrt (Hee/lkeee'i)
ledil eejie tke i)erte|lee'e F velee (ei
Jiicrlkee it eeetlee 1.1.1), rue (er ell
leitire Lete Seroti leiii le tke
¦•rtkteefere US.
leeleiel ¦•It*' ikodli* reflect* tke Hltete
e•poi111•• iieeiiiti le rieeci tke ANC ef
the nut leeeltlt* leke li eeek «nll te 0.
ii!i
m
!ii
I'imiro C-f>. IHsiiilmliou of sulfate critical loads in the northeastern United States by acid sensitivity subrcgion. Subrcgions are
shaded to represent the sulfate deposition (kg/lw/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual siibregion to 0. Critical loads calculated with the SSWC model using the Norwegian F-factor.
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vO
to
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Environmnntal R • * • a c h Laboratory - C o r«a I I is
Badrock Sanaitivity CIoimi
Using Sulfate Dapoiition required
to achieve 0 ANC
Steady Stole Voter Chemietry Model
•ilk Norwegian "F" Valuee
SULFATE kj/ho/yr
¦ < l
¦ l-il
COS 11-14
r~~i >n
O »• D«t«
Steely Stele leter Ckenletri (Henrlleee'e)
Ii4iT eilee IK* iUr«t|lei'l 'r' »elee tee
Jeetrlked ?e eeetlee 1.1.t). ree ler ell
I)
eitere leke Sirter lekee le Ike
•rlkteiUn US.
£
•eeleeel eelti' ekeOlee reflecte Ike eelfete
dieentiei eeteieeri le refece Ike ANC e(
IM eiiet teeeltln lele le ietk eelt te 0.
Figure C-6. Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity class. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found
in a given class to 0. Critical loads calculated with the SSWC model using the Norwegian F-factor.
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vO
CO
Figure (1-7. Pistributiou of sulfide critical loads iu the northeastern United States by bedrock sensitivity subregion. Subregions arc
shaded to represent tin* sulfide deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
.111 indivi< liiiil subregion to 0. (Critical loads calculated with the SSWC model using the Norwegian F-factor.
•)
•t
Stilly Still liter Ckinlitrj IHnrlliin'
Mill! ulii Iki ¦•rtiflnri 'f iilii (it
luerlkil !• tieucR 1.1.1), rai lir ill
llitin III! Sarnjt Lit it li Iki
Mirtkiiitiri US.
IiiIimI mlti* ikillii riflitti tki ulliti
liiiiltiii iiciiiim ti rilici tki tltC if
tki mut miltlTi laki li nek mil ti 0.
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agency
Environmental Rtstoch Laboratory - Corvollii
Bedrock Sensitivity. By Subregion
Using Sulfite Deposition reqairtd
to achieve 0 ANC
Steady State Voter Chemistry Model
»ith Norwegian "f Velvet
SULFATE kg/ha/yr
¦ < I
¦ l-il
tna ii - 2«
~ > j«
~ »• Data
-------
vo
Critical Loads Project
Northeastern United States
U.S. Environmental P r o t • e I i«n Agency
environmental Riioaeh Loborotory - Corvallie
JtitrlbH Ti MCtiM M.I), ru fit <11
CiiUii Lilt Svni Lflift l> III
NrtkMtliu US (n«7ii).
flit Ucatlu tynbil riflttti lit i»lfit«
JmiltiM ne«tiif» W iiliiti tl« ADC if
Ikl lok» t» 15.
SULFATE kg/ho/yr
loke Location!, Volun
Using Sulfate D«po*ition required
to achieve 25 ANC
Steady State Voter Chemietry Model
vith Norwegian 'F' Voloee
Figure C-8. Point map indicating individual lake location and sulfate deposition (kg/ha/yr) required to reduce the lake ANC to
2"> nc.(\/\.. Critical loads calculated with the SSWC model using the Norwegian F-factor.
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0
01
Sridded Voiuee
Uoing Sulfate Deposition required
to achieve 25 ANC
Steady Stole Veter Chemistry Model
with Nervosa* 'F' Veleee
SULFATE kg/he/yr
¦
< •
¦
1 - H
ma
tl - l«
m
> 14
~
Date
St tUy Stat* (ittr Ckamlatrt (Haarlkaaa'a)
S«4*l ailaa tha Nartaflaa a 'F' ralaa (aa
jaatrlka* In aaatiaa i.e.I), rn far (II
(aatara Lata Sviai lataa la tka
latlkaaatara US.
laalaul aalta* akatlaa rallacta tka aillata
Jaaaaltlaa aaaaaaari la ratoea tka ADC a(
tka mail aaaaltlia laka la aaek anil ta IS.
