Measuring the Effectiveness of a Pilot Scale Bioreactor for Removing

Oct 20, 2008 - A pilot scale fluidized bed bioreactor to control the cyanobacterium, Microcystis, was tested in an outdoor experimental pond system (2...
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Environ. Sci. Technol. 2008, 42, 8498–8503

Measuring the Effectiveness of a Pilot Scale Bioreactor for Removing Microcystis in an Outdoor Pond System T O M O A K I I T A Y A M A , * ,† N O R I O I W A M I , ‡ M I T S U Y O K O I K E , †,∇ TAKASHI KUWABARA,| NIWOOTI WHANGCHAI,⊥ AND YUHEI INAMORI# Environmental Chemistry Division, National Institute for Environmental Studies (NIES), 16-2, Onogawa, Tsukuba, Ibaraki, 305-8506, Japan, Department of environmental systems, Meisei University, 2-1-1 Hodokubo, Hino, Tokyo, 191-8506, Japan, Department of Chemistry, Toho University, 2-2-1 Miyama, Funabashi, Chiba, 274-8510, Japan, Department of Biological Environment, Akita Prefectural University, 241-438 Kaidoubata-nishi, Nakano, Shimo-shinjo, Akita 010-0195, Japan, Faculty of Fisheries Technology and Aquatic Resource, Maejo University, Sansai, Chiangmai, 50290, Thailand, Faculty of Symbiotic Systems Science, Fukushima University, 1 Kanayagawa, Fukushima 960-1296, Japan

Received January 11, 2008. Revised manuscript received April 15, 2008. Accepted April 17, 2008.

A pilot scale fluidized bed bioreactor to control the cyanobacterium, Microcystis, was tested in an outdoor experimental pond system (28 m3) over a 57 day period. The pond system was inoculated with a wild bloom of Microcystis, and the bioreactor was preinoculated with an oligochaete, Aeolosoma hemprichi, which is known to prey on colonial Microcystis. This and other Microcystis predators such as the rotifer, Philodina erythrophthalma were observed to colonize the bioreactor during the experiment. The bioreactor performance in removing Microcystis was estimated using a mathematical model and a multiple regression analysis of the chlorophyll-a concentration, which was a satisfactory surrogate for the Microcystis cell density in the ponds. The estimated specific decrease in chlorophyll-a concentration due to bioreactor treatment was 0.04 day-1, which was equal to the net removal of 4.3 × 1011 Microcystis cells day-1 from the treated pond.

Introduction Toxic blooms of cyanobacteria such as Microcystis often occur in eutrophic lakes (1–3), causing potentially serious ecological and public health problems (4–6). Algicides are commonly used to control toxigenic cyanobacteria (7). However, algi* Corresponding author phone: +81-29-850-2721; fax +81-29-8502573; e-mail: [email protected]. † National Institute for Environmental Studies (NIES). ‡ Meisei University. † Toho University. | Akita Prefectural University. ⊥ Maejo University. # Fukushima University. ∇ Current address: Water Quality Division, Chiba Prefectural Government, 1-1, Ichibamachi Cyuouku, Chiba, Chiba, 260-8667, Japan. 8498

