Transcript Slide 1

5. EUTROPHICATION
OF LAKES
Like winds and sunsets, wild things were
taken far granted until progress began to
do away with them.
- Aldo Leopold, A Sand County Almanac
5.1. INTRODUCTION
• Eutrophication: excessive rate of addition of nutrients (usually:
anthropogenic activities and the addition of phosphorus and
nitrogen to natural waters).
• Nutrient additions result in the excessive growth of plants
including phytoplankton (free-floating algae), periphyton
(attached or benthic algae), and macrophytes (rooted, vascular
aquatic plants).
• Natural process taking place over geologic time that is greatly
accelerated by human activities. For example, due to soil erosion
and biological production, lakes normally fill with sediments
over thousands of years; but eventually the process is
accelerated to decades.
• Undesirable effects of water quality:
– Excessive plant growth (green color, decreased transparency,
excessive weeds).
– Hypolimnetic loss of dissolved oxygen (anoxic conditions).
– Loss of species diversity (loss of fishery).
– Taste and odor problems.
• Not all eutrophic lakes will exhibit all of these water quality
problems, but they are likely to have one or more of them.
• Eutrophication is excessive rate of addition of nutrients.
• Water quality may become so degraded that the lake's original
uses are lost, being no longer be swimmable or fishable.
Figure 5.1. Schematic of
lake eutrophication and
nutrient recycle.
Thermal stratification
causes a high concentration of oxygen in nearsurface waters, but the
dissolved oxygen cannot
mix vertically, and deep
water eventually becomes
anoxic. Anoxic conditions
in the bottom waters and
sediments cause anaerobic
decomposition and the
release of nutrients (phosphate, ammonia, dissolved
iron).
• The degree of eutrophication is a continuum. It is referred to as the
“trophic status” of the water body:
– Oligotrophic (undernourished – biological production is limited
by nutrient additions);
– Mesotrophic;
– Eutrophic (well-nourished).
• Phosphate is often the limiting nutrient for algal growth, and point
sources are reduced by precipitation of phosphate in wastewater
with iron chloride or iron sulfate.
• Nonpoint sources are caused by agricultural runoff, stormwater
runoff, or combined-sewer overflow. Both particulate and
dissolved forms of nutrient additions are important.
• Bioavailability of nutrients debate as to the relative contribution of
particulate and adsorbed nutrients to the eutrophication of lakes.
5.2. STOICHIOMETRY
• Primary production in natural waters is the photosynthetic
process whereby carbon dioxide and nutrients are converted to
plant protoplasm.
• Respiration is the reverse process in which protoplasm
undergoes endogenous decay and/or lysis and oxidation:
• Algal protoplasm: C106H263O110N16P1
• Algal respiration occurs during both dark and daylight hours,
but photosynthetic primary production can occur only in the
presence of sunlight.
• Algae settle and decay, and the nutrients recycle from the
sediment back to the overlying water.
Figure 5.2. Regulation of the chemical composition of
natural waters by algae.
• Stoichiometry helps to determine the ratios of elements
assimilated during primary production for mathematical
modeling. It also helps us to understand the regulation of the
chemical composition of natural waters.
• The molar ratio of nitrogen to phosphorus is 16:1, as indicated
by the stoichiometric equations. Algae assimilate nutrients in
these ratios and when algal cells lysis and decompose (Figure
5.3), they release the N and P in the same molar ratios, thus
regulating, to some extent, the chemical composition of natural
waters.
• Martin and co-workers: relatively high concentrations of nitrate
and phosphate in Antarctic seas is due to the extremely small
amounts of dissolved iron in solution, which limits phytoplankton growth.
Figure 5.3.
Ratio of nitrate
to phosphate in
surface ocean
waters.
Example 5.1. Nutrient Assimilation Rates Based on Algal Uptake
Stoichiometry
Assuming the stoichiometric relationship for algal protoplasm,
estimate the nutrient uptake rate for nitrate and CO2 in Lake
Ontario. It was observed that 5µg per liter of phosphate was
removed from the euphotic zone during the month of May in Lake
Ontario. What is the rate of phytoplankton production in biomass,
dry weight?
Solution: Use stoichiometric ratios to convert from phosphorus to
biomass.
