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In-stream nitrification rate prediction
War. Res. Vol. 22, No. 6, pp. 723-732, 1988 Printed in Great Britain. All rights reserved
0043-1354/88 $3.00 + 0.00 Copyright © 1988 Pergamon Press plc
IN-STREAM NITRIFICATION RATE PREDICTION JOHN J. WARWICKand PRADEEP SHETTY Graduate Program in Environmental Sciences, The University of Texas at Dallas, Richardson, TX 75080, U.S.A.
(Received February 1987; accepted in revised form December 1987) Abstract--A simultaneous nitrogen and dissolved oxygen mass balancing technique is used to accurately assess actual in-stream nitrification rate coefficients. These rate coefficients are compared against estimations from observed spatial changes in various bulk fluid nitrogen species concentrations. Total Kjeldahl nitrogen change was found to be the most accurate predictor of in-stream nitrification. Proposed nitrification rate coefficient estimation functions have r 2 values in excess of 0.90.
Key words--nitrification, stream, model, kinetics, water quality simultaneous, nitrogen and dissolved oxygen mass balancing technique was employed to accurately assess actual in-stream nitrification rates. Numerous data sets provided by the Texas Water Commission (formerly Texas Department of Water Resources) were analyzed. The computed, actual, in-stream rate coefficients were statistically compared with simple estimations based upon observed spatial changes in bulk fluid nitrogen species.
NOMENCLATURE D = Stream depth (m) F = Fraction of aquatic plant nitrogen uptake that is nitrate-N Ka = In-stream reaeration rate coefficient (day-~) K, = Actual in-stream, zero-order, nitrification rate coefficient (mg 021- t day- l) K~ = Actual in-stream, zero-order, nitrification rate coefficient (g 02 m -2 day -l) /'max= Maximum gross photosynthetic oxygen production rate (mg 021- l day- t) Rp = Aquatic plant respiration rate (mg 021-1 day- 1) tf = Flow time between stations (days) TKN = Total Kjeldahl nitrogen (mgN 1-~) V = Stream velocity (m s- i)
IN-STREAM NITRIFICATION RATE COMPUTATION
Ultimate NBOD reduction INTRODUCTION
The concentration of dissolved oxygen is an important measure of stream water quality and a significant factor in the evaluation of pollution impact. The dissolved oxygen level reflects the capacity of a fiver to maintain and propagate a balanced aquatic habitat. Nitrogenous biochemical oxygen demand (NBOD) has long been recognized as a major sink in the oxygen balance of streams (Courchaine, 1968; Wezernak and Gannon, 1968; O'Connor and DiToro, 1970). It is therefore important to accurately assess the magnitude of in-stream nitrification when performing dissolved oxygen computations. NBOD removal represents a relatively complex process involving the sequential oxidation of ammonia nitrogen ( N H ~ - N ) to nitrite nitrogen ( N O r - N ) and then to nitrate nitrogen ( N O r - N ) . The deoxygenation rate associated with in-stream nitrification has generally been determined by one of two methods: (1) quantifying the rate of ultimate NBOD (NBODL) change in the downstream direction or (2) analysis of the rate of individual nitrogen species change in the downstream direction. The purpose of this research was to determine which nitrogen species transformation was the best predictor of in-stream nitrification rates. A complex,
This technique involves plotting NBOD L values vs flow times between stations. The slope of the best fit line gives an estimate of the nitrification rate coefficient. The method is questionable due to inaccuracies in ascertaining the NBODL values and the problem is further aggravated by the 20 day test duration. Several investigators have studied this approach and have emphasized additional problems associated with it. DeMarco et al. (1967) showed that nitrification in the standard bottle BOD test was dependent on the turbulence and initial nitrifying seed concentration. In addition, nitrification in the bottle does not begin for an appreciable time lag, usually 3-10 days since surfaces precolonized by nitrifying bacteria are absent (Finstein et al., 1977). Furthermore, the standard 20°C temperature used in the bottle test is below the optimum range of 25-28°C for nitrifying bacteria (Courchaine, 1968). The problems and expense associated with this technique dictated exclusion from further consideration.
723 WR. 22/~-E
Nitrogen species transformations This method is usually accomplished by calculating the rate of change of individual nitrogen species in the downstream direction. The nitrogen species typically used are total Kjeldahl (organic + ammonia), ammonia, nitrate and nitrite plus nitrate nitrogen. Since the
724
JOHN J. WARWICKand PRADEEPSHETTY
transformation rate for each of these compounds is dependent not only on nitrification but also on other processes involved in the nitrogen cycle, one typically obtains different nitrification rates depending on the nitrogen species utilized. In fact, several studies have shown significant nitrogen imbalances, where the rates of observed decreases in ammonia-N have not been paralleled by corresponding increases in the rate of nitrite-N and nitrate-N buildup (e.g. Schwert and White, 1974; Ruane and Krenkel, 1978; Cirello et al., 1979; Wilber et al., 1979; Warwick and McDonnell, 1985b). Due to the lack of sufficient information researchers have been inconsistent in selecting a nitrogen species to use when assessing in-stream nitrification. Blanc and O'Shaughnessy (1974), Ruane and Krenkel (1978) and Cirello et al. (1979) used both NH4+ - N and N O f - N transformations; Wild et al. (1971) and Huang and Hopson (1974) followed NH~--N changes; Somville (1978) based his assessment on N O a - N increase; and Gowda (1983) suggested that the nitrification rate could be estimated either by total Kjeldahl nitrogen (TKN) decrease of N O ~ - N increase. SNOAP
model
The Stream Nitrogen and Dissolved Oxygen Analysis Program (SNOAP) is a one-dimensional, pseudo unsteady state water quality model developed by Warwick and McDonnell (1985a) to predict instream nitrification rates based on observed changes in all nitrogen species and dissolved oxygen. The model follows any number of sequential parcels of water downstream, thus allowing the handling of unsteady water quantity and quality values at each station. The model neglects longitudinal dispersion and therefore application should be limited to situations where this assumption is valid. The nitrogen mass balance equations include the following transformation pathways: ammonification, nitrification, bacterial and aquatic plant nitrogen assimilation, ammonia exsolution and denitrification. The SNOAP model calculates reaction rates from the water quality data input at each station. The kinetic order for certain nitrogen reaction rates is set in the model. Ammonia exsolution is established as a first-order reaction whereas nitrogen uptake by aquatic plants and bacteria is modeled as a zero-order reaction. The other major nitrogen transformation pathways like ammonification, nitrification and denitrification can be optionally modeled either as zero- or first-order reactions. The dissolved oxygen mass balance equations include: carbonaceous and nitrogenous deoxygenation, aquatic plant respiration, benthal demands, atmospheric reaeration and photosynthetic oxygen production. The reaeration rate, carbonaceous deoxygenation rate and benthal oxygen demand terms are established as input parameters leaving nitrification and aquatic plant respiration as two
unknowns. Combining the nitrogen and dissolved oxygen mass balance equations and using an iterative solution algorithm results in singular values of the nitrification rate and aquatic plant respiration rate. This simultaneous solution results in a quick and stable convergence in typically < l0 iterations. The iterative algorithm may not converge in the case of erroneous data for nitrogen and/or dissolved oxygen concentrations. Furthermore, if major sources or sinks (e.g. unidentified pollutant loading, benthal demands, reaeration) have been ignored or incorrectly estimated a simultaneous solution may not be possible. This nonconvergence feature is a significant attribute of the SNOAP model since it helps in screening out faulty input data values. The model was successfully calibrated and verified for three independent water quality surveys of Marsh Creek, a tributary to Pine Creek, located in the Pine Creek watershed on the Appalachian Plateau in Northcentral Pennsylvania (Warwick and McDonnell, 1985a, b). Additionally, the SNOAP model has been used to compute the diel variation of in-stream nitrification rates (Warwick, 1986). The original SNOAP model was modified for this study to incorporate preferential uptake of ammoniaN by aquatic plants. Several studies in a variety of aquatic systems have shown that ammonia-N is preferentially assimilated by aquatic plants in the presence of nitrate-N (MacIssac and Dugdale, 1969; Eppley and Rogers, 1970; McCarthy and Eppley, 1972; Eppley et al., 1977; McCarthy et al., 1977). The suppression of nitrate-N assimilation depends on the ambient concentration of ammonia-N. Based on a study in the Chesapeak Bay, McCarthy et al. (1977) indicated that for ammonia-N concentrations in excess of 0.08 mg 1- J of N H ~ - N , an insignificant amount of nitrate-N uptake can be expected. Figure 1 shows a graph of the fraction of total nitrogen assimilation that is associated with nitrate-N uptake (nitrate-N assimilation divided by the sum of nitrateN and ammonia-N uptake) vs the ammonia-N concentration as plotted by Stanley and Hobbie (1977). Equation (1) (dashed line in Fig. 1) was developed to explain the noted variation: F = 0.00457 [NH~ ] 0.9149
(1)
where F = fraction of total nitrogen uptake that is nitrate-N, and [NHg] = ammonia-N concentration (mg N 1-~). This equation was incorporated into the SNOAP model to account for the preferential uptake of ammonia-N by aquatic plants in the presence of nitrate-N. Further details regarding SNOAP model modification can be found elsewhere (Shetty, 1985). Nitrification reaction order
Variation reaction rates have been used to describe in-stream nitrification. O'Connor (1967) proposed a model including nitrification as a first-order reaction. Knowles et al. (1965) and Stratton and McCarty (1967) used Monod kinetics in their rate expression
In-stream nitrification rate prediction
SNOAP model was furnished in the reports. The screening criteria included the following:
l.O0
I
1 ta
÷q-
z
(1) data availability, (a) flow, nitrogen species concentrations and ultimate carbonaceous biochemical oxygen demand (CBODL); along with flow times between stations, (b) diel variation of dissolved oxygen, temperature and pH; (2) flow times had to be ~ 1.0 mg l-~ to ensure that nitrification was uninhibited, and (4) evidence of substantial nitrogen species transformations.