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cides are typically non specific and their impact can be devastating to the health and sustainability of the ecosystem. Moreover, algicides control blooms by disrupting the phytoplankton cells. If toxic cyanobacteria are present, soluble cyanotoxins such as microcystin are released into the water upon cell lysis, significantly increasing the public health risks (8–11). For these reasons, methods for the control and purification of Microcystis affected waters, without concomitant public health and ecological problems, are desirable. Biomanipulation or ecosystem intervention has been widely used to control phytoplankton abundance and composition (12–25). The so-called “top down” approach introduces piscivorous fish into ponds and lakes to reduce predation pressure on zooplankton grazers, and thus increase the predation of phytoplankton (22–25). However, the most effective zooplankton grazers, such as Daphnia are selective feeders that do not consume Microcystis, either because the colonies are too large or because the cells contain toxins (26–29). Thus there is considerable interest in identifying microorganisms that can prey on large and or toxic Microcystis colonies. We have previously isolated three of these predators; the microflagellate Mastigophoran, Monas guttula, which consumes and detoxifies unicellular toxic Microcystis (30–34); the oligochaete Aeolosoma hemprichi; and the rotifer, Philodina erythrophthalma both of which can prey on colonial Microcystis (35, 36). However, these and other nonplanktonic consumers are usually unable to access planktonic Microcystis cells until they settle out of the water column. A previous laboratory investigation of Microcystis control methods showed a bioreactor rapidly reduced the concentration of a Microcystis monoculture in suspension, in a process which followed first order kinetics (37). The current study has moved closer to natural conditions by growing Microcystis in an outdoor continuous culture system, where the cell density was affected by changes in light and nutrient availability, water temperature, flushing rate, and intra specific competition. This variability also meant the performance of the bioreactor was more difficult to measure. So this paper has used a novel method to assess the performance of a new bioreactor design based on a simple mathematical model and a multiple regression analysis that quantified Microcystis growth in terms of changes in the chlorophyll-a concentration.

Experimental Section Experimental Pond. The experiment was conducted in outdoor ponds, arranged side by side (see S1 in the Supporting Information) at the National Institute for Environmental Studies in Tsukuba, Japan. The control and the Treated Ponds were inoculated on September 4 1998, with 40 L of pond water containing a bloom of Microcystis sp. collected from Teganuma Marsh (E140° 01′ 32.7”, N35° 51′ 31.5”, Abiko City, Chiba, Japan). Filtered groundwater was added continuously to each pond to simulate the retention time for a small lake or reservoir in Japan. Initially the hydraulic retention time (HRT) was set to about 10 days. After September 23 the HRT was reduced to about 18 days. The inflow rates were checked and adjusted on all sampling days using a measuring cylinder (1 L) and a stopwatch and the relative error in flow rate in both ponds was within (2%. Moreover, since the relative variation of effective capacity was ( 0.1 m3/28 m3 ) ( 0.4% (see Figure S1 caption in the Supporting Information), the relative variation of dilution rate between both ponds was estimated (2.4%. Thus, the difference of the dilution rate between both ponds was estimated at ( 0.0024 day-1 at HRT 10.1021/es703172z CCC: $40.75