(31 days) (18.5 μg L-1 d-1) = 573 µg L-1
algae
0.57 mg L-1 of algae grew during the month of May in Lake Ontario
5.3. PHOSPHORUS AS A LIMITING NUTRIENT
• It is possible that one of many nutrients could limit algal growth.
• Parsons and Takahash: the following micronutrients are important for photosynthesis.
• Macronutrients: CO2 (the carbon source), phosphorus, nitrogen
(ammonia or nitrate), Mg, K, Ca, and dissolved silica for diatom
frustule formation.
• Algae and rooted aquatic plants are photoautotrophs-they use
sunlight as their energy source and CO2 for their carbon source.
• The terminal electron acceptor is usually dissolved oxygen in the
electron transfer of photosynthesis.
• Any of these nutrients could become limiting for growth, as per
Liebig's law of the minimum from the 19th century.
• Liebig envisioned a saturated response growth curve for each
nutrient, similar to that shown for Monod kinetics (Figure 5.4).
μmax – maximum growth rate, S – substrate or nutrient concentration, Ks –
half-saturation constant.
• It is possible that more than one nutrient will become limiting for
growth at the same time. Di Toro - evaluated the expressions for an
electrical resistance analogue in parallel:
and a multiplicative analogue:
Figure 5.4. Response curves of algal growth rates to limiting
nutrients, light, and temperature.
Example 5.2. Limiting Nutrient Concentrations and Overall Growth
Rates
Estimate the resulting growth rate for diatom phytoplankton in the
Great Lakes by three different methods for the following data if the
maximum growth rate is 1.0 per day.
- Liebig’s law of the minimum.
- Electrical resistance analogy.
- Multiplicative algorithm.
Solution: a) Monod kinetic expressions:
Liebig: the min growth rate is the appropriate choice  the answer
is 0.2 d-1.
b) Electrical resistance analogy:
All three nutrients contribute to an overall growth rate.
c) Multiplicative algorithm:
The multiplicative law also includes limitation due to all three
nutrients, and it result in the lowest predicted growth rate of all
three methods.
Experiment studies with all three nutrients in combination
would be needed to confirm which model is most accurate.
5.4. MASS BALANCE ON TOTAL PHOSPHORUS
IN LAKES
• A simple mass balance can be developed on the limiting nutrient
in a lake, e.g., total phosphorus.
• Here: total phosphorus – unfiltered concentration of inorganic,
organic, dissolved, and particulate forms of phosphorus.
• For steady flow and constant volume: lake is a completely mixed
flow-through system (Pout = Plake).
• The average lake concentration is equal to the concentration of
total phosphorus in the outflow.
V – lake volume; P – total phosphorus concentration; Qin – inflow rate;
Pin – inflow total phosphorus concentration; ks – first-order sedimentation coefficient, Q – outflow rate.
• The sedimentation coefficient is surrogate parameter for the
mean settling velocity, reciprocal mean depth, and an alpha
factor for the ratio of particulate phosphorus to total
phosphorus
α – ratio of particulate P to total P, vs – mean particle settling velocity,
H – mean depth of the lake
• Under conditions of steady-state the eq`n can be simplified:
• If evaporation can be neglected, the inflow rate is approximately
equal to the outflow rate.
τ – hydraulic detention time.
• Total P concentration is directly related to the total P concentration in the inflow (Pin), and it is inversely related to the hydraulic detention time and the sedimentation rate constant (the main
removal mechanism of total phosphorus from the water column).
The fate of total phosphorus in lakes is determined by an important dimensionless number (ksτ).
• There is a trade-off between the detention time of the lake and
the sedimentation rate constant — it is the product of these two
parameters that determines the ratio of total phosphorus in the
lake to the inflowing total P:
• The fraction of total phosphorus that is trapped or removed
from the water column:
Example 5.3. Mass Balance on Total Phosphorus in Lake Lyndon
B. Johnson
Lake Lyndon B. Johnson is a flood control and recreational
reservoir along a chain of reservoirs in central Texas along the
Colorado River. It has an average hydraulic detention time of 80
days, a volume of 1.71 × 108 m3, and a mean depth of 6.7 m. The
ratio of particulate to total phosphorus concentration in the lake is
0.7, and the mean particle settling velocity is 0.1 m d-1. If the flowweighted average inflow concentration of total phosphorus to Lake
LBJ is 72 µg L-1, estimate the average annual total phosphorus
concentration in the lake.
