Determination of 5-d biochemical oxygen demand (BOD5) is the most commonly practiced test to assess the water quality of surface waters and the waste loading. However, BOD5 is not a good parameter for the control of water or wastewater treatment processes because of its long test period. It is very difficult to produce consistent and reliable BOD5 results without using careful laboratory quality control practices. This study was performed to develop software sensors to predict the BOD5 of river water and wastewater. The software sensors were based on the multiple regression analysis using the dissolved organic carbon (DOC) concentration, UV light absorbance at 254 nm, and synchronous fluorescence spectra. River water samples and wastewater treatment plant (WWTP) effluents were collected at 1-hour interval to evaluate the feasibility of the software sensors. In short, the software sensors developed in this study could well predict the BOD5 of river water (
The determination of 5-d biochemical oxygen demand (BOD5) is the standardized experimental procedure to determine the relative oxygen requirements for aqueous microbes to consume organic materials in wastewaters, wastewater treatment plant (WWTP) effluent, or natural waters [
In order to overcome the shortcoming of the conventional BOD5 test, biosensors, UV-visible spectrophotometry, fluorescence measurements, and software sensor (virtual sensors) have been suggested as an alternative method to determine the BOD5 of a water sample.
Most BOD5 biosensors rely on the measurement of the respiratory activity of cells by a suitable transducer. In addition, the ones using an oxygen electrode, a carbon dioxide analyzer, an optical transducer or a microbial fuel cell have recently been reported [
Single or mixed cultures are used in biosensors. Since a single strain is not able to oxidize the entire range of organic contaminants in water samples, and the DO consumption is, thus, not always directly proportional to the concentration of biodegradable organics, mixed cultures like activated sludge have been preferred [
Dissolved organic compounds with aromatic structures strongly absorb UV radiation [
Fluorescence measurements have been applied to determine the presence of humic substances and organic matters in natural waters. Among a few fluorescence analysis methods, synchronous fluorescence spectroscopy is the best way to scan the entire section of excitation wavelengths by fixing the excitation and emission wavelengths uniformly. This method allows obtaining a better resolution and producing various information regarding the DOMs in water [
Recently, a few researchers have utilized both UV-visible spectrophotometry and fluorescence measurements together to estimate the BOD5 in waters. Applying the sensors to the environmental monitoring is advantageous since they are rapid and versatile. In addition, they require low operating costs, no chemicals, and no sample pretreatment for measurements. However, their application to water samples can be very limited if the SS concentration of the samples is high [
A software sensor (in other words, virtual sensors) generates virtual signals for the water quality parameter of interest through the calculation of a model fed with real signals from reliable, available sensors for other parameters [
In this study, the synchronous fluorescence spectra, the UV light absorbance at 254 nm, and DOC of water samples were analyzed to predict their BOD5 values. Since the fluorescence spectra vary depending on the characteristics of DOMs that are site specific in this study, therefore, all the fluorescence spectra of a sample were utilized.
The specific purposes of this study are as follows: (1) the analysis of synchronous fluorescence spectra of river waters and wastewaters, (2) correlation analyses between water BOD5 and organic parameters (i.e., DOC) and between water BOD5 and optical parameters (i.e., UV light absorbance at 254 nm, synchronous fluorescence spectra (at 270~300 nm, 310~370 nm, 370~400 nm, and 400~530 nm)), and (3) development of multiple regression models for the BOD5 prediction using DOC, UV absorbance at 254 nm, and synchronous fluorescence spectra.
A total of 23 river samples were collected from the Gyeong-An River which flows through the City of Yong In, Korea, at 1-h intervals. In addition, a total of wastewater samples were collected from the Hwa-Do WWTP in the City of Namyang-ju, Korea, at 1-h intervals. The river samples contained low concentrations of BOD5, while the wastewater samples contained a wider range of BOD5. Once the samples were collected, they were stored under refrigerated condition (4°C) and transported to the laboratory, in which they were analyzed immediately. Using prewashed GF/F filters (Whatman, USA; nominal pore size: 0.7
The dissolved organic matters in the filtered samples were measured with an UV spectrophotometer (Shimadzu UV-1800) at 254 nm. The BOD5 of each sample was determined by calculating the decreased amount of the DO over 5 d (APHA, 2010). The DOC of the samples was calculated by subtracting dissolved inorganic carbon (DIC) from dissolved carbon (DC). The DIC and DC were measured using a TOC analyzer (Shimadzu TOC-V-CPH, Japan). DC concentration of a water sample was determined by combusting a water sample at 680°C in the presence of a platinized alumina catalyst and by measuring the resulting CO2 production. On the other hand, the DIC of a water sample was determined through the phosphoric acid digestion of the water followed by the determination of the CO2 production.
