\
o
^r
P*
Risk and Exposure Assessment for Review
of the Secondary National Ambient Air Quality
Standards for Oxides of Nitrogen and
Oxides of Sulfur
Second Draft:
Appendices
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EPA-452/P-09-004b
June 5, 2009
RISK AND EXPOSURE ASSESSMENT FOR REVIEW OF THE SECONDARY
NATIONAL AMBIENT AIR QUALITY STANDARDS FOR OXIDES OF NITROGEN
AND OXIDES OF SULFUR
SECOND DRAFT:
APPENDICES
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC
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i DISCLAIMER
2 This draft document has been prepared by staff from the Health and Environmental
3 Impacts and Air Quality Analysis Divisions of the Office of Air Quality Planning and Standards,
4 the Clean Air Markets Division, Office of Air Programs, the National Center for Environmental
5 Assessment, Office of Research and Development, and the National Health and Environmental
6 Effects Research Laboratory, Office of Research and Development, U.S. Environmental
7 Protection Agency. Any opinions, findings, conclusions, or recommendations are those of the
8 authors and do not necessarily reflect the views of EPA. This document is being circulated to
9 obtain review and comment from the Clean Air Scientific Advisory Committee (CASAC) and
10 the general public. Comments on this draft document should be addressed to Dr. Anne Rea, U.S.
11 Environmental Protection Agency, Office of Air Quality Planning and Standards, C539-02,
12 Research Triangle Park, North Carolina 27711 (email: rea.anne@epa.gov).
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1 June 5, 2009
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6 Appendix 1
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8 Community Multiscale Air Quality (CMAQ) Model
9
10 Description of CMAQ Applications and Model Performance Evaluation
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20 Prepared by
21
22 U.S. Environmental Protection Agency
23 Office of Air Quality Planning and Standards
24 Research Triangle Park, NC 27709
25
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Description of CMAQ Applications and Model Performance Evaluation
i Table of Contents
2 1. Introduction 1
3 1.1 Overview of CMAQ Model Application 1
4 2. CMAQ Model Performance Evaluation 4
5 2.1 CMAQv4.6 2002 Predictions vs Observations for the Eastern U.S 5
6 2.2 CMAQv4.6 2002 Predictions vs Observations for the Western U.S 9
7 2.3 CMAQv4.7 2002 Through 2005 Predictions vs Observations 13
8
9 List of Figures
10 Figure 1.1-1. CMAQ Continental U.S. and Eastern and Western modeling domains 2
11 Figure 2.1-1. 2002 CMAQv4.6 Annual Average SO2 Predicted Concentrations vs
12 Observations at CASTNet Sites in the Eastern Domain 5
13 Figure 2.1-2. 2002 CMAQv4.6 Annual Average SO42" Predicted Concentrations vs
14 Observations at CASTNet Sites in the Eastern Domain 6
15 Figure 2.1-3. 2002 CMAQv4.6 Annual Average TNO3 Predicted Concentrations vs
16 Observations at CASTNet Sites in the Eastern Domain 6
17 Figure 2.1-4. 2002 CMAQv4.6 Annual Average NH4+Predicted Concentrations vs
18 Observations at CASTNet Sites in the Eastern Domain 7
19 Figure 2.1-5. 2002 CMAQv4.6 Annual Average SO42" Predicted Wet Deposition vs
20 Observations atNADP Sites in the Eastern Domain 7
21 Figure 2.1-6. 2002 CMAQv4.6 Annual Average NO3" Predicted Wet Deposition vs
22 Observations atNADP Sites in the Eastern Domain 8
23 Figure 2.1-7. 2002 CMAQv4.6 Annual Average NH4+Predicted Wet Deposition vs
24 Observations atNADP Sites in the Eastern Domain 8
25 Figure 2.2-1. 2002 CMAQv4.6 Annual Average SO42" Predicted Concentrations vs
26 Observations at CASTNet Sites in the Western Domain 9
27 Figure 2.2-2. 2002 CMAQv4.6 Annual Average SO2 Predicted Concentrations vs
28 Observations at CASTNet Sites in the Western Domain 10
29 Figure 2.2-3. 2002 CMAQv4.6 Annual Average TNO3 Predicted Concentrations vs
30 Observations at CASTNet Sites in the Western Domain 10
31 Figure 2.2-4. 2002 CMAQv4.6 Annual Average NH4+ Predicted Concentrations vs
32 Observations at CASTNet Sites in the Western Domain 11
33 Figure 2.2-5. 2002 CMAQv4.6 Annual Average SO42" Predicted Wet Deposition vs
34 Observations atNADP Sites in the Western Domain 11
35 Figure 2.2-6. 2002 CMAQv4.6 Annual Average NO3" Predicted Wet Deposition vs
36 Observations atNADP Sites in the Western Domain 12
37 Figure 2.2-7. 2002 CMAQv4.6 Annual Average NH4+ Predicted Wet Deposition vs
38 Observations atNADP Sites in the Western Domain 12
39 Figure 2.3-1. 2002 - 2005 Domainwide Average SO42" Predicted Concentrations and
40 Observations by Month at CASTNet Sites in the Eastern Domain 14
41 Figure 2.3-2. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
42 Statistics for SO42" Concentrations Based on CASTNet Sites in the Eastern
43 Domain 15
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1 - i
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Description of CMAQ Applications and Model Performance Evaluation
1 Figure 2.3-3. 2002 - 2005 Domainwide Average TNOs Predicted Concentrations and
2 Observations by Month at CASTNet Sites in the Eastern Domain 15
3 Figure 2.3-4. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
4 Statistics for TNO3 Concentrations Based on CASTNet Sites in the
5 Eastern Domain 16
6 Figure 2.3-5. 2002 - 2005 Domainwide Average NH4+Predicted Concentrations and
7 Observations by Month at CASTNet Sites in the Eastern Domain 16
8 Figure 2.3-6. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
9 Statistics for NH4+ Concentrations Based on CASTNet Sites in the Eastern
10 Domain 17
11 Figure 2.3-7. 2002 - 2005 Domainwide Average SO42" Predicted Deposition and
12 Observations by Month at NADP Sites in the Eastern Domain 17
13 Figure 2.3-8. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
14 Statistics for SO42" Deposition Based on NADP Sites in the Eastern
15 Domain 18
16 Figure 2.3-9. 2002 - 2005 Domainwide Average NOs" Predicted Deposition and
17 Observations by Month at NADP Sites in the Eastern Domain 18
18 Figure 2.3-10. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
19 Statistics for NOs" Deposition Based on NADP Sites in the Eastern
20 Domain 19
21 Figure 2.3-11. 2002 - 2005 Domainwide Average NH4+Predicted Deposition and
22 Observations by Month at NADP Sites in the Eastern Domain 19
23 Figure 2.3-12. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
24 Statistics for NH4+ Deposition Based on NADP Sites in the Eastern
25 Domain 20
26
27
28 List of Tables
29 Table 1.1-1. CMAQ Nitrogen and Sulfur Deposition Species 3
30 Table 1.1-2. Formulas for Calculating Nitrogen and Sulfur Deposition 3
31 Table 2.3-1. Normalized mean bias statistics for predicted and observed pollutant
32 concentration 13
33 Table 2.3-2. Normalized mean bias statistics for predicted and observed pollutant wet
34 deposition 13
35
36
37
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1 - ii
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Description of CMAQ Applications and Model Performance Evaluation
i 1. INTRODUCTION
2 This appendix provides an overview of the CMAQ model and the modeling system used
3 for simulating pollutant concentrations and deposition for the years 2002 through 2005. Included
4 in this appendix are the results of a model performance evaluation in which model predictions of
5 SO2, NO2, SO42", total MV1, and NH4+ concentrations and SO42", NO3", and NH4+ wet deposition
6 are compared to observations.
7 1.1 OVERVIEW OF CMAQ MODEL APPLICATION
8 The Community Multiscale Air Quality (CMAQ) model is a comprehensive three-
9 dimensional grid-based Eulerian air quality model designed to simulate the formation and fate of
10 gaseous and particle (PM) species including ozone, oxidant precursors, and primary and
11 secondary PM concentrations and deposition over urban, regional, and larger spatial scales2'3'4.
12 CMAQ is run for user-defined input sets of meteorological conditions and emissions. For this
13 analysis we are using predictions from several existing CMAQ runs. These runs include annual
14 simulations for 2002 using CMAQv4.6 and annual simulations for each of the years 2002
15 through 2005 using CMAQv4.7. CMAQv4.6 was released by EPA's Office of Research and
16 Development (ORD) in October 2007. CMAQv4.7 along with an updated version of CMAQ's
17 meteorological preprocessor (MCIPv3.4)5 were released in October 20086. The CMAQ modeling
18 regions (i.e., modeling domains), are shown in Figure 1.1-1. The 2002 simulation with
19 CMAQv4.6 was performed for both the Eastern and Western domains. The horizontal spatial
20 resolution of the CMAQ grid cells in these domains is approximately 12 x 12 km. The 2002
1 Total NO3 includes the mass of nitric acid gas and paniculate nitrate.
2 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A., 1996. The next generation of
integrated air quality modeling: EPA's Models-3, Atmospheric Environment, 30, 1925-1938.
3 U.S. Environmental Protection Agency, Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA
Models-3 Community Multiscale Air Quality (CMAQ modeling system, EPA/600/R-99/030, Office of Research and
Development).
4 Byun, D.W., and Schere, K.L., 2006. Review of the Governing Equations, Computational Algorithms, and Other
Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, J. Applied
Mechanics Reviews, 59 (2), 51-77.
5 The scientific updates in CMAQ v4.7 and MCIP v3.4 can be found at the following web links:
http://www.cmascenter.org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt
http://www.cmascenter.org/help/model_docs/mcip/3.4/ReleaseNotes
6 The differences in nitrogen and sulfur deposition in the case study areas between CMAQ v4.6 and v4.7 for 2002
are small, as described in Chapter 3.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1-1
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Description of CMAQ Applications and Model Performance Evaluation
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through 2005 simulations with CMAQv4.7 were preformed for the Eastern 12 km domain and
for the continental U.S. (CONUS) domain which has a grid resolution of 36 x 36 km. The
CMAQv4.6 and v4.7 annual simulations feature year-specific meteorology, as well as year-
specific emissions inventories for key source sectors such as utilities, on-road vehicles, nonroad
vehicles, wild fires, and natural biogenic sources. Emissions for other sectors of the inventory for
each of the years modeled rely on inventories for 2002. Details on the development of emissions,
meteorology, and other inputs to the 2002 CMAQv4.6 runs can be found in a separate report7.
Inputs for the CMAQv4.7 runs for 2002 through 2005 were derived using procedures similar to
those for the CMAQv4.6 2002 runs.
CONUS Domain
10
11
Eastern Domain
Western Domain
Figure 1.1-1. CMAQ Continental U.S. and Eastern and Western modeling domains.
7 Technical Support Document for the Final Locomotive/Marine Rule: Air Quality Modeling Analyses. EPA 454/R-
08-002, U.S.EPA, Office of Air Quality Planning and Standards. January 2008.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 1-2
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Description of CMAQ Applications and Model Performance Evaluation
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Each CMAQ model run produces hourly concentrations and wet and dry deposition of
individual pollutant species in each grid cell within the domain. Concentration predictions for
NOy8 and SO2, both in units of parts per billion (ppb), are produced as part of our standard
model output. The CMAQ deposition data for nitrogen and sulfur species are used to calculate
oxidized and reduced wet and dry nitrogen deposition, wet and dry sulfur deposition, and total
reactive nitrogen and total sulfur deposition. These composite deposition variables are derived
from the species identified in Table 1.1-1 as applied in the formulas shown in Table 1.1-2. The
CMAQ deposition data are in units of kilograms per hectare (kg/ha). We are also including in the
analysis gridded precipitation data that were input to the CMAQ runs to help understand the
temporal and spatial behavior of wet deposition.
Table 1.1-1. CMAQ Nitrogen and Sulfur Deposition Species.
CMAQ Species
ANO3
HNO3
N2O5
HONO
NO
NO2
PAN
NTR
ASO4
SO2
Chemical Name
Particle Nitrate
Nitric Acid
Nitrogen Pentoxide
Nitrous Acid
Nitric Oxide
Nitrogen Dioxide
Peroxyacyl Nitrate
Organic Nitrate
Particle Sulfate
Sulfur Dioxide
Table 1.1-2. Formulas for Calculating Nitrogen and Sulfur Deposition.
Deposition Type
Oxidized Nitrogen
Reduced Nitrogen
Sulfur
Formula
0.2258*ANO3 4
+ 0.1157*PAN-
0.7777* NH4+ +
0.3333*ASO4 +
0.2222*HNO3 + 0.4667*NO + 0.3043*NO2 + 0.2592*N2O5
f 0.2978*HONO + 0.1052*NTR
0.8235*NH3
0.5000*SO2
8 NOy is defined as the sum of CMAQ predictions for NO, NO2, HNO3, and PAN.
2nd Draft Risk and Exposure Assessment
Appendix 1-3
June 5, 2009
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Description of CMAQ Applications and Model Performance Evaluation
i 2. CMAQ MODEL PERFORMANCE EVALUATION
2 The CMAQ predictions of 862, SC>42", total N(V9, and NH4+ concentrations as well as
3 SC>42", N(V, and NH4+ wet deposition were compared to the corresponding measured data for
4 the years 2002 through 2005. The purpose of this evaluation is to provide information on how
5 well model predictions match the observed data on a regional basis, and not to evaluate
6 performance for an individual location or area. In this analysis we compare the annual average
7 predictions of SC>2, SC>42", total NCV, and NH4+ concentrations to measurements from CASTNet
8 sites10. We compare the CMAQ annual total SO42", NO3", and NH4+ wet deposition to
9 measurements of these species at NADP sites. In all cases, the model predictions and
10 observations were paired in space and time to align with the corresponding observations.
11 For the 2002 CMAQv4.6 runs we provide model performance information for both
12 Eastern and Western modeling domains. For the 2002 through 2005 CMAQv4.7 runs have
13 performance results for concentrations and deposition for the Eastern modeling domain.11 The
14 CMAQ v4.7 performance results for 2002 through 2005 are courtesy of EPA's Office of
15 Research and Development.12 The equations used to calculate model performance statistics for
16 the CMAQv4.6 and v4.7 simulations are described elsewhere13.
17 The "acceptability" of model performance is judged by comparing the CMAQ
18 performance results to the range of performance found in other recent regional photochemical
19 model applications.14'15'16 These other modeling studies represent a wide range of modeling
20 analyses which cover various models, model configurations, domains, years and/or episodes,
9 Total nitrate includes nitric acid gas and paniculate nitrate.
10 There are insufficient non-urban measurements of NO2 to provide for a meaningful evaluation of this pollutant
for the purposes of this assessment.
11 CMAQv4.7 was not run for the Western 12 km domain for 2002 through 2005.
12 Personnel communication with Wyat Appel, U.E. EPA, Office of Research and Development, National
Environmental Research Laboratory, Research Triangle Park, NC.
13 U.S. EPA, 2008. Technical Support Document for the Final Locomotive/Marine Rule: Air Quality Modeling
Analyses. EPA 454/R-08-002, U.S.EPA, Office of Air Quality Planning and Standards, Research Triangle Park,
NC, January 2008.
14 U.S. E PA, 2006. Technical Support Document for the Final Clean Air Interstate Rule: Air Quality Modeling;
Office of Air Quality Planning and Standards, Research Triangle Park, NC, March 2005 (CAIR Docket OAR-2005-
0053-2149).
15 U.S. Environmental Protection Agency, 2006. Technical Support Document for the Final PM NAAQS Rule:
Office of Air Quality Planning and Standards, Research Triangle Park, NC, NC.
16 Dentener FJ; Crutzen PJ. (1993). Reaction of N2O5 on tropospheric aerosols: impact on the global distributions
of NOX, O3, and OH. J Geophys Res, 98, 7149-7163.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1-4
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Description of CMAQ Applications and Model Performance Evaluation
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chemical mechanisms, and aerosol modules. Our CMAQv4.6 and v4.7 performance results are
within the range found by these other studies. Thus, the model performance results give us
confidence that our applications provide a scientifically credible approach for the purposes of
this assessment.
2.1 CMAQV4.6 2002 PREDICTIONS VS OBSERVATIONS FOR THE
EASTERN U.S.
The figures below display the comparison of CMAQv4.6 predictions of annual average
concentrations and annual average wet deposition for monitoring CASTNet (concentrations) and
NADP (wet deposition) sites the Eastern domain. Each data point in the figures represents an
annual average paired observation and CMAQ prediction at a particular CASTNet or NADP site.
Solid lines indicate the factor of 2 around the 1:1 line shown between them.
Using the normalized mean bias (NMB) statistic, we see that the 2002 CMAQ run tends
to underpredict concentrations of SO42" (NMB = -12.0%) and NH4+ (NMB = -3.4%) and
overpredict SO2 (NMB = 33.8%) and TNO3 (NMB = 11.2%) within the Eastern domain for
2002. For wet deposition, the 2002 CMAQ run tends to underpredict NO3" (NMB = -11.4%) and
NH4+ (-6.1%) and overpredict SO42" wet deposition (9.4%).
20Q2sc met2v33 12kmE SO2 for 20020101 to 20021231
6 8 10
Gbservatron
Figure 2.1-1. 2002 CMAQv4.6 Annual Average SO2 Predicted
Concentrations vs Observations at CASTNet Sites in the Eastern Domain
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 1-5
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Description of CMAQ Applications and Model Performance Evaluation
20Q2ac met2v33 12km £ SO4 for 20020101 to 20021231
1
2
3
ug,-m3)
0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0 5,5
Observation
Figure 2.1-2. 2002 CMAQv4.6 Annual Average SO42" Predicted
Concentrations vs Observations at CASTNet Sites in the Eastern Domain.
2002ac meI2v33 12kmE TNO3 lor 20020101 lo 20021231
CASTNei !2Q02ac me)2v33 12kmE)
5
6
7
0,0 0,5 1,0 1,5 2.0 2.5 3.0 3,5 4,0 4,5 5.0 5,5 6.0
Observation
Figure 2.1-3. 2002 CMAQv4.6 Annual Average TNO3 Predicted
Concentrations vs Observations at CASTNet Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 1-6
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Description of CMAQ Applications and Model Performance Evaluation
2002ac m»t2v33 l2kmE NH4 lor 20020101 IO 20021231
1
2
3
0,0
0.5
1,0 1,5 2,0
Ob&ervatfOn
2,5
Figure 2.1-4. 2002 CMAQv4.6 Annual Average NH4+Predicted
Concentrations vs Observations at CASTNet Sites in the Eastern Domain.
20023C met2v33 12kmE SO4 lor 20020101 to 20021231
5
6
7
NADP depCaOQZac met2v33 12ki7iE)
2-
Figure 2.1-5. 2002 CMAQv4.6 Annual Average SO4 Predicted Wet
Deposition vs Observations at NADP Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 1-7
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Description of CMAQ Applications and Model Performance Evaluation
2002ac met2v33 12kmE NO3 for 20020101 to 20021231
1
2
3
NADP dep(2002ac me!2v33 12km£)
Ob&ervatfOn
Figure 2.1-6. 2002 CMAQv4.6 Annual Average NO3" Predicted Wet
Deposition vs Observations at NADP Sites in the Eastern Domain.
2002ac m«t2v33 12kmE NH4 foi 20020101 lo 20021231
5
6
7
Figure 2.1-7. 2002 CMAQv4.6 Annual Average NH4+Predicted Wet
Deposition vs Observations at NADP Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 1-8
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Description of CMAQ Applications and Model Performance Evaluation
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2.2 CMAQV4.6 2002 PREDICTIONS VS OBSERVATIONS FOR THE
WESTERN U.S.
The figures below display the comparison of CMAQv4.6 predictions of annual average
concentrations and annual average wet deposition for monitoring CASTNet (concentrations) and
NADP (wet deposition) sites the Western domain. Each data point in the figures represents an
annual average paired observation and CMAQ prediction at a particular CASTNet or NADP site.
Solid lines indicate the factor of 2 around the 1:1 line shown between them.
Using the normalized mean bias (NMB) statistic, we see that the 2002 CMAQ run tends
to underpredict concentrations of SO42" (NMB = -20.8%), NH4+ (NMB = -16.2%), and TNO3
(NMB = -19.6%) and overpredict SO2 (NMB = 31.2%) within the Western domain for 2002. For
wet deposition, the 2002 CMAQ run tends to underpredict NO3" (NMB = -43.6%), NH4+ (NMB
= -43.4%), and SC>42" wet deposition (NMB = -20.5%). In general, model performance for the
Western domain is degraded somewhat compared to the results found for the Eastern domain.
2M2ac J2km WUSSO4 for 20020101 1020021231
a CASTNst (20Q2ae 12km WUS)
(A
RMSE
BMSEs
RMSEu
MS
ME
08® HUB = -208
0 31 WilE = 250
0 27 NWnfl ^ -13.6
0 13 l*1dn£ » 14.7
-0 18 PB - -19.0
D 21 FE = 24.4
-0 OS
1.5 2.0
Observation
Figure 2.2-1. 2002 CMAQv4.6 Annual Average SO42" Predicted
Concentrations vs Observations at CASTNet Sites in the Western Domain.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 1-9
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Description of CMAQ Applications and Model Performance Evaluation
2002ac 12km WUSSO2 lor 20020101 1020021231
1
2
3
a CASTNet (2002ac_12km__WUS)
RMSE =
RMS£s =
RMSEu s
CAfrtE
MdsnE
O 1I I ! I ! ! I I I I !
0,0 0,2 0,4 0.6 0.8 1.0 1.2 1.4 1.6 1,8 2.0 2.2
Observation
Figure 2.2-2. 2002 CMAQv4.6 Annual Average SO2 Predicted
Concentrations vs Observations at CASTNet Sites in the Western Domain.
2002SC 12km WUSTNO3fOf 20020101 io 20021231
4
5
6
1
o
D CASTNet (2002ac 12km_WUS
1.5
2.0 2.5
Qbservalion
3.0
3.5
4.0
Figure 2.2-3. 2002 CMAQv4.6 Annual Average TNO3 Predicted
Concentrations vs Observations at CASTNet Sites in the Western Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-10
June 5, 2009
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Description of CMAQ Applications and Model Performance Evaluation
2002ac 12km WUS NH4 for 20020101 to 20021231
1
2
O
0.2
0.4
0.6 0.8
Observation
1.0
1.2
Figure 2.2-4. 2002 CMAQv4.6 Annual Average NH4+Predicted
Concentrations vs Observations at CASTNet Sites in the Western Domain.
20023C 12km WUS SO4 for 20020101 to 20021231
4
5
6
a NADP dep(2Q02ae 12km WUS)
Petiod Accymulated
SO4 (Kg/ha)
0123
5 6
Observation
10 11
Figure 2.2-5. 2002 CMAQv4.6 Annual Average SO42" Predicted Wet
Deposition vs Observations at NADP Sites in the Western Domain.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1-11
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Description of CMAQ Applications and Model Performance Evaluation
2002ac 12km WUS NO3 for 20020101 lo 20021231
1
2
3
a NADP dep(2002ac 12km WUSj
10 11
Figure 2.2-6. 2002 CMAQv4.6 Annual Average NO3" Predicted Wet
Deposition vs Observations at NADP Sites in the Western Domain.
2002acJ2km_WUS NH4 for 20020101 to20021231
5
6
7
o
<
NADP dep(2002ac 12km WUS|
IA
RMSE
RMSEJ
Period Accumulated
NH4 f Kg/ha )
0-0
o.s
1.Q
1.5 2.0
Observation
2.5
3.0
3.5
4.0
Figure 2.2-7. 2002 CMAQv4.6 Annual Average NH4+Predicted Wet
Deposition vs Observations at NADP Sites in the Western Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-12
June 5, 2009
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Description of CMAQ Applications and Model Performance Evaluation
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2.3 CMAQV4.7 2002 THROUGH 2005 PREDICTIONS VS
OBSERVATIONS
The annual normalized mean bias statistics for the CMAQv4.7 2002 through 2005
simulations are presented in Table 2.3-1 and Table 2.3-2 for annual average concentrations and
annual total wet deposition, respectively. In general, model performance for each species is
similar in each of the four years. The CMAQv4.7 performance for 2002 is similar to that of
CMAQv4.6 for most species. Notable differences are seen for concentrations of TNOs and NH4+
For TNOs the overprediction in CMAQv4.7 is about twice that of CMAQv4.6. For NH4+,
CMAQv4.7 slightly overpredicts observations (4%) while CMAQv4.7 slightly underpredicts
observations (-3%).
Table 2.3-1. Normalized mean bias statistics for predicted and observed pollutant concentration.
Pollutant
Concentrations
SO2
SO42
TNO3
NH4+
2002
45%
-13%
22%
4%
2003
39%
-9%
26%
11%
2004
47%
-13%
22%
7%
2005
41%
-17%
24%
2%
11
Table 2.3-2. Normalized mean bias statistics for predicted and observed pollutant wet
deposition.
Pollutant
Deposition
SO42
NO3
NH4+
2002
11%
-13%
-11%
2003
6%
-17%
-16%
2004
11%
-14%
-9%
2005
6%
-17%
-13%
12
13
14
15
16
17
18
Figures 2.3-1 through 2.3-12 provide a comparison of observed and predicted monthly
concentrations and monthly total wet deposition for an aggregate of monitoring sites the Eastern
domain. This time series format is intended to reveal differences and similarities in performance
within and across the four year period. Figures 2.3-1 and 2.3-7 indicate that the predictions of
SC>42" concentration and SC>42" wet deposition closely track the temporal patterns exhibited by the
observations. However, the correlation is higher and the error is lower for SC>42" concentrations
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1-13
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Description of CMAQ Applications and Model Performance Evaluation
1 than the corresponding statistics for SO42" wet deposition (see Figures 2.3.2 and 2.3-8).
2 Predictions of NCV concentrations, although highly correlated with the observations in most
3 months, show relatively large error and positive bias in the fall with a peak in October for each
4 year. Model performance for wet deposition of NCV also has a seasonal pattern with
5 underprediction of approximately 40% in the late spring and summer and overprediction from
6 October through December. Observed concentrations of NH4+ are overpredicted by CMAQ in the
7 spring and fall and underpredicted in the summer. The overprediction in the spring peaks in
8 March/April while the peak overprediction in the fall occurs in October/November of each year.
9 Model predictions of NH4+ wet deposition more closely track the temporal patterns of
10 observations than do the predictions of NH4+ concentrations. There does not appear to be strong
11 seasonal differences in performance across the four years as seen in NH4+ concentrations.
12 However, the greatest underprediction appears to occur in May in each year. The differences and
13 similarities in the seasonal patterns in model performance for various species are being analyzed
14 by EPA to understand and explain these relationships with the goal of improving model
15 performance through improvements to emissions and meteorological inputs and scientific
16 formulation.
CDCJ>HASE RUNS CASTNET SO4 for 20020101 to 20051231; State: All; Site: All
17
18
19
7,0
6,5
6.0
5.5
5,0
4.5
3,0
2.5
2.0
1.5
1,0
0.5
CASTNET
CMAQ
2002
2C03
2004
2005
13579 11 13579 11 13579 11 13579 11
yonths
Figure 2.3-1. 2002 - 2005 Domainwide Average SO42 Predicted Concentrations
and Observations by Month at CASTNet Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-14
June 5, 2009
-------
Description of CMAQ Applications and Model Performance Evaluation
CDC_PHASE_RUNS CASTNET SO4 for 20020101 to ZOOS 1231; State: All; Site: All
1
2
3
4
40 -i
30-
20-
-10 -
-20 -
_dn -
Correlation
-A- NMB
-*-. NME
0 °'
O 3
< x x /
x A/\/\ xx'
V x x x /
X XX
A -AA
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2002
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01
t
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3579 11 13579 11 13579 11 135791]
Months
Figure 2.3-2. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
Statistics for SC>42~ Concentrations Based on CASTNet Sites in the Eastern
Domain.
CDC_PHASE_RUNS CASTNET TNO3 lor 20020101 to 20051231; State: All; Site: All
5
6
1
13579 11 13579 11 13579 11 13579 11
1.5 -
Figure 2.3-3. 2002 - 2005 Domainwide Average TNOs Predicted Concentrations and
Observations by Month at CASTNet Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-15
June 5, 2009
-------
Description of CMAQ Applications and Model Performance Evaluation
CDC_PHASE_RUNS CASTNET TNO3 lor ZODZ0101 to ZDOS1231; State: All; Site: All
1
2
3
90 -
80 -
g
50 -
LU
I 40 -
•Jj
^ 30 -
Z
20 -
10 -
0 -
-20 -
Correlation
-A- NMF
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V'l
f
i
:
00
oo V
x
li
A
11
1 T
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11
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2002
i
2003
v OO - O /
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,
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0
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£
-0.4 0
- 0.3
- 0.2
- 0.1
- o.o
3579 11 13579 11 13579 11 135791]
Months
Figure 2.3-4. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
Statistics for TNOs Concentrations Based on CASTNet Sites in the Eastern Domain.
CDC PHASE RUNS CASTNET NH4 lor 20020101 to 20051231; State: All; Site: All
4
5
6
2.0 -
13579 11 13579 11 13579 11 13579 11
0.6 -
Months
Figure 2.3-5. 2002 - 2005 Domainwide Average NELt"1" Predicted Concentrations
and Observations by Month at CASTNet Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-16
June 5, 2009
-------
Description of CMAQ Applications and Model Performance Evaluation
CDC_PHASE_RUNS CASTNET NH4 for 20020101 to ZOOS 1231; State: All; Site: All
1
2
3
4
90 -
70 -
60 -
50 -
g 40-
J 30-
2
m 20 ~
Z 10 -
-10 -
-20 -
•Srt
— JU
-40 -
Correlation
-A- NMB
-*- NME
°\ ?" f °*
oM W
A x XS
/A Vxx//lx
/ / \ x x / ^
** \
/ a fL
/ \ /
A \ / /
k i \i
\ 1 a
A'A
2002
•J o '
o \ .0-0 /\
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k
X
x/X/txx'X->
1 *f 1
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- 0.5 ra
i
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— n T
U.iJ
^ 0.2
- 0.1
- 0,0
3579 11 13579 11 13579 11 135791]
Months
Figure 2.3-6. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
Statistics for NH4+ Concentrations Based on CASTNet Sites in the Eastern
Domain.
CDC PHASE RUNS NADP SO4_dep for 2002D101 to 20051231; State: All; Site: All
5
6
1
13579 11 13579 11 13579 11 13579 11
Months
Figure 2.3-7. 2002 - 2005 Domainwide Average SC>42~ Predicted Deposition and
Observations by Month at NADP Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 1-17
-------
Description of CMAQ Applications and Model Performance Evaluation
CDC PHASE_RUNS NADP SO4_dep for 2DD2D1D1 to 20051231; State: All; Site: All
1
2
3
£
1 nn
on
80 -
70 -
60 -
SO -
40 -
30 -
ZO -
10 -
-10 -
-20 -
Correlation
-fir- NMB
-*- NME
X
/\.
A,
\
\ 0 /
A \ / \ O
/\ r
i ?
i
V A J
k/ A
^
2005
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- 0.5 ra
£
-0.40
- 0.3
- 0.2
- 0.1
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3579 11 13579 11 13579 11 135791]
Months
Figure 2.3-8. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
Statistics for SC>42~ Deposition Based on NADP Sites in the Eastern Domain.
CDC_PHASE_RUNS NADP NO3 dep lor 20020101 to 20051231; State: All; Site: All
4
5
6
13579 11 13579 11 13579 11 13579 11
Months
Figure 2.3-9. 2002 - 2005 Domainwide Average NOs" Predicted Deposition and
Observations by Month at NADP Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-18
June 5, 2009
-------
Description of CMAQ Applications and Model Performance Evaluation
CDC_PHASE_RUNS NADP NO3_dep (or ZODZ0101 to 20051231; State: All; Site: All
1
2
3
100 -
on _
DU
60 -
g 40-
LU
Z 20 -
CO
Z n
-20 -
-40 -
-60 -
Correlation
-a- NMB
-*- NME
,* /
I, "
P
I «£
<7
I
I
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-
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2005
- 1.0
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t_
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3579 11 13579 11 13579 11 135791]
Months
Figure 2.3-10. 2002 - 2005 Domainwide Monthly Aggregate Model Performance
Statistics for N(V Deposition Based on NADP Sites in the Eastern Domain.
CDC PHASE RUNS NADP NH1 dep tor 20020101 to 20051231; State: All; Site: All
4
5
6
13579 11 13579 11 13579 11 13579 11
Months
Figure 2.3-11. 2002 - 2005 Domainwide Average NELt"1" Predicted Deposition and
Observations by Month at NADP Sites in the Eastern Domain.
2nd Draft Risk and Exposure Assessment
Appendix 1-19
June 5, 2009
-------
Description of CMAQ Applications and Model Performance Evaluation
CDC PHASE_RUNSNADP NH4_dep for 20020101 to 20051231; State: All; Site: All
1
2
3
100
80 -
40 -
20 -
-20 -
Correlation
-Sr- NMB
-x- NME
x ,
/\A
x, x x \j
^- K }
/ \ H
h
l\ Ik/1
/ \ \ / 1
A \/ V A
x A
2002
/\ ,
VXx>
-------
1 June 5, 2009
2
O
4
5
6 Appendix 2
7
8 Trends in Wet Deposition of Inorganic Nitrogen and
9 Sulfate at National Atmospheric Deposition Program
10 Sites in or Near Case Study Areas
11
12
13
14
15
16
17
18
19
20 Prepared by
21
22 U.S. Environmental Protection Agency
23 Office of Air Quality Planning and Standards
24 Research Triangle Park, NC 27709
25
26
27
28
-------
1
2
-------
Trends in Wet Deposition at NADP Sites
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23 [Placeholder for map of case study areas and NADP sites]
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 2-1
-------
Trends in Wet Deposition at NADP Sites
1
2
3
4
5
6
7
8
9
10
11 [This page intentionally left blank.]
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 2-2
-------
Trends in Wet Deposition at NADP Sites
1
2
4
5
Trends in Wet Deposition of Inorganic Nitrogen
(Arrow on plots indicate the year 2002)
NADP/NTN Site NY20
Annual inorganic N wet depositions, 1978-2007
re
0
I t I i I I < I I I 1. 1 I I
I I I
I I I
197719791981 19631985198719891991 19931995 199719992001 200320052007
» Met criteria A Did no! rneeicriteria /Trend line
Adirondack Case Study Area; Site: Huntington Wildlife Forest, NY
NTN = National Trends Network
6
7
NADP/NTN Site NY98
Annual inorganic N wet depositions, 1984-2007
OS
4—+-
4-
-+—I—i—I—i—\—+-
0
1383 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
• Met criteria A Did not meet criteria /Trend line
Adirondack Case Study Area; Site: White Face Mountain, NY
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 2-3
-------
Trends in Wet Deposition at NADP Sites
1
2
NADP/NTN Site NH02
Annual inorganic N wet depositions, 1978-2007
8 r
i l i l i ' i l i l i [ i l i l i l i l i i i l i
0
197719791981 19831985198719891991 19931995199719992001
# Met criteria A Did no! meet criteria /Trend line
Hubbard Brook Experimental Forest Case Study Area
4
5
ttf
NADP/NTN Site PA29
Annual inorganic N wet depositions, 1978-2007
10
0
I I I I I I ' I I
-1-4
1977 1 979 1 981 1 983 1 985 1 987 1 989 1 991 1 993 1995 1997 1999 2001 2007
# Met criteria A Did not meet criteria /Trend line
Kane Experimental Forest Case Study Area
June 5, 2009
2nd Draft Risk and Exposure Assessment
Appendix 2-4
-------
Trends in Wet Deposition at NADP Sites
NADP/NTN Site PAOO
Annual inorganic N wet depositions, 1999-2007
Of
01
H
4 - 1
1998 2000 2002
» Met criteria
2004 2006
/Trend line
20Qi
Potomac River/Potomac Estuary Case Study Area; Site: Arendtsville, PA
4
5
NADP/NTN Site WV18
Annual inorganic N wet depositions, 1978-2007
10 r
0" I I I I I I I I I I I I I I I I I I I 1 I I i I ( I
1977 1 979 1 981 1 983 1 985 1 987 1 989 1 991 1 993 1995 1997 1999 2001 2007
# Met criteria
A Did not meet criteria /Trend line
Potomac River/Potomac Estuary Case Study Area; Site: Parsons, WV
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 2-5
-------
Trends in Wet Deposition at NADP Sites
NADP/NTN SiteMD13
Annual inorganic N wet depositions, 1983-2007
o*
* *
w
!
f
^
1182 1984 1988 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 200i
• Met criteria A Did not meet criteria /Trend line
Potomac River/Potomac Estuary Case Study Area; Site: Wye, MD
4
5
NADP/NTN Site VA28
Annual inorganic N wet depositions, 1981-2007
a r
O)
0
j i i i
ii
i i i i i i
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2008
# Met criteria A Did not meet criteria /Trend line
Shenandoah Case Study Area; Site: Shenandoah National Park, VA
June 5, 2009
2nd Draft Risk and Exposure Assessment
Appendix 2-6
-------
Trends in Wet Deposition at NADP Sites
NADP/NTNSiteVA13
Annual inorganic N wet depositions, 1978-2007
6 p
o*
. . • • * ' *
* A I
-t-l—i-
1377 1979 1981 1983 1985 1987 1969 1991 1993 1995 1997 1999 2001 2003 2005 2001
• Met criteria A Did not meet criteria /Trend line
Shenandoah Case Study Area; Site: Horton's Station, VA
4
5
NADP/NTN Site NC41
Annual inorganic N wet depositions, 1978-2007
10 r
OI
0
1977197919811983198519871989199119931 995 1997 1999 2001 2007
» Met criteria A Did not meet criteria /Trend line
Neuse River/Neuse River Estuary Case Study Area; Site: Finley Farm, NC
June 5, 2009
2nd Draft Risk and Exposure Assessment
Appendix 2-7
-------
Trends in Wet Deposition at NADP Sites
NADP/NTN Site NC06
Annual inorganic N wet depositions, 1999-2007
5 r
o*
1S98 2000
• Met criteria
2002 2004 200S
A Did not meet criteria / Trend line
2008
Neuse River/Neuse River Estuary Case Study Area; Site: Beaufort, NC
4
5
NADP/NTN Site CO19
Annual inorganic N wet depositions, 1980-2007
4
3
CS
•C 2
OI
1;
0
19
*
•'.>.• ^^^f-"
•
- 4 I
*
A
I I I I I I ' I I I I I I I I I I I I I i • r | | | |
79 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 20i07
• Met criteria A Did not meet criteria /Trend line
Rocky Mountain National Park Supplemental Area; Site: Beaver Meadows, CO
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 2-8
-------
Trends in Wet Deposition at NADP Sites
1
2
3
4
5
6
or
NADP/NTN Site CA99
Annual inorganic N wet depositions, 1981-2007
^ \ -
* • * *• -v
0
1,980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2008
» Met criteria 4 Did not meet criteria /Trend line
Mixed Conifer Forest (Sierra Nevada Range) Case Study Area;
Site: Yosemite National Park, CA
NADP/NTN Site CA67
Annual inorganic N wet depositions, 2000-2007
2.5 r
2.0
1.5
10
0.5
0.0
1999 2001 2003 200?
* Met criteria 4 Did not meet criteria /Trend line
Mixed Conifer Forest (Transverse Range) Case Study Area;
Site: Joshua Tree National Park, CA
June 5, 2009
4
^
i i
^— -^
4
»
1 , i 4 i
2nd Draft Risk and Exposure Assessment
Appendix 2-9
-------
Trends in Wet Deposition at NADP Sites
1
2
3
4
5
NADP/NTN Site CA42
Annual inorganic N wet depositions, 1982-2007
^
I
-4-4-H-
i i i 1 i I i { i
1381 1i83 1985 1987 1989 1991 1993 1995 1997 lift 2001 2003 2005 2001
• Met criteria A Did not meet criteria /Trend line
Mixed Conifer Forest (Transverse Range) Case Study Area;
Site: Tanbark Flat, CA
2nd Draft Risk and Exposure Assessment
Appendix 2 - 10
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
4
5
6
Trends in Wet Deposition of Sulfate
(Arrow on plots indicate the year 2002)
NADP/NTN Site NY20
Annual S04 wet depositions, 1978-2007
1
30 r
25
20
15
10
W I
I I I 1 i I' I I I I I I I
I I I I I I I I I
0
19?? 1979 1981 19831985 198? 1981 19931995 199? 19992001 200320052007
* Met criteria A Did not meet criteria /Trend line
Adirondack Case Study Area; Site: Huntington Wildlife Forest, NY
NADP/NTN Site NY98
Annual S04 wet depositions, 1984-2007
30 r
25
20
15
10
0
-I h
-t-
•+•
1983 1985 198? 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
* Met criteria A Did nol meet criteria / Trend line
Adirondack Case Study Area; Site: White Face Mountain, NY
2nd Draft Risk and Exposure Assessment
Appendix 2-11
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
NADP/NTN Site NH02
Annual SO4 wet depositions, 1978-2007
40 r
30:
20
10
1977 1979 1981 19831985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2001
• Met criteria A Did not meet criteria /Trend line
Hubbard Brook Experimental Forest Case Study Area
3
4
OJ
60 r
50
40:
30
20
10
NADP/NTN Site PA29
Annual SO4 wet depositions, 1978-2007
I I i I i ! i \ t t i 1 a ' t t i I
0
1977 1979 1981 19831985 1987 1969 1991 1993 1995 1997 1999 2001 2003 2005 200?
it Met criteria 4 Did not meet criteria /Trend line
Kane Experimental Forest Case Study Area
2nd Draft Risk and Exposure Assessment
Appendix 2-12
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
30 r
25
m
15
10
NADP/NTN Site PAOO
Annual SO4 wet depositions, 1999-2007
-I-
—-
1998 2000 2002
* Met criteria
2004 200®
/Trend line
Potomac River/Potomac Estuary Case Study Area; Site: Arendtsville, PA
NADP/NTN Site WV18
Annual SO4 wet depositions, 1978-2007
50
40
w
i r -t H -1- >- I- I-
197 719791931 19831985 1967 1989 1991 199319951997 1999 2001 2003 2005 200?
* Met criteria A Did not meet criteria /Trend line
Potomac River/Potomac Estuary Case Study Area; Site: Parsons, WV
2nd Draft Risk and Exposure Assessment
Appendix 2 - 13
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
NADP/NTNSiteMD13
Annual SO4 wet depositions, 1983-2007
40 r
30
20
10
* *
•
1982 1984 193S 1988 1990 1992 1S94 I§90 1998 2000 2002 2004 2008 2008
• Met criteria A Did not meet criteria /Trend line
Potomac River/Potomac Estuary Case Study Area; Site: Wye, MD
30
25
20
15
10
NADP/NTN Site VA28
Annual SO4 wet depositions, 1981-2007
Ol ' ' ' ' I I I I I I \ \ ' I \ i ! i I i I '
1080 1392 1984 1188 1388 1990 1992 1S84 1396 1998 2000 2002 20W 2006 2003
* Met criteria
A Did not meet criteria /Trend line
Shenandoah Case Study Area; Site: Shenandoah National Park, VA
2nd Draft Risk and Exposure Assessment
Appendix 2-14
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
25
20:
15
10
NADP/NTN SiteVA13
Annual SO4 wet depositions, 1978-2007
•+•
o
13171979 19811983198519871989199119931 f95 1997 i 999 2001 2003 2005 2007
* Met criteria A Did not meet criteria /Trend line
Shenandoah Case Study Area; Site: Horton's Station, VA
3
4
OJ
30
25
20:
15
10
NADP/NTN Site NC41
Annual SO4 wet depositions, 1978-2007
i i i
i a i t ' i i i i i i t \-
1977 1979 1981 19831985 1967 1969 1991 1993 1995 199M 999 2001 2003 2005 20i?
* Met criteria A Did not meet criteria /Trend line
Neuse River/Neuse River Estuary Case Study Area; Site: Finley Farm, NC
2nd Draft Risk and Exposure Assessment
Appendix 2-15
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
20 r
15
1:0
o*
NADP/NTN Site NC06
Annual SO4 wet depositions, 1999-2007
• • t
1998 2000
» Met criteria
2002 2004
A Did not meet criteria / Trend line
2008
Neuse River/Neuse River Estuary Case Study Area; Site: Beaufort, NC
3
4
NADP/NTN Site CO19
Annual SO4 wet depositions, 1980-2007
rtJ
OJ
4 -
3 -
1 -
1 -
1979 1981 1983 1i85 1S87 1989 1991 1903 1995 1997 1999 2001 2003 2005 20'0?
it Met criteria 4 Did not meet criteria /Trend line
Rocky Mountain National Park (Supplemental Area); Site: Beaver Meadows, CO
2nd Draft Risk and Exposure Assessment
Appendix 2 - 16
June 5, 2009
-------
Trends in Wet Deposition at NADP Sites
1
2
3
4
5
6
of
NADP/NTN Site CA99
Annual SO4 wet depositions, 1981-2007
8 r
^ V * *^^M~^/ *
* A * * » ^*^% f *
-f-
1180 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 200-1 2006 2008
• Met criteria A Did not meet criteria /Trend line
Mixed Conifer Forest (Sierra Nevada Range) Case Study Area;
Site: Yosemite National Park, CA
NADP/NTN Site CA67
Annual SO4 wet depositions, 2000-2007
3.0 r
2.5
2.0
15
10
0,5
o.o
4.
2001
1999 2001 2003
• Met criteria A Did not meet criteria /Trend line
Mixed Conifer Forest (Transverse Range) Case Study Area;
Site: Joshua Tree National Park, CA
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 2 - 17
-------
Trends in Wet Deposition at NADP Sites
1
2
3
NADP/NTN Site CA42
Annual SO4 wet depositions, 1982-2007
10
0 !—I—I—I—I—I—I—I—I-
1931 1983 1185 198? 1889 1991 1993 1995 1997 1999 2001 2003 2005 200?
• Met criteria A Did not meet criteria /Trend line
Mixed Conifer Forest (Transverse Range) Case Study Area;
Site: Tanbark Flat, CA
2nd Draft Risk and Exposure Assessment
Appendix 2-18
June 5, 2009
-------
1 June 5, 2009
2
O
4
5
6 Appendix 3
7
8 Components of Reactive Nitrogen Deposition Based on
9 Average Deposition Over the Period 2002-2005
10
11 Dry Deposition from CMAQ/Wet Deposition from NADP
12
13
14
15
16
17
18
19
20
21 Prepared by
22
23 U.S. Environmental Protection Agency
24 Office of Air Quality Planning and Standards
25 Research Triangle Park, NC 27709
26
27
28
29
30
31
-------
-------
Components of Reactive Nitrogen Deposition: 2002-2005
Re N - Dry
7%
Re N - Wet
23%
Ox N - Wet
35%
Ox N - Dry
35%
Adirondack Case Study Area: 2002-2005
Re N - Dry
6%
Re N - Wet
19%
Ox N - Wet
35%
Ox N - Dry
40%
4
5
Hubbard Brook Experimental Forest Case Study Area: 2002-2005
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 3-1
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Components of Reactive Nitrogen Deposition: 2002-2005
Re N - Dry
4%
Re N - Wet
22%
Ox N - Dry
41%
Ox N - Wet
33%
1
2
Kane Experimental Forest Case Study Area: 2002-2005
Re N - Dry
37%
Ox N - Dry
30%
Ox N - Wet
15%
Re N - Wet
18%
4
5
Neuse River/Neuse River Estuary Case Study Area: 2002-2005
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 3-2
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Components of Reactive Nitrogen Deposition: 2002-2005
Re N - Dry
15%
Re N - Wet
20%
Ox N - Wet
23%
Ox N - Dry
42%
1
2
Potomac River/Potomac Estuary Case Study Area: 2002-2005
Re N - Dry
16%
Re N - Wet
18%
Ox N - Wet
24%
Ox N - Dry
42%
4
5
Shenandoah Case Study Area: 2002-2005
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 3-3
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Components of Reactive Nitrogen Deposition: 2002-2005
Re N - Dry
7%
Re N - Wet
36%
Ox N - Dry
32%
Ox N - Wet
25%
Rocky Mountain National Park (Supplemental Area): 2002-2005
Re N - Dry
23%
Re N - Wet
18%
Ox N - Wet
13%
Ox N - Dry
46%
4
5
Mixed Conifer Forest (Sierra Nevada Range) Case Study Area: 2002-2005)
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 3-4
-------
Components of Reactive Nitrogen Deposition: 2002-2005
Re N - Dry
14%
Re N - Wet
6%
Ox N - Wet
6%
Ox N - Dry
74%
1
2
3
4
5
Mixed Conifer Forest (Transverse Range) Case Study Area: 2002-2005
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 3-5
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1 June 5, 2009
2
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4
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6 Appendix 4
? Aquatic Acidification Case Study
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19
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25
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27
28
29 Prepared by
30
31 U.S. Environmental Protection Agency
32 Office of Atmospheric Programs
33 Washington, DC
34
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38
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Aquatic Acidification Case Study
1 CONTENTS
2 Acronyms and Abbreviations vii
3 1. Purpose 1
4 2. Background 1
5 2.1 Acidification 1
6 2.2 Indicators of Acidification 2
7 2.3 Biological Response to Acidification and Acid Neutralizing Capacity 4
8 3. Case Studies 6
9 3.1 Surface Waters Acidification in the Eastern United States 6
10 3.2 Objectives 8
11 3.3 Adirondack Case Study Area 9
12 3.3.1 General Description 9
13 3.3.2 Levels of Air Pollution and Acidifying Deposition 10
14 3.3.3 Levels of Sulfate,Nitrate, and ANC Concentrations in Surface Water 10
15 3.4 Shenandoah Case Study Area 13
16 3.4.1 General Description 13
17 3.4.2 Levels of Air Pollution and Acidifying Deposition 14
18 3.4.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water 14
19 4. Methods 17
20 4.1 Biological Response to Acidification 17
21 4.2 Past, Present, and Future Surface Water Chemistry—the MAGIC Modeling
22 Approach 21
23 4.3 Connecting Current Nitrogen and Sulfur Deposition to Acid-Base Conditions of
24 Lakes and Streams: The Critical Load Approach 22
25 4.3.1 Regional Assessment of Adirondack Case Study Area Lakes and
26 Shenandoah Case Study Area Trout Streams 24
27 4.3.1.1 Adirondack Case Study Area 24
28 4.3.1.2 Shenandoah Case Study Area 25
29 5. Results 26
30 5.1 Adirondack Case Study Area 26
31 5.1.1 Current and Preacidification Conditions of Surface Waters 26
32 5.1.2 ANC Inferred Condition—Aquatic Status Categories 28
33 5.1.3 The Biological Risk from Current Nitrogen and Sulfur Deposition:
34 Critical Load Assessment 30
35 5.1.4 Representative Sample of Lakes in the Adirondack Case Study Area 34
36 5.1.5 Recovery from Acidification Given Current Emission Reductions 35
37 5.2 Shenandoah Case Study Area 36
38 5.2.1 Current and Preacidification Conditions of Surface Waters 36
39 5.2.2 ANC Inferred Condition—Aquatic Status Categories 38
40 5.2.3 The Biological Risk from Current Nitrogen and Sulfur Deposition:
41 Critical Load Assessment 40
42 5.2.4 Regional Assessment of Trout Streams in the Shenandoah Case Study
43 Area 42
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Appendix 4 - i
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Aquatic Acidification Case Study
1 5.2.5 Recovery from Acidification Given Current Emission Reductions 42
2 6. References 44
3 ATTACHMENT A A-l
4 1.0 Modeling Descriptions A-l
5 1.1 MAGIC A-l
6 1.1.1 InputData and Calibration A-2
7 1.1.2 Lake, Stream, and Soil Data for Calibration A-2
8 1.1.3 Wet Deposition and Meteorology Data for Calibration A-3
9 1.1.4 Wet Deposition Data (Reference Year and Calibration Values) A-5
10 1.1.5 Dry and Occult Deposition Data and Historical Deposition
11 Sequences A-6
12 1.1.6 Protocol for MAGIC Calibration and Simulation at Individual
13 Sites A-7
14 1.1.7 Combined Model Calibration and Simulation Uncertainty A-9
15 1.2 Critical Loads: Steady-State Water Chemistry Model A-10
16 1.2.1 Preindustrial Base Cation Concentration A-11
17 1.2.2 F-factor A-12
18 1.2.3 ANC Limits A-13
19 1.2.4 Sea Salt Corrections A-13
20 1.2.5 Uncertainty and Variability A-14
21 ATTACHMENTS B-l
22 1.0 EMAP/TIME/LTM Programs B-l
23 2.0 Temporally Integrated Monitoring of Ecosystems and Long-Term Monitoring
24 Programs B-2
25 2.1 TIME Program B-2
26 2.2 LTM Project B-3
27
28
29 LIST OF FIGURES
30 Figure 2.3-1. (a) Number offish species per lake or stream versus acidity, expressed as
31 ANC for Adirondack Case Study Area lakes (Sullivan et al., 2006). (b)
32 Number offish species among 13 streams as a function of ANC in the
33 Shenandoah Case Study Area. Values of ANC are means based on
34 quarterly measurements from 1987 to 1994. The regression analysis shows
35 a highly significant relationship (p < .0001) between mean stream ANC
36 and the number offish species 6
37 Figure 3.1-1. Regions containing ecosystems sensitive to acidifying deposition in the
38 eastern United States (U.S. EPA, based onNAPAP, 2005) 7
39 Figure 3.3-1. Annual average total wet deposition (kg/ha/yr) for the period 1990 to 2006
40 in SO42" (green) and NO3" (blue) from eight NADP/NTN sites in the
41 Adirondack Case Study Area 10
42 Figure 3.3-2. Trends over time for SO42- (blue), NO3" (green), and ANC (red)
43 concentrations in LTM-monitored lakes in the Adirondack Case Study
44 Area. Both SC>42" and N(V concentrations have decreased in surface
45 waters by approximately 26% and 13%, respectively 11
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Appendix 4 - ii
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Aquatic Acidification Case Study
1 Figure 3.3-3. Current (a) SC>42", (b) N(V, and (c) ANC concentrations (ueq/L) in surface
2 waters from 94 monitored lakes in the TIME/LTM monitoring network in
3 the Adirondack Case Study Area 12
4 Figure 3.4-1. Air pollution concentrations and deposition for the period 1990 to 2006
5 using one CASTNET and seven NADP/NTN sites in the Shenandoah Case
6 Study Area, (a) Annual average atmospheric concentrations of SC>2 (blue),
7 oxidized nitrogen (red), SC>42" (green), and reduced nitrogen (black), (b)
8 Annual average total wet deposition (kg/ha/yr) of SO42" (green) and NO3"
9 (blue) 14
10 Figure 3.4-2. Trends over time for SO42" (blue), NO3" (green), and ANC (red)
11 concentrations in VTSSS LTM-monitored streams in the Shenandoah
12 Case Study Area 15
13 Figure 3.4-3. Current (a) SC>42", (b) N(V, and (c) ANC concentrations (ueq/L) in surface
14 waters from 68 monitored streams in the SWAS-VTSSS LTM network in
15 the Shenandoah Case Study Area 16
16 Figure 4.1-1. Relationship between summer and spring ANC values at LTM sites in New
17 England, the Adirondack Mountains, and the Northern Appalachian
18 Plateau. Values are mean summer values for each site for the period 1990
19 to 2000 (horizontal axis) and mean spring minima for each site for the
20 same time period. On average, spring ANC values are at least 30 ueq/L
21 lower than summer values 20
22 Figure 4.1-2. Number offish species per lake or stream versus ANC level and aquatic
23 status category (represented by color) for lakes in the Adirondack Case
24 Study Area (Sullivan et al., 2006). The five aquatic status categories are
25 described in Table 4.1-1 20
26 Figure 4.3-1. (a) The location of lakes in the Adirondack Case Study Area used for
27 MAGIC (red dots) and critical load (green dots) modeling, (b) The
28 location of streams in the Shenandoah Case Study Area used for both
29 MAGIC and critical load modeling 23
30 Figure 5.1-1 Average N(V (orange), SO42"(red), and ANC (blue) concentrations for the
31 44 lakes in the Adirondack Case Study Area modeled using MAGIC for
32 the period 1850 to 2050 26
33 Figure 5.1-2. (a) N(V and (b) SC>42" concentrations (ueq/L) of preacidification (1860) and
34 current (2006) conditions based on hindcasts of 44 lakes in the
35 Adirondack Case Study Area modeled using MAGIC 28
36 Figure 5.1-3. Percentage of lakes in the five aquatic status categories of acidification
37 (Acute, Severe, Elevated, Moderate, Low) for preacidification (1860) and
38 current (2006) conditions for 44 lakes in the Adirondack Case Study Area
39 modeled using MAGIC. Error bars indicate the 95% confidence interval 30
40 Figure 5.1-4. ANC concentrations of preacidification (1860) and current (2006)
41 conditions based on hindcasts of 44 lakes in the Adirondack Case Study
42 Area modeled using MAGIC 30
43 Figure 5.1-5. Critical loads of acidifying deposition that each surface water location can
44 receive in the Adirondack Case Study Area while maintaining or
45 exceeding an ANC concentration of 50 ueq/L based on 2002 data.
46 Watersheds with critical load values <100 meq/m2/yr (red and orange
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Appendix 4 - iii
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Aquatic Acidification Case Study
1 dots) are most sensitive to surface water acidification, whereas watersheds
2 with values >100 meq/m2/yr (yellow and green dots) are the least sensitive
3 sites 32
4 Figure 5.1-6. Critical load exceedances (red dots) in the Adirondack Case Study Area
5 based on the year 2002 deposition magnitudes for waterbodies where the
6 critical limit ANC concentration is 0, 20, 50, and 100 ueq/L, respectively.
7 Green dots represent lakes where deposition is below the critical load. See
8 Table 5.1-3 33
9 Figure 5.1-7. Percentage of lakes in each of the five aquatic status categories of
10 acidification (Acute, Severe, Elevated, Moderate, Low) for the years 2006,
11 2020, and 2050 for 44 lakes in the Adirondack Case Study Area modeled
12 using MAGIC, where current emissions are held constant. Error bars
13 indicate the 95% confidence interval 35
14 Figure 5.2-1. Average NCV (orange), SO42"(red), and ANC (blue) concentrations for the
15 60 streams in the Shenandoah Case Study Area modeled using MAGIC
16 for the period 1850 to 2050 36
17 Figure 5.2-2. (a) N(V and (b) SC>42" concentrations (ueq/L) of 1860 (preacidification) and
18 2006 (current) conditions based on hindcasts of 60 streams in the
19 Shenandoah Case Study Area modeled using MAGIC 37
20 Figure 5.2-3. Percentage of streams in the five aquatic status categories of acidification
21 (Acute, Severe, Elevated, Moderate, Low) for preacidification (1860) and
22 current (2006) conditions for 60 streams in the Shenandoah Case Study
23 Area modeled using MAGIC. The number of streams in each category is
24 above the bar. Error bars indicate the 95% confidence interval 39
25 Figure 5.2-4. ANC concentrations of 1860 (preacidification) and 2006 (current)
26 conditions based on hindcasts of 60 streams in the Shenandoah Case Study
27 Area modeled using MAGIC 39
28 Figure 5.2-5. Critical loads of surface water acidity for an ANC concentration of 50
29 ueq/L for Shenandoah Case Study Area streams. Each dot represents an
30 estimated amount of acidifying deposition (i.e., critical load) that each
31 stream's watershed can receive and still maintain a surface water ANC
32 concentration >50 ueq/L. Watersheds with critical load values <100
33 meq/m2/yr (red and orange dots) are most sensitive to surface water
34 acidification, whereas watersheds with values >100 meq/m2/yr (yellow
35 and green dots) are the least sensitive sites 40
36 Figure 5.2-6. Critical load exceedances for ANC concentrations of 0, 20, 50, and 100
37 ueq/L for Shenandoah Case Study Area streams. Green dots represent
38 streams where current nitrogen and sulfur deposition is below the critical
39 load and that maintain an ANC concentration of 0, 20, 50, and 100 ueq/L,
40 respectively. Red dots represent streams where current nitrogen and sulfur
41 deposition exceeds the critical load, indicating they are currently impacted
42 by acidifying deposition. See Table 5.2-3 41
43 Figure 5.2-7. Percentage of streams in the five categories of acidification (Acute, Severe,
44 Elevated, Moderate, Low) for the years 2006, 2020, and 2050 for 60
45 streams in the Shenandoah Case Study Area modeled using MAGIC. The
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4 - iv
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Aquatic Acidification Case Study
1 number of streams in each category is above the bar. Error bars indicate
2 the 95% confidence interval 43
4 LIST OF TABLES
5 Table4.1-l. Aquatic Status Categories 18
6 Table 5.1-1. Estimated Average Concentrations of Surface Water Chemistry for 44 Lakes
7 in the Adirondack Case Study Area Modeled Using MAGIC for
8 Preacidification (1860) and Current (2006) Conditions 27
9 Table 5.1-2. Percentage of Lakes in the Five Aquatic Status Categories Based on Their
10 Surface Water ANC Concentrations for 44 Lakes Modeled Using MAGIC
11 and 94 Lakes in the TIME/LTM Monitoring Network. Results Are for the
12 Adirondack Case Study Area for the Year 2006 29
13 Table 5.1-3. Adirondack Case Study Area Critical Load Exceedances, Where Nitrogen
14 and Sulfur Deposition Is Larger Than the Critical Load for Four Different
15 ANC Critical Limit Thresholds, for 169 Modeled Lakes within the
16 TEVIE/LTM and EMAP Monitoring and Survey Programs. "No.
17 Exceedances" Indicates the Number of Lakes at or below the Given ANC
18 Critical Limit, and "% Lakes" Indicates the Total Percentage of Lakes at
19 or below the Given ANC Critical Limit 32
20 Table 5.1-4. Critical Load Exceedances for the Regional Population of 1,842 Lakes in the
21 Adirondack Case Study Area That Are from 0.5 to 2,000 ha in Size and at
22 Least 1 m in Depth for the Four Critical Limit ANC Levels (0, 20, 50, and
23 100 ueq/L). Estimates Use the Exceedances for the Subset of 169 Lakes
24 Using 2002 Deposition Magnitudes (Table 5.1-3) and Are Extrapolated to
25 the Full Population Based on the EMAP Lake Probability Survey of 1991
26 to 1994. "No. Exceedances" Indicates the Number of Lakes at or Below
27 the Given ANC Critical Limit; "% Lakes" Indicates the Total Percentage
28 of Lakes at or Below the Given ANC Critical Limit 34
29 Table 5.2-1. Estimated Average Concentrations of Surface Water Chemistry for 60
30 Streams in the Shenandoah Case Study Area Modeled Using MAGIC for
31 Preacidification (1860) and Current (2006) Conditions 38
32 Table 5.2-2. Percentage of Streams in the Five Aquatic Status Categories Based on Their
33 Surface Water ANC Concentrations for 60 Streams Modeled Using
34 MAGIC and 68 Streams in the SWAS-VTSSS LTM Network. Results are
35 for the Shenandoah Case Study Area for the Year 2006 38
36 Table 5.2-3 Critical Load Exceedances (Nitrogen + Sulfur Deposition > Critical Load)
37 for 60 Modeled Streams Within the VTSSS LTM Program in the
38 Shenandoah Case Study Area. "No. Exceedances" Indicates the Number
39 of Streams at the Given ANC Limit; "% Streams" Indicates the Total
40 Percentage of Streams at the Given ANC Limit 42
41
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Appendix 4 - v
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Appendix 4 - vi
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ACRONYMS AND ABBREVIATIONS
2
O
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
A13+
A1(OH)3
ANC
ASTRAP
Ca2+
cr
CL(A)
CO2
DDF
EMAP
eq/ha/yr
F
H+
H4SiO4
ha
ISA
K+
kg/ha/yr
km
LTM
m
m/yr
MAGIC
MAHA
meq/m2'yr)
Mg2+
Na+
NADP
NH4+
NO3"
NOX
NSWS
NTN
03
PnET-BGC
Si
S02
SO42-
sox
SWAS
sswc
TIME
aluminum
aluminum hydroxide
acid neutralizing capacity
Advanced Statistical Trajectory Regional Air Pollution
calcium
chloride
critical loads of acidity
carbon dioxide
dry and occult deposition factor
Environmental Monitoring and Assessment Program
equivalents per hectare per year
fluoride
hydrogen ion
silicic acid
hectare
Integrated Science Assessment
potassium
kilograms/hectare/year
kilometer
Long-Term Monitoring
meter
meters/year
Model of Acidification of Groundwaters in Catchments
Mid-Atlantic Highlands Assessment
milliequivalents per square meter per year
magnesium
sodium
National Atmospheric Deposition Program
ammonium
nitrate
nitrogen oxides
National Lake/Stream Surveys
National Trends Network
ozone
biogeochemical model
silicon
sulfur dioxide
sulfate
sulfur oxides
Shenandoah Nation Park Surface Water Acidification
Steady- State Water Chemistry
Temporally Integrated Monitoring of Ecosystems
Study
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Appendix 4 - vii
June 5, 2009
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Aquatic Acidification Case Study
1 ueq/L microequivalents per liter
2 uM micrometer
3 VTSSS Virginia Trout Stream Sensitivity Survey
4
5
6
7
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Appendix 4 - viii
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Aquatic Acidification Case Study
i 1. PURPOSE
2 This case study is intended to estimate the ecological exposure and risk to aquatic
3 ecosystems from acidification associated with the deposition of nitrogen and sulfur for two
4 sensitive regions of eastern United States: the Adirondack Mountains in New York (hereafter
5 referred to as the Adirondack Case Study Area) and Shenandoah National Park and the
6 surrounding areas of Virginia (hereafter referred to as the Shenandoah Case Study Area).
7 2. BACKGROUND
8 2.1 ACIDIFICATION
9 Sulfur oxides (SOX) and nitrogen oxides (NOX) compounds in the atmosphere undergo a
10 complex mix of reactions and thermodynamic processes in gaseous, liquid, and solid phases to
11 form various acidic compounds. These acidic compounds are removed from the atmosphere
12 through deposition: either wet (e.g., rain, snow), occult (e.g., fog, mist), or dry (e.g., gases,
13 particles). Deposition of these acidic compounds leads to ecosystem exposure and effects on
14 ecosystem structure and function. Following deposition, these compounds can, in some
15 instances, leach out of the soils in the form of sulfate (SC>42") and nitrate (NCV), leading to the
16 acidification of surface waters. The effects on ecosystems depend on the magnitude of
17 deposition, as well as a host of biogeochemical processes occurring in the soils and waterbodies.
18 When sulfur or nitrogen migrates from soils to surface waters in the form of SC>42" or
19 NCV, an equivalent amount of positive cations, or countercharge, is also transported. This
20 maintains the balance of electric charge. If the countercharge is provided by base cations, such as
21 calcium (Ca2+), magnesium (Mg2+), sodium (Na+), or potassium (K+), rather than hydrogen (H+)
22 and aluminum (A13+), the acidity of the soil water is neutralized, but the base saturation of the
23 soil is reduced. Continued SC>42" or NCV leaching can further deplete the base cation supply of
24 the soil. As the base cations are removed, continued deposition and leaching of SC>42" and/or
25 NCV (with H+ and A13+) leads to acidification of soil water, and by connection, surface water.
26 Loss of soil base saturation is a cumulative effect that increases the sensitivity of the watershed
27 to further acidifying deposition. Base cations are replenished through the natural weathering of
28 the rocks and soils, but weathering is a slow process, which results in the depletion of cations in
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Appendix 4-1
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Aquatic Acidification Case Study
1 the soil in the presence of SC>42" and/or N(V pollution. A watershed's ability to buffer acidic
2 deposition is determined by a host of biogeophysical factors, including base cation
3 concentrations, weathering rates, uptake by vegetation, rate of surface water flow, soil depth, and
4 bedrock.
5 Following deposition, SC>42" can absorb to, or bind with, soil particles, a process that
6 removes it from the aqueous soil solution, and therefore, prevents the leaching of base cations (at
7 least temporarily) and further acidifying of the soil water. This process results in an
8 accumulation of sulfur in the soil. This process is potentially reversible and can contribute to soil
9 acidification if, and when, the SC>42" is desorbed and released back into the soil water solution.
10 The degree to which SC>42" adsorbs on soil is dependent on soil characteristics. The locations of
11 soils in the United States that most effectively adsorb SO42" are found south of the areas that
12 experienced glaciation during the most recent ice age (Rochelle and Church, 1987; Rochelle et
13 al., 1987). SC>42" adsorption is strongly pH-dependent, and a decrease in soil pH resulting from
14 acidifying deposition can enhance the ability of soil to adsorb SC>42". Consequently, as deposition
15 increases, the soil potentially stores a disproportionate amount of SC>42". When deposition
16 decreases, this stored SC>42" is slowly, but continually, released, keeping soil water acidified
17 and/or depleting the base cation supply.
18 2.2 INDICATORS OF ACIDIFICATION
19 The chemistry of the surface water is directly related to the biotic integrity of freshwater
20 ecosystems. There are numerous chemical constituents in surface water that can be used to
21 indicate the acidification condition of lakes and streams and to assess the effects of acidifying
22 deposition on ecosystem components. These include surface water pH (log[H+]) and
23 concentrations of SC>42", NCV, A13+, and Ca2+; the sum of base cations; the recently developed
24 base cation surplus; and the acid neutralizing capacity (ANC). Each of these chemical indicators
25 provides direct links to the health of individual biota and the overall health and integrity of
26 aquatic ecosystems as a result of surface water acidification.
27 Although ANC does not directly affect the health of biotic communities, it is calculated
28 (or measured) based on the concentrations of chemical constituents that directly contribute to or
29 ameliorate acidity-related stress, in particular, pH, Ca2+, and A13+. Furthermore, numerical
30 models of surface water acidification can more accurately estimate ANC than all of the
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Appendix 4-2
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Aquatic Acidification Case Study
1 individual constituents that comprise it. Consequently, for the purpose of this case study, annual
2 average ANC of surface waters was used as the primary metric to quantify the current acidic
3 conditions and biological impacts for a subset of waterbodies in the study areas. The remainder
4 of this section focuses on a description of ANC.
5 ANC reflects the relative balance between base cations and strong acid anions. It
6 accounts for the cumulative effects of all of the ionic interactions that occur as the acidic
7 compounds are removed from the atmosphere to the catchment and drainage water to emerge in
8 a stream or lake. ANC of surface waters is defined (in this study) as the total amount of strong
9 base ions minus the total amount of strong acid anions:
10 ANC = (Ca2+ + Mg2++K+ + Na+ + NH4+)-(SO42" + NO3"+Cr) (1)
11 The unit of ANC is microequivalents per liter (ueq/L), which is a concentration. If the
12 sum of the equivalent concentrations of the base cations exceeds those of the strong acid anions,
13 then the ANC of a waterbody will be positive. To the extent that the base cation sum exceeds the
14 strong acid anion sum, the ANC will be higher. Higher ANC is generally associated with high
15 pH and Ca2+ concentrations, and lower ANC is generally associated with low pH and high A13+
16 concentrations and a greater likelihood of toxicity to biota.
17 ANC samples from waterbodies are typically measured using the Gran titration approach.
18 Process-based numerical models, such as Model of Acidification of Groundwaters in Catchments
19 (MAGIC) and the biogeochemical model PnET-BGC utilize the ANC calculated from the charge
20 balance. For assessment purposes, including resource characterization and Long-Term
21 Monitoring (LTM) programs, it is always best to use both directly measured and numerically
22 estimated ANC values. The difference between the two can be used to quantify uncertainty and
23 reveal the influences of natural organic acidity and/or dissolved aluminum (Al) on the overall
24 acid-base chemistry of the water.
25 Relative to some individual chemical parameters, such as pH, ANC concentration reflects
26 sensitivity to acidifying deposition input and effects on surface water chemistry in a linear
27 fashion across the full range of ANC values. Consequently, ANC is a preferred indicator variable
28 for surface water acidification. Other parameters, such as surface water pH, can complement the
29 assessment of surface water acidification; however, the response of this parameter to inputs is
30 not necessarily linear throughout its range. For example, at pH values >6.0, pH is not a good
31 indicator of either sensitivity to acidification or level of biological effect. In addition, pH
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Appendix 4-3
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Aquatic Acidification Case Study
1 measurements (especially at these higher values) are sensitive to and can be confounded by the
2 level of dissolved carbon dioxide (CO?) in the water.
3 2.3 BIOLOGICAL RESPONSE TO ACIDIFICATION AND ACID
4 NEUTRALIZING CAPACITY
5 Ecological effects occur at four levels of biological organization: (1) the individual; (2)
6 the population, which is composed of a single species of individuals; (3) the biological
7 community, which is composed of many species; and (4) the ecosystem. Low ANC
8 concentrations are linked with negative effects on aquatic systems at all four of these biological
9 levels. For the individual level, impacts are assessed in terms of fitness (i.e., growth,
10 development, and reproduction) or sublethal effects on condition. Surface water with low ANC
11 concentrations can directly influence aquatic organism fitness or mortality by disrupting ion
12 regulation and can mobilize Al3+, which is highly toxic to fish under acidic conditions (i.e., pH
13 <6 and ANC <50 ueq/L). For example, research showed that as the pH of surface waters
14 decreased to <6, many aquatic species, including fish, invertebrates, zooplankton, and diatoms,
15 tended to decline sharply causing species richness to decline (Schindler, 1988). Van Sickle et al.
16 (1996) also found that blacknose dace (Rhinichthy spp.) were highly sensitive to low pH and
17 could not tolerate inorganic Al concentrations greater than about 3.7 micromolar (uM) for
18 extended periods of time. For example, they found that after 6 days of exposure to high inorganic
19 Al, blacknose dace mortality increased rapidly to nearly 100%.
20 At the community level, species richness and community structure can be used to
21 evaluate the effects of acidification. Species composition refers to the mix of species that are
22 represented in a particular ecosystem, whereas species richness refers to the total number of
23 species in a stream or lake. Acidification alters species composition and richness in aquatic
24 ecosystems. There are a number of species common to many oligotrophic waterbodies that are
25 sensitive to acidification and cannot survive, compete, or reproduce in acidic waters. In response
26 to small to moderate changes in acidity, acid-sensitive species are often replaced by other more
27 acid-tolerant species, resulting in changes in community composition and richness, but with little
28 or no change in total community biomass. The effects of acidification are continuous, with more
29 species being affected at higher degrees of acidification. At a point, typically a pH <4.5 and an
30 ANC <0 ueq/L, complete to near-complete loss of many classes of organisms occur, including
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Appendix 4-4
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Aquatic Acidification Case Study
1 fish and aquatic insect populations, whereas others are reduced to only a few acidophilic forms.
2 These changes in species integrity are because energy cost in maintaining physiological
3 homeostasis, growth, and reproduction is high at low ANC levels (Schreck, 1981, 1982;
4 Wedemeger et al., 1990).
5 Decreases in species richness related to acidification have been observed in the
6 Adirondack Mountains and Catskill Mountains of New York (Baker et al., 1993), the Upper
7 Midwest of the United States (Schindler et al., 1989), New England and Pennsylvania (Haines
8 and Baker, 1986), and Virginia (Bulger et al., 2000). Studies on fish species richness in the
9 Adirondack Case Study Area demonstrated the effect of acidification; of the 53 fish species
10 recorded in Adirondack Case Study Area lakes, only 27 species were found in lakes with a pH
11 <6.0. The 26 species missing from lakes with a pH <6.0 include important recreational species,
12 such as Atlantic salmon, tiger trout (Salmo trutta X Salvelinusfontinalis), redbreast sunfish
13 (Lepomis auritus), bluegill (Lepomis macrochirus), tiger musky (Esox masquinongy X Indus),
14 walleye (Sander vitreus), alewife (Alosapseudoharengus), and kokanee (Oncorhynchus nerkd)
15 (Kretser et al., 1989), as well as ecologically important minnows that are commonly eaten by
16 sport fish. A survey of 1,469 lakes in the late 1980s found 346 lakes to be devoid offish. Among
17 lakes with fish, there was a relationship between the number offish species and lake pH, ranging
18 from about one species per lake for lakes having a pH <4.5 to about six species per lake for lakes
19 having a pH >6.5 (Driscoll et al., 2001; Kretser et al., 1989).
20 These decreases in species richness due to acidifying deposition are positively correlated
21 with ANC concentrations (Kretser et al., 1989; Rago and Wiener, 1986). Most notably, Sullivan
22 et al. (2006) found a logistic relationship between fish species richness and ANC category for
23 Adirondack Case Study Area lakes (Figure 2.3-1, a), which indicates the probability of
24 occurrence of an organism for a given value of ANC. In addition, a similar relationship has been
25 found for the Shenandoah Case Study Area, where a statistically robust relationship between
26 ANC concentration and fish species richness was documented (Figure 2.3-1, b). In fact, ANC
27 has been found in studies to be the best single indicator of the biological response and health of
28 aquatic communities in acid-sensitive systems (Lien et al., 1992; Sullivan et al., 2006).
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Appendix 4-5
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Aquatic Acidification Case Study
i
2
3
4
5
6
7
(a)
« ,,,
t& 12 -
*o
i.10-
fift A
.* jgr
E -i
o
w 2 -
E
3 -2 -
-4 _
-2
si
'«
V* -'V* " *.
•• ,A&c*^i*^"iiNM»
•^•PwP^"
i T T f T r i
30 .100 0 100 200 300 400 5C
(b)
9 -i
"a
i
«
£
«
E
•5
1 3
E
z -
1
)0 -!
-^" !
^ 1 i
X '
^ t 1
x
T y "^ , ,
i " H
: . _ ' t , _ -' ' - t
5 0 25 50 ?5 100 125 150 175 203 225 ZSC' 275 ;
Average AMC (|jec|/L)
ANC(peqlL)
Figure 2.3-1. (a) Number offish species per lake or stream versus acidity, expressed
as ANC for Adirondack Case Study Area lakes (Sullivan et al., 2006). (b) Number
offish species among 13 streams as a function of ANC in the Shenandoah Case
Study Area. Values of ANC are means based on quarterly measurements from 1987
to 1994. The regression analysis shows a highly significant relationship (p < .0001)
between mean stream ANC and the number offish species.
3. CASE STUDIES
8 3.1 SURFACE WATERS ACIDIFICATION IN THE EASTERN UNITED
9 STATES
10 The regions of the United States with low average annual surface water ANC values are
11 the locations that are sensitive to acidifying deposition. The majority of lakes and streams in the
12 United States have ANC levels >200 ueq/L and are not sensitive to the acidifying deposition of
13 NOX and SOX air pollution at their existing ambient concentration levels. Figure 3.1-1 shows the
14 acid-sensitive regions of the eastern United States with the potential for low surface water ANC,
15 as determined by geology and surface water chemistry.
16 Freshwater surveys and monitoring in the eastern United States have been conducted by
17 many programs since the mid-1980s, including the National Lake/Stream Surveys (NSWS),
18 EPA's Environmental Monitoring and Assessment Program (EMAP), the Temporally Integrated
19 Monitoring of Ecosystems (TIME) monitoring program (Stoddard, 1990), and LTM project
20 (Ford et al., 1993; Stoddard et al., 1998) (Appendix Attachment B). The purpose of these
21 programs is to determine the current state and document the trends over time in surface water
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June 5, 2009
Appendix 4-6
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Aquatic Acidification Case Study
1 chemistry for regional populations of lakes or streams impacted by acidifying deposition. Based
2 on extensive surveys and surface water data from these programs, it was determined that the
3 most sensitive lakes and streams (i.e., ANC less than about 50 ueq/L) in the eastern United States
4 are found in New England, the Adirondack Mountains, the Appalachian Mountains (northern
5 Appalachian Plateau and Ridge/Blue Ridge region), northern Florida, and the Upper Midwest.
6 These areas are estimated to contain 95% of the lakes and 84% of the streams in the United
7 States that have been anthropogenically acidified through deposition (see Annex 4.3.3.2 of the
8 Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur-Ecological Criteria
9 (Final Report) (ISA) (U. S. EPA, 2008).
10
11
12
13
14
15
16
17
18
Figure 3.1-1. Regions containing ecosystems sensitive to acidifying deposition in
the eastern United States (U.S. EPA, based on NAPAP, 2005).
The number and proportion of acidic waterbodies in these regions, defined as having a
pH <5.0 and ANC <0 ueq/L, are substantial. The Adirondack Case Study Area had a large
proportion of acidic surface waters (14%) in the NSWS; from 1984 to 1987, the Adirondack
Lakes Survey Corporation sampled 1,469 Adirondack Case Study Area lakes >0.5 hectares (ha)
in size and estimated that many more (26%) were acidic (Driscoll et al., 1991). The proportion of
lakes estimated by NSWS to be acidic was smaller in New England and the Upper Midwest (5%
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June 5, 2009
Appendix 4-7
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Aquatic Acidification Case Study
1 and 3%, respectively), but because of the large number of lakes in these regions, there were
2 several hundred acidic waters in each of these two regions. The Shenandoah Case Study Area
3 had 5.5% and 6% acidic sites, respectively, based on data from the early 1990s. Portions of
4 northern Florida also contain many acidic and low-ANC lakes and streams, although the role of
5 acidifying deposition in these areas is less clear. In 2002, Stoddard et al. (2003) comprehensively
6 reexamined the levels of acidification within all of these regions. Although improvement in ANC
7 occurred, they still found that about 8% of 1,469 lakes in the Adirondack Case Study Area and
8 6% to 8% of streams in the northern Shenandoah Case Study Area (Appalachian Plateau and
9 Ridge/Blue Ridge region) were acidic at base-flow conditions.
10 The Adirondack Case Study Area and the Shenandoah Case Study Area provide ideal
11 areas to assess the risk to aquatic ecosystems from NOX and SOX acidifying deposition. Four
12 main reasons support the selection of these two areas. First, both regions fall within the areas of
13 the United States known to be sensitive to acidifying deposition because of a host of
14 environmental factors that make these regions predisposed to acidification. Second, these areas
15 are representative of other areas sensitive to acidification, which will allow the results of this
16 case study to be generalized. Third, these regions have in the past and continue to experience
17 substantial exposure to NOX and SOX air pollution. Fourth, these areas have been extensively
18 studied (e.g., from atmospheric concentrations, soil characteristics, surface water chemistry, to
19 the changes in biological communities in response to aquatic acidification) over the last 3
20 decades (see Section 4 of the ISA Report) (U.S. EPA, 2008). For example, extensive water
21 quality data exists from monitoring networks in operation since the 1980s, along with numerous
22 research studies that directly link the biological harm of individuals, populations, communities,
23 and ecosystems to aquatic acidification. The sections below describe each of the case studies
24 areas, in turn, indicating past impacts of acidifying deposition, and identifying research linking
25 biological and acidic conditions for each region.
26 3.2 OBJECTIVES
27 For the two case study areas, the Adirondack and the Shenandoah, conditions of the
28 aquatic ecosystems and responses to nitrogen and sulfur deposition were evaluated by using
29 multiple approaches that rely on monitoring data and modeled output. Current conditions were
30 evaluated by a three-step process:
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Aquatic Acidification Case Study
1 "By evaluating the status and trends of surface water chemistry data to establish linkages
2 between current ambient air pollution levels of nitrogen and sulfur and the total amount
3 of deposition
4 "By evaluating the biological risk to individuals, populations, and communities from
5 acidification
6 "By evaluating the response of the aquatic ecosystem to current and future deposition
7 compared with the likelihood for recovery of currently impacted aquatic waterbodies.
8 In evaluating these conditions, this case study addresses the welfare effects of
9 acidification by building linkages between ambient pollutant levels, deposition, surface water
10 chemistry, and the resulting response in the biological communities.
11 3.3 ADIRONDACK CASE STUDY AREA
12 3.3.1 General Description
13 The Adirondack Case Study Area is situated in northeastern New York and is
14 characterized by dense forest cover of evergreen and deciduous trees and abundant surface
15 waters, with 46 peaks that extend up to 1,600 meters. The Adirondack Case Study Area has long
16 been a nationally important recreation area for fishing, hiking, boating, and other outdoor
17 activities. The area includes the headlands of five major drainage basins: Lake Champlain and
18 the Hudson, Black, St. Lawrence, and Mohawk rivers, which all draw water from the preserve.
19 There are more than 2,800 lakes and ponds, and more than 1,500 miles of rivers that are fed by
20 an estimated 30,000 miles of brooks and streams. The Adirondack Case Study Area, particularly
21 its southwestern section, is sensitive to acidifying deposition because it receives high
22 precipitation amounts with high concentrations of pollutants, has shallow base-poor soils, and is
23 underlain by igneous bedrock with low weathering rates and buffering ability (Driscoll et al.,
24 1991; Sullivan et al., 2006). This case study area is among the most severely acid-impacted
25 regions in North America (Driscoll et al., 2003; Landers et al., 1988; Stoddard et al., 2003). It
26 has long been used as an indicator of the response of forest and aquatic ecosystems to changes in
27 emissions of sulfur dioxide (802) and NOX resulting, in part, from the Clean Air Act
28 Amendments of 1990 (NAPAP, 1998; U.S. EPA, 1995).
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Appendix 4-9
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Aquatic Acidification Case Study
4
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6
7
9
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13
14
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16
3.3.2 Levels of Air Pollution and Acidifying Deposition
Wet deposition in the Adirondack Case Study Area has been monitored by the National
Atmospheric Deposition Program/National Trends Network (NADP/NTN) since 1978 at two
sites (i.e., Huntington Forest and Whiteface Mountain) and at seven other sites since the 1980s.
Since 1990, wet SO42" and NO3" deposition at these NADP/NTN sites in the Adirondack Case
Study Area has declined by about 45% and 40%, respectively (Figure 3.3-1). However, annual
total wet deposition is still >15 and 10 kilograms/hectare/year (kg/ha/yr) of SC>42" and N(V,
respectively.
1990 1991 1992 1993 1994 199E 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Source: NftDP
NftDP Sites: NY08,Nt20,NtS2,Ht68,Nlf98,Ht99,UT01,UT99
Figure 3.3-1. Annual average total wet deposition (kg/ha/yr) for the period 1990 to 2006 in
SO42" (green) and NO3" (blue) from eight NADP/NTN sites in the Adirondack Case Study Area.
3.3.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water
Environmental monitoring data reported above demonstrate decreasing trends in
depositional loading, reflecting decreases in air pollution. Figure 3.3-2 shows trends in SC>42",
N(V, and ANC in surface water for Adirondack Case Study Area lakes monitored through the
Adirondack LTM program. As a result of decreases in air pollution and depositional loading,
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Aquatic Acidification Case Study
1
2
3
4
5
6
7
10
11
12
13
14
15
16
17
18
regional SC>42" concentrations in these lakes has dropped by approximately 26% since the mid-
1990s. While inter-annual variability in N(V concentrations is evident in the Adirondack Case
Study Area monitored lakes, the overall trend is modestly downward (13% over the entire
period). An increase in long-term ANC concentrations of+0.8 ueq/L/yr has corresponded to the
declines in N(V and SC>42". However, this increase in ANC also correlates with reductions in
base cations of calcium (Ca2+) and magnesium (Mg2+) during the same period of time (data not
shown). This decline in base cation concentration is important because base cations buffer the
inputs of N(V and SC>42", which will likely limit future recovery of ANC concentrations. In the
Adirondack Case Study Area, toxic levels of organic Al also declined slightly (data not shown).
Annual Average Surface Water Trends 1990-2006
(ftdirondack LTM Lakes)
ueq/L
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
Source: TIME/LTM
Figure 3.3-2. Trends over time for SC>42- (blue), N(V (green), and ANC (red) concentrations in
LTM-monitored lakes in the Adirondack Case Study Area. Both SC>42~ and N(V concentrations
have decreased in surface waters by approximately 26% and 13%, respectively.
Despite decreases in deposition and surface water concentrations of SC>42" and N(V,
levels remain elevated in monitored lakes. Figure 3.3-3 shows current concentrations of SC>42",
N(V, and ANC for Adirondack Case Study Area lakes monitored through the Adirondack
TIME/LTM programs. The annual averages for the period 2005 to 2006 of SO42", NO3", and
ANC are 70.96 ± 19.7.1, 9.07 ± 10.3, and 43.43 ± 32.3 ueq/L, respectively.
2nd Draft Risk and Exposure Assessment
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June 5, 2009
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Aquatic Acidification Case Study
(a)
(b)
TIME/LTM - 2006
Sulfate (SO42")
TIME/LTM - 2006
Nitrate (NO3")
CD"
(c)
TIME/LTM - 2006
ANC
1 Figure 3.3-3. Current (a) SO42", (b) NO3", and (c) ANC concentrations (ueq/L) in
2 surface waters from 94 monitored lakes in the TIME/LTM monitoring network in
3 the Adirondack Case Study Area.
4 There are still a significant number of lakes in the Adirondack Case Study Area that have
5 low ANC values (<50 ueq/L) based on the observed annual average concentration of ANC from
6 the years 2005 and 2006 for the waterbodies in the TIME/LTM monitoring network. Of the 94
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Appendix 4-12
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Aquatic Acidification Case Study
1 monitored lakes, 22% have ANC values >50 ueq/L, whereas 78% of the monitored lakes have
2 ANC values <50 ueq/L. Of the 73 monitored lakes with >50 ueq/L, 17 are chronically acidic
3 (ANC <0 ueq/L). Twenty-nine of the lakes have <20 ueq/L, making their biological communities
4 susceptible to episodic acidification.
5 3.4 SHENANDOAH CASE STUDY AREA
6 3.4.1 General Description
7 The Shenandoah Case Study Area straddles the crest of the Blue Ridge Mountains in
8 western Virginia, on the eastern edge of the central Appalachian Mountain region. Several areas
9 in Shenandoah National Park have been designated Class 1 Wilderness areas. Shenandoah
10 National Park is known for its scenic beauty, outstanding natural features, and biota. The Skyline
11 Drive, a scenic 165-kilometer (km) parkway, provides the opportunity for views of the Blue
12 Ridge Mountains and surrounding areas. The Appalachian National Scenic Trail is the backbone
13 of the park's trail system. The natural features and biota of the park include the well-exposed
14 rock strata of the Appalachians, which is one of the oldest mountain ranges in the world. The
15 park comprises one of the nation's most diverse botanical reserves and wildlife habitats. A
16 congress!onally designated wilderness area within the park is the largest in the mid-Atlantic
17 states and provides a comparatively accessible opportunity for solitude, study, and experience in
18 a natural area.
19 Air pollution within the Shenandoah Case Study Area, including concentrations of sulfur,
20 nitrogen, and ozone (Os), is higher than in most other national parks in the United States. This
21 area is sensitive to acidifying deposition because of the noncarbonate composition and
22 weathering-resistant characteristics of much of the underlying bedrock, which result in base-poor
23 soils with low weathering rates and poor buffering capacity. At base flow conditions, Lynch and
24 Dise (1985) determined that stream water ANC, pH, and base cation concentrations in this region
25 are strongly correlated with bedrock geology. This landscape includes three major bedrock types:
26 siliceous (e.g., quartzite and sandstone), felsic (e.g., granitic), and mafic (e.g., basaltic). Each of
27 these bedrock types influence about one-third of the stream miles in this region. ANC
28 concentrations for streams associated with siliceous bedrock are extremely low. Almost half of
29 the sampled streams had ANC in the chronically acidic range (<0 ueq/L). The balance of the
30 streams associated with siliceous bedrock had ANC in the episodically acidic range (0 to 20
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Appendix 4 - 13
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Aquatic Acidification Case Study
1 ueq/L). Consequently, this region is among the most severely acid-impacted areas in North
2 America (Stoddard et al., 2003; Webb et al., 2004).
4
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21
2-
3.4.2 Levels of Air Pollution and Acidifying Deposition
Annual average atmospheric concentrations of sulfur dioxide (802), NOX, SC>4Z", and
reduced nitrogen at the Big Meadow air monitoring location within the Shenandoah National
Park have all decreased since the 1990s, with the exception of reduced nitrogen (Figure 3.4-1 a).
As a result, wet deposition in the Shenandoah National Park monitored at 7 sites by the
NADP/NTN since the 1980s shows wet SC>42" and N(V deposition declining by about 28% and
20%, respectively (Figure 3.4-1 b). However, annual total deposition is still 15 and 10 kg/ha/yr
of SC>42" and N(V, respectively.
(a)
(b)
Annual Average Ajr Concentrations 1960-2007
Annual Average Wet Deposition 1990—2006
Figure 3.4-1. Air pollution concentrations and deposition for the period 1990 to
2006 using one CASTNET and seven NADP/NTN sites in the Shenandoah Case
Study Area, (a) Annual average atmospheric concentrations of 862 (blue),
oxidized nitrogen (red), SC>42" (green), and reduced nitrogen (black), (b) Annual
average total wet deposition (kg/ha/yr) of SC>42" (green) and N(V (blue).
3.4.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water
Figure 3.4-2 shows trends in surface water concentrations of SC>42", NCV, and ANC for
streams in the Shenandoah Case Study Area monitored through the Shenandoah Nation Park
Surface Water Acidification Study (SWAS), Virginia Trout Stream Sensitivity Survey (VTSSS),
and LTM programs. The annual average for the period 2005 to 2006 of SC>42", N(V, and ANC
are 57.34 ± 71.4, 3.37 ± 6.5, and 61.22 ± 4,326.9 ueq/L, respectively (Figure 3.4-3). Despite the
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June 5, 2009
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Aquatic Acidification Case Study
1
2
3
4
5
6
7
9
10
11
12
decreases in pollution and regional acidifying deposition, SC>42" and NO3" concentrations in these
streams have not seen improvements since the mid-1990s. There is a slight decline in SC>42"
concentrations (-0.09 ueq/L/yr) in surface waters, whereas NO3" declined by only -0.1 ueq/L/yr.
On the other hand, average ANC concentrations of the 68 streams increased to 75 ueq/L until the
year 2002, from about 50 ueq/L in the early 1990s. However, since 2002, ANC levels have
declined back to early 1990s levels. Despite improvement in deposition, surface water
concentrations of SO42" and NO3" levels remain elevated in monitored streams in the Shenandoah
Case Study Area.
*~*~* SIM
* • * HH3
*-*-* AM;
1338 I3S3
2-
Figure 3.4-2. Trends over time for SO4 (blue), NO3" (green), and ANC (red)
concentrations in VTSSS LTM-monitored streams in the Shenandoah Case Study Area.
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Aquatic Acidification Case Study
(a)
(b)
VTSSS/LTM - 2006
Sulfate (S042")
VTSSS/LTM - 2006
Nitrate (NO3")
-.-...
(c)
VTSSS/LTM - 2006
ANC
I |
. .,
1 Figure 3.4-3. Current (a) SC>42", (b) N(V, and (c) ANC concentrations (ueq/L) in surface
2 waters from 68 monitored streams in the SWAS-VTSSS LTM network in the
3 Shenandoah Case Study Area.
4 There are a significant number of streams in SWAS-VTSSS and LTM programs that
5 currently have ANC <50 ueq/L based on the observed annual average ANC concentrations
6 (Figure 3.4-3). Fifty-five percent of all monitored streams have ANC values >50 ueq/L, whereas
7 55% have <50 ueq/L. Of the 55% <50 ueq/L, 18% experience episodic acidification (<20 ueq/L)
8 and 12% are chronically acidic (<0 ueq/L) at the current level of acidifying deposition and
9 ambient concentrations of NOX and 862.
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Aquatic Acidification Case Study
i 4. METHODS
2 4.1 BIOLOGICAL RESPONSE TO ACIDIFICATION
3 Because there is a continuum in the relationship between ANC concentrations and
4 resulting biological effects, a range of ANC values related to specific biological effects is needed
5 for the following reasons:
6 (1) ANC concentrations within a waterbody are not constant; there is variation with time
7 and season. For example, during spring, following snowmelt and the resulting influx of
8 acidifying compounds, surface water ANC levels can substantially drop. There is also
9 spatial uncertainty. Consequently, the length of exposure (i.e., chronic vs. episodic) can
10 affect biological responses.
11 (2) The biological effects of particular ANC values vary between individual organisms
12 because of differences in developmental stage and size, innate differences between
13 different species of the same general types of organisms, and differences between
14 different kingdoms, phyla, and classes of organisms.
15 Therefore, five categories of ANC concentrations were used that link specific biological
16 health conditions to the effects of aquatic communities, ranging from no impacts to complete
17 loss of populations. These five classes are based on the relationships among ANC and ecological
18 attributes, including richness, diversity, community structure, and individual fitness of
19 organisms. The following paragraphs describe the biological impacts, given a range of ANC
20 values and the scientific research that supports the grouping. Section AX4 of the Annexes to the
21 ISA (U.S. EPA, 2008) presents a more in-depth description of the biological relationship used in
22 this case study.
23 For freshwater systems, ANC concentrations are grouped into five major categories:
24 Acute Concern (<0 ueq/L), Severe Concern (0 to 20 ueq/L), Elevated Concern (20 to 50 ueq/L),
25 Moderate Concern (50 to 100 ueq/L), and Low Concern (>100 ueq/L), with each range
26 representing a probability of ecological damage to the community (Table 4.1-1).
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Appendix 4 - 17
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Aquatic Acidification Case Study
Table 4.1-1. Aquatic Status Categories
Category Label ANC Levels* Expected Ecological Effects
Acute
Concern
<0 ueq/L
Complete loss offish populations is expected. Planktonic
communities have extremely low diversity and are dominated by
acidophilic forms. The number of individuals in plankton species
that are present are greatly reduced.
Severe
Concern
0-20
ueq/L
Highly sensitive to episodic acidification. During episodes of
high acidifying deposition, brook trout populations may
experience lethal effects. Diversity and distribution of
zooplankton communities decline sharply.
Elevated
Concern
20-50
ueq/L
Fish species richness is greatly reduced (i.e., more than half of
expected species can be missing). On average, brook trout
populations experience sublethal effects, including loss of health,
reproduction capacity, and fitness. Diversity and distribution of
zooplankton communities decline.
Moderate
Concern
50-100
ueq/L
Fish species richness begins to decline (i.e., sensitive species are
lost from lakes). Brook trout populations are sensitive and
variable, with possible sublethal effects. Diversity and
distribution of zooplankton communities also begin to decline as
species that are sensitive to acidifying deposition are affected.
Low
Concern
>100 ueq/L
Fish species richness may be unaffected. Reproducing brook
trout populations are expected where habitat is suitable.
Zooplankton communities are unaffected and exhibit expected
diversity and distribution.
1
2 Low Concern - Biota is generally not harmed when ANC values are >100 ueq/L. For
3 example, the number offish species tend to peak at ANC values >100 ueq/L (Bulger et al., 1999;
4 Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006). Typically, with ANC
5 concentrations >100 ueq/L, the diversity of the aquatic community is more influenced by other
6 environmental factors, such as habitat availability, than the acid-base balance of the surface
7 water.
8 Moderate Concern - At ANC levels 50 to 100 ueq/L, declines in the fitness and
9 recruitment of species sensitive to acidity (e.g., some fish and invertebrate organisms) have been
10 demonstrated and may result in decreases in community-level diversity as the few highly acid-
11 sensitive species are lost (Figure 2.3-1). However, minimal (no measurable) change in total
12 community abundance or production generally occurs, resulting in good overall health of the
13 community.
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Aquatic Acidification Case Study
1 Elevated Concern - When ANC concentrations drop between 20 and 50 ueq/L, they are
2 generally associated with negative effects on the fitness and recruitment of aquatic biota. Kretser
3 et al. (1989) showed that a 50% reduction in the number of fish species occurred when ANC
4 concentrations dropped to <50 ueq/L in lakes that were surveyed. Furthermore, Dennis and
5 Bulger (1995) showed that when ANC concentrations drop to between 20 and 50 ueq/L, the
6 overall fitness of most fish species are reduced, such as sensitive species of minnows and daces
7 (e.g., fathead minnow and blacknose dace), and recreation fish species (e.g., lake trout and
8 walleye). In addition to the changes in the fish community, a drop in ANC concentrations can
9 cause some loss of common invertebrate species from zooplankton and benthic communities,
10 which include many species of snails, clams, mayflies, and amphipods. These losses of sensitive
11 species often result in distinct decreases in species richness and changes in species composition
12 of the biota. However, the total community abundance or production remains high, with little if
13 any change.
14 Severe Concern - When ANC concentrations drop <20 ueq/L, almost all biota exhibit
15 some level of negative effects. Fish and plankton diversity and the structure of the communities
16 continue to decline sharply to levels where acid-tolerant species begin to outnumber all other
17 species (Driscoll et al., 2001; Matuszek and Beggs, 1988). Loss of several important sport fish
18 species is possible, including lake trout, walleye, and rainbow trout, and losses of additional
19 nongame species, such as creek chub, occur. In addition, several other invertebrate species,
20 including all snails, most slams, and many species of mayflies, stoneflies, and other benthic
21 invertebrates, are lost or greatly reduced in population size, which further depresses species
22 composition and community richness. Also, at <20 ueq/L, surface waters are susceptible to
23 episodic acidification, and a total loss of biota can occur when ANC concentration goes to
24 <0 ueq/L for a short period of time. Stoddard et al. (2003) showed that to protect biota from
25 episodic acidification in the spring, base flow (i.e., summer nonstorm event) ANC concentrations
26 had to have an ANC of at least 30 to 40 ueq/L (Figure 4.1-1).
27 Acute Concern - Complete loss offish populations and extremely low diversity of
28 planktonic communities occur with ANC concentrations <0 ueq/L. Only acidophilic species are
29 present, but their population numbers are sharply reduced. For example, lakes in the Adirondack
30 Case Study Area have been shown to be fishless when the average ANC is <0 ueq/L (Sullivan et
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4-19
-------
Aquatic Acidification Case Study
1 al., 2006). A summary of the five categories of ANC and expected ecological effects can be
2 found in Figure 4.1-2 and Table 4.1-1
200
3
4
5
6
7
8
9
10
11
12
i
<
E
ZJ
150
100
50
CO
New England Lakes
Adirondack Lakes
Appalachian Streams
0 50 100 150
Mean Summer ANC (|jeq/L)
200
Figure 4.1-1. Relationship between summer and spring ANC values at LTM sites
in New England, the Adirondack Mountains, and the Northern Appalachian
Plateau. Values are mean summer values for each site for the period 1990 to 2000
(horizontal axis) and mean spring minima for each site for the same time period.
On average, spring ANC values are at least 30 ueq/L lower than summer values.
.2 12 -
CD 10 -
O.
CO 8 -
to 6 -
iE ^
O
*oj
E °
= 2
Severe Elevated Moderate
V t ^
Acute
Low
*
_ R^ - ::
/
-200 -100 0 100 200 300 400 500
ANC(Meq/L)
Figure 4.1-2. Number offish species per lake or stream versus ANC level and aquatic
status category (represented by color) for lakes in the Adirondack Case Study Area
(Sullivan et al., 2006). The five aquatic status categories are described in Table 4.1-1.
2nd Draft Risk and Exposure Assessment
Appendix 4-20
June 5, 2009
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Aquatic Acidification Case Study
1 4.2 PAST, PRESENT, AND FUTURE SURFACE WATER
2 CHEMISTRY—THE MAGIC MODELING APPROACH
3 The preacidification condition of a waterbody is rarely known because it is not easily
4 measured. Likewise, it is also difficult to determine if a waterbody has or will recover from
5 acidification as acidifying deposition inputs decline, because recovery may take many years to
6 decades to occur. For these reasons, hydrological models, such as MAGIC, enable estimates of
7 past, present, and future water quality levels that can be used to evaluate the associated risk and
8 uncertainty of the current levels of acidification compared with estimated preacidification
9 conditions and to evaluate whether a system will recover as a result of reduction in acidifying
10 deposition.
11 Dynamic hydrological models use surface water measurements of multiple parameters
12 from the long-term record, information about the current exposure (i.e., ambient pollutant
13 concentrations, deposition estimates), and known/measurable biogeochemical factors to
14 characterize a watershed and estimate its preindustrial (i.e., preacidification) state and to estimate
15 its response to changes in deposition in the future.
16 In both case study areas, MAGIC was used to estimate the past (i.e., preacidification),
17 present (i.e., the years 2002 and 2008), and future (i.e., the years 2020 and 2050) acidic
18 conditions of 44 lakes in the Adirondack Case Study Area and 60 streams in the Shenandoah
19 Case Study Area (Figure 4.3-1). Furthermore, MAGIC was used to quantify the associated
20 uncertainty in these estimates, as well as in input parameters used in MAGIC. The MAGIC
21 model output for each waterbody was summarized into five ANC levels that correspond to the
22 aquatic status categories in Table 4.1-1. This grouping permits an assessment of the risk to the
23 biological communities for each of the conditions. The hydrological model, MAGIC, along with
24 all the necessary inputs and calibration procedure, is described in detail in Appendix
25 Attachment A.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4 -21
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Aquatic Acidification Case Study
1 4.3 CONNECTING CURRENT NITROGEN AND SULFUR
2 DEPOSITION TO ACID-BASE CONDITIONS OF LAKES AND
3 STREAMS: THE CRITICAL LOAD APPROACH
4 Using the relationships established between the biogeochemical state of the environment,
5 current pollutant deposition, the surface water chemistry, and the response of the biological
6 communities to that deposition using the MAGIC model, it is possible to relate specific amounts
7 of deposition to particular ANC levels for individual waterbodies. Conversely, it is possible to
8 specify a "critical limit" ANC level and to estimate the "critical load" of deposition required to
9 cause the stream to have that specified ANC level. The past, current, or estimated future levels of
10 deposition can be compared with the critical load estimate. For example, a critical limit ANC
11 value of 50 ueq/L could be specified for a particular stream or lake. The amount of deposition
12 that the stream or lake could take and maintain an ANC of 50 ueq/L would be its critical load.
13 Clearly, if the critical limit ANC value is lower (20 ueq/L), the critical load would increase—it
14 would take more deposition to lower the stream's ANC to that new value.
15 A critical load estimate is analogous to a "susceptibility" estimate, relating the sensitivity
16 of the waterbody to become acidified from the deposition of nitrogen and sulfur to the critical
17 limit ANC concentration. Low critical load values (e.g., less than 50 milliequivalents per square
18 meter per year (meq/m2/yr)) mean that the watershed has a limited ability to neutralize the
19 addition of acidic anions, and hence, it is at risk or susceptible to acidification and the resulting
20 deleterious effects. The greater the critical load value, the greater the ability of the watershed to
21 neutralize the additional acidic anions and resist acidification, thereby protecting the aquatic
22 ecosystem.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4 - 22
-------
Aquatic Acidification Case Study
(a)
'C?
Adirondack Case Study Area
(b)
Shenandoah Case Study Area
* MAGIC Locations
I I Cas* Study Bounty
1 Figure 4.3-1. (a) The location of lakes in the Adirondack Case Study Area used for
2 MAGIC (red dots) and critical load (green dots) modeling, (b) The location of streams in
3 the Shenandoah Case Study Area used for both MAGIC and critical load modeling.
4 Applied at many locations over a region, the critical load approach provides a method to
5 quantify the number of lakes or streams in a given area that receive harmful levels of deposition.
6 The magnitude of the biological harm is defined by the critical limit ANC concentration (e.g.,
7 ANC of 50 ueq/L) (see Table 4.1-1). Critical load exceedance (i.e., the amount of actual
8 deposition above the critical load, if any) can be calculated for each waterbody in the region.
9 Lakes and streams with positive exceedance values, where actual deposition was above its
2nd Draft Risk and Exposure Assessment
Appendix 4-23
June 5, 2009
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Aquatic Acidification Case Study
1 critical load, are not protected at that critical limit, whereas negative exceedance values, where
2 deposition of nitrogen and sulfur was below its critical load, are protected.
3 Critical loads and their exceedances were calculated for four critical limit thresholds (i.e.,
4 ANC of 0, 20, 50, and 100 ueq/L), separating the five ANC categories of biological protections
5 (Table 4.1-1) for 169 lakes in the Adirondack Case Study Area and 60 streams in the
6 Shenandoah Case Study Area. The Steady-State Water Chemistry (SSWC) model was used to
7 estimate the critical load for each of the critical limit ANC levels for each waterbody. For each
8 waterbody, the actual current total deposition in the year 2002 was compared with the estimated
9 critical loads for the four critical limit thresholds to determine which sites exceed their critical
10 limit of deposition and biological protection level. Estimates of actual current deposition were
11 based on the sum of measured wet deposition values from the year 2002 NADP network and
12 modeled dry deposition values based on the year 2002 emissions and meteorology using the
13 Community Multiscale Air Quality (CMAQ) model, respectively.
14 The actual deposition was compared with the critical load for each of the waterbodies
15 within the case study areas for each of the critical limit levels, and exceedances were determined.
16 Results for an individual lake were grouped by whether or not the lake exceeded its critical load.
17 For each of two case study areas, the number and percentage of lakes that receive acidifying
18 deposition above their critical load for each of the ANC critical limits of 0, 20, 50, and 100 ueq/L
19 were determined.
20 4.3.1 Regional Assessment of Adirondack Case Study Area Lakes and Shenandoah
21 Case Study Area Trout Streams
22 4.3.1.1 Adirondack Case Study Area
23 In the Adirondack Case Study Area, critical load exceedances were extrapolated to lakes
24 defined by the New England EMAP probability survey. The EMAP probability survey was
25 designed to estimate, with known confidence, the status, extent, change, and trends in condition
26 of the nation's ecological resources, such as surface water quality. In probability sampling, the
27 inclusion probability for each sampled lake represents a proportion of the target population.
28 Lakes selected with relatively high probability represent relatively few lakes in the population;
29 therefore, they carry relatively low weight and influence the final inferences less than lakes
30 selected with low probability. These inclusion probabilities (i.e., weighting or expansion factors)
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4-24
-------
Aquatic Acidification Case Study
1 are used to infer or estimate population frequency distributions and to evaluate sampling
2 uncertainty.
3 For the Adirondack Case Study Area, the regional EMAP probability survey of 117 lakes
4 (i.e., weighting factors) were used to infer the number of lakes and percentage of lakes that
5 receive acidifying deposition above their critical load of a target population of 1,842 lakes. The
6 target population of 1,842 lakes represents all lakes from 0.5 to 2,000 ha with a depth of
7 >1 meter (m) and > 1,000 m2 of open water in the Adirondack Case Study Area. ANC limits of
8 20, 50, and 100 ueq/L were examined.
9 The 117 lakes in the regional Adirondack probability survey represent a subset of 344
10 sampled lakes throughout New England (e.g., lakes in Maine, New Hampshire, Vermont, Rhode
11 Island, Massachusetts, Connecticut, New York, New Jersey) from 1991 through 1994. For New
12 England, 11,076 lakes are represented in the target population (Larsen et al., 1994).
13 4.3.1.2 Shenandoah Case Study Area
14 In the Shenandoah Case Study Area, critical load exceedances were extrapolated using
15 the SWAS-VTSSS LTM quarterly monitored sites to the population of brook trout streams that
16 do not lie on limestone bedrock and/or are not significantly affected by human activity within the
17 watershed. The total number of brook trout streams represented by the SWAS-VTSSS LTM
18 quarterly monitored sites is approximately 310 streams out of 440 mountain headwater streams
19 known to support reproducing brook trout in the Shenandoah Case Study Area.
20 The SWAS-VTSSS LTM programs were designed to track the effects of acidifying
21 deposition and other factors that determine water quality and related ecological conditions in the
22 Shenandoah Case Study Area's native trout streams. The SWAS-VTSSS LTM began in spring
23 1987, when water samples were collected from 440 streams known to have brook trout.
24 Following the 1987 survey, a representative subset of 69 streams was selected for long-term
25 quarterly monitoring of water quality, mostly located on National Forest lands or within the
26 Shenandoah National Park Case Study Area (14 SWAS and 55 VTSSS streams). These streams
27 were selected to achieve geographic distribution and representation of major bedrock types
28 (Webb et al., 1994), allowing the streams to be stratified into bedrock type. This enabled results
29 from the monitored streams (n=69) to be extrapolated to the entire regional population of trout
30 streams (440). Webb et al. (1994) identified six bedrock classes that account for much of the
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4-25
-------
Aquatic Acidification Case Study
1 spatial variation in ANC among the SWAS-VTSSS LTM quarterly sampled streams. The
2 landscape classes adopted for this study and the number of selected stream sites within each of
3 the classes include Blue Ridge siliciclastic (16 streams), Blue Ridge granitic (18 streams), Blue
4 Ridge basaltic (four streams), and Valley and Ridge siliciclastic (22 streams). Streams in the
5 carbonate classes (13) were not included because they are not considered susceptible to
6 acidification. A weighting scheme based on the number of monitoring streams in each of the
7 bedrock classes was used to extrapolate to the regional population of trout streams. For example,
8 103 VTSSS streams lie on granitic bedrock, of which 18 were monitored quarterly, resulting in a
9 weighting factor of 5.7 (=103/18). The weights for streams in the other bedrock classes are 6.25
10 (= 25/4) for basaltic, 4.3 (= 69/16) for Blue Ridge siliciclastic, and 4.86 (=107/22) for Valley and
11 Ridge siliciclastic. Thus, the total number of brook trout streams represented in the Shenandoah
12 Case Study Area is approximately 310; these are all brook trout streams that do not lie on
13 limestone (10% of 440 streams) and/or have not been significantly affected by human activity
14 within the watersheds (20% of the streams).
is 5. RESULTS
16 5.1 ADIRONDACK CASE STUDY AREA
17 5.1.1 Current and Preacidification Conditions of Surface Waters
18 Since the mid-1990s, lakes in the Adirondack Case Study Area have shown signs of
19 improvement in ANC, N(V, and SC>42" concentrations in surface waters, as shown in Figure 3.3-
20 2. However, current average surface water concentrations of N(V and SC>42" are still well above
21 preacidification conditions based on MAGIC model simulations of 44 lakes (Figure 5.1-1),
22 resulting in lower than average ANC surface water concentrations.
0 140
is 120
1^100
g sr80
°a eo
O 40
< 20
0
1850 1900 1950 2000 2050
23
24 Figure 5.1-1 Average NOs (orange), 864 (red), and ANC (blue) concentrations for the 44 lakes
25 in the Adirondack Case Study Area modeled using MAGIC for the period 1850 to 2050.
2nd Draft Risk and Exposure Assessment Years ^une ^' 2009
Appendix 4-26
-------
Aquatic Acidification Case Study
1
2
3
4
5
6
7
9
10
11
12
13
On average, simulated SC>42" and NCV surface water concentrations are 5- and 17-fold
higher today, respectively, compared with 1860 acidification levels, whereas ANC has dropped
by a factor of 2 (Table 5.1-1, Figure 5.1-2). Although NO3" deposition can be an important
factor in acid precipitation and waterbody acidification, the strong inverse relationship between
SC>42" and ANC, in combination with the low levels of NCV in surface waters, suggests that
acidification in the Adirondack Case Study Area has and continues to be driven by SO42"
deposition.
Table 5.1-1. Estimated Average Concentrations of Surface Water Chemistry for 44 Lakes in the
Adirondack Case Study Area Modeled Using MAGIC for Preacidification (1860) and Current
(2006) Conditions
ueq/L
ANC
SO42"
NCV
NH4+
Preacidification
Ave.
120.3
12.4
0.2
0.0
(+/-)
13.6
2.1
1.7
0.0
Current
Ave.
62.1
66.1
3.4
0.1
(+/-)
15.7
1.24
14.8
0.1
An estimate of how much of this current condition is attributed to the effects of
industrially generated acidifying deposition can be made by examining the hindcast conditions of
the streams. Based on the MAGIC model simulations, the preacidification average ANC
concentration of 44 modeled lakes is 120.3 ueq/L (95% CI 106.8 to 134.0) compared with 62.1
ueq/L (95% CI 50.5 to 81.8) for today (Table 5.1-1).
2nd Draft Risk and Exposure Assessment
Appendix 4-27
June 5, 2009
-------
Aquatic Acidification Case Study
(a)
Nitrate Preacidification (1860) and Current (2006) Conditions
Preacidification (1860) Current (2006)
Nitrate (M«l L)
• 3-6
(b)
Sulfate Preacidification (1860) and Current (2006) Conditions
Preacidification (1860) Current (2006)
Sulfate ([ieq L)
• : r:
• 15 - 50
1 Figure 5.1-2. (a) N(V and (b) SC>42" concentrations (ueq/L) of
2 preacidification (1860) and current (2006) conditions based on hindcasts
3 of 44 lakes in the Adirondack Case Study Area modeled using MAGIC.
4 5.1.2 ANC Inferred Condition—Aquatic Status Categories.
5 The deposition of sulfur and nitrogen and resulting changes in water quality has effects
6 on ANC values, and by consequence, the biological integrity of the water ecosystem. By
7 comparing their current surface water condition with their preindustrial (i.e., preacidification or
8 1860) condition through the MAGIC model simulations of 44 lakes, it is possible to estimate
2nd Draft Risk and Exposure Assessment
Appendix 4 - 28
June 5, 2009
-------
Aquatic Acidification Case Study
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
how the distribution of affected lakes within the Adirondack Case Study Area has changed over
time. Furthermore, grouping each lake into aquatic status categories provides the range of
biological condition. The percentage of lakes in each of the five aquatic status categories is
shown in Table 5.1-2. Eighty-nine percent of the modeled lakes were likely Low Concern or
Moderate Concern (i.e., ANC concentrations >50 ueq/L) prior to the onset of acidifying
deposition (Figure 5.1-3), whereas the remaining 11% of lakes have ANC concentrations >20
ueq/L.
Table 5.1-2. Percentage of Lakes in the Five Aquatic Status Categories Based on Their Surface
Water ANC Concentrations for 44 Lakes Modeled Using MAGIC and 94 Lakes in the TEVIE/LTM
Monitoring Network. Results Are for the Adirondack Case Study Area for the Year 2006.
Concern
Low
Moderate
Elevated
Severe
Acute
ANC
(^leq/L)
>100
50-100
20-50
0-20
<0
Modeled Pre-
Acidification
(% of Lakes)
50
39
11
0
0
Modeled Current
Condition
(% of Lakes)
20
36
23
9
11
Measured Current
Condition
(% of Lakes)
6
16
32
29
17
In contrast, the current simulated condition of the lakes has shifted toward the chronically
acidified categories. Only 20% and 36% of the lakes currently experience the Low Concern or
Moderate Concern condition, having ANC concentrations >50 ueq/L, whereas 43% of the lakes
are in either the Acute Concern, Severe Concern, or Elevated Concern condition. The result that
the hindcast simulations produced no lakes with Acute Concern or Severe Concern
preacidification suggests that current and recent historical ambient concentrations of NOX and
SOX and their associated levels of N(V and SC>42" deposition have substantially contributed to
acidification (<50 ueq/L) to approximately 32% of modeled lakes. Figure 5.1-4 shows the
spatial extent of preacidification and the current annual average ANC levels in the Adirondack
Case Study Area.
2nd Draft Risk and Exposure Assessment
Appendix 4-29
June 5, 2009
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Aquatic Acidification Case Study
1
2
3
4
5
6
7
10
11
12
13
14
/u
60
en
40
30
on
2\J
10
n
I
1
I
J_
E_
f_
T
T_
Acute (Below 0 |jeq/L)
Severe (0-20
Elevated (20-50
Moderate (50-100
Low (Above 100
1860
2006
Figure 5.1-3. Percentage of lakes in the five aquatic status categories of
acidification (Acute, Severe, Elevated, Moderate, Low) for preacidification
(1860) and current (2006) conditions for 44 lakes in the Adirondack Case Study
Area modeled using MAGIC. Error bars indicate the 95% confidence interval.
ANC Preacidification (1860) and Current (2006) Conditions
Preacidification (1860) Current (2006)
ANC ||je
0-30
JO s
50-100
[ jA
FFVU£AMD M09
Figure 5.1-4. ANC concentrations of preacidification (1860) and current (2006)
conditions based on hindcasts of 44 lakes in the Adirondack Case Study Area
modeled using MAGIC.
5.1.3 The Biological Risk from Current Nitrogen and Sulfur Deposition: Critical
Load Assessment
The amount of acidifying deposition of sulfur and nitrogen that a watershed can receive
and effectively neutralize varies with location, depending on the biogeochemical properties of
the watershed. A critical load of deposition approach, where the amount of deposition that a
2nd Draft Risk and Exposure Assessment
Appendix 4-30
June 5, 2009
-------
Aquatic Acidification Case Study
1 watershed can receive and maintain an ANC critical limit level, can provide insight into the
2 sensitivity of the waterbody to deposition and can allow an assessment of what the current
3 condition of the lake might be under current deposition loads.
4 A critical load of deposition analysis for a critical limit ANC threshold level of 50 ueq/L
5 was done for 169 waterbodies in the Adirondack Case Study Area. Sites that are unable to
6 maintain the critical limit ANC level of 50 ueq/L while experiencing 100 meq/m2/yr or less of
7 deposition are classified as "highly" or "moderately sensitive," indicating that they have a
8 limited ANC ability and could shift toward acidic aquatic status levels with modest acidifying
9 deposition inputs. Figure 5.1-5 shows the locations and relative sensitivity of the 169
10 waterbodies for the critical load analysis (with the 50 ueq/L ANC critical limit). Sites labeled by
11 red or orange dots have less buffering ability than lakes labeled with yellow and green dots, and
12 therefore, are those lakes most sensitive to acidifying deposition. Approximately 50% of the 169
13 lakes modeled in the Adirondack Case Study Area are sensitive, or at risk, to acidifying
14 deposition.
15 In Figure 5.1-6, a critical load exceedance "value" indicates combined sulfur and
16 nitrogen deposition for the year 2002 is greater than the amount of deposition the lake could
17 buffer and still maintain an ANC level at or above the critical limit threshold. For the deposition
18 load for the year 2002, 18%, 28%, 44%, and 58% of the 169 lakes modeled received levels of
19 combined sulfur and nitrogen deposition that exceeded their critical load for the critical limit
20 ANC values of 20, 50, and 100 ueq/L, respectively (Table 5.1-3).
21
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4-31
-------
Aquatic Acidification Case Study
Current Condition of Acidity
and Sensitivity
CrittciaJ Load
2 Figure 5.1-5. Critical loads of acidifying deposition that each surface water
3 location can receive in the Adirondack Case Study Area while maintaining or
4 exceeding an ANC concentration of 50 ueq/L based on 2002 data. Watersheds
5 with critical load values <100 meq/m2/yr (red and orange dots) are most sensitive
6 to surface water acidification, whereas watersheds with values >100 meq/m2/yr
7 (yellow and green dots) are the least sensitive sites.
Table 5.1-3. Adirondack Case Study Area Critical Load Exceedances, Where Nitrogen and
Sulfur Deposition Is Larger Than the Critical Load for Four Different ANC Critical Limit
Thresholds, for 169 Modeled Lakes within the TIME/LTM and EMAP Monitoring and Survey
Programs. "No. Exceedances" Indicates the Number of Lakes at or below the Given ANC
Critical Limit, and "% Lakes" Indicates the Total Percentage of Lakes at or below the Given
ANC Critical Limit.
ANC
Critical Limit
100 ueq/L
50 ueq/L
20 ueq/L
0 ueq/L
No. Exceedances
(out of 169)
98
74
47
30
%
Lakes
58
44
28
18
2nd Draft Risk and Exposure Assessment
Appendix 4-32
June 5, 2009
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Aquatic Acidification Case Study
2
3
4
5
Critical Load Exceedances
{100 [jeq/L)
Critical Load Exc«ed#nces
( > ANC of 100 i]':fi U
• Opawiwi does net £xcc*d CMC* Load
. _.'-- c ..;•. -'ML . J
Critical Load Exceedances
(50 peq/L)
Critical Load Excfledences
\ > ANC of 50 | leq 'U
• CwposOon dam net Exceed CnvcM La:
Critical Load Exceedances
Critical Load Exceedenc«s
( > ANC of 20 peq/L)
[ | A
• . , ,, „•_'.[
Critical Load Exceedances
(0 Meq/L)
Critical 1 • i i;i Exceedences
f > ANC of 0(jeqTL|
I I Ai3r0r,d3tk
- : • "i
Figure 5.1-6. Critical load exceedances (red dots) in the Adirondack Case Study
Area based on the year 2002 deposition magnitudes for waterbodies where the
critical limit ANC concentration is 0, 20, 50, and 100 iieq/L, respectively. Green
dots represent lakes where deposition is below the critical load. See Table 5.1-3.
2nd Draft Risk and Exposure Assessment
Appendix 4-33
June 5, 2009
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Aquatic Acidification Case Study
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
5.1.4 Representative Sample of Lakes in the Adirondack Case Study Area
Estimating the acidification risk to the entire population of lakes in the Adirondack Case
Study Area from the current the levels of NOX and SOX ambient concentrations and deposition
requires extrapolating from the 169 modeled lakes to the broader population of lakes in the
Adirondack Case Study Area. One hundred seventeen lakes of the 169 lakes modeled for critical
loads are part of a subset of 1,842 lakes in the Adirondack Case Study Area, which include all
lakes from 0.5 to 2000 ha in size and at least 1 m in depth. Using weighting factors for frequency
of occurrence derived from the EMAP probability survey and critical load calculations from the
117 lakes, exceedances estimates were derived for the 1,842 lakes in the Adirondack Case Study
Area. Based on this approach, 945, 666, 242, and 135 lakes exceed their critical load for the year
2002 deposition with critical limits of 100, 50, 20, and 0 ueq/L, respectively (Table 5.1-4). The
current effect level for a moderate protective (i.e., ANC limit of 20 and 50 ueq/L) is 13% and
36% of lakes, whereas it is 51% of lakes for the most protective level (i.e., ANC limit of 100
ueq/L).
Table 5.1-4. Critical Load Exceedances for the Regional Population of 1,842 Lakes in the
Adirondack Case Study Area That Are from 0.5 to 2,000 ha in Size and at Least 1 m in Depth for
the Four Critical Limit ANC Levels (0, 20, 50, and 100 ueq/L). Estimates Use the Exceedances
for the Subset of 169 Lakes Using 2002 Deposition Magnitudes (Table 5.1-3) and Are
Extrapolated to the Full Population Based on the EMAP Lake Probability Survey of 1991 to
1994. "No. Exceedances" Indicates the Number of Lakes at or Below the Given ANC Critical
Limit; "% Lakes" Indicates the Total Percentage of Lakes at or Below the Given ANC Critical
Limit.