Solution:
The answer is a total phosphorus concentration of 39 µg L-1 in the
lake, 70% of which is particulate material and the remaining 30%
is dissolved (most of the dissolved P is PO43- available for
phytoplankton uptake). The mass flux of total P to the bottom
sediment is ksPV, a flux rate equal to 69 kg d-1.
A plot of the fraction of total P remaining in any lake as a function
of the dimensionless number ksτ is given by Figure 5.5. The fraction
of total P removed to the lake sediments is equal to 0.454 (1 - P/Pin);
the mass of total phosphorus entering the lake is 152 kg d-1 and the
mass outflow is 83 kg d-1 (Figure 5.6).
Figure 5.5. Fraction of total P remaining in a lake as a function of
the sedimentation coefficient ks times the detention time τ
Figure 5.6. Total P mass balance for Lake Lyndon B.
Johnson in central Texas
5.5. NUTRIENT LOADING CRITERIA
• Clair Sawyer was one of the first investigators to consider
adoption of a criterion for the classification of eutrophic lakes.
• Vollenweider: not the nutrient concentration that mattered as
much as the nutrient supply rate. He published "permissible"
and ''dangerous" loading rates for lakes based on a log-log plot
of annual phosphorus loading versus mean depth, which became
widely adopted as nutrient loading criteria - classify lakes as
oligotrophic, mesotrophic, and eutrophic.
• Actually, both approaches to classifying the trophic status are
valid and interrelated.
• Dillon and Rigler modified the Vollenweider approach to
account for lakes of varying hydraulic detention times and
phosphorus retention (fraction removed).
– improved the empirical fit of the observed lake conditions
and their trophic classification;
– firmly grounded the classification criteria on mass balance
principles.
• Now possible to determine the mean total phosphorus
concentrations that defined the classification scheme (Figure
5.7).
• Oligotrophic lakes: less than 10 µg L-1 total phosphorus on an
annual average basis.
• Eutrophic lakes: greater than 20 µg L-1 total phosphorus
concentrations.
• Mesotrophic lakes: intermediate with total P concentrations
between 10 and 20 µg L-1.
Figure 5.7.
Nutrient loading
criteria and
classification.
• Areal nutrient (phosphorus) loading rate – L  the mass balance:
• Dividing through by the volume of the lake (V = AsurfH):
νs – mean apparent settling velocity of total phosphorus.
• At steady-state (dP/dt = 0) the eq`n is simplified to:
• The final equation describing Figure 5.7 is:
ρ – hydraulic flushing rate of the lake (1/τ).
• A log-log plot of nutrient loading remaining, L(1-R)/ρ, versus
mean depth (H) will have a slope of 1.0 and an intercept where
H = 1 m of log P.
• Lorenzen, Larsen: similar simple mass balance models for
predicting total P concentration in lakes – the concepts of total P
loading on an areal basis and an apparent settling velocity at
steady state.
• Multiplying by the mean depth H:
qs – surface overflow rate for the lake (Q/Asurf)
5.6. RELATIONSHIP TO STANDING CROP
• The nuisance conditions of a eutrophic lake are not only nutrient
concentrations, but rather:
– excessive algal blooms;
– decreased transparency;
– decaying algae in the sediment (consumes oxygen, ruins
aquatic habitats, causes taste and odor problems).
• Previous mass balance models: predict steady-state or annual
average total P, but do not predict biomass or chlorophyll α
(phytoplankton pigments as a measure of standing crop).
• Other investigations: it is possible to correlate the summer
concentration of total P to the chlorophyll concentration (Figure
5.8).
Figure 5.8.
Relationship
between summer
levels of chlorophyll
a and measured
total phosphorus
concentration for
143 lakes.
• Phytoplankton primary use ortho-phosphate (PO4-3) measured as
molybdate-reactive phosphoruse. Figure 5.8 is valid because total
P is correlated with ortho-phosphorus, which is bioavailable to
phytoplankton.
• Other limnological measures can be correlated with chlorophyll a
or trophic status of lakes.
• Other indicators of eutrophic conditions:
– Cyanobacteria (blue-green algae) blooms.
