~
Pergamon PII: S0043-1354(97)00089-4
War. Res. Vol. 31, No. 10, pp. 255(~2556, 1997 :~ 1997 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0043-1354/97 $17.00 + 0.00
M E A S U R I N G BACTERIAL BIOMASS-COD IN WASTEWATER CONTAINING PARTICULATE MATTER E L I S A B E T H v. M O N C H *~ a n d P E T E R C. POLLARDS':@ The Advanced Wastewater Management Centre, tDepartment of Chemical Engineering and -'Department of Microbiology, The University of Queensland, St Lucia, Queensland 4072, Australia
(Received April 1996; accepted in revised.form March 1997) Abstract--The paper describes a method to determine bacterial biomass-COD in wastewater treatment systems in the presence of particulate matter. A quantitative measure of biomass-COD concentration could greatly enhance the accuracy and reliability of wastewater treatment models that rely on the component "biomass concentration", which is usually expressed in terms of COD. Rarely is biomass-COD determined directly, despite the existence of microbiological methods for this measurement. Here, we have used a classic microbiological method, acridine orange stain direct counting (AODC), to count the number of bacteria. The average cell volume of wastewater bacteria was used to determine a conversion factor of 20 × I0-H mg-COD/cell for calculating bacterial biomass-COD with a fast, reliable and simple method applied to wastewater samples in the presence of particulate substrate. Bacterial biomass-COD was measured in five different wastewater treatment systems and compared to the particulate COD. © 1997 Elsevier Science Ltd
Key words--biomass measurement, bacteria, biomass-COD, particulate COD, mathematical modelling, acridine orange stain direct counting (AODC), epifluorescence, wastewater treatment
NOMENCLATURE Afilter = total area of the filter (mm'-) Asqua~ = a r e a of smallest square of ocular graticule (#m 2) Cblomass-COD = concentration of biomass in terms of COD (mg-COD/litre) Co:lls = number of cells per litre (cells/litre) CpcoD = concentration of particulate COD (mgCOD/litre) of soluble COD CscoD = concentration (mg-COD/litre) of total COD CTCOD = concentration (mg-COD/litre) Jdilution = sample dilution factor /COD/cell = COD equivalent of one bacterial cell (mg-COD/cell) equivalent of biomass iCOD'X= COD (mg-COD/mg-biomass) /filler = fraction of filter area counted teell = length of a bacterial cell (/tin) /'F/cell = weight of a bacterial cell (mg/cell) ni = sample size of measurement j Nce.s = number of cells counted in Nsq..... squares gsquares = number of squares of graticule for which N¢~,~ was determined So/Xo = initial substrate-to-biomass ratio (mgCOD/mg-COD) Vcel~ = volume of a bacterial cell (//m 3) Vsample.dil, = volume of diluted sample (ml) Wcell = width of a bacterial cell (/~m) 6c, = absolute error of measurement j (mgCOD/litre) *Author to whom all correspondence should be addressed [Fax: 0061-7-3365-4726, E-mail:
[email protected]. edu.au, E-mail:
[email protected]].
crj = standard deviation of measurement j (mg-COD/mg-COD)
INTRODUCTION
Why measure bacterial biomass in terms o f COD? M a t h e m a t i c a l modelling o f biological carbon, nitrogen a n d p h o s p h o r u s removal from domestic or industrial wastewaters is n o w a widely accepted tool used to design, simulate, control a n d optimise biological wastewater t r e a t m e n t plants. The developm e n t a n d application o f such models require reliable quantitative estimates o f the bacterial biomass. This is because " s u b s t r a t e " a n d " b i o m a s s " are two key c o m p o n e n t s (or state variables) in these models. The c o m p o n e n t " b i o m a s s " is defined in terms of its chemical oxygen d e m a n d ( C O D ) in models o f activated sludge wastewater t r e a t m e n t processes including biological nutrient removal ( I A W Q Activated Sludge M o d e l no. 1 a n d no. 2, Henze et al., 1987; Gujer et al., 1995; Oies a n d Wilderer, 1991); anaerobic sludge digestion (Bryers, 1985; Tschui, 1989; Siegrist et al., 1993); a n d a n a e r o b i c wastewater t r e a t m e n t ( G u p t a et al., 1994). C O D is used as the s t a n d a r d unit in these m a t h e m a t i c a l models because it links the organic substrate a n d bacterial biomass in a c o m m o n reaction, the reduction o f oxygen (Henze et al., 1987). Thus, mass balances can be m a d e in terms o f C O D , even t h o u g h the physical n a t u r e o f the substrate is u n k n o w n .