ANC Critical Limit
100 ueq/L
50 ueq/L
20 ueq/L
0 ueq/L
No. Exceedances
(out of 1842)
945
666
242
13
% Lakes
51
36
13
7
Because some lakes in the Adirondack Case Study Area have natural sources of acidity,
they would never have ANC concentrations >50 or 100 ueq/L, even in the absence of all
anthropogenic-derived acidifying deposition. Based on the hindcast simulations of 44 lakes using
the MAGIC model, no modeled lakes have ANC levels <20 ueq/L. However, 5 modeled lakes,
or 11%, have ANC concentrations between 22 and 47 ueq/L. This equates to approximately 300
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 4-34
-------
Aquatic Acidification Case Study
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
lakes, or 16%, of the representative population of lakes in the Adirondack Case Study Area that
likely had preacidification ANC concentrations <50 ueq/L. On the other hand, potentially >52%
of lakes likely had preacidification ANC concentrations <100 ueq/L. The higher percentage of
lakes in the regional population compared with the modeled population is because the lake
classes or sizes that are likely to have preacidification ANC concentrations <50 or 100 ueq/L are
more abundant in the Adirondack Case Study Area than lakes with preacidification ANC
concentrations >50 or 100 ueq/L.
5.1.5 Recovery from Acidification Given Current Emission Reductions
The question is whether lakes can recover to healthy systems (i.e., ANC >50, or 100
ueq/L) given the current surface water and ecosystem conditions of the lakes and current
emission and deposition levels in the Adirondack Case Study Area. The forecast model runs of
44 lakes using MAGIC were used to determine if current deposition could lead to recovery of the
acidified lakes. Based on a deposition scenario that maintains current emission levels to the years
2020 and 2050, the simulation forecast indicates no improvement in water quality. The
percentage of lakes within the Elevated Concern to Acute Concern categories remains the same
in the years 2020 and 2050 (Figure 5.1-7). Moreover, the percentage of modeled lakes classified
as not acidic remains the same, suggesting that current emission levels will likely not lead to
further improvements in the acidification of lakes in the Adirondack Case Study Area.
2006
2020
2050
• Acute (Below 0 ueq/L)
D Elevated (20-50 ueq/L)
• Low (Above 100 ueq/L)
n Severe (0-20 ueq/L)
• Moderate (50-100 ueq/L)
Figure 5.1-7. Percentage of lakes in each of the five aquatic status categories of
acidification (Acute, Severe, Elevated, Moderate, Low) for the years 2006, 2020, and
2050 for 44 lakes in the Adirondack Case Study Area modeled using MAGIC, where
current emissions are held constant. Error bars indicate the 95% confidence interval.
2nd Draft Risk and Exposure Assessment
Appendix 4 - 35
June 5, 2009
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Aquatic Acidification Case Study
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
5.2 SHENANDOAH CASE STUDY AREA
5.2.1 Current and Preacidification Conditions of Surface Waters
Since the mid-1990s, streams in the Shenandoah Case Study Area have shown slight
signs of improvement in NCV and SC>42" concentrations in surface waters Figure 3.4-2.
However, current concentrations of NCV and SC>42" are still well above preacidification
conditions based on MAGIC model simulations, resulting in lower ANC concentration of surface
water (Figure 5.2-1).
1850
1900
1950
Years
2000
2050
Figure 5.2-1. Average NCV (orange), SO42"(red), and ANC (blue) concentrations
for the 60 streams in the Shenandoah Case Study Area modeled using MAGIC for
the period 1850 to 2050.
Figure 5.2-2 shows the condition of the streams in the year 1860 (i.e., preacidification)
and in the year 2006 (i.e., current) conditions. On average, NCV and SC>42" concentrations are 32-
and 10-fold higher today (Table 5.2-1). These results also demonstrate that acidification in most
streams in the Shenandoah Case Study Area are currently being driven by SC>42" deposition
because the current average SO42" concentration is 11-folder greater than NCV concentrations in
surface waters.
An estimate of how much of this current condition is attributed to the effects of
industrially generated acidifying deposition can be made by examining the hindcast conditions of
the streams. Based on the MAGIC model simulations, preacidification average ANC
concentration of the 60 modeled streams is 101.4 (95% CI 91.9. to 110.9) ueq/L compared with
57.9 (95% CI. 53.4 to 62.4) ueq/L for today (Table 5.2-1).
2nd Draft Risk and Exposure Assessment
Appendix 4-36
June 5, 2009
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Aquatic Acidification Case Study
(a)
Nitrate Preacidification (1860) and Current (2006) Conditions
Preacidification (1860) Current (2006)
Nitrate (|jeq/L)
* 0 5
All results are based an modelling using MAGIC
(b)
Sulfate Preacidification (1860) and Current (2006) Conditions
Preacidification (1860) Current (2006)
Sulfate die'| LI
• 0-2S
All i«ults ait based on modelling using MAGIC
S«K» EPA/CAMD2009
1
2
Figure 5.2-2. (a) NO3" and (b) SO42" concentrations (ueq/L) of 1860
(preacidification) and 2006 (current) conditions based on hindcasts of 60 streams
in the Shenandoah Case Study Area modeled using MAGIC.
2nd Draft Risk and Exposure Assessment
Appendix 4-37
June 5, 2009
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Aquatic Acidification Case Study
1
2
3
4
5
6
7
Table 5.2-1. Estimated Average Concentrations of Surface Water
Chemistry for 60 Streams in the Shenandoah Case Study Area Modeled
Using MAGIC for Preacidification (1860) and Current (2006) Conditions
jieq/L
ANC
SO42"
N03-
NH4+
Preacidification
Avg.
101.4
2.1
0.6
n/a
(+/-)
9.5
0.1
0.01
n/a
Current
Avg.
57.9
68.0
6.2
n/a
(+/-)
4.5
8.4
0.1
n/a
5.2.2 ANC Inferred Condition—Aquatic Status Categories
The percentage of streams in each of the five aquatic status categories is shown in Table
5.2-2 and in the graph in Figure 5.2-3. Ninety-two percent of the modeled streams likely were at
the Low Concern or Moderate Concern conditions prior to the onset of acidifying deposition.
The other 8% of streams have ANC of >27 ueq/L. The hindcast simulations produced no streams
with Acute Concern or Severe Concern conditions.
Table 5.2-2. Percentage of Streams in the Five Aquatic Status Categories Based on Their
Surface Water ANC Concentrations for 60 Streams Modeled Using MAGIC and 68 Streams in
the SWAS-VTSSS LTM Network. Results are for the Shenandoah Case Study Area for the Year
2006.
Concern
Low
Moderate
Elevated
Severe
Acute
ANC
(neq/L)
>100
50-100
20-50
0-20
<0
Modeled
Preacidification
Condition
(% of Streams)
42
50
8
0
0
Modeled Current
Condition
(% of Streams)
27
20
28
12
13
Measured Current
Condition
(% of Streams)
15
30
25
18
12
9
10
11
12
In contrast, the current simulated condition of the streams has shifted toward the
chronically acidified categories. Only 47% of the streams currently experience the Low Concern
or Moderate Concern conditions, whereas 53% of the streams experience the Acute Concern,
Severe Concern, or Elevated Concern conditions. These results based on model reconstructions
2nd Draft Risk and Exposure Assessment
Appendix 4 - 38
June 5, 2009
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Aquatic Acidification Case Study
1 suggest that anthropogenic acidifying deposition is responsible for acidifying (ANC <50 ueq/L)
2 approximately 45% of streams modeled in the Shenandoah Case Study Area. Figure 5.2-4 shows
3 the spatial extent of preacidification and current annual average ANC levels in the Shenandoah
4 Case Study Area.
5
6
7
8
9
10
ou
50
40
30
on
£\J
10
n
I
i
1
n
\
|
T
J_
I
J_
• Acute (Below 0 |Jeq/L)
• Severe (0-20 ueq/L)
D Elevated (20-50 ueq/L)
D Moderate (50-100 ueq/L)
D Low (Above 100 ueq/L)
1860
2006
Figure 5.2-3. Percentage of streams in the five aquatic status categories of
acidification (Acute, Severe, Elevated, Moderate, Low) for preacidification
(1860) and current (2006) conditions for 60 streams in the Shenandoah Case
Study Area modeled using MAGIC. The number of streams in each category is
above the bar. Error bars indicate the 95% confidence interval.
11
12
13
14
ANC Preacidification (1860) and Current (2006) Conditions
Preacidification (1860) Current (2006)
l results are based on modelling using MAGIC
Soiro EBVCAMD2009
Figure 5.2-4. ANC concentrations of 1860 (preacidification) and 2006 (current)
conditions based on hindcasts of 60 streams in the Shenandoah Case Study Area
modeled using MAGIC.
2nd Draft Risk and Exposure Assessment
Appendix 4-39
June 5, 2009
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Aquatic Acidification Case Study
1
2
3
4
5
6
9
10
11
12
13
14
15
16
17
18
5.2.3 The Biological Risk from Current Nitrogen and Sulfur Deposition: Critical
Load Assessment
In Figure 5.2-5, sites labeled by red or orange dots have less buffering ability than
streams labeled with yellow and green dots, and therefore, are streams most sensitive to
acidifying deposition. Approximately 75% of the 60 streams modeled in the Shenandoah Case
Study Area are sensitive or at risk to acidifying deposition.
Current Condition of Acidity
and Sensitivity
cmiciai mad
m>"-: TO fyi
0 Hl::^ s TM::,<
101 -HID
f 'II • -vrnlr.i- i ,'n
I I yrqria &HJWETY
Figure 5.2-5. Critical loads of surface water acidity for an ANC concentration of
50 ueq/L for Shenandoah Case Study Area streams. Each dot represents an
estimated amount of acidifying deposition (i.e., critical load) that each stream's
watershed can receive and still maintain a surface water ANC concentration >50
ueq/L. Watersheds with critical load values <100 meq/m2/yr (red and orange dots)
are most sensitive to surface water acidification, whereas watersheds with values
>100 meq/m2/yr (yellow and green dots) are the least sensitive sites.
In Figure 5.2-6, a critical load exceedance "value" indicates combined sulfur and
nitrogen deposition in the year 2002 that is greater than the amount of deposition the stream
could buffer and still maintain the ANC level of above each of the four different ANC limits of
0, 20, 50, and 100 ueq/L. For the year 2002, 52%, 72%, 85%, and 92% of the 60 streams
2nd Draft Risk and Exposure Assessment
Appendix 4-40
June 5, 2009
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Aquatic Acidification Case Study
1 modeled receive levels of combined sulfur and nitrogen deposition that exceeded their critical
2 load with critical limits of 0, 20, 50, and 100 ueq/L, respectively (Table 5.2-3).
3
Critical Load Exccedances
(100
Critical Load Exccedances
(50 jjeq/L)
*
CD
•
CD- -
Critical Load Exccedances
(20
Critical Load Exccedances
(0 Meq/L)
CD-
5
6
7
8
9
10
11
Figure 5.2-6. Critical load exceedances for ANC concentrations of 0, 20, 50, and 100
ueq/L for Shenandoah Case Study Area streams. Green dots represent streams where
current nitrogen and sulfur deposition is below the critical load and that maintain an
ANC concentration of 0, 20, 50, and 100 ueq/L, respectively. Red dots represent
streams where current nitrogen and sulfur deposition exceeds the critical load,
indicating they are currently impacted by acidifying deposition. See Table 5.2-3.
2nd Draft Risk and Exposure Assessment
Appendix 4-41
June 5, 2009
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Aquatic Acidification Case Study
Table 5.2-3 Critical Load Exceedances (Nitrogen + Sulfur Deposition > Critical Load) for 60
Modeled Streams Within the VTSSS LTM Program in the Shenandoah Case Study Area. "No.
Exceedances" Indicates the Number of Streams at the Given ANC Limit; "% Streams" Indicates
the Total Percentage of Streams at the Given ANC Limit.
ANC Critical Limit
No. Exceedances
(out of 60)
% Streams
100
ueq/L
55
92
50
ueq/L
51
85
20
ueq/L
43
72
0
ueq/L
31
52
1
2 5.2.4 Regional Assessment of Trout Streams in the Shenandoah Case Study Area
3 The 60 trout streams modeled are characteristic of first- and second-order streams on
4 nonlimestone bedrock in the Blue Ridge Mountains of Virginia. Because of the strong
5 relationship between bedrock geology and ANC in this region, it is possible to consider the
6 results in the context of similar trout streams in the Southern Appalachians that have the same
7 bedrock geology and size. In addition, the 60 streams are a subset of 344 streams sampled by the
8 Virginia Trout Stream Sensitivity Study, which can be applied to 304 of the original 344 streams.
9 Using the 304 streams to which the analysis applies directly as the total, 279, 258, 218, and 157
10 streams exceed their critical load for the year 2002 deposition with critical limits of 100, 50, 20,
11 and 0 ueq/L, respectively. However, it is likely that many more of the -12,000 trout streams in
12 Virginia would exceed their critical load, given the extent of similar bedrock geology outside of
13 the case study area in the southern Appalachian Mountains.
14 5.2.5 Recovery from Acidification Given Current Emission Reductions
15 Based on a deposition scenario that maintains current emission levels to the years 2020
16 and 2050, there will still be a large number of streams in Virginia that have Elevated Concern to
17 Acute Concern problems with acidity (Figure 5.2-7). In the short term (i.e., by the year 2020)
18 and in the long term (i.e., by the year 2050), the response of the 60 modeled streams shows no
19 improvement in the number of streams that have Moderate Concern conditions. In fact, under
20 current emission levels, the modeling suggests that conditions may get worse by the year 2050.
21 In Figure 5.2-7 the percentage of streams in Acute Concern condition increases by 5%, whereas
22 streams in Moderate Concern condition decreases by 5%.
2nd Draft Risk and Exposure Assessment
Appendix 4-42
June 5, 2009
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Aquatic Acidification Case Study
2006
2020
2050
• Acute (Below 0 |jeq/L)
D Elevated (20-50 |jeq/L)
• Low (Above 100 |jeq/L)
D Severe (0-20 |jeq/L)
• Moderate (50-100 |jeq/L)
2 Figure 5.2-7. Percentage of streams in the five categories of acidification (Acute,
3 Severe, Elevated, Moderate, Low) for the years 2006, 2020, and 2050 for 60 streams in
4 the Shenandoah Case Study Area modeled using MAGIC. The number of streams in
5 each category is above the bar. Error bars indicate the 95% confidence interval.
2nd Draft Risk and Exposure Assessment
Appendix 4-43
June 5, 2009
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Aquatic Acidification Case Study
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14 Chemistry, Inc., Corvallis, OR.
15 Sullivan, T.J., C.T. Driscoll, B.J. Cosby, I.J. Fernandez, A.T. Herlihy, J. Zhai, R. Stemberger,
16 K.U. Snyder, J.W. Sutherland, S.A. Nierzwicki-Bauer, C.W. Boylen, T.C. McDonnell,
17 and N.A. Nowicki. 2006. Assessment of the Extent to which Intensively-Studied Lakes are
18 Representative of the Adirondack Mountain Region. Final report. New York State Energy
19 Research and Development Authority (NYSERDA), Albany, NY. Available at
20 http://nysl.nysed.gOv/uhtbin/cgisirsi/Qcwd6NzFby/NYSL/138650099/8/4298474
21 (accessed November 1, 2007.
22 Sverdrup, H., W. de Vries, and A. Henriksen. 1990. Mapping Critical Loads. Miljorapport 14.
23 Nordic Council of Ministers, Copenhagen, Denmark.
24 Urquhartetal., 1998
25 U.S. EPA (Environmental Protection Agency). 1995. Acid Deposition Standard Feasibility
26 Study. Report to Congress. EPA 430-R-95-001a. U.S. Environmental Protection Agency,
27 Office of Air and Radiation, Washington, DC.
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Appendix 4-50
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1 U.S. EPA (Environmental Protection Agency). 2008. Integrated Science Assessment (ISA) for
2 Oxides of Nitrogen and Sulfur-Ecological Criteria (Final Report). EPA/600/R-
3 08/082F. U.S. Environmental Protection Agency, National Center for Environmental
4 Assessment-RTF Division, Office of Research and Development, Research Triangle
5 Park, NC. Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485.
6 Van Sickle, 1, J.P. Baker, H.A. Simonin, B.P. Baldigo, W.A. Kretser, and W.E. Sharpe. 1996.
7 Episodic acidification of small streams in the northeastern United States: Fish mortality
8 in field bioassays. Ecological Applications (5:408-421.
9 Webb J.R., Cosby B.J., Galloway J.N., and Hornberger G.M.. 1989. Acidification of native brook
10 trout streams in Virginia. Water Resources Research 25:1367-1377.
11 Webb etal., 1994
12 Webb, J.R., BJ. Cosby, F.A. Deviney, J.N. Galloway, S.W. Maben, and AJ. Bulger. 2004. Are
13 brook trout streams in western Virginia and Shenandoah National Park recovering from
14 acidification? Environmental Science and Technology 35:4091-4096.
15 Webster, K. E., Brezonik, P. L., Holdhsen, B. J. 1993. Temporal trends in low alkalinity lakes of
16 the upper Midwest (1983-1989). Water Air Soil Pollution 67: 397-414
17 Wedemeyer, G.A., B.A. Barton, and DJ. MeLeay. 1990. Stress and acclimation. Pp. 178-198 in
18 Methods for Fish Biology. Edited by C.B. Schreck and P.B. Moyle. Bethesda, MD:
19 American Fisheries Society.
20 Whitehead, P.G., S. Bird, M. Hornung, BJ. Cosby, C. Neal, and P. Paricos. 1988. Stream
21 acidification trends in the Welsh Uplands: A modelling study of the Llyn Brianne
22 catchments. Journal of Hydrology 101:191-212.
23 Whittier et al., 2002
24 Wright, R.F., BJ. Cosby, M.B. Flaten, and J.O. Reuss. 1990. Evaluation of an acidification
25 model with data from manipulated catchments in Norway. Nature 343:53-55.
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1 Wright, R.F., E. Lotse, and E. Semb. 1994. Experimental acidification of alpine catchments at
2 Sogndal, Norway: results after 8 years. Water, Air, and Soil Pollution 72:297-315.
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i ATTACHMENT A
2 1.0 MODELING DESCRIPTIONS
3 1.1 MAGIC
4 Model of Acidification of Groundwaters in Catchments (MAGIC) is a lumped-parameter
5 model of intermediate complexity, developed to predict the long-term effects of acidic deposition
6 on surface water chemistry (Cosby et al., 1985a, b). The model simulates soil solution chemistry
7 and surface water chemistry to predict the monthly and annual average concentrations of the
8 major ions in these waters. MAGIC consists of (1) a 2-10 submodel in which the concentrations
9 of major ions are assumed to be governed by simultaneous reactions involving sulfate (SC>42")
10 adsorption, cation exchange, dissolution-precipitation- speciation of aluminum (Al), and
11 dissolution-speciation of inorganic carbon; and (2) a mass balance submodel in which the flux of
12 major ions to and from the soil is assumed to be controlled by atmospheric inputs, chemical
13 weathering, net uptake and loss in biomass, and losses to runoff. At the heart of MAGIC is the
14 size of the pool of exchangeable base cations in the soil. As the fluxes to and from this pool
15 change over time in response to changes in atmospheric deposition, the chemical equilibria
16 between soil and soil solution shift, resulting in changes in surface water chemistry. Thus, the
17 degree and rate of change of surface water acidity depend both on flux factors and the inherent
18 biogeochemical characteristics of the affected soils.
19 Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with
20 selectivity coefficients for each base cation and Al. SC>42" adsorption is represented by a
21 Langmuir isotherm. Al dissolution and precipitation are assumed to be controlled by equilibrium
22 with a solid phase of aluminum hydroxide (A1(OH)3). Al speciation is calculated by considering
23 hydrolysis reactions, as well as complexation with SC>42" and fluoride (F). The effects of carbon
24 dioxide (CO2) on pH and on the speciation of inorganic carbon are computed from equilibrium
25 equations. Organic acids are represented in the model as tri-protic analogues. Weathering and the
26 uptake rate of nitrogen are assumed to be constant. A set of mass balance equations for base
27 cations and strong acid anions are included (Cosby et al., 1985).
28 Given a description of the historical deposition at a site, the model equations are solved
29 numerically to give long-term reconstructions of surface water chemistry (for complete details of
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1 the model, see Cosby et al., 1985 a, b; Cosby et al., 1989). MAGIC was successfully used to
2 reconstruct the history of acidification and to simulate the future trends on a regional basis and in
3 a large number of individual catchments in both North America and Europe (e.g., Cosby et al.,
4 1989, 1990, 1996; Hornberger et al., 1989; Jenkins et al., 1990 a, b, c; Lepisto et al., 1988;
5 Norton et al., 1992; Sullivan and Cosby, 1998; Sullivan and Cosby, 2004; Whitehead et al.,
6 1988; Wright et al., 1990, 1994).
7 The input data required in this project for aquatic and soils resource modeling with the
8 MAGIC model (i.e., stream water, catchment, soils, deposition data) were assembled and
9 maintained in databases for each site modeled (electronic spreadsheets, text-based MAGIC
10 parameter files). Model outputs for each site were archived as text-based time-series files of
11 simulated variable values. The outputs were also concatenated across all sites and maintained in
12 electronic spreadsheets.
13 1.1.1 Input Data and Calibration
14 The calibration procedure requires that streamwater chemistry, soil chemical and physical
15 characteristics, and atmospheric deposition data be available for each watershed. The surface
16 water chemistry data needed for calibration are the concentrations of the individual base cations
17 (calcium [Ca2+], magnesium [Mg2+], sodium [Na+], and potassium [K+]) and acid anions
18 (chlorine [Cl~], SC>42", nitrate [N(V]) and the stream pH. The soil data used in the model
19 comprise physical properties, including soil depth and bulk density, and chemical properties,
20 such as soil pH, soil cation-exchange capacity, and exchangeable bases in the soil (Ca2+, Mg2+,
21 Na+, and K+). The deposition inputs required for calibration include the concentrations and
22 magnitudes of all major ions from wet, dry, and cloud deposition.
23 The acid-base chemistry modeling for this project was conducted using the year 2002 as
24 the Base Year. The effects models were calibrated to the available atmospheric deposition and
25 water chemistry data and then interpolated or extrapolated to yield Base Year estimates of lake
26 water chemistry in the year 2002, which served as the starting point for modeling of current
27 water chemistry (e.g., the years 2002 to 2100)
28 1.1.2 Lake, Stream, and Soil Data for Calibration
29 Several water chemistry databases were acquired for use in model calibration. Data were
30 derived primarily from the Environmental Monitoring and Assessment Program (EMAP) and
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1 Temporally Integrated Monitoring of Ecosystems (TIME) survey and monitoring efforts. The
2 required lake water and soil composition data for the modeling efforts included the following
3 measurements:
4 • Stream water composition— pH, acid neutralizing capacity (ANC), Ca2+, Mg2+, K+, Na+,
5 SO42", NCV, and Cl'
6 • Soil properties— thickness and total cation exchange capacity, exchangeable bases (Ca2+,
7 Mg2+, Na+, and K+) bulk density, porosity, and pH where available; the stream water
8 chemistry database also included dissolved organic and inorganic carbon, silicic acid
9 (H4SiO4), and inorganic monomeric Al (i.e., Ali).
10 1.1.3 Wet Deposition and Meteorology Data for Calibration
11 MAGIC requires, as atmospheric inputs for each site, estimates of the total annual
12 deposition (eq/ha/yr) of eight ions, and the annual precipitation volume (meters/year [m/yr]). The
13 eight ions are calcium (Ca2+), magnesium (Mg2+), Na+, K+, ammonium (NH4+), wet sulfate (SO4),
14 chlorine (Cl"), and nitrate (NOs). Total deposition of an ion at a particular site for any year can be
15 represented as combined wet, dry, and occult (i.e., cloud and fog) deposition:
16 TotDep = WetDep + DryDep + OccDep. (1)
17 Inputs to the MAGIC model are specified as wet deposition (the annual flux in
18 meq/m2/yr) and a dry and occult deposition factor (DDF, unitless), which is multiplied by the
19 wet deposition in order to get total deposition:
20 TotDep = WetDep x DDF, (2)
21 where DDF is the ratio of total deposition to wet deposition. It usually prescribed as equal to a
22 constant fraction of the wet deposition.
23 Given an annual wet deposition flux (WetDep), the ratio of dry deposition to wet
24 deposition (DryDep/WetDep), and the ratio of occult deposition to wet deposition
25 (OccDep/WetDep) for a given year at a site, the total deposition for that site and year is uniquely
26 determined.
27 In order to calibrate MAGIC, a time-series of total deposition is needed, beginning with a
28 reference calibration year and including the 140 years preceding the calibration year. The
29 procedure for providing a time-series of total deposition inputs to MAGIC follows.
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1 The absolute values of wet deposition and DDF for each ion are provided for a Reference
2 Year at each site. For all the case study sites, a Reference Year of 2002 was used. Given the
3 Reference Year deposition values, deposition data for the historical and calibration periods, and
4 potentially any future deposition scenarios, can be estimated as a fraction of the Reference Year
5 value. For instance, to calculate the total deposition of a particular ion in some historical or
6 future year, j:
7 TotDepG) = [WetDep(O) x WetDepScaleG) ] x [ DDF(O) x DDF ScaleQ)], (3)
8 where:
9 WetDep(O) = the Reference Year wet deposition (meq/m2/yr) of the ion
10 WetDepScale(j) = the scaled value of wet deposition in year j (expressed as a fraction of the
11 wet deposition in the Reference Year)
12 DDF(O) = the dry and occult deposition factor for the ion for the Reference Year
13 DDFScale(j) = the scaled value of the dry and occult deposition factor in year j (expressed
14 as a fraction of the DDF in the Reference Year).
15 The absolute value of wet deposition used for the Reference Year is time and space
16 specific—varying geographically within the region, varying locally with elevation, and varying
17 from year to year. It is desirable to have the estimates of wet deposition take into account the
18 geographic location and elevation of the site, as well as the year for which calibration data are
19 available. Therefore, estimates of wet deposition used for the Reference Year should be derived
20 from either direct measurements or a procedure (i.e., model) that has a high spatial resolution and
21 considers elevation effects. As described in Section 4.2.1.4, the absolute wet deposition values
22 used for the Reference Year in this project were derived from observed data from NADP
23 hybridized with high-spatially resolved estimates of rainfall.
24 The value of the DDF used for the Reference Year specifies the ratio between the
25 absolute amounts of wet and total deposition. While the wet deposition component varies
26 spatially and temporally, this ratio is not nearly so responsive. In large part, this is because the
27 varying wet deposition parameter is usually a large component of the total deposition and is
28 included in both the numerator and denominator of the ratio. For example, if in a given year at a
29 particular site, the wet deposition goes up, then the total deposition usually goes up; or, if the
30 elevation or aspect of a given site results in lower wet deposition, the total deposition also will
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1 often be lower. Therefore, estimates of the absolute values of DDF may be derived from a model
2 that has a relatively low spatial resolution and/or temporally smoothes the data. Estimates of the
3 absolute values of the DDF for the Reference Year at each site in this project were derived from
4 the Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model (Shannon, 1998),
5 as described below.
6 The long-term scaled sequences used to specify time-series of deposition inputs for
7 MAGIC simulations usually do not require detailed spatial or temporal resolution. Scaled
8 sequences of wet deposition or DDF (normalized to the same reference year) at neighboring sites
9 will be similar, even if the absolute wet deposition or DDF at the sites are different because of
10 factors such as local aspect or elevation. Therefore, if the scaled long-term patterns of any of
11 these do not vary much from place to place, estimates of the scaled sequences (as for estimates of
12 absolute DDF values) may be derived from a model that has a relatively low spatial resolution.
13 As described in the following sections, output from the ASTRAP model was used to construct
14 scaled sequences of both wet deposition and DDF for these case study areas.
15 1.1.4 Wet Deposition Data (Reference Year and Calibration Values)
16 The absolute values of wet deposition used for defining the Reference Year and for the
17 MAGIC calibrations must be highly site-specific. Estimated wet deposition data was used for
18 each site derived from the spatial interpolation model of Grimm and Lynch (2004), referred to
19 here as the Grimm model. The Grimm model is based on observed wet deposition concentrations
20 at NADP monitoring stations and radar-based precipitation estimates adjusted by elevation
21 effects, and provides a spatially-resolved estimate of wet deposition for each of the eight ions
22 required by MAGIC. The Grimm model makes a correction for changes in precipitation volume
23 (and thus wet deposition) based on the elevation at a given site. This correction arises from a
24 model of orographic effects on precipitation magnitudes derived from regional climatological
25 data.
26 The latitude, longitude, and elevation of the case study sites were provided as inputs to
27 the Grimm model. Estimates of quarterly and annual wet deposition and precipitation estimates
28 for each modeling site were found for the time period from 1983 through 2002. These annual
29 data were used to define the Reference Year and were used in conjunction with the ASTRAP
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1 historical deposition sequences for MAGIC calibration and simulation. The ASTRAP historical
2 sequences were scaled to match the Grimm estimates at each site.
3 1.1.5 Dry and Occult Deposition Data and Historical Deposition Sequences
4 Historical sequences of wet deposition and DDF were estimated using the ASTRAP
5 model. The ASTRAP model provided estimates of historical wet, dry, and occult deposition of
6 sulfur and oxidized nitrogen at modeled sites for the two case study areas. The ASTRAP sites
7 included 10 NADP deposition sites. For each of the modeled sites, ASTRAP produced wet, dry,
8 and occult deposition estimates of sulfur and oxidized nitrogen every 10 years, starting in 1900
9 and ending in 1990. The model outputs are smoothed estimates of deposition roughly equivalent
10 to a 10-year moving average centered on each of the output years. The wet, dry, and occult
11 deposition outputs of ASTRAP were used to estimate the absolute DDF for each site (using the
12 DryDep/WetDep and OccDep/WetDep ratios from the ASTRAP 19 output) and to set up the
13 scaled sequences of historical wet deposition and historical DDF for the calibration of each site
14 modeled in this project. Using the values and rates of change from the year 1900 ASTRAP
15 estimates, values for each time period going back to 1850 were estimated through linear
16 interpolation.
17 Because the ASTRAP sites are in the same region, but are not in the identical locations as
18 the MAGIC sites, and since deposition magnitudes are spatially- and elevation-sensitive, the
19 historical sequences of deposition at the ASTRAP sites were scaled to align with the deposition
20 estimates from the Grimm model for the MAGIC case study areas. First, the time series of wet
21 deposition estimates for each ASTRAP site were used to construct historical scaled sequences of
22 wet deposition. The absolute wet deposition outputs for the period 1850 to 1990 from each site
23 modeled in ASTRAP were normalized using their year 1990 values, converting them into scaled
24 sequences. It was then necessary to couple these historical scaled wet deposition sequences from
25 the year 1990 to the MAGIC Reference Year 2002. This coupling was accomplished using the
26 observed changes in wet deposition for the period 1983 to 2002 derived from the Grimm model.
27 With these site-specific deposition magnitudes and rates of change, the normalized ASTRAP
28 values were converted back into concentrations, though, now scaled to the deposition at the
29 MAGIC sites.
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1 Because DDF is much less sensitive to location, the actual (nonscaled) estimates from
2 ASTRAP were used at the MAGIC sites. The value of DDF for the year 1990 was used as the
3 value of DDF for the Reference Year (i.e., no change was assumed for DDF for the period 1990
4 to 2002). The resulting time series of DDF values for the period 1900 to 2002 for each ASTRAP
5 site were normalized to the year 2002 values to provide historical scaled sequences of DDF at
6 each ASTRAP site.
7 1.1.6 Protocol for MA GIC Calibration and Simulation at Individual Sites
8 The aggregated nature of the MAGIC model requires that it be calibrated with observed
9 data from a system before it can be used to forecast potential system response to changes in
10 deposition. Calibration is achieved by specifying values of certain parameters within the model
11 that can be directly measured or observed in the system of interest (called fixed parameters). The
12 model is then run (using observed and/or assumed atmospheric and hydrologic inputs), and the
13 outputs (streamwater and soil chemical variables called criterion variables) are compared with
14 observed values of these variables. If the observed and simulated values differ, the values of
15 another set of parameters in the model (called optimized parameters) are adjusted to improve the
16 fit. After a number of iterations adjusting the optimized parameters, the simulated-minus-
17 observed values of the criterion variables usually converge to zero (within some specified
18 tolerance, or uncertainty). The model is then considered calibrated.
19 There are eight observed fixed parameters that are used to drive the estimate (i.e., current
20 soil exchangeable pool size and current output flux of each of the four base cations), and there
21 are eight parameters to be optimized in this procedure (i.e., the weathering and the selectivity
22 coefficient of each of the four base cations). If new assumptions or new values for any of the
23 observed fixed parameters or inputs to the model are adopted, the model must be recalibrated by
24 readjusting the optimized parameters until the simulated-minus-observed values of the criterion
25 variables again fall within the specified tolerance.
26 Estimates of the fixed parameters, the deposition inputs, and the target variable values to
27 which the model is calibrated all contain uncertainties. A "fuzzy optimization" procedure was
28 used for these case study sites to provide explicit estimates of the effects of these uncertainties.
29 The procedure consists of performing multiple calibrations at each site using random values of
30 the fixed parameters drawn from a range of fixed parameter values (representing uncertainty in
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1 knowledge of these parameters) and random values of Reference Year deposition drawn from a
2 range of total deposition estimates (representing uncertainty in these inputs). The final
3 convergence (i.e., completion) of the calibration is determined when the simulated values of the
4 criterion variables are within a specified "acceptable window" around the nominal observed
5 value. This acceptable window represents uncertainty in the target variable values being used to
6 calibrate the site.
7 Each of the multiple calibrations at a site begins with (1) a random selection of values of
8 fixed parameters and deposition, and (2) a random selection of the starting values of the
9 adjustable parameters. The adjustable parameters are then optimized using an algorithm seeking
10 to minimize errors between simulated and observed criterion variables. Calibration success is
11 judged when all criterion values simultaneously are within their specified acceptable windows,
12 (which may occur before the absolute possible minimum error is achieved). This procedure is
13 repeated 10 times for each site.
14 For this project, the acceptable windows for base cation concentrations in streams were
15 taken as +/- 2 microequivalents per liter (ueq/L) around the observed values. Acceptable
16 windows for soil exchangeable base cations were taken as +/- 0.2% around the observed values.
17 Fixed parameter uncertainty in soil depth, bulk density, cation exchange capacity, stream
18 discharge, and stream area were assumed to be +/- 10% of the estimated values. Uncertainty in
19 total deposition was +/- 10% for all ions.
20 The final calibrated model at the site is represented by the ensemble of parameter values
21 of all of the successful calibrations at the site. When performing a simulation of the site, each of
22 the calibrated parameter sets are run for a given historical or future scenario, generating an
23 ensemble of results. The results include multiple simulated values of each variable for each year,
24 all of which are acceptable in the sense of the calibration constraints applied in the fuzzy
25 optimization procedure. The median of all the simulated values within a year is taken to be "the
26 most likely" response for the site in that year. For this project, whenever single values for a site
27 are presented or used in an analysis, these values are the median of the ensemble values derived
28 from running each of the parameter sets for the site.
29 An estimate of the uncertainty (or reliability) of a simulated response to a given scenario
30 can also be derived from the multiple simulated values within a year resulting from the ensemble
31 simulations. For any year in a given scenario, the largest and smallest values of a simulated
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1 variable define the upper and lower 95% confidence bounds for that site's response for the
2 scenario under consideration. Thus, for all variables and all years of the scenario, a band of
3 simulated values can be produced from the ensemble simulations at a site that encompasses the
4 likely response (and provides an estimate of the simulation uncertainty) for any point in the
5 scenario. For these case study areas, whenever uncertainty estimates are presented, the estimate
6 is based on the range of values from the ensemble simulations for each of its sites.
7 In addition, uncertainty estimates for three classes of the major inputs of the model were
8 made through a sensitivity study, examining response of parameters and ability of the model to
9 attain calibration in response to variation in the following inputs:
10 • Soils data for calibration
11 • Stream water data calibration
12 • Deposition data calibration.
13 1.1.7 Combined Model Calibration and Simulation Uncertainty
14 The sensitivity analyses described above were designed to address specific assumptions
15 or decisions that had to be made in order to assemble the data for the 44 or 60 modeled sites in a
16 form that could be used for calibration of the model. In all cases, the above analyses address the
17 questions of what the effect would have been if alternate available choices had been taken. These
18 analyses were undertaken for a subset of sites for which the alternate choices were available at
19 the same sites. As such, the analyses above are informative, but they provide no direct
20 information about the uncertainty in calibration or simulation arising from the choices that were
21 incorporated into the final modeling protocol for all sites. That is, having made the choices about
22 soils assignments, high elevation deposition, and stream samples for calibration (and provided an
23 estimate of their inherent uncertainties), the need arises for a procedure for estimating
24 uncertainty at each and all of the individual sites using the final selected calibration and
25 simulation protocol.
26 These simulation uncertainty estimates were derived from the multiple calibrations at
27 each site provided by the "fuzzy optimization" procedure employed in this project. For each of
28 the modeled sites, 10 distinct calibrations were performed with the target values, parameter
29 values, and deposition inputs for each calibration, reflecting the uncertainty inherent in the
30 observed data for the individual site. The effects of the uncertainty in the assumptions made in
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1 calibrating the model (and the inherent uncertainties in the data available) can be assessed by
2 using all successful calibrations for a site when simulating the response to different scenarios of
3 future deposition. The model then produces an ensemble of simulated values for each site. The
4 median of all simulated values in a year is considered the most likely response of the site. The
5 simulated values in the ensemble can also be used to estimate the magnitude of the uncertainty in
6 the projection. Specifically, the difference in any year between the maximum and minimum
7 simulated values from the ensemble of calibrated parameter sets can be used to define an
8 "uncertainty" (or a "confidence") width for the simulation at any point in time. All 10 of the
9 successful model calibrations will lie within this range of values. These uncertainty widths can
10 be produced for any variable and any year to monitor model performance.
11 1.2 Critical Loads: Steady-State Water Chemistry Model
12 The critical load of acidity for lakes or streams was derived from present-day water
13 chemistry using the SSWC model. The Steady-State Water Chemistry (SSWC) model is based
14 on the principle that excess base cation production within a catchment area should be equal to or
15 greater than the acid anion input, thereby maintaining the ANC above a preselected level
16 (Reynolds and Norris, 2001). This model assumes steady-state conditions and assumes that all
17 SO42 in runoff originates from sea salt spray and anthropogenic deposition. Given a critical
18 ANC protection level, the critical load of acidity is simply the input flux of acid anions from
19 atmospheric deposition (i.e., natural and anthropogenic) subtracted from the natural (i.e.,
20 preindustrial) inputs of base cations in the surface water.
21 Critical loads of acidity CL(A) were calculated for each waterbody from the principle
22 that the acid load should not exceed the nonmarine, nonanthropogenic base cation input and
23 sources and sinks in the catchment minus a buffer to protect selected biota from being damaged:
24 CL(A) = BC*dep + BCW - Bcu - ANCiimit (4)
25 where
26 BC*deP = (BC*=Ca*+Mg*+K*+Na*), nonanthropogenic deposition flux of base cations and
27 BCW = the average weathering flux, creating base cations,
28 Bcu (Bc=Ca*+Mg*+K*) = the net long-term average uptake flux of base cations in the biomass
29 (i.e., the annual average removal of base cations due to harvesting),
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it = the lowest ANC-flux that protects the biological communities.
2 Since the average flux of base cations weathered in a catchment and reaching the lake is
3 difficult to measure or compute from available information, the average flux of base cations and
4 the resulting critical load estimation were derived from water quality data (Henriksen and Posch,
5 2001; Henriksen et al., 1992; Sverdrup et al., 1990). Weighted annual mean water chemistry
6 values were used to estimate average base cation fluxes, which were calculated from water
7 chemistry data collected from the Temporally Integrated Monitoring of Ecosystems (TIME) and
8 Long-Term Monitoring (LTM) project monitoring networks (see Section 4. 1 .2.1).
9 The preacidification nonmarine flux of base cations for each lake or stream, BC*o, is
10 BC*o = BC*deP + BCw-Bcu (5)
1 1 Thus, critical load for acidity can be rewritten as
12 CL(A) = BC*0 - ANCHmit = Q ([BC*]0 - [ANC]Hmit), (6)
13 where the second identity expresses the critical load for acidity in terms of catchment runoff (Q)
14 m/yr and concentration ([x] = X/Q),
15 1.2.1 Preindustrial Base Cation Concentration
16 Present-day surface water concentrations of base cations are elevated above their steady -
17 state preindustrial concentrations because of base cation leaching through ion exchange in the
18 soil due to anthropogenic inputs of SC>42" to the watershed. For this reason, present-day surface
19 water base cation concentrations are higher then natural or preindustrial levels, which, if not
20 corrected for, would result in critical load values not in steady-state condition. To estimate the
21 preacidification flux of base cations, the present flux of base cations was estimated, BC t, given
22 by
23 BCt = BCdep + BCw-Bcu+BCexc, (7)
24 where
25 BCexc = the release of base cations due to ion-exchange processes.
26 Assuming that deposition, weathering rate, and net uptake have not changed over time,
27 BCexc can be obtained by subtracting Eq. 5 from Eq. 7:
28 BCexc = BC*t-BC*o (8)
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1 This present-day excess production of base cations in the catchment was related to the
2 long-term changes in inputs of nonmarine acid anions (ASO*2 + ANOs) by the F-factor (see
3 below):
4 BCexc = F (ASO*2 + ANO3) (9)
5 For the preacidification base cation flux, solving Eq. 5 for BC o and then substituting Eq.
6 8 for BCexc and explicitly describing the long-term changes in nonmarine acid ion inputs:
7 BC*0 = BC*t - F (SO*4;t - SO*4)o + NO*3)t - NO*3,0) (10)
8 The preacidification N(V concentration, NO*3,o, was assumed to be zero.
9 Finally, using all of the information about critical loads, the weathering rates, critical
10 limits of ANC, and deposition magnitudes, it is possible to calculate exceedances of the critical
1 1 load of acidity, Ex(A) as:
12 Ex(A) = SdeP + Nieach -CL(A), (H)
13 where S*dep is the amount of sulfur deposited in the catchment (assuming that all SC>42" deposited
14 leaches into the waterbody) and Nieach is the amount of deposited nitrogen, Ndep, that moves into
1 5 the water.
16 While SC>42" is assumed to be a mobile anion (Sieach = S*dep), nitrogen is to a large extent
17 retained in the catchment by various processes; therefore, Ndep cannot be used directly in the
18 exceedances calculation. Therefore, only present-day exceedances can be calculated from the
19 leaching of nitrogen, Nieach, which is determined from the sum of measured concentrations of
20 NOs" and ammonia in the stream chemistry. No nitrogen deposition data are required for
21 exceedance calculations; however, Ex(A) quantifies only the exceedances at present rates of
22 retention of nitrogen in the catchment.
23 1.2.2 F-factor
24 An F-factor was used to correct the concentrations and estimate preindustrial base
25 concentrations for lakes in the Adirondack Case Study Area. In the case of streams in the
26 Shenandoah Case Study Area, the preindustrial base concentrations were derived from the
27 MAGIC model as the base cation supply in 1860 (hindcast) because the F-factor approach is
28 untested in this region. An F-factor is a ratio of the change in nonmarine base cation
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1 concentration due to changes in strong acid anion concentrations (Henriksen, 1984; Brakke et al.,
2 1990):
3 F =([BC*]t. [BC*]0)/([SO;]t - [S04*]o + [NO3*]t - [NO3*]o), (12)
4 where the subscripts t and 0 refer to present and preacidification conditions, respectively. If F=l,
5 all incoming protons are neutralized in the catchment (only soil acidification); at F=0, none of
6 the incoming protons are neutralized in the catchment (only water acidification). The F-factor
7 was estimated empirically to be in the range 0.2 to 0.4, based on the analysis of historical data
8 from Norway, Sweden, the United States, and Canada (Henriksen, 1984). Brakke et al. (1990)
9 later suggested that the F-factor should be a function of the base cation concentration:
10 F = sin (Ti/2 Q[BC*]t/[S]) (13)
11 where
12 Q = the annual runoff (m/yr)
13 [S] = the base cation concentration at which F=l; and for [BC*]t>[S] F is set to 1. For
14 Norway [S] has been set to 400 milliequivalents per cubic meter (meq/m3)(circa.
15 8 mg Ca/L) (Brakke et al., 1990).
16 The preacidification SC>42" concentration in lakes, [SO4*]o, is assumed to consist of a
17 constant atmospheric contribution and a geologic contribution proportional to the concentration
18 of base cations (Brakke et al., 1989).
19 1.2.3 ANCLimits
20 Four classes of ANC limits were estimated: Suitable ANC >50 ueq/L; Indeterminate
21 ANC 20 to 50 ueq/L; Marginal ANC 0 to 20 ueq/L and Unsuitable ANC <0 ueq/L.
22 1.2.4 Sea Salt Corrections
23 The model applies a sea salt correction to the water chemistry concentrations. The
24 equations below were applied to all lakes and streams, and to all the New England states and
25 eastern Canadian provinces for the New England Governors and Eastern Canadian Premier
26 assessment. The equations correct for sea salt. An asterisk (*) indicates the value has been
27 corrected for sea salt. Units are in ueq/L.
28 Ca* = (Ca-(CLx 0.0213)) (14)
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Mg =(Mg-(CLx 0.0669)) (15)
Na* = (Na - (CL x 0.557)) (16)
K* = (K - (CL x 0.0.0206)) (17)
= (SO4-(CLx0.14)) (18)
5 1.2.5 Uncertainty and Variability
6 Although the F-factor approach and SSWC models have been widely published and
7 analyzed in Canada and Europe and have been applied in the United States (e.g.. Dupont et al.,
8 2005), their utility and uncertainty in estimating critical load values are unclear at this time. For
9 this reason, an uncertainty analysis of the SSWC critical load model was completed to evaluate
10 the uncertainty in the modeling parameters. A probabilistic analysis using a range of parameter
1 1 uncertainties was used. The probabilistic framework is Monte Carlo, whereby each SSWC input
12 parameter varies according to specified probability distributions. Within Monte Carlo analysis,
13 models are run a sufficient number of times (i.e., 2,000 times) to capture the range of behaviors
14 represented by all variable inputs to the SSWC model. In this case study, multiple values were
15 used for several parameters in the SSWC calculation. This analysis tabulated the number of
16 lakes where the confidence interval is entirely below the critical load, where the confidence
17 interval is entirely above the critical load, and where the confidence interval straddles zero.
18 Similar results are given for the number of lakes with all realizations above the critical load, all
19 realizations below the critical load, and some realizations above and some below the critical load
20
21
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i ATTACHMENT B
2 1.0 EMAP/TIME/LTM PROGRAMS
3 The EPA Environmental Monitoring and Assessment Program (EMAP) began regional
4 surveys of the nation's surface waters in 1991 with a survey of northeastern United States lakes.
5 Since then, EMAP and Regional-EMAP (REMAP) surveys have been conducted on lakes and
6 streams throughout the country. The objective of these EMAP surveys is to characterize
7 ecological condition across populations of surface waters. EMAP surveys are probability surveys
8 where sites are picked using a spatially balanced systematic randomized sample so that the
9 results can be used to make estimates of regional extent of condition (e.g., number of lakes,
10 length of stream). EMAP sampling typically consists of measures of aquatic biota (e.g., fish,
11 macroinvertebrates, zooplankton, periphyton), water chemistry, and physical habitat. Of
12 particular interest with respect to acidifying deposition effects were two EMAP surveys
13 conducted in the 1990s, the Northeastern Lake Survey and the Mid-Atlantic Highlands
14 Assessment of streams (MAHA). The Northeastern Lake Survey was conducted in summer from
15 1991 to 1994 and consisted of 345 randomly selected lakes in New York, New Jersey, Vermont,
16 New Hampshire, Maine, Rhode Island, Connecticut, and Massachusetts (Whittier et al., 2002).
17 To make more precise estimates of the effects of acidic deposition, the sampling grid was
18 intensified to increase the sample site density in the Adirondack Mountains and New England
19 Uplands areas known to be susceptible to acidic deposition. The MAHA study was conducted on
20 503 stream sites from 1993 to 1995 in the states of West Virginia, Virginia, Pennsylvania,
21 Maryland, Delaware, and the Catskill Mountain region of New York (Herlihy et al., 2000).
22 Sampling was done during spring baseflow. Sample sites were restricted to first through third
23 order streams as depicted on the USGS 1:100,000 digital maps used in site selection. To make
24 more precise estimates of the effects of acidic deposition, the sampling grid was intensified to
25 increase the sample site density in the Blue Ridge, Appalachian Plateau, and Ridge section of the
26 Valley and Ridge ecoregions. Results from both of these surveys were used to develop and select
27 the sampling sites for the Temporally Integrated Monitoring of Ecosystems (TIME) program,
28 which is described below.
29
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1 2.0 TEMPORALLY INTEGRATED MONITORING OF ECOSYSTEMS
2 AND LONG-TERM MONITORING PROGRAMS
3 There are two surface water chemistry monitoring programs, administered by EPA, that
4 are especially important to inform the assessment of aquatic ecosystem responses to changes in
5 atmospheric deposition. These are the TIME program (Stoddard et al., 2003) and the Long-term
6 Monitoring (LTM) project (Ford et al., 1993; Stoddard et al., 1998). These efforts focus on
7 portions of the United States most affected by the acidifying influence of sulfur and nitrogen
8 deposition, including lakes in the Adirondack Mountains of New York and in New England, and
9 streams in the Northern Appalachian Plateau and Blue Ridge in Virginia and West Virginia.
10 Both projects are operated cooperatively with numerous collaborators in state agencies, academic
11 institutions, and other federal agencies. The TIME program and LTM project have slightly
12 different objectives and structures, which are outlined below. Stoddard et al. (2003) conducted a
13 thorough trends analysis of the TIME and LTM data.
14 2.1 TIME Program
15 At the core of the TIME project is the concept of probability sampling, whereby each
16 sampling site is chosen statistically from a predefined target population. Collectively, the
17 monitoring data collected at the sites are representative of the target population of lakes or
18 streams in each study region. The target populations in these regions include lakes and streams
19 likely to be responsive to changes in acidifying deposition, defined in terms of acid neutralizing
20 capacity (ANC), which represents an estimate of the ability of water to buffer acid. Measurement
21 of Gran ANC uses the Gran technique to find the inflection point in an acid-base titration of a
22 water sample (Gran, 1952). In the Northeast, the TIME target population consists of lakes with a
23 Gran ANC <100 microequivalents per liter (ueq/L). In the mid-Atlantic, the target population is
24 upland streams with Gran ANC <100 ueq/L. In both regions, the sample sites selected for future
25 monitoring were selected from the EMAP survey sites in the region (Section AX3.2.1.1) that met
26 the TIME target population definition. Each lake or stream is sampled annually (in summer for
27 lakes; in spring for streams), and results are extrapolated with known confidence to the target
28 population(s) as a whole using the EMAP site population expansion factors or weights (Larsen
29 and Urquhart, 1993; Larsen et al., 1994; Stoddard et al., 1996; Urquhart et al., 1998). TIME sites
30 were selected using the methods developed by the EMAP (Herlihy et al., 2000; Paulsen et al.,
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Aquatic Acidification Case Study
1 1991;). The TIME program began sampling northeastern lakes in 1991. Data from 43 lakes in the
2 Adirondack Mountains can be extrapolated to the target population of low ANC lakes in that
3 region. There are about 1,000 low-ANC Adirondack lakes, out of a total population of 1,842
4 lakes with surface area greater than 1 hectare (ha). Data from 30 lakes (representing about 1,500
5 low-ANC lakes, out of a total population of 6,800) form the basis for TIME monitoring in New
6 England. Probability monitoring of mid-Atlantic streams began in 1993. Stoddard et al. (2003)
7 analyzed data from 30 low-ANC streams in the Northern Appalachian Plateau (representing
8 about 24,000 kilometer (km) of low-ANC stream length out of a total stream length of 42,000
9 km).
10 The initial 1993 to 1995 EMAP-MAHA sample in the mid-Atlantic was not dense
11 enough to obtain enough sites in the TIME target population in the Blue Ridge and Valley and
12 Ridge ecoregions. In 1998, another denser random sample was conducted in these ecoregions to
13 identify more TIME sites. After pooling TIME target sites taken from both MAHA and the 1998
14 survey, there are now 21 TIME sites in the Blue Ridge and Ridge and Valley that can be used for
15 trend detection in this aggregate ecoregion in the mid-Atlantic in addition to the northern
16 Appalachian Plateau ecoregion.
17 2.2 LTM Program
18 As a complement to the statistical lake and stream sampling in TIME, the LTM Program
19 samples a subset of generally acid-sensitive lakes and streams that have long-term data, many
20 dating back to the early 1980s. These sites are sampled 3 to 15 times per year. This information
21 is used to characterize how some of the most sensitive aquatic systems in each region are
22 responding to changing deposition, as well as giving information on seasonal variation in water
23 chemistry. In most regions, a small number of higher-ANC (e.g., Gran ANC >100 ueq/L) sites
24 are also sampled, and these help to separate temporal changes due to acidifying deposition from
25 those attributable to other disturbances (e.g., climate, land use change). Because of the
26 availability of long-term records (i.e., more than two decades) at many LTM sites, their trends
27 can also be placed in a better historical context than those of the TIME sites, where data are only
28 available starting in the 1990s. Monitored water chemistry variables include pH, ANC, major
29 anions and cations, monomeric aluminum (Al), silicon (Si), specific conductance, dissolved
30 organic carbon, and dissolved inorganic carbon. The field protocols, laboratory methods, and
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Aquatic Acidification Case Study
1 quality assurance procedures are specific to each team of investigators. This information is
2 contained in the cited publications of each research group. The EMAP and TIME protocols and
3 quality assurance methods are generally consistent with those of the LTM cooperators. Details of
4 LTM data from each region are given below.
5 New England lakes: The LTM project collects quarterly data from lakes in Maine
6 (sampled by the University of Maine) (Kahl et al., 1991; Kahl et al., 1993) and Vermont (data
7 collected by the Vermont Department of Environmental Conservation) (Stoddard and Kellogg,
8 1993; Stoddard et al., 1998). Data from 24 New England lakes were available for the trend
9 analysis reported by Stoddard et al. (2003) for the period 1990 to 2000. In addition to quarterly
10 samples, a subset of these lakes have outlet samples collected on a weekly basis during the
11 snowmelt season; these data are used to characterize variation in spring chemistry. The majority
12 of New England LTM lakes have mean Gran ANC values ranging from 20 to 100 ueq/L; two
13 higher ANC lakes (i.e., Gran ANC between 100 and 200 ueq/L) are also monitored.
14 Adirondack lakes: The trend analysis of Stoddard et al. (2003) included data from 48
15 Adirondack lakes, sampled monthly by the Adirondack Lake Survey Corporation (Driscoll and
16 Van Dreason, 1993; Driscoll et al., 1995); a subset of these lakes are sampled weekly during
17 spring snowmelt to help characterize spring season variability. Sixteen of the lakes have been
18 monitored since the early 1980s; the others were added to the program in the 1990s. The
19 Adirondack LTM dataset includes both seepage and drainage lakes, most with Gran ANC values
20 in the range -50 to 100 ueq/L; three lakes with Gran ANC between 100 ueq/L and 200 ueq/L are
21 also monitored.
22 Appalachian Plateau streams: Stream sampling in the Northern Appalachian Plateau is
23 conducted about 15 times per year, with the samples spread evenly between baseflow (e.g.,
24 summer and fall) and high flow (e.g., spring) seasons. Data from four streams in the Catskill
25 Mountains (collected by the U.S. Geological Survey) (Murdoch and Stoddard, 1993), and five
26 streams in Pennsylvania (collected by Pennsylvania State University) (DeWalle and Swistock,
27 1994) were analyzed by Stoddard et al. (2003). All of the northern Appalachian LTM streams
28 have mean Gran ANC values in the range 25 to 50 ueq/L.
29 Upper Midwest lakes: Forty lakes in the Upper Midwest were originally included in the
30 LTM project, but funding in this region was terminated in 1995. The Wisconsin Department of
31 Natural Resources (funded by the Wisconsin Acid Deposition Research Council, the Wisconsin
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1 Utilities Association, the Electric Power Research Institute, and the Wisconsin Department of
2 Natural Resources) has continued limited sampling of a subset of these lakes, as well as carrying
3 out additional sampling of an independent subset of seepage lakes in the state. The data reported
4 by Stoddard et al. (2003) included 16 lakes (both drainage and seepage) sampled quarterly
5 (Webster et al., 1993), and 22 seepage lakes sampled annually in the 1990s. All of the Upper
6 Midwest LTM lakes exhibit mean Gran ANC values from 30 to 80 ueq/L.
7 Ridge/Blue Ridge streams: Data from the Ridge and Blue Ridge provinces consist of a
8 large number of streams sampled quarterly throughout the 1990s as part of the Virginia Trout
9 Stream Sensitivity Study (Webb et al., 1989), and a small number of streams sampled more
10 intensively (as in the Northern Appalachian Plateau). A total of 69 streams, all located in the
11 Ridge section of the Ridge and Valley province, or within the Blue Ridge province, and all
12 within the state of Virginia, had sufficient data for the trend analyses by Stoddard et al. (2003).
13 The data are collected cooperatively with the University of Virginia and the National Park
14 Service. Mean Gran ANC values for the Ridge and Blue Ridge data range from 15 to 200 ueq/L,
15 with 7 of the 69 sites exhibiting mean Gran ANC >100 ueq/L.
16
17
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Appendix 5
Terrestrial Acidification Case Study
Final Draft
EPA Contract Number EP-D-06-003
Work Assignment 3-62
Project Number 0209897.003.062
Prepared for
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27709
Prepared by
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1 TABLE OF CONTENTS
2 Acronyms and Abbreviations xi
3 1. Background 1
4 1.1 Indicators, Ecological Endpoints, and Ecosystem Services 2
5 1.1.1 Indicators 2
6 1.1.2 Ecological Endpoints 7
7 1.1.3 Ecosystem Services 10
8 1.2 Case Study Areas 10
9 1.2.1 GIS Analysis of National Sensitivity 10
10 1.2.2 Selection of Case Study Areas 12
11 1.2.3 Sugar Maple 17
12 1.2.3.1 Kane Experimental Forest 17
13 1.2.3.2 Plot Selection for Kane Experimental Forest Case Study Area 18
14 1.2.4 Red Spruce 20
15 1.2.4.1 Hubbard Brook Experimental Forest 20
16 1.2.4.2 Plot Selection for Hubbard Brook Experimental Forest Case
17 Study Area 23
18 2. Approach and Methods 26
19 2.1 Chosen Method 30
20 2.1.1 Critical Load Equations and Calculations 32
21 2.1.1.1 Simple Mass Balance Calculations 32
22 2.1.1.2 Deposition Relative to Critical Load Calculations 37
23 2.1.1.3 Critical Load Function 38
24 2.1.2 Critical Load Data Requirements 39
25 2.1.2.1 Data Requirements and Sources 39
26 2.1.2.2 Select!on of Indicator Values 43
27 2.1.2.3 Case Study Input Data 46
28 2.2 Critical Load Function Response Curves Associated with the Three Levels of
29 Protection 50
30 3. Results 51
31 3.1 Critical Load Estimates 51
32 3.1.1 Sugar Maple 51
33 3.1.2 Red Spruce 59
34 3.2 Recommended Parameter Values and Critical Loads 63
35 3.3 Current Conditions 64
36 4. Expansion of Critical Load Assessments for Sugar Maple and Red Spruce 76
37 4.1 Critical Load Assessments 76
38 4.2 Relationship between Atmospheric Nitrogen and Sulfur Deposition and Tree
39 Growth 92
40 5. Uncertainty Analysis 93
41 5.1 Kane Experimental Forest and Hubbard Brook Experimental Forest Case Study
42 Areas 93
43 5.2 Expansion of Critical Load Assessments 94
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1 6. References 97
2 Attachment A: Relationship Between Atmospheric Nitrogen and Sulfur Deposition and
3 Sugar Maple and Red Spruce Tree Growth 1
4 1. Introduction 1
5 2. Source of Data for Analyses 2
6 3. Regression Analyses Methodology and Results 5
7 4. Additional Sources of Variability Influencing the Critical Load-to-Tree Growth
8 Relationship 22
9 4.1 State-Specific Variables 22
10 4.2 Dead Trees 23
11 4.3 Other Factors 23
12 5. Conclusions 24
13
14 LIST OF FIGURES
15 Figure 1.1-1. Conceptual impacts of acidifying deposition on soil Ca2+ depletion, tree
16 physiology, and forest ecosystem health and sustainability (recreated from
17 DeHayes et al., 1999) 6
18 Figure 1.1-2. Areal coverages of red spruce and sugar maple tree species within the
19 continental United States (USFS, 2006) 9
20 Figure 1.2-1. Map of areas of potential sensitivity of red spruce and sugar maple to
21 acidification in the United States (see Table 1.2-1 for listing of data
22 sources to produce this map) 12
23 Figure 1.2-2. Location of the Kane Experimental Forest (Horsley etal., 2000) 17
24 Figure 1.2-3. The seven plots used to evaluate critical loads of acidity in the Kane
25 Experimental Forest 19
26 Figure 1.2-4. Location of theHubbard Brook Experimental Forest 21
27 Figure 1.2-5. Vegetation cover (NLCD, 2001) and location of Watershed 6 of Hubbard
28 Brook Experimental Forest 24
29 Figure 1.2-6. Grid units within Watershed 6 of Hubbard Brook Experimental Forest. The
30 red outline delineates the spruce-fir forest type. The dotted grid cell areas
31 indicate the grid units with high proportions of red spruce and represents
32 the composite plot area for the Hubbard Brook Experimental Forest Case
33 Study Area 25
34 Figure 1.2-7. Location of case study plots within Watershed 6 of Hubbard Brook
35 Experimental Forest 26
36 Figure 2.1-1. The critical load function created from the calculated maximum and
37 minimum levels of total nitrogen and sulfur deposition (eq/ha/yr). The
38 grey areas show deposition levels less than the established critical loads.
39 The red line is the maximum critical level of sulfur deposition (valid only
40 when nitrogen deposition is less than the minimum critical level of
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Terrestrial Acidification Case Study
1 nitrogen deposition [blue dotted line]). The flat line portion of the curves
2 indicates nitrogen deposition corresponding to the CLm;n(N) (i.e., nitrogen
3 absorbed by nitrogen sinks within the system) 39
4 Figure 2.1-2. The relationship between the Bc/Al ratio in soil solution and the percentage
5 of tree species (found growing in North America) exhibiting a 20%
6 reduction in growth relative to controls (after Sverdrup and Warfvinge,
7 1993b) 44
8 Figure 2.1-3. The relationship between soil solution Bc/Al ratio and stem or root growth
9 in sugar maple (from Sverdrup and Warfvinge, 1993b) 45
10 Figure 2.1-4. The relationship between soil solution Bc/Al ratio and biomass or root
11 growth in red spruce (from Sverdrup and Warfvinge, 1993b) 45
12 Figure 2.2-1. An example of the critical load function response curves associated with the
13 three (Bc/Al)crit ratios and the associated levels of protection of tree health.
14 The flat line portion of the curves indicates total nitrogen deposition
15 corresponding to the CLm;n (nitrogen absorbed by nitrogen sinks within
16 the system) 51
17 Figure 3.1-1. The critical load function response curves detailing the lowest critical load
18 estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1
19 for the parameters corresponding to each of the curves). The flat line
20 portion of the curves indicates total nitrogen deposition corresponding to
21 the CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system) 57
22 Figure 3.1-2. The critical load function response curves detailing the highest critical load
23 estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1
24 for the parameters corresponding to each of the curves). The flat line
25 portion of the curves indicates total nitrogen deposition corresponding to
26 the CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system 58
27 Figure 3.1-3. The critical load function response curves detailing the lowest critical load
28 estimates for the Hubbard Brook Experimental Forest Case Study Area
29 (refer to Table 3.1-10 for the parameters corresponding to each of the
30 curves). The flat line portion of the curves indicates total nitrogen
31 deposition corresponding to the CLm;n(N) (nitrogen absorbed by nitrogen
32 sinks within the system) 61
33 Figure 3.1-4. The critical load function response curves detailing the highest critical load
34 estimates for the Hubbard Brook Experimental Forest Case Study Area
3 5 (refer to Table 3.1-10 for the parameters corresponding to each of the
36 curves). The flat line portion of the curves indicates total nitrogen
37 deposition corresponding to the CLm;n(N) (nitrogen absorbed by nitrogen
38 sinks within the system) 61
39 Figure 3.3-1. Plot 1 Kane Experimental Forest critical load function response curves,
40 detailing the lowest critical load estimates for Kane Experimental Forest
41 Case Study Area (refer to Table 3.1-1 for the parameters corresponding to
42 each of the curves). The 2002 CMAQ/NADP total nitrogen and sulfur
43 deposition levels ((N+S)comb) were greater than the critical loads of
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1 nitrogen and sulfur at all levels of protection ((Bc/Al)crit= 0.6, 1.2, and
2 10.0). The flat line portion of the curves indicates total nitrogen deposition
3 corresponding to the CLm;n (N) (nitrogen absorbed by nitrogen sinks
4 within the system) 70
5 Figure 3.3-2. Plot 1 critical load function response curves, detailing the highest maximum
6 deposition load estimates for Kane Experimental Forest Case Study Area
7 (refer to Table 3.1 -1 for the parameters corresponding to each of the
8 curves). The 2002 CMAQ/NADP total nitrogen and sulfur deposition
9 levels ((N+S)comb) were greater than the critical load of total nitrogen and
10 sulfur deposition calculated with the highest level of protection (Bc/Al)crit
11 = 10.0). The flat line portion of the curves indicates total nitrogen
12 deposition corresponding to the CLm;n(N) (nitrogen absorbed by nitrogen
13 sinks within the system) 71
14 Figure 3.3-3. Critical load function response curves, detailing the lowest critical load
15 estimates for the Hubbard Brook Experimental Forest Case Study Area
16 (refer to Table 3.1 -10 for the parameters corresponding to each of the
17 curves). The 2002 CMAQ/NADP total nitrogen and sulfur deposition
18 levels ((N+S)comb) were greater than the critical load of total nitrogen and
19 sulfur calculated with the highest and the intermediate levels of protection
20 ((Bc/Al)crit= 1.2 and 10.0). The flat line portion of the curves indicates
21 total nitrogen deposition corresponding to the CLm;n(N) (nitrogen absorbed
22 by nitrogen sinks within the system) 71
23 Figure 3.3-4. Critical load function response curves, detailing the highest critical load
24 estimates for the Hubbard Brook Experimental Forest Case Study Area
25 (refer to Table 3.1-10 for the parameters corresponding to each of the
26 curves). The 2002 CMAQ/NADP total nitrogen and sulfur deposition
27 levels ((N+S)comb) were less than the critical loads associated with all three
28 (Be/Al)crit ratios. The flat line portion of the curves indicates total nitrogen
29 deposition corresponding to the CLm;n(N) (nitrogen absorbed by nitrogen
30 sinks within the system) 72
31 Figure 3.3-5. Critical load function response curves for the three selected critical loads
32 conditions (corresponding to the three levels of protection) for the Kane
33 Experimental Forest Case Study Area. The 2002 CMAQ/NADP total
34 nitrogen and sulfur deposition levels ((N+S)comb) were greater than the
35 highest and intermediate level of protection critical loads. The flat line
36 portion of the curves indicates total nitrogen deposition corresponding to
37 the CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system) 73
38 Figure 3.3-6. Critical load function response curves for the three selected critical loads
39 conditions (corresponding to the three levels of protection) for the
40 Hubbard Brook Experimental Forest Case Study Area. The 2002
41 CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)COmb) were
42 greater than the highest level of protection critical loads. The flat line
43 portion of the curves indicates total nitrogen deposition corresponding to
44 the CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system) 74
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1 Figure 3.3-7. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur
2 deposition levels (NHX-N) on the critical load function response curve, and
3 in turn, the maximum critical loads of sulfur (CLmax(S)) and oxidized
4 nitrogen (NOX-N) for the selected highest protection critical load for the
5 Kane Experimental Forest Case Study Area. The critical load of oxidized
6 nitrogen (NOX-N) is 661 eq/ha/yr (910 - 249 eq/ha/yr). The CLmin(N)
7 (nitrogen absorbed by nitrogen sinks within the system) corresponds to the
8 value depicted in Figure 3.3-5 75
9 Figure 3.3-8. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur
10 deposition levels (NHX-N) on the critical load function response curve and,
11 in turn, the maximum critical loads of sulfur (CLmax(S)) and oxidized
12 nitrogen (NOX-N) for the selected highest protection critical load for the
13 Hubbard Brook Experimental Forest Case Study Area. The critical load of
14 oxidized nitrogen (NOX-N) is 328 eq/ha/yr (487 - 159 eq/ha/yr). The
15 CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system)
16 corresponds to the value depicted in Figure 3.3-6 76
17 Figure 4.1-1. States where sugar maple is found and where 2002 CMAQ/NADP total
18 nitrogen and sulfur deposition levels exceeded the lowest protection
19 critical load (Bc/Al(crit) = 0.6) in the following: none of the sugar maple
20 plots, <50% of the sugar maple plots, and >50% of the sugar maple plots 87
21 Figure 4.1-2. States where sugar maple is found and where 2002 CMAQ/NADP total
22 nitrogen and sulfur deposition levels exceeded the intermediate protection
23 critical load (Bc/Al(crit) = 1.2) in the following: none of the sugar maple
24 plots, <50% of the sugar maple plots, and >50% of the sugar maple plots 88
25 Figure 4.1-3. States where sugar maple is found and where 2002 CMAQ/NADP total
26 nitrogen and sulfur deposition levels exceeded the highest protection
27 critical load (Bc/Al(crit) = 10.0) in the following: none of the sugar maple
28 plots, <50% of the sugar maple plots, and >50% of the sugar maple plots 89
29 Figure 4.1-4. States where red spruce is found and where 2002 CMAQ/NADP total
30 nitrogen and sulfur deposition levels exceeded the lowest protection
31 critical load (Bc/Al(crit) = 0.6) in the following: none of the red spruce
32 plots, <50% of the red spruce plots, and >50% of the red spruce plots 90
33 Figure 4.1-5. States where red spruce is found and where 2002 CMAQ/NADP total
34 nitrogen and sulfur deposition levels exceeded the intermediate protection
35 critical load (Bc/Al(crit) = 1.2) in the following: none of the red spruce
36 plots, <50% of the red spruce plots, and >50% of the red spruce plots 91
37 Figure 4.1-6. States where red spruce is found and where 2002 CMAQ/NADP total
38 nitrogen and sulfur deposition levels exceeded the highest protection
39 critical load (Bc/Al(crit) = 10.0) in the following: none of the red spruce
40 plots, <50% of the red spruce plots, and >50% of the red spruce plots 92
41 Figure 1-1. Hypothetical relationship between tree growth and critical load exceedance
42 (based on curve describing forest productivity as a function of long-term
43 chronic nitrogen additions outlined in Aber et al., 1995) 2
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5 - v
-------
Terrestrial Acidification Case Study
1 LIST OF TABLES
2 Table 1.1-1. Literature Support for Selected Indicators of Acidification 2
3 Table 1.1-2. Key Indicators of Acidification Due to Nitrogen and Sulfur 3
4 Table 1.1-3. Summary of Linkages between Acidifying Deposition, Biogeochemical
5 Processes That Affect Ca2+, Physiological Processes That Are Influenced
6 by Ca2+, and Effect on Forest Function 8
7 Table 1.2-1. Summary of Mapping Layers, Selected Indicator, and Selected Ecological
8 Endpoint for the Terrestrial Acidification Case Study 11
9 Table 1.2-2. Compilation of Potential Areas for the Terrestrial Acidification Case Study
10 (i.e., for Studying Red Spruce) as Identified in the Literature 14
11 Table 1.2-3. Major Studies at the Kane Experimental Forest 18
12 Table 1.2-4. Characteristics of the Case Study Plots in the Kane Experimental Forest 20
13 Table 1.2-5. Major Studies at the Hubbard Brook Experimental Forest 22
14 Table 2.1-1. Soil Texture Classes as a Function of Clay and Sand Content 34
15 Table 2.1-2. Parent Material Classes for Common FAO Soil Types 35
16 Table 2.1-3. Weathering Rate Classes as a Function of Texture and Parent Material
17 Classes 35
18 Table 2.1-4. Data Requirements and Sources for Calculating Critical Loads for Total
19 Nitrogen and Sulfur Deposition in Hubbard Brook Experimental Forest
20 and Kane Experimental Forest 40
21 Table 2.1-5. The Three Indicator (Bc/Al)crit Soil Solution Ratios Used in This Case Study
22 and the Corresponding Levels of Protection to Tree Health and Critical
23 Loads 44
24 Table 2.1-6. Input Values for the Calculation of Critical Load in Hubbard Brook
25 Experimental Forest and Kane Experimental Forest 47
26 Table 2.1-7. Soil Characteristics in the Seven Plots of the Kane Experimental Forest Case
27 Study Area for the Calculation of the Base Cation Weathering Rate
28 Parameters 49
29 Table 2.1-8. Annual Volume Growth by Tree Species in Each of the Seven Plots of the
30 Kane Experimental Forest Case Study Area for the Calculation of Nutrient
31 Uptake (Bcu and Nu) 49
32 Table 2.1-9. Specific Gravity and Nutrient Concentrations by Biomass Component (Bark
33 and Bole Wood) and by Tree Species for the Calculation of Nutrient
34 Uptake (Bcu and Nu) in the Kane Experimental Forest Case Study Area 50
3 5 Table 3.1-1. Critical Loads Calculated with the Different Base Cation Weathering,
36 Gibbsite Equilibrium Constant (^gibb), and Base Cation (Bcu) and Nitrogen
37 (Nu) Uptake Parameter Values in Plot 1 of the Kane Experimental Forest
38 Case Study Area 52
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5 - vi
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Terrestrial Acidification Case Study
1 Table 3.1-2. Critical Loads Calculated with the Different Base Cation Weathering,
2 Gibbsite Equilibrium Constant (K^), and Base Cation (Bcu) and Nitrogen
3 (Nu) Uptake Parameter Values in Plot 2 of the Kane Experimental Forest
4 Case Study Area 53
5 Table 3.1-3. Critical Loads Calculated with the Different Base Cation Weathering,
6 Gibbsite Equilibrium Constant (K^), and Base Cation (Bcu) and Nitrogen
7 (Nu) Uptake Parameter Values in Plot 3 of the Kane Experimental Forest
8 Case Study Area 53
9 Table 3.1-4. Critical Loads Calculated with the Different Base Cation Weathering,
10 Gibbsite Equilibrium Constant (K^), and Base Cation (Bcu) and Nitrogen
11 (Nu) Uptake Parameter Values in Plot 4 of the Kane Experimental Forest
12 Case Study Area 54
13 Table 3.1-5. Critical Loads Calculated with the Different Base Cation Weathering,
14 Gibbsite Equilibrium Constant (^g;bb) and Base Cation (Bcu) and Nitrogen
15 (Nu) Uptake Parameter Values in Plot 5 of the Kane Experimental Forest
16 Case Study Area 54
17 Table 3.1-6. Critical Loads Calculated with the Different Base Cation Weathering,
18 Gibbsite Equilibrium Constant (K^), and Base Cation (Bcu) and Nitrogen
19 (Nu) Uptake Parameter Values in Plot 6 of the Kane Experimental Forest
20 Case Study Area 55
21 Table 3.1-7. Critical Loads Calculated with the Different Base Cation Weathering,
22 Gibbsite Equilibrium Constant (^gibb), and Base Cation (Bcu) and Nitrogen
23 (Nu) Uptake Parameter Values in Plot 7 of the Kane Experimental Forest
24 Case Study Area 55
25 Table 3.1-8. Ranges of Critical Load Values (eq/ha/yr) (with and without the Influence of
26 Nutrient Uptake and Removal with Tree Harvest) for the Seven Plots of
27 the Kane Experimental Forest Case Study Area (Both Kgibb values and
28 methods to estimate BCW were used in these calculations to present the
29 range of critical loads estimated using all combinations of the parameter
30 values.) 56
31 Table 3.1-9. Comparison of the Critical Load Values Determined in This Case Study and
32 the Critical Load Values Determined by McNulty et al. (2007) for the
33 Seven Plots in the Kane Experimental Forest Case Study Area 59
34 Table 3.1-10. Critical Load Calculated with the Different Base Cation Weathering and
35 Gibbsite Equilibrium Constant (^gibb) Parameter Values in the Hubbard
36 Brook Experimental Forest Case Study Area 60
37 Table 3.1-11. Summary of the Critical Load Values Determined by Other Studies
38 Conducted in the Hubbard Brook Experimental Forest (negative values are
39 equal to 0 eq/ha/yr) 62
40 Table 3.2-1. Critical Loads Selected to Represent the Three Levels of Protection in the
41 Kane Experimental Forest and Hubbard Brook Experimental Forest Case
42 Study Areas 64
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5 - vii
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Terrestrial Acidification Case Study
1 Table 3.3-1. Ranges of Differences between the 2002 CMAQ/NADP Total Nitrogen and
2 Sulfur Deposition Levels ((N+S)comb) and the Estimated Critical Load
3 Values (with and without the Influence of Nutrient Uptake and Removal
4 [Nu and Bcu]) for the Seven Plots of the Kane Experimental Forest Case
5 Study Area 65
6 Table 3.3-3. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
7 Deposition Levels ((N+S)COmb) and the Critical Load Values (with Two
8 Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
9 Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
10 Parameter Values) in Plot 2 of the Kane Experimental Forest Case Study
11 Area 66
12 Table 3.3-4. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
13 Deposition Levels ((N+S)comb) and the Critical Load Values (with Two
14 Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
15 Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
16 Parameter Values) in Plot 3 of the Kane Experimental Forest Case Study
17 Area 67
18 Table 3.3-5. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
19 Deposition Levels ((N+S)COmb) and the Critical Load Values (with Two
20 Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
21 Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
22 Parameter Values) in Plot 4 of the Kane Experimental Forest Case Study
23 Area 67
24 Table 3.3-6. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
25 Deposition Levels ((N+S)comb) and the Critical Load Values (with Two
26 Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
27 Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
28 Parameter Values) in Plot 5 of the Kane Experimental Forest Case Study
29 Area 68
30 Table 3.3-7. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
31 Deposition Levels ((N+S)COmb) and the Critical Load Values (with Two
32 Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
33 Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
34 Parameter Values) in Plot 6 of the Kane Experimental Forest Case Study
35 Area 68
36 Table 3.3-8. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
37 Deposition Levels ((N+S)comb) and the Critical Load Values (with Two
38 Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
39 Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
40 Parameter Values) in Plot 7 of the Kane Experimental Forest Case Study
41 Area 69
42 Table 3.3-9. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
43 Deposition Levels ((N+S)COmb) and the Critical Load Values (with Two
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5 - viii
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Terrestrial Acidification Case Study
1 Base Cation Weathering Estimation Methods and Two Gibbsite
2 Equilibrium Constants [^gibb]) in the Hubbard Brook Experimental Forest
3 Case Study Area 69
4 Table 4.1-1. Number and Location of USFS FIA Permanent Sampling Plots Used in the
5 Analysis of Critical Loads For the Full Ranges of Sugar Maple and Red
6 Spruce 77
7 Table 4.1-2. Gibbsite Equilibrium (Kgibb) Determined by Percentage of Soil Organic
8 Matter 80
9 Table 4.1-3. Parent Material Acidity Classifications for Base Cation (BCW) Estimations 80
10 Table 4.1-4. Parent Material and Descriptive Modifier Characteristics (within the
11 SSURGO Soils [USDA-NRCS, 2008c] and USGS Geology [USGS,
12 2009b] Databases) Used to Classify Parent Material Acidity 81
13 Table 4.1-5. Ranges of Critical Load Values by Level of Protection (Bc/Al(crit) = 0.6, 1.2,
14 and 10.0) and by State for the Full Distribution Ranges of Sugar Maple
15 and Red Spruce 84
16 Table 4.1-6. Percentages of Plots, by Protection Level (Bc/Al(crit) = 0.6, 1.2, and 10.0) and
17 by State, where 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
18 Levels Were Greater Than the Critical Loads for Sugar Maple and Red
19 Spruce 86
20 Table 5.1-1. Differences and Percentage Differences in Plot-Level Critical Load
21 Estimates Associated with the Misclassification of Parent Material Acidity
22 for the Full Range Assessment of Sugar Maple 95
23 Table 5.1-2. Differences and Percentage Differences in Plot-Level Critical Load
24 Estimates Associated with the Misclassification of Parent Material Acidity
25 for the Full Range Assessment of Red Spruce 96
26 Table 2-1. Summary of Plot-Level Data for Sugar Maple Volume, Tree Growth and
27 Critical Load Exceedance (4,047 Plots) 3
28 Table 2-2. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical
29 Load Exceedance (613 plots) 4
30 Table 3-la. Results from the Ordinary Least Squares Regression Analyses of the
31 Quadratic Model for Critical Load Exceedance and Sugar Maple Tree
32 Growth (for critical load exceedances based on Bc/Al=10.0 critical loads) 6
33 Table 3-lb. Results from the Ordinary Least Squares Regression Analyses of the
34 Quadratic Model for Critical Load Exceedance and Sugar Maple Tree
35 Growth (for critical load exceedances based on Bc/Al=1.2 critical loads) 7
36 Table 3-lc. Results from the Ordinary Least Squares Regression Analyses of the
37 Quadratic Model for Critical Load Exceedance and Sugar Maple Tree
38 Growth (for critical load exceedances based on Bc/Al=0.6 critical loads) 8
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5 - ix
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Terrestrial Acidification Case Study
1 Table 3-2a. Results from the Ordinary Least Squares Regression Analyses of the
2 Quadratic Model for Critical Load Exceedance and Red Spruce Tree
3 Growth (for critical load exceedances based on Bc/Al=10.0 critical loads) 9
4 Table 3-2b. Results from the Ordinary Least Squares Regression Analyses of the
5 Quadratic Model for Critical Load Exceedance and Red Spruce Tree
6 Growth (for critical load exceedances based on Bc/Al=1.2 critical loads) 9
7 Table 3-2c. Results from the Ordinary Least Squares Regression Analyses of the
8 Quadratic Model for Critical Load Exceedance and Red Spruce Tree
9 Growth (for critical load exceedances based on Bc/Al=0.6 critical loads) 10
10 Table 3-3. Summary of Plot-Level Data for Sugar Maple Volume and Growth and
11 Critical Load Exceedances (for plots with positive critical load exceedance
12 values and based on critical loads calculated with Be/Al = 10.0) 12
13 Table 3-4. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical
14 Load Exceedances (for plots with positive critical load exceedance values
15 and based on critical loads calculated with Be/Al = 10.0) 13
16 Table 3-5. Results from the Multivariate Ordinary Least Squares Linear Regression
17 Analyses of Positive Critical Load Exceedances and Sugar Maple Tree
18 Growth (for critical loads calculated with Be/Al = 10.0) 14
19 Table 3-6. Results from the multivariate Ordinary Least Squares linear regression
20 analyses of positive critical load exceedances and red spruce tree growth
21 (for critical loads calculated with Be/Al = 10.0) 15
22 Table 3-7. Summary of Plot-Level Data for Sugar Maple Volume and Growth and
23 Critical Load Exceedances North of the Glaciation Line (for plots with
24 positive critical load exceedance values and based on critical loads
25 calculated with Be/Al = 10.0) 17
26 Table 3-8. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical
27 Load Exceedances North of the Glaciation Line (for plots with positive
28 critical load exceedance values and based on critical loads calculated with
29 Bc/Al= 10.0) 19
30 Table 3-9. Results from the Multivariate Ordinary Least Squares Linear Regression
31 Analyses of Positive Critical Load Exceedances and Sugar Maple Tree
32 Growth, North of the Glaciation Line (for critical loads calculated with
33 Bc/Al= 10.0) 21
34 Table 3-10. Results from the Multivariate Ordinary Least Squares Linear Regression
35 Analyses of Positive Critical Load Exceedances and Red Spruce Tree
36 Growth, North of the Glaciation Line (for critical loads calculated with
37 Bc/Al= 10.0) 22
38
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5 - x
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Terrestrial Acidification Case Study
ACRONYMS AND ABBREVIATIONS
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
Al
A13+
ANC
ANCle,cnt
Be
(Bc/Al)crit
BC
BCdep
Bcu
BCW
n 2+
Ca
cr
Cldep
CLmax(N)
CLmax(S)
CLmin
CLmm(N)
CLF
CLRTAP
cm
CMAQ
eq/ha/yr
FAO
Fe3+
FGROWCFAL
FIA
ft3
GIS
ha
HBEF
HNO3
ICP
ISA
K+
Kgibb
KEF
kg
km
m
Mg2+
mm
Mn
aluminum2+'3+
trivalent aluminum
acid neutralizing capacity
forest soil acid neutralizing capacity of critical load leaching (calculated
value)
base cation (Ca2+ + K+ + Mg2+)
base cation (Ca2+ + K+ + Mg2+) to aluminum ratio (selected indicator value)
base cation (Ca2+ + K+ + Mg2+ + Na+)
base cation (Ca2+ + K+ + Mg2+ + Na+) deposition
uptake of base cations (Ca2+ + K+ + Mg2+) by trees
base cation (Ca2+ + K+ + Mg2+ + Na+) weathering
calcium
chloride
chloride deposition
maximum critical load of nitrogen
maximum critical load of sulfur
minimum critical load
minimum critical load of nitrogen
critical load function
Convention on Long-Range Transboundary Air Pollution
centimeter
Community Multiscale Air Quality
equivalents per hectare per year
Food and Agriculture Organization
trivalent (ferrous) iron
Net annual sound cubic-foot growth of a live tree on forest land
Forest Inventory and Analysis National Program
cubic feet
geographic information systems
hectare
Hubbard Brook Experimental Forest
nitric acid
International Cooperative Programme
Integrated Science Assessment
potassium
the gibbsite equilibrium constant
Kane Experimental Forest
kilogram
kilometer
meter
magnesium
millimeter
2+4+
manganese
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5 - xi
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Terrestrial Acidification Case Study
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
mol
N
Nde
N,
Nu
(N+S)comb
Na+
NADP
NEG/ECP
NH4+
NHX
NCV
NOX
NOy
NRCS
OLS
S
8MB
S02
SO42"
SOX
SSURGO
STATSGO
UNECE
USDA
USFS
USGS
VOLCFNET
ybp
mole
nitrogen
denitrification
nitrogen immobilization
uptake of nitrogen by trees
combined nitrogen and sulfur deposition
sodium
National Atmospheric Deposition Program
Conference of New England Governors and Eastern Canadian Premiers
ammonium
total reduced nitrogen
nitrate
nitrogen oxides
oxidized nitrogen
Natural Resources Conservation Service
ordinary least squares
sulfur
Simple Mass Balance
sulfur dioxide
sulfate
sulfur oxides
Soil Survey Geographic Database
State Soil Geographic Database
United Nations Economic Commission for Europe
U.S. Department of Agriculture
U.S. Forest Service
U.S. Geological Survey
total net volume per tree
years before present
2nd Draft Risk and Exposure Assessment
Appendix 5 - xii
June 5, 2009
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Terrestrial Acidification Case Study
i 1. BACKGROUND
2 The selection and performance of case studies represent Steps 3 and 4, respectively, of
3 the 7-step approach to planning and implementing a Risk and Exposure Assessment of total
4 nitrogen, nitrogen oxides (NOX) (as a component of total nitrogen), and sulfur oxides (SOX)
5 deposition on ecosystems, as presented in the April 2008 Scope and Methods Plan for Risk
6 Exposure Assessment (U.S. EPA, 2008a). Step 4 entails evaluating the current Community
7 Multiscale Air Quality Model (CMAQ) modeling results for 2002 and the 2002 National
8 Atmospheric Deposition Program (NADP) monitoring data for total nitrogen and sulfur
9 deposition loads on, and effects to, a chosen case study assessment area, including ecosystem
10 services. This case study evaluates the current wet and dry atmospheric nitrogen and sulfur
11 deposition load to terrestrial ecosystems and the role atmospheric deposition can play in the
12 acidification of a terrestrial ecosystem.
13 Deposition of NOX and SOX can result in acidification of certain terrestrial ecosystems.
14 Because ecosystems and species may respond differently, case studies have been used to
15 illustrate potential effects of acidification on sensitive species. This report presents the
16 quantitative approach used to analyze the acidification effects of total nitrogen, NOX (as a
17 component of total nitrogen), and SOX deposition on red spruce and sugar maple.
18 Acidification
19 Acidification is the process of increasing the acidity of a system (e.g., lake, stream, forest
20 soil). Within soils, acidification occurs through increases in hydrogen ions or protons. Terrestrial
21 acidification occurs as a result of both natural biogeochemical processes and acidifying
22 deposition where strong acids are deposited into the soil. Acidifying deposition increases
23 concentrations of nitrogen and sulfur in the soil, which accelerates the leaching of sulfate (SC>42")
24 and nitrate (N(V) from the soil to drainage water. Under natural conditions (i.e., low
25 atmospheric deposition of nitrogen and sulfur), the limited mobility of anions in the soil controls
26 the rate of base cation leaching. However, acidifying deposition of nitrogen and sulfur species
27 can significantly increase the concentration of anions in the soil, leading to an accelerated rate of
28 base cation leaching, particularly the leaching of calcium (Ca2+) and magnesium (Mg2+) cations.
29 If soil base saturation (i.e., the concentration of exchangeable base cations as a percentage of the
30 total cation exchange capacity. Cation exchange capacity, the sum total of exchangeable cations
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-1
-------
Terrestrial Acidification Case Study
1
2
3
4
5
that a soil can absorb, is 20% to 25%, or lower, inorganic aluminum2+'3+ (Al) can become
mobilized, leading to the leaching of Al into soil waters and surface waters (Reuss and Johnson,
1985). This is an important effect of acidifying deposition because inorganic Al is toxic to tree
roots, fish, algae, and aquatic invertebrates (U.S. EPA, 2008c, Sections 3.2.1.5, 3.2.2.1, and
3.2.3).
6 1.1 Indicators, Ecological Endpoints, and Ecosystem Services
7 1.1.1 Indicators
8 The Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur-Ecological
9 Criteria (Final Report) (ISA) (U.S. EPA, 2008c) identified a variety of indicators supported by
10 the literature that can be used to measure the effects of acidification in soils (Table 1.1-1). Table
11 1.1-2 provides a general summary of these indicators by indicator groups.
12 Table 1.1-1. Literature Support for Selected Indicators of Acidification
Citation
Main Finding
Soil Base Saturation
Reuss, 1983
Cronan and Grigal, 1995
Lawrence et al., 2005
Bailey et al., 2004
Johnson et al., 1991; Joslin and Wolfe,
1992
When base saturation less than 15% to 20%, then
exchange ion chemistry is dominated by inorganic
Al.
When base saturation below about 15% in the
soil, B-horizon could lead to impacts from Al
stress.
When base saturation declines from 30% to 20%
in the upper soil, B-horizon showed decreases in
diameter growth of Norway spruce.
Sugar maple mortality found at Ca2+ saturation
less than 2% and Mg2+ saturation less than 0.5%
in the upper soil B-horizon.
When base saturation below about 20%, base
cation reserves are so low that Al exchange
dominates.
Al Concentrations
Johnson et al., 1991; Joslin and Wolfe,
1992
When base saturation below about 20%, base
cation reserves are so low that Al exchange
dominates.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5-2
-------
Terrestrial Acidification Case Study
Citation
Cronan and Grigal, 1995; Eagar et al.,
1996
Johnson et al., 1994a, b
DeWitt et al., 2001
Main Finding
There is a 50% chance of negative effects on tree
growth if the molar ratio of Ca2+/Al in soil
solution is as low as 1.0. There is a 100% chance
for negative effects on growth at molar ratio value
below 0.2.
Ca2+/Al ratios above 1.0 were found in a
forestland experiencing high mortality over the
course of 4 years.
Ca2+/Al ratios of Norway spruce stand below 0.5
showed reduced Mg2+ concentrations in needles in
the third year.
Carbon/Nitrogen Ratio
Aber et al., 2003; Ross et al., 2004
Increased effects of nitrification occur only in soil
with carbon/nitrogen ratio below about 20 to 25.
1 Source: U.S. EPA 2008c, Section 3.2.2.1.
2 Table 1.1-2. Key Indicators of Acidification Due to Nitrogen and Sulfur
Key Indicator Group
Acid anions
Base cations
Acidity
Carbon
Metals
Examples of Indicators
SO42", NO3"
Ca2+, Mg2+, BC (sum of
Ca2+, Mg2+, K+ and Na+),
Bc(sumofCa2+,Mg2+,
and K+)
pH, acid neutralizing
capacity
Carbon/nitrogen ratio
Al3+,Fe3+
Description
Trends in these concentrations reflect
recent trends in atmospheric deposition
(especially SC>42") and in ecosystem
responses to long-term deposition (notably
NO3" and desorbed SO42")
These cations are mobilized by weathering
reactions and cation exchange. They
respond indirectly to decreases in SC>42" and
NOs" because a reduced input of acids will
lead to a reduction of neutralizing
processes in the soil, thereby reducing the
release of base cations to soil water and
runoff water. (Base saturation is included
within this category.)
These indicators reflect the outcomes of
interactions between the changing
concentration of acid anions and base
cations.
The carbon/nitrogen ratio of soil indicates
alterations to the nitrogen biogeochemical
cycle.
These metals are mobilized as a response to
the deposition of SC>42" and N(V.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5-3
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Terrestrial Acidification Case Study
Key Indicator Group
Examples of Indicators
Description
Biological
Tree health, community
structure, species
composition, taxonomic
richness, Index of Biotic
Integrity
Ecological effects occur at four levels:
individual, population, community, and
ecosystem. Metrics have been developed
for each level to assess the negative effects
of acidification.
1 Note: BC = base cation (Caz + K+ + Mgz+ + Na ); Be = base cation (Caz + K+ + Mgz+); K+ =
2 potassium; Na+ = sodium; A13+ = trivalent aluminum ; Fe3+ = trivalent (ferrous) iron
3 Much of the literature discussing terrestrial acidification focuses on Ca2+ and Al as the
4 primary indicators of detrimental effects for trees and other terrestrial vegetation. As such, this
5 discussion of indicators of terrestrial acidification focuses on these two parameters and the
6 interaction between them. The use of these indicators—in combination and through the
7 evaluation framework that will be described within this case study—ultimately combines all
8 indicator categories described in Table 1.1-1 except the carbon category. Ca2+ and Al are the
9 focus of the analysis because both of these indicators are strongly influenced by soil acidification
10 and both have been shown to have quantitative links to tree health, including aluminum's
11 interference with Ca2+ uptake and Al toxicity to roots.
12 A detailed description of the influences of Al on Ca2+ is provided by Schaberg et al.
13 (2001)1:
14 Decreases in concentrations of exchangeable calcium are generally attributed to
15 displacement by hydrogen ions, which can originate from either acidifying
16 deposition or uptake of cations by roots (Johnson et al., 1994a; Richter et al.,
17 1994). A regional survey of soils in northeastern red spruce forests in 1992-93
18 (fig. 2)2 has revealed that decreases in exchangeable calcium concentrations in the
19 Oa horizon (a layer within the forest floor, where uptake of nutrients is greatest)
20 can also result from increased concentrations of exchangeable aluminum, which
21 originated in the underlying mineral soil (Lawrence et al., 1995). By lowering the
22 pH of the aluminum-rich mineral soil, acid deposition can increase aluminum
23 concentrations in soil water through dissolution and ion-exchange processes.
24 Once in solution, the aluminum (although not a nutrient) is taken up by roots and
25 transported through the trees to be eventually deposited on the forest floor in
26 leaves and branches.
27 A continued buildup of aluminum in the Oa horizon can (1) decrease the
28 availability of calcium for roots (Lawrence et al., 1995), (2) lower the efficiency
29 of calcium uptake because aluminum is more readily taken up than calcium when
1 References contained within this quotation are not included in the References section of this case study report.
2 Figure 2 is not included in this case study report.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-4
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Terrestrial Acidification Case Study
1 the ratio of calcium to aluminum in soil water is less than 1 (Cronan and Grigal,
2 1995), and (3) be toxic to roots at high concentrations (Lawrence et al., 1995).
3 The relationship between Ca2+ and Al and tree health is summarized in the ISA (U.S.
4 EPA, 2008c, Section 3.2.2.1), as excerpted below3:
5 Al may be toxic to tree roots. Plants [exposed to] high Al concentration in soil
6 solution often have reduced root growth, which restricts the ability of the plant to
7 take up water and nutrients, especially Ca (Parker et al., 1989) (Figure 3-5 [of
8 U.S. EPA, 2008c]). Ca is well known as an ameliorant for Al toxicity to roots in
9 soil solution, as well as to fish in a stream. However, because inorganic Al tends
10 to be increasingly mobilized as soil Ca is depleted, elevated concentrations of
11 inorganic Al tend to occur with low levels of Ca in surface waters. Mg, and to a
12 lesser extent Na and K, have also been associated with reduced Al toxicity.
13 Dissolved Al concentrations in soil solution at spruce-fir study sites in the
14 southern Appalachian Mountains frequently exceed 50 uM and sometimes exceed
15 100 uM (Eagar et al., 1996; Johnson et al., 1991; Joslin and Wolfe, 1992a). All
16 studies reviewed by Eagar [et al.] (1996) showed a strong correlation between Al
17 concentrations and NOs concentrations in soil solution. They surmised that the
18 occurrence of periodic large pulses of NOs in solution were important in
19 determining Al chemistry in the soils of southern Appalachian Mountain spruce-
20 fir forests.
21 The negative effect of Al mobilization on Ca uptake by tree roots was proposed
22 by Shortle and Smith (1988). Substantial evidence of this relationship has
23 accumulated over the past two decades through field studies (Kobe et al., 2002;
24 McLaughlin and Tjoelker, 1992; Minocha et al., 1997; Schlegel et al., 1992;
25 Shortle et al., 1997) and laboratory studies (see review by Cronan and Grigal,
26 1995; Sverdrup and Warfvinge, 1993). Based on these studies, it is clear that high
27 inorganic Al concentration in soil water can be toxic to plant roots. The toxic
28 response is often related to the concentration of inorganic Al relative to the
29 concentration of Ca, expressed as the molar ratio of Ca to inorganic Al in soil
30 solution. As a result, considerable effort has been focused on determining a
31 threshold value for the ratio of Ca to Al that could be used to identify soil
32 conditions that put trees under physiological stress.
33 Building on the explanation of the relationship between Ca2+, Al, and tree health, a figure
34 developed by DeHayes et al. (1999), clearly shows the connections between nitrogen and sulfur
35 acidifying deposition and Ca2+ within an ecosystem (Figure 1.1-1). The authors used solid lines
36 to denote known connections and dotted lines to present potential impacts. While the authors did
3 References contained within this quotation are not included in the References section of this case study report.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-5
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Terrestrial Acidification Case Study
1 not specify that increases in Al within the soils will occur with reductions in biologically
2 available Ca2+ pools, this impact is expected as detailed in the previous paragraphs. The final
3 process represented in Figure 1.1-1 completes the linkage from the indicator of Ca2+ (and
4 therefore Al) to the effects on the ecosystem services for the terrestrial area.
Acid Rain
T
Soil Calcium
Depletion
Reductions in Biologically
Important Calcium Pools
Disruptions in Stress
Response Systems
Predisposition to Stress-induced Injury
Potential Secondary
Environmental Stresses
Air pollutants
Temperature perturbation
(high, low, variable)
Insects
Pathogens
Drought
Heavy metals
5
6
1
8
9
10
11
12
13
14
Disruptions in Forest Ecosystem Health/Stability
2+
Figure 1.1-1. Conceptual impacts of acidifying deposition on soil Ca depletion,
tree physiology, and forest ecosystem health and sustainability (recreated from
DeHayes et al., 1999).
2+
In summary, based on the ISA (U.S. EPA, 2008c) and supporting literature, soil Ca and
Al are suitable chemical indicators to represent the impacts of acidification in soils and to
provide a linkage between soil acidification and tree health. Therefore, the Ca/Al ratio in soil
solution was selected as the basis for the indicator in this case study to evaluate critical loads of
acidity in terrestrial systems. Within the calculations of critical loads, the base cation (Be) to Al
ratio (Bc/Al) was used to represent the Ca/Al indicator (described further in Section 2.1). The Be
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5-6
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Terrestrial Acidification Case Study
,2+
2 variable consists of Ca , Mg , and potassium (K ),
,2+
4 withCa often representing a large proportion of Be.
.2+
6 In addition, as stated earlier, Mg and K are also
Indicator: The Bc/AI ratio in the soils
solution was selected as the indicator to
estimate critical loads of acidity in this
case study.
7 impacted by terrestrial acidification and are associated with reduced Al toxicity. The Bc/AI ratio
8 is, therefore, a good surrogate for the Ca2+/Al indicator and is the most commonly used indicator
9 or critical ratio (Bc/Al(crit)) in estimations of acid load (McNulty et al., 2007; Ouimet et al., 2006;
10 UNECE, 2004).
11 1.1.2 Ecological Endpoints
12 The tree species most commonly associated with the impacts of acidification due to
13 atmospheric nitrogen and sulfur deposition include red spruce (Picea rubens), a coniferous tree
14 species, and sugar maple (Acer sacchamm), a deciduous tree species. Both species are found in
15 the eastern United States, and soil acidification is widespread throughout this area (Warby et al.,
16 2009).
17 Red spruce is found scattered throughout high-elevation sites in the Appalachian
18 Mountains, including the southern peaks (Figure 1.1-2). Noticeable levels of the canopy red
19 spruce died within the Adirondack, Green, and White mountains in the 1970s and 1980s.
20 Acidifying deposition has been implicated in this decline due to links between tree stress from Al
21 toxicity and increased freezing injury (DeHayes et al., 1999). Within the southeastern United
22 States, periods of red spruce growth decline slowed after the 1980s, when a corresponding
23 decrease in sulfur dioxide (802) emissions was recorded in the United States (Webster et al.,
24 2004). The Ca2+/Al ratios in forest floor soil are also important to the overall health of red spruce
25 trees in the Northeast. Red spruce has been shown to have an increased instance of foliar winter
26 injury and bud mortality due to imbalanced Al and Ca2+ levels in soils at locations in Vermont
27 and surrounding states. A decrease in cold and winter weather tolerance leads to an increase in
28 freezing injuries to red spruce, placing the species at a greater chance of declining overall forest
29 health. Soil nutrient imbalances and deficiencies can reduce the ability of a tree to respond to
30 stresses, such as insect defoliation, drought, and cold weather damage (DeHayes et al., 1999;
31 Driscoll et al., 2001). Based on the research conducted to date, important factors related to the
32 high mortality rates and decreased growth trends of red spruce include depletion of base cations
33 in upper soil horizons by acidifying deposition, Al toxicity to tree roots, and accelerated leaching
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-7
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Terrestrial Acidification Case Study
1 of base cations from foliage as a consequence of acidifying deposition (U.S. EPA, 2008c,
2 Section 3.2.2.3). Additional linkages between acidifying deposition and red spruce physiological
3 responses are indicated in Table 1.1-3.
4 Sugar maple is found throughout the northeastern United States and the central
5 Appalachian Mountain region (Figure 1.1-2). This species has been declining in the eastern
6 United States since the 1950s. Studies on sugar maple have found that this decline in growth is
7 related to both acidifying deposition and base-poor soils on geologies dominated by sandstone or
8 other base-poor substrates (Bailey et al., 2004; Horsley et al., 2000). These site conditions are
9 representative of the conditions expected to be most susceptible to impacts of acidifying
10 deposition because of probable low initial base cation pools and high base cation leaching losses
11 (U.S. EPA, 2008c, Section 3.2.2.3). The probability of a decrease in crown vigor or an increase
12 in tree mortality has been noted to increase at sites with low Ca2+ and Mg2+ as a result of
13 leaching caused by acidifying deposition (Drohan and Sharpe, 1997). Low levels of Ca2+ in
14 leaves and soils have been shown to be related to lower rates of photosynthesis and higher
15 antioxidant enzyme activity in sugar maple stands in Pennsylvania (St. Clair et al., 2005).
16 Additionally, plots of sugar maples in decline were found to have Ca2+/Al ratios less than 1, as
17 well as lower base cation concentrations and pH values compared to plots of healthy sugar
18 maples (Drohan et al., 2002). These indicators have all been shown to be related to the
19 deposition of atmospheric nitrogen and sulfur. Additional linkages between acidifying deposition
20 and sugar maple physiological responses are indicated in Table 1.1-3.
21 Table 1.1-3. Summary of Linkages between Acidifying Deposition, Biogeochemical Processes
22 That Affect Ca2+, Physiological Processes That Are Influenced by Ca2+, and Effect on Forest
23 Function
Biogeochemical Response to
Acidifying Deposition
Leach Ca2+ from leaf
membrane
Reduce the ratio of Ca2+/Al in
soil and soil solutions
Reduce the ratio of Ca2+/Al in
soil and soil solutions
Physiological Response
Decrease the cold tolerance
of needles in red spruce
Dysfunction in fine roots of
red spruce blocks uptake of
^ 2+
Ca
More energy is used to
acquire Ca2+ in soils with low
Ca2+/Al ratios
Effect on Forest Function
Loss of current-year needles
in red spruce
Decreased growth and
increased susceptibility to
stress in red spruce
Decreased growth and
increased photosynthetic
allocation to red spruce roots
2nd Draft Risk and Exposure Assessment
Appendix 5-8
June 5, 2009
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Terrestrial Acidification Case Study
1
2
3
4
6
8
10
12
13
14
15
Biogeochemical Response to
Acidifying Deposition
Reduce the availability of
nutrient cations in marginal
soils
Physiological Response
Sugar maples on drought-
prone or nutrient-poor soils
are less able to withstand
stresses
Effect on Forest Function
Episodic dieback and growth
impairment in sugar maple
Source: Fenn et al., 2006a.
In summary, the acidification of soils negatively impacts the health, growth, and vigor of
red spruce and sugar maple. Mortality and susceptibility to disease and injury can be increased,
and growth can be decreased with acidifying deposition. Therefore, the health of sugar maple
and red spruce was used as the biological endpoint in this case study to evaluate critical loads of
acidity in terrestrial systems. Estimation of site-specific
critical loads offers a means by which to determine a
cause of reduced tree health and growth.
Endpoint: The health of sugar maple
and red spruce was selected as the
biological endpoint to estimate critical
loads of acidity in this case study.
States
^] Red Spruce Range
| Sugar Maple Range
Source:USFS Forest Inventory and Analysis
Program (2006)
Figure 1.1-2. Areal coverages of red spruce and sugar maple tree species
within the continental United States (USFS, 2006).
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5-9
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Terrestrial Acidification Case Study
1 1.1.3 Ecosystem Services
2 Ecosystem services are generally defined as the benefits individuals and organizations
3 obtain from ecosystems. In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem
4 services are classified into four main categories:
5 • Provisioning—includes products obtained from ecosystems
6 • Regulating—includes benefits obtained from the regulation of ecosystem processes
7 • Cultural—includes the nonmaterial benefits people obtain from ecosystems through
8 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic
9 experiences
10 • Supporting—includes those services necessary for the production of all other ecosystem
11 services (MEA, 2005).
12 A number of impacts on the ecological endpoints of forest health, water quality, and
13 habitat exist, including the following:
14 • Decline in forest aesthetics—cultural
15 • Decline in forest productivity—provisioning
16 • Increases in forest soil erosion and reductions in water retention—cultural and regulating.
17 Recognizing that many ecosystem services have not been adequately studied, the
18 ecosystem services highlighted in this case study will include economic values associated with
19 red spruce and sugar maple wood volume production.
20 1.2 Case Study Areas
21 The selection of case study areas to evaluate terrestrial acidification was based on
22 geographic information systems (GIS) mapping (locations recommended by the ISA [U.S. EPA,
23 2008c; Sections 3.2, 4.2, and Annex B]), and the availability of data for the selected indicators
24 and ecological endpoints, as presented in relevant literature and databases.
25 1.2.1 GIS Analysis of National Sensitivity
26 A GIS analysis was performed on datasets and datalayers of physical, chemical, and
27 biological properties to map areas of potential sensitivity to acidification in the United States
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-10
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Terrestrial Acidification Case Study
1 (Table 1.2-1). Ranges of sugar maple and red spruce were mapped by extracting counties with
2 plots that contained either sugar maple or red spruce from the U.S. Forest Service (USFS) Forest
3 Inventory and Analysis (FIA) database (http://fia.fs.fed.us/tools-data/). To characterize soil
4 acidity, soil pH was mapped with State Soil Geographic Database (STATSGO) soils
5 (http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov) and USFS Forest Soils
6 datalayers. Soil thickness was also extracted from the STATSGO soils data. Areas with bedrock
7 with high acid neutralizing capacity (ANC) were determined by using the karst topography
8 dataset from the National Atlas of the United States (Tobin and Weary, 2005). Karst topography
9 is a landscape formed by the dissolution of soluble rock (e.g., limestone, dolomite); caves,
10 springs, and sinkholes are common features of this type of landscape (USGS, 2009a). Locations
11 with sugar maple or red spruce, soil pH <5.0, soils <51 centimeters (cm) in depth, and low ANC
12 bedrock (not dominated by carbonate rocks) were selected to represent areas with potential
13 sensitivity to acidification (Figure 1.2-1).
14
15
Table 1.2-1. Summary of Mapping Layers, Selected Indicator, and Selected Ecological Endpoint
for the Terrestrial Acidification Case Study
Targeted
Ecosystem
Effect
Terrestrial
acidification
due to
nitrogen and
sulfur
Selected
Indicator
Ca2+/Al
ratio
Selected
Biological
Endpoint
Reduced
health of
red spruce
and sugar
maple
Mapping Layers
Sugar maple and red spruce coverages (USFS, 2006)
Soil pH (USD A, 1994; USFS, 2008, Dr. Charles
Perry, personal communication)
Soil depth (USD A, 1994)
Karst topography (Tobin and Weary, 2005)
16
17
The Bc/Al (Be = Ca +, Mg +, and K+) is used to represent the Ca +/A1 ratio indicator in the acid
load calculations (described further at the end of Section 1.1.1).
2nd Draft Risk and Exposure Assessment
Appendix 5-11
June 5, 2009
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Terrestrial Acidification Case Study
\ States
Potentially Sensitive to Terrestrial Acidification
1,000
I km
2 Figure 1.2-1. Map of areas of potential sensitivity of red spruce and sugar maple to
3 acidification in the United States (see Table 1.2-1 for listing of data sources to produce
4 this map).
5 1.2.2 Selection of Case Study Areas
6 Following the identification of regions of potential sensitivity to acidification, Risk and
7 Exposure Assessment sites recommended by the Science Advisory Board—Ecological Effects
8 Subcommittee (U.S. EPA, 2005) and found in the ISA (U.S. EPA, 2008c, Appendix A) and in
9 the body of published and unpublished literature were reviewed to determine the most suitable
10 locations for the Hubbard Brook Experimental Forest (FffiEF) and Kane Experimental Forest
11 (KEF) case study areas.
12 Selection of a location for studying the sugar maple focused on the Allegheny Plateau
13 region in Pennsylvania, where a large proportion of published and unpublished research has been
14 focused. A significant amount of the research work in the Allegheny Plateau region has been
15 sponsored by the USFS and has produced extensive datasets of soil and tree characteristics
2nd Draft Risk and Exposure Assessment
Appendix 5-12
June 5, 2009
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Terrestrial Acidification Case Study
1 (Horsley et al., 2000; Bailey et al., 2004; Hallett et al., 2006). The USFS-designated KEF was
2 selected as the area for studying the sugar maple as part of the Terrestrial Acidification Case
3 Study. The KEF has been the focus of several long-term studies since the 1930s.
4 Selection of a case study area for studying the red spruce involved the review of a variety
5 of regions. Four studies that examined the relationship between the Ca2+/Al soil solution ratio
6 and tree health were identified, and relevant soil and tree information for each of the study
7 regions was compiled (Table 1.2-2). A review of this information led to the selection of the
8 FffiEF in New Hampshire's White Mountains as the area for the study of red spruce in the
9 Terrestrial Acidification Case Study. The HBEF was also recommended by the ISA (U.S. EPA,
10 2008c, Appendix A) as a good location for the Risk and Exposure Assessment. This forest has
11 experienced high atmospheric nitrogen and sulfur deposition levels and low Ca2+/Al soil solution
12 ratios. It has been the subject of extensive nutrient investigations and has provided a large dataset
13 from which to work on the case study.
14
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-13
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Terrestrial Acidification Case Study
1 Table 1.2-2. Compilation of Potential Areas for the Terrestrial Acidification Case Study (i.e., for Studying Red Spruce) as Identified
2 in the Literature
Site Name
Balsam High
Top,NC
Clingman's
Dome, TN
Double Spring
Gap, TN
Mount LeConte,
TN
Mount Sterling,
TN
Richland Balsam
Mountain, NC
Spruce Mountain,
NC
Sleepers River,
VT
Groton, VT
Elevation
(m)
1,641
2,020
1,678
2,010
1,772
1,941
1,695
NA
520
Size of Tree
Population
Spruce-fir forest6
Spruce-fir forest6
Spruce-fir forest6
Spruce-fir forest6
Spruce-fir forest6
Spruce-fir forest6
Spruce-fir forest6
Red spruce
dominated with low
exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Availability of
Field Data
University study
University study
University study
University study
University study
University study
University study
Not selected in
studies
USFS study
location
Ecological
Importance
Great Smoky
Mountains
National Park
Great Smoky
Mountains
National Park
Great Smoky
Mountains
National Park
Great Smoky
Mountains
National Park
Great Smoky
Mountains
National Park
Blue Ridge
Parkway
Great Smoky
Mountains
National Park
Reported Impacts
Nearly 100% chance of
negative forest health
effects
Nearly 100% chance of
negative forest health
effects
Nearly 100% chance of
negative forest health
effects
75% chance of a negative
forest health effects
Nearly 100% chance of
negative forest health
effects
Nearly 100% chance of
negative forest health
effects
Nearly 100% chance of
negative forest health
effects
Site did not contain
sufficient number of
healthy, mature red
spruce for study
No specific references at
this time, but
disturbances are known
to have occurred
Ca2+:Al and
Al/Ca2+
Ratios
0.094a
0.084a
0.053a
0.567a
0.07a
0.07a
0.128a
NA
0.3b
Deposition
Load
(kg/ha/yr)
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
5.3C
Source(s)
Bintz and
Butcher, 2007
Bintz and
Butcher, 2007
Bintz and
Butcher, 2007
Bintz and
Butcher, 2007
Bintz and
Butcher, 2007
Bintz and
Butcher, 2007
Bintz and
Butcher, 2007
Shortle et al.,
1997
Shortle et al.,
1997;Wargoet
al., 2003
2nd Draft Risk and Exposure Assessment
Appendix 5-14
June 5, 2009
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Terrestrial Acidification Case Study
Site Name
Rowland, ME
Bartlett, NH
Kossuth, ME
Hubbard Brook,
NH
Whiteface
Mountain, NY
Crawford Notch,
NH
Elevation
(m)
60
525
100
755
950
670
Size of Tree
Population
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Availability of
Field Data
USFS study
location
USFS
Experimental
Forest (1,052 ha);
red spruce covers
highest slopes
USFS study
location
USFS
Experimental
Forest (3, 138 ha);
red spruce
abundant at
higher elevations
and on rock
outcrops
USFS study
location
USFS study
location
Ecological
Importance
Within the White
Mountains
Within the White
Mountains
Within the White
Mountains
Reported Impacts
No specific references at
this time, but
disturbances are known
to have occurred
No specific references at
this time, but
disturbances are known
to have occurred
No specific references at
this time, but
disturbances are known
to have occurred
Acid-extractable Al in the
forest floor increased
over the past two decades
at the HBEF, and ratios
of Al to Ca2+ in mineral
soil solutions (but not
forest floor solutions)
were strongly correlated
with exchangeable Al
content in the forest floor.
Contained neither
evidence of unusual
mortality or current tree
decline; winter injury
events reported (Lazarus
et al, 2004)
50% chance of negative
forest health effects;
mortality of red spruce
was significant, but most
of the remaining trees
were in good to fair
health
Ca2+:Al and
Al/Ca2+
Ratios
0.4b
0.8b
0.8b
0.8b
0.8b
l.lb
Deposition
Load
(kg/ha/yr)
3.1C
4.9C
2.8C
6.0C
7.9C
5.5C
Source(s)
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997;Wargoet
al. 2003
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997;Wargoet
al., 2003
2nd Draft Risk and Exposure Assessment
Appendix 5-15
June 5, 2009
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Terrestrial Acidification Case Study
Site Name
Big Moose Lake,
NY
Bear Brook, ME
Cone Pond, NH
Mt. Abraham,
VT
Mt. Ascutney,
VT
Elevation
(m)
550
400
610
NA
762
Size of Tree
Population
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
gradient of forest
floor exchangeable
Al/Ca2+ ratios
Red spruce
dominated with a
high exchangeable
Al/Ca2+ ratios
Series of high
elevation spruce-fir
forest nitrogen
addition plots f
Availability of
Field Data
USFS study
location
USFS study
location
USFS study
location
Not selected in
studies
USFS study
location
Ecological
Importance
Within the White
Mountains
Within the Green
Mountains
Nitrogen additions
to system
Reported Impacts
50% chance of negative
forest health effects
75% chance of negative
forest health effects
Nearly 100% chance of
negative forest health
effects
Site did not contain
sufficient number of
healthy, mature red
spruce for study; forest
floor solution Al/ Ca2+
ratio above the 50%
chance level
Reduction in live basal
area on the high nitrogen
addition plots versus
control plots
Ca2+:Al and
Al/Ca2+
Ratios
1.2b
1.9b
5.2b
7.1b
NA
Deposition
Load
(kg/ha/yr)
6.4C
3.8C
5.4C
NA
Additions'1
Source(s)
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997;Wargoet
al., 2003
Shortle et al.,
1997
McNulty et al.,
2005
1 a Molar Ca2+/Al ratio (Bintz and Butcher, 2007).
2 b Oa horizon Al/Ca2+ratios (Wargoetal., 2003).
3 ° Estimated wet nitrogen deposition (Lilleskov et al., 2008).
4 d In addition to ambient total nitrogen deposition, paired plots each received 15.7 kilograms (kg) N/ha/yr (low nitrogen addition), 31.4 kg N/ha/yr (high nitrogen
5 addition) or no nitrogen addition (control) from 1988 to 2002.
6 e High elevation sites in the Southern Appalachians—The sites are located in the Great Smoky Mountains National Park and Richland Balsam Mountain on the
7 Blue Ridge Parkway. Sites were selected because of the presence of a spruce-fir forest with a northwest slope aspect within 10 km of a trailhead at elevations
8 between 1,650 and 2,025 meters (m).
9 f Red spruce grew in large patches (>1 ha) at elevations above 725 m. Red spruce comprised > 80% of the total basal area in all plots; the remainder of the other
10 tree species were divided among balsam fir, red maple, mountain maple, and birch.
11 NA= Not available
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5-16
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Terrestrial Acidification Case Study
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
1.2.3 Sugar Maple
1.2.3.1 Kane Experimental Forest
The KEF (USFS, 1999, 2008b) is located on the eastern edge of the Allegheny National
Forest, 5.6 kilometers (km) south of Kane, PA (Figure 1.2-2). It is comprised of 703 hectare (ha)
of forestland and ranges in elevation from about 550 to 640 meters (m) above sea level, primarily
on flat to gently sloping land. The climate of the KEF is humid temperate; the average annual
temperature is 6°C. The forest receives approximately 110 cm of precipitation per year, mostly
as rain, including 10 cm/month during the growing season.
X
rCHAfJTA
CL
BO
CATTARAVGUS
LV
:ON
NEW [YORK
Figure 1.2-2. Location of the Kane Experimental Forest (Horsley et al., 2000).
The forest soils on the Allegheny Plateau are derived from shales and sandstones. In
general, these soils are very stony and exist as extremely stony loams and sandy loams. They are
strongly acidic. The major soil series are the well-drained Hazelton series, the moderately well-
drained to somewhat poorly drained Cookport series, and the somewhat poorly drained Cavode
series.
The forest stands on the KEF are typical of the Allegheny Plateau. They resulted from a
series of cuttings made in the original hemlock-beech-maple stands starting as early as the mid-
1880s. Currently, the KEF contains second-growth stands ranging from 60 to about 100 years of
age, several third-rotation stands 20 or 40 years old, and one tract with remnant old growth. Most
stands are even-aged, with black cherry, maples, and beech being the main overstory species.
2nd Draft Risk and Exposure Assessment
Appendix 5-17
June 5, 2009
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Terrestrial Acidification Case Study
1 The KEF was formally established in 1932, although research there began as early as
2 1927 or 1928. The forest's primary mission has been forest management research, and the
3 current research focus is centered on three topics: regeneration and forest renewal stand
4 dynamics, silviculture, and sugar maple decline. Table 1.2-3 summarizes major studies at the
5 KEF related to the sugar maple and chemical criterion that can be used in calculating critical
6 loads of atmospheric nitrogen and sulfur deposition.
7 Table 1.2-3. Major Studies at the Kane Experimental Forest
Authors
Year
Title
Key Finding
Horsley.et al.
2000
Factors
Associated with
the Decline-
Disease of Sugar
Maple on the
Allegheny
Plateau
The most important factors determining sugar
maple health were foliar levels of Mg2+ and Mn
and defoliation history. The decline disease of
sugar maple appears to be the result of an
interaction between Mg2+ (and perhaps Mn)
nutrition and stress caused by defoliation.
Bailey et al.
2005
Thirty Years of
Change in Forest
Soils of the
Allegheny
Plateau,
Pennsylvania
Between 1967 and 1997, there were significant
decreases in exchangeable Ca2+ and Mg2+
concentrations and pH at all soil depths.
Exchangeable Al concentrations increased at all
depths at all sites; however, increases were only
significant in upper soil horizons. At most of the
sites, losses of Ca2+ and Mg2+ on a pool basis
were much larger than could be accounted for in
biomass accumulation, suggesting the leaching
of nutrients off site.
8
9
10
1 1
12
13
14
15
16
17
Note: Mn = manganese.
1. 2. 3. 2 Plot Selection for Kane Experimental Forest Case Study Area
Plots were selected for the KEF Case Study Area based on the location of permanent
sampling plots and the presence of sugar maple trees. Permanent sampling plots are 0. 1 acres
(0.04 ha) in size and are evenly distributed and spaced (200 m east-west between plots)
throughout the forest. Only plots that were not located within an active research study and
contained a basal area of at least 20% sugar maple were selected (Table 1.2-4). A total of seven
plots (0.28 ha total) were identified based on these criteria and were used for the KEF Case
Study Area (Figure 1.2-3). Site, stand, and soil characteristics of these seven plots are presented
in Table 1.2-4
2nd Draft Risk and Exposure Assessment
Appendix 5-18
June 5, 2009
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Terrestrial Acidification Case Study
Kane Experimental Forest
3 4
Legend
• Case Study Plots (0.1 acre)
NLCD Classifications
Developed, Open Space
^B Deciduous Forest
^H Evergreen Forest
~^\ Mixed Forest
I | Scrub/Shrub
^] Grassland/Herbaceous
~^\ Pasture/Hay
1
2
3
New York
Ohio
Pennsylvania
West Virginia
West Virginia
Maryland
Legend
I I Kane Forest Location
Figure 1.2-3. The seven plots used to evaluate critical loads of acidity in the Kane
Experimental Forest.
2nd Draft Risk and Exposure Assessment
Appendix 5-19
June 5, 2009
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Terrestrial Acidification Case Study
1 Table 1.2-4. Characteristics of the Case Study Plots in the Kane Experimental Forest
Plot
Number
1
2
O
4
5
6
7
KEF Plot
ID
2,920
2,150
950
850
760
650
560
Location
78°47'35"W
4F35'41"N
78°46'33"W
4F36'5"N
78°44'50"W
4F35'50"N
78°44'40"W
41°35'51"N
78°44'33"W
41°36'01"N
78°44'24"W
41°35'56"N
78°44'16"W
41°35'59"N
Elevation
(m)
583.9
616.5
530.2
548.9
589.0
530.6
527.7
Stand
Basal
Area
(m2/ha)
62.8
44.8
28.8
22.4
24.0
30.7
23.63
Representation
of Sugar
Maple in
Stand (% of
Basal Area)
32%
23%
36%
44%
28%
35%
33%
Soil Type
(Soil
Series)
CpB
(Cockport)
HaC
(Harleton)
BxD
(Buchanan)
HaF
(Harleton)
CpD
(Cockport)
HaF
(Harleton)
HaF
(Harleton)
2 Note: W = west; N = north.
3 1.2.4 Red Spruce
4 1.2.4.1 Hubbard Brook Experimental Forest
5 The HBEF is located in the southern part of the White Mountain National Forest in
6 Grafton County, central New Hampshire (Figure 1.2-4). The experimental forest consists of an
7 oblong basin approximately about 8-km long by 5-km wide, and covers 3,138 ha. Hubbard
8 Brook is the single major stream draining the basin. Elevations within the HBEF range from 222
9 to 10,015 m. The climate of HBEF is predominantly continental, with a January temperature
10 average of -9°C and an average July temperature of 18°C. Annual precipitation at the HBEF
11 averages about 1,400 millimeters (mm), with one-third to one-quarter as snow.
2nd Draft JAisk and Exposure Assessment
Appendix 5-20
June 5, 2009
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Terrestrial Acidification Case Study
Franklin
x Cumberland
O
i County
State
| White Mountain National Fore
Hubbard Brook Experimental
Forest Location
2 Figure 1.2-4. Location of the Hubbard Brook Experimental Forest.
3 Soils at the HBEF are predominantly well-drained Spodosols (Typic Haplorthods)
4 derived from glacial till, with sandy loam textures. Principal soil series are the sandy loams of
5 the Berkshire series, along with the Skerry, Becket, and Lyman series. These soils are acidic (i.e.,
6 pH about 4.5 or less) and relatively infertile (i.e., base saturation of mineral soil ~ 10%).