– Loss of benthic invertebrates such as mayfly larvae
(Hexagenia spp.).
– Secchi disk depths less than 2.0 meters.
– Loss of fishery and presence of "rough" fish.
– Taste and odor problems.
– Aquatic weeds in littoral zones.
– Chlorophyll a concentrations greater than 10 µg L-1.
5.7. LAND USE AND BIOAVAILABILITY
• Major inputs, to streams and lakes that present a difficult
challenge for water quality management and control:
– nonpoint source runoff, particularly from intensive
agriculture;
– urban stormwater runoff;
– combined-sewer overflow.
• Bioavailability of the nutrients becomes important when much of
the runoff from nonpoint sources is in organic or particulate
forms that algae cannon use directly. Also, the oxygen content of
bottom waters has an effect on bioavailability.
• Several empirical models for estimating nonpoint source nutrient
loadings to natural waters and the net phosphorus retention of
lakes. It is too simplistic to assume that all of the phosphorus that
settles to the bottom sediments of a lake remains there.
• When costly decisions are necessary for water quality
management, it is desirable to use a more detailed and
mechanistic approach to the eutrophication problem
(nonlinear interactions between nutrients, plankton, and
dissolved oxygen).
• Limitations and assumptions:
– Steady state, completely mixed assumptions.
– One limiting nutrient assumption.
– No recycle of nutrients from sediments.
– Lack of a dissolved oxygen mass balance that triggers
sediment release of nutrients under anoxic conditions.
5.5. DYNAMIC ECOSYSTEM MODELS FOR
EUTROPHICATION ASSESSMENTS
• Many cases: a better methodology is needed rather than a
steady-state, total phosphorus model for assessing the
eutrophication of lakes.
• Chapra: extended the concept of the Vollenweider approach to
a dynamic (time-variable) lake model for total phosphorus in
the Great Lakes with sedimentation as the major loss
mechanism (Figure 5.9).
• At the same time (late 1970s), a variety of dynamic ecosystem
models were developed by DiToro, Thomann, and O'Connor
that included nitrogen, silica, and phosphorus limitation, as
well as zooplankton grazing and loss terms.
Figure 5.9.
Dynamic model
simulation of total
phosphorus in the
Great Lakes by
Chapra.
• Figure 5.10: general schematic of a dynamic ecosystem model
– Water quality includes nutrients, chlorophyll a, transparency,
and dissolved oxygen.
• Figure 5.11: typical flowchart for phosphorus in the Lake Erie
model
– Available phosphorus is commonly taken to be orthophosphate (PO43-) that passes a 0.45-µm membrane filter.
– Unavailable particulate phosphorus is everything else that is
measured in a whole water sample for total phosphorus.
• More than one taxa of phytoplankton contribute to the aggregate
measurement of chlorophyll a in lakes.
– In northern temperate lakes, a spring bloom of diatoms (coldwater phytoplankton with silica frustules) is often followed by
a summer bloom of green algae or a late summer-early fall
bloom of cyanobacteria (blue-green algae).
Figure 5.12 illustrates a double phytoplankton bloom in Lake
Ontario.
Figure 5.10.
Schematic of an
aquatic ecosystem
Figure 5.11.
Flowchart for
phosphorus
kinetics in a
dynamic ecosystem
model
Figure 5.12.
A dynamic
eutrophication
model calibration
for Lake Ontario
with two
phytoplankton
species (diatoms
and nondiatoms)
• Blue-green algae especially can cause water quality problems
because they
– float (because of the gas vacuoles in the cell formed during
nitrogen-fixation of some blue-green algae with heterocysts)
– are associated with taste and odor problems and toxins during
their decay following the bloom
– difficult to control (do not need many nutrients and have very
low loss rates – no sinking)
• Phytoplankton taxa:
Diatoms
Green algae
Dinoflagellates
Blue-green algae
Dissolved silica limiting nutrient (frustules)
Cold water, P- or Si-limited
Usually P-limited, summer
Flagellated, motile, occasional toxins (red tides)
Low N requirements (N-fixing)
Low sinking velocities (gas vacuoles)
Warm water, late summer or early fall
• Phytoplankton must be modeled within the context of their
transport regime.
