823B05002
AOUATOX Release 2.1 Technical Documentation Addendum
AQUATOX FOR WINDOWS
A MODULAR FATE AND EFFECTS
MODEL FOR AQUATIC ECOSYSTEMS
RELEASE 2.1
ADDENDUM TO RELEASE 2
TECHNICAL DOCUMENTATION
Jonathan S. Clough, Warren Pinnacle Consulting, Inc.
and
Richard A. Park, Eco Modeling
Prepared under
EPA Contract 68-C-98-010
with AQUA TERRA Consultants
Prepared for
U.S. Environmental Protection Agency
Risk Assessment Division (7403M)
Office of Pollution Prevention and Toxics
Washington, D.C. 20460
October 2005
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AQUATOX Release 2.1 Technical Documentation Addendum
TABLE OF CONTENTS
1 INTRODUCTION TO RELEASE 2.1 4
Background 4
What's New 4
3 PHYSICAL CHARACTERISTICS 5
Dynamic Mean Depth 5
4 BIOTA 5
4.1 Algae 5
Periphyton Code Changes 6
Phytoplankton and Zooplankton Residence Time 7
Periphyton-Phytoplankton Link 8
4.4 Steinhaus Similarity Index 9
5 REMINERALIZATION 10
5.2 Nitrogen 10
Assimilation 11
Nitrification and Denitrification 12
lonization of Ammonia 14
5.3 Phosphorus 16
5.4 Nutrient Mass Balance 17
Variable Stoichiometry 18
Nutrient Loading Variables 18
Nutrient Output Variables 19
Mass Balance of Nutrients 19
5.7 Modeling Dynamic pH 25
7 Toxic Organic Chemicals 28
7.6 Nonequilibrium Kinetics 28
7.7 Alternative Uptake Model: Entering BCFs, Kl, and K2 28
7.8 Half Life Calculation Refinement DT50&DT95 29
8 ECOTOXICOLOGY 30
8.3 External Toxicity 30
9 REFERENCES 32
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AQUATOX Release 2.1 Technical Documentation Addendum
1 INTRODUCTION TO RELEASE 2.1
Background
Nutrients (nitrogen and phosphorus) are leading causes of water quality impairment in the
Nation's rivers, lakes and estuaries. To address this problem, states need the technical resources
to establish nutrient criteria, adopt them into their water quality standards, and implement them
in regulatory programs. Ecosystem models such as AQUATOX that mechanistically simulate
nutrient dynamics can be one tool for deriving and implementing nutrient criteria.
To further assist in modeling nutrients AQUATOX has been significantly updated since EPA
Release 2 was released. There have also been several enhancements related to toxicity, along
with improvements to the user interface. This document is an addendum to the AQUATOX
Release 2 Technical documentation (EPA-823-R-04-002, January 2004). The document
describes changes in the model that distinguish Release 2.1 from Release 2.
What's New
The capability to model mean depth dynamically has been included.
« Various modifications to periphyton modeling, phytoplankton modeling, and a
periphyton-phytoplankton linkage may be found in the section on biota.
. The capability to export Steinhaus similarity matrices has been added to provide a
measure of community effects.
. The fraction of ammonia that is un-ionized is estimated and reported.
« Variable stoichiometry, new nutrient loading variables, new nutrient output variables, and
strict mass balance of nutrients have all been added to AQUATOX since Release 2.
• pH may now be modeled dynamically as a function of a site's total alkalinity, carbon
dioxide, and dissolved organic matter.
« Additional flexibility has been added to the modeling of toxic organic chemicals
including new uptake and depuration modeling options and the ability to model toxicity
based on external concentrations.
• Libraries now can be viewed in a "GridMode" spreadsheet form to facilitate comparison
of chemical or organism parameters.
• The complete setup of a study, including state variable parameter values, loadings, and
site constants, can be exported to a text file.
« The linkage to BASINS has been expanded to include a variety of phosphorus loadings.
A revised User's Manual for the BASINS Extension to AQUATOX has been released
that describes these changes. (EPA-823-B-05-001, October 2005)
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AQUATOX Release 2.1 Technical Documentation Addendum
3 PHYSICAL CHARACTERISTICS
The following should be inserted before "Habitat Disaggregation " on p. 3-6.
Dynamic Mean Depth
AQUATOX normally uses an assumption of unchanging mean depth (i.e., mean over the site
area). However, under some circumstances, and especially in the case of streams or rivers, the
depth of the system can change considerably, which could result in a significantly different light
climate for algae. For this reason, an option to import mean depth in meters has been added. A
daily time-series of mean depth values may be imported into the software (using an interface
found within the site screen by pressing the "Show Mean Depth Panel" button.) A time-series of
mean depth values can be estimated given known water volumes or can be imported from a
linked water hydrology model.
The user-input dynamic mean depth affects the following portions of AQUATOX:
Light climate, see (38);
. Calculation of biotic volumes for sloughing calculations, see (66);
. Calculation of vertical dispersion for stratification calculations, Thick in equation (18);
. Calculation of sedimentation for plants & detritus, Thick in (135);
. Oxygen reaeration, see (158).
. Toxicant photolysis and volatilization, Thick in (221) and (230)
4 BIOTA
4.1 Algae
(There have been minor refinements added to Algae Derivatives, and the following should
replace equations 29 and 30 in the Release 2 Technical Documentation)
dBiomassPlnto
= Loading + Photosynthesis - Respiration - Excretion
— Mortality — Predation ± Sinking - Washout ± TurbDiff + •
dBiomass „„„ ,. n, , .
!ML = Loading + Photosynthesis ~ Respiration - Excretion
dt (30)
- Mortality - Predation + SedPeri
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AQUATOX Release 2.1 Technical Documentation Addendum
where:
Slough = Scour of Periphyton to Phytoplankton, see (la);
Sedperi = Sedimentation of Phytoplankton to Periphyton, see (7a).