7 Although highly variable, soil depths, including unweathered till, average about 2.0 m from
8 surface to bedrock.
9 The HBEF is entirely forested, mainly with deciduous northern hardwoods. Red spruce is
10 abundant at higher elevations and on rock outcrops. Logging in the area began in the late 1880s
11 and ended around 1917. The present second-growth forest is even-aged and composed of about
12 80% to 90% hardwoods and 10% to 20% conifers.
13 The FffiEF was established in 1955 as a major center for hydrologic research in New
14 England, and in 1963, the Hubbard Brook Ecosystem Study was founded. In 1988, the HBEF
2nd Draft Risk and Exposure Assessment
Appendix 5-21
June 5, 2009
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Terrestrial Acidification Case Study
1 was designated as a Long-Term Ecological Research site. Research at the HBEF has been in
2 progress for more than 50 years and has focused on hydrometeorological monitoring,
3 biogeochemical nutrient cycling, and stand dynamics. Table 1.2-5 summarizes major studies that
4 were related to red spruce and calculated critical loads of nitrogen and sulfur at the HBEF
5 (HBES, 2008b; Pardo and Driscoll, 1996; USFS, 2008a).
6 Table 1.2-5. Major Studies at the Hubbard Brook Experimental Forest
Authors
Year
Title
Key Finding
Driscoll et al.
1989
Changes in the chemistry of
surface waters: 25-year
results at the HBEF
A decline in the sum of base cations
in surface water paralleled the
sulfate decline in atmospheric
deposition, preventing any long-
term decrease in stream acidity.
There have been no significant long-
term trends in precipitation inputs or
stream outflow of N(V.
Pardo and
Driscoll
1996
Critical loads for nitrogen
deposition: case studies at
two northern hardwood
forests
Critical loads for nitrogen
deposition with respect to acidity
ranged from 0 to 630 eq/ha/yr;
critical loads with respect to effects
of elevated nitrogen (eutrophication
and nutrient imbalances) ranged
from 0 to 1,450 eq/ha/yr.
Palmer et al.
2004
Long-term trends in soil
solution and stream water
chemistry at the HBEF;
relationship with landscape
position
Significant declines in strong acid
anion concentrations were
accompanied by declines in base
cation concentrations in soil
solutions draining the Oa and Bs
soil horizons at all elevations.
Persistently low Ca2+/Al; ratios (<1)
in Bs-horizon soil solutions at these
sites may be evidence of continuing
Al stress to trees.
Siccama et al.
2007
Population and biomass
dynamics of trees in a
northern hardwood forest at
HBEF
Tree data from 1991 to 2001,
including total aboveground
biomass, in-growth of >10 cm DBH
trees, mortality, biomass by type,
aboveground net primary
productivity, and net ecosystem
productivity.
7 Note: eq/ha/yr = equivalents per hectare per year; DBH = diameter at breast height.
2nd Draft Risk and Exposure Assessment
Appendix 5-22
June 5, 2009
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Terrestrial Acidification Case Study
1 1.2.4.2 Plot Selection for Hubbard Brook Experimental Forest Case Study Area
2 Selection of plots for the HBEF Case Study Area was restricted to Watershed 6 (Figure
3 1.2-5). This watershed is 13.2 ha and is maintained as the biogeochemical control watershed for
4 research studies. It consists of typical northern hardwood species (e.g., sugar maple, beech,
5 yellow birch) on the lower 90% of its area and by a montane boreal transition forest of red
6 spruce, balsam fir, and white birch (e.g., spruce-fir forest type) on the highest 10% of its area.
7 The watershed is divided into 208 25x25-m2 grid cells. This grid system and the 2002 Forest
8 Inventory for the watershed were used to identify the nine grid units (units 9, 14, 15, 21 to 24,
9 32, and 33) within the northeast portion of the watershed that contain large portions of red spruce
10 trees (Figure 1.2-6). These nine grid cells were combined into a 0.56-ha plot for the HBEF Case
11 Study Area (Figure 1.2-7). This case study plot is located at 43°57'N, 71°44'W and is 762.0
12 to769.3 m in elevation. Soils within the plot are from the Tunbridge-Lyman soil association and
13 consist of Tunbridge and Lyman soil series with smaller inclusions of Marlowe and Peru soils.
14 Red spruce accounts for 18.8% of the total basal area (131.3 m2/ha) in the plot area.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-23
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Terrestrial Acidification Case Study
Hubbard Brook Experimental Forest
Legend
I | Watershed Boundaries
NLCD Classifications
I I Developed, Open Space
H Deciduous Forest
j^B Evergreen Forest
] Mixed Forest
I | Scrub/Shrub
I I Pasture/Hay
^B Cultivated Crops
] Woody Wetlands
1
2
3
New
York
New
Hampshire
Maine
Massachusetts
Legend
I 1 Hubbard Brook
' ' Forest Location
Figure 1.2-5. Vegetation cover (NLCD, 2001) and location of Watershed 6 of Hubbard
Brook Experimental Forest.
2nd Draft Risk and Exposure Assessment
Appendix 5-24
June 5, 2009
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Terrestrial Acidification Case Study
1
2
3
l qs I 3V ra I i»
. __;-:„ tg -i
I 01 wr I Wf Iff
-i-di-A,. it -)
}:
- ,t _i_ i! _ , -
..•:.J±i
l;fcl*l*]i
1ML"L" l'i-
- •— * • =• »
JJT«uLT^p
,:. , ._
u, |.|
(' K "' ^ 'j
S»-ft-9
«* I
-I
•T«utt*
-|l)-k- i-ii-
Jrt I n
,-k_
!» ' •
I
M X
' r
!,i
Figure 1.2-6. Grid units within Watershed 6 of Hubbard Brook Experimental Forest. The
red outline delineates the spruce-fir forest type. The dotted grid cell areas indicate the
grid units with high proportions of red spruce and represents the composite plot area for
the Hubbard Brook Experimental Forest Case Study Area.
2nd Draft Risk and Exposure Assessment
Appendix 5-25
June 5, 2009
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Terrestrial Acidification Case Study
Legend
l~l Watershed Boundaries
11' Case Study Location
Land Cover Classifications
I I Developed. Open Space
^H Deciduous Forest
•• Evergreen Forest
I I Mixed Foresl
I I Scrub/Shrub
1 I Pasture/Hay
•• Cultivated Crops
I Woody Wetlands
1
2 Figure 1.2-7. Location of case study plots within Watershed 6 of Hubbard Brook
3 Experimental Forest.
4 2. APPROACH AND METHODS
5 The ISA (U.S. EPA, 2008c, Section 3.1.1) identified critical load assessments as a
6 suitable approach to evaluate the potential impacts of anthropogenic pollution on biological end
7 points and ecosystem impairment. A critical load is "a quantitative estimate of ecosystem
8 exposure to one or more pollutants below which significant harmful effects on specified sensitive
9 elements of the environment do not occur, according to present knowledge" (McNulty et al.,
10 2007). Critical loads of acidity from atmospheric nitrogen and sulfur deposition for an ecosystem
11 have been specifically defined as "the highest deposition of acidifying compounds that will not
12 cause chemical changes leading to long-term harmful effects on ecosystem structure and
13 function" (Nilsson and Grennfelt, 1988). "The basic idea of the critical load concept is to balance
14 the depositions that an ecosystem is exposed to with the capacity of this ecosystem to buffer the
2nd Draft Risk and Exposure Assessment
Appendix 5-26
June 5, 2009
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Terrestrial Acidification Case Study
1 input (e.g., the acidity input buffered by the weathering rate), or to remove it from the system
2 (e.g., nitrogen by harvest) without harmful effects within or outside the system" (UNECE, 2004).
3 European countries have been using critical load assessments for many years to
4 determine the impacts of atmospheric nitrogen and sulfur deposition in forest ecosystems. These
5 studies have served as the platform for informing policy related to the control and reduction of
6 emissions of acidifying pollutants. The International Cooperative Programme (ICP) on
7 Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends
8 has published a series of manuals (the most recent in 2004) to provide guidance on calculating
9 and mapping critical loads. These manuals helped parties to the United Nations Economic
10 Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution
11 (CLRTAP) meet their obligations and conduct effects and risk assessments (UNECE, 2004).
12 Canada has also completed critical load evaluations in support of efforts to design emission-
13 reduction programs (Jeffries and Lam, 1993; RMCC, 1990). Critical load modeling was included
14 in the 7997 Canadian Acid Rain Assessment (Jeffries, 1997) for several regions in eastern
15 Canada.
16 The establishment and analysis of critical loads within the United States is relatively new.
17 The Conference of New England Governors and Eastern Canadian Premiers (NEG/ECP) funded
18 studies that used critical load-based methods to estimate sustainable acidifying deposition rates
19 and exceedences for upland forests representative of the New England states and the eastern
20 Canadian Provinces in 2000 to 2001 (NEG/ECP Forest Mapping Group, 2001). More recently,
21 McNulty et al. (2007) completed a national critical load assessment for U.S. forest soils at a 1-
22 km2 scale.
23 Within the ISA (U.S. EPA, 2008c, Section D.2.2), EPA detailed an 8-step protocol to
24 define the basic critical load question in any analysis. Those steps are repeated here:
25 1. Identify the ecosystem disturbance that is occurring (e.g., acidification, eutrophication).
26 Not all disturbances will occur in all regions or at all sites, and the degree of disturbance
27 may vary across landscape areas within a given region or site.
28 2. Identify the landscape receptors that are subjected to the disturbance (e.g., forests,
29 surface waters, crops). Receptor sensitivity may vary locally and/or regionally, and the
30 hierarchy of those receptors that are most sensitive to a particular kind of disturbance
31 may vary as well.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-27
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Terrestrial Acidification Case Study
1 3. Identify the biological indicators within each receptor that are affected by atmospheric
2 deposition (i.e., individual organism, species, population, or community characteristics).
3 Indicators will vary geographically and perhaps locally within a given receptor type.
4 4. Establish the critical biological responses that define "significant harm" to the biological
5 indicators (e.g., presence/absence, loss of condition, reduced productivity, species shifts).
6 Significant harm may be defined differently for biological indicators that are already at
7 risk from other stressors or for indicators that are perceived as "more valued."
8 5. Identify the chemical indicators or variables that produce or are otherwise associated
9 with the harmful responses of the biological indicators (e.g., streamwater pH, lake Al
10 concentration, soil base saturation). In some cases, the use of relatively easily measured
11 chemical indicators (e.g., surface water pH or acid neutralizing capacity [ANC]) may be
12 used as a surrogate for chemical indicators that are more difficult to measure (e.g., Al
13 concentration).
14 6. Determine the critical chemical limits for the chemical indicators at which the harmful
15 responses to the biological indicators occur (e.g., pH < 5, base saturation < 5%, inorganic
16 Al concentration greater than 2 umol). Critical limits may be thresholds for indicator
17 responses, such as presence/absence, or may take on a continuous range of values for
18 continuous indicator responses, such as productivity or species richness. Critical limits
19 may vary regionally or locally depending on factors such as temperature, existence of
20 refugia, or compensatory factors (e.g., high Ca2+ concentration mitigates the toxicity of
21 Al to fish and plant roots).
22 7. Identify the atmospheric pollutants that control (affect) the pertinent chemical indicators
23 (e.g., deposition of SC>42", N(V, ammonium [NH4+], nitric acid [HNOs]). Multiple
24 pollutants can affect the same chemical variable. The relative importance of each
25 pollutant in producing a given chemical response can vary spatially and temporally.
26 8. Determine the critical pollutant loads (e.g., kg/ha/yr total deposition of sulfur or
27 nitrogen) at which the chemical indicators reach their critical limits. Critical pollutant
28 loads usually include both wet and dry forms of pollutant deposition. The critical
29 pollutant load may vary regionally within a receptor or locally within a site (e.g., as
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-28
-------
Terrestrial Acidification Case Study
1 factors such as elevation or soil depth vary) and may vary temporally at the same location
2 (e.g., as accumulated deposition alters chemical responses).
3 As shown in the eight steps above, a variety of indicators and responses can be
4 incorporated into the estimation of a critical load, the point at which ecological impacts occur.
5 Varying any one of these will result in a different critical load estimate. As a result, there is no
6 single definitive critical load for an ecosystem. In this case study, terrestrial acidification was
7 evaluated using the chemical indicator of the Bc/Al ratio in the soil solution (as a surrogate for
8 Ca2+/Al—discussed earlier at the end of Section 1.1.1) and biological indicators (ecological
9 endpoints) of red spruce and sugar maple tree health and growth. The critical chemical limits
10 discussed above allow for the calculation of multiple critical loads, depending on the level of
11 protection of interest. Three base cation to aluminum ratio - critical load (Bc/Al)crit ratio values
12 were applied in this case study to provide a range of protection (i.e., low, intermediate, high) to
13 tree health and growth, and these values (Bc/Al)crit ratios) are detailed in Section 2.1.2.2.
14 Several methodological approaches can be taken to estimate critical loads in terrestrial
15 ecosystems. Three of the most commonly used methods are empirically derived estimations,
16 steady-state mass-balance model estimations, and dynamic model estimations (Bull et al., 2001;
17 Bobbink et al., 2003; Jenkins et al., 2003; McNulty et al., 2007; UNECE, 2004).
18 The UNECE CLRTAP has used the empirically-derived estimation approach within their
19 mapping framework. Empirically derived critical load estimates of atmospheric nitrogen
20 deposition for specific receptor groups within natural and seminatural terrestrial ecosystems and
21 wetland ecosystems were first presented in a background document for the 1992 workshop on
22 critical loads held under the UNECE CLRTAP Convention at Lokeberg (Sweden) (Bobbink et
23 al., 1992). Updates to the empirically derived loads were completed for a 2007 update to the
24 2004 Manual on Methodologies and Criteria for Modeling and Mapping Critical Loads and
25 Levels and Air Pollution Effects, Risks, and Trends (henceforth referred to as the TCP Mapping
26 and Modeling Manual) (UNECE, 2004). Empirically derived critical loads can provide good
27 estimates of the impacts of acidifying deposition on terrestrial systems. However, they require
28 data from studies that establish the impacts of varying loads (e.g., amount and duration) on
29 ecosystem processes and attributes and have a limited ability to extrapolate to other systems with
30 different characteristics.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-29
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Terrestrial Acidification Case Study
1 The mass-balance model estimation method to calculate critical loads consists of simple
2 models that relate chemical indicators (e.g., related to or indicative of biological impact of
3 acidifying deposition) to the deposition levels observed in an ecosystem. The chemical indicator
4 used in the mass balance calculations must have a proven relationship to the biological indicator.
5 With the mass-balance approach, critical loads are calculated by relating the flow of acidifying
6 agents (i.e., base cations and other ions) and nutrients into, out of, and within an ecosystem.
7 These mass-balance models are steady state and offer estimates of critical loads for time frames
8 based on the data used to evaluate the mass balance (UNECE, 2004). To accurately characterize
9 the steady-state ecosystem condition and impacts of acidifying deposition, it is important to use
10 long-term averages of input fluxes in the mass-balance calculations. Benefits of the simple mass-
11 balance approach are its ease of use, moderate data requirements, and applicability over a large
12 area (Pardo and Driscoll, 1996). Disadvantages, however, include an inability to incorporate
13 changes or ecosystem responses into the modeled critical load estimates.
14 Dynamic-model estimation methods simulate the processes of pollutant fate and transport
15 into, out of, and within a system on a temporally varying basis. They are more data intensive
16 than mass-balance models and require the modeling of temporal rates and processes in addition
17 to the mass balance of acidifying agents, base cations, and nutrients. Some dynamic models
18 involve the integration of hydrologic, geochemical, and biological processes, but such models
19 are still of limited use in determining critical loads (Pardo and Driscoll, 1996). An advantage of
20 dynamic models is that they allow for an estimation or prediction of ecosystem response over
21 time and under different acidifying deposition scenarios (Pardo and Duarte, 2007).
22 2.1 Chosen Method
23 The Simple Mass Balance (8MB) model, outlined in the TCP Mapping and Modeling
24 Manual (UNECE, 2004) to determine terrestrial critical loads, was used to estimate the critical
25 loads of acidifying nitrogen and sulfur deposition in the KEF and HBEF (i.e., for sugar maple
26 and red spruce, respectively) case study areas. This model is currently the most commonly used
27 approach to estimate critical loads and has been widely applied in Europe (Sverdrup and de
28 Vries, 1994), the United States (McNulty et al., 2007; Pardo and Duarte, 2007), and Canada
29 (Watmough et al., 2006; Ouimet et al., 2006). Although a limitation of the 8MB model is that it
30 is a steady-state model, as stated by the UNECE (2004), "Since critical loads are steady-state
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-30
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Terrestrial Acidification Case Study
1 quantities, the use of dynamic models for the sole purpose of deriving critical loads is somewhat
2 inadequate." In addition, if dynamic models are "used to simulate the transition to a steady state
3 for the comparison with critical loads, care has to be taken that the steady-state version of the
4 dynamic model is compatible with the critical load model" (UNECE, 2004). Therefore, the
5 selection of the 8MB model is the most suitable approach for this case study examining critical
6 loads for sugar maple and red spruce.
7 The 8MB model examines a long-term, steady-state balance of base cation, chloride, and
8 nutrient inputs, "sinks," and outputs within an ecosystem. With this model, base cation
9 equilibrium is assumed to equal the system's critical load. It is a single-layer model, where
10 assumptions stipulate that the soil layer is a homogeneous unit at least as deep as the rooting
11 zone, so that the nutrient cycle can be ignored. This allows the model to focus directly on growth
12 and uptake processes. There are several additional assumptions that are included with application
13 of the 8MB model (UNECE, 2004):
14 • All evapotranspiration occurs on the top of the soil profile
15 • Percolation is constant through the soil profile and occurs only vertically
16 • Physico-chemical constants are assumed to be uniform throughout the whole soil profile
17 • Internal fluxes (e.g., weathering rates, nitrogen immobilization) are independent of soil
18 chemical conditions (e.g., pH).
19 The 8MB model relates atmospheric nitrogen and sulfur deposition to a critical load by
20 incorporating mass balances for nitrogen and sulfur within the soils with the charge balance of
21 ions in the soil leaching flux. This model accounts for the processes that add and remove
22 nitrogen and sulfur, as well as base cations and other charged elemental species, from the soil.
23 Although this model analyzes both total nitrogen and sulfur deposition loads, it does not
24 allow for the analysis of the specific effects of the different total reactive nitrogen species.
25 However, as stated in Chapter 5 of the ICP Mapping and Modeling Manual, "the possible
26 differential effects of the deposited nitrogen species (oxidized nitrogen [NOy] or reduced
27 nitrogen [NHX]) are insufficiently known to make a differentiation between these nitrogen
28 species for critical load establishment" (UNECE, 2004). Therefore, attempting an analysis of the
29 impacts of different nitrogen species was not seen as necessary.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-31
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Terrestrial Acidification Case Study
1 2.1.1 Critical Load Equations and Calculations
2 2.1.1.1 Simple Mass Balance Calculations
3 The 8MB model used to estimate critical loads of acidity in this case study is presented in
4 Equation 1. The full derivation of this equation is detailed in the TCP Mapping and Modeling
5 Manual (UNECE, 2004). Unless otherwise stated, all variables are expressed in units of eq/ha/yr.
6 Equivalent, or "eq," is a unit that removes the influence of molecular weight and is equivalent to
7 "mole." For example, 1 g of Ca is equal to 0.25 eq (1 g/the molecular weight of Ca = 40.08).
8 CL(S + N) = BCdep - Cldep + BCW -Bcu + N, + Nu + Nde - ANCle,cnt (1)
9 where
10 CL(S+N) = forest soil critical load for combined nitrogen and sulfur acidifying
11 deposition ((N+S)comb)
12 BCdep = base cation (Ca2+ + K+ + Mg2+ + Na+) deposition4
13 Cldep = chloride deposition
14 BCW = base cation (Ca2+ + K+ + Mg2+ + Na+) weathering
15 Bcu = uptake of base cations (Ca2+ + K+ + Mg2+) by trees
16 N; = nitrogen immobilization
17 Nu = uptake of nitrogen by trees
18 Nde = denitrification
19 ANCie,crit = forest soil acid neutralizing capacity of critical load leaching
20 NOTE: There is a distinction between the base cation variables base cation (BC) and Be. BC
21 includes all four base cations (Ca2+ + K+ + Mg2+ and K+), whereas Be only includes three
22 cations—those that are taken up by vegetation (Ca2+ + K+ + Mg2+) (UNECE, 2004). Terms in the
23 8MB equations that are directly related to or impact vegetation use the Be variable.
24 Some of these parameters had defined or selected input values (BCdep, Cldep, N;, Nu and
25 Nde), while four of these parameters, including BCW, Bcu, Nu and ANCie,crit, required calculation.
4 The ICP Mapping and Modeling Manual (UNECE, 2004) recommends that wet deposition be corrected for sea salt
on sites within 70 km of the coast. Both the HBEF and KEF case study areas are greater than 70 km from the
coast, so this correction was not used.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-32
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Terrestrial Acidification Case Study
1 Two methods were used to calculate BCW in this case study; the clay-substrate method
2 and the soil type-texture approximation method. The clay-substrate method has been used by
3 many researchers in North America (Ouimet et al., 2006; Watmough et al., 2006; McNulty et al.,
4 2007; Pardo and Duarte 2007), and the soil type-texture approximation is one of the methods
5 outlined in the TCP Mapping and Modeling Manual (UNECE, 2004). Base cation weathering is
6 the most influential and most difficult-to-estimate parameter within the 8MB model (Whitfield et
7 al., 2006; Li and McNulty, 2007). Therefore, these two methods were chosen to provide a range
8 of BCW estimates within which the correct value probably lies (discussed further in Section 5).
9 Base cation weathering was calculated with the clay-substrate method using equations
10 outlined by McNulty et al. (2007) (Equations 1 to 3). This method relies on a combination of
11 parent material and clay percentage to determine the soil weathering rate. Parent material acidity
12 was determined by silica content (see Table 3 in McNulty et al., 2007).
13 Acid Substrate: BCe = (56.7x%clay)-(o.32x(%clay)2) (2)
14 Intermediate Substrate: BCe = 500 + (53.6 x %clay)- (o. 18 x (%clay)2) (3)
15 Basic Substrate: BCe = 500 + (59.2 x %clay) (4)
16 where
17 BCe = empirical soil base cation (Ca2+ + K+ + Mg2+ + Na+) weathering rate
18 (eq/ha/yr)
19 % clay = the percentage of clay within the soil.
20 The empirical base cation weathering rate was corrected for soil temperature and depth of
21 the rooting zone soil (Sverdrup and de Vries, 1994; Hodson and Langan, 1999; van der Salm and
22 de Vries, 2001; UNECE, 2004; Watmough et al., 2006; Whitfield et al., 2006; Pardo and Duarte
23 2007; NEG/ECP Forest Mapping Group, 2001) to determine the final BCW as outlined in
24 Equations 5 and 6.
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Appendix 5-33
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Terrestrial Acidification Case Study
3
4
5
6
7
9
10
11
12
13
14
15
16
BC =BC xe
2.6+273 J I 273+T,
BCW = BCC x depth
(5)
(6)
where
BCC
A
Tm
Depth
base cation (Ca + K + Mg
temperature (eq/ha/yr/m)
Arrhenius constant (3,600 kelvin [K])
mean annual soil (or air) temperature (°C)
the depth of rooting zone mineral soil (m).
Na+) weathering rate corrected for
Base cation weathering was calculated with the soil type-texture approximation method
using Tables 2.1-1, 2.1-2, and 2.1-3 and Equation 7 (UNECE, 2004). Similar to the clay-
substrate method, the soil type-texture approximation method also requires a combination of soil
texture and parent material acidity to calculate base cation weathering. The soil texture class was
determined by percentages of clay and sand (Table 2.1-1), and the parent material acidity was
classified according to Food and Agriculture Organization (FAO) soil types (Table 2.1-2). Soil
texture class and parent material acidity was then combined to determine the weathering rate
class (Table 2.1-3)
17 Table 2.1-1. Soil Texture Classes as a Function of Clay and Sand Content
Texture Class
1
2
3
4
5
Name
Coarse
Medium
Medium fine
Fine
Very fine
Definition
clay <18% and sand >65%
clay <35% and sand >15%,
but clay >18% if sand >65%
clay <35% and sand <15%
35%60%
18 Source: UNECE, 2004.
2nd Draft Risk and Exposure Assessment
Appendix 5-34
June 5, 2009
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Terrestrial Acidification Case Study
1 Table 2.1-2. Parent Material Classes for Common FAO Soil Types
Parent Material
Acidic
Intermediate
Basic
Organic
FAO Soil Type
Ah, Ao, Ap, B, Ba, Bd, Be, Bf, Bh, Bm, Bx, D, Dd, De, Dg, Gx, I, Id, le,
Jd, P, Pf, Pg, Ph, PI, Po, Pp, Q, Qa, Qc, Qh, Ql, Rd, Rx, U, Ud, Wd
A, Af, Ag, Bv, C, Cg, Ch, Cl, G, Gd, Ge, Gf, Gh, Gi, Gl, Gm, Gs, Gt, H,
Hg, Hh, HI, J, Je, Jm, Jt, L, La, Ld, Lf, Lg, Lh, Lo, Lp, Mo, R, Re, V, Vg,
Vp, W, We
F, T, Th, Tm, To, TV
O, Od, Oe, Ox
2 Source: UNECE, 2004.
3 Table 2.1-3. Weathering Rate Classes as a Function of Texture and Parent Material Classes
Parent
Material
Acidic
Intermediate
Basic
Organic
Texture Class
1
1
2
2
2
O
4
5
3
O
4
5
4
6
6
6
5
6
6
6
Class 6 for Oe and class 1 for other organic soils
4 Source: UNECE, 2004.
273+J
(7)
6 where
7 z = rooting zone soil depth (m)
8 WRc = weathering rate class
9 T = average annual soil temperature (°C)
10 A = Arrhenius constant (3,600 K)
11 Base cation (Bcu) and nitrogen (Nu) uptake were calculated for this case study using the
12 equation outlined by McNulty et al. (2007) (Equation 8). These terms represent nutrients that are
13 taken up from the soil and used to support tree growth and maintenance but are eventually
14 returned to the system through litter senescence and decay. In a forest stand that does not
15 experience biomass removal, these nutrients are internally cycled and not lost from the system.
16 Under this scenario, both Bcu and Nu would be given values of 0 equivalents per hectare per year
2nd Draft Risk and Exposure Assessment
Appendix 5-35
June 5, 2009
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Terrestrial Acidification Case Study
1 (eq/ha/yr) in the 8MB calculations. However, in a managed stand that is harvested, base cations
2 and nitrogen taken up by the trees are removed from the forest system with tree harvesting and
3 are, therefore, considered a loss or output from the system within the 8MB calculations.
4 Watershed 6 in HBEF is a reference watershed and is not harvested. Therefore, for the
5 HBEF Case Study Area, the Bcu and Nu variables were assumed to have a value of 0 eq/ha/yr
6 because biomass and nutrients are not removed from these plots. In contrast, most of the stands
7 in the KEF are harvested, and therefore, as discussed further in Section 3.1.1 Bcu and Nu were
8 estimated for the seven plots in the KEF Case Study Area. Equation 8 was modified, as
9 necessary, to estimate uptake in the bark and bole for nitrogen, Ca2+, Mg2+, and K+. These
10 calculations were conducted for each species on each plot.
11 Uptake (eq/ha/yr) = AVI xNCxSGx% bark x 0.65 (8)
12 where
13 AVI = average forest volume increment (m3/ha/yr)
14 NC = base cation ((Ca2+ + K+ + Mg2+ ) or nitrogen nutrient concentration in bark and
15 bole(%)
16 SG = specific gravity of bark and bole wood (g/cm3)
17 %bark= percentage of volume growth that is allotted to bark
18 65% = average aboveground tree volume that is removed from the site (Birdsey,
19 1992; Hall et al., 1998; Martin et al., 1998).
20 Acid neutralizing capacity (ANC(ie,Crit)) represents the buffering capacity of the soil, and
21 the selection of the chemical indicator for the effects on the biological receptor or ecological
22 endpoint occurs within the calculation of ANC(ie,Crit). Several formulations for ANC(ie,Crit) exist,
23 depending on which indicator is being used to examine the critical load for the biological
24 receptor (endpoint), sensitivity to pH conditions, or sensitivity to the toxic effects of Al. A large
25 proportion of the research indicates Al toxicity in relation to Ca2+ depletion as the main indicator
26 of red spruce and sugar maple mortality and decline. Therefore, for the estimates of critical loads
27 for these two species at HBEF or KEF, Ca2+ and Al concentrations applied through the base
28 cation to aluminum (Bc/Al)crit indicator ratio were used in the ANC(ie,Crit) calculations according
29 to Equation 9. As outlined in the end of Section 1.1.1, the Bc/Al ratio is a good surrogate for the
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-36
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Terrestrial Acidification Case Study
1 Ca2+/Al indicator and is the most commonly used indicator (Bc/Al(crit)) in estimations of acid
2 load (McNulty et al., 2007; Ouimet et al., 2006; UNECE, 2004).
ANC(le,cnt)=-e2/3x
^1/3
1.5x
Bcdep+Bcw-Bcu
Bc
AT
Bcdep+Bcw-Bcu
Be
A!
(9)
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
where
Bcw
Bcu
(Bc/Al)crit =
annual runoff in m3/ha/yr
base cation (Ca2+ + K+ + Mg2+ ) deposition5
soil base cation (Ca2++ K+ + Mg2+) weathering6
base cation (Ca2++ K+ + Mg2+) uptake by trees
the gibbsite equilibrium constant (a function of forest soil organic matter
content that affects Al solubility) (UNECE, 2004)
the base cation to aluminum ratio (indicator)
Base cation weathering (Bcw) in the ANC(ie,Crit) parameter was calculated using the two
methods described earlier: the clay-substrate and soil type-texture approximation methods.
However, sodium (Na+) typically accounts for 10% to 30% of base cation weathering (BCW)
(Sverdrup and deVries 1994), and therefore, the Bcw, which only consists of Ca2+, K+, and Mg2+,
was determined by multiplying BCW by 0.80, the mid-range of the Na+ proportional content.
2. 1. 1. 2 Deposition Relative to Critical Load Calculations
If total nitrogen and sulfur deposition (combined) ((N+S)comb) is greater than the
calculated critical load for acidity, the soil is no longer able to buffer the acidifying deposition,
and there is increased likelihood of environmental harm (McNulty et al., 2007). Deposition of
(N+S)comb (expressed as eq/ha/yr) that is greater than the critical load was calculated in this case
5 Bcdep is not the same as BCdep used in Equation 1. BCdep includes Ca2+, K+, Mg2+, and Na+, whereas Bcdep includes
base cations that are taken up by vegetation (i.e., only includes Ca2+, K+, and Mg2+),
6 Bcw is not the same as BCW used in Equation 1. BCW includes Ca2+, K+, Mg2+, and Na+, whereas Bcw includes base
cations that are taken up by vegetation (i.e., only includes Ca2+, K+, and Mg2+).
2nd Draft Risk and Exposure Assessment
Appendix 5-37
June 5, 2009
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Terrestrial Acidification Case Study
1 study by comparing the 8MB estimated critical load to the CMAQ/NADP total nitrogen and
2 sulfur deposition levels as outlined in Equation 3.
3 Ex(S + N)dep = Sdep + Ndep - CL(S + N) (10)
4 where
5 Ex = exceedance of the forest soil critical nitrogen and sulfur loads
6 (S+N)deP = the deposition of sulfur and nitrogen.
7 2.1.1.3 Critical Load Function
8 The critical load function (CLF) expresses the relationship between all combinations of
9 total nitrogen and sulfur deposition ((N+S)comb) and the critical load of an ecosystem. To define
10 the CLF, minimum and maximum critical load levels for both total nitrogen and sulfur
1 1 deposition must be determined (UNECE, 2004). These maximum and minimum levels were
12 calculated in this case study using Equations 1 1 through 13 (UNECE, 2004).
13 CLmax(s)=BCdep -Cldep +BCW -BCU - ANCle,cnt (11)
14 CLmm(N) = NI+Nu+Nde (12)
CL
15 CLmax(N) = CLmm(N)+ - (13)
16 where
17 fde = denitrificati on fraction (0
-------
Terrestrial Acidification Case Study
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
when there is no sulfur deposition and all acidity due to deposition comes from nitrogen.
Translated into an equation (Equation 13), this critical load can be calculated as the sum of
CLmin(N) and CLmax(S) (corrected for denitrification).
An example of a CLF is depicted in Figure 2.1-1. All combinations of total nitrogen and
sulfur deposition that fall on the black line representing the CLF are at the critical load level.
Any deposition combination that falls below the line or within the grey area is below the critical
load level. All combinations of total nitrogen and sulfur deposition that are located above the line
or within the white area are greater than the critical load.
CLmax(S)
o
in
O
a
4*
Q
CLmm(N)
CLmax(N)
N Deposition
Figure 2.1-1. The critical load function created from the calculated maximum and
minimum levels of total nitrogen and sulfur deposition (eq/ha/yr). The grey areas show
deposition levels less than the established critical loads. The red line is the maximum
critical level of sulfur deposition (valid only when nitrogen deposition is less than the
minimum critical level of nitrogen deposition [blue dotted line]). The flat line portion of
the curves indicates nitrogen deposition corresponding to the CLm;n(N) (i.e., nitrogen
absorbed by nitrogen sinks within the system).
17 2.1.2 Critical Load Data Requirements
18 2.1.2.1 Data Requirements and Sources
19 Atmospheric, hydrologic, soil, bedrock geology, and tree measurement data are necessary
20 to evaluate critical loads associated with total nitrogen and sulfur deposition. The specific data
21 requirements to satisfy Equations 1 through 13 and calculate critical loads and CLF for this case
22 study are presented in Table 2.1-4. This table also outlines the sources of these data specific to
23 the two case study areas. Cloud deposition of nitrogen was not included in the critical load
2nd Draft Risk and Exposure Assessment
Appendix 5-39
June 5, 2009
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Terrestrial Acidification Case Study
1 calculations because of the lack of available data. However, it should be noted that cloud
2 deposition coupled with wet and dry deposition can result in 6 to 20 times greater total nitrogen
3 deposition at high elevation relative to low elevation sites (Baumgardner et al., 2003). Therefore,
4 total nitrogen deposition and the degree to which total nitrogen deposition exceeds the critical
5 load at the HBEF Case Study Area may be underestimated.
6 Table 2.1-4. Data Requirements and Sources for Calculating Critical Loads for Total Nitrogen
7 and Sulfur Deposition in Hubbard Brook Experimental Forest and Kane Experimental Forest
DATA
Total nitrogen
and sulfur
deposition —
wet and dry
Base cation
(Ca2+, Mg2+,
Na+, K+)
deposition —
wet
Base cation
(Ca2+, Mg2+,
Na+, K+)
deposition —
dry
Chlorine (Cl")
deposition —
wet
Chlorine (Cl")
deposition —
dry
Runoff
DATA NAME AND TYPE
Name
CMAQ/
NADP
NADP
CASTNET
NADP
CASTNET
Annual run-
off (1:
7,500,000
scale)
Type
GIS
datalayers
GIS
datalayer
GIS
datalayer
GIS
datalayer
GIS
datalayer
GIS
datalayer
DATA SOURCE
Hubbard Brook
Experimental Forest
Provided by U.S.
Environmental
Protection Agency
(EPA)/NADP, 2003a,e,
h
NADP, 2003b, d, f, g
U.S. EPA, 2008b
NADP, 2003 c
U.S. EPA, 2008b
Gebertetal., 1987
Kane Experimental
Forest
Provided by EPA/
NADP, 2003a,e, h
NADP, 2003b, d, f, g
U.S. EPA, 2008b
NADP, 2003 c
U.S. EPA, 2008b
Gebertetal., 1987
2nd Draft Risk and Exposure Assessment
Appendix 5-40
June 5, 2009
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Terrestrial Acidification Case Study
DATA
Mean annual
soil
temperature
Soil horizon
depth
Percentage of
clay by soil
horizon
Percentage of
sand by soil
horizon
Percentage of
organic matter
by soil horizon
Gibbsite
equilibrium
constant (Kgibb)
Parent
material/
bedrock
Food and
Agriculture
Organization
(FAO) soil
type
Nitrogen
immobilization
(NO
DATA NAME AND TYPE
Name
Soil
temperature
data (HBEF/
KEF)
SSURGO
SSURGO
SSURGO
SSURGO
Selected
-Kgibb values
Map of
bedrock
geology
Map of
dominant
soil types
Selected N;
value
Type
Database
(HBEF)/
Peer-
reviewed
journal
articles
(KEF)
GIS
datalayer
GIS
datalayer
GIS
datalayer
GIS
datalayer
Peer-
reviewed
journal
articles and
literature
GIS
datalayer
GIS
datalayer
Peer-
reviewed
journal
article and
literature
DATA SOURCE
Hubbard Brook
Experimental Forest
HBES, 2008c
USDA-NRCS, 2008b
USDA-NRCS, 2008b
USDA-NRCS, 2008b
USDA-NRCS, 2008b
Ouimet et al., 2006;
Watmough et al., 2006;
UNECE, 2004
USGS, 2000
FAO, 2007
McNulty et al., 2007;
UNECE, 2004
Kane Experimental
Forest
Carter and Ciolkosz,
1980
USDA-NRCS, 2008a
USDA-NRCS, 2008a
USDA-NRCS, 2008a
USDA-NRCS, 2008a
Ouimet et al., 2006;
Watmough et al.,
2006; UNECE, 2004
PADCNR, 2001
FAO, 2007
McNulty et al., 2007;
UNECE, 2004
2nd Draft Risk and Exposure Assessment
Appendix 5-41
June 5, 2009
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Terrestrial Acidification Case Study
DATA
Denitrification
(Nde)
Stand
composition
Annual volume
increment
(AVI) by
species
Percentage
allocation of
growth to bark
by species
Specific
gravity (bark
and bole wood)
by species
Nutrient
concentration
(bark and bole
wood) by
species —
nitrogen, Ca2+,
Mg2+, K+
Percentage
biomass (bark
and bole)
removal during
harvest
DATA NAME AND TYPE
Name
Selected Nde
value
Forest
inventory
(HBEF)/
SILVAH
(KEF)
Forest
inventory
(HBEF) /
SILVAH
(KEF)
Selected %
allocation
values
Selected
specific
gravity
values
Nitrogen,
Ca2+, K+,
Mg2+
concentra-
tions in bark
and bole
wood by
species
Selected
value
Type
Peer-
reviewed
journal
articles
Database
(HBEF)/
mensuration
model
(KEF)
Database
(HBEF)/
mensuration
model
(KEF)
Peer-
reviewed
journal
article
Peer-
reviewed
journal
article
Forest
Service
Report
Peer
reviewed
journal
article
DATA SOURCE
Hubbard Brook
Experimental Forest
McNulty et al., 2007;
Ouimet et al., 2006;
Watmough et al., 2006
HBES, 2008a
HBES, 2008a
McNulty et al., 2007
Jenkins etal., 2001
Pardo et al., 2004
McNulty et al., 2007
Kane Experimental
Forest
McNulty et al., 2007;
Ouimet et al., 2006;
Watmough et al., 2006
Thomasma et al., 2008
Thomasma et al., 2008
McNulty et al., 2007
Jenkins etal., 2001
Pardo et al., 2004
McNulty et al., 2007
1 Note: CMAQ = Community Multiscale Air Quality Model; NADP = National Atmospheric
2 Deposition Program; CASTNET = Clean Air Status and Trends Network; GIS = Geographic
3 Information System; SSURGO = Soil Survey Geographic Database; SILVAH = Silviculture of
4 Allegheny Hardwoods
2nd Draft Risk and Exposure Assessment
Appendix 5-42
June 5, 2009
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Terrestrial Acidification Case Study
1 2.1.2.2 Selection of Indicator Values
2 As described at the end of Section 1.1.1, the Bc/Al ratio ((Bc/Al)crit) in the soil solution
3 was selected as the indicator for the calculation of critical loads in this case study. The (Bc/Al)crit
4 connects the acid-influenced chemical status of the soil with the tree response: as the ratio
5 decreases, tree health and growth can be impaired because of reduced uptake of base cations and
6 increased Al toxicity. Most studies that calculate critical loads of acidity set the (Bc/Al)crit ratio
7 to 1.0 or 10.0 (McNulty et al., 2007; NEG/ECP Forest Mapping Group, 2001; Pardo and Duarte,
8 2007; UNECE, 2004). The (Bc/Al)crit ratio of 1.0 is a common default value in European forests
9 (UNECE, 2004) and has been applied to coniferous forests in the United States (McNulty et al.,
10 2007). A (Bc/Al)crit ratio of 10.0 is a more conservative ratio and has been applied to hardwood
11 forests in the United States (McNulty et al., 2007), in Canadian forests (Ouimet et al., 2006;
12 Watmough et al., 2006), and in systems where maintained tree health is required (NEG/ECP
13 Forest Mapping Group, 2001). Soil solution Bc/Al ratios of 10.0 are less likely to reduce soil
14 base saturation and are not known to impair tree vigor or growth.
15 Cronan and Grigal (1995) conducted a meta-analysis of research investigating the
16 relationship between soil solution Ca2+/Al ratio and growth of 18 tree species. They found a 50%
17 chance of negative impacts on tree growth or nutrition when the soil solution Ca2+/Al ratio was
18 as low as 1.0, a 75% chance when the soil solution ratio was as low as 0.5, and nearly a 100%
19 chance of impaired tree growth or nutrition when the soil solution Ca2+/Al molar ratio was as low
20 as 0.2. In a similar meta-analysis of studies that explored the relationship between Bc/Al and tree
21 growth, Sverdrup and Warfvinge (1993b) reported the Bc/Al ratio at which growth was reduced
22 by 20% relative to control trees. Figure 2.1-2 presents the findings of Sverdrup and Warfvinge
23 (1993b) based on 46 of the tree species that grow in North America. This summary indicates that
24 there is a 50% chance of negative tree response (i.e., greater than 20% reduced growth) at a soil
25 solution Bc/Al ratio of 1.2. Sverdrup and Warfvinge (1993b) also presented the results of studies
26 conducted on individual tree species. Figures 2.1-3 and 2.1-4 show growth in sugar maple and
27 red spruce, respectively. According to these figures, sugar maple growth was reduced by 20%
28 and red spruce growth was reduced by 35% (relative to controls) at a Bc/Al ratio of 0.6.
29 Three Bc/Al ratio ((Bc/Al)crit) values were used in this case study to evaluate different
30 levels of protection associated with total nitrogen and sulfur deposition: 0.6, 1.2, and 10 (Table
31 2.1-5). The (Bc/Al)crit ratio of 0.6 represents the highest level of impact (lowest level of
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-43
-------
Terrestrial Acidification Case Study
1 protection) to tree health and growth; as much as 75% of 46 tree species found in North America
2 experience reduced growth at this ratio (Sverdrup and Warfvinge, 1993b). Both red spruce and
3 sugar maple show at least a 20% reduction in growth at the 0.6 (Bc/Al)crit ratio. The (Bc/Al)crit
4 ratio of 1.2 is considered to represent a moderate level of impact; the growth of 50% of tree
5 species (found growing in North America) were negatively impacted at this soil solution ratio
6 (Figure 2.1-4). The (Bc/Al)crit ratio of 10.0 was selected to represent the lowest level of impact
7 (greatest level of protection) to tree growth; it is the most conservative value used in studies that
8 have calculated critical loads in the United States and Canada (NEG/ECP Forest Mapping
9 Group, 2001; McNulty et al., 2007; Watmough et al., 2004).
10
1 1
Table 2.1-5. The Three Indicator (Bc/Al)crit Soil Solution Ratios Used in This Case Study and the
Corresponding Levels of Protection to Tree Health and Critical Loads
Indicator (Bc/Al)crit Soil
Solution Ratio
0.6
1.2
10.0
Level of Protection to Tree
Health
Low
Intermediate
High
Critical Load
High
Intermediate
Low
12
13
14
15
10
.o
o
t/3
m
(Bc/Al)nt = 1.
(Bc/Al)nt = 0.6
4.
0.01
0 25 50 75 100
Cumulative % of Species Exhibiting Reduced Growth Response
Figure 2.1-2. The relationship between the Bc/Al ratio in soil solution and the percentage
of tree species (found growing in North America) exhibiting a 20% reduction in growth
relative to controls (after Sverdrup and Warfvinge, 1993b).
2nd Draft Risk and Exposure Assessment
Appendix 5-44
June 5, 2009
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Terrestrial Acidification Case Study
1
2
3
g
c
o
o
I
o
I
CO
120
100
80
60
40
20
0
O Sugar Maple
k=0,000004
0.01 0.1 1 10 100 1,000
Soil solution (Ca«fMg+K)/Al molar ratio
Figure 2.1-3. The relationship between soil solution Bc/Al ratio and stem or root growth
in sugar maple (from Sverdrup and Warfvinge, 1993b).
4
5
6
CS
o
o
•5
I
5
|
|
.8
ffl
120
100
80
60
40
20
0
McQuattie and Schier (1990)
Schier(1984)
Ohnoetal,(1988)
Hutchinsonetal. (1985)
Thornton et al. (1987)
JoslinandWolfc(l989)
0,01 0.1 1 10 100 1,000
Soil solution (Ca4Mg+K)/Al molar ratio
Figure 2.1-4. The relationship between soil solution Bc/Al ratio and biomass or root
growth in red spruce (from Sverdrup and Warfvinge, 1993b).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-45
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Terrestrial Acidification Case Study
1 2.1.2.3 Case Study Input Data
2 The data used to calculate critical loads for sugar maple and red spruce in the KEF and
3 HBEF of this case study are presented in Tables 2.1-5, 2.1-6, 2.1-7 and 2.1-8. The majority of
4 the data was specific to the case study areas and was compiled from published research studies
5 and models, site-specific databases, or spatially-explicit GIS datalayers. However, several of the
6 parameters, including denitrification (Nde), nitrogen immobilization (N;), the gibbsite equilibrium
7 constant (^gibb), and rooting zone soil depth required the use of default values or values used in
8 published critical load assessments. Denitrification loss of nitrogen (Nde) was assumed to be 0.0
9 eq/ha/yr because both the KEF and HBEF study plots are upland forests and denitrification is
10 considered negligible in such forests (McNulty et al., 2007; Ouimet et al., 2006; Watmough et
11 al., 2006). The TCP Mapping and Modeling Manual (UNECE, 2004) reported values of N; in the
12 soil, ranging from 14.3 to 35.7 eq/ha/yr in colder climates and up to 71.4 eq/ha/yr in warmer
13 climates. Nitrogen immobilization (N;) was set to 42.86 eq/ha/yr (the average of the colder and
14 warmer climate immobilization rates) for both forests in this case study. This approach and value
15 was also used by McNulty et al. (2007) for forests in the United States. Two values of the ^g;bb,
16 300 and 3,000 m6/eq2, were used in the calculations of critical loads because the 300 m6/eq2
17 value is a widely used default value (UNECE, 2004; McNulty et al., 2007), and the 3,000 m6/eq2
18 value has been used to map critical loads in Canada (Ouimet et al., 2006; Watmough et al.,
19 2006). The 3,000 m6/eq2 constant is also the highest K^\,\, value associated with soils with low
20 organic matter contents (UNECE, 2004). Fifty cm (0.5 m) was selected to represent the depth of
21 the rooting zone layer in this case study. Fine roots, which are responsible for the vast majority
22 of nutrient uptake, are typically concentrated in the upper 10 to 20 cm of soil (van der Salm and
23 de Vries, 2001). These roots are most susceptible to the impacts of acidification. Therefore, a 0.5
24 m depth has been suggested as a suitable rooting zone depth in the calculation of critical loads
25 for forest soils (Sverdrup and de Vries, 1994; Hodson and Langan, 1999).
26 As detailed in the preceding section, the three (Bc/Al)crit ratio values, associated with
27 three levels of forest protection, were used in the critical load calculations for this case study.
28 The 0.6, 1.2, and 10.0 (Bc/Al)crit ratios were applied to both the KEF and HBEF case study areas.
29 As outlined earlier, base cation weathering rates were calculated using two methods; the
30 clay-substrate method and the soil type-texture association method. The data presented in
31 Tables 2.1-6 and 2.1-7 were used for these calculations. Similarly, base cation (Bcu) and
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-46
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Terrestrial Acidification Case Study
1 nitrogen (Nu) uptake values were calculated in two different ways for the two case study areas. In
2 HBEF, Bcu and Nu were assumed to be 0 eq/ha/yr because Watershed 6 is a reference watershed
3 and does not have a history or future of harvesting. Biomass (and the nutrients contained therein)
4 would, therefore, not have been removed from site. In KEF, two sets of values were used to
5 model two scenarios and estimate Bcu and Nu in the 8MB model calculations. In the first
6 scenario, it was assumed that the tree biomass was not harvested. Therefore, Nu and Bcw, in this
7 scenario, were set to 0 eq/ha/yr. In the second scenario, the case study plots were assumed to be
8 managed and harvested on a regular basis. Values of Bcu and Nu for this scenario were therefore
9 calculated using tree data (Tables 2.1-6, 2.1-8 and 2.1-9) and Equation 8 in Section 2.1.1.1. The
10 calculation of critical loads with the different Bcu and Nu values allowed for a comparison of the
11 influence of forest harvesting on the estimates of critical loads. The removal of nitrogen and base
12 cations with harvesting can significantly reduce the critical load of total nitrogen and sulfur
13 acidifying deposition in an ecosystem; the uptake and removal of base cations reduces the
14 capacity of the system to buffer against acidifying deposition.
15 Table 2.1-6. Input Values for the Calculation of Critical Load in Hubbard Brook Experimental
16 Forest and Kane Experimental Forest
DATA
2002 CMAQ/NADP total nitrogen
and sulfur deposition levels — wet
and dry (eq/ha)
Average annual (2000 to 2007) base
cation (Ca2+, Mg2+, Na+, K+)
deposition — wet (eq/ha)
Average annual (2000 to 2007) base
cation (Ca2+, Mg2+, Na+, K+)
deposition — dry (eq/ha)
Average annual (2000 to 2007)
chloride (Cl") deposition — wet
(eq/ha)
Average annual chloride (Cl")
deposition — dry (eq/ha)
Runoff (m3/ha/yr)
CASE STUDY AREA
Hubbard Brook
Experimental Forest
Nitrogen = 60 1.07,
Sulfur = 23 3. 08 11
Ca2+= 15.87, Mg2+ = 5.69,
K+ = 4.27,Na+ = 35.78
Ca2+ = 0.29, Mg2+ = 0.14,
K+ = 0.18,Na+ = 0.83
45.48
(2004 to 2007) 0.37
7,620
Kane Experimental Forest
Nitrogen = 967.54,
Sulfur = 646.37
Ca2+ = 33.03,Mg2+ = 7.96,
K+ = 6.33,Na+=18.31
Ca2+= 1.08, Mg2+ = 0.38,
K+ = 0.34, Na+ = 0.82
35.36
(2003 to 2007) 0.1 7
6,350
2nd Draft Risk and Exposure Assessment
Appendix 5-47
June 5, 2009
-------
Terrestrial Acidification Case Study
DATA
Average annual soil temperature (to
0.5 m) (°C)
Rooting Zone Soil depth (m)
Percentage clay (in top 0.5 m of
soil)a
Percentage sand (in top 0.5 m of
soil)a
Percentage organic matter (in top
0. 5m of soil) a
Gibbsite equilibrium constant
(Agibb) (m6/eq2)
Parent material/bedrock13
Food and Agriculture Organization
(FAO) soil type
Nitrogen immobilization (N;)
(eq/ha/yr)
Denitrification (Nde) (eq/ha/yr)
Stand composition (in common
name alphabetical order)
Annual volume increment (AVI) by
species (m3/ha/yr)
Percentage allocation of growth to
bark by species
Specific gravity (bark and bole
wood) by species (g/cm3)
Nutrient concentration (% nitrogen,
Ca2+, Mg2+, K+) (bark and bole
wood) by tree species
CASE STUDY AREA
Hubbard Brook
Experimental Forest
(1989 to 1998)7.29
0.5
6.4
57.4
3.6
300 and 3,000
Quartz, mica, schist, and
quartzite
Orthic Podzol (Po)
42.86
0
American Beech (Fagus
grandifolia), Balsam Fir
(Abies balsamea), Birch spp.
(Betula spp.)., Mountain Ash
(Sorbus americana), Red
Maple (Acer mbrum), Red
Spruce, Striped Maple (Acer
pensylvanicuni) and Sugar
Maple
-
-
—
Kane Experimental Forest
(1976 to 1979) 7.90
0.5
See Table 2.1-7
See Table 2.1-7
See Table 2.1-7
300 and 3,000
Sandstone, conglomerate,
and shale
Dystric Cambisol (Bd)
42.86
0
American Beech (Fagus
grandifolid), Birch spp.
(Betula spp.), Black cherry
(Prunus serotina), Cucumber
Tree (Magnolia acuminata),
Eastern Hemlock (Tsuga
canadensis), Red Maple
(Acer rubrum), and Sugar
Maple
See Table 2.1-8
1 1% — coniferous species /
1 5% — deciduous species
See Table 2.1-9
See Table 2.1-9
2nd Draft Risk and Exposure Assessment
Appendix 5-48
June 5, 2009
-------
Terrestrial Acidification Case Study
DATA
Percentage biomass (bark and bole)
removal during harvest
CASE STUDY AREA
Hubbard Brook
Experimental Forest
—
Kane Experimental Forest
65
1 a Determined by weighted average by horizon depth and soil series coverage
2 b Based on dominant mineralogy
3 Table 2.1-7. Soil Characteristics in the Seven Plots of the Kane Experimental Forest Case Study
4 Area for the Calculation of the Base Cation Weathering Rate Parameters
Soil Attribute (in
top 0.5 m of soil)
Percentage Clay
Percentage Sand
Percentage
Organic Matter
PLOT
1
23.3
39.9
1.4
2
20.1
27.0
0.7
3
21.3
26.2
1.1
4
20.1
27.0
0.7
5
23.3
39.9
1.4
6
20.1
27.0
0.7
7
20.1
27.0
0.7
6 Table 2.1-8. Annual Volume Growth by Tree Species in Each of the Seven Plots of the Kane
7 Experimental Forest Case Study Area for the Calculation of Nutrient Uptake (Bcu and Nu)
PLOT
1
2
3
4
5
6
7
TREE SPECIES ANNUAL VOLUME GROWTH (m3/ha/yr)
American
Beech
NA
NA
0.8
0.9
0.9
NA
0.6
Birch
spp.
NA
NA
0.2
NA
3.0
NA
0.1
Black
Cherry
3.3
3.0
1.3
NA
NA
1.8
0.8
Cucumber
Tree
NA
NA
NA
NA
0
NA
NA
Eastern
Hemlock
NA
NA
0.1
NA
NA
NA
0.1
Red
Maple
0.8
NA
0.3
1.1
NA
0.1
NA
Sugar
Maple
2.8
1.4
1.3
0.4
0.6
1.3
0.7
8 NA = Not applicable.
2nd Draft Risk and Exposure Assessment
Appendix 5-49
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 2.1-9. Specific Gravity and Nutrient Concentrations by Biomass Component (Bark and
2 Bole Wood) and by Tree Species for the Calculation of Nutrient Uptake (Bcu and Nu) in the Kane
3 Experimental Forest Case Study Area
SPECIES
American
Beech
Birch spp
Black
Cherry
Cucumber
Tree
Eastern
Hemlock
Red
Maple
Sugar
Maple
BARK
Specific
Gravity
(g/cm3)
0.50
0.61
0.50
0.50
0.34
0.55
0.54
%N
0.75
0.40
0.00
0.54
0.27
0.43
0.51
»*
0.22
0.12
0.00
0.21
0.15
0.20
0.31
Mg°2+
0.05
0.04
0.00
0.06
0.03
0.05
0.06
%Ca2+
2.81
0.78
2.69
2.15
0.74
1.30
2.23
BOLE
Specific
Gravity
(g/cm3)
0.56
0.61
0.47
0.52
0.38
0.49
0.56
%N
0.11
0.09
0.00
0.10
0.08
0.09
0.10
»*
0.07
0.05
0.00
0.09
0.09
0.08
0.07
Mg°2+
0.02
0.02
0.00
0.02
0.01
0.02
0.02
Ca°2+
0.07
0.08
0.08
0.11
0.07
0.11
0.13
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Note: N = nitrogen.
2.2 Critical Load Function Response Curves Associated with the Three
Levels of Protection
The three (Bc/Al)crit ratio values (0.6, 1.2, and 10.0) used to evaluate level of protection
to tree health and growth correspond to three different critical load values of total nitrogen and
sulfur deposition ((N+S)comb). A critical load based on 0.6 (Bc/Al)crit ratio is the least stringent
load and offers the least protection to forests of the three critical loads. The 1.2 (Bc/Al)crit ratio
results in an intermediate critical load of total nitrogen and sulfur deposition with moderate
protection to tree health. A critical load based on a 10.0 (Bc/Al)crit ratio would be the most
stringent load and offers the greatest protection to the health of trees.
As outlined in Section 2.1.1.3, critical loads of acidity can be translated into CLF
relationships; the CLF defines the combinations of (N+S)COmb that are equal to the calculated
combined critical load of an ecosystem. Therefore, to provide an indication of all combinations
of total nitrogen and sulfur deposition associated with the three different levels of protection,
2nd Draft Risk and Exposure Assessment
Appendix 5-50
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
5
6
7
8
9
10
11
CLF response curves were produced with the three (Bc/Al)crit ratios. From the 0.6, 1.2, and 10.0
(Bc/Al)crit ratios, the critical loads of (N+S)COmb and corresponding CLmax(N), CLm;n(N) and
CLmax(S) values were calculated to generate CLF response curves for each case study area.
Figure 2.2-1 provides an example of the CLF response curves associated with the three levels of
protection.
o
o
a
CLF based on lowest
protection level
(Bc/Al(crit)) = 0.6
CLF based on
intermediate protection
level (Bc/Al(crit)) = 1.2
CLF based on highest
protection level
(Bc/Al(crit)) = 10.0
N Deposition
Figure 2.2-1. An example of the critical load function response curves associated with
the three (Be/Al)crit ratios and the associated levels of protection of tree health. The flat
line portion of the curves indicates total nitrogen deposition corresponding to the CLm;n
(nitrogen absorbed by nitrogen sinks within the system).
3. RESULTS
12 3.1 Critical Load Estimates
13 3.1.1 Sugar Maple
14 The estimates of critical loads for sugar maple in the seven plots in KEF are presented in
15 Tables 3.1-1 through 3.1-7. The critical load estimates for all seven plots are summarized in
16 Table 3.1-8 and ranged from 728 eq/ha/yr to 2,998 eq/ha/yr of combined total nitrogen and
17 sulfur deposition ((N+S)comb). The ranges of critical loads associated with the three (Bc/Al)crit
18 ratios differed by level of impact to tree health, but there was some overlap between the ranges
19 of values. The lowest level of protection, (Be/Al)crit ratio = 0.6, had the highest critical loads that
20 ranged from 1,132 eq/ha/yr to 2,998 eq/ha/yr of (N+S)comb. The intermediate level of protection,
21 (Be/Al)cnt ratio = 1.2, had critical loads ranging from 1,033 eq/ha/yr to 2,079 eq/ha/yr. The
22 (Bc/Al)crit ratio of 10.0, corresponding to the most protective level for tree health, had critical
2nd Draft Risk and Exposure Assessment
Appendix 5-51
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
load values ranging from 728 eq/ha/yr to 1,139 eq/ha/yr of (N+S)COmb. Plot location, the two
different methods to estimate base cation weathering, the two K&bb values, and the inclusion of
the influence of nutrient uptake and removal (Bcu and Nu greater than 0 eq/ha/yr) in the critical
load calculations all influenced the critical loads for the three (Be/Al)crit ratio values. In general,
Plot 1 had both the lowest and highest critical load values. This was largely due to the method
used to calculate BCW (clay-substrate method) and the relative high percentages of clay and
organic matter in the soil, which accounted for the high critical load values. The low critical load
values in Plot 1 were caused by the comparatively high amount of base cation and nitrogen
uptake by the trees. The K&bb constant also influenced the critical load values, with 300 m6/eq2
causing higher critical loads than 3000 m6/eq2. Similarly, the clay-substrate method to estimate
BCW produced higher critical load values than did the soil type-texture approximation method.
The inclusion of the influence of nutrient uptake and removal by trees (Bcu and Nu greater than 0
eq/ha/yr) in the calculations of critical loads resulted in a large decrease in the critical values,
especially for the (Be/Al)crit ratio of 0.6, which offers the lowest level of protection to tree health.
Table 3.1-1. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
in Plot 1 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,998
2,079
1,139
1,940
1,473
960
tfgibb = 3000
m6/eq2
2,678
1,825
1,014
1,692
1,276
862
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
1,553
1,208
814
tfgibb = 3000
m6/eq2
2,328
1,591
883
1,332
1,033
728
18
2nd Draft Risk and Exposure Assessment
Appendix 5-52
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 3.1-2. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
2 Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
3 in Plot 2 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,711
1,885
1,032
2,009
1,481
910
tfgibb = 3000
m6/eq2
2,403
1,641
911
1,749
1,275
808
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
1,928
1,426
879
tfgibb = 3000
m6/eq2
2,328
1,591
883
1,673
1,223
779
5 Table 3.1-3. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
6 Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
7 in Plot 3 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,817
1,957
1,071
2,228
1,626
983
tfgibb = 3,000
m6/eq2
2,504
1,709
949
1,954
1,408
875
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
2,039
1,497
912
tfgibb = 3,000
m6/eq2
2,328
1,591
883
1,776
1,288
809
2nd Draft Risk and Exposure Assessment
Appendix 5-53
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 3.1-4. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
2 Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
3 in Plot 4 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,711
1,885
1,032
2,388
1,707
990
tfgibb = 3,000
m6/eq2
2,403
1,641
911
2,101
1,479
878
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
2,308
1,653
960
tfgibb = 3,000
m6/eq2
2,328
1,591
883
2,025
1,429
850
5 Table 3.1-5. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
6 Equilibrium Constant (Kgibb) and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
7 in Plot 5 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr )
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,998
2,079
1,139
2,545
1,849
1,118
tfgibb = 3,000
m6/eq2
2,678
1,825
1,014
2,256
1,620
1,004
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
2,173
1,596
978
tfgibb = 3,000
m6/eq2
2,328
1,591
883
1,903
1,382
873
2nd Draft Risk and Exposure Assessment
Appendix 5-54
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 3.1-6. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
2 Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
3 in Plot 6 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr )
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,711
1,885
1,032
2,212
1,599
946
tfgibb = 3,000
m6/eq2
2,403
1,641
911
1,936
1,380
838
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
2,132
1,545
916
Kvbb = 3,000
m6/eq2
2,328
1,591
883
1,861
1,329
810
5 Table 3.1-7. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite
6 Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values
7 in Plot 7 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Load (eq/ha/yr )
Clay-Substrate Method
tfgibb = 300
m6/eq2
2,711
1,885
1,032
2,368
1,693
981
tfgibb = 3,000
m6/eq2
2,403
1,641
911
2,082
1,466
869
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,633
1,832
1,002
2,289
1,639
951
tfgibb = 3,000
m6/eq2
2,328
1,591
883
2,007
1,415
840
2nd Draft Risk and Exposure Assessment
Appendix 5-55
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
5
Table 3.1-8. Ranges of Critical Load Values (eq/ha/yr) (with and without the Influence of
Nutrient Uptake and Removal with Tree Harvest) for the Seven Plots of the Kane Experimental
Forest Case Study Area (Both Kgibb values and methods to estimate BCW were used in these
calculations to present the range of critical loads estimated using all combinations of the
parameter values.)
Nutrient
Uptake
in
Critical
Load
Bcu and
NUNOT
Included
Bcu and
Nu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Critical Loads (eq/ha/yr )
Plotl
2,328 to
2,998
1,591 to
2,079
883 to
1,139
1,332 to
1,940
1,033 to
1,473
728 to
960
Plot 2
2,328 to
2,711
1,591 to
1,885
883 to
1,032
1,673 to
2,009
1,223 to
1,481
779 to
910
PlotS
2,328 to
2,817
1,591 to
1,957
883 to
1,071
1,776 to
2,228
1,288 to
1,626
809 to
983
Plot 4
2,328 to
2,711
1,591 to
1,885
883 to
1,032
2,025 to
2,388
1,429 to
1,707
850 to
990
PlotS
2,328 to
2,998
1,591 to
2,079
883 to
1,139
1,903 to
2,545
1,382 to
1,849
873 to
1,118
Plot 6
2,328 to
2,711
1,591 to
1,885
883 to
1,032
1,861 to
2,212
1,329 to
1,599
8 10 to
946
Plot?
2,328 to
2,711
1,591 to
1,885
883 to
1,032
2,007 to
2,368
1,415 to
1,693
840 to
981
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Two series of CLF response curves corresponding to the three (Be/Al)crit ratio values (i.e.,
0.6, 1.2, and 10.0) for Plot 1 of the KEF Case Study Area are shown in Figures 3.1-1 and 3.1-2.
Plot 1 had the highest and lowest critical load estimates. Therefore, the extreme values of this
plot were graphed to capture the range of critical loads associated with the three levels of
protection in the KEF Case Study Area. Figure 3.1-1 presents the CLF response curves
associated with the lowest critical load estimates. This scenario corresponded to the BCW
calculated using the soil type-texture approximation method, the 3000 m6/eq2 Kg{\,b constant, and
the inclusion of the influence of nutrient uptake and removal (Bcu and Nu greater than 0 eq/ha/yr)
in the calculation of critical load. Nitrogen uptake (Nu) substantially increased the minimum
critical load of nitrogen (CLmin(N)) in this CLF relationship. Figure 3.1-2 shows the CLF
response curves associated with the highest critical load estimates in Plot 1. These estimates
occurred with BCW calculated using the clay-substrate method and the 300 m6/eq2 Xgibb constant.
Nutrient uptake (i.e., Bcu and Nu) was set to 0 eq/ha/yr in this calculation of critical load.
2nd Draft Risk and Exposure Assessment
Appendix 5-56
June 5, 2009
-------
Terrestrial Acidification Case Study
3500
P
P &
^
1026
422
Low Protection (Bc/Al = 0.6)
Intermediate Protection (Bc/Al =1.2)
High Protection (Bc/Al = 10.0)
CLmin(N)
306 728 1033 1332
1
2
3
4
5
6
N Deposition
(eq/ha/yr)
3500
Figure 3.1-1. The critical load function response curves detailing the lowest critical load
estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1 for the
parameters corresponding to each of the curves). The flat line portion of the curves
indicates total nitrogen deposition corresponding to the CLm;n(N) (nitrogen absorbed by
nitrogen sinks within the system).
2nd Draft Risk and Exposure Assessment
Appendix 5-57
June 5, 2009
-------
Terrestrial Acidification Case Study
3500
2955
Q
t/3
1096
Low Protection (Bc/Al = 0.6)
"" Intermediate Protection (Bc/Al =1.2)
- High Protection (Bc/Al = 10.0)
--CLmin(N)
1139
2998
3500
N Deposition
(eq/ha/yr)
2 Figure 3.1-2. The critical load function response curves detailing the highest critical load
3 estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1 for the
4 parameters corresponding to each of the curves). The flat line portion of the curves
5 indicates total nitrogen deposition corresponding to the CLm;n(N) (nitrogen absorbed by
6 nitrogen sinks within the system.
7 The critical loads calculated for the KEF Case Study Area are consistent with critical
8 loads determined by other studies conducted on forests in the Allegheny Plateau. McNulty et al.
9 (2007), in their evaluation of critical loads across the United States, calculated loads of 1,061 to
10 1,146 eq/ha/yr , corresponding to the location of the seven case study plots in KEF (Table 3.1-9).
11 These values are very similar to the ranges (910 to 1,139 eq/ha/yr) determined in this case study,
12 using similar parameter values. McNulty et al. (2007) used the 8MB model to calculate critical
13 load, the clay-substrate method to estimate BCW and the indicator value of 10.0 for (Bc/Al)crit for
14 hardwood tree species. It is not known which K^ constant was used or if nutrient uptake and
15 removal (Bcu and Nu greater than 0 eq/ha/yr) was included in their calculations.
2nd Draft Risk and Exposure Assessment
Appendix 5-58
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 3.1-9. Comparison of the Critical Load Values Determined in This Case Study and the
2 Critical Load Values Determined by McNulty et al. (2007) for the Seven Plots in the Kane
3 Experimental Forest Case Study Area
Case Study
Plot
1
2
3
4
5
6
7
Critical Load (eq/ha/yr)
Case Study*
Nutrient Uptake (Bcu
and Nu) NOT
Included
1,139
1,032
1,071
1,032
1,139
1,032
1,032
Nutrient Uptake (Bcu
and Nu) Included
960
910
983
990
1,118
946
981
McNulty et al. (2007)
1,146
1,144
1,061
1,061
1,061
1,061
1,064
4 The case study values in this table are those calculated with K^bb = 300 m /eq and (Bc/Al)crit =
5 10.0, and the clay-substrate method to estimate base cation weathering.
6 3.1.2 Red Spruce
7 The estimates of critical loads of acidity for red spruce in the HBEF Case Study Area are
8 presented in Table 3.1-10. The critical load estimates for this case study area were lower than
9 those for KEF, and ranged from 391 eq/ha/yr to 2,568 eq/ha/yr of combined total nitrogen and
10 sulfur deposition ((N+S)COmb). Similar to the KEF Case Study Area, the ranges of critical loads
11 associated with the three (Bc/Al)crit ratios differed by level of protection to tree health. The least
12 stringent, least protective level, (Be/Al)crit ratio = 0.6, had the highest critical loads that ranged
13 from 991 eq/ha/yr to 2,568 eq/ha/yr of (N+S)comb The intermediate level of protection, (Bc/Al)crit
14 ratio = 1.2, had critical loads ranging from 697 eq/ha/yr to 1,801 eq/ha/yr. The (Be/Al)crit ratio of
15 10.0, corresponding to the most stringent, most protective level for tree protection, had critical
16 load values ranging from 391 eq/ha/yr to 987 eq/ha/yr of (N+S)comb. The Ksibb and method to
17 calculate BCW also influenced the critical load estimates for the FffiEF Case Study Area, with the
18 Kgibb value of 300 m6/eq2 resulting in higher critical load values than 3000 m6/eq2. In contrast,
19 the soil type-texture approximation method to estimate BCW caused higher critical load values in
20 the FffiEF Case Study Area. These trends in the results were largely due to the relatively low
21 clay and organic matter concentrations in the soils in HBEF Case Study Area compared to the
2nd Draft Risk and Exposure Assessment
Appendix 5-59
June 5, 2009
-------
Terrestrial Acidification Case Study
1 KEF Case Study Area; this lower clay content resulted in much lower BCW rates using the clay-
2 substrate compared to soil type-texture method.
3 Table 3.1-10. Critical Load Calculated with the Different Base Cation Weathering and Gibbsite
4 Equilibrium Constant (Ksibb) Parameter Values in the Hubbard Brook Experimental Forest Case
5 Study Area
(Bc/Al)crit Ratio
0.6
1.2
10.0
Critical Load (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300 m6
eq2
1,237
892
487
tfgibb = 3,000
m6/eq2
991
697
391
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
2,568
1,801
987
tfgibb = 3,000
m6/eq2
2,232
1,534
856
7 Two series of CLF response curves that indicate the combined total nitrogen and sulfur
8 deposition ((N+S)comb) levels for the three (Be/Al)crit ratios (i.e., 0.6, 1.2, and 10.0) for the HBEF
9 Case Study Area are shown in Figures 3.1-3 and 3.1-4. These two sets of critical load estimates
10 were selected to provide an indication of the range of critical loads associated with the three
11 levels of protection for red spruce health in the HBEF Case Study Area.
12 Figure 3.1-3 shows the CLF response curves corresponding to the lowest critical loads.
13 This scenario occurred with the BCW calculated using the clay-substrate method and the 300
14 m6/eq2 Kg^b constant. Figure 3.1-4 shows the CLF response curves associated with the highest
15 critical load estimates in the HBEF Case Study Area. These estimates were calculated with BCW
16 estimated using the soil type-texture approximation method and the 3,000 m6/eq2 ^g;bb constant.
2nd Draft Risk and Exposure Assessment
Appendix 5-60
June 5, 2009
-------
Terrestrial Acidification Case Study
3000
1
2
3
4
5
6
9
10
11
12
.2 ^
948
654
348
0
Low Protection (Bc/Al = 0.6)
— ""Intermediate Protection (Bc/Al = 1.2)
- - - High Protection (Bc/Al = 10.0)
CLmin(N)
391
697
991
3000
N Deposition
(eq/ha/yr)
Figure 3.1-3. The critical load function response curves detailing the lowest critical load
estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table
3.1-10 for the parameters corresponding to each of the curves). The flat line portion of
the curves indicates total nitrogen deposition corresponding to the CLm;n(N) (nitrogen
absorbed by nitrogen sinks within the system).
3000
2525
tf
1) ^H
Q i)
GO V—'
944
Low Protection (Bc/Al = 0.6)
• ~ Intermediate Protection (Bc/Al = 1.2)
- - High Protection (Bc/Al = 10.0)
CLmin(N)
0 43
987
2568
3000
N Deposition
(eq/ha/yr)
Figure 3.1-4. The critical load function response curves detailing the highest critical load
estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table
3.1-10 for the parameters corresponding to each of the curves). The flat line portion of
the curves indicates total nitrogen deposition corresponding to the CLm;n(N) (nitrogen
absorbed by nitrogen sinks within the system).
2nd Draft Risk and Exposure Assessment
Appendix 5-61
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
The HBEF Case Study Area has been the site of extensive research, and several studies
have estimated critical loads of acidity for the experimental forest (Table 3.1-11). Using the
8MB model and parameter values similar or equivalent to those used in this case study, McNulty
et al. (2007) calculated a critical load of 516 eq/ha/yr for the location of the case study plot. The
NEG/ECP Forest Mapping Group (2005) that conducted a detailed assessment of critical loads in
several of the northeastern states and the eastern provinces of Canada determined critical load
values around 1,500 eq/ha/yr for the area of the case study plot in FffiEF (NEG/ECP Forest
Mapping Group, 2005). This group used a modified version of the 8MB model to estimate
critical loads. Pardo and Driscoll (1996) also calculated critical loads for the HBEF. They
evaluated four charge- and mass-balance models (steady-state water chemistry, nitrogen mass
balance, base cation mass balance, and steady-state mass balance models) over three different
time periods (between 1965 and 1988) and found values ranging from -433 to 1,452 eq/ha/yr
(negative values are equal to 0 eq/ha/yr). Therefore, the critical load values of 391 to 2,181
eq/ha/yr calculated in this case study are consistent with those from earlier studies that used
similar and different methods or models to estimate critical loads. The higher estimates (i.e.,
2,568 eq/ha/yr) in this case study can be attributed to the soil type-texture approximation method
of calculating BCW and the lowest (Bc/Al)crit (0.6) value.
Table 3.1-11. Summary of the Critical Load Values Determined by Other Studies Conducted in
the Hubbard Brook Experimental Forest (negative values are equal to 0 eq/ha/yr)
Critical Load Model
Used for Estimation
Simple Mass Balance
Steady-State Water
Chemistry
Nitrogen Mass
Balance
Basic Cation Mass
Balance
Modified Basic
Cation Mass Balance
Steady-State Balance
Critical Load Values for Each Study (eq/ha/yr)
Pardo and
Driscoll (1996)
-
-4 to 1 1
133 to 1,452
62 to 133
Oto 1,405
-433 to 630
McNulty et
al. (2007)
516
-
-
-
-
-
NEG/ECP
Forest Mapping
Group (2005)
-1,500
-
-
-
-
-
Case Study
391 to 2,568
-
-
-
-
-
20 Source: Pardo and Driscoll, 1996.
2nd Draft Risk and Exposure Assessment
Appendix 5-62
June 5, 2009
-------
Terrestrial Acidification Case Study
1 3.2 Recommended Parameter Values and Critical Loads
2 Within the ranges of critical loads estimated for the KEF and HBEF case study areas,
3 three critical loads were selected to represent the conditions associated with the three levels of
4 protection (Bc/Al(crit) = 0.6, 1.2, and 10.0) for sugar maple in KEF and for red spruce in FffiEF
5 (Table 3.2-1). For the KEF Case Study Area, these critical load values, in order of lowest to
6 highest level of protection were 2,009, 1,481 and 910 eq/ha/yr (for Bc/Al(crit)= 0.6, 1.2, and 10.0,
7 respectively). For the HBEF Case Study Area, these values, in order of lowest to highest level of
8 protection, were 1,237, 892, and 487 eq/ha/yr (for Bc/Al(crit)= 0.6, 1.2, and 10.0, respectively).
9 These critical load estimates were derived using the clay-substrate method to estimate
10 BCW and a Ksibb of 300 m6/eq2. For the KEF Case Study Area, nutrient uptake and removal with
11 tree harvest (Bcu and Nu) was also included in the critical load estimates, and within the
12 constraints of the selected parameters, the plot with the most conservative (i.e., lowest critical
13 load) was selected to represent the full KEF Case Study Area. The parameter values were set to 0
14 eq/ha/yr in the FffiEF Case Study Area because the study plots in this experimental forest are not
15 actively managed or harvested. The selection of these parameters and methods was based on the
16 best available recommendations of scientists and research efforts, to date. When field
17 assessments and measurements are not possible, the clay-substrate method is one of the most
18 commonly used methods to estimate base cation weathering in North America (Ouimet et al.,
19 2006; Watmough et al., 2006; McNulty et al., 2007; Pardo and Duarte 2007), and the 300 m6/eq2
20 value of the Kg^b is a recommended default value (UNECE, 2004). For the KEF Case Study
21 Area, the influence of nutrient uptake and removal (i.e., Bcu and Nu greater than 0 eq/ha/yr) was
22 included because the forest has been and will likely continue to be actively harvested (USFS,
23 1999).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-63
-------
Terrestrial Acidification Case Study
1 Table 3.2-1. Critical Loads Selected to Represent the Three Levels of Protection in the Kane
2 Experimental Forest and Hubbard Brook Experimental Forest Case Study Areas
Protection Level (Bc/Al(crit)
ratio)
Low (Bc/Al(crit) = 0.6)
Medium (Bc/Al(crit) = 1.2)
High (Bc/Al(cnt)= 10.0)
Critical Load Values for Each Case Study Area
(eq/ha/yr)
KEF
2,009
1,481
910
HBEF
1,237
892
487
4 3.3 Current Conditions
5 This section discusses the impact of the 2002 CMAQ/NADP total nitrogen and sulfur
6 deposition levels relative to the critical loads estimated for the KEF and FffiEF case study areas.
7 The atmospheric deposition of total nitrogen and sulfur ((N+S)comb) in both the FffiEF and KEF
8 case study areas was elevated. According to 2002 CMAQ output, the KEF Case Study Area
9 received 13.6 kilograms (kg) N/ha (967.5 eq/ha) and 20.7 kg S/ha (646.4 eq/ha), and the HBEF
10 Case Study Area experienced 8.4 kg N/ha (601.1 eq/ha) and 7.5 kg S/ha (233.1 eq/ha). When
11 these deposition levels were compared to the critical loads calculated using the three (Bc/Al)crit
12 ratio values, the CMAQ-modeled (N+S)comb deposition loads were found to be both greater than
13 and less than the three critical loads for the two case study areas (Tables 3.3-1 to 3.3-9, Figures
14 3.3-1 to 3.3-4) . In all plots of the KEF Case Study Area, the 2002 CMAQ/NADP total nitrogen
15 and sulfur deposition levels ((N+S)comb) were greater than the range of total nitrogen and sulfur
16 allowable for the most stringent critical load (where (Bc/Al)crit= 10.0). Similarly, in the HBEF
17 Case Study Area, the modeled (N+S)comb deposition was greater than the critical loads estimated
18 using the (Bc/Al)crit = 10.0 ratio and the clay-substrate method to estimate BCW:. However,
19 combined 2002 CMAQ/NADP total nitrogen and sulfur deposition levels were less than the
20 critical loads estimated with (Bc/Al)crit= 10.0 and BCW determined by the soil type-texture
21 approximation. The 2002 CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)comb)
22 were less than the ranges of total nitrogen and sulfur allowable for the least stringent critical load
23 (where (Bc/Al)crit = 0.6) for both the HBEF Case Study Area and all plots of the KEF Case Study
24 Area. The only exception to this trend was in Plot 1 of the KEF Case Study Area, where the
25 removal of base cations and nitrogen with harvesting (Bcu and Nu greater than 0 eq/ha/yr) were
2nd Draft Risk and Exposure Assessment
Appendix 5-64
June 5, 2009
-------
Terrestrial Acidification Case Study
1 included in the load calculations. The variability in the results comparing 2002 CMAQ/NADP
2 total nitrogen and sulfur deposition levels to calculated acid loads shows the strong influence of
3 the Bc/Al(Crit) indicator ratio (reflecting the level of tree protection) in critical load estimates.
4
5
6
7
Table 3.3-1. Ranges of Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur
Deposition Levels ((N+S)COmb) and the Estimated Critical Load Values (with and without the
Influence of Nutrient Uptake and Removal [Nu and Bcu]) for the Seven Plots of the Kane
Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu and Nu
NOT
Included
Bcu and Nu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)comb Deposition and Estimated
Critical Loads (eq/ha/yr)
Plotl
-1,384 to
-714
-465 to
23*
475* to
731*
-327 to
282*
141* to
581*
654* to
886*
Plot 2
-1,098 to
-714
-271 to
23*
582* to
731*
-395 to
-59
132* to
391*
704* to
835*
Plot3
-1,203 to
-714
-343 to
23*
543* to
731*
-6 14 to
-162
-12 to
326*
631* to
805*
Plot 4
-1,098 to
-714
-271 to
23*
582* to
731*
-774 to
-411
-93 to
185*
624* to
764*
PlotS
-1,384 to
-714
-465 to
23*
475* to
731*
-931 to
-289
-235 to
231*
496* to
741*
Plot 6
-1,098
to -714
-271 to
23*
582* to
731*
-598 to
-247
15* to
285*
668* to
804*
Plot?
1,098 to
-714
-271 to
23*
582* to
731*
-755 to
-393
-79 to
199*
633* to
774*
9
10
: Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
2nd Draft Risk and Exposure Assessment
Appendix 5-65
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
5
Table 3.3-2. Differences between the 2002 CMAQ/NADP Total Nitrogen and
Sulfur Deposition Levels ((N+S)comb) and the Critical Load Values (with Two
Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium
Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake
Parameter Values) in Plot 1 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)COmb Deposition and
Estimated Critical Deposition Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
-1,384
-465
475*
-327
141*
654*
tfgibb = 3,000
-1,064
-211
600*
-78
338*
751*
Soil Type-Texture
Approximation Method
tfgibb = 300
-1,019
-218
612*
61*
406*
800*
tfgibb = 3,000
-714
23*
731*
282*
581*
886*
6
7
: Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
9
10
11
12
Table 3.3-3. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods, Two Gibbsite Equilibrium Constants [^gibb], and Two Base Cation (Bcu) and Nitrogen
(Nu) Uptake Parameter Values) in Plot 2 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)comb Deposition and
Estimated Critical Loads ( eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
-1,098
-271
582*
-395
132*
704*
tfgibb = 3,000
-789
-27
703*
-135
339*
806*
Soil Type-Texture
Approximation Method
tfgibb = 300
-1,019
-218
612*
-314
188*
735*
tfgibb = 3,000
-714
23*
731*
-59
391*
835*
13
14
* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
2nd Draft Risk and Exposure Assessment
Appendix 5-66
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
Table 3.3-4. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods, Two Gibbsite Equilibrium Constants [£gibb], and Two Base Cation (Bcu) and Nitrogen
(Nu) Uptake Parameter Values) in Plot 3 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)comb Deposition and
Estimated Critical Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
-1,203
-343
543*
-614
-12
631*
tfgibb = 3,000
m6/eq2
-890
-95
665*
-340
206*
738*
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
-1,019
-218
612*
-425
117*
702*
Kvbb = 3,000
m6/eq2
-714
23*
731*
-162
326*
805*
5
6
* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
7
8
9
10
Table 3.3-5. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods, Two Gibbsite Equilibrium Constants [£gibb], and Two Base Cation (Bcu) and Nitrogen
(Nu) Uptake Parameter Values) in Plot 4 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)COmb Deposition and
Estimated Critical Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
-1,098
-271
582*
-774
-93
624*
Kstbb = 3,000
m6/eq2
-789
-27
703*
-487
134*
736*
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
-1,019
-218
612*
-694
-39
654*
tfgibb = 3,000
m6/eq2
-714
23*
731*
-411
185*
764*
11
12
: Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
2nd Draft Risk and Exposure Assessment
Appendix 5-67
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
Table 3.3-6. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods, Two Gibbsite Equilibrium Constants [£gibb], and Two Base Cation (Bcu) and Nitrogen
(Nu) Uptake Parameter Values) in Plot 5 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)comb Deposition and
Estimated Critical Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
-1,384
-465
475*
-931
-235
496*
tfgibb = 3,000
m6/eq2
-1,064
-211
600*
-642
-6
609*
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
-1,019
-218
612*
-559
18*
636*
Kvbb = 3,000
m6/eq2
-714
23*
731*
-289
231*
741*
5
6
* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
7
8
9
10
Table 3.3-7. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods, Two Gibbsite Equilibrium Constants [£gibb], and Two Base Cation (Bcu) and Nitrogen
(Nu) Uptake Parameter Values) in Plot 6 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)COmb Deposition and
Estimated Critical Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
-1,098
-271
582*
-598
15*
668*
Kstbb = 3,000
m6/eq2
-789
-27
703*
-322
234*
776*
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
-1,019
-218
612*
-518
69*
698*
tfgibb = 3,000
m6/eq2
-714
23*
731*
-247
285*
804*
11
12
: Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
2nd Draft Risk and Exposure Assessment
Appendix 5-68
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
Table 3.3-8. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods, Two Gibbsite Equilibrium Constants [£gibb], and Two Base Cation (Bcu) and Nitrogen
(Nu) Uptake Parameter Values) in Plot 7 of the Kane Experimental Forest Case Study Area
Nutrient
Uptake in
Critical
Load
Estimate
Bcu andNu
NOT
Included
Bcu andNu
Included
(Bc/Al)crit
Ratio
0.6
1.2
10.0
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)comb Deposition and
Estimated Critical Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300
m6/eq2
-1,098
-271
582*
-755
-79
633*
tfgibb = 3,000
m6/eq2
-789
-27
703*
-469
148*
745*
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
-1,019
-218
612*
-675
-25
663*
Kvbb = 3,000
m6/eq2
-714
23*
731*
-393
199*
774*
5
6
7
8
9
10
* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
Table 3.3-9. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition
Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation
Methods and Two Gibbsite Equilibrium Constants [^gibb]) in the Hubbard Brook Experimental
Forest Case Study Area
(Bc/Al)erit Ratio
0.6
1.2
10.0
Difference between CMAQ/NADP (N+S)COmb Deposition and Estimated
Critical Loads (eq/ha/yr)
Clay-Substrate Method
tfgibb = 300 m6
eq2
-403
-58
347*
tfgibb = 3,000
m6/eq2
-157
137*
443*
Soil Type-Texture
Approximation Method
tfgibb = 300
m6/eq2
-1,734
-967
-153
Kstbb = 3,000
m6/eq2
-1,398
-700
-21
* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 (N+S)comb
deposition.
2nd Draft Risk and Exposure Assessment
Appendix 5-69
June 5, 2009
-------
Terrestrial Acidification Case Study
3500
1026
422
• Low Protection (Bc/Al = 0.6)
' Intermediate Protection (Bc/Al =1.2)
High Protection (Bc/Al = 10.0)
CLmin(N)
2002 CMAQ N and S Deposition
1332
1
2
3
4
5
6
7
N Deposition
(eq/ha/yr)
3500
Figure 3.3-1. Plot 1 Kane Experimental Forest critical load function response curves,
detailing the lowest critical load estimates for Kane Experimental Forest Case Study Area
(refer to Table 3.1-1 for the parameters corresponding to each of the curves). The 2002
CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)COmb) were greater than
the critical loads of nitrogen and sulfur at all levels of protection ((Bc/Al)crit = 0.6, 1.2,
and 10.0). The flat line portion of the curves indicates total nitrogen deposition
corresponding to the CLm;n (N) (nitrogen absorbed by nitrogen sinks within the system).
2nd Draft Risk and Exposure Assessment
Appendix 5-70
June 5, 2009
-------
Terrestrial Acidification Case Study
3500
1
2
3
4
5
6
7
10
11
12
13
14
15
16
17
18
N
.
Is
If
Low Protection (Bc/Al = 0.6)
— -" Intermediate Protection (Bc/Al = 1.2)
- - - High Protection (Bc/Al = 10.0)
CLmin(N)
• 2002 CMAQ N and S Deposition
X
X
X
X
X
X
X
X
X
X
2998
N Deposition
(eq/ha/yr)
Figure 3.3-2. Plot 1 critical load function response curves, detailing the highest
maximum deposition load estimates for Kane Experimental Forest Case Study Area
(refer to Table 3.1-1 for the parameters corresponding to each of the curves). The 2002
CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)COmb) were greater than
the critical load of total nitrogen and sulfur deposition calculated with the highest level of
protection (Bc/Al)crit= 10.0). The flat line portion of the curves indicates total nitrogen
deposition corresponding to the CLmui(N) (nitrogen absorbed by nitrogen sinks within the
system).
3000
O ,q,
'•S >,
O J
o, "9
948
654
348
Low Protection (Bc/Al = 0.6)
— - Intermediate Protection (Bc/Al = 1.2)
- - - High Protection (Bc/Al = 10.0)
CLmin(N)
• 2002 CMAQ N and S Deposition
391
697
991
3000
N Deposition
(eq/ha/yr)
Figure 3.3-3. Critical load function response curves, detailing the lowest critical load
estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table
3.1-10 for the parameters corresponding to each of the curves). The 2002 CMAQ/NADP
total nitrogen and sulfur deposition levels ((N+S)COmb) were greater than the critical load
of total nitrogen and sulfur calculated with the highest and the intermediate levels of
protection ((Bc/Al)crit= 1.2 and 10.0). The flat line portion of the curves indicates total
nitrogen deposition corresponding to the CLmui(N) (nitrogen absorbed by nitrogen sinks
within the system).
2nd Draft Risk and Exposure Assessment
Appendix 5-71
June 5, 2009
-------
Terrestrial Acidification Case Study
3000
2525
If
ft
D &
944
Low Protection (Bc/Al = 0.6)
__ _ intermediate Protection (Bc/Al = 1.2)
- - - High Protection (Bc/Al = 10.0)
CLmin(N)
• 2002 CMAQ N and S Deposition
X
X
X
X
043
987
N Deposition
(eq/ha/yr)
2568
3000
1
2 Figure 3.3-4. Critical load function response curves, detailing the highest critical load
3 estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table
4 3.1-10 for the parameters corresponding to each of the curves). The 2002 CMAQ/NADP
5 total nitrogen and sulfur deposition levels ((N+S)comb) were less than the critical loads
6 associated with all three (Be/Al)crit ratios. The flat line portion of the curves indicates total
7 nitrogen deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen sinks
8 within the system).
9 As outlined in Section 3.2, critical loads of 2,009, 1,481 and 910 eq/ha/yr were selected
10 to represent the three levels of increasing protection for the KEF Case Study Area, and 1,237,
11 892 and 487 eq/ha/yr were the critical loads selected for the HBEF Case Study Area. These
12 estimates are based on the critical load parameters suggested and most frequently used by
13 scientists and previous research. When compared to the 2002 CMAQ-modeled deposition levels,
14 it was evident that the deposition levels were greater than the most protective critical load
15 (Bc/Al(crit) = 10.0) for both case study areas and also greater than the intermediate protection
16 critical load (Bc/Al(crit) = 1.2) for KEF (Figures 3.3-5 and 3.3-6)). In these comparisons, 2002
17 CMAQ/NADP total nitrogen and sulfur deposition levels exceeded the KEF Case Study Area
18 critical load by 132 to 704 eq/ha/yr and exceeded the FffiEF Case Study Area's critical load by
19 347 eq/ha/yr. Similar results have been reported in other studies which have assessed the two
20 case study areas. McNulty et al. (2007) and Pardo and Driscoll (1996) found that deposition
21 levels were greater than the estimated critical loads in the HBEF area. McNulty et al. (2007) also
2nd Draft Risk and Exposure Assessment
Appendix 5-72
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
reported that 2002 CMAQ/NADP total nitrogen and sulfur deposition levels in the KEF
exceeded the calculated critical loads for the KEF Case Study Area. These results suggest that
the health of red spruce at HBEF and sugar maple at KEF may have been compromised by the
acidifying nitrogen and sulfur deposition received in 2002.
3500
o
CX
%
Q
1796
1268
697
0
Low Protection (Bc/Al = 0.6)
Intermediate Protection (Bc/Al =1.2)
- - - - High Protection (Bc/Al = 10.0)
CLmin(N)
• 2002 CMAQ N and S Deposition
0 213
910
1481
2009
3500
5
6
7
8
9
10
11
N Deposition
(eq/ha/yr)
Figure 3.3-5. Critical load function response curves for the three selected critical loads
conditions (corresponding to the three levels of protection) for the Kane Experimental
Forest Case Study Area. The 2002 CMAQ/NADP total nitrogen and sulfur deposition
levels ((N+S)comb) were greater than the highest and intermediate level of protection
critical loads. The flat line portion of the curves indicates total nitrogen deposition
corresponding to the CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system).
2nd Draft Risk and Exposure Assessment
Appendix 5-73
June 5, 2009
-------
Terrestrial Acidification Case Study
3000
ex
(L>
Q
GO
1194
849
444
Low Protection (Bc/Al = 0.6)
Intermediate Protection (Bc/Al =1.2)
- - - - High Protection (Bc/Al = 10.0)
CLmin(N)
• 2002 CMAQ N and S Deposition
43
487
892
1237
3000
N Deposition
(eq/ha/yr)
1
2 Figure 3.3-6. Critical load function response curves for the three selected critical loads
3 conditions (corresponding to the three levels of protection) for the Hubbard Brook
4 Experimental Forest Case Study Area. The 2002 CMAQ/NADP total nitrogen and sulfur
5 deposition levels ((N+S)comb) were greater than the highest level of protection critical
6 loads. The flat line portion of the curves indicates total nitrogen deposition corresponding
7 to the CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system).
8 Acidifying nitrogen deposition consists of both reduced (NHX) and oxidized (NOX) forms
9 of nitrogen. However, only NOX is currently regulated as a criteria pollutant. Therefore, to gain
10 an understanding of the relationship between the two states (i.e., reduced and oxidized) of total
11 nitrogen deposition and the critical loads for the KEF and HBEF case study areas, total nitrogen
12 deposition must be separated into NHX-N and NOX-N. Examples of the separation of nitrogen
13 species are presented in Figure 3.3-7 and Figure 3.3-8 which indicate the CLF response curves
14 for the highest level of protection critical load condition selected for the KEF and HBEF case
15 study areas, respectively. In these relationships, the CLF function has been modified by
16 maintaining NHX-N deposition at the 2002 CMAQ-modeled deposition level; only sulfur and
17 NOX-N deposition levels vary to indicate the combined critical load. Based on 2002
18 CMAQ/NADP total nitrogen and sulfur deposition levels, NHX-N accounted for 25.7% (249
19 eq/ha) and 26.4% (159 eq/ha) of total nitrogen deposition in KEF and HBEF case study areas,
20 respectively. These fixed amounts of NHX-N influenced the highest protection CLF response
21 curves for both areas. For both case studies, the maximum sulfur critical load (CLmax(S)) and the
2nd Draft Risk and Exposure Assessment
Appendix 5-74
June 5, 2009
-------
Terrestrial Acidification Case Study
1
2
3
4
5
6
7
8
9
10
11
12
13
maximum nitrogen critical load (CLmax(N)), as NOX, were lowered.. In the calculations for the
KEF Case Study Area, the CLmax(S) was reduced by 5% to 661 eq/ha/yr, and in the HBEF Case
Study Area calculations, the CLmax(S) was reduced by 26% to 328 eq/ha/yr. Similarly, the
CLmax(N) (as NOX) for KEF was reduced by 27% to 661 eq/ha/yr, and the CLmax(N) (as NOX) for
HBEF was reduced by 33% to 328 eq/ha/yr.
1200
c
•~H !-H
o
ex <
CLmax(S) 697
Adj. CLmax(S) 661
Q
GO
i^X.
i
^
^,
\
v
*
—
\
V
*
ta.
_
Q NHX-N deposition (fixed amount)
H NOX-N deposition
• 2002 CMAQ N and S deposition
CLmm(N)
•
^
V
w
v
V
^ ^
V
V
TV
rv
V
. TV
213 249
CLmin(N)
910
1200
N Deposition
(eq/ha/yr)
Figure 3.3-7. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur
deposition levels (NHX-N) on the critical load function response curve, and in turn, the
maximum critical loads of sulfur (CLmax(S)) and oxidized nitrogen (NOX-N) for the
selected highest protection critical load for the Kane Experimental Forest Case Study
Area. The critical load of oxidized nitrogen (NOX-N) is 661 eq/ha/yr (910 - 249
eq/ha/yr). The CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system)
corresponds to the value depicted in Figure 3.3-5.
2nd Draft Risk and Exposure Assessment
Appendix 5-75
June 5, 2009
-------
Terrestrial Acidification Case Study
750
1
2
3
4
5
6
7
9
10
1?
o jS
ex ^
GO
CLmax(S)
Adj. CLmax(S) 328
4.
O NHX-N deposition (fixed amount)
H NOX-N deposition
• 2002 CMAQ N and S deposition
CLmm(N)
0
CLmin(N)
159
487
750
N Deposition
(eq/ha/yr)
Figure 3.3-8. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur
deposition levels (NHX-N) on the critical load function response curve and, in turn, the
maximum critical loads of sulfur (CLmax(S)) and oxidized nitrogen (NOX-N) for the
selected highest protection critical load for the Hubbard Brook Experimental Forest Case
Study Area. The critical load of oxidized nitrogen (NOX-N) is 328 eq/ha/yr (487 - 159
eq/ha/yr). The CLm;n(N) (nitrogen absorbed by nitrogen sinks within the system)
corresponds to the value depicted in Figure 3.3-6.
EXPANSION OF CRITICAL LOAD ASSESSMENTS FOR
SUGAR MAPLE AND RED SPRUCE
11 4.1 Critical Load Assessments
12 Although the KEF and FffiEF case studies estimated critical loads for red spruce and
13 sugar maple in two locations and established that the 2002 CMAQ/NADP total nitrogen and
14 sulfur deposition levels were greater than the calculated loads, these results cannot be
15 extrapolated directly to represent the critical load condition for the full distribution ranges of the
16 two tree species. Critical loads are largely determined by soil characteristics, and these
17 characteristics vary by location. Therefore, to gain an understanding of the range of critical load
18 values experienced by sugar maple and red spruce, it is necessary to calculate critical loads in
19 multiple locations throughout the ranges of the two species.
2nd Draft Risk and Exposure Assessment
Appendix 5-76
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Critical load calculations were applied to multiple locations within 24 states for sugar
2 maple and in 8 states for red spruce. Individual site locations within each State were determined
3 by the USFS FIA database permanent sampling plots' locations on forestland7 (timberland8 for
4 New York, Arkansas, Kentucky and North Carolina), each covering 0.07 ha. Only database
5 information for nonunique9, permanent sampling plots that supported the growth of sugar maple
6 or red spruce and had the necessary soil, parent material, atmospheric deposition, and runoff data
7 were included in the analyses. With these restrictions, 4,992 of the 14,669 sugar maple plots and
8 763 of the 2,875 red spruce plots were included in the calculations of the plot-specific critical
9 loads (Table 4.1-1). Although only a subset of the total sugar maple and red spruce plots were
10 included in the analyses, the results are thought to capture accurately the range and trends of
11 critical loads of the two species. Due to the randomness of the plot restrictions, it is unlikely that
12 a bias was incorporated into the analyses.
13 Table 4.1-1. Number and Location of USFS FIA Permanent Sampling Plots Used in the
14 Analysis of Critical Loads For the Full Ranges of Sugar Maple and Red Spruce
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kansas
Kentucky
Maine
Sugar Maple
13
10
35
29
306
13
NA
14
271
Red Spruce
-
-
-
-
-
-
-
-
560
7 Forestland is defined as, "land at least 10 percent stocked by forest trees of any size, or formerly having such tree
cover, and not currently developed for non-forest uses, with a minimum area classification of 1 acre." (USFS,
2002a).
8 Timberland is defined as, "forest land capable of producing in excess of 20 cubic feet per acre per year and not
legally withdrawn from timber production, with a minimum area classification of 1 acre." (USFS, 2002b).
9 Nonunique permanent sampling plot locations are those that have critical load attribute values (e.g., soils, runoff,
and atmospheric deposition) that are not distinct and are repeated within a 250-acre area of the plot location. This
"confidentiality" filter is a requirement of the USFS to prevent the disclosure of data that can be directly linked to
a location on private land. To comply with the necessary "confidentiality," full coverages of the data required for
the critical load deposition calculations were given to the USFS, and the USFS matched and provided the data to
each nonunique, permanent sampling plot.
2nd Draft Risk and Exposure Assessment
Appendix 5-77
June 5, 2009
-------
Terrestrial Acidification Case Study
State
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Rhode Island
South Carolina
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
TOTAL
Sugar Maple
4
33
633
289
147
82
6
485
17
374
285
NA
NA
319
114
175
378
960
4,992
Red Spruce
-
O
-
-
-
55
-
52
1
-
NA
-
-
1
11
NA
7
-
763
1 Note: NA = data not available for State; "-" = tree species not present on forestland
2 in State.
3
4 The parameter values selected for the 8MB calculations of critical loads for all plots
5 within the ranges of sugar maple and red spruce included wet deposition of base cations (Na+,
6 Ca+2, Mg+2, and K+) and chlorine, the clay-substrate method to estimate BCW, three levels of
7 protection ((Bc/Al)crit ratio = 0.6, 1.2 and 10.0), a 0.5 m rooting zone soil depth, and the N;
8 (42.86 eq/ha/yr) and Nde (0 eq/ha/yr) default values used for the HBEF and KEF case study
9 areas. The Kgibb constant ranged from 100 to 950 m6/eq2, and was determined by average organic
10 matter content, as outlined by McNulty et al. (2007) (Table 4.1-2). Nutrient (Nu) and base cation
11 (BCU) uptake were not included in the 8MB calculations because it was not possible to determine
12 the harvesting status of the individual sampling plots. Corrections for sea salt influence were not
13 applied to the wet deposition because such corrections were found to over-correct deposition
14 estimates (McNulty et al., 2007).
2nd Draft Risk and Exposure Assessment
Appendix 5-78
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Similar to the KEF and HBEF case studies, the U.S. Department of Agriculture- Natural
2 Resources Conservation Service (USDA-NRCS) SSURGO soils database (USDA-NRCS,
3 2008c) was the main source used to estimate BCW in the calculations of critical loads for the full
4 distribution ranges of sugar maple and red spruce. The U.S. Geological Survey (USGS) state-
5 level integrated map database for the United States (USGS, 2009b) was used as a secondary
6 source of information, when necessary. Parent material acidity was inferred from the parent
7 material attribute in the SSURGO soils database. The estimated total silica and ferromagnesium
8 content, relative to the mineral assemblage typical of the rock or sediment type, were used to
9 classify parent material as acidic, intermediate, or basic, according to the classification table
10 (Table 4.1-3) outlined by McNulty et al. (2007) from Gray and Murphy (1999). When possible,
11 classification of the parent material silica content was determined by the range of rock types
12 provided as examples in Table 4.1-3. When rock types were not clearly indicated in the parent
13 material attribute, parent material acidity was classified using a systematic protocol involving the
14 consideration of descriptive modifiers that suggest a probable range of silica or ferromagnesium
15 content (Table 4.1-4). In cases where the parent material attribute in the SSURGO soils database
16 was not populated or was too nondescriptive to classify, acidity rating of parent material was
17 inferred from the USGS state-level spatial geology databases. The criteria applied to the soils
18 data were also used in interpretation of these USGS spatial datasets, along with general
19 observation related to spatial patterns of local and regional geologic settings that suggested
20 characteristics of igneous and metamorphic petrogenesis and implied sedimentary deposit!onal
21 mechanisms and environments. If parent material acidity was classified as "organic" or "other"
22 or could not be determined by either the SSURGO or USGS geology databases, the critical load
23 was not estimated for the location. The BCW values in the critical load assessments were not
24 corrected for temperature because the soil temperature attribute in the SSURGO soils database
25 was missing data for most of the plot locations. Average air temperature was not used as a
26 substitute because McNulty et al. (2007) determined that corrections for air temperatures were
27 more suitable for northern climates, presumably where the temperature corrections were derived
28 (i.e., Scandinavia).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-79
-------
Terrestrial Acidification Case Study
1 Table 4.1-2. Gibbsite Equilibrium (Kgibb) Determined by Percentage of Soil Organic Matter
Soil Type Layer
Mineral soils: C layer
Soils with low organic matter: B/C layers
Soils with some organic material: A/E
layers
Peaty and organic soils: organic layers
Organic
Matter %
<5
5tol5
15 to 70
>70
Kg,bb (m6/eq2)
950
300
100
9.5
Source: Modification of table from McNulty et al. (2007).
Table 4.1-3. Parent Material Acidity Classifications for Base Cation (BCW) Estimations
Parent
Material
Classification
Acidic
Intermediate
Parent
Material
Category
Extremely
siliceous
Highly
siliceous
Transitional
siliceous/
Intermediate
Intermediate
Silica
Content
>90%
72% to
90%
62% to
72%
52% to
62%
Calcium-
Ferromagnesium
Content
Extremely low
(generally <3%)
Low
(generally 3% to
7%)
Moderately low
(generally 7% to
14%)
Moderate
(generally 14% to
20%)
Examples
Quartz sands (i.e., beach,
alluvial, or Aeolian), chert,
quartzite, quartz reefs, and
silicified rocks
Granite, rhyolite,
adamellite, dellenite,
quartz sandstone, and
siliceous tuff
Granodiorite, dacite,
trachyte, syenite, most
greywacke, most lithic
sandstone, most
argillaceous rocks, and
siliceous/intermediate tuff
Monzonite, trachy-
andesite, diorite, andesite,
intermediate tuff, as well
as some greywacke, lithic
sandstone, and
argillaceous rock
2nd Draft Risk and Exposure Assessment
Appendix 5-80
June 5, 2009
-------
Terrestrial Acidification Case Study
Parent
Material
Classification
Basic
Organic
Other
Parent
Material
Category
Mafic
Ultramafic
Calcareous
Organic
Alluvial
Sesquioxide
Silica
Content
45% to
52%
<45%
Low
Low
Variable
Variable*
Calcium-
Ferromagnesium
Content
High
(generally 20% to
30%)
Very high
(generally >30%)
CaCOs dominate
other bases
variable
Organic matter
dominates bases
variable
Variable
Variable,
dominated by
sesquioxides
Examples
Gabbro, dolerite, basalt,
and mafic tuff
(uncommon)
Serpentinite, dunite,
peridotite, amphibolite,
and tremolite-chlorite-talc
schists
Limestone, dolomite,
calcareous shale, and
calcareous sands
Peat, coal, and humified
vegetative matter
Variable
Laterite, bauxite,
ferruginous sandstone, and
ironstone
* Category not defined by silica content
2 Source: Modified from McNulty et al. (2007).
3 Table 4.1-4. Parent Material and Descriptive Modifier Characteristics (within the SSURGO
4 Soils [USDA-NRCS, 2008c] and USGS Geology [USGS, 2009b] Databases) Used to Classify
5 Parent Material Acidity
Parent Material or
Modifier Characteristic
Glacial deposits, till,
outwash, or drift
(without modifiers)
Glacial lacustrine
(without modifiers)
Glaciomarine (without
modifiers)
Marine deposits (without
modifiers)
Loess and eolian
(without modifiers)
Acidity
Classification
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Rational
Probable mixture of different
mineralogies
Probable mixture of different
mineralogies
Probable mixture of different
mineralogies
Probable mixture of different
mineralogies
Probable mixture of different
mineralogies
2nd Draft Risk and Exposure Assessment
Appendix 5-81
June 5, 2009
-------
Terrestrial Acidification Case Study
Parent Material or
Modifier Characteristic
Alluvium (without
modifiers)
Residuum (without
modifier)
Colluvium (without
modifiers)
Deposits, till, or outwash
(without modifiers)
Saprolite (without
modifiers)
Sandy modifier
Loamy modifier
Skeletal modifier
Red, brown, ferric, iron
modifier
Opposing mineralogies
Multiple layers described
Acidity
Classification
Acidic
Not able to classify
Intermediate
Not able to classify
Not able to classify
Acidic
Intermediate
Classification based
on top two layer
descriptions
Basic
Intermediate
Classification based
on top layer
description
Rational
Probable composition, predominantly
quartz
Mineralogy unknown
Probable mixture of different
mineralogies located close to source
area
Mineralogy unknown
Mineralogy unknown
Probable high silica content
Equal contributions of chemical
properties (intermediate ion exchange
capacity) from the three soil textures
(i.e., sand, silt, clay)
Skeletal layer is very thin and spotty, so
top two layers considered main source
of parent material
Probable high iron content (associated
with potentially higher) cation ion-
exchange capacity
Considered average of two
mineralogies
Top layer considered main source of
soil parent material
4
5
6
7
8
9
The calculated critical loads for the three levels of protection (Bc/Al(crit) = 0.6, 1.2 and
10.0) were compared to 2002 CMAQ/NADP total nitrogen and sulfur deposition levels to
determine which plots with sugar maple and/or red spruce experienced deposition levels greater
than the critical load values.
Based on the 8MB calculations for the three levels of protection (Bc/Al(crit) = 0.6, 1.2 and
10.0), critical loads of acidifying deposition for sugar maple in the 24 states were found to range
from 107 to 6,008 eq/ha/yr (Table 4.1-5). Critical loads for red spruce in the 8 states ranged from
180 to 4,278 eq/ha/yr. In a comparison of the 2002 CMAQ/NADP total nitrogen and sulfur
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-82
-------
Terrestrial Acidification Case Study
1 deposition levels and calculated critical loads, 3% to 75% of all sugar maple plots and 3% to
2 36% of all red spruce plots were found to have 2002 CMAQ/NADP total nitrogen and sulfur
3 deposition levels greater than the critical loads; the highest protection critical loads (Bc/Al(crit) =
4 10.0) had the highest frequency of exceedance (Table 4.1-6). Aggregated by State, a large
5 proportion of the sugar maple and red spruce plots showed high levels of critical load
6 exceedance for the highest protection level (Bc/Al(Crit) = 10.0) and comparatively lower
7 exceedance frequency at the lowest protection level ((Bc/Al(crit) = 0.6)) (Table 4.1-6, Figures
8 4.1-1 to 4.1-6). In general, New Hampshire displayed the greatest degree of critical load
9 exceedance at all protection levels for both species.
10 Collectively, these results suggest that the health of at least a portion of the sugar maple
11 and red spruce growing in the United States may have been compromised with the 2002
12 CMAQ/NADP total nitrogen and sulfur deposition levels; even with the lowest level of
13 protection, half the states contained sugar maple and red spruce stands that were negatively
14 impacted by acidifying deposition. At the highest level of protection (Bc/Al(crit) = 10.0), the
15 apparent impact of the 2002 CMAQ/NADP total nitrogen and sulfur deposition levels was much
16 greater. A large proportion of sugar maple (>80% of plots in 13 of 24 states) and the majority of
17 red spruce (100% of plots in 5 of 8 states) experienced deposition levels that exceeded the
18 critical loads. If this high protection critical load accurately represents the conditions of the two
19 species, a large proportion of both sugar maple and red spruce, throughout their ranges, were
20 most likely negatively impacted by 2002 CMAQ/NADP total nitrogen and sulfur deposition
21 levels.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-83
-------
Terrestrial Acidification Case Study
1 Table 4.1-5. Ranges of Critical Load Values by Level of Protection (Bc/Al(crit) = 0.6, 1.2, and 10.0) and by State for the Full
2 Distribution Ranges of Sugar Maple and Red Spruce
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kansas
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Ranges of Critical Load Values (eq/ha/yr)
Sugar Maple
Bc/Al = 0.6
1,592 to 5,337
2,239 to 4,290
1,5 19 to 2,468
2,543 to 3,671
1,478 to 5,859
2,260 to 3,791
NA
2,044 to 3,994
746 to 4,284
2,066 to 3,090
791 to 2,4 14
400 to 6,008
220 to 4,9 16
978 to 4,891
5 80 to 1,994
1,452 to 2,651
503 to 4,467
1,415 to 3,444
1,226 to 4,986
1,026 to 4,047
Bc/Al = 1.2
1,1 14 to 3,638
1,536 to 2,913
1,05 8 to 1,702
1,730 to 2,485
1,020 to 3,971
1,533 to 2,560
NA
1,390 to 2,707
535 to 2,983
1,417 to 2,122
566 to 1,661
294 to 4,070
166 to 3,3 18
681 to 3,304
419 to 1,439
1,029 to 1,824
370 to 3,039
1,0 10 to 2,426
855 to 3,366
723 to 2,752
Bc/Al = 10.0
617 to 2,015
857 to 1,623
581 to 941
965 to 1,390
573 to 2,214
854 to 1,424
NA
749 to 1,497
295 to 1,620
929 to 1,178
319 to 919
169 to 2,269
107 to 1,861
377 to 1,843
236 to 780
566 to 1,012
209 to 1,686
558 to 1,319
469 to 1,877
402 to 1,530
Red Spruce
Bc/Al = 0.6
-
-
-
-
-
-
-
-
599 to 4,278
-
1,706 to 1,736
-
-
-
418 to 1,994
-
526 to 3,146
1256
-
NA
Bc/Al = 1.2
-
-
-
-
-
-
-
-
439 to 2,979
-
1,191 to 1,213
-
-
-
324 to 1,439
-
386 to 2,156
926
-
NA
Bc/Al = 10.0
-
-
-
-
-
-
-
-
249 to 1,623
-
656 to 669
-
-
-
180 to 780
-
217 to 1,195
501
-
NA
2nd Draft Risk and Exposure Assessment
Appendix 5-84
June 5, 2009
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Terrestrial Acidification Case Study
2
3
4
State
Rhode Island
South Carolina
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
Combined (all plots)
Ranges of Critical Load Values (eq/ha/yr)
Sugar Maple
Bc/Al = 0.6
NA
NA
921 to 5,755
479 to 5,660
1,036 to 5,852
369 to 4,134
400 to 5,031
220 to 6,008
Bc/Al = 1.2
NA
NA
653 to 3,901
351 to 3,846
726 to 3,968
270 to 2,8 19
290 to 3,393
166 to 4,070
Bc/Al = 10.0
NA
NA
351 to 2, 175
201 to 2, 142
4 10 to 2,208
152 to 1,560
166 to 1,898
107 to 2,269
Red Spruce
Bc/Al = 0.6
-
-
2,065
1,462 to 2,141
NA
2,300 to 3,634
-
418 to 4,278
Bc/Al = 1.2
-
-
1,433
1,036 to 1,534
NA
1,610 to 2,533
-
324 to 2,979
Bc/Al = 10.0
-
-
788
574 to 825
NA
884 to 1,382
-
180 to 1,623
Note: NA = data not available for state; "-" = tree species not present on forestland in state.
2nd Draft Risk and Exposure Assessment
Appendix 5-85
June 5, 2009
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Terrestrial Acidification Case Study
1
2
3
Table 4.1-6. Percentages of Plots, by Protection Level (Bc/Al(crit) = 0.6, 1.2, and 10.0) and by
State, where 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition Levels Were Greater
Than the Critical Loads for Sugar Maple and Red Spruce
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kansas
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Rhode Island
South Carolina
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
Combined
(all plots)
Percentage of Plots Where Critical Load is Exceeded (%)
Sugar Maple
Bc/Al = 0.6
0
0
0
0
0.3
0
NA
0
0
0
6
6
2
0.7
29
0
6
0
1
7
NA
NA
0.3
2
2
2
2
3
Bc/Al = 1.2
23
0
23
0
12
0
NA
0
0.7
25
33
14
7
2
38
67
20
6
16
22
NA
NA
3
7
9
8
10
12
Bc/Al = 10.0
31
10
100
66
87
23
NA
86
20
100
100
70
30
46
84
100
95
71
95
98
NA
NA
50
99
59
95
82
75
Red Spruce
Bc/Al = 0.6
-
-
-
-
-
-
-
-
0.2
-
0
-
-
-
27
-
14
0
-
NA
-
-
0
2
NA
0
-
3
Bc/Al = 1.2
-
-
-
-
-
-
-
-
0.5
-
100
-
-
-
38
-
15
0
-
NA
-
-
0
6
NA
0
-
5
Bc/Al = 10.0
-
-
-
-
-
-
-
-
16
-
100
-
-
-
78
-
79
100
-
NA
-
-
100
100
NA
100
-
36
4 Note: NA = data not available for state; "-" = tree species not present on forestland in state
2nd Draft Risk and Exposure Assessment
Appendix 5-86
June 5, 2009
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Terrestrial Acidification Case Study
Sugar Maple (Bc/AI=0.6)
1
2
3
4
5
Legend
no exceedances
^50% exceedance
>50%exceedance
Sugar Maple slate
with data nol available
Figure 4.1-1. States where sugar maple is found and where 2002 CMAQ/NADP total
nitrogen and sulfur deposition levels exceeded the lowest protection critical load
(Bc/Al(crit) = 0.6) in the following: none of the sugar maple plots, <50% of the sugar
maple plots, and >50% of the sugar maple plots.
2nd Draft Risk and Exposure Assessment
Appendix 5-87
June 5, 2009
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Terrestrial Acidification Case Study
Sugar Maple (Bc/AI=1.2)
1
2
3
4
5
Legend
no exceedances
^50% exceedance
>50% exceedance
Sugar Maple slate
with data nol available
Figure 4.1-2. States where sugar maple is found and where 2002 CMAQ/NADP total
nitrogen and sulfur deposition levels exceeded the intermediate protection critical load
(Bc/Al(crit) = 1.2) in the following: none of the sugar maple plots, <50% of the sugar
maple plots, and >50% of the sugar maple plots.
2nd Draft Risk and Exposure Assessment
Appendix 5-88
June 5, 2009
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Terrestrial Acidification Case Study
Sugar Maple (Bc/AI=10.0)
1
2
3
Legend
no exceedances
^50% exceedance
>50% exceedance
Sugar Maple slate
with data nol available
Figure 4.1-3. States where sugar maple is found and where 2002 CMAQ/NADP total
nitrogen and sulfur deposition levels exceeded the highest protection critical load
(Bc/Al(crit) = 10.0) in the following: none of the sugar maple plots, <50% of the sugar
maple plots, and >50% of the sugar maple plots.
2nd Draft Risk and Exposure Assessment
Appendix 5-89
June 5, 2009
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Terrestrial Acidification Case Study
Red Spruce (Bc/AI=0.6)
1
2
3
4
5
Legend
no exoeedances
^50% exceedance
i50% exceedance
Red Spruce state
with data nol available
Figure 4.1-4. States where red spruce is found and where 2002 CMAQ/NADP total
nitrogen and sulfur deposition levels exceeded the lowest protection critical load
(Bc/Al(crit) = 0.6) in the following: none of the red spruce plots, <50% of the red spruce
plots, and >50% of the red spruce plots.
2nd Draft Risk and Exposure Assessment
Appendix 5-90
June 5, 2009
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Terrestrial Acidification Case Study
Red Spruce (Bc/AI=1.2)
1
2
3
Legend
no exoeedances
^50% exceedance
250% exceedance
Red Spruce state
with data nol available
Figure 4.1-5. States where red spruce is found and where 2002 CMAQ/NADP total
nitrogen and sulfur deposition levels exceeded the intermediate protection critical load
(Bc/Al(crit) = 1.2) in the following: none of the red spruce plots, <50% of the red spruce
plots, and >50% of the red spruce plots.
2nd Draft Risk and Exposure Assessment
Appendix 5-91
June 5, 2009
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Terrestrial Acidification Case Study
Red Spruce (Bc/AI=10,0)
1
2
3
6
7
Legend
no exoeedances
^50% exceedance
250% exceedance
Red Spruce state
with data nol available
4.2
Figure 4.1-6. States where red spruce is found and where 2002 CMAQ/NADP total
nitrogen and sulfur deposition levels exceeded the highest protection critical load
(Bc/Al(crit) = 10.0) in the following: none of the red spruce plots, <50% of the red spruce
plots, and >50% of the red spruce plots.
Relationship between Atmospheric Nitrogen and Sulfur Deposition and
Tree Growth
8 The impacts of the 2002 CMAQ/NADP total nitrogen and sulfur deposition and critical
9 load exceedances on sugar maple and red spruce growth throughout the full ranges of the two
10 species is presented and discussed in Attachment A.
2nd Draft Risk and Exposure Assessment
Appendix 5-92
June 5, 2009
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Terrestrial Acidification Case Study
i 5. UNCERTAINTY ANALYSIS
2 5.1 Kane Experimental Forest and Hubbard Brook Experimental Forest
3 Case Study Areas
4 Despite the extensive use of the 8MB model to estimate critical loads, there is uncertainty
5 regarding the output from the model and calculations. To a large degree, this uncertainty comes
6 from the dependence of the 8MB calculations on assumptions made by the researcher and the
7 use of default values. Parameters including base cation weathering (BCW and Bcw), ANCie,Crit,
8 .Kgibb, Nu, N;, Nde, and Bcu are rarely measured at each location and must be selected based on the
9 literature or on other calculations and models. In an analysis conducted by Li and McNulty
10 (2007), it was determined that BCW and ANCie,Crit were the main sources of uncertainty, with
11 each respectively contributing 49% and 46% to the total variability in critical load estimates. It
12 has, therefore, been suggested that the calculation of critical loads using a relevant range of
13 parameter values can provide the foundation for an uncertainty analysis (Li and McNulty, 2007;
14 Hall et al., 2001; Hodson and Langan 1999); it is likely that the correct critical load of a system
15 will be contained within the range of load estimates from such an approach. If all or a large
16 majority of estimates indicate that the critical load of a system is exceeded with 2002
17 CMAQ/NADP total nitrogen and sulfur deposition levels, the probability is high that deposition
18 is greater than the critical load and that the trees and vegetation in that system are being
19 negatively impacted by acidification. Conversely, if deposition is not greater than the majority of
20 critical load estimates, there can be greater confidence that the system is not being impacted by
21 acidifying deposition. Under a scenario of a near equal number of estimates indicating
22 exceedance and nonexceedance, however, there is low probability that the actual acidification
23 status of a system can be accurately determined. Nonetheless, such results do suggest that the
24 system is near the critical load level and should be monitored or assessed more thoroughly
25 In this case study, multiple values were used for several parameters in the 8MB
26 calculations for KEF and HBEF; BCW was calculated with two methods, two values of Xgibb
27 constant were used, and three indicator values of (Bc/Al)crit were evaluated. Therefore, it was
28 possible to use the range of output values from the calculations to access the certainty of the
29 acidification status of the HBEF and KEF case study areas. For both sugar maple and red spruce,
30 a similar number of estimates indicated deposition levels greater than the critical loads; the
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-93
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Terrestrial Acidification Case Study
1 critical loads associated with the most stringent, most protective Bc/Alcrit ratio indicator (Bc/Alcrit
2 = 10.0) were frequently lower than the 2002 CMAQ/NADP total nitrogen and sulfur deposition
3 levels. Conversely, the critical loads calculated with the (Bc/Al)crit ratio indicative of a high risk
4 to tree health (Bc/Alcrit = 0.6) were higher than the 2002 CMAQ/NADP total nitrogen and sulfur
5 deposition levels. The intermediate indicator ratio ((Bc/Al)crit) had critical load estimates that
6 were either exceeded or not exceeded by 2002 CMAQ/NADP total nitrogen and sulfur
7 deposition levels . The patterning of the results suggests that the 2002 CMAQ/NADP total
8 nitrogen and sulfur deposition levels were very close to, if not greater than, the critical loads of
9 the two case study areas, and both ecosystems are likely to be sensitive to any future changes in
10 the levels of nitrogen and sulfur acidifying deposition.
11 A more thorough, quantified uncertainty analysis of the parameters that are selected for
12 the 8MB method calculations of critical acid loads is recommended for future analyses.
13 5.2 Expansion of Critical Load Assessments
14 Critical load estimates for individual plots within the distribution ranges of sugar maple
15 and red spruce were calculated using the clay-substrate method to estimate BCW. As discussed
16 earlier, the BCW term within the 8MB model is one of the most influential terms in the
17 calculation of a critical load, and the determination of this BCW value is strongly influenced by
18 the classified acidity of the soil parent material. In large-scale analyses, descriptions of the
19 mineralogy of parent material underlying the soil may be missing, nondescriptive, only
20 suggestive of mineralogy, or may only represent the dominant mineralogy in a large area (and
21 therefore not accurately capture the smaller-scale variation in mineralogy). Therefore, it is
22 possible to misclassify the parent material acidity in the BCW term.
23 In the analyses of critical loads for the full distribution ranges of sugar maple and red
24 spruce in this report, two fine-scale databases (i.e., SSURGO soils [USDA-NRCS, 2008c] and
25 USGS state-level geology [USGS, 2009b] databases) were used as the sources for parent
26 material mineralogy to allow for location-specific mineralogy descriptions. In addition, a
27 systematic protocol similar to that used in Europe (UNECE, 2004) and Australia (Gray and
28 Murphy, 1999), and based on known and probable silica and ferromagnesium content, spatial
29 patterns of local and geologic settings, and implied deposit!onal mechanisms and environments
30 was used to determine the parent material acidity classifications. Therefore, steps were taken to
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5-94
-------
Terrestrial Acidification Case Study
1 determine accurate, location-specific acidity classifications. Nonetheless, parent material in some
2 of the plots may have been misclassified.