.80
.60
Z
+ -
~
.40
\V *---"EOOATION, o
725
•02
~
.06
.10
.14
38
AMMONIA CONCENTRATION (mg- N/L)
Fig. 1. Preferential aquatic plant ammonia-N uptake (after Stanley and Hobble, 1977). for nitrification studies. Wezernak and Gannon (1968) utilized an autocatalytic growth reaction in evaluating the nitrification rate in the Clinton River. Wild et al. (1971) studied the nitrification process with a laboratory reaeration unit and found that nitrification followed a zero-order reaction, when the ammonia-N concentration was varied from 6 to 60 mg 1-1. Huang and Hopson (1974) compared zeroorder, first-order, Monod and autocatalytic growth equations to model nitrification and concluded on the basis of laboratory studies that nitrification follows zero-order kinetics for ammonia-N concentrations > 2 . 5 m g l -~. Based on field studies Finstein and Matulewich (1974) and Curtis et al. (1975) found that nitrification took place extensively on rock surfaces and sediments, which further suggests that nitrification follows zero-order kinetics and occurs due to the action of pregrown cells. The SNOAP model has the flexibility to simulate nitrification with either zero- or first-order reaction rates. Due to the relatively high concentration of ammonia-N in all data sets analyzed, zero-order kinetics will be used to model the nitrification reaction in this paper. DATA ANALYSIS
Twenty-seven intensive surveys of rivers in Texas were acquired from the Texas Department of Water Resources (TDWR). Each of these surveys was accomplished in accordance with the Texas Water Quality Act, Section26.127, as amended in 1977. They were to be used in developing and maintaining the State Water Quality Strategy published in 4 0 C F R 35.1511-2 pursuant to Section 303(e) of the Federal Clean Water Act of 1977. There were a total of 451 individual reaches reported in the 27 intensive surveys. These segments were studied to see if all the data required by the
This scrutiny eliminated most of the 451 possible segments resulting in a final data base of only 9 reaches. Table 1 gives a brief description of the selected segments. S N O A P application
SNOAP model input includes flowtime between stations; along with diel variations of flow, dissolved oxygen, nitrogen species, temperature and pH at both the upstream and downstream stations. The diel variations of nitrogen species and flow were not available in the analyzed data sets, so average (constant) values were used for these parameters. The SNOAP model requires input of CBODL measurements and a reaction rate. Measured in-stream CBODL removal was used to evaluate the CBODL reaction rate. The SNOAP model also allows for the direct input of a zero-order benthal oxygen demand rate. Unfortunately, sediment oxygen demand was not measured and a zero value was therefore used. Ammonification, nitrification and denitrification were set as zero-order reactions, due to the relatively
Table 1. Selected data set identification Data set
Title*
Segment
Intensnve survey of Rowlett Creek, IS-25
10-9
Intensive survey of San Antonio River, segment 1901, IS-59
C-D
Intensive survey of San Antonio River, segment 1901, IS-59
FD-FE
Intensive survey of East Fork Trinity River, segment 0819, IS-58
B-C
Intensive survey of East Fork Trinity River, segment 0819, IS-58
E-F
Intensive survey of Aransas River-above Tidal, segment 2004, IS-48
N-M
Intensive survey of the Trinity River, segment 0805, IS-53
B-3
Intensive survey of Sims Bayou, IS-24
D--C
Intensive survey of Halls Bayou, IS-31
D-C
*As published and distributed by the Texas Department of Water Resources, P.O. Box 13087, Austin, TX 78711, U.S.A.
726
JOHN J. WARWICK a n d PRADEEP SHETTY Table 2. Average water quantity and quality data
Data set
Q (m3s l)
D.O. (mgl-i)
Temp. (°C)
pH
Total P ( m g P l -I)
1 2 3 4 5 6 7 8 9
0.20 5.34 1.78 1.99 2.33 0.10 2.88 0.67 0.42
5.97 1.76 4.10 3.48 1.92 4.74 6.75 4.92 4.72
I 1.5 29.5 27.5 19.2 19.6 25.0 23.1 30.4 19.4
7.24 7.28 7.70 7.58 7.31 7.82 6.62 7.52 7.69
7.68 4.43 1.35 1.16 1.56 6.42 2.53 2.98 4.66
high nitrogen species concentrations analyzed. Table 2 summarizes selected water quantity and quality data for each data set. The SNOAP model solution framework computes hourly values for ammonification, nitrification, ammonia exsolution, denitrification, aquatic plant respiration and gross photosynthetic oxygen production. These reaction rate values are computed to exactly match all input dissolved oxygen and nitrogen species data. SNOAP model validation can be performed by inspecting the magnitude and variability of the computed reaction rates.
SNOAP calibration The reaeration rate coefficient (Ka) was selected as the model calibration parameter. This choice was dictated by the fact that reaeration was not measured for most of the reaches analyzed. Calibration was achieved by adjusting K~ to achieve a desired or target value for the ratio of Pmax/Rp, where Pmax= maximum photosynthetic rate (mgO21 ~day -:) and R p = p l a n t respiration rate (mgO21 ~day-~). Estimates of Pmax and Rp are calculated by the SNOAP model within the simultaneous dissolved oxygen and nitrogen mass balancing framework. Table 3 presents a partial summary of ratios available in the literature. A target ratio value equal to 3.10 was used in all analyses. The overall level of photosynthetic activity can be controlled by nutrient availability. Ranges of Michaelis-Menton half-saturation growth constants (Ks) have been reported for a variety of different photosynthetic organisms and environmental conditions in "Rates, Constants, and Kinetics Formulations in Surface Water Quality Modelling" (EPA, 1978). The reported K~ range for nitrogen was 0.01-0.40 mg N 1- 1 and 0.002-0.05 mg P 1-1 for phosphorus. The nutrient values given in Table 2 demonstrate that the selected data sets are not nutrient limiting. The Pmax/Rp ratio has been shown to be a function of light intensity (Harris and Lott, 1973) and Table 3. P,na,/Rp ratios References Geike and Parasher (1978) Harris and Lott (1973) Nihei el al. (1954)
Organism
Pm,x/Rp
Chlorella pyrenoidosa Cosrnarium botrytis Chlorella ellipsoidea
3.05 3.25 3.10
Nitrogen species (mg N 1-I) . . . . . . . . . . . TKN NH~NO2 NO i
p,~ Rp (mgO21 i day- i)
8.40 12.60 4.21 6.20 6.49 7.55 3.85 2.86 5.12
29.7 6.7 5.5 60.4 18.0 90.9 2.0 22.9 21.2
8.11 9.18 2.90 4.74 5.95 5.75 1.74 2. I 0 4.80
0.28 0.30 0.76 0.04 0.12 0.66 0.18 0.42 0.32
2.00 0.46 2.10 0.11 0.04 5.73 4.69 2.08 0.34
9.6 2.1 1.8 19.5 5.8 29.3 0.6 7.4 6.8
the concentration of various environmental contaminants. Geike and Parasher (1978) demonstrated that oxygen evolution was reduced by 33.3% at a hexachlorobenzene (HCB) concentration of 0.1 mg l-lq while respiration was unaffected. Table 4 presents the adjusted reaeration (Ka) values used for SNOAP model calibration. Reaeration coefficients were also computed as a function of average hydraulic stream conditions using empirical formulations by O'Connor and Dobbins (1958) and Owens et al. (1964). Application of these empirical expressions was based upon criteria set forth by Covar (1976). The SNOAP model computed instream nitrification rate coefficients were very insensitive to changes in Ka, while the target ratio (Pmax/Rp = 3.10) was extremely sensitive to variation of Ks. Achieving the desired Pm~x/Rpratio sometimes resulted in Ks values which were somewhat inconsistent with field measurements and empirical computation. Fortunately, this amount of Ka error had an insignificant effect on the in-stream nitrification rate coefficients as computed by the SNOAP model. Reaeration was measured for data sets 7-9 (Table 4) using radioactive krypton-85 and hydrogen-3 (Neal, 1979). Data sets 7-9 were re-analyzed using the measured Ka values instead of adjusting to achieve the target Pmax/Rp ratio. The changes in SNOAP computed nitrification rate coefficients were +0.32, - 4 . 5 9 and - 0 . 1 5 % for data sets 7-9, respectively.