U.S. Environmental Prelection Agency
Environmental Reeooch Laboratory - Corvallle
Figure C-<>. Distribution of sulfate critical loads in the northeastern United Slates by grid. Grids are shaded to represent the sulfate
deposition (kg/ha/vr) required to reduce llic ANC of the most sensitive ELS lake within an individual grid cell to
l2."> /u-t| L Critical loads calculated with the SSWC model using the Norwegian F-factor.
-------
o
On
Critical Loads Project
Northeastern United States
U.S. Ea* I r o itiM»1 • I Protoetlon Ajonc;
En«irouna11a I Raitoch L a k o r a t » r y - C «r v• I li •
Major Land Rooourco Arut
Uiinj Sulfata Dopoiltion raqulrad
to acklava 25 AMC
Staady Slate fotor Chamialry Modal
•1th Norvafian T* Voliaa
SULFATE kj/ha/yr
¦ < •
¦ I - 1«
nag it - 2<
ESI >n
~ it »iti
i«i«rlk*4 l« aiatiaa 1.1.1), rai In tU
(••lira lata Sana; lakia la tka
fiarltantara US.
Malaial a
daaaaltlaa
Ika aiaat
' ahaltaa raflacli Ik* aaltala
Maari la ra4a*< Ita ANC af
»« lata la aaak aatl ta 19.
>
Figure C-10. Distribution of sulfate criticnl loads in the northeastern United States by MLRA subregion. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an
individual subregion to 25 ficq/L. Critical loads calculated with the SSWC model using the Norwegian F-factor.
-------
vO
^3
Critical Loads Project
Northeastern United States
U.S. E lit I r o one n t o I Protection Ajency
En»l ronmtnta I Reieoch Laboratory - Corveliii
Jti.fj ltd* later Ck.-lftr,
M*d*l etjaa tk« N*rtMlM.i F fin* (••
7**«rlk«a It iiiiln l.l.l), rn far *11
(*it*r* l*li Wii) [*k*a la Ik*
R*rlk**it*r* US.
•*|l*>*l nit*' itillii r*fl*cti tli* •¦ltd*
e*i**ltl** *•••••«> It f*4aci tki ARC at
tki M*tl ••••lllf* laki I* (Kl ¦nit I* 19.
AeM S«n»itivity Clonti
Using Sulfot* Dopooitlon raqairod
to achlovo 25 ANC
Steady Stoti Votor Chemistry Model
with Norwegian 'F' Valuoe
SULFATE kj/he/yr
¦ < I
¦ I - w
BED W - 14
m > M
O II* tat*
Figure C-l 1. Distribution of sulfate critical loads in the northeastern United States by acid sensitivity class. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found in a given
class In 25 jfcieq/L Critical loads calculated with the SSWC model using the Norwegian F-factor.
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\o
CO
Critical Loads Project
Northeastern United States
U.S. E • » i r » nmi n I c I Protect Ion Ajencjr
Envi ronmtnta I R o I o o c h Loborotory - C o r v o I I i *
Add By S«br«gi»n
Utinj Sulfato Dopooition required
to ichievt 25 ANC
Steady Stele later Chemietry Model
with Norwegian "F" Valuei
SULFATE kg/ho/yr
¦ I - N
una ti - >4
CD > J4
~ *o Dete
(••c/IkH le eeetlee J.I.I), ree ler ell
feetere Like Jeriej lekek le lit
US.
Neleeej nlli' ati^lii reflecle tk« ealfete
Opeeltlee Mtiiitri te fllltl lit ANC i)
tki Reel leeiltlM like le eeek eell te IS.
\
Figure C-12. Distribution of sulfate critical loads in the northeastern United States by acid sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 25 /xeq/L. Critical loads calculated with the SSWC model using the Norwegian F-factor.
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o
o
Critical Loads Project
Northeastern United States
U.S. Environmantol Protect ion Ajinty
E n v i r o nm« n t a I Rmach Laboratory - Corvallii
Bedrock Sontitivity CIoimi
Uiinj Sulfat* Doposition riquirod
to achim 25 ANC
Study Stato Wator Ckomiatry Modol
tith Norttgion "f Voluti
SUlfATE kj/ho/yr
¦ i-ii
E2D H - 24
CD >14
~ »« Oil*
StllO ttltl «I|M J (HtMltiM'i)
¦•<•1 iiIm tk« Ntrtiiltn'i f nhi lit
4>«crlk«4 "» Mttiti 1.1.1). r>« Ur all
Lih S»r«M lain It tk«
*»ttku(Uf> US.