2550
Measuring bacterial biomass-COD in wastewater A model becomes more precise the more parameters and state variables are directly measured and the less that are assumed or estimated• Hence, more information will be gained about the treatment process the more state variables are measurable. The direct measurement of the biomass-COD concentration is particularly important for the simulation of batch reactors where the initial biomass-COD concentration can lzLave a pronounced effect on all the simulated reactions (Oles and Wilderer, 1991; M/inch, 1994). Present methods used to measure biomass-COD Traditional mea:~urements of active biomass concentrations, such as volatile suspended solids or particulate C O D , are often inappropriate because particulate substrate is present in most wastewater treatment systems. Determining the biomass concentration in such wastewaters is generally regarded as very difficult, if not impossible (Holmberg and Ranta, 1982; Jorgensen et al., 1992; Nov~tk et al., 1994) Modellers rarely quantify the biomass concentration in their systems• Instead, they estimate biomass concentrations by indirect techniques, which are mostly based on measurements of the metabolic activity. F o r example, in measuring the rate of oxygen or nitrate uptake in batch tests, the uptake rates are used to estimate the biomass concentration in the system (Henze, 1986; Henze and Mladenovski, 1991; Kappeler and Gujer, 1992). However, such measurements are tedious and require a separate experimental system which implies that the experimental conditions are not necessarily representative of the process conditions. Also, they cannot be applied to anaerobic systems. Active biomass concentrations in wastewater samples can also be measured by determining A T P or dehydrogenase concentrations (Chung and Neethling, 1988, 1989). However, these techniques are unreliable because they depend on the metabolic state of the process. ATP-to-biomass ratios can vary considerably (Jorgensen et al., 1992): Changes in A T P concentration do not always relate to changes in biomass concent~:ation. The present paper will show how to directly measure bacterial biomass-COD with a classic microbiological fluorescent staining and ceil-counting technique. Counting of acridine orange stained bacteria with an epifluorescence microscope is an accepted quantitative method for determining the number of bacteria in aquatic environments (Bratbak, 1993). The value of using microscopic methods to determine bacterial biomass is that they allow a direct and specific measurement of the number of bacteria. The method is independent of extracellular material, abiological material and eucaryotic microorganisms, such as protozoans. Also derived is a factor to convert bacterial cell numbers to biomassC O D , an identifiable engineering term, for use in WR 31/10
F
modelling processes.