SNOAP validation Classically, model validation involves reapplying the calibrated model to a substantially different data set. Unfortunately, multiple data sets for the investigated stream segments were unavailable. Validation was therefore limited to a more qualitative inspection of three parameters; (1) input reaeration (Ka) values, (2) computed nitrification rate coefficient of variability, and (3) minimum computed aquatic plant respiration (Rp). Table 5 summarizes these 3 parameters for all 9 data sets. Two comparisons are possible when evaluating the validity of reaeration assignment. First a comparison can be made between calibration reaeration assignments and empirically computed values (Error~) (see Table 5). Rathbun (1977) evaluated the performance
In-stream nitrification rate prediction
727
Table 4. S N O A P calibration (K~) values ~
K~ ( d a y - I)
)
Data
(m)
(m
set
[It]
[ft s - ']
(h)
SNOAP
Measured
Computed
I
0.579 [1.90]
0.130 [0.427]
4.47
5.79
--
3.73*
2
0.892 [2.93]
0.205 [0.673]
4.43
5.09
--
2.lit
3
0.585 [1.92]
0.165 [0.541]
9.43
4.34
--
4.28*
4
0.531 [1.74]
0.226 [0.741]
5.35
9.63
--
6.34*
5
0.656 [2.15]
0.208 [0.682]
4.30
2.96
--
3.38t
6
0.203 [0.667[
0.085 [0.279]
5.87
12.00
--
19.48"
7
1.448 [4.75]
0.133 [0.436]
3.75
4.84
1.70
0.82t
8
0.507 [1.66]
0.147 [0.482]
4.93
1.93
0.95
5.19"
9
--
0.139
2.00
4.03
3.72
S - I
tf
1
*Owens et aL (1964); K "da - b at
21"7(v)°'67 for 15 ~<2.0ft
Y )=
~
/5[=lft,
g[=]fts-"
t O ' C o n n o r and Dobbins (1958); i 12.9(g) °.s° for 15 > 2 . 0 f t Ko ( d a y - ) = ~ /5 [ = ] ft, g [ = ]ft s_l.
of predictive reaeration formulae and found that for five streams (comprising 482 total observations) the normalized mean percentage error ranged from 5 to 216 and from - 12 to 120 for Owen et al. (1964) and O'Connor and Dobbins (1958), respectively. Based upon this criteria, most reaeration assignments appear quite reasonable with data sets 2, 6, 7 and 8 being most questionable. A second comparison can be made between measured values and calibration assignments (Error2). Data sets 7 and 8 appear most questionable under this criteria. Simultaneous evaluation of both criterion indicates that the assigned reaeration values used in data set 8 lies between the measured and empirically computed value. The assigned reaeration value is therefore assumed valid for data set 8. In summary, data sets 2, 6 and 7 still appear questionable. The SNOAP model computes hourly estimates of
in-stream nitrification. Since zero-order kinetics have been used in all analyses, temperature corrected nitrification rate coefficients should remain reasonably constant over the entire study period. Table 5 presents the coefficients of variation for computed hourly, temperature corrected, in-stream nitrification rate coefficients. Data sets 2 and 9 are most questionable based upon this validation condition. The SNOAP model also computes hourly estimates of aquatic plant respiration (Rp). Computation of negative Rp values is unreasonable. Therefore, data sets 2, 7 and 9 must all be viewed as questionable under this validation criteria (Table 5). Data sets 2, 7 and 9 have been identified as questionable under two or more of the designated validation criterion. In the analyses which follow results will be presented for the full data set (1-9) and for a partial data set excluding data sets 2, 7 and 9.
Table 5. S N O A P validation Reaeration (K~) Data set l 2 3 4 5 6 7 8 9
Errort
Error 2
35.6% 58.5% 1.4% 34.2% -14.2% -62.3% 83.1% - 167.0% --
------64.9% 50.8% 7.7%
S N O A P nitrification Coefficient o f variation
Error I = [(SNOAP - computed)/SNOAP]* 100. Error: = [(SNOAP - measured)/SNOAP]* 100.
5.0% 10.0% 1.7% 3.1%
M i n i m u m Rp
(m8 02 I- ' d a y - ')
7.2% 3.2% 8.3%
7.5 -3.3 1.1 15.6 4.9 22.7 -0.3 5.4
~.7%
-4.4
3.1%
728
JOHN
J. WARWICK
and
PRADEEP
SHETTY
Table 6. Computed in-stream nitrificationrate coefficients Nitrificationrate coefficients (rag02 I- ~day ~) Data set
TKN
NH~-
NO.~
23.25 19.20 15.65 42.74 9.67 40.72 13.86 10.54 26.50
18.60 -3.12 15.21 I 1.46 7.73 21.24 9.42 4,85 10.91
-7.44 -5.56 7.48 2.68 -0.11 -11.81 -52.89 -13.18 10.37
Estimation of nitrification rate coefficients Four different estimations of the nitrification rate coefficient were made for each data set. All estimations were based upon the observed spatial gradient of a selected nitrogen species. The 4 specific estimates were computed as follows: (1) observed spatial decrease of bulk fluid TKN-N, (2) observed spatial decrease of bulk fluid NH2 - N , (3) observed spatial increase of bulk fluid NO~- - N , (4) observed spatial increase of bulk fluid NO~--N. Table 6 presents a comparison of these estimates and the SNOAP computed in-steam nitrification rate coefficients. The stoichiometric equivalents used were 3.22mg of oxygen required per mg of N H ~ - N oxidized to N O / - - N and 1.11 mg of oxygen required per mg of N O / - - N oxidized to N O 3 - N (Haug and McCarthy, 1972). RESULTS AND DISCUSSION
The nitrification rate coefficient estimations were statistically compared with the rate coefficient calculated by SNOAP. It was assumed that the value from A O
NO~
SNOAP
-7.44 -2.88 5.84 3,69 0,24 --20.89 -55.98 -13.49 7.27
19.41 12.29 13.81 30.60 4.99 27,51 I 1,65 7.59 21.89
the SNOAP model represented the actual in-stream nitrification rate coefficient. The statistical analyses included a linear regression technique and evaluation of a root mean square error. The full and partial data sets were analyzed separately to illucidate any impact from the questionable data sets (2, 7 and 9).
Regression analysis The actual nitrification rate coefficient (SNOAP computation) was regressed with the 4 estimations using PROC REG, a general purpose regression procedure available through SAS (SAS Inst. Inc., 1982). The predicted mean regression lines (solid) along with 95% confidence intervals about the predicted mean (dashed) are presented in Figs 2-5. Circled data points identify data sets 2, 7 and 9; which were excluded during the analysis of the partial data set. Figures 2-5 were constructed for the full data set. The predictions based upon the rate of observed T K N decrease (Fig. 2) resulted in the smallest confidence interval, signifying superior predictive power. The 95% confidence interval lines, in Figs 3-5, indicate increasing uncertainty in estimating the actual nitrification rate coefficient. These results were also found for the partial data set.
60 / /
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ta
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ill
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60
--60
I
I
-40
i
I
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I
I
0
I
I
I
20
J
40
60
TKN ESTIMATED COEFFICIENT (mg-Oa/L/doy)
Fig. 2. Total Kjeldahl nitrogen estimation on in-stream nitrification rate coefficient.
In-stream nitrification rate prediction
729
60
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o-
//
40
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20
8 IM
// ////////
rw
Z
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-20
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i-
-40
Z r,
//
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-60 -60
¢/
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-40
I
I
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-20
I
0
l
i
I
20
I
40
60
NH 4 ESTIMATED COEFFICIENT (rag- 02/L/doy)
Fig. 3. Ammonia nitrogen estimation of in-stream nitrification rate coefficient. Computing the correlation coefficient r 2, slope, and the intercept gave an additional level of insight into the comparison of the SNOAP computed and estimated nitrification rate coefficients. Tables 7 and 8 present this information for the full and partial data sets; respectively. The 95% confidence intervals for the slope and intercept are also presented. The estimations computed by NOx increase and N O r increase can be discounted based on the low r 2 values and extremely large 95% confidence intervals. The NH + decrease was superior to both N O r and NO~" increase, but was still substantially inferior to the predictions made using T K N decrease. The significant number of negative predictions from observed changes in NO~- and NO~- (Table 6) reflect the
real possibility of significnt in-stream denitrification. The 95% confidence intervals are slightly larger for the partial data set due to the reduced sample size. The slope and intercept values for the full and partial data sets (Tables 7 and 8) are not significantly different. Therefore, the impact of the questionable data sets was not statistically significant. R o o t mean square error
A measure of the total error between the values calculated by the SNOAP model and the estimated rate coefficients is also given by the root mean square (RMS) error. The RMS error is statistically well behaved and provides a direct measure of model error (Thomann, 1982). The RMS error values are also
6O
0
40
pZ
'" UM. W 0
o
20
0
bJ ne
zo
-20
~
-40
Z Q. 0 Z
-60
~
-60
i
-40
i
I
-20
i
I
0
¢
i
I
20
i
40
i
60
NOx ESTIMATED COEFFICIENT (mg-O=/L/d0y)
Fig. 4. Nitrite plus nitrate nitrogen estimation on in-stream nitrification rate coefficient.