3 To evaluate the degree to which critical load estimates could change with a
4 misclassification of parent material acidity, a simple analysis of absolute (eq/ha/yr) and
5 percentage change associated with misclassifications of parent materials was conducted, using
6 the critical loads associated with the three levels of protection ((Bc/Al)crit = 0.6. 1.2 and 10. 0) for
7 sugar maple and red spruce. The differences between all combinations of critical loads calculated
8 with basic, intermediate, and acidic parent materials were determined, and these difference
9 values were expressed as a percentage of the original critical load estimates (Table 5.1-1 and
10 5.1-2). For example, the percentage difference associated with the misclassification of an
11 intermediate parent material as acidic would be calculated as the absolute value of (CLintermediate ~
lZ ^J-^acidJ/ Lx^intermediate •
13 Table 5.1-1. Differences and Percentage Differences in Plot-Level Critical Load Estimates
14 Associated with the Misclassification of Parent Material Acidity for the Full Range Assessment
15 of Sugar Maple
Bc/Al(critt
Ratio
0.6
1.2
Misclassification of
Parent Material
Acidic as Intermediate
Acidic as Basic
Intermediate as Basic
Intermediate as Acidic
Basic as Acidic
Basic as Intermediate
Acidic as Intermediate
Acidic as Basic
Intermediate as Basic
Intermediate as Acidic
Basic as Acidic
Basic as Intermediate
Difference between Critical Loads
(eq/ha/yr)
Range of
Values
737tol,559
784 to 3, 631
0 to 2,072
737 to 1,559
784 to 3, 631
0 to 2,072
493 to 1,045
527 to 2,433
0 to 1,388
493 to 1,045
527 to 2,433
0 to 1,388
Average
797
1,018
222
797
1,018
222
537
686
149
537
686
149
Median
784
936
156
784
936
156
529
634
105
529
634
105
% Difference between Critical
Loads (%)
Range of
Values
25 to 492
42 to 492
Oto36
20 to 83
30 to 83
Oto26
24 to 428
40 to 428
Oto36
19 to 81
29 to 81
Oto26
Average
58
70
8
35
40
7
56
67
8
34
39
7
Median
51
61
7
34
38
7
50
60
7
33
37
6
16
2nd Draft Risk and Exposure Assessment
Appendix 5-95
June 5, 2009
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Terrestrial Acidification Case Study
Bc/Al^
Ratio
10.0
Bc/Al(crit) Ratio
Acidic as Intermediate
Acidic as Basic
Intermediate as Basic
Intermediate as Acidic
Basic as Acidic
Basic as Intermediate
Misclassification of Parent
Material
Range of
Values
275 to 583
293 to 1,357
Oto 774
275 to 583
293 to 1,357
0 to 774
Range of
Values
298
381
83
298
381
83
Average
294
351
58
294
351
58
Difference between Critical
Loads (eq/ha/yr)
Median
24 to 376
41 to 376
Oto 36
20 to 79
29 to 79
Oto 26
Range of
Values
56
67
8
34
39
7
Average
50
60
7
33
37
6
3 Table 5.1-2. Differences and Percentage Differences in Plot-Level Critical Load Estimates
4 Associated with the Misclassification of Parent Material Acidity for the Full Range Assessment
5 of Red Spruce
Bc/Al(crit)
Ratio
0.6
1.2
10.0
Misclassification of
Parent Material
Acidic as Intermediate
Acidic as Basic
Intermediate as Basic
Intermediate as Acidic
Basic as Acidic
Basic as Intermediate
Acidic as Intermediate
Acidic as Basic
Intermediate as Basic
Intermediate as Acidic
Basic as Acidic
Basic as Intermediate
Acidic as Intermediate
Acidic as Basic
Intermediate as Basic
Intermediate as Acidic
Basic as Acidic
Basic as Intermediate
Difference between Critical Loads
(eq/ha/yr)
Range of
Values
772 to 1,340
853 to 1,584
0 to 655
772 to 1,340
853 to 1,584
0 to 655
521 to 969
578 to 1,075
0 to 444
521 to 969
578 to 1,075
0 to 444
289 to 511
320 to 594
0 to 246
289 to 511
320 to 594
0 to 246
Average
831
965
134
831
965
134
567
658
91
567
658
91
312
362
50
312
362
50
Median
829
932
99
829
932
99
566
637
67
566
637
67
311
350
37
311
350
37
% Difference between Critical
Loads (%)
Range of
Values
27 to 453
44 to 453
Oto 16
22 to 82
31 to 82
Oto 14
27 to 392
43 to 392
Oto 16
21 to 80
30 to 80
Oto 14
27 to 345
45 to 345
Oto 16
21 to 78
31 to 78
Oto 14
Average
69
78
6
40
43
5
66
74
5
38
42
5
66
75
5
39
42
5
Median
67
74
5
40
43
5
64
71
5
39
42
4
65
73
5
39
42
5
2nd Draft Risk and Exposure Assessment
Appendix 5-96
June 5, 2009
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Terrestrial Acidification Case Study
1 The comparisons of critical loads revealed that changes in critical load values could range
2 from 0 to 3,631 eq/ha/yr for sugar maple and 0 to 1,584 eq/ha/yr for red spruce with the
3 misclassification of parent material acidity. These ranges correspond to percentage differences
4 ranging from 0% to 492% and 0% to 453% for sugar maple and red spruce, respectively. The
5 results also indicate that the biggest impacts of a misclassification on critical load estimates
6 would occur with an acidic parent material being misclassified as basic; the average percentage
7 changes in the estimated critical loads, in such a scenario, were 67% to 70% for sugar maple and
8 74% to 78% for red spruce, and the median percentage changes were 60% to 61% and 71% to
9 74% for the two species, respectively. In contrast, the smallest impacts on critical load estimates
10 would occur when a basic parent material was incorrectly classified as intermediate and vice
11 versa. In this scenario, the average and median percentage changes in critical load estimates were
12 only 7% to 8% and 6% to 7% for sugar maple and 5% to 6% and 4% to 5% for red spruce. Given
13 the potential significant impacts of a misclassification of parent material acidity on critical load
14 estimates, this potential source of error should be considered in the accuracy and application of
15 the critical load estimates.
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i ATTACHMENT A
2 RELATIONSHIP BETWEEN ATMOSPHERIC
3 NITROGEN AND SULFUR DEPOSITION AND SUGAR
4 MAPLE AND RED SPRUCE TREE GROWTH
5 1. Introduction
6 Nitrogen and sulfur deposition in forest systems can have either positive or negative
7 impacts on tree growth. The growth of many forests in North America is limited by nitrogen
8 availability (Chapin et al., 1993; Killam, 1994; Miller, 1988). Therefore, nitrogen fertilization is
9 often a key component of forest management (Allen, 2001), and multiple research trials have
10 established significant increases in tree growth following nitrogen fertilization. However,
11 nitrogen additions can sometimes be greater than what trees require and can negatively impact
12 tree health and growth (Aber et al., 1995; McNulty et al., 2005). Systems where atmospheric
13 deposition of nitrogen and sulfur is greater than the critical load may be examples of such a
14 forest condition. When critical loads are exceeded, tree health and growth may be compromised
15 both directly and indirectly because of soil nutrient deficiencies and imbalances caused by acidic
16 deposition and the leaching of base cations from the soil. Tree growth may be reduced and/or
17 trees may have an increased susceptibility to drought and pest damage, aluminum (Al) toxicity in
18 roots, reduced tolerance to cold, and a greater propensity to frost injury (Driscoll et al., 2001;
19 DeHayes et al., 1999; Fenn et al., 2006b; McNulty et al. 2005; Ouimet et al., 2008). In the
20 context of acidifying deposition of nitrogen and sulfur, the positive versus negative impact of
21 deposition on tree growth may depend largely upon whether the critical load is exceeded by the
22 deposition level, and it may follow an inverted U-shape relationship similar to that which was
23 hypothesized by Aber et al. (1995) for temperate forest systems that receive chronic, long-term
24 nitrogen additions (Figure 1-1). If nitrogen and sulfur deposition is less than the critical load,
25 tree growth may be stimulated because of a fertilizer effect. In contrast, when the deposition is
26 greater than the critical load, tree vigor and growth may be reduced because of direct or indirect
27 causes. The transition point between growth stimulation and impairment would occur when
28 deposition is equal to the critical load.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 1
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Terrestrial Acidification Case Study
not exceeded
Critical Load
£
£
2
O
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Terrestrial Acidification Case Study
1 individual tree volume growth10 and tree volume11 for all live sugar maple and red spruce trees in
2 each plot were obtained from the USFS FIA database. When critical load exceedances, tree
3 volume, and growth data were not available for a plot, the plot was not included in the analysis.
4 Only trees that had both volume and growth measurements were included in the analyses. The
5 tree volumes and growth were from the most recent measurement period, and the interval
6 between measurements (to determine growth rates) for the plots ranged from 1 to 11 years. Trees
7 with negative growth values were included in the analyses to account for the potential indirect
8 impacts of nitrogen and sulfur deposition. All trees that had "0" volume values were excluded
9 from the analyses. Given these data restrictions, a total of 4,047 sugar maple and 613 red spruce
10 plots were included in the analyses. Volumes and volume growth for the sugar maple and red
11 spruce trees in each plot were averaged to produce single values of each parameter for each
12 species. Tables 2-1 and 2-2 summarize the plot-level FIA sugar maple and red spruce data used
13 to model the relationship between critical load exceedance and tree growth.
14 Table 2-1. Summary of Plot-Level Data for Sugar Maple Volume, Tree Growth and Critical
15 Load Exceedance (4,047 Plots)
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kentucky
Maine
Total
Number of
Plots
12
8
33
25
266
8
14
242
Average
Tree
Volume
Growth
(m3/yr)
0.011
0.010
0.009
0.008
0.017
0.010
0.015
0.008
Average
Tree
Volume
(m3)
0.424
0.348
0.279
0.232
0.407
0.206
0.364
0.281
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=10
-372.87
-254.93
487.76
72.75
368.49
-150.49
343.02
-177.78
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=1.2
-1635.93
-1159.38
-84.61
-844.44
-519.67
-1105.39
-442.67
-768.57
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=0.6
-2946.47
-2110.66
-645.39
-1822.38
-1455.10
-2136.60
-1253.76
-1321.36
10 FGROWCFAL or GROWCFAL (i.e., the net annual sound cubic-foot growth of a live tree (of trees
>12.7 cm diameter). Growth values may be negative because of loss of volume due to death or damage, rot,
broken top, or other natural causes. Source: FIA database (USDA-USFS, 2008).
11 VOLCFNET (i.e., the net cubic foot volume of wood in the central stem of a tree, beginning at 12.7 cm in
diameter or larger up the stem to a minimum 10.16 cm top outer-bark diameter. This measurement does not
include rotten, missing, and form cull. Source: FIA database (USFS, 2008c).
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 3
June 5, 2009
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Terrestrial Acidification Case Study
State
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
TOTAL
Observations
(used in
calculations)
Total
Number of
Plots
4
27
596
257
122
72
6
280
13
55
270
264
162
104
337
870
4047
Average
Tree
Volume
Growth
(m3/yr)
0.010
0.003
0.009
0.008
0.006
0.008
0.013
0.010
0.010
0.011
0.012
0.012
0.008
0.013
0.011
0.009
4047
Average
Tree
Volume
(m3)
0.373
0.366
0.305
0.251
0.244
0.286
0.357
0.355
0.450
0.380
0.358
0.333
0.410
0.294
0.282
0.305
4047
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=10
599.93
473.31
127.28
-125.76
-145.34
305.51
601.39
409.27
121.57
541.32
542.07
5.47
290.91
30.11
326.78
131.45
4047
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=1.2
-184.96
-59.01
-431.89
-811.96
-1061.78
-97.89
32.93
-258.37
-609.45
-337.79
-258.63
-914.28
-275.99
-859.27
-560.07
-484.31
4047
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=0.6
-992.34
-578.12
-997.53
-1554.76
-2040.75
-471.46
-514.67
-920.02
-1318.17
-1261.99
-1078.63
-1863.62
-821.40
-1786.85
-1467.53
-1124.14
4047
2 Table 2-2. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical Load
3 Exceedance (613 plots)
State
Maine
Massachusetts
New Hampshire
New York
Tennessee
Total
Number of
Plots
483
3
42
18
1
Average
Tree
Volume
Growth
(m3/yr)
0.008
0.004
0.006
0.005
0.020
Average
Tree
Volume
(m3)
0.248
0.203
0.230
0.309
0.463
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=10
-245.67
628.54
267.01
197.07
420.24
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=1.2
-898.77
88.86
-110.50
-409.34
-224.54
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=0.6
-1509.34
-431.22
-448.26
-959.70
-856.47
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 4
June 5, 2009
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Terrestrial Acidification Case Study
State
Vermont
West Virginia
TOTAL
Observations
(used in
calculations)
Total
Number of
Plots
60
6
613
Average
Tree
Volume
Growth
(m3/yr)
0.007
0.011
613
Average
Tree
Volume
(m3)
0.328
0.329
613
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=10
292.43
257.62
613
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=1.2
-290.96
-672.99
613
Average
Critical
Load
Exceedance
(eq/ha/yr)
Bc/Al=0.6
-834.32
-1555.06
613
2 3. Regression Analyses Methodology and Results
3 The relationship between net individual tree growth and critical load exceedances for
4 sugar maple and red spruce were examined empirically using multivariate ordinary least squares
5 (OLS) regression analyses. A quadratic functional form was used to test for evidence of the
6 inverted U-shaped relationship represented in Figure 1-1. In these analyses, the explanatory
7 variables included the linear and squared terms of critical load exceedance (expressed as
8 equivalents per hectare per year (eq/ha/year)) for each plot, linear and squared terms of average
9 tree volumes in cubic meters (m3), and a categorical (dummy) variable for each State (with
10 Connecticut arbitrarily selected as the reference category for sugar maple and Vermont selected
11 for red spruce). Tree volume was included as an explanatory variable of tree growth because tree
12 age, the preferred explanatory variable, was not available for this dataset. However, tree age and
13 tree volume are highly correlated, and volume growth is influenced by tree size, so tree volume
14 was seen as an appropriate surrogate explanatory variable. Linear and squared terms of tree
15 volume were included as regressors in the analyses. The State variables were included in the
16 analyses to control for unobserved sources of variation in tree growth related to a plot's general
17 geographic location. Examples of potential unobserved factors include differences in data
18 collection methods and measurements across reporting State, climatic factors, and geological
19 characteristics.
20 The results of the multivariate OLS quadratic regression analyses to test the quadratic
21 model for the three critical load exceedance scenarios and sugar maple and red spruce are
22 reported in Tables 3-la-c and 3-2a-c, respectively.
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 5
June 5, 2009
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Terrestrial Acidification Case Study
1 Table 3-la. Results from the Ordinary Least Squares Regression Analyses of the Quadratic
2 Model for Critical Load Exceedance and Sugar Maple Tree Growth (for critical load
3 exceedances based on Bc/Al=10.0 critical loads)
Explanatory Variables
Intercept
Critical Load Exceedance
Square of Critical Load Exceedance
Average Tree Volume
Square of Average Tree Volume
Alabama
Arkansas
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.003850
3.398 xlO'7
-8.098 x 10'10
0.019910
-7.588 xlO'4
-0.000792
-0.000643
-0.000645
0.005467
0.002074
0.004095
-0.000778
-0.000817
-0.008013
-0.000494
-0.000377
-0.002168
-0.001104
0.001827
-0.000589
-0.002767
0.000379
0.001051
0.001691
-0.004022
0.003308
0.001340
-0.000771
t-statistic
1.3
0.31
-0.64
14.13
-1.44
-0.14
-0.1
-0.14
1.77
0.31
0.77
-0.25
-0.09
-1.85
-0.16
-0.12
-0.65
-0.31
0.25
-0.19
-0.5
0.1
0.34
0.54
-1.26
0.98
0.44
-0.26
p-value
0.1952
0.7553
0.5234
<.0001
0.1495
0.8901
0.9228
0.8849
0.0769
0.7541
0.4428
0.8064
0.9265
0.0649
0.8697
0.9049
0.5171
0.7538
0.8056
0.8483
0.6142
0.9182
0.7339
0.5885
0.2092
0.3268
0.6607
0.7964
4047
0.1229
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 6
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 3-lb. Results from the Ordinary Least Squares Regression Analyses of the Quadratic
2 Model for Critical Load Exceedance and Sugar Maple Tree Growth (for critical load
3 exceedances based on Bc/Al=l .2 critical loads)
Explanatory Variables
Intercept
Critical Load Exceedance
Square of Critical Load Exceedance
Average Tree Volume
Square of Average Tree Volume
Alabama
Arkansas
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.003716
-1.351 xlO'6
-4.230 x 10'10
0.019900
-7.641 x 10'4
-0.001827
-0.001619
-0.001331
0.005049
0.001177
0.003754
-0.001492
-0.001019
-0.007972
-0.000814
-0.001077
-0.003006
-0.001055
0.001936
-0.000766
-0.003220
0.000038
0.000791
0.000970
-0.004184
0.002675
0.000881
-0.001137
t-statistic
1.27
-1.3
-0.72
14.13
-1.45
-0.32
-0.24
-0.3
1.63
0.18
0.7
-0.48
-0.12
-1.84
-0.27
-0.34
-0.9
-0.3
0.26
-0.25
-0.59
0.01
0.26
0.31
-1.31
0.79
0.29
-0.38
p-value
0.2048
0.1943
0.4742
<0001
0.1466
0.7519
0.8069
0.7656
0.1034
0.8589
0.482
0.6343
0.9083
0.0662
0.7861
0.7305
0.3682
0.764
0.7942
0.8036
0.5573
0.9918
0.7978
0.7569
0.1908
0.4284
0.774
0.7027
4047
0.1232
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 7
June 5, 2009
-------
Terrestrial Acidification Case Study
1 Table 3-lc. Results from the Ordinary Least Squares Regression Analyses of the Quadratic
2 Model for Critical Load Exceedance and Sugar Maple Tree Growth (for critical load
3 exceedances based on Bc/Al=0.6 critical loads)
Explanatory Variables
Intercept
Critical Load Exceedance
Square of Critical Load Exceedance
Average Tree Volume
Square of Average Tree Volume
Alabama
Arkansas
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.003009
-1.406xlO'6
-2.001 x 10'10
0.019880
-7.624 xlO'4
-0.002426
-0.001983
-0.001679
0.004725
0.000777
0.003539
-0.001498
-0.001271
-0.007924
-0.000801
-0.001240
-0.003375
-0.000841
0.001940
-0.000859
-0.003348
-0.000306
0.000529
0.000643
-0.004148
0.002363
0.000586
-0.001190
t-statistic
1.01
-1.42
-0.7
14.11
-1.45
-0.42
-0.3
-0.38
1.52
0.12
0.66
-0.48
-0.14
-1.83
-0.27
-0.4
-1.01
-0.24
0.26
-0.28
-0.61
-0.08
0.17
0.21
-1.3
0.7
0.19
-0.4
p-value
0.3115
0.155
0.486
<0001
0.1475
0.6753
0.7645
0.707
0.1285
0.9066
0.5074
0.631
0.8858
0.0678
0.7889
0.6909
0.3121
0.8112
0.7937
0.7802
0.5415
0.934
0.864
0.8374
0.1942
0.4842
0.8489
0.6889
4047
0.1236
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 8
June 5, 2009
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Terrestrial Acidification Case Study
1 Table 3-2a. Results from the Ordinary Least Squares Regression Analyses of the Quadratic
2 Model for Critical Load Exceedance and Red Spruce Tree Growth (for critical load exceedances
3 based on Bc/Al=10.0 critical loads)
Explanatory Variables
Intercept
Critical Load Exceedance
Square of Critical Load Exceedance
Tree Volume
Square of Tree Volume
Maine
Massachusetts
New Hampshire
New York
West Virginia
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.005549
-1.705 xlO'6
-2.041 x 10'9
0.009840
-S.SlOxlO'3
-0.000365
-0.001453
-0.000575
-0.002697
0.003209
t-statistic
6.57
-1.73
-1.27
4.45
-1.64
-0.44
-0.5
-0.6
-2.11
1.58
p-value
<.0001
0.0844
0.2051
<.0001
0.1014
0.6623
0.6165
0.5496
0.0356
0.1157
613
0.0762
5 Table 3-2b. Results from the Ordinary Least Squares Regression Analyses of the Quadratic
6 Model for Critical Load Exceedance and Red Spruce Tree Growth (for critical load exceedances
7 based on Be/Al= 1.2 critical loads)
Explanatory Variables
Intercept
Critical Load Exceedance
Square of Critical Load Exceedance
Tree Volume
Square of Tree Volume
Maine
Massachusetts
New Hampshire
New York
West Virginia
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.004409
-1.738 xlO'6
-7.104xlO-10
0.009840
-3.332 xlO'3
0.000095
-0.002031
-0.000246
-0.002547
0.002940
t-statistic
5.5
-1.57
-1.17
4.45
-1.65
0.13
-0.72
-0.25
-1.99
1.43
p-value
<.0001
0.1166
0.2419
<.0001
0.0995
0.8944
0.4747
0.8035
0.0468
0.1518
613
0.0756
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 9
June 5, 2009
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Terrestrial Acidification Case Study
1
2
Table 3-2c. Results from the Ordinary Least Squares Regression Analyses of the Quadratic
Model for Critical Load Exceedance and Red Spruce Tree Growth (for critical load exceedances
based on Bc/Al=0.6 critical loads)
Explanatory Variables
Intercept
Critical Load Exceedance
Square of Critical Load Exceedance
Tree Volume
Square of Tree Volume
Maine
Massachusetts
New Hampshire
New York
West Virginia
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.003998
-1.296xlO'6
-3.251 x 10'10
0.009820
-3.312 xlO'3
0.000280
-0.002275
-0.000183
-0.002497
0.002977
t-statistic
4.19
-1.39
-1.08
4.44
-1.64
0.41
-0.8
-0.18
-1.95
1.45
p-value
<.0001
0.164
0.2805
<.0001
0.1017
0.6854
0.4214
0.8552
0.0514
0.1475
613
0.0749
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Although the coefficients on the linear and squared terms of critical load exceedance
were not statistically significant at the 5% level for any of the analyses, the curves produced by
the analyses do suggest that both sugar maple and red spruce follow the general inverted U-
shaped growth pattern in response to critical load exceedance. For all three critical load
exceedance scenarios (based on critical loads determined for the three different protection levels:
Bc/Al = 0.6, 1.2, and 10.0), the results indicate patterns of increasing growth followed by
plateauing and then decreasing growth, with increased critical load exceedance. In theory, the
inverted U-shape distribution and the point of inversion, or zero slope, should occur when
nitrogen and sulfur deposition is equal to the critical load. This is the turning point, before which
deposition is less than the critical load and may stimulate growth, and after which the critical
load is exceeded by total nitrogen and sulfur deposition and tree health and growth may be
decreased. For sugar maple, the point of inversion between growth stimulation and growth
impairment, or the point of zero slope, was estimated to occur at a critical load exceedance of
210 eq/ha/yr (e.g., deposition is slightly greater than the critical load) for the most protective
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 10
June 5, 2009
-------
Terrestrial Acidification Case Study
1 critical load (Bc/Al = 10.0). For red spruce, the zero slope corresponding to the critical load
2 calculated with a Bc/Al = 10.0 was estimated to occur at -420 eq/ha/yr (e.g., deposition is
3 slightly less than the critical load). Given the range of exceedance values
4 (i.e., -1,660 to 1,630 eq/ha/yr for sugar maple and -1,070 to 810 eq/ha/yr for red spruce) for the
5 Bc/Al=10.0 critical load scenario, both of these estimated inversion points are near the 0 eq/ha/yr
6 outlined in the hypothetical tree growth/critical load exceedance relationship (Figure 1-1).12 In
7 contrast, the zero slope values corresponding to the low (Bc/Al=0.6) and intermediate
8 (Bc/Al=1.2) protection-level critical loads were located along the critical load exceedance axis at
9 -3,500 and -1,600 eq/ha/yr, respectively, for sugar maple. For red spruce, the zero slopes
10 occurred at -2,000 eq/ha/yr for the Bc/Al=0.6 critical load exceedances and at -1,200 eq/ha/yr
11 for the Bc/Al=l .2 critical load exceedances.
12 The location of zero slope or inversion point along the critical load exceedance axis
13 appears to provide an indirect evaluation of the most accurate critical load value for sugar maple
14 and red spruce and offer a field-based validation of lab studies which evaluated reduced growth
15 in response to different Bc/Al ratios (Sverdrup and Warfvinge, 1993b). Based on the trends
16 suggested by these analyses (in particular, the estimated inversion points being near the 0
17 eq/ha/yr, as outlined by the hypothesized relationship between critical load exceedance and tree
18 growth [Figure 1-1]), the critical loads calculated with Bc/Al = 10.0 appear to offer the most
19 accurate representation of the critical load value for both sugar maple and red spruce growing in
20 forests in the northeastern United States; when nitrogen and sulfur deposition exceed this critical
21 load, tree health and vigor may be impaired. It should be noted that this Bc/Al indicator value is
22 larger than the 0.6 and 1.2 values determined in laboratory studies for sugar maple and red
23 spruce seedlings, respectively (Sverdrup and Warfvinge, 1993b). However, these lower Bc/Al
24 indicator values represent conditions that were found to cause a 20% reduction in root or
25 biomass growth, whereas the Bc/Al soil solution ratio of 10, as suggested by the regression
26 analyses, represents the point at which atmospheric nitrogen and sulfur deposition may start
27 having negative impacts on tree health and growth.
12 The estimated inversion point is close to the mid-point of the range of critical load exceedance values for Bc/Al =
10.0 for sugar maple. For red spruce, this point of inversion is slightly to the left of the mid-point of the range of
values.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 11
-------
Terrestrial Acidification Case Study
1 To provide a more focused assessment of the potentially negative impacts of atmospheric
2 nitrogen and sulfur deposition on tree growth, multivariate OLS linear regression analyses were
3 conducted on the critical load exceedance values that were positive (i.e., values where combined
4 nitrogen and sulfur atmospheric deposition was greater than the critical load) and on net tree
5 volume growth of sugar maple and red spruce. These analyses were only conducted for the
6 critical load exceedance scenarios determined with the critical loads estimated with the
7 Bc/Al=10.0 parameter. This critical load scenario was chosen because it has the inversion point
8 nearest 0 eq/ha/yr critical load exceedance. A total of 75% of all sugar maple plots and 32% of
9 all red spruce plots had positive exceedance values and were included in these analyses (Tables
10 3-3 and 3-4).
11 Table 3-3. Summary of Plot-Level Data for Sugar Maple Volume and Growth and Critical Load
12 Exceedances (for plots with positive critical load exceedance values and based on critical loads
13 calculated with Bc/Al = 10.0)
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
Total
Number
of Plots
12
8
33
25
266
8
14
242
4
27
596
257
122
72
6
280
Number of
Plots with
Positive CL
Exceedance
Values
O
1
33
17
235
2
12
51
4
27
418
79
58
60
6
264
Average CL
Exceedance
(eq/ha/yr)
434.81
105.75
487.76
145.10
439.82
48.07
412.29
130.42
599.93
473.31
242.17
156.06
171.50
378.40
601.39
437.94
Average
Tree
Volume
Growth
(m3/yr)
0.009
0.010
0.009
0.007
0.017
0.005
0.015
0.011
0.010
0.003
0.011
0.010
0.005
0.009
0.013
0.010
Average
Tree
Volume
(m3)
0.117
0.354
0.279
0.201
0.386
0.123
0.356
0.323
0.373
0.366
0.307
0.256
0.246
0.304
0.357
0.344
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 12
June 5, 2009
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Terrestrial Acidification Case Study
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
TOTAL
Observations
(used in
calculations)
13
55
270
264
162
104
337
870
4047
9
54
263
132
160
63
318
719
2988
299.25
554.56
557.91
161.75
301.67
291.64
352.08
185.16
2988
0.016
0.012
0.012
0.013
0.008
0.013
0.010
0.009
2988
0.378
0.385
0.358
0.349
0.411
0.301
0.282
0.304
2988
4
Table 3-4. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical Load
Exceedances (for plots with positive critical load exceedance values and based on critical loads
calculated with Bc/Al = 10.0)
State
Maine
Massachusetts
New Hampshire
New York
Tennessee
Vermont
West Virginia
TOTAL
Observations
(used in
calculations)
Total
Number
of Plots
483
3
42
18
1
60
6
613
Number of
Plots with
Positive CL
Exceedance
Values
78
3
32
14
1
60
6
194
Average CL
Exceedance
(eq/ha/yr)
133.10
628.54
368.95
282.54
420.24
292.43
257.62
194
Average
Tree
Volume
Growth
(m3/yr)
0.007
0.004
0.006
0.004
0.020
0.007
0.011
194
Average
Tree
Volume
(m3)
0.245
0.203
0.245
0.221
0.463
0.328
0.329
194
5
6
7
The results of the linear regression analyses showed that the coefficient of critical load
exceedance for both species was negative, supporting the theory that when critical loads are
exceeded by atmospheric nitrogen and sulfur deposition, tree health and growth can be impaired
(Tables 3-5 and 3-6)
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 13
June 5, 2009
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Terrestrial Acidification Case Study
1 Table 3-5. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses of
2 Positive Critical Load Exceedances and Sugar Maple Tree Growth (for critical loads calculated
3 with Bc/Al= 10.0)
Explanatory Variables
Intercept
Critical Load Exceedance
Average Tree Volume
Square of Average Tree Volume
Alabama
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.004302
-1.432 x 10'6
0.020010
2.578 x 10'4
0.003413
-0.001118
0.005437
-0.001421
0.004491
-0.000112
-0.000789
-0.008142
0.000777
0.000848
-0.003892
-0.001202
0.001904
-0.000542
0.004307
0.000185
0.000833
0.001717
-0.004483
0.002610
0.000914
-0.001513
t-statistic
1.35
-0.92
10.81
0.34
0.32
-0.21
1.65
-0.11
0.74
-0.03
-0.08
-1.76
0.24
0.23
-1
-0.31
0.24
-0.17
0.64
0.05
0.25
0.49
-1.32
0.68
0.28
-0.48
p-value
0.1787
0.3571
<.0001
0.7365
0.7523
0.8347
0.0991
0.9135
0.4564
0.9776
0.9337
0.0786
0.8095
0.8194
0.3192
0.7556
0.8108
0.8685
0.5226
0.9626
0.7994
0.6225
0.1879
0.4957
0.7783
0.6347
2988
0.1329
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 14
June 5, 2009
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Terrestrial Acidification Case Study
1
2
Table 3-6. Results from the multivariate Ordinary Least Squares linear regression analyses of
positive critical load exceedances and red spruce tree growth (for critical loads calculated with
Bc/Al = 10.0)
Explanatory Variables
Intercept
Critical Load Exceedance
Tree Volume
Square of Tree Volume
Maine
Massachusetts
New Hampshire
New York
West Virginia
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.006281
-5.618 xlO'6
0.006110
4.632 xlO'3
0.000013
-0.000173
0.000268
-0.002004
0.003306
t-statistic
5.15
-2.3
1.38
1.11
0.01
-0.06
0.25
-1.43
1.64
p-value
<0001
0.0223
0.1685
0.2705
0.989
0.9524
0.7993
0.1549
0.102
194
0.2009
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
For red spruce, the critical load exceedance coefficient was negative and significant at the
5% level (p-value of 0.022), indicating that red spruce health and growth may be negatively
impacted by deposition levels that exceed the critical load. Although the coefficient was also
negative for sugar maple, it was not statistically significant (p-value of 0.357). As discussed in
Appendix 5, the base cation weathering (BCW) term that accounts for the contributions of base
cations from the weathering of soil minerals and parent material is one of the most influential
terms in the simple mass balance model. Li and McNulty (2007) determined that 49% of the
variability in critical load estimates was due to this term. Within the United States and Canada,
BCW for critical load assessments is commonly estimated using the clay-substrate model (Ouimet
et al., 2006; Watmough et al., 2006; McNulty et al., 2007; Pardo and Duarte, 2007), and this
model was also used in this case study. Critical load experts from both the United States and
Canada have commented that the clay-substrate model method performs well in young soils that
formed since the last glaciation (20,000 years before present (ybp)), but may not be suitable for
older, more weathered soils south of the most recent glacial advancement. Therefore, to account
for the potential influence of a poorer estimation of BCW in plots south of the glaciation line
(map), a second set of multivariate, OLS linear regression analyses were conducted, using the
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 15
June 5, 2009
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Terrestrial Acidification Case Study
1 same specifications (i.e., plots with positive critical load exceedance values, critical load
2 exceedance scenario based on critical loads calculated with Bc/Al=10.0), as described above.
3 However, these analyses were restricted to data from plots that had been covered by the last
4 glaciation (i.e., north of the glaciation line). Limiting the analysis to plots north of the glaciation
5 line led to analyzing 3.6% fewer red spruce plots and 26.2% fewer sugar maple plots (Tables 3-7
6 and 3-8).
7
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 16
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Terrestrial Acidification Case Study
Table 3-7. Summary of Plot-Level Data for Sugar Maple Volume and Growth and Critical Load Exceedances North of the Glaciation
Line (for plots with positive critical load exceedance values and based on critical loads calculated with Bc/Al = 10.0)
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
Total
Number
of Plots
12
8
33
25
266
8
14
242
4
27
596
257
122
72
6
280
Number of
Plots North
of Glaciation
Line
0
0
33
20
234
8
0
242
0
27
596
257
31
72
6
280
Number of Plots
with Positive
Critical Load
Exceedance
Values
3
1
33
17
235
2
12
51
4
27
418
79
58
60
6
264
Number of Plots
with Positive
Critical Load
Exceedance Values
North of
Glaciation Line
0
0
33
12
204
2
0
51
0
27
418
79
18
60
6
264
Average
Critical Load
Exceedance
(eq/ha/yr)
NA
NA
487.76
117.17
390.90
48.07
NA
130.42
NA
473.31
242.17
156.06
84.02
378.40
601.39
437.94
Average Tree
Volume
Growth
(m3/yr)
NA
NA
0.009
0.007
0.018
0.005
NA
0.011
NA
0.003
0.011
0.010
0.012
0.009
0.013
0.010
Average
Tree
Volume
(m3)
NA
NA
0.279
0.227
0.397
0.123
NA
0.323
NA
0.366
0.307
0.256
0.246
0.304
0.357
0.344
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 17
June 5, 2009
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Terrestrial Acidification Case Study
State
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
TOTAL
Observations (used
in calculations)
Total
Number
of Plots
13
55
270
264
162
104
337
870
4047
Number of
Plots North
of Glaciation
Line
0
27
133
0
162
0
0
870
2998
Number of Plots
with Positive
Critical Load
Exceedance
Values
9
54
263
132
160
63
318
719
2988
Number of Plots
with Positive
Critical Load
Exceedance Values
North of
Glaciation Line
0
26
126
0
160
0
0
719
2205
Average
Critical Load
Exceedance
(eq/ha/yr)
NA
452.60
387.35
NA
301.67
NA
NA
185.16
2205
Average Tree
Volume
Growth
(m3/yr)
NA
0.013
0.011
NA
0.008
NA
NA
0.009
2205
Average
Tree
Volume
(m3)
NA
0.545
0.366
NA
0.411
NA
NA
0.304
2205
NA = not applicable. Average Critical Load Exceedance, Average Tree Volume Growth, and Average Tree Volume values could not
be determined because there were no sugar maple plots north of the glaciation line.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5, Attachment A - 18
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Terrestrial Acidification Case Study
Table 3-8. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical Load Exceedances North of the Glaciation
Line (for plots with positive critical load exceedance values and based on critical loads calculated with Bc/Al = 10.0)
State
Maine
Massachusetts
New Hampshire
New York
Tennessee
Vermont
West Virginia
TOTAL
Observations (used
in calculations)
Total
Number
of Plots
483
3
42
18
1
60
6
613
Number of
Plots North
of Glaciation
Line
483
3
42
18
0
60
0
606
Number of Plots
with Positive
Critical Load
Exceedance
Values
78
3
32
14
1
60
6
194
Number of Plots
with Positive
Critical Load
Exceedance Values
North of
Glaciation Line
78
3
32
14
0
60
0
187
Average
Critical Load
Exceedance
(eq/ha/yr)
133.1048
628.5439
368.9527
282.5433
NA
292.4329
NA
187
Average Tree
Volume
Growth
(m3/yr)
0.007
0.004
0.006
0.004
NA
0.007
NA
187
Average
Tree
Volume
(m3)
0.245
0.203
0.245
0.221
NA
0.328
NA
187
NA = not applicable. Average Critical Load Exceedance, Average Tree Volume Growth, and Average Tree Volume values could not
be determined because there were no red spruce plots north of the glaciation line.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 5, Attachment A - 19
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Terrestrial Acidification Case Study
1 The results from the linear regression analyses for sugar maple and red spruce, north of
2 the glaciation line, are presented in Tables 3-9 and 3-10, respectively. Similar to the first linear
3 regression analyses that included all positive exceedance plots, the coefficient of the critical load
4 exceedance was negative for both species and was statistically significant at the 5% level (p-
5 value of 0.035) for red spruce. However, in contrast to the first linear regression analyses for
6 sugar maple, the coefficient in this regression analysis north of the glaciation line was significant
7 at the 10% (p-value of 0.101), and occurred despite the 26% reduction of plots used in the
8 analysis. These results suggest that a larger portion of the variation in sugar maple growth could
9 be accounted for by critical load exceedance (e.g., positive values) when the analysis was
10 restricted to plots north of the glaciation.
11 One potential reason for this difference between the linear regression analyses for all
12 sugar maple plots versus only sugar maple plots north of the glaciation line may be inaccuracy in
13 critical load estimates introduced by the use of the clay-substrate model to estimate BCW. As has
14 been suggested by critical load experts, the clay-substrate model may not provide good estimates
15 of BCW on the older, more weathered soils south of the glaciation line. Therefore, including sugar
16 maple plots from south of the glaciation line in the analysis may have increased the error in the
17 data used in the analysis. The clay-substrate model is an empirically based correlation model and
18 does not factor in soil mineralogy (H. Sverdrup, personal communication, 2009), and may not be
19 suitable to estimate BCW in certain soils. Other models, such as PROFILE (Sverdrup and
20 Warfvinge, 1993 a), which are based on soil mineralogy, may provide better estimates of the
21 contribution of base cations from soil weathering. Future assessment of critical loads may,
22 therefore, want to consider exploring other BCW models to estimate critical loads of atmospheric
23 nitrogen and sulfur deposition.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 20
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Terrestrial Acidification Case Study
1 Table 3-9. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses of
2 Positive Critical Load Exceedances and Sugar Maple Tree Growth, North of the Glaciation Line
3 (for critical loads calculated with Bc/Al = 10.0)
Explanatory Variables
Intercept
Critical Load Exceedance
Average Tree Volume
Square of Average Tree Volume
Illinois
Indiana
Iowa
Maine
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Vermont
Wisconsin
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.004875
-3.344xlO'6
0.021150
8.944 xlO'4
-0.001884
0.005452
-0.002052
-0.000895
-0.008403
0.000222
0.000210
0.001850
-0.001647
0.001956
-0.000817
-0.002104
-0.000803
-0.005168
-0.002195
t-statistic
1.48
-1.64
10.12
1.1
-0.31
1.63
-0.16
-0.22
-1.82
0.07
0.06
0.35
-0.43
0.25
-0.25
-0.45
-0.23
-1.51
-0.68
p-value
0.1385
0.1008
<.0001
0.27
0.755
0.1029
0.8743
0.8245
0.0685
0.9456
0.9553
0.7255
0.6696
0.8042
0.8035
0.6522
0.8177
0.131
0.4958
2205
0.1722
2nd Draft Risk and Exposure Assessment
Appendix 5, Attachment A - 21
June 5, 2009
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Terrestrial Acidification Case Study
1 Table 3-10. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses
2 of Positive Critical Load Exceedances and Red Spruce Tree Growth, North of the Glaciation
3 Line (for critical loads calculated with Bc/Al = 10.0)
Explanatory Variables
Intercept
Critical Load Exceedance
Tree Volume
Square of Tree Volume
Maine
Massachusetts
New Hampshire
New York
Number of Observations
Adjusted R2
Dependent Variable: Average Tree Growth (m3/yr)
Coefficient
0.006034
-5.162xlO'6
0.005590
S.lOOxlO'3
0.000285
-0.000132
0.000435
-0.001805
t-statistic
4.96
-2.12
1.26
1.23
0.32
-0.05
0.42
-1.32
p-value
<.0001
0.0354
0.2093
0.2218
0.7489
0.9629
0.6736
0.1897
187
0.1963
5 4. Additional Sources of Variability Influencing the Critical Load-to-Tree
6 Growth Relationship
7 In addition to potential inaccuracy in the BCW variable in the estimation of critical loads,
8 there are additional sources of variation that may have influenced the relationship between
9 critical load exceedance and the growth of red spruce and sugar maple.
10 4.1 State-Specific Variables
11 Although State dummy variables and current tree volume were included as covariates in
12 the regression analyses to account for the influences of location and tree size on tree growth,
13 additional factors could be included in future regression analyses. For example, incorporating
14 latitude/longitude and elevation in analyses could remove the influence of location on tree
15 growth. Similarly, site index could remove the influence of site quality on the growth of red
16 spruce and sugar maple. Measurement year and time between measurements could help remove
17 the influence of year-to-year variation in conditions and measurement methodology on the
18 growth data. Climatic variation (e.g., rainfall, temperature) could be included to account for the
19 influence of drought and frost conditions (McNulty and Boggs, in press) on tree growth. It is
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 22
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Terrestrial Acidification Case Study
1 recommended that future analyses comparing tree growth and critical load exceedance take
2 additional sources of variability into account.
3 4.2 Dead Trees
4 The regression relationship between critical load exceedance and tree growth may also be
5 improved with the inclusion of dead trees in the analyses. In the FIA database, when a tree is
6 recorded as dead for the first time, the total volume of that tree is considered negative volume
7 growth over the most recent measurement period.13 As described earlier, atmospheric deposition
8 of nitrogen and sulfur can indirectly results in tree mortality. Therefore, it may be appropriate to
9 include tree mortality in an evaluation of the relationship between critical load exceedance and
10 tree growth. However, the validity of including dead tree negative volume growth measurement
11 (as calculated by the FIA database) in analyses that compare load exceedance and tree growth is
12 uncertain, and, therefore, the inclusion of dead trees was not pursued in the analyses reported in
13 this Addendum.
14 4.3 Other Factors
15 The FIA sugar maple and red spruce tree data used in the analyses may have also
16 introduced variability and a source of error in the analyses. As discussed in Appendix 5, due to
17 restriction factors, not all sugar maple and red spruce plots were included in the analyses. It is
18 uncertain to what degree, if any, these restrictions may have biased the results. The influence of
19 tree ingress14 may also not have been completely accounted for in the analyses and may have
20 introduced another source of error. According to USFS FIA database methodology, trees must be
21 at least 12.7 centimeters (cm) in diameter to be included in the VOLCFNET (total net volume
22 per tree) tree volume table. When they reach this size, the full volume of the stem is incorporated
23 into the volume growth measurements, and, in many cases, these measurements would be larger
24 than the actual annual growth rates. Trees with 0 m3 VOLCFNET volume values were excluded
13 Dead tree volume growth is calculated as a difference in volumes (v2-vl) divided by the time between sequential
measurement period (t2-tl). When a tree is recorded as dead, it is assigned a v2 value of "0." Therefore, the
associated volume growth is equal to the entire tree volume divided by the difference in the number of years
between the current and last measurement cycle.
14 "Ingress" refers to trees that appear that are included in the dataset for the first time. Usually ingress is a result of
trees reaching a certain size, but occasionally, ingress can also occur when new trees are included in the
measurements. Steps are taken to identify these new trees and estimate previous size and growth, but sometimes
they may be missed.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 23
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Terrestrial Acidification Case Study
1 from the analyses to at least partially account for this influence. With the data that were made
2 available for the analyses, it was not possible to determine other possible ingress trees. The use
3 of VOLCFNET tree volume as a covariate of FGROWCFAL (net annual sound cubic-foot
4 growth of a live tree on forest land) tree growth may have also introduced a small source of
5 error. VOLCFNET is based on merchantable volume (e.g., pulp and sawlog), whereas
6 FGROWCFAL is based on growth of sound wood. The difference between the two
7 measurements is cull wood that is sound but not merchantable due to circumstances such as the
8 location on the tree or tree branchiness. The removal of trees with 0 m3 VOLCFNET volume
9 from the analyses removed at least a portion of the trees that would increase the influence of cull
10 wood on the covariate relationship between the two variables. Measurement error may also have
11 introduced another source of error to the analyses. Tree volumes and volume growth are based
12 on the measurements conducted on the main stem of the tree. Slight differences in measurements
13 conducted by different crews and in different years could have introduced some error to the
14 volume and growth estimates. Based on the FIA data provided by the USFS, it was not possible
15 to determine the degree to which each of these various source of error influenced the data, nor
16 was it possible to determine if reduction or elimination of these sources of error would change
17 and/or improve the regression analyses. Attempts to minimize these potential sources of error are
18 recommended in future analyses of the relationship between critical load exceedance and tree
19 growth.
20 5. Conclusions
21 In conclusion, the results from these analyses do suggest that there is an inverted U-
22 shaped relationship between nitrogen and sulfur deposition and tree growth. The results also
23 indicate a negative relationship between growth and exceedances for deposition above the
24 critical load. Exceedance of critical loads by nitrogen and sulfur deposition appeared to
25 negatively impact the vigor and growth of sugar maple and red spruce. In addition, the results
26 suggest that quadratic regression analyses comparing critical load exceedance and tree growth
27 could serve as a field test or validation of the well-established, lab-based impacts of Bc/Al on
28 tree growth, and could also assist in the determination of the most appropriate level-of-protection
29 critical load (based on Bc/Al = 0.6, 1.2, or 10.0) to use for each tree species. Based on these
30 findings, it is recommended that critical load exceedance-tree growth regressions be used as a
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 24
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Terrestrial Acidification Case Study
1 tool in future assessments and determinations of critical loads for forest systems and tree species
2 and for evaluating potential negative impacts of positive critical load exceedances on tree health
3 and growth.
4
5
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 5, Attachment A - 25
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1 June 5, 2009
2
O
4
5
6 Appendix 6
? Aquatic Nutrient Enrichment
s Case Study
9
10 Final Draft
11
12 EPA Contract Number EP-D-06-003
13 Work Assignment 3-62
14 Project Number 0209897.003.062
15
16
17
18
19
20
21
22
23 Prepared for
24
25 U.S. Environmental Protection Agency
26 Office of Air Quality Planning and Standards
27 Research Triangle Park, NC 27709
28
29
30
31 Prepared by
32
3 3 RTI Internati onal
34 3040 Cornwall!s Road
3 5 Research Triangle Park, NC 27709-2194
36
37
38
39
40
41 INTERNATIONAL
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3
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5
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7
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Aquatic Nutrient Enrichment Case Study
1 CONTENTS
2 Acronyms and Abbreviations v
3 1. Background 1
4 1.1 Indicators, Ecological endpoints, and Ecosystem Services 3
5 1.2 Case Studies 9
6 1.2.1 National Overview of Sensitive Areas 9
7 1.2.2 Use of ISA Information and Rationale for Site Selection 13
8 1.2.3 Potomac River and Potomac Estuary 19
9 1.2.4 Neuse River andNeuse River Estuary 22
10 2. Approach and Methods 28
11 2.1 Modeling 29
12 2.2 Chosen Method 32
13 2.2.1 SPARROW 33
14 2.2.1.1 Background and Description 33
15 2.2.1.2 Key Definitions for Understanding SPARROW Modeling 38
16 2.2.1.3 Concepts of Importance to Case Study—SPARROW
17 Application 40
18 2.2.2 ASSETS Eutrophication Index 42
19 2.2.2.1 Background and Description 42
20 2.2.2.2 Applications and Updates 48
21 2.2.3 Assessments Using Linked SPARROW and ASSETS El 48
22 3. Results 61
23 3.1 Current Conditions 61
24 3.1.1 Summary of Results for the Potomac River/Potomac Estuary Case Study
25 Area 61
26 3.1.1.1 SPARROW Assessment 62
27 3.1.1.2 ASSETS El Assessment 68
28 3.1.2 Summary of Results for the Neuse River/Neuse River Estuary Case
29 Study Area 72
30 3.1.2.1 SPARROW Assessment 72
31 3.1.2.2 ASSETS El Assessment 79
32 3.2 Alternative Effects Levels 82
33 3.2.1 Potomac River Watershed 82
34 3.2.2 Neuse River Watershed 91
35 4. Implications for Other Systems 100
36 5. Uncertainty 103
37 6. Conclusions 107
38 7. References 108
39
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6 - i
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Aquatic Nutrient Enrichment Case Study
1 FIGURES
2 Figure 1.1-1. Descriptions of the five eutrophication indicators used in NOAA' s NEEA
3 (Bricker et al., 2007a) 6
4 Figure 1.1 -2. A simplified schematic of eutrophication effects on an aquatic ecosystem 6
5 Figure 1.1 -3. An illustrated representation of eutrophication measures through the use of
6 indicators and influencing factors from NOAA's NEEA (Bricker et al.,
7 2007a) 7
8 Figure 1.2-1. The relationship between mean dissolved inorganic nitrogen concentration
9 and mean wet inorganic nitrogen in unproductive lakes in different regions
10 in North America and Europe (Bergstrom and Jansson, 2006) 11
11 Figure 1.2-2. Areas potentially sensitive to aquatic nutrient enrichment 13
12 Figure 1.2-3. The Potomac River Watershed and Potomac Estuary 20
13 Figure 1.2-4. TheNeuse River Watershed andNeuse River Estuary 25
14 Figure 2.2-1. Modeling methodology for case study 33
15 Figure 2.2-2. Mass balance description applied to the SPARROW model formulation 34
16 Figure 2.2-3. Conceptual illustration of a reach network 35
17 Figure 2.2-4. SPARROW model components (Schwarz et al., 2006) 37
18 Figure 2.2-5. Influencing factors/Overall Human Influence index description and decision
19 matrix (Bricker et al., 2007a) 43
20 Figure 2.2-6. Overall Eutrophic Condition index description and decision matrix (Bricker
21 etal., 2007a) 45
22 Figure 2.2-7. Detailed descriptions of primary and secondary indicators of eutrophication
23 (Bricker et al., 2007a) 46
24 Figure 2.2-8. Determined Future Outlook index description and decision matrix (Bricker
25 etal., 2007a) 47
26 Figure 2.2-9. Example response curve of instream total nitrogen concentrations to
27 atmospheric deposition loads 50
28 Figure 2.2-10. Example of response for case study analysis (Bricker et al., 2007b) 51
29 Figure 2.2-11. ASSETS El response curve 53
30 Figure 2.2-12a. Back calculation analysis scenario A: no uncertainly 57
31 Figure 2.2-12b. Back calculation analysis scenario B: uncertainty in ASSETS El
32 assessment 58
33 Figure 2.2-12c. Back calculation analysis scenario C: uncertainty in both ASSETS El
34 assessment and nitrogen loading assessment 59
35 Figure 2.2-13. Example for improvement by one ASSETS El score category in a back
36 calculation assessment 60
37 Figure 2.2-14. Example for resulting change in atmospheric nitrogen loads due to
38 improvement in ASSETS El score in back calculation assessment 61
39 Figure 3.1-1 a. Atmospheric deposition yields of oxidized nitrogen over the Potomac
40 River and Potomac Estuary watershed 64
41 Figure 3.1-lb. Atmospheric deposition yields of reduced nitrogen over the Potomac River
42 and Potomac Estuary watershed 65
43 Figure 3.1-lc. Atmospheric deposition yields of total nitrogen over the Potomac River
44 and Potomac Estuary watershed 66
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Appendix 6 - ii
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Aquatic Nutrient Enrichment Case Study
1 Figure 3.1-2. Total nitrogen yields from all sources as predicted using the Version 3 of
2 the Chesapeake Bay SPARROW application with updated 2002
3 atmospheric deposition inputs 67
4 Figure 3.1-3. Source contributions to Potomac Estuary nitrogen load 68
5 Figure 3.1-4. The ASSETS El scores for the Potomac Estuary (Bricker et al., 2006) 70
6 Figure 3.1-5a. Atmospheric deposition yields of oxidized nitrogen over the Neuse River
7 and Neuse River Estuary watershed 74
8 Figure 3. l-5b. Atmospheric deposition yields of reduced nitrogen over the Neuse River
9 and Neuse River Estuary watershed 75
10 Figure 3. l-5c. Atmospheric deposition yields of total nitrogen over the Neuse River and
11 Neuse River Estuary watershed 76
12 Figure 3.1-6. Total nitrogen yields from all sources in the Neuse River watershed as
13 predicted by a SPARROW modeling application for the Neuse, Tar-
14 Pamlico, and Cape Fear rivers' watersheds with 2002 data inputs 78
15 Figure 3.1-7. Source contributions to Neuse River Estuary total nitrogen load 79
16 Figure 3.2-1. Response curve relating instream total nitrogen concentration to total
17 nitrogen atmospheric deposition load for the Potomac River watershed 83
18 Figure 3.2-2. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, median
19 TNatm*i (i = run 280) 89
20 Figure 3.2-3. Response curve relating instream total nitrogen concentration to total
21 nitrogen atmospheric deposition load for the Neuse River/Neuse River
22 Estuary Case Study Area 92
23 Figure 3.2-4. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, median
24 TNatm*i (i = run 287) 97
25 Figure 3.2-5. Theoretical SPARROW response curves demonstrating relative influence of
26 sources on nitrogen loads to an estuary 99
27 Figure 4-1. Preliminary classifications of estuary typology across the nation (Bricker et
28 al., 2007a) 102
29
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Appendix 6 - iii
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Aquatic Nutrient Enrichment Case Study
1 TABLES
2 Table 1.1-1. Key Indicators of Nutrient Enrichment Due toNr, Including NOX 4
3 Table 1.1-2. Assessment Ecological Endpoints for Nutrient Enrichment Due to
4 Deposition of Total Reactive Nitrogen, Including NOX 7
5 Table 1.1-3. Ecosystem Services for Aquatic Systems Affected by Nutrient Enrichment 7
6 Table 1.2-1. Summary of Indicators, Mapping Layers, and Models for Targeted
7 Ecosystems 10
8 Table 1.2-2. Nitrogen Deposition Level vs. EPA Total Nitrogen Criteria for Lakes and
9 Reservoirs 12
10 Table 1.2-3. Science Advisory Board/Ecological Effects Subcommittee Listing of
11 Potential Assessment Areas for Evaluation of Benefits of Reductions in
12 Atmospheric Deposition with Respect to Aquatic Nutrient Enrichment 15
13 Table 1.2-4. Potential Assessment Areas for Aquatic Nutrient Enrichment Identified in
14 the ISA 16
15 Table 1.2-5. Physical Characteristics of the Potomac Estuary 20
16 Table 1.2-6. Hydrological Characteristics of the Potomac Estuary 21
17 Table 1.2-7. Physical Characteristics of the Neuse River Estuary 25
18 Table 1.2-8. Neuse River Watershed Land Use and Population 26
19 Table 1.2-9. Hydrological Characteristics of the Neuse River Estuary 27
20 Table 2.1-1. Examples of SPARROW Applications 38
21 Table 3.1-1. Potomac Estuary Current Condition Overall Human Influence Index Score 71
22 Table 3.1-2. Model Parameters for 2002 Current Condition SPARROW Application for
23 the Neuse River Watershed 76
24 Table 3.1-3. Model Evaluation Statistics for 2002 Current Condition SPARROW
25 Application for the Neuse River Watershed 77
26 Table 3.1-4. Current Condition Overall Eutrophic Condition Index Score for the Neuse
27 River/Neuse River Estuary Case Study Area 81
28 Table 3.2-1. Potomac River Watershed Alternative Effects Levels 83
29 Table 3.2-2. Historical Potomac River Total Nitrogen Loads and Concentrations 84
30 Table 3.2-3. Additional Potomac Estuary Overall Eutrophic Condition Index Scores for
31 Alternative Effects Levels 85
32 Table 3.2-4. Summary Statistics for Target ASSETS El Scenarios for the Potomac
33 Estuary 89
34 Table 3.2-5. Neuse River/Neuse River Estuary Case Study Area Alternative Effects
35 Levels 92
36 Table 3.2-6. Annual Average Instream Total Nitrogen Concentrations in the Neuse River 93
37 Table 3.2-7. Additional Neuse River Estuary Overall Eutrophic Condition Index Scores
38 for Alternative Effects Levels 94
39 Table 3.2-8. Summary Statistics for Target ASSETS El Scenarios for the Neuse
40 River/Neuse River Estuary Case Study Area 97
41 Table 4-1. Typology Group Categorizations 101
42
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Appendix 6 - iv
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Aquatic Nutrient Enrichment Case Study
ACRONYMS AND ABBREVIATIONS
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
ASSETS El
AQUATOX
CASTNet
CBP
CE-QUAL
CMAQ
C02
DFO
DIN
EDA
EES
EMAP
GIS
GT/MEL
HAB
HNO3
HSPF
INCA
ISA
km
km2
LANDSCAN
m
m2
m3
MAGIC
MEA
mg/L
mi2
MODMON
N2O
NADP
NCCR
NCDWQ
NEEA
NH3
NH4+
NOAA
NO
NO2
N03
NOX
Nr
Assessment of Estuarine Trophic Status eutrophication index
a simulation model for aquatic systems
Clean Air Status and Trends Network
Chesapeake Bay Program
a mathematical model of water quality
Community Multiscale Air Quality model.
carbon dioxide
determined future outlook
dissolved inorganic nitrogen
Estuarine Drainage Areas
Ecological Effects Subcommittee
Environmental Monitoring and Assessment Program
geographic information systems
Georgia Tech Hydrologic Model/Multiple Element Limitation
harmful algal bloom
nitric acid
Hydrologic Simulation Program-FORTRAN
Integrated Nitrogen in Catchments
Integrated Science Assessment
kilometer
square kilometer
a worldwide population database
meter
square meter
cubic meter
Model of Acidification of Groundwater in Catchments
Millennium Ecosystem Assessment
milligrams per liter
square mile
Modeling and Monitoring Project
nitrous oxide
National Atmospheric Deposition Program
National Coastal Condition Report
North Carolina Department of Water Quality
National Estuarine Eutrophication Assessment
ammonia gas
ammonium
National Oceanic and Atmospheric Administration
nitric oxide
nitrogen dioxide
nitrate
nitrogen oxides
reactive nitrogen
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Appendix 6 - v
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Aquatic Nutrient Enrichment Case Study
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
OEC
OKI
psu
PnET-BCG
QA
QC
QUAL2K
RCA/ECOMSED
RF1
RHESSys
RTI
SAGT
SAV
sox
SPARROW
STORE!
TN
TNatm
TNS
Hg/L
USGS
VIF
VIMS
WASP
WATERSN
Overall Eutrophic Condition
Overall Human Influence
practical salinity unit
biogeochemical model
quality assurance
quality control
Enhanced Stream Water Quality Model
Row Column AESOP/Estuary and Coastal Ocean Model with Sediment
Transport
Reach File version 1
Regional Hydro-Economic Simulation System
RTI International
Atlantic and the eastern Gulf of Mexico, as well as the Tennessee River
basin
submerged aquatic vegetation
sulfur oxides
Spatially Referenced Regression on Watershed
STOrage and RETrieval
total nitrogen
total nitrogen atmospheric deposition load
instream total nitrogen concentration
microgram per liter
U.S. Geological Survey
Variance Inflation Factor
Virginia Institute of Marine Science
Water Quality Analysis Simulation Program
Watershed Assessment Tool for Evaluating Reduction Strategies for
Nitrogen
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Appendix 6 - vi
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Aquatic Nutrient Enrichment Case Study
i 1. BACKGROUND
2 One classification of effects targeted for this Risk and Exposure Assessment is nitrogen
3 and sulfur enrichment of ecosystems in response to deposition of nitrogen oxides (NOX) and
4 sulfur oxides (SOX). Nutrient enrichment effects are caused by nitrogen or sulfur deposition, but
5 are dominated by nitrogen deposition, which is the focus of this case study. Nutrient enrichment
6 can result in eutrophication in aquatic systems (see Section 4.3 of the Integrated Science
1 Assessment (ISA) for Oxides of Nitrogen and Sulfur-Ecological Criteria (Final Report) (ISA)
8 (U.S. EPA, 2008a).
9 Because ecosystems may respond differently to nutrient enrichment, it is necessary to
10 first perform Risk and Exposure Assessment case studies unique to the effect and ecosystem
11 type. The feasibility of consolidating the effects and/or ecosystems in the Risk and Exposure
12 Assessment was assessed, and where feasible, a broader characterization was performed.
13 However, some ecosystems and their effects may be too unique to consolidate into a broad
14 characterization.
15 Upon completion of all risk and exposure assessment case studies, the results of the
16 assessments performed for unique combinations of effects and ecosystem types are presented
17 together to facilitate decision making on the total effects of nitrogen and sulfur deposition.
18 Ecosystem services that relate to the effects are identified and valued, if possible. Ecosystem
19 services provide an additional way to compare effects across various ecosystems.
20 The selection and performance of case studies represent Steps 3 and 4, respectively, of
21 the seven-step approach to planning and implementing a risk and exposure assessment, as
22 presented in the April 2008 Draft Scope and Methods Plan for Risk/Exposure Assessment:
23 Secondary NAAQS Review for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA, 2008b). Step
24 4 entails evaluating the current nitrogen and sulfur loads and effects to a chosen case study
25 assessment area, including ecosystems services. This case study evaluates the current nitrogen
26 deposition load to aquatic ecosystems; in particular, estuarine systems and the role atmospheric
27 deposition can play in the eutrophi cation of an aquatic ecosystem.
28 Eutrophication
29 Eutrophication is the process whereby a body of water becomes over-enriched in
30 nutrients, resulting in increased productivity (e.g., of algae or aquatic plants). As productivity
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Appendix 6-1
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Aquatic Nutrient Enrichment Case Study
1 increases, dissolved oxygen levels in the waterbody may decrease and result in hypoxia (i.e., low
2 dissolved oxygen levels). Total reactive nitrogen (Nr) can promote eutrophication in inland
3 freshwater ecosystems, as well as in estuarine and coastal marine ecosystems, ultimately
4 reducing biodiversity because of the lack of available oxygen needed for the survival of many
5 species of aquatic plants and animals. Total Nr includes all biologically, chemically, and
6 radiatively active nitrogen compounds in the atmosphere and biosphere, such as ammonia gas
7 (NH3), ammonium (NH4+), nitric oxide (NO), nitrogen dioxide (NO2), nitric acid (HNO3),
8 nitrous oxide (N2O), nitrate (NO3 ), and organic compounds (e.g., urea, amines, nucleic acids)
9 (U.S. EPA, 2008b).
10 Freshwater Aquatic Ecosystems
11 A freshwater lake or stream must be nitrogen-limited to be sensitive to nitrogen-mediated
12 eutrophi cation. Although conventional wisdom holds that most lakes and streams in the United
13 States are limited by phosphorus, recent evidence illustrates examples of lakes and streams that
14 are limited by nitrogen and show symptoms of eutrophi cation in response to nitrogen addition.
15 For example, surveys of lake nitrogen concentrations and trophic status along gradients of
16 nitrogen deposition show increased inorganic nitrogen concentrations and productivity to be
17 correlated with atmospheric nitrogen deposition (Bergstrom and Jansson, 2006). Additional
18 information supporting the connection between nitrogen loading and eutrophication in freshwater
19 systems is provided in EPA's ISA (U.S. EPA, 2008a, Sections 3.3.2.3 and 3.3.3.2).
20 Estuarine and Coastal Marine Ecosystems
21 Estuarine and coastal marine ecosystems are highly important to human and ecological
22 welfare through the ecosystem services they provide (e.g., fisheries, recreation). "Because the
23 productivity of estuarine and nearshore marine ecosystems is generally limited by the availability
24 of Nr, an excessive contribution of Nr from sources of water and air pollution can contribute to
25 eutrophication" (U.S. EPA, 2008a, Section 4.3.4.1). The National Oceanic and Atmospheric
26 Administration's (NOAA's) National Estuarine Eutrophication Assessment (NEEA) examined
27 more than 140 estuaries along the coasts of the conterminous United States. The assessment
28 examined a range of symptoms of eutrophication, including algal blooms, hypoxia, and
29 vegetation growth. Findings from the study concluded that 65% of the assessed systems had
30 moderate to high overall eutrophic conditions (OECs) (Bricker et al., 2007a). Increasingly,
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Appendix 6-2
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Aquatic Nutrient Enrichment Case Study
1 individual estuarine ecosystems have become the center of intensive studies on nutrient
2 enrichment/eutrophication causes and effects. Within the Chesapeake Bay, studies of the
3 frequency of phytoplankton blooms and the extent and severity of hypoxia revealed overall
4 increases in these detrimental effects (Officer et al., 1984). Within the Pamlico Estuary in North
5 Carolina, similar trends have been observed and studied by Paerl et al. (1998). Sources identified
6 within these assessments range from atmospheric deposition to fertilizer applications and other
7 land use-based applications.
8 Estuarine and coastal marine ecosystems experience a range of ecological problems
9 associated with nutrient enrichment. Because the productivity of estuarine and nearshore marine
10 ecosystems is generally limited by the availability of Nr, an excessive contribution of Nr from
11 sources of water and atmospheric pollution can contribute to eutrophication. Some of the most
12 important environmental effects include increased algal blooms, the occurrence of bottom-water
13 hypoxia, and reductions in fishery populations and the abundance of seagrass habitats (Boynton
14 et al., 1995; Valiela and Costa, 1988; Howarth et al., 1996; Paerl, 1995, 1997; Valiela et al.,
15 1990).
16 There is broad scientific consensus that nitrogen-driven eutrophi cation in shallow U.S.
17 estuaries has increased over the past several decades and that environmental degradation of
18 coastal ecosystems is now a widespread occurrence (Paerl et al., 2001). For example, the
19 frequency of phytoplankton blooms and the extent and severity of hypoxia have increased in the
20 Chesapeake Bay (Officer et al., 1984), the Pamlico Estuary in North Carolina (Paerl et al., 1998),
21 and along the continental shelf adjacent to the Mississippi and Atchafalaya river discharges to
22 the Gulf of Mexico (Eadie et al., 1994). A recent national assessment of eutrophic conditions in
23 estuaries found that 65% of the assessed systems had moderate to high OECs (Bricker et al.,
24 2007a). Estuaries with high OECs were generally those that received the greatest nitrogen loads
25 from all sources, including atmospheric and land-based sources (Bricker et al., 2007a).
26 1.1 INDICATORS, ECOLOGICAL ENDPOINTS, AND ECOSYSTEM
27 SERVICES
28 Major indicators for nutrient enrichment to aquatic systems from atmospheric deposition
29 of total Nr require measurements based on available monitoring stations for wet deposition
30 (National Atmospheric Deposition Program [NADPJ/National Trends Network) and limited
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Appendix 6-3
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Aquatic Nutrient Enrichment Case Study
1 networks for dry deposition (Clean Air Status and Trends Network [CASTNet]). Wet deposition
2 monitoring stations can provide more information on an extensive range of nitrogen species than
3 is possible for dry deposition monitoring stations. This creates complications in developing
4 estimates for total nitrogen (TN) deposition levels because dry deposition data sources will likely
5 be underestimated because of the use of fixed deposition velocities that do not reflect local
6 conditions at the time of measurement, under-representation of monitoring sites in certain
7 landscapes, and omission of some Nr species in the measurements (U.S. EPA, 2008a, Section
8 2.5).
9 For aquatic ecosystems, the indicators for "nutrient enrichment" effects reflect a
10 combination of inputs from all media (e.g., air, discharges to water, diffuse runoff, groundwater
11 inputs). Major aquatic system indicators include nutrient loadings (Heinz Center for Science,
12 2007), excess algal standing crops, or in larger waterbodies, anoxia (i.e., absence of dissolved
13 oxygen) and/or hypoxia in bottom waters (see Table 1.1-1). For nitrogen, loadings or
14 concentration values related to total Nr (a combination of nitrates, nitrites, organic nitrogen, and
15 total ammonia) are encouraged for inclusion in numeric criteria as part of EPA-approved state
16 water quality standards (U.S. EPA, 2000). Given the nature of the major indicators for
17 atmospheric deposition and indicators for aquatic and terrestrial ecological systems, a data-fusion
18 approach that combines monitoring indicators with modeling inputs and outputs is often used
19 (Howarth, 2007).
Table 1.1-1. Key Indicators of Nutrient Enrichment Due to Nr, Including NOX
Key Indicator
Group
Nitrogen deposition
Nitrogen
throughfall
deposition
Nitrogen loadings
and fluxes to
receiving waters
Examples of Indicators
Nitrate or ammonia
Nitrate, ammonia,
organic nitrogen
Total nitrogen or
constituent species
combined with flow data
from gauged stations
Description
From wet or dry deposition monitoring
stations and networks
Special measurements in terrestrial
ecosystem with corrections for nitrogen
intercepted by plant canopies
Reflects a combination of inputs from all
media (e.g., air, discharges to water, diffuse
runoff, and groundwater inputs); relative
role of air deposition should ideally be
compared with air deposition data and also
with available (preferably multimedia)
models
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 6-4
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Aquatic Nutrient Enrichment Case Study
Key Indicator
Group
Examples of Indicators
Description
Other indicators of
aquatic system
nutrient enrichment
(eutrophication)
Algal standing crop
(plankton and
periphyton);
anoxia/hypoxia for
estuaries and large rivers
Reflects a combination of inputs from all
media (e.g., air, discharges to water, diffuse
runoff, and groundwater inputs); relative
role of air deposition should ideally be
compared with air deposition data and also
with available (preferably multimedia)
models
1 Nitrogen is an essential nutrient for estuarine and marine ecosystem fertility and is often
2 the algal growth-limiting nutrient (U.S. EPA, 2008a; Section 3.3.5.3). Excessive nitrogen
3 contributions can cause habitat degradation, algal blooms, toxicity, hypoxia, anoxia, fish kills,
4 and decreases in biodiversity (Paerl et al., 2002). To evaluate these impacts, five ecological
5 indicators were used in NOAA's recent NEEA of estuary trophic condition: chlorophyll a,
6 macroalgae, dissolved oxygen, nuisance/toxic algal blooms, and submerged aquatic vegetation
7 (SAV) (Bricker et al., 2007a).
8 Figure 1.1-1, excerpted from the NOAA's NEEA Update, provides a brief description of
9 each of the indicators. Further interactions between the indicators are described in the following
10 text. For greater detail on each of the indicators, including previous findings and study areas,
11 refer to the ISA (U.S. EPA, 2008a, Sections 3.3.2, 3.3.3, 3.3.5, 3.3.8, 4.3.4, and C.5) and the
12 NEEA Update (Bricker et al., 2007a).
13 Figure 1.1-2 provides a simplified progression of the indicators as the estuarine waters
14 become more eutrophic. In the NEEA Update (Bricker et al., 2007a), an illustrated relationship
15 between the OEC, water quality and ecological indicators, and influencing factors (e.g., nitrogen
16 loads) is presented (Figure 1.1-3).
17 Indicators of eutrophication do not provide a direct link to the ecological benefits of the
18 ecosystem. Because of this, the eutrophication-impact ecological endpoints and the ecosystem
19 services affected must be identified and related to the quantifiable indicators. Table 1.1-2
20 provides some examples of the ecological endpoints associated with the indicators of
21 eutrophication. As described in the introduction, the ecological endpoints are ecological entities
22 and their impacts. For instance, an indicator may be low dissolved oxygen, but the ecological
23 endpoint or impact of having low dissolved oxygen is a decrease in the populations offish that
24 are highly sensitive to dissolved oxygen conditions.
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Appendix 6-5
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Aquatic Nutrient Enrichment Case Study
1
2
3
Primary symptoms
Description
t orophyfl a
i i/toplankton)
Secondary symptoms
A measure used to indicate the amount of microscopic algae
(phytoplankton) growing in a water body, High concentrations can lead
to low dissolved oxygen levels as a resytt of decomposition.
Large algae commonly referred to as "seaweed," Blooms can cause
losses of submerged aquatic vegetation by blocking sunlight.
Additionally, blooms may smother immobile shellfish, corals, or other
habitat. The unsightly nature of some blooms may impact tourism due
to the declining value of swimming, fishing, and boating.
Description
\ Dissolved
J
Submerged
aquatic vegetation
;'*** jrj) Nuisance/toxic
\4r***j*/' blooms
Low dissolved oxygen is a eutrophic symptom because it occurs as a
result of decomposing organic matter (from dense algal blooms), which
sinks to the bottom and uses oxygen during decay. Low dissolved
oxygen can cause fish kills habitat loss, and degraded aesthetic values,
resulting in the loss of tourism and recreational water use,
Loss of submerged aquatic vegetation (SAV) occurs when dense al^al
blooms caused by excess nutrient additions (and absence of grazers)
decrease water clarity and light penetration. Tyrbidity caused by other
factors (e.g,, wave energy, color) similarly affects SAV. The loss of SAV can
have negative effects on an estuary's functionality and may impact
some fisheries dye to loss of a critical nursery habitat.
Thought to be caused by a change in the natural mixture of nutrients
that occurs when nutrient inputs increase over a long period of time.
These blooms may release toxins that kill fish and shellfish. Human
health problems may also occur due to the consumption of
contaminated shellfish or from inhalation of airborne toxins. Many
nuisance/lode blooms occur naturally, some are advected into
estuaries from the ocean; the role of nutrient enrichment is unciear.
Figure 1.1-1. Descriptions of the five eutrophication indicators used in NOAA's NEEA
(Bricker et al., 2007a).
Nutrient inputs
Excessive growth of phytoplankton and macroalgae (grazers cannot
control growth)
Decreased water clarity/decreased light penetration/decreased
dissolved oxygen
SAV inhibition
Nuisance/toxic algal blooms
Low dissolved oxygen/hypoxia
4
5
Invertebrates and fish kills
Figure 1.1-2. A simplified schematic of eutrophication effects on an aquatic ecosystem.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 6-6
-------
Aquatic Nutrient Enrichment Case Study
£
£
a.
>-
^W
No Problem /low Moderate low Moderate Moderate high High
v"*. ~~ "••:." ** :»" ' '" "»:•»:.
"•*» w •••*.. \n\v
'"' ^ IC't-^ '1 s *• 'l"'?fi- it', 'f- *. -Y - .*;i ft fl ^ .'^, ** '' I f\ (fJ •
I VwMV f itelf-'^^f ''Lcf 1 '*t"w ^4i'.*'^--
Key to symbols:
1,5;. Submerged aquatic
''1 ' vegetation
; Chlorophyll a
**» Nuisjnce/toxic
*^!*fl
^ blooms
Few symptoms occur Symptoms occur Symptoms occur Symptoms occur Symptoms occur
atroorethan episodically and/or tessregufariy less reguiaHy and/or pertodicallyor
' minimal levels. over a small to and/or over a over a medium to persistently and,'or
\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\^^
Dissolved oxygen
1
2
Figure 1.1-3. An illustrated representation of eutrophication measures through the use of
indicators and influencing factors from NOAA's NEEA (Bricker et al., 2007a).
Table 1.1-2. Assessment Ecological Endpoints for Nutrient Enrichment
Due to Deposition of Total Reactive Nitrogen, Including NOX
Assessment Ecological Endpoint
Fish abundance/population
Water quality, color, clarity
Species richness/community structure
Habitat quality, including benthos and shoreline
Surface scum, odors
4
5
6
7
Continuing to link the indicators and ecological endpoints to the ecological processes of
value to society brings us to the ecosystem services related to eutrophi cation. Examples are
provided in Table 1.1-3. The example of dissolved oxygen and the resulting decrease in fish
population was used to identify the ecosystem services offish catch rate and fish kills, which
support both food and materials and recreational uses of the ecosystem.
Table 1.1-3. Ecosystem Services for Aquatic
Systems Affected by Nutrient Enrichment
Ecosystem Service
Fisheries
• Fish catch rate
• Fishable area
• Size/extent of fish kills
2nd Draft Risk and Exposure Assessment
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Appendix 6-7
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Aquatic Nutrient Enrichment Case Study
Ecosystem Service
Recreation
• Boating
• Swimming
• Beach conditions
Tourism
• Aesthetics
Risk of illness
• Drinking water quality
• Contaminated fish
1 The methods of connecting the ecological endpoints and ecosystem services related to
2 eutrophication are beyond the scope of this case study, but they have been examined in another
3 study (RTI, 2008). Rather, the remaining discussion focuses on determining and detailing the
4 indicator measures as a function of the changing atmospheric deposition inputs of Nr, including
5 NOX.
6 Ecosystem services are generally defined as the benefits individuals and organizations
7 obtain from ecosystems. In the Millennium Ecosystem Assessment (MEA), ecosystem services
8 are classified into four main categories:
9 • Provisioning. Includes products obtained from ecosystems.
10 • Regulating. Includes benefits obtained from the regulation of ecosystem processes.
11 • Cultural. Includes the nonmaterial benefits people obtain from ecosystems through
12 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic
13 experiences.
14 • Supporting. Includes those services necessary for the production of all other ecosystem
15 services (MEA, 2005).
16 A number of impacts on the ecological endpoints offish population, water quality, and
17 habitat quality and the related ecosystem services exist, including the following:
18 • Fish kills - provisioning and cultural
19 • Surface scum - cultural
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Appendix 6-8
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Aquatic Nutrient Enrichment Case Study
1 • Fish/water contamination - provisioning and cultural
2 • Decline in fish population - provisioning and cultural
3 • Decline in shoreline quality (e.g., erosion) - cultural and regulating
4 • Poor water clarity and color - cultural
5 • Unpleasant odors - cultural.
6 The goal of the Aquatic Nutrient Enrichment Case Study was to focus on fisheries,
7 recreation, and tourism. Attempts have been made to link fisheries (e.g., closings, decreased
8 species richness) quantitatively to eutrophication symptoms through monitoring data, and
9 recreation activities qualitatively through user surveys. The symptoms of eutrophication defined
10 by Bricker et al., (2007a) were pursued as the ecosystem ecological endpoints to link to these
11 eco sy stem servi ce s.
12 1.2 CASE STUDIES
13 1.2.1 National Overview of Sensitive Areas
14 The selection of case study areas specific to eutrophication began with national
15 geographic information systems (GIS) mapping. Spatial datasets were reviewed that included
16 physical, chemical, and biological properties indicative of eutrophication potential in order to
17 identify sensitive areas of the United States (Table 1.2-1). The analysis then led to combining
18 the eutrophic estuaries from NOAA's Coastal Assessment Framework, along with areas that
19 exceed the nutrient criteria for lakes/reservoirs (U.S. EPA, 2002), as compared with wet nitrogen
20 deposition, to define areas of national aquatic nutrient enrichment sensitivity.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-9
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Aquatic Nutrient Enrichment Case Study
Table 1.2-1. Summary of Indicators, Mapping Layers, and Models for Targeted Ecosystems
1
2
O
4
Targeted
Ecosystem
Effect
Aquatic nutrient
enrichment and
eutrophication
Indicator(s)
• Nitrate and ammonia, total
nitrogen (major reactive
nitrogen species)
• Al toxicity data
• Chlorophyll a (e.g., algal
standing crop)
• Anoxia/hypoxia(e.g.,
primarily estuaries and tidal
rivers)
• Nitrogen loadings for sub-
watersheds or larger basins
and EDAs
• EPA NCCR Water Quality
Index and NOAA Estuarine
Coastal Eutrophication Index
• Diatom data for nitrogen-
limited systems
Mapping Layers
• STORET retrievals
• USGS National Water Quality
Assessment Program information
• USGS SPARROW attributes,
information
• Water quality standards nutrient
criteria for rivers and lakes
• EPA, NCCR, and NOAA
estuarine eutrophication
indicators
• NOAA ED As
• EPA/NOAA airsheds for major
Atlantic and Gulf estuaries
CMAQ (e.g., nitrogen) by
hydrological unit code
Model(s)
• USGS
SPARROW
• PnET-BCG
Note: EDAs = Estuarine Drainage Areas; NCCR = National Coastal Condition Report;
STORET = STOrage and RETrieval; USGS = U.S. Geological Survey; SPARROW = Spatially
Referenced Regression on Watershed; CMAQ = Community Multiscale Air Quality; PnET-
BCG = a biogeochemical model
5 Bergstrom and Jansson (2006) compiled dissolved inorganic nitrogen (DIN) data from
6 4,296 lakes (i.e., 195 lakes from United States/Canada and 4,101 lakes from Europe). They
7 found that the mean lake DIN concentrations were strongly correlated to the mean wet DIN
8 deposition over large areas of Europe and North America (Figure 1.2-1). The equation for this
9 correlation is:
10
Iog7= 1.34xlogX-1.55 (r =0.70; PO.001)
(1)
2nd Draft Risk and Exposure Assessment
Appendix 6-10
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1 where
2 Y is lake water DIN (microgram per liter [ug/L]), and
3 X is wet deposition (kilograms [kg] N/km2/yr).
4 EPA recommended TN criteria for lakes and reservoirs for 12 aggregated ecoregions in
5 2002 (U.S. EPA, 2002).
6 Based on equation (1), nitrogen deposition level (X: kg N/ha/yr) associated with EPA TN
7 criteria (Y: |ig/L) for each aggregated ecoregion can be calculated by equation (2), and the
8 results are listed in Table 1.2-2.
r+1.55)71.34
7100
(2)
800 -i
10
11
12
13
0 200 400 600 800 1000 1200 1400 1600
Wet DIN-deposition (kg N km-2 yr-1)
Figure 1.2-1. The relationship between mean dissolved inorganic nitrogen concentration
and mean wet inorganic nitrogen in unproductive lakes in different regions in North
America and Europe (Bergstrom and Jansson, 2006).
2nd Draft Risk and Exposure Assessment
Appendix 6-11
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Table 1.2-2. Nitrogen Deposition Level vs. EPA Total Nitrogen Criteria for Lakes and
Reservoirs
TNEPA
criteria
(Hg/L)
N wet
dep
(kg
N/ha/yr)
NADP
Mean
wetN
dep (kg
N/ha/yr)
Agg
Ecor
n
100
4.46
1.19
Agg
Ecor
m
400
12.55
1.16
Agg
Ecor
IV
440
13.47
2.36
Agg
Ecor
V
560
16.13
3.02
Agg
Ecor
VI
780
20.65
5.01
Agg
Ecor
VII
660
18.23
6.36
Agg
Ecor
vm
240
8.57
5.21
Agg
Ecor
IX
360
11.60
4.44
Agg
Ecor
XI
460
13.93
4.93
Agg
Ecor
XII
520
15.26
3.28
Agg
Ecor
xm
1270
29.72
3.35
Agg
Ecor
XIV
320
10.62
4.22
1 Source: Prepared by Lingli Liu, U.S. EPA Office of Research and Development and
2 transmitted in communication from Tara Graever, U.S. EPA Office of Research and
3 Development, May 2008. (Comparable information was not available for rivers and streams.)
4 Note: kg N/ha/yr = kilograms of nitrogen per hectare per year; |ig/L = micrograms per liter.
5 The resulting map reveals areas of highest potential sensitivity to nitrogen deposition as
6 shown in Figure 1.2-2.These areas are identified in blue as nutrient sensitive estuaries contained
7 in NOAA's Coastal Assessment Framework, and in red in areas where deposition exceeds the
8 nutrient criteria. Yellow areas indicate those areas that are below the nutrient criteria, but are
9 within 5 kg/ha/yr of exceeding the criterion.
2nd Draft Risk and Exposure Assessment
Appendix 6-12
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Total Nitrogen Nutrient Criteria for Lake/Reservoirs by Zone
zone i ii ii iv
TN (mg/L)
N dep (kg N ha/yr)
NADP Mean wet N dep (kg N'ha.'yr)
NOAA CAP Total Wet N Dep Exceedance
I I Nutrient Criteria Bdy
IZZI States ^H-26.41- -15
Lakes I I -14.99--10
II II IV V VI VII VIII IX X XI Ml XIII XIV
0.10' 0.40 0.44' 0.56- 0.78 0.66 0.24 036 0.46 0.52 1.27 0.32
4.46 12.55 13.47 16.13 20.65 18.23 8.57 11.6 '13.93 15.26 29,72 10.62
1.19 1.16 2.36 3.02 5.01 6.36 5.21 4.44 493 3.28 3.35 4.22
Exceedance levels determined by first converting N concentration
nutrient criteria amounts(mg/litre) to wet N deposition amounts (kg/ha/yr)
using a formula published by Bergstrom and jansson (2006). These N
; -9.99 --5
-4.99 - 0
deposition amounts were then compared to wet NADP N deposition
(2002) amounts to determine areas of the US that are either above
or below the nutrient criteria levels for lakes/reservoirs.
* Blank areas do not have an EPA nutrient criteria for lake/reservoirs.
1
2 Figure 1.2-2. Areas potentially sensitive to aquatic nutrient enrichment.
3 1.2.2 Use of ISA Information and Rationale for Site Selection
4 The potential case study areas identified by the Ecological Effects Subcommittee (EES)
5 of the Advisory Council on Clean Air Compliance Analysis were considered for examining the
6 ecological benefits of reducing atmospheric deposition. Nutrient enrichment-relevant case study
7 areas suggested by the EES (U.S. EPA, 2005) are reproduced in Table 1.2-3. The ISA (U.S.
8 EPA, 2008b) also recommends case study areas as candidates for risk and exposure assessments;
9 Table 1.2-4 contains potential assessment areas for aquatic nutrient enrichment. Additionally,
10 Howarth and Marino (2006) provide a comprehensive summary of the literature and scientific
11 findings on eutrophication over the past 3 decades. This summary has led to the general
12 consensus that freshwater lakes and estuaries differ in terms of nutrient limitation as the cause of
13 eutrophi cation, and that nitrogen is the limiting element to primary production in coastal marine
14 ecosystems in the temperate zone.
2nd Draft Risk and Exposure Assessment
Appendix 6-13
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Aquatic Nutrient Enrichment Case Study
1 For purposes of the Risk and Exposure Assessment, two regions were selected for case
2 study analysis to which a common methodology could be applied—Chesapeake Bay and the
3 Pamlico Sound. For aquatic nutrient enrichment, special emphasis was given to the Chesapeake
4 Bay region because it has been the focus of many previous studies and modeling efforts, and it is
5 currently one of the few systems within the United States in which economic-related ecosystem
6 services studies have been conducted. The Pamlico Sound, an economically important estuary
7 because of its fisheries, has been studied and modeled greatly by the universities and has also
8 been known to exhibit symptoms of extreme eutrophication. The following factors were
9 considered in choosing these case study areas:
10 • Availability of atmospheric deposit!on data
11 • Availability of existing water quality modeling that accounts for the role of atmospheric
12 deposition
13 "A large, mainstem river that feeds a system with adequate hydrologic unit code delineation
14 and point- and nonpoint-source input data
15 • Scientific stature of the case study area
16 • Scalability and generalization opportunities for risk analysis results from the case studies.
17 These estuarine ecosystems have been the subjects of extensive research that provides the
18 data needed for a first phase of quantitative analysis of the role of nitrogen deposition in
19 eutrophi cation. Other candidate estuarine systems could be evaluated for potential future
20 analyses, whereas freshwater ecosystems in the western United States would most likely require
21 a separate analysis.
22 Because the Chesapeake Bay and Pamlico Sound are fed by multiple river systems, the
23 case study was scaled to one main stem river and associated estuary for each system: the
24 Potomac River and Potomac Estuary for the Chesapeake Bay and the Neuse River and Neuse
25 River Estuary for the Pamlico Sound. Details on each estuarine system are provided below.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-14
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Aquatic Nutrient Enrichment Case Study
Table 1.2-3. Science Advisory Board/Ecological Effects Subcommittee Listing of Potential
Assessment Areas for Evaluation of Benefits of Reductions in Atmospheric Deposition with
Respect to Aquatic Nutrient Enrichment
Ecosystem/
Region
Main CAA
Pollutant(s)
Percentage(s)
Attributable to
Atmospheric
Deposition
Quantitative
Ecological and
Economic
Information
EES Comments
Coastal
Waquoit Bay
Chesapeake
Bay
Long Island
Sound
Barnegat Bay
Tampa Bay
Gulf of
Maine
Casco Bay
Rocky
Mountains
Nitrogen
Nitrogen
Nitrogen;
mercury
Nitrogen
Nitrogen;
mercury
Nitrogen
Nitrogen;
mercury
Nitrogen
30%
20% to 30%
Nitrogen = 23%
to 35%; Mercury
= ?
50% total;
direct deposition
30% to 3 9%
Nitrogen = 25%
to 30%
Low
Nitrogen = 30%
to 40%
Mercury = 84% to
92%
Nearly 100%
Yes
Yes
Yes
Yes
Yes
?
Yes
Yes
High priority. Higher loading
from nondepositional sources
may confound analysis.
High priority. Loading from
diverse sources, particularly
agricultural, may confound
analysis.
High priority. High nitrogen
loading from wastewater
treatment plants may
confound analysis.
High priority. Direct linkage
of ecological effects with
atmospheric deposition;
quantitative economic data
exist.
Medium priority. Examined in
previous EPA efforts.
Variability in loading data
may confound analysis.
Low priority. Linkage of
nitrogen loadings and
ecological impacts is not well
established. Major source of
nitrogen is open-ocean influx.
Medium priority. Good data
on ecological and economic
impacts are available.
Medium priority. Levels of
nitrogen loading much
lower than for northeastern
locations. Economic data
may be lacking.
2nd Draft Risk and Exposure Assessment
Appendix 6-15
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Table 1.2-4. Potential Assessment Areas for Aquatic Nutrient Enrichment Identified in the ISA
Area
Adirondack
Mountains
Chesapeake
Bay
Indicator
Aquatic
nutrient
enrichment;
terrestrial
nutrient
enrichment;
mercury
methylation
Aquatic
nutrient
enrichment;
aquatic
nitrogen-
limited
eutrophication
Detailed
Indicator
Foliar N
concentration;
MV leaching;
C:N ratio; N
mineralization;
nitrification;
denitrification
Watershed N
sources;
chlorophyll a;
dissolved
oxygen;
submerged
aquatic
vegetation
Area Studies
PIRLA I and II;
Adirondack
Lakes Survey;
Episodic
Response
Project; EMAP
NA
Models
MAGIC;
PnET-BGC
NA
References in U.S. EPA, 2008a
Baker and Laflen, 1983; Baker et al.,
1990b; Baker et al., 1990c; Baker et al.,
1996; Benoit et al., 2003; Chen and
Driscoll, 2004; Confer et al., 1983;
Cumming et al., 1992; Driscoll et al.,
1987a; Driscoll et al., 1991; Driscoll et
al., 1998; Driscoll et al., 2001a; Driscoll
et al., 2001b; Driscoll et al., 2003b;
Driscoll et al., 2003c; Driscoll et al.,
2007a; Driscoll et al., 2007b; Evers et al.,
2007; GAO, 2000; Havens et al., 1993;
Ito et al., 2002; Johnson et al., 1994b;
Landers et al., 1988; Lawrence et al.,
2007; NAPAP, 1998; Siegfried et al.,
1989; U.S. EPA, 2003; Sullivan et al.,
1990; Sullivan et al., 2006a; Sullivan et
al., 2006b; U.S. EPA, 1995b; Van Sickle
et al., 1996; Whittier et al., 2002;
Wigington et al., 1996; Zhai et al., 2007
Bricker et al., 1999; Bricker et al., 2007;
Boesch et al., 2001; Boyer et al., 2002;
Boyer and Howarth, 2002; Cooper and
Brush, 1991; Fisher and Oppenheimer,
1991; Harding and Perry, 1997; Howarth,
2007; Kemp et al., 1983; Malone, 1991,
1992; Officer et al., 1984; Orth and
Moore, 1984; Twilley et al., 1985
Source
ISA,
Section
3.2.4,
3.4.1,
4.2.2,
Annex
B
ISA,
Section
3.3.2,
3.3.8,
4.3.4,
Annex
C
2nd Draft Risk and Exposure Assessment
Appendix 6-16
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Area
Alpine and
subalpine
communities
of the
eastern slope
of the Rocky
Mountains,
CO
Beartooth
Mountain,
WY
Pamlico
Estuary, NC
Indicator
Aquatic
nutrient
enrichment;
terrestrial
nutrient
enrichment
Aquatic
nutrient
enrichment
Aquatic
nitrogen limited
eutrophication
Detailed
Indicator
Biomass
production;
MV leaching;
species
richness
Algae
composition
switch
Hypoxia;
phytoplankton
bloom
Area Studies
NA
NA
NA
Models
NA
NA
NA
References in U.S. EPA, 2008a
Baron et al., 1994; Baron et al., 2000;
Baron, 2006; Bowman, 2000; Bowman
and Steltzer, 1998; Bowman et al., 1993;
Bowman et al., 1995; Bowman et al.,
2006; Burns, 2004; Fenn et al., 2003 a;
Fisk et al., 1998; Korb and Ranker, 2001;
Rueth et al., 2003; Seastedt and Vaccaro,
2001; Sherrod and Seastedt, 2001;
Steltzer and Bowman, 1998; Suding et
al., 2006; Williams and Tonnessen, 2000;
Williams et al.,1996a; Wolfe et al., 2001
Saros et al., 2003
Paerletal., 1998
Source
ISA,
Sections
3.2.1,
3.2.2,
3.3.2,
3.3.3,
3.3.5,
3.3.8,
4.5,
Annex
CandD
ISA,
Sections
3.3.5,
4.4.3,
4.5,
Annex
BandC
ISA,
Sections
3.3.2,
3.3.3
2nd Draft Risk and Exposure Assessment
Appendix 6-17
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Area
Rocky
Mountain
National
Park, CO
Lake Tahoe,
CA
Indicator
Aquatic
nutrient
enrichment
Aquatic
nutrient
enrichment
Detailed
Indicator
Diatom shifts
Primary
productivity;
chlorophyll a
Area Studies
NA
NA
Models
NA
NA
References in U.S. EPA, 2008a
Interlandi andKilham, 1998
Goldman, 1988; Jassby et al., 1994
Source
ISA,
Sections
3.3.3,
3.3.5,
3.3.8,
4.3.3,
4.5,
Annex
C
ISA,
Sections
3.3.3,
3.3.5,
3.3.8,
Annex
C
1 Source: U.S. EPA, 2008a
2 Note: CAA = Clean Air Act; PIRLA = paleoecological investigation of recent lake acidification; EMAP = Environmental Monitoring
3 and Assessment Program; MAGIC = Model of Acidification of Groundwater in Catchments; PnET-BGC = a biogeochemical model;
4 N(V = nitrate; NA = not applicable.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 6-18
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Aquatic Nutrient Enrichment Case Study
1 1.2.3 Potomac River and Potomac Estuary
2 The Chesapeake Bay is the largest of 130 estuaries in the United States. This commercial
3 and recreational resource serves more than 15 million people who live in and near its watershed
4 (i.e., drainage basin). The bay produces approximately 500 million pounds of oysters, crabs, and
5 other seafood per year. The richness of its species can be seen in the value of the bay's annual
6 fish harvest, which is estimated at more than $100 million. The Chesapeake Bay estuary receives
7 approximately 50% of its water from the Atlantic Ocean in the form of saltwater. The other half
8 of the water (i.e., fresh water) drains into the bay from a large 165,800-square-kilometer (km2)
9 (64,000-square-mile [mi2]) drainage watershed. Among the 150 major rivers and streams in the
10 Chesapeake Bay drainage basin are the James, Potomac, York, Rappahannock, Patuxent, and
11 Susquehanna rivers. The Potomac River watershed comprises about 22% of the land area and
12 30% of the population of the total Chesapeake Bay watershed. As a result, pollution loads from
13 the Potomac River have a significant impact on the health of the bay. The Chesapeake Bay
14 contains on average more than 68 trillion liters (18 trillion gallons) of water (Atkins and
15 Anderson, 2009).
16 The Potomac River is approximately 413 miles (665 km) long, with a drainage area of
17 approximately 14,670 mi2 (38,000 km2) and a population of approximately 5,350,000 people. It
18 begins at Fairfax Stone, WV, and runs to Point Lookout, MD. In terms of area, this makes the
19 Potomac River the fourth largest river along the Atlantic Coast of the United States and the
20 twenty-first largest in the United States as a whole (Fact-index.com, 2009). As shown in Figure
21 1.2-3, as well as in Table 1.2.5 and Table 1.2-6, the Potomac River contains diverse watersheds
22 in terms of topography, elevation (e.g., extending into the Shenandoah Mountains), and nutrient
23 point and nonpoint sources (e.g., forestland, farmland, and the Washington, DC, metropolitan
25 area). The Potomac River watershed lies in five geological
27 provinces: the Appalachian Plateau, Ridge and Valley, Blue
29 Ridge, Piedmont Plateau, and Coastal Plain. The watershed
Atmospheric deposition accounts
for between 5% and 15% to 20%
of the Potomac River watershed's
total nitrogen load according to
published research (U.S. EPA,
2000; Boyeretal., 2002,
respectively). Additional expert
., estimates put the contribution by
35 to contribute from 5% to 15%-20% of the watershed s TN atmospheric deposition at 30% to
37 load (U.S. EPA, 2000; Boyer et al., 2002, respectively). 40% of the total load-
31 is approximately 12% urbanized, 36% agricultural use, and
33 52% forested. Atmospheric deposition has also been reported
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-19
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Aquatic Nutrient Enrichment Case Study
1
2
Potomac River Watershed Study Area
Atlantic
Ocean
Watershed boundary layers and mapping data were provided by
The site URL is: http://md.water.usgs.gov/gis/chesbay/sparrowS/doc/rewrJi
Legend
I I Potomac River
Watershed
— Potomac River Basin
Stream Network
<> TIME/LTM Sites
® Calibration Sites
HUC8 Watersheds
| | 2070001
| | 2070002
| | 2070003
| | 2070004
| | 2070005
| | 2070006
| | 2070007
| | 2070008
| | 2070009
|, | 2070010
| | 2070011
—i Segmented
-1 SPARROW
Watersheds
0 15 30 60
• Milt;
Figure 1.2-3. The Potomac River Watershed and Potomac Estuary.
Table 1.2-5. Physical Characteristics of the Potomac Estuary
Parameter
Estuary area (km2)
Tidal fresh zone area (km2)
Mixing zone area (km2)
Saltwater zone area (km2)
Estuary volume (m3)
Estuary depth (m)
Estuary perimeter (km)
Value
1,260
183
1,077
0
6.46 x 109
5.13
1,350
Metadata
Estuary area, calculated from NOAA shapefiles
Tidal fresh area, calculated from NOAA
shapefiles
Mixing zone area, calculated from NOAA
shapefiles
Saltwater area, calculated from NOAA shapefiles
Best estimate of volume from digital bathymetric
chart if available; otherwise, NOAA planimetry
From digital bathymetric chart if available;
otherwise, NOAA planimetry
Perimeter of estuary, based on shapefile; can be
used to calculate various aspect ratios
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Appendix 6-20
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Parameter
Percentage of estuary open
(%)
Catchment area (km2)
Catchment mean elevation
(m)
Catchment maximum
elevation (m)
Catchment/estuary area ratio
Value
1.33
36,804
330
1,433
29.2
Metadata
Percentage of the perimeter that is the "open" (or
oceanic) boundary; somewhat subjective
Not available
Calculated from catchment shapefiles + HydrolK
(a global 1-km grid of elevation)
Calculated from catchment shapefiles + HydrolK
(a global 1-km grid of elevation)
Area ratio, based on catchment and area data
given above
1 Source: NEEA Estuaries Database
2 Note: m = meter
Table 1.2-6. Hydrological Characteristics of the Potomac Estuary
Parameter
Tide height (m)
Tide volume (m3)
Tides/day (#)
Tide volume/day (m'.d"1)
Tide ratio
Stratification ratio
Percent freshwater (%)
Percent mixed water (%)
Percent seawater (%)
Average salinity (psu)
Tidal exchange (days)
Tidal freshwater flush
(days)
Value
0.55
6.93 x 108
2
1,339,130,435
0.11
0.02649
14.5
85.5
0
11
121
36
Metadata
NOAA estimate of tide height, back-calculated
from tide volume; in some cases, guessed from
nearby systems
Tide height (m) x estuary area (km2) x 106
NOAA designation
Calculated from tide volume and tides per day
Tide height divided by estuary depth; a clean-
up of a NOAA variable
Total freshwater flux per day divided by tide
volume per day
Based on NOAA shapefiles of the three zones
according to their designation
Based on NOAA shapefiles of the three zones
according to their designation
Based on NOAA shapefiles of the three zones
according to their designation
Based on NOAA estimate of freshwater
volume, but scaled to "local coastal salinity,"
below
Exchange time as (Est_V/net fw_V per d) *
(coastal_sal - avg_sal)/coastal_sal); a salinity-
based estimate of exchange
NOAA-based calculation, using (daily tide +
freshwater volume)/system volume
2nd Draft Risk and Exposure Assessment
Appendix 6-21
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Parameter
Daily freshwater/estuary
area (m.d"1)
Daily freshwater (m3/day)
(best)
Flow/estuary area (m/day)
(best)
Total freshwater Volume
(I/day)
Daily precipitation
(m3/day)
Daily evaporation
(m3/day)
Daily precipitation/estuary
area (mm/day)
Daily evaporation/estuary
area (mm/day)
Flow (m3/day)
Value
27.063
34,100,000
27.063
0.00549
3.64 x 106
2.26 x 106
2.889
1.794
2.33 x 107
Metadata
NOAA estimate of daily flow/estuary area
NOAA estimate above or (if not available)
NCPDI estimate
Best estimate/estuary area
Best estimate/estuary volume (= hydraulic
exchange rate)
Direct precipitation on system, derived from
PRISM Shapefile
Direct evaporation from system, derived from
LOICZ 0.5 degree database, originally from
Wilmott
Daily precipitation/estuary area
Daily evaporation/estuary area
NCPDI_1 982-1 991
1 Source: NEEA Estuaries Database
2 Note: m = meter; psu = practical salinity unit; NCPDI = National Coastal Pollution Discharge
3 Inventory; PRISM = Parameter-elevation Regressions on Independent Slopes Model; LOICZ =
4 Land-Ocean Interactions in the Coastal Zone; mm = millimeters.
5 1.2.4 Neuse River and Neuse River Estuary
6 The Neuse River is the longest river in North Carolina, and the Neuse River watershed is
7 the third largest river watershed in the state (Figure 1.2-4). The Neuse River is a mainstem river
8 to the Pamlico Sound—one of the two largest estuaries on the Atlantic Coast. The river
9 originates in north-central North Carolina and flows southeasterly until it reaches tidal waters
10 upstream of New Bern, NC. At New Bern, the river broadens dramatically and changes from a
11 free-flowing river to a sound. While the Neuse River itself is 399 kilometers (km)_(248 miles)
12 long, there are 5,628 freshwater stream kilometers (3,497 miles), 6,643 hectares (16,414 acres)
13 of freshwater reservoirs and lakes, 149,724 estuarine hectares (369,977 acres), and 33.8
14 kilometers (21 miles) of Atlantic coastline within the entire Neuse River watershed. The drainage
15 area for the watershed is approximately 14,210 mi2 (36,804 km2). There are 19 major reservoirs
16 in the Neuse River watershed; most of these are located in the upper portion of the watershed.
2nd Draft Risk and Exposure Assessment
Appendix 6-22
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1 The watershed starts in the eastern Piedmont physiographic region, with approximately two-
2 thirds of the watershed located in the Coastal Plain (NC DENR, 2002).
3 The Neuse River watershed encompasses all or portions of 18 counties and 74
4 municipalities. The watershed has a population of approximately 1,320,379 according to the
5 2000 census. Fifty-six percent of the land in the watershed is forested, and approximately 23% is
6 in cultivated cropland. Only 8% of the land falls into the urban/built-up category. Despite the
7 large amount of cultivated cropland and the relatively small amount of urban area, the basin has
8 seen a significant decrease (-72,800 hectares [-180,000 acres]) in cultivated cropland and forest
9 and an increase (+91,900 hectares [+227,000 acres]) in developed areas over the past 15 years
10 (NRCS, 2001). The Neuse River watershed is divided into 14 subbasins (6-digit North Carolina
11 Division of Water Quality subbasins) (NC DENR, 2002). Table 1.2-7 through Table 1.2-9,
12 respectively, provide physical, land use and population, and hydrological characteristics of the
13 Neuse River and Neuse River Estuary.
14 There are 134,540 estuarine hectares (332,457 acres) classified for shellfish harvesting
15 (Class SA [shellfishing]) in the Neuse River Estuary. The Neuse River Estuary is important to
16 the commercial blue crab (Callinectes sapidus) fishery in the eastern United States and
17 accounted for approximately one-quarter of the blue crab harvest from 1994 to 2002 (Smith and
18 Crowder, 2005). Eutrophication became a water quality concern in the lower Neuse River and
19 Neuse River Estuary in the late 1970s and early 1980s. Nuisance algal blooms prevalent in the
20 upper estuary prompted investigations by the state. These investigations, as well as other studies,
21 indicated that algal growth was being stimulated by excess nutrients entering the estuarine waters
22 of the system. In 1988, a phosphate detergent ban was put in place, and the lower Neuse River
23 and Neuse River Estuary received the supplemental classification of nutrient-sensitive waters.
24 Phosphorus loading was greatly reduced, and algal blooms in the river and freshwater portions of
25 the system were reduced as a result of this action. However, the 1993 Neuse River Basin-wide
26 Water Quality Plan (NC DENR, 1993) recognized that eutrophication continued to be a water
27 quality problem in the estuary below New Bern. Extensive fish kills in 1995 prompted further
28 study of the problem. Low dissolved oxygen levels associated with algal blooms were
29 determined to be a probable cause of many of the fish kills. The algal blooms and
30 correspondingly high levels of chlorophyll a prompted the state to place the Neuse River and
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-23
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Aquatic Nutrient Enrichment Case Study
1 Neuse River Estuary on the 1994, 1996, 1998, and 2000 303(d) List of Impaired Waters. It was
2 determined that control of nitrogen was needed to reduce the extent and duration of algal blooms.
3 Atmospheric deposition is believed to play a role in nutrient loading to the Neuse River
4 and Neuse River Estuary. As excerpted from Whitall and Paerl (2001), the following discusses
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
the role of atmospheric deposition to nutrient loading for sensitive waterbodies:
Excessive nitrogen loading to nitrogen-sensitive waters, such as the Neuse River
Estuary (North Carolina) has been shown to promote changes in microbial and
algal community composition and function (harmful algal blooms), hypoxia and
anoxia, and fish kills. Previous studies have estimated that wet atmospheric
deposit!on of nitrogen (WAD-N), as deposition of dissolved inorganic nitrogen
(DIN: NO's, NH3/NH+4) and dissolved organic nitrogen, may contribute at least
15% of the total externally supplied or "new" nitrogen flux to the coastal waters
of North Carolina. In a 3-year study from June 1996 to June 1999, Whitall and
Paerl calculated the weekly wet deposition of inorganic and organic nitrogen at 11
sites on a northwest-southeast transect in the watershed. The annual mean total
(wet DIN + wet organics) WAD-N flux for the Neuse River watershed was
calculated to be 956 mg N/m2/yr (15,026 Mg N/yr). Seasonally, the spring
(March-May) and summer (June-August) months contain the highest total weekly
nitrogen deposition; this pattern appears to be driven by nitrogen concentration in
precipitation. There is also spatial variability in WAD-N deposition; in general,
the upper portion of the watershed receives the lowest annual deposition and the
middle portion of the watershed receives the highest deposition. Based on a range
of watershed nitrogen retention and in-stream riverine processing values, we
estimate that this flux contributes approximately 24% of the total "new" nitrogen
flux to the estuary (Whitall and Paerl, 2001).
Of these atmospheric deposition measurements, it is
expected that the contributions will be greater from reduced
forms of nitrogen than from oxidized forms because of the large
amounts of agriculture within the watershed. One of the reasons
for selecting this case study area is to evaluate the impact of a
NOx-based standard on an area dominated by reduced forms of
nitrogen.
According to Whitall and
Paerl (2001), atmospheric
deposition accounts for
approximately 24% of the
Neuse River watershed's
total nitrogen loading, with
reduced forms of nitrogen
making up a larger portion of
the total than oxidized forms.
2nd Draft Risk and Exposure Assessment
Appendix 6-24
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1
2
Neuse River Watershed Study Area
Atlantic Ocean
Watershed boundary layers and mapping data was provided by USGS. The site
URL is: http://md.water.usgs.gov/gis/chesbay/sparrowa'doc/retv3.htrn#section1
Atlantic
Ocean
Legend
o Calibration Stations
Watershed Stream
Segments
I Neuse/Tar Parnlico
' ' /Cape Fear
Watershed Area
Neuse River Basin
HUCS Watersheds
^B 03020201
| \ 03020202
\_^\ 03020203
| | 03020204
-i Segmented
J SPARROW
Watersheds
0 20 40 80
Figure 1.2-4. The Neuse River Watershed and Neuse River Estuary.
Table 1.2-7. Physical Characteristics of the Neuse River Estuary
Parameter
Estuary area (km2)
Tidal fresh zone area
(km2)
Mixing zone area (km2)
Saltwater zone area
(km2)
Estuary volume (m3)
Estuary depth (m)
Estuary perimeter (km)
Value
456
5
451
0
1.304 x 109
2.86
523
Metadata
Estuary area, calculated from NOAA shapefiles
Tidal fresh area, calculated from NOAA shapefiles
Mixing zone area, calculated from NOAA
shapefiles
Saltwater area, calculated from NOAA shapefiles
Best estimate of volume from digital bathymetric
chart if available; otherwise, NOAA planimetry
From digital bathymetric chart if available;
otherwise, NOAA planimetry
Perimeter of estuary, based on shapefile; can be
used to calculate various aspect ratios
2nd Draft Risk and Exposure Assessment
Appendix 6-25
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Parameter
Percentage estuary
open (%)
Catchment area (km2)
Catchment mean
elevation (m)
Catchment maximum
elevation (m)
Catchment/estuary area
ratio
Value
2.1
14,066
56
245
30.8
Metadata
Percentage of the perimeter that is the "open" (or
oceanic) boundary; somewhat subjective
Not available
Calculated from catchment shapefiles + HydrolK
(a global 1-km grid of elevation)
Calculated from catchment shapefiles + HydrolK
(a global 1-km grid of elevation)
Area ratio, based on catchment and area data given
above
1
2
Source: NEEA Estuaries Database
Note: m = meter
Table 1.2-8. Neuse River Watershed Land Use and Population
Parameter
Urban (km2)
Agriculture (km2)
Forest (km2)
Wetland (km2)
Range (km2)
Total (km2)
Population (#)
Population/estuary area
(#.km'2)
Value
1,328.66
(9.5%)
4,983.14
(35.6%)
6,648.5
(47.5%)
1,020.46
(7.3%)
5.17998(0%)
13,985.93998
1,015,059
2,226
Metadata
USGS (LUDA) for entire watershed 1972 with
census 1990 information, base year early 1990s
USGS LUDA for entire watershed 1972 with
census 1990 information, base year early 1990s
USGS LUDA for entire watershed 1972 with
census 1990 information, base year early 1990s
USGS LUDA for entire watershed 1972 with
census 1990 information, base year early 1990s
USGS LUDA for entire watershed 1972 with
census 1990 information, base year early 1990s
USGS LUDA for entire watershed 1972 with
census 1990 information, base year early 1990s
Based on gridded (1-km) U.S. 1990 census data,
corrected for catchments extending outside the
United States (with LAND SCAN)
Population based on gridded (1-km) U.S. 1990
census data, corrected for catchments extending
outside the United States (with LANDSCAN).
Estuary area, calculated from NOAA shapefiles.
3
4
5
Source: NEEA Estuaries Database
Note: USGS = U.S. Geological Survey; LUDA = Land Use and Land Cover; LANDSCAN = a
global population database
2nd Draft Risk and Exposure Assessment
Appendix 6-26
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Table 1.2-9. Hydrological Characteristics of the Neuse River Estuary
Parameter
Tide height (m)
Tide volume (m3)
Tides/day (#)
Tide volume/day
(m3/day)
Tide ratio
Stratification ratio
Percent freshwater (%)
Percent mixed water (%)
Percent seawater (%)
Average salinity (psu)
Tidal exchange (days)
Tidal freshwater flush
(days)
Daily freshwater/estuary
area (m/day)
Daily freshwater
(m3/day) (best)
Flow/estuary area
(m/day) (best)
Total freshwater volume
(I/day)
Daily precipitation
(m3/day)
Daily evaporation
(m3/day)
Value
0.15
6.84 x 107
2
132,173,913
0.05
0.08318
1.1
98.9
0
13
74
73
22.368
10,200,000
22.368
0.00843
1.72 x 106
926,000
Metadata
NOAA estimate of tide height, back-calculated from
tide volume; in some cases, guessed from nearby
systems
Tide height (m) x estuary area (km2) x 106
NOAA designation
Calculated from tide volume and tides per day
Tide height divided by estuary depth; a clean-up of a
NOAA variable
Total freshwater flux per day divided by tide volume
per day
Based on NOAA shape files of the three zones
according to their designation
Based on NOAA shape files of the three zones
according to their designation
Based on NOAA shape files of the three zones
according to their designation
Based on NOAA estimate of freshwater volume, but
scaled to "local coastal salinity," below
Exchange time as (Est V/net fw V per
d)*(coastal sal - avg sal)/coastal sal); a salinity-
based estimate of exchange
NOAA-based calculation, using (daily tide +
freshwater volume)/system volume
NOAA estimate of daily flow/estuary area
NOAA estimate above or (if not available) NCPDI
estimate
Best estimate/estuary area
Best estimate/estuary volume (= hydraulic exchange
rate)
Direct precipitation on system, derived from PRISM
shapefile
Direct evaporation from system, derived from
LOICZ 0.5 degree database, originally from Wilmott
2nd Draft Risk and Exposure Assessment
Appendix 6-27
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Parameter
Daily precipitation/
estuary area (mm/day)
Daily evaporation/
estuary area (mm/day)
Flow (m3/day)
Value
3.772
2.031
7.95 x 106
Metadata
Daily precipitation/estuary area
Daily evaporation/estuary area
NCPDI_1 982-1 991
1 Source: NEEA Estuaries Database
2 Note: m = meter; psu = practical salinity unit; NCPDI = National Coastal Pollution Discharge
3 Inventory; PRISM = Parameter-elevation Regressions on Independent Slopes Model; LOICZ =
4 Land-Ocean Interactions in the Coastal Zone; mm = millimeters
5 Ammonia emissions from intensive livestock feeding operations are believed to
6 contribute to nitrogen deposition in eastern North Carolina watersheds. During a 10-year
7 legislative mandated moratorium on new operations, poultry populations increased in two Neuse
8 River watershed counties, according to the U.S. Department of Agriculture's 2002 Ag Census.
9 Statewide, the census reported an increase in poultry farms from 5,094 in 1997 to 6,251 in 2002
10 statewide (USD A, 2002). (As of this writing, the 2007 Ag Census is not complete.) The
11 continued contribution of poultry operations' growth to nitrogen deposition during the moratoria
12 has not been assessed, particularly in terms of its deposition in the Neuse River watershed.
13 2. APPROACH AND METHODS
14 Since it was necessary for this case study to span both terrestrial and aquatic systems to
15 accommodate indirect (i.e., to the watershed) and direct (i.e., to the water surface) deposition
16 effects, as well as a variety of indicators, a modeling approach was necessary to examine the
17 impacts due to aquatic nutrient enrichment from nitrogen deposition.
18 There are several complicating factors to carrying out an analysis of eutrophication in
19 waterbodies when one of the requirements is to include modeled output of atmospheric
20 deposition from a high-level, detailed atmospheric model. This analysis is considered a
21 multimedia analysis where the air, land, and water are involved. Typically, models or analysis
22 methods existing in the literature focus on only one of those components. Links between the
23 components with the desired output of eutrophi cation indicators are rare in the current literature
24 or modeling environments. Additionally, the few instances that are available in the literature tend
25 to focus on specific case study areas or on being highly empirical and difficult to scale or extend
2nd Draft Risk and Exposure Assessment
Appendix 6-28
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1 to alternate locations. All these facts must be considered when developing a method to examine
2 the effects of Nr, including NOX, deposition on aquatic nutrient enrichment.
3 2.1 MODELING
4 There are four basic steps necessary to undertake a modeling effort to examine the effects
5 of nitrogen deposition (RTI, 2007):
6 1. Choose the specific question/problem to address.
7 2. Choose the best models based on model formulation (e.g., are biological processes
8 considered?), desired output, study area, data availability, and necessary
9 uncertainty/sensitivity analyses for the models.
10 3. Determine and set up any processes/algorithms necessary to match atmospheric modeling
11 output (assumed to be from Community Multiscale Air Quality [CMAQ]) to the chosen
12 receiving water or terrestrial/watershed model.
13 4. Obtain the data needed for model parameterization.
14 The problem to be addressed in this analysis is assessment of the effects of deposition of
15 Nr, including NOX, on aquatic nutrient enrichment. The impacts of both direct (i.e., deposition on
16 the waterbody surface) and indirect (i.e., deposition within the watershed and transport to the
17 waterbody) deposition need to be identified. A method is needed to provide measures of the
18 eutrophication indicators that were previously described in Section 1.1.
19 A previous RTI International (RTI)1 report (RTI, 2007) detailed the difficulty, along with
20 the desire, to utilize atmospheric modeling in combination with the receiving-water and
21 terrestrial/watershed models for analyzing the effects of Nr, including NOX, deposition. The
22 multimedia approach to modeling is still in development; therefore, at this time, not many
23 models are set up to immediately accept the output from an atmospheric model such as CMAQ.
24 In the previous model investigation, RTI examined 35 receiving-water and terrestrial/watershed
25 models, which represent a wide diversity of types of ecosystems; history, location, and
26 spatial/temporal scales of application; scientific acceptance levels and organizational and agency
27 support; complexity and requirements; state variables and processes; and management uses.
28 Several existing models accept atmospheric concentration or flux data, but the time-step,
29 spatial resolution, and exact species required might all differ from the atmospheric model output.
30 The RTI report (2007) provided a list of models that could fulfill the multimedia approach while
31 using CMAQ output as input for the atmospheric component to the model. These models include
1 RTI International is a trade name of Research Triangle Institute
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-29
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Aquatic Nutrient Enrichment Case Study
1 the Hydrologic Simulation Program-FORTRAN (HSPF), Regional Hydeo-Economic Simulation
2 System (RHESSys), the Georgia Tech Hydrologic Model/Multiple Element Limitation
3 (GT/MEL), Model of Acidification of Groundwater in Catchments (MAGIC), PnET-BGC,
4 Integrated Nitrogen in Catchments (INC A), Spatially Referenced Regression on Watershed
5 attributes (SPARROW), AQUATOX, Water Quality Analysis Simulation Program (WASP),
6 Enhanced Stream Water Quality Model (QUAL2K), CE-QUAL family of models, and Row
7 Column AESOP/Estuary and Coastal Ocean Model with Sediment Transport
8 (RCA/ECOMSED). These models are very different from one another in terms of the system
9 components included, process representations, data requirements, and output parameters (for
10 comprehensive details for each model, refer to the RTI report [2007]).
11 After determining which models could utilize CMAQ data, the ecosystem component
12 encompassed by the models was examined. The choice of case study areas that include estuaries
13 dictated that the model chosen must provide nutrient loads to an estuary waterbody and examine
14 the impacts of those loads within the estuary itself. Although AQUATOX and QUAL2K are
15 receiving-water models, they do not function for estuaries, nor do they account for indirect
16 deposition over the contributing watershed. The WASP, CE-QUAL family of models, and
17 RCA/ECOMSED are receiving-water models, which can be parameterized for estuaries, but they
18 do not simulate terrestrial processes. Several of the other models account for indirect deposition
19 and are strictly terrestrial models. These models include Regional Hydro-Economic Simulation
20 System (RHESSys) and GT/MEL. Other models include both the indirect deposition and direct
21 deposition, but only over streams and lakes within the watershed. These models are HSPF,
22 MAGIC, PnET-BGC, INCA, and SPARROW.
23 From this analysis, it was apparent that a multiple step (i.e., linked processes or
24 calculations) or model (i.e., separate but linked models) analysis would be optimal, including
25 both a step/model to examine the indirect deposition and a step/model to examine the estuarine
26 effects. The challenge was balancing analysis power against data, effort, and scalability
27 requirements. Higher-level modeling approaches could be used to evaluate the eutrophication
28 effects of interest if significant data resources, time, and expertise were available for a specific
29 site. An approach of this kind would not be scalable or applicable to wider regions, but it would
30 provide estimates with less uncertainty for a studied system.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-30
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Aquatic Nutrient Enrichment Case Study
1 The list of models above was used to identify several models that could be used to
2 produce nutrient loads to the estuary, the obvious critical component of an eutrophication
3 analysis. The best model for determining nitrogen loading to the estuary would track the
4 atmospheric deposition of nitrogen through the watershed and to the estuary. This requirement
5 eliminated models that did not provide stream networking (i.e., PnET-BGC, MAGIC) or that
6 lumped land-use categories together (i.e., INCA). The remaining models of HSPF and
7 SPARROW are very different. HSPF is a highly parameterized, dynamic model that requires
8 extensive data inputs and calibration. SPARROW is a hybrid statistical and process-based,
9 steady-state model that requires much less data for parameterization, but still includes spatial
10 variation and source investigation. Therefore, the SPARROW model was chosen to estimate
11 nitrogen loadings to the estuaries.
12 Next, the most applicable method for examining eutrophi cation effects in an estuary was
13 assessed. The three identified models that could represent estuarine processes (i.e., WASP, CE-
14 QUAL family of models, and RCA/ECOMSED) were systematically ruled out as possibilities.
15 RCA/ECOMSED is a proprietary model with extensive data requirements and requires a high
16 level of expertise. The CE-QUAL family of models has primarily been used by the U.S. Army
17 Corps of Engineers. The various versions of CE-QUAL all have extensive data requirements,
18 and no indications of model integration have been uncovered in the literature. WASP provides
19 the output desired, but requires parameterization for each system of study. Considering that the
20 SPARROW model will provide TN loads to the estuary and the fact that that the chosen method
21 needs to be scalable and applicable to a variety of future case study areas, the SPARROW model
22 was selected for this case study.
23 With the elimination of the three identified dynamic modeling applications, a more
24 descriptive method of evaluation was sought. The method developed by NOAA and used in their
25 NEEA was identified as a likely candidate for eutrophication assessment.
26 The screening process that led to the decision to use SPARROW and a more descriptive
27 eutrophication evaluation technique considered the level of effort needed for an analysis of this
28 scale in the time available. Additionally, as summarized in recent literature (Howarth and
29 Marino, 2006), the complex processes that cause and express eutrophication within an estuary
30 are not greatly understood and could lead to under- and mis-representation within dynamic
31 models. The loss of a temporally varying analysis with the use of a steady-state or annual
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-31
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Aquatic Nutrient Enrichment Case Study
1 average model results in the loss in detailing seasonal changes and some of the intricate
2 processes that may occur on a daily or even monthly time scale rather than over an entire year.
3 This trade-off allows for development toward the ultimate goal of providing a scalable
4 methodology that can be applied to various sites across the nation. With the acknowledged
5 uncertainties in eutrophication process details across different systems, a more screening-level,
6 scalable approach was deemed appropriate for this initial study to link atmospheric nitrogen
7 deposition to eutrophicatic conditions.
8 2.2 CHOSEN METHOD
9 After examining several estuarine assessment options, the most comprehensive
10 evaluation technique that could be applied on a wide scale was revealed to be an assessment of
11 eutrophi cation as conducted in NO AA' s NEEA. This assessment method is titled Assessment of
12 Estuarine Trophic Status eutrophi cation index (ASSETS El) (Bricker et al., 2007a). NOAA's
13 ASSETS El results in an estimation of the likelihood that the estuary is experiencing
14 eutrophi cation or will experience eutrophi cation in the future.
15 The ASSETS El incorporates indirect deposition over the watershed through the
16 evaluation of nitrogen loading to the estuary. Thus, a decision was required on how to derive the
17 nitrogen load to the estuary based on the 2002 CMAQ-modeled deposition data. Because the
18 ASSETS El is more of a screening-level approach to assessing eutrophi cation, the nitrogen load
19 to the estuary is only required to be an annual estimate of TN loading. For these reasons, The
20 SPARROW model was chosen to provide the estimates of nitrogen loading to the estuary.
21 The combination of SPARROW modeling and the ASSETS method to developing an El
22 (Figure 2.2-1) provides a sound basis for conducting an eutrophi cation assessment. Both
23 SPARROW and the ASSETS El are supported by federal agencies and have been through
24 several improvement iterations. As shown in the following sections, the method provides a
25 screening-level approach that includes an appropriate level of detail for determining the impacts
26 on the degree of eutrophi cation in an estuary based on changes in atmospheric deposition
27 loadings.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-32
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Aquatic Nutrient Enrichment Case Study
Atmos
Depo
Load
Land
Use
Streams
& Catch-
ments
Point
Sources
/Other
Non-
point
Sources /
Suscep-
tibility
2 Figure 2.2-1. Modeling methodology for case study.
3 Note: DO = dissolved oxygen; HAB = harmful algal bloom.
4 ASSETS El scores were available for both the Potomac and Neuse River estuaries, and
5 both estuaries were the subject of past and ongoing SPARROW modeling of point and nonpoint
6 sources, including atmospheric deposition.
7 2.2.1 SPARROW
8 2.2.1.1 Background and Description
9 SPARROW is a watershed modeling technique designed and supported by the U.S.
10 Geological Survey (USGS). The model relies on a nonlinear regression formulation to relate
11 water quality measurements throughout the watershed of interest to attributes of the watershed.