• ¦|Uial mill' shtiln* rdlicti lk« ulltlt
JiMtltlll !•(•••• ri t« r»4«t tk« ANC «f
lk» m#it iMtltln l«k» In itck mil to 29.
Figure C-13.
Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity class. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found
in a given class to 25 /Lteq/L. Critical loads calculated with the SSWC model using the Norwegian F-factor.
-------
o
o
Critical Loads Project
Northeastern United States
U.S. En»Ir»ninento I Protection Agency
Envi ronmontol Ruioch Laboratory - Corvallis
Bedrock Sensitivity. By Subregion
Using Sulfate Deposition required
to achieve 25 ANC
Steady State Vator Chemistry Model
with Norwegian 'F' Values
SULFATE kg/ho/yr
¦ l-il
ra 24
~ «• lite
S1ee4; Stela latir Ckeeilairv (Henrlkitn'i
IMil iilii I III Nersa|lea i V Mill (ai
tfncrlkie la aattlaa 21.1). ru far all
(aitara lata Saner lain la Iba
Nuikaaitira US.
Daalatal aalti' ibitlii raflacta I ha aalt
matmia latiiairi la ritaci (la tut
till nail eiaillln lata la eock aalt ta
>
Figure C-14. Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
;in individual subregion to 25 /xeq/L. Critical loads calculated with the SSWC model using the Norwegian F-factor.
-------
Critical Load
Northeastern
U.S. Eit»1 r»nmi n til P r • t • c
E n v i r o ium n t a 1 Ritioeh Lot
laki Locations, Valutt
Uiing Sulfate Daposition r• quir«d
to oclliivi 0 ANC
Stiody Stall Votir Chimiitry Modil
ilth Polio 'F' Voluii
SULFATE kg/ho/yr
» < I
« I - il
* 11-24
. > >4
Stiilf Stilt Willi Ckinlitrr (Hiiflkiii'i)
Modil. illn cilcilltil tr»m cltmliuy
cktifit ilmi 111! it lifirril Inn lit!
eilliitil Inn lili MllMit uni li tli
Allriilnk Mimtitii. "f" vdlm *iri
utriiililtl to tki tktli kirtkiiit Inn
thi Mlrtnliei liti ml tki atlil til rat
tor ill Entirn Liki 5«r»ij liku II til
•irtkuitiri US (»-7ll).
Stti liiitlia ifakil ritlMti tki itlfiti
dipoilllin Meiiiir; ti nOll III ARC if
s Project x\^A^\
United States
_JL— \ \ • \*m • \ V
11 • n A11 it ey \ ^ A \
oratory - Cirvollii \
r\
i i j-- A—\ A
\ I——r~—\ \ \ ' %
\ \ _Jt—¦—"\ \ -TSA \' \
\ .—\—" \ \ V———¦""~jF\ «\ • I \ v i. \ \ —
Z3\ \_^-\—\/\ '8 V
V.— \ \ V"| \ l—-HX *" \ \
—Air"—\ \ \ .—wrTv/r \ «Jr-T \ ; '>Wn \ ^-V"^
i- V—A^f' JL^llWf^vA
h-—\
rK/C-A
\ "Jk T~^\ • V. —*\ • A ,
——A—ft \ 1-- \\ ' VY'isSw&^Tjk \
VL^ ,r _JL- 1 \ * . \ ' \
/ \ ¦*>—¦ 1 \ \ \ ' \ %\ *—\' Jl— \
\_-4-—
Figure C-15. Point map indicating individual lake location and sulfate deposition (kg/ha/yr) required to reduce the lake ANC to 0.
Critical loads calculated with the SSWC model using the Paleo F-factor (see text for description).
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o
to
Critical Loads Project
Northeastern United States
U.S. £nviron>*«ntol Protect ion A 5 a n c y
Envf ronmonta I Ritioch Laboratory - C»r * a 11 la
Grlddod Valval
Uiinj Sulftta Dipoiition raqulrad
to achlota 0 ANC
Staady Stota Votor Chamiatry Modal
with Poleo "F* Valuta
SULFATE kj/ho/yr
¦ < •
¦ 1 - ta
GBI 11-24
ED > t<
~ lit Data
ckatttt tittt 1150 at laf*rri4 Irtit Itti
ttllttUI trim loll itJlatat urn It tkt
iali ¦ ' - ' ' ¦
IE
llrtttttk Itnttltt. *f* (ilui nri
:::a"
far *11 Cittiin Itkt tirut Litn
Ntrtktttlirt UJ.