730
JOHN J. WARWICK a n d PRADEEP SHETTY A
60 .J
/ /
O i
40
E I-z bd
20
--W
U. It. tlJ O O IM r¢ Z
*
O
_o
-20
(J EL n nr
-40
I--
®
Z
IX ,¢ O Z O9
,
-6C -
,
-40
60
,
.
-20
,
,
0
.
,
20
.
,
40
60
NO3 ESTIMATED COEFFICIENT (rng-Oz/L/doy)
Fig. 5. N i t r a t e n i t r o g e n e s t i m a t i o n o f i n - s t r e a m n i t r i f i c a t i o n r a t e coefficient.
summarized in Tables 7 and 8 with the smallest error being for the T K N technique of evaluating the nitrification rate coefficient.
Proposed in-stream nitrification prediction model From the regression analysis of the full data set, the equation for estimating the actual, zero-order, nitrification rate coefficient is: K, = 0.70, (ATKN • 4.33/tr) + 0.93 mg O2 l- l day- 1 (2) where Kn=actual, zero-order, nitrification rate (mg 02 l- l d a y - 1); ATKN = observed change in bulk
fluid total Kjeldahl nitrogen concentration between stations (mgNl-~); and tf= flow time between stations (days). Analysis of the partial data set results in an expression which is very similar to equation (2). Kn = 0.69" (ATKN* 4.33/tf) + 0.82 mg 021-1 day- J (3) Equation (3) is herein proposed as the best predictor of zero-order, in-stream nitrification as a function of bulk fluid spatial changes in nitrogen species concentrations. All data sets included in the partial data set analysis had average depths of flow less than 0.66 m
Table 7, Statistical results for data set Nitrogen species
r2
Slope
TKN
0.949
0.700 (0.5554).845)*
NH 4 N
0.315
NO, N NO3 N
Intercept (mgO21 i d a y ,)
RMS error (mgO21 t d a y i)
0.927 ( - 2.72~4.58)*
2.12
0.673 ( - 0 . 2 1 3 1.56)
9.44 ( - 1 . 8 5 20.73)
777
0.063
0.117 ( - 0.288-4).522)
17.56 (9.72- 25.40)
9.09
0.029
0.075 (4).3154).466)
17.34 (9.19 25.49)
9.26
*95% Confidence intervals. Table 8. Statistical results for partial data set Nitrogen species
r2
Slope
TKN
0.964
0.694 (0.5214).868)*
NH4 N
0.411
NO~-N
0.017
NO3-N
< 0.00t
Intercept ( m g O 2 1 - 1 d a y ~)
RMS error (mgO21 ' d a y ')
0.825 ( -3.91-5.56)*
2.22
3.40 ( - 19.98-2677)
8,94
16.62 (2.73-30.51)
11.56
(-- 1.40--1.14) 0.009 ( - 1.60-1.61)
17.35 (3.73-30.97)
I 1,66
1.056 ( - 0.568-2.68) 0.130
*95% Confidence intervals.
In-stream nitrification rate prediction
731
Table 9. Statistical results for partial data set using areal units for in-stream nitrification rate coefficients Nitrogen species
•2
Slope
Intercept (g 02 rn - 2day - i)
RMS error (g 02 m - 2day- i)
TKN
0.952
0.755 (0.538-0.971)*
-0.163 ( - 2.84-2.52)*
1.21
NH4-N
0.268
0.840 ( - 0.947-2.63)
2.78 ( - 9.48-15.04)
4.76
NOx-N
0.105 0.163
8.56 (2.71-14.41) 8.86 (3.01-14.70)
5.27
NO 3-N
0.403 ( - 1.11-1.91) 0.494 ( - 0.946-1.93)
5.09
*95% Confidence intervals.
(2.2 ft). Nitrification could be expected to occur as a result of attached organisms in these shallow systems, and there may be some correlation with stream contact area. The analysis of the partial data set was therefore repeated for nitrification rates computed on an areal basis (g O2m -2 day -l) (see Table 9). Equation (4) represents the best predictor of zero-order, in-stream nitrification expressed in areal units. /
K n = 0 . 7 6 , ( A T K N * 4.33/tf) * / )
- 0 . 1 6 gO2 m-2day -l
(4)
where K~=actual, zero-order, nitrification rate (g 02 m -2 day- 1); and/~ = average stream depth (m). Equations (2)-(4) were developed for zero-order nitrification rate coefficients only. Development of parallel expressions for first-order reaction kinetics will require analyses of additional data sets which are characterized by lower nitrogen species concentrations.
SUMMARY AND CONCLUSIONS
A complex, simultaneous nitrogen and dissolved oxygen mass balancing program (SNOAP) was used to accurately assess actual in-stream nitrification rate coefficients. These computed coefficients were compared with simple estimations based upon observed bulk fluid spatial changes in various nitrogen species concentrations. Observed changes in TKN concentration were found to most accurately predict instream nitrification. However, calculating the instream nitrification rate coefficient directly from observed decreases in TKN, without using equations (3) or (4), will result in significant overestimation. The substantial impact of nitrification on stream dissolved oxygen budgets is well known. Therefore, it is important to accurately assess the level of in-stream nitrification. Performing a complete nitrogen and dissolved oxygen mass balance (e.g. SNOAP model) requires an extensive data base, which may be practically unattainable in many situations. It is therefore hoped that use of expressions like equations (3) and (4) will facilitate a relatively quick, inexpensive and accurate means for assessing the magnitude of instream nitrification.
REFERENCES Blanc F. C. and O'Shaughnessy J. C. (1974) The biokinetics of the Assabet River. Northeastern University, Department of Civil Engineering, Environmental Engineering Laboratories, Report to Commonwealth of Massachusetts, Division of Water Pollution Control, Project 73-05. CireUo H. et al. (1979) The question of nitrification in the Passaic River, New Jersey: analysis of historical data and experimental investigation. Wat. Res. 13, 525-537. Courchaine R. J. (1968) Significance of nitrification in stream analysis-effects on oxygen balance. J. Wat. Pollut. Control Fed. 40, 835-847. Covar A. J. (1976) Selecting the proper reaeration coet~cient for use in water quality models. Presented at the U.S. EPA Conference on Environmental Simulation and Modeling. Curtis E. J. C., Durrant K. and Harman M. M. I. (1975) Nitrification in rivers in the Trent Basin. Wat. Res. 9, 255-268. DeMarco J. et al. (1967) Influence of environmental factors on the nitrogen cycle in water. J. Am. ;Vat. Wks Ass. 59, 580-592. EPA (1978) Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling. Environmental Research Laboratory, United States Environmental Protection Agency, Athens, Ga. Eppley R. W. and Rogers J. N. (1970) Inorganic nitrogen assimilation on Ditylum brightwellii, a marine plankton diaton. J. phycol. 6, 912-920. Eppley R. W., Sharp J. H., Renger E. H., Perry M. J. and Harrison W. G. (1977) Nitrogen assimilation by phytoplankton and other microorganisms in the surface waters o f the central North Pacific Ocean. Mar. Biol. 39, 111-120. Finstein M. S. and Matulewich V. A. (1974) Distribution o f autotrophic nitrifying bacteria in a polluted stream. Research Project Technical Completion Report of Project A-030-N.J., Office of Water Resources Research U.S. Department of the Interior. Finstein M. S. et al. (1977) Evalution o f nitrification in the water column o f the Passaic River. Report A-053-N.J., Water Resources Research Institute, New Brunswick, NJ. Geike F. and Parasher C. D. (1978) Effect o f hexachlorobenzene (HCB) on photosynthetic oxygen evolution and respiration o f Chlorella pyrenoidosa. Bull. envir, contam. Toxic. 20, 647~i51. Gowda T. P. H. (1983) Modelling nitrification effects on the dissolved oxygen regime of the Speed River. War. Res. 17, 1917-1927. Harris G. P. and Lott J. N. A. (1973) Light intensity and photosynthetic rates in phytoplankton. J. Fish. Res. B Can. 30, 1771-1778. Haug R. T. and McCarthy P. L. (1972) Nitrification with submerged filters. J. Wat. Pollut. Control Fed. 44, 2086-2102.