(See page 4-2 of Release 2 Technical Documenation for other terms and equations.)
Periphyton Code Changes
The following should replace the text and equations on p 4-22 and 4-23, up to "Detrital
Accumulation in Periphyton" on p. 4-23.
Suboptimal light, nutrients, and temperature cause senescence of cells that bind the periphyton
and keep them attached to the substrate. This effect is represented by a factor, Suboptimal,
which is computed in modeling the effects of environmental conditions on photosynthesis.
Suboptimal decreases the critical force necessary to cause sloughing. If the drag force exceeds
the critical force for a given algal group modified by the Suboptimal factor and an adaptation
factor, then sloughing occurs:
If DragForce > SuboptimalOra • FCrit0ro • Adaptation
then Slough = Biomass • FracSloughed
else Slough = 0 /j •>
where:
Suboptimalorg = factor for Suboptimal nutrient, light, and temperature effect on
senescence of given periphyton group (unitless);
FCritorg = critical force necessary to dislodge given periphyton group (kg
m/s2);
Adaptation - factor to adjust for mean discharge of site compared to reference
site (unitless);
Slough = biomass lost by sloughing (g/m3);
FracSloughed = fraction of biomass lost at one time (97%, unitless).
SuboptimalOrg = NutrLimif0rg • LtLimit0ra • TCorr0rg • 20
If SuboptimalOra > 1 then SuboptimalOro - 1
where:
NutrLimit - nutrient limitation for given algal group (unitless) computed by
AQUATOX, see (47);
LtLimitorg = light limitation for given algal group (unitless) computed by
AQUATOX, see (33); and
TCorr - temperature limitation for a given algal group (unitless) computed
by AQUATOX, see (51);
20 = factor to desensitize construct.
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AQUATOX Release 2.1 Technical Documentation Addendum
The sloughing construct was tested and calibrated (U.S. E.P.A., 2001) with data from
experiments with artificial and woodland streams in Tennessee (Rosemond, 1993, ). However,
in modeling periphyton at several sites, it was observed that sloughing appears to be triggered at
greatly differing mean velocities. The working hypothesis is that periphyton adapt to the
ambient conditions of a particular channel. Therefore, a factor is included to adjust for the
velocity of a given site compared to the reference site in Tennessee. It is still necessary to
calibrate FCrit for each site to account for intangible differences in channel and flow conditions,
analogous to the calibration of shear stress by sediment modelers, but the range of calibration
needed is reduced by the Adaptation factor:
Vel2
Adaptation =
0.006634 (3a)
where:
Vel = velocity for given site (m/s), see (14);
0.006634 = mean velocity2 for reference experimental stream (m/s).
The following two sections should be added to the end of Section 4.1, on p. 4-24
Phytoplankton and Zooplankton Residence Time
Phytoplankton and zooplankton can quickly wash out of a short reach, but they may be able to
grow over an extensive reach of a river, including its tributaries. Somehow the volume of water
occupied by the phytoplankton needs to be taken into consideration. To solve this problem,
AQUATOX takes into account the "Total Length" of the river being simulated, as opposed to the
length of the river reach, or "SiteLength" so that phytoplankton and zooplankton production
upstream can be estimated. This parameter can be directly entered on the Site Data screen or
estimated based on watershed area based on Leopold et al. 1964.
TotLength = 1.609 • 1.4 • (Watershed • 0.386)°6 (4a)
where:
TotLength = total river length (km);
Watershed = land surface area contributing to flow out of the reach (square km);
1.609 = km per mile;
0.386 = square miles per square km.
If the total length or watershed area is entered as zero, the phytoplankton and zooplankton
residence time equations are not used and Eqs. 63 and 105 of Release 2 are used to calculate
washout. Otherwise, to simulate the inflow of plankton from upstream reaches plankton
upstream loadings are estimated as follows:
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AQUATOX Release 2.1 Technical Documentation Addendum
Loadingupslream = Washoutbwta -\
Washoutblota \
TotLength I SiteLength )
(5a)
where:
Loadingupstream
Washoutbiota
TotLength
SiteLength
= loading of plankton due to upstream production (mg/L);
= washout of plankton from the current reach (mg/L);
= total river length (km);
= length of the modeled reach (km).
An integral assumption in this approach is that upstream reaches being modeled have identical
environmental conditions as the reach being modeled and that plankton production in each mile
up-stream will be identical to plankton production in the given reach. Residence time for
plankton within the total river length is estimated as follows:
residence
Volume ( TotLength |
Discharge { SiteLength )
(6a)
where:
'residence
Volume
Discharge
TotLength
SiteLength
residence time for floating biota within the total river length (d);
volume of modeled segment reach (m3); see (2, Rel. 2);
discharge of water from modeled reach (m3/d); see Table 1, Rel. 2;
total river length (km);
length of the modeled reach (km).
Periphyton-Phytoplankton Link
Periphyton may slough or be scoured, contributing to the suspended algae; this may be reflected
in the chlorophyll a observed in the water column. Previously, AQUATOX assumed that
sloughed periphyton became detritus. Periphyton may now be linked to a phytoplankton
compartment so that chlorophyll a results reflect the results of periphyton sloughing. One-third
of periphyton is assumed to become phytoplankton and two thirds is assumed to become
suspended detritus in a sloughing event.
Additionally, when phytoplankton undergoes sedimentation it will now be incorporated into the
linked periphyton layer if such a linkage exists. If multiple periphyton species are linked to a
single phytoplankton species, biomass is distributed to periphyton weighted by the mass of each
periphyton compartment.
h A
— Sink
phyta
Mass
(7a)
All Linked Pen
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AQUATQX Release 2.1 Technical Documentation Addendum
where:
= sedimentation that goes to periphyton compartment A;
= total sedimentation of linked phytoplankton compartment, see (61,
Rel.2);
= mass ofperiphyton compartment A;
Mass AH Lmked Pen = mass of all periphyton compartments linked to the
relevant phytoplankton compartment.
