The Use Of Ultra Violet Light For The Treatment Of Filtered Drinking Water
DEVELOPMENT OF A NITRATE CONCENTRATON PREDICTION MODEL USING UV
1. DEVELOPMENT OF A NITRATE CONCENTRATON PREDICTION MODEL
USING UV/VIS SPECTROSCOPY IN MUNICIPAL WASTEWATER
by
Evan Thomas Forney
A research project submitted to the faculty of
The University of North Carolina at Charlotte
in partial fulfillment of the requirements
for the degree of Master of Science in Engineering
Environmental Engineering
Charlotte
2016
Approved by:
______________________________
Dr. Olya Keen
______________________________
Dr. James Bowen
______________________________
Dr. Mei Sun
3. 3
ABSTRACT
EVAN FORNEY. Development of a nitrate concentration prediction model using
UV/Vis spectroscopy in municipal wastewater. (Under the direction of Dr. Olya Keen).
In the present work, UV/Vis spectroscopy was used to measure absorbance in
samples of wastewater effluent in order to develop a model that uses absorbance values to
predict a concentration of nitrate. Samples were collected from McDowell municipal
wastewater treatment plant effluent and measured for actual concentrations of nitrate,
nitrite, and TOC. The prediction model was developed from 30 samples using
absorbance measurements at two wavelengths; [NO3] = ( A219 – ( A245 × 2.71 )) / (ɛNO3, 219
nm) × (1cm). The model’s accuracy was tested on 10 samples, and a Pearson’s r
correlation coefficient of 0.89 was determined for measured and predicted values within
1:1 linearity. The average difference in magnitude between measured and predicted
concentrations of nitrate was +/- 0.21 mg-N/L, and the difference range was 0.02 – 0.53
mg-N/L. The range of percent error for the nitrate prediction residuals in this data set
was 0.53-12.9%. The results suggest that UV/vis spectroscopy can be used to quickly
monitor and survey concentrations of nitrate, as well as identify possible treatment upsets
in nitrification/denitrification.
4. 4
TABLE OF CONTENTS
Literature Review……………………………………………………………. 5
Materials and Methods……………………………………………………….15
Model Creation……………………………………………………………… 17
Results & Model Testing……………………………………………………. 20
Conclusions…………………………………………………………………. 29
Acknowledgements……………………………………………………….… 32
References…………………………………………………………………... 33
5. 5
LITERATURE REVIEW
Monitoring wastewater quality is a major concern for treatment plant operators
who need to achieve a regulated level of contaminant removal mandated by the U.S.
EPA. In general, wastewater treatment aims to achieve a healthy water quality effluent
by limiting environmental release of several potentially harmful water characteristics
such as organics, nitrogen, BOD, ammonia, and several others regulated by The Clean
Water Act (WEF, 2007). In order to comply with discharge permit requirements, plant
personnel must constantly monitor these characteristics in the event of a potential
treatment upset.
Ammonium can be especially problematic when discharged into streams due to its
high oxygen demand. The conversion of ammonia nitrogen to nitrite and nitrate is
particularly important, as the nitrification process is governed by sensitive microbe
populations that can take several days to develop (Metcalf & Eddy, 2014). In wastewater
treatment, ammonium is converted to nitrite during aeration by slow growing populations
of bacteria called ammonia oxidizing bacteria (AOB), or Nitrosomonas. Ammonium
conversion is the limiting step in the nitrification reaction as well as the most sensitive to
changes in environmental conditions. Nitrite is then converted to nitrate by a different
population of aerobic nitrite oxidizing bacteria (NOB) or Nitrobacter. This reaction
however, is less likely to be disrupted because NOB are faster growing and can tolerate
harsher environmental conditions than AOB (Moorcroft et al., 2001).
6. 6
If the plant discharges into a sensitive water body, then denitrification may also be
required by permit. Denitrification is the conversion and release of nitrate into nitrogen
gas that happens in the anoxic zone utilizing facultative anaerobic bacteria that can utilize
bound oxygen. Changes in environmental conditions or the presence of dissolved oxygen
in the anoxic zone can disrupt the process, resulting in elevated nitrate levels that could
violate discharge permits for sensitive water bodies. An abundance of nitrogen in a
receiving stream can cause eutrophication, a process that removes oxygen from the water
caused by uncontrollable algae blooms that thrive in the presence of nutrients.