and
simulating
2551 wastewater
treatment
MATERIALS AND METHODS
Origin of wastewater samples The wastewater samples analysed for bacterial biomassCOD originated from four different sources. (1) A full-scale biological nutrient removal (BNR) plant at Loganholme, Queensland, Australia. The overfow and the underflow of the primary clarifier was sampled as well as a mixture (1:2 ratio) of prefermenter sludge and primary clarifier underfow sludge. (2) A pilot-scale BNR activated sludge plant at Gibson Island, Brisbane, Australia. Mixed liquor and raw wastewater were mixed in a ratio of 1:3. (3) A continuously operated bench-scale two-stage anaerobic digestion system currently being operated at The University of Queensland, Australia (Ramsay, 1997). The synthetic wastewater fed to this system used glucose as the carbon source. Samples were drawn from the first stage of this system, which was a continuously stirred acidification reactor. (4) A bench-scale sequencing batch reactor (SBR) system treating abattoir wastewater currently being operated at The University of Queensland, Australia. Samples were drawn at the beginning of the 3-h aerated period of the cycle• COD measurements were performed in duplicate or triplicate using Merck COD cell tests (Merck, Darmstadt, Germany). Soluble COD was determined after filtering the sample through a 0.45-#m filter. Sample preparation Samples (5 ml) were fixed in 0.5 ml formaldehyde (36% v/v) and stored at 4°C until they could be counted. The bacteria were dispersed and collected on a polycarbonate filter. Cells must not be clumped or associated with particulate material (Daley, 1979). Samples were diluted, chilled and sonicated with a Vibracell Sonicator (Branson, type 250, Danburg, CT, USA) fitted with a 3-mm stepped microtip. The tip was immersed just below the surface of the liquid and sonicated for 30 s at a duty cycle of 80% and a power setting of 5. The present authors, and others, have found that sonicating activated sludge samples with a high intensity probe for 30 s, as described here, is the most convenient method for dispersing the flocs of activated sludge. It is also the least disruptive to the bacterial cell integrity (Pike, 1975; Jorand et al., 1995). Bacteria grown on artificial, soluble substrates did not require dispersing. Sterile, filtered (0.2 pm), Milli-RQ~ water was used to dilute samples and wash filters (see below). This ensured that no bacterial cells contaminated the samples. Staining, collecting and counting of bacterial cells To determine bacterial cell numbers with the AODC technique, the following three pieces of equipment were used: e a membrane filter apparatus (25mm) that was connected via a liquid trap to a vacuum; • an epifluorescence microscope; and • an eye piece with a graticule where the smallest square was 9.8 #m wide, (the size of the grid was checked prior to use with a stage micrometer). The AODC technique is detailed in Hobble et al. (1977) and Bitton et al. (1993). The following describes the adaptation of this classic microbiological technique to wastewater samples. A sample volume of 0.2 ml (the volume depended
2552
E.v. Mfinch and P. C. Pollard
on the biomass concentration) of the diluted and dispersed sample (Vs,mp~o.d,) was filtered onto polycarbonate filters (0.2-#m pore size) that had been stained black. The filters were stained by soaking them overnight in 0.5% (w/v) irgalan black that was dissolved in a 2% (v/v) solution of acetic acid. Filters were rinsed in Milli-RQ water prior to use to remove excess dye. A filter was mounted on a 25-mm-diameter filtration apparatus (Millipore, Bedford Massachusetts, USA), and 5 ml of sterile filtered water was added above the filter, followed by the sample. The bacteria were then stained for about 2-5 min by adding three to four drops of dissolved acridine orange (Sigma Chemicals, St Louis, Missouri, USA; c. 5% w/v) to this solution. Cells were then collected on the black polycarbonate filter. It is important to wash excess acridine orange stain through the filter with a few millilitres of sterile filtered MilIi-RQ water. The filter was then placed on a damp microscope slide and not allowed to dry. One drop of non-fluorescent immersion oil was added (Cargille immersion oil) followed by a coverslip and another drop of immersion oil. Bacteria were then counted with an epifluorescence microscope. Acridine orange attaches to the bacterial DNA molecules that then become fluorescent when viewed with an epifluorescence microscope. The eye piece of the epiftuorescence microscope had a graticule where the size of the smallest square was 9.8 x 9.8/~m (measured with a stage micrometer) (Fig. 1). Approximately 30 bacteria were counted in 1-6 squares of the total grid, depending on the dilution factor ~i~,oo°). Counting bacterial cells is a subjective task. This subjectivity comes mainly from deciding when bacteria are single cells, beginning to divide or aligned in filaments (Fig. 1). Cell counts can therefore vary considerably (see results below), so counting was repeated at least 12 times per sample for different areas of the same filter to increase confidence in the measurement.