The fluorescence spectra of a water sample were measured using a fluorescence spectrometer (Scinco FS-2, Korea). For each sample, synchronous fluorescence spectra for excitation wavelengths ranging from 200 to 600 nm were recorded using a constant offset (i.e., Δ
The correlation coefficients between the BOD5 of water samples and the UV light absorbance at 254 nm and between the BOD5 and fluorescence spectra were analyzed using the correlation function of Microsoft Excel (Microsoft, USA). The multiple regression with the parameters (i.e., DOC, UV absorbance, and fluorescence spectra) for the development of a model to predict the water BOD5 were carried out using the Data Analysis function of Microsoft Excel.
In this study, all the spectra obtained at the wavelengths of 270 nm~300 nm, 310 nm~370 nm, 370 nm~400 nm, and more than 460 nm for monoaromatic compounds and tryptophan, diaromatic compounds, fulvic acid, humic acids, and other compounds, respectively, were selected as fluorescence parameters after the synchronous fluorescence spectra of 200~600 nm had been examined. Ferrari and Mingazzini [
Figure
Synchronous fluorescence spectra of (a) DI water (b) river water, and (c) wastewater.
The fluorescence intensity ratio of river waters to wastewaters is 1 to 5.6. With synchronous fluorescence spectra (Δ
To estimate compounds in the DOM of samples, the whole spectrum area obtained for each sample type was divided into four subareas for the excitation wavelengths of 270–300 nm, 310–370 nm, 370–400 nm, and more than 460 nm (Figure
Pie chart of synchronous fluorescence spectra for (a) river water and (b) WWTP effluent.
In order to rapidly estimate BOD5 of a water, the UV spectra [
Thomas et al. [
Therefore, this study used both organic parameter and optical parameters to improve the accuracy of the BOD5 estimation: DOC as the organic parameter, UV absorbance at 254 nm, and fluorescence spectra at 270~300 nm, at 310~370 nm, at 370~400 nm, and at 400~530 nm as the optical parameters (Table
BOD5 and different parameters measured for river waters and wastewaters.
Sample | BOD5 mg L−1 | DOC mg L−1 | Absorbance at 254 nm | Fluorescence intensity (AU) | |||
---|---|---|---|---|---|---|---|
270 |
310 |
370 |
400 |
||||
River waters | |||||||
Mean | 1.6 | 2.0 | 0.049 | 53600 | 450202 | 201142 | 300654 |
S.D. | 0.2 | 0.2 | 0.008 | 19294 | 57116 | 30772 | 64592 |
Min | 1.3 | 1.6 | 0.036 | 28252 | 381099 | 158019 | 188096 |
Max | 1.9 | 2.5 | 0.064 | 84575 | 595808 | 254822 | 461216 |
| |||||||
Waste-waters | |||||||
Mean | 62.0 | 21.8 | 0.219 | 366183 | 3050004 | 528135 | 849118 |
S.D. | 37.9 | 13.4 | 0.079 | 192547 | 1771048 | 116813 | 213009 |
Min | 6.5 | 3.5 | 0.090 | 114609 | 1629452 | 350192 | 549098 |
Max | 139.9 | 55.2 | 0.310 | 755116 | 9233331 | 748122 | 1272352 |
The wastewater samples contained 6.5~140 mg L−1 of BOD5 and 3.5~55.2 mg L−1 of DOC. In addition, 0.090~0.310 of the UV absorbance at 254 nm and 114609~755116 of fluorescence intensity at 270~300 nm, 1629452~9233331 at 310~370 nm, 350192~748122 at 370~400 nm, and 549098~1272352 at 400~530 nm were observed (Table
In Figure
Time profile of BOD5 and correlation between measured BOD5 and other parameters for (a) river waters and (b) wastewaters.
River waters
Wastewaters
The correlation coefficients between BOD5 and the UV absorption or other fluorescence intensity at different wavelengths were obtained for river waters and wastewaters (Table
Correlation coefficients between parameters for (a) river waters and (b) wastewaters.