If no linkage is present, settling phytoplankton are assumed to contribute to sedimented detritus.
4.4 Steinhaus Similarity Index
This section should be added to page 4-45.
Within the differences graph portion of the output interface, a user may now select to write a set
of Steinhaus similarity indices in Microsoft Excel format. The Steinhaus index (Legendre and
Legendre 1998) measures the concordance in values (usually numbers of individuals, but
biomass in this application) between two samples for each species. A Steinhaus index of 1.0
indicates that all species have identical biomass in both simulations (i.e., the perturbed and
control simulations); an index of 0.0 indicates a complete dissimilarity between the two
simulations.
The equation for the Steinhaus index is as follows:
n ,
2•^mm(Biomass ,„,„„„; , Biomass ^pc,,u,lva
t \
^(Biomass , ioalrol + Biomass , pmmbed)
(8a)
where:
S = Steinhaus similarity index at time t;
Biomass ,_COntroi = biomass of species i, control scenario at time t;
Biomass , pcnurbeci = biomass of species i, perturbed scenario at time t.
A time-series of indices is written for each day of the simulation representing the similarity on
that date. Separate indices are written out for plants, all animals, invertebrates only, and fish
only.
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AQUATOX Release 2.1 Technical Documentation Addendum
5 REMINERALIZATION
5.2 Nitrogen
Replace Section 5.2 with the following section. Note: equations 138 and 139 have been
removed as they are now replaced with the Remineralization calculation below (9a).
Two nitrogen compartments, ammonia and nitrate, are modeled. Nitrite occurs in very low
concentrations and is rapidly transformed through nitrification and denitrification (Wetzel,
1975); therefore, it is modeled with nitrate. Un-ionized ammonia (NHs) is not modeled as a
separate state variable but is estimated as a fraction of ammonia (lOa). Ammonia is assimilated
by algae and macrophytes and is converted to nitrate as a result of nitrification:
dAmmonia T ,. „ ,. ,,. .f
= Loading + Remineralization - Nitrify - Assimilation Ammoma „ ...
at (137)
- Washout ± TurbDiff
where:
dAmmonia/dt = change in concentration of ammonia with time (g/m3-d);
Loading = loading of nutrient from inflow (g/m3-d);
Remineralization = ammonia derived from detritus and biota (g/m3-d), see (9a);
Nitrify = nitrification (g/m3-d), see (144);
Assimilation = assimilation of nutrient by plants (g/m -d), see (141) and (142);
Washout - loss of nutrient due to being carried downstream (g/m3-d), see (16)
TurbDiff = depth-averaged turbulent diffusion between epilimnion and
hypolimnion if stratified (g/m3-d), see (22) and (23).
Remineralization includes all processes by which ammonia is produced from animal, plants, and
detritus, including decomposition, excretion, and other processes required to maintain variable
stoichiometry (see Table 2 on page 22):
Remineralization = PhotoResp + DarkResp + AnimalResp + AnimalExcr
+ DetritalDecomp + AnimalPredation + NutrRelDefecation
+ NutrRelPlantSink + NutrRelMortality + NutrRelGameteLoss
+ NutrRelColonization + NutrRelPeriScour
where:
PhotoResp = algal excretion of ammonia due to photo respiration (g/m3-d);
DarkResp = algal excretion of ammonia due to dark respiration (g/m3-d);
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AQUATQX Release 2.1 Technical Documentation Addendum
AnimalResp = excretion of ammonia due to animal respiration (g/m-d);
AnimalExcr = animal excretion of excess nutrients to ammonia to maintain
constant org. to n ratio as required (g/m3-d);
DetritalDecomp = nitrogen release due to detrital decomposition (g/m3-d);
AnimalPredation — change in nitrogen content necessitated when an animal consumes
prey with a different nutrient content (g/m3-d);
NutrRelDefecation = ammonia released from animal defecation (g/m3-d);
NutrRelPlantSink = ammonia balance from sinking of plants and conversion to detritus
(g/m3-d);
NutrRelMortality = ammonia balance from biota mortality and conversion to detritus
(g/m3-d);
NutrRelGameteLoss = ammonia balance from gamete loss and conversion to detritus
(g/m3-d);
NutrRelColonization = ammonia balance from colonization of refractory detritus into labile
detritus (g/m3-d);
NutrRelPeriScour = ammonia balance when periphyton is scoured and converted to
phytoplankton and suspended detritus. (g/m3-d);
Nitrate is assimilated by plants and is converted to free nitrogen (and lost) through
denitrification:
dNitrate
= Loading + Nitrify - Denitrify - AssimM,,ra!e - Washout ± TurbDiff (140)
dt
where:
dNitrate/dt = change in concentration of nitrate with time (g/m3-d);
Loading = user entered loading of nitrate, including atmospheric deposition;
and
Denitrify = denitrification (g/m3-d).
Free nitrogen can be fixed by blue-green algae. Both nitrogen fixation and denitrification are
subject to environmental controls and are difficult to model with any accuracy; therefore, the
nitrogen cycle is represented with considerable uncertainty.
Assimilation
Nitrogen compounds are assimilated by plants as a function of photosynthesis in the respective
groups (Ambrose et al, 1991):
Assimilation Ammoma= I.PI™, ( Photosynthesisplant • Uptake Nitrooen • NH4PreJ) (141)
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AQUATOX Release 2.1 Technical Documentation Addendum _
Assimilation Ni,,a!e = I/>w (Photosynthesis Plant • Uptake N[!rogen • (1 - NH4PreJ)) (142)
where:
Assimilation = assimilation rate for given nutrient (g/m3-d);
Photosynthesis = rate of photosynthesis (g/m3-d), see (31);
UptakeNitrogen = fraction of photosynthate that is nitrogen (unitless, 0.01975
if nitrogen-fixing, otherwise 0.079);
NH4Pref - ammonia preference factor (unitless).