732
JOHN J. WARWICK and PRADEEP SHETTY
Huang C. S. and Hopson N. E. (1974) Nitrification rate in biological processes. J. envir. Engng Div. Am. Soc. cir. Engrs 108, 409-422. Knowles G., Downing A. L. and Barrett N. J. (1965) Determination of kinetic constants for nitrifying bacteria in mixed culture with the aid of an electronic computer. J. gen. Microbiol. 38, 263-278. Maclssac J. J. and Dugdale R. C. (1969) The kinetics of nitrate and ammonia uptake by natural populations of marine phytoplankton. Deep-Sea Res. 16, 45-57. McCarthy J. J. and Eppley R. W. (1972) A comparison of chemical, isotropic, and enzymatic methods for measuring assimilation in marine phytoplankton. Limnol. Oceanogr. 17, 371-382. McCarthy J. J., Taylor W. R. and Taft J. L. (1977) Nitrogenous nutrition of the plankton in the Chesapeake Bay I. Nutrient availability and phytoplankton preferences. Limnol. Oceanogr. 22, 996-1011. Neal L. A. (1979) Method for tracer measurement o f reaeration in free-flowing Texas streams. Law Engineering and Testing Company, Atlanta, Ga. Nihei T., Sasa T., Miyachi S., Suzuki K. and Tamiya H. (1954) Change of photosynthetic activity of chlorella cells during the course of their normal life cycle. Archs Microbiol. 21, 155-164. O'Connor D. J. (1967) The temporal and spatial distribution of dissolved oxygen in streams. Wat. Resour. Res. 3, 65-78. O'Connor D. J. and DiToro D. M. (1970) Photosynthesis and oxygen balance in streams. J. sanit. Engng Div. Am. Soc. cir. Engrs 96, 547-571. O'Connor D. J. and Dobbins W. E. (1958) Mechanism of reaeration in natural streams. Trans. Am. Soc. cir. Engrs 123, 641~84. Owens M., Edwards R. W. and Gibbs J. W. (1964) Some reaeration studies in streams. Int. J. Air Wat. Pollut. 8, 469-486. Rathbun R. E. (1977) Reaeration coefficients of streams-state-of-the-art. J. Hydraul. Div. Am. Soc. civ. Engrs 103, 409-424. Ruane R. J. and Krenkel P. A. (1978) Nitrification and
other factors affecting nitrogen in the Holston River. J. Wat. Pollut. Control Fed. 50, 2016-2028. SAS User's Guide (1982) Statistics, SAS Institute Inc., Box 8000, Cary, NC 27511. Schwert D. P. and White J. P. (1974) Method tbr in-situ measurement of nitrification in a stream. Appl. Microbiol. 28, 1082-1083. Shetty P. V. (1985) Nitrogen species transformations as predictors of in-stream nitrification. Thesis, presented to the University of Texas at Dallas, Richardson, Tex., in partial fulfillment of the requirements for the degree of Master of Science. Somville M. (1978) A method for the measurement of nitrification rates in water. Wat. Res. 12, 843-848. Stanley D. W. and Hobbie J. E. (1977) Nitrogen cycling in the Chowan River. Water Resources Research Institute of the University of North Carolina, Report No. 121. Chapel Hill, N.C. Stratton F. E. and McCarty P. L. (1967) Prediction of nitrification effects on the dissolved oxygen balance of streams. Envir. Sci. Technol. I, 405410. Thomann R. V. (1982) Verification of water quality models. J. envir. Engng Div. Am. Soc. cir. Engrs 108, 923-940. Warwick J. J. (1986) Diel variation of in-stream nitrification. Wat. Res. 20, 1325 1332. Warwick J. J. and McDonnell A. J. (1985a) Simultaneous in-stream nitrogen and D.O. balancing. J. envir. Engng Div. Am. Soc. cir. Engrs 111,401-416. Warwick J. J. and McDonnell A. J. (1985b) Nitrogen accountability for fertile streams. J. envir. Engng Div. Am. Soc. civ. Engrs 111, 417-430. Wezernak C. T. and Gannon J. J. (1968) Evaluation of nitrification in streams. J. sanit. Engng Div. Am. Soc. cir. Engrs 94, 827-837. Wilber W. G., Peters J. G., Ayers M. A. and Crawford C. G. (1979) ,4 one-dimensional, steady-state, dissolved oxygen model and waste-load assimilation study for Cedar Creek, Dekalband Allen Counties, Indiana. United State Geological Survey, USDI, Open-File Report 79-1062. Wild H. E., Sawyer C. N. and McMahon T. C. (1971) Factors affecting nitrification kinetics. J. Wat. Pollut. Control Fed. 43, t845 1854.
0043-1354/88 $3.00 + 0.00 Copyright © 1988 Pergamon Press plc
IN-STREAM NITRIFICATION RATE PREDICTION JOHN J. WARWICKand PRADEEP SHETTY Graduate Program in Environmental Sciences, The University of Texas at Dallas, Richardson, TX 75080, U.S.A.
(Received February 1987; accepted in revised form December 1987) Abstract--A simultaneous nitrogen and dissolved oxygen mass balancing technique is used to accurately assess actual in-stream nitrification rate coefficients. These rate coefficients are compared against estimations from observed spatial changes in various bulk fluid nitrogen species concentrations. Total Kjeldahl nitrogen change was found to be the most accurate predictor of in-stream nitrification. Proposed nitrification rate coefficient estimation functions have r 2 values in excess of 0.90.
Key words--nitrification, stream, model, kinetics, water quality simultaneous, nitrogen and dissolved oxygen mass balancing technique was employed to accurately assess actual in-stream nitrification rates. Numerous data sets provided by the Texas Water Commission (formerly Texas Department of Water Resources) were analyzed. The computed, actual, in-stream rate coefficients were statistically compared with simple estimations based upon observed spatial changes in bulk fluid nitrogen species.
NOMENCLATURE D = Stream depth (m) F = Fraction of aquatic plant nitrogen uptake that is nitrate-N Ka = In-stream reaeration rate coefficient (day-~) K, = Actual in-stream, zero-order, nitrification rate coefficient (mg 021- t day- l) K~ = Actual in-stream, zero-order, nitrification rate coefficient (g 02 m -2 day -l) /'max= Maximum gross photosynthetic oxygen production rate (mg 021- l day- t) Rp = Aquatic plant respiration rate (mg 021-1 day- 1) tf = Flow time between stations (days) TKN = Total Kjeldahl nitrogen (mgN 1-~) V = Stream velocity (m s- i)
IN-STREAM NITRIFICATION RATE COMPUTATION
Ultimate NBOD reduction INTRODUCTION
The concentration of dissolved oxygen is an important measure of stream water quality and a significant factor in the evaluation of pollution impact. The dissolved oxygen level reflects the capacity of a fiver to maintain and propagate a balanced aquatic habitat. Nitrogenous biochemical oxygen demand (NBOD) has long been recognized as a major sink in the oxygen balance of streams (Courchaine, 1968; Wezernak and Gannon, 1968; O'Connor and DiToro, 1970). It is therefore important to accurately assess the magnitude of in-stream nitrification when performing dissolved oxygen computations. NBOD removal represents a relatively complex process involving the sequential oxidation of ammonia nitrogen ( N H ~ - N ) to nitrite nitrogen ( N O r - N ) and then to nitrate nitrogen ( N O r - N ) . The deoxygenation rate associated with in-stream nitrification has generally been determined by one of two methods: (1) quantifying the rate of ultimate NBOD (NBODL) change in the downstream direction or (2) analysis of the rate of individual nitrogen species change in the downstream direction. The purpose of this research was to determine which nitrogen species transformation was the best predictor of in-stream nitrification rates. A complex,
This technique involves plotting NBOD L values vs flow times between stations. The slope of the best fit line gives an estimate of the nitrification rate coefficient. The method is questionable due to inaccuracies in ascertaining the NBODL values and the problem is further aggravated by the 20 day test duration. Several investigators have studied this approach and have emphasized additional problems associated with it. DeMarco et al. (1967) showed that nitrification in the standard bottle BOD test was dependent on the turbulence and initial nitrifying seed concentration. In addition, nitrification in the bottle does not begin for an appreciable time lag, usually 3-10 days since surfaces precolonized by nitrifying bacteria are absent (Finstein et al., 1977). Furthermore, the standard 20°C temperature used in the bottle test is below the optimum range of 25-28°C for nitrifying bacteria (Courchaine, 1968). The problems and expense associated with this technique dictated exclusion from further consideration.