• Ecological niches within a lake:
– Littoral – shallow water, near shore.
– Pelagic – open water, deep.
– Euphotic zone – light penetration to 1-10% of near-surface
light.
– Epilimnion – above the thermocline, well-mixed.
– Hypolinmion – below the thermocline, well-mixed.
– Sediment-water interface – bioturbation, diffusion of pore
water, variable redox conditions.
• Figure 5.13: transport regime for a large lake ecosystem. It is
necessary to compartmentalize the lake into a number of
compartments to account for transport and unique ecological
features of each zone – six-compartment model with three
surface layers (epilimnetic compartments) and three deep layers
(hypolimnetic compartments).
Figure 5.13. Transport regime for a compartmentalized lake
ecosystem model. Large arrows represent current flow
(advection) and double; small arrows – mixing (dispersion)
between compartments.
• The first step in developing a dynamic ecosystem water quality
model is to determine the transport regime quantitatively. This is
usually accomplished by simulating momentum, heat, or mass
transport of a constituent that is well known and independent of
water quality.
• The method of determining the correct transport regime is one of
the following:
– Velocity field (momentum).
– Temperature distribution (heat).
– Conservative tracer (chloride or total dissolved solids).
•
For a conservative tracer such as chloride in large 3-D lakes, for j-th
compartment:
Vj – volume of the j-th comp.;
Cj – concentration of a conservative tracer in the j-th comp.;
Qij – flowrate from the j-th to the i-th adjacent comp.;
Eij – bulk dispersion coefficient between the j-th and the i-th adjacent comp.;
Aij – interfacial area between the j-th and the i-th adjacent comp.;
Ci – concentration of a conservative tracer in the i-th comp.;
lij – half-distance (connecting distance between the middle of the two adjacent
compartments).
•
Bulk dispersion coefficient is scale dependent as an aggregate formulation
of all mixing processes (may be thought of as a bulk exchange flow going
each way between compartments in Figure 5.13)
• Procedures for model calibration and verification are:
– Calibrate the model using field data for a conservative substance (e.g.,
chloride) or heat to determine Qij and Eij values.
– Using the same advective flows and bulk dispersion coefficients
between compartments, calibrate the model for all water quality
constituents for the same set of field data to determine all other
adjustable parameters and coefficients (reaction rate constants,
stoichiometric coefficients, etc.).
– Verify the model with an independent set of field data, keeping all
transport and reaction coefficients the same. Determine the
"goodness of fit" between the model results and field data.
• A suitable criterion for acceptance of the model calibration and
verification should be determined a priori, depending on the use of the
model in research or water quality management.
• A median relative error of 10 - 20%, model results/field data, is typical in
water quality models.
• Ecosystem models consist of a series of mass balance equations
within each compartment.
• The number of compartments will vary depending on the spatial
extent of water quality data that is available and the resolution that
is needed from the model.
• Figure 5.14: a schematic of a typical ecosystem model.
– nine state variables shown, but there could be more or less
depending on the design of the model.
– each of the nine state variables will undergo advection and
dispersion as shown in the previous equation; however, fish and
zooplankton would not disperse at the same rate as other water
quality constituents because they are motile.
– assume four limiting nutrients and a multiplicative expression
here.
– biomass, rather than chlorophyll a or carbon, is simulated.
Figure 5.14. Ecosystem model for eutrophication assessments. Boxes
represent state variables; solid arrows are mass fluxes; and dotted
lines represent external forcing functions.
• Temperature affects every rate constant, increasing the rate of the
reaction.
where:
• These equations describe quantitatively the interactions of Figure
5.14.
• Table 5.5 gives a range and a typical value for the parameters,
coefficients, and rate constants used in the model.
Table 5.5.
Rate Constants
and Stoichiometric
Coefficients for
Dynamic
Ecosystem Models
• Eutrophication remains one of the world’s greatest water quality
problems. In developed countries the issue has evolved from
controlling point sources to one of watershed control and nonpoint source runoff.
• When state-of-the-art modeling is needed, a comprehensive
ecological model of the type presented in this section should be
considered.
• Sediment dynamics, oxygen balances at the sediment - water
interface, and diffusive flux modeling will be crucial to obtaining
long-term reliable estimates of eutrophication in lakes and
estuaries.