Only 23 percent of nitrate is nitrogen, but 78 percent of ammonia is nitrogen. This results in an
apparent preference for ammonia. The preference factor is calculated with an equation
developed by Thomann and Fitzpatrick (1982) and cited and used in WASP (Ambrose et al.,
1991):
Arrr,n f N2NH4 • Ammonia • N2NO3 • Nitrate
NH4Pref =
(KN + N2NH4 • Ammonia) • (KN + N2NO3 • Nitrate)
(143)
N2NH4 • Ammonia • KN
(N2NH4 • Ammonia + N2NO3 • Nitrate) • (KN + N2NO3 • Nitrate)
where:
N2NH4 = ratio of nitrogen to ammonia (0.78);
N2NO3 = ratio of nitrogen to nitrate (0.23);
KN = half-saturation constant for nitrogen uptake (g N/m3);
Ammonia = concentration of ammonia (g/m3); and
Nitrate = concentration of nitrate (g/m3).
For algae other than blue-greens, Uptake is the Redfield (1958) ratio; although other ratios (cf.
Harris, 1986) may be used by editing the parameter screen. At this time nitrogen-fixation by
blue-greens is represented by using a smaller uptake ratio, thus "creating" nitrogen.
Nitrification and Denitrification
Nitrification is the conversion of ammonia to nitrite and then to nitrate by nitrifying bacteria; it
occurs primarily at the sediment-water interface (Effler et al., 1996). The maximum rate of
nitrification, corrected for the area to volume ratio, is reduced by limitation factors for
suboptimal dissolved oxygen and pH, similar to the way that decomposition is modeled, but
using the more restrictive correction for suboptimal temperature used for plants and animals:
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AOUATOX Release 2.1 Technical Documentation Addendum
Nitrify = KNitri
Area
Volume
DOCorrection • TCorr • pHCorr • Ammonia
(144)
where:
Nitrify
KNitri
Area -
Volume
DOCorrection
TCorr -
pHCorr
Ammonia '•
nitrification rate (g/m3-d);
maximum rate of nitrification (m/d);
area of site or segment (m );
volume of site or segment (m3); see (2);
correction for anaerobic conditions (unitless) see (131);
correction for suboptimal temperature (unitless); see (51);
correction for suboptimal pH (unitless), see (133); and
concentration of ammonia (g/m3).
The nitrifying bacteria have narrow environmental optima; according to Bowie et al. (1985) they
require aerobic conditions with a pH between 7 and 9.8, an optimal temperature of 30 , and
minimum and maximum temperatures of 10 and 60 respectively (Figure 60, Figure 61).
Figure 60
Response to pH, Nitrification
EFFECT OF pH
6.4 7.8 9.2 106
57 71 8,5 9.9
PH
Figure 61
Response to Temperature, Nitrification
STROGANOV FUNCTION
NITRIFICATION
Q
ropt
10 20 30 40 50 60
TEMPERATURE (C)
In contrast, denitrification (the conversion of nitrate and nitrite to free nitrogen) is an anaerobic
process, so that DOCorrection enhances the process (Ambrose et al., 1991):
Denitrify = KDenitri • (1 - DOCorrection) • TCorr • pHCorr • Nitrate
(145)
where:
Denitrify
KDenitri
Nitrate
denitrification rate (g/m3-d);
maximum rate of denitrification (g ammonia/g nitrate); and
concentration of nitrate (g/m3).
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AQUATOX Release 2.1 Technical Documentation Addendum
Furthermore, it is accomplished by a large number of reducing bacteria under anaerobic
conditions and with broad environmental tolerances (Bowie et al., 1985; Figure 62, Figure 63).
Figure 60
Response to pH, Denitrification
EFFECT OF pH
48
6.6
8.4
10.2
5.7
7.5
9.3
PH
Figure 61
Response to Temperature, Denitrification
STROGANOV FUNCTION
DECOMPOSITION
TOpt
10
20 30 40 50
TEMPERATURE (C)
60
lonization of Ammonia
The un-ionized form of ammonia, NHa, is toxic to invertebrates and fish. Therefore, it is often
singled out as a water quality criterion. Un-ionized ammonia is in equilibrium with the
ammonium ion, NH4+, and the proportion is determined by pH and temperature. Previous
versions of AQUATOX did not differentiate the forms of ammonia. However, now that pH is a
dynamic variable (see new section 5.7), it is useful to report NHs as well as total ammonia (Nth
+ NH4+).
The computation of the fraction of total ammonia that is un-ionized is relatively straightforward
(Bowie etal. 1985):
FracNHl
10*** p»
NH3 = FracNH3 • Ammonia
2729.92
pkh = 0.09018 +
TKelvin
(lOa)
(lla)
(12a)
where:
FracNHS
pkh
NH3
Ammonia
TKelvin
fraction of un-ionized ammonia (unitless);
hydrolysis constant;
un-ionized ammonia (mg/L);
total ammonia (mg/L); see (137, Rel. 2)
temperature (°K).
The relative contributions of temperature and pH can be seen by graphing the fraction of un-
ionized ammonia against each of those variables in simulations of Lake Onondaga (Figures 1 and
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AQUATOX Release 2.1 Technical Documentation Addendum
2). As inspection of the construct would suggest, un-ionized ammonia has a linear relationship
to temperature and a logarithmic relationship to pH, which causes it to be sensitive to extremes
in pH.
Fraction NH3
-FracNHS
Temp
0
Sep-88 Apr-89 Oct-89 May-90 Nov-90 Jun-91
Figure 1. Fraction of un-ionized ammonia roughly following temperature.
Fraction NH3
-FracNHS
PH
Sep-88 Apr-89 Oct-89 May-90 Nov-90 Jun-91
Figure 2. Fraction of un-ionized ammonia affected by extreme values of pH.