12 Both point and diffuse sources within the watershed are considered along with nonconservative
13 transport processes (i.e., loss and storage of contaminants within the watershed). SPARROW
2nd Draft Risk and Exposure Assessment
Appendix 6-33
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1 follows the rules of mass balance while using a hybrid statistical and process-based approach
2 (Figure 2.2-2). "Because the dependent variable in SPARROW models (i.e., the mass of
3 contaminant that passes a specific stream location per unit time) is, in mathematical terms,
4 linearly related to all sources of contaminant mass in the model, all accounting rules relating to
5 the conservation of mass will apply" (Schwarz et al., 2006). Additionally, since SPARROW is a
6 statistical model at its core, it provides measures of uncertainty in model coefficient and water
7 quality predictions. Utilization of the SPARROW model results in estimates of long-term,
8 steady-state water quality in a stream. In most applications, SPARROW estimates represent
9 mean annual stream loadings of a contaminant.
Load generated within Load originating within the
Load leaving the _ upstream reaches and + reach's incremental watershed
reach transported to the reach via and delivered to the reach
the stream network segment
10 Figure 2.2-2. Mass balance description applied to the SPARROW model formulation.
11 A key component of SPARROW is its reliance on the spatial distribution of watershed
12 characteristics and sources. The stream reach network is spatially referenced against all
13 monitoring stations, GIS data for watershed properties, and source information. This structure
14 allows for the simulation of fate and transport of contaminants from sources to streams and
15 downstream ecological endpoints. "Spatial referencing and the mechanistic structure in
16 SPARROW have been shown to improve the accuracy and interpretability of model parameters
17 and the predictions of pollutant loadings as compared to those estimated in conventional linear
18 regression approaches" (e.g., Smith et al., 1997; Alexander et al., 2000) (Schwarz et al., 2006).
19 This spatially distributed model structure based on a defined stream network allows separate
20 statistical estimation of land and water parameters that quantify the rates of pollutant delivery
21 from sources to streams and the transport of pollutants to downstream locations within the stream
22 network (i.e., reaches, reservoirs, and estuaries) (Schwarz et al., 2006). Figure 2.2-3 shows how
23 each watershed and stream reach within the stream network defined for the SPARROW
24 application (represented by different colors in the figure) is processed separately and linked to
25 derive a final loading at a downstream location (the star labeled X). The SPARROW model is
26 calibrated at each monitoring station (represented by stars in Figure 2.2-3) by comparing the
27 modeled loads (i.e., a total of loads from each watershed segment and any upstream loads from
28 previous calibrations) against monitored data at the station. In this case, the modeled load at
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-34
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Aquatic Nutrient Enrichment Case Study
1 downstream monitoring station X would include loads from upstream monitoring station Y and
2 the five watershed segments between the two monitoring stations.
3
4
5
6
7
8
9
10
11
12
13
14
Stream
reach
segment
Downstream
monitoring
station, X
Upstream
monitoring
station, Y
Reservoir
Reach
contributing
area
Point source
Figure 2.2-3. Conceptual illustration of a reach network.
Within this Aquatic Nutrient Enrichment Case Study, the mathematical formulation of
the basic version of SPARROW presented by McMahon et al., (2003) is shown for consideration
in Equations 3 to 5. "The additive contaminant source components and multiplicative land and
water transport terms are conceptually consistent with the physical mechanisms that explain the
supply and movement of contaminants in watersheds" (Schwarz et al., 2006). Preservation of
mass, accounting for transport and decomposition at individual sources, is accomplished within
SPARROW through the spatial referencing of all processes with respect to the stream network
and the specific reach in which the process is carried out. Decomposition processes are
represented through losses in delivery to the stream and within the stream reach itself (Equation
4) or within a reservoir (Equation 5).
15
(3)
16
17
18
19
where
Load =
n,N =
J(i) =
Nitrogen load or flux in reach /', measured in metric tons
Source index where TV is the total number of individual n
Set of all reaches upstream, including reach /
sources
2nd Draft Risk and Exposure Assessment
Appendix 6-35
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1
2
5
6
1
8
9
10
11
12
13
14
fa = Estimated source coefficient for source n
Sn.j = Nitrogen mass from source n drainage to reach y'
a = Estimated vector of land to water delivery coefficients
Zj = Land-surface characteristics associated with drainage to reach y'
fFjj = Fraction of nutrient mass present in water body y transported to water
body /' as a function of first-order loss process associated with stream
channels
HRtj = Fraction of nutrient mass present in water body y transported to water
body /' as a function of first-order loss process associated with lakes
and reservoirs
8, = Multiplicative error term assumed to be independent and identically
distributed across separate subbasins defined by intervening drainage
areas between monitoring stations.
(4)
15
16
17
18
19
20
21
22
where
m =
J-'i.j.m
First-order loss coefficient (km"1) (A A: value of 0.08, for example,
indicates that nitrogen is removed at a rate of approximately 8% per
km of channel length.)
Number of discrete flow classes
Length of the stream channel between water bodiesy and /' in flow
class m.
(5)
23 where
24 k = Estimated first-order loss rate (or settling velocity; units = m/yr)
25 qil = Reciprocal areal hydraulic load of lake or reservoir (ratio of water-
26 surface area to outflow discharge; units = yr/m) for each of the lakes
27 and reservoirs (/) located between water bodiesy and /'.
28 SPARROW has been designed to identify and quantify pollution sources that contribute
29 to the water quality conditions predicted by the model. Several different types of sources may be
30 examined, and sources may be for an individual stream location or summarized for a grouping of
31 stream locations. Examples of sources modeled within SPARROW include atmospheric
32 deposition, point sources, animal agriculture, or land use-based supply of contamination. "The
2nd Draft Risk and Exposure Assessment
Appendix 6-36
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
ability to develop quantitative information on pollution sources in SPARROW models stems
from the ability to trace, for each contaminant category, the predicted in-stream flux through a
given stream reach to the individual sources in each of the upstream reach watersheds
contributing contamination to that reach" (Schwarz et al., 2006). Figure 2.2-4 highlights some of
these sources in a conceptualization of the SPARROW model process.
Diffuse Sources
Industrial / Municipal
Point Sources
Landscape
Transport
SPARROW
Model Components
Aquatic Transport
Streams
Reservoirs
In-Stream Flux Prediction
Calibration minimizes differences
between predicted and calculated
mean-annual loads at the
monitoring stations
Water-Quality and
Flow Data
Periodic measurements
at monitoring stations
Monitoring
Station Flux
Estimation
Rating Curve Model
of Pollutant Flux
Station calibration to
monitoring data
Mean-Annual Pollutant
Flux Estimation
Evaluation of Model
Parameters and Predictions
Figure 2.2-4. SPARROW model components (Schwarz et al., 2006).
Complete procedures, such as calculation of monitoring station flux estimation (Figure
2.2-4) and details on data formatting, will not be discussed here. The reader is pointed to the
documentation for the recently released SAS version of the SPARROW model available from
the USGS SPARROW Web site (http://water.usgs.gov/nawqa/sparrow/sparrow-mod.html) for
full details on the model. The reader may also review some of the previous SPARROW
applications presented in Table 2.1-1. The following sections describing SPARROW provide
basic definitions of terms that aid in understanding SPARROW inputs and outputs and discuss
some details that pertain to an application focused on atmospheric deposition inputs. Finally, an
alternate formulation of SPARROW is described that highlights contributions of ammonia to the
total Nr load for use in the Neuse River watershed.
2nd Draft Risk and Exposure Assessment
Appendix 6-37
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Table 2.1-1. Examples of SPARROW Applications
Location
National
Major estuaries of the United States
Chesapeake Bay
State of Kansas waters
Connecticut River Basin
State of New Jersey waters
New England waters
New Zealand river basins
North Carolina coastal watersheds
Tennessee and Kentucky watersheds
Citation
Smith and Alexander, 2000
Alexander et al., 2001
Preston and Brakebill, 1999; Brakebill and
Preston, 2004
Kansas Department of Health
2004
and Environment,
NEIWPCC, 2004
Smith etal., 1994
Moore et al., 2004
Alexander et al., 2002; Elliott
et al., 2005
McMahon et al., 2003
Hoos, 2005
2 2.2.1.2 Key Definitions for Understanding SPARRO W Modeling
3 The following definitions have been summarized from the documentation accompanying
4 the SAS application of the SPARROW model available from the USGS (Schwarz et al., 2006).
5 Additional references are noted when used.
6 • Bootstrapping. This is the practice of estimating the properties associated with the model
7 coefficients by estimating those properties when sampling from a specified distribution
8 using replacement (e.g., the model coefficients are estimated a number of times until the
9 best evaluation properties of the coefficients are found).
10 • Delivered Yield (load per area). This is the amount of nutrients generated locally for
11 each stream reach and weighted by the amount of in-stream loss that would occur with
12 transport from the reach to the receiving water. The cumulative loss of nutrients from
13 generation to delivery to the receiving water is dependent on the travel time and in-stream
14 loss rate of each individual reach (Preston and Brakebill, 1999).
15 • Incremental Yield (load per area). This yield represents the local generation of nutrients.
16 It is the amount of nutrients generated locally (independent of upstream load) and
17 contributed to the downstream end of each stream reach. Each stream reach and associated
2nd Draft Risk and Exposure Assessment
Appendix 6-38
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
1 watershed is treated as an independent unit, quantifying the amount of nutrient generated
2 (Preston and Brakebill, 1999).
3 • In-Stream Loss. This refers to stream attenuation processes that act on contaminant flux
4 as it travels along stream reaches. A first-order decay process implies that the rate of
5 removal of the contaminant from the water column per unit of time is proportional to the
6 concentration or mass that is present in a given volume of water. According to a first-order
7 decay process, the fraction of contaminant removed over a given stream distance is
8 estimated as an exponential function of a first-order reaction rate coefficient (expressed in
9 reciprocal time units) and the cumulative water time of travel over this distance. Within
10 SPARROW, the in-stream loss rate is assumed to vary as a function of stream channel
11 length and various flow classes.
12 • Landscape Variables. These variables describe properties of the landscape that relate to
13 climatic, or natural- or human-related terrestrial processes affecting contaminant transport.
14 These typically include properties for which there is (1) some conceptual or empirical
15 basis for their importance in controlling the rates of contaminant processing and transport,
16 and (2) broad-scale availability of continuous measurements of the properties for use in
17 model estimation and prediction. Examples include precipitation, evapotranspiration, soil
18 properties like organic content or permeability, topographic index, or slope. Particular
19 types of land-use classes, such as wetlands or impervious cover, may also potentially be
20 used to describe transport properties of the landscape.
21 • Land-to-Water Delivery Factor. This factor describes the influence of landscape
22 characteristics in the delivery of diffuse sources of contamination to the stream. The
23 interaction of particular land-to-water delivery factors with individual sources may also be
24 important to consider in SPARROW models.
25 • Monitoring Station Flux Estimation. This refers to the estimates of long-term flux used
26 as the response variable in the model. Flux estimates at monitoring stations are derived
27 from station-specific models that relate contaminant concentrations from individual water
28 quality samples to continuous records of streamflow and time. These estimates are used to
29 calibrate the model in each application.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-39
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Aquatic Nutrient Enrichment Case Study
1 • Non-linear Regression. The SPARROW model equation is a nonlinear function of its
2 parameters. As such, the model must be estimated using nonlinear techniques. The errors
3 of the model are assumed to be independent across observations and have zero mean; the
4 variance of each observation may be observation-specific. A general method commonly
5 used for these types of problems, one in which it is not necessary to assume the precise
6 distribution of the residuals, is nonlinear weighted least squares. This is the estimation
7 method used by SPARROW.
8 • Segmented Watershed Network. This network relates to the system of joined stream
9 reaches that define the watershed of interest. Previous SPARROW applications have relied
10 on the River Reach File 1 (RF1) hydrography developed by U.S. EPA (1996) and the
11 1:100,000 scale National Hydrologic Dataset (USGS, 1999). These datasets may be used
12 in their original form or modified as needed depending on application requirements
13 • Source. SPARROW distinguishes between source categories (e.g., point sources,
14 atmospheric sources, and animal agriculture) and individual sources (i.e., the rate of
15 supply of contaminant of a particular category originating in the watershed and draining to
16 a specific stream reach). A variety of sources based on knowledge of the watershed and
17 inferences from literature may be examined with SPARROW.
18 • Stream Reach. This is the most elemental spatial unit of the infrastructure used to
19 estimate and apply the basic SPARROW models. Stream reaches define the stream
20 channel length that extends from one stream tributary junction to another. Each reach has
21 an associated contributing drainage catchment.
22 • Total Yield (load per area). This is the amount of nutrients, including upstream load,
23 contributed to each stream reach. These estimates are calculated by stream reach and
24 account for all potential sources cumulatively and individually (Preston and Brakebill,
25 1999).
26 2.2.1.3 Concepts of Importance to Case Study—SPARRO W Application
27 Previous SPARROW applications have typically relied on atmospheric deposition
28 measurements from NADP and have used wet NOs deposition as a surrogate for nitrogen
29 deposition over the watershed of interest. Within the case studies conducted, CMAQ-modeled
30 and NADP-monitored atmospheric deposition was used. Several differences in the final
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-40
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Aquatic Nutrient Enrichment Case Study
1 parameterization of the SPARROW model will most likely result from this variation in input
2 data.
3 Expected rules of model coefficient estimation based on source type are described below.
4 When using direct measures of contaminant mass as a source estimate, "the source-specific
5 parameter (an) is expressed as a dimensionless coefficient that, together with standardized
6 expressions of the land-to-water delivery factor, describes the proportion or fraction of the source
7 input that is delivered to streams (note that source and land-to-water delivery coefficients that are
8 standardized in relation to the mean values of the land-to-water delivery variables are necessary
9 to compare and interpret the physical meaning of source coefficients). This fraction would be
10 expected to be <1.0 but >0, reflecting the removal of contaminants in soils and ground water"
11 (Schwarz et al., 2006).
12 An example of a source of this type would include atmospheric deposition where the
13 model input would be the mass of nitrogen deposited over the watershed. When using only wet
14 NOs" deposition as an estimate of nitrogen deposition, the model would be expected to account
15 for the additional nitrogen species (e.g., organic nitrogen, dry deposition of nitrate) to the extent
16 that they are correlated with the measured inputs of NOs" (Alexander et al., 2001). This
17 accounting is revealed by estimation within the model application of a land-to-water delivery
18 fraction for wet N(V deposition (i.e., product of the deposition coefficient and the exponential
19 land-to-water delivery function) that exceeds 1.0.
20 Although available estimates for the estuarine watersheds indicate that wet NOs
21 deposition is highly correlated with dry NO3~plus NH4+ and organic wet deposition, and
22 estimates of the ratio of (dry and wet TN) deposition to NOs^wet deposition for the estuarine
23 watersheds range from 3.2 to 4.0 with an average of 3.6 (Alexander et al., 2001), the use of
24 NADP wet NOs" measurements requires the assumption that the spatial distribution of the
25 various nitrogen species across a watershed does not vary. With the inclusion of explicit nitrogen
26 species in atmospheric deposition measures, this assumption will not be required, and the land-
27 to-water delivery fraction for the atmospheric deposition source term estimation is expected to be
28 <1.0. This variation was explored within the case studies as was the general model fit with the
29 improved atmospheric deposition inputs.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-41
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Aquatic Nutrient Enrichment Case Study
1 2.2.2 ASSETS Eutrophication Index
2 2.2.2.1 Background and Description
3 The NEEA Program defined and developed a Pressure-State-Response framework to
4 assess the potential for eutrophication termed the ASSETS El. It is categorical, where each of
5 three indices results in a score that, when combined, result in a final overall score, also known as
6 the ASSETS El score or rating, which is representative of the health of the estuary. The indices
7 are as follows:
8 • OHI. Physical, hydrologic, and anthropogenic factors that characterize the susceptibility
9 of the estuary to the influences of nutrient inputs (also quantified as part of the index) and
10 eutrophi cation.
11 • OEC. An estimate of current eutrophic conditions derived from data for five symptoms
12 known to be linked to eutrophi cation.
13 • DFO. A qualitative measure of expected changes in the system.
14 The following excerpt from Whitall et al., (2007) describes the objectives in applying the
15 ASSETS method:
16 The ASSETS assessment method should be applied on a periodic basis to track
17 trends in nutrient-related water quality over time in order to test management
18 related hypotheses and provide a basis for more successful management. The null
19 hypothesis being tested in this approach is: The change in anthropogenic pressure
20 as a result of management response does not result in a change of state. The
21 hypothesis is tested, e.g., to verify whether decreased pressure improves State, or
22 whether increased pressure deteriorates State. In many cases, a reduction in
23 pressure will result in an improvement of State, but in some cases, such as
24 naturally occurring harmful algal bloom (HAB) advected from offshore, it will
25 not (Whitall et al., 2007).
26 Influencing Factors/Overall Human Influence
27 Influencing factors help to establish a link between a system's natural sensitivity to
28 eutrophi cation and the nutrient loading and eutrophic symptoms actually observed. This
29 understanding also helps to illustrate the relationship between eutrophic conditions and use
30 impairments (Bricker et al., 2007a). Influencing factors are determined by calculating two factors
31 of susceptibility and nitrogen load, where "susceptibility" provides a measure of a system's
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-42
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Aquatic Nutrient Enrichment Case Study
1 nutrient retention based upon flushing and dilution, and "nitrogen loads" are a ratio between the
2 nitrogen input to the system from the oceans versus from the land (Figure 2.2-5).
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Calculating influencing factors Determination of influencing factors
Overall, the impact of influencing factors for
an estuarine system is determined by a matrix
(figure at right). Several calculations were made
to create the matrix. First, both susceptibility
and load were determined for each estuary and
placed in one of three categories: low, moderate,
or high. The load refers to a ratio of land-based
to oceanic nitrogen inputs, with a high rating
indicating primarily land-based inputs (Bricker
et al. 2003; Ferriera et al. 2007). The estuary's
susceptibility and nutrient loads were compared
in a matrix and given an influencing factors
rating. For example, an estuary with low
nutrient loads and moderate susceptibility is
moderately/slightly influenced. Each of the
systems in the survey can fall into one of five
categories: slightly influenced, moderately/
slightly influenced, moderately influenced,
highly/moderately influenced, and highly
influenced (see Bricker et al. 1999 for details).
lift
=
Moderately
influenced
influenced*
Hmh|,, jr,flu
-------
Aquatic Nutrient Enrichment Case Study
1 example, a high rating means that >80% of the nutrient load comes from land, whereas a
2 low rating signifies a land percentage of <20%. This rating also provides insight into
3 loading management because loads to systems with primarily ocean-derived nitrogen are
4 not easily controlled. Understanding the sizes of current and expected future loads
5 provides further insight into the application and success of management measures.
6 Overall Eutrophic Condition
7 To assess the eutrophic conditions of a system, the NEEA relies on five symptoms. Each
8 of the five symptoms, divided into primary and secondary categories, is assessed based on a
9 combination of the following factors: concentration or occurrence, duration, spatial coverage,
10 frequency of occurrence, and confidence in the data (Figure 2.2-6). The two primary symptoms,
11 chlorophyll a and macroalgal abundance (Figure 2.2-7), were chosen as indicators of the first
12 possible stage in the process of water quality degradation leading to eutrophication. The
13 secondary symptoms, which in most coastal systems will develop from the primary symptoms,
14 include low dissolved oxygen levels, loss of SAV, and occurrences of nuisance/toxic algal
15 blooms (Figure 2.2-7). At times, the secondary symptoms may also be present or develop
16 without expression of primary symptoms. Nutrient concentrations are not employed as a
17 symptom indicator because concentrations may vary between low and high values based on a
18 number of factors, such as estuary susceptibility, which invalidates the use of nutrient
19 concentrations alone as an indicator. As stated by Bricker et al., "Through the use of a simple
20 model, the current framework was established to help understand the sequence, processes, and
21 symptoms associated with nutrient enrichment. Despite its limitations, it represents an attempt to
22 synthesize enormous volumes of data and derive a single value for eutrophi cation in each
23 estuary, essentially representing a complex process in a simple way" (Bricker et al., 2007a).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-44
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Aquatic Nutrient Enrichment Case Study
Mtafogzone
Step 1: Determine expression value for each eutrophic symptom in each salinity zone,
Eutrophic symptom expression
values are determined for each
symptom in each salinity zone
(seawater. mixing, and tidal fresh),
resulting in a total of 15 calculations.
The expression is based on a set of
IF, AND, THEN, decision rules that
incorporate the symptom level (e,g,
concentration), spatial coverage,
and frequency.
"1
r * b
Chlo
o. Concentration
WJ Hrg),
i/ IF Medlum AND
Unlnowi
Spatial ewer
High
AND
Loo
Verrlow
Anyccncr
Unkn,3«ii
Frequency
Episodic
*"*•"•
Expression
ugh
THEN [
Lcr»'
Flag"
Value
'"I
0,25
OS
Step 2: Calculate estuary- wide symptom expressions (using chlorophyll a as an example).
Th e express ion val Lies are then used ID __
Each symptom value is m ukiplied
by the estuary area ratio.
calculate estuary-wide symptom
expressions for each symptom. First,
each expression value is multiplied by
the area of the salinity zone and
divided by the entire area of the
system to establish the weighted
value. Then, the weighted expression
values in the tidal fresh, mixing, and
seawater zone for each symptom are
totaled to calculate the estuary-wide
symptom expression value. This
process Is repeated for all five
eutrophic symptoms. Note that "no
problem" Is the rating assigned if the
value is 0. but that "no problem"
and low are combined for discussion
and tabulation throughout the report.
Expression Value
Expression
High
Low
Flag*
Value
1.0
0.25
0.5
weighted
expression value for
tidal fresh zone
For each symptom, the weighted expression values for the three salinity zones are added,
Step 3: Assign categories For primary and secondary symptoms.
Primary and secondary estuary-wide symptom expression
values are determined in a two step process:
Theaverageof the primary
symptoms is calculated to represent
the estuary-wide primary symptom
value. The highest of the secondary
symptom values Is chosen to
represent the estuary-wide
secondary symptom expression
value and rating. The highest value is
chosen because an average might
obscure the severity of a symptom if
the other two have very low values
(a precautionary approach).
1)
Estuary-wide
primary symptom value
— estuary-wicie
express!on value for
chlorophyll a
Estuary-wide symptom rating is determined:
Symptom expression value Symptom rating
20 to £0.3 lQW
>0.3 to < 0,6
Medium
or
or
— Estuary-wide
>0.6 to < 1
High
secondary symptom value
(Highest value is selected)
Step 4: Determine overall eutrophic condition.
1.0
A matrix is used to combine the High
estuary-wide primary and secondary p'"Mr' Moderate
symptom values into an overall o.e—
eutrophic condition rating according
to the categories at right. Thresholds
between racing categories were
agreed on by the scientific advisory
committee and participants from the
1999 assessment (Brickeretal. 1999).
Moderate low
Low
Low •
Primacy
0 Low Secondary
.Moderate high
Moderate
Moderate low Moderate hig
C.? High SeojnJsrf 1.0
'Flags are used to identify components for which data were inadequate or unknown. In these cases, assumptions were made based on conservative estimates
]^ that unknown spatial coverage is at least 10% of a zone, frequency at least episodic, and duration at least days.
2 Figure 2.2-6. Overall Eutrophic Condition index description and decision matrix
3 (Bricker et al., 2007a).
2nd Draft Risk and Exposure Assessment
Appendix 6-45
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Symptom
Typical high concentration
(ug L'1) in an annual cycle
determined as the 90th
percentile value.
x- — -^
'rtt^B^1
Macroalgae , 'NjjKiL J
\*ljV/
Causes a detrimental impact
on any natural resource.
Typical low concentration
(determined as the 10"
percentile value) in an
annual cycle.
A change in SAV spatial area
observed since 1990.
^..,---N
Nuisance/ toxic /VT^ •%%!
blooms \^j 4l^ /
Causes detrimental impact
on any natural resources.
Parameters
Spatial coverage: Frequency:
High >50% Episodic
Moderate 25-5094 Periodic
Low 10-2594 Persistent
Very low o-io*
Concentration:
High iIOugL1
Medium 5-20 ug L'1
Low 0-SugL'1
Frequency of problem:
Episodic (occasional, 'random)
Periodic (seasonal, annual,
predictable)
Persistent (always/continuous)
Spa tial co verage: Frequency:
High >50% Episodic
Moderate 25-50% Periodic
Low 10-25% Persistent
Very low 0-1094
State:
Anoxia 0 mg L1
Hypoxia 0-2 rng L1
Biol. stress 2-5 mgL-1
M agniaide o( change:
High >sote
Moderate 25-50*
Low 10-25%
Very low 0-10%
Duration:
Persistent, seasonal, months, variable,
weeks, days, weeks to seasonal.
weeks to months, or days to weeks
Frequency:
Episodic, periodic or persistent
Low
Low symptom expression:
Cone. Coverage Frequency
low any any
medium mod. - v. low episodic
high low-v. low episodic
No rnacroalgal bloom problems
have been observed.
Low symptom expression:
State Coverage Frequency
anoxia mod. - low episodic
anoxia very low periodic
hypoxia low-v. low periodic
hypoxia moderate episodic
stress any episodic
stress mod. -v. low periodic
The magnitude of SAV loss is
low to very low.
Blooms are either a) short in
duration (days) and periodic in
frequency; or b) moderate in
duration (days to weeks) and
episodic in frequency.
Expression
Moderate
Moderate symptom expression:
Cane. Coverage Frequency
medium high episodic
medium moderate periodic
high low-v. low periodic
high moderate episodic
Episodic rnacroalgal bloom
problems have been observed.
Moderate symptom expression:
State Coverage Frequency
anoxia high episodic
anoxia low periodic
hypoxia moderate periodic
hypoxia high episodic
stress high periodic
The magnitude of SAV loss is
moderate.
Blooms are either a) moderate in
duration (days to weeks) and
periodic in frequency; or b) long
in duration (weeks to months)
and episodic in frequency.
High
High symptom expression:
Cone. Coverage Frequency
medium high periodic
high mod.- high periodic
high high episodic
Periodic or persistent rnacroalgal
bloom problems have been
observed.
High symptom expression:
State Coverage Frequency
anoxia moderate- high periodic
hypoxia high periodic
The magnitude of SAV loss
is high.
Blooms are long in duration
(weeks, months, seasonal)
and periodic in frequency.
1
2
Tor further technical documentation of the methods, refer to Bricker et al. 1999 and Brickeretal. 2003.
Figure 2.2-7. Detailed descriptions of primary and secondary indicators of eutrophication (Bricker et al., 2007a).
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 6-46
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Aquatic Nutrient Enrichment Case Study
Determined Future Outlook
The future outlook relies on a similar combination of factors as the influencing factors
(i.e., a rating of the system susceptibility and nutrient loading in the future). The aim of this
index is to estimate future changes in the system through a combination of any physical,
hydrologic, or pollutant loadings to the system itself or to its contributing watershed through
such actions as watershed management plans, development restrictions, or policy changes
resulting in nutrient reductions. The matrix in Figure 2.2-8 is used to determine the DFO index
rating.
/"&-•:,
jjB Calculating future outlook
.
The analysis for future outlook is an attempt
to determine whether conditions in an estuary
will worsen, improve, or remain unchanged
over the next 20 years.
In this analysis, expected nutrient input
changes were used to predict whether
eutrophic conditions will improve or worsen.
The system's susceptibility to nutrients is
then used to determine the magnitude of
this change. Population projections are used
as a primary indicator of the level of future
nutrient input changes. However, population
projections are unpredictable. Therefore,
experts at the NEEA update workshop were
asked to predict changes in nutrient load,
based on their knowledge of likely changes in
land use, management measures, and other
activities that affect nutrient loading.
Determination of the future outlook
e
1
bo
.5
1|
v — •
5
•H
•Q
I
[f — Q/
V^ \j
OtS
frl|
Hi
E 3
2-
: .STJ Q.
1
Improve high
Symptoms likely to
imp rove substantially
Improve low
Symptoms likely to
improve
Improve low
Symptoms likely to
improve somewhat
decreasing nitrogen load
'"•*.
No change
Symptoms will most
likely remain
unchanged
No change
Symptoms will most
likely remain
unchanged
No change
Symptoms will most
likely remain
unchanged
no change
"V
Worsen low
Symptoms likely to
worsen only
minimally
Worsen higli
'r/rnproire are likely
to substantially
worsen
Worsen high
Symptoms are likely
to substantially
worsen
^^^^^^^^^^^^^^^^^^^^1
increasing nitrogen load
"*""*'•..
yw Expected future load (nitrogen input)
Figure 2.2-8. Determined Future Outlook index description and decision matrix (Bricker
et al., 2007a).
The last step is to combine the OHI, OEC, and DFO index scores into a single overall
ASSETS El score. The ASSETS El scores fall into one of six categories: High, Good, Moderate,
Poor, Bad, or Unknown. These ratings can be summarized as follows (Bricker et al., 2007a):
• High: Low pressure from influencing factors, low OEC, and any expected improvement or
no future change in eutrophic condition.
• Good: Low to moderate pressure, low to moderate-low eutrophic condition, and any
expected future change in condition.
2nd Draft Risk and Exposure Assessment
Appendix 6-47
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
• Moderate: Any pressure, moderate-low to moderate-high eutrophic condition, and any
expected future change in eutrophic condition.
• Poor: Moderate-low to high pressure, moderate to moderate-high eutrophic condition, and
any expected future change in condition.
• Bad: Moderate to high pressure, moderate-high to high eutrophic condition, and any
expected future change in eutrophic condition.
• Unknown: Insufficient data for analysis.
2.2.2.2 Applications and Updates
The ASSETS El method developed out of the NEEA was first reported in 1999. Since
that time, it has been used in several assessments across the country and internationally and has
undergone revision and validation (Bricker et al., 1999, 2003, 2007a; Ferreira et al., 2007;
Whitall et al., 2007). The original NEEA ASSETS El assessment relied on questionnaires to
experts for each estuary considered (Bricker et al., 1999). Later assessments determined that
reliance on monitored data and less on reports from experts provided a more valid assessment
tool (Bricker et al., 2006, 2007a). With the NEEA Update in 2007 (Bricker et al., 2007a), an
online database was completed in which data users and data holders could access and input data.
Additional datasets have also been collected for smaller study areas (Bricker et al., 2006). These
data systems provide a wealth of information from which analyses may be conducted.
The original formulation of the ASSETS El within the NEEA used watershed nutrient
model estimates from SPARROW (Bricker et al., 1999). Although the updated ASSETS El
methodology has further apportioned nitrogen sources using the Watershed Assessment Tool for
Evaluating Reduction Strategies for Nitrogen (WATERSN) model (Whitall et al., 2007),
SPARROW is still appropriate for this study because atmospheric deposition inputs relative to
other nitrogen sources can be defined.
2.2.3 Assessments Using Linked SPARROW and ASSETS El
The link between the SPARROW model and the ASSETS El occurs when the
SPARROW output is used as the nitrogen load in the OHI index calculation of the ASSETS El
score. For the purposes of this study, a complete analysis from atmospheric deposition loading to
ecological endpoint of the ASSETS El score required an assessment of the relative changes in
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-48
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Aquatic Nutrient Enrichment Case Study
the deposition load, the resulting instream nitrogen load to the estuary, and the change in
ASSETS El score. An iterative assessment of the various possible ecological endpoints due to
changing nitrogen loads has not been previously undertaken. The methods that follow have been
designed to allow for set up of a process to link the SPARROW and ASSETS El assessment,
which includes uncertainty analysis. As will be detailed below, at this time the developed process
includes an uncertainty analysis for the ASSETS El assessment, whereas the uncertainty
surrounding the corresponding atmospheric deposition reduction loads predicted by SPARROW
has not yet been implemented. The process is described beginning with the individual
components of each assessment, followed by a description of the iterative processing designed to
sample among the assessments, producing a distribution of results.
The first step in setting up the linked analysis was to create a series of response curves.
The SPARROW model can be used to assess the different atmospheric deposition loads that will
result from changes in the NOX concentrations enforced with any new policy scenarios.
Therefore, a change in atmospheric deposition load produces a corresponding change in TN
loading at the outlet of the watershed, in this case, to the estuary. Converting the TN load to a
concentration value using the flow values employed in the SPARROW modeling allows for the
creation of a relationship between reduction in atmospheric deposition load from the current
condition scenario and instream total nitrogen concentration (TNS). A theoretical representation
of this relationship in an ideal situation is presented in Figure 2.2-9 (dotted lines represent
uncertainty bounds). The red "x" indicates the nitrogen concentration during the current
condition assessment where there is no load reduction in atmospheric deposition. The vertical
asymptote of the curve approaches the nitrogen concentration of the river if there were no
atmospheric inputs. In theory, the watershed response to changes in any loading would be
nonlinear because of various retention and loss processes occurring within the watershed.
However, because SPARROW is a statistical model, the empirical representation of these
processes does not produce a nonlinear response as shown in Figure 2.2-9. As will be discussed
in the results section, the response to changes in atmospheric deposition load is predicted to be
linear by SPARROW. This highlights one area of uncertainty due to the steady-state, statistical
nature of SPARROW. A benefit of using SPARROW is that the various atmospheric deposition
levels can be run through the model in a short amount of time.
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Appendix 6-49
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Aquatic Nutrient Enrichment Case Study
A second response curve is set up for the ASSETS El based on the Influencing Factors/
OKI and OEC index scores (Section 2.2.2), which are functions of TN load. The ASSETS El
assessment is essentially a pressure-state-response scenario (Figure 2.2-10) where the pressure is
the nitrogen load (represented by TNS) and the state is the current OEC index and ASSETS El
scores for the system. Response would be the change in state of the estuary (represented in the
ASSETS El by the DFO index). Bricker et al. (2007b), noted that the shape of the response curve
would vary based on the susceptibility of the system. Therefore, if the susceptibility is known
and held constant, a curve can be created.
T3
ro
en
o
T3 O
I!
S ro
o ^j
Instream Total Nitrogen Concentration
Figure 2.2-9. Example response curve of instream total nitrogen concentrations to
atmospheric deposition loads.
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Aquatic Nutrient Enrichment Case Study
Figure 2.2-10. Example of response for case study analysis (Bricker et al., 2007b).
With the case study analysis, response curves were created for two different estuaries
where the susceptibility is known. As previously described, the ASSETS El score is a
combination of OEC, OHI, and DFO index scores. It is possible to combine all three of these
scores with the ASSETS El into a single response curve when the susceptibility and DFO are
held constant. The DFO may be held constant when alternative effects levels are being evaluated
based on a current condition scenario, such as was done in this study. The susceptibility rating is
based on physical and hydrological conditions. Physical conditions are unlikely to change. The
hydrologic conditions may change because of extreme conditions, such as prolonged drought or
hurricane events, but overall the conditions should average to a steady value that can be used in
the analysis. Figure 2.2-11 highlights this combination of scores where the susceptibility is
"High" and the DFO is set at "Improve." Additionally, by holding the susceptibility constant, the
OHI index score becomes a function of the TNS. This is evident in the double x-axis. The state
response is the OEC index score along the y-axis. Underlying these combinations of OHI and
OEC index scores is the ASSETS El score.
The categorical nature of the assessment produces a mix of a continuous curve based on
TNS and blocks where different ASSETS El scores are valid. The shape of the curve can be
determined by fitting a logistic relationship through a series of points (red "x"s) within the
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Appendix 6-51
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Aquatic Nutrient Enrichment Case Study
pressure-state realm based on compilations of historical data. The logistic function is suggested
because of the pressure-state-response nature of the system meaning that initial, lower pressures
are thought to affect the system slowly, then changes become more rapid up until a point where
the pressure is so great that the state changes little because it has almost reached a maximum
level. A logistic response to inputs (e.g., nutrients) is a common pattern in biological systems and
is well documented. A variety of logistic (or sigmoidal) functions are available. In this case
study, the following four-parameter function was used:
OEC(TNS) = A + ^—-^ (6)
1 + exp
where
OEC(TNS) = OEC index score as a function of TNS (unitless)
TNS = TNs(mgN/L)
A, B, C, D = Parameters to be estimated.
Applying this logistic function as in Figure 2.2-11, parameter^ affects the OEC
intercept value, b in Figure 2.2-11; parameters A and B affect the OEC asymptote value; C
affects the "S" shape; and D shifts the "S" horizontally. The curve is fit through a nonlinear
optimization routine using a modified version of Box's algorithm (Box, 1965). Constraints on
the boundary conditions for the function are described below. At this time, 500 iterations of the
algorithm were used to define the function. A future assessment must complete a Monte Carlo
simulation analysis to determine a justifiable estimate of the number of iterations needed to bring
the algorithm to convergence for the problem as thus defined.
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Appendix 6-52
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Aquatic Nutrient Enrichment Case Study
VI
o
LLJ
O
Nitrogen Instream Concentration
Low Moderate Low Moderate Moderate High High
OHI Score (assuming constant susceptibility)
Figure 2.2-11. ASSETS El response curve.
Within the analysis space created by both the OHI and OEC index scores, the axes are
limited to the scores of zero (really one) to five, but the corresponding TNS must be determined
separately. Point "a" represents the background nitrogen concentration that would occur in the
system with no anthropogenic inputs (assuming the system is not naturally eutrophic) or the
system at a pristine state. In almost all cases, this value will be unknown because of the extent to
which anthropogenic inputs have influenced the nation's ecosystems. A lower bound and upper
bound on this value, between which the algorithm randomly selects a different realization for
each iteration, were specified. The lower bound was specified as the offshore TN concentration
(used in the OHI index score) and the upper bound was specified at 1.0 milligrams per liter
(mg/L) for the Potomac River/Potomac Estuary Case Study Area based on best professional
judgment and at 0.1 mg/L for the Neuse River/Neuse River Estuary Case Study Area based on a
combination of the lowest monitored nitrogen values over a number of years and best
professional judgment. The upper bound of the TNS is the maximum nitrogen concentration at
which the system is nitrogen-limited; above this point, the nitrogen inputs to the system no
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Aquatic Nutrient Enrichment Case Study
longer affect the eutrophication condition. Several attempts to quantify this limit were made
through historical data analysis, examination of nitrogen to phosphorus (N:P) ratios within the
estuary, and consideration of the underlying eutrophication processes. For this first assessment, a
lower and upper bound on this maximum TNS between which the algorithm randomly selects a
different realization for each iteration were specified. The lower bound was specified as the
maximum observed TNS, whereas the upper bound was specified as 50% greater than this value
based on best professional judgment.
Further measures of uncertainty could be taken into account using upper and lower
bounds on the logistic curve. The horizontal spread at each OEC index score reflects the range of
nitrogen concentrations that may relate to that specific OEC index score. The vertical spread
relates to the range in OEC index scores that may occur at each nitrogen concentration. As with
the TNS axis, the lower and upper bounds on both extremes of the OEC scale were specified
again. At both ends of the scale (i.e., 0 and 5), the OEC maximum and OEC minimum values
could be randomly selected at each iteration within a range of 1 OEC unit or could be set at
constant values of 1 and 5. If randomly selected, the actual OEC minimum would be between 0
and 1 at the "0" end of the scale and between 4 and 5 at the "5" end of the scale. This would
account for the likelihood that an estuary may not physically be capable of reaching either a
"pure, pristine" condition (e.g., OEC = 5) or a "completely hypereutrophic" condition (e.g.,
OEC = 0). Consideration of this allowance and of holding the scores constant is discussed in the
uncertainty section.
Back Calculation Method
The creation of the two response curves allows an analyst to work backward from the
ecological endpoint to the source of the impairment; in this case from the ASSETS El score to
the atmospheric deposition loading of oxidized nitrogen. To accomplish this back calculation, a
computer program has been generated that processes through a series of user inputs, defined
boundary conditions, and set cases for determining the ASSETS El score to perform the iterative
calculations described in setting the logistic curve and determining the TNS. Currently, the
program is coded in the Visual Basic language and requires an input file defined by the user to
contain:
• The desired ASSETS El ecological endpoint (i.e., score)
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Appendix 6-54
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Aquatic Nutrient Enrichment Case Study
• Oceanic nitrogen and salinity values
• Instream salinity values
• The regression equation relating the reduction of atmospheric deposition of nitrogen to
TNS
• The number of realizations on which to iterate the model calculations.
Uncertainty Bounds on TNS, OEC Min/Max Values
Based on the degree of uncertainty, three different scenarios exist for the analysis (Figure
2.2-12a through Figure 2.2-12c). There are two relationships, or functions, involving uncertainty
in the proposed methodology. The first is the SPARROW-predicted TNS at the head of the
estuary given a total nitrogen atmospheric deposition load (TNatm). (Note that SPARROW model
predictions are actually provided in terms of instream TN loads [mass per time]; however,
concentrations [mass per volume] can be calculated by dividing the instream TN load by the
flow rate.) This functional relationship is denoted as TNs(TNatm) where the TNS is actually
evaluated on changes in the loading of oxidized nitrogen, not all nitrogen species, in the TN
atmospheric deposition load. This means that reductions are applied to the NOX load within the
atmospheric load, and a new TNatm is calculated). (For instance if the NOX load contribution is 10
kg N/yr and TNatm is 20 kg N/yr, then a 40% reduction would result in a TNatm equal to 16 kg
N/yr after reducing the 10 kg N/yr by 40%.) Because of the SPARROW regression uncertainties,
this function is a probability distribution (i.e., given an TNatm, there are many alternative TNS
values). This distribution function is denoted as TNspdf(TNatm). Under the standard assumptions
of regression modeling, this is a normal distribution with a mean value represented by the
SPARROW estimate of TN load. The variability around the mean value represents uncertainty in
the SPARROW parameter estimates. However, the SPARROW regression model does not give
rise to normally distributed residuals, and some nonparameteric methods have been developed by
the SPARROW developers to estimate SPARROW'S confidence limits on predictions (Schwarz
et al., 2006). This measure of uncertainty has not yet been incorporated into this methodology,
and that is why the results for the Potomac River/Potomac Estuary Case Study Area and the
Neuse River/Neuse River Estuary Case Study Area are based on uncertainty Scenario B
(explained below; Figure 2.2-12b). Accordingly, the uncertainty presented in those results will
be an underestimate of the total uncertainty.
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Appendix 6-55
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Aquatic Nutrient Enrichment Case Study
The second uncertain function is the semi quantitative relationship between the ASSETS
El score and the TN load. The ASSETS El model yields a result based on three index "scores,"
the OEC, the OKI, and the DFO index scores as described in Section 2.2.2. The OEC and OKI
index scores are functions of TNS. Thus, the ASSETS El model can be written as ASSETS El =
f(OEC(TNs), OHI(TNS), DFO) where "f' is the functional relationship that is the ASSETS El
methodology. Based on the methodology developed for creation of the response curve, the
OEC(TNS) function also involves uncertainty, and the OEC index score resulting from any
particular TNS is also a probability distribution, which can be assumed to be a uniform
distribution for this first analysis. Incorporating this uncertainty, the ASSETS El model can be
expressed for this study as ASSETS El = f(OECpdf(TNs), OHI(TNS), DFO). Figure 2.2-12b and
Figure 2.2-12c present the iterative steps in which the probability distribution function of OEC
results is created in the coded model.
In setting up Scenario B for the evaluation of alternative effects levels, the goal was set to
determine the change in oxidized nitrogen load required to improve the ASSETS El score by
one, two, and three categories from its current level set in the 2002 current condition analysis.
Improvement in the ASSETS El score by categorical values means moving along the logistic
curve set to data points determined through the gathering of historical monitoring data and
reports. Figure 2.2-13 provides a visualization of what it means graphically to improve by one
ASSETS El score category. In reality, the exact results of improving by one, two, or three
categories will vary depending on the estuary and the baseline state of the estuary (i.e., where on
the logistic curve the analysis begins). In the example shown in Figure 2.2-13, improving the
ASSETS El score by one category also improves the OEC index score by one category but
allows for a decrease in the TNS, which results in the same OHI index score as the baseline. Also
in this example, the baseline for the estuary is an ASSETS El score of "bad," thus it is possible
to improve by three categories although, in doing so, the OEC and OHI/TNS would have to
improve to an almost pristine state. If a system begins with an ASSETS El score of "Moderate"
or "Good," the assessment will only be able to examine an improvement in two or one ASSETS
El score categories, respectively. Also noted in Figure 2.2-13 is the direction of the category
movement. In this example, improvement of the ASSETS El score along the determined logistic
curve is a vertical movement down rather than a horizontal move to the left (not illustrated with
this example); therefore, the OHI index score may remain in the same or similar state. If the
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Appendix 6-56
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Aquatic Nutrient Enrichment Case Study
movement were to the left, the OHI index score would have to improve, whereas the eutrophic
condition of the system (i.e., OEC index score) could remain the same or similar to the baseline
conditions.
ASSETS model: El = f(OEC(TNs), OHI(TNS), DFO)
SPARROW model response curve: TNs(TNatm) = a + b TNatm
Terms: El Eutrophication Indicator
f Denotes functional relationship between
parameters (i.e., f is the ASSETS methodology)
OEC Overall Eutrophic Condition
OHI Overall Human Influence
DFO Determined Future Outlook
Eutrophication Indicator of interest
Total Nitrogen concentration of interest
Total atmospheric nitrogen deposition load evaluated
by decreasing the A/Ox contribution to deposition
a, b constants
i, j number of function interations
Cl Confidence interval
A) No uncertainty in OEC(TNS) or in TNs(TNatm)
El*
TNS
Find TNS* to satisfy
El* = f(OEC(TNs*), OHI(TNS*), DFO)
Find TNatm* to satisfy TNS*
From SPARROW response curve
Jatm*
TN*
Result: TNatm*
(scalar value)
Figure 2.2-12a. Back calculation analysis scenario A: no uncertainty.
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Aquatic Nutrient Enrichment Case Study
B) Uncertainty in OEC(TNS); No uncertainty in TNs(TNatm)
No
Select number of iterations to
sample OEC(TNS) function (n)
Select a OEC(TNS) function (i)
(Fit of a logistic function to OEC, TNS data pairs where
endpoints are randomly selected within a specified range)
Find TNS* to satisfy
El* = f(OEC(TNs*), OHI(TNS*), DFO)
Find TNatm* to satisfy TNS
From SPARROW response curve
atm*
b
TN*
Present Results
m* fori = 1, ..., n
Figure 2.2-12b. Back calculation analysis scenario B: uncertainty in ASSETS El assessment.
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June 5, 2009
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Aquatic Nutrient Enrichment Case Study
C) Uncertainty in OEC(TNS) and TNs(TNatm)
No
Select number of iterations to
sample OEC(TNS) function (n)
Select number of iterations to sample
probability distribution of TNs*(TNatm*j) (m)
Select a OEC(TNS) function (i)
(Fit of a logistic function to OEC, TNS data pairs where
endpoints are randomly selected within a specified range)
Find TNS* to satisfy
El* = f(OEC(TNs*), OHI(TNS*), DFO)
For a given percentile (j) of the TNs*(TNatm*j) probability distribution
TNs*i = a + b TNatmV Cl
Find the TNatm*ij that satisfies TNS*
^No-
Present Results
TNatm*i,j for i = 1,...,n
j = 1, ..., m
Figure 2.2-12c. Back calculation analysis scenario C: uncertainty in both ASSETS El
assessment and nitrogen loading assessment.
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Appendix 6-59
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
o
UJ
O
(2) Resulting
Change in N
Cope/Load
Neededto
jlVleet
Eridpoints
Nitrogen mstream Concentration
Cor
Low Moderate Low Moderate Moderate High High
OHI Score (assuming constant susceptibility)
Figure 2.2-13. Example for improvement by one ASSETS El score category in a back
calculation assessment.
In the second piece of the back calculation, the newly determined TNS needed to make an
improvement in the ASSETS El score is examined on the response curve of the TNS to TNatm.
Figure 2.2-14 presents an example similar to those found in the case studies where the response
is linear (note that the axes have been flipped from original representation in Figure 2.2-9 to
reflect how the response curve is handled within the coded program developed for this study).
While the linear nature of this curve during this preliminary assessment is a function of the use
of SPARROW, a statistical watershed model, valuable information on the required reduction in
TNatm, can still be determined. The scale of the axes of the response curve also provides a great
deal of information on the system of interest. For instance, if the TNS changes that result from a
small change in the TNatm are much greater (e.g., 5% change in atmospheric load versus 20%
change in instream concentration), then the system is highly influenced by the atmospheric
deposition of nitrogen. If the situation is reversed and large changes in TNatm result in only small
changes in the TNS, then the system is not greatly influenced by the atmospheric deposition of
nitrogen.
2nd Draft Risk and Exposure Assessment
Appendix 6-60
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
o
o
o
.fc
c
Specified change in
instream total nitrogen
concentration from
improvements in ASSETS El
I
Resulting change in
atmospheric deposition
and percent of
oxidized nitrogen
Total Nitrogen Atmospheric Deposition Load
Percent Reduction in Atmospheric Oxidized n
Nitrogen Load
Figure 2.2-14. Example for resulting change in atmospheric nitrogen loads due to
improvement in ASSETS El score in back calculation assessment.
3. RESULTS
3.1 CURRENT CONDITIONS
The available air quality data for this review are based on the 2002 CMAQ model year
and NADP data; therefore, current conditions for this case study evaluated ecosystem responses
for the year 2002. In both case study areas, the best attempts were made to use monitoring and
modeling data from that time period. The methods designed for the current condition ecosystem
analysis required for this study produce annual averages for 2002.
3.1.1 Summary of Results for the Potomac River/Potomac Estuary Case Study
Area
The 2002 current condition analysis of the Potomac River/Potomac Estuary Case Study
Area relied on a previous SPARROW application calibrated for the late 1990s in the Chesapeake
Bay watershed. The evaluation of the ASSETS El used raw data compiled from organizations
reporting on various issues in the Chesapeake Bay, including the Chesapeake Bay Program
(CBP) and the Virginia Institute of Marine Science (VIMS).
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Appendix 6-61
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Aquatic Nutrient Enrichment Case Study
3.1.1.1 SPARROW Assessment
The Version 3 Chesapeake Bay SPARROW application modeled the watershed for the
time period of the late 1990s. Stream nitrogen load estimates from 87 sites were used to calibrate
the model. The stream reach network used in this analysis relied on a modified version of the
RF1 used in previous Chesapeake Bay SPARROW applications, but included 68 reservoirs that
were not previously included. This analysis examined the sources of atmospheric deposition,
fertilizer and manure application, point sources, and land use. Details on the compilation of each
of these GIS-based datasets can be found in the work by Brakebill and Preston (2004).
Watershed characteristics considered in the model as loss and decay variables include
precipitation, temperature, slope, soil permeability, and hydrogeomorphic regions.
The Version 3 calibrated model was selected to create the results for the 2002 current
condition analysis because of its temporal proximity to the desired base year. The 5-year
difference from 1997 to 2002 was not expected to result in a large change in the model if it were
recalibrated to more recent data (S. Preston, personal communication, 2008). Future updates to
this study should consider recalibrating the model to 2002 data to ensure that this assumption
holds true.
The SPARROW assessment for the 2002 current condition analysis used the same source
inputs and watershed characteristics, except in the case of atmospheric deposition. The Version 3
Chesapeake Bay SPARROW application relied on 1997 mean deposition values of wet-
deposition atmospheric NOs" using the 191-point measurements in the NADP program across the
country. As described in Section 2.2.1.3, relying on wet NOs" deposition as a surrogate for TN
deposition requires an assumption of spatial homogeneity between the nitrogen species. The
2002 current condition analysis used the CMAQ/NADP data that were prepared for the Risk and
Exposure Assessment as atmospheric deposition inputs to the model. Although the Version 3
Chesapeake Bay SPARROW model was calibrated against only wet NOs deposition loads, it
was decided that the 2002 current condition analysis should incorporate all available forms of
nitrogen deposition in order to fully reflect the result of changing oxidized nitrogen loads.
The use of the 2002 atmospheric deposition loads with the 1997 Version 3 of SPARROW
introduces a source of uncertainty to the modeled results because the source parameter for
atmospheric deposition was calibrated to the spatial variability and magnitude of the original
atmospheric source. This degree of uncertainty will be omitted from any future analyses when
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-62
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Aquatic Nutrient Enrichment Case Study
the time and data exist to recalibrate the SPARROW model to a full set of 2002 current condition
data. At this time, the study's goal, which is to reflect on the changes in magnitude of nitrogen
loads to the estuary due to changes in the deposition of oxidized nitrogen, can still be assessed
and described using this data compilation. The 1997 data used in the original Version 3
Chesapeake Bay SPARROW application shows wet NOs"yields highest in the western,
mountainous region of the Potomac River watershed with lower values around the southern
central portion and areas surrounding the Potomac Estuary (Brakebill and Preston, 2004). There
is a clear trend of interpolation from the NADP data. In comparison, Figure 3.1-la through
Figure 3.1-lc reveal highly different spatial patterns in oxidized, reduced, and TN atmospheric
deposition yields across the Potomac River and Potomac Estuary watershed. Note that the scales
across the three figures use the same increments and colors so that they can be directly
compared. For the current condition 2002 analysis of the Potomac River and Potomac Estuary,
an estimated 40,770,000 kg of TN was deposited in the 3.2-million hectares (ha) Potomac River
watershed, for an average TN deposition of 12.9 kg N/ha/yr.
Application of the SPARROW model provides estimates of the incremental flux derived
within each catchment of the Potomac River watershed, as well as estimates of how much of that
incremental flux (i.e., delivered flux) ultimately reaches the estuary (Figure 3.1-2). By looking at
catchment-scale results, the spatial variability among flux/load contributions across the
watershed can be shown. Differences between the incremental and delivered yields/loads reflect
the instream losses that occur as the load from each catchment travels downstream to the target
estuary.
For this first application and analysis of the 2002 current condition case, SPARROW was
used to model the loads from the Potomac River and its watershed to the upper portions of the
Potomac Estuary. The most downstream modeled catchment in the analysis lies downstream of
several major point sources between Washington, DC, and the mixing zone of the estuary. These
point sources were major contributors of nutrients to the estuary, and by including them in the
analysis a more accurate load from the Potomac River watershed is defined than if the modeling
stopped at the fall line of the river. Direct runoff from catchments surrounding the Potomac
Estuary and direct deposition to the estuary were not considered in this preliminary model
application. The majority of the nitrogen loading to the estuary was expected to derive within the
Potomac River watershed because of its overall larger land area and applications of fertilizer and
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-63
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Aquatic Nutrient Enrichment Case Study
manure. Additionally, the major point sources to the Potomac Estuary were included in the most
downstream watersheds at the mouth of the estuary modeled in this application.
Potomac River Watershed: Atmospheric Deposition - Oxidized Nitrogen
Legend
^ Potomac River V^tershed
I I NOAAUV* HUC8 Border
Atmospheric Deposition "
By Watershed Unit
(All units ,ire in kg Imyr)
I |4-B
| 6 - 8
^B B- 1°
^H 10- 12
** Oxidized nitrogen species and
sources: wet- nitrate (NADP); dry-
particulate nitrate, nitric acid,
nitrogen pentoxide, nitrous acid,
nitric oxide, nitrogen dioxide.
peroxycyl nitrate, and organic
nitrate (CMAQ)
0 10 20
40
60
nMiles
Watershed boundary layers
and mapping data were provided
by USGS. The site URL is:
http://rnci.waler.usgs.gov/gis/chesbay/
sparrow3/doc/retv3.htrn#section1
Figure 3.1-la. Atmospheric deposition yields of oxidized nitrogen over the Potomac
River and Potomac Estuary watershed.
2nd Draft Risk and Exposure Assessment
Appendix 6-64
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Potomac River Watershed: Atmospheric Deposition - Reduced Nitrogen
Legend
~| Potomac River Vtetershed
NOMUW\HUC8 Border
Atmospheric Deposition "
By Watershed Unit
(All units are in kg.liayr)
| 2-4
| |4-B
^B 8- 10
IHi 1°-|2
^H 12- 14
"Reduced nitrogen species and
sources: wet- ammonium (NADP);
dry - ammonia and ammonium
(CMAQ).
0 10 20
40
60
3 Mile?
Watershed boundary layers
and mapping data were provided
bylJSGS. The site URL is:
http://rnd.water.usgs.gov/gis/chesbay/
sparrow3/doc/retv3.htm#section1
Figure 3.1-lb. Atmospheric deposition yields of reduced nitrogen over the Potomac River and
Potomac Estuary watershed.
2nd Draft Risk and Exposure Assessment
Appendix 6-65
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Potomac River Watershed: Atmospheric Deposition - Total Nitrogen
Legend
Potomac River V\atershed
NOAAUVVft HUC8 Border
Atmospheric Deposition "
By Watershed Unit
(All units .lie in Kg luiyi I
I h-8
I |B-10
**Total nitrogen is the sum of
oxidized and reduced nitrogen
species. Oxidized nitrogen
species and sources: wet-
nitrate (NADP); dry • participate
nitrate, nitric acid, nitrogen
pentoxide, nitrous acid, nitric
oxide, nitrogen dioxide,
peroxycyl nitrate, and organic
nitrate (CMAQ). Reduced
nitrogen species and sources:
wet- ammonium (NADP); dry-
amnionia and ammonium (CMAQ).
0 10 20 40 60
V&tershed boundary layers end mapping
data were prodded by U SOS. TheateURLis
http: AVnd .water.usgs.gov/gis/chestoay/
SB'atuj'.^j.'i.i .. ...'! t : I ;! i^i^ctiQnl _
Figure 3.1-lc. Atmospheric deposition yields of total nitrogen over the Potomac River and
Potomac Estuary watershed.
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June 5, 2009
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Aquatic Nutrient Enrichment Case Study
2002 Base Case Results for Potomac Watershed
Map#1: Incremental Nitrogen Yield
A-it
Map #2: Delivered Nitrogen Yield
Legend
| | Potomac River
Watershed
~| Cathments not included
J in SPARROW Modeling
Nitrogen Yield
by Watershed Unit
(all units are in kg/hr/yr)
Map #1
Incremental Yield
I I 4-6
| | 6-8
I I 8-10
^H 10-12
Map #2
Delivered Yield
| 6-8
8-10
0 10 20
«
60
80
• Miles
Watershed boundary layers
and mapping data were provided
by Uses. The site URL is:
http:tfmd. water, usgs.cjov/gisfchesbayl
sp arrows' do c/retw 3. htrn#se cti on 1
Figure 3.1-2. Total nitrogen yields from all sources as predicted using the Version 3 of the
Chesapeake Bay SPARROW application with updated 2002 atmospheric deposition inputs.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-67
-------
Aquatic Nutrient Enrichment Case Study
Overall, the model produced an estimate
of TN loading to the Potomac Estuary of
36,660,000 kgN/yr. The atmospheric deposition
SPARROW modeling for 2002 predicts that
atmospheric deposition was 20% of the total
nitrogen loading to the Potomac River's
estuary, producing an TNS of 3.4 mg/L.
load was estimated at 7,380,000 kg N/yr or 20% of the total loading (Figure 3.1-3). These
modeling estimates are consistent with previous modeling estimates for the system (Preston and
Brakebill, 1999). The TNS resulting from this loading was approximately 3.4 mg/L.
Relative Contributions of Nitrogen Sources to Potomac Estuary
20%
25%
D Manure
D Fertilizer
• Urban Area
D Point Sources
D Atmospheric Deposition
46%
Total load to estaury:
36,660,000 kg N/yr
Figure 3.1-3. Source contributions to Potomac Estuary nitrogen load.
3.1.1.2 ASSETS El Assessment
An ASSETS El assessment was completed for the Potomac Estuary (Figure 3.1-4) in a
2006 NOAA project on the Gulf of Maine (Bricker et al., 2006). The data used to complete the
scoring were from 2002. That assessment showed that the system has a high susceptibility to
pressures and a high score for nutrient inputs, resulting in a score of High for influencing factors
(i.e., OHI index). Individual scores for the primary and secondary indicators varied, but resulted
in an overall score of High for the OEC (i.e., OEC index). The score of Improve Low for the
future outlook (i.e., DFO index) is based on the expectations that future nutrient pressures will
decrease and there will be significant population and development increases.
The ratings for the nutrient inputs and the OEC index were recreated and verified using
methods consistent with the 2007 NEEA Update (Table 3.1-1; Bricker et al., 2007a), which
included separate areal-weighted consideration of the tidal fresh, mixing, and saltwater zones
2nd Draft Risk and Exposure Assessment
Appendix 6-68
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
within the estuary (Bricker et al., 2007a). (Note that the Potomac Estuary is split approximately
between tidal fresh [14.5%] and mixing [85.5%] zones.) Data for dissolved oxygen and
chlorophyll a were downloaded from the CBP water quality database (CBP, 2008). Harmful
algal bloom (HAB) data were taken from data compilations available from VIMS. Conflicting
results were found for the SAV coverage where an unreasonably large increase was reported in
the original analysis (Bricker et al., 2006). The source of the data for the updated analysis was
the VIMS (VIMS, 2008), which was the same source cited for the original analysis. Possible
explanations for the discrepancy are different baseline periods for comparisons between the gains
and losses in SAV areas. The updated analysis compared only annual changes in areal growth of
SAV (in contrast to previous NEEA studies that used a baseline year of comparison [Bricker et
al., 1999; Bricker et al., 2007a]). Additionally, the areas of SAV measured for 2001 were listed
as partial measures, making the estimates in small gains and losses in the tidal fresh and mixing
areas, respectively, of the estuary uncertain. In the 2007 NEEA Update (Bricker et al., 2007a), a
value of 0.5 was used for any uncertain index score. A value of 0.5 was, therefore, given to the
uncertain loss estimated in the mixing zone of the Potomac Estuary for 2002.
Index scores for the updated analysis were compiled using the scoring methods and
matrices as shown in Figure 2.2-6 and Figure 2.2-7. Although there were uncertain/unknown
values for macroalgae in the primary symptoms and SAV in the secondary symptoms, the high
rankings of chlorophyll a within the primary symptoms and HAB in the secondary symptoms
overweighed the uncertainty of the other parameters in the final scores. Even if a value of 0.5
(denoting uncertainty) had been used for the macroalgae score, as was done with the mixing zone
index for SAV, the overall score for that indicator would have resulted in a primary score greater
than 0.6 and a High ranking. Combination of the primary and secondary scores (both High)
provided an overall OEC index score of High, which agreed with the original analysis.
The OHI index score (confirmed with the modeled nitrogen load from the 2002
SPARROW application) and the DFO index score remain the same as in the original analysis.
Therefore, the ASSETS El score for the 2002 current condition scenario is Bad.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-69
-------
Aquatic Nutrient Enrichment Case Study
Indkes Methods Parameters/ Values /EAR Index category ASSETS grade
Pressure
OHI index
State
OEC index
Response
DFO index
Susceptibility
Nutrient inputs
Primary
Symptom
Method
Secondary
Symptom
Method
Future nutrient
pressures
Dilution potential
Fl ushi ng potential
High
Low
High
Susceptibility
High
Chlorophyll a
iVacroalgae
Di ssQhffidoaygen
Submerged
aquatic/egetation
Nuisance and
Toxic Blooms
High
NoProb
Low
Large Increase
Problem (1)
High
High
Futurenutrientpres5uresdecrease,5ignifi cant population/
development increases - Improve Low
High
High
Improve Low
OHI =1
OEC = »
DFO = 4
Bad
Figure 3.1-4. The ASSETS El scores for the Potomac Estuary (Bricker et al., 2006).
2nd Draft Risk and Exposure Assessment
Appendix 6-70
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Table 3.1-1. Potomac Estuary Current Condition Overall Human Influence Index Score
Year
2002
Parameter
CHLA
CHLA
Macroalgae
DO
DO
SAV
SAV
HAB
Zone
MX
TF
ALL
MX
TF
MX
TF
ALL
Value
13.25
22.805
NA
3.6
5.8
NA
NA
NA
Concentration
MEDIUM
HIGH
UNKNOWN
BIO STRESS
NO
PROBLEM
LOSS
(Uncertain)
GAIN
(Uncertain)
NA
Spatial
Coverage
HIGH
LOW
UNKNOWN
HIGH
HIGH
NA
NA
NA
Frequency
PERSISTENT
PERIODIC
UNKNOWN
PERIODIC
PERSISTENT
NA
NA
NA
Expression
HIGH
MODERATE
UNKNOWN
MODERATE
NO PROBLEM
UNCERTAIN
(0.5)
UNCERTAIN
(0)
HIGH
Score
0.9275
NA
0.4275
0.43
1
Primary/
Secondary
Scores
0.9275
1
OEC
Score
HIGH (1)
CHLA: Chlorophyll a
DO: Dissolved Oxygen
SAV: Submerged Aquatic Vegetation
HAB: Harmful/Toxic Algal Blooms
MX: Mixing Zone
TF: Tidal Fresh Zone
ALL: All Estuary Zones
NA: Not Applicable
2nd Draft Risk and Exposure Assessment
Appendix 6-71
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
3.1.2 Summary of Results for the Neuse River/Neuse River Estuary Case Study
Area
The 2002 current condition analysis of the Neuse River and Neuse River Estuary used
recently released data from the USGS to calibrate a new SPARROW application for 2002 to the
Neuse, Tar-Pamlico, and Cape Fear river watersheds. Developing the ASSETS El score for the
Neuse River Estuary proved to be a greater challenge than for the Potomac Estuary because of
the availability of data sources that were less consolidated and more varied.
3.1.2.1 SPARROW Assessment
The release of digital data by the USGS for its Southeast Major River Basin SPARROW
Assessment (area includes all of the river basins draining to the south Atlantic and the eastern
Gulf of Mexico, as well as the Tennessee River basin [referred to collectively as the SAGT
area]) in the summer of 2008 provided the opportunity to calibrate a new SPARROW model for
the 2002 current condition analysis (Hoos et al., 2008). The SAGT data were compiled for 2002,
providing the necessary data inputs and calibration TN loads for model development. Because of
a limited number of calibration points within the Neuse River watershed itself, the SPARROW
model was expanded to include the Tar-Pamlico and Cape Fear river watersheds, providing a
total of 41 calibration points on which to base the SPARROW model. The river network within
these basins was again based on the RF1, with enhancements for calibration points. Source
variables investigated in the new model development included atmospheric deposition (modified
from the original SAGT dataset to use the CMAQ/NADP data developed for this study),
fertilizer application to farmland, manure from livestock production, point sources, and land
cover (urban, forest, and nonagriculture categories). Decay and loss terms considered in the
model included soil permeability, mean annual temperature, and slope.
Figure 3.1-5a through Figure 3.1-5c show the atmospheric deposition inputs used within
the modeling effort. The model was based on TN loads from deposition, but oxidized and total
Nr yields are also presented to highlight source information within the watershed. The Neuse
River watershed is the location of major agricultural operations focusing on swine facilities.
These operations are evident in the high levels of reduced nitrogen found within the south-central
catchments of the watershed (Figure 3.1-5b). For the current condition 2002 analysis of the
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-72
-------
Aquatic Nutrient Enrichment Case Study
Neuse River and Estuary, an estimated 18,340,000 kg of TN was deposited in the 1.3 million-ha
Neuse River watershed, for an TN average deposition of 14.0 kg N/ha/yr.
Development of the new SPARROW model used the USGS SAS SPARROW application
in predictive mode, with bootstrap analyses completed for additional analysis and exploratory
options (Schwarz et al., 2006). The model used two flow classes based on the work by McMahon
et al. (2003) and a reservoir decay factor. In model set up, the decay factors were constrained to
be nonnegative. Tables 3.1-2 and 3.1-3 present the calibrated model parameters and model
evaluation statistics, respectively. The model used point sources, atmospheric deposition,
fertilizer application, manure production, and urban land area. There was some lack of
significance in the parameter estimations for manure production and urban land area, but these
sources were deemed likely sources based on watershed knowledge and were left in the model.
The land-to-water delivery factor used in the model was the soil permeability factor. As
expected, the parameter estimate for this factor was less than one. The three decay terms (i.e.,
two flow classes and reservoir decay) all lacked significance in their parameter estimates but are
required for the model as it was configured. Future analyses should consider alternate flow
classes to investigate ways to improve statistical significance of these estimates. Overall, the
model evaluation criteria reveal a strong significance in the estimated model, with high
prediction values (i.e., R-squared values close to 1), little multicollinearity (i.e., Eigen value
spread less than 100), and normally distributed weighted model residuals (i.e., probability plot
correlation coefficient close to 1).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-73
-------
Aquatic Nutrient Enrichment Case Study
Neuse River Watershed: Atmospheric Deposition - Oxidized Nitrogen
Vii'jiiii.i
Legend
Atmospheric Deposition'
By Watershed Unit
(All units are in kg'ha'yr)
I 14-6
I |6-8
j^H B- 10
** Oxidized nitrogen species and
sources: wet- nitrate (NADP); dry-
particulate nitrate, nitric acid,
nitrogen pentoxide, nitrous acid,
nitric oxide, nitrogen dioxide,
peroxycyl nitrate, and organic
nitrate (CMAQ)
0 5 10 20 30 40 50
Watershed boundary layers
and mapping data were provided
by USGS. The site URL is:
http://md.water.usgs.gov/gis/chesbay/
spa rrow3/do c/retv3 .htm#sectio n1
Figure 3.1-5a. Atmospheric deposition yields of oxidized nitrogen over the Neuse River
and Neuse River Estuary watershed.
2nd Draft Risk and Exposure Assessment
Appendix 6-74
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Neuse River Watershed: Atmospheric Deposition - Reduced Nitrogen
** Reduced nitrogen species and
sources: wet- ammonium (NADP);
dry - ammonia and ammonium
(CMAQ).
16 24 32 40
Watershed boundary layers
and mapping data were provided
by USGS. The site URL is:
http://rnd.water.usgs.gov/gis/chesbay/
spa trow3/doc/retv3.htm#section1
Figure 3.1-5b. Atmospheric deposition yields of reduced nitrogen over the Neuse River
and Neuse River Estuary watershed.
2nd Draft Risk and Exposure Assessment
Appendix 6-75
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Neuse River Watershed: Atmospheric Deposition - Total Nitrogen
Atmospheric Deposition
Bj Watershed Unit
1AII units aie in ky hayr)
**Total nitrogen is the sum of
oxidized and reduced nitrogen
species. Oxidized nitrogen
ecies and sources: wet-
nitrate (NADP); dry - particulate
nitrate, nitric acid, nitrogen
pentoxide, nitrous acid, nitric
oxide, nitrogen dioxide,
peroxycyl nitrate, and organic
nitrate (CMAQ). Reduced
nitrogen species and sources:
wet- ammonium (NADP); dry-
ammonia and ammonium (CMAQ).
16 24 32 40
Miles
V^fertershed boundary layers end mapping
data vere provided by USGS. ThesteURLis
rttp: //md .vvater.usgs.gov/gis/diesbay/
•sparrow.;:•.-. ,-•,.-' hj .-• te-gcti onl
Figure 3.1-5c. Atmospheric deposition yields of total nitrogen over the Neuse River and
Neuse River Estuary watershed.
Table 3.1-2. Model Parameters for 2002 Current Condition SPARROW Application for the
Neuse River Watershed
Source Parameters
Point Sources
Atmospheric Deposition
Fertilizer
Manure
Urban Area
Parameter
Estimate
0.84
0.082
0.13
0.017
1.6
Standard
Error
0.121
0.045
0.038
0.019
1.7
t-statistic
6.95
1.80
3.38
0.91
0.96
p-value
<0.001
0.081
0.002
0.368
0.345
VIF
1.4
13.3
7.1
4.6
2.5
Decay and Loss Parameters
Soil Permeability
Reach Decay Group 1
Reach Decay Group 2
Reservoir Decay
-0.52
0.17
0.029
3.4
0.217
0.266
0.058
9.6
-2.41
0.64
0.49
0.35
0.022
0.528
0.627
0.728
4.8
2.3
2.3
1.3
Note: VIF = Variance Inflation Factor.
2nd Draft Risk and Exposure Assessment
Appendix 6-76
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Table 3.1-3. Model Evaluation Statistics for 2002 Current Condition SPARROW Application
for the Neuse River Watershed
Evaluation Criteria
Number of Observations
Degrees of Freedom — model error
Degrees of Freedom — coefficients
Sum of the Squared Errors
Mean Square Error
Root Mean Square Error
R Squared
Adjusted R Squared
Eigen Value Spread
Probability Plot Correlation Coefficient
Value
40
31
9
1.79
0.058
0.24
0.99
0.98
84.2
0.984
As with the Potomac River watershed results, the Neuse SPARROW application modeled
watershed loads to the upper edges of the estuary. Both the incremental and delivered yields are
presented in Figure 3.1-6. The TN load estimated to enter the estuary from the Neuse River is
4,3 80,000 kg N/yr, equating to a TNS of 1.11
mg/L. The load from atmospheric deposition
was estimated to be 1,150,000 kg N/yr, or 26%
SPARROW modeling for 2002 predicts that
atmospheric deposition was 26% of the total
nitrogen loading to the Neuse River's estuary,
producing a TNS of 1.1 mg/L
of the total load (Figure 3.1-7). These estimates fall in line with instream monitoring data and
previous loadings from the Neuse River, estimated at 9.61 million pounds or 4,359,000 kg N/yr
(Spruill et al., 2004).
2nd Draft Risk and Exposure Assessment
Appendix 6-77
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
2002 Base Case Results for Neuse River Watershed
Map#1: Incremental Nitrogen Yield
Map #2: Delivered Nitrogen Yield
Legend
Nitrogen Yield
by Watershed Unit
(all units are in kg/hr/yr)
Map 81
Incremental Yield
| | 4-6
\ 6-8
Eg| 8-10
Map #2
Delivered Yield
| |2-4
| |4-B
| |6-B
0 10 20
40
60
Watershed boundary layers
and mapping data were
provided by USGS. The
site URL is:
http://md .water.usgs.gov/
gis/chesbay/sparrow3/
d o c/retv3 .htm#se cti o n 1
Figure 3.1-6. Total nitrogen yields from all sources in the Neuse River watershed as
predicted by a SPARROW modeling application for the Neuse, Tar-Pamlico, and Cape
Fear rivers' watersheds with 2002 data inputs.
2nd Draft Risk and Exposure Assessment
Appendix 6-78
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Relative Contributions of Nitrogen Sources to Neuse Estuary
7%
D Point Sources
• Atmospheric Deposition
D Fertilizer
D Manure
• Urban Areas
48%
26%
Total load to estuary:
4,382,000 kg N/yr
Figure 3.1-7. Source contributions to Neuse River Estuary total nitrogen load.
3.1.2.2 ASSETS El Assessment
Previous work was completed using the ASSETS El assessment on the Neuse River
Estuary as part of the NEEA Update (Bricker et al., 2007a). The exact source of this load
estimate and the exact timeframe of the data used to calculate the ASSETS El score are still
unknown at this time, although the data should fall within the period of 2000 to 2002 (S. Bricker,
personal communication, 2008). That analysis revealed a Highly/Moderately Influenced or High
score for influencing factors (i.e., OHI index) where the nitrogen load was ranked as Moderate to
High and the ASSETS El score for the estuary was a Bad overall.
To develop an updated ASSETS El score specific to the 2002 current conditions, raw
data were compiled from several sources, including EPA's Storage and Retrieval System
(STORET), the Neuse River Estuary Modeling and Monitoring Project (MODMON), North
Carolina Department of Water Quality (NC DWQ), and journal articles. While there were a
variety of sources of data, information on macroalgae and S AV for the period of interest could
not be obtained. Monitoring data for chlorophyll a, dissolved oxygen, and nitrogen were
obtained from MODMON records. NC DWQ has only recently begun to measure SAV within
defined areas and on defined intervals, so no data were available for the 2002 time period.
Monitoring experts within the Neuse River/Neuse River Estuary Case Study Area could not
identify any sources of macroalgae data (B. Peierls, personal communication, 2008). HAB data
were gleaned from notations in journal papers (Burkholder et al., 2006) and in the reports
2nd Draft Risk and Exposure Assessment
Appendix 6-79
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
beginning in 1997 tracked by NC DWQ (NC DWQ, 2008). The available data were combined to
form a 2002 OEC index score (Table 3.1-4). Because both the chlorophyll a and HAB data were
available and overwhelmingly pointed to a system with both High primary and secondary scores,
a rating of High is given to the OEC index with confidence for 2002.
The High susceptibility ranking combined with the TN loads estimated by the
SPARROW assessment rank the OHI index as High as well. The DFO index score set during the
2007 NEEA Update remains unchanged, with a ranking of Worsen High due to nutrient
reductions from improved management practices in recent years being offset by increases in
human populations and factors related to swine production (Burkholder et al., 2006). Combining
the three index scores together results in an overall ASSETS El score of Bad for the Neuse
River/Neuse River Estuary Case Study Area for 2002.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-80
-------
Aquatic Nutrient Enrichment Case Study
Table 3.1-4. Current Condition Overall Eutrophic Condition Index Score for the Neuse River/Neuse River Estuary Case Study Area
Year
2002
2002
2002
2002
2002
2002
2002
Parameter
CHLA
CHLA
Macroalgae
DO
DO
SAV
HAB
Zone
MX
TF
ALL
MX
TF
ALL
ALL
Value
35
9.0
NA
1.6
2.7
NA
NA
Concentration
HIGH
MEDIUM
UNKNOWN
HYPOXIA
BIO STRESS
NA
NA
Spatial
Coverage
HIGH
MODERATE
UNKNOWN
MODERATE
HIGH
NA
NA
Frequency
PERSISTENT
PERIODIC
UNKNOWN
PERIODIC
PERIODIC
NA
NA
Expression
HIGH
MODERATE
UNKNOWN
MODERATE
MODERATE
UNKNOWN
HIGH
Score
0.9945
NA
0.5
NA
1
Primary/
Secondary
Scores
0.9945
1
OEC
Score
HIGH (1)
CHLA: Chlorophyll a
DO: Dissolved Oxygen
SAV: Submerged Aquatic Vegetation
HAB: Harmful/Toxic Algal Blooms
MX: Mixing Zone
TF: Tidal Fresh Zone
ALL: All Estuary Zones
NA: Not Applicable
2nd Draft Risk and Exposure Assessment
Appendix 6-81
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
3.2 ALTERNATIVE EFFECTS LEVELS
Alternative effects levels were assessed for both the Potomac River/Potomac Estuary and
the Neuse River/Neuse River Estuary case study areas (separately) by applying percentage
reductions to the oxidized nitrogen loads in the estimated atmospheric deposition. Model
estimates then relied on the SPARROW models used (for the Potomac River/Potomac Estuary
Case Study Area) or developed (for the Neuse River/Neuse River Estuary Case Study Area) for
the 2002 current condition analysis to determine how the changing atmospheric inputs (i.e., TN
load evaluated with changes in oxidized nitrogen deposition, TNatm) affect the overall TN load to
the estuary of interest. These results were used to create the response curve relating TNS to
TNatm, as first described in Section 2.2.3. The second response curve described in Section 2.2.3
was defined for the alternative effects level analysis using historical data compilations of OEC
index scores and TNS, while holding the susceptibility portion of the OHI index (at its 2002
current condition level—in both cases a ranking of High) and the DFO index constant (at a
ranking of No Change [3]).
Upon creation of the two response curves, the back-calculation-coded program described
in Section 2.2.3 (referred to as BackCalculation through the remainder of this document) was
applied to the curves with the intent of defining the atmospheric loads that are needed to improve
the ASSETS El from a score of Bad (I) to Poor (2), Moderate (3), Good (4), or High (5). These
improvements represent improvements by 1, 2, 3, and 4 categories. The BackCalculation
program was run under Uncertainty Scenario B (Figure 2.2-12b) for both case studies.
The following sections describe the data used to create the two response curves and the
application of the BackCalculation program for each estuary.
3.2.1 Potomac River Watershed
Beginning with the data and model used for the current condition analysis, the
atmospheric deposition inputs derived from national coverage of CMAQ and NADP data were
altered to create various alternative effects levels by reducing the oxidized nitrogen loads by
rates of 5%, 10%, 20%, 30%, and 40% from their original 2002 levels. A zero percent reduction
corresponds to the 2002 current condition analysis (Table 3.2-1). The remaining inputs to the
SPARROW model remained the same, and the model was rerun for each of these alternative
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-82
-------
Aquatic Nutrient Enrichment Case Study
effects level scenarios. The TN load to the estuary calculated from the model was then converted
to TNS using the annual average flow of the Potomac River. Plotting these concentrations against
the new TNatm incorporating the oxidized nitrogen reductions leads to the development of the
desired response curve and relationship (Figure 3.2-1).
Table 3.2-1. Potomac River Watershed Alternative Effects Levels
Percent
Reduction
in Oxidized
Nitrogen
0
5
10
20
30
40
Total Nitrogen
Atmospheric
Deposition
Load, TNatm
(kg/yr)
40,770,000
39,450,000
38,130,000
35,480,000
32,840,000
30,200,000
Reduction in Oxidized
Nitrogen (NOX)
Atmospheric Deposition
Load (kg/yr)
0
1,320,000
2,640,000
5,290,000
7,930,000
10,580,000
Instream
Total
Nitrogen
Load
(kg/yr)
36,660,000
36,420,000
36,180,000
35,700,000
35,220,000
34,740,000
Instream Total
Nitrogen
Concentration,
TNs(mg/L)
3.41
3.39
3.37
3.32
3.28
3.23
Instream Total Nitrogen Concentration
(mg/L)
o en
0 A C
•3 Af)
•3 -3C
o on
0 OC
•3 on
0 -I C
o -| n
•3 nc
3nn
y = 1.69E-08x
R2 =
= 1
+ 2.72
Jt
*r
X
X
9T
.UU ill
o o o o o
o o o o
0000
o" o" o" o"
o o o o
0000
in" o" in" o"
T- T- CM
Total Nitrogen Atmospheric
o o o o o
o o o o o
00000
o" o" o" o" o"
o o o o o
0 0 0 0 0
lO O IO O IO
CM CO CO -
-------
Aquatic Nutrient Enrichment Case Study
Table 3.2-2 details the historical modeling data used to determine TN loads to the
Potomac Estuary, which are then combined with annual average flow values to calculate a final
TNS. These instream concentrations were then combined with the OEC index scores, which were
also determined from historical data (Table 3.2-3), to create the data points needed to create the
logistic response curve in the BackCalculation program. The years chosen for this analysis relate
to those years in which a modeled TN load to the estuary could be estimated. OEC data were
then gathered for those years to find the corresponding effects.
Table 3.2-2. Historical Potomac River Total Nitrogen Loads and Concentrations
Year
1985
1992
1997
2002
2005
Nitrogen Load to
Estuary (kg/yr)
32,110,000
49,750,000
39,380,000
31,160,000
23,790,000
Source
CBM Phase
4.3
CB V2
SPARROW
CB V3
SPARROW
Model Run
CBM Phase
4.3
Flow (m3/s)
332
340
340
196
307
Source
USGS gage
records
CB V2
SPARROW
Input Data
CB V3
SPARROW
Input Data
USGS gage
records
USGS gage
records
Concentration
(mg/L)
3.07
4.63
3.67
5.04
2.46
CBM Phase 4.3: Chesapeake Bay Model Phase 4.3 reported results
CB V2 SPARROW: Chesapeake Bay SPARROW Version 2 model results (Brakebill et al., 2001)
CB V3 SPARROW: Chesapeake Bay SPARROW Version 3 model results (Brakebill and Preston, 2004)
Model run: Current condition analysis results as determined using modeling efforts based on CB V3
SPARROW
2nd Draft Risk and Exposure Assessment
Appendix 6-84
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Table 3.2-3. Additional Potomac Estuary Overall Eutrophic Condition Index Scores for Alternative Effects Levels
Year
1985
1987
1992
Parameter
CHLA
CHLA
Macroalgae
DO
DO
SAV
SAV
HAB
CHLA
CHLA
Macroalgae
DO
DO
SAV
SAV
HAB
CHLA
CHLA
Macroalgae
DO
DO
SAV
SAV
Zone
MX
TF
ALL
MX
TF
MX
TF
ALL
MX
TF
ALL
MX
TF
MX
TF
ALL
MX
TF
ALL
MX
TF
MX
TF
Value
26
32
NA
1.6
5.6
NA
NA
NA
26
20
NA
2.2
6.3
NA
NA
NA
12
14
NA
2.6
6.3
NA
NA
Concentration
HIGH
HIGH
UNKNOWN
HYPOXIA
NO PROBLEM
GAIN
GAIN
NA
HIGH
HIGH
UNKNOWN
BIO STRESS
NO PROBLEM
GAIN
LOSS
NA
MEDIUM
MEDIUM
UNKNOWN
BIO STRESS
NO PROBLEM
GAIN
LOSS
Spatial Coverage
MODERATE
HIGH
UNKNOWN
MODERATE
HIGH
NA
NA
NA
MODERATE/
HIGH
MODERATE
UNKNOWN
HIGH
HIGH
NA
NA
NA
HIGH
MODERATE
UNKNOWN
HIGH
HIGH
NA
NA
Frequency
PERIODIC
PERIODIC
UNKNOWN
PERIODIC
PERSISTENT
NA
NA
NA
PERIODIC
PERIODIC
UNKNOWN
PERIODIC
PERSISTENT
NA
NA
NA
PERSISTENT
PERIODIC/
PERSISTENT
UNKNOWN
PERIODIC
PERSISTENT
NA
NA
Expression
HIGH
HIGH
UNKNOWN
MODERATE
NO PROBLEM
NO PROBLEM
NO PROBLEM
LOW/
UNKNOWN
HIGH
HIGH
UNKNOWN
MODERATE
NO PROBLEM
NO PROBLEM
VERY LOW
LOW/
UNKNOWN
HIGH
MODERATE
UNKNOWN
MODERATE
NO PROBLEM
NO PROBLEM
MODERATE
Score
1
NA
0.86
0
0.25
1
NA
0.86
0.04
0.25
0.93
NA
0.43
0.07
Primary/
Secondary
Scores
1
0.86
1
0.86
0.93
0.43
OEC Score
HIGH (1)
HIGH (1)
MODERATE
HIGH (2)
2nd Draft Risk and Exposure Assessment
Appendix 6-85
June 5, 2009
-------
Aquatic Nutrient Enrichment Case Study
Year
1997
2005
Parameter
HAB
CHLA
CHLA
Macroalgae
DO
DO
SAV
SAV
HAB
CHLA
CHLA
Macroalgae
DO
DO
SAV
SAV
HAB
Zone
ALL
MX
TF
ALL
MX
TF
MX
TF
ALL
MX
TF
ALL
MX
TF
MX
TF
ALL
Value
NA
28
34
NA
3.5
6.6
NA
NA
NA
21
15
NA
2.2
5.7
NA
NA
NA
Concentration
NA
HIGH
HIGH
UNKNOWN
BIO STRESS
NO PROBLEM
GAIN
LOSS
NA
HIGH
MEDIUM
UNKNOWN
BIO STRESS
NO PROBLEM
GAIN
GAIN
NA
Spatial Coverage
NA
HIGH
HIGH
UNKNOWN
HIGH
HIGH
NA
NA
NA
MODERATE
HIGH
UNKNOWN
HIGH
HIGH
NA
NA
NA
Frequency
NA
PERIODIC/
PERSISTENT
PERIODIC/
PERSISTENT
UNKNOWN
PERIODIC
PERSISTENT
NA
NA
NA
PERIODIC
PERSISTENT
UNKNOWN
PERIODIC
PERSISTENT
NA
NA
NA
Expression
LOW/
UNKNOWN
HIGH
HIGH
UNKNOWN
MODERATE
NO PROBLEM
NO PROBLEM
LOW
MODERATE/
UNKNOWN
HIGH
HIGH
UNKNOWN
MODERATE
NO PROBLEM
NO PROBLEM
NO PROBLEM
HIGH
Score
0.25
1
NA
0.43
0.04
0.5
1
NA
0.43
0
1
Primary/
Secondary
Scores
1
0.5
1
1
OEC Score
MODERATE
HIGH (2)
HIGH (1)
CHLA:
DO:
SAV:
HAB:
MX:
TF:
ALL:
NA:
Chlorophyll a
Dissolved Oxygen
Submerged Aquatic Vegetation
Harmful/Toxic Algal Blooms
Mixing Zone
Tidal Fresh Zone
All Estuary Zones
Not Applicable
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 6-86
-------
Aquatic Nutrient Enrichment Case Study
The 2002 Potomac Estuary TNatm (evaluated in terms of decrease in oxidized nitrogen)
loading is estimated to be 4.08 x 107 kg/yr (Table 3.2-1). The response curve relationship
between atmospheric deposition and TNS (TNS [mg/L] = 2.72 + 1.69 x 10"8 xTNatm [kg/yr]) can
be found in Figure 3.2-1. Outside data specified for the model include the following:
• Mean salinity in estuary = 11 (relative units)
• Mean salinity offshore = 33 (relative units)
• Mean offshore TN concentration = 0.028 mg/L.
There were five sets of observed OEC and TNS data points used to fit the OEC(TNS)
logistic model (not including ecological endpoints) from the data presented in Table 3.2-2 and
Table 3.2-3
For the purpose of estimating the OEC(TNS) function at each iteration, the range on the
OEC minimum value was specified as 0 to 0 (i.e., fixed at 0). In addition, the OEC maximum
value was fixed at 5. Notwithstanding the capability to vary OEC minimum and OEC maximum
from iteration to iteration, the decision was made to not do so. Varying OEC minimum and OEC
maximum would ideally be performed to reflect natural limitations on an estuary. For instance,
in its most pristine state, it may not fully attain OEC = 5 or, in its most degraded state, it may not
fully attain OEC = 1. However, because of the difficulty in knowing these natural limitations, the
option to vary these levels was not implemented, but rather left OEC minimum = 0 and OEC
maximum = 5. The range on the TNS minimum value was specified from 0.028 mg/L (i.e., the
offshore value) to 1.0 mg/L (i.e., a value determined by best professional judgment at this time
due to lack of supporting data). The TNS maximum value range was specified from 4.63 mg/L
(maximum observed above) to 1.5x4.63 = 6.95 mg/L.
For the purpose of illustrating the overall back calculation uncertainty analysis
methodology, each of the four ASSETS El scores constituting state improvements (i.e., Poor-2,
Moderate-It, Good-4, High-5) was treated as a "target" ASSETS El score, and 5002 iterations
were run under each target El scenario. As previously discussed, at each iteration, the
BackCalculation program first fits the four-parameter logistic function to the observed data,
including the randomly sampled ecological endpoints, using a weighted least squares criterion
2 A convergence test was performed to see how stable the various quantiles of the resulting NOx*i distribution were
to 500 iterations. There was still some variability, suggesting that future analyses should use more than 500
iterations (particularly if SPARROW model uncertainty is included).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-87
-------
Aquatic Nutrient Enrichment Case Study
(Figure 3.2-2). (In this application, only the TN ecological endpoints were varied.) After the
nonlinear, least squares optimization is performed (see Figure 2.2-12b for an example), the
program then iteratively finds that TNS concentration, TNS*;, that satisfies the target ASSETS El
score. Once TNS ; is found, the SPARROW response model is then evaluated to find the TN
atmospheric deposition load, TNatm i, which results in TNS ;. That TNatm i is saved, and the
process is repeated 500 times. The percentage reduction in NOX deposition load to reach TNatm i
is calculated as 100 x (TNatm current load - TNatm ;)/ TNatm current load, and it is also saved. The
result is a distribution of 500 TNatm*i and percentage reduction values, each of which is equally
likely to result in the target El (because the OEC(TNS) function is assumed to be uniformly
distributed within its uncertain envelope). These 500 values for each result were then rank-
ordered (i.e., ascending order) to find statistics of interest (e.g., the mean, median, 5th percentile,
and 95th percentile).
It should be carefully noted that the TNatm i value is allowed to be determined at each
iteration without regard to what the current TNatm is. Thus, if a TNatm i value is greater than the
current TNatm (4.08 x 108 kg/yr), then the implication is that more TNatm would be required (i.e.,
added to the system) to attain the target ASSETS El score. Obviously, in this situation, the
estuary is currently at a better/higher ASSETS El score than the target ASSETS El score, and the
target score represents a more polluted scenario. The TNatm*i value is not prevented from
becoming negative. As can be seen from the general form of the linear, SPARROW response
model, TNS = a + b x TNatm, a TNS*; value less than parameter "a" would require more than a
reduction in TNatm to zero. It would actually require a "negative" TNatm load. Clearly, the lower
bound on TNatm load is zero, so the negative TNatm i value would represent additional nitrogen
(beyond decreasing the TNatm [i.e., total atmospheric nitrogen] deposition load, including NOX to
zero) in the system that must be removed to achieve the target ASSETS El score. (The actual
value of the negative load would be valid only if the additional nitrogen removed had the same
characteristics [e.g., spatial distribution, speciation, sources/sinks] as the TNatm. This is unlikely;
nonetheless, it is of interest to report the values with this caveat.)
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-88
-------
Aquatic Nutrient Enrichment Case Study
• Calibration Points
Fit Logistic Curve
0.0 1.0 2.0 3.0
TN (mg/L)
4.0
5.0
6.0
Figure 3.2-2. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2,
median TNatm*i (i = run 280)
The summary statistics of the 500 iterations for each target ASSETS El scenario for the
Potomac Estuary are presented in Table 3.2-4.
Table 3.2-4. Summary Statistics for Target ASSETS El Scenarios for the Potomac Estuary
Statistic
TNatm*,(kgN/yr)
%TN
atm i Reduction
ASSETS El = 2 (Poor)
Mean
Median
5th Percentile
95th Percentile
-1.78 x 106
-1.46 x 106
-3.67 x 106
9.02 x 106
104
104
109
78
ASSETS El = 3 (Moderate)
No feasible solutions found
ASSETS EI=4 (Good) and ASSETS El =
All TN^ = -1.61 x 108, i.e., TNSV
= 5 (High)
Omg/L
Target ASSETS El = 2 is the most interesting scenario and illustrates the power of the
uncertainty analysis. The mean and median TNatm ; values are negative, meaning again that not
2nd Draft Risk and Exposure Assessment
Appendix 6-89
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
only must all TNatm (including all NOX) be removed, but additional nitrogen as well. However,
there is a slim chance that ASSETS El = 2 can be attained only from TNatm reduction, as
indicated by the positive 95th percentile TNatm*i value of 9.02 x 106 kg N/yr (representing a 78%
reduction).
Target ASSETS El = 3 is a unique case because all solutions were infeasible. With a
TNS ; value of 0 mg/L, the other (i.e., fixed) components of the ASSETS El scoring methodology
(i.e., DFO index and Susceptibility Score) preclude satisfying any of the 95 combinations of
DFO, OEC, and OKI indices that comprise the ASSETS EI=3 combinations in the ASSETS El
lookup table. (At TNS*; = 0, an ASSETS El = 4 can be achieved, but not ASSETS El = 3,
according to the 95 score combinations defined by Bricker et al., (2003) for ASSETS El scores.)
Bricker et al. (2003) acknowledge that "not all combinations" were included in the lookup table
within their paper, so this scenario falls into that gap. Each of the intermediate scores (i.e., OEC,
OHI, and DFO index scores) can take on integer values of 1 to 5. Thus, there are 5x5x5=125
different possible combinations, yet the lookup tables presented by Bricker et al. (2003) include
only 95 combinations, so there are 30 "missing" combinations. (These are evenly distributed
among the 5 possible DFO index scores. Each DFO index score has associated with it 19
combinations of OEC and OHI index scores.) It is possible that these combinations were not seen
as likely combinations in nature by the experts that defined the scoring matrix (e.g., a TNs*i = 0
means there is no nitrogen coming in through the surface water and, hence, it is not feasible that
a system would score below Good in the ASSETS El score). Future assessments will explore the
"missing" combinations further with the ASSETS El experts.
Target ASSETS El = 4 and 5 had identical results. All 500 iterations returned a TNS*;= 0,
and a corresponding TNatm*i negative load equal to TNatm*i= (0 - 2.72)71.69 x 10"8= -1.61 x ICT8
kg/yr. Clearly, target ASSETS Els equaling 4 and 5 are very much unattainable when reducing
the TNatm is the only policy option. To reach the target ASSETS El, all atmospheric nitrogen
(i.e., TNatm) must be removed plus an additional amount (represented by the negative resultant
load corresponding to TNS*;= 0) that is approximately equal to one order of magnitude greater
than the original atmospheric deposition load. These amounts could be compared to the other
nitrogen sources in the watershed (e.g., fertilizer and manure application or point sources) that
were used as inputs to the SPARROW model to determine the relative nature of the required
removal with other sources in the watershed. However, consideration must be given that this load
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-90
-------
Aquatic Nutrient Enrichment Case Study
is a reflection of the characteristics of the source in the SPARROW model (e.g., spatial
distribution, magnitude of loads, sources/sinks), and a reduction required in atmospheric load is
not equal to a reduction in another source. Relative proportions can be examined by comparing
the source characteristics and model parameters.
The SPARROW response curve can also be used to examine the role of atmospheric
nitrogen deposition in achieving specified reductions in TN estuarine load. For example, the
SPARROW modeling results predict that the 41 x 106 kg N/yr deposited (i.e., atmospheric
deposition input) over the Potomac River watershed in 2002 results in a loading of 7,380,000 kg
N/yr, or 20% of the annual TN load, to the Potomac Estuary. If a 30% reduction in annual TN
load to the estuary (i.e., a reduction of 11 x 106 kg N/yr) were desired, a reduction of 61 x io6 kg
N/yr in nitrogen inputs to the watershed would be required according to the SPARROW response
curve based on atmospheric deposition. This represents a 100% reduction in the atmospheric
deposition inputs (i.e., 41 x io6 kg N/yr) plus an additional 20 x IO6 kg N/yr removal of nitrogen
from other sources in the Potomac River watershed (i.e., point and nonpoint sources). Note that
this value of 20 x IO6 kg N/yr is an approximate value when applied to the other sources because
they differ in characteristics (e.g. spatial distribution and magnitude) from atmospheric
deposition which was used to estimate the loading.
3.2.2 Neuse River Watershed
The same methods for creating alternative effects levels were applied to the data from the
Neuse River/Neuse River Estuary Case Study Area as to data from the Potomac River/Potomac
Estuary Case Study Area. The oxidized nitrogen atmospheric deposition loads were reduced by
rates of 5%, 10%, 20%, 30%, and 40% from their original 2002 levels. A zero percent reduction
corresponds to the 2002 current condition analysis (Table 3.2-5). With the remaining inputs to
the SPARROW model kept the same, the SAS-developed model was rerun for each of these
alternative effects level scenarios. The TN load to the estuary calculated from the model was
then converted to a TNS using the annual average flow of the Neuse River. Plotting these
concentrations against the new TNatm and incorporating the oxidized nitrogen reductions leads to
the development of the desired response curve and relationship (Figure 3.2-3).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-91
-------
Aquatic Nutrient Enrichment Case Study
Table 3.2-5. Neuse River/Neuse River Estuary Case Study Area Alternative Effects Levels
Percent
Reduction
in
Oxidized
Nitrogen
0
5
10
20
30
40
Total Nitrogen
Atmospheric
Deposition
Load, TNatm
(kg/yr)
18,340,000
17,920,000
17,510,000
16,680,000
15,850,000
15,020,000
Reduction in
Oxidized Nitrogen
Atmospheric
Deposition Load
(kg/yr)
0
410,000
830,000
1,660,000
2,490,000
3,320,000
Instream Total
Nitrogen Load
(kg/yr)
4,382,000
4,378,000
4,374,000
4,366,000
4,358,000
4,351,000
Instream Total
Nitrogen
Concentration,
TNS (mg/L)
1.112
1.111
1.110
1.108
1.106
1.104
c
O -1 -| -| c
•f 1 . II 0
re
il
C"
Q)
O
C
o 1.110
O
c
Q) "7
O) ==
| ~ 1.105
5
,2
I 1.100
y = 2.0E-09X
R2 = 1
+ 1.07
/
Ji
*r
/
/
t ooo
in OOO
C '000
~ o" o" o"
OOO
OOO
CM" •*" CD"
0
0
o
o"
o
o
oo"
o
o
o
o"
0
o
o"
Total Nitrogen Atmospheric
o o
o o
o o
o" o"
0 0
o o
CN" •*"
Deposition
o
o
o
o"
0
o
CD"
Load
o o
o o
o o
o" o"
0 0
o o
oo o"
t- CN
(kg/yr)
Figure 3.2-3. Response curve relating instream total nitrogen concentration to total
nitrogen atmospheric deposition load for the Neuse River/Neuse River Estuary Case
Study Area.
Table 3.2-6 details the historical monitoring data used to determine TNS at the
downstream end of the Neuse River where the SPARROW model was used to determine current
condition and alternative effects levels nitrogen loads. The monitoring data were derived from
data downloaded from EPA's STORE! Web site for monitoring location J8290000 from NC
DWQ. These instream concentrations were then combined with the OEC index scores, which
2nd Draft Risk and Exposure Assessment
Appendix 6-92
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
were also determined from historical data (Table 3.2-7), to create the data points needed to
create the logistic response curve in the BackCalculation program. Data from as many years as
possible were gathered for both the TNS and OEC index scores. However, because of the limited
amount of complete data from the various sources identified under the current condition analysis,
only three corresponding years of data were found. These years are highlighted in Table 3.2-6.
Table 3.2-6. Annual Average Instream Total Nitrogen Concentrations in the Neuse River
Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Annual Average TNS (mg/L)
1.08
0.93
0.99
1.10
0.96
0.91
1.02
1.09
1.12
1.07
0.97
NC DWQ Station J8290000; Results from EPA's
STORET Summation of Total Kjeldahl Nitrogen and
Nitrate/Nitrite
2nd Draft Risk and Exposure Assessment
Appendix 6-93
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Table 3.2-7. Additional Neuse River Estuary Overall Eutrophic Condition Index Scores for Alternative Effects Levels
Year
1992
1993
2003
2007
Parameter
CHLA
Macroalgae
DO
SAV
HAB
CHLA
Macroalgae
DO
SAV
HAB
CHLA
CHLA
Macroalgae
DO
DO
SAV
HAB
CHLA
CHLA
Macroalgae
DO
DO
SAV
Zone
MX
ALL
MX
ALL
ALL
MX
ALL
MX
ALL
ALL
MX
TF
ALL
MX
TF
ALL
ALL
MX
TF
ALL
MX
TF
ALL
Value
28
NA
6.1
NA
NA
30
NA
3.4
NA
NA
46
7.2
NA
2.0
4.5
NA
NA
52
27
NA
2.6
3.1
NA
Concentration
HIGH
UNKNOWN
NO PROBLEM
NA
NA
HIGH
UNKNOWN
BIO STRESS
NA
NA
HIGH
MEDIUM
UNKNOWN
HYPOXIA
BIO STRESS
NA
NA
HIGH
HIGH
UNKNOWN
BIO STRESS
BIO STRESS
NA
Spatial
Coverage
VERY LOW
UNKNOWN
HIGH
NA
NA
HIGH
UNKNOWN
HIGH
NA
NA
HIGH
HIGH
UNKNOWN
HIGH
HIGH
NA
NA
HIGH
MODERATE
UNKNOWN
HIGH
HIGH
NA
Frequency
PERIODIC
UNKNOWN
PERSISTENT
NA
NA
PERIODIC
UNKNOWN
PERIODIC
NA
NA
PERSISTENT
PERIODIC
UNKNOWN
PERIODIC
PERIODIC
NA
NA
PERSISTENT
PERIODIC
UNKNOWN
PERIODIC
PERIODIC
NA
Expression
MODERATE
UNKNOWN
NO PROBLEM
UNKNOWN
HIGH
HIGH
UNKNOWN
MODERATE
UNKNOWN
HIGH
HIGH
HIGH
UNKNOWN
HIGH
MODERATE
UNKNOWN
MODERATE
HIGH
HIGH
UNKNOWN
MODERATE
MODERATE
UNKNOWN
Score
0.5
NA
0.25
NA
1
0.99
0.5
NA
1
1
NA
0.99
NA
0.5
1
NA
0.5
NA
Primary/
Secondary
Scores
0.5
1
0.99
1
1
0.99
1
0.5
OEC Score
HIGH(l)
HIGH (1)
HIGH (1)
MODERATE
HIGH (2)
2nd Draft Risk and Exposure Assessment
Appendix 6-94
June 5, 2009
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Aquatic Nutrient Enrichment Case Study
Year
Parameter
HAB
Zone
ALL
Value
NA
Concentration
NA
Spatial
Coverage
NA
Frequency
NA
Expression
LOW/
MODERATE
Score
0.5
Primary/
Secondary
Scores
OEC Score
CHLA: Chlorophyll a
DO: Dissolved Oxygen
SAV: Submerged Aquatic Vegetation
HAB: Harmful/Toxic Algal Blooms
MX: Mixing Zone
TF: Tidal Fresh Zone
ALL: All Estuary Zones
NA: Not Applicable
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 6-95
-------
Aquatic Nutrient Enrichment Case Study
1 The current estuary TNatm (evaluated in terms of reduction in oxidized nitrogen) loading
2 is estimated to be 1.83 x 107 kg/yr (Table 3.2-5). The response curve relationship between TNatm
3 and TNS (TNS [mg/L] = 1.07 + 2.0 x 10'8 xTNatm [kg/yr]) can be found in Figure 3.2-3. Outside
4 data specified for the model include the following:
5 • Mean salinity in estuary = 13 (relative units)
6 • Mean salinity offshore = 35 (relative units)
7 • Mean offshore TN concentration = 0.014 mg/L.
8 There were three sets of observed OEC and TNS data points used to fit the OEC(TNS)
9 logistic model (not including ecological endpoints) from the data presented in Table 3.2-6 and
10 Table 3.2-7
11 For purposes of estimating the OEC(TNS) function at each iteration, the range on the
12 OEC minimum value was specified as 0 to 0 (i.e., fixed at 0). In addition, the OEC maximum
13 value was fixed at 5. Notwithstanding the capability to vary OEC minimum and OEC maximum
14 from iteration to iteration, the decision was made to not do so. The range on the TNS minimum
15 value was specified from 0.014 mg/L (i.e., the offshore value) to 0.1 mg/L (i.e., a value
16 determined by best professional judgment at this time due to lack of supporting data). The TNS
17 maximum value range was specified from 2.57 mg/L (i.e., maximum observed value from
18 STORET) to 1.5x2.57 = 3.86 mg/L.
19 As for the Potomac River/Potomac Estuary Case Study Area, each of the four ASSETS
20 El scores representing state improvements (i.e., Poor-2, Moderate-3, Good-4, High-5) was
21 treated as a "target" ASSETS El score, and 500 iterations were run under each target ASSETS El
22 scenario. Figure 3.2-4 shows one of the curve fits of the logistic function to the observed OEC
23 and TNS data, including the randomly sampled ecological endpoints for TNS.
24 The summary statistics of the 500 iterations for each target ASSETS El scenario are
25 presented in Table 3.2-8.
26 For target ASSETS El = 2, all reductions are positive, but exceed 100%, meaning that not
27 only must all TNatm be removed to meet ASSETS El = 2, but considerably more nitrogen from
28 other sources must be removed as well. Given these results, the Neuse River Estuary is clearly
29 currently somewhere between these two ASSETS El scores as was the Potomac Estuary. There
30 is some evidence that it is slightly more eutrophic than the Potomac Estuary because there was at
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 6-96
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Aquatic Nutrient Enrichment Case Study
1 least a slim chance for the Potomac Estuary (at the 95th percentile) that TNatm reductions (of less
2 than 100%) would achieve ASSETS El = 2.
3
4
5
6
7
9
10
Calibration Points
Fit Logistic Curve
0.0
0.5
1.0
1.5 2.0
TN (mg/L)
2.5
3.0
3.5
Figure 3.2-4. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, median
TNatm*i (i = run 287).
Table 3.2-8. Summary Statistics for Target ASSETS El Scenarios for the
Neuse River/Neuse River Estuary Case Study Area
Statistic
TN^VkgN/yr)
%TN
atm*i Reduction
ASSETS El = 2 (Poor}
Mean
Median
5th Percentile
95th Percentile
-1.43 x 108
-1.43 x 108
-1.47 x 108
-1.01 x 108
880
880
901
653
ASSETS El = 3 (Moderate)
No feasible solutions found
ASSETS EI=4 (Good) and ASSETS El =
All TNatm*i = -5.35 x 108, i.e. TNS*; =
= 5 (High)
Omg/L
Target ASSETS El = 3 is again a unique case because all solutions were infeasible. With
a TNS ; value of 0 mg/L, the other (i.e., fixed) components of the ASSETS El scoring
methodology (i.e., DFO index and Susceptibility Scores) preclude satisfying any of the 95
combinations of DFO, OEC, and OHI index scores that comprise the ASSETS EI=3
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Aquatic Nutrient Enrichment Case Study
1 combinations in the ASSETS El lookup table. This result again depends on the experts who set
2 up the ASSETS El scoring table, defining only 95 out of 125 possible combinations. The
3 likelihood that any of the 30 "missing" combinations are feasible in nature and could result in
4 reaching Target ASSETS El = 3 for this scenario will be examined in future analyses.
5 Target ASSETS El = 4 and 5 had identical results. All 500 iterations returned a TNS*; = 0
6 mg/L, and a corresponding TNatm*i negative load equal to TNatm*i = (0 - 1.07)72.0 x 10"9= -5.35 x
7 108 kg/yr. Clearly, target ASSETS El equal 4 and 5 are very unattainable when reducing the
8 TNatm (including all NOX) is the only policy option. Again, the reduction required includes all of
9 the TNatm source plus an additional amount that is one order of magnitude greater than the
10 original atmospheric deposition load of 108 kg/yr). These amounts could be compared to the
11 other nitrogen sources in the watershed that were used as inputs to the SPARROW model, giving
12 consideration to the characteristics of each of these sources.
13 As with the Potomac River and Potomac Estuary watershed analysis, the SPARROW
14 response curve can be used to examine the role of nitrogen deposition in achieving desired
15 reductions load to the Neuse River Estuary. In the Neuse River watershed, modeling results
16 indicate that 7 x 106 kg N/yr was deposited in 2002. SPARROW modeling predicts that this
17 deposition input results in a loading of 1.2 x 106 kg N/yr (i.e., 20% of the annual TN load) to the
18 Neuse River Estuary. Unlike the Potomac River and Potomac Estuary analysis, little change is
19 seen in the TN loading to the Neuse River Estuary with large decreases in the nitrogen
20 deposition. If all atmospheric nitrogen deposition inputs were eliminated (i.e., 100% reduction),
21 the total annual nitrogen load to the Neuse River Estuary would only decrease by 4%. There are
22 two apparent reasons for this lack of change in loadings. The first reason is a characteristic of the
23 Neuse River watershed. The second reason is an inherent characteristic of the SPARROW model
24 formulation.
25 First, the TN loadings to the Neuse River Estuary are highly dependent on the sources
26 other than atmospheric deposition within the SPARROW model. There are differences in
27 characteristics among the sources within the watershed, where fertilizer, in particular, has a
28 strong signature (i.e., indicating the large influence of agriculture within the watershed). This
29 result demonstrates that the SPARROW response curves of TN load to other sources would be
30 quite different, and the current response curve cannot be used to predict the relative magnitudes
31 of loads needed to produce reductions greater than this 4%. Figure 3.2-5 illustrates the
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Appendix 6-98
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Aquatic Nutrient Enrichment Case Study
1
2
3
4
5
6
10
11
12
13
14
15
16
17
18
19
20
theoretical response curves that may result when the SPARROW modeled loads are plotted
against the other TN source inputs. The green curve, or least influential source, displays the
behavior of the atmospheric deposition for the Neuse River Estuary. The red curve, or highly
influential source, likely corresponds to how agricultural sources within the watershed behave.
These response curves will depend on the source magnitudes, spatial distributions, and other
characteristics.
I?
C D)
O ^
e ^
c ^
CD
o
o
O
CO
LJJ
E
CD
CD
T3
CD
O
Current conditions concentration or loading
Least influential source where a large
change in input loading produces a
small change in load to the estuary
Moderately influential source where a
large change in input loading produces
a large change in load to the estuary
Highly influential source where a small
change in input loading produces a
large change in load to the estuary
Source Input Load (kg N/yr)
Figure 3.2-5. Theoretical SPARROW response curves demonstrating relative influence
of sources on nitrogen loads to an estuary.
Second, the decay rates used by SPARROW play a part in the differing percentage
reductions seen. The changes in the loadings reaching the estuary in the alternate effects analyses
also occur because of the instream and reservoir decay terms. Within SPARROW, the decay
term is applied to the total runoff load from each catchment (i.e., the sum of the source loads) in
the upstream to downstream cumulative load calculations. When the atmospheric deposition
loads are decreased, the TOTAL load from a catchment is decreased, even though the other
sources remain constant. The same rate of decay is applied no matter the magnitude of the source
loads is because the decay rate is based on stream travel characteristics or reservoir
characteristics. So when the total load decreases, the amount of decay decreases, leading to a
proportionally higher delivered load. Thus, the same percentage decrease in atmospheric
deposition and fertilizer loads would not produce the same decrease in loads to the estuary.
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Aquatic Nutrient Enrichment Case Study
i 4. IMPLICATIONS FOR OTHER SYSTEMS
2 Selection of the analysis method for aquatic nutrient enrichment considered applications
3 beyond a small number of case studies. The chosen method, consisting of a combination of
4 SPARROW modeling for nitrogen loads and assessment of estuary conditions under the NOAA
5 ASSETS El, provides a highly scalable and widely applicable analysis method. Both components
6 have been applied on a national scale—the national nutrient assessment using SPARROW
7 (Smith and Alexander, 2000) and the NEEA using the ASSETS El (Bricker et al., 1999, 2007a).
8 Additionally, both have been used on a smaller scale. These previous analyses supply a large
9 body of work—data, methods, and supporting experts—to draw from when conducting
10 additional analyses or updating past applications.
11 Requirements for applying this method to other systems include mandatory data inputs,
12 the ability to formulate a SPARROW application on a reliable stream network, and an estuary
13 under suspicion of eutrophication. Data requirements and model formulations have been
14 described and detailed throughout this report.
15 The method is not currently designed to assess eutrophi cation impacts on inland waters.
16 SPARROW modeling can still be applied to determine nitrogen loadings to an inland waterway,
17 but the ASSETS El assessment would not apply, and as such, the indicators and overall
18 likelihood of eutrophication could not be assessed. For these inland waters, an alternate
19 methodology would be necessary to examine the effects of changing nitrogen loads within the
20 waterbody. A variety of methods could possibly be applied, including empirical relationships or
21 dynamic modeling. It is beyond the scope of this case study to further assess these inland waters
22 beyond the sensitive areas analysis in Section 1.2.1. An additional case study in this project
23 examines the effects of aquatic acidification on inland waters using dynamic modeling (See
24 Appendix 4).
25 The scalability of the methods and approaches taken in these case studies will rely on the
26 ability to group estuaries across the country into patterns of similar behavior either in terms of
27 nitrogen sources or eutrophication effects. In 2003 and 2004, NOAA and the Kansas Geological
28 Survey conducted a series of workshops to develop a type classification system for the 138
29 estuarine systems assessed in the original NEEA (Bricker et al., 1999). Participants considered
30 70 classification variables for grouping the estuarine systems. These variables included 51
31 physical characteristics (e.g., estuary depth and volume, tidal range, salinity, nitrogen and
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Appendix 6-100
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Aquatic Nutrient Enrichment Case Study
1 phosphorus concentrations, estimates of flushing time, evaporation), 10 perturbation
2 characteristics (e.g., population in watershed, estimates of nutrient loading), and nine response
3 characteristics (e.g., SAV loss, presence of nuisance and toxic blooms). Ultimately, the
4 workgroup selected five variables (i.e., depth, openness of estuary mouth, tidal range, mean
5 annual air temperature, and the log of freshwater inflow/estuarine area) deemed to be the most
6 critical physical and hydrological characteristics influencing nutrient processing and the
7 expression of eutrophic symptoms in a waterbody. Based on these five variables, the 138
8 estuarine systems were classified into 10 groups (Table 4-1; Figure 4-1). The two estuary
9 systems included in this case study, the Potomac and Neuse River estuary systems, were in
10 groups one and nine, respectively (Bricker et al., In prep).
Table 4-1. Typology Group Categorizations
Group
Group 0
Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
Group 7
Group 8
Group 9
Number of Systems
13
35
5
8
18
O
2
16
17
21
Overriding Characteristics
Low freshwater inflow:estuarine area ratio;
low depth; low estuary mouth openness
Medium depth, medium openness, high
annual air temperature
High depth, low annual air temperature
High estuary mouth openness; high depth
Low estuary mouth openness; high
freshwater inflow: estuarine area ratio; low
annual air temperature
High estuary mouth openness; high depth
High depth; high estuary mouth openness
High tidal range; medium estuary mouth
openness; low annual air temperature
High freshwater inflow: estuarine area ratio;
low depth
Low depth, medium estuary mouth openness;
high annual air temperature
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Aquatic Nutrient Enrichment Case Study
# of estuaries in
each cluster
• 35
EH
21
is
16
EH 3
2 Figure 4-1. Preliminary classifications of estuary typology across the nation (Bricker et
3 al.,2007a).
4 Given that the response curve of the OEC index score to TNS is expected to change
5 shapes with different values of susceptibility, the typology classes thus defined in Table 4-1
6 provide an opportunity to assess the validity of this expectation. The first step in assessing this
7 statement would be to examine the nutrient loadings in other estuaries that fall within groups 1 or
8 9, the groups corresponding to the two case study areas. Once the shape and behavior of the
9 response curve for the estuary grouping is confirmed, work can begin to scale the results between
10 estuaries of that group. The ASSETS El score for an estuary may also be considered within this
11 analysis. For the 48 systems for which an ASSETS El score was developed in the 2007 NEEA
12 Update, only one system was rated as High (i.e., Connecticut River), whereas five were rated as
13 Good (i.e., Biscayne Bay, Pensacola Bay, Blue Hill Bay, Sabine Lake, Boston Harbor). Eighteen
14 systems were rated as Moderate, and 24 systems were rated as Poor or Bad. Those estuaries that
15 fall within groups 1 or 9 and are rated as Poor or Bad would be the most appropriate candidates
16 to start the scaling analysis.
17 Scaling of results will also need to account for the response of the watershed to
18 atmospheric nitrogen deposition inputs. If SPARROW is used, either through the in-development
19 Web-enabled national SPARROW application or through regional or site-specific applications,
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Aquatic Nutrient Enrichment Case Study
1 the shape of the response curve will be determined by the model and its parameters. If a different
2 approach is taken to develop TN loadings, then the systems will need to be grouped according to
3 the shape and behavior of the response curve. Additional consideration should be given to the
4 magnitude of the percentage contributions of the atmospheric deposition to the TN load to the
5 watershed and the resulting TN load to the estuary.
6 5. UNCERTAINTY
7 There are several areas of uncertainty with this method of assessment for aquatic nutrient
8 enrichment, which are summarized below.
9 • Data inputs to SPARROW. The compilation of data needed for creation or application of
10 SPARROW relies on geographic and temporal analyses. For this study, the data used were
11 developed under separate studies and published by the USGS. Because the data were
12 independently verified before publication by the USGS, only quality checks were
13 performed on the data rather than full validation exercises. For any future analyses that
14 require new compilations of data, close attention should be paid to the source and
15 geographic and temporal precision and accuracy of the data because SPARROW relies on
16 the distribution of sources across the watershed to create model parameters on the annual
17 average basis.
18 • Modeling uncertainty in SPARROW estimates. With any measured or modeled results,
19 there is a certain amount of uncertainty that should be quantified. Because SPARROW
20 relies on a nonlinear regression basis, a number of parameters can be used to assess the
21 uncertainty within the model and provide confidence intervals around the estimates. The
22 Version 3 Chesapeake Bay SPARROW application met evaluation criteria based on
23 degrees of freedom, model error, and R-squared values. The calibration of the Neuse River
24 watershed SPARROW model using SAS examined the standard deviation, t-statistics, p-
25 values, and VIFs for each estimated parameter. The overall model was evaluated based on
26 minimizing model error, maximizing R-squared values, and ensuring that the Eigen value
27 range was below 100 while the probability plot correlation coefficient was close to one.
28 The model derived for the Neuse River/Neuse River Estuary Case Study Area did produce
29 some model parameters (i.e., manure production, urban area, and decay terms) that did not
30 reach desired statistical significance levels. The estimation of decay term parameters may
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Appendix 6-103
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Aquatic Nutrient Enrichment Case Study
1 be rectified by adjusting the flow classes among which the parameters are split. This
2 should be examined in any future analyses. The manure production and urban area source
3 terms should also be examined as to their distribution throughout the watershed and
4 overall contributions to the load.
5 • Sensitivity of SPARROW formulation due to atmospheric inputs in the Potomac
6 River/Potomac Estuary Case Study Area. The parameterization of a SPARROW
7 application for the Potomac River watershed is expected to change when recalibration is
8 completed using the atmospheric deposition of TN based on the combination of CMAQ
9 and NADP data created for this study, rather than the interpolated values of wet deposition
10 of nitrate. As discussed in Section 3.1.1, the spatial gradient as well as the magnitude of
11 the atmospheric deposition of different nitrogen species varies across the watershed. While
12 it is certain that the parameter estimated to apply to the atmospheric deposition source will
13 change, what is uncertain at this point is the extent to which the other model parameters
14 and the overall nitrogen load estimates will be affected by using the CMAQ/NADP
15 estimates in the model calibrated against the wet NOs~ deposition values. Sensitivity of the
16 model parameters and nitrogen load estimate can be evaluated in future studies where
17 SPARROW is recalibrated against the 2002 data.
18 • Calibration data for SPARROW estimates. Monitoring data will be used to calibrate the
19 SPARROW model. By relying on data from federally recognized data systems, the aim is
20 to use data that has undergone quality assurance/quality control (QA/QC) procedures.
21 Additionally, collaboration has been completed with the researchers who have conducted
22 the previous SPARROW applications in each case study area to provide a rigorous check
23 on the data used.
24 • Data inputs to the ASSETS El. Because of the numerous data requirements and sources
25 required to conduct a full ASSETS El analysis, there is a large range of uncertainty that
26 can enter into the calculations. For the water quality data evaluation of dissolved oxygen
27 and chlorophyll a, the numerical values of the 10th and 90th percentiles used in the
28 evaluation were subjected to QA/QC procedures as processed through regulated databases
29 with checks. The frequency of occurrence of these indicators and HABs events relied more
30 on subjective judgment of temporal variations of concentrations across the year. Best
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Appendix 6-104
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Aquatic Nutrient Enrichment Case Study
1 attempts were made to apply standardized evaluation methods in order to minimize any
2 uncertainties due to subjectivity or processing differences.
3 • Heuristic estimates of DFO index scores. The estimation of the DFO index score in the
4 ASSETS El assessment currently relies on heuristic estimates from systems experts.
5 Future NOAA efforts will seek to provide more scientifically structured estimates for this
6 parameter, but at this time, reliance must be on expert judgment on whether there will be
7 increased or decreased pressures because of nutrient loads, population growth, and land
8 use change.
9 • Steady-state estimates/mean annual estimates. Both SPARROW and the ASSETS El
10 methods currently provide only longer-term estimates of the system conditions. There is
11 the possibility of conducting the analyses on a seasonal basis, which may be appropriate
12 because the trends in eutrophication indicators are likely to vary seasonally. Producing
13 annual averages actually introduces some leverage to the uncertainty in the input data as
14 previously discussed. Because the ultimate values used to base the analysis on are
15 averages, there is less reliance on the certainty of individual measures.
16 • Use of a screening method. The methods used in this study are only of the screening
17 level. The screening level was more appropriate for a scalable, widely applicable set of
18 case studies than for a highly detailed modeling effort. Undoubtedly, details, such as the
19 degree to which the soil-groundwater system affects atmospherically deposited nitrogen,
20 will be less quantified than detailed processes using this method. However, for an initial
21 approach to determining the aquatic nutrient enrichment effects on a system, the screening
22 method provides a response curve that can be used in the evaluation of ecosystem services.
23 Additionally, many of the complex concepts linking the indicators of eutrophication to the
24 effects of eutrophication are not highly developed or understood at this time (Howarth and
25 Marino, 2006). While some targeted studies may produce the type of linked results from
26 indicators to ecological endpoints that are the goal of this study, these results can not be
27 readily expanded to multiple areas in multiple climate zones without great levels of effort.
28 As the base of literature and results expands, the concepts applied in this methodology can
29 be expanded to more deterministic, temporarily varying analyses.
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Appendix 6-105
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Aquatic Nutrient Enrichment Case Study
1 Uncertainties in Back Calculation Methods
2 • Missing ASSETS El scores per combinations of index scores. The combinations of
3 OHI, OEC, and DFO index scores provided by Bricker et al. (2003) leave out 30 of the
4 possible 125 combinations that represent overall ASSETS El scores. In both case study
5 analyses, these missing scores have led to a conclusion of infeasible reduction scenarios
6 because an overall ASSETS El score could not be determined from the resulting instream
7 nitrogen load found during the back calculation method. The methods used to determine
8 the 95 combinations will be investigated, and the missing 30 scores pursued for future case
9 study analyses.
10 • Better rationale for TNS minimum and maximum uncertainty range. The uncertainty
11 about how to best quantify the TNS ecological endpoint uncertainty is the biggest
12 limitation of the current analyses. This is particularly true for the TNS high end (i.e., the
13 maximum TNS that would be expected to result in an OEC index score of 1). The assigned
14 uncertainty ranges were based on best professional judgment, but more research is needed.
15 It is expected for the results of the back calculation methodology to be very sensitive to
16 these ecological endpoint ranges, especially on the maximum TNS end. Because of this
17 limitation, the results presented herein for the Potomac and Neuse River estuaries should
18 be interpreted as illustrative of the methodology, not strictly valid.
19 • Methodology to incorporate uncertainty in the SPARROW model. Estimates of TNS at
20 the head of the estuary, predicted by SPARROW and driven by the TNatm (i.e., TN
21 deposition evaluated on reductions in NOX) over the watershed and other nitrogen sources,
22 are uncertain. That uncertainty was not considered in these two case studies; therefore, the
23 probability distributions of TNatm*i presented are artificially "tight" (i.e., the true
24 distributions would exhibit more variability). There is a need to explore the SPARROW
25 literature more thoroughly to determine how to incorporate the non-parametric confidence
26 limits that have been developed for the SPARROW model. Once such limits are
27 incorporated, it is very unlikely that one would be able to explicitly solve the SPARROW
28 model, including these confidence limit terms explicitly for a TNatm y as a function of the
29 TNs*i value and the "jth" probability of confidence limit term. Some sort of implicit,
30 iterative method would be needed. An application of the Newton's method algorithm has
31 already been developed for these purposes and tested using an artificial confidence limit
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Appendix 6-106
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Aquatic Nutrient Enrichment Case Study
1 term. It seems to work remarkably well (i.e., convergence within a few iterations to very
2 tight convergence criteria), and the researchers are optimistic that expanding the overall
3 methodology to include SPARROW uncertainty is very tractable.