Discharging moderate levels of nitrate can be acceptable, however if the receiving
waterbody is shared with a drinking water facility, controlling the release of nitrate may
be required or strictly regulated. While nitrate can exist in the environment at moderate
to low concentrations, nitrite is of particular concern in drinking water because it is a
more reactive species and the presence of one is rarely found without the other
(Moorcroft et al., 2001). Although nitrite typically exists in much lower concentrations
than nitrate, human ingestion of nitrate can lead to gastric cancer or cause
methemoglobinemia, also known as “blue baby syndrome.” The nitrate is converted into
nitrite in the stomach and reduces the ability of hemoglobin to carry oxygen. The U.S.
EPA has set a Maximum Contaminant Level of 10 mg/L as nitrate (Mackenzie, 2010).
Due to the sensitivity of biological processes involved in nitrogen conversion and
removal, it is important that plants obtain real-time data so that treatment upsets can be
identified and addressed quickly.
There are two processes typically used in wastewater treatment to monitor nitrate
concentrations: ion chromatography, and colorimetric methods. These methods are
7. 7
essentially limited in their ability to provide in-situ measurements or are too time and
resource consuming to maintain a vigilant sample frequency. Ion chromatography
requires frequent calibration and a great deal of pre-treatment in order to obtain accurate
measurements. Samples must be filtered for turbidity and with some methods nitrate
must be converted to nitrite prior to analysis, often using hazardous chemicals such as
cadmium in the process. There is also additional training required for employees to
perform ion chromatography, as well as the costs associated with instrumentation and
chemical supply. Colorimetric methods typically involve the addition of a reagent to
create a colored nitrate complex that can be measured photometrically using visible or
ultraviolet light, such as with the HACH TNT-835 Nitrate detection kits. Supplying
materials for frequent testing can be costly, as well as the constraints associated with
waste disposal and having to handle multiple vials and liquids during testing. Additional
methods for measuring nitrate that are less common include electrochemical methods
such as nitrate ion selective electrode probes (OMEGA, 1993). These probes are often
used to monitor water quality in lakes and streams but can also be applied to monitor
nitrate in water treatment. However, these probes require daily and sometimes hourly
calibration with standards of known concentrations, and can be tedious when sample
frequency is high. There is also the possibility of interference from other ions sometimes
found in wastewater, as well as measurement error associated with high or low ionic
strength (OMEGA, 1993). Additionally, probes can be fragile and expensive, and are
typically susceptible to bio film growth that will disrupt electrochemical readings.
In wastewater treatment, sampling frequency may be several times a day using
large sample volumes, causing a high demand for testing materials that can be costly or
8. 8
hazardous. Due to these current limitations, the use of UV/Vis spectral absorbance has
surfaced as a possible alternative to standard methods for fast, routine monitoring of
nitrate and other water quality parameters.
The use of UV/vis spectral absorbance data is seldom studied and applied in
wastewater treatment. Absorbance (A) is unitless and is normally measured using a
spectrophotometer, which is a device capable of scanning multiple wavelengths of a
sample in the ultraviolet/ visible light range of 190-800 nm, usually using a quartz
cuvette or vial of a particular path length. As light radiation moves through a solution
containing absorbing molecules, the intensity decreases as the analyte absorbs photons
(Holler et al., 2007). Transmittance (T) is the ratio between the light that passes through
the cuvette (P) and the initial radiation intensity (Po), which is logarithmically related to
the absorbance value through the following equation.
[1] A = -log T = -log (P/Po) = log Po/P
The absorbance value is directly proportional to analyte concentration (c) through the
Beer-Lambert law:
[2] A = ɛ b c
Where (b) is path length usually chosen as 1 cm, and concentration is given in units of
molarity. The quantity (ɛ) is referred to as molar absorptivity and is usually given in
units of (L mol-1
cm-1
) to make the absorbance value unitless. Molar absorptivity is the
characteristic of a substance that determines how much light radiation is absorbed by a 1
M solution at a given wavelength and path length (Harris, 2010).
9. 9
Figure 1: Absorption coefficients for nitrate and nitrite in the spectral range of 200 – 400 nm (Keen et al.,
2012). The dashed line separates the units scale on the y-axis.
For nitrate, peak absorption happens in the range of 200 – 220 nm with molar
absorptivity increasing from 3,200 (L mol-1
cm-1
) at 220 nm, to 8,500 (L mol-1
cm-1
) at 200
nm. Figure 1 shows a graph of molar absorption coefficient (ɛ) for nitrate and nitrite
(Keen et al., 2012). Outside of the excitement range for nitrate (200-220 nm), molar
absorptivity drastically reduces to a point where there is zero absorption due to nitrate in
the spectral range of 245 – 260 nm. Nitrites peak absorbance is similar to that of nitrate
(200 – 220 nm), and it also approaches zero in the spectral range of 245 – 260 nm.