Irtttltlta ta tkt tktlt Nartkiait (raai
• ulrntei acta iM tkt ntltl aia ria
Situ) Itktt li tkt
I Mlta' tktaitt r«fl*ct> tlx iilfitt
aa tttttttri It ra4att tkt AIC at
*tit mtlllft itkt It tttk ttll tt 0.
Haiti
t/tt
Figure C-16.
Distribution of sulfate critical loads in the northeastern United States by grid. Grids are shaded to represent the sulfate
deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an individual grid cell to 0.
Critical loads calculated with the SSWC model using the Paleo F-factor.
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o
C-O
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Agoncy
Environmental Roeeach Laboratory - Corvallie
Major Land Roiource Aroao
Using Sulfate Deposition required
to achieve 0 ANC
Stood; State Voter Chemistry Model
»ith Peleo "F* Vaiaei
SULFATE kg/ha/yr
m i -1«
Q33 11-24
ED > i«
~ It tlti
$tti<» Stat* Iitif
i«*ii'r mim cticil
at ItftrrtO Irtm felt
etiiin tint 1151 „ ....
cellittH frta lilt Mtlatit ttm It lit
Mlrtiliek imtilii. 'F* vtltta itrt
iitrtMleUd It (hi tkili Nirtkint frta
III i4lrii4iel till ml tki ailil tit rn
tr til Ctitira lilt Sertti Lektt It tkt
•rtktitliu IIS.
Ii|ltttl nlte'
tktdltt ritlieti tkt iiljeti
ttiirt It rtwet III AIC if
iitilllii Itki li iith till It 0.
Figure C-17.
Distribution of sulfate critical loads in the northeastern United States by MLRA subregion. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an
individual subregion to 0. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
o
4*
Acid Sensitivity Clones
Uiinf Sulfate Deposition required
to achieve 0 ANC
Steady State later Chemietry Model
with Paleo "F" Volues
SULFATE kj/de/yr
¦
< 1
¦
1 - 11
BED
11 - 24
fl
> 14
~
Re tete
ekeeeee elite lite ee jeforree free eete
telleetel Tree lake teyaeit tine it tke
Mlriueek Keeetelee. f nlin tire
eitrneletel te tke tkele Rertkeeet tree
tke Mlreedeek fete en< tke aelel tee ree
for ell Eeeteie Like Sertei lekei le tke
Rertkeeetere US.
fteeloeil mite' ekeJIie reflecte tke eiUete
(eieeltlee leeeeeeri le ratnee tke Alt e)
kt eeel eeeeltlee leke le e«ek aelt le 0.
Figure C-18.
Distribution of sulfate critical loads in the northeastern United States by acid sensitivity class. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found in a given
class to 0." Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
o
U1
tlHelil frIlk* u
-------
o
c*
Critical Loads Project
Northeastern United States
U.S. Eiivl r»nm«ntal Protection Agency
Environmental Rtinch Laboratory - Corxlllt
Bodrock Siniititity Claim
Uoing Sulfite Ooposition roquirod
to ochlovo 0 ANC
Stoody Stoto Wotor Chomistry Modil
with Polio 'F' Values
SULFATE kj/ho/yr
¦ I - H
cm ti -«
CD > H
~ »• »ltl
il«|H line 1151 II lilerrH lte» (lie
eelleetel Ires liki eelleieel eeree le ike
MliteOek Nfietilei. *F" veliei »re
ulroiletie te tke ekele lirtkeiil Ire*
ki Hiiimki lite n< tke lew tee ru
r ill Entire Leke Sirii) Letei le tki
rlkeeetire US.
111
He
iiffi
nlti' ekellee reflietl tke
it iiceieirr Ii riliie tie .
it eeeiltWe Ilka Ii nek unit
>
Figure C-20.
Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity class. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found
in a given class to 0. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
Critical Loads Project
Northeastern United States
U.S. E n»I r o nme n (a I Protection Agency
Envi ronmintol Reieoeh Laboratory - Coriollio
Bedrock Sonoitivity. By Subregion
Using Sulfite Deposition required
to achieve 0 ANC
Steady Stote Voter Chemistry Model
with Poleo "F" Values
SULFATE kg/ho/yr
¦ 1-11
BB3 11-24
ED > 14
~ It tele
Steel* Stele letei Ckeplitri (Hearlktee'e)
*rv telle celeeleteJ IreA eleolelrf
tkeeeee eleee till « inferred Ireo 4ete
.......... ..._ ..... _.ai_ ;kl
... et left
telleelel tree leke eedleeet eeree
A4lree*ock ViieUlii. H' telee* tire
8
itrueleted te tke fhole Hortkoeet (res
e Mlrn4«ti Me
,er eli teeter*
Kertkeeitere US
4«te •»< tke »M tet ret
leke Ser>er lelee le tke
teeleeel eelte' ehedlne retteete tke eellete
dijoiltlee eeteeeeri le re4ete tke ANC el
Ike moil eooeftlv* leke le letk enlt te 0.