4 • More convergence testing to determine appropriate numbers of samples. As briefly
5 mentioned, some modest convergence testing was completed to determine how many
6 samples of the OEC(TNS) function need to be used for the statistics of interest for the
7 resulting TNatm*i distributions to be reasonably stable. The answer is something more than
8 500, which will undoubtedly increase when SPARROW uncertainty is incorporated. More
9 convergence testing is needed.
10 • Crossing of a categorical ranking system with a continuous nitrogen concentration
11 scale. Several assumptions and considerations had to be made in order to create and
12 evaluate the logistic response curve because the OEC index score is a categorical ranking
13 of 1 through 5, whereas TNS is a continuous variable. The functions evaluated in
14 BackCalculation treat the OEC index score as a continuous function. Until higher level
15 models are developed to relate the nitrogen concentrations in the system to eutrophication
16 effects, these assumptions are necessary. Future applications with additional data should
17 be used to test and validate these assumptions and results.
is 6. CONCLUSIONS
19 "A screening-level method has been determined to be an appropriate approach to assessing
20 the effects of atmospheric deposition of oxidized nitrogen on eutrophi cation/nutrient
21 enrichment because there is a lack of a generalized link development between these
22 characteristics in the literature.
23 • Both the Potomac and Neuse River estuaries have an ASSETS El score of Bad for 2002,
24 meaning that both systems are highly eutrophic and are not expected to improve greatly in
25 the near future. Atmospheric deposition over the watersheds account for approximately
26 24% and 26% of the instream loads to the Potomac and Neuse River estuaries,
27 respectively.
28 • The BackCalculation program designed and set up for this study succeeded in assessing
29 the links between TNS responding to changes in TNatm and the OEC index and ASSETS El
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Appendix 6-107
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Aquatic Nutrient Enrichment Case Study
1 scores. Results of this assessment for the Potomac Estuary reveal that it is possible to
2 improve the ASSETS El score by only one category when the only change is in a
3 reduction of the oxidized nitrogen component of the atmospheric deposition (Table 3.2-4).
4 This result showed that there was a 5% chance (i.e., 95th Percentile of results) that
5 reducing the TNatm by 78% would result in the one category improvement in the ASSETS
6 El score. Within the Neuse River Estuary, this analysis revealed that it would not be
7 possible to improve the ASSETS El score by reducing the oxidized nitrogen in the
8 atmospheric deposition loading to the estuary alone (Table 3.2-8; all percentage
9 reductions greater than 100). Additional source reductions would be necessary to produce
10 OHI and OEC index scores good enough to improve the ASSETS El score.
11 • Scaling of this methodology was a priority in development. Demonstration of the back
12 calculation methods was the first step to expanding the results to estuaries across the
13 nation. Alternative evaluation methods of eutrophication will be needed to assess nutrient
14 enrichment in inland waters.
is 7. REFERENCES
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20 Hoos, A.B. 2005. Evaluation of the SPARROW model for estimating transport of nitrogen and
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26 Data to Support Statistical Modeling oflnstream Nutrient Load Based on Watershed
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8 Oceanography 57(1 part 2):364-376.
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14 Kansas Department of Health and Environment, Bureau of Water, Topeka, KS. Available
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21 Current State and Trends, Volume 1. Edited by R. Hassan, R. Scholes, and N. Ash.
22 Washington: Island Press. Available at
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24 Moore, R.B., C.M. Johnston, K.W. Robinson, and J.R. Deacon. 2004. Estimation of total
25 nitrogen and phosphorus in New England streams using spatially referenced regression
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1 Interior, U.S. Geological Survey, Pembroke, NH. Available at
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4 River Basinwide Water Quality Management Plan. North Carolina Department of
5 Environment and Natural Resources, Division of Environmental Management, Water
6 Quality Section, Raleigh, NC.
7 NC DENR (North Carolina Department of Environment and Natural Resources). 2002. Neuse
8 River Basin-wide Water Quality Plan. North Carolina Department of Environment and
9 Natural Resources, Division of Water Quality/Planning, Raleigh, NC. Available at
10 http://h2o.enr.state.nc.us/basinwide/Neuse/2002/Section A Chapter 2.pdf.
11 NC DWQ (Division of Water Quality). 2008. North Carolina Department of Environment and
12 Natural Resources. Environmental Sciences Section - Fish Kill Event Update. Online
13 information. North Carolina Department of Environment and Natural Resources,
14 Division of Water Quality, Raleigh, NC. Available at:
15 http://www.esb.enr.state.nc.us/Fishkill/fishkillmain.htm (accessed December 2008).
16 NEIWPCC (New England Interstate Water Pollution Control Commission). 2004. New England
17 SPARROW water quality model. Interstate Water Report 7(3):6-7. Available at
18 http://www.neiwpcc.org/PDF_Docs/iwr_s04.pdf
19 NRCS (Natural Resources Conservation Service). 2001. 1997 National Resources Inventory,
20 Updated June 2001. Natural Resources Conservation Service, Washington, DC.
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22 Chesapeake Bay anoxia: origin, development, and significance. Science 223:22-27'.
23 Paerl, H.W. 1995. Coastal eutrophication in relation to atmospheric nitrogen deposition: current
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25 Paerl, H.W. 1997. Coastal eutrophi cation and harmful algal blooms: Importance of atmospheric
26 deposition and groundwater as "new" nitrogen and other nutrient sources. Limnology and
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1 Paerl, H. W. 2002. Connecting atmospheric nitrogen deposition to coastal eutrophication.
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4 watershed organic matter loading: Consequences for hypoxia in the eutrophying Neuse
5 River Estuary, NC, USA. Marine Ecology Progress Series 166:17-25.
6 Paerl, H.W., R.S. Fulton, P.H. Moisander, and J. Dyble. 2001. Harmful freshwater algal blooms,
7 with an emphasis on cyanobacteria. The Scientific World 7:76-113.
8 Paerl, H., J. Dyble, L. Twomey, J. Pinckney, J. Nelson, and L. Kerkhof. 2002. Characterizing
9 man-made and natural modifications of microbial diversity and activity in coastal
10 ecosystems. Antonie van Leeuwenhoek S7(l-4):487-507.
11 Peierls, B. 2008. Personal communication.
12 Preston, S. 2008. Personal communication.
13 Preston, S.D., and J.W. Brakebill. 1999. Applications of Spatially Referenced Regression
14 Modeling for the Evaluation of Total Nitrogen Loading in the Chesapeake Bay
15 Watershed. USGS Water-Resources Investigations Report 99-4054. U.S. Department of
16 the Interior, U.S. Geological Survey, MD-DE-DC Water Science Center, Baltimore, MD.
17 Available at http://md.usgs.gov/publications/wrir-99-4054.
18 RTI (RTI International). 2007. Review of Candidate Fate and Transport and Ecological Models.
19 Report prepared for the U. S. Environmental Protection Agency under Contract No.
20 EP-D-06-003. RTI International, Research Triangle Park, NC.
21 RTI (RTI International). 2008. Methodology Development for Linking Ecosystem Indicators to
22 Ecosystem Services. Report prepared for the U.S. Environmental Protection Agency,
23 Office of Air Quality Planning and Standards under Contract No. EP-D-06-003. RTI
24 International, Research Triangle Park, NC.
25 Schwarz, G.E., A.B. Hoos, R.B. Alexander, and R.A. Smith. 2006. The SPARROW Surface
26 Water-Quality Model—Theory, Applications and User Documentation. USGS
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2 U.S. Geological Survey, Reston, VA.
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4 Managing Nutrients and Pathogens from Animal Agriculture. Proceedings from the
5 Natural Resource, Agriculture, and Engineering Service Conference for Nutrient
6 Management Consultants, Extension Educators, and Producer Advisors, Camp Hill, PA,
7 March 28-30. Natural Resource, Agriculture, and Engineering Service, Cooperative
8 Extension, Ithaca, NY.
9 Smith, M.D., and L.B. Crowder. 2005. Valuing ecosystem services with fishery rents: a lumped
10 parameter approach to hypoxia in the Neuse River Estuary. Milan, Italy: Fondazione Eni
11 Enrico Mattel (FEEM), NRM Nota di Lavoro (Natural Resources Management Working
12 Papers), 115.05. Available at http://ssrn.com/abstract=825587 (accessed November 5,
13 2007).
14 Smith, R.A., R.B. Alexander, G.D. Tasker, C.V. Price, K.W. Robinson, and D.A. White. 1994.
15 Statistical modeling of water quality in regional watersheds. Pp. 751-754 in Proceedings
16 of Watershed '93—A National Conference on Watershed Management, Alexandria, VA,
17 March 21-24, 1993. EPA 840-R-94-002. U.S. Environmental Protection Agency,
18 Washington, DC.
19 Smith, R.A., G.E. Schwarz, and R.B. Alexander. 1997. Regional interpretation of water-quality
20 monitoring data. Water Resources Research 33(12):2781-2798.
21 Spruill, T.B., AJ. Tesoriero, H.E. Mew, Jr., K.M. Farrell, S.L. Harden, A.B. Colosimo, and S.R.
22 Kraemer. 2004. Geochemistry and Characteristics of Nitrogen Transport at a Confined
23 Animal Feeding Operation in a Coastal Plain Agricultural Watershed, and Implications
24 for Nutrient Loading in the Neuse River Basin, North Carolina, 1999-2002. Scientific
25 Investigations Report 2004-5283. U.S. Department of the Interior, U.S. Geological
26 Survey, Reston, VA.
27 University of North Carolina. 2008. The Neuse Rover Estuary Modeling and Monitoring Project
28 (ModMon). Online information. University of North Carolina, Institute of Marine
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1 Sciences, Morehead City, NC. Available at
2 http://www.unc.edu/ims/neuse/modmon/index.htm (accessed December 2008).
3 U.S. EPA (Environmental Protection Agency). 1996. Air quality criteria for ozone and related
4 photochemicaloxidants. EPA/600/AP-93/004aF-cF. 3v. U.S. Environmental Protection
5 Agency, Office of Research and Development, Research Triangle Park, NC. Available at
6 http://cfpub2.epa.gov/ncea/.
7 U.S. EPA (Environmental Protection Agency). 2000. Deposition of air pollutants to the great
8 waters. Third report to Congress. EPA-453/R-00-005. U.S. Environmental Protection
9 Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
10 Available at http://www.epa.gov/air/oaqps/gr8water/3rdrpt/ (accessed January 16, 2008).
11 U.S. EPA (Environmental Protection Agency). 2002. Summary Table for Nutrient Criteria
12 Documents. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
13 Available at:
14 http://www.epa.gov/waterscience/criteria/nutrient/ecoregions/files/sumtable.pdf.
15 U. S. EPA (Environmental Protection Agency). 2005. Advisory on Plans for Ecological Effects
16 Analysis in the Analytical Plan for EPA 's Second Prospective Analysis—Benefits and
17 Costs of the Clean Air Act, 1990-2020. U.S. Environmental Protection Agency, Office of
18 the Administrator, Science Advisory Board, Washington, DC. June 23.
19 U.S. EPA (Environmental Protection Agency). 2008a. Integrated Science Assessment for Oxides
20 of Nitrogen and Sulfur—Ecological Criteria. Final Report. EP A/600/R-08/082F .U.S.
21 Environmental Protection Agency, National Center for Environmental Assessment-RTF
22 Division, Office of Research and Development, Research Triangle Park, NC. Available at
23 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485.
24 U. S. EPA (Environmental Protection Agency). 2008b. Draft Scope and Methods Plan for
25 Risk/Exposure Assessment: Secondary NAAQS Review for Oxides of Nitrogen and Oxides
26 of Sulfur. U.S. Environmental Protection Agency, Office of Air Quality Planning and
27 Standards, Research Triangle Park, NC Available at
28 http://www.epa.gov/ttn/naaqs/standards/no2so2sec/cr_pd.html.
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1 USDA (U. S. Department of Agriculture). 2002. 2002 Census of Agriculture - State Data: Table
2 13 "Poultry - Inventory and Sales: 2002 and 1997. " U. S. Department of Agriculture,
3 National Agricultural Statistics Service, Washington, DC. Available at
4 http://www.nass. usda.gov/census/census02/volume l/us/st99_2_013_013.pdf.
5 USGS (U.S. Geological Survey). 1999. Digital representation of "Atlas of United States Trees"
6 by ElbertL. Little, Jr. Digital Version 1.0. U.S. Department of the Interior, U.S.
7 Geological Survey, Denver, CO. Available at http://esp.cr.usgs.gov/data/atlas/little
8 (accessed July 25, 2008).
9 Valiela, I, and J. Costa. 1988. Eutrophication of Buttermilk Bay, a Cape Cod coastal
10 embayment: Concentrations of nutrients and watershed nutrient budgets. Environmental
11 Management 72:53 9-5 51.
12 Valiela, I, J. Costa, K. Foreman, J.M. Teal, B. Howes, and D. Aubrey. 1990. Transport of
13 groundwater-borne nutrients from watersheds and their effects on coastal waters.
14 Biogeochemistry 70:177-197.
15 VIMS (Virginia Institute of Marine Science). 2008. Submerged Aquatic Vegetation (SAV) in
16 Chesapeake Bay andDelmarva Peninsula Coastal Bays. Online information. Virginia
17 Institute of Marine Science, Gloucester Point, VA. Available at
18 http://web.vims.edu/bio/sav/?svr=www (accessed December 2008).
19 Whitall, D., and H.W. Paerl. 2001. Spatiotemporal variability of wet atmospheric nitrogen
20 deposition to the Neuse River Estuary, North Carolina. Journal of Environmental Quality
21 30:1508-1515.
22 Whitall, D., S. Bricker, J. Ferreira, A.M. Nobre, T. Simas, and M. Silva. 2007. Assessment of
23 eutrophication in estuaries: pressure-state-response and nitrogen source apportionment.
24 Environmental Management 40:678-690.
25
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5 Appendix 7
e Terrestrial Nutrient Enrichment
7 Case Study
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11 EPA Contract Number EP-D-06-003
12 Work Assignment 3-62
13 Project Number 0209897.003.062
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21
22 Prepared for
23
24 U.S. Environmental Protection Agency
25 Office of Air Quality Planning and Standards
26 Research Triangle Park, NC 27709
27
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30 Prepared by
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32 RTI International
33 3040 Cornwall!s Road
34 Research Triangle Park, NC 27709-2194
35
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38
39 INTERNATIONAL
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Terrestrial Nutrient Enrichment Case Study
1 CONTENTS
2 Acronyms and Abbreviations iv
3 Introduction 1
4 1. Background 2
5 1.1 Indicators, Ecological Endpoints, and Ecosystem Services 3
6 1.1.1 What Is Known 3
7 1.1.2 What Is Not Known 6
8 1.1.3 Benchmarks Selected for This Case Study 7
9 1.1.4 Ecosystem Services 8
10 1.2 Case Study Site Selection 9
11 1.2.1 National Overview of Sensitive Areas 9
12 1.2.1.1 Presence of Acidophytic Lichens 9
13 1.2.1.2 Anthropogenic Land Cover 9
14 1.2.1.3 Nitrogen-Sensitive Species Identified in Literature 10
15 1.2.1.4 Excluded Datasets 10
16 1.2.1.5 Overlay Results 10
17 1.2.2 Use of ISA Information and Rationale for Site Selection 12
18 1.3 Ecosystem Overview 14
19 1.3.1 Coastal Sage Scrub 14
20 1.3.2 Mixed Conifer Forest 18
21 1.4 Historical Trends 23
22 1.4.1 Coastal Sage Scrub 23
23 1.4.2 Mixed Conifer Forests 24
24 2. Approach and Methodology 25
25 2.1 Published Research 26
26 2.2 GIS Methodology 26
27 2.2.1 Overview 26
28 2.2.2 Available Data Inputs 26
29 2.2.2.1 Nitrogen Deposition 26
30 2.2.2.2 Range of Coastal Sage Scrub 27
31 2.2.2.3 Fire Threat 29
32 2.2.2.4 Changes in Coastal Sage Scrub Communities 29
33 2.2.2.5 Distribution of Invasive Species 29
34 2.2.2.6 Threatened and Endangered Species Habitat 29
35 2.2.2.7 Range of Mixed Conifer Forest 30
36 2.2.2.8 Distribution of Acid-Sensitive Lichens 30
37 3. Results 30
38 3.1 Literature Review Findings 31
39 3.1.1 Coastal Sage Scrub 31
40 3.1.1.1 Atmospheric Nitrogen Deposition 34
41 3.1.1.2 Nonnative Grasses 36
42 3.1.1.3 Mycorrhizae 36
43 3.1.1.4 Soil Nitrogen 37
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1 3.1.2 Fire 38
2 3.1.3 Coastal Sage Scrub Model 39
3 3.1.4 MCF Ecosystems 40
4 3.1.4.1 Nitrogen and Ozone Effects 40
5 3.1.4.2 Nitrogen Effects on Lichens 44
6 3.1.4.3 Nitrogen Saturation 46
7 3.2 Results Summary 48
8 4. Implications for Other Systems 50
9 5. Uncertainty 55
10 5.1 Coastal Sage Scrub 55
11 5.2 Mixed Conifer Forest 56
12 6. Conclusions 56
13 7. References 57
14
15 FIGURES
16 Figure 1.1-1. Observed effects from ambient and experimental atmospheric nitrogen
17 deposition loads in relation to 2002 CMAQ/NADP deposition data.
18 Citations for effect results can be found in the ISA, Table 4.4 (U.S. EPA,
19 2008) 5
20 Figure 1.2-1. Areas of highest potential nutrient enrichment sensitivity. (Acidophytic
21 lichens, tree species, and the extent of the Mojave Desert are from data
22 obtained from the USFS. The extents of coastal sage scrub and California
23 mixed conifer are from the California Fire and Resource Assessment
24 Program. Grasslands are from the National Land Cover Dataset [USGS]) 11
25 Figure 1.3-1. Range of coastal sage scrub ecosystems 15
26 Figure 1.3-2. Presence of three threatened and endangered species in California's coastal
27 sage scrub ecosystem 16
28 Figure 1.3-3. Range of California's mixed conifer forests 19
29 Figure 1.3-4. Presence of two threatened and endangered species and mixed conifer
30 forests 19
31 Figure 1.4-1. Change in coastal sage scrub extent from 1977 to 2002 24
32 Figure 3.1-1. Coastal sage scrub range and total nitrogen deposition using CMAQ 2002
33 modeling results andNADP monitoring data 35
34 Figure 3.1-2. Current fire threats to coastal sage scrub ecosystems 39
35 Figure 3.1-3. Model of coastal sage scrub ecosystem in relation to fire and atmospheric
36 nitrogen deposition 40
37 Figure 3.1-4. Mixed conifer forest range and total atmospheric nitrogen deposition using
38 CMAQ 2002 modeling results andNADP monitoring data 42
39 Figure 3.1-5. Conceptual model for increased susceptibility to wildfire in mixed conifer
40 forests (Grulke et al., 2008) 44
41 Figure 3.1-6. Importance of lichens as an indicator of ecosystem health (Jovan, 2008) 44
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Appendix 7 - ii
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1 Figure 3.1-7. Presence of acidophyte lichens and total nitrogen deposition in the
2 California mountain ranges using CMAQ 2002 modeling results and
3 NADP monitoring data 46
4 Figure 3.2-1. Illustration of the range of terrestrial ecosystem effects observed relative to
5 atmospheric nitrogen deposition 49
6 Figure 4.1-1. 2002 CMAQ-modeled and NADP monitoring data for deposition of total
7 nitrogen in the western United States 51
8 Figure 4.1-2. Benchmarks of atmospheric nitrogen deposition for several ecosystem
9 indicators 52
10 Figure 4.1-3. Habitats that may experience ecological benchmarks similar to coastal sage
11 scrub and mixed conifer forest 53
12
13 TABLES
14 Table 1.2-1. Potential Assessment Areas for Terrestrial Nutrient Enrichment Identified in
15 the ISA (U.S. EPA, 2008) 13
16 Table 1.3-1. Selected Flora and Fauna Associated with the Coastal Sage Scrub Habitat 17
17 Table 1.3-2. Selected Flora and Fauna Associated with the Mixed Conifer Forest Habitat 20
18 Table 1.3-3. List of Lichen Species Present in the Sierra Nevada Range and San
19 Bernardino Mountains (Jovan, 2008; Sigal and Nash, 1983) 22
20 Table 3.1-1. Summary of Selected Experimental Variables across Multiple Coastal Sage
21 Scrub Study Areasa 32
22 Table 3.1-2. Coastal Sage Scrub Ecosystem Area and Nitrogen Deposition 36
23 Table 3.1-3. Research Evidence of Ecosystem Responses to Nitrogen Relevant to Coastal
24 Sage Scrub 37
25 Table 3.1-4. Mixed Conifer Forest Ecosystem Area and Nitrogen Deposition 48
26 Table 4.1-1. Areas of Coastal Sage Scrub and Mixed Conifer Forest That Exceed
27 Benchmark Nitrogen Deposition Levels 53
28
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ACRONYMS AND ABBREVIATIONS
2
3
4
5
6
7
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10
11
12
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15
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20
21
22
23
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27
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AM
C:N
cm
CMAQ
CO2
CSS
FIA
FRAP
FWS
GAP
GIS
HNO3
ISA
kg
km
m
MCF
MEA
mm
NADP
NH3
NH4+
NHX
NO
N03-
NOX
03
PNV
8MB
TM
l^g/g
USFS
USGS
VTM
arbuscular mycorrhizae
carbon to nitrogen ratio
centimeter
Community Multiscale Air Quality model
carbon dioxide
coastal sage scrub
Forest Inventory and Analysis National Program
Fire and Resource Assessment Program
U.S. Fish and Wildlife Service
Gap Analysis Project
geographic information systems
nitric acid
Integrated Science Assessment
kilogram
kilometer
meter
mixed conifer forest
Millennium Ecosystem Assessment
millimeter
National Atmospheric Deposition Program
ammonia gas
ammonium
reduced nitrogen
nitric oxide
nitrate
nitrogen oxides
ozone
Potential Natural Vegetation
Simple Mass Balance
Thematic Mapper
micrograms per gram
U.S. Forest Service
U.S. Geological Survey
Vegetation Type Map
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Appendix 7 - iv
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i INTRODUCTION
2 For the last half century, landscapes in the United States have been exposed to
3 atmospherically deposited nitrogen from anthropogenic activities. Some of the highest deposition
4 has occurred in Southern California, where researchers have documented measurable ecological
5 changes related to atmospheric deposition. This case study investigated the coastal sage scrub
6 (CSS) and the mixed conifer forest (MCF) ecosystems. Research was conducted on these
7 complex ecosystems to understand the relationships among the effects of nitrogen loads, fire
8 frequency and intensity, and invasive plants. The breadth of spatial and temporal data needed for
9 quantitative modeling of ecological response in the CSS and MCF ecosystems is not currently
10 available. However, biologically meaningful ecological endpoints were identified and compared
11 to ecological endpoints identified in the other case studies presented in the Risk and Exposure
12 Assessment (Chapters 4 and 5), as well as similar ecological endpoints from ecosystems in
13 different parts of the United States. The results in this case study report are based on geospatial
14 analysis and published empirical research.
15 Evidence from the two ecosystems discussed in this case study report supports the
16 finding that nitrogen alters the CSS and MCF ecosystems. For this analysis, the loss of the native
17 shrubs in the CSS and the increase in nonnative annual grasses were investigated. In MCF on the
18 slopes of the San Bernardino and Sierra Nevada Range, lichen communities associated with the
19 forest stands and nitrogen saturation were investigated to identify the effects of nitrogen
20 loadings. Changes in nitrogen loading may also affect the ecological services provided by the
21 CSS and MCF ecosystems, including regulation (e.g., water, habitat), cultural and aesthetic value
22 (e.g., recreation, natural landscape, sense of place), and provisioning (e.g., timber) (MEA, 2005).
23 In addition, these locations have the following characteristics that make them good candidates
24 for case studies:
25 • There is public interest
26 • Data were available to begin investigation (especially geographic information systems
27 [GIS] datasets)
28 • Effects observed have implications for other ecosystems and ecosystem services
29 • Ecological endpoints related to nitrogen deposition can be identified
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1 • Observed effects, such as mycorrhizal responses, increase in nonnative annual grasses,
2 decrease in certain lichen species, and nitrate (N(V) leaching are considered by
3 researchers to be linked to atmospheric nitrogen deposition.
4 Section 3.3 of the Integrated Science Assessment (ISA) for Oxides of Nitrogen and
5 Sulfur-Ecological Criteria (FinalReport) (ISA) (U.S. EPA, 2008) describes the ecosystems and
6 species of concern, identifies trends in the ecosystems and the effects of these trends, and
7 discusses research efforts that investigated the variables and driving forces that may affect the
8 communities. The Community Multiscale Air Quality (CMAQ) 2002 modeling results and 2002
9 National Atmospheric Deposition Program (NADP) data were used to gain an understanding of
10 how atmospheric deposition of nitrogen is spatially distributed. GIS data on the spatial extent of
11 the habitat and changes in that extent, the location of fire threat (an important variable in both
12 CSS and MCF ecosystems), and the location of species of concern were used to compare these
13 patterns to the CMAQ/NADP data. In sum, spatial information and observed, experimental
14 effects were used to help identify the trends in these ecosystems and describe the past and current
15 spatial extent of the ecosystems.
16 The following ecological endpoints were identified for CSS:
17 • Loss of CSS native shrubs
18 • Mycorrhizal (a symbiotic association of fungi and plant roots) responses
19 • Nonnative annual grass biomass.
20 The following ecological endpoints were identified for MCF:
21 • Lichen community species
22 • NO3" leaching.
23 1. BACKGROUND
24 Current analysis of the effects of terrestrial nutrient enrichment from atmospheric
25 nitrogen deposition in both CSS and MCF seeks to improve scientific understanding of the
26 interactions among nitrogen deposition, fire events, and community dynamics. The available
27 scientific information is sufficient to identify ecological endpoints that are affected by nitrogen
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1 deposition. These ecological endpoints can be compared to the ecological endpoints identified
2 from modeling conducted for other case studies in the Risk and Exposure Assessment
3 (Chapters 4 and 5). These ecological endpoints can also be compared to similar
4 ecological endpoints from different ecosystems.
5 1.1 INDICATORS, ECOLOGICAL ENDPOINTS, AND ECOSYSTEM
6 SERVICES
7 1.1.1 What Is Known
8 Determining an acceptable ambient air concentration of nitrogen oxides (NOX) for this
9 case study required knowledge of ecosystem sensitivity to subsequent atmospheric deposition.
10 Terrestrial nutrient enrichment research has measured ecosystems' exposure to deposition of
11 various atmospheric nitrogen species, including nitrogen oxides, reduced nitrogen, and total
12 nitrogen. The ISA (U.S. EPA, 2008, Section 3.3) documents current understanding of the effects
13 of nitrogen nutrient enrichment on terrestrial ecosystems. The report concludes that there is
14 sufficient information to suggest a causal relationship between atmospheric nitrogen deposition
15 and biogeochemical cycling and fluxes of nitrogen in terrestrial systems. The ISA further
16 concludes that there is a causal relation between atmospheric nitrogen deposition and changes in
17 species richness, species composition, and biodiversity in terrestrial systems. These conclusions
18 are based on an extensive literature review that is summarized in Table 4-4 of the ISA. The
19 research involves both observational and experimental (e.g., nitrogen addition) projects. Alpine
20 ecosystems, grasslands (e.g., arid and semiarid ecosystems), forests, and deserts were included.
21 This extensive documentation was used to assist in selecting the case study areas to identify and
22 compare ecological endpoints from different habitats.
23 CSS is subject to several pressures, such as land conversion, grazing, fire, and pollution,
24 all of which have been observed to induce declines in other ecosystems (Allen et al., 1998).
25 Research suggests that both fire and increased nitrogen can enhance the growth of nonnative
26 grasses in established CSS ecosystems. It is hypothesized that many stands are no longer limited
27 by nitrogen and have instead become nitrogen-saturated due to atmospheric nitrogen deposition
28 (Allen et al., 1998; Westman, 1981a). Nitrogen availability may favor the germination and
29 growth of nonnative grasses, which can create a dense network of shallow roots that slow the
30 diffusion of water through soil, decrease the percolation depth of precipitation, and decrease the
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-3
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Terrestrial Nutrient Enrichment Case Study
1 water storage capability of the soil and underlying bedrock (Wood et al., 2006). Establishment of
2 CSS species may be reduced because of decreased water and nitrogen availability at depths
3 where more woody CSS tap roots are found (Keeler-Wolf, 1995; Wood et al., 2006).
4 The ISA (U. S. EPA, 2008, Section 3.3) notes that there are areas of CSS of Southern
5 California where dry nitrogen deposition approaches 30 kilograms (kg) N/ha/yr (Bytnerowicz
6 and Fenn, 1996). Seedlings of native shrubs and nonwoody plants in these areas of high nitrogen
7 deposition are unable to compete with dense stands of exotic grasses, and thus are gradually
8 replaced by grasses, particularly following disturbances, such as fire (Eliason and Allen, 1997;
9 Yoshida and Allen 2001; Clone et al., 2002). CSS has been declining in land area and in shrub
10 density for the past 60 years, and in many places is being replaced by nonnative annual grasses
11 (Allen et al., 1998; Padgett and Allen, 1999). Nitrogen deposition has been suggested as a
12 possible cause or factor in this ecosystem alteration (U.S. EPA, 2008, Section 3.3).
13 The ISA (U.S. EPA, 2008, Section 3.3) discusses the extensive land areas in the western
14 United States that receive low levels of atmospheric nitrogen deposition and which are
15 interspaced with areas of relatively higher atmospheric deposition downwind of large
16 metropolitan centers and agricultural areas. Fenn et al. (2008) determined empirical critical loads
17 for atmospheric nitrogen deposition in MCF, based on changes in leached N(V in receiving
18 waters and reduced fine-root biomass in Ponderosa pine (Pinusponderosa), and based on
19 changes in epiphytic lichen communities. An atmospheric nitrogen deposition of 17 kg N/ha/yr
20 was found to be associated with NO3"leaching and an approximately 25% reduction in fine root
21 biomass. The study further noted that lichens are good early indicators of atmospheric nitrogen
22 deposition effects on other MCF species because lichens rely entirely on atmospheric nitrogen
23 and cannot regulate uptake. From the lichen data, Fenn et al. (2008) predicted that a critical load
24 of 3.1 kg N/ha/yr would be protective for all components of the forest ecosystem.
25 Figure 1.1-1 displays a map of observed effects from ambient and experimental
26 atmospheric nitrogen deposition loads in relation to 2002 CMAQ-modeled deposition results.
27 The map depicts the areas where empirical effects of terrestrial nutrient enrichment have been
28 observed and the area's proximity to elevated levels of nitrogen deposition. The ISA (U.S. EPA,
29 2008, Section 3.3) also identifies areas of the western United States where atmospheric nitrogen
30 deposition effects have been reported.
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Appendix 7-4
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Terrestrial Nutrient Enrichment Case Study
11,12
I'1 -•
,«r
..
Legend
Total N Deposition
(kgfha/yr)
•i High. 66.507
IB National Parks
.... LOW: 0 761 .,• Nalmnal Forests
1
2 ~\. Nitrogen enrichment or eutrophication of lakes (Loch Vale, CO: 0.5 to1.5 kg/ha/yr; Niwot Ridge, CO: 4.71 kg/ha/yr)
3 2. Alpine lakes increase shift in diatom species (Rocky Mountains, CO: 2 kg/ha/yr)
4 3. Alpine meadows' elevated NO3" levels in runoff (Colorado Front Range: 20, 40, 60 kg/ha/yr)
5 4. Alpine meadows' shift toward hairgrass (Niwot Ridge, CO: 25 kg/ha/yr)
6 5. Nitrogen enrichment or nitrogen saturation (e.g., soil and foliar nitrogen concentration) (eastern slope of Rocky Mountains: 1.2;
7 3.6 kg/ha/yr; Fraser Forest, CO: 3.2 to 5.5 kg/ha/yr)
8 6. Increased nitrogen mineralization rates and nitrification (Loch Vale, CO (spruce): 1.7 kg/ha/yr)
9 7. Alpine tundra with increased plant foliage and reduced species richness (Niwot Ridge, CO: 50 kg/ha/yr)
10 8. Nitrogen saturation, high NO3" in streamwater, soil, leaves; high nitric oxide (NO) emissions (Los Angeles, CA air basin:
11 saturation at 24 to 25 kg/ha/yr (dry) and at 0.8 to 45 kg/ha/yr (wet); northeastern U.S.: 3.3 to 12.7 kg/ha/yr)
12 9. Nitrogen saturation, high NO3"in streamwater (San Bernardino Mountains, CA (coniferous): 2.9 and 18.8 kg/ha/yr)
13 10. NO3" leaching (New England: Adirondack lakes: 8to10 kg/ha/yr)
14 11. Nitrogen saturation, high dissolved inorganic nitrogen (San Bernardino Mountains, San Gabriel Mountains, CA, chaparral,
15 hardwood, coniferous): 11 to 40 kg/ha/yr)
16 12. Increased tree mortality and beetle activity (San Bernardino Mountains, CA (Ponderosa): 8 and 82 kg/ha/yr)
17 13. Enhanced growth of black cherry and yellow poplar; possible decline in red maple vigor; increased foliar nitrogen (Fernow
18 Forest, VW: 35.5 kg/ha/yr)
19 14. Impacts on lichen communities (California MCF: 3.1 kg/ha/yr; Columbia R. Gorge, OR/WA: 11.5 to 25.4)
20 15. Evidence that threatened and endangered species impacted San Francisco Bay, CA (checkerspot butterfly and serpentinitic
21 grass invasion: 10 to15 kg/ha/yr; Jasper Ridge, CA: 70 kg/ha/yr)
22 16. Decreased diversity of mycorrhizal communities (Southern California: -10 kg/ha/yr; Northern Michigan (maple/sugar maple): 5
23 to 9 kg/ha/yr)
24 17. Decreased abundance of CSS (Southern California: 3.3 kg/ha/yr)
25 18. Loss of grasslands (Cedar Creek, MN: 5.3 [1.3 to 9.8] kg/ha/yr)
26 19. Decrease in abundance of desert creosote bush, increase in nonnative grasses (Mojave Desert and Chihuahuan Desert, CA:
27 1.7 kg/ha/yr and up)
28 20. Decrease in pitcher plant (Hawley Bog, MA; Molly Bog, VA: 10 to 14 kg/ha/yr)
29 Figure 1.1-1. Observed effects from ambient and experimental atmospheric
30 nitrogen deposition loads in relation to 2002 CMAQ/NADP deposition data.
31 Citations for effect results can be found in the ISA, Table 4-4 (U.S. EPA, 2008).
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Appendix 7-5
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Terrestrial Nutrient Enrichment Case Study
1 1.1.2 What Is Not Known
2 The ISA (U. S. EPA, 2008, Section 3.3) indicates that information is limited about the
3 spatial extent and distribution of terrestrial ecosystems most sensitive to nutrient enrichment
4 from atmospheric nitrogen deposition: "Effects are most likely to occur where areas of relatively
5 high atmospheric nitrogen deposition intersect with nitrogen-limited plant communities. The
6 factors that govern the sensitivity of terrestrial ecosystems to nutrient enrichment from
7 atmospheric nitrogen deposition include the degree of nitrogen limitation, rates and form of
8 atmospheric nitrogen deposition, elevation, species composition, length of growing season, and
9 soil nitrogen retention capacity." Examples of sensitive ecosystems include the following:
10 • Alpine tundra (low rates of primary production, short growing season, low temperature,
11 wide moisture variation, low nutrient supply).
12 • Western U.S. ecosystems, such as the alpine ecosystems of the Colorado Front Range,
13 chaparral watersheds of the Sierra Nevada Range, lichen communities in the San
14 Bernardino Mountains and the Pacific Northwest, and CSS communities in Southern
15 California.
16 • Eastern U.S. ecosystems where sensitivities are typically assessed in terms of the degree of
17 NCV leaching from soils into ground and surface waters. These ecosystems are expected to
18 include hardwood forests, semiarid lands, and grassland ecosystems, but effects on
19 individual plant species have not been studied well.
20 Major indicators for nutrient enrichment to terrestrial systems from atmospheric
21 deposition of total reactive nitrogen, such as those described above, require measurements based
22 on available monitoring stations for wet deposition (NADP/National Trends Network) and
23 limited networks for dry deposition (Clean Air Status and Trends Network [CASTNet]).).
24 However, data have been limited, particularly at the spatial scale required for a more accurate
25 analysis. Wet deposition monitoring stations can provide more information on an extensive range
26 of nitrogen species than can dry deposition monitoring stations. In the Mediterranean systems of
27 Southern California where rainfall is concentrated during some months of the year, dry
28 deposition is particularly important. Individual studies measuring atmospheric nitrogen
29 deposition to terrestrial ecosystems that involve throughfall estimates for forested ecosystems
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Appendix 7-6
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Terrestrial Nutrient Enrichment Case Study
1 can provide better approximations for total atmospheric nitrogen deposition levels; however,
2 such estimates and related bioassessment data are not available for the entire country.
3 Finally, in the area of what is still unknown, the exact relationship between atmospheric
4 nitrogen loadings, fire frequency and intensity, and nonnative plants, particularly in the CSS
5 ecosystem, have not been quantified. Various conceptual models linking these factors have been
6 developed, but an understanding of cause and effect, seasonal influences, and benchmarks
7 remains undeveloped. These potential confounders are discussed at greater length in Section 3.
8 1.1.3 Benchmarks Selected for This Case Study
9 The data limitations on atmospheric nitrogen deposition (described above), along with
10 current data to describe the full extent and distribution of nitrogen-sensitive U.S. ecosystems,
11 presented a barrier to designing a case study that used quantitative monitoring and modeling
12 tools. Instead, this case study used published research results to identify meaningful ecological
13 endpoints associated with different levels of atmospheric nitrogen deposition.
14 The ecological endpoints that were identified for the CSS and the MCF are included in
15 the suite of ecological endpoints identified in the ISA (U.S. EPA, 2008, Section 3.3). There are
16 sufficient data to confidently relate the ecological effect to a loading of atmospheric nitrogen.
17 For the CSS community, the following ecological benchmarks were identified:
18 • 3.3 kg N/ha/yr—the amount of nitrogen uptake by a vigorous stand of CSS; above this
19 level, nitrogen may no longer be limiting
20 • 10 kg N/ha/yr—mycorrhizal community changes
21 For the MCF community, the following ecological benchmarks were identified:
22 • 3.1 kg N/ha/yr—shift from sensitive to tolerant lichen species
23 • 5.2 kg N/ha/yr—dominance of the tolerant lichen species
24 • 10.2 kg N/ha/yr—loss of sensitive lichen species
25 • 17kg N/ha/yr—leaching of N(V into streams.
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Appendix 7-7
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Terrestrial Nutrient Enrichment Case Study
1 1.1.4 Ecosystem Services
2 Ecosystem services are generally defined as the benefits individuals and organizations
3 obtain from ecosystems. In the 2005 Millennium Ecosystem Assessment (MEA), ecosystem
4 services are classified into four main categories:
5 • Provisioning—includes products obtained from ecosystems
6 • Regulating—includes benefits obtained from the regulation of ecosystem processes
7 • Cultural—includes the nonmaterial benefits people obtain from ecosystems through
8 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic
9 experiences
10 • Supporting—includes those services necessary for the production of all other ecosystem
11 services (MEA, 2005).
12 Atmospheric nitrogen deposition affects CSS and MCF ecological processes that, in turn,
13 are related to ecosystem services. These processes include the following:
14 For CSS:
15 • Decline in CSS habitat, shrub abundance, species of concern—cultural and
16 regulating
17 • Increased abundance of nonnatives—cultural and regulating
18 • Increase in wildfires—cultural and regulating.
19 For MCF:
20 • Change in habitat suitability and increased tree mortality—cultural and regulating
21 • Decline in MCF aesthetics—cultural
22 • Increase in fire intensity, change in forest's nutrient cycling, other nutrients
23 becoming limiting—regulating
24 • Decline in surface water quality—regulating.
25 Terrestrial nutrient enrichment for CSS potentially affects ecosystem services, such as
26 biodiversity; threatened and endangered species and rare species (both national and state);
27 landscape view; water quality; and fire hazard mitigation. Linking ecological endpoint to
28 services involves the measurement of changes in biodiversity and abundance and distribution of
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Appendix 7-8
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Terrestrial Nutrient Enrichment Case Study
1 threatened and endangered species, comparison of past and present photography, and
2 measurement of the distribution of soil moisture with depth and possible N(V leaching. The
3 relationship between fire frequency, CSS ecosystem, and property values will be investigated in
4 the ecosystem services analysis.
5 The case study approach for MCF focused on ecosystem services, such as visual and
6 recreational aesthetics provided by the CSS community and water quality. Linking ecological
7 endpoints to services includes measurement of the density of stands, shifts in tree dominance,
8 shifts in lichen communities, foliar nitrogen increases, and increased NOs concentrations in
9 streams due to leaching.
10 1.2 CASE STUDY SITE SELECTION
11 1.2.1 National Overview of Sensitive Areas
12 The selection of case study areas specific to terrestrial nutrient enrichment began with
13 national GIS mapping. GIS datasets of physical, chemical, and biological properties that were
14 indicative of potential terrestrial nutrient enrichment were considered in order to identify
15 sensitive areas in the United States. The publicly available geospatial datasets outlined in the
16 following paragraphs have been identified as important contributors to terrestrial nutrient
17 enrichment and met the selection criteria.
18 1.2.1.1 Presence of A cidophytic Lichens
19 Acidophytic lichens are known to be sensitive to increased levels of nitrogen loading.
20 Other species are dependent upon lichens for both food and habitat. For this exercise, the list of
21 acidophytic species from Fenn et al. (2008) was used. Data on these species were available for
22 the years 2001 to 2006. Geospatial data were obtained from the U.S. Forest Service (USFS)
23 Forest Inventory and Analysis National Program (FIA) (USFS, 2008a). Locations where
24 acidophytic lichen were identified were defined as being sensitive.
25 1.2.1.2 Anthropogenic Land Cover
26 Urban and agricultural land covers were mapped to so that they could be used to exclude
27 areas that are not sensitive to terrestrial nutrient enrichment, such as agricultural areas and
28 urbanized areas. This information was obtained from the U.S. Geological Survey (USGS)
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Appendix 7-9
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Terrestrial Nutrient Enrichment Case Study
1 National Atlas of the United States (USGS, 2006) and covered the continental United States at a
2 spatial resolution of 1-km grid cells.
3 1.2.1.3 Nitrogen-Sensitive Species Identified in Literature
4 Although there is no known nationwide species that has shown range loss because of
5 additional nitrogen, it was possible to assemble a "patchwork quilt" of species and forest types
6 from across the United States that are identified as sensitive in the published literature. A range
7 was extracted from national datasets for each species or forest type where the range existed. The
8 cumulative extent of all ranges allowed for the definition of sensitive areas in the United States.
9 1.2.1.4 Excluded Datasets
10 The publicly available spatial datasets outlined below were considered for inclusion in
11 the national sensitivity assessment, but were not used.
12 • Soil Nitrogen Content. This pre-1980 dataset was requested but not received at the time of
13 this report's production. The quality of data is uncertain.
14 • Presence of Mountains. The physiographic provinces of the United States were considered
15 to provide leeward sides of mountains that tend to receive a greater amount of atmospheric
16 nitrogen deposition. Continental U.S. data identified were from USGS and dated 1946.
17 The spatial resolution was a scale of 1:7,000,000. If used, the benchmark value would have
18 been for mountain ranges only. However, this dataset was not used because terrain is
19 already taken into account by the CMAQ modeling.
20 1.2.1.5 Overlay Results
21 The extraction of the areas of greatest nutrient enrichment sensitivity was constrained by
22 the relative lack of available national datasets. Therefore, the review involved two steps within
23 the GIS. First, the ranges of sensitive species identified in the literature were combined with a
24 layer of acidophytic lichen distribution. Second, areas of human use (i.e., urban and agricultural
25 land covers) were removed. The resulting map illustrates the area of highest potential sensitivity
26 (see Figure 1.2-1 )
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-10
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Terrestrial Nutrient Enrichment Case Study
1
2
3
4
5
Acidophytic Lichen | | Red Pine J Sugar Maple/Beech/Yellow Birch Coastal Sage Scrub
| Ponderosa Pine ^^| Black Cherry |^| Engelmann Spruce Mojave Desert
j Red Maple/Oak | Pitch Pine ] Engelmann Spruce/Subalpine Fir Grasslands
I CA Mixed Conifer
250
500
750 1,000km
Figure 1.2-1. Areas of highest potential nutrient enrichment sensitivity. (Acidophytic lichens, tree species, and the
extent of the Mojave Desert are from data obtained from the USFS. The extents of coastal sage scrub and California
mixed conifer are from the California Fire and Resource Assessment Program. Grasslands are from the National Land
Cover Dataset [USGS]).
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June 5, 2009
Appendix 7-11
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Terrestrial Nutrient Enrichment Case Study
1 1.2.2 Use of ISA Information and Rationale for Site Selection
2 Potential case study areas identified in the ISA (U.S. EPA, 2008) were considered in site
3 selection along with information gathered in the national GIS analysis. Table 1.2-1 contains the
4 relevant nutrient enrichment areas identified in the ISA.
5 After considering this information, California's CSS and MCF ecosystems were selected
6 for this case study analysis. The following selection factors supplement those listed in the
7 Introduction:
8 • Availability of atmospheric ambient and deposition data (monitored or modeled)
9 • Availability of digitized datasets of biotic communities; fire-prone areas; and sensitive,
10 rare species
11 • Scientific results of research on nitrogen effects for the case study area
12 • Representation of western U.S. ecosystems potentially impacted by atmospheric nitrogen
13 deposition
14 • Scalability and generalization opportunities for risk analysis results from the case studies.
15 California's CSS has been the subject of intensive research in the past 10 years, which
16 has provided the data needed for a first phase of GIS analysis of the role of atmospheric nitrogen
17 deposition in terrestrial ecosystems. California's MCF have an even longer record of study that
18 includes investigations into the effects of atmospheric pollution, changes to forest structure,
19 changes to the lichen communities, and measurements of nitrogen saturation. Another ecosystem
20 that was considered but not selected for this case study was the alpine ecosystem in the Rocky
21 Mountains. As noted in the ISA (U.S. EPA, 2008, Section 3.3), results from a number of studies
22 indicate that nitrates may be leaching from alpine catchments, and there appear to be changes in
23 plant communities related to the deposition of atmospheric nitrogen. The amount of data from
24 these alpine ecosystems is more limited than that from the CSS and MCF. However, the
25 ecological benchmarks suggested for alpine ecosystems were comparable to the benchmarks
26 from CSS and MCF ecosystems.
27 In summary, CSS and MCF were selected as case study areas for the following reasons:
28 • The two ecosystems have significant geographic coverage and are located where urban
29 areas interface with wilderness areas.
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Appendix 7 pg 12
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Terrestrial Nutrient Enrichment Case Study
1 • Both sites are located in areas of sharp atmospheric nitrogen deposition gradients, ranging
2 from low background levels to some of the highest deposition levels recorded in the
3 United States.
4 • The two ecosystems have been researched for extended periods to understand the
5 interactive effects of deposition, climate change, fire, and other stressors.
6 • The results of these research investigations for CSS and MCF result are well documented
7 in the peer-reviewed literature.
8 Table 1.2-1. Potential Assessment Areas for Terrestrial Nutrient Enrichment Identified in the
9 IS A (U.S. EPA, 2008)
Area
Alpine and
subalpine
communities
of the eastern
slope of the
Rocky
Mountains,
CO
Fernow
Experimental
Forest near
Parsons, WV
Bear Brook,
ME
Harvard
Forest, MA
Southern
California
Indicator
Terrestrial
nutrient
enrichment
Terrestrial
nutrient
enrichment
Terrestrial
acidification
Terrestrial
nutrient
enrichment
Terrestrial
nutrient
enrichment
Detailed
Indicator
Biomass
production;
NO3
leaching;
species
richness
Forest
growth
Sugar
maple; red
spruce
Forest
growth —
species
Forest
growth —
species;
coastal sage
scrub
References in U.S. EPA, 2008
Baron et al., 1994; Baron et al., 2000;
Baron, 2006; Bowman, 2000; Bowman
and Steltzer, 1998; Bowman et al., 1993;
Bowman et al., 1995; Bowman et al.,
2006; Burns, 2004; Fenn et al., 2003a;
Fisk et al., 1998; Korb and Ranker, 2001;
Rueth et al., 2003; Seastedt and Vaccaro,
2001; Sherrod and Seastedt, 2001;
Steltzer and Bowman, 1998; Suding et
al., 2006; Williams and Tonnessen, 2000;
Williams et al.,1996a; Wolfe et al., 2001
Adams et al., 1997, 2000; DeWalle et al.,
2006; Edwards and Helvey, 1991;
Gilliam et al., 2006; Peterjohn, 1996
Elvir et al., 2003
Magill et al., 2004; Magill, 2004
Bytnerowicz and Fenn, 1996, 2003a;
Takemoto et al., 2001
Source
ISA, Section
3.3,4.3,
Annex A,
Annex C, and
Annex D
ISA, Section
3.3,4.3,
Annex A,
Annex B
ISA, Section
3.3,4.3,
Annex A,
Annex B,
Annex C
ISA, Sections
2.8,2.10,3.2,
3.3,4.3,
Annex B,
Annex C
ISA, Section
3.2,3.3,3.4,
4.3, Annex B,
Annex C
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Appendix 7 pg 13
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
Area
Jasper Ridge
Biological
Preserve, CA
Loch Vale,
CO
Rocky
Mountain
National Park,
CO
Indicator
Terrestrial
nutrient
enrichment
Terrestrial
nutrient
enrichment
Terrestrial
nutrient
enrichment
Detailed
Indicator
Grasslands
Old-spruce
growth
Tundra
composition
switch
References in U.S. EPA, 2008
Zavaleta et al., 2003
Rueth et al., 2003
Interlandi and Kilham, 1998
Source
ISA, Sections
3.3,4.3
ISA, Section
3.3,4.3,
Annex B
ISA, Section
3.3,4.3,
Annex C
Source: U.S. EPA, 2008.
1 1.3 ECOSYSTEM OVERVIEW
2 1.3.1 Coastal Sage Scrub
3 CSS consists of more than 50 aromatic shrub and subshrub species, which range from
4 approximately 0.5 meters (m) to 2 m in height (Burger et al., 2003; Westman, 1981a). The range
5 of CSS extends from north of San Francisco down to Baja California in the lower elevation
6 coastal range of California (see Figure 1.3-1); however, the species composition may vary with
7 location (Westman, 1981b). According to the California Natural Diversity Database, there are 22
8 floristic alliances of CSS (e.g., Riversidian Sage Scrub, Venturan Sage Scrub, and Diegan Sage
9 Scrub). These alliances consist of similar species that help determine the significance, rarity, and
10 growth patterns of California vegetation types.
11 CSS grows in a warm Mediterranean climate and is characterized by approximately 300
12 millimeters (mm) of annual rainfall falling from December through March and little or no
13 rainfall from April through November (Egerton-Warburton and Allen, 2000; Westman, 1981b).
14 Underlying substrate types of CSS vary greatly across the CSS stands, although many CSS
15 floristic alliances are found on unconsolidated sand, sandstone, conglomerate, and shale
16 (Westman, 1981b).
17 CSS is also known as "soft chaparral" because of its semideciduousness, drought-tolerant
18 nature, and less-rigid leaves, respective to chaparral species (Westman, 1981b). CSS is
19 considered a fire-adapted community, meaning that although vegetation layers may be destroyed
20 in fires, CSS soil seed banks can withstand fire, and in some species, require fire to open the seed
21 cases. However, many CSS species can flourish and propagate in the absence of any fire (Keeler-
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Appendix 7 pg 14
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Terrestrial Nutrient Enrichment Case Study
1 Wolf, 1995). CSS has been observed to maintain a permanent cover without fire or other
2 disturbance regimes (e.g., land conversion, grazing) for at least a century (Westman, 1981a).
4
5
6
7
8
9
10
11
12
13
I CMMal Sa^e Scrub 2032
County
SOUIM or CSS range t* me CaJilomia Decwtmorti
61 Fwaitiy fln^ F*o Pr«Kl»n
This data «« ptiMi&lvd ri 3002 tiaymg bean
Figure 1.3-1. Range of coastal sage scrub ecosystems.
The resprouting and competition of species post-fire is generally dependent upon fire
intensity, fire frequency, and seasonal timing (Keeler-Wolf, 1995). CSS species are generally
poor colonizers after a fire (Minnich and Dezzani, 1998). Annual forbs and any grass seedlings
present in the post-fire soils are usually dominant in the first few growth cycles. Significant
shrub growth is most likely to occur in later cycles, further disturbance not withstanding (Keeler-
Wolf, 1995).
The CSS ecosystem also supports the growth of more than 550 herbaceous annual and
perennial species between and beneath the shrub canopy. Of these herbs, nearly half are
endangered, sensitive, or of special status (Burger et al., 2003). Additionally, several avian,
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Appendix 7 pg 15
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1
2
3
4
5
6
arthropod, reptilian, amphibian, and mammalian species depend on CSS habitat for foraging,
breeding, and/or residence. These include several threatened and endangered species, such as the
coastal California gnatcatcher (Polioptila californica californica), the Stephens' kangaroo rat
(Dipodomys stephenst), and the Quino checkerspot butterfly (Euphydryas editha quino). Figure
1.3-2 presents the range of these three species. Table 1.3-1 presents a selected list of flora and
fauna species that are associated with CSS habitat.
7
8
9
lOS'ANGELES
***£ LOS^KS
SAM BERNARDINO
® Cities
[ J County
Quino Checkerspot Butterfly
Kangaroo Rat
^^| Coastal CA Gnatcatcher
| Coastal Sage Scaib 1998
Source Qf CSS range is the California Departmenl
of Forestry and Fire Prolsction.
Source of critical tiabitafs s (he US Fish and Wildlife
Service Critical Wildlife Portal.
Figure 1.3-2. Presence of three threatened and endangered species in
California's coastal sage scrub ecosystem.
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Terrestrial Nutrient Enrichment Case Study
1 Table 1.3-1. Selected Flora and Fauna Associated with the Coastal Sage Scrub Habitat
Scientific Name
Buteo swainsoni
Polioptila californica californica
Dipodomys merriami parvus
Dipodomys stephensi
Bufo microscaphus californicus
Euphydryas editha quino
Rhaphiomidas terminatus
abdominalis
Allium munzii
Rosa minutifolia
Deinandra conjugens
Cordylanthus orcuttianus
Ambrosia pumila
Acanthomintha ilicifolia
Campylorhynchus brunneicapillus
couesi
Athene cunicularia
Cnemidophorus hyperythrus
Phrynosoma coronatum blainvillei
Masticophis lateralis euryxanthus
Common Name
Swainson's Hawk
Coastal California Gnatcatcher
San Bernardino Kangaroo Rat
Stephens' Kangaroo Rat
Arroyo Toad
Quino Checkerspot Butterfly
Delhi Sands Flower-Loving Fly
Munz's Onion
Small-Leaved Rose
Otay Tarplant
Orcutt's Bird's Beak
San Diego Ambrosia
San Diego Thorn-Mint
Coastal Cactus Wren
Burrowing Owl
Orange-Throated Whiptail
San Diego Horned Lizard
Alameda Whipsnake
Life Form
Bird
Bird
Mammal
Mammal
Amphibian
Insect
Insect
Perennial Forb
Shrub
Annual Forb
Annual Forb
Perennial Forb
Annual Forb
Bird
Bird
Reptile
Reptile
Reptile
Federal Listing*
Not listed
Threatened
Endangered
Endangered
Endangered
Endangered
Endangered
Endangered
Not listed
Threatened
Not listed
Proposed Endangered
Threatened
Not listed
Not listed
Not listed
Not listed
Threatened
State Listing*
Threatened
Not listed
Not listed
Threatened
Not listed
Not listed
Not listed
Threatened
Endangered
Endangered
Not listed
Not listed
Endangered
Not listed
Not listed
Not listed
Not listed
Threatened
* Status listed for threatened and endangered species only. Others may be species of concern, on federal watch lists, or state special status.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 7-17
-------
Terrestrial Nutrient Enrichment Case Study
1 The principal source of nitrogen to the CSS ecosystem is atmospheric nitrogen (e.g.,
2 NOX, reduced nitrogen (NHX)). These nitrogen species are transported and deposited onto the
3 historically nitrogen-limited CSS soil in the form of nitrates and nitric acid. In the soil, these
4 nitrogen species are potentially available for plant uptake and nutrient cycles. The effects of
5 increased availability of nitrogen species in the CSS ecosystem were the focus of this case study.
6 1.3.2 Mixed Conifer Forest
7 MCF stand approximately 30 to 50 m tall and consist of conifer species that dominate
8 mid-range elevations (1300 to 2800 m) of the California San Bernardino and Sierra Nevada
9 mountain ranges. The San Bernardino Mountains lie east of the Los Angeles air basin, and the
10 Sierra Nevada Range span the majority of the state longitudinally. Figure 1.3-3 illustrates the
11 range of MCF in California. MCF have historically adapted to withstand fire at low, medium,
12 and even high intensities. As in the range of CSS, the climate is Mediterranean, where 80% of
13 rainfall occurs from October through March (Takemoto et al., 2001).
14 Dominant tree species shift along a precipitation gradient. Ponderosa pine (Pinus
15 ponder osa}, white fir (Abies concolor), sugar pine (P. lambertiana\ and incense cedar
16 (Calocedrus decurrens) are the predominant species on moist windward slopes, whereas Jeffrey
17 pine (P. jeffreyf) and white fir are commonly found on leeward slopes and at higher elevations in
18 the mixed conifer elevation range. Important deciduous components of the MCF are canyon live
19 oak (Quercus chrysolepis), black oak (Quercus kelloggi), and quaking aspen (Popus
20 tremuloides). These stands support a number of shrubs, subshrubs, and annual and perennial
21 forbs, as well as mountain meadows (Minnich, 2007). Federal-listed species, Rana sierrae and
22 Rana muscosa (both called the mountain yellow-legged frog), and a number of state-listed
23 species, such as the Peninsular bighorn sheep (Ovis canadensis nelsoni), utilize MCF habitat.
24 The range of two of these selected species is illustrated in Figure 1.3-4. Table 1.3-2 shows
25 selected flora and fauna associated with MCF habitat.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-18
-------
Terrestrial Nutrient Enrichment Case Study
| County
| San Bernard-no rjF
Sierra tsejacis
SourM or mud coniter rang* » dw California Dwanmtin
or Fonclry m) Fi
Figure 1.3-3. Range of California's mixed conifer forests.
County
Peninsular Bighorn Sheep
Mln Yellow-legged Frog
I Mixed Conifer
Source of the Mountain Yellow-legged Frog and Peninsular Bighorn Sheep ranges
is trie US Forest Service critical HabiEai Portal.
Source ol Mixed Conifer is CA Dept.of Forestry and Fire Protection.
Figure 1.3-4. Presence of two threatened and
endangered species and mixed conifer forests.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 7-19
-------
Terrestrial Nutrient Enrichment Case Study
1 Table 1.3-2. Selected Flora and Fauna Associated with the Mixed Conifer Forest Habitat
Scientific Name
Abies concolor
Pinus ponder osa
Pinus lambertiana
Calocedrus decurrens
Rana sierrae
Spea hammondii
Rana muscosa
Glaucomys sabrlnus
Glaucomys sabrlnus californicus
Ovis canadensis nelsoni
Odocoileus hemionus
Charina umbratica
Packera bernardlna
Sidalcea pedata
Perideridia parishii ssp. parishii
Taraxacum californicum
Gilia leptantha ssp. leptantha
Piranga rubra
Haliaeetus leucocephalus
Strix occidentalis occidentalis
Strix nebulosa
Common Name
White Fir
Ponderosa Pine
Sugar Pine
Incense Cedar
Sierra Madre Yellow-Legged Frog
Western Spadefoot
Sierra Madre Yellow-Legged Frog
Northern Flying Squirrel
San Bernardino Flying Squirrel
Peninsular Bighorn Sheep
Black-Tailed Deer
Southern Rubber Boa
San Bernardino Ragwort
Bird-Foot Checkerbloom
Parish's Yampah
California Dandelion
San Bernardino Gilia
Summer Tanager
Bald Eagle
California Spotted Owl
Great Gray Owl
Life Form
Tree
Tree
Tree
Tree
Amphibian
Amphibian
Amphibian
Mammal
Mammal
Mammal
Mammal
Reptile
Perennial Forb
Perennial Forb
Perennial Forb
Perennial Forb
Shrub
Bird
Bird
Bird
Bird
Federal Listing*
Not listed
Not listed
Not listed
Not listed
Endangered
Not listed
Endangered
Not listed
Not listed
Endangered
Not listed
Not listed
Not listed
Endangered
Not listed
Endangered
Not listed
Not listed
Delisted
Not listed
Not listed
State Listing*
Not listed
Not listed
Not listed
Not listed
Not listed
Not listed
Not listed
Not listed
Not listed
Threatened
Not listed
Threatened
Not listed
Endangered
Not listed
Not listed
Not listed
Not listed
Endangered
Not listed
Endangered
* Status listed for Threatened and Endangered species only. Others may be species of concern, on federal watch lists, or state special status
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 7-20
-------
Terrestrial Nutrient Enrichment Case Study
1 Additionally, several lichen species are associated with the MCF habitats. Lichens are
2 formed by a symbiotic relationship between fungus and algae or cyanobacterium. In the MCF
3 ecosystem, lichens are generally epiphytic, living on conifers and obtaining nutrients from the
4 atmosphere. Epiphytic lichens serve as food, habitat, and nesting material for various species in
5 the pine stands (Fenn et al., 2008). The presence of individual species is determined by the
6 amount of nitrogen present and the pH of the vegetation on which it grows; however, general
7 categories for lichens have been developed according to species' sensitivity to nitrogen. These
8 categories include nitrophytes, neutrophytes, and acidophytes (Jovan, 2008). Nitrophytes are
9 generally associated with ammonia and high pH environments. Neutrophytes tolerate increased
10 pH and ammonia, but exhibit slower growth patterns than nitrophytes when exposed to these
11 conditions. Acidophytes are sensitive to nitrogen species and deteriorate or die after relatively
12 small increments of exposure to nitrogen species (Fenn et al., 2008). Table 1.3-3 presents a list
13 of lichen species, classified by nitrogen sensitivity, that have been observed in the San
14 Bernardino Mountains and Sierra Nevada Range.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-21
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Terrestrial Nutrient Enrichment Case Study
1 Table 1.3-3. List of Lichen Species Present in the Sierra Nevada Range and San Bernardino Mountains (Jovan, 2008; Sigal and Nash,
2 1983)
Nitrophytes
Candelaria concolor
Flavopunctelia flaventiorb
Phaeophyscia orbicularis
Physcia adscendens
Physcia aipolia
Physcia dimidiate
Physcia stellaris
Physcia tenella
Physconia enteroxantha
Physconia perisidiosa
Xanthomendoza fallax
Xanthomendoza fitlva
Xanthomendoza hasseana
Xanthomendoza oregano
Xanthoria candelaria
Xanthoria polycarpa
Potential Acidophytes
Bryoria fremontii
Cetraria canadensis
Cetraria chlorophylla
Cetraria merrillii
Cetraria orbata
Cetraria pallidula
Cetraria platyphylla
Evernia prunastri
Hypogymnia enteromorpha
Hypogymnia imshaugii
Hypogymnia occidentalis
Parmeliopsis ambigua
Platismatia glauca
Usnea filipendula
-
-
Potential Neutrophytes
Melanelia elegantula
Melanelia exasperatula
Melanelia glabra
Melanelia subargentifera
Melanelia subelegantula
Melanelia subolivacea
Parmelia hygrophilab
Parmelia sulcata
Ramalina subleptocarphab
-
-
-
-
-
-
-
Unknown
Ahtiana sphaerosporella
Alectoria sarmentosa
Collema furfuraceum
Esslingeriana idahoensis
Leptogium lichenoides
Letharia columbiana
Letharia vulpina
Nodobryoria abbreviata
Nodobryoria oregana
Parmelina quercina
Parmelina elegantula
Physcia biziana
Physconia americana
Physconia isidiigera
-
-
2nd Draft Risk and Exposure Assessment
Appendix 7-22
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1 1.4 HISTORICAL TRENDS
2 1.4.1 Coastal Sage Scrub
3 The CSS ecosystem is a unique system that has experienced a significant decline in
4 coverage since vegetation types in Southern California were inventoried in 1929. Subsequently,
5 this community was designated for special status in California (CA DFG, 1993). This decline is
6 due to urban encroachment and sprawl, increased fire frequencies, and pollution (Minnich and
7 Dezzani, 1998). CSS is decreasing at a higher rate than habitat destruction alone would indicate
8 (Allen et al., 1998; Fenn et al., 2003; Minnich and Dezzani, 1998).
9 Nonnative grasses were introduced to California by explorer expeditions and Franciscan
10 missionaries arriving in the region prior to documentation of indigenous vegetation. However,
11 accounts of herbaceous vegetation in the coastal range exist from the late 1700s and throughout
12 the 1800s (Minnich and Dezzani, 1998). CSS was first scientifically inventoried during the
13 California Forest and Range Experiment Station Vegetation Type Map (VTM) Survey,
14 beginning in 1929. Recently, 54 of the VTM sites in Southern California that were dominated by
15 CSS cover in the 1930s were resampled (Talluto and Suding, 2008). Since the 1930s, CSS
16 declined 49% and was mainly replaced by nonnative annual grasses (Talluto and Suding, 2008).
17 Figure 1.4-1 illustrates the decline in CSS from 1977 to 2002.
18 Based on changes in CSS cover from VTM data since the early 1930s, it is estimated that
19 approximately 18% of the extent of Riverside County CSS had been completely converted to
20 nonnative grasses, and an additional 42% of the cover had nonnative grasses intermixed with
21 CSS. Therefore, only 40% of the original extent of CSS in Riverside County remained intact and
22 contiguous. The 2005 resampling of part of Riverside and Orange counties indicated that 15% of
23 the remaining CSS had not been invaded by annual grasses (Talluto and Suding, 2008). Across
24 the entire CSS range, Westman (1981a) estimated that only 10% to 15% of the historical CSS
25 extent remained in the late 1970s. This estimate is based upon the fraction of potential CSS land
26 cover (in the absence of pressures) in which CSS vegetation was actually observed at the time of
27 the study. The potential CSS land cover estimates may also be supported by the broad range in
28 which specimens of the Quino checkerspot butterfly have historically been observed and
29 collected (Mattoni et al., 1997). Therefore, the remaining extent of CSS is most likely 10% to
30 82% of the historical CSS coverage, depending on the development pressures and the spread of
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-23
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Terrestrial Nutrient Enrichment Case Study
1 nonnative grasses in each stand. Additionally, these nonnative grasses are less diverse and are
2 not likely to support the majority of the sensitive, threatened, and endangered species that
3 currently rely on CSS (Allen et al., 2005).
4
5
6
7
8
9
10
11
12
13
14
County
| Coastal Sage Scrub 1977
Swuw o' CSS rango
Q. the Cjnoma Pdpsivwnl
of Fix«sy anH fft
j County
| Coastal Sage Scrub ZOT2
Source of CSS iwo*
•stne C*Sfwrx) Oepwnonl
or Fwestry »n
-------
Terrestrial Nutrient Enrichment Case Study
1 Additionally, a 79% increase in the average number of tree branches was reported in the San
2 Bernardino conifer forests. Studies have indicated that increasing stand densities are also
3 occurring within the Sierra Nevada Range (Minnich et al., 1995).
4 Increased litter on the forest floor has also been observed across the conifer ecosystems,
5 particularly in the MCF stands in the San Bernardino Mountains. These MCF stands have been
6 observed to shed needles approximately six times faster than more remote northern Sierra
7 Nevada Range conifer stands (Takemoto et al., 2001). Additionally, litterfall depths up to 15
8 centimeters (cm) have been noted in MCF stands near Camp Paivika in the eastern San
9 Bernardino Mountains (Grulke et al., 2008).
10 Across the San Bernardino Mountains, a tree community composition shift was also
11 noted. In MCF stands where Ponderosa pine has been historically dominant, trees in the youngest
12 age bracket are now predominantly white fir and incense cedar. Additional research is needed to
13 determine if a shift in community composition is also occurring in the Sierra Nevada Range
14 MCF (Minnich et al., 1995). Although research on understory communities revealed no clear
15 trends with atmospheric nitrogen deposition and ozone (Os), it was noted that native diversity
16 had declined in those areas receiving the highest loads of atmospheric nitrogen (Allen et al.,
17 2007).
18 Lichen communities associated with the MCF habitat have also been dramatically altered
19 (Fenn et al., 2003, 2008; Sigal and Nash, 1983). Of the!6 lichen species reported to be associated
20 with the San Bernardino Mountains MCF in the early part of the 20th century, only 8 species
21 were found 60 years later. Additionally, deterioration was observed on some of the lichens,
22 particularly in the areas with the highest levels of air pollution (Sigal and Nash, 1983). Lichens
23 are significant members of the MCF community. They serve as forage for wildlife, and changes
24 in the lichen community are considered by some to be a warning signal for deteriorating
25 conditions in the rest of the forest.
26 2. APPROACH AND METHODOLOGY
27 Using the approach and methodology described below, a number of significant ecological
28 endpoints have been identified. These results come from empirical results and from spatial
29 databases. Dose/response relationships beyond benchmark values were investigated, but these
30 have not yet been well quantified. Nitrogen deposition data was available at a 12-km resolution,
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-25
-------
Terrestrial Nutrient Enrichment Case Study
1 and many of the ecosystems, especially CSS, are fragmented into smaller areas. The analysis is,
2 therefore, somewhat limited by the discrepancy between resolution of the nitrogen deposition
3 data and the distribution of habitats, as well as by the specific areas where ecological processes
4 were measured. Further, some models have been tested, but with limited results. For example,
5 the steady-state simple mass-balance model (UNECE, 2004) still has many unresolved
6 uncertainties. Uncertainty exists in establishing the linkage between soil and biological impacts
7 and the ability to account for forest management and wildfires (Fenn et al., 2008). The DayCent
8 biogeochemical model is not a watershed-scale model and may not represent N(V leaching
9 accurately. Although, application of DayCent yielded results more comparable to empirically
10 based findings than the steady-state model (Fenn et al., 2008).
11 For the above reasons, empirical data, in tandem with GIS analysis, was deemed more
12 suitable to develop potential correlations between atmospheric nitrogen deposition and
13 ecological endpoints.
14 2.1 PUBLISHED RESEARCH
15 The ISA (U.S. EPA 2008, Sections 3.3, 4.3) was used as the basis for identifying the
16 published scientific literature on CSS and MCF ecosystems.
17 2.2 GIS METHODOLOGY
18 2.2.1 Overview
19 For both the CSS and MCF ecosystems, spatially distributed data are available. Some of
20 the variables that are known to influence terrestrial nutrient enrichment and have been cited in
21 the literature are available as either state-level or national-level datasets. It is important that
22 spatial data are temporally and spatially compatible and have well-documented metadata. It is
23 also desirable that they possess the ability to be scaled-up for a national characterization.
24 2.2.2 Available Data Inputs
25 2.2.2.1 Nitrogen Deposition
26 Wet nitrogen deposition in the forms of NOs and ammonium (NH4+)are available
27 nationally from the NADP. This national network of 321 sampling stations is the best wet
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-26
-------
Terrestrial Nutrient Enrichment Case Study
1 monitored data available. Scientists at the NADP have created continuous surfaces of deposition
2 values by using interpolation algorithms to estimate values between measurements at known
3 locations. Dry nitrogen deposition can be estimated using the output from the CMAQ 2002
4 modeling system. This model produced estimates of many nitrogen species aggregated to 12-
5 kilometer (km) squares. Although these data are fairly coarse spatially, they are the best that are
6 currently available.
7 2.2.2.2 Range of Coastal Sage Scrub
8 Several publicly available spatial vegetation datasets were examined for this analysis.
9 They range in dates from 1945 to 2002 and are compiled from a combination of field data and
10 remotely-sensed imagery.
11 The Wieslander VTM (USFS, 2008b) collection is a dataset published in 1945 that
12 brought together data recorded on photos, species inventories, plots maps, and vegetation maps.
13 This dataset was obtained from the California Spatial Information Library at the University of
14 California, Davis. It divided the entire state of California into polygons that were attributed with
15 23 different vegetation types (i.e., communities) such as "coastal sagebrush" or "chaparral."
16 Individual species were not recorded.
17 Another vegetation layer named CALVEG (California Vegetation) was created in 1977
18 from LANDS AT imagery that was used to create 1:1,000,000 scale maps. This dataset is
19 available from the California Department of Forestry and Fire Protection's Fire and Resource
20 Assessment Program (FRAP) Web site (California Department of Forestry and Fire Protection,
21 2007a). This dataset contains land cover/land use polygons for California digitized from the
22 1:1,000,000 scale maps. The minimum mapping unit is approximately 400 acres, and the data
23 contains vegetation attributes for series-level species groups only.
24 A land cover change dataset is also available from the FRAP Web site that uses Thematic
25 Mapper (TM) data from 1993 and 1997 to determine areas of change (California Department of
26 Forestry and Fire Protection, 2007b). This dataset also contains information on the cause of the
27 change. The spatial resolution of this dataset is 30-meter pixels. These data do not contain
28 species-level data; these contain only community-level data.
29 California Gap Analysis Project (GAP) data is available from the University of
30 California, Santa Barbara Biogeography Lab (Davis et al., 1998). It contains vegetation attributes
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-27
-------
Terrestrial Nutrient Enrichment Case Study
1 for landscape scale map units, including canopy dominant species, canopy density, presence of
2 regional endemic species, and inclusion of wetland habitats. These data were published in 1998
3 and used a variety of sources including TM data, aerial photography, Wieslander VTM data, and
4 field maps.
5 The most recent land cover data for the state of California is also available from the
6 FRAP website. It was published in 2002 and was created by compiling the best available land
7 cover data into a single data layer. This agency classified California's vegetation into 59
8 different categories, including CSS, at a spatial resolution of 100 m. Decision rules were
9 developed that controlled which layers were given priority in areas of overlap. Cross-walks were
10 used to compile the various sources into the common California Wildlife Habitat Relationships
11 system classification. No species specific data are available.
12 One of the central analytical tasks for this case study was to quantify the amount of CSS
13 and MCF extent loss and to see if loss corresponded spatially to areas of high nitrogen
14 deposition, fire threat, or both. The land cover change layer created by the California Department
15 of Forestry and Fire Protection was used for this case study analysis. While the temporal
16 difference for this layer depicting land cover change was fairly small (i.e., 5 years), the two
17 datasets used to create the change layer were fully compatible, and the results were verified by
18 field confirmation.
19 The feasibility of using the 1945 VTM, the 1977 CALVEG, the 1998 GAP, and the 2002
20 FRAP land cover data to determine changes in the extent of CSS and MCF ecosystems was
21 evaluated. In each case, the data sources, spatial resolution, and classification schemes were
22 different enough to prevent any meaningful measurement of change in these communities.
23 In addition to publicly available datasets, research datasets were obtained and plotted.
24 Field data were obtained directly from Talluto and Suding (2008). While these data provided
25 very detailed measurements of species distribution and percentage ground cover for their study
26 areas, they were not sufficiently spatially dispersed across the CSS range, nor were they
27 compatible with the very spatially coarse (i.e., 12-km grid size) CMAQ-modeled nitrogen
28 deposition data.
29 Additionally, The Kuchler Potential Natural Vegetation (PNV) Groups (Kuchler, 1988)
30 data layer that was created to show "climax" vegetation was not used because the intent of this
31 case study was to quantify known changes to the extent of CSS. The PNV data illustrates where
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-28
-------
Terrestrial Nutrient Enrichment Case Study
1 the vegetation might potentially be found without disturbance or climate change. PNV is an
2 expression of environmental factors, such as topography, soils, and climate across an area.
3 2.2.2.3 Fire Threat
4 The California Department of Forestry and Fire Protection's FRAP also compiles data
5 about fire threat. These data consider fire rotation (i.e., how frequently fire occurs) and potential
6 fire behavior, which take into account topography and potential vegetative fuels. Fire threat is
7 classified into four unique categories that range from moderate to extreme.
8 2.2.2.4 Changes in Coastal Sage Scrub Communities
9 Although spatial datasets mapping CSS communities exist for 1945, 1977, 1998, and
10 2002, none are compatible enough to calculate meaningful change (e.g., the methods used to
11 ascertain CSS extent and define ecosystems were not consistent across datasets). Therefore, a
12 spatial dataset published by the California Department of Forestry and Fire Protection was
13 chosen. This dataset documented change to CSS and other ecosystems between 1993 and 1997.
14 2.2.2.5 Distribution of Invasive Species
15 Two data sources for invasive species were found for California. The first is the PLANTS
16 program, which is part of the U.S. Department of Agriculture (USDA) (USDA, 2009;
17 http://plants.usda.gov/index.html). This resource posts maps that indicate whether a species is
18 present or not in a given county, but not the distribution of that species within the county. The
19 second is the California Invasive Plant Council (2008), (http://www.cal-
20 ipc.org/ip/mapping/statewide_maps/index.php), which lists the relative abundance by county of a
21 select number of species.
22 2.2.2.6 Threatened and Endangered Species Habitat
23 The U.S. Fish and Wildlife Service (FWS) publishes critical habitat range information for
24 threatened and endangered species by state, county, and species through the Critical Habitat
25 Portal (http://crithab.fws.gov/) (U.S. FWS, 2008). For example, the Critical Habitat Portal
26 locates 16 species for Riverside County, 5 of which are associated with CSS habitat.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-29
-------
Terrestrial Nutrient Enrichment Case Study
1 2.2.2.7 Range of Mixed Conifer Forest
2 The most recent (2002) land cover dataset from the California Department of Forestry
3 and Fire Protection's FRAP Web site was used to extract the range of MCF. Research data were
4 also obtained from a series of sample plot locations documented in Fenn et al. (2008), The
5 locations of the field sites were listed as latitude and longitude coordinates, which were
6 converted into a GIS layer with atmospheric nitrogen deposition as an attribute.
7 2.2.2.8 Distribution of Acid-Sensitive Lichens
8 The USFS FIA datasets were the source of lichen distributions.
9 3. RESULTS
10 Effects of elevated atmospheric nitrogen deposition on the CSS and MCF ecosystems are
11 the result of increased long-term chronic, rather than short-term pulsed, nitrogen deposition. It is
12 difficult to quantify effects in both ecosystems because of confounding stressors, such as fire and
13 Os. The literature available on long-term research and application of robust models on these
14 ecosystems is extremely limited.
15 The CSS analysis relies upon peer-reviewed literature and spatial analyses to derive
16 major conclusions regarding the effects of nitrogen. Spatial analyses were used to determine the
17 changes in the extent of CSS ecosystems and their associated habitat, as well as to investigate the
18 effects of nitrogen and fire, another driving component in the alteration of the CSS ecosystem.
19 The reviewed literature includes greenhouse experiments, field observations, and field
20 manipulation experiments that document the observed and measured effects of nitrogen.
21 The MCF analysis also contains a summary of the peer-review literature; however, this
22 case study focused on the empirical loading benchmarks derived from an analysis by Fenn et al.
23 (2008), which employed observational data and the Simple Mass Balance (8MB) model and the
24 DayCent simulation model to estimate critical loads. However, there are identified limitations to
25 both models (e.g., 8MB does not account for the effects of prescribed burns or wildfires on
26 nutrient uptake, and DayCent is not a watershed-scale model, and thus, does not accurately
27 represent N(V concentrations in surface and groundwater). Fenn et al. (2008) conclude that the
28 empirical approach is the most reliable source of information.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-30
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Terrestrial Nutrient Enrichment Case Study
1 3.1 LITERATURE REVIEW FINDINGS
2 3.1.1 Coastal Sage Scrub
3 CSS is subject to several pressures, such as land conversion, grazing, fire, and pollution,
4 all of which have been observed to induce declines in other ecosystems (Allen et al., 1998). At
5 one extreme, development pressure (i.e., the conversion of CSS to residential and commercial
6 land uses) will simply eliminate acres of CSS. Other pressures will come into play in modifying
7 the remaining habitat. Research suggests that both fire and increased atmospheric nitrogen
8 deposition can enhance the growth of nonnative grasses in established CSS ecosystems.
9 Additionally, CSS declines have been observed when fire frequency is held constant and/or
10 nitrogen is held constant, suggesting that both fire and nitrogen play a role in CSS decline when
11 direct destructive factors are not an imminent threat. Table 3.1-1 contains a summary of selected
12 experimental variables across multiple CSS study areas.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-31
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Terrestrial Nutrient Enrichment Case Study
1 Table 3.1-1. Summary of Selected Experimental Variables across Multiple Coastal Sage Scrub Study Areasa
Study Locations
Riverside-Ferris Plainb
Santa Margarita Ecological
Reserve
Santa Monica Mountains
Orange County b
Rancho Jamul Ecological
Reserve
Voorhis Ecological Reserve
Riverside-Ferris Plain b
Sedgwick Ranch Natural
Reserve
Southern California fuel
breaks b
Critical review b
Southern California burn sites
b
Riverside-Ferris Plain b
Greenhouse experiment
Riverside-Ferris Plain b
University of California-
Riverside Agricultural
Research Station
Riverside-Ferris Plain b
Soil
Nitrogen
X
X
X
X
X
X
X
Atmospheric
Nitrogen
X
X
Vegetation
Change
X
X
X
X
X
X
X
X
X
X
X
X
Mycorrhizae
Change
X
X
Fire
Cycle
X
X
X
X
Author
Allen etal., 1998
Burger et al., 2003
Carrington and Keeley, 1999
Diffendorfer et al., 2007
Drus, 2004
Egerton-Warburton and Allen,
2000
Fierer and Gabet, 2002
Merriam et al., 2006
Keeley, 2001
Keeley et al., 2005
Minnich and Dezanni, 1998
Padgett et al., 1999
Padgett and Allen, 1999
Padgett et al., 2000
Siguenza et al., 2006
2nd Draft Risk and Exposure Assessment
Appendix 7-32
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
Study Locations
Riverside-Ferris Plain b
Lake Skinner
Riverside-Ferris Plain b
67 sites across CSS range b
Riverside-Ferris Plain b
Lake Skinner Western
Riverside County Multi-
Species Reserve
Greenhouse experiment
Soil
Nitrogen
X
X
X
X
Atmospheric
Nitrogen
Vegetation
Change
X
X
X
Mycorrhizae
Change
X
X
X
Fire
Cycle
Author
Sirulnik et al., 2007a
Sirulnik et al., 2007b
Vourlitis et al., 2007
Westman, 1979, 1981a,b
Wood et al., 2006
Yoshida and Allen, 2001
a Empty cells indicate not studies identified.
b Multiple data sites within the study location.
2nd Draft Risk and Exposure Assessment
June 5, 2009
Appendix 7-33
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Terrestrial Nutrient Enrichment Case Study
1 3.1.1.1 Atmospheric Nitrogen Deposition
2 Increased atmospheric nitrogen deposition has been observed to alter vegetation types
3 when nitrogen is a limiting nutrient to growth. This has been observed in alpine plant
4 communities in the Colorado Front Range, as well as in lichen communities in the western Sierra
5 Nevada region (Fenn et al., 2003, 2008); however, in the case of CSS, it is hypothesized that
6 many stands are no longer limited by nitrogen and have instead become nitrogen-saturated due to
7 atmospheric nitrogen deposition (Allen et al., 1998; Westman, 1981a). This is supported by the
8 positive correlation between atmospheric nitrogen and soil nitrogen, increased long-term
9 mortality of CSS shrubs, and increased nitrogen-cycling rates in soil and litter and soil fertility
10 (Allen et al., 1998; Padgett et al., 1999; Sirulnik et al., 2007a; Vourlitis et al., 2007). Figure
11 3.1-1 illustrates the levels of atmospheric nitrogen deposition on CSS ecosystems using 2002
12 CMAQ/NADP data.
13 Wood et al. (2006) investigated the amount of nitrogen used by healthy and degraded
14 CSS ecosystems. In healthy stands, the authors estimated that 3.3 kg N/ha/yr was used for CSS
15 plant growth (Wood et al., 2006). It is assumed that 3.3 kg/ha/yr is near the point where nitrogen
16 is no longer limiting in CSS. Therefore, this amount can be considered an ecological benchmark
17 for CSS. Figure 3.1-1 displays the spatial extent of CSS where nitrogen deposition is above the
18 ecological benchmark of 3.3 kg/ha/yr. As shown in Figure 3.1-1 and Table 3.1-2, almost all of
19 CSS receive >3.3 kg/ha/yr of nitrogen through atmospheric deposition. Note that CSS is
20 observed in areas receiving >3.3 kg/N/ha/yr. This distribution may result from time lags (i.e.,
21 years may be required for the CSS ecosystem to completely disappear), or it may indicate that
22 3.3 kg nitrogen, although ecologically meaningful, may not be the benchmark value.
23
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-34
-------
Terrestrial Nutrient Enrichment Case Study
\
j j Counties
^^| Coastal Sage Scrub
Total N Deposition
kg/ha/yr
| | less than 3.3
| | 3.3-9.9
| 10 or greater
Source of CSS range is the California Department
of Forestry and Fire Prelection.
2
3
Figure 3.1-1. Coastal sage scrub range and total nitrogen deposition using CMAQ
2002 modeling results and NADP monitoring data.
2nd Draft Risk and Exposure Assessment
Appendix 7-35
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
Table 3.1-2. Coastal Sage Scrub Ecosystem Area and Nitrogen Deposition
N Deposition
(kg/ha/yr)
>3.3
>10
Area (hectares)
654,048
138,019
Percent of
CSS Area, %
93.51
19.73
2 3.1.1.2 Normative Grasses
3 The ecological effects of increased nitrogen are most easily explained by considering the
4 seasonal stages of a semiarid Mediterranean ecosystem. In the rainy, winter season, deposited
5 surface nitrogen is transported deeper into the soil and is rapidly mineralized by microbes, thus
6 making it available for plants. Faster nitrogen availability may favor the germination and growth
7 of nitrophylous colonizers, more specifically nonnative grasses (e.g., Bromus madritensis., Avena
8 fatua, and Hirschfeldia incand). This earlier flourishing of grasses can create a dense network of
9 shallow roots, which slows the diffusion of water through soil, decreases the percolation depth of
10 precipitation, and decreases the amount of water for soil and groundwater recharge (Wood et al.,
11 2006). Growth of CSS species, such as Artemisia californica, Eriogonumfasciculatum, and
12 Encelia farinose, may be reduced because of decreased water and nitrogen availability at the
13 deeper soil layers where more woody CSS tap roots are found (Keeler-Wolf, 1995; Wood et al.,
14 2006). Furthermore, an increased percentage of shrub species is established during wet years,
15 suggesting that percolation of nutrient-carrying water may be limited in years with average or
16 below average precipitation (Keeley et al., 2005).
17 3.1.1.3 Mycorrhizae
18 Elevated nitrogen may also play a role in altering the nutrient uptake of CSS plants by
19 decreasing the species richness and abundance of mutualistic fungal communities, such as
20 arbuscular mycorrhizae (AM) (Egerton-Warburton and Allen, 2000; Siguenza et al., 2006).
21 Although both CSS and nonnative grass species have AM and other mycorrhizal associations,
22 which increase the surface area and capacity for nutrient uptake, CSS is predominantly colonized
23 by a coarse AM species, and nonnative grasses are more likely mutualistic with finer AM
24 species. In the presence of elevated nitrogen, coarse AM colonizations were depressed in number
25 and volume. Egerton-Warburton and Allen (2000) documented shifts in AM species as well as
26 declines in spore abundance and colonization at approximately 10 kg N/ha/yr. In areas with the
27 highest levels of soil nitrogen tested (e.g., 57 micrograms per gram (ug/g) average annual soil
2nd Draft Risk and Exposure Assessment
Appendix 7-36
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1 nitrogen present in Jurupa Hills, Riverside County), a shift in the timing of AM growth was also
2 observed. Therefore, it is suggested that these reduced mutualistic associations may contribute to
3 a decline in the overall health of CSS via a loss in nutrient uptake capacity and may represent an
4 ecological endpoint for the CSS ecosystem. Figure 3.1-1 displays the levels of atmospheric
5 nitrogen deposition on CSS ecosystems above the ecological benchmark of 10 kg N/ha/yr using
6 2002 CMAQ/NADP data. The 12-km resolution CMAQ/NADP data indicate that CSS within the
7 Los Angeles and San Diego airsheds are likely to experience the noted effects at the 10 kg
8 N/ha/yr ecological benchmark.
9 3.1.1.4 Soil Nitrogen
10 In a greenhouse fertilization experiment, soil nitrogen levels of 50 ug/g ammonium N(V
11 had a 100% mortality rate after 9 months of continuous growth. The plants began to senesce at
12 approximately 6 months, whereas all lower-exposure individuals were still healthy and remained
13 healthy for more than 1 year (Allen et al., 1998). In the field, seasonal changes do not allow for
14 12 months of uninterrupted growth; therefore, the increased mortality shown in this study may be
15 realized over much longer periods of time in situ. Additionally, studies have suggested that soil
16 nitrogen may now be increasing because of soil fertility in conjunction with atmospheric
17 deposition, so that the soil itself becomes an intrinsic source (Padgett et al., 1999). In
18 combination with decreased establishment and the capacity for nutrient uptake, these responses
19 to elevated nitrogen levels may represent a detrimental and long-term pressure on CSS at varying
20 levels of nitrogen additions. Table 3.1-3 summarizes the various ecosystem responses to
21 nitrogen levels that affect CSS communities.
22 Table 3.1-3. Research Evidence of Ecosystem Responses to Nitrogen Relevant to
23 Coastal Sage Scrub
Environmental Impact
Enhanced growth of
nonnative species
Nutrient enrichment of
soil and plants
Decreased growth regulation
of shrubs
Decreased diversity of
mycorrhizal communities
Location
Southern California
Riverside-Perris Plain,
San Diego County
Greenhouse experiment
Riverside-Perris Plain
Reference
Minnich and Dezanni, 1998; Allen et al.,
1998; Weiss, 2006; Westman, 1981a,b
Sirulnik et al., 2007a; Allen et al., 1998;
Padgett et al., 1999; Vourlitis et al., 2007
Padgett and Allen, 1999
Egerton-Warburton and Allen, 2000;
Siguenza et al., 2006
2nd Draft Risk and Exposure Assessment
Appendix 7-37
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Terrestrial Nutrient Enrichment Case Study
Environmental Impact
Increased runoff and nutrient
loss
Altered fire cycle
Increased dependent species
vulnerability
Increased erosion
Location
Santa Barbara
Riverside-Ferris Plain
All CSS; San Diego
County
California shrublands
Reference
Fierer and Gabet, 2002
Wood et al, 2006
Weiss, 2006; Weaver, 1998
Keeler-Wolf, 1995
2 3.1.2 Fire
3 Fire is also an inextricable and significant component in CSS losses. Although CSS
4 species are fire resilient, nonnative grass seeds are quick to establish in burned lands, reducing
5 the water and nutrient amounts available to CSS species for reestablishment (Keeler-Wolf,
6 1995). Additionally, when nonnative annual grasses have established dominance, these species
7 alter and increase the fire frequency by senescing earlier in the annual season and increasing the
8 dry, ignitable fuel availability (Keeley et al., 2005). With increased fire frequencies and faster
9 nonnative colonizations, CSS seed banks are eventually eradicated from the soil, and the
10 probability of re-establishment decreases significantly (Keeley et al., 2005). Figure 3.1-2
11 represents the fire threats to CSS ecosystems.
2nd Draft Risk and Exposure Assessment
Appendix 7-38
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1
2
3
4
5
6
7
Fresno
•
Sanra «srra
® Cities
^H Coastal Sage Scrub 2002
Fire Threat
1 Moderate
I I High
] Very High
Extreme
Jrl
Source ol CSS range artf fire threat A the
Calrfoma Department
of Forestry and Fire Protection,
Figure 3.1-2. Current fire threats to coastal sage scrub ecosystems.
3.1.3 Coastal Sage Scrub Model
It appears that both atmospheric nitrogen deposition and fire are critical factors involved
in the decline of CSS. Figure 3.1-3 presents a model of CSS ecosystem response to nitrogen and
fire. Note that the model indicates that both nitrogen and fire play critical roles, and that there
may be positive feedback loops and possible synergies between fire and nitrogen loadings.
2nd Draft Risk and Exposure Assessment
Appendix 7-39
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
Atmospheric
Nitrogen
Modified Fire
Cycle
Coastal
Sage Scrub
f No n- native
Grasses
X
Myconrhizae
Associations
Modified
Nutrient and
Water
Retention
1
2 Figure 3.1-3. Model of coastal sage scrub ecosystem in relation to fire and
3 atmospheric nitrogen deposition.
4 3.1.4 MCF Ecosystems
5 The MCF ecosystem has been a subject of study for many years. There are a number of
6 important stressors on the community, including atmospheric fire, bark beetles, 63, particulates,
7 and nitrogen. Although fire suppression in the 20th century is probably the most significant
8 change that has led to alterations in morphology and perhaps to shifts in forest composition
9 (Minnich et al., 1995), stress from elevated levels of ambient atmospheric nitrogen
10 concentrations is the subject of increasing research.
11 3.1.4.1 Nitrogen and Ozone Effects
12 Measurements documenting increases in atmospheric nitrogen deposition have been
13 recorded with some regularity since the 1980s (Bytnerowicz and Fenn, 1996); however, the Los
14 Angeles area has seen elevated ambient atmospheric nitrogen concentrations for the last 50 years
15 (Bytnerowicz and Fenn, 1996). Also, some data have been published for the primary nitrogen
16 species of dry atmospheric nitrogen deposition in the San Bernardino Mountains (i.e., nitric acid
2nd Draft Risk and Exposure Assessment
Appendix 7-40
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1 [HNOsJand ammonia gas [NFL?]) from passive samplers (Bytnerowicz et al., 2007). The
2 pressures exerted on MCF ecosystems in California form a gradient across the Sierra Nevada
3 Range and San Bernardino Mountains. Nitrogen throughfall levels in the northern Sierra Nevada
4 Range are as low as 1.4 kg N/ha/yr, whereas forests in the western San Bernardino Mountains
5 experience measured throughfall nitrogen levels up to 33 to 71 kg N/ha/yr. (Note that the high
6 levels of nitrogen seen in some measured throughfall values are not reflected in the CMAQ
7 modeled results. This may be an artifact of using a 12-km grid.) The primary source of nitrogen
8 in the western San Bernardino Mountains stems from fossil fuels combustion, such as vehicle
9 exhaust. Other sources, such as agricultural processes, also play a prominent role in the western
10 portions of the San Bernardino Mountains and Sierra Nevada Range (Grulke et al., 2008).
11 Figure 3.1-4 illustrates the current total atmospheric nitrogen deposition on MCF in California.
12 At the individual tree level, elevated atmospheric nitrogen can shift the ratio of
13 aboveground to belowground biomass. Elevated pollution levels allow increased uptake of
14 nutrients via the canopy, reduced nitrogen intake requirements on root structures, and increased
15 demand for carbon dioxide (CC^) uptake and photosynthetic structures to maintain the carbon
16 balances. Therefore, the increased nutrient availability stimulates aboveground growth and
17 increases foliar production, while reducing the demand for belowground nutrient uptake (Fenn et
18 al., 2000). Carbon allocation gradually shifts from root to shoot, and fine-root biomass is reduced
19 (Fenn and Bytnerowicz, 1997; U.S. EPA, 2008, Section 3.3). Grulke et al. (1998) observed a 6-
20 to 14-fold increase in fine-root mass in areas of low atmospheric nitrogen deposition compared
21 to areas of high deposition. Medium roots also declined at high levels (Fenn et al., 2008).
22 At the stand level, elevated atmospheric nitrogen has been associated with increased
23 stand density, although other factors, such as fire suppression and Os, also contribute to
24 increased density and can increase mortality rates (U.S. EPA, 2008, Section 3.3). As older trees
25 die, they are replaced with younger, smaller trees. Smaller trees allow more sunlight through the
26 canopy and, combined with an increased availability of nitrogen, may allow for more trees to be
27 established. Increased stand densities with younger-age classes are observed in the San
28 Bernardino Mountains, where air pollution levels are among the highest found in the California
29 MCF ranges studied (Minnich et al., 1995; Fenn et al., 2008). These shifts in stand density and
30 age distribution result in vegetation structure shifts which, in turn, may impact population and
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-41
-------
Terrestrial Nutrient Enrichment Case Study
1 community dynamics of understory plants and animals, including threatened and endangered
2 species.
3
4
5
| | Sierra Nevada
| | San Bernardino NF
| Mixed Conifer
CMAQ
kg/ha/yr
less than 3.1
3.1 -5.1
5.2- 10.1
10.2-16.9
17 or greater
.
Yosemite National Park
Kings Canyon National Park
on
\
Sequoia National Park
Source of Mixed Conifer: California Fire and
Resource Assessment Program
Figure 3.1-4. Mixed conifer forest range and total atmospheric nitrogen
deposition using CMAQ 2002 modeling results and NADP monitoring data.
2nd Draft Risk and Exposure Assessment
Appendix 7-42
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1 It should be noted that the effects of 63 and atmospheric nitrogen are difficult to separate.
2 The atmospheric transformation of NOX can yield moderate concentrations of 63 as a byproduct
3 (Grulke et al., 2008). Therefore, since elevated nitrogen levels are generally correlated with O3
4 concentrations, researchers often report changes in tree health and physiology as being the result
5 of both (i.e., Grulke and Balduman, 1999).
6 High concentrations of Os and atmospheric nitrogen can generate increased needle and
7 branch turnover. In areas subjected to low pollution, conifers may retain needles across 4 or 5
8 years; however, in areas of high pollution, such as Camp Paivika in the San Bernardino
9 Mountains, needle retention is generally less than 1 year (Grulke and Balduman, 1999; Grulke et
10 al, 2008). Needle turnover significantly increases litterfall. Litter biomass has been observed to
11 increase in areas with elevated atmospheric nitrogen deposition up to 15 times more than in areas
12 with low deposition (Fenn et al., 2000; Grulke et al., 2008). The increased litter deposition may
13 facilitate faster rates of microbial decomposition initially, but it may reduce decomposition over
14 the long term because of changes in the carbon to nitrogen (C:N) ratio and increasing lignin
15 content over time (Grulke et al., 2008; U.S. EPA, 2008, Section 3.3). The increased litter depth
16 may then affect subcanopy growth and stand regeneration over long periods of time.
17 At the highest levels of nitrogen deposition, native understory species were seen to
18 decline (Allen et al., 2007). In addition to this decline in native understory diversity, changes in
19 decreased fine-root mass, increased needle turnover, and the associated chemostructural
20 alterations, MCF that are exposed to elevated pollutant levels have an increasing susceptibility to
21 drought and beetle attack (Grulke et al., 1998, 2001; Takemoto et al., 2001). These stressors
22 often result in the death of trees, producing an increased risk of wildfires. This complex model is
23 displayed in Figure 3.1-5 as a graphic developed by Grulke et al. (2008).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-43
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Terrestrial Nutrient Enrichment Case Study
Rapid population increase,
Change in land use
^
Fire suppression,
Reduced
Increased Qs & N
Periodic drought
/ \
Increased 8"ee
susceptibility to
drought stress
Increased success
of bark beetle,
1
2
3
4
5
6
7
^ Increased fire starts,
Continued fire suppression
= WILDFIRE
Figure 3.1-5. Conceptual model for increased susceptibility to wildfire in mixed
conifer forests (Grulke et al., 2008).
3.1.4.2 Nitrogen Effects on Lichens
Lichens emerged as an indicator of nutrient enrichment from the research on the effects
of acid rain. Lichen species can be sensitive to air pollution; in particular, atmospheric nitrogen
deposition. Since the 1980s, information about lichen communities has been gathered, and
lichens have been used as indicators to detect changes in forest communities. Jovan (2008)
depicts how lichens might be considered as sentinels in the MCF community (Figure 3.1-6).
l.ICHF.N
COMMUNITY
INDICATI-S
CONDITION OF
RESOURCE
t'orcst productivity.
biodivcrsin. health
CAUSE-
:!FFECT
10
11
ENVIRONMENTAL
STRESSORS
N- and S-based air pollutants:
direct lo\icily and acidifying and
Ccniliiting effects
Figure 3.1-6. Importance of lichens as an indicator of ecosystem health (Jovan, 2008).
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-44
-------
Terrestrial Nutrient Enrichment Case Study
1 As atmospheric nitrogen deposition increases, the relative abundance of acidophytic
2 lichens decreases, and the concentration of nitrogen in one of those species, Letharia vulpine.,
3 increases (Fenn et al., 2008). Fenn et al. (2008) were able to quantify the change in the lichen
4 community, noting that for every 1 kg N/ha/yr increase, the abundance of acidophytic lichens
5 declined by 5.6%. Figure 3.1-7 illustrates the presence of acidophyte lichens and the total
6 atmospheric nitrogen deposition in the California ranges.
7 In addition to abundance changes, species richness, cover, and health are affected in areas
8 of high Os and nitrogen concentrations. Fifty percent fewer lichen species were observed after 60
9 years of elevated air pollution in San Bernardino Mountains MCF, with the areas of highest
10 pollution levels exhibiting low species richness, decreased abundance and cover, and
11 morphological deterioration of existing lichens (Sigal and Nash, 1983).
12 Ecological endpoints relating to shifts in the abundance of acidophilic lichens were
13 identified by Fenn et al. (2008). They found that at 3.1 kg N/ha/yr, the community of lichens
14 begins to change from acidophilus to tolerant species; at 5.2 kg N/ha/yr, the typical dominance
15 by acidophilus species no longer occurs; and at 10.2 kg N/ha/yr, acidophilic lichens are totally
16 lost from the community. Additional studies in the Colorado Front Range of the Rocky Mountain
17 National Park support these findings and are summarized in Chapter 5 of the Risk and Exposure
18 Assessment. These three values are one set of ecologically meaningful benchmarks for the MCF.
19 As shown in Figure 3.1-7, much of the MCF receives nitrogen deposition levels above the 3.1 N
20 kg/ha/yr ecological benchmark according to the 2002 CMAQ/NADP data, with the exception of
21 the easternmost Sierra Nevada Range. MCF in the southern portion of the Sierra Nevada forests
22 and nearly all MCF communities in the San Bernardino forests receive nitrogen deposition levels
23 above the 5.2 N kg/ha/yr ecological benchmark. Figure 3.1-7 also displays the potential areas
24 where acidophilic lichens are extirpated due to nitrogen deposition levels >10.2 kg N kg/ha/yr.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-45
-------
Terrestrial Nutrient Enrichment Case Study
1
2
3
4
Acidophyte Lichens
San Bernardino NF
Sierra Nevada
CMAQ Total N Dep
kg/ha/yr
less than 3.1
I 13.1-5.1
5.2- 10.1
10.2 or greater
Ba*«ral7eW
1
8
9
10
Figure 3.1-7. Presence of acidophyte lichens and total nitrogen deposition in the
California mountain ranges using CMAQ 2002 modeling results and NADP
monitoring data.
3.1.4.3 Nitrogen Saturation
The established signs of nitrogen saturation have been shown within the MCF ecosystem.
These symptoms include the following:
• Increased carbon and nitrogen cycling. The foliar turnover rates and changes in
microbial decomposition both suggest that carbon and nitrogen cycles have been altered as
a result of elevated nitrogen. Additionally, nitrogen fluxes in San Bernardino Mountains
2nd Draft Risk and Exposure Assessment
Appendix 7-46
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1 soils are elevated when compared to MCF in the northern Sierra Nevada Range
2 (Bytnerowicz and Fenn, 1996).
3 • Decreased nitrogen uptake efficiency of plants. Changes in root: shoot ratio demonstrate
4 structural alterations in response to increasing available nitrogen.
5 • Increased loss of forest nitrates to streamwater (i.e., NOs leachate). Elevated N(V
6 leachate levels are estimated to have begun in the late 1950s and have been observed from
7 the western MCF in the San Bernardino Mountains since 1979 (Fenn et al., 2008). These
8 losses are a result of high soil nitrogen driven by the combined litter, needle turnover, and
9 throughfall nitrogen exerted in these areas (Bytnerowicz and Fenn, 1996).
10 Changes in root biomass and stream leachate, in addition to lichen species compositional
11 shifts, have been used to develop benchmarks for nitrogen benchmarks in the MCF ecosystem.
12 These critical loading benchmarks, or empirical loads, are designed to estimate the levels at
13 which atmospheric nitrogen concentrations and subsequent deposition begin to affect selected
14 components of the ecosystem, such as forest growth, health, and composition. Some benchmarks
15 aim to estimate individual changes to an ecosystem, whereas others assess the levels at which the
16 entire ecosystem will not be altered because of atmospheric nitrogen deposition. The possibility
17 of using the MCF as a model for benchmarking is discussed below.
18 Fenn et al. (2008) established a critical loading benchmark of 17 kg throughfall N/ha/yr
19 in the San Bernardino Mountains and Sierra Nevada Range MCF ecosystems. This benchmark
20 represents the level of atmospheric nitrogen deposition at which elevated concentrations of
21 streamwater N(V leachate or potential nitrogen saturation may occur. At this deposition level, a
22 26% reduction in fine-root biomass is anticipated (Fenn et al., 2008). Rootshoot ratios are,
23 therefore, altered, and changes in nitrogen uptake efficiencies, litterfall biomass, and microbial
24 decomposition are anticipated to be present at this atmospheric nitrogen deposition level. This
25 benchmark is based on 30 to 60 years of exposure to elevated atmospheric concentrations. At
26 longer exposure levels, the benchmark is lower because of decreased nitrogen efficiencies of the
27 ecosystem. This benchmark is exceeded in areas of the western San Bernardino Mountains, such
28 as Camp Paivika.
29 MV leaching is a symptom that an ecosystem is saturated by nitrogen. NCV leaching is
30 also known to cause acidification in adjacent surface waters. The ecological benchmark of 17 kg
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-47
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Terrestrial Nutrient Enrichment Case Study
1
2
3
4
5
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
N/ha/yr is the last benchmark identified in this study. At this level of atmospheric nitrogen
deposition, N(V is observed in streams in the MCF (Fenn et al., 2008), denoting a change in
ecosystem function.
Table 3.1-4 displays the area in hectares of MCF experiencing different nitrogen
deposition levels.
Table 3.1-4. Mixed Conifer Forest Ecosystem Area and Nitrogen Deposition
N Deposition
(kg/ha/yr)
>3.1
>5.2
>10.2
>17
Area
(hectares)
1,099,133
130,538
11,963
0
Percent of MCF
Area, %
38.62
4.59
0.42
0.00
3.2 RESULTS SUMMARY
A range of ecological benchmarks were developed in the results. All benchmarks are tied
to a level of atmospheric nitrogen deposition, but include a number of different ecological
processes and ecological endpoints. All of the benchmarks are ecologically significant in that
changes are seen that are related to community structure and function. The benchmarks span a
range from 3.1 to 17 kg N/ha/yr and include:
• 3.1 kg N/ha/yr—shift in lichen communities are first observed in MCF
• 3.3 kg N/ha/yr—nitrogen no longer limiting in CSS
• 5.2 kg N/ha/yr—dominance of tolerant lichen species in MCF
• 10 kg N/ha/yr—AM community shift in CSS
• 10.2 kg N/ha/yr—loss of acidophilic lichen species from MCF
• 17 kg N/ha/yr—NO3" leaching in MCF.
This range of ecological benchmarks may be used to develop a "green line/red line"
schematic, similar to the forest screening model discussed in Lovett and Tear (2007) that
illustrates the levels at which ecosystem effects may occur or are known to occur. In Figure
3.2-1, the green area/line denotes that point at which there do not appear to be any effects and the
red line the point at which known negative effects occur.
2nd Draft Risk and Exposure Assessment
Appendix 7-48
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
20'
— High Probability of Negative Effects
Nitrogen Leaching to Streams
-------
Terrestrial Nutrient Enrichment Case Study
1 increases (Jovan and McCune, 2005; Geiser and Neitlich, 2007). In Europe, acidophyte decline
2 has been identified in regions with 8 to 10 kg N/ha/yr (Bobbink, 1998; Bobbink et al., 1998).
3 4. IMPLICATIONS FOR OTHER SYSTEMS
4 This Terrestrial Nutrient Enrichment Case Study examined the effects of atmospheric
5 nitrogen on two ecosystem types in California: CSS and MCF. Figure 4.1-1 presents the
6 coverage of 2002 CMAQ/NADP data for total nitrogen deposition in the western United States,
7 including California. Ecological effects have been documented across the United States where
8 elevated nitrogen deposition has been observed. Benchmarks documented in the literature for the
9 negative effects on ecosystems are summarized in Figure 4.1-2 and are discussed in this case
10 study report. Looking across the United States, Figure 4.1-3 illustrates the occurrence of these
11 ecosystems which are sensitive to nitrogen and/or have similar characteristics to the ecosystems
12 explored in this case study. These ecosystems may also experience levels of atmospheric
13 nitrogen deposition that exceed the benchmark levels identified in Figure 4.1-2. Table 4.1-1 lists
14 the area of CSS and MCF that exceed benchmark nitrogen levels.
15 In the western United States, other arid and forested ecosystems exposed to deposition at
16 levels discussed in this case study may experience altered effects. As noted in Section 3, research
17 on grasslands and chaparral habitats is underway. These arid systems may respond to
18 benchmarks similar to those observed for CSS, as was shown by Clark and Tilman (2008) for
19 bluestem grasslands in Minnesota. N(V leaching in forests with elevated deposition (similar to
20 the range found in this study) may result in nutrient enrichment in streams which can affect
21 aquatic ecosystems (Aber et al., 2003). Research is also being conducted on lichen species in the
22 Pacific Northwest and in Central California that are exposed to elevated levels of atmospheric
23 nitrogen deposition (Jovan, 2008). Extensive research on the eastern Front Range of the Rocky
24 Mountain National Park has been conducted in alpine and subalpine terrestrial and aquatic
25 systems at elevations above 3,300 m, where communities are typically adapted to low nutrient
26 availability but are now being exposed to >10 kg N/ha/yr in some study areas (Baron et al. 2000;
27 Baron, 2006). (Chapters 5 and 6 of the Risk and Exposure Assessment also provide discussion on
28 this topic.)
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 7-50
-------
Terrestrial Nutrient Enrichment Case Study
1
2
3
Sierra Nevada
Range
Mountain
National Park
0
Total N Deposition
kg/ha/yr
^| 0.8 to < 1.5
^H >= 1.5 to < 3
>= 3 to < 6
| | >= 6 to < 9
>=9to<12
>= 12to< 18
>= 18 to 20
Deposition data is the result of
combining CMAQ (dry) and
NADP (wet) over 12-km grid cells
250 500 750 1.000
Figure 4.1-1. 2002 CMAQ-modeled and NADP monitoring data for deposition of
total nitrogen in the western United States.
2nd Draft Risk and Exposure Assessment
Appendix 7-51
June 5, 2009
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Terrestrial Nutrient Enrichment Case Study
1
2
3
Rocky Mountain alpine lakes: shift in diatom community dominance (Baron, 2006)
Ecosystem Effect
• Southern California: CSS loss (Wood et al., 2006)
EjUll • San Bernardino Mountains and Sierra Nevada Mountains: acidophytic lichen
) decline in MCF (Fenn et al., 2008)
/ 71
• Eastern Rock}
3-5- al., 2000)
3-6 1 • Eastern Rock}
(Rarnn pt al :
5.2- • San Bernar
5.3 to neutral o
' • Minnesota (
' >
I Mountain Slope: low carbon:nitrogen; low lignin:nitrogen (Baron et
/ Mountain Slope: increased foliar nitrogen; increased mineralization
2000)
dino Mountains and Sierra Nevada Mountains: shift from acidophytic
r nitrogen-tolerant lichen in MCF (Fenn et al., 2008)
grasslands (Clark and Tilman, 2008)
7_12 • Northeast U.S.: NO3 leaching (Aber et al., 2003)
/
' >
• Bay A
1°-15 grasse
/
• San B
lichen
102 • South*
and A
/ • Snnthf
/• / °008)
•ea, CA: Increased cover of nonnative grasses; decreased native
s (Weiss, 1999)
ernardino Mountains and Sierra Nevada Mountains: loss of acidophytic
in MCF (Fenn etal., 2008)
3rn California: shift in mycorrhizal species in CSS (Egerton-Warburton
len, 2000)
srn California: shift from native species to invasive grasses in CSS (Allen,
• San Bernardino Mountains: high dissolved organic nitrogen (Meixner
11'40 and Fenn, 2004)
) • San Bernardino Mountains: nitrogen saturation (Fenn et al., 2000)
/ /|
• Increased nitrogen in lichen (Fenn et al., 2007)
1 1 .5-25.4
J
/
17
• MCF: NO3 leaching (Fenn et al., 2008)
• MCF: 25% decrease in fine-root biomass (Fenn et al., 2008)
/
— -71 • Southern California: NO3 leaching (Fenn etal., 2003)
Southern California: high foliar nitrogen (Byternowicz and
Fenn, 1996)
Los Angeles Basin, California: High NO emissions
(Byternowicz and Fenn, 1996)
Fraser Experimental Forest, CO:
increased foliar nitrogen; increased
mineralization (Rueth et al., 2003)
0246 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Nitrogen Deposition, kg/ha/yr
Figure 4.1-2. Benchmarks of atmospheric nitrogen deposition for several
ecosystem indicators.
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Appendix 7-52
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Terrestrial Nutrient Enrichment Case Study
Coverage Areas of Interest
Alpine Areas
^ PNW and CA Regions with Lichen
^H NE Forests
| Bluestem Grasslands
^B CA Mixed-Conifer Forest
Coastal Sage Scrub
| California Grassland OverSerpentinite Bedrock
I California Grasslands
5
6
Note: PNW = Pacific Northwest; NE = Northeast.
Figure 4.1-3. Habitats that may experience ecological benchmarks similar to
coastal sage scrub and mixed conifer forest.
Table 4.1-1. Areas of Coastal Sage Scrub and Mixed Conifer Forest That Exceed Benchmark
Nitrogen Deposition Levels.
CSS:
N Deposition (kg/ha/yr)
>3.3
>10
Area (hectares)
654,048
138,019
Percentage of CSS Area, %
93.51
19.73
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Terrestrial Nutrient Enrichment Case Study
MCF
N Deposition (kg/ha/yr)
>3.1
>5.2
£10.2
>17
Area (hectares)
1099,133
130,538
11,963
0
Percentage of MCF Area, %
38.62
4.59
0.42
0.00
2
3 Other systems with the following characteristics may also be found to be sensitive:
4 • Ecosystems with nitrogen-sensitive epiphytes, such as lichens or mycorrhizae. Such
5 systems may demonstrate shifts in community structure through changes in nutrient
6 availability or modified provisioning services.
7 • Ecosystems that may have been exposed to long periods of elevated atmospheric
8 nitrogen deposition. The established signs of nitrogen saturation are increased leaching of
9 N(V into streamwater, decreased nitrogen uptake efficiency of plants, and increased
10 carbon and nitrogen cycling. At prolonged elevated nitrogen levels, ecosystems are
11 generally less likely to use, retain, or recycle nitrogen species efficiently at both the
12 species and community levels.
13 • Critical habitats. Ecosystems that are necessary for endemic species or special ecosystem
14 services should be monitored for possible changes due to nitrogen.
15 • Locations where there are seasonal releases of nitrogen. In both the California CSS and
16 MCF ecosystems discussed in this case study report, a large portion of nitrogen is dry-
17 deposited and remains on the foliage and soil surface until the beginning of the winter
18 rainy season when nitrogen will be flushed into the soil.
19 Current analysis of the effects of terrestrial nutrient enrichment from atmospheric
20 nitrogen deposition in both CSS and MCF seeks to improve scientific understanding of the
21 interactions among nitrogen deposition, fire events, and community dynamics. The available
22 scientific information is sufficient to identify ecological thresholds that are affected by nitrogen
23 deposition, and ecological thresholds have been identified for CSS and MCF. This case study
24 report has examined the sensitivity and effects of nutrient enrichment on terrestrial ecosystems,
25 and although a diverse array of U.S. ecosystems exist, exposure levels and thresholds for effects
26 appear to be generally comparable to levels identified in other sensitive U.S. ecosystems (e.g.,
2nd Draft Risk and Exposure Assessment
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Terrestrial Nutrient Enrichment Case Study
1 thresholds range from 3.1 to 30.5 kg N/ha/yr), including thresholds identified from modeling
2 conducted for other case studies in the Risk and Exposure Assessment (Chapters 4 and 5).
3 Knowledge and understanding of such relevant exposure levels can help inform decision makers.
4 5. UNCERTAINTY
5 5.1 COASTAL SAGE SCRUB
6 There are several areas of uncertainty associated with this case study of CSS.
7 • Although current research indicates that both atmospheric nitrogen deposition and fire
8 have contributed to the decline of CSS, the interaction between the variables and the extent
9 of their contributions requires further research. CSS declines have been observed in the
10 absence of fire when elevated nitrogen levels are present, and declines have also been
11 observed in the absence of elevated nitrogen, but due to fire. Therefore, there is still a need
12 for quantifiable and predictive results to indicate the pressure of each variable, as well as
13 the pressure of the combined variables (if synergism is present). Additional studies are also
14 required to test the proposed nitrogen-fire feedback loop and the associated
15 biogeochemical elements (e.g., changes in water availability and mycorrhizal associations)
16 that contribute to CSS decline.
17 • Many studies allude to a degradation of CSS by assessing species richness and abundance,
18 but it is not clear that indicators of CSS ecosystem health have been adequately explored.
19 Assessing the health of CSS ecosystems may help to identify a response curve to the
20 factors associated with CSS decline.
21 • Ongoing experiments are beginning to show changes in CSS in response to elevated
22 nitrogen over relatively long periods of time (Allen, personal communication, 2008). The
23 incremental process may be occurring slower than previous field research experiments
24 have lasted, making the reasons for the decline appear variable or imperceptible over the
25 duration of a typical study.
26 • At this point, CSS is fragmented into many relatively small parcels. The CMAQ/NADP
27 2002 data is being modeled at 4-km resolution. The availability of these 4-km resolution
28 data will provide a better sense of the relationship between the current distribution of CSS
29 and atmospheric nitrogen loads and fire threat.
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Terrestrial Nutrient Enrichment Case Study
1 • Very little research exists regarding the effects of 63 on CSS. Although there is some
2 support that 63 is negatively correlated with CSS, the role has yet to be quantified or
3 consistently studied (Westman, 198la).
4 • The last area of uncertainty is the relationship between current CSS distribution and the
5 changing climate.
6 5.2 MIXED CONIFER FOREST
7 The currently known areas of uncertainty for MCF are as follows:
8 • The long-term consequences of increased nitrogen on conifers are unclear. Although the
9 results indicate an increased susceptibility to wildfire and disease, the long-term health of
10 the stands and risk of cascading effects into the ecosystem require further investigation.
11 • The effects of Os for both MCF and lichens confound the effects of nitrogen.
12 • The intermingling of fire and nitrogen cycling require additional research.
13 • Research suggests that critical loading benchmarks can decrease over time if the nitrogen
14 benchmark is exceeded for long periods of time because of decreasing nitrogen
15 efficiencies within nitrogen-saturated ecosystems (Fenn et al., 2008). This may indicate
16 that a sliding-scale approach will be required when evaluating ecosystems of varying
17 nitrogen responses.
18 • There remains considerable uncertainty in the potential response of soil carbon to increases
19 in total reactive nitrogen additions.
20 6. CONCLUSIONS
21 Evidence from the two ecosystems discussed in this case study report supports the
22 finding that nitrogen alters CSS and MCF. For this analysis, the loss of the native shrubs in CSS
23 and the increase in nonnative annual grasses were investigated. In MCF on the slopes of the San
24 Bernardino Mountains and Sierra Nevada Range, lichen communities associated with the forest
25 stands and nitrogen saturation were investigated to identify the effects of nitrogen loadings.
26 California's CSS and MCF have important recreational value, protect water resources, and
27 provide habitats for many other species. In the CSS ecosystem, there is compelling evidence that
28 elevated atmospheric nitrogen deposition is a driving force in the degradation of CSS. A CSS
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Terrestrial Nutrient Enrichment Case Study
1 model was developed to help identify and parse the pressures and changes occurring within the
2 ecosystem. In the MCF ecosystem, lichen communities and nitrogen saturation can provide a
3 means to monitor and quantify the effects of nitrogen loadings.
4 Ecological benchmarks for a suite of indicators were identified in both ecosystems:
5 • 3.1 kg N/ha/yr—shift from sensitive to tolerant lichen species in MCF
6 • 3.3 kg N/ha/yr—the amount of nitrogen uptake by a vigorous stand of CSS; above this
7 level, nitrogen may no longer be limiting
8 • 5.2 kg N/ha/yr—dominance of tolerant lichen species in MCF
9 • 10 kg N/ha/yr—mycorrhizal community changes in CSS
10 • 10.2 kg N/ha/yr—loss of sensitive lichen species from MCF
11 • 17 kg N/ha/yr—NO3" leaching in MCF
12 Because these benchmarks are comparable to levels identified in other sensitive U.S.
13 ecosystems and are also comparable to modeled values found in the other case studies, this set of
14 ecological benchmarks supports the need for continued monitoring, research, and protection of
15 sensitive ecosystems and informs the decision-making process.
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12 Ecology (52:170-184.
13 Westman, W.E. 1981b. Factors influencing the distribution of species of Californian coastal sage
14 scrub. Ecology 62:439-455.
15 Westman, W. 1979. Oxidant effects on California coastal sage scrub. Science 205:1001-1003.
16 Wood, Y., T. Meixner, PJ. Shouse, and E.B. Allen. 2006. Altered Ecohydrologic response
17 drives native shrub loss under conditions of elevated N-deposition. Journal of
18 Environmental Quality 35:76-92.
19 Yoshida, L.C., and E.B. Allen. 2001. Response to ammonium and nitrate by a mycorrhizal
20 annual invasive grass and a native shrub in southern California. American Journal of
21 Botany 88:1430-1436.
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Appendix 7-66
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JuneS, 2009
Appendix 8
Analysis of Ecosystem Services
Impacts for the NOX/SOX Secondary
NAAQS Review
Final Report
Prepared for
Christine Davis
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards (OAQPS)
Climate, International and Multi-Media Group (CIMG)
(MD-C504-04)
Research Triangle Park, NC 27711
Prepared by
George Van Houtven
Paramita Sinha
Carol Mansfield
Jennifer Phelan
Ross Loomis
RTI International
Research Triangle Park, NC 27709
Maura Flight
Industrial Economics, Inc.