Additionally, nitrite does have a small peak that may be discernable from nitrate around
350 nm. Nitrite has been shown to affect absorbance in the spectral range of 200-220
nm, however it is often overlooked or combined with nitrate absorbance due to low
0
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200 250 300 350 400
Molarabsorptionatλ>250nm,M-1cm-1
Molarabsorptionatλ<250nm,M-1cm-1
Wavelength, nm
Nitrate
Nitrite
10. 10
concentrations found in wastewater (Drolc & Vrtovsek, 2010). Collos et al. (1999) found
that using absorbance and the molar absorption coefficients for nitrate + nitrite yielded a
stronger correlation (r2
= 0.983) in cultures of marine phytoplankton, than between
absorbance and nitrate concentration alone (r2
= 0.815). In this study, nitrite was added
in incremental steps in an attempt to distinguish between absorbance due to nitrate and
absorbance due to nitrite. It may be possible to identify the secondary absorbance peak
for nitrite around 350 nm, and use this peak to factor out nitrite from total absorbance. If
the model can make the distinction between nitrate and nitrite, this method can be used to
monitor for less common nitrification process upsets, such as nitrogen conversion
stopping at nitrite. While uncommon, decreases in NOB populations have been shown to
occur if oxygen is restricted immediately following ammonia oxidation, or if high levels
of ammonia or nitrous acid are present, leading to an accumulation of nitrite that is not
normally present (Lemaire et al., 2008, Moorcroft, 2001).
While the value of epsilon can be calculated for an individual molecular species at
a particular wavelength, absorbance measurements reflect the summation of absorbance
from all species in a solution, and thus direct measurements of analyte concentration can
be difficult (Skoog et al., 2013). Writing an expression for total absorbance contributed
by two species would resemble equation [3] below, where ɛ1 would be the molar
absorption coefficient of the first species at a given wavelength, and ɛ2 would be a
different absorption coefficient for the second species at that same wavelength.
[3] Atotal = A1+A2 … Atotal = ɛ1 b c1 + ɛ2 b c2
One of the biggest limitations to the general application of UV/vis absorbance is
interferences among different wastewater characteristics. Variables such as total organic
11. 11
carbon (TOC), chemical oxygen demand (COD), total suspended solids (TSS) and
nitrate/nitrite have been shown to affect absorbance measurements in the range of 200-
300 nm, however their individual contributions can be subtracted from overall
absorbance using calibration modeling (Langergraber et al., 2003; Liu & Wang, 2015).
In general, most organic matter reacts to UV radiation in the same way and produces low
level absorbance in the range of 200-380 nm. However, due to the variety of organic
compounds found in wastewater, absorbance due to organic matter is not defined by a
single or several wavelengths like for other distinct species. The carbon bonds of organic
molecules become increasingly excited proportional to their concentration when exposed
to increasing radiation energy at shorter wavelengths. This results in a somewhat linear
increase in absorbance due to organic matter as wavelength is decreased from 400 – 200
nm (Harris, 2010). Lourenco et al. (2008), successfully used UV-spectra to factor out
and estimate TOC concentrations in a wastewater treatment plant that treated both
domestic waste and industrial fuel waste. The authors concluded that while their model
did work well for predicting the TOC concentration for the wastewater used in the study,
caution be exercised when applying a single model to another water matrix. By using the
molar absorption of a known species such as nitrate at a particular wavelength, it may be
possible to determine the absorbance contribution from other unknown species, such as
compounds that make up TOC.
In order to account for overlapping absorbance spectra from background
components such as TOC, a calibration model is often developed to improve prediction
accuracy for a particular type of water. Calibration determines the relationship between
the analyte concentration and the analytical response, and usually involves the use of
12. 12
prepared standards of known concentration. A study by Collos et al. (1999), measured
nitrate in marine phytoplankton cultures and found that while UV spectroscopy tended to
be non-specific and subject to interferences, a calibration using an enriched seawater
artificial water (ESAW) medium with a known composition could be used along with
absorbance readings at 210 nm and 220 nm to determine individual contributions from
each species. When a water consists of many different characteristics, such as in
groundwater, seawater, or industrial wastewater, analysis using UV/vis absorbance can
still be performed using multivariate calibration techniques defined as using
measurements at multiple wavelengths to account for interferences (Skoog et al., 2013;
Langergraber et al., 2003).