723 WR. 22/~-E
Nitrogen species transformations This method is usually accomplished by calculating the rate of change of individual nitrogen species in the downstream direction. The nitrogen species typically used are total Kjeldahl (organic + ammonia), ammonia, nitrate and nitrite plus nitrate nitrogen. Since the
724
JOHN J. WARWICKand PRADEEPSHETTY
transformation rate for each of these compounds is dependent not only on nitrification but also on other processes involved in the nitrogen cycle, one typically obtains different nitrification rates depending on the nitrogen species utilized. In fact, several studies have shown significant nitrogen imbalances, where the rates of observed decreases in ammonia-N have not been paralleled by corresponding increases in the rate of nitrite-N and nitrate-N buildup (e.g. Schwert and White, 1974; Ruane and Krenkel, 1978; Cirello et al., 1979; Wilber et al., 1979; Warwick and McDonnell, 1985b). Due to the lack of sufficient information researchers have been inconsistent in selecting a nitrogen species to use when assessing in-stream nitrification. Blanc and O'Shaughnessy (1974), Ruane and Krenkel (1978) and Cirello et al. (1979) used both NH4+ - N and N O f - N transformations; Wild et al. (1971) and Huang and Hopson (1974) followed NH~--N changes; Somville (1978) based his assessment on N O a - N increase; and Gowda (1983) suggested that the nitrification rate could be estimated either by total Kjeldahl nitrogen (TKN) decrease of N O ~ - N increase. SNOAP
model
The Stream Nitrogen and Dissolved Oxygen Analysis Program (SNOAP) is a one-dimensional, pseudo unsteady state water quality model developed by Warwick and McDonnell (1985a) to predict instream nitrification rates based on observed changes in all nitrogen species and dissolved oxygen. The model follows any number of sequential parcels of water downstream, thus allowing the handling of unsteady water quantity and quality values at each station. The model neglects longitudinal dispersion and therefore application should be limited to situations where this assumption is valid. The nitrogen mass balance equations include the following transformation pathways: ammonification, nitrification, bacterial and aquatic plant nitrogen assimilation, ammonia exsolution and denitrification. The SNOAP model calculates reaction rates from the water quality data input at each station. The kinetic order for certain nitrogen reaction rates is set in the model. Ammonia exsolution is established as a first-order reaction whereas nitrogen uptake by aquatic plants and bacteria is modeled as a zero-order reaction. The other major nitrogen transformation pathways like ammonification, nitrification and denitrification can be optionally modeled either as zero- or first-order reactions. The dissolved oxygen mass balance equations include: carbonaceous and nitrogenous deoxygenation, aquatic plant respiration, benthal demands, atmospheric reaeration and photosynthetic oxygen production. The reaeration rate, carbonaceous deoxygenation rate and benthal oxygen demand terms are established as input parameters leaving nitrification and aquatic plant respiration as two
unknowns. Combining the nitrogen and dissolved oxygen mass balance equations and using an iterative solution algorithm results in singular values of the nitrification rate and aquatic plant respiration rate. This simultaneous solution results in a quick and stable convergence in typically < l0 iterations. The iterative algorithm may not converge in the case of erroneous data for nitrogen and/or dissolved oxygen concentrations. Furthermore, if major sources or sinks (e.g. unidentified pollutant loading, benthal demands, reaeration) have been ignored or incorrectly estimated a simultaneous solution may not be possible. This nonconvergence feature is a significant attribute of the SNOAP model since it helps in screening out faulty input data values. The model was successfully calibrated and verified for three independent water quality surveys of Marsh Creek, a tributary to Pine Creek, located in the Pine Creek watershed on the Appalachian Plateau in Northcentral Pennsylvania (Warwick and McDonnell, 1985a, b). Additionally, the SNOAP model has been used to compute the diel variation of in-stream nitrification rates (Warwick, 1986). The original SNOAP model was modified for this study to incorporate preferential uptake of ammoniaN by aquatic plants. Several studies in a variety of aquatic systems have shown that ammonia-N is preferentially assimilated by aquatic plants in the presence of nitrate-N (MacIssac and Dugdale, 1969; Eppley and Rogers, 1970; McCarthy and Eppley, 1972; Eppley et al., 1977; McCarthy et al., 1977). The suppression of nitrate-N assimilation depends on the ambient concentration of ammonia-N. Based on a study in the Chesapeak Bay, McCarthy et al. (1977) indicated that for ammonia-N concentrations in excess of 0.08 mg 1- J of N H ~ - N , an insignificant amount of nitrate-N uptake can be expected. Figure 1 shows a graph of the fraction of total nitrogen assimilation that is associated with nitrate-N uptake (nitrate-N assimilation divided by the sum of nitrateN and ammonia-N uptake) vs the ammonia-N concentration as plotted by Stanley and Hobbie (1977). Equation (1) (dashed line in Fig. 1) was developed to explain the noted variation: F = 0.00457 [NH~ ] 0.9149
(1)
where F = fraction of total nitrogen uptake that is nitrate-N, and [NHg] = ammonia-N concentration (mg N 1-~). This equation was incorporated into the SNOAP model to account for the preferential uptake of ammonia-N by aquatic plants in the presence of nitrate-N. Further details regarding SNOAP model modification can be found elsewhere (Shetty, 1985). Nitrification reaction order
Variation reaction rates have been used to describe in-stream nitrification. O'Connor (1967) proposed a model including nitrification as a first-order reaction. Knowles et al. (1965) and Stratton and McCarty (1967) used Monod kinetics in their rate expression
In-stream nitrification rate prediction
SNOAP model was furnished in the reports. The screening criteria included the following:
l.O0
I
1 ta
÷q-
z
(1) data availability, (a) flow, nitrogen species concentrations and ultimate carbonaceous biochemical oxygen demand (CBODL); along with flow times between stations, (b) diel variation of dissolved oxygen, temperature and pH; (2) flow times had to be ~
.80
.60
Z
+ -
~
.40
\V *---"EOOATION, o
725
•02
~
.06
.10
.14
38
AMMONIA CONCENTRATION (mg- N/L)
Fig. 1. Preferential aquatic plant ammonia-N uptake (after Stanley and Hobble, 1977). for nitrification studies. Wezernak and Gannon (1968) utilized an autocatalytic growth reaction in evaluating the nitrification rate in the Clinton River. Wild et al. (1971) studied the nitrification process with a laboratory reaeration unit and found that nitrification followed a zero-order reaction, when the ammonia-N concentration was varied from 6 to 60 mg 1-1. Huang and Hopson (1974) compared zeroorder, first-order, Monod and autocatalytic growth equations to model nitrification and concluded on the basis of laboratory studies that nitrification follows zero-order kinetics for ammonia-N concentrations > 2 . 5 m g l -~. Based on field studies Finstein and Matulewich (1974) and Curtis et al. (1975) found that nitrification took place extensively on rock surfaces and sediments, which further suggests that nitrification follows zero-order kinetics and occurs due to the action of pregrown cells. The SNOAP model has the flexibility to simulate nitrification with either zero- or first-order reaction rates. Due to the relatively high concentration of ammonia-N in all data sets analyzed, zero-order kinetics will be used to model the nitrification reaction in this paper. DATA ANALYSIS
Twenty-seven intensive surveys of rivers in Texas were acquired from the Texas Department of Water Resources (TDWR). Each of these surveys was accomplished in accordance with the Texas Water Quality Act, Section26.127, as amended in 1977. They were to be used in developing and maintaining the State Water Quality Strategy published in 4 0 C F R 35.1511-2 pursuant to Section 303(e) of the Federal Clean Water Act of 1977. There were a total of 451 individual reaches reported in the 27 intensive surveys. These segments were studied to see if all the data required by the
This scrutiny eliminated most of the 451 possible segments resulting in a final data base of only 9 reaches. Table 1 gives a brief description of the selected segments. S N O A P application
SNOAP model input includes flowtime between stations; along with diel variations of flow, dissolved oxygen, nitrogen species, temperature and pH at both the upstream and downstream stations. The diel variations of nitrogen species and flow were not available in the analyzed data sets, so average (constant) values were used for these parameters. The SNOAP model requires input of CBODL measurements and a reaction rate. Measured in-stream CBODL removal was used to evaluate the CBODL reaction rate. The SNOAP model also allows for the direct input of a zero-order benthal oxygen demand rate. Unfortunately, sediment oxygen demand was not measured and a zero value was therefore used. Ammonification, nitrification and denitrification were set as zero-order reactions, due to the relatively
Table 1. Selected data set identification Data set
Title*
Segment
Intensnve survey of Rowlett Creek, IS-25
10-9
Intensive survey of San Antonio River, segment 1901, IS-59
C-D
Intensive survey of San Antonio River, segment 1901, IS-59
FD-FE
Intensive survey of East Fork Trinity River, segment 0819, IS-58
B-C
Intensive survey of East Fork Trinity River, segment 0819, IS-58
E-F
Intensive survey of Aransas River-above Tidal, segment 2004, IS-48
N-M
Intensive survey of the Trinity River, segment 0805, IS-53
B-3
Intensive survey of Sims Bayou, IS-24
D--C
Intensive survey of Halls Bayou, IS-31
D-C
*As published and distributed by the Texas Department of Water Resources, P.O. Box 13087, Austin, TX 78711, U.S.A.
726
JOHN J. WARWICK a n d PRADEEP SHETTY Table 2. Average water quantity and quality data
Data set
Q (m3s l)
D.O. (mgl-i)
Temp. (°C)
pH
Total P ( m g P l -I)
1 2 3 4 5 6 7 8 9
0.20 5.34 1.78 1.99 2.33 0.10 2.88 0.67 0.42
5.97 1.76 4.10 3.48 1.92 4.74 6.75 4.92 4.72
I 1.5 29.5 27.5 19.2 19.6 25.0 23.1 30.4 19.4
7.24 7.28 7.70 7.58 7.31 7.82 6.62 7.52 7.69
7.68 4.43 1.35 1.16 1.56 6.42 2.53 2.98 4.66
high nitrogen species concentrations analyzed. Table 2 summarizes selected water quantity and quality data for each data set. The SNOAP model solution framework computes hourly values for ammonification, nitrification, ammonia exsolution, denitrification, aquatic plant respiration and gross photosynthetic oxygen production. These reaction rate values are computed to exactly match all input dissolved oxygen and nitrogen species data. SNOAP model validation can be performed by inspecting the magnitude and variability of the computed reaction rates.