River waters
Parameter | BOD5 |
DOC |
Absorbance |
Fluorescence at 270~300 nm | Fluorescence at 310~370 nm | Fluorescence at 370~400 nm | Fluorescence at 400~530 nm |
---|---|---|---|---|---|---|---|
BOD5 (mg L−1) | 1 | ||||||
DOC (mg L−1) | 0.72 | 1 | |||||
Absorbance |
0.80 | 0.91 | 1 | ||||
Fluorescence at 270~300 nm | 0.32 | −0.05 | 0.02 | 1 | |||
Fluorescence at 310~370 nm | 0.38 | 0.15 | 0.26 | 0.75 | 1 | ||
Fluorescence at 370~400 nm | 0.62 | 0.58 | 0.73 | 0.41 | 0.70 | 1 | |
Fluorescence at 400~530 nm | 0.50 | 0.37 | 0.46 | 0.78 | 0.80 | 0.75 | 1 |
Wastewaters
Parameter | BOD5 |
DOC |
Absorbance |
Fluorescence at 270~300 nm | Fluorescence at 310~370 nm | Fluorescence at 370~400 nm | Fluorescence at 400~530 nm |
---|---|---|---|---|---|---|---|
BOD5 (mg L−1) | 1 | ||||||
DOC (mg L−1) | 0.91 | 1 | |||||
Absorbance |
0.81 | 0.76 | 1 | ||||
Fluorescence at 270~300 nm | 0.36 | 0.27 | 0.42 | 1 | |||
Fluorescence at 310~370 nm | 0.42 | 0.30 | 0.51 | 0.54 | 1 | ||
Fluorescence at 370~400 nm | 0.24 | 0.31 | 0.47 | 0.61 | 0.39 | 1 | |
Fluorescence at 400~530 nm | 0.27 | 0.32 | 0.54 | 0.63 | 0.37 | 0.97 | 1 |
Multivariate relationships require a multiple regression analysis involving several explanatory variables for predictors of theoretical interest and control variables [
If many independent variables are used to explain a dependent variable, the most appropriate regression model should be selected based on the coefficient of determination such as
A regression model is selected if it increases
To select an appropriate regression model for the BOD5 prediction, a total of seven models were developed for river waters and wastewaters and presented in Table
Summary of model development for predicting BOD5 of (a) river waters and (b) wastewaters.
River waters
Number in Model | Variables in model |
|
Adjust |
|
MSE |
---|---|---|---|---|---|
1 | UV254 | 0.6322 | 0.6147 | 11.10 | 0.9839 |
2 | DOC | 0.5117 | 0.4885 | 7.86 | 1.3062 |
3 | F1, F2, F3, and F4 | 0.4163 | 0.2866 | 0.71 | 0.3904 |
4 | DOC, UV254 | 0.6326 | 0.5959 | 9.11 | 0.4914 |
5 | DOC, F1, F2, F3, and F4 | 0.6636 | 0.5646 | −3.95 | 0.1500 |
6 | UV254, F1, F2, F3, and F4 | 0.7770 | 0.7114 | 7.00 | 0.1193 |
7 | DOC, UV254, F1, F2, F3, and F4 | 0.7770 | 0.6934 | 7.00 | 0.0994 |
Wastewaters
Number |
Variables in model |
|
Adjust |
|
MSE |
---|---|---|---|---|---|
1 | UV254 | 0.6538 | 0.6345 | 3.29 | 9441.1632 |
2 | DOC | 0.8310 | 0.8217 | 7.61 | 4607.0759 |
3 | F1, F2, F3, and F4 | 0.2271 | 0.0210 | 35.52 | 5268.8259 |
4 | DOC, UV254 | 0.8609 | 0.8445 | 7.45 | 1896.5335 |
5 | DOC, F1, F2, F3, and F4 | 0.8901 | 0.8509 | −3.24 | 599.2478 |
6 | UV254, F1, F2, F3, and F4 | 0.7140 | 0.6118 | 7.59 | 1559.9272 |
7 | DOC, UV254, F1, F2, F3, and F4 | 0.9024 | 0.8574 | 7.00 | 443.5252 |
In Table
Multiple linear regression models for predicting BODs of river waters and wastewaters.
Sample | Multiple regression model |
---|---|
River waters |
|
| |
Wastewaters |
|
The developed multiple regression models for river waters and wastewaters were, respectively, applied to predict the BOD5 of different sets of river water and wastewater samples. The model predictions were then compared with manual measurements (Figure
Correlation between manually measured BOD5 and model predictions for (a) river waters and (b) wastewaters.
In this study, two multiple regression models were developed to predict the BOD5 of two types of environmental waters: one for river waters and the other for wastewaters. The model for river waters predicts BOD5 using the data of the UV absorbance at 254 nm and fluorescence intensities at 270~300 nm, at 310~370 nm, at 370~400 nm, and at 400~530 nm. The model for wastewaters was utilizing the data of DOC, the UV absorbance at 254 nm, and fluorescence intensities at 270~300 nm, at 310~370 nm, at 370~400 nm, and at 400~530 nm. The developed models reasonably well predicted the BOD5 of the river waters and the wastewater samples; correlation coefficients between the model-predicted and manually measured BODs were 0.78 for the river waters and 0.90 for wastewaters.
In fact, the data used for predicting the BOD5 of two types of water samples can be measured using an on-line optical sensors. Therefore, if the BOD5 is estimated using the approach proposed in this study, its measurement can be done rapidly. In addition, this approach can be applied to develop a software sensor for the BOD5 measurement which can be implemented in an on-line water quality monitoring system for streams or WWTP discharges.
This work was supported by the R&D program of MKE/KEIT (R&D Program no.: 10037331, Development of Core Water Treatment Technologies based on Intelligent BT-NT-IT Fusion Platform).