A calibration function can be constructed graphically or mathematically, but it is
often desirable to achieve a linear calibration curve between instrument response and
known analyte concentrations. Statistical methods such as the method of least squares,
and partial least squares (PLS) regression are used frequently in literature to construct
multivariate calibration models used to predict nitrate and other wastewater components
for a specific matrix. A study by Karlsson et al. 1995, determined that UV absorbance
measurements at 220 nm and 275 nm could be used in municipal wastewater plants to
determine nitrate concentrations given that a calibration technique such as PLS accounts
for interferences. While their model calibrated well with municipal wastewater from
their geographical area, it would not be adequate for predicting industrial influents or
wastewater from another area. One study that looked at predicting nitrate and non-
purgeable organic carbon (NPOC) concentrations in groundwater used UV/Vis
absorption, PLS, cross-validation models, and eight different variables commonly found
13. 13
in groundwater to calibrate absorbance readings in the 200-300 nm range with nitrate and
organic carbon concentrations. They concluded that the method was adequate for fast
routine monitoring and surveying of several groundwater characteristics (Dahlen et al.,
2000). Similarly, a study by Liu & Wang (2015) looked at the use of UV spectroscopy to
monitor seawater quality and predict concentrations of TOC and COD. They used a PLS
model to calibrate and predict concentrations of TOC and COD in the suggested range of
240-380 nm for detecting organics.
Several studies also highlight the potential for monitoring wastewater quality
using on-line or in-situ UV/vis probes. A study by Drolc & Vtrovsek (2010) attempted to
construct a calibration model to monitor nitrate and nitrite concentrations using an on-line
UV probe calibrated with off-line reference testing. They collected data from municipal
wastewater, industrial wastewater, and landfill leachate and found that the UV sensor
would not give consistent readings between water matrixes. There findings suggest that
there is a large potential for UV on-line monitoring, given that the sensor is calibrated
specifically to local wastewater and all interferences are identified and accounted for. In
a similar study by Langergraber et al. (2003), multivariate calibration was used with an
on-line UV spectrometer probe to measure nitrate and organic matter concentrations in
municipal wastewater. They predicted that by also measuring absorbance outside the
range of nitrate they could account for organic and suspended solid interferences within
nitrate’s spectral range. What they found was a discrepancy in probe performance
between the global calibration procedure and local calibration to a specific wastewater,
however they concluded that global calibration would still produce measurements that
would be satisfactory. By taking absorbance in the 200-750 nm range, they were able to
14. 14
produce a correlation coefficient of 0.68 for predicting nitrate concentrations using an on-
line UV spectrometer.
Review of the literature has highlighted the potential for use of UV/Vis
absorbance as an alternative method for predicting and monitoring nitrate concentrations
in municipal wastewater. Interferences from organic matter and other variables have
been effectively reduced though calibration modeling at multiple wavelengths. The
purpose of this study, is to create a mathematical model that can be used to predict an
unknown nitrate concentration given an absorbance reading at two or more wavelengths.
15. 15
MATERIALS AND METHODS
All samples were obtained from McDowell Wastewater Treatment Plant located
outside of Charlotte, North Carolina. McDowell WWTP receives a majority of their
water from domestic sources and achieves nutrient removal utilizing biological treatment
and nitrification. Composite samples were collected from McDowell effluent in March
and April of 2016 and were stored in a refrigerator at 4o
C until testing. A Hach DR6000
Version 1.05 UV/vis spectrometer was used for all wavelength scans and to perform
Hach TNT 835 nitrate and TNT 839 nitrite test kit measurements. Stock solutions used
for nitrate and nitrite additions were prepared at 500 mg-N/L using sodium nitrate,
sodium nitrite, and ultrapure water.
The sample set used to build the nitrate prediction model consists of 30 effluent
samples which were tested initially for nitrate and nitrite. Ten additional samples were
tested to evaluate the accuracy of nitrate predictions and the chosen wavelengths (219 nm
and 245 nm). Nitrate was measured and recorded in mg-N/L using the Hach TNT 835
nitrate test kit procedure, and nitrite was measured similarly using the Hach TNT-839 test
kit procedure. A Shimadzu TOC – LCPN instrument was used to measure concentrations
of TOC in mg/L using the standard methods 5310B – 2000. Initial nitrite concentrations
were measured below the detection limit (0.015 mg-N/L) in all samples. Nitrite was
added in three incremental steps (0.1, 0.2, 0.3 mg-N/L) for 26 of the samples in order to
achieve a detectable concentration of nitrite. A 5 mL solution was prepared for all nitrite
spike additions and a diluted stock of 5 mg-N/L was added in 100 μL increments to
achieve concentrations of 0.1, 0.2, and 0.3 mg-N/L of nitrite. These concentrations for
16. 16
nitrite were chosen because they are representative of concentrations typically found in
domestic wastewater effluent (Metcalf & Eddy, 2014). One additional sample was tested
using concentration spikes of 1.0, 2.0 and 3.0 mg-N/L for nitrite, in order to identify
absorbance from extreme concentrations. Nitrate was also spiked in 5 additional samples
to test the models accuracy at higher concentrations. Nitrate spikes were prepared in
5mL of effluent by adding 30 μL increments of 500 mg-N/L nitrate stock to raw samples
to achieve concentrations of roughly 8, 11, 14, 17 & 20 mg-N/L. All samples and spikes
were scanned for absorbance using the UV/Vis spectrometer at 1 nm intervals from 200
nm to 400 nm. A 10 mm (1 cm) quartz cuvette was used in the spectrometer to measure
each sample. Ultrapure water was used as blanks.