SNOAP calibration The reaeration rate coefficient (Ka) was selected as the model calibration parameter. This choice was dictated by the fact that reaeration was not measured for most of the reaches analyzed. Calibration was achieved by adjusting K~ to achieve a desired or target value for the ratio of Pmax/Rp, where Pmax= maximum photosynthetic rate (mgO21 ~day -:) and R p = p l a n t respiration rate (mgO21 ~day-~). Estimates of Pmax and Rp are calculated by the SNOAP model within the simultaneous dissolved oxygen and nitrogen mass balancing framework. Table 3 presents a partial summary of ratios available in the literature. A target ratio value equal to 3.10 was used in all analyses. The overall level of photosynthetic activity can be controlled by nutrient availability. Ranges of Michaelis-Menton half-saturation growth constants (Ks) have been reported for a variety of different photosynthetic organisms and environmental conditions in "Rates, Constants, and Kinetics Formulations in Surface Water Quality Modelling" (EPA, 1978). The reported K~ range for nitrogen was 0.01-0.40 mg N 1- 1 and 0.002-0.05 mg P 1-1 for phosphorus. The nutrient values given in Table 2 demonstrate that the selected data sets are not nutrient limiting. The Pmax/Rp ratio has been shown to be a function of light intensity (Harris and Lott, 1973) and Table 3. P,na,/Rp ratios References Geike and Parasher (1978) Harris and Lott (1973) Nihei el al. (1954)
Organism
Pm,x/Rp
Chlorella pyrenoidosa Cosrnarium botrytis Chlorella ellipsoidea
3.05 3.25 3.10
Nitrogen species (mg N 1-I) . . . . . . . . . . . TKN NH~NO2 NO i
p,~ Rp (mgO21 i day- i)
8.40 12.60 4.21 6.20 6.49 7.55 3.85 2.86 5.12
29.7 6.7 5.5 60.4 18.0 90.9 2.0 22.9 21.2
8.11 9.18 2.90 4.74 5.95 5.75 1.74 2. I 0 4.80
0.28 0.30 0.76 0.04 0.12 0.66 0.18 0.42 0.32
2.00 0.46 2.10 0.11 0.04 5.73 4.69 2.08 0.34
9.6 2.1 1.8 19.5 5.8 29.3 0.6 7.4 6.8
the concentration of various environmental contaminants. Geike and Parasher (1978) demonstrated that oxygen evolution was reduced by 33.3% at a hexachlorobenzene (HCB) concentration of 0.1 mg l-lq while respiration was unaffected. Table 4 presents the adjusted reaeration (Ka) values used for SNOAP model calibration. Reaeration coefficients were also computed as a function of average hydraulic stream conditions using empirical formulations by O'Connor and Dobbins (1958) and Owens et al. (1964). Application of these empirical expressions was based upon criteria set forth by Covar (1976). The SNOAP model computed instream nitrification rate coefficients were very insensitive to changes in Ka, while the target ratio (Pmax/Rp = 3.10) was extremely sensitive to variation of Ks. Achieving the desired Pm~x/Rpratio sometimes resulted in Ks values which were somewhat inconsistent with field measurements and empirical computation. Fortunately, this amount of Ka error had an insignificant effect on the in-stream nitrification rate coefficients as computed by the SNOAP model. Reaeration was measured for data sets 7-9 (Table 4) using radioactive krypton-85 and hydrogen-3 (Neal, 1979). Data sets 7-9 were re-analyzed using the measured Ka values instead of adjusting to achieve the target Pmax/Rp ratio. The changes in SNOAP computed nitrification rate coefficients were +0.32, - 4 . 5 9 and - 0 . 1 5 % for data sets 7-9, respectively.
SNOAP validation Classically, model validation involves reapplying the calibrated model to a substantially different data set. Unfortunately, multiple data sets for the investigated stream segments were unavailable. Validation was therefore limited to a more qualitative inspection of three parameters; (1) input reaeration (Ka) values, (2) computed nitrification rate coefficient of variability, and (3) minimum computed aquatic plant respiration (Rp). Table 5 summarizes these 3 parameters for all 9 data sets. Two comparisons are possible when evaluating the validity of reaeration assignment. First a comparison can be made between calibration reaeration assignments and empirically computed values (Error~) (see Table 5). Rathbun (1977) evaluated the performance
In-stream nitrification rate prediction
727
Table 4. S N O A P calibration (K~) values ~
K~ ( d a y - I)
)
Data
(m)
(m
set
[It]
[ft s - ']
(h)
SNOAP
Measured
Computed
I
0.579 [1.90]
0.130 [0.427]
4.47
5.79
--
3.73*
2
0.892 [2.93]
0.205 [0.673]
4.43
5.09
--
2.lit
3
0.585 [1.92]
0.165 [0.541]
9.43
4.34
--
4.28*
4
0.531 [1.74]
0.226 [0.741]
5.35
9.63
--
6.34*
5
0.656 [2.15]
0.208 [0.682]
4.30
2.96
--
3.38t
6
0.203 [0.667[
0.085 [0.279]
5.87
12.00
--
19.48"
7
1.448 [4.75]
0.133 [0.436]
3.75
4.84
1.70
0.82t
8
0.507 [1.66]
0.147 [0.482]
4.93
1.93
0.95
5.19"
9
--
0.139
2.00
4.03
3.72
S - I
tf
1
*Owens et aL (1964); K "da - b at
21"7(v)°'67 for 15 ~<2.0ft
Y )=
~
/5[=lft,
g[=]fts-"
t O ' C o n n o r and Dobbins (1958); i 12.9(g) °.s° for 15 > 2 . 0 f t Ko ( d a y - ) = ~ /5 [ = ] ft, g [ = ]ft s_l.
of predictive reaeration formulae and found that for five streams (comprising 482 total observations) the normalized mean percentage error ranged from 5 to 216 and from - 12 to 120 for Owen et al. (1964) and O'Connor and Dobbins (1958), respectively. Based upon this criteria, most reaeration assignments appear quite reasonable with data sets 2, 6, 7 and 8 being most questionable. A second comparison can be made between measured values and calibration assignments (Error2). Data sets 7 and 8 appear most questionable under this criteria. Simultaneous evaluation of both criterion indicates that the assigned reaeration values used in data set 8 lies between the measured and empirically computed value. The assigned reaeration value is therefore assumed valid for data set 8. In summary, data sets 2, 6 and 7 still appear questionable. The SNOAP model computes hourly estimates of
in-stream nitrification. Since zero-order kinetics have been used in all analyses, temperature corrected nitrification rate coefficients should remain reasonably constant over the entire study period. Table 5 presents the coefficients of variation for computed hourly, temperature corrected, in-stream nitrification rate coefficients. Data sets 2 and 9 are most questionable based upon this validation condition. The SNOAP model also computes hourly estimates of aquatic plant respiration (Rp). Computation of negative Rp values is unreasonable. Therefore, data sets 2, 7 and 9 must all be viewed as questionable under this validation criteria (Table 5). Data sets 2, 7 and 9 have been identified as questionable under two or more of the designated validation criterion. In the analyses which follow results will be presented for the full data set (1-9) and for a partial data set excluding data sets 2, 7 and 9.
Table 5. S N O A P validation Reaeration (K~) Data set l 2 3 4 5 6 7 8 9
Errort
Error 2
35.6% 58.5% 1.4% 34.2% -14.2% -62.3% 83.1% - 167.0% --
------64.9% 50.8% 7.7%
S N O A P nitrification Coefficient o f variation
Error I = [(SNOAP - computed)/SNOAP]* 100. Error: = [(SNOAP - measured)/SNOAP]* 100.
5.0% 10.0% 1.7% 3.1%
M i n i m u m Rp
(m8 02 I- ' d a y - ')
7.2% 3.2% 8.3%
7.5 -3.3 1.1 15.6 4.9 22.7 -0.3 5.4
~.7%
-4.4
3.1%
728
JOHN
J. WARWICK
and
PRADEEP
SHETTY
Table 6. Computed in-stream nitrificationrate coefficients Nitrificationrate coefficients (rag02 I- ~day ~) Data set
TKN
NH~-
NO.~
23.25 19.20 15.65 42.74 9.67 40.72 13.86 10.54 26.50
18.60 -3.12 15.21 I 1.46 7.73 21.24 9.42 4,85 10.91
-7.44 -5.56 7.48 2.68 -0.11 -11.81 -52.89 -13.18 10.37
Estimation of nitrification rate coefficients Four different estimations of the nitrification rate coefficient were made for each data set. All estimations were based upon the observed spatial gradient of a selected nitrogen species. The 4 specific estimates were computed as follows: (1) observed spatial decrease of bulk fluid TKN-N, (2) observed spatial decrease of bulk fluid NH2 - N , (3) observed spatial increase of bulk fluid NO~- - N , (4) observed spatial increase of bulk fluid NO~--N. Table 6 presents a comparison of these estimates and the SNOAP computed in-steam nitrification rate coefficients. The stoichiometric equivalents used were 3.22mg of oxygen required per mg of N H ~ - N oxidized to N O / - - N and 1.11 mg of oxygen required per mg of N O / - - N oxidized to N O 3 - N (Haug and McCarthy, 1972). RESULTS AND DISCUSSION
The nitrification rate coefficient estimations were statistically compared with the rate coefficient calculated by SNOAP. It was assumed that the value from A O
NO~
SNOAP
-7.44 -2.88 5.84 3,69 0,24 --20.89 -55.98 -13.49 7.27
19.41 12.29 13.81 30.60 4.99 27,51 I 1,65 7.59 21.89
the SNOAP model represented the actual in-stream nitrification rate coefficient. The statistical analyses included a linear regression technique and evaluation of a root mean square error. The full and partial data sets were analyzed separately to illucidate any impact from the questionable data sets (2, 7 and 9).