After counting the number of cells (Nco.s) in N~q...... the number of cells per litre can be calculated as follows: Cco~ =
Ncells ",/dilution ili~cr " V~am ple,dil
"103
(1)
where the fraction of filter area counted in the graticule (i~ter) can be determined as /filter = Asquare " Nsquares A~l,er ' 10 6
(2)
For the filtration apparatus used in this study, the total filter area (A~,or) was 346.4 mm 2.
Statistical error analysis A 95% confidence interval has been used to quantify the error (scatter) of the measurements. The 95% confidence interval in absolute terms (6C~) was calculated according to Eq. 3, by taking the standard deviation of the measurement, acj, and the sample size, nj, into account (Walpole and Myers, 1990): 6Cj = 1.96.
(3)
RESULTS AND DISCUSSION
Conversion of bacterial cell numbers into bacterial biomass-COD T o c o n v e r t the n u m b e r o f bacterial cells per litre (Cc0.s) into r a g - C O D p e r litre (Cb~...... coD), t w o f a c t o r s m u s t be k n o w n : • the d r y w e i g h t o f o n e bacterial cell (mce,), a n d • t h e C O D p e r bacterial m a s s (/coD/x) as m g C O D p e r m g d r y weight o f bacteria.
Fig. 1. Bacteria from a full-scale plant dispersed, stained and viewed (1000x magnified) with an epifluorescence microscope. A graduated graticule is overlaid; the smallest square is 9.8 x 9.8/~m.
Measuring bacterial biomass-COD in wastewater The biomass-COD is then simply Cbi..... -COD= Cc¢,,~'mcel," iCOD:X
2553
120
(4)
The weight of one bacterial cell (race,) can be estimated by assuming that there is a constant ratio between the cell carbon content and the volume of the bacterial cell. There has been some debate as to the relationship between cell carbon content and volume of a bacterium. Bratbak and Dundas (1984) reported 220 fg carbon per #m 3 of cell volume, whereas the average of the repotted values for the carbon content per cell volume is around 350 fg-C//tm 3 (Norland, 1993). In the present study, the lower carbon content of 196 + 69 fg-C//~m 3 (mean _ standard deviation, sample size n = 12),determined by Bloem et al. (1995) has been used because larger bacteria, such as bacteria in high nutrient micro-environments (Pollard and Moriarty, 1991), generally have lower biomass-to-volume ratios (Norland, 1993). Bacterial cell w31umes can vary considerably, especially in pure cultures, depending on the type of bacteria and their growth rates (Lee, 1993). To determine the average cell volume of bacteria in wastewater treatment plants, photographs of acridine orange stained wasIewater bacteria, such as the one shown in Fig. 1, were used. These photographs, together with a photograph of a stage micrometer, were analysed via the image analysis software package NIH Image 1.57 (a public-domain freeware program). This soft'ware package allows simple sizing of bacteria once a reference length is known. Measurements could also be made directly from the photograph (Bratbak, 1993). Also measured were the dimensions (length and width) of 341 bacteria in a wastewater sample which was a 1:2 mixture of sludge from the prefermenter and the underflow of the primary clarifier of a full-scale BNR wastewater treatment plant at Loganhoime, Aust~:alia. For each bacteria, the cell volume was detern~ined with the following formula:
This formula is based on bacteria that are straight rods with hemispherical ends, and it works equally well for cocci (Bratbak, 1993). On average, the bacteria in the wastewater sample had a cell volume of 0.39 + 0.02/~m~ (mean + 95% confidence interval, n = 341). The average bacterial cell length was 1.30___0.03vm and average width was 0.66 ___0.01/~m. Note that when calculating the average cell volume: the average cell length and width cannot be used, bul: the volume of each cell has to be calculated and the average of that taken. The spread of cell volumes iis illustrated in Fig. 2. When comparing this ,,;ample to a sample from a bench-scale SBR l:reating abattoir wastewater, no significant differences in cell sizes were observed between the two samples (average length and width
I°
60
t~
40 20 0 d
d
" d
o
. . . .