EPA Contract Number EP-D-06-003
RTI Project Number 0209897.003.066
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
CONTENTS
2 1. Introduction 1
3 1.1 Ecosystem Service Categories 3
4 1.1.1 Descriptions and Examples of MEA Ecosystem Services 5
5 1.2 References 7
6 2. Aquatic Acidification 9
7 2.1 Overview of Affected Ecosystem Services 9
8 2.1.1 Provisioning Services 9
9 2.1.2 Cultural Services 10
10 2.1.3 Regulating Services 10
11 2.2 Changes in Ecosystem Services Associated with Alternative Levels of
12 Ecological Indicators 11
13 2.2.1 Improvements in Recreational Fishing Services due to Increased Acid
14 Neutralizing Capacity Levels in Adirondack and Other New York Lakes 15
15 2.2.2 Improvements in Total Ecosystem Services due to Increased Acid
16 Neutralizing Capacity Levels in Adirondack Lakes 26
17 2.3 References 33
18 3. Terrestrial Acidification 35
19 3.1 Overview of Affected Ecosystem Services 35
20 3.1.1 Provisioning Services 35
21 3.1.2 Cultural Services 37
22 3.1.3 Regulating Services 41
23 3.2 Changes in Ecosystem Services Associated with Alternative Levels of
24 Ecological Indicators 42
25 3.2.1 Increased Provisioning Services from Sugar Maple Timber Harvests due
26 to Elimination of Critical Load Exceedances 42
27 3.3 References 55
28 4. Aquatic Enrichment 59
29 4.1 Overview of Affected Ecosystem Services 61
30 4.1.1 Provisioning Services 61
31 4.1.2 Cultural Services 65
32 4.1.3 Regulating Services 65
33 4.2 Changes in Ecosystem Services Associated with Alternative Levels of
34 Ecological Indicators 66
35 4.2.1 The Chesapeake Bay Estuary 68
36 4.2.2 Neuse River Estuary 92
37 4.3 References 97
38 5. Terrestrial Enrichment 103
39 5.1 Overview of Affected Ecosystem Services 103
40 5.1.1 Cultural 104
41 5.1.2 Regulating 115
42 5.2 Value of Coastal Sage Scrub and Mixed Conifer Forest Ecosystem Services 119
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 5.3 References 120
2 6. Conclusion 122
3 6.1 Benefits from Enhanced Provisioning Services 122
4 6.2 Benefits from Enhanced Cultural Services 124
5 6.3 Benefits from Enhanced Regulating Services 125
6 Attachment A: Annual Recreational Fishing Benefit Estimates for Reductions in
7 New York Lake Acidification Levels, 2002-2100 A-l
LIST OF FIGURES
9 Figure 1.1-1. Conceptual Framework for Linking Changes in Ambient NOX and SOX
10 Levels to Changes in Ecosystem Services and Public Welfare 2
11 Figure 1.1-2. MEA Categorization of Ecosystem Services and their Links to Human
12 Weil-Being (Source: MEA, 2005b) 5
13 Figure 2.2-1. Summary of Acid Neutralizing Capacity Values Relevant for Lake and
14 Fish Health (Source: Industrial Economics, Inc., 2008) 15
15 Figure 3.1-1. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry
16 Deposition and NADP Wet Deposition Estimates) and the Range of Sugar
17 Maple in the United States 36
18 Figure 3.1-2. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry
19 Deposition and NADP Wet Deposition Estimates) and the Range of Red
20 Spruce in the United States 37
21 Figure 3.1-3. Annual Value of Sugar Maple and Red Spruce Harvests and Maple Syrup
22 Production, 2006 38
23 Figure 3.2-1. Estimated Time Path of Welfare Gains in the Forestry and Agricultural
24 Sector due to Increased Sugar Maple and Red Spruce Growth (2000-
25 2065) 53
26 Figure 4-1. Conceptual Model of Eutrophication Impacts in Estuaries (Source: Adapted
27 from Bricker et al. [2007] and Bricker, Ferreira, and Simas [2003]) 60
28 Figure 4.2-1. Chesapeake Bay Coastal Block Groups 85
29 Figure 5.1-1. Coastal Sage Scrub Areas and Population 105
30 Figure 5.1-2. Mixed Conifer Forest Areas and Population 106
31 Figure 5.1-3. Boundaries of the NCCP Region and Subregions for Coastal Sage Scrub
32 (Source: California Department of Fish and Game, n.d.) 107
33 Figure 5.1-4. Mixed Conifer Forest Areas and National and State Park Boundaries 109
34 Figure 5.1-5. Coastal Sage Scrub Areas and Housing Values 112
35 Figure 5.1-6. Presence of Three Threatened and Endangered Species in California's
36 Coastal Sage Scrub Ecosystem 113
37 Figure 5.1-7. Presence of Two Threatened and Endangered Species in California's
38 Mixed Conifer Forest 114
39 Figure 5.1-8. Coastal Sage Scrub Areas and Fire Threat 117
40 Figure 5.1-9. Mixed Conifer Forest Areas and Fire Threat 118
41 Figure 5.1-10. Mixed Conifer Forest Areas and Major Lakes and Rivers 119
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Appendix 8 - ii
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 LIST OF TABLES
2 Table 2.2-1. Participation in Freshwater Recreational Fishing in Northeastern States in
3 2006 11
4 Table 2.2-2. Acid Neutralizing Capacity Levels (in |ieq/L) at 44 MAGIC-Modeled Lakes
5 in the Adirondacks 12
6 Table 2.2-3. Random Effects Model Results 17
7 Table 2.2-4. Count of Impacted Lakes 21
8 Table 2.2-5. Per Capita Willingness to Pay (2007 $) 22
9 Table 2.2-6. Present Value and Annualized Benefits, Adirondack Region 24
10 Table 2.2-7. Present Value and Annualized Benefits, New York State 24
11 Table 2.2-8. Comparison of Resources for the Future Contingent Valuation Scenarios
12 and EPA Zero-Out Scenario 28
13 Table 2.2-9. Aggregate Benefit Estimates for the Zero-Out Scenario 31
14 Table 3.1-1. Participation in Hunting and Wildlife Viewing in Northeastern States in
15 2006 40
16 Table 3.2-1. Summary of Plot-Level Data on Sugar Maple Growth and Exceedances (for
17 Plots above the Glaciation Line) 44
18 Table 3.2-2. Summary of Plot Level Data on Sugar Maple Growth and Exceedances (for
19 Plots above the Glaciation Line) 45
20 Table 3.2-3. Linear Exposure-Response Model for Exceedances (above a Critical Load
21 Calculated with Bc/Al = 10) and Sugar Maple Tree Growth: OLS
22 Regression Results (for Plots above the Glaciation Line) 47
23 Table 3.2-4. Linear Exposure-Response Model for Exceedances (above a Critical Load
24 Calculated with Bc/Al = 10) and Red Spruce Tree Growth: OLS
25 Regression Results (for Plots above the Glaciation Line) 48
26 Table 3.2-5. Estimated Increments in Sugar Maple and Red Spruce Timber Volume
27 (Resulting from Elimination of Critical Load Exceedances), by
28 FASOMGHG Region 50
29 Table 3.2-6. Proportions of Hardwood in Sugar Maple Production and Proportions of
30 Softwood in Red Spruce Production, by FASOM Region 51
31 Table 3.2-7. Proportion of Timberland under Private and Public Ownership by FIA
32 Regiona:2002 52
33 Table 4.1-1. Annual Values of East Coast Commercial Landings (in millions) 62
34 Table 4.1-2. Value of Commercial Landings for Selected Species in 2007 (Chesapeake
35 Bay Region) 63
36 Table 4.2-1. Participation in Selected Marine Recreation Activities in East Coast States
37 in 1999-2000 67
38 Table 4.2-2. Regression Analysis of the Chesapeake Water Quality Index on Water
39 Quality Parameters 70
40 Table 4.2-3. Average Catch Rate per Fishing Trip in the Chesapeake Bay, by State and
41 Targeted Fish Species 72
42 Table 4.2-4. Aggregate Number of Fishing Trips to the Chesapeake Bay, by State and
43 Targeted Fish Species 74
44 Table 4.2-5. Input Estimates for the Chesapeake Bay Boating Benefit Transfer Model 77
45 Table 4.2-6. Input Estimates for the Chesapeake Bay Beach-Use Benefit Transfer Model 81
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Appendix 8 - iii
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Table 4.2-7. Summary of Housing Unit Numbers and Average Prices in Chesapeake
2 Coastal Block Groups in 2007 86
3 Table 5.1-1. Recreational Activities in California in 2006 by Residents and Nonresidents 110
4 Table 6-1. Summary of Aggregate Benefit Estimates for Selected Ecosystem Services
5 and Areas (Zero Out of Nitrogen and Sulfur Deposition)21 123
6
7
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Appendix 8 - iv
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
i 1. INTRODUCTION
2 The U.S. Environmental Protection Agency (EPA) is conducting a review of the
3 secondary National Ambient Air Quality Standards (NAAQS) for nitrogen oxides (NOX) and
4 sulfur oxides (SOX). As part of the review, EPA is interested in linking changes in NOX and SOX
5 ambient air concentrations to the changes in ecosystem services and ultimately to changes in
6 public welfare. This process of linking changes in ambient NOX and SOX levels to public welfare
7 through the effects on ecosystem services is illustrated in Figure 1.1-1. Reducing NOX and SOX
8 concentrations will reduce the stresses on aquatic and terrestrial ecosystems by reducing
9 atmospheric deposition of nitrogen and sulfur compounds. As shown in the figure, EPA has
10 identified four main categories of adverse ecosystem effects—aquatic acidification, terrestrial
11 acidification, aquatic nutrient enrichment, and terrestrial nutrient enrichment.1 For each of these
12 categories, EPA has identified key ecological indicators, which provide quantitative measures of
13 adverse impacts on the affected ecosystems.
14 The purpose of this report is to identify, characterize, and, to the extent possible, quantify
15 the ecosystem services that are primarily impacted by nitrogen and sulfur deposition (see Section
16 1.1 for the definition and categorization framework used to define ecosystem services) and the
17 changes in ecosystem services that are expected to result from changes in the ecological
18 indicators. By linking indicators of ecological function to ecosystem service provision through
19 risk and economic assessments, the objective is to inform decisions regarding the adequacy of
20 current NAAQS and the ecosystem protection afforded by potential revisions to the current
21 primary standards forNOx and SOX.
22 This report includes four main sections (after this one), each dedicated to one of the main
23 ecosystem effect categories defined above and in Figure 1.1-1. Section 2 focuses on aquatic
24 acidification and provides an overview of the main ecosystem services affected by acidification
25 of freshwater. The section then applies the results of the Aquatic Acidification Case Study of
26 Adirondack lakes to quantify specifically the value of improved recreational fishing and other
27 cultural services caused by reductions in lake acidification in this part of the country.
1 Although other effects exist, the magnitude and/or scientific evidence of these effects is much more limited.
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Appendix 8-1
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
Ambient Air Quality
NOX/SOX Levels Under
Current Conditions
Stressor
I
NOX/SOX Levels Under
Alternative Conditions
Atmospheric N and S Deposition
Affected Ecosystems
I
Aquatic
Ecosystem Effects/
Symptoms
I
I
Terrestrial
• Acidification
• Nutrient Enrichment
I
• Acidification
• Nutrient Enrichment
Ecological Indicators
• Lake ANC Levels
• Eutrophication Indicators
• Forest Soil Chemistry
• Lichen Community Changes
Affected Ecosystem
Services
• Provisioning
• Cultural
• Regulating
Changes in
Ecosystem
Services
Figure 1.1-1. Conceptual Framework for Linking Changes in Ambient NOX and
SOX Levels to Changes in Ecosystem Services and Public Welfare
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June 5, 2009
Appendix 8-2
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Section 3 focuses on terrestrial acidification, providing an overview of the main
2 ecosystem services affected by acidification of forest soils. It then applies the results of the
3 Terrestrial Acidification Case Study and additional analyses of impacts on sugar maple trees to
4 quantify the value of improved provisioning services associated with expected enhancements to
5 forest productivity.
6 Section 4 focuses on aquatic nutrient enrichment. It describes and characterizes the
7 ecosystem services that are primarily affected by the eutrophication processes in estuaries that
8 result from excess nitrogen loadings. It then applies the results of the Aquatic Nutrient
9 Enrichment Case Study of the Potomac River/Potomac Estuary Case Study Area and the Neuse
10 River/Neuse River Estuary Case Study Area to quantify improvements in provisioning and
11 cultural services associated with reduced nitrogen loadings and improvements in eutrophic
12 conditions in the Chesapeake Bay and Neuse estuaries.
13 Section 5 focuses on terrestrial nutrient enrichment. It provides an overview of the
14 ecosystem services that are primarily affected by excess nitrogen loadings in two main terrestrial
15 ecosystems—California coastal sage scrub (CSS) and mixed conifer forest (MCF) habitats. It
16 also applies the findings from the Terrestrial Nutrient Enrichment Case Study of these affected
17 ecosystems; however, like the case study, because of data limitations and current knowledge
18 gaps, Section 5 does not quantify expected changes due to reductions in nitrogen and sulfur
19 deposition.
20 1.1 ECOSYSTEM SERVICE CATEGORIES
21 Ecosystem services are generally defined as the benefits individuals and organizations
22 obtain from ecosystems. This report uses the classification framework for ecosystem services
23 developed by the Millennium Ecosystem Assessment (MEA) (2005a, 2005b). In the MEA,
24 ecosystem services are defined to include both natural and human-modified ecosystems. Services
25 are further defined to encompass both tangible and intangible benefits that individuals and
26 organizations derive from ecosystems. In the MEA, ecosystem services are classified into four
27 main categories:
28 • Provisioning: includes products obtained from ecosystems.
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Appendix 8-3
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 • Cultural: includes the nonmaterial benefits people obtain from ecosystems through
2 spiritual enrichment, cognitive development, reflection, recreation, and aesthetic
3 experiences.
4 • Regulating: includes benefits obtained from the regulation of ecosystem processes.
5 • Supporting: includes those services necessary for the production of all other ecosystem
6 services.
7 Figure 1.1-2, taken from the MEA, displays the impact of the ecosystem services on
8 human well-being. The first three categories directly affect human well-being and economic
9 measures of welfare change. Supporting services do not have a direct effect on human well-being
10 but are vital to the functioning of the ecosystem.2 While other authors have proposed
11 categorizing ecosystem services using different systems, the MEA framework was chosen
12 because it is a well-developed and widely accepted system.3 The valuation of ecosystem services
13 benefits, however, is based on a careful linking of the MEA framework with neoclassical
14 economics.
2 One of the criticisms of the MEA framework from the perspective of economic analysis is that even some of the
regulating services, such as climate regulation, are more like ecosystem functions/processes or "supporting
services" that are only indirectly related to welfare (Boyd and Banzhaf [2007], Wallace [2007]).
3 Alternatives to the MEA ecosystem service classifications include, for example, Daily et al. (1997), National
Research Council (2005), and Wallace (2007).
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Appendix 8-4
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
ECOSYSTEM SERVICES
Provisioning
IFOOD H
FRESHWATER •)
WOOD AND FIBER
,
SuF
Nl
sc
PF
porting Regulating
JTRIENT CYCLING CLIMATE REGULATION Y\
)IL FORMATION FLOOD REGULATION
IMARY PRODUCTION DISEASE REGULATION JfJ
Cultural
AESTHETIC
SPIRITUAL
EDUCATIONAL
RECREATIONAL
/
f
LIFE ON EARTH - BIODIVERSITY
CONSTITUENTS OF WELL-BEING
Security
PERSONAL SAFETY
SECURE HESOUHCEACCESS
SECURITY FROM DISASTERS
Basic material
for good life
ADEQUATE LIVEUHOODS
SUFFICIENT NUTRITIOUS FOOD
SHELTER
ACCESS TO GOODS
Health
STRENGTH
FEELING WELL
ACCESS TO CLEAN AIR
AND WATER
Good social relations
SOCIAL COHESION
MUTUAL RESPECT
ABILITY TO HELP OTHERS
Freedom
of choice
and action
OPPORTUNITY TO BE
ABLE TO ACHIEVE
WHAT AN INDIVIDUAL
VALUES DOING
AND BEING
Goinc*: Millennium EoXiVstem Ass^cmb-nt
1
2 Figure 1.1-2. MEA Categorization of Ecosystem Services and their Links to
3 Human Well-Being (Source: MEA, 2005b).
4 1.1.1 Descriptions and Examples of MEA Ecosystem Services
5 For each service category, the MEA identifies a variety of subcategories. The list below
6 (adapted from MEA [2005b]) highlights the services that are most relevant to this report;
7 however, the MEA framework contains more services than those listed. Note that supporting
8 services, which do not link directly to welfare, are not included, and that there is some overlap
9 between the categories.
10 1.1.1.1 Provisioning Services
11 • Food and fiber: This includes the vast range of food products derived from plants, animals,
12 and microbes, as well as materials such as wood, jute, hemp, silk, and many other products
13 derived from ecosystems.
14 • Fuel: Wood, manure, and other biological materials serve as sources of energy.
15 • Genetic resources: This includes the genes and genetic information used for animal and
16 plant breeding and biotechnology.
17 • Biochemicals, natural medicines, and pharmaceuticals: Many medicines, biocides, food
18 additives such as alginates, and biological materials are derived from ecosystems.
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June 5, 2009
Appendix 8-5
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 • Fresh water: Fresh water is another example of linkages between categories—in this case,
2 between provisioning and regulating services.
3 1.1.1.2 Regulating Services
4 • Air quality maintenance: Ecosystems both contribute chemicals to and extract chemicals
5 from the atmosphere, influencing many aspects of air quality.
6 • Climate regulation: Ecosystems influence climate both locally and globally. For example,
7 on a local scale, changes in land cover can affect both temperature and precipitation. On a
8 global scale, ecosystems play an important role in climate regulation by either sequestering
9 or emitting greenhouse gases.
10 • Water regulation: The timing and magnitude of runoff, flooding, and aquifer recharge can
11 be strongly influenced by changes in land cover, including, in particular, alterations that
12 change the water storage potential of the system, such as the conversion of wetlands or the
13 replacement of forests with croplands or croplands with urban areas.
14 • Erosion control: Vegetative cover plays an important role in soil retention and the
15 prevention of landslides.
16 • Water purification and waste treatment: Ecosystems can be a source of impurities in
17 freshwater but also can help filter out and decompose organic wastes introduced into
18 inland waters and coastal and marine ecosystems.
19 • Biological control: Ecosystem changes affect the prevalence of crop and livestock pests
20 and diseases.
21 • Biological control—food chain: Ecosystem changes affect the availability of vegetation
22 and, in turn, animals that comprise and sustain delicate food chains within an ecosystem.
23 • Storm protection: The presence of coastal ecosystems such as mangroves and coral reefs
24 can dramatically reduce the damage caused by hurricanes or large waves.
25 1.1.1.3 Cultural Services
26 • Spiritual and religious values: Many religions attach spiritual and religious values to
27 ecosystems or their components.
28 • Educational values: Ecosystems and their components and processes provide the basis for
29 both formal and informal education in many societies.
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Appendix 8-6
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 • Inspiration: Ecosystems provide a rich source of inspiration for art, folklore, national
2 symbols, architecture, and advertising.
3 • Aesthetic values: Many people find beauty or aesthetic value in various aspects of
4 ecosystems, as reflected in the support for parks, "scenic drives," and the selection of
5 housing locations.
6 • Recreation and ecotourism: People often choose where to spend their leisure time based, in
7 part, on the characteristics of the natural or cultivated landscapes in a particular area.
8 Environmental economists also have identified a category of services associated with
9 ecological benefits termed "nonuse values" (also referred to as "existence values" or "passive
10 use values"). As the name implies, nonuse values capture those values people have for the
11 environment or natural resources separate from the direct or indirect use value the resources
12 provide. The value some individuals hold for wilderness areas that they will never visit is one
13 type of nonuse value. This report includes nonuse values as a subcategory of cultural services.
14 1.2 REFERENCES
15 Boyd, J., and S. Banzhaf. 2007. "What are Ecosystem Services? The Need for Standardized
16 Environmental Accounting Units." Ecological Economics 63:616-626.
17 Daily, G.C., S. Alexander, P.R. Ehrlich, L. Goulder, J. Lubchenco, P.A. Matson, H.A. Mooney,
18 S. Postel, S.H. Schneider, D. Tilman, and G.M. Woodwell. 1997. "Ecosystem Services:
19 Benefits Supplied to Human Societies by Natural Ecosystems." Issues in Ecology 2:1-16.
20 Millennium Ecosystem Assessment (MEA). 2005a. Ecosystems and Human Well-being: Current
21 State and Trends, Volume 1. R. Hassan, R. Scholes, and N. Ash, eds. Washington, DC:
22 Island Press. Available at http://www.millenniumassessment.org/documents/
23 document.766.aspx.pdf.
24 Millennium Ecosystem Assessment (MEA). 2005b. Ecosystems and Human Well-being:
25 Synthesis. Washington, DC: World Resources Institute.
26 National Research Council. 2005. Valuing Ecosystem Services: Toward Better Environmental
27 Decision-Making. Washington, DC: National Academies Press.
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Appendix 8-7
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Wallace, KJ. 2007. "Classification of Ecosystem Services: Problems and Solutions." Ecological
2 Conservation 139:235-246.
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Appendix 8-8
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
i 2. AQUATIC ACIDIFICATION
2 High levels of nitrogen and sulfur deposition, particularly in areas with soils containing
3 relatively low levels of alkaline chemical bases such as calcium or magnesium ions, often lead to
4 acidification of surface waters such as lakes and streams. These processes contribute to low pH
5 levels and other chemical changes that can be toxic to fish and other aquatic life. Evidence of
6 both chronic and episodic acidification of surface waters is particularly evident in the Eastern
7 and northeastern United States, where levels of nitrogen and sulfur deposition have also been
8 relatively high in recent decades. These surface waters support a wide variety of ecosystem
9 services, many of which can be affected adversely by acidification.
10 2.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES
11 Because acidification primarily affects the diversity and abundance of aquatic biota, it
12 also primarily affects the ecosystem services that are derived from the fish and other aquatic life
13 found in these surface waters.
14 2.1.1 Provisioning Services
15 Food and freshwater are generally the most important provisioning services provided by
16 inland surface waters (Millennium Ecosystem Assessment [MEA], 2005). Whereas acidification
17 is unlikely to have serious adverse effects on, for example, water supplies for municipal,
18 industrial, or agricultural uses, it can limit the productivity of surface waters as a source of food
19 (i.e., fish). In the northeastern United States, the surface waters affected by acidification are not a
20 major source of commercially raised or caught fish; however, they are a source of food for some
21 recreational and subsistence fishers and for other consumers. Although data and models are
22 available for examining the effects on recreational fishing (see Section 2.1.2), relatively little
23 data are available for measuring the effects on subsistence and other consumers. For example,
24 although there is evidence that certain population subgroups in the northeastern United States,
25 such as the Hmong and Chippewa ethnic groups, have particularly high rates of self-caught fish
26 consumption (Hutchison and Kraft, 1994; Peterson et al., 1994), it is not known if and how their
27 consumption patterns are affected by the reductions in available fish populations caused by
28 surface water acidification.
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Appendix 8-9
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 2.1.2 Cultural Services
2 Inland surface waters support several cultural services, such as aesthetic and educational
3 services; however, the type of service that is likely to be most widely and significantly affected
4 by aquatic acidification is recreational fishing, since it depends directly on the health and
5 abundance of aquatic wildlife. Other recreational activities such as hunting and birdwatching are
6 also likely to be affected, to the extent that fish eating birds and other wildlife are harmed by the
7 absence offish in acidic surface waters.
8 Recreational fishing in lakes and streams is among the most popular outdoor recreational
9 activities in the northeastern United States. Data from the 2006 National Survey of Fishing,
10 Hunting, and Wildlife Associated Recreation (FHWAR), as summarized in Table 2.2-1, indicate
11 that more than 9% of adults in this part of the country participate annually in freshwater
12 (excluding Great Lakes) fishing. The total number of freshwater fishing days occurring in those
13 states (by both residents and nonresidents) in 2006 was 140.8 million days. Roughly two-thirds
14 of these fishing days were at ponds, lakes, or reservoirs in these states, and the remaining one-
15 third were at rivers or streams. Based on studies conducted in the northeastern United States,
16 Kaval and Loomis (2003) estimated an average consumer surplus value per day of $35.91 for
17 recreational fishing (in 2007 dollars). Therefore, the implied total annual value of freshwater
18 fishing in the northeastern United States was $5.06 billion in 2006.
19 2.1.3 Regulating Services
20 In general, inland surface waters such as lakes, rivers, and streams provide a number of
21 regulating services, such as hydrological regime regulation and climate regulation. There is little
22 evidence that acidification of freshwaters in the northeastern United States has significantly
23 degraded these specific services; however, freshwater ecosystems also provide biological control
24 services by providing environments that sustain delicate aquatic food chains. The toxic effects of
25 acidification on fish and other aquatic life impair these services by disrupting the trophic
26 structure of surface waters (Driscoll et al., 2001). Although it is difficult to quantify these
27 services and how they are affected by acidification, it is worth noting that some of these services
28 may be captured through measures of provisioning and cultural services. For example, these
29 biological control services may serve as "intermediate" inputs that support the production of
30 "final" recreational fishing and other cultural services.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
3
4
5
6
7
8
9
10
11
2.2 CHANGES IN ECOSYSTEM SERVICES ASSOCIATED WITH
ALTERNATIVE LEVELS OF ECOLOGICAL INDICATORS
This section estimates values for changes in ecosystem services associated with
reductions in lake acidification in Adirondack State Park and in other lakes in the state of New
York. Using the results of the Adirondack Case Study Area of aquatic acidification effects, the
value of reducing nitrogen and sulfur deposition in the affected areas to background levels (i.e., a
"zeroing out" of anthropogenic sources of nitrogen and sulfur) was estimated. Although this
scenario is not realistic from a policy perspective, it allows the examination of the upper bound
of ecosystem service gains that would result from reducing the number of acidified (i.e., low acid
neutralizing capacity [ANC]) lakes to the lowest possible level.
Table 2.2-1. Participation in Freshwater Recreational Fishing in Northeastern States in 2006
State
Connecticut
Delaware
Illinois
Indiana
Maine
Maryland
Massachusetts
Michigan
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Rhode Island
Vermont
West Virginia
Wisconsin
Total
Participation
Rates by State
Residents"
7.3%
6.4%
9.5%
13.8%
19.9%
6.6%
6.0%
12.3%
10.0%
4.0%
4.6%
11.6%
8.3%
5.3%
13.2%
20.9%
21.4%
9.3%
Activity Days by Residents and Nonresidents (in
thousands)
Ponds, Lakes, or
Reservoirs
2,856
764
10,318
6,843
3,734
2,882
4,494
15,175
2,144
2,377
8,548
9,781
7,507
504
1,264
3,069
13,026
95,286
Rivers or
Streams
2,409
770
5,088
1,819
1,521
2,379
978
4,426
627
1,116
5,086
3,710
6,998
104
453
3,617
4,439
45,540
Total
5,265
1,534
15,406
8,662
5,255
5,261
5,472
19,601
2,771
3,493
13,634
13,491
14,505
608
1,717
6,686
17,465
140,826
a Ages 16 or older.
2nd Draft Pvisk and Exposure Assessment
Appendix 8-11
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Source: U.S. Department of the Interior (DOI), Fish and Wildlife Service, and U.S. Department of Commerce, U.S.
2 Census Bureau, 2007.
3 The case study analysis focused on 44 lakes in the Adirondacks. It estimated ANC levels
4 at each of these lakes under the alternative scenarios shown in Table 2.2-2. Using the MAGIC
5 model, it predicted median ANC levels for the years 2005, 2020, 2050, and 2100 under
6 "business-as-usual" conditions (i.e., accounting for expected emission controls associated with
7 Title IV regulations but no additional measures to reduce nitrogen and sulfur deposition). In
8 contrast, the model run for the year 1860 represents ANC levels for "background" conditions by
9 simulating the effect of zeroing out anthropogenic sources of nitrogen and S.
Table 2.2-2. Acid Neutralizing Capacity Levels (in jieq/L) at 44 MAGIC-Modeled Lakes in the
Adirondacks
Year:
Lake Name
Clear Pond (61)
Long Pond (65)
Hope Pond
Second Pond
Squaw Lake
Indian Lake
Big Alderbed
Long Lake
Gull Pond
Little Lilly Pond
Upper Sister Lake
Dry Channel Pond
Bennett Lake
Effley Falls Pond
Parmeter Pond
North Lake
Razorback Pond
Snake Pond
South Lake
Boottree Pond
Horseshoe Pond
Rock Pond
Observed
2002
218.1
66.1
62.6
71.3
19.5
-8.0
57.4
-32.1
160.9
47.0
31.0
23.8
33.0
50.8
75.6
-1.6
33.2
3.6
-7.7
53.2
51.4
86.7
MAGIC Model Simulations
2005
233.0
73.5
72.9
75.8
25.6
1.4
67.5
-20.8
166.8
54.4
37.4
31.7
37.5
59.8
85.7
6.9
39.6
12.3
0.1
59.0
63.0
95.1
2020 a
243.2
78.3
78.4
77.0
27.1
6.2
72.6
-15.4
170.7
57.9
39.9
34.3
39.2
64.2
91.7
10.9
42.4
15.5
3.7
63.2
70.0
98.8
2050a
246.7
80.4
81.1
75.3
24.9
6.2
74.5
-16.0
173.0
58.5
39.4
33.2
37.8
64.2
94.3
10.0
40.5
14.5
2.3
65.3
73.4
99.5
2100a
247.6
81.2
82.8
72.5
21.3
5.1
75.6
-17.6
174.6
58.6
38.0
31.6
35.0
63.7
95.4
8.1
37.4
13.4
-0.2
66.1
74.8
99.5
I860"
("Background")
290.3
106.4
126.5
121.5
73.8
52.2
124.1
34.4
208.8
95.5
80.3
78.6
69.7
132.4
134.8
66.0
94.3
78.5
56.6
84.5
117.6
151.5
2nd Draft Risk and Exposure Assessment
Appendix 8-12
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Observed
1
2
3
4
5
6
7
8
9
Year:
Lake Name
Antediluvian Pond
Seven Sisters Pond
Canada Lake
Bickford Pond
Wolf Pond
Blue Mountain Lake
Carry Falls Reservoir
Rocky Lake
Bog Pond
Clear Pond (82)
Seventh Lake
Trout Pond
Hitchins Pond
Piseco Lake
Mccuen Pond
Arbutus Pond
Witchhopple Lake
Willys Lake
Lower Beech Ridge
Pond
Dismal Pond
Payne Lake
Whitney Lake
2002
66.2
-14.1
53.8
19.9
-5.7
118.0
121.2
43.7
88.2
85.9
198.4
39.9
150.2
98.2
37.6
88.0
27.2
-48.4
-19.6
-23.6
53.5
22.3
a Based on predicted future scenarios for nitroj
b Represents background levels and levels that
of nitrogen+sulfur deposition.
2005
70.1
-9.1
69.4
33.6
4.8
126.7
133.2
58.6
107.0
97.1
217.3
53.4
162.7
114.7
46.0
101.6
35.7
-38.8
-10.8
-12.0
56.2
30.7
MAGIC Model Simulations
2020 a
72.0
-6.9
77.6
41.3
9.8
129.3
140.8
66.3
117.2
104.1
223.4
61.9
170.0
123.7
50.2
108.6
39.4
-33.5
-6.9
-7.6
58.1
33.7
2050a
71.4
-7.2
80.1
45.2
11.2
127.8
144.1
68.3
120.5
107.4
227.1
65.7
172.6
127.2
51.7
111.3
38.9
-33.3
-7.4
-7.3
59.0
32.9
2100a
69.9
-8.1
81.4
46.9
11.9
125.2
145.8
68.8
121.6
108.2
229.1
67.6
173.8
128.6
52.4
113.1
37.6
-33.4
-8.8
-7.6
59.4
31.5
I860"
("Background")
95.3
21.9
151.2
101.3
58.3
184.3
205.8
113.7
178.1
145.6
317.6
127.2
214.7
186.2
90.0
187.1
91.7
47.5
41.5
40.4
75.1
84.3
;en+sulfur deposition, accounting for Title IV emissions controls.
would eventually result from a "zero-out" of anthropogenic sources
In the following subsections, ecosystem
business-as-usual reference
that the zero out of nitrogen
service gains
associated with
conditions to the zero-out condition are
estimated
going from the
. It was assumed
and sulfur deposition would occur in 2010 and that it would take 10
years for the full effect of these reductions on lake ANC levels to occur. In other words, by the
year 2020, lake ANC levels
would increase and
fully return to their
estimated
1860 background
levels, as shown in Table 2.2-2.
2nd Draft Risk and Exposure Assessment
Appendix 8-13
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 In Section 2.2.1, a model that focuses specifically on recreational fishing services is
2 applied, and the value of current and future enhancements to these services from Adirondack and
3 other New York lakes is estimated. In Section 2.2.2, a model that takes a broader perspective on
4 ecosystem services is applied, and the value of improving all of the ecosystem services that are
5 affected by acidification of Adirondack lakes is estimated.
6 In both cases, the analysis focuses on ANC and evaluates the sensitivity of different ANC
7 thresholds for aquatic functioning. In general, moderate shifts in ANC levels may result in
8 changes in species composition, where acid-sensitive species are replaced by less sensitive
9 species. At more extreme acidification levels, however, species richness, defined as the total
10 number of species occupying a system, may be affected. Research has shown that the number of
11 fish species present is positively correlated with ANC (Driscoll et al., 2003). In the Adirondacks,
12 recent research indicates that aquatic biota begin to exhibit effects at an ANC of 50
13 microequivalents per liter (|j,eq/L) (Chen and Driscoll, 2004). Uncertainty exists regarding
14 threshold levels of ANC: the levels at which predictable effects occur. Several ANC thresholds
15 have been observed, however, at which lakes and fish are affected, as summarized in Figure 2.2-
16 1. To account for the uncertainty in the threshold level of acidification above which Adirondack
17 lakes may support recreational fishing, this analysis considers three threshold levels: 20 |j,eq/L,
18 50 neq/L, and 100
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-14
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
ANC THRESHOLD VALUES (UEQ/L)
20 40 50
100
200
Species richness
increases between 50
and 100 |jeq/L
Lakes are not sensitive to
acidification at values approaching
200 |jeq/L
Level below which aquatic biota are affected
Lakes are sensitive to episodic acidification
Level below which all biota are affected
Lakes are chronically acidic and fishless
2 Figure 2.2-1. Summary of Acid Neutralizing Capacity Values Relevant for Lake
3 and Fish Health (Source: Industrial Economics, Inc., 2008).
4 2.2.1 Improvements in Recreational Fishing Services due to Increased Acid
5 Neutralizing Capacity Levels in Adirondack and Other New York Lakes
6 To estimate the value of improved services, this analysis relied on commonly accepted
7 economic models to relate the predicted changes in lake acidity to a change in recreational
8 fishing behavior throughout the study area. First, a random effects model was used to extrapolate
9 lake ANC levels from the ecological model forecast for a subset of lakes to a broader suite of
10 regional lakes. This random effect model does this by relating acidification levels to lake
11 characteristics and geographic location. That is, the forecast ANC levels of the lakes modeled in
12 MAGIC for each year in the study period are tied to explanatory variables so that the forecast
13 changes in ANC can be extrapolated to other potentially affected lakes in the region. This model
14 was first applied to forecast ANC levels at lakes in the Adirondack region and then repeated to
15 forecast ANC levels for lakes in New York State (with the exception of New York City). The
16 result of this effort is a full time-series dataset of ANC levels for Adirondack and New York
17 State lakes.
18 The second economic model applied describes changes in behavior of recreational fishers
19 in response to changes in lake acidification levels. This step of the process relies on the
20 assumption that below the specified ANC threshold (of 20 |j,eq/L, 50 |j,eq/L, or 100 |j,eq/L) lakes
2nd Draft Risk and Exposure Assessment
Appendix 8-15
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 are no longer fishable. The specific type of model applied here is a "discrete choice model."
2 Generally, a discrete choice model predicts a binary decision (which may be thought of as "yes"
3 or "no") regarding whether to fish at a given site made by an individual as a function of a
4 number of independent variables (Greene, 2003). The independent variable is the catch rate at
5 the water body (itself a function of lake acidity). Additional independent variables may include
6 travel time required to reach the site and the concentration of fisherman at the site, among others.
7 A specific form of discrete choice model called a "random utility model," or RUM is
8 applied. In the study of economics, utility is defined as a measure of the happiness or satisfaction
9 gained from a good or service. In keeping with the tenets of neoclassical economics, this utility is
10 sought to be maximized subject to a constraint (often represented by income or time). Put more
11 simply, the model assumes that the fisherman will seek the most happiness at the lowest cost.
12 Section 2 describes the application of these models and the results of this analysis.
13 2.2.1.1 Analytic Method
14 The following steps were followed to connect the modeled changes in lake ANC levels to
15 the benefits of improved recreational fishing services.
16 Step 1: Development of the Random Effects Model
17 To develop this model, it was first necessary to compare the subset of lakes considered in
18 the ecological model (see Table 2.2-2) with the subset of lakes included in the database of lake
19 characteristics contained within the RUM. Nine of the 44 lakes were not usable for the analysis
20 because they did not appear in the database of lake characteristics within the RUM.l As a result,
21 the analysis relied on data for a subset of 35 Adirondack lakes.
22 Because forecasted ANC levels were provided for the 35 lakes only, the next step of the
23 analysis was extrapolating these forecasts to the broader suite of lakes within the Adirondack
24 region. To this end, a random effects model was developed to determine the statistical
25 relationship between the lakes' ANC levels and their characteristics. Significant uncertainty
26 exists regarding the relationships between lake characteristics and ANC levels. Ecologists at the
27 Environmental Protection Agency (EPA) are researching the characteristics that best explain a
28 lake's sensitivity to acidification.
lrrhose lakes excluded include the following: Bickford Pond, Bog Pond, Hope Pond, Little Lilly Pond, Lower Beech
Ridge, Razorback Pond, Seven Sisters Pond, Snake Pond, and Witchhopple Lake.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-16
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 The random effects model used in this analysis to forecasted lake ANC levels was also
2 limited by the lake characteristic data that are currently available; in this case, elevation, surface
3 area, shoreline, and county location were considered as potential explanatory variables in
4 forecasting ANC levels. The relationship between these characteristics and the forecast ANC
5 levels for the 35 lakes informed the extrapolation of the results from the MAGIC model to the
6 broader population of lakes, first in the Adirondack region and then in New York State. These
7 variables that describe the lake characteristic and geographic location are the explanatory
8 variables in the model. The random effects model helps identify the influences of these
9 explanatory variables, net of other factors that are unknown and cannot be controlled.2
10 The model cannot perfectly predict ANC levels in lakes; there are not enough available
11 data and there is no existing knowledge about the best determinants of ANC levels. Given that
12 there is some uncertainty and limited information available to explain ANC levels, a method
13 must be used that can remove the net effects of the unknown data and identify the effects of the
14 available information. The random effects model generates estimates of the net effects of the
15 explanatory variables.
16 Furthermore, random effects models are appropriate for situations where the study
17 sample is a random sample of a larger universe and one wishes to make inferences about the
18 larger universe of data (Kennedy, 2003).3 In this case, the group of lakes analyzed is sampled
19 from the total number of lakes for which ANC levels are forecast (the lakes to which the ANC
20 levels are forecast is the universe).
21 The modeled ANC levels for the 35 aforementioned lakes, along with the lake
22 characteristic information, served as inputs for a random effects regression analysis to isolate the
23 impact of each variable on ANC. Table 2.2-3 details the results of the random effects model.
Table 2.2-3. Random Effects Model Results
Variable Coefficient Std. Error
constant -106.171 75.050
2 Several important conditions must be satisfied for the random effects model to be appropriate. In this case, these
conditions are met. For both models, the Breusch-Pagan test for random effects rejects the null hypothesis of no
random effects in the data. The Hausman specification test (against a fixed effects alternative) rejects the null
hypothesis of systematic differences between random and fixed effects models for the Clean Air Interstate Rule
(CAIR) and t variables, which indicates that omitted variables are not biasing the coefficients for those variables.
3 This criterion assumes that there are no omitted variable effects present; the previous footnote explains that there is
no evidence of this.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-17
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
elevation
area
In(shoreline)
T
Hamilton
Essex
Fulton
Franklin
Herkimer
Lewis
Warren
-0.047
0.125
-36.005
0.108
9.430
55.149
-16.793
49.538
-38.655
-19.160
24.924
0.128
0.074
18.802
0.013
27.760
46.894
80.273
39.176
40.142
45.899
66.423
1
2 Variables describing elevation, total area, and shoreline length were included to capture
3 physical differences between lakes. While the coefficients are not statistically significant, the
4 variables do lend some explanatory power to the model. The variable identified as "T" is an
5 annual time trend included to capture changes through time manifested in the greater system and
6 not a specific lake. The final seven variables listed in the table are binary variables indicating the
7 counties in which the lakes occur. The omitted variable is for St. Lawrence County. These
8 variables are intended as a proxy for a host of location-specific factors, including subsurface
9 geology and degree of forest cover because data were not available for these variables.
10 Step 2: Extrapolation to All Lakes in Adirondack Region/New York State
11 The Montgomery-Needelman RUM includes lake characteristic data for a total of 2,586
12 lakes in New York State. As described previously, the MAGIC model predicts ANC levels for
13 35 lakes within the Adirondack region that could be included in the random effects model. These
14 35 lakes are located in Hamilton, Essex, Fulton, Franklin, Herkimer, Lewis, Warren, and St.
15 Lawrence counties. Their explanatory value for lakes outside of this eight-county region is
16 uncertain. Therefore, this study performed a "tiered" extrapolation, where the random effects
17 model results were first extrapolated only to lakes in the Adirondack region represented by the
18 modeled lakes; this exercise was then repeated for the full suite of New York State lakes.
19 For the first tier (for the Adirondack region), the analysis was limited by two dimensions:
20 (1) only including lakes within the eight counties containing the 35 modeled lakes and (2)
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-18
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 limiting the analysis to lakes within the size range of the modeled lakes. Because none of the 35
2 modeled lakes occur in Clinton, Saratoga, and Oneida counties (all within the Adirondack
3 region), this analysis did not apply the model to forecast lake acidification in these three
4 counties. This assumption may lead to an understatement of the total benefits associated with
5 decreased lake acidification in the Adirondack region, but it avoids some uncertainty associated
6 with extrapolating ANC outside of the scope of the modeled region.
7 The second tier of the analysis (for all of New York State except New York City) was
8 also limited to consider only lakes within the size range of the modeled lakes. This portion of the
9 analysis required consideration of lakes outside of the eight-county geographic scope, however.
10 Therefore, an average of the eight-county binary variable coefficients for all lakes outside of the
11 eight counties was used. Further, as with the first tier of the analysis, all lakes with an area
12 greater than the largest lake in the ecological subset of 35 were "hardwired" to be unimpaired,
13 because changes in their ANC levels are unlikely to be represented by the subset of modeled
14 lakes.4 A total of 62 lake sites were determined to be too large to be represented by the sample
15 MAGIC data and were, therefore, hardwired.
16 Step 3: Application of ANC Thresholds
17 This analysis employs three ANC threshold assumptions—20 |j,eq/L, 50 |j,eq/L, and 100
18 |j,eq/L—to indicate whether a lake is fishable. A lake was deemed to be affected if it was above
19 the threshold (fishable) in the "zero-out" scenario and below the threshold (impaired) in the
20 baseline scenario. As previously described and shown in Table 2.2-2, zero-out conditions are
21 defined by lake ANC levels in the year 1860 as estimated by MAGIC. MAGIC provided these
22 data for the subset of 35 lakes within the Adirondack region. To determine zero-out conditions
23 for the broader suite of lakes in the Adirondacks and in New York State, a simple ordinary least
24 squares (OLS) regression was run to determine whether lake size is a reasonable indicator of the
25 difference between the observed ANC level in 2002 and the pristine condition in 1860 for the 35
26 lakes. This analysis determined that no statistically significant relationship existed. Therefore, an
4 Hardwired lakes (in order of decreasing size) include Lake Ontario, Lake Erie, Great Sacandaga Lake, Oneida
Lake, Seneca Lake, Lake Champlain, Cayuga Lake, Lake George, Canandaigua Lake, Ashokan Reservoir,
Cranberry Lake, Owasco Lake, Chautauqua Lake, Tupper Lake, Stillwater Reservoir, Keuka Lake, Pepacton
Reservoir, Allegheny Reservoir, Raquette Lake, Cannonsville Reservoir, Indian Lake, Skaneateles Lake, Black
Lake, Long Lake, Otsego Lake, Saratoga Lake, Mount Morris Reservoir, Salmon River Reservoir, Great Sodus
Bay, Conesus Lake, Whitney Point Reservoir, and Onondaga Lake.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-19
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 average difference in ANC level between the 2002 observed level and the 1860 pristine
2 condition for the 35 lakes was calculated; the average difference is 64.6 |j,eq/L. This average
3 difference was then added to the 2002 ANC levels for each lake (forecast by extrapolation using
4 the random effects model), and the resulting value was considered to be the "pristine" ANC
5 value in 2020, 2050, and 2100.
6 Table 2.2-4 reports the number of "impacted" lakes in each year, where impact means
7 that the lake is predicted to be below the ANC threshold under business-as-usual conditions and
8 above the threshold under zero-out conditions. This definition of impacted lakes is needed
9 because the RUM framework only estimates benefits accruing from lakes that switch from
10 nonfishable to fishable status. The lake counts for 2005 are zero because in this year no change
11 occurs in ANC level relative to the baseline (i.e., the reduction in emissions beginning the return
12 to pristine conditions was not assumed to have occurred in those years). The zero-out scenario
13 was assumed to be implemented in 2010, with lakes reaching their pristine conditions by 2020. It
14 should be noted that the nature of this model allows for lakes to switch between impaired and
15 unimpaired between years. As a result, the lake counts reported in Table 2.2-4 are not cumulative
16 counts and, in fact, may reflect different subsets of lakes.
17 Step 4: Application of the Random Utility Model
18 The Montgomery-Needelman model applied in this analysis is a repeated discrete choice
19 RUM that describes lake fishing behavior of New York residents (Montgomery and Needelman,
20 1997). In particular, the model characterizes decisions regarding (1) the number of lake fishing
21 trips to take each season and (2) the specific lake sites to visit on each fishing trip. The model
22 can be used to develop estimates of economic losses or gains associated with changes in the set
23 of lakes available to anglers.
24
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-20
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 2.2-4. Count of Impacted Lakes
ANC Threshold
(in jieq/L)
20
20
20
20
50
50
50
50
100
100
100
100
Year
2005
2020
2050
2100
2005
2020
2050
2100
2005
2020
2050
2100
Adirondack
0
107
95
74
0
244
222
200
0
430
404
354
Lake Count
Region New York State
0
110
97
75
0
365
316
254
0
1,500
1,399
1,228
1 Note: There are 1,076 lakes in the "Adirondack Region" and 2,586 lakes in New York State (less New York City).
2 The data used to estimate the RUM were obtained from a 1989 repeat-contact telephone
3 survey of New York residents conducted as part of the National Acid Precipitation and
4 Assessment Program (NAPAP).5 This survey provided information on the destinations of
5 anglers' fishing trips (day trips only) taken during the 1989 fishing season. The survey data were
6 supplemented with lake characteristics data obtained from New York State Department of
7 Environmental Conservation's (NYSDEC's) Characteristics of New York State Lakes: Gazetteer
8 of Lakes and Ponds and Reservoirs., New York State's Fishing Guide, and New York's 305(b)
9 report for 1990. Travel distances between anglers' homes and lake fishing sites were calculated
10 using Hy ways/By ways. The model and data used in the present analysis are described in greater
11 detail in a 1997 journal article by Montgomery and Needelman.6
12 The list of affected lakes generated in the previous step serves as the primary input to the
13 RUM. The model estimates the economic welfare value of enhancements to recreational fishing
5 New York City counties were excluded from the sampling frame.
6 The published version of the model has had several minor updates, all of which have been discussed with Mark
Montgomery.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-21
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 services derived from shifting specific lakes from nonfishable to fishable status.7 The economic
2 benefits estimates represent New York State residents' average willingness to pay (WTP) to
3 improve recreational fishing services by reducing lake acidification levels. Table 2.2-5 reports
4 the estimated per capita values generated by the RUM. These values have been adjusted from
5 1989 dollars to 2007 dollars using the Consumer Price Index-All Urban Consumers (CPI-U).
6 Note that the zero-out scenario is assumed to begin at the end of 2010; therefore, the benefits do
7 not begin to accrue until the following year, and they are zero in 2005 and 2010.
Table 2.2-5. Per Capita Willingness to Pay (2007 $)
ANC Threshold
(in (ieq/L)
20
20
20
20
20
50
50
50
50
50
100
100
100
100
100
Per Capita Benefits of a Return to Pristine Conditions
by 2020
Year
2005
2010
2020
2050
2100
2005
2010
2020
2050
2100
2005
2010
2020
2050
2100
7 Since the RUM uses travel distances and travel
to the spatial locations and distributions of the
the number or percentage of lakes impacted).
Adirondack Region
$0.00
$0.00
$0.41
$0.34
$0.28
$0.00
$0.00
$0.74
$0.73
$0.70
$0.00
$0.00
$0.79
$0.77
$0.68
New York State
$0.00
$0.00
$0.47
$0.38
$0.32
$0.00
$0.00
$2.55
$2.26
$1.47
$0.00
$0.00
$11.05
$10.61
$9.40
costs to infer economic values, the benefit estimates are sensitive
impacted lakes (i.e., the benefit estimates do not depend only on
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8 - 22
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Step 5: Interpolation of RUM Output
2 The RUM provided per capita loss estimates (reported in 1989 nominal dollars) for 2010,
3 2020, 2050, and 2100.8 Rather than running this model separately for each year, estimates for the
4 intervening years (between the four point estimates provided by the RUM) were generated via
5 simple linear interpolations.
6 Step 6: Estimation of Aggregate Benefits through Application of Per Capita Results to
1 Affected Population
8 To estimate aggregate benefits for New York residents, the per capita benefit estimates
9 must be multiplied by the corresponding population of residents. To match the characteristics of
10 the population surveyed in developing the RUM, this analysis required estimating the population
11 of New York State that will be over 18 years old and reside outside of New York City for each
12 year from 2011 through 2100. The U.S. Census Bureau provides estimated population figures for
13 2002 through 2008 and projected population through 2030 at the state level. Absent projection
14 information, the population was held constant from 2030 through the period of the analysis
15 (through 2100). The ratio of the New York State population residing outside New York City
16 (that is, the five counties of Bronx County, Kings County, New York County, Queens County,
17 and Richmond County) was calculated for 2006 and assumed to remain constant throughout the
18 analysis. The U.S. Census also estimates and projects the 18+ population at the state level
19 through 2030. The 18+ population was held constant from 2030 through the end of the analysis
20 in 2100. The ratio of adults (18+) to the entire population was calculated for New York State,
21 and that ratio was applied to the population residing outside New York City.
22 2.2.1.2 Results
23 Table 2.2-6 summarizes the estimated present value and annualized benefits for each
24 acidification threshold assumption applying discount rates of 3% and 7%. The estimated present
25 value of benefits in 2010 range from $60.1 million to $298.7 million depending on the threshold
26 and discount rate assumptions applied. In comparison, a previous study of the recreational
27 fishing benefits in the Adirondacks associated with the Clean Air Act Amendments (CAAA)
28 estimated benefits ranging from $13.7 million to $100.6 million (EPA, Office of Air and
8 As mentioned previously, the CPI-U, provided by the U.S. Bureau of Labor Statistics, was used to inflate these
estimates to 2007 dollars.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Radiation, 1999).9 Annualizing these benefits over the period 2010 to 2100 results in annual
2 benefit estimates ranging from $3.9 million to $9.3 million per year. Six tables containing
3 detailed results for each scenario (threshold assumption and geographic scope) by year are
4 included in Attachment A.
5 Table 2.2-7 describes total present value and annualized benefits associated with reduced
6 lake acidification in all of New York State. Estimated present value benefits in 2010 range from
7 $68.3 million to $4.16 billion, depending on the threshold and discount rate assumptions applied.
8 Annualizing these benefits over the period 2010 to 2100 results in annual benefit estimates
9 ranging from $4.5 million to $130 million per year.
Table 2.2-6. Present Value and Annualized Benefits, Adirondack Region
. mT._ Present Value Benefits" Annualized Benefits'5
ANC
Threshold (in million of 2007 dollars) (in million of 2007 dollars)
10
11
(in (ieq/L) 3% Discount Rate 7% Discount Rate
20 $142.59 $60.05
50 $285.15 $114.18
100 $298.67 $120.61
a Annual benefits for 2010 to 2100 discounted to 2010.
b Present value benefits annualized over 2009 to 2 100.
3% Discount Rate
$4.46
$8.91
$9.33
7% Discount Rate
$3.94
$7.49
$7.91
Table 2.2-7. Present Value and Annualized Benefits, New York State
. mT._ Present Value Benefits" Annualized Benefits'5
ANC
Threshold (in million of 2007 dollars) (in million of 2007 dollars)
12
13
(in (ieq/L) 3% Discount Rate 7% Discount Rate
20 $161.76 $68.34
50 $897.20 $378.00
100 $4,159.64 $1,685.80
a Annual benefits for 2010 to 2100 discounted to 2010.
b Present value benefits annualized over 2010 to 2100.
3% Discount Rate
$5.05
$28.04
$129.98
7% Discount Rate
$4.48
$24.78
$110.52
9 For comparison to the results in our analysis, presented in 2007 dollars, the estimated benefits from the Clean Air
Act report were inflated from 1999 to 2007 dollars using the GDP deflator
(http://www.bea.gov/bea/dn/nipaweb/SelectTable.asp?Selected=Y).
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1 2.2.1.3 Assumptions and Caveats
2 The following assumptions and caveats are particularly important for interpreting the
3 results and the application of the ecological model for lake acidification:
4 • This analysis assumed that the level of impairment is binary as applied to a specific lake:
5 that is, the ANC threshold indicates whether a lake is fishable.
6 • The available literature suggests that ANC levels between 20 and 100 cover the range
7 where ecological affects are realized. Three points within this range (20, 50, and 100) were
8 tested as point estimates at which the fishability of lakes is affected.
9 • This analysis assumed that the 35 modeled lakes are a representative subset of lakes in the
10 Adirondacks (for the first tier of the analysis) and in New York State (for the second tier of
11 the analysis).
12 • This analysis used the ANC levels of the 35 modeled lakes in the year 1860 as a proxy for
13 "pristine" acidification levels.
14 • In the first tier, the analysis is not used to forecast acidification effects in Clinton,
15 Saratoga, and Oneida counties, which are generally considered to be part of the
16 Adirondack region because they are not represented by the subset of lakes subject to the
17 ecological model. This restriction contributes to an underestimation of total benefits.
18 • Pristine ANC levels for the full population of New York State lakes are estimated by first
19 finding the average difference between 2002 observed ANC levels and the 1860 ANC
20 values for the 35 lakes modeled by MAGIC and then adding this average difference to the
21 2002 ANC values for all lakes (as estimated by extrapolating using the random effects
22 model). The ANC levels assumed to represent pristine lake conditions are therefore subject
23 to significant uncertainty.
24 The following assumptions and caveats are particularly important for interpreting the
25 application of the RUM model for estimating recreational fishing benefits to New York
26 residents:
27 • The RUM only considers the behavior of New York State residents. It may be reasonable
28 to assume that residents of neighboring jurisdictions (the Canadian provinces of Ontario
29 and Quebec, along with the State of Vermont) may also take day trips to these lakes and
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 respond in a rational manner comparable to New York State residents. This restriction
2 contributes to an underestimation of benefits.
3 • The output of the RUM is on a per capita basis. The results are presented in terms of
4 impacts to the entire population. This requires an extrapolation of the population through
5 2100. Absent specific projection information beyond 2030, the population was held
6 constant beyond this year.
7 • This analysis assumed that the demand for fishing, in other words, an individual's
8 propensity to fish, has remained constant from the time of the survey underlying the RUM
9 to the present. That is, this analysis does not account for any potential change in interest in
10 both recreational fishing and park use since the survey was conducted in 1989. In the case
11 that general demand for recreational fishing has decreased, this analysis may overstate
12 benefits. This restriction contributes to an overestimation of benefits.
13 • This analysis did not take into account income adjustments through time. The RUM holds
14 income to be constant and a lack of detailed demand elasticity functions precludes the
15 incorporation of an adjustment. Other EPA analyses have shown that increases in real
16 income over time lead to increases in WTP for a wide range of health effects and some
17 welfare effects, such as recreational visibility. This restriction contributes to an
18 underestimation of benefits.
19
20 2.2.2 Improvements in Total Ecosystem Services due to Increased Acid
21 Neutralizing Capacity Levels in Adirondack Lakes
22 To develop estimates of the overarching ecological benefits associated with reducing lake
23 acidification levels in Adirondacks National Park, researchers at Resources for the Future (RFF)
24 conducted a detailed contingent valuation (CV) survey (Banzhaf et al., 2006). Unlike other
25 valuation studies described in this report, the RFF study did not identify the specific categories of
26 ecosystem services that would be enhanced by improving aquatic conditions. Rather, the survey
27 described and elicited values for specific improvements in acidification-related water quality and
28 ecological conditions in Adirondack lakes. For this reason, and because the survey was
29 administered to a random sample of New York households, in this section the benefit estimates
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1 from the RFF study are interpreted as measures that incorporate values for all ecosystem services
2 adversely affected by lake acidification.
3 In this section, the RFF study results were adapted and transferred to estimate the
4 ecological benefits of the zero-out scenario for Adirondack lakes. The fundamental benefit
5 transfer model can be summarized as follows:
where
AggBlAdr = WTPAdr * Nm * A%IL , (2.1)
8 AggBAdr = aggregate annual benefits (in 2007 dollars) to New York households in 2010
9 due to lake ecosystem improvements resulting from the zero-out scenario,
10 WTPAdr = average annual household WTP (in 2007 dollars) per unit of long-term
1 1 change in the percentage of Adirondack lakes impaired by acidification,
12 .A/NY = projected total number of households in New York in 2010, and
13 A%7L = long-term change in the percentage of Adirondack lakes impaired by
14 acidification as a result of the zero-out scenario.
15 To develop estimates of WTPAdr, the estimates from the RFF study were used with results
16 reported in Banzhaf et al. (2006). The CV survey for the study was distributed to a random
17 sample of nearly 6,000 New York residents in 2003 to 2004 through the Internet and mail. As
18 part of the design and development of the survey instrument, experts were interviewed on the
19 ecological damages, and a summary of the science was used as the foundation for the description
20 of the park's existing condition and the hypothetical changes to be valued. The scientific review
21 indicated that there was significant uncertainty regarding the future status of lakes in the Park in
22 the absence of specific programs to improve lake acidification conditions. To bracket the range
23 of uncertainty in the science as well as to test the sensitivity of respondents' WTP to the scale of
24 ecological improvements, two versions of the survey instrument were developed and randomly
25 administered to separate subsamples.
26 Table 2.2-8 summarizes key features of the two survey versions. In both survey versions,
27 respondents were provided with information on the current (circa 2004) condition of the 3,000
28 lakes in the Park. Both versions describe half (1,500) of them as "lakes of concern" (i.e.,
29 unhealthy lakes where "fish and other aquatic life have been reduced or eliminated because of air
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
pollution in the past"), and both versions propose policies that would improve the lakes over a
period of 10 years (using lime to neutralize the excess acidity).
Table 2.2-8. Comparison of Resources for the Future Contingent Valuation Scenarios and EPA
Zero-Out Scenario
Percentage
of Adirondack
Current No Program3
(A) (B)
RFF "Base" Scenario
RFF "Scope" Scenario
Year = 2004
50%
Year = 2004
50%
50%
55%
Lakes that Are "Unhealthy"
Future
With Program15
(C)
Year = 20 14
30%
Year = 20 14
10%
Reduction
(B)-(C)
20%
45%
EPA "Zero-Out" Scenario
ANC Threshold
20 (ieq/L
50 (ieq/L
100 (ieq/L
Year = 20 10
22%
43%
79%
22%
42%
77%
Year = 2020
0%
11%
51%
22%
31%
26%
a Business-as-usual conditions.
bLake liming program for the RFF survey scenarios and a zero-out policy for the EPA scenario.
The "base" version of the survey asserts that, in the absence of any direct policy
intervention, the condition of the 1,500 unhealthy lakes and 1,500 healthy lakes is expected to
remain unchanged over the next 10 years. However, if a liming program is undertaken, it would
improve 20% (600) of the lakes in the Park relative to their expected 2014 condition without the
program.
In contrast, the "scope" version describes a gradually worsening status quo without the
liming program, in which 5% (150) of the healthy lakes are expected to gradually become
unhealthy. In other words, without the program, 55% (1,650) of the lakes would be unhealthy in
2014. With the liming program, however, only 10% of the lakes would be unhealthy in 2014, so
the program improves 45% (1,350) of the lakes relative to their expected 2014 condition without
the program.
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1 Although scientific evidence indicates that a liming policy would not significantly
2 improve the condition of birds and forests, pretesting of the survey indicated that respondents
3 nonetheless tended to assume that these other benefits would occur. Therefore, to make the
4 scenarios more acceptable to respondents, other nonlake effects were added to the two survey
5 versions. In the base case, the red spruce (covering 3% of the forests' area) and two aquatic bird
6 species (common loon and hooded merganser) are said to be affected. In this version, the health
7 of birds and forests is described as unchanged in the absence of intervention, and minor
8 improvements are said to result from the program. In the scope version, a broader range of
9 damages is associated with acid rain—two additional species of trees (sugar maple and white
10 ash, all together covering 10% of forest area) and two additional birds (wood thrush and tree
11 swallow) are said to be affected. The scope version describes a gradually worsening status quo
12 along with large improvements due to the program.
13 Each respondent was presented with one of these (base or scope) policy scenarios and
14 then asked how they would vote in a referendum on the program, if it were financed by an
15 increase in state taxes for 10 years. To estimate the distribution of WTP, the annual tax amounts
16 were randomly varied across respondents.
17 Based on a detailed analysis of the survey data, Banzhaf et al. (2006) defined a range of
18 best WTP estimates, which were converted from 10-year annual payments to permanent annual
19 payments using discount rates of 3% and 5%. For the base version, the best estimates ranged
20 from $48 to $107 per year per household (in 2004 dollars), and for the scope version they ranged
21 from $54 to $154.
22 To specify values for WTPAdr, these estimates were converted to 2007 dollars using the
23 CPI and each of them was divided by the corresponding change in the percentage of lakes that
24 are unhealthy (20% for the base version and 45% for the scope version). For the base version, the
25 WTPAdr estimates range from $2.63 to $5.87 per percentage decrease in unhealthy lakes, and for
26 the scope version they range from $1.32 to $3.76.
27 To estimate NNY, the Census population projection for New York for 2010 was used,
28 which is 19.26 million people, and this amount was divided by the ratio of population size to the
29 number of households in New York (2.69) in the year 2000 (assuming that this ratio stays
30 constant from 2000 to 2010).
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1 Finally, to estimate A%7L the MAGIC model results reported in Table 2.2-2 were used,
2 and it was assumed that the distribution of ANC levels for these 44 lakes is representative of all
3 3,000 lakes in the Adirondacks Park. For each of the three ANC thresholds, column (A) of Table
4 2.2-8 reports the estimated percentage of "unhealthy" (below the ANC threshold) lakes in 2010.
5 In columns (B) and (C), it also reports the percentage of unhealthy lakes in 2020 for the
6 reference and zero-out conditions, respectively. In 2020, the reduction in the percentage of lakes
7 that are unhealthy in the zero-out condition compared to the reference condition is 22% for the
8 20 ueq/L threshold. For the 50 ueq/L, and 100 ueq/L thresholds, it is 31% and 26%, respectively.
9 These 3% reduction values were used as the main estimates of A%IL.
10 To estimate aggregate benefits for the zero-out scenario using the RFF survey results, it is
11 important to use the results from the survey version that most closely match this scenario. Table
12 2.2-8 provides direct comparisons of the percentage of lakes that are defined as unhealthy under
13 the different conditions and scenarios. Although both RFF survey versions use 2004 as the
14 "current" year instead of 2010, they both use a 10-year horizon, which corresponds to the zero-
15 out scenario. Although no direct matches exist, the closest correspondence is between the zero-
16 out scenario assuming a 50 ueq/L threshold and the RFF scope survey. Under current and future
17 conditions with no additional policy interventions, the RFF scope scenario assumes a small
18 increase in unhealthy lakes from 50% to 55%, whereas the 50 ueq/L threshold is expected to
19 result in a small decrease from 43% to 42%. With the program, the RFF scope survey describes a
20 45% decrease in unhealthy lakes, whereas the zero-out scenario projects a 31% decrease.
21 Moreover, although the RFF survey does not specify ANC thresholds, the survey's description of
22 unhealthy lakes is arguably closest to what the science defines for a 50 ueq/L threshold (as
23 summarized in Figure 2.2-1).
24 2.2.2.1 Results: Aggregate Benefits from Reduced Acidification in Adirondack Lakes
25 Table 2.2-9 reports the aggregate benefit estimates for the zero-out scenario using the 50
26 ueq/L threshold. As described above, the projected long-term decrease in the percentage of
27 unhealthy lakes (A%7L) for this scenario is 31%. Using the range of WTPAdr values from the RFF
28 scope survey and the projected number of New York households in 2010 and applying
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Table 2.2-9. Aggregate Benefit Estimates for the Zero-Out Scenario
ANC
Threshold
20 (ieq/L
50 (ieq/L
100 (ieq/L
Reduction in
Percentage of
Unhealthy
Lakes
t±%IL
22%
31%
26%
Range of Average
Household WTP per
Percentage
Reduction
WTPAdr
$2.63 $5.87
$1.32 $3.76
$2.63 $5.87
Number of NY
Households
(in millions)
NNY
7.162
7.162
7.162
Range of Aggregate
Benefits
(in millions of 2007
dollars)
AggBAdr
$410.6 $916.4
$291.2 $829.4
$491.6 $1,097.2
1
2 Equation (2.1), the aggregate annual benefits of the zero-out scenario are estimated to
3 range from $291 million to $829 million.
4 Table 2.2-9 also reports aggregate benefit estimates for the zero-out scenarios using the
5 20 ueq/L and 100 ueq/L thresholds for ANC. Neither of these scenarios corresponds well with
6 the baseline descriptions of either the base or scope version of the RFF survey. The baseline
7 percentage of unhealthy lakes using the 20 ueq/L threshold (22%) is much lower than in either
8 the survey version. In contrast, the percentage using the 100 ueq/L threshold (77%) is much
9 higher. Nevertheless, the future reductions in the percentage of unhealthy lakes (22% and 26%)
10 are closest to the reductions described in the base version of the RFF survey. Therefore, the
11 aggregate benefits of the zero-out scenario with these thresholds are evaluated using the range of
12 WTPAdr values from the RFF base survey. With the 20 ueq/L threshold, the aggregate benefits are
13 estimated to range from $411 million to $916 million per year. With the 100 ueq/L threshold, the
14 aggregate benefits are estimated to range from $492 million to $1.1 billion per year.
15 2.2.2.2 Limitations and Uncertainties
16 The benefit transfer model summarized in Equation (2.1) estimates the aggregate benefits
17 to New York households in 2010 due to lake ecosystem improvements resulting from the zero-
18 out scenario. To do this, estimates from two different studies were linked and combined. The
19 measures of improvements in lake ecosystems were obtained from the MAGIC model (as
20 described in Table 2.2-2), and the value estimates were obtained from the RFF survey study.
21 Uncertainties are associated with the estimates drawn from each study, and additional
22 uncertainties arise when these estimates were combined in the analysis. Some of these main
23 uncertainties and limitations are described below.
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1 First, uncertainties are associated with extrapolating results from the 44 MAGIC-modeled
2 lakes to all (roughly 3,000) Adirondack lakes. The 44 modeled lakes are drawn from a larger,
3 randomly drawn sample of lakes; however, the representativeness of these 44 lakes for the
4 Adirondacks as a whole is uncertain.
5 Second, the time frame required for the zero-out scenario to match 1860 conditions is
6 uncertain. It was assumed that it takes 10 years for lakes to fully adjust to the reductions in
7 nitrogen and sulfur deposition and that conditions equivalent to "background" 1860 conditions
8 are achieved in 2020. The present value and annualized benefits would be lower if a longer time
9 frame were assumed.
10 Third, there is also some uncertainty related to the exact types of ecosystem services that
11 are included in these RFF study values, particularly regarding provisioning and regulating
12 services, which survey respondents may have been less likely to consider when formulating
13 responses to the CV questions. Importantly though, the values estimated by the RFF study are
14 likely to include (1) recreational fishing services, which means they cannot be added to the RUM
15 results, and (2) other cultural services, in particular recreational and nonuse services.
16 Fourth, the inclusion of other ecosystem changes (trees, birds, etc.) in the RFF CV survey
17 scenarios implies that respondents' stated values will overstate WTP for just changes in lake
18 acidification. This feature, therefore, contributes to potential overestimation of benefits.
19 Fifth, the lack of direct correspondence between the RFF CV scenarios and the zero-out
20 scenario requires assumptions for making the benefit transfer. In particular, baseline and future
21 levels (percentage of unhealthy lakes) are very different from those in the RFF survey if one uses
22 a 20 or 100 ANC threshold. Although the percentage changes are reasonably close to the RFF
23 20% and 45% decline scenarios, they are not exact and may not be applicable when applied to a
24 different baseline (something that was not specifically tested in the CV survey). Reseating the
25 WTP estimates for different percentage changes in unhealthy lakes also requires the somewhat
26 strong assumption that there is a constant WTP per percentage decline in unhealthy lakes.
27 Finally, the reported results only apply to Adirondack lakes and to New York residents.
28 The Adirondack region is more sensitive to acidity in contrast to many other areas of New York
29 State, which have calcium-rich limestone deposits that neutralize the acid. The bedrock soil and
30 shallow soil deposits have a lower buffering capacity. These geological factors together with
31 high and acidic precipitation levels contribute to the vulnerability of this region to acidification.
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1 The uniqueness of the Park makes simple extrapolations of ecological conditions and human
2 values to other lakes very uncertain. Similarly, residents of other states are likely to value
3 improved ecosystem services from Adirondack lakes, but the magnitude of these values is
4 difficult to assess and, therefore, not included in the reported benefit estimates.
5 2.3 REFERENCES
6 Banzhaf, S., D. Burtraw, D. Evans, and A. Krupnick. 2006. "Valuation of Natural Resource
7 Improvements in the Adirondacks." Land Economics 82:445-464.
8 Chen, L., and C.T. Driscoll. 2004. "Modeling the Response of Soil and Surface Waters in the
9 Adirondack and Catskill Regions of New York to Changes in Atmospheric Deposition
10 and Historical Land Disturbance." Atmospheric Environment 38:4099-4109.
11 Driscoll, C.T. et al. 2003. "Effects of Acidic Deposition on Forest and Aquatic Ecosystems in
12 New York State." Environmental Pollution 123:327-336.Driscoll, C.T., G.B. Lawrence,
13 AJ. Bulger, TJ. Butler, C.S. Cronan, C. Eagar, K.F. Lamber, G.E. Likens, J.L. Stoddard,
14 and K.C. Weathers. 2001. Acidic Deposition in the Northeastern United States: Sources
15 and Inputs, Ecosystem Effects and Management Strategies. BioScience 51:180-198.
16 Greene, W.H. 2003. Econometric Analysis, 5th Ed. New Jersey: Prentice Hall.
17 Hutchison, R., and C.E. Kraft. 1994. "Hmong Fishing Activity and Fish Consumption." Journal
18 of Great Lakes Research 20(2):471-487.
19 Industrial Economics, Inc. June 2008. "The Economic Impact of the Clean Air Interstate Rule on
20 Recreational Fishing in the Adirondack Region of New York State." Prepared for the
21 Clean Air Markets Division, Office of Air and Radiation, U.S. EPA.
22 Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On
23 National Park Recreation. Final Report October 2003, under Cooperative Agreement CA
24 1200-99-009, Project number EVIDE-02-0070.
25 Kennedy, P. 2003. A Guide to Econometrics, pp. 312-313. Cambridge, MA: MIT Press.
2nd Draft Risk and Exposure Assessment June 5, 2009
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1 Millennium Ecosystem Assessment (MEA). 2005. Ecosystems and Human Well-being:
2 Wetlands and Water. Synthesis. A Report of the Millennium Ecosystem Assessment.
3 Washington, DC: World Resources Institute.
4 Montgomery, M. and M. Needelman. 1997. "The Welfare Effects of Toxic Contamination in
5 Freshwater Fish." Land Economics 73(2):211-223.
6 Peterson, D.E., M.S. Kanarek, M.A. Kuykendall, J.M. Diedrich, H.A. Anderson, P.L.
7 Remington, and T.B. Sheffy. 1994. "Fish Consumption Patterns and Blood Mercury
8 Levels in Wisconsin Chippewa Indians." Archives of Environmental Health 49(l):53-58.
9 U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce,
10 U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and Wildlife-
11 Associated Recreation.
12 U.S. Environmental Protection Agency (EPA), Office of Air and Radiation. November 1999.
13 The Benefits and Costs of the Clean Air Act 1990 to 2010: EPA Report to Congress.
14 EPA-410-R-99-001. Washington, DC: U.S. Environmental Protection Agency.
15
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i 3. TERRESTRIAL ACIDIFICATION
2 Terrestrial acidification is the result of natural processes and anthropogenic sources of
3 acidic deposition. Elevated levels of atmospheric deposition of nitrogen and sulfur can alter the
4 chemical composition of soils by accelerating rates of base cation (e.g., calcium and magnesium)
5 leaching, which depletes available plant nutrients, and by mobilizing and leaching aluminum,
6 which can be toxic to tree roots. Consequently, among the most visible and significant effects of
7 acid deposition are damages to forest health and resulting reductions in tree growth.
8 Evidence of adverse effects due to terrestrial acidification is particularly strong for two
9 common tree species in the northeastern United States where levels of nitrogen and sulfur
10 deposition have historically been relatively high—sugar maples and red spruce. Therefore, the
11 discussion of ecosystem service effects focuses on these two species; however, more widespread
12 impacts that include other tree species are also possible.
13 3.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES
14 The existing ecosystem services that are primarily affected by the terrestrial acidification
15 resulting from nitrogen and sulfur deposition are described and, to the extent possible, quantified
16 using the classification system outlined in Section 1.
17 3.1.1 Provisioning Services
18 Forests in the northeastern United States provide several important and valuable
19 provisioning services, which are reflected in measures of production and sales of tree products.
20 Sugar maples (also referred to as hard maples) are a particularly important commercial
21 hardwood tree species in the United States. As shown in Figure 3.1-1, the main range of the
22 sugar maple covers most of the United States east of the Mississippi River and north of Alabama
23 and Georgia. This range is also the area with the highest levels of nitrogen and sulfur deposition
24 in the country, according to monitored estimates from the National Atmospheric Deposition
25 Network (NADP) and modeled estimates from the Community Multiscale Air Quality (CMAQ)
26 modeling system.
27 The two main types of products derived from sugar maples are wood products and maple
28 syrup. The wood from sugar maple trees is particularly hard, and its primary uses include
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
construction, furniture, and flooring (Luzadis and Gossett, 1996). According to data from the
U.S. Forest Service's National Forest Inventory and Analysis (FIA) databases
(http://199.128.173.26/fido/mastf/index.html), in 2006, the total removal of sugar maple saw
timber from timberland in the United States was almost 2.12 million cubic meters. Assuming an
average price of $169.5 per cubic meter, the total value of these removals in 2006 was
approximately $358 million.
| | Sugar Maple Distribution
Combined N and S
Value (Kgrtiatyr)
I High 150.0
_ Low: 1.0
Source of Deposition 2002 CMAQ hybrid diy deposition plus 2002 NADP wet deposition
Source of Sugar Maple Distribution: United Slates Forest Service 2002-2006
Figure 3.1-1. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry
Deposition and NADP Wet Deposition Estimates) and the Range of Sugar Maple
in the United States
During winter and early spring (depending, in part, on location and diurnal temperature
differences), sugar maple trees also generate sap that is used to produce maple syrup. From 2005
to 2007, annual production of maple syrup in the United States varied between 1.2 million and
5.3 million liters, which accounted for roughly 19% of worldwide production. The total annual
value of U.S. production in these years varied between $157 million and $168 million (National
Agricultural Statistics Service [NASS], 2008).
Red spruce is a common commercial softwood species. As shown in Figure 3.1-2, its
range in the United States is much more limited than the sugar maple's range, but it also
primarily grows in areas with relatively high levels of nitrogen and sulfur deposition. Red spruce
2nd Draft Risk and Exposure Assessment
Appendix 8-36
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
is now mainly found in northern New England, New York, and in a few high-elevation areas of
the Appalachian Mountain range. Wood from red spruce is used in a variety of products
including lumber, pulpwood, poles, plywood, and musical instruments. According to FIA data, in
2006, the total removal of red spruce saw timber from timberland in the United States was 0.77
million cubic meters. Assuming an average price of $42.37 per cubic meter, the total value of
these removals in 2006 was approximately $33 million.
| | Red Spruce Distribution
Combined N and S
Value (kgma/yr)
• High 150 0
_, Low 1.0
Source of Deposition 2002 CMAQ hybrid dry deposMmn plus 2002 NADP wel deposition
Source of Red Spruce Distribution: Unned Slates Foresl Service 3002-2006
Figure 3.1-2. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry
Deposition and NADP Wet Deposition Estimates) and the Range of Red Spruce
in the United States
Figure 3.1-3 shows and compares the value of annual production of sugar maple and red
spruce wood products and of maple syrup in 2006. Across states in the northeastern United
States, sugar maple timber harvests consistently generated the highest total sales value of the
three products. Although total sales of red spruce saw timber and maple syrup were of roughly
the same magnitude in the United States as a whole, the red spruce harvest was concentrated in
Maine, whereas maple syrup production was largest in Vermont and New York.
3.1.2 Cultural Services
Forests in the northeastern United States are also an important source of cultural
ecosystem services—in particular recreational and aesthetic services. Forestlands support a wide
2nd Draft Risk and Exposure Assessment
Appendix 8-37
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
3
4
5
6
7
8
9
10
11
12
13
variety of outdoor recreational activities, including fishing, hiking, camping, off-road driving,
hunting, and wildlife viewing. Regional statistics on recreational activities that are specifically
forest based are not available; however, more general data on outdoor recreation provide some
insights into the overall level of recreational services provided by forests. For example, most
recent data from the National Survey on Recreation and the Environment (NSRE) indicate that,
from 2004 to 2007, 31% of the U.S. adult (16 or older) population visited a wilderness or
primitive area during the previous year, and 32% engaged in day hiking (Cordell et al., n.d.).
From 1999 to 2004, 16% of adults in the northeastern United States1 participated in off-road
vehicle recreation, for an average of 27 days per year (Cordell et al., 2005). Using the meta-
analysis results reported by Kaval and Loomis (2003), which found that the average consumer
surplus value per day of off-road driving in the United States was $25.25 (in 2007 dollars), the
implied total annual value of off-road driving recreation in the northeastern United States was
more than $9.25 billion.
14
15
16
Figure 3.1-3. Annual Value of Sugar Maple and Red Spruce Harvests and Maple
Syrup Production, 2006
1 This area includes Connecticut, Delaware, District of Columbia, Illinois, Indiana, Maine, Maryland,
Massachusetts, Michigan, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont,
West Virginia, and Wisconsin.
2nd Draft Risk and Exposure Assessment
Appendix 8-38
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 State-level data on other outdoor recreational activities associated with forests are also
2 available from the 2006 FHWAR (U.S. Department of the Interior [DOT], 2007). As summarized
3 in Table 3.1-1, 5.5% of adults in the northeastern United States participated in hunting, and the
4 total number of hunting days occurring in those states was 83.8 million. Data from the survey
5 also indicated that 10% of adults in northeastern states participated in wildlife viewing away
6 from home. The total number of away-from-home wildlife viewing days occurring in those states
7 was 122.2 million in 2006. For these recreational activities in the northeastern United States,
8 Kaval and Loomis (2003) estimated average consumer surplus values per day of $52.36 for
9 hunting and $34.46 for wildlife viewing (in 2007 dollars). The implied total annual value of
10 hunting and wildlife viewing in the northeastern United States was, therefore, $4.38 billion and
11 $4.21 billion, respectively, in 2006.
12 As previously mentioned, it is difficult to estimate the portion of these recreational
13 services that are specifically attributable to forests and to the health of specific tree species.
14 However, one recreational activity that is directly dependent on forest conditions is fall color
15 viewing. Sugar maple trees, in particular, are known for their bright colors and are, therefore, an
16 essential aesthetic component of most fall color landscapes. Thus, declines in sugar maple stocks
17 due to terrestrial acidification are expected to have detrimental effects on these landscapes.
18 Statistics on fall color viewing are much less available than for the other recreational and tourism
19 activities; however, a few studies have documented the extent and significance of this activity.
20 For example, based on a 1996 to 1998 telephone survey of residents in the Great Lakes area,
21 Spencer and Holecek (2007) found that roughly 30% of residents reported at least one trip in the
22 previous year involving fall color viewing. In a separate study conducted in Vermont, Brown
23 (2002) reported that more than 22% of households visiting Vermont in 2001 made the trip
24 primarily for the purpose of viewing fall colors. Unfortunately, data on the total number or value
25 of these trips are not available, although the high rates of participation suggest that numbers
26 might be similar to the wildlife viewing estimates reported above.
27 Although these statistics provide useful indicators of the total recreational and aesthetic
28 services derived from forests in the northeastern United States, they do not provide estimates of
29 how these services are affected by terrestrial and forest acidification. Very few empirical studies
30 have directly addressed this issue; however, two studies have estimated values for protecting
31 high-elevation spruce forests in the Southern Appalachians. Kramer, Holmes, and Haefele (2003)
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-39
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
3
4
5
6
7
9
10
11
conducted a CV study estimating households' WTP for programs to protect remaining high-
elevation spruce forests from damages associated with air pollution and insect infestation
(Haefele, Kramer, and Holmes, 1991; Holmes and Kramer, 1995). The study collected data from
Table 3.1-1. Participation in Hunting and Wildlife Viewing in Northeastern States in 2006
Participation Rates by State
Residents"
State
Hunting
Wildlife Viewing"
Activity Days by Residents and
Nonresidents (in thousands)
Hunting Wildlife Viewing15
Connecticut
Delaware
Illinois
Indiana
Maine
Maryland
Massachusetts
Michigan
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Rhode Island
Vermont
West Virginia
Wisconsin
Total
1.2%
3.1%
2.8%
5.3%
13.6%
3.5%
1.3%
9.2%
5.0%
1.3%
3.3%
5.4%
9.5%
1.2%
11.3%
13.6%
15.0%
5.5%
11.0%
7.0%
8.0%
13.0%
20.0%
7.0%
11.0%
11.0%
12.0%
8.0%
8.0%
13.0%
11.0%
11.0%
16.0%
9.0%
10.0%
10.0%
509
654
4,688
4,808
2,283
2,262
1,149
11,905
1,057
1,457
10,289
10,633
16,863
155
1,111
3,939
10,059
83,821
4,184
855
5,686
24,013
4,778
4,782
8,461
10,043
3,165
7,965
13,521
7,816
11,972
2,948
2,459
4,005
5,547
122,200
a Ages 16 or older.
b Wildlife viewing away from home.
Source: U.S. Department of the Interior (DOI), Fish and Wildlife Service, and U.S. Department of
Commerce, U.S. Census Bureau, 2007.
486 households using a mail survey of residents living within 500 miles of Asheville,
North Carolina. The survey presented respondents with photographs representing three stages of
forest decline and explained that, without forest protection programs high-elevation spruce
forests would all decline to worst conditions (with severe tree mortality). The survey then
2nd Draft Risk and Exposure Assessment
Appendix 8-40
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 presented two potential forest protection programs—one would prevent further decline in forests
2 along roads and trail corridors (one-third of the at-risk ecosystem) and the other would prevent
3 decline in all at-risk forests. Both programs would be funded by tax payments going to a
4 conservation fund. Median household WTP was estimated to be roughly $29 (in 2007 dollars)
5 for the first program and $44 for the more extensive program.
6 Jenkins, Sullivan, and Amacher (2002) conducted a very similar study in 1995 using a
7 mail survey of households in seven Southern Appalachian states. In this study, respondents were
8 presented with one potential program, which would maintain forest conditions at initial (status
9 quo) levels. It was explained that, without the program, forest conditions would decline to worst
10 conditions (with 75% dead trees). In contrast to the previously described study, in this survey the
11 initial level of forest condition was varied across respondent. In one version of the survey, the
12 initial condition was described and shown as 5% dead trees, while the other version described
13 and showed 30% dead trees. Household WTP was elicited from 232 respondents using a
14 dichotomous choice and tax payment format. The overall mean annual WTP for the forest
15 protection programs was $208 (in 2007 dollars), which is considerably larger than the WTP
16 estimates reported by Kramer, Holmes, and Haefele (2003). One possible reason for this
17 difference is that respondents to the Jenkins, Sullivan, and Amacher (2002) survey, on average,
18 lived much closer to the affected ecosystem. Multiplying the average WTP estimate from this
19 study by the total number of households in the seven-state Appalachian region results in an
20 aggregate annual value of $3.4 billion for avoiding a significant decline in the health of high-
21 elevation spruce forests in the Southern Appalachian region.
22 3.1.3 Regulating Services
23 Forests in the northeastern United States also support and provide a wide variety of
24 valuable regulating services, including soil stabilization and erosion control, water regulation,
25 and climate regulation (Krieger, 2001). As terrestrial acidification contributes to root damages,
26 reduced biomass growth, and tree mortality, all of these services are likely to be affected;
27 however, the magnitude of these impacts is very uncertain. Forest vegetation plays an important
28 role in maintaining soils in order to reduce erosion, runoff, and sedimentation that can adversely
29 impact surface waters. In addition to protecting the quality of water in this way, forests also help
30 store and regulate the quantity and flows of water in watersheds. Finally, forests help regulate
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-41
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 climate locally by trapping moisture and globally by sequestering carbon. The total value of
2 these ecosystem services is very difficult to quantify in a meaningful way, as is the reduction in
3 the value of these services associated with nitrogen and sulfur deposition.
4 3.2 CHANGES IN ECOSYSTEM SERVICES ASSOCIATED WITH
5 ALTERNATIVE LEVELS OF ECOLOGICAL INDICATORS
6 This section estimates values for changes in ecosystem services associated with
7 reductions in damages to commercial forests resulting from terrestrial acidification. With high
8 levels of acidifying nitrogen and sulfur deposition, trees may experience an increased
9 susceptibility to drought and pest damage, aluminum toxicity in roots, a reduced tolerance to
10 cold, and a greater propensity to frost injury (DeHayes et al., 1999; Driscoll et al., 2001; Fenn et
11 al., 2006). As a result, total stand volume and growth may be reduced. The tree growth response
12 and value of reducing nitrogen+sulfur deposition loads across the range of sugar maples and red
13 spruces (as shown in Figures 3.1-1 and 3.1-2, respectively) was estimated using a critical load
14 assessment methodology (described in the case study analysis) of terrestrial acidification. More
15 specifically, the beneficial effects of eliminating all exceedances of critical load for sugar maples
16 and red spruces in this range were estimated.
17 3.2.1 Increased Provisioning Services from Sugar Maple Timber Harvests due to
18 Elimination of Critical Load Exceedances
19 A three-stage approach was used to estimate the value of increased provisioning services
20 from sugar maple and red spruce timber harvests. In the first stage, exposure-response models2
21 for sugar maple and red spruce trees were estimated, which measure the empirical relationship
22 between exceedances of critical loads and growth in volume of live trees. In the second stage,
23 these exposure-response models were applied to estimate the average increase in sugar maple
24 and red spruce growth rates (in three regions) that would result from eliminating critical load
25 exceedances in the range of these tree species. In the third stage, these increased growth rates
26 were incorporated into an existing forest market model for North America and the value (i.e.,
27 increase in consumer and producer surplus) of expected future increases in sugar maple and red
28 spruce timber harvests and sales was estimated. Each of these stages is described below in detail.
See the case study report for alternative models of exposure-response.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8 - 42
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Stage 1: Estimation of the Exposure-Response Model
2 The analysis of the relationship between critical load exceedances and sugar maple and
3 red spruce trees' growth was conducted using data from the USFS FIA database for 16 states in
4 the sugar maple range and 5 states in the red spruce range. Each data point in the analysis
5 corresponds with a permanent sampling plot location on classified forestland (timberland for
6 New York) covering 0.07 ha. Estimation of critical loads for each plot was based on a Simple
7 Mass Balance (8MB) modeling approach (described in the case study). FIA plots were only
8 included in the analysis if they (1) were nonunique3 permanent sampling plots; (2) provided
9 necessary soil, parent material, atmospheric deposition, and run-off data to apply the 8MB model
10 for critical load estimation; (3) were located to the north of the glaciation line (this line
11 represents the southernmost extension of the most recent glacial advancement)4; and (4) had
12 positive exceedances in deposition above the most protective critical load (Bc/Al = 10.0).5 With
13 these restrictions, 2,205 sugar maple plots and 187 red spruce plots were included in the analysis.
14 Tables 3.2-1 and 3.2-2 summarize the plot-level FIA sugar maple and red spruce data
15 used to model the exposure-response relationship. For each plot, exceedances of critical loads
16 were calculated by subtracting the results of the 8MB analysis (estimated critical loads
17 estimates) from corresponding 2002 CMAQ nitrogen+sulfur deposition estimates. Overall, 74%
18 of sugar maple plots above the glaciation line exceeded the critical loads, ranging from 21% in
19 Maine to over 95% in Connecticut, Massachusetts, New Jersey, New York, Pennsylvania, Ohio,
20 and Vermont. Thirty-one percent of red spruce above the glaciation line exceeded the critical
21 loads, ranging from 16% in Maine to 100% in Massachusetts and Vermont. For sugar maple
22 plots with positive exceedances, the average exceedance ranged from less than 100 eq/ha/yr in
23 Missouri and Iowa to over 450 eq/ha/yr in Connecticut, Massachusetts, New Jersey, and Ohio.
24 For red spruce plots with positive exceedances, the average exceedance ranged from less than
25 150 eq/ha/yr in Maine to over 600 eq/ha/yr in Massachusetts.
3 Nonunique permanent sampling plot locations are those that have maximum critical load attribute values (soils,
runoff, and atmospheric deposition) that are not distinct and are repeated within a 250-acre area of the plot
location. This "confidentiality" filter is a requirement of the USFS to prevent the disclosure of data that can be
directly linked to a location on private land. To comply with the necessary "confidentiality," full coverages of the
data required for the critical load calculations were given to the USFS, and the USFS matched and provided the
data for each nonunique permanent sampling plot.
4 This is because the base cation weathering term, one of the key components of critical load, may not have been
accurately estimated for plots from south of the glaciation line (see case study for details) that have older, more
weathered soils. Thus, using such plots in the analysis may potentially increase error in the data used.
5 For analysis using lower levels of protection, see the case study report.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-43
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Net annual individual tree volume growth and tree volumes for all live sugar maple and
2 red spruce trees6 (greater than 12.7 cm diameter at 1 .3 m) were acquired from the USFS FIA
3 database for each plot. The volume growth calculations were based on the most recent
4 measurement period, and the time interval between measurements for the plots (to determine
5 annual growth rates) ranged from 1 to 1 1 years. These calculations included the influences of
6 growth and volume reductions or losses due to natural damage (pest, wind, frost) or natural
7 mortality. Average volume growth ranged from 0. 1 in Massachusetts to 0.62 in Indiana for sugar
8 maple and from 0. 14 in Massachusetts to 0.26 in Maine for red spruce. Volumes and volume
9 growth measures for the sugar maple and red spruce trees in each plot were averaged to produce
10 single values of each measure for each species.
Table 3.2-1. Summary of Plot-Level Data on Sugar Maple Growth and Exceedances (for Plots
above the Glaciation Line)
State
Alabama
Arkansas
Connecticut
Illinois
Indiana
Iowa
Kentucky
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
Total
Number
of Plots
12
8
33
25
266
8
14
242
4
27
596
257
122
72
6 All trees with reported volumes of "
Number of
Plots
North of
Glaciation
Line
0
0
33
20
234
8
0
242
0
27
596
257
31
72
Number
of Plots
with
Positive
CL
Exceed-
ance
Values
3
1
33
17
235
2
12
51
4
27
418
79
58
60
0" were excluded from the
Number of
Plots with
Positive CL
Exceedance
Values
North of
Glaciation
Line
33
12
204
2
51
27
418
79
18
60
analyses.
Average
CL
Exceed-
ance
(eq/ha/yr)
487.76
117.17
390.90
48.07
130.42
473.31
242.17
156.06
84.02
378.40
Average
Tree
Volume
Growth
(m3/yr)
0.009
0.007
0.018
0.005
0.011
0.003
0.011
0.010
0.012
0.009
Average
Tree
Volume
(m3)
0.279
0.227
0.397
0.123
0.323
0.366
0.307
0.256
0.246
0.304
2nd Draft Risk and Exposure Assessment
Appendix 8-44
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
State
New Jersey
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Vermont
Virginia
West Virginia
Wisconsin
TOTAL
Observations (used
in calculations)
CL = critical load
Total
Number
of Plots
6
280
13
55
270
264
162
104
337
870
4,047
Number of
Plots
North of
Glaciation
Line
6
280
0
27
133
0
162
0
0
870
2,998
Number
of Plots
with
Positive
CL
Exceed-
ance
Values
6
264
9
54
263
132
160
63
318
719
2,988
Number of
Plots with
Positive CL
Exceedance Average
Values
North of
Glaciation
Line
6
264
26
126
160
719
2,205
Table 3.2-2. Summary of Plot Level Data on Sugar Maple Growth
above the Glaciation
State
Maine
Massachusetts
New Hampshire
New York
Tennessee
Line)
Total
Number
of Plots
483
3
42
18
1
Number
of Plots
North of
Glacia-
tion Line
483
3
42
18
0
Number
of Plots
with
Positive
CL
Exceed-
ance
Values
78
3
32
14
1
Number of
Plots with
Positive CL
Exceedance
Values
North of
Glaciation
Line
78
3
32
14
0
CL
Exceed-
ance
(eq/ha/yr)
601.39
437.94
452.60
387.35
301.67
185.16
2,205
Average
Tree
Volume
Growth
(m3/yr)
0.013
0.010
0.013
0.011
0.008
0.009
2,205
and Exceedances (for
Average
CL
Exceed-
ance
(eq/ha/yr)
133.104
8
628.543
9
368.952
7
282.543
3
Average
Average
Tree
Volume
(m3)
0.357
0.344
0.545
0.366
0.411
0.304
2,205
Plots
Tree Average
Volume
Growth
(ni3/yr)
0.007
0.004
0.006
0.004
Tree
Volume
(m3)
0.245
0.203
0.245
0.221
2nd Draft Risk and Exposure Assessment
Appendix 8-45
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
State
Vermont
West Virginia
TOTAL Observations
(used in
calculations)
Total
Number
of Plots
60
6
613
Number
of Plots
North of
Glacia-
tion Line
60
0
606
Number
of Plots
with
Positive
CL
Exceed-
ance
Values
60
6
194
Number of
Plots with
Positive CL
Exceedance
Values
North of
Glaciation
Line
60
0
187
Average
CL
Exceed-
ance
(eq/ha/yr)
292.432
9
187
Average
Tree
Volume
Growth
(m3/yr)
0.007
187
Average
Tree
Volume
(m3)
0.328
187
1
2 The results of a multivariate OLS regression, using average tree growth (measured in
3 cubic meters per year) as the dependent variable, are reported in Table 3.2-3 and 3.2-4. The
4 explanatory variables include the critical load exceedance (measured in eq/ha/year) for each plot,
5 linear and squared terms of average tree volumes (measured in cubic meters), and a categorical
6 (dummy) variable for each state (with Connecticut as the reference category for sugar maple and
7 Vermont for red spruce). The purpose of the state variables is to control for other unobserved
8 sources of variation in tree growth, which are related to a plot's general geographic location.
9 Examples of potential unobserved factors include differences in data collection methods and
10 measurements across reporting state, climatic factors, and geological characteristics. An F test
11 applied to the state categorical variables indicated that their coefficients are jointly significant at
12 the 5% level for sugar maple. In general, the growth of a tree rises with age but at a decreasing
13 rate. Because data on the age were unavailable, average tree volume was instead included as a
14 proxy variable in the regression to control for this relationship.
15 The coefficient of the critical load exceedance was negative for both species and was
16 statistically significant at the 5% level (p-value of 0.035) for red spruce and at the 10% (p-value
17 of 0.101) for sugar maple, thus supporting the theory that when critical loads are exceeded by
18 atmospheric nitrogen and sulfur deposition, tree health and growth can be impaired.
2nd Draft Risk and Exposure Assessment
Appendix 8-46
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 3.2-3. Linear Exposure-Response Model for Exceedances (above a Critical Load
Calculated with Bc/Al = 10) and Sugar Maple Tree Growth: OLS Regression Results (for Plots
above the Glaciation Line)
Explanatory Variables
Intercept
Critical load exceedance
Average tree volume
Square of average tree volume
Illinois
Indiana
Iowa
Maine
Massachusetts
Michigan
Minnesota
Missouri
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Vermont
Wisconsin
Number of Observations
Adjusted R2
Dependent
Variable:
Average Tree
Growth (m3/yr)
Coefficient
0.004875
-3.344E-06
0.021150
8.944E-04
-0.001884
0.005452
-0.002052
-0.000895
-0.008403
0.000222
0.000210
0.001850
-0.001647
0.001956
-0.000817
-0.002104
-0.000803
-0.005168
-0.002195
2,205
0.1722
t-statistic
1.48
-1.64
10.12
1.1
-0.31
1.63
-0.16
-0.22
-1.82
0.07
0.06
0.35
-0.43
0.25
-0.25
-0.45
-0.23
-1.51
-0.68
p-value
0.1385
0.1008
<0001
0.27
0.755
0.1029
0.8743
0.8245
0.0685
0.9456
0.9553
0.7255
0.6696
0.8042
0.8035
0.6522
0.8177
0.131
0.4958
1 Stage 2: Estimation of Average Increments in Tree Volume
2 In this stage of the analysis, the effect of eliminating all critical load exceedances in the
3 range of sugar maples and red spruce was simulated and the resulting average (at a region level)
4 percentage increase in tree volume was estimated.
2nd Draft Risk and Exposure Assessment
Appendix 8-47
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 3.2-4. Linear Exposure-Response Model for Exceedances (above a Critical Load
Calculated with Bc/Al =10) and Red Spruce Tree Growth: OLS Regression Results (for Plots
above the Glaciation Line)
Dependent Variable:
Average Tree
Growth (m3/yr)
Explanatory Variables
Intercept
Critical load exceedance
Tree volume
Square of tree volume
Maine
Massachusetts
New Hampshire
New York
Number of Observations
Adjusted R2
Coefficient
0.006034
-5.162E-06
0.005590
5.100E-03
0.000285
-0.000132
0.000435
-0.001805
187
0.1963
t-statistic
4.96
-2.12
1.26
1.23
0.32
-0.05
0.42
-1.32
p-value
<.0001
0.0354
0.2093
0.2218
0.7489
0.9629
0.6736
0.1897
1
2 Based on the results of the regression equation reported in Table 3.2-3 and 3.2-4, for each
3 plot /' with a positive critical loads exceedance, the following equation was first used to estimate
4 what tree volume growth would be under conditions with no critical loads exceedances:
5 g}=g!+b*(~CLE?) (3.1)
6 where
7 CLEf = critical load exceedance at plot /' under observed conditions
8 g° = annual tree volume growth on plot /' under observed conditions (in m3/yr)
9 g] = annual tree volume growth on plot /' under conditions with no exceedance of
10 critical load (CLE, = 0) (in m3/yr)
11 B = regression coefficient (slope) for critical load exceedance (from Table 3.2-3 and
12 3.2-4, equals -3.344E-06 for sugar maple and -5.162E-06 for red spruce)
13 Since this study calculated the effects of eliminating positive exceedances with the aim of
14 estimating reductions in damages to sugar maple and red spruce forests resulting from terrestrial
15 acidification, it was assumed that there is no change in growth for plots without positive critical
16 load exceedances. In practice, however, some reduced growth may be possible due to lower
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Appendix 8-48
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 nitrogen availability in plots that are below the critical load. Thus, the calculations should be
2 interpreted as an upper bound to the value of reducing nitrogen+sulfur deposition loads.
3 To apply these results in the market model used in the next stage of the analysis, these
4 volume growth estimates were then converted into an average percentage increment in volume.
5 In other words, in each period t, tree volume on plot /' is expected to be greater (by a factor/)
6 under conditions with no critical loads exceedance, compared to conditions with currently
7 observed critical loads exceedances. In formal terms:
8 Vl =(! + /. Wu =(! + /• )(1 + (g- IV^ W^ = (1 + (g\ IC, Wl-i (3.2)
9 where
10 V° = average tree volume on plot /' under observed conditions in period t (in m3)
11 Vl = average tree volume on plot /' under conditions with no exceedance of critical load
12 in period t (in m3)
13 Solving Equation (3.2) for/ results in
14 (i+(g;/o (33)
Jl d+^/o ( }
15 Using the plot-level estimates of g1, g°, and V°,ft for each plot in the data set was
16 estimated, and these estimates were then averaged across each region.
17 Stage 3: Estimation of Increased Market-Based Benefits from Sugar Maple and Red Spruce
18 Timber Harvests
19 The next critical step in establishing the link between changes in nitrogen and sulfur
20 deposition and the changes in forest provisioning services is modeling the effect of the average
21 increase in tree growth (obtained in Stage 2) on public welfare. This section describes the
22 approach to obtaining valuation estimates for this incremental increase in the volume of
23 commercial sugar maple and red spruce stands.7 To implement this approach, the increase in the
24 percentage volume of timber was applied to all age categories, and FASOMGHG (Forest and
25 Agricultural Sector Optimization Model—Green House Gas version) was used to calculate the
26 resulting market-based welfare effects in the forest and agricultural sectors of the United States.
7 Holmes (1992) describes a similar approach to estimate welfare effects for a decline in southern pine forest
productivity in the United States.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Data obtained from the FIA were used as inputs into FASOMGHG, which enabled the
2 adaptation of the model for this application. The different components of these input data are
3 described below.
4 FASOMGHG is a price-endogenous, dynamic, nonlinear programming model of the
5 forest and agricultural sectors in the United States (Adams et al., 2005). The model simulates the
6 allocation of land over time to competing activities in these two sectors and the resultant
7 consequences for the commodity markets supplied by these lands. It was developed to evaluate
8 the welfare and market impacts of public policies that cause changes in land use and activities
9 both between and within the two sectors. The results from this model yield a dynamic simulation
10 of prices, production, management, consumption, greenhouse gas (GHG) effects, and other
11 environmental and economic indicators within these two sectors. For this application,
12 FASOMGHG's key outputs include economic welfare measures, such as changes in producer
13 and consumer surplus.8
14 The following discussion summarizes the other main features of FASOMGHG and
15 describes how they were used and adapted for this application:
16 • Temporal Frame: The time frame of this model is typically 70 to 100 years, and the
17 model is solved on a 5-year time-step basis. The base year for this model is 2002.
18 • Geographical Regions: FASOMGHG models forest and agricultural activity across the
19 conterminous United States, which is broken into 11 market regions. Forestry production
20 occurs in nine of these regions. The selection of FASOMGHG regions for this model
21 application was determined by comparing maps showing the regions where sugar maples
22 grow with a list of FASOM regions. Table 3.2-5 lists the states in each of the FASOM
23 regions used in this application. It also shows the average increase in tree growth (obtained
24 from Stage 2) for each of these regions.
Table 3.2-5. Estimated Increments in Sugar Maple and Red Spruce Timber Volume (Resulting
from Elimination of Critical Load Exceedances), by FASOMGHG Region
Average Average Percentage
Percentage Increment in Red
Increment in Sugar Spruce Tree Volume
Maple Tree (i.e., average/)
Key Region States Volume
8 For a detailed documentation of FASOMGHG, please see Adams et al. (2005).
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
(i.e., average/)
NE Northeast Connecticut, Delaware, Maine, 0.59% 0015%
Maryland, Massachusetts, New
Hampshire, New Jersey, New
York, Pennsylvania, Rhode
Island, Vermont, West Virginia
LS
CB
Lake
States
Corn Belt
Michigan, Minnesota, Wisconsin
Illinois, Indiana, Iowa, Missouri,
Ohio
0.28%
0. 57%
1 Source: Adams et al., 2005.
2 • Types of Forests: Two types of forests are considered when evaluating policy effects in
3 FASOMGHG—softwood and hardwood. To adapt these categories for the application,
4 sugar maples and red spruce trees needed to be expressed as a proportion of hardwoods
5 and softwoods, respectively. This was done for each of the regions modeled in this
6 application. These relevant data were obtained from FIA (Table 3.2-6) and are a
7 component of the input into FASOMGHG.
Table 3.2-6. Proportions of Hardwood in Sugar Maple Production and Proportions of Softwood
in Red Spruce Production, by FASOM Region
Sugar Maple
Red Spruce
FASOM Regions
NE
LS
CB
NE
Proportion of Hardwood/Softwood
13%
11%
14.5%9
8 Source: U.S. Department of Agriculture, Forest Service, 2002.
9 • Forestland: The FASOMGHG model does not track land under forest cover that produces
10 less than 0.57 m3/yr (called unproductive forestland) or on timberland that is reserved for
11 other uses, because these are not a part of the U.S. timber base. Endogenous land use
12 modeling is only done for privately held land, not publicly owned or managed timberlands.
13 The model assumes that the amount of public land in forests does not adjust to market
14 conditions but is set by the government. Thus, the average percentage increase in volume
9 The RPA Assessment tables report the proportion of the spruce and balsam fir category as 29%. We assume that
half of this is due to red spruce.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 is applied to only forests growing on private land. The proportions of the timberland under
2 private and public ownership are shown in Table 3.2-7 (obtained from FIA data).
3 • Welfare Measure: Mathematically, FASOMGHG solves an objective function to
4 maximize net market surplus. This is represented by the area under the product demand
5 function (an aggregate measure of consumer welfare) less the area under the factor supply
6 curves (an aggregate measure of producer costs). The value of the resultant objective
7 function is consumers' and producers' surplus. The welfare effects of a productivity
8 improvement are obtained from FASOMGHG as the difference in annual net market
9 surplus between a base case (without the policy in place) and a control case (with the
10 policy in place).
11 To apply FASOMGHG for this analysis, the main input required for the model is the
12 annual percentage increase in total hardwood and total softwood volume by region. To address
13 this requirement, the estimate of the average percentage increment in sugar maple tree volume
14 (average/, shown in Table 3.2-5) was multiplied by the proportion of hardwoods in sugar maple
15 production (shown in Table 3.2-6) for each FASOM region, which ranges from 11% to 13%.
16 Similarly, the estimate of the average percentage increment in red spruce tree volume (average
17 fj, shown in Table 3.2-5) was multiplied by the proportion of softwoods in red spruce production
18 (shown in Table 3.2-6) for the NE region.
Table 3.2-7. Proportion of Timberland under Private and Public Ownership by FIA Regiona: 2002
FIA Region Private Timberland Public Timberland
Northeast 87% 13%
NorthCentral 28% 72%
19 a The states in the Northeast FIA region correspond exactly to states in NE in FASOM.
20 The states in the NorthCentral FIA region correspond exactly to states in LS and CB in FASOM.
21 Source: U.S. Department of Agriculture, Forest Service, 2002, Table 10.
22 3.2.1.4 Results: Aggregate Benefit Estimates
23 Figure 3.2-1 summarizes the FASOMGHG model results. These results are reported as
24 the present discounted values of future welfare changes in the forestry sector (in 5-year
25 increments from 2000 to 2065) due to increased tree growth as well as the future welfare changes
26 in the agricultural sector. Summing over this 65-year period, the value of gains to the forestry
27 sector is $17.1 million (in 2007 dollars, using a 4% discount rate). The agricultural sector has a
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Appendix 8-52
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 welfare loss of $1 million. This loss is possibly due to a shift in land use from agriculture to
2 forestry. The total present value of these welfare changes due to both the sectors is $16.09
3 million (in 2007 dollars, using a 4% discount rate). On an annualized basis (at 4%), this is
4 equivalent to $684,000 per year. Figure 3.2-1 presents the time path of these welfare estimates
5 for the forestry and agricultural sector as well as the total welfare estimates. The cyclical pattern
6 of the estimates is most likely driven by the fact that if more harvesting is done in any period,
7 this leads to less stock to harvest from in the next period.
Welfare Changes in Forestry and Agriculture
4000 T-
~ 3000
01
£ Ta
I I 2000
.E«
w Is-
0) O
O) 2
c f"
1000 -
O c
O »
fl
^ -1000
LU
-2000
- Forestry
-Agriculture
Total Welfare
2030
2010
2020
2040
2050
2060
Year
10 Figure 3.2-1. Estimated Time Path of Welfare Gains in the Forestry and Agricultural
11 Sector due to Increased Sugar Maple and Red Spruce Growth (2000-2065)
12 Limitations and Uncertainties
13 This analysis links two separate models to estimate values of reductions in damages to
14 sugar maple forests due to terrestrial acidification. The first is an exposure-response model
15 relating maximum acid deposition load exceedances with tree growth. Simulating the effect of
16 eliminating all exceedances, an average percentage increase in tree volume was obtained, a
17 market model of the forest and agricultural sectors (FASOMGHG) was then used to calculate the
18 welfare effects of this increased volume.
19 In doing this, certain limitations and uncertainties are associated with each component of
20 the analysis as well as with the linkages between them. These are described below.
21 Linking changes in deposition levels to changes in tree growth:
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1 "In interpreting the results of this model, readers should keep certain data limitations in
2 mind. First, plot-level data were completely unavailable for some of the states and partially
3 unavailable for others for both the species. Second, only plots that were above the
4 glaciation line were used in this analysis. It is not known whether the plots in other states
5 and also those below the glaciation line that are part of the FASOMGHG regions have
6 characteristics that are correlated with the critical load exceedances, which might lead to
7 biased estimates of the exposure-response relationship.
8 • The estimated reduction in the forest damages, as explained in Section 3.2.1.2, should be
9 interpreted as an upper bound on the benefits of reducing nitrogen+sulfur deposition, since
10 it only includes the gains from reducing critical load exceedances. Nitrogen deposition
11 below the critical load may actually promote tree growth through fertilization effects;
12 therefore, reducing deposition may have potential counteracting effects on tree growth.
13 The current analysis does not estimate or include these counteracting effects.
14 • Although this analysis of tree growth response is done for sugar maple and red spruce,
15 gains are also expected for other commercial species. Thus, we are underestimating the
16 total benefits of reducing nitrogen+sulfur deposition.
17 • Because of data limitations, the exposure-response analysis does not control for other
18 factors that may affect tree growth, such as elevation, slope, density (to account for
19 sunlight and competition among trees for nutrients), age (though tree volume was used as a
20 crude proxy for this variable), different management practices, and climate. Also,
21 differences in measurement and reporting across plots and states may result in
22 discrepancies in the data. Although this study attempted to capture the differences in
23 measurement and in climate by using state dummies, this is not a perfect control, since, for
24 example, there is substantial variation in climate within a state. Inadequate controls for
25 these other factors could potentially lead to omitted variable bias. Other uncertainties and
26 limitations associated with the estimation of the exposure-response relationships are
27 discussed in the case study.
28 Linking changes in tree growth to economic welfare changes:
29 • In applying the estimates from the exposure-response model to the FASOM model, it was
30 assumed that the plots used to calculate the percentage increase in tree volume are
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 representative of the FASOMGHG regions. However, because data on all plots in the
2 region are unavailable, there is some uncertainty regarding the representativeness of plots
3 used.
4 • The tree volume growth estimates used in the exposure-response model were calculated
5 based on live trees on forestland, not timberland, which is what FASOMGHG uses. This
6 may potentially give rise to some uncertainty in applying the results to FASOMGHG
7 because the estimates of the slopes (b) may be different for timberland than for forestland.
8 • The exposure-response model uses data from both private and public lands, while in
9 FASOMGHG the growth is applied to private lands only. This is an additional source of
10 uncertainty because different management practices could potentially affect the
11 relationship between exposure and growth differently.
12 • The age structure (and consequently volume of trees) may not be the same. So the stands
13 to which the change in growth rates are applied in FASOMGHG may be different from the
14 ones used in the exposure-response model, and this study may be assuming a change in
15 growth rate that is not realistic for these stands.
16 "A general limitation when using FASOMGHG is that it is a very aggregated region-level
17 model; thus, effects pertaining to areas particularly vulnerable to acidification cannot be
18 identified. Also, to make future timber market projections, FASOMGHG requires several
19 assumptions regarding future product demands, production capacity, and timber inventory.
20 • It must also be emphasized that the economic welfare changes reported in this section are
21 only those associated with markets for sugar maple and red spruce timber. They do not
22 include potential gains associated with other provisioning services, such as sugar maple
23 syrup production or production of other hardwood or softwood species affected by
24 terrestrial acidification. They also do not include gains outside the United States (in
25 particular, Canada) or in other sectors of the U.S. economy.
26 3.3 REFERENCES
27 Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. February 2005. FASOMGHG Conceptual
28 Structure, and Specification: Documentation. Available at
29 http://agecon2.tamu.edu/people/faculty/mccarl-bruce/FASOM.html. Accessed on
30 October 22, 2008.
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1 Brown, L.H. 2002. Profile of the Annual Fall Foliage Tourist in Vermont: Travel Year 2001.
2 Report prepared for the Vermont Department of Tourism and Marketing. Burlington, VT:
3 Vermont Tourism Data Center, University of Vermont.
4 Cordell, H.K., CJ. Betz, G. Green, and M. Owens. 2005. Off-Highway Vehicle Recreation in the
5 United States, Regions and States: A National Report from the National Survey on
6 Recreation and the Environment (NSRE). Report prepared for the Forest Service's
7 National OHV Policy & Implementation Teams. USDA Forest Service.
8 Cordell, K., B. Leeworthy, G.T. Green, C. Betz, and B. Stephens, n.d. The National Survey on
9 Recreation & the Environment. Research Work Unit 4953. Athens, GA: Pioneering
10 Research on Changing Forest Values in the South and Nation USDA Forest Service
11 Southern Research Station. Available at www.srs.fs.fed.us/trends.
12 DeHayes, D.H., P.G. Schaberg, GJ. Hawley, and G.R. Strimbeck, 1999. "Acidic Rain Impacts
13 on Calcium Nutrition and Forest Health." BioScience 49:789-800.
14 Driscoll, C.T., G.B. Lawrence, A.J. Bulger, T.J. Butler, C.S. Cronan, C. Eagar, K.F. Lamber,
15 G.E. Likens, J.L. Stoddard, and K.C. Weathers. 2001. "Acidic Deposition in the
16 Northeastern United States: Sources and Inputs, Ecosystem Effects and Management
17 Strategies." BioScience 51:180-198.
18 Fenn, M.E., T.G. Huntington, S.B. McLaughlin, C. Eager, A. Gomez, and R.B. Cook. 2006.
19 "Status of Soil Acidification in North America." Journal of Forest Science 52 (special
20 issue):3-13.
21 Haefele, M., R.A. Kramer, and T.P. Holmes. 1991. Estimating the Total Value of a Forest
22 Quality in High-Elevation Spruce-Fir Forests. The Economic Value of Wilderness:
23 Proceedings of the Conference. Gen. Tech. Rep. SE-78 (pp. 91-96). Southeastern For.
24 Exper. Station. Asheville, NC: USDA Forest Service.
25 Holmes, T.P. 1992. Economic Welfare Impacts of Air Pollution Damage to Forests in the
26 Southern United States. Asheville, NC: U.S. Dept. of Agriculture, Forest Service,
27 Southeastern Forest Experiment Station, pp. 19-26.
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1 Holmes, T., and R. Kramer. 1995. "An Independent Sample Test of Yea-Saying and Starting
2 Point Bias in Dichotomous-Choice Contingent Valuation. " Journal of Environmental
3 Economics and Management 28:121-132.
4 Jenkins, D.H., J. Sullivan, and G.S. Amacher. 2002. "Valuing High Altitude Spruce-Fir Forest
5 Improvements: Importance of Forest Condition and Recreation Activity." Journal of
6 Forest Economics 8:77-99.
7 Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On
8 National Park Recreation. Final Report October 2003, under Cooperative Agreement CA
9 1200-99-009, Project number EVIDE-02-0070.
10 Kramer, A., T. Holmes, and M. Haefele. 2003. "Contingent Valuation of Forest Ecosystem
11 Protection." In Forests in a Market Economy., E.O. Sills and K.L. Abt, eds., pp. 303-320.
12 Dordrecht, The Netherlands: Kluwer Academic Publishers.
13 Krieger, DJ. 2001. Economic Value of Forest Ecosystem Services: A Review. Washington, DC:
14 The Wilderness Society.
15 Luzadis, V.A. and E.R. Gossett. 1996. "Sugar Maple." In Forest Trees of the Northeast, J.P.
16 Lassoie, V.A. Luzadis, and D.W. Grover, eds., pp. 157-166. Cooperative Extension
17 Bulletin 235. Cornell Media Services. Available at
18 http://maple.dnr.cornell.edu/pubs/trees.htm.
19 National Agricultural Statistics Service (NASS). June 12, 2008. "Maple Syrup Production Up 30
20 Percent Nationwide." New England Agricultural Statistics, NASS, USDA.
21 Spencer, D.M., and D.F. Holecek. 2007. "Basic Characteristics of the Fall Tourism Market."
22 Tourism Management 28:491 -504.
23 U.S. Department of Agriculture, Forest Service. Forest Inventory and Analysis National
24 Program, RPA Assessment Tables. 2002. Available at
25 http://Ncrs2.Fs.Fed.Us/4801/Fiadb/Rpa_Tabler/Draft_RPA_2002_Forest_Resource_Tabl
26 es.pdf.
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1 U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce,
2 U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and Wildlife-
It Associated Recreation.
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i 4. AQUATIC ENRICHMENT
2 One of the main adverse ecological effects resulting from nitrogen deposition,
3 particularly in the Mid-Atlantic region of the United States, is the effect associated with nutrient
4 enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the National
5 Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) suffered
6 from moderately high or high levels of eutrophication due to excessive inputs of both nitrogen
7 and phosphorus, and a majority of these estuaries are located in the coastal area from North
8 Carolina to Massachusetts (NOAA, 2007). By several measures, the aquatic ecosystem of the
9 Chesapeake Bay estuary is particularly suffering from the effects of excessive nitrogen loads,
10 and roughly one-third of these loads are associated with atmospheric deposition of nitrogen in
11 the watershed (Sweeney, 2007).l For other estuaries in the Mid-Atlantic region, the contribution
12 of atmospheric distribution to total nitrogen loads is estimated to range between 10% and 58%
13 (Valiguraetal., 2001).
14 Eutrophication in estuaries is associated with a range of adverse ecological effects. Using
15 the conceptual framework developed by NOAA, Figure 4-1 illustrates the main links between
16 nutrient loadings and ecological symptoms in estuaries. The framework emphasizes four main
17 types of eutrophication effects—low dissolved oxygen (DO), harmful algal blooms (HABs), loss
18 of submerged aquatic vegetation (SAV), and low water clarity.
19 Low DO (i.e., hypoxia) has become a chronic problem in several estuaries, particularly
20 during summer months. Five of the 22 estuaries evaluated by NOAA in the Mid-Atlantic region
21 suffer from serious DO problems. The mainstem of the Chesapeake Bay has been a particular
22 area of concern. For example, between 2005 and 2007, only about 12% of the Bay met DO
23 standards during the summer months (Chesapeake Bay Program, n.d.). Low DO disrupts aquatic
24 habitats, causing stress to fish and shellfish, which, in the short term, can lead to episodic fish
25 kills and, in the long term, can damage overall growth in fish and shellfish populations. Low DO
26 also degrades the aesthetic qualities of surface water.
27 HABs were also rated by NOAA as a major problem in five Mid-Atlantic estuaries,
28 including the mainstem of the Chesapeake Bay and the Potomac River estuary. In addition to
1 Phosphorus loads, primarily from agricultural runoff and wastewater dischargers in the Chesapeake Bay
watershed, are the other main source of nutrients contributing to eutrophication in the Bay.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 often being toxic to fish and shellfish and leading to fish kills and aesthetic impairments of
2 estuaries, HABs can, in some instances, also be harmful to human health.
Nutrient Inputs to Estuary
Primary Symptoms of Secondary Symptoms
Eutroohication of Eutroohication
Ecosystem Endpoints
Affected Ecosystem Services
Nitrogen Loadings from Direct
and Indirect Deposition
Nitrogen and Phosphorous
Loadings from Other Sources
Provisioning Services
production of fish
production of shellfish
Cultural Services
• recreational fishing,
boating, swimming,
etc.
aesthetic enjoyment
Low Water
. Clarity and
Light
Availability
i
Loss of
Submerged
Aquatic
Vegetation
(SAV)
— »
Declines in
Fish/Shellfish
Abundance
Declines in
Shoreline
Quality
4 — 1
• nonuse services
T
Regulating services
• erosion control
• storm protection
3
4 Figure 4-1. Conceptual Model of Eutrophication Impacts in Estuaries (Source:
5 Adapted from Bricker et al. [2007] and Bricker, Ferreira, and Simas [2003]).
6 SAV provides critical habitat for many aquatic species in estuaries and, in some
7 instances, can also protect shorelines by reducing wave strength; therefore, declines in SAV due
8 to nutrient enrichment are an important source of concern. Although less prevalent than low DO
9 and HABs as a problematic symptom of eutrophication, it is nonetheless rated by NOAA as a
10 serious problem in the mainstem of the Chesapeake Bay and the New Jersey Inland Bays.
11 Low water clarity is the result of accumulations of both algae and sediments in estuarine
12 waters. In addition to contributing to declines in SAV, high levels of turbidity also degrade the
13 aesthetic qualities of the estuarine environment. Although NOAA's assessment of estuaries did
14 not focus on turbidity separately as an indicator of eutrophi cation, it is nonetheless a common
15 problem in the Mid-Atlantic region.
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1 4.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES
2 Figure 4-1 also extends the NOAA framework to include links to the main types of
3 ecosystem services that are affected by the primary and secondary symptoms of eutrophication.
4 The following sections provide a discussion and overview of the primarily affected provisioning,
5 cultural, and regulating services.
6 4.1.1 Provisioning Services
7 Estuaries in the eastern United States are an important source of food production, in
8 particular fish and shellfish production. The estuaries are capable of supporting large stocks of
9 resident commercial species, and they serve as the breeding grounds and interim habitat for
10 several migratory species.
11 To provide an indication of the magnitude of provisioning services associated with
12 coastal fisheries, Table 4.1-1 reports the annual value of commercial landings in recent years for
13 15 East Coast states. From 2005 to 2007, the average value of total catch was $1.5 billion per
14 year. It is not known, however, what percentage of this value is directly attributable to or
15 dependent upon the estuaries in these states. Table 4.1-2 focuses specifically on commercial
16 landings in Maryland and Virginia in 2007, and it reports values for the main commercial species
17 in these states. Although these values also include fish caught outside of the Chesapeake Bay, the
18 values for two key species—blue crab and striped bass—are predominantly from the estuary
19 itself. These data indicate that blue crab landings in 2007 totaled nearly $44 million in the Bay.
20 The value of striped bass and menhaden totaled about $9 million and $25 million, respectively.
21 To most accurately assess how eutrophication in East Coast estuaries is related to the
22 long-term provisioning services from their fishery resources requires bioeconomic models (i.e.,
23 models that combine biological models offish population dynamics with economic models
24 describing fish harvesting and consumption decisions). In most cases, these models address the
25 dynamic feedback effects between fish stocks and harvesting behavior, and they characterize
26 conditions for a "steady-state" equilibrium, where stocks and harvest levels are stabilized and
27 sustainable over time.
28 Section 4.2 describes one bioeconomic model linking blue crab harvests to nutrient loads
29 in the Neuse River estuary, and it applies the model to estimate how reductions in nitrogen loads
30 to the estuary would affect the societal value of future blue crab harvests. In practice, however,
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1 very few other studies have developed empirical bioeconomic models to estimate how changes
2 in environmental quality affect fish harvests and the value of these services (Knowler, 2002).
3 One exception is Kahn and Kemp (1985), which estimated a bioeconomic model of commercial
4 and recreational striped bass fishing using annual data from 1965 to 1979, measuring the effects
5 of SAV levels on fish stocks, harvests, and social welfare. They estimated, for example, that a
6 50% reduction in SAV from levels existing in the late 1970s (similar to current levels
7 [Chesapeake Bay Program, 2008]) would decrease the net social benefits from striped bass by
8 roughly $16 million (in 2007 dollars).
9 In a separate analysis, Anderson (1989) developed an empirical dynamic simulation
10 model of the effects of SAV changes on commercial blue crab harvests in the Virginia portion of
11 the Chesapeake Bay. Applying the empirical model results, long-run (15-year) dynamic
12 equilibria were estimated under baseline conditions (assuming SAV area constant at 1987 levels)
13 and under conditions with "full restoration" of SAV (i.e., 284% increase). In equilibrium, the
14 increase in annual producer surplus and consumer surplus with full restoration of SAV was
15 estimated to be $3.5 million and $4.4 million (in 2007 dollars), respectively.
Table 4.1-1. Annual Values of East Coast Commercial Landings (in millions)
State
Connecticut
Delaware
Florida, East Coast
Georgia
Maine
Maryland
Massachusetts
New Hampshire
New Jersey
New York
North Carolina
Pennsylvania
Rhode Island
2004
$33.40
$5.42
$39.98
$14.37
$367.09
$49.29
$326.00
$17.21
$145.86
$46.89
$79.70
$0.07
$76.25
2005
$37.57
$6.11
$35.49
$13.46
$391.90
$63.67
$427.07
$22.12
$159.01
$56.45
$64.89
$0.04
$91.58
2006
$36.89
$5.69
$42.00
$11.53
$361.85
$53.58
$437.05
$18.84
$136.05
$57.73
$70.12
$0.10
$98.58
2007
$42.08
$7.58
$42.74
$10.08
$319.52
$52.27
$457.18
$19.09
$152.46
$58.94
$82.31
$0.13
$76.79
2nd Draft Pvisk and Exposure Assessment June 5, 2009
Appendix 8-62
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
State
South Carolina
Virginia
Total
2004
$18.54
$160.51
$1,380.60
2005
$17.57
$155.26
$1,542.20
2006
$17.03
$109.07
$1,456.11
2007
$15.57
$130.56
$1,467.31
1 Source: National Oceanic and Atmospheric Administration (NOAA), 2007.
Table 4.1-2. Value of Commercial Landings for Selected Species in 2007 (Chesapeake Bay
Region)
State Species
Maryland
Blue crab
Striped bass
Clams or bivalves
Sea scallop
Oyster, Eastern
Other
Virginia
Sea scallop
Menhaden
Blue crab
Croaker, Atlantic
Striped bass
Clam, Northern Quahog
Summer flounder
Other
Value
$30,433,777
$5,306,728
$5,007,952
$2,808,984
$2,524,045
$6,190,474
Total $52,271,960
$62,891,848
$25,350,740
$13,222,135
$4,615,924
$3,834,906
$3,691,319
$3,186,229
$16,954,893
Total $130,561,765
2 Source: National Oceanic and Atmospheric Administration (NOAA), 2007.
3 One study examining the short-term effects of DO levels on crab harvests is Mistiaen,
4 Strand, and Lipton (2003). Focusing on three
Chesapeake Bay tributaries — the Patuxent,
5 Chester, and Choptank rivers — they estimated a "stress-availability" model measuring the effects
6 of DO levels on the availability of blue crabs
7 number of fishing vessels. The model results
for commercial harvest, given the stock levels and
indicated that, below a threshold of 5 mg/L,
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 reductions in DO cause a statistically significant reduction in commercial harvest and revenues.