Regression analysis The actual nitrification rate coefficient (SNOAP computation) was regressed with the 4 estimations using PROC REG, a general purpose regression procedure available through SAS (SAS Inst. Inc., 1982). The predicted mean regression lines (solid) along with 95% confidence intervals about the predicted mean (dashed) are presented in Figs 2-5. Circled data points identify data sets 2, 7 and 9; which were excluded during the analysis of the partial data set. Figures 2-5 were constructed for the full data set. The predictions based upon the rate of observed T K N decrease (Fig. 2) resulted in the smallest confidence interval, signifying superior predictive power. The 95% confidence interval lines, in Figs 3-5, indicate increasing uncertainty in estimating the actual nitrification rate coefficient. These results were also found for the partial data set.
60 / /
o~
I
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ta
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ill
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--60
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i
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-20
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I
0
I
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J
40
60
TKN ESTIMATED COEFFICIENT (mg-Oa/L/doy)
Fig. 2. Total Kjeldahl nitrogen estimation on in-stream nitrification rate coefficient.
In-stream nitrification rate prediction
729
60
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o-
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rw
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-20
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0
l
i
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20
I
40
60
NH 4 ESTIMATED COEFFICIENT (rag- 02/L/doy)
Fig. 3. Ammonia nitrogen estimation of in-stream nitrification rate coefficient. Computing the correlation coefficient r 2, slope, and the intercept gave an additional level of insight into the comparison of the SNOAP computed and estimated nitrification rate coefficients. Tables 7 and 8 present this information for the full and partial data sets; respectively. The 95% confidence intervals for the slope and intercept are also presented. The estimations computed by NOx increase and N O r increase can be discounted based on the low r 2 values and extremely large 95% confidence intervals. The NH + decrease was superior to both N O r and NO~" increase, but was still substantially inferior to the predictions made using T K N decrease. The significant number of negative predictions from observed changes in NO~- and NO~- (Table 6) reflect the
real possibility of significnt in-stream denitrification. The 95% confidence intervals are slightly larger for the partial data set due to the reduced sample size. The slope and intercept values for the full and partial data sets (Tables 7 and 8) are not significantly different. Therefore, the impact of the questionable data sets was not statistically significant. R o o t mean square error
A measure of the total error between the values calculated by the SNOAP model and the estimated rate coefficients is also given by the root mean square (RMS) error. The RMS error is statistically well behaved and provides a direct measure of model error (Thomann, 1982). The RMS error values are also
6O
0
40
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20
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zo
-20
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-40
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-60
~
-60
i
-40
i
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-20
i
I
0
¢
i
I
20
i
40
i
60
NOx ESTIMATED COEFFICIENT (mg-O=/L/d0y)
Fig. 4. Nitrite plus nitrate nitrogen estimation on in-stream nitrification rate coefficient.
730
JOHN J. WARWICK a n d PRADEEP SHETTY A
60 .J
/ /
O i
40
E I-z bd
20
--W
U. It. tlJ O O IM r¢ Z
*
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-40
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,
-40
60
,
.
-20
,
,
0
.
,
20
.
,
40
60
NO3 ESTIMATED COEFFICIENT (rng-Oz/L/doy)
Fig. 5. N i t r a t e n i t r o g e n e s t i m a t i o n o f i n - s t r e a m n i t r i f i c a t i o n r a t e coefficient.
summarized in Tables 7 and 8 with the smallest error being for the T K N technique of evaluating the nitrification rate coefficient.
Proposed in-stream nitrification prediction model From the regression analysis of the full data set, the equation for estimating the actual, zero-order, nitrification rate coefficient is: K, = 0.70, (ATKN • 4.33/tr) + 0.93 mg O2 l- l day- 1 (2) where Kn=actual, zero-order, nitrification rate (mg 02 l- l d a y - 1); ATKN = observed change in bulk
fluid total Kjeldahl nitrogen concentration between stations (mgNl-~); and tf= flow time between stations (days). Analysis of the partial data set results in an expression which is very similar to equation (2). Kn = 0.69" (ATKN* 4.33/tf) + 0.82 mg 021-1 day- J (3) Equation (3) is herein proposed as the best predictor of zero-order, in-stream nitrification as a function of bulk fluid spatial changes in nitrogen species concentrations. All data sets included in the partial data set analysis had average depths of flow less than 0.66 m
Table 7, Statistical results for data set Nitrogen species
r2
Slope
TKN
0.949
0.700 (0.5554).845)*
NH 4 N
0.315
NO, N NO3 N
Intercept (mgO21 i d a y ,)
RMS error (mgO21 t d a y i)
0.927 ( - 2.72~4.58)*
2.12
0.673 ( - 0 . 2 1 3 1.56)
9.44 ( - 1 . 8 5 20.73)
777
0.063
0.117 ( - 0.288-4).522)
17.56 (9.72- 25.40)
9.09
0.029
0.075 (4).3154).466)
17.34 (9.19 25.49)
9.26
*95% Confidence intervals. Table 8. Statistical results for partial data set Nitrogen species
r2
Slope
TKN
0.964
0.694 (0.5214).868)*
NH4 N
0.411
NO~-N
0.017
NO3-N
< 0.00t
Intercept ( m g O 2 1 - 1 d a y ~)
RMS error (mgO21 ' d a y ')
0.825 ( -3.91-5.56)*
2.22
3.40 ( - 19.98-2677)
8,94
16.62 (2.73-30.51)
11.56
(-- 1.40--1.14) 0.009 ( - 1.60-1.61)
17.35 (3.73-30.97)
I 1,66
1.056 ( - 0.568-2.68) 0.130
*95% Confidence intervals.
In-stream nitrification rate prediction
731
Table 9. Statistical results for partial data set using areal units for in-stream nitrification rate coefficients Nitrogen species
•2
Slope
Intercept (g 02 rn - 2day - i)
RMS error (g 02 m - 2day- i)
TKN
0.952
0.755 (0.538-0.971)*
-0.163 ( - 2.84-2.52)*
1.21
NH4-N
0.268
0.840 ( - 0.947-2.63)
2.78 ( - 9.48-15.04)
4.76
NOx-N
0.105 0.163
8.56 (2.71-14.41) 8.86 (3.01-14.70)
5.27
NO 3-N
0.403 ( - 1.11-1.91) 0.494 ( - 0.946-1.93)
5.09
*95% Confidence intervals.
(2.2 ft). Nitrification could be expected to occur as a result of attached organisms in these shallow systems, and there may be some correlation with stream contact area. The analysis of the partial data set was therefore repeated for nitrification rates computed on an areal basis (g O2m -2 day -l) (see Table 9). Equation (4) represents the best predictor of zero-order, in-stream nitrification expressed in areal units. /
K n = 0 . 7 6 , ( A T K N * 4.33/tf) * / )
- 0 . 1 6 gO2 m-2day -l
(4)
where K~=actual, zero-order, nitrification rate (g 02 m -2 day- 1); and/~ = average stream depth (m). Equations (2)-(4) were developed for zero-order nitrification rate coefficients only. Development of parallel expressions for first-order reaction kinetics will require analyses of additional data sets which are characterized by lower nitrogen species concentrations.
SUMMARY AND CONCLUSIONS
A complex, simultaneous nitrogen and dissolved oxygen mass balancing program (SNOAP) was used to accurately assess actual in-stream nitrification rate coefficients. These computed coefficients were compared with simple estimations based upon observed bulk fluid spatial changes in various nitrogen species concentrations. Observed changes in TKN concentration were found to most accurately predict instream nitrification. However, calculating the instream nitrification rate coefficient directly from observed decreases in TKN, without using equations (3) or (4), will result in significant overestimation. The substantial impact of nitrification on stream dissolved oxygen budgets is well known. Therefore, it is important to accurately assess the level of in-stream nitrification. Performing a complete nitrogen and dissolved oxygen mass balance (e.g. SNOAP model) requires an extensive data base, which may be practically unattainable in many situations. It is therefore hoped that use of expressions like equations (3) and (4) will facilitate a relatively quick, inexpensive and accurate means for assessing the magnitude of instream nitrification.
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JOHN J. WARWICK and PRADEEP SHETTY
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