The construct was verified with the same set of data from Lake Onondaga as was used for the pH
verification (Effler et al. 1996). It fits the observed data well (Figure 3).
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AQUATOX Release 2.1 Technical Documentation Addendum
Fraction NH3
- Frac NH3
Obs frac NH3
Poly. (Obs frac IMH3)|
Feb-
89
Apr-
89
May- Jul-89 Aug- Oct-
89 89 89
Dec-
89
Figure 3. Comparison of predicted and observed fraction of NH3 for Lake Onondaga, NY.
Data from (Effler et al. 1996).
5.3 Phosphorus
Replace Section 5.2 with the following section. Note: equations 147 and 148 have been
removed as they are now replaced with the Remineralization calculation below (13a).
dt
where:
^Phosphate/At =
Loading =
Remineralization =
Assimilation =
Washout =
TurbDiff
dPhosphate
- = Loading + Remineralization - Assimilation phospha,e
(146)
- Washout ± TurbDiff
change in concentration of phosphate with time (g/m3-d);
loading of nutrient from inflow (g/m3-d);
phosphate derived from detritus and biota (g/m3-d), see (13a);
assimilation of nutrient by plants (g/m3-d), see (149);
loss of nutrient due to being carried downstream (g/m3-d), see (16)
depth-averaged turbulent diffusion between epilimnion and
hypolimnion if stratified (g/m3-d), see (22) and (23).
As was the case with ammonia, Remineralization includes all processes by which phosphate is
produced from animal, plants, and detritus, including decomposition, excretion, and other
processes required to maintain mass balance given variable stoichiometry (see Table 3 on page
24):
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AQUATQX Release 2.1 Technical Documentation Addendum
Remineralization = PhotoResp + DarkResp + AnimalResp + AnimalExcr
+ DetritalDecomp + AnimalPredation + NutrRelDefecation
+ NutrRelPlantSink + NutrRelMortality + NutrRelGameteLoss
+ NutrRelColonization + NutrRelPeriScour
(13a)
where:
PhotoResp
DarkResp
AnimalResp
AnimalExcr
DetritalDecomp
A nimalPredation
NutrRelDefecation
NutrRelPlantSink
NutrRelMortalitv
NutrRelGameteLoss =
NutrRelColonization =
NutrRelPeriScour =
= algal excretion of phosphate due to photo respiration (g/m3-d);
algal excretion of phosphate due to dark respiration (g/m -d);
excretion of phosphate due to animal respiration (g/m3-d);
animal excretion of excess nutrients to phosphate to maintain
constant org. to p ratio as required (g/m3-d);
phosphate release due to detrital decomposition (g/m -d);
change in phosphate content necessitated when an animal consumes
prey with a different nutrient content (g/m3-d);
phosphate released from animal defecation (g/m3-d);
phosphate balance from sinking of plants and conversion to detritus
(g/m3-d);
phosphate balance from biota mortality and conversion to detritus
(g/m3-d);
phosphate balance from gamete loss and conversion to detritus
(g/m3-d);
phosphate balance from colonization of refractory detritus into
labile detritus (g/m3-d);
phosphate balance when periphyton is scoured and converted to
phytoplankton and suspended detritus. (g/m3-d);
At this time AQUATOX models only phosphate available for plants; a correction factor in the
loading screen allows the user to scale total phosphate loadings to available phosphate. A future
enhancement could be to consider phosphate precipitated with calcium carbonate, which would
better represent the dynamics of marl lakes; however, that process is ignored in the current
version. A default value is provided for average atmospheric deposition, -but this should be
adjusted for site conditions. In particular, entrainment of dust from tilled fields and new
highway construction can cause significant increases in phosphate loadings. As with nitrogen,
the uptake parameter is the Redfield (1958) ratio; it may be edited if a different ratio is desired
(cf. Harris, 1986).
5.4 Nutrient Mass Balance
This section should be inserted on p. 5-17 as the new section 5.4. Current Sections 5.4
(Dissolved Oxygen) and 5.5 (Carbon Dioxide), should be renumbered as 5.5 and 5.6,
respectively
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AQUATOX Release 2.1 Technical Documentation Addendum
Variable Stoichiometry
A notable simplification in AQUATOX has been the assumption of constant Stoichiometry
across trophic levels. However, in order to better model nutrients, the latest version of
AQUATOX allows the ratios of elements in organic matter to vary considerably. This is
accomplished by providing editable fields for N:organic matter and P:organic matter for each
compartment. Furthermore, the wet to dry ratio is editable for all compartments; it had been
hard-wired with a value of 5.
In order to maintain the specified ratios for each compartment, the model now explicitly
accounts for processes that balance the ratios during transfers, such as excretion coupled with
consumption and nutrient uptake coupled with colonization. Nutritional value is not
automatically related to Stoichiometry in the model, but it is implicit in default egestion values
provided with various food sources. Table 1 shows the default stoichiometric values suggested
for the model based on two references (Elser et al. 2000) (Sterner and Elser 2002).