>
Figure C-21. Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 0. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
h'gvrG C-22
P./« ^ re7"'^ to T _
-------
o
o
Critical Loads Project
Northeastern United States
U.S. E*»ir•n m•«t< I Protection A f • n c y
E n » i r o nrno n t o I Rooooch Laboratory - C • r v • I I I •
Griddod Viluoo
Uiin| Sulfate D*p«sltion roquirod
to ochiovo 25 ANC
Stood; Stat* lotor Chomiotry Modal
with Poloo V Voluos
SULFATE kj/ho/yr
¦ I - M
ma i« - li
E3 > 24
~ »• Dili
I lilt 11)0 « Infirr*"
,"n
iliffid In* ill?
(Wil li III
tllM llllll
ill 1130 I)
• •initio rrn
illniliik Mi
III! Hi III til (II
•r ill EiiImi Liii Sinii Liku li (hi
irtkiutiri UJ.
Kiiloool Oilir itidlii rifliclo tbi iilfiti
IioiiIIIii iicimri li film III lit if
iti nut miftIii Mki li ink nil li 15.
. . Mill! »lfl
ikili. Hirtkiiot fna
"g
s
5
Figure C-23.
Distribution of sulfate critical loads in the northeastern United States by grid. Grids are shaded to represent the sulfate
deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an individual grid cell to
25 fxcq/L. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
Critical Loads Project
Northeastern United States
U.S. En»Ir0nmt* I < I Protect ion Agoncy
En«lronmontal R 0 1« a c h Laboratory - C 0 r * a I I is
Major loud Rttourco Artoi
Using Sulloti Diptiititn riqilrid
to achlno 25 ANC
Stood; State Vator Chomistry Modal
•ith Polio "F* Valuas
SULFATE kg/ha/yr
¦ l-il
Effl W - 14
ED > 14
O #« Diti
itr«Mltt(4 U tl( ft«l« MrttMit
h XtlnriM Mi ai4 Iti ¦•4*1 111 rii
tr <11 E«ittr« Lit) Siriti Litii It lk<
UrtkMittri US.
i»
nltt' ik«4li| riflotti IM
III f< .—
••eiiurt !• r«4i
Figure C-24.
Distribution of sulfate critical loads in the northeastern United States by MLRA subregion. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an
individual subregion to 25 /neq/L. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
Critical Loads Project
Northeastern United States
U.S. E *» i r • (111 a t « I Protection A g • n c y
Environmental Reteach Laboratory - C o r * a 1 I i i
Acid Sintitivity Claim
Using Suifot« Deposition r«quir«d
to flctiiovi 25 AKC
Steady Stoto later Chamiitry Model
with Paleo *F* Values
SULFATE kj/ha/yr
¦ l-ll
BED H - 24
CD >24
~ »• Dite
(kM|M ilin Hit ee Ielerre4 Ire* 4«t«
-------
N2
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Ajency
Enviranmontal Ruiach Laboratory - Corvollli
Add Sonoitivity. By Subroglon
Using Sulfate Demolition roqairod
to achiovo 25 ANC
Stood; Stoto Voter Chemistry Model
lith Poleo "F" Valuei
SULFATE kg/ho/yr
¦ < I
¦ 1-11
GSO 11-14
ED >14
~ Hi Data
SImI? it lit (elii
*»4»i. *r» ttiM c«ii>
tkeaaaa alaaa IIH
•tlltiue !m Mi
A4il
ei inferred Iron 4itl
•tllaiil <•(•! )• lit
. "f1 u
• ilrueleted la the
fki Mir«>4«i lata
far ill Caataia lilt
Hartkaaitara UJ.