Bactertmi e e l volume (p,m ~)
Fig. 2. Distribution of bacterial cell volumes in a sample from a full-scale BNR plant. Average cell volume is 0.39 + 0.02 # m 3 (mean ___95% confidence interval). The total number of cells counted was 341. was 1.24 _+ 0.05/~m and 0.65 + 0.03 gm, respectively). It is recommended that bacterial cell volumes are determined again for samples where the nutrient loads are significantly different to those studied here or for very different types of sludges, e.g. bulking sludges. With the carbon content per cell volume (196 fg/ #m 3) and the cell volume (0.39 #m3), the bacterial mean biomass in terms of carbon was calculated to be 76 _ 16 fg-C/cell (mean + 95% confidence interval; the confidence interval was calculated by combining the errors of the two single values, carbon content per cell volume and cell volume). A similar value, namely 63fg-C/cell, was determined by Frolund et aL (1996) for bacteria from activated sludge samples. The carbon content of a bacterial cell is 53.1% (w/w) based on the average bacterial composition of CdHTNO2 (Hoover and Porges, 1952). Thus, the dry weight of one bacterial cell (m~,) in the wastewater environment is approximately 14 x 10-" ___3 x 10-11 mg/cell (mean ___95% confidence interval). This value compares well with the weight of one cell of Nitrobacter sp. in pure culture, 9 x 10-~ mg/cell, which Sand6n et al. (1996) have reported. COD is a recognised term in the wastewater industry, used in models to simulate wastewater treatment processes. There is about 1.416 mg COD per mg dry weight of bacteria (Jain et al., 1992; Lazarova and Manem, 1995) based on the oxidation of bacterial biomass (composed of CdHTNO2) according to CdH7NO2 + 5 0 : ~ 5
CO2 + 2 H20 + NH3
(6)
To obtain the COD content per cell (iCOD/~,) the bacterial dry weight (m~,) and the COD content per dry weight of biomass (/coD/x) are multiplied with each other. Hence, cell numbers are converted to biomass-COD according to Cbi...... COD= /COD/co,'C~l,~
(7)
For bacteria living in nutrient rich wastewaters, icoo/=, has been determined to be 20 x 1 0 - " _ 4 x 10-" mg-COD/cell (mean + 95% confidence interval).
2554
E.v. Miinch and P. C. Pollard
Measuring biomass-COD in wastewater and activated sludge The AODC technique was used in a number of different wastewater treatment systems to determine bacterial biomass-COD. These results together with measurements of total and particulate COD concentrations in the same samples are summarised in Fig. 3. The origin of these samples has been detailed in the Materials and Methods section of this paper. Particulate COD is defined as total COD minus soluble COD. The error bars in Fig. 3 show the 95% confidence intervals of the data. For the biomassCOD concentrations, these 95% confidence intervals were calculated as follows. Firstly, the absolute error (absolute 95% confidence interval) of the cell concentration (6C~e~) was determined by repeating the cell counting at least 12 times across the filter surface (therefore, n = t2 or higher). Secondly, the law of error propagation was applied to equation (7). The relative error of the biomass-COD measurement (rb~...... coD) is then equal to the square root of the sum of the squares of individual relative errors, i.e. the relative error in cell concentration and the relative error in the conversion factor iCODce,. The biggest contributor to the uncertainty in the biomass-COD concentration is usually the error in icoo/ce,, which is around 20%. For the particulate COD measurements, the absolute error of the particulate COD concentration (6CpcoD) is the square root of the sum of squares of the absolute errors in the total and in the soluble COD concentrations (6CTcoDand 6CscoD). Wastewater biomass-COD concentrations should always be equal or less than the particulate COD concentration: Cbi...... coo < CPcoD= CrcoD - CscoD
(8)
For a bioreactor that is fed synthetic wastewater that contains soluble COD only, the concentrations of 25~
~
.~coD
I
IIF
511110
0
Fig. 3. Total COD, particulate COD and biomass-COD are compared for five different wastewater treatment processes. Error bars represent 95% confidence intervals of the measurements.