Table 1: Default Stochiometric Values in AQUATQX
Compartment
Refrac. detritus
Labile detritus
Phytoplankton
Bl-greens
Periphyton
Macrophytes
Cladocerans
Copepods
Zoobenthos
Minnows
Shiner
Perch
Smelt
Bluegill
Trout
Bass
Frac. N
(dry)
0.002
0.059
0.059
0.059
0.04
0.018
0.09
0.09
0.09
0.097
0.1
0.1
0.1
0.1
0.1
0.1
Frac. P
(dry)
0.0002
0.007
0.007
0.007
0.0044
0.002
0.014
0.006
0.014
0.0149
0.025
0.031
0.016
0.031
0.031
0.031
Reference
Sterner & Elser 2002
Same as phytoplankton
Sterner & Elser 2002
same as phytoplankton for now
Sterner & Elser 2002
Sterner & Elser 2002
Sterner & Elser 2002
Sterner & Elser 2002
same as cladocerans for now
Sterner 2000
Sterner 2000
Sterner 2000
Sterner 2000
same as perch for now
same as perch for now
same as perch for now
Nutrient Loading Variables
Often water quality data are given as total nitrogen and phosphorus. In order to improve
agreement with monitoring data, AQUATOX can now accept both loadings and initial
conditions as "Total N" and "Total P." This is made possible by accounting for the nitrogen and
phosphorus contributed by suspended and dissolved detritus and phytoplankton and back-
calculating the amount that must be available as freely dissolved nutrients. The precision of this
conversion is aided by the model's variable Stoichiometry. For nitrogen:
18
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AQUATOX Release 2.1 Technical Documentation Addendum
N = N - N - N
Dissolved Total SuipendedDetritus SuspendedPIants
where:
^Dissolved = bioavailable dissolved nitrogen (g/m3 d); see (137 & 140, Rel. 2);
Njotai - loadings of total nitrogen as input by the user (g/m3 d);
NsuspendedDetntus = nitrogen in suspended detritus loadings (g/m3 d);
N SuspendedPIants - nitrogen in suspended plant loadings (g/m3 d).
In acknowledgment of the way it is used in the model, the phosphorus state variable is now
designated "Total Soluble P." Phosphorus that is not bioavailable (i.e. immobilized phosphorus/
acid soluble phosphorus) may be specified using the FracAvail parameter as shown here:
TSP = FracAvail(PTolal - PSuspendedDetntus - PSuspendedPlants } ( 1 5a)
where:
TSP = bioavailable phosphorus (g/m3 d); see (146, Rel. 2);
FracAvail = user input bioavailable fraction of phosphorus;
P Total = loadings of total phosphorus (g/m3 d);
= phosphorus in suspended detritus loadings (g/m3 d);
= phosphorus in suspended plant loadings (g/m3 d).
Nutrient Output Variables
In order to compare model results with monitoring data, total phosphorus, and total nitrogen are
now calculated as output variables. This is accomplished by the reverse of the calculations for
the loadings: the contributions of the nutrient in the freely dissolved state and tied up in
phytoplankton and dissolved and particulate organic matter are calculated and summed.
Biochemical oxygen demand (BOD5) is computed as the sum of the contributions from
phytoplankton and labile dissolved and particulate organic matter using a conversion of 1.35
BOD/organic matter.
Mass Balance of Nutrients
New variables for tracking mass balance and nutrient fate have been added to the output as
detailed below. Phosphorus and Nitrogen now balance mass to machine accuracy. To maintain
mass balance, nutrients are tracked through many interactions. The mass balance and nutrient
fate tracking variables are:
Nutrient Tot. Mass: Total mass of nutrient in the system in kg
Nutrient Tot. Loss: Total loss of nutrient from system since simulation start, kg
Nutrient Tot. Washout: Total washout since simulation start, kg
19
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AQUATOX Release 2.1 Technical Documentation Addendum
Nutrient Wash, Dissolved: Washout in dissolved form since simulation start, kg
Nutrient Wash, Animals: Washout in animals since start, kg
Nutrient Wash, Detritus: Washout in detritus since start, kg
Nutrient Wash, Plants: Washout in plants since start, kg
Nutrient Loss Emergel: Loss of nutrients in emerging insects since start, kg
Nutrient Loss Denitrif.: Denitrification since start, kg
Nutrient Burial: Burial of nutrients since start, kg
Nutrient Tot. Load: Total nutrient load since start, kg
Nutrient Load, Dissolved: Dissolved nutrient load since start, kg
Nutrient Load as Detritus: Nutrient load in detritus since start, kg
Nutrient Load as Biota: Nutrient load in biota since start, kg
Nutrient Root Uptake: Load of nutrients into sytem via macrophyte roots since start, kg
Nutrient MB Test: Mass balance test, total Mass + Loss - Load: Should stay constant
Nutrient Exposure: Exposure of buried nutrients
Nutrient Net Layer Sink: For stratified systems, sinking since start, kg
Nutrient Net TurbDiff: For stratified systems, Turbdiff since start, kg
Nutrient Net Layer Migr.: For stratified systems, migration since start, kg
Nutrient Total Net Layer: Net movement over layers, kg
Nutrient Mass Dissolved: Total mass of dissolved nutrient in system, kg
Nutrient Mass Detritus: Total mass of nutrient in detritus in system, kg
Nutrient Mass Animals: Total mass of nutrient in animals in system, kg
Nutrient Mass Plants: Total mass of nutrient in plants in system, kg
Please make careful note of the units presented in the list above. Load and loss terms are
calculated in terms of "kg since the start of the simulation," total mass units are "kg at the current
moment."
A simplified diagram of the nitrogen and phosphorus cycles can be found in Figures 4 and 5. A
full accounting of the 18 nutrient linkages and all external loads and losses for nitrogen and
phosphorus is also provided in Tables 2 and 3.
20
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AOUATQX Release 2.1 Technical Documentation Addendum
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AQUATOX Release 2.1 Technical Documentation Addendum
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AQUATOX Release 2.1 Technical Documentation Addendum
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24
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AQUATQX Release 2.1 Technical Documentation Addendum
There are instances in which nutrients can be moved to and from compartments that are not in
the model domain. For example, when NOs undergoes denitrification and becomes free nitrogen
the free nitrogen is no longer tracked within AQUATOX. An example of nutrients entering the
model domain comes with the growth of macrophytes. Rooted macrophytes are not limited by a
lack of nutrients in the water column as nutrients are derived from the sediment. Therefore,
when photosynthesis of macrophytes produces growth, the nutrient content within the leaves of
the macrophytes is assumed to originate from the pore waters of the sediments which is not
modeled in this version of AQUATOX.