•)
i7
mink luililn. T mini ttra
tka tkilt M*rtk«i«t Iran
i e*4 Iki aa4al tat r»
Sana) lekaa la tka
M,
•alia' atitlii ralletla tka
(•
. tlaa aataaaarr la ralna tka
tka mail laaalllta laka la aack nil
>
Figure C-26. Distribution of sulfate critical loads in the northeastern United States by acid sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 25 /xeq/L. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
w
Critical Loads Project
Northeastern United States
U.S. Cn*ironm«ntal Protection A9• ncy
Environmental Roeoech Laboratory - Corvollis
Bsdroek Sensitivity Clstsss
Using Sulfot« Deposition rsqslrsd
to ochiovo IS ANC
Steady State later Chemistry Model
with Paleo "F" Value*
SULFATE kj/hs/yr
¦ l-il
EE) 11-14
ED > 24
~ lie tote
Figure C-27. Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity class. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found
in a given class to 25 /xeq/L. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Ajenty
Envi ronmonta I Rijtoch Laborotorjr - Carvallis
Bedrock Sensitivity, By Subrejion
Using Sulfate Deposition required
to achieve 25 ANC
Steady State Water Chemistry Model
with Poleo "F" Vales*
SULFATE kg/ho/yr
m i-i«
O « - it
r~i >m
~ St Data
ihi|ii aim use •• alnrri Ina « •
cillut<4 Irea lake aallaaul aarai li Ike
Mniriitl Imtilii. 'r* veltia nri
•itriiilotal ta tba »kali Nirtkieit tfia
Ika MlitaSeti 4«te nl tka aaM
Iir ell Etataia .
Swtkaiatiri US.
Mi III tki »|M «H r»i
lit* Sinijr Liku I* tki
tiiltia.l anita* ikillii raflacta Ike aalfata
eiMilIlM iieiiMri la rHici tka ASC il
tka not iimIHk leka li tick mil ta 15.
>
Figure C-28.
Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 25 /u.eq/L. Critical loads calculated with the SSWC model using the Paleo F-factor.
-------
Critical Loads Project
Northeastern United States
U.S. Environmental Prottclion A 9 • n c jr \ —-—V\ \
Environmental Reieach Laboratory - Corvellii V \ JLV» \\
\ \ \ —/ \ \ •
\ \ —\ \ / \ e\
V^—'"\ \ \ ' \ —-—y\
Lake Location!. Values
Using Sulfate Deposition required
to achieve 0 ANC
MAGIC Model
\ \ ——¦— "T \ \ ^ \ \ 0 » \
V—v \ Ju-—t \ '-W^r \
\ -—\ \ L\ v —" \S \ • ^rASv \ v,——
——r I 1 \ —-—***T / \ e \ —"T A k Uw \ ^--1;
. -1 I \ 1— \ w \ 11 tt ^<»^e j/r» \
J\ \ 1 — I 1 / \ -*^" \ \ \ \e PA \
\ /u^-ArvA
T*^i V*"*\ U—t'V\ V"^
SULFATE kg/ha/yr
0 <1
« 1 - II
. 11-14
. > 14
T \ VA \ \ \
\ / \ \. / \ \ \ J>4-— > \ \
\ r y< \ \ e " V \S» .»\ —~—' \ \
—TTTHr A—-—t—\ \^-\—"\ \
\ ' n. 1 \ u—\ \ 4. i\* M \ \
1 a. \ \ —V-^» \ \ \ \ T^>i 1 \
/'i /\ "T \ \ \ \ • \ \ * \
~^3ar\—7T \ —"T Tv^A \^X^\ ->^T * Jr-" \ V^--"
\ — \ \ \ ^_.—V e a / ^*-4 \ \ \
tkt MAGIC ariil ffi ra» for Olriet-tileyri
Hmsobh frtlttl (Ooftfl likn la tkt
N«rtk*«tt*rn US till 4eti tttlliklt (••111).
sill Itctlltl tyrnksl rtllttlt tkt Itllott
fipiltltt itctttirt It rrinet tkt ADC •!
Ik* lata U 0.
-\"~Zs\ \ \ -——\ 0 \ 0 y Ur Jar — \ \
jt \ \ \ V-- \ \ «jL--^ V/^ -—-^x \ _L--*— \
Figure C-20. Point map indicating individual lake location and sulfate deposition (kg/ha/yr) required to reduce the lake ANC to 0.
Critical loads calculated using the MAGIC model (see text for description).
-------
C*
Figure C-30. Distribution of sulfate crilical loads in the northeastern United States by grid. Grids are shaded to represent the sulfate
deposition (kg/ha/yr) required to reduce llie ANC of the most sensitive ELS lake within an individual grid cell to 0.
Critical loads calculated using the MACIC model.