Interpretation of absorbance data compared to molar absorption values suggested
the instrument may have reached a sensitivity limit while measuring undiluted samples.
Previous studies mention the possibility of signal saturation in absorbance readings above
1.5 absorbance units in undiluted samples, and suggest dilution ratios (1:3, and 1:10) to
compensate for lack of sensitivity (Lourenco et al., 2008; Liu & Wang, 2015). Due to
these technological limitations, all 30 samples were measured again with a dilution ratio
of 1:5 using ultrapure water. Absorbance measurements of diluted samples were then
back calculated in order to represent the total absorbance at each wavelength.
17. 17
MODEL CREATION
Absorbance and concentration data collected from 30 wastewater samples was
used to construct a calibration model that predicts a concentration of nitrate given an
absorbance reading at two distinct wavelengths (219 and 245 nm) chosen by statistical
evaluation of mean square error (MSE) for the possible wavelength pairs from the 215-
230 nm and 245-260 nm range. Wavelengths in the range of 210 – 220 nm are
commonly suggested in literature for being significantly indicant of differences in nitrate
concentration (Collos et al., 1999; Karlsson et al., 1995). However, because total
absorbance at any given wavelength represents the summation of absorbance’s from each
individual chemical species, identifying a relationship between absorbance and
concentration of nitrate requires that interferences are accounted for and factored out in
the model function (Skoog et al., 2013).
For construction of this model the focus was on two of the three major absorbing
species in McDowell wastewater effluent: nitrate and total organic carbon (TOC). The
third species nitrite, did not produce a measurable response that could be distinguished
from nitrate and was left out of the final prediction model. The absorbance attributed to
TOC cannot be determined on its own because the molecular composition of organic
carbon is not globally consistent, and the absorbance measurements in the 200 – 400 nm
range will be affected by composition (Lourenco et al., 2008). According to Beer’s Law,
the absorbance of TOC is proportional to its concentration. Additionally, because
organic absorbance increases almost linearly with increased UV radiation due to carbon
bond excitation, it is then possible to identify a ratio between TOC absorbance at two
18. 18
different wavelengths for the purposes of predicting TOC absorbance at any one
wavelength (Harris, 2010). In order to identify the portion of absorbance from TOC, it is
important to first identify the absorbance attributed by nitrate.
If the concentration of nitrate is known, absorbance is determined at a given
wavelength using the molar absorption coefficient (ɛ) and Beer’s Law. Figure 1 shows a
graph of molar absorption coefficients for nitrate and nitrite in the range of 200 – 400 nm
(Keen et al., 2012). Nitrate and nitrite show an absorbance peak between 200 nm and
220 nm, then absorbance steadily approaches zero in the 245 – 260 nm range. This area
of the spectrum (240 – 380 nm) has been identified in the literature as containing
information related to organic carbon (Lourenco et al., 2008; Liu & Wang, 2015).
Choosing a wavelength where TOC absorbs UV but nitrate does not (~250 nm) allows
for measuring absorbance from only organic carbon. Additionally, if nitrate absorbance
is calculated using a known concentration and the molar absorption coefficient at (~220
nm), it can be subtracted from the total absorbance, resulting in absorbance due to TOC
at (~220 nm).
[4] Atotal, 220 = (ANO3, 220) + (ATOC, 220)
[5] ATOC, 220 = (Atotal, 220) – ([NO3] × ɛNO3, 220 × 1cm)
By taking the ratio of TOC absorbance calculated at wavelength 220 nm (ATOC, 220 nm) and
TOC absorbance measured at wavelength 250 nm (ATOC, 250 nm), a coefficient (k) can be
derived that is used to predict TOC absorbance in the range of nitrate.
[6] k = (ATOC, 220 nm) / (ATOC, 250 nm)
19. 19
By multiplying (k) by the absorbance value at 250 nm, it is possible to estimate the
portion of absorbance at 220 nm that comes from TOC.