In some cases, when concentrations of nutrients in the water column drop to zero, perfect mass
balance of nutrients will not be maintained. Nutrient to organic matter ratios within organisms
do not vary over time, therefore transformation of organic matter (e.g. consumption, mortality,
sloughing, and sedimentation) occasionally requires that a nutrient difference be made up from
the water column. If there are no available nutrients in the water column, a slight loss of mass
balance is possible.
The mass associated with each component can be plotted, as in Figure 6.
ONONDAGA LAKE, NY (PERTURBED) 7/5/2004 723:35 PM
(Epilimnion Segment)
— N Tot. Mass (kg)
—- N Mass Dissolved (kg)
— N Mass Detritus (kg)
— N Mass Animals (kg)
N Mass Plants (kg)
N Wash, Animals (kg)
— N Wash, Detritus (kg)
NWash, Plants (kg)
N Loss Emerge! (kg)
— N Loss Denitrif. (kg)
N Burial (kg)
— N Load as Detritus (kg)
N Load as Biota (kg)
— N Root Uptake (kg)
1/18/1989 5/18/1989 9/15/1989 1/13/1990 5/13/1990 9/10/1990 1/8/1991
Figure 6 Distribution of predicted mass of nitrogen in Lake Onondaga NY.
5.7 Modeling Dynamic pH
(add this section to the end of chapter 5)
25
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AQUATOX Release 2.1 Technical Documentation Addendum
Dynamic pH is important in simulations for several reasons. As demonstrated in section 5.2,
ionization of ammonia is sensitive to pH. Furthermore, hydrolysis of organic chemicals can be
sensitive to pH. Both these relationships are modeled in AQUATOX. In addition, the viability
of organisms and bioaccumulation and toxiciry of organic chemicals can be dependent on pH;
these relationships are not currently modeled by AQUATOX.
Many models follow the example of Stumm and Morgan (1996) and solve simultaneous
equations for pH, alkalinity, and the complete carbonate-bicarbonate equilibrium system.
However, this approach requires more data than are often available, and the iterative solution of
the equations entails an additional computational burden—all for a precision that is unnecessary
for ecosystem models. The alternative is to restrict the range of simulated pH to that of normal
aquatic systems and to make simplifying assumptions that allow a semi-empirical computation of
pH (Marmorek et al. 1996, Small and Sutton 1986). That is the approach taken for AQUATOX.
The computation is good for the pH range of 4 to 8.25, where the carbonate ion is negligible and
can thus be ignored. The derivation is given by Small and Sutton (1986), with a correction for
dissolved organic carbon (Marmorek et al. 1996). It incorporates a quadratic function of carbon
dioxide; and it is a nonlinear function of mean alkalinity and the concentration of refractory
dissolved organic carbon (humic and fulvic acids), by means of an inverse hyperbolic sine
function:
DOC}
„„ , ADA „.„( Alkalinity-5.1
pHCalc = A + B • ArcSmH
I C
(16a)
where:
pHCalc
ArcSinH
Alkalinity =
DOC
5.1 =
A = -Log-J Alpha
B = l/ln(10)
C = 2 • JAlpha
Alpha = H2CO3 * • CCO2 + pkw
pH;
inverse hyperbolic sine function;
mean Gran alkalinity (ueq CaCOs/L);
refractory dissolved organic carbon (mg/L); calc. from (114, 115,
Rel 2);
average ueq of organic ions per mg of DOC;
H2CO3* = 10
-(657-00118 J + 000012 T T) 092
where:
H2CO3*
CCO2
pkw
T
0.92
first acidity constant;
COi expressed as ueq/L; see (164, Rel. 2);
ionization constant for water (le-14);
temperature (°C); see (24, Rel. 2);
correction factor for dissolved CO2
Calibration and verification of the construct used data from nine lakes and ponds in the National
Eutrophication Survey (U.S. Environmental Protection Agency, 1977), two observations on Lake
26
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AQUATQX Release 2.1 Technical Documentation Addendum
Onondaga, NY, from before and after closure of a chlor-alkali plant (Effler et al., 1996), and one
observation in a river (Figure 7). The correction factor for CC>2 was obtained by fitting the data
to the unity line, but ignoring the two highest points because the construct does not predict pH
above 8.25.
6.0
Observed vs. Predicted pH
7.0
8.0
Predicted pH
9.0
10.0
Figure 7 Comparison of predicted and observed pHs from selected lakes.
The construct also was verified using time-series data from Lake Onondaga, NY (Figure 8). The
observed data were interpolated from the 2-m depth pH isopleths on a graph (Effler et al. 1996),
introducing some uncertainty into the comparison.
Predicted pH, Lake Onondaga NY
AQUATOX
• Observed
• Poly. (Observed)
Feb- Apr- May- Jul-89 Aug- Oct- Dec-
89 89 89 89 89 89
Figure 8. Comparison of predicted and observed pH values for Lake Onondaga, NY.
Data from (Effler et al. 1996).
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AQUATOX Release 2.1 Technical Documentation Addendum
7 Toxic Organic Chemicals
7.6 Nonequilibrium Kinetics
The following text and equation should replace Equation (251) on page 7-24 and the
descriptive paragraph preceding it.
Given the latest model formulations and testing, it is not necessary to normalize an uptake rate
constant (Difj) based on competing uptake rates. The Runge-Kutta dufferential equation solver
effectively removes any issues of excessive chemical uptake and toxicant mass balance is
maintained at all times. Therefore:
Diff = \ti (251)
7.7 Alternative Uptake Model: Entering BCFs, K1, and K2
The following should be added to the end of Chapter 7, on page 7-36.