Critical Loads Project
Northeastern United States
SULFATE kj/ha/yr
¦ < I
¦ 1-11
QH 11-24
ED >14
a *» »•««
U.S. En»IronmentaI Protection Agency
Environmental Reieoch Laboratory - C e r v a 11 I a
Gridded Yoluei
Ueing Suit at* Deposition required
to ickieie 0 ANC
MACIC Model
ItrUtttlirt \)S.
fttfltttl Mitt' iMIii rttlttli tilt iilflll
iiitiltlti ¦tctitirr l» ri4ni the AMC it
Flit ntil ititltlit lili li hcI nil tt 0.
-------
Critical Loads Project
Northeastern United States
U.S. Env i rtniMfi to I Protection Ajency
En»ironme»toI Reitoeh Laboratory - C o r v a I t I *
Major Land Resource Areoi
Using Sulfite Demolition required
to echieve 0 ANC
MAGIC Model
SULFATE kg/he/yr
¦ I - H
BED W - 14
ESI > ti
~ »• leti
III MACIC eatol III rii let Dlriel-DilijrH
frt^iel (60RP) LiIii It Iki
IteltMl mill' ikiilH reflect* lk« Hlfllt
ira'.Kif.Vn-u: t!
>
Figure C-31. Distribution of sulfate critical loads in the northeastern United States by MLRA subregion. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an
individual subregion to 0. Critical loads calculated using the MAGIC model.
-------
00
Critical Loads Project
Northeastern United States
U.S. En»ironmen I o I Protection Agency
£ n »i r o nine n t o I Hunch Laboratory - C • r v a I I I i
Acid Siniitivlty Claim
Uiinj Sulfate Oaposition raqairad
to achieve 0 ANC
MAGIC Model
SULFATE kj/ho/yr
¦ < I
H 1-11
tffl 11 - 14
m > J4
~ He Oete
Ike HACIC «~<•! til.CM let Olrecl-Deleyea
lekee li let
le|leoot Mile* ekeOee relletle Ike eelfelt
aeeeelllee eeeeeeeri le reduce Ike Alt el
(he eeel eeeeltlve let* le eeck unit te 0.
>
Figure C-32.
Distribution of sulfate critical loads in the northeastern United States by acid sensitivity class. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found in a given
class to 0. Critical loads calculated using the MAGIC model.
-------
vO
Critical Loads Project
Northeastern United States
U.S. Environmantal Protection Agency
Environmental Reeeaeh Laboratory - Corvalllt
Acid Sensitivity. By Sabragion
Ueing Sulfite Deposition required
to achieve 0 ANC
MAGIC Model
SULFATE kj/ha/yr
¦ < •
¦ I - it
BSD 11-24
ED > 14
~ *• Site
US.
¦•iltMl Mill' IU4IM rituiti Ik* lellete
Kra'eSJfiWUi e
Figure C-33. Distribution of sulfate critical loads in the northeastern United States by acid sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 0. Critical loads calculated using the MAGIC model.
-------
N2
O
Bedrock Sensitivity Cloeeei
Using Sulfate Deposition required
to echieve 0 ANC
MAGIC Model
SULFATE kj/ho/jr
¦ < I
¦ I - 14
sno w - H
ED > 14
~ lit tile
S3?5®Wi',v"w
Rt|ltttl ulli' tkillM rtlUeti lk« iillttt
etMiitun iiiiiiki (• iW)ci tu arc »i
|M Bill Itllltlft Ilk* It lick Hit It I.
Figure C-34.
Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity class. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found
in a given class to 0. Critical loads calculated using the MAGIC model.
-------
fO
Critical Loads Project
Northeastern United States
U.S. Environmental Protect I on Agency
En«ir•amintsI Reieaeh Laboratory - Corvtllli
Bedrock Smitirlty, By Subrtgion
Using Sulfate Deposition required
to achiovo 0 AMC
MAGIC Model
SULFATE kg/ha/yr
¦ < I
¦ I - H
USD It - 14
ED > t«
~ Re lete
R^eVettl
Derfkeeetere TJ5.
leeleeel ee
tbi met i
l*|leeel eelti' ikeAee relleete tie eelfete
ceeeeri le riiiti tie MC ft
eeeeltlie lete li lock eelt te 0.
>
Figure C-35.
Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 0. Critical loads calculated using the MAGIC model-
-------
N>
f\S
Pi
S^JtQ Q
36.
C r l' t '
N ° r ( l c o / I
u. , 1 n e o „ , L 0 Q d
1Sl-
>'•<».