[7] (ATOC, 220 nm) = k × (ATOC, 250 nm)
Subtracting the term (ATOC, 220 nm) from total absorbance (Atotal,220 nm), gives the calculated
absorbance from nitrate (ANO3, 220 nm) at 220 nm. Beer’s Law can then be applied by
dividing the calculated absorbance from nitrate by the molar absorption coefficient for
nitrate at 220 nm, yielding a prediction for nitrate concentration in moles of nitrogen.
[8] [NO3] = (ANO3, 220 nm) / (ɛNO3, 220 nm)×(1cm)
When combined with equation [5], the prediction model becomes:
[9] [NO3] = ( A220 – ( A250 × k )) / (ɛNO3, 220 nm) × (1cm)
20. 20
RESULTS & MODEL TESTING
Figure 2 shows the absorbance results from the 30 wastewater samples used to
construct the model and coefficient “k.” In order to determine the best wavelengths to
model nitrate and TOC concentrations, a statistical determination of mean squared error
(MSE) was used to select the two wavelengths and k-coefficient to be used in the final
model function. The k-coefficients for 255 combinations of wavelength pairs were
calculated by taking the mean of absorbance ratios for each sample. The range of 215-
230 nm was evaluated for nitrate absorbance and was compared to the range of 245-260
nm for TOC absorbance.
Figure 2: Absorbance readings for 30 samples used to construct the nitrate prediction model.
For testing the accuracy of this model outside the sample set used to create it, ten
additional samples were scanned for absorbance. Absorbance from these samples was
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
200
205
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215
220
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AbsorbanceUnits
Wavelength nm
21. 21
used to predict nitrate concentrations using equation [9] and the substituted k-coefficient
and wavelength pairs from each of the 255 combinations. These predictions were
compared to measured concentrations and evaluated using the sum of the mean squared
error difference. The lowest value for the sum of MSE determined that 219 nm and 245
nm were the best wavelengths for modeling nitrate and TOC absorbance, and the
resulting coefficient was (k = 2.71). This yields the final prediction model function [10].
[10] Model Function [NO3] = ( A219 – ( A245 × 2.71 )) / (ɛNO3, 219 nm) × (1cm)
To obtain a molar concentration for nitrate select ɛ219 = 3620 M-1
cm-1
, or for a
concentration in mg/L divide this coefficient by 14,000 (mg N/ mol), to get ɛ219 = 0.259
L mg-1
cm-1
.
The final model function was used to predict nitrate concentrations in the 10
sample set shown in Table 1 and Figure 3.
Table 1: Measured and predicted concentration and absorbance values from experimental sample set.
Sample
ID
TOC
(mg/L)
Measured
Nitrate
(mg/L)
Predicted
Nitrate
(mg/L) A-219 A-245
1 5.50 5.08 5.15 1.685 0.130
2 5.76 4.10 4.08 1.395 0.125
3 5.75 4.61 4.56 1.505 0.120
4 6.34 3.72 4.06 1.405 0.130
5 6.13 4.45 4.41 1.480 0.125
6 6.22 3.74 4.04 1.425 0.140
7 5.80 4.95 4.79 1.675 0.160
8 5.99 5.45 5.32 1.825 0.165
9 6.34 4.28 3.86 1.365 0.135
10 6.40 4.11 3.58 1.320 0.145
22. 22
Figure 3: Correlation graph of predicted and measured nitrate concentrations calculated using the
experimental sample set. A Pearson’s r coefficient of 0.89 was determined for the fit to a 1:1 plot.
The data suggests that the model worked well for predicting concentrations of
nitrate. The difference in magnitude for the 10 samples was on average 0.21 mg-N/L
above or below measured concentration values. The difference range was 0.02 – 0.53
mg-N/L, indicating the model performance was within an acceptable range and there
were no serious outliers. A Pearson’s r correlation test was performed for the measured
and predicted concentrations shown in Table 1 and Figure 3. A Pearson’s r correlation
coefficient compares the linearity of two variables and how well a data set fits a 1:1 plot.
A coefficient of (r = 0.89) suggests a strong linear relationship between model predictions
and actual concentrations.