2 For the Patuxent River alone, a simulated reduction of DO from 5.6 to 4.0 mg/L was estimated to
3 reduce crab harvests by 49% and reduce total annual earnings in the fishery by $275,000 (in
4 2007 dollars). However, this is an upper-bound estimate because it does not account for changes
5 in fishing effort that would likely occur, and if the measured changes are due to migration of crab
6 populations to other areas rather than to crab mortality, then the broader net effects on crab
7 harvests may also be considerably smaller.2
8 In addition to affecting provisioning services through commercial fish harvests,
9 eutrophication in estuaries may also affect these services through its effects on the demand for
10 seafood. For example, a well-publicized toxic pfiesteria bloom in the Maryland Eastern Shore in
11 1997, which involved thousands of dead and lesioned fish, led to an estimated $56 million (in
12 2007 dollars) in lost seafood sales for 360 seafood firms in Maryland in the months following the
13 outbreak (Lipton, 1999). Additional evidence regarding potential losses in provisioning services
14 due to eutrophication-related fish kills is provided by Whitehead, Haab, and Parsons (2003) and
15 Parsons et al. (2006). The survey used in both studies was conducted with more than 5,000
16 respondents in states bordering the Chesapeake Bay area and in North Carolina. The survey
17 asked respondents to consider how their consumption patterns would change in response to news
18 about a large fish kill caused by a toxic pfiesteria bloom. To address the fact that not all fish kills
19 are the same, the size and type of the described fish kill—either "major," involving more than
20 300,000 dead fish and 75% with pfiesteria lesions, or "minor," involving 10,000 dead fish and
21 50% with lesions—were randomized across respondents. Based on respondents' stated
22 behaviors, the studies estimated reductions in consumer surplus per seafood meal ranging from
23 $2 to $5.3 The survey also found that 42% of residents in the four-state area (Maryland, Virginia,
24 Delaware, and North Carolina) were seafood consumers and that the average number of seafood
25 meals per month among these consumers was between four and five. As a result, they estimated
2 The estimated relationship between harvest and DO is discontinuous at 5 mg/L. The size of the measured effect on
harvests is relatively small below 5 mg/L and zero above the 5 mg/L threshold; therefore, any sizable benefits
would require DO to cross the 5 mg/L threshold. Moreover, the 5 mg/L threshold was an assumption of the model
rather than a tested hypothesis, which raises additional questions about the accuracy of benefit estimates for
changes across the threshold.
3 Surprisingly, these estimates were not sensitive to whether the fish kill was described as major or minor or to the
different types of information included in the survey.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 aggregate consumer surplus losses of $43 million to $84 million (in 2007 dollars) in the month
2 after a fish kill.
3 4.1.2 Cultural Services
4 Estuaries in the eastern United States also provide an important and substantial variety of
5 cultural ecosystem services, including water-based recreational and aesthetic services. One of the
6 difficulties with quantifying recreational services from estuaries is that much of the national and
7 regional statistics are jointly collected and reported for estuarine and other coastal areas.
8 Nevertheless, even these combined statistics provide several useful indicators of recreational
9 service flows. For example, data from the FHWAR indicate that, in 2006, 4.8% of the 16 or older
10 population in coastal states from North Carolina to Massachusetts participated in saltwater
11 fishing. The total number of days of saltwater fishing in these states was 26.1 million in 2006.
12 Based on estimates from Kaval and Loomis (2003), the average consumer surplus value for a
13 fishing day was $35.91 (in 2007 dollars) in the Northeast and $87.23 in the Southeast. Therefore,
14 the total recreational consumer surplus value from these saltwater fishing days was
15 approximately $1.28 billion (in 2007 dollars).
16 Recreational participation estimates for several other coastal recreational activities are
17 also available for 1999 to 2000 from the NSRE. These estimates are summarized in Table 4.2-1
18 based on data reported in Leeworthy and Wiley (2001). Almost 6 million individuals aged 16 or
19 older participated in motorboating in coastal states from North Carolina to Massachusetts, for a
20 total of nearly 63 million days annually during 1999-2000. Using a national daily value estimate
21 of $32.69 (in 2007 dollars) for motorboating from Kaval and Loomis (2003), the aggregate value
22 of these coastal motorboating outings was $2.08 billion per year. Almost 7 million people
23 participated in birdwatching, for a total of almost 175 million days per year, and more than 3
24 million participated in visits to nonbeach coastal waterside areas, for a total of more than 35
25 million days per year. In contrast, fewer than 1 million individuals per year participated in
26 canoeing, kayaking, or waterfowl hunting.
27 4.1.3 Regulating Services
28 Estuaries and marshes have the potential to support a wide range of regulating services,
29 including climate, biological, and water regulation; pollution detoxification; erosion prevention;
30 and protection against natural hazards (Millennium Ecosystem Assessment [MEA], 2005). It is
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1 more difficult, however, to identify the specific regulating services that are significantly
2 impacted by changes in nutrient loadings. One potentially affected service is provided by SAV,
3 which can help reduce wave energy levels and thus protect shorelines against excessive erosion.
4 Declines in SAV may, therefore, also increase the risks of episodic flooding and associated
5 damages to near-shore properties or public infrastructure. In the extreme, these declines may
6 even contribute to shoreline retreat, such that land and structures are lost to the advancing
7 waterline.
8 4.2 CHANGES IN ECOSYSTEM SERVICES ASSOCIATED WITH
9 ALTERNATIVE LEVELS OF ECOLOGICAL INDICATORS
10 This section estimates values for changes in several ecosystem services associated with
11 reduced nutrient enrichment effects in the Chesapeake Bay and Neuse River estuaries. Using the
12 results of the Potomac River and Neuse River Case Studies, the value of removing all
13 atmospheric sources of nitrogen loadings to these estuaries was estimated. Although such a large
14 change represents an upper bound on possible loading reductions through controls on
15 atmospheric sources, it corresponds with the findings of the case studies, which indicate that
16 reductions of this magnitude are the minimum required to improve the eutrophication index (El)
17 score (based on NOAA's ASSETS framework) from current "bad" conditions (El = 1) in these
18 two estuaries to somewhat better "poor" conditions (El = 2).
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 4.2-1. Participation in Selected Marine Recreation Activities in East Coast States in 1999-2000
Visiting
Watersides besides
Beaches
State
Connecticut
Delaware
Maryland
Massachusetts
New Jersey
New York
North Carolina
Rhode Island
Virginia
Total
Na
0.18
0.08
0.47
0.47
0.45
0.56
0.44
0.27
0.48
3.41
Days"
2.41
C
5.89
2.93
4.58
3.74
4.16
3.31
8.27
35.29
Motorboating
Na
0.39
0.38
0.97
0.61
0.89
0.90
0.55
0.38
0.60
5.67
Days"
6.76
4.56
8.13
6.05
12.45
9.48
7.25
4.37
4.54
63.59
Canoeing
Na
0.05
0.04
0.16
0.07
0.07
0.07
0.04
0.15
0.15
0.79
Kayaking
Na
0.10
0.02
0.03
0.17
0.10
0.06
0.12
0.11
0.06
0.76
Bird Watching
Na
0.45
0.43
0.82
1.02
0.80
0.88
1.04
0.56
0.86
6.84
Days"
15.19
14.03
19.76
26.10
18.80
24.55
20.52
19.01
17.00
174.96
Waterfowl
Hunting
Na
0.00
0.02
0.03
0.00
0.01
0.00
0.03
0.00
0.04
0.13
a Number of resident and nonresident participants annually (in millions).
b Number of days by residents and nonresidents annually (in millions).
0 Insufficient data for estimate.
Source: Leeworthy and Wiley, 2001
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 4.2.1 The Chesapeake Bay Estuary
2 For the Chesapeake Bay analysis, the results of the Potomac River/Potomac Estuary Case
3 Study were applied. Other than the mainstem of the Bay (6,074 km2), the Potomac estuary is the
4 largest subestuary within the Chesapeake Bay estuary system (1,260 km2), and other than the
5 Susquehanna River, which flows directly into the mainstem, it contributes the largest portion of
6 freshwater (19%) to the Bay. Eutrophic conditions within the Potomac estuary are also reflective
7 of more widespread conditions in the Bay. For example, when assessing estuarine conditions
8 across the country in 2004, NOAA (2007) evaluated nine subestuaries of the Bay, including the
9 mainstem and the Potomac. Five subestuaries in the Bay, including the mainstem and the
10 Potomac, rated "high" with respect to overall eutrophic conditions (the worst level on a 5-point
11 scale from low to high). The remaining four subestuaries were all rated as "moderate high" (the
12 second worst level). Therefore, for this analysis, it was assumed that the results of the Potomac
13 River estuary case study are representative of the Chesapeake Bay as a whole.
14 According to the Aquatic Nutrient Enrichment Case Study, atmospheric deposition is
15 estimated to contribute 24% (7.38 million kg nitrogen/year) of total nitrogen loadings to the
16 Potomac estuary. This percentage falls within the range of the 23% to 33% that has been
17 estimated for the Chesapeake Bay as a whole (Valigura et al., 2001). The case study also
18 estimates that a reduction in nitrogen loadings roughly equivalent to the contribution from
19 atmospheric deposition in the Potomac River watershed would be required to improve the
20 Potomac estuary from "bad" to "poor" on the 5-point ASSETS El.4
21 For the Chesapeake Bay analysis, the change in selected ecosystem services associated
22 with a 24% reduction in loadings to the Chesapeake Bay as a whole was estimated, and it was
23 assumed that this reduction would also improve the Bay's overall El score from "bad" to "poor."
24 The selection of ecosystem services for this analysis, which includes recreational, aesthetic, and
25 nonuse services (i.e., specific cultural services), was based on availability of existing models,
26 data, and empirical results.
27 For each of the ecosystem service categories addressed in this section, the geographic
28 extent of aggregate benefits to residents and recreators in Maryland, Virginia, and Washington,
29 DC (DC) was limited. Because these areas are directly adjacent to the Bay, this approach is
4 The mean and median estimate of required loading reductions is 104% of the annual atmospheric deposition
component (almost 25% of all N loadings).
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 expected to include a large majority of the beneficiaries; however, this approach also will
2 unavoidably contribute to some underestimation of aggregate benefits. Other specific limitations
3 and uncertainties in the proposed methods are described in each of the subsections below.
4 4.2.1.1 Recreational Fishing Services
5 This section describes and applies a three-part "benefit transfer" framework for
6 estimating the recreational fishing benefits of improved eutrophic conditions in the Chesapeake
7 Bay. The first component translates changes in the 5-point El into equivalent changes in average
8 DO levels in the Bay. This step is required to link eutrophic conditions to existing recreational
9 catch rate models.
10 The second component predicts the effect of changes in average DO levels on
11 recreational fishing catch rates. These catch rates can be interpreted as indicators of the
12 recreational fishing services provided by the Bay. Two catch rate models are described: one
13 based on a study of striped bass fishing in the Bay and the other based on a study of summer
14 flounder fishing in the Maryland coastal bays.
15 The third component estimates the benefits of catch rate improvements using willingness
16 to pay (WTP) estimates derived from a meta-analysis study by Johnston et al. (2005) and annual
17 fishing trip estimates to the Bay using data from the Marine Recreation Fishing Statistics Survey
18 (MRFSS).
19 4.2.1.1.1 Converting Changes in El to Changes in DO
20 As described above, low DO is one of several ecosystem symptoms associated with
21 estuarine eutrophication; therefore, DO levels are one of several factors included in the ASSETS
22 framework to derive the composite 5-point El.
23 To derive changes in DO that are equivalent to a 1-unit change on the El, data for a
24 comparable water quality index were used. In collaboration with the University of Maryland's
25 Center for Environmental Science, NOAA has also developed a 100-point Chesapeake Water
26 Quality Index (CWQI) based on three main eutrophication symptom indicators—chlorophyll a,
27 water clarity, and DO (Chesapeake Eco-Check, 2007). Using annual data for the three water
28 quality parameters, a water quality index score was generated for 141 monitoring stations across
29 the Bay in 2007. Table 4.2-2 reports the results of regressing the CWQI score for each station
30 against four corresponding water quality measures—(1) average surface DO, (2) average bottom
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1 DO, (3) average secchi depth, and (4) average chlorophyll a. Both DO measures have positive
2 and statistically significant effects (with a p-value less than 0.05) on the index score, although
3 the estimated effect of bottom DO is somewhat larger. A 1-unit change in both bottom and
4 surface DO is predicted to change the CWQI by a combined effect of 8.3 points. If it was
5 assumed that the 100-point CWQI and the 5-point El are directly proportional, then a 1-unit
6 change in both bottom and surface DO is predicted to change the El by 0.415 (= 8.3/20) points.
7 Alternatively, a 1-point increase in the El (e.g., from "bad" to "good") would be predicted to
8 result from a 2.41-unit increase in both surface and bottom DO.5 Therefore, going forward, the
9 effects on recreational fishing resulting from an increase in bottom and surface DO of 2.41 mg/L,
10 which is assumed to be equivalent to a 1-unit increase in the El, were estimated.
Table 4.2-2. Regression Analysis of the Chesapeake Water Quality Index on Water Quality
Parameters
Water Quality
Parameter
Regression Results
Coefficient P-value
Summary Statistics
Mean
Std. Dev.
Min Max
CWQI (dependent
variable)
Explanatory variables
Chlorophyll a (ug/L)
Secchi depth (m)
Bottom DO (mg/L)
Surface DO (mg/L)
Constant
48.85
22.30
-0.556
0.163
5.069
3.235
6.296
0.000
0.443
0.000
0.028
0.556
15.99
2.32
5.01
6.91
14.09
7.44
2.00
0.97
2.0
0.1
0.1
4.5
100
82.0
50.4
9.3
9.1
11
12
13
14
Observations
R-squared
141
0.357
4.2.1.1.2 Estimating Changes in Recreation Services (Catch Rates)
In two related papers, Lipton and Hicks (1999, 2003) reported the results of a travel cost
study of recreational striped bass fishing in the Chesapeake Bay. One of the main focuses of the
5 Based on the results in Table 4.2-2, other combinations of bottom DO and surface DO changes can also produce a
1-point increase in the El; however, for simplicity, equivalent changes in the two DO measures were considered.
June 5, 2009
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1 study was measuring the effect of DO levels on striped bass catch rates. The fishing data for this
2 study were drawn from the National Marine Fisheries Service's (NMFS's) 1994 MRFSS, which
3 included 407 intercept sites in the Bay and 1,806 striped bass angler respondents. The DO water
4 quality data were from biweekly summer sampling at 207 locations in the Bay.
5 The striped bass catch model assumes that the number of fish caught per trip (in
6 logarithmic form) at a site is a linear function of several factors, including the hours spent by the
7 angler at the site on the trip, the angler's experience and skill in saltwater fishing, and water
8 quality conditions at the site. Water quality is characterized in the model by surface temperature
9 (57), bottom temperature (BT), surface DO (SDO), and bottom DO (EDO). According to the
10 functional form of the estimated model, the change in the expected striped bass catch rate per
1 1 trip due to a water quality change can be expressed as
12 Ag = ft - Q0 = exp(/B (AWQ) + In Q0 ) - Q0 , (4.1)
13 where Qt is the expected number of striped bass caught per trip under conditions /', such
14 that / = 0 represents reference conditions and / = 1 represents conditions after the water quality
15 change. The function fs(/\WQ) represents the combined effect of changes in temperature and DO
16 on expected catch rates. Using the parameter estimates from the empirical catch rate model, this
17 function for striped bass can be expressed as
Q) = In Ql -In Q0 = -0.2548(
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 4.2-3. Average Catch Rate per Fishing Trip in the Chesapeake Bay, by State and Targeted
Fish Species
Fishing Trip
Maryland residents
Striped bass
Summer flounder
Other species
All species
Virginia residents
Striped bass
Summer flounder
Other species
All species
2001
1.20
0.09
0.29
0.34
0.42
0.96
0.34
0.37
2002
1.58
0.08
0.32
0.38
0.44
0.80
0.40
0.42
2003
1.99
0.09
0.32
0.40
0.52
0.91
0.26
0.29
2004
1.81
0.34
0.27
0.35
0.82
0.93
0.27
0.32
2005 Average 2001-2005
1.70
0.04
0.45
0.51
0.68
0.69
0.34
0.36
1.65
0.12
0.33
0.39
0.59
0.86
0.32
0.35
1 Source: National Oceanic and Atmospheric Administration (NOAA), 2009.
2 It is more difficult to develop catch rate predictions for other recreational species,
3 because of the apparent lack of any other empirical studies that have estimated the relationship
4 between water quality conditions and recreational catch rates in the Bay.7 One alternative is to
5 assume that the striped bass model described above is applicable to other species; however, the
6 resulting catch rate change estimates would inevitably have higher levels of uncertainty
7 associated with them.
8 A second approach is to use catch rate models developed in areas outside the Bay;
9 however, only one such study was found.8 Massey, Newbold, and Gentner (2006) used data from
10 the Maryland coastal bays to estimate a catch rate model for recreational summer flounder
11 fishing. They found significant effects from DO, temperature (7), and water clarity (secchi depth
12 [SD]) on recreational catch. Using the parameter estimates from this model, the following
13 function summarizes the measured effects of water quality on summer flounder catch rates:
14 fF(AWQ) = 0.117(00, -£>O0) +0.126(^-7;) +1392(SDl-SD0). (4.3)
7 Bricker et al. (2006) described similar models for the Potomac and Patuxent River estuaries and other East Coast
estuaries; however, they did not provide parameter estimates for these models.
8 Kaoru, Smith, and Liu (1995) also estimated the effects of estuarine water quality on recreational fishing in North
Carolina; however, rather than using ambient water quality measures, he used estimates of nutrient and
biochemical oxygen demand loadings as proxies for water quality conditions.
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1 Applying this function to Equation (4. 1) in place affs^WQ), a 2.41-unit increase in DO
2 (with no change in T or SD) is predicted to increase summer flounder catch by an additional 0.04
3 fish per trip in Maryland and 0.28 fish per trip in Virginia (a 32.6% increase). Transferring this
4 model from the Maryland coastal bays to the Chesapeake Bay also contributes to the uncertainty
5 in catch rate predictions for summer flounder, although arguably less so than transferring models
6 from other species (i.e., striped bass) within the Bay.
7 4.2.1.1.3 Valuing Changes in Catch Rates
8 The second component of the proposed benefit transfer model for recreational fishing can
9 be summarized as follows:
10 AggBftsh = Zj (WTPfish x Tj) x AQ,-, (4.4)
1 1 where
12 AggBfish = aggregate annual benefits (in 2007 dollars) to Chesapeake Bay anglers for
13 specified increases in species-specific average catch rates per trip (Ag,,
14 where y is the species indicator),
15 A<27 = predicted change in average catch rate per trip for speciesy in the
16 Chesapeake Bay (as described in Section 4.2.1.1.2),
17 WTPflsh = average WTP per additional fish caught per trip, and
18 TJ = total number of annual fishing trips (in 2007) targeting speciesy in the
19 Chesapeake Bay.
20 A large number of revealed- and stated-preference studies have estimated welfare
21 changes associated with changes in recreational fishing catch rates in the United States. Most of
22 these results have been synthesized in a meta-analysis study by Johnston et al. (2006), which
23 estimated meta-regression models controlling for differences across studies in type of water
24 resource, context, angler attributes, and in-study methods. Using these summary models, they
25 predicted average WTP per fish per trip for different species categories. For both Atlantic small
26 game (including striped bass) and Atlantic flatfish (including summer flounder), they predicted
27 WTP ranging from $3 to $1 1 in 2003 dollars. This meta-analysis study included one WTP
28 estimate from a Chesapeake Bay striped bass study (Bockstael, McConnell, and Strand, 1989),
29 which falls slightly below this range ($2.23), but it did not include a more recent striped bass
30 estimate from the Lipton and Hicks (1999) study, which falls within the upper end of the range
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1 ($10.91). Johnston et al.'s (2006) study also did not include the estimate for summer flounder in
2 Maryland coastal bays from Massey, Newbold, and Gentner (2006), which falls within the lower
3 end of the range ($4.22 in 2002 dollars).
4 Based on these WTP results from the literature, a value range of $2.50 to $12.50 for
5 WTPflsh, with a midpoint of $7.50, was selected.
6 To quantify annual trips by species (7J), recent MRFSS data for the Bay, which are
7 summarized in Table 4.2-4, were again used. The table reports total annual trips for striped bass
8 and other key recreational species from 2001 to 2005. To approximate trips in 2007, the average
9 number of trips from 2001 to 2005 by species were used. The same methodology was used to
10 approximate baseline catch rates for 2007.
Table 4.2-4. Aggregate Number of Fishing Trips to the Chesapeake Bay, by State and Targeted
Fish Species
Average
Fishing Trip 2001 2002 2003 2004 2005 2001-2005
Maryland
residents
Striped bass 2,594,971 2,014,818 2,579,771 2,176,824 2,351,145 2,343,506
Summer 2,106,810 1,268,048 1,598,484 1,486,154 1,734,101 1,638,719
flounder
Other species 33,457,937 31,349,971 48,352,248 39,740,106 34,503,965 37,480,845
All species 38,159,717 34,632,837 52,530,503 43,403,083 38,589,211 41,463,070
Virginia residents
Striped bass 2,043,025 1,911,180 2,369,576 2,525,057 2,549,248 2,279,617
Summer 2,285,628 1,982,130 2,300,633 2,556,902 2,549,248 2,334,908
flounder
Other species 49,915,214 47,535,158 67,839,883 65,345,054 58,036,434 57,734,349
All species 54,243,868 51,428,468 72,510,092 70,427,013 63,134,930 62,348,874
11 Source: National Oceanic and Atmospheric Administration (NOAA), 2009.
12 4.2.1.1.4 Results: Aggregate Recreational Fishing Benefits
13 Combining the three model components, the aggregate recreational fishing benefits from
14 a 1-point El increase in Chesapeake Bay from "bad" to "poor" can now be estimated. Assuming
15 that this change is equivalent to a 2.41 mg/L increase in both surface and bottom DO, the result
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1 is an annual benefit of $37.2 million to striped bass anglers in Maryland and Virginia and an
2 annual benefit of $5.4 million to summer flounder anglers.
3 Recognizing the uncertainties associated with transferring these models to other species,
4 the same benefit transfer framework can also be applied to other recreational fishing trips.
5 Striped bass and summer flounder fishing only accounted for 7.4% of the total number of trips
6 and 15.5% of the total catch from 2001 to 2005. If the striped bass catch rate model
7 (Equation [4.2]) is applied to all other types offish species, then a 2.41 mg/L increase in surface
8 and bottom DO would result in an estimated aggregate benefit of $217 million for recreational
9 anglers targeting these other species in the Bay.
10 4.2.1.1.5 Limitations and Uncertainties
11 Although the objective of the previously described approach is to make the best use of
12 existing research to quantify the relationship between changes in eutrophic conditions and
13 recreational fishing benefits in the Bay, the following limitations and uncertainties must also be
14 noted.
15 First, the conversion of changes in El to changes in DO requires several strong
16 assumptions. One key assumption is that the El and CWQI are directly proportionate to one
17 another. The reasonableness of this assumption rests on the fact that the two indexes use similar
18 symptom indicators (DO, SD, and chlorophyll a) and both have been designed and used by
19 NOAA as summary metrics of eutrophic conditions in estuaries. Another key assumption is that
20 the regression model in Table 4.2-2 can be used to generate equivalent changes in DO. Because
21 several water quality parameters besides DO are also measures of eutrophic symptoms, there is
22 no guarantee that a 1-unit change in El is uniquely associated with a 2.41 mg/L change in surface
23 and bottom DO (i.e., that the other factors are held constant).
24 Second, the catch rate models summarized in Equations (4.2) and (4.3) are most likely to
25 understate the effects of long-term changes (i.e., over several years) in water quality across the
26 entire Bay. Both models are based on analyses that use spatial and short-term (during a single
27 year's fishing season) temporal variation to measure the relationship between catch rates and
28 water quality conditions. Therefore, these measured relationships cannot be expected to capture
29 the dynamic effects of long-term changes in DO on the overall growth and abundance of the
30 striped bass and summer flounder populations in the Bay.
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Appendix 8-75
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Third, as previously noted, empirical catch rate models are only available for striped bass
2 and summer flounder, and the model for the latter species is based on data from outside the Bay.
3 Although it is not difficult to apply these models to estimate catch rate changes for other species
4 within the Bay, the resulting estimates are subject to significant uncertainty, because there is
5 little evidence about how well these models transfer to other species.
6 Fourth, the valuation model summarized in Equation (4.4) uses a number of simplifying
7 assumptions. In particular, the value per fish caught is assumed to be constant, but within a large
8 range — $2.50 to $12.50 — which can significantly affect the aggregate benefit estimates. In
9 addition, the total number of fishing trips is assumed to be unaffected by changes in catch rates.
10 This restriction is expected to understate the true aggregate benefits of increased catch rates,
1 1 because higher catch rates would most likely increase the number of fishing trips.
12 4.2.1.2 Boating
13 To estimate benefits to Chesapeake Bay boaters, a benefit transfer approach that uses
14 value estimates developed by Lipton (2004) is described. That study used a CV method and
15 survey data from 755 Maryland boaters in 2000 to estimate the individual and aggregate benefits
16 of a 1-unit improvement in respondents' water quality rating (on a 1 to 5 scale from "poor" to
17 "excellent") for the Bay. The benefit transfer model based on this study can be summarized as
18 follows:
19 AggBboat = Zt Lj(WTPboat.t x NtJ xbj)x &WQ5, (4.5)
20 where
21 &WQs = change in Chesapeake Bay water quality, expressed on a 5-point rating scale
22 (from "poor" to "excellent");
23 AggBboat = aggregate annual benefits (in 2007 dollars) to Maryland, Virginia, and DC
24 boat owners who use the Chesapeake Bay as their principal boating area for
25 a specified AWQs increase in water quality;
26 WTPboat.i = average annual WTP (in 2007 dollars) per boater for a 1-unit increase in
27 water quality on the WQs scale (/' = sailboat, trailered powerboat, or in-water
28 powerboat);
29 Nij = total number of boats by type /' and locationy (j = Maryland, Virginia, or
30 DC) of boat ownership in 2007; and
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 bj = the ratio of (1) registered boat owners whose principal boating area is the
2 Chesapeake Bay to (2) the total number of registered boats (by location7).
3 Lipton (2004) reported estimates of average WTP by boat owners in three different
4 categories for a 1-unit increase in water quality (AWQ$ = 1) in the Chesapeake Bay. Sailboat
5 owners had the highest average WTP of $93.26 (in 2000 dollars). Trailered and in-water
6 powerboat owners had an average WTP of $30.25 and $77.98, respectively.
7 Converting these Lipton (2004) estimates to 2007 dollars with the consumer price index
8 (CPI) results in WTPboat estimates of $112.29, $36.42, and $93.89 for sailboat, trailered
9 powerboat, and in-water powerboat owners, respectively (Table 4.2-5).
Table 4.2-5. Input Estimates for the Chesapeake Bay Boating Benefit Transfer Model
Number of Registered Boats
Boat Type
Sailboat
Trailered powerboat
In-water powerboat
Total
NMD
8,200
93,300
23,600
125,100
NVA
9,200
104,600
26,400
140,300
NDC
100
1,100
300
1,500
Adjustment Factor
bMD bVA bDC WTP boat
60.76% 56.92% 60.76% $112.29
60.77% 56.93% 60.77% $36.42
60.77% 56.93% 60.77% $93.89
10
11 NMD was estimated for the three boater categories using data on Maryland boat ownership
12 from Lipton (2008) and Lipton (2006). The former data source quantifies sailboat and powerboat
13 ownership for 2007, but it does not break out powerboats according to whether they were
14 trailered or in-water boats. To develop separate estimates for these two subcategories, the
15 proportions reported for 2005 (Lipton, 2006), which indicated that 79.8% of powerboats in
16 Maryland were trailered, were applied. To estimate NVA and NDC, the total number of registered
17 boats in Virginia and DC in 2006 was obtained from the National Marine Manufacturers
18 Association (2008), and this number was augmented by the observed growth rate in Maryland
19 boat ownership from 2006 to 2007. To separate these total numbers into the three categories of
20 boat ownership, the same proportions estimated for Maryland registered boats in each category
21 were applied.
22 The value bMo represents a two-part adjustment to the total number of registered boats in
23 Maryland, as estimated by Lipton (2004). The first converts the total number of registered boats
24 to the total number of boat owners, because some boat owners own more than one boat. The
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 second adjusts for the fact that, for some Maryland boaters, the Chesapeake Bay is not their
2 principal boating area. Every 100 registered boats correspond to an estimated 60.8 boat owners
3 whose principal boating area is the Chesapeake Bay. The same adjustment factor for registered
4 boaters in DC was applied to estimate hoc-
5 To estimate bvA, the expected 6.3% of registered boats in Virginia Beach (Murray and
6 Lucy, 1981), which is the main Virginia coastal area outside the Bay, was first excluded; then the
7 same adjustment factor developed for Maryland and DC was applied. Thus, in Virginia, for
8 every 100 registered boats, there are 56.9 boat owners whose principal boating area is the
9 Chesapeake Bay.
10 4.2.1.2.1 Results: Aggregate Recreational Boating Benefits
11 To apply the previously described framework, it was first assumed that there is a direct
12 one-to-one correspondence between the 5-point El and the 5-point subjective WQs index. Based
13 on this assumption, a 1-unit increase in Chesapeake Bay water quality (&WQ5 = 1 and AE7 = 1)
14 was estimated to yield an annual aggregate benefit of $8.2 million for Maryland, Virginia, and
15 DC boat owners whose principal boating area is the Chesapeake Bay.
16 4.2.1.2.2 Limitations and Uncertainties
17 A potential limitation of the proposed benefit transfer model for boating services is the
18 uncertainty associated with directly translating the WQs index into the El index. Although both
19 metrics are summary 5-point measures of Chesapeake Bay water quality, the first is a subjective
20 index based on boaters' perceptions and experience. These perceptions may be based on
21 observations unrelated to eutrophic conditions (e.g., trash in the water or advisories based on
22 pathogen levels) or boaters may implicitly assign more or less importance to eutrophic
23 conditions than is assigned by the El.
24 The other main source of uncertainty is with the number of affected boaters. As in the
25 recreational fishing model, the affected number of recreators is assumed to be unaffected by the
26 change in water quality. This assumption is likely to lead to an underestimate of the aggregate
27 benefit to boaters of a water quality improvement.
28 One alternative approach is to use value estimates from Bockstael, McConnell, and
29 Strand (1989), who also estimated changes in consumer surplus for trailered boat owners in
30 Maryland resulting from a 20% decrease in the product of total nitrogen and phosphorus (TNP)
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 levels in the Bay. By reseating and updating their estimates to 2007 dollars, the implied average
2 WTP per Maryland trailered boat owner per 1% decrease in TNP is $5.38. Applying this value to
3 the estimated total number of trailered powerboat owners in Maryland, Virginia, and DC (see
4 Table 4.2-1), implies that the aggregate benefits to these boaters per 1% decrease in TNP in the
5 Bay would be $120,000. Assuming that a 24% decrease in nitrogen loadings would result in a
6 24% reduction in TNP levels in the Bay, the resulting estimate of annual aggregate benefits is
7 $2.9 million. The main advantage of this approach compared with the model summarized in
8 Equation (4.5) is that it is based on an objective measure of water quality. The fact that it is
9 based on values estimated through a revealed-preference travel cost model of actual boating
10 behavior, compared with a stated-preference CV approach, may be seen as an advantage.
1 1 However, this approach also has several drawbacks: (1) it is based on considerably older data
12 (from 1984), (2) it only includes direct estimates for trailered boaters, and (3) it includes a
13 potentially narrower measure of value than the Lipton (2004) study because it uses revealed-
14 rather than stated-preference data. This approach also requires the assumption that decreases in
15 nitrogen loads to the Bay are proportional to decreases in TNP levels in the Bay.
16 4.2.1.3 Beach Use
17 To estimate benefits to Chesapeake Bay beach users, the benefit transfer approaches
18 developed by Morgan and Owens (2001) and Krupnick (1988) were adapted and updated. Both
19 of these studies estimated the aggregate benefits to Maryland, Virginia, and DC households of
20 percentage reductions in levels in the Bay. The fundamental benefit transfer model can be
21 summarized as follows:
22 AggBbeach=(WTPbeach*(Nlbl +N2b2rtbeachr^%WQmp, (4.6)
23 where
24 A%WQrNp = percentage change in Chesapeake Bay water quality, expressed in terms
25 of the average TNP levels, each measured in parts per million (ppm);
26 AggBbeach = aggregate annual benefits (in 2007 dollars) to Maryland, Virginia, and
27 DC households for a specified A%WQmp increase in water quality in the
28 Bay;
29 WTP beach = average annual household WTP (in 2007 dollars) per trip for a 1%
30 reduction in TNP levels in the Bay;
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Ni = total number of households in the 1980 Baltimore and DC standard
2 metropolitan statistical areas (SMSA) in 2007;
3 .A/2 = total number of Maryland and Virginia households outside the SMS A in
4 2007;
5 hi = portion of SMSA households with at least one Chesapeake Bay beach trip
6 in the year;
7 &2 = portion of non-SMSA households in Maryland and Virginia with at least
8 one Chesapeake Bay beach trip in the year; and
9 tbeach = average number of Chesapeake Bay beach trips per year for beach-going
10 Maryland, Virginia, and DC households.
11 Table 4.2-6 summarizes value estimates for these model components. Values for
12 WTPbeach were derived using estimates from Bockstael, McConnell, and Strand (1988, 1989).
13 Using data from 408 summer beach users in 1984 at nine Maryland western shore beaches and
14 average county-level summer TNP values, they estimated a varying parameter travel cost model.
15 Based on the model results, they reported aggregate annual consumer surplus gains of $34.66
16 million (in 1987 dollars) for beachgoers residing in the SMSA associated with a 20% decrease in
17 TNP in the Bay. The study also reported that (1) 401,000 SMSA households per year (in the
18 early 1980s) visited Chesapeake Bay beaches and (2) the average number of trips per year for
19 these beach-going households was 4.35,9 which implies that there were an estimated 1,745,000
20 trips to the Bay by SMSA households in 1984. Dividing the aggregate benefit estimate by this
21 number of trips implies an average per-trip benefit of $19.86 (in 1987 dollars), for a 20%
22 reduction in TNP.
9 This number is actually inferred from a description of values Bockstael, McConnell, and Strand (1989) derived
from an alternate model. The value per household user ($4.70) was divided by the value per trip ($1.08) to get
trips per household (4.35).
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 4.2-6. Input Estimates for the Chesapeake Bay Beach-Use Benefit Transfer Model
Beach Use
SMSA
Non-SMSA
Total
Number of
Households
(N)
2,744,217
2,540,214
5,284,431
Percentage of Bay
Beachgoers
(b)
21.00%
3.08%
12.38%
Average Beach
Trips per Year
(0
4.35
4.35
4.35
WTPbeach
$1.81
$1.81
$1.81
1
2 To estimate WTPbeach, the $19.86 estimate was divided by 20 (i.e., it was assumed that
3 each percentage reduction in TNP has the same value), and the estimate was converted to 2007
4 dollars using the CPI to adjust for inflation. The resulting estimate for WTPbeach is $1.81.
5 NI and N2 were estimated using the Census estimates of population by county in 2007,
6 multiplied by the ratio of households to population by county in the 2000 U.S. Census. From this
7 calculation, it was estimated that a total of 5.28 million households are in Maryland, Virginia,
8 and DC, and 2.74 million of these are within the SMSA.
9 For bj, the Bockstael, McConnell, and Strand (1989) estimate that 21% of households in
10 the SMSA take at least one beach trip to the Chesapeake Bay a year was applied. To derive 62,
11 this estimate was combined with data from the 2006 Virginia Outdoors Survey (Virginia
12 Department of Conservation and Recreation, 2007), which reports that 8% of all the households
13 in Virginia take at least one beach trip to the Chesapeake Bay (or other tidal bays) per year.
14 Taken together, these estimates imply that approximately 3% ofnon-SMSA Virginia households
15 take at least one beach trip per year to the Bay. Applying this estimate to Maryland non-SMSA
16 households as well, it was assumed that b2 equals 3%.
17 To estimate tbeach, the Bockstael, McConnell, and Strand (1989) estimate of 4.35 trips per
18 year was applied, recognizing that it is most likely an overestimate for non-SMSA beach-going
19 households.
20 4.2.1.3.1 Results: Aggregate Beach Use Benefits
21 To apply this benefit estimation framework, it was assumed that changes in nitrogen
22 loads to the Bay are directly proportional to changes in average TNP concentrations in the Bay
23 (i.e., a 24% reduction in loadings results in a 24% decline in TNP). It was estimated that the
24 aggregate annual benefits to Maryland, Virginia, and DC Chesapeake Bay beachgoers
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 (AggB beach) per 1% decrease in TNP is $5. 16 million (in 2007 dollars); therefore, the benefit of a
2 24% decrease is $124 million.
3 4.2.1.3.2 Limitations and Uncertainties
4 One of the main limitations of the beach-use valuation model described above is that it is
5 based on value estimates that are from 1984 and, therefore, may be outdated. Beach conditions
6 and recreator preferences in the Bay may have changed significantly since then. In addition,
7 several uncertainties are associated with the estimated number of beach trips by Maryland,
8 Virginia, and DC households in 2007. These estimates are based on limited and, in some cases,
9 relatively old data regarding the percentage of households in each state that use the Bay's
10 beaches and the average number of annual beach trips for those who do. A second limitation is
1 1 that it, again, requires the assumption that decreases in nitrogen loads to the Bay are proportional
12 to decreases in TNP levels in the Bay.
13 4.2.1.4 Aesthetic Services
14 To estimate the benefits of improved aesthetic services due to improvements in
1 5 Chesapeake Bay water quality, a benefit transfer model that is based on estimates of near-shore
16 residents' values for small water-quality changes was developed and applied. The transfer
17 function has the following form:
1 8 AggBhome = ZkMWTPk x AZW* x Nk, (4.7)
19 where
20 AD/TV/t = reduction in dissolved inorganic nitrogen (DIN) levels in the portion of the
21 Chesapeake Bay closest to coastal Census block group k;
22 AggBhome = aggregate annual benefits (in 2007 dollars) to homeowners in all
23 Chesapeake Bay coastal block groups for specified ADINk changes in water
24 quality;
25 Nk = estimated number of specified owner-occupied homes in block group k in
26 2007; and
27 MWTPk = estimated annual marginal WTP (in 2007 dollars) for a 1-unit reduction in
28 water quality, ADINk = 1, in block group k.
29 To parameterize this function, results from a hedonic housing price study by Poor,
30 Pessagno, and Paul (2007) were used. Using data on 1,377 residential home sales from 1993 to
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Appendix 8-82
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 2003 in St. Mary's River watershed in Maryland, this study regressed the natural log of real
2 home prices (in 2003 dollars) against structural, neighborhood, and environmental water quality
3 characteristics. It specifically estimated the effect of differences in DIN (mg/L), as measured by
4 the annual average in the year of sale at the closest water monitoring station, on log home
5 prices.10 The study found a statistically significant effect with a model coefficient estimate of
6 -0.0878.
7 To convert this semielasticity coefficient, which measures the marginal effect of DIN on
8 the log of home price, to MWTPk, which represents the annualized average dollar value of a
9 1-unit reduction in DIN for homes in block group k, the following conversion equation was used:
10 MWTPk =0.0878 *Pk*A(i,T), (4.8)
11 where
12 Pk = average price of specified owner-occupied homes in block group k and
13 A = annualization factor, which is a function of the assumed interest rate (r) and
14 average lifetime of homes in years (I).11 For r = 0.05 and T = 50, A = 0.0522.
15 To implement the model, Chesapeake Bay coastal block groups were defined as those
16 block groups with a Chesapeake Bay coastline, as delineated by the Census block group
17 boundary files (Environmental Systems Research Institute, Inc. [ESRI], 2002), as well as those
18 block groups whose geographic centroids are located within 1 mile of the coast. This second
19 condition was added to ensure that a majority of the included properties are located within
20 roughly 2 miles of the coast. As shown in Figure 4.2-1, 1,066 block groups met these criteria.
21 Within these block groups, the study focused on Census "specified owner-occupied
22 housing units," which include only single-family houses on less than 10 acres without a business
23 or medical office on the property. These properties match best with the types of properties
24 analyzed in the hedonic study described above, and the decennial Census provides both count
25 and property value estimates for these homes. Thirty-six of the identified 1,066 block groups had
26 no specified owner-occupied homes and were excluded from the analysis.
10 A separate model reported in Poor, Pessagno, and Paul (2007) used total suspended solids (mg/L) instead of DIN
as the water quality measure. It was also found to have a statistically significant effect on home prices.
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Appendix 8-83
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 To estimate Nk, the number of specified owner-occupied homes in each block group in
2 2000 was augmented by the growth rate in housing units in the block group's county from 2000
3 to 2007 (U.S. Census Bureau, 2008b).
4 To estimate P/t, the average price of specified owner-occupied homes in 2000 in each
5 block group was adjusted to 2007 using the CPI-Shelter values for Washington-Baltimore, DC-
6 MD-VA-WV.12 Table 4.2-7 summarizes the estimated values for Nk and Pk.
1 4.2.1.4.1 Results: Aggregate Aesthetic Benefits to Near-Shore Residents
8 To approximate the aggregate annual benefits from a 24% reduction in nitrogen loadings
9 to the Bay, the benefit transfer model summarized in Equations (4.7) and (4.8) was applied,
10 assuming uniform 24% reductions in DIN across all Chesapeake Bay waters. Based on 2005
11 monitoring data for the Potomac River estuary, the 25th and 75th percentile values of average
12 DIN levels were 0.46 mg/L and 1.22 mg/L, respectively. Using this range of initial DIN values, a
13 reduction of 24% translates to DIN decreases of between 0.11 mg/L and 0.29 mg/L. It was
14 estimated that these changes would result in aggregate annual benefits of between $38.7 million
15 and $102.2 million to residents of specified owner-occupied homes in the Chesapeake coastal
16 block groups.
12 In the decennial Census, values of specified owner-occupied homes were grouped into ranges of values (e.g.,
from $250,000 to $300,000). With the exception of the highest range, which is $1,000,000 and greater (no upper
bound), the midpoint of each range was used to calculate the mean value for each block group. For the highest
range, a central value of $1,250,000 was selected.
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-84
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Legend
1
2
Ches Bay Coastal BGs
State BGs
Ches Bay Coastline
Miles
Figure 4.2-1. Chesapeake Bay Coastal Block Groups
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Appendix 8-85
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table 4.2-7. Summary of Housing Unit Numbers and Average Prices
State, County
Maryland
Anne Arundel County
Baltimore County
Calvert County
Caroline County
Cecil County
Charles County
Dorchester County
Harford County
Kent County
Prince George's County
Queen Anne's County
St. Mary's County
Somerset County
Talbot County
Wicomico County
Worcester County
Baltimore city
Virginia
Accomack County
Charles City County
Essex County
Gloucester County
Isle of Wight County
James City County
Number
of Coastal
Block
Groups
163
101
25
2
18
6
20
23
13
1
18
29
14
20
7
1
116
9
4
5
16
10
7
in Chesapeake Coastal Block Groups in 2007
Number of Specified Single-
Unit Dwellings per Block
Group (Nk)
Mean
470
339
583
346
408
466
263
397
294
175
584
458
258
431
304
148
168
247
270
255
438
515
817
Std.
Dev.
258
160
380
37
206
208
125
296
105
0
249
195
118
181
129
0
95
93
46
69
248
204
551
Min
13
16
190
310
195
302
46
20
55
175
158
92
81
192
163
148
10
128
219
135
174
292
85
Max
1,604
859
1,957
383
1,088
909
584
926
418
175
1,104
910
528
943
576
148
436
388
340
348
1,063
1,076
1,734
Average Value of Specified Units
per Block Group (Pk)
Mean
$298,348
$154,975
$257,582
$145,589
$206,114
$263,744
$167,317
$187,685
$225,531
$296,739
$277,352
$252,720
$130,391
$341,180
$145,763
$98,596
$106,483
$127,159
$163,344
$175,598
$194,628
$210,389
$306,926
Std. Dev.
$128,985
$54,654
$68,476
$3,499
$41,341
$46,708
$61,534
$39,578
$48,381
$0
$73,004
$41,564
$30,635
$162,586
$29,367
$0
$52,800
$56,731
$43,411
$52,691
$40,091
$42,265
$173,652
Min
$81,409
$65,209
$174,459
$142,091
$138,614
$196,436
$86,191
$116,187
$143,163
$296,739
$153,496
$174,343
$77,591
$128,864
$110,355
$98,596
$23,628
$69,539
$104,671
$132,168
$127,058
$136,806
$88,149
Max
$686,879
$343,455
$498,410
$149,088
$270,798
$319,093
$310,723
$303,797
$306,886
$296,739
$478,396
$317,879
$181,198
$658,874
$186,045
$98,596
$345,380
$236,974
$227,234
$278,618
$285,437
$273,345
$569,755
2nd Draft Pvisk and Exposure Assessment
June 5, 2009
Appendix 8-86
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
State, County
King and Queen County
King George County
King William County
Lancaster County
Mathews County
Middlesex County
New Kent County
Northampton County
Northumberland County
Prince George County
Richmond County
Surry County
Westmoreland County
York County
Chesapeake city
Hampton city
Newport News city
Norfolk city
Poquoson city
Portsmouth city
Suffolk city
Virginia Beach city
Number
of Coastal
Block
Groups
2
3
1
11
4
9
1
9
9
4
3
2
13
14
29
53
40
109
10
53
5
18
Number of Specified Single-
Unit Dwellings per Block
Group (Nk)
Mean
200
227
398
325
700
301
497
267
363
362
307
317
311
549
347
301
281
168
348
296
1,426
391
Std.
Dev.
7
175
0
78
335
88
0
48
124
220
33
67
83
257
200
149
287
118
150
242
644
210
Min
193
3
398
145
279
138
497
178
219
88
276
250
181
97
38
17
5
7
156
14
545
14
Max
206
431
398
447
1,131
415
497
365
670
591
353
384
458
1,114
747
810
1,374
528
733
1,031
2,198
829
Average Value of Specified Units
per Block Group (Pk)
Mean
$121,113
$193,030
$128,864
$255,256
$203,309
$205,784
$188,507
$151,509
$233,922
$197,887
$172,231
$172,904
$161,391
$239,291
$140,654
$134,117
$122,332
$168,857
$226,740
$115,948
$214,610
$218,105
Std. Dev.
$3,296
$31,483
$0
$51,809
$28,957
$36,392
$0
$34,176
$35,330
$46,549
$19,788
$12,214
$32,705
$54,067
$51,189
$43,962
$59,341
$98,567
$58,274
$40,479
$35,399
$81,187
Min
$117,817
$151,588
$128,864
$157,850
$153,167
$146,531
$188,507
$100,355
$182,242
$144,641
$157,244
$160,690
$98,923
$123,412
$61,354
$67,363
$37,130
$51,507
$132,643
$54,008
$168,310
$132,151
Max
$124,409
$227,847
$128,864
$339,499
$220,637
$249,250
$188,507
$214,862
$303,884
$269,254
$200,192
$185,119
$228,513
$333,513
$261,780
$269,833
$316,437
$590,748
$311,350
$243,782
$254,324
$396,073
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June 5, 2009
Appendix 8-87
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1 4.2.1.4.2 Limitations and Uncertainties
2 Many of the limitations and uncertainties surrounding this benefit transfer model are
3 associated with the limitations and uncertainties inherent in the hedonic "implicit price" estimate,
4 MWTPk. From a strictly conceptual standpoint, the hedonic implicit price provides a correct
5 measure of the welfare gains to residents of relatively small and localized improvements in the
6 amenity, in this case changes in DIN water quality. However, caution is required when using this
7 implicit price to estimate the benefits of either a large water-quality change or a change that
8 affects many housing consumers. The accuracy of the benefit transfer model summarized by
9 Equation (4.7) will tend to decline as the value ofADINk increases and as ^increases. This is
10 because changes that are larger and that affect more consumers are also more likely to cause
11 shifts in the housing market, resulting in potentially large transaction (e.g., moving) costs and
12 changes in the market price equilibrium. Nevertheless, Bartik (1988) has shown that, under many
13 common conditions, models such as Equation (4.7) can be interpreted as providing an upper-
14 bound estimate of aggregate benefits.
15 From an empirical standpoint, there are other potential limitations and uncertainties. First,
16 there are potential errors in the hedonic parameter estimate. For example, DIN may be correlated
17 with other influential housing or neighborhood characteristics that are not included in the
18 hedonic model, in which case the parameter estimate is likely to overstate the implicit price of
19 DIN. Second, for this benefit transfer model, it was assumed that the Census block groups along
20 the Chesapeake Bay coast represent the areas in which the hedonic estimates can most
21 reasonably be applied; however, this spatial extrapolation has inherent limitations. In particular,
22 the implicit price estimates are expected to be less accurate as a measure of WTP in areas that are
23 farther from the hedonic study area (e.g., St. Mary's River watershed), particularly areas that are
24 more urban and densely populated. By excluding homes in other noncoastal Census block groups
25 that are also near the Bay, the benefit transfer model is also likely to exclude some beneficiaries
26 of improved aesthetic services and, therefore, underestimate aggregate benefits. Third, the
27 implicit price was measured using data on individual homes and water quality measures within at
28 most a few miles from these homes; however, the model summarized in Equation (4.7) uses
29 properties aggregated at the Census block group level and (most likely) more spatially averaged
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1 water quality. These differences are likely to reduce the accuracy of applying Equation (4.7) to
2 estimate benefits.
3 It is also important to recognize the expected overlap in ecosystem services captured by
4 the hedonic implicit price estimates and the WTP estimates summarized in Section 4.2. In
5 principle, the hedonic price estimate includes residents' values for all of the use-related services
6 they receive that depend on water quality. Therefore, in addition to capturing the aesthetic
7 services received by living near the Bay, the hedonic implicit price should include values for
8 recreational services received by near-shore residents. Unfortunately, the hedonic estimates do
9 not provide separate value estimates for these different use-related services. Decomposing the
10 value estimates into separate use-related categories requires additional assumptions, data, or
1 1 analysis.
12 Finally, to specify reductions in DIN levels across the Bay resulting from a 24%
13 reduction in nitrogen loadings, strong assumptions were made that DIN levels decline by the
14 same percentage. In addition, to address variation in initial DIN levels across the Bay, this
15 percentage reduction was applied to the range (25th to 75th percentile) of recently observed
16 values in the Potomac River estuary.
17 4.2.1.5 Nonuse Services
18 Some of the ecosystem services provided by the Chesapeake Bay may be independent of
19 individuals' recreational or other specific uses of the estuary. Measuring values for these nonuse
20 services is more difficult and involves more uncertainty than for recreational and aesthetic
21 services. Nevertheless, several stated-preference studies have estimated water quality values
22 using sample populations that include nonusers. Evidence from these studies indicates that,
23 compared with users of water resources, nonusers have significantly lower but still positive WTP
24 for water quality improvements. Based on this evidence, the following simple benefit transfer
25 equation was specified for estimating nonuse benefits:
26 AggBNU = NNU * WTPNU (AWQW ) , (4.9)
27 where
28 AWQio = change in Chesapeake Bay water quality, expressed on a 10-point
29 rating scale;
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1 AggBNU = aggregate annual benefits (in 2007 dollars) to nonusers of the
2 Chesapeake Bay in Maryland, Virginia, and DC for a specified
3 AWQio increase in water quality;
4 WTPNU(AWQio) = average annual WTP (in 2007 dollars) per nonuser, as a function of
5 the AWQw increase in water quality; and
6 NNU = total number of nonusers in Maryland, Virginia, and DC in 2007.
7 To estimate the WTP^u function, results from two meta-analytic studies summarizing
8 evidence from the water quality valuation literature were used. The first, Johnston et al. (2005),
9 included 81 WTP estimates from 34 stated-preference studies. Although these studies addressed
10 a wide variety of water quality changes, for the meta-analysis, they were all converted to a 10-
1 1 point index (where 0 and 10 represent the worst and best possible water quality, respectively)
12 based on the "Resources for the Future (RFF) water quality ladder" (Vaughan, 1986). The meta-
13 analysis regressed average WTP estimates on water quality measures (baseline and change),
14 characteristics of the water resource and study population, and several study method descriptors.
15 The resulting WTP function can be simplified and summarized as follows:13
"2.45 + (0.6827 * \n(AWQw )) - (0. 129 * WQwbase )"
16 WTPNU = exp
17 where
(0.005 *INC/CP02)
*CP02, (4.10)
18 WQwbase = baseline Chesapeake Bay water quality, expressed on the 10-point rating
19 scale;
20 INC = average annual household income of Maryland, Virginia, and DC nonusers
21 in 2007; and
22 CP02 = price adjustment factor for 2002 to 2007.
23 The second study, Van Houtven, Powers, and Pattanayak (2007), conducted a similar
24 meta-analysis using a somewhat different sample of studies (18 studies, including 11 for
25 freshwater resources) and WTP estimates (131). A 10-point index based on the RFF ladder was
13 The function is a simplified version of the translog unweighted parameter estimate model (Model 2) in Johnston
et al. (2005). This model includes several explanatory variables and coefficients, which are summarized in the
constant term (2.45). To derive this constant, values were assigned to the other explanatory variables as follows:
year is 2007 (yearjndex = 37), study method is a dichotomous choice through a personal interview
(discrete_ch = 1 and interview = 1) to a nonuser-only population (nonusers = 1) with a high response rate
(hi_response = 1), protest and outlier bids are excluded (protest_bids = 1 and outlier jbids = 1), and the species
benefiting from the water quality change are unspecified (InWQnon = lnwq_change).
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1 also used to convert water quality changes to a common scale. The resulting WTP function from
2 this study can also be simplified and summarized as follows:14
"-1.197 + (0.823 *
WTPNU = exp
+ (0.8969 *ln(/M:/CPOO))
*Q300, (4.11)
4 where
5 CPOO = price adjustment factor for 2000 to 2007.
6 Using these functions, WTPNU can be estimated for selected values ofAWQio, WQ whose,
1 and INC. To estimate WTP^u for a 1-unit change in the 5-point El scale, it was assumed that the
8 El scale is directly proportional to the 10-point WQw scale. In other words, it was assumed that
9 El = 1 is equivalent to WQwbase = 2, and a 1-unit increase in El is equivalent to AWQio = 2. For
10 INC, U.S. average household income in 2007 of $67,610 was used (U.S. Census Bureau, 2008a).
11 Based on these inputs, WTPNU for a 2-unit change in water quality is estimated to be $16.33
12 using the Johnston et al. (2005) function and $27.75 using the Van Houtven, Powers, and
13 Pattanayak (2007) function.
14 Estimates of the percentage of Maryland, Virginia, and DC residents who are nonusers of
15 the Chesapeake Bay are not readily available; however, they can be roughly approximated from
16 recreational participation statistics for the area. For example, data from the 2006 Virginia
17 Outdoors Survey suggest that (1) 92% of households in Virginia did not take any beach trips to
18 the Chesapeake Bay, (2) 84% did not engage in saltwater fishing, and (3) 92% did not engage in
19 powerboating. Assuming that these proportions represent independent probabilities of nonuse,
20 then the combined probability (proportion) of nonuse for these primary activities is roughly 70%.
21 Applying this percentage to the Maryland, Virginia, and DC population in 2007, which was
22 13,918,727, suggests that the number of nonusers (NNU) is approximately 9,743,109.
14 This function is a simplified version of the parsimonious log-linear model in Table 5 in Van Houtven, Powers,
and Pattanayak (2007). This model also includes several explanatory variables and coefficients, which are
summarized in the constant term (-1.197). To derive this constant in a way that is consistent with the previous
function, values were assigned to the other explanatory variables as follows: year is 2007 (studyyr73 = 34), study
method is a personal interview (inperson = 1) to a nonuser-only population (pctuser = 0) with a high response rate
(responserate = 100), publication outlet is peer reviewed (dpubjrlbk = 1), and the water quality change is not
expressed in terms of recreational uses (InwqlOchru = 0).
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1 4.2.1.5.1 Results: Aggregate Nonuse Benefits
2 Applying these estimates to the benefit transfer models summarized in Equations (4.9)
3 through (4.11), the aggregate annual nonuse benefits of a 1-unit improvement in the El scale
4 (kWQw = 2) are estimated to range from $159.1 million to $270.4 million.
5 4.2.1.5.2 Limitations and Uncertainties
6 As with the recreational boating services model described in Section 4.2.1.2, one of the
7 main practical limitations of applying these meta-analysis models is the water quality index used.
8 Translating changes in the El scale to the WQw metric requires strong assumptions. Another
9 inherent limitation of using the meta-analytic models as benefit transfer functions is their lack of
10 sensitivity to the spatial scale of water quality changes.
11 In addition to the limitations that primarily contribute uncertainty in the WTPNU
12 estimates, there is also significant uncertainty associated with the measurement ofNmj. First,
13 defining criteria for distinguishing users and nonusers of the Bay is somewhat inherently
14 subjective. Second, statistics on overall rates of visitation and use of the Bay by Maryland,
15 Virginia, and DC households are not readily available.
16 A final caveat for this approach to estimating nonuse values for water quality
17 improvements in the Bay is that, by design, it only includes nonuse values for nonusers.
18 However, it is not unreasonable to suspect that users also benefit to some extent from nonuse
19 services from the Bay. Whereas these types of nonuse values are likely to be captured in, for
20 example, the Lipton (2004) WTP values for boaters used in Equation (4.5), they are not included
21 in the benefit estimates in Equations (4.4), (4.6), and (4.7) for recreational anglers, beach users,
22 and residents, respectively.
23 4.2.2 Neuse River Estuary
24 To analyze changes in ecosystem services for the Neuse River, the results of the Neuse
25 River/Neuse Estuary Case Study were applied. This case study concluded that atmospheric
26 deposition contributes 26% (1.15 million kg nitrogen/year) of total nitrogen loadings to the
27 estuary. In contrast to the Potomac River/Potomac Estuary Case Study, it estimates that a much
28 larger reduction in nitrogen loadings than this 26% would be required to improve the Neuse
29 River estuary from "bad" to "poor." Therefore, for this analysis, the change in selected
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1 ecosystem services associated with a 26% reduction (100% of atmospheric deposition) in
2 nitrogen loadings to the Neuse estuary was estimated.
3 4.2.2.1 Provisioning Services from the Blue Crab Fishery
4 As discussed in Section 4.1.1, there are few examples of empirical bioeconomic models
5 that link changes in nutrient-related water quality to changes in productivity of commercial
6 fisheries; however, one exception is a study by Smith (2007). This study, which is applied to the
7 Neuse River estuary, estimated the dynamic effects of a 30% reduction in nitrogen loads to the
8 estuary on blue crab stocks, commercial catch levels, and the producer and consumer surplus
9 derived from this fishery.
10 Smith (2007) applied a two-patch predator-prey model that incorporated both direct and
11 indirect effects of hypoxia (i.e., low DO) on blue crab communities. Direct effects include the
12 movement of blue crab to water habitats with higher DO content. Indirect effects include the
13 dying off of blue crab prey. The model compares producer and consumer surplus changes under
14 the existing open-access institutional structure to a 30% reduction of nitrogen loadings in the
15 same structure. The model was parameterized using results and estimates derived from several
16 other studies. To address uncertainty, the values of three key parameters—economic speed of
17 adjustment under open-access conditions, biological spatial connectivity, and price elasticity of
18 demand—were each allowed to take on three different values. For a 30% reduction in nitrogen
19 loadings to the estuary, the present value (100-year time horizon and 4.5% discount rate) of
20 producer benefits ranged from $0.7 million to $5.9 million (in 2002 dollars), and the present
21 value of consumer surplus ranged from $3.15 million to $425.20 million. The combined present
22 value of producer and consumer surplus changes was estimated to range from $3.8 to $31.0
23 million.
24 To estimate the annual aggregate benefits from the blue crab fishery due to a 26%
25 reduction in nitrogen loads, (1) the results reported in Smith (2007) were rescaled by the
26 percentage difference between 30% and 26%, (2) the benefit estimates (using the 100-year
27 horizon and 4.5% discount rate) were annualized, and (3) the estimates were converted to 2007
28 dollars using the CPI.
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1 4.2.2.1.1 Results: Aggregate Benefits from the Blue Crab Fishery
2 Applying this modeling framework, the aggregate annual benefits to Neuse River crab
3 fishers and consumers from a 26% reduction in nitrogen loadings is estimated to range from
4 $0.12 million to $1.01 million.
5 4.2.2.1.2 Limitations and Uncertainties
6 The large range of the benefit estimates reported above reflects uncertainty in three key
7 model parameters—economic speed of adjustment under open-access conditions, biological
8 spatial connectivity, and price elasticity of demand. However, the model includes at least 16
9 other parameters whose values are drawn from other studies; thus, the overall uncertainty in
10 these benefit estimates is most likely understated by this range. In addition, by simply reseating
11 the results reported in Smith (2007) to address a 26% rather than a 30% reduction in nitrogen
12 loads, it was assumed that benefits are directly proportional to the percentage reduction in
13 nitrogen loads. This assumption adds additional (albeit, most likely small) uncertainty to the
14 reported benefit estimates.
15 4.2.2.2 Recreational Fishing Services
16 To estimate the benefits from improvements in recreational fishing services due to
17 reductions in nitrogen loadings to the Neuse, a benefit transfer model originally developed to
18 assess the nutrient-reduction benefits of EPA's effluent guidelines for Consolidated Animal
19 Feeding Operations (CAFOs) (EPA, 2002) was applied. For that analysis, EPA conducted a case
20 study evaluating the potential economic benefits of a reduction in nutrient loadings via changes
21 in recreational fishing opportunities in North Carolina's Albemarle and Pamlico Sounds (APS)
22 estuary (Van Houtven and Sommer, 2002). The Neuse River estuary is a subestuary within the
23 APS system.
24 To estimate the value of reductions in nitrogen loads, the APS case study relied on
25 economic value estimates obtained from two related studies—Kaoru (1995) and Kaoru, Smith,
26 and Liu (1995). Both studies used recreational data obtained from a 1981-1982 intercept survey
27 of recreational fishermen conducted at 35 boat ramps or marinas within the APS estuary.
28 Kaoru (1995) used a three-level nested random utility model (RUM), which broke the
29 recreational fishing decision into three stages: a decision on the duration of the trip (1, 2, 3, or
30 more than 3 days), a decision on which of the five regions to visit, and a decision on which of the
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1 individual sites within the region to visit. The impact of nitrogen (and phosphorus) loadings was
2 specifically investigated in the second stage of the decision process (regional choice). A 25%
3 reduction in nitrogen loadings for the entire APS estuary resulted in a benefit estimate of $4.70
4 (in 1982 dollars) per person-trip.
5 Kaoru, Smith, and Liu (1995) also used a RUM approach to estimate the value of
6 improving water quality. First, a household production function (HPF) was estimated to predict
7 expected catch rates for individuals based on variables such as equipment used; effort exerted;
8 and the physical characteristics of the fishing site, including pollutant loadings. Second, the HPF
9 model was used to predict the impact of a 36% reduction in nitrogen loadings on expected catch
10 rates. The estimated values ranged from $0.76 to $6.52 (in 1982 dollars) per person-trip.
1 1 Based on a systematic review of the value estimates reported in these studies, the CAFO
12 case study selected three estimates to include in the benefit transfer model — $4.70 per person-
13 trip, for a 25% reduction in nitrogen loads (Kaoru, 1995) and $3.95 and $6.52 per person, for a
14 36% reduction (Kaoru, Smith, and Liu, 1995).
15 To apply these estimates, they were converted to comparable units. First, they were
16 converted to 2007 dollars using the CPI. Second, they were rescaled to values per 1% reduction
17 in loadings (i.e., dividing by 25 and 36, respectively). The resulting three unit values are $0.40,
18 $0.24, and $0.39 per person-trip per 1% reduction in nitrogen loads to the APS.
19 A further adjustment is necessary to convert these values into per-ton units. According to
20 Kaoru (1995), the average nitrogen load to the APS estuary at the time the study was conducted
21 was 1,741 tons per bordering county per year, which translates to a total of 22,633 tons of
22 nitrogen loadings per year because of the 13 counties bordering the APS estuary in North
23 Carolina. The resulting three unit values are $0.0018, $0.0010, and $0.0017 per person-trip per
24 1-ton reduction in nitrogen loads to the APS.
25 To estimate the aggregate annual recreational fishing benefits of total reductions in
26 nitrogen loads to the APS estuary, the following benefit transfer equation was specified:
27 AggBAPsfish= V*AL*T, (4.12)
28 where
29 AggBAPsfish = the aggregate annual recreational fishing benefits from reductions in
30 nitrogen loads to the APS estuary (in 2007 dollars),
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1 V = the annual per trip value per unit (either in tons per year or percentage)
2 reduction in nitrogen (in 2007 dollars),
3 AL = reduction in nitrogen loadings (either in tons per year or percentage) to
4 the APS estuary, and
5 T = the total number of annual fishing trips to the APS estuary (person-trips
6 per year).
7 Although the unit value (F) estimates derived from Kaoru (1995) and Kaoru, Smith, and
8 Liu (1995) are based on data only for boating anglers, it was assumed that they apply to all
9 recreational fishing trips (7) in the APS. Data on visitation rates for recreational anglers in the
10 APS estuary are available from the MRFSS, which contains information on the number, type,
11 and destination of recreational fishers for several coastal regions in the United States. For 2006,
12 the MRFSS data provide an estimate of 753,893 person-trips to the APS for recreational fishing.
13 4.2.2.2.1 Results: Aggregate Recreational Fishing Benefits
14 As noted above, the findings of the Neuse River/Neuse Estuary Case Study indicate that
15 eliminating atmospheric deposition of nitrogen to the Neuse watershed would reduce nitrogen
16 loads to the Neuse estuary (and, thus, the APS estuary as well) by 1.15 million kg per year,
17 which is equivalent to 1,268 tons of nitrogen per year. Assuming that annual recreational fishing
18 levels in the APS remain at 2006 levels and applying Equation (4.12), the resulting aggregate
19 annual benefits (AggBAPsftsh) of such a reduction are estimated to be between $1.0 million and
20 $1.7 million.
21 If the Neuse case study results regarding the portion of nitrogen loadings attributable to
22 atmospheric deposition (26%) are extended to the entire APS system, then this extrapolation
23 implies that eliminating all atmospheric nitrogen loads to the APS watershed would also reduce
24 annual nitrogen loads to the APS estuary by 26%. Applying Equation (4.11) to this scenario
25 suggests that the aggregate recreational fishing benefits of zeroing out nitrogen deposition in the
26 entire APS watershed would be between $4.6 million and $7.9 million.
27 4.2.2.2.2 Limitations and Uncertainties
28 The following limitations and uncertainties should be considered when interpreting these
29 recreational fishing benefit estimates. First, the value estimates are based on fishing activity data
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1 that are more than 2 decades old. The analysis assumes that the benefits of water quality changes
2 have remained constant (in real terms) over this period.
3 Second, the value estimates obtained from the two existing studies were based on
4 percentage reductions in nutrients that were uniform across the APS estuary. By converting these
5 estimates into per-ton terms and applying them only to the Neuse River nitrogen load reductions,
6 the analysis implicitly assumes that average per-trip benefits do not vary with respect to the
7 spatial distribution of the loadings reductions.
8 Third, the original value estimates are based on data only from boat fishermen; however,
9 the analysis assumes that these values are appropriate for both boat and nonboat fishers.
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21 Grant Extension Program. Available at
22 ftp://ftp.mdsg.umd.edu/Public/MDSG/rec_boat05.pdf.
23 Lipton, D. 2008. "Economic Impact of Maryland Boating in 2007." University of Maryland Sea
24 Grant Extension Program. Available at
25 ftp://ftp.mdsg.umd.edu/Public/MDSG/rec_boat07.pdf.
2nd Draft Risk and Exposure Assessment June 5, 2009
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1 Lipton, D.W., and R.W. Hicks. 1999. "Linking Water Quality Improvements to Recreational
2 Fishing Values: The Case of Chesapeake Bay Striped Bass." In Evaluating the Benefits of
3 Recreational Fisheries. Fisheries Centre Research Reports 7(2), TJ. Pitcher, ed., pp.
4 105-110. Vancouver: University of British Columbia.
5 Lipton, D.W., and R.W. Hicks. 2003. "The Cost of Stress: Low Dissolved Oxygen and the
6 Economic Benefits of Recreational Striped Bass Fishing in the Patuxent River." Estuaries
1 26(2A):310-315.
8 Massey, M., S. Newbold, and B. Gentner. 2006. "Valuing Water Quality Changes Using a
9 Bioeconomic Model of a Coastal Recreational Fishery." Journal of Environmental
10 Economics and Management 52:482-500.
11 Millennium Ecosystem Assessment (MEA). 2005. Ecosystems and Human Well-being:
12 Wetlands and Water. Synthesis. A Report of the Millennium Ecosystem Assessment.
13 Washington, DC: World Resources Institute.
14 Mistiaen, J.A., I.E. Strand, and D. Lipton. 2003. "Effects of Environmental Stress on Blue Crab
15 (Callinectes sapidus) Harvests in Chesapeake Bay Tributaries." Estuaries 26(2a): 316-
16 322.
17 Morgan, C., and N. Owens. 2001. "Benefits of Water Quality Policies: The Chesapeake Bay."
18 Ecological Economics 39:271-284.
19 Murray, T., and J. Lucy. 1981. "Recreational Boating in Virginia: A Preliminary Analysis."
20 Special Report in Applied Marine Science and Ocean Engineering No. 251. Available at
21 http://www.vims.edu/GreyLit/VIMS/sramsoe251 .pdf
22 National Marine Manufacturers Association (NMMA). 2008. "2007 Recreational Boating
23 Statistical Abstract." Available at
24 http://www.nmma.org/facts/boatingstats/2007/files/Abstract.pdf.
25 National Oceanic and Atmospheric Administration (NOAA). (2007, August). "Annual
26 Commercial Landing Statistics." Available at http://www.st.nmfs.noaa.gov/
27 stl/commercial/landings/annual_landings.html.
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1 National Oceanic and Atmospheric Administration (NOAA). "Recreational Fisheries."
2 Available at www.st.nmfs.noaa.gov/stl/recreational/overview/overview.html. Last
3 updated February 12, 2009.
4 Parsons, G., A.O. Morgan, J.C. Whitehead, and T.C. Haab. 2006. "The Welfare Effects of
5 Pfiesteria-Related Fish Kills: A Contingent Behavior Analysis of Seafood Consumers."
6 Agricultural and Resource Economics Review 3 5 (2): 1 -9.
7 Poor, P.I, K.L. Pessagno, and R.W. Paul. 2007. "Exploring the Hedonic Value of Ambient
8 Water Quality: A Local Watershed-Based Study." Ecological Economics 60:797-806.
9 Smith, M.D. 2007. "Generating Value in Habitat-Dependent Fisheries: The Importance of
10 Fishery Management Institutions." Land Economics 83(l):59-73.
11 Sweeney, J. 2007. "Impacts of CAMD 2020 CAIR on Nitrogen Loads to the Chesapeake Bay."
12 University of Maryland, Chesapeake Bay Program Office.
13 U.S. Census Bureau. 2008a. "Annual Social and Economic (ASEC) Supplement." Available at
14 http://pubdb3.census.gov/macro/032008/hhinc/new02_001.htm.
15 U.S. Census Bureau. 2008b. "Housing Unit Estimates for Counties of MD and VA: April 1/2000
16 to July 1/2007." Available at http://www.census.gov/popest/housing/HU-EST2007-
17 CO.html.
18 U.S. Environmental Protection Agency (EPA). 2002. December. Environmental and Economic
19 Benefit Analysis of Final Revisions to the National Pollutant Discharge Elimination
20 System Regulation and the Effluent Guidelines for Concentrated Animal Feeding
21 Operations (EPA-821-R-03-003). Washington, DC: U.S. Environmental Protection
22 Agency, Office of Water, Office of Science and Technology.
23 Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and R.E.
24 Turner. 2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric Perspective.
25 Washington, DC: American Geophysical Union.
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1 Van Houtven, G. and A. Sommer. December 2002. Recreational Fishing Benefits: A Case Study
2 of Reductions in Nutrient Loads to the Albemarle-Pamlico Sounds Estuary. Final Report.
3 Prepared for the U.S. Environmental Protection Agency. Research Triangle Park, NC:
4 RTI Internati onal.
5 Van Houtven, G.L., J. Powers, and S.K. Pattanayak. 2007. "Valuing Water Quality
6 Improvements Using Meta-Analysis: Is the Glass Half-Full or Half-Empty for National
7 Policy Analysis?" Resource and Energy Economics 29:206-228.
8 Vaughan, WJ. 1986. "The Water Quality Ladder." Included as Appendix B in R.C. Mitchell,
9 and R.T. Carson, eds. The Use of Contingent Valuation Data for Benefit/Cost Analysis in
10 Water Pollution Control. CR-810224-02. Prepared for the U.S. Environmental Protection
11 Agency, Office of Policy, Planning, and Evaluation.
12 Virginia Department of Conservation and Recreation. 2007. "2006 Virginia Outdoors Survey."
13 Available at
14 http://www.dcr.virginia.gov/recreati onal_planning/documents/vopsurvey06.pdf
15 Whitehead, J.C., T.C. Haab, and G.R. Parsons. 2003. "Economic Effects of Pfiesteria." Ocean &
16 Coastal Management 46(9-10):845-858.
17
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i 5. TERRESTRIAL ENRICHMENT
2 Terrestrial enrichment occurs when terrestrial ecosystems receive nitrogen loadings in
3 excess of natural background levels, either through atmospheric deposition or direct application.
4 Evidence presented in the Integrated Science Assessment (Environmental Protection agency
5 [EPA], 2008) supports a causal relationship between atmospheric nitrogen deposition and
6 biogeochemical cycling and fluxes of nitrogen and carbon in terrestrial systems. Furthermore,
7 evidence summarized in the report supports a causal link between atmospheric nitrogen
8 deposition and changes in the types and number of species and biodiversity in terrestrial systems.
9 The Terrestrial Nutrient Enrichment Case Study focuses on the coastal sage scrub (CSS)
10 ecosystem and San Bernardino and Sierra Nevada mixed conifer forests (MCF), both located in
11 California. CSS is a unique and endemic ecosystem that provides habitat to several threatened
12 and endangered species. Additionally, CSS is generally less fire prone than the nitrophyllous
13 species that tend to amass dominance in abundance and richness with increased nutrient
14 enrichment. MCF provide habitat for animals as well as contribute other ecosystem services such
15 as timber, recreation, and water cycling. Nitrogen enrichment occurs over a long time period; as
16 a result, it may take as much as 50 years or more to see changes in ecosystem conditions and
17 indicators. This long time scale also affects the timing of the ecosystem service changes.
18 The Terrestrial Nutrient Enrichment Case Study differs from the other case studies in that
19 it focuses on geographic information system (GIS) analyses and existing nitrogen loading
20 threshold investigations as the basis for describing endpoints. The CSS investigation analyzed
21 GIS data in conjunction with the results from Community Multiscale Air Quality (CMAQ) 2002
22 modeling to assess the relationship between atmospheric nitrogen deposition and changes in the
23 CSS ecosystem. In the San Bernardino and Sierra Nevada MCF, nitrogen loading thresholds
24 obtained in situ and through simulation modeling were investigated for potential endpoints and
25 applicability to the San Bernardino and Sierra Nevada MCF system.
26 5.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES
27 The ecosystem service impacts of terrestrial nutrient enrichment include primarily
28 cultural and regulating services. In CSS, concerns focus on a decline in CSS and an increase in
29 nonnative grasses and other species, impacts on the viability of threatened and endangered
30 species associated with CSS, and an increase in fire frequency. Changes in MCF include changes
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1 in habitat suitability and increased tree mortality, increased fire intensity, and a change in the
2 forest's nutrient cycling that may affect surface water quality through nitrate leaching (EPA,
3 2008).
4 Both CSS and MCF are located in areas of California valuable for housing, recreation,
5 and development. CSS runs along the coast through densely populated areas of California (see
6 Figure 5.1-1). From Figure 5.1-2, MCF covers less densely populated areas that are valuable for
7 recreation. The proximity of CSS and MCF to population centers and recreational areas and the
8 potential value of these landscape types in providing regulating ecosystem services suggest that
9 the value of preserving CSS and MCF to California could be quite high. The value that
10 California residents and the U.S. population as a whole place on CSS and MCF habitats is
11 reflected in the various federal, state, and local government measures that have been put in place
12 to protect these habitats. Threatened and endangered species are protected by the Endangered
13 Species Act. The State of California passed the Natural Communities Conservation Planning
14 Program (NCCP) in 1991, and CSS was the first habitat identified for protection under the
15 program (see www.dfg.ca.gov/habcon/nccp). Figure 5.1-3 shows the boundaries of the NCCP
16 region and subregions for CSS. Private organizations such as The Nature Conservancy, the
17 Audubon Society, and local land trusts also protect and restore CSS and MCF habitat. According
18 to the 2005 National Land Trust Census Report (Land Trust Alliance, 2006), California has the
19 most land trusts of any state with a total of 1,732,471 acres either owned, under conservation
20 easement, or conserved by other means.
21 5.1.1 Cultural
22 The primary cultural ecosystem services associated with CSS and MCF are recreation,
23 aesthetic, and nonuse values. The possible ecosystem service benefits from reducing nitrogen
24 enrichment in CSS and MCF are discussed below, and a general overview of the types and
25 relative magnitude of the benefits is provided.
26 CSS, once the dominant landscape type in the area, is a unique ecosystem that provides
27 cultural value to California and the nation as a whole. Culturally, the remaining patches of CSS
28 contain a number of threatened and endangered species, and patches of CSS are present in a
29 number of parks and recreation areas. More generally the patches of CSS represent the iconic
30 landscape type of Southern California and serve as a reminder of what the area looked like
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
3
4
5
predevelopment. Changes that might impact cultural ecosystem services in CSS resulting from
nutrient enrichment potentially include
• decline in CSS habitat, shrub abundance, and species of concern;
• increased abundance of nonnative grasses and other species; and
• increase in wildfires.
6
7
HHI Coastal Sage Scrub 2002
Block Groups
Projected 2007 Population
0 - 1.500
1.501 -3,000
3.O01 - 6.000
^^B 6.OO1 - 1 5,OOO
^^B 15.001 - 38.798
Figure 5.1-1. Coastal Sage Scrub Areas and Population
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:
f,
T " •"
\
i
2
Legend
|^H Mixed Conifer Forest
Block Groups
Projected 2007 Population
0-1,500
1,501 -3,000
3,001 - 6,000
^B 6,001 -15,000
^H 15,001 - 38,798
Figure 5.1-2. Mixed Conifer Forest Areas and Population
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1
2
3
4
5
San Bernardino
San Bernardino
~" L_
Riverside
Riverside .
Hemet
I I NCCP Region
Subregional Planning Areas
Camp Pendleton Resource Management Plan
Coastal/Central Orange County NCCP
Northern Orange County Subregion
PalosVerdes Peninsula NCCP
San Bernardino Valley-wide Multi-Species Habitat Conservation Plan
San Diego Multiple Habitat Conservation and Open Space Program (MHCOSP)
San Diego Multiple Species Conservation Program (MSCP)
San Diego Multiple Habitat Conservation Program (MHCP)
San Diego Northern MSCP Subarea
Southern Orange County NCCP
Western Riverside County Multiple Species Habitat Conservation Plan
Figure 5.1-3. Boundaries of the NCCP Region and Subregions for Coastal Sage Scrub
(Source: California Department of Fish and Game, n.d.).
For MCF, the changes from nutrient enrichment that might impact cultural ecosystem
services include
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1 • change in habitat suitability and increased tree mortality and
2 • decline in MCF aesthetics.
3 5.1.1.1 Recreation
4 CSS and MCF are found in numerous recreation areas in California. Three national parks
5 and monuments in California contain CSS, including Cabrillo National Monument, Channel
6 Islands National Park, and Santa Monica National Recreation Area. All three parks showcase
7 CSS habitat with educational programs and information provided to visitors, guided hikes, and
8 research projects focused on understanding and preserving CSS. Together a total of 1,456,879
9 visitors traveled through these three parks in 2008. MCF is highlighted in Sequoia and Kings
10 Canyon National Park, Yosemite National Park, and Lassen Volcanic National Park, where a
11 total of 5,313,754 people visited in 2008. Figure 5.1-4 maps national and state parkland against
12 MCF areas.
13 In addition, numerous state and county parks encompass CSS and MCF habitat. Visitors
14 to these parks engage in activities such as camping, hiking, attending educational programs,
15 horseback riding, wildlife viewing, water-based recreation, and fishing. For example,
16 California's Torrey Pines State Natural Reserve protects CSS habitat (see
17 http://www.torreypine.org/).
18 Table 5.1-1 reports the results from the 2006 National Survey of Fishing, Hunting, and
19 Wildlife Associated Recreation (FHWAR) for California (DOI, 2007) on the number of
20 individuals involved in fishing, hunting, and wildlife viewing in California. Millions of people
21 are involved in just these three activities each year. The quality of these trips depends in part on
22 the health of the ecosystems and their ability to support the diversity of plants and animals found
23 in important habitats. Based on estimates from Kaval and Loomis (2003), in the Pacific Coast
24 region of the United States, a day of fishing has an average value of $48.86 (in 2007 dollars),
25 based on 15 studies. For hunting and wildlife viewing in this region, average day values were
26 estimated to be $50.10 and $79.81 from 18 and 23 studies, respectively. Multiplying these
27 average values by the total participation days reported in Table 5.1-1, the total benefits in 2006
28 from fishing, hunting, and wildlife viewing away from home in California were approximately
29 $947 million, $169 million, and $3.59 billion, respectively.
30 In addition, data from California State Parks (2003) indicate that in 2002 68.7% of adult
31 residents participated in trail hiking for an average of 24.1 days per year. Applying these same
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1
2
3
4
5
rates to Census estimates of the California adult population in 2007 suggests that residents in
California hiked roughly 453 million days in 2007. According to Kaval and Loomis (2003), the
average value of a hiking day in the Pacific Coast region is $25.59, based on a sample of 49
studies. Multiplying this average-day value by the total participation estimate indicates that the
aggregate annual benefit for California residents from trail hiking in 2007 was $11.59 billion.
Legend
Parks
National
State
California Counties
Mixed Conifer Forest
6
7
Figure 5.1-4. Mixed Conifer Forest Areas and National and State Park
Boundaries
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Table 5.1-1. Recreational Activities in California in 2006 by Residents and Nonresidents
Activities in California by Residents and Nonresidents
Fishing
Anglers 1,730,000
Days of fishing 19,394,000
Average days per angler 11
Hunting
Hunters 281,000
Days of hunting 3,376,000
Average days per hunter 12
Wildlife Watching
Total wildlife-watching participants 6,270,000
Away-from-home participants 2,894,000
Around-the-home participants 5,259,000
Days of participation away from home 45,010,000
Average days of participation away from home 16
Activities in California by Residents
Fishing
Anglers 1,578,000
Days of fishing 18,310,000
Average days per angler 12
Hunting
Hunters 274,000
Days of hunting 3,339,000
Average days per hunter 12
Wildlife Watching
Total wildlife-watching participants 5,704,000
Away-from-home participants 2,328,000
Around-the-home participants 5,259,000
Days of participation away from home 41,436,000
Average days of participation away from home 18
1 Source: U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce, U.S. Census
2 Bureau, 2007.
3 The potential impacts of an increase in wildfires on recreation are discussed in Section
4 5.1.2, Regulating.
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1 5.1.1.2 Aesthetic
2 Beyond the recreational value, the CSS landscape and MCF provide aesthetic services to
3 local residents and homeowners who live near CSS or MCF. Aesthetic services not related to
4 recreation include the view of the landscape from houses, as individuals commute, and as
5 individuals go about their daily routine in a nearby community. Studies find that scenic
6 landscapes are capitalized into the price of housing. Although no studies came to light that look
7 at the value of housing as a function of the view in landscapes that include CSS or MCF, other
8 studies document the existence of housing price premia associated with proximity to forest and
9 open space (Acharya and Bennett, 2001; Geoghegan, Wainger, and Bockstael, 1997; Irwin,
10 2002; Mansfield, et al., 2005; Smith, Poulos, and Kim, 2002; Tyrvainen and Miettinen, 2000).
11 The CSS landscape itself is closely associated with Southern California, which should increase
12 the aesthetic value of the landscape in general. Figure 5.1-5 presents home values in 2000 by
13 Census block and CSS areas. CSS areas border a number of areas along the coast near large
14 cities with very high home values, as well as areas between the cities where home values are
15 lower.
16 5.1.1.3 Nonuse Value
17 Nonuse value, also called existence value or preservation value, encompasses a variety of
18 motivations that lead individuals to place value on environmental goods or services that they do
19 not use. The values individuals place on protecting rare species, rare habitats, or landscape types
20 that they do not see or visit and that do not contribute to the pleasure they get from other
21 activities are examples of nonuse values.
22 While measuring the public's willingness to pay (WTP) to protect endangered species
23 poses theoretical and technical challenges, it is clear that the public places a value on preserving
24 endangered species and their habitat. Data on charitable donations, survey results, and the time
25 and effort different individuals or organizations devote to protecting species and habitat suggest
26 that endangered species have intrinsic value to people beyond the value derived from using the
27 resource (recreational viewing or aesthetic value). CSS and MCF are home to a number of
28 important and rare species and habitat types. CSS displays richness in biodiversity with more
29 than 550 herbaceous annual and perennial species. Of these herbs, nearly half are endangered,
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1 sensitive, or of special status (Burger et al., 2003). Additionally, avian, arthropod, herpetofauna,
2 and mammalian species live in CSS habitat or use the habitat for breeding or foraging.
3
4
5
6
7
Legend
^^B Coastal Sage Scrub 2002
Home Value by Census Tract
Year 2000
$0 00 -$150,000
$150,000- $275,000
$275,000 - $450,000
^H $450,000 - $725.000
^^H $725,000 - $1,OOO,OO1
Figure 5.1-5. Coastal Sage Scrub Areas and Housing Values
Figure 5.1-6 shows communities of CSS and three important federally endangered
species. MCF is home to one federally endangered species and a number of state-level sensitive
species. Figure 5.1-7 provides a map of MCF habitat and two threatened and endangered
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1 species. The Audubon Society lists 28 important bird areas in CSS habitat and at least 5 in MCF
2 in California (http://ca.audubon.org/iba/index.shtml). 1
3
4
5
• , •*•
Coasiai Sage Scrub 2002
Quino Checfcsrap
Kangaroo Ral
Coaslal CA, Gnatcatcher
cities
County
Source of CSS range is (He C-ahtocrna Department
of Foreslry artu Fire Prolecllon.
Source of critical hefeisate
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
| Peninsular &g Nocr Shwp
Mixed Conifer
]] County
Source cr the Mountain Yeitw-legged Frog and
Peninsular Big Horn She*?* rang* is the US Fores)
Sert«e Cm ital Nabiial Phonal
2
3 Figure 5.1-7. Presence of Two Threatened and Endangered Species in
4 California's Mixed Conifer Forest
5 To the authors' knowledge, only one study has specifically estimated values for
6 protecting CSS habitat in California. Stanley (2005) uses a contingent valuation (CV) survey to
7 measure WTP to support recovery plans for endangered species in Southern California. The
8 survey of Orange County, California, residents asked respondents to value the recovery of a
9 single species (the Riverdale fairy shrimp) and a larger bundle of 32 species found in the county.
10 The acquisition of critical habitat and implementation of the recovery plan were the specific
11 goods being valued in the WTP question and the programs would be financed by an annual tax
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 payment. The average WTP for fairy shrimp recovery was roughly $29 (in 2007 dollars) and for
2 all 32 species was $61 per household, depending on the model used. Aggregating benefits
3 (multiplying average household WTP by the number of households in the county) results in total
4 estimated WTP of over $27 million annually for protecting fairy shrimp and $57 million
5 annually for all 32 species.
6 In a more general study valuing endangered species protection, Loomis and White (1996)
7 synthesize key results from 20 threatened and endangered species valuation studies using meta-
8 analysis methods. They find that annual WTP estimates range from a low of $11 for the Striped
9 Shiner fish to a high of $178 for the Northern Spotted Owl (in 2007 dollars). None of the studies
10 summarized by Loomis and White are found in CSS or MCF, but the study provides another
11 indication of the value that the public places on preserving endangered species in general.
12 5.1.2 Regulating
13 Excessive nitrogen deposition upsets the balance between CSS and nonnative plants,
14 changing the ability of an area to support the biodiversity found in CSS. The composition of
15 species in CSS changes fire frequency and intensity, as nonnative grasses fuel more frequent and
16 more intense wildfires. More frequent and intense fires also reduce the ability of CSS to
17 regenerate after a fire and increase the proportion of nonnative grasses (EPA, 2008). A healthy
18 MCF ecosystem supports native species, promotes water quality, and helps regulate fire
19 intensity. Excess nitrogen deposition leads to changes in the forest structure, such as increased
20 density and loss of root biomass, which in turn can result in more intense fires and water quality
21 problems related to nitrate leaching (EPA, 2008).
22 The importance of CSS and MCF as homes for sensitive species and their aesthetic
23 services are discussed in Section 5.1.1. Here the contribution of CSS and MCF to fire regulation
24 and water quality are discussed.
25 5.1.2.1 Fire Regulation
26 The Terrestrial Nutrient Enrichment Case Study identified fire regulation as a service that
27 could be affected by enrichment of the CSS and MCF ecosystems. Wildfires represent a serious
28 threat in California and cause billions of dollars in damage. Over the 5-year period from 2004 to
29 2008, Southern California experienced, on average, over 4,000 fires a year burning, on average,
30 over 400,000 acres (National Association of State Foresters [NASF], 2009). Improved fire
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 regulation leads to short-term and long-term benefits. The short-term benefits include the value
2 of avoided residential property damages, avoided damages to timber, rangeland, and wildlife
3 resources; avoided losses from fire-related air quality impairments; avoided deaths and injury
4 due to fire; improved outdoor recreation opportunities; and savings in costs associated with
5 fighting the fires and protecting lives and property. For example, the California Department of
6 Forestry and Fire Protection (CAL FIRE) estimated that average annual losses to homes due to
7 wildfire from 1984 to 1994 were $163 million per year (CAL FIRE, 1996) and were over $250
8 million in 2007 (CAL FIRE, 2008). In fiscal year 2008, CAL FIRE's costs for fire suppression
9 activities were nearly $300 million (CAL FIRE, 2008). Therefore, even a 1% reduction in these
10 damages and costs would imply benefits of over $5 million per year.
11 Figure 5.1-8 is a map of the overlap between fire threat and CSS habitat. CSS overlaps
12 with areas of very to extremely high fire threat. MCF is found in some areas closer to the coast
13 with extremely high fire threat and in areas up in the mountains also under very high fire threat,
14 as seen in Figure 5.1-9.
15 In the long term, decreased frequency of fires could result in an increase in property
16 values in fire-prone areas. Mueller, Loomis, and Gonzalez-Caban (2007) conducted a hedonic
17 pricing study to determine whether increasing numbers of wildfires affect house prices in
18 southern California. They estimated that house prices would decrease 9.71% ($30,693 in 2007
19 dollars) after one fire and 22.7% ($71,722; $102,417 cumulative) after a second wildfire within
20 1.75 miles of a house in their study area. After the second fire, the housing prices took between 5
21 and 7 years to recover. The results come from a sample of 2,520 single-family homes located
22 within 1.75 miles of one of five fires during the 1990s.
23 Long-term decreases in wildfire risks are also expected to provide outdoor recreation
24 benefits. The empirical literature contains several articles measuring the relationship between
25 wildfires and recreation values; however, very few address fires in California, particularly in
26 CSS areas. One exception is Loomis et al. (2002), which estimates the changes in deer harvest
27 and deer hunting benefits resulting from controlled burns or prescribed fire in the San Bernardino
28 National Forest in Southern California. Using a CV survey of deer hunters in California, they
29 estimated that the net economic value of an additional deer harvested is on average $122 (in
30 2007 dollars). Based on predicted changes in deer harvest in response to a prescribed fire, they
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Appendix 8-116
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 estimated annual economic benefits for an additional 1,000 acres of prescribed burning ranges
2 from $3,328 to $3, 893.
3
4
Fresno
\ ;-^:- v-
v^' "-1- »N ,.
^ • *
* ; • '•
+ ~-
.!-_ Santa'Maria
%;* ' . •
• cities
| California Counties
^^| Coastal Sage Scrub 2002
Fire Threat
| | Moderate
| | High
| | Very High
I Extreme
Bakersfield
t^"'- .">sT4nge/es**.-
••,
Riverside
4
Figure 5.1-8. Coastal Sage Scrub Areas and Fire Threat
2nd Draft Risk and Exposure Assessment
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1
2
3
4
5
6
7
8
® cities
| | California Counties
^^| fv'i xed Conifer Forest
Fire Threat
J Moderate
j | High
| | Very High
I Extreme
®
San Diego
Figure 5.1-9. Mixed Conifer Forest Areas and Fire Threat
5.1.2.2 Water Quality
In the MCF case study, maintaining water quality emerged as a regulating service that
can be upset by excessive nitrogen. When the soil becomes saturated, nitrates may leach into the
surface water and cause acidification. Several large rivers and Lake Tahoe cut through MCF
areas, presented in Figure 5.1-10. Additional nitrogen from MCF areas could further degrade
waters that are already stressed by numerous other sources of nutrients and pollution.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Legencl
M .---»j or Rivers
Major I .:ih. <•-
Mixed Conifer Forest
2 Figure 5.1-10. Mixed Conifer Forest Areas and Major Lakes and Rivers
3 5.2 VALUE OF COASTAL SAGE SCRUB AND MIXED CONIFER
4 FOREST ECOSYSTEM SERVICES
5 The CSS and MCF were selected as case studies for terrestrial enrichment because of the
6 potential that these areas could be adversely affected by excessive nitrogen deposition. To date,
7 the detailed studies needed to identify the magnitude of the adverse impacts due to nitrogen
8 deposition have not been completed. Based on available data, this report provides a qualitative
9 discussion of the services offered by CSS and MCF and a sense of the scale of benefits
10 associated with these services. California is famous for its recreational opportunities and
11 beautiful landscapes. CSS and MCF are an integral part of the California landscape, and together
12 the ranges of these habitats include the densely populated and valuable coastline and the
13 mountain areas. Through recreation and scenic value, these habitats affect the lives of millions of
14 California residents and tourists. Numerous threatened and endangered species at both the state
15 and federal levels reside in CSS and MCF. Both habitats may play an important role in wildfire
2nd Draft Risk and Exposure Assessment
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June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 frequency and intensity, an extremely important problem for California. The potentially high
2 value of the ecosystem services provided by CSS and MCF justify careful attention to the long-
3 term viability of these habitats.
4 5.3 REFERENCES
5 Acharya, G., and L.L. Bennett. 2001. "Valuing Open Space and Land-Use Patterns in Urban
6 Watersheds." Journal of Real Estate Finance and Economics 22(2/3): 221-23 7.
7 Burger, J.C., R.A. Redak, E.B. Allen, J.T. Rotenberry, and M.F. Allen. 2003. "Restoring
8 Arthropod Communities in Coastal Sage Scrub." Conservation Biology 17(2):460-467.
9 CAL FIRE (California Department of Forestry and Fire Protection). 1996. California Fire Plan.
10 Available at http://cdfdata.fire.ca.gov/fire_er/fpp_planning_cafireplan.
11 CAL FIRE (California Department of Forestry and Fire Protection). 2008. CAL FIRE 2007
12 Wildland Fire Summary.
13 California Department of Fish and Game. n.d.
14 http://www.dfg.ca.gov/habcon/nccp/images/region.gif.
15 Geoghegan, J., L.A. Wainger, and N.E. Bockstael. 1997. "Spatial Landscape Indices in a
16 Hedonic Framework: An Ecological Economics Analysis Using GIS." Ecological
17 Economics 23:251 -264.
18 Irwin, E.G. 2002. "The Effects of Open Space on Residential Property Values." Land Economics
19 78(4):465-480.
20 Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On
21 National Park Recreation. Final Report October 2003, under Cooperative Agreement CA
22 1200-99-009, Project number IMDE-02-0070.
23 Land Trust Alliance. 2006. The 2005 National Land Trust Census Report. Washington, D.C.:
24 Land Trust Alliance, November 30, 2006.
2nd Draft Risk and Exposure Assessment June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 Loomis, J., D. Griffin, E. Wu, and A. Gonzalez-Caban. 2002. "Estimating the Economic Value
2 of Big Game Habitat Production from Prescribed Fire Using a Time Series Approach."
3 Journal of Forest Economics 2:119-29.
4 Loomis, J.B., and D.S. White. 1996. "Economic Benefits of Rare and Endangered Species:
5 Summary and Meta-Analysis." Ecological Economics 18(3): 197-206.
6 Mansfield, C.A., S.K. Pattanayak, W. McDow, R. MacDonald, and P. Halpin. 2005. "Shades of
7 Green: Measuring the Value of Urban Forests in the Housing Market." Journal of Forest
8 Economics 11(3):177-199.
9 Mueller, J., J. Loomis, and A. Gonzalez-Caban. 2007. "Do Repeated Wildfires Change
10 Homebuyers' Demand for Homes in High-Risk Areas? A Hedonic Analysis of the Short
11 and Long-Term Effects of Repeated Wildfires on House Prices in Southern California."
12 Journal of Real Estate Finance and Economics, 1-18.
13 National Association of State Foresters (NASF). 2009. Quadrennial Fire Review
14 2009.Washington, DC: NASF. Quadrennial Fire and Fuel Review Final Report 2009.
15 National Wildfire Coordinating Group Executive Board January 2009.
16 Smith, V.K., C. Poulos, and H. Kim. 2002. "Treating Open Space as an Urban Amenity."
17 Resource and Energy Economics 24:107-129.
18 Tyrvainen, L., and A. Miettinen. 2000. "Property Prices and Urban Forest Amenities." Journal of
19 Economics and Environmental Management 39:205-223.
20 U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce,
21 U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and Wildlife-
22 Associated Recreation.
23 U.S. Environmental Protection Agency (EPA). 2008. Integrated Science Assessment for Oxides
24 of Nitrogen and Sulfur-Environmental Criteria.EPA/600/R-08/082. U.S. Environmental
25 Protection Agency, Office of Research and Development, National Center for
26 Environmental Assessment - RTF Division, Research Triangle Park, NC.
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
i 6. CONCLUSION
2 This report has identified, characterized, and, to the extent possible, quantified the
3 ecosystem services that are primarily affected by changes in nitrogen and sulfur deposition and
4 associated ecological indicators. The discussion has focused on four main categories of
5 ecosystem effects—aquatic and terrestrial acidification and aquatic and terrestrial nutrient
6 enrichment—and on three main categories of ecosystem services—provisioning, cultural, and
7 regulating services.
8 The report demonstrates that nitrogen and sulfur deposition have wide-ranging
9 detrimental effects on the services provided by ecosystems across the United States; however,
10 there continues to be significant uncertainty regarding the overall magnitude of these effects. To
11 partially address this uncertainty, where data and scientific evidence permit, this study has
12 estimated how reducing nitrogen and sulfur deposition in specific areas would affect the value of
13 selected ecosystem services. These estimates are summarized in Table 6-1.
14 6.1 BENEFITS FROM ENHANCED PROVISIONING SERVICES
15 Provisioning services are derived from goods and commodities whose production
16 depends directly on inputs from healthy ecosystems. Two main examples of provisioning
17 services that are constrained by nitrogen and sulfur deposition are the production and
18 consumption of forest products and seafood.
19 Terrestrial acidification has been shown to cause forest damages, and much of the
20 specific evidence has focused on two tree species—sugar maples and red spruce. The value of
21 commercial harvests from these two species in 2006 was roughly $400 million, but the more
22 relevant question is how much would the value of these services increase with reductions in
23 nitrogen and sulfur deposition? This study estimates that eliminating the growth suppression
24 effects of terrestrial acidification on sugar maples and red spruce would generate market benefits
25 of about $684,000 per year.
26 Aquatic enrichment resulting from excess inputs of nitrogen is also known to contribute
27 to eutrophic conditions in surface waters, which limits the growth and abundance of commercial
28 fish species. Evidence regarding the magnitude of these effects is limited, largely due to the
29 complexities involved in modeling the dynamic ecosystem processes and links between fish
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 stocks and commercial fishing behaviors. One exception is a model of commercial blue crab
2 fishing in the Neuse River estuary (Smith, 2007). Using the results of this study, it is estimated
3 that eliminating the contribution of atmospheric nitrogen deposition to the Neuse would generate
4 market benefits from blue crab fishery ranging between $0.1 million and $1 million per year.
Table 6-1. Summary of Aggregate Benefit Estimates for Selected Ecosystem Services and Areas
(Zero Out of Nitrogen and Sulfur Deposition)21
Ecosystem Effect
T-, , o Range (in millions of 2007 dollars/year)
Ecosystem Service °_- £ -
Area Low High
Aquatic Acidification
Recreational Fishing
Adirondack Lakes 3.9 9.3
New York Lakes 4.5 130.0
General
Adirondack Lakes 291.2 1097.2
Terrestrial Acidification
Commercial Timber (Sugar Maple and Red
Spruce)
Northeastern U.S. 0.684 0.684
Aquatic Enrichment
Commercial Fishing (Blue Crab)
Neuse 0.1 1.0
Recreational Fishing
Chesapeake 42.6 217.0
APS 1.0 7.9
Recreational Boating
Chesapeake 2.9 8.2
Recreational Beach Use
Chesapeake 124.0 124.0
Aesthetic (Nearshore Residents)
Chesapeake 38.7 102.2
Nonuse
Chesapeake 159.1 270.4
5 a Because of overlaps in the services covered, the value estimates reported in the table should not be added together.
2nd Draft Risk and Exposure Assessment June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 6.2 BENEFITS FROM ENHANCED CULTURAL SERVICES
2 Cultural services are derived from the nonmaterial benefits that individuals receive from
3 ecosystems, including spiritual enrichment, cognitive development, reflection, recreation, and
4 aesthetic experiences. As this report discusses, acidification and enrichment effects from
5 nitrogen and sulfur deposition have the potential to affect a wide variety of these services;
6 however, most of the available evidence concerns recreation and aesthetic services.
7 As discussed in Sections 3 and 5, much of the evidence of adverse terrestrial ecosystem
8 impacts from acidification and enrichment centers on forests in the northeastern portion of the
9 United States and coastal sage scrub (CSS) and mixed conifer forest (MCF) ecosystems in the
10 west. These ecosystems support a wide variety of land-based outdoor recreational activities,
11 including hunting, wildlife viewing, and hiking, worth several billions of dollars each year to the
12 general public. Unfortunately, relatively little evidence is available to quantity how the benefits
13 of these recreational services are affected by terrestrial acidification or enrichment due to
14 nitrogen and sulfur deposition.
15 As discussed in Sections 2 and 4, aquatic ecosystem impacts due to the acidification and
16 nutrient enrichment of surface waters also adversely affect a broad and valuable range of outdoor
17 recreation services. In contrast to terrestrial effects, however, the impacts of these aquatic effects
18 on recreational services are relatively easier to quantify (at least for selected activities and
19 geographic areas). For example, based on the results of an Aquatic Acidification Case Study of
20 the Adirondacks, the benefits to recreational anglers in New York from zeroing out the
21 acidification effects of nitrogen and sulfur deposition on Adirondack lakes are estimated to be
22 roughly equivalent to $4 million to $9 million per year. If the zero-out conditions are extended to
23 all New York lakes, the annual benefits could be an order of magnitude higher.
24 This study also used the results of the aquatic enrichment case studies to estimate the
25 value of enhancements to several recreational activities in the Chesapeake and Albemarle-
26 Pamlico estuaries, as a result of zeroing out nitrogen deposition to their watersheds. In the
27 Chesapeake, the benefits to striped bass and summer flounder anglers were estimated to be
28 roughly $43 million per year. Extending the estimation methodology to all recreational species
29 implies benefits of nearly $220 million, but with a much higher degree of uncertainty. For
30 Chesapeake boaters and beach users, the main benefit estimates were $8 million and $124
31 million per year, respectively. In the Albemarle and Pamlico Sounds (APS), the benefits to
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
1 recreational anglers were $1 million if the nitrogen loading reductions only occurred in the
2 Neuse and almost $8 million if they applied to the entire APS.
3 In addition to the benefits from enhanced recreational services, this report also examines
4 benefits to other cultural ecosystem services as a result of reduced aquatic acidification and
5 nutrient enrichment effects. Using the results of the Resources for the Future (RFF) contingent
6 valuation ( CV) study of New York residents, total annual benefits (assumed to primarily be for
7 improved cultural services, including recreational fishing services) of between $291 million and
8 $1.1 billion per year for a zero out of nitrogen and sulfur deposition were estimated. In the
9 Chesapeake Bay, benefits to nearshore residents (assumed to be mainly from improved aesthetic
10 and recreation services) of $39 million to $102 million were estimated. Total nonuse benefits of
11 between $159 million and $271 million per year were also estimated.
12 6.3 BENEFITS FROM ENHANCED REGULATING SERVICES
13 Terrestrial and aquatic ecosystems provide a variety of regulating services, such as fire,
14 flood, and erosion control and hydrological and climate regulation; however, there is relatively
15 little evidence regarding the magnitude of impairments to these services due to the effects of
16 nitrogen and sulfur deposition. Therefore, this report provides more of a qualitative assessment
17 of these services. It describes how aquatic acidification and enrichment can affect biological food
18 chain control services through their effects on the growth and mortality offish species. It also
19 describes the regulating services provided by forests, including erosion and sedimentation
20 control, water storage, and carbon sequestration, which may be adversely affected by nitrogen
21 and sulfur deposition. Finally, it describes potential changes in wildfire risks and fire regulation
22 services as a result of changes in CSS ecosystems that have been altered through nitrogen
23 enrichment.
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1
2
3
4
5
6
7
8
9
10
11 [This page intentionally left blank.]
12
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8-126
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i ATTACHMENT A
2 ANNUAL RECREATIONAL FISHING BENEFIT ESTIMATES
3 FOR REDUCTIONS IN NEW YORK LAKE ACIDIFICATION
4 LEVELS, 2002-2100
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-l. Adirondack Region—20 |ieq/L Threshold
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Per Capita Benefit
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0680
0.1360
0.2039
0.2719
0.3399
0.4079
0.4759
0.5438
0.6118
0.6798
0.6760
0.6723
0.6685
0.6647
0.6610
0.6572
0.6534
0.6497
0.6459
0.6421
0.6384
0.6346
0.6309
0.6271
Population
8,333,023
8,391,727
8,440,400
8,471,658
8,511,465
8,551,313
8,665,048
8,595,000
8,630,872
8,661,363
8,685,935
8,706,365
8,721,517
8,729,057
8,731,768
8,731,621
8,730,281
8,733,574
8,733,759
8,731,785
8,728,824
8,725,132
8,720,953
8,716,339
8,712,372
8,709,340
8,707,278
8,705,815
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$0
$0
$0
$0
$0
$0
$0
$0
$0
$588,805
$1,180,951
$1,775,593
$2,371,578
$2,967,035
$3,561,548
$4,155,069
$4,747,921
$5,343,426
$5,937,266
$5,903,042
$5,868,169
$5,832,830
$5,797,195
$5,761,304
$5,725,873
$5,691,082
$5,656,945
$5,623,210
$5,589,950
$5,557,169
$5,524,387
$5,491,606
$5,458,824
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A-l
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-l. Adirondack Region—20 |ieq/L Threshold (continued)
Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
Per Capita Benefit
0.6233
0.6196
0.6158
0.6120
0.6083
0.6045
0.6007
0.5970
0.5932
0.5894
0.5857
0.5819
0.5781
0.5744
0.5706
0.5668
0.5647
0.5625
0.5603
0.5581
0.5559
0.5538
0.5516
0.5494
0.5472
0.5450
0.5429
0.5407
0.5385
0.5363
0.5341
0.5320
0.5298
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$5,426,042
$5,393,261
$5,360,479
$5,327,698
$5,294,916
$5,262,134
$5,229,353
$5,196,571
$5,163,790
$5,131,008
$5,098,226
$5,065,445
$5,032,663
$4,999,882
$4,967,100
$4,934,318
$4,915,346
$4,896,373
$4,877,400
$4,858,428
$4,839,455
$4,820,482
$4,801,510
$4,782,537
$4,763,565
$4,744,592
$4,725,619
$4,706,647
$4,687,674
$4,668,701
$4,649,729
$4,630,756
$4,611,783
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 2
June 5, 2009
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Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-l. Adirondack Region—20 |ieq/L Threshold (continued)
Year
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Per Capita Benefit
0.5276
0.5254
0.5232
0.5211
0.5189
0.5167
0.5145
0.5123
0.5102
0.5080
0.5058
0.5036
0.5014
0.4993
0.4971
0.4949
0.4927
0.4906
0.4884
0.4862
0.4840
0.4818
0.4797
0.4775
0.4753
0.4731
0.4709
0.4688
0.4666
0.4644
0.4622
0.4600
0.4579
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$4,592,811
$4,573,838
$4,554,865
$4,535,893
$4,516,920
$4,497,947
$4,478,975
$4,460,002
$4,441,029
$4,422,057
$4,403,084
$4,384,111
$4,365,139
$4,346,166
$4,327,193
$4,308,221
$4,289,248
$4,270,275
$4,251,303
$4,232,330
$4,213,357
$4,194,385
$4,175,412
$4,156,439
$4,137,467
$4,118,494
$4,099,521
$4,080,549
$4,061,576
$4,042,603
$4,023,631
$4,004,658
$3,985,685
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8, Attachment A - 3
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-2. Adirondack Region—50 |ieq/L Threshold
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Per Capita Benefit
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.1232
0.2463
0.3695
0.4927
0.6159
0.7390
0.8622
0.9854
1.1086
1.2317
1.2310
1.2302
1.2294
1.2287
1.2279
1.2271
1.2264
1.2256
1.2248
1.2241
1.2233
1.2226
1.2218
1.2210
Population
8,333,023
8,391,727
8,440,400
8,471,658
8,511,465
8,551,313
8,665,048
8,595,000
8,630,872
8,661,363
8,685,935
8,706,365
8,721,517
8,729,057
8,731,768
8,731,621
8,730,281
8,733,574
8,733,759
8,731,785
8,728,824
8,725,132
8,720,953
8,716,339
8,712,372
8,709,340
8,707,278
8,705,815
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$0
$0
$0
$0
$0
$0
$0
$0
$0
$1,066,847
$2,139,747
$3,217,170
$4,297,025
$5,375,925
$6,453,113
$7,528,505
$8,602,686
$9,681,672
$10,757,642
$10,748,533
$10,738,213
$10,727,000
$10,715,194
$10,702,859
$10,691,327
$10,680,945
$10,671,759
$10,663,309
$10,655,746
$10,649,090
$10,642,433
$10,635,777
$10,629,121
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 4
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-2. Adirondack Region—50 |ieq/L Threshold (continued)
Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
Per Capita Benefit
1.2203
1.2195
1.2187
1.2180
1.2172
1.2164
1.2157
1.2149
1.2141
1.2134
1.2126
1.2118
1.2111
1.2103
1.2096
1.2088
1.2079
1.2070
1.2061
1.2051
1.2042
1.2033
1.2024
1.2015
1.2006
1.1997
1.1987
1.1978
1.1969
1.1960
1.1951
1.1942
1.1933
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$10,622,464
$10,615,808
$10,609,151
$10,602,495
$10,595,839
$10,589,182
$10,582,526
$10,575,869
$10,569,213
$10,562,557
$10,555,900
$10,549,244
$10,542,587
$10,535,931
$10,529,275
$10,522,618
$10,514,670
$10,506,723
$10,498,775
$10,490,827
$10,482,879
$10,474,931
$10,466,983
$10,459,035
$10,451,087
$10,443,139
$10,435,191
$10,427,243
$10,419,296
$10,411,348
$10,403,400
$10,395,452
$10,387,504
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 5
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-2. Adirondack Region—50 |ieq/L Threshold (continued)
Year
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Per Capita Benefit
1.1924
1.1914
1.1905
1.1896
1.1887
1.1878
1.1869
1.1860
1.1851
1.1841
1.1832
1.1823
1.1814
1.1805
1.1796
1.1787
1.1777
1.1768
1.1759
1.1750
1.1741
1.1732
1.1723
1.1714
1.1704
1.1695
1.1686
1.1677
1.1668
1.1659
1.1650
1.1641
1.1631
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$10,379,556
$10,371,608
$10,363,660
$10,355,712
$10,347,764
$10,339,817
$10,331,869
$10,323,921
$10,315,973
$10,308,025
$10,300,077
$10,292,129
$10,284,181
$10,276,233
$10,268,285
$10,260,338
$10,252,390
$10,244,442
$10,236,494
$10,228,546
$10,220,598
$10,212,650
$10,204,702
$10,196,754
$10,188,806
$10,180,859
$10,172,911
$10,164,963
$10,157,015
$10,149,067
$10,141,119
$10,133,171
$10,125,223
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8, Attachment A - 6
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-3. Adirondack Region—100 |ieq/L Threshold
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Per Capita Benefit
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.1307
0.2614
0.3920
0.5227
0.6534
0.7841
0.9148
1.0454
1.1761
1.3068
1.3056
1.3044
1.3031
1.3019
1.3007
1.2995
1.2982
1.2970
1.2958
1.2946
1.2934
1.2921
1.2909
1.2897
Population
8,333,023
8,391,727
8,440,400
8,471,658
8,511,465
8,551,313
8,665,048
8,595,000
8,630,872
8,661,363
8,685,935
8,706,365
8,721,517
8,729,057
8,731,768
8,731,621
8,730,281
8,733,574
8,733,759
8,731,785
8,728,824
8,725,132
8,720,953
8,716,339
8,712,372
8,709,340
8,707,278
8,705,815
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$0
$0
$0
$0
$0
$0
$0
$0
$0
$1,131,871
$2,270,164
$3,413,256
$4,558,928
$5,703,587
$6,846,430
$7,987,367
$9,127,018
$10,271,769
$11,413,319
$11,400,066
$11,385,532
$11,370,052
$11,353,948
$11,337,287
$11,321,479
$11,306,893
$11,293,575
$11,281,036
$11,269,438
$11,258,798
$11,248,159
$11,237,519
$11,226,879
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 7
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-3. Adirondack Region—100 |ieq/L Threshold (continued)
Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
Per Capita Benefit
1.2885
1.2872
1.2860
1.2848
1.2836
1.2824
1.2811
1.2799
1.2787
1.2775
1.2762
1.2750
1.2738
1.2726
1.2714
1.2701
1.2674
1.2647
1.2621
1.2594
1.2567
1.2540
1.2513
1.2486
1.2459
1.2432
1.2405
1.2378
1.2351
1.2324
1.2297
1.2270
1.2243
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$11,216,240
$11,205,600
$11,194,961
$11,184,321
$11,173,681
$11,163,042
$11,152,402
$11,141,763
$11,131,123
$11,120,484
$11,109,844
$11,099,204
$11,088,565
$11,077,925
$11,067,286
$11,056,646
$11,033,188
$11,009,730
$10,986,272
$10,962,814
$10,939,356
$10,915,897
$10,892,439
$10,868,981
$10,845,523
$10,822,065
$10,798,607
$10,775,149
$10,751,691
$10,728,233
$10,704,775
$10,681,317
$10,657,859
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 8
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-3. Adirondack Region—100 |ieq/L Threshold (continued)
Year
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Per Capita Benefit
1.2216
1.2189
1.2162
1.2135
1.2109
1.2082
1.2055
1.2028
1.2001
1.1974
1.1947
1.1920
1.1893
1.1866
1.1839
1.1812
1.1785
1.1758
1.1731
1.1704
1.1677
1.1650
1.1623
1.1597
1.1570
1.1543
1.1516
1.1489
1.1462
1.1435
1.1408
1.1381
1.1354
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$10,634,400
$10,610,942
$10,587,484
$10,564,026
$10,540,568
$10,517,110
$10,493,652
$10,470,194
$10,446,736
$10,423,278
$10,399,820
$10,376,362
$10,352,903
$10,329,445
$10,305,987
$10,282,529
$10,259,071
$10,235,613
$10,212,155
$10,188,697
$10,165,239
$10,141,781
$10,118,323
$10,094,865
$10,071,406
$10,047,948
$10,024,490
$10,001,032
$9,977,574
$9,954,116
$9,930,658
$9,907,200
$9,883,742
2nd Draft Risk and Exposure Assessment June 5, 2009
Appendix 8, Attachment A - 9
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-4. New York State—20 jieq/L Threshold
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Per Capita Benefit
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0777
0.1555
0.2332
0.3110
0.3887
0.4665
0.5442
0.6220
0.6997
0.7775
0.7727
0.7680
0.7632
0.7585
0.7537
0.7490
0.7442
0.7395
0.7347
0.7300
0.7252
0.7205
0.7157
0.7110
Population
8,333,023
8,391,727
8,440,400
8,471,658
8,511,465
8,551,313
8,665,048
8,595,000
8,630,872
8,661,363
8,685,935
8,706,365
8,721,517
8,729,057
8,731,768
8,731,621
8,730,281
8,733,574
8,733,759
8,731,785
8,728,824
8,725,132
8,720,953
8,716,339
8,712,372
8,709,340
8,707,278
8,705,815
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$0
$0
$0
$0
$0
$0
$0
$0
$0
$673,415
$1,350,651
$2,030,742
$2,712,368
$3,393,391
$4,073,334
$4,752,142
$5,430,186
$6,111,264
$6,790,438
$6,747,422
$6,703,667
$6,659,383
$6,614,765
$6,569,857
$6,525,479
$6,481,834
$6,438,935
$6,396,496
$6,354,599
$6,313,245
$6,271,891
$6,230,538
$6,189,184
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 10
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-4. New York State—20 |ieq/L Threshold (continued)
Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
Per Capita Benefit
0.7062
0.7015
0.6967
0.6920
0.6872
0.6825
0.6777
0.6730
0.6682
0.6635
0.6587
0.6540
0.6492
0.6445
0.6397
0.6350
0.6327
0.6305
0.6283
0.6261
0.6238
0.6216
0.6194
0.6171
0.6149
0.6127
0.6105
0.6082
0.6060
0.6038
0.6015
0.5993
0.5971
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$6,147,830
$6,106,477
$6,065,123
$6,023,769
$5,982,416
$5,941,062
$5,899,708
$5,858,355
$5,817,001
$5,775,647
$5,734,294
$5,692,940
$5,651,586
$5,610,233
$5,568,879
$5,527,525
$5,508,117
$5,488,710
$5,469,302
$5,449,894
$5,430,487
$5,411,079
$5,391,671
$5,372,264
$5,352,856
$5,333,448
$5,314,041
$5,294,633
$5,275,225
$5,255,818
$5,236,410
$5,217,002
$5,197,595
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 11
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-4. New York State—20 |ieq/L Threshold (continued)
Year
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Per Capita Benefit
0.5948
0.5926
0.5904
0.5882
0.5859
0.5837
0.5815
0.5792
0.5770
0.5748
0.5726
0.5703
0.5681
0.5659
0.5636
0.5614
0.5592
0.5569
0.5547
0.5525
0.5503
0.5480
0.5458
0.5436
0.5413
0.5391
0.5369
0.5347
0.5324
0.5302
0.5280
0.5257
0.5235
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$5,178,187
$5,158,779
$5,139,372
$5,119,964
$5,100,556
$5,081,149
$5,061,741
$5,042,333
$5,022,926
$5,003,518
$4,984,110
$4,964,703
$4,945,295
$4,925,887
$4,906,480
$4,887,072
$4,867,664
$4,848,257
$4,828,849
$4,809,441
$4,790,034
$4,770,626
$4,751,218
$4,731,811
$4,712,403
$4,692,995
$4,673,588
$4,654,180
$4,634,772
$4,615,365
$4,595,957
$4,576,549
$4,557,142
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 12
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-5. New York State—50 jieq/L Threshold
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Per Capita Benefit
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.4224
0.8447
1.2671
1.6894
2.1118
2.5341
2.9565
3.3788
3.8012
4.2235
4.2077
4.1918
4.1759
4.1601
4.1442
4.1284
4.1125
4.0967
4.0808
4.0649
4.0491
4.0332
4.0174
4.0015
Population
8,333,023
8,391,727
8,440,400
8,471,658
8,511,465
8,551,313
8,665,048
8,595,000
8,630,872
8,661,363
8,685,935
8,706,365
8,721,517
8,729,057
8,731,768
8,731,621
8,730,281
8,733,574
8,733,759
8,731,785
8,728,824
8,725,132
8,720,953
8,716,339
8,712,372
8,709,340
8,707,278
8,705,815
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$0
$0
$0
$0
$0
$0
$0
$0
$0
$3,658,144
$7,337,045
$11,031,452
$14,734,201
$18,433,674
$22,127,279
$25,814,723
$29,498,013
$33,197,783
$36,887,209
$36,740,406
$36,589,530
$36,435,696
$36,279,955
$36,122,540
$35,967,946
$35,817,317
$35,670,765
$35,526,718
$35,385,658
$35,247,618
$35,109,578
$34,971,538
$34,833,498
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 13
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-5. New York State—50 |ieq/L Threshold (continued)
Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
Per Capita Benefit
3.9857
3.9698
3.9539
3.9381
3.9222
3.9064
3.8905
3.8747
3.8588
3.8429
3.8271
3.8112
3.7954
3.7795
3.7637
3.7478
3.7216
3.6955
3.6693
3.6432
3.6170
3.5909
3.5647
3.5386
3.5124
3.4863
3.4601
3.4340
3.4078
3.3816
3.3555
3.3293
3.3032
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$34,695,458
$34,557,418
$34,419,378
$34,281,337
$34,143,297
$34,005,257
$33,867,217
$33,729,177
$33,591,137
$33,453,097
$33,315,057
$33,177,016
$33,038,976
$32,900,936
$32,762,896
$32,624,856
$32,397,187
$32,169,517
$31,941,848
$31,714,179
$31,486,509
$31,258,840
$31,031,171
$30,803,502
$30,575,832
$30,348,163
$30,120,494
$29,892,824
$29,665,155
$29,437,486
$29,209,816
$28,982,147
$28,754,478
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 14
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-5. New York State—50 |ieq/L Threshold (continued)
Year
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Per Capita Benefit
3.2770
3.2509
3.2247
3.1986
3.1724
3.1463
3.1201
3.0940
3.0678
3.0416
3.0155
2.9893
2.9632
2.9370
2.9109
2.8847
2.8586
2.8324
2.8063
2.7801
2.7540
2.7278
2.7017
2.6755
2.6493
2.6232
2.5970
2.5709
2.5447
2.5186
2.4924
2.4663
2.4401
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$28,526,808
$28,299,139
$28,071,470
$27,843,800
$27,616,131
$27,388,462
$27,160,793
$26,933,123
$26,705,454
$26,477,785
$26,250,115
$26,022,446
$25,794,777
$25,567,107
$25,339,438
$25,111,769
$24,884,099
$24,656,430
$24,428,761
$24,201,092
$23,973,422
$23,745,753
$23,518,084
$23,290,414
$23,062,745
$22,835,076
$22,607,406
$22,379,737
$22,152,068
$21,924,398
$21,696,729
$21,469,060
$21,241,390
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 15
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-6. New York State—100 jieq/L Threshold
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Per Capita Benefit
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1.8336
3.6671
5.5007
7.3342
9.1678
11.0014
12.8349
14.6685
16.5021
18.3356
18.3111
18.2865
18.2620
18.2374
18.2129
18.1883
18.1638
18.1392
18.1147
18.0901
18.0656
18.0410
18.0165
17.9919
Population
8,333,023
8,391,727
8,440,400
8,471,658
8,511,465
8,551,313
8,665,048
8,595,000
8,630,872
8,661,363
8,685,935
8,706,365
8,721,517
8,729,057
8,731,768
8,731,621
8,730,281
8,733,574
8,733,759
8,731,785
8,728,824
8,725,132
8,720,953
8,716,339
8,712,372
8,709,340
8,707,278
8,705,815
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$0
$0
$0
$0
$0
$0
$0
$0
$0
$15,881,141
$31,852,394
$47,890,966
$63,965,753
$80,026,316
$96,061,405
$112,069,745
$128,060,056
$144,121,906
$160,138,854
$159,888,275
$159,619,755
$159,338,035
$159,047,621
$158,749,471
$158,463,334
$158,194,349
$157,943,136
$157,702,858
$157,475,737
$157,262,020
$157,048,303
$156,834,587
$156,620,870
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 16
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-6. New York State—100 jieq/L Threshold (continued)
Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
Per Capita Benefit
17.9674
17.9428
17.9183
17.8937
17.8691
17.8446
17.8200
17.7955
17.7709
17.7464
17.7218
17.6973
17.6727
17.6482
17.6236
17.5991
17.5589
17.5187
17.4786
17.4384
17.3982
17.3580
17.3179
17.2777
17.2375
17.1973
17.1572
17.1170
17.0768
17.0366
16.9965
16.9563
16.9161
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$156,407,153
$156,193,436
$155,979,720
$155,766,003
$155,552,286
$155,338,569
$155,124,852
$154,911,136
$154,697,419
$154,483,702
$154,269,985
$154,056,269
$153,842,552
$153,628,835
$153,415,118
$153,201,401
$152,851,672
$152,501,943
$152,152,214
$151,802,485
$151,452,756
$151,103,027
$150,753,298
$150,403,569
$150,053,840
$149,704,111
$149,354,382
$149,004,652
$148,654,923
$148,305,194
$147,955,465
$147,605,736
$147,256,007
(continued)
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 17
June 5, 2009
-------
Analysis of Ecosystem Services Impacts for the NOX/SOX Secondary NAAQS Review
Table A-6. New York State—100 jieq/L Threshold (continued)
Year
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Per Capita Benefit
16.8759
16.8358
16.7956
16.7554
16.7152
16.6751
16.6349
16.5947
16.5545
16.5144
16.4742
16.4340
16.3938
16.3537
16.3135
16.2733
16.2331
16.1930
16.1528
16.1126
16.0724
16.0323
15.9921
15.9519
15.9117
15.8716
15.8314
15.7912
15.7510
15.7109
15.6707
15.6305
15.5903
Population
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
8,705,075
Undiscounted Benefit
$146,906,278
$146,556,549
$146,206,820
$145,857,091
$145,507,362
$145,157,633
$144,807,903
$144,458,174
$144,108,445
$143,758,716
$143,408,987
$143,059,258
$142,709,529
$142,359,800
$142,010,071
$141,660,342
$141,310,613
$140,960,883
$140,611,154
$140,261,425
$139,911,696
$139,561,967
$139,212,238
$138,862,509
$138,512,780
$138,163,051
$137,813,322
$137,463,593
$137,113,864
$136,764,134
$136,414,405
$136,064,676
$135,714,947
2nd Draft Risk and Exposure Assessment
Appendix 8, Attachment A - 18
June 5, 2009
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-452/P-09-004b
Environmental Protection Health and Environmental Impacts Division June 5, 2009
Agency Research Triangle Park, NC
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