When performing bioaccumulation calculations, the default behavior of the AQUATOX model is
to allow the user to enter elimination rate constants (K2) for all plants and animals for a
particular organic chemical. K2 values may also be estimated based on the LogKow of the
chemical. Uptake in plants is a function of log KQW while gill uptake in animals is a function of
respiration and chemical uptake efficiency. The AQUATOX default model works well for a
wide variety of bioaccumulative organic chemicals, but some chemicals are subject to very rapid
uptake and depuration are not effectively modeled using these relationships.
For this reason, an alternative uptake model is provided to the user. In the chemical toxicity
record, the user may enter two of the three factors defining uptake (BCF, Kl, K2) and the third
factor is calculated using the below relationship:
K2
(17a)
where: BCF = bioconcentration factor (L/kg dry);
Kl = uptake rate constant (L/kg dry day);
K2 — elimination rate constant (1/d).
Given these parameters, AQUATOX calculates uptake and depuration in plants and animals as
kinetic processes.
Uptake = Kl • ToxState • Biomass • 1 e - 6 /< o \
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AQUATOX Release 2.1 Technical Documentation Addendum
Depuration = K2-ToxState
where: Uptake = uptake rate within organism (|ng/L day);
Kl = uptake rate constant (L/kg dry day);
ToxState = concentration of toxicant in organism in water
Biomass = concentration organism in water (mg/L)
le-6 = (kg/mg)
Depuration = loss rate within organism (^ig/L day);
K2 = elimination rate constant (1/d).
Dietary uptake of chemicals by animals is not affected by this alternative parameterization.
7.8 Half Life Calculation Refinement DT50 & DT95
The half-life estimation capability with AQUATOX has been significantly upgraded since
Release 2. AQUATOX now estimates time to 50% (half-lives, DTSOs) and time to 95%
chemical loss (DT95s) independently in bottom sediment and in the water column. Estimates are
produced at each output time-step depending on the average loss rate during that time-step in that
medium.
Hydrolysis Water + Photolysis + Microbial Waler + Washout + Volat. + Sorption
(20a)
where: Loss
Hydrolysis
Photolysis
Microbial
Microbial Sed + Hydrolysis w + Desorption
= — -
Mass.
3 Sed
(21a)
= loss rate within media (1/d);
= hydrolysis rate in given media (|ag/L d), see (212, Rel. 2);
= photolysis rate in the water column (^ig/L d), see (219, Rel. 2);
= rate of microbial metabolism in given media (ug/L d), see (225, Rel.
2);
= rate of toxicant washout from the water column ((J.g/L d); see (16, Rel.
2)
= rate of chemical volatilization in the water column (jag/L d), see (230,
Rel. 2);
Sorption — sorption of toxicant to detritus, plants, and animals (|ag/L d), see (249,
Rel. 2);
Mass Media = mass of chemical in the media (ug/L);
Desorption — desorption of toxicant from bottom sediment, see (250, Rel. 2).
Washout =
Volat =
Loss rates are converted into time to 50% and 95% loss using the following formulae for first-
order reactions:
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AQUATQX Release 2.1 Technical Documentation Addendum
where:
LoSS
uedm
DT95Media =2.9961 Loss Media
(22a)
(23a)
time in which 50% of chemical will be lost at current loss rate (d);
time in which 95% of chemical will be lost at current loss rate (d);
loss rate within media (1/d);
The following should be inserted at the end of Chapter 8, on p. 8-10
8 ECOTOXICOLOGY
8.3 External Toxicity
Chemicals that are taken up very rapidly and those that have an external mode of toxicity, such
as affecting the gills directly, are best simulated with an external toxicity construct. AQUATOX
has an alternative computation for CumFracKilled, when calculating toxic effects based on
external concentrations, using the two-parameter Weibull distribution as in Christiensen and
Nyholm(1984):
CumFracKiled = 1 - exp(-fe ta)
(24a)
where: z = external concentration of toxicant (ug/L);
CumFracKilled = cumulative fraction of organisms killed for a given period of exposure
(fraction/d);
k and Eta = fitted parameters describing the dose response curve.
Rather than require the user to fit toxicological bioassay data to determine the parameters for k
and Eta, these parameters are derived to fit the LC50 and the slope of the cumulative mortality
curve at the LC50 (in the manner of the RAMAS Ecotoxicology model, Spencer and Person,
1997):
k =
-ln(0.5)
IC50
Eta
(25a)
Eta =
-2-LC50-slope
ln(0.5)
(26a)
where: slope = slope of the cumulative mortality curve at LC50 (unitless).
LC50 = concentration where half of individuals are affected (jag/L).
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AQUATOX Release 2.1 Technical Documentation Addendum
AQUATOX assumes that each chemical's dose response curve has a distinct shape, relevant to
all organisms modeled. In this manner, a single parameter describing the shape of the Weibull
parameter can be entered in the chemical record rather than requiring the user to derive slope
parameters for each organism modeled.
However, as shown below, the slope of the curve at the LC50 is both a function of the shape of
the Weibull distribution and also the magnitude of the LC50 in question.
Figures 9 and 10 show two Weibull distributions with identical shapes, but with slopes that are
significantly different due to the scales of the x axes:
Weibull Distribution, LC50=1, Slope=1
Weibull Distribution, LC50=100, Slope=0.1
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Slope
,y
0.5 1 1.5
Concentration
50 100 150 200
Concentration
Figures 9 and 10: Weibull distributions with identical shapes, but with slopes that are significantly different
due to the scales of the x axes
For this reason, rather than have a user enter "the slope at LC50" into the chemical record,
AQUATOX asks that the user enter a "slope factor" defined as "the slope at LC50 multiplied by
LC50." In the above example, the user would enter a slope factor of 1.0 and then, given an
LC50 of 1 or an LC50 of 100, the above two curves would be generated.
When modeling toxicity based on external concentrations, organisms are assumed to come to
equilibrium with external concentrations (or the toxicity is assumed to be based on external
effects to the organism).
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AQUATOX Release 2.1 Technical Documentation Addendum
9 REFERENCES
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