The prediction residuals for nitrate were determined by subtracting the measured
concentration from the predicted value. A percent error calculation was performed using
the nitrate residuals over the actual concentrations. The range was determined to be 0.53-
3.50
3.70
3.90
4.10
4.30
4.50
4.70
4.90
5.10
5.30
5.50
3.5 3.7 3.9 4.1 4.3 4.5 4.7 4.9 5.1 5.3 5.5
PredictedNitrateConcentration(mg-N/L)
Measured Nitrate Concentration (mg-N/L)
23. 23
12.9%, which describes what percentage of the actual value is part of the residual and
confirms the models precision. This residual was plotted against actual nitrate and TOC
concentrations, as well as absorbance at 219 nm and 245 nm. The resulting residual plots
are shown in Figures 4, 5, 6, & 7. A trend line and an R2
value was calculated for each
residual comparison and displayed on Figures 4-7. The R2
value tells how closely a data
set fits a statistical model such as a curve or line, and can be used to evaluate a possible
correlation between nitrate prediction residuals and either TOC, A-219 or A-245 nm.
Examining the correlation coefficients in Figures 4-7 would indicate that the residual
plots did not show any strong correlations, however TOC was further analyzed using the
magnitude of predicted minus measured concentrations and the results displayed in
Figure 8. Figure 8 does indicate an R2
correlation of 0.68 between TOC concentrations
and the difference in magnitude for nitrate predictions. Figure 8 suggests that the model
may be less accurate or bias with increasing TOC concentration. However, because only
10 samples were tested it may be possible that this correlation is merely a coincidence.
In order to confirm this correlation, additional testing would need to be performed
measuring a greater range of TOC concentrations than what was examined in this
research (5.5 – 6.6 mg/L).
26. 26
Figure 8: Possible correlation between the difference in magnitude of nitrate predictions and TOC concentrations.
The models performance was then tested against irregular concentrations that
would most likely result from a treatment failure during the nitrification or denitrification
process. If nitrification failed, ammonia concentrations would most likely be high and
there would be an observable lack of nitrate measured in plant effluent. It is important
that the model be able to identify this issue, and that ammonia absorbance does not
interfere with absorbance from nitrate. In order to evaluate this scenario, a solution of 50
mg-N/L of ammonia in ultrapure water was scanned for absorbance in the range of 200-
400 nm and the results displayed in Figure 9. A concentration of 50 mg-N/L of ammonia
would be considered “strong” compared to other domestic wastes, and it is clear from
Figure 9 that even at high concentrations ammonia does not absorb in the range of nitrate.
A nitrification upset would actually be identified by a lack of absorbance at wavelength
219 nm for measuring nitrate.
y = 0.4784x - 2.6747
R² = 0.6776
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
5.4 5.6 5.8 6 6.2 6.4 6.6
MagnitudeofNitratePrediction(mg-N/L)
Measured TOC Concentration (mg/L)
27. 27
Figure 9: Absorbance readings for three samples of 50 mg-N/L ammonia solution.
In addition to nitrification failure, the denitrification process can also fail resulting
in high concentrations of nitrate in plant effluent. To evaluate the models ability to
identify abnormally high concentrations of nitrate, 5 additional samples were tested using
nitrate spike additions. Nitrate was added to raw samples to achieve concentrations of
roughly 8, 11, 14, 17 and 20 mg-N/L, and tested using the Hach TNT-835 nitrate
colorimetric test kits. Absorbance from these samples was used to plot predicted
concentrations outlined in Table 2 and Figure 10. It is clear from the data that the model
performed exceptionally well when predicting high concentrations of nitrate. A
Pearson’s r correlation of (r = 0.99) was determined from predicted and measured
concentrations and describes a close fit to a 1:1 plot. This suggests that the model can
accurately distinguish an abnormally high concentration of nitrate that could be
associated with denitrification failure. Overall, the linearity between concentration and
absorbance confirms the models accuracy and relation to the Beer-Lambert Law.
0
0.05
0.1
0.15
0.2
0.25
0.3
200
208
216
224
232
240
248
256
264
272
280
288
296
304
312
320
328
336
344
352
360
368
376
384
392
400
Absorbance
Wavelength (nm)
28. 28
Table 2: Concentration and absorbance data for the nitrate spike additions.
Sample
ID
TOC
(mg/L)
Measured
Nitrate
(mg-N/L)
Predicted
Nitrate
(mg-N/L) A-219 A-245
1 5.75 7.87 8.67 2.665 0.155
2 5.61 11.70 13.11 3.815 0.155
3 5.80 13.44 13.99 4.110 0.180
4 5.99 17.24 17.41 5.010 0.185
5 6.26 19.84 19.73 5.625 0.190
Figure 10: Correlation graph of predicted and measured nitrate spike concentrations. A Pearson’s r
coefficient of 0.99 was determined for the fit to a 1:1 plot.