250 G)
200 150
e~
~ 100 | 50 I
I
I
l
2
3
time0l) Fig. 4. Changes in bacterial biomass-COD during a batch experiment with activated sludge determined via the AODC technique. Error bars represent standard errors of the AODC technique for the respective measurements (n = 12). biomass-COD and particulate COD are expected to be the same. Indeed, it was found that, in the sample that was taken from the bench-scale anaerobic digestion system fed by soluble COD only, the biomass-COD and particulate COD concentrations were similar, within the error of the experimental techniques (see acidogenic reactor in Fig. 3). The other samples taken from wastewater processes containing high amounts of particulate substrate, e.g. in the primary clarifier underflow, in the SBR or in the prefermenter, revealed that biomass-COD represented only a low fraction of the particulate COD (between about 9 and 14%). This shows that biomass-COD estimates that are based on TCOD or volatile suspended solids (VSS) measurements can grossly overestimate the amount of biomass present in a wastewater sample whenever large amounts of particulate substrates are present. Also monitored were changes of biomass-COD over time during a batch experiment originally designed to determine the maximum specific growth rate of heterotrophs. The mixture of raw wastewater and activated sludge for this experiment originated from the BNR pilot plant at Gibson Island, Brisbane, Australia. The 4-1itre reactor was aerated for 3 h and the bacterial biomass-COD concentrations determined via the AODC technique at different time intervals. Figure 4 shows that even relatively small changes in biomass-COD (increase about 1/5-fold in 3 h) can be observed by the technique described in this paper. Another application of the biomass-COD measurement is the direct estimate of the initial substrate-tobiomass ratio (So/Xo) in wastewater samples. This ratio is of importance whenever batch tests are carried out where the aim is to obtain kinetic constants of a corresponding continuous system (Nov~.k et al., 1994; Grady et al., 1996). Assuming that all COD (excluding biomass-COD) represents a substrate for the bacteria, the So/Xo can be determined according to
SO
Xoo ~
CTCOD-- Cbiomass-COD
Cbi. . . . . .
COD
(9)
Measuring bacterial biomass-COD in wastewater Traditionally, the iniitial biomass concentration X0 is often approximated by the VSS concentration and the initial substrate concentration So by the T C O D concentration of the sample. Therefore, for the sample from the SBR (with a VSS concentration of about 3.5 g/litre) the authors would have estimated the So/Xo ratio to be around 2.3 mg-COD/mg-VSS. On the other hand, if we use the directly measured biomass concentration in the calculation of So/Xo (using equation 9), one will find the more accurate estimate of this ratio to be around 6.0. CONCLUSIONS Measuring bacterial biomass concentrations will improve the predictive capabilities of mathematical models which are important to the successful design and operation of biological wastewater treatment plants. The authors have directly measured the state variable "biomass concentration" in terms of C O D by applying a simple, classic microbiological technique to wastewatcr treatment systems. Bacterial counts via the A O D C technique combined with the conversion factor of 20 × 10-" mg-COD/celi give a reliable and reproducible measure of bacterial biomass C O D in wastewater and wastewater sludges. Biomass-COD measurements can be used to track (short-term) change:~ of biomass in batch experiments and can also be incorporated into modelling and simulation work. Acknowledgements--This work was funded by the CRC for Waste Management and Pollution Control Limited, a centre established and supported under the Australian Government's Co-operative Research Centre Program, and an Australian Postgraduate Award granted to Peter C. Pollard. The authors would also like to acknowledge Dr Paul Lant for his valuable contributions to this paper. REFERENCES
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