V ,e""# Pa'"'0, ,
Blc *•<„ A"C "«
S0LFAT* kt/,
9/h*/>r
"»» 4
' Ug9/L
c"'0"'-,
C"'i°sl /
°«
-------
to
Critical Loads Project
Northeastern United States
U.S. En»i r 0 nm« n I o I Protaction Ajincjr
En* i ronmantol Rinoch Laboratory - C o r v a II I ¦
Griddtd Valuaa
Uoinj Sulfoto D«p«iltion required
to «chi«M 2$ ANC
MAGIC Modal
SULFATE kg/ha/yr
¦ 1-11
ea it - u
E3 >14
~ •» B*t«
ggvsf w&Pew*"
Milt' itillM rtflttli tka tilftU
4 • yoi !tI • * iitiuvi (• ri
-------
ts5
Critical Loads Project
Northeastern United States
U.S. E«»I r onm« n t a I Protoctlon Afincjr
Envi ronm«ntal Rmoch Laboratory - Corvillii
Major Land Rnouret Araat
Utinf Sulfat* Oopoaition roquirod
to ochim 25 AHC
MAGIC Modal
SULFATE kj/ho/yr
B » - 1«
MO 11-14
n >14
~ Hi fi«(«
Ski MACIC
V,'""
tmlHi
l*r Mrtct-ttltyri
loktt li tk.
l«|ltiil nlti' iliflit ritUeti tt< iilfiU
Optilllti I» liOti tfc* ARC tl
III I Kit 111. li ttck (til t. li.
Figure C-38.
Distribution of sulfate critical loads in the northeastern United States by MLRA subregion. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within an
individual subregion to 25 /xeq/L. Critical loads calculated using the MAGIC model.
-------
Cn
Critical Loads Project
Northeastern United States
U.S. Environmental Protection Ajency
En*Ironmtnto I R o i a a c h Laboratory - Corvollli
Acid Sonoitivity Clonu
Uiinj Sulfate Otpoiitlon roquirod
to oehlaya 25 ANC
MAGIC Modil
SULFATE kj/ho/»r
¦i i - ta
CHS 11-14
~ >1«
~ It Data
ft"™*1''
I«|UmI mIIi' akadlna rtlUeli t)» aallata
?:r:"lr.s:sf«rw: ti
>
Figure C-39. Distribution of sulfate critical loads in the northeastern United States by acid sensitivity class. Subregions are shaded to
represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found in a given
class to 25 /ieq/L Critical loads calculated using the MAGIC model.
-------
to
On
SULFATE kj/hc/yr
Itrfatitiri V$.
R<|loil Mill' ik<4la« ftfUell lha MlffU
diMiltlw Mcainri It rtdvci Iki »NC ol
III* «»»l uulllx liki la #«tk mil to 29.
Figure C-40.
Distribution of sulfate critical loads in the northeastern United States by acid sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
an individual subregion to 25 /xeq/L. Critical loads calculated using the MAGIC model.
-------
to
Critical Loads Project
Northeastern United States
U.S. environmental Protection Af•ncy
E 11 * I r o nine n I a I Rtitoch Laboratory - C • r « « 11 I ¦
Bedrock Seneitivlty Clones
Using Sulfoto Deposition required
to ecklove 25 ANC
MAGIC Model
SULFATE kg/ho/yr
¦ I - II
^ 11-24
E3 >24
~ «« let*
SImiI mill' ikellM riflieli tko iillilt
••ItUi Meiinri It rrixi lit ANC il
•ml iHiltlii *«kt It lock mil to 29.
>
Figure C-41 Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity class. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake found
in a given class to 25 /i.eq/L. Critical loads calculated using the MACIC model.
-------
to
CO
Critical Loads Project
Northeastern United States
U.S. En»i r onrnin to I P r«t • c t i«n Agincy
Environmental Rotaach Laboratory - Corvollii
Bodrock Soniitivity, By Subragion
Uting Sulfate Daposition raqulrad
to achiavi 25 ANC
MAGIC Model
SULFATE kj/ho/yr
¦ 1-11
mi w - 14
o > m
a »• D«it
•iflbmtiri VS.
IiiIimI Mill' ikiJIii riflieti tki HlliU
?ifnltlii Mcimri It ri4ic> til ANC tl
hi nut mifllti lot• la nek mil ti IS.
>
Figure C-42
Distribution of sulfate critical loads in the northeastern United States by bedrock sensitivity subregion. Subregions are
shaded to represent the sulfate deposition (kg/ha/yr) required to reduce the ANC of the most sensitive ELS lake within
ail individual subregion to 25 fieq/L. Critical loads calculated using the MAGIC model.
------- |