7
9
11
13
15
17
19
21
7 9 11 13 15 17 19 21
PredictedNitrateConcentration(mg-N/L)
Measured Nitrate Concentration (mg-N/L)
29. 29
CONCLUSIONS
Experimental results confirm the accuracy of the model in relation to Beer’s Law
and the ability to predict concentrations of nitrate using absorbance. The model
performed well for all nitrate concentrations within the range of 2.8 and 19.8 mg-N/L,
that would typically be found in wastewater effluent. Differences in nitrate predictions
using this model ranged from 0.02-0.53 mg-N/L outside of measured concentrations, with
an average magnitude difference of 0.21 mg-N/L. Percent error for this model was 0.53-
13%, and predicted concentrations correlated with measured concentrations by a
coefficient of 0.89, suggesting a good linear fit. Typical performance values for
alternative nitrate detection methods are outlined in Table 3. The USEPA establishes
quality assurance and quality control standards for permit reporting of water
characteristics. For ion chromatography, nitrate measurement must fall within 15% of
actual values to be used in permit reporting. This level of precision was achieved by this
prediction model, however, in order to use this method for permit reporting purposes, a
long-term monitoring study should be conducted that accounts for seasonal changes in
wastewater composition.
Table 3: Typical performance values for alternative nitrate detection methods (Hach, 2013; Jackson P., E.,
2000; OMEGA, 1993).
Method Application Range
Precision
(error) Mean Recovery % Detection Limit
Ion Chromatography 0.42-14.0 mg-N/L <15% 101% 0.002 mg/L
Colorimetric method 0.23-13.5 mg-N/L 10% 90-110% 0.05 mg/L
Electrochemical ion probe 1-14,000 ppm nitrate 2% unknown 10 ppm as N
30. 30
Therefore, UV/Vis absorption can successfully be used to quickly predict nitrate
concentrations in wastewater for the purposes of monitoring water quality or treatment
performance. Measuring absorbance at 219 nm and 245 nm allows to effectively factor
out TOC interference from nitrate absorption and predict concentrations of nitrate with
reasonable accuracy. The coefficient (k = 2.71) is specific to this wastewater
composition and should only be used when measuring absorbance at wavelengths 219
and 245 nm. While the proposed function does predict concentration well for McDowell
effluent, it is not suggested that the exact model be used for other treatment plants or
other water matrices. Wastewater from different geographical areas or industrial inputs
have been shown to respond differently to UV/Vis radiation and produce inconsistent
absorbance readings (Karlsson et al., 1995). A similar procedure can be followed in
order to derive k-coefficients and prediction functions for other wastewater compositions.
Nitrite could not be mathematically included in the model function due to
undetectable concentrations found in McDowell effluent. Even concentrations as high as
3.0 mg-N/L of nitrite were not detectable in the area of nitrites secondary peak (350 nm),
or were not distinguishable from nitrate absorbance fluctuations. Nitrite does absorb in
the 200 – 220 nm range of nitrate absorbance and has been shown to directly affect
absorbance readings in this region (Moorcroft et al., 2001). However, nitrate
concentrations in wastewater effluent are typically very low and should not cause
significant interference from nitrate. Additional work would need to be conducted if
absorbance is to be used to monitor for less common process upsets, such as an
accumulation of nitrite caused by a decrease in NOB populations (Lemaire et al., 2008,
Moorcroft, 2001). Using the molar absorption coefficient for nitrite and nitrate against
31. 31
known concentrations allows both species to be subtracted from total absorbance,
yielding a more accurate absorbance reading proportional to TOC concentration that can
be used to adjust the calibration function.
In conclusion, this model can be used to quickly identify treatment upsets in both
the nitrification and denitrification processes for domestic wastewater. Deriving a
prediction function that is more globally applicable would require measuring many
samples of varying composition from multiple wastewater treatment plants. However, a
global model is likely to be less accurate than calibrating a local model such as the one
created from this study. Individual wastewater plants will have more success creating a
model based on data from their effluent matrix, because the coefficient (k) and the chosen
wavelengths will be more reflective of absorbance attributed to TOC and nitrate. This
allows individual plants to quickly monitor and survey water quality without the need for
excessive testing materials or procedures, improving conditions for wastewater
professionals and the environment.
32. 32
ACKNOWLEDGMENT
The author would like to acknowledge Xueying Wang for collecting effluent samples and
performing measurements of TOC concentrations using the standard method 5310B-
2000. The author also acknowledges Thomas Siddons for his help performing statistical
analysis on model wavelengths using excel. The author would also like to acknowledge
Dr. Olya Keen as head advisor for the research, and Dr. Mei Sun and Dr. James Bowen
as fellow advisors and committee members.
33. 33
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