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of 10 days, and ( 0.0013 day-1 at HRT of 18 days. The bioreactor operation commenced on October 2 1998 and it operated continuously until October 27. Nutrient Additions. The nutrient content of the experimental ponds was made up to match the source of the microalgal inoculum (Teganuma Marsh; TN 3.5 mg-N L-1 TP of 0.4 mg-P L-1) by adding 590 g of NaNO3 and 40 g of NaH2PO4 on September 4, 1998. Further additions of NaNO3 were made (480 g on September 8 and 770 g on September 23) to maintain the pond nutrient levels at the starting concentrations. No further orthophosphate additions were required. Water Sampling and Analysis. The water temperature was measured at 15, 60, and 120 cm below the surface between 16:00 and 16:30 on each sample date. A single composite (3 L) sample for determining the nutrient and chlorophyll-a concentration was prepared from 1 L samples collected at the 3 points shown in Figure S1 in the Supporting Information. The pond was stirred with a dipper to homogenize the water bloom before sampling. A matched sample of bioreactor effluent (2 L) was collected 2.5 h after the pond water samples (2.5 h ) bioreactor HRT). The water samples were immediately filtered (Whatman Ltd. GF/C 1.2 µm mesh), and the concentration of nitrate, ammoniacal nitrogen and orthophosphate in the filtrate was measured using an autoanalyzer (Bran Luebbe TRACCS 2000). The concentration of TN and TP in the unfiltered samples was measured using the potassium persulfate method (38). Chlorophyll-a was extracted from the cell residue on a GF/C filter using methanol, and quantified spectrometrically (39). The cell density of phytoplankton in the pond water was measured by a direct count using a TATAI cell counter (Enosinophil counter, SLGC Ltd.) under a microscope. Separate samples (5 mL) for counting Microcystis were pretreated by sonication (Ultrasonicator UR-20P, TOMY SEIKO CO., Ltd. 20W) at half of maximum power for 5 s to minimize cell disruption. A composite water sample was used for each analysis as a preliminary investigation showed the variation in chlorophyll-a concentration between the three points was less than (10% and the analytical error was (5%. The variation of the nutrient data was within 5% and repeating error was within 5%. The counting error for the Microcystis cells was (25% which is consistent with the statistical precision of the method (40). Operation of the Bioreactor. The bioreactor was modified from the unit described previously (37), by adding a rotary fluidized bed. Details of the structure are provided in Figure S2 in the Supporting Information. The fluidized design was expected to distribute the phytoplankton cells rapidly and uniformly within the bioreactor, to facilitate predation by A. hemprichi and other microfauna. We estimated the population of A. hemprichi in the sponge carrier by the following method. Three carriers were randomly collected from bioreactor and placed in a Petri dish (9 cm in diameter) with 10 mL of distilled water. After 1 h, the A. hemprichi that had left the carriers were counted under the stereomicroscope. We provide qualitative observations of other microfauna as few of these animals left the carriers. A diaphragm pump (flow rate of 2.5L min-1) pumped pond water into the bioreactor (reactor capacity VR ) 500 L) and the treated water was returned to the treated pond under gravity. The reactor was stocked with A. hemprichi (106 organisms) at the start of the experiment (37) and the bioreactor was continuously sparged with air (5 L min-1) to oxygenate the carriers. Theoretical Basis of the Data Analysis. One purpose of this study is to clarify the process of Microcystis removal by the bioreactor. However, it is difficult to quantify the process, when algal growth in the outdoor pond is continuously and

significantly affected by fluctuating growth conditions. Thus we used a mathematical model with first order kinetics to simulate the removal process in the bioreactor in a fluctuating environment (37). The following differential eq 1 is assumed to represent the process of algae removal by the bioreactor in the treated pond and eq 2 is for the control pond. dA ) µAA - kA dt

(1)

dB ) µBB dt

(2)

where A and B denote the density of algal biomass in treatment pond and control pond, respectively. The net specific rate of increase in algal biomass, µA (B), can be defined as a time fluctuating function because of time dependent factors such as water temperature, illumination, nutrient concentration and interferences from other pond biota. The term -kA represents the bioreactor purification process with the kinetic rate k, which has the following relation: k ) DR(t)

(3)

where the removal ratio R(t) is defined as Ceff(t) Cin(t)

R(t) ) 1 -

(4)

Cin(t) and Ceff(t) are the algal biomass density of the bioreactor influent and effluent respectively. The coefficient D [day-1] is the rate of circulation of pond water through the bioreactor. Thus D can be represented as follows: D)

Qr VL

(5)

where Qr [m3 day-1] is the flow rate into the bioreactor and VL [m3] is the pond volume (33). We can derive the following regression equation from eq 1 and 2 (see “On the derivation of the regression model equation” in the Supporting Information). hi ) h0 + ∆µ · ti - D · ri + εi

(6)

where hi ) ln ∆µ )

1 T

∫

T

0

Ai Bi

(7)

(µA(t) - µB(t))dτ

ri ) r(ti) )

∫

ti

0

(8)

R(τ)dτ

(9)

The response variable hi in the regression eq 9 was calculated from eq 7. The Ai and Bi (i ) 0. . .n) in eq 7 was the measured algal biomass in the treated pond and the control pond at the sampling time ti (i ) 0. . .n, t0 ) 0) respectively. The coefficient ∆µ represents the trend in the difference in the rate of algal biomass production between the ponds during the experimental period (0-T days). On the other hand, D (the kinetic rate of reduction in algae biomass) in eq 6 can be determined by a multiple regression. Here the ri () r(ti)) can be calculated from eq 9 using the trapezoidal rule for numerical integration of R(t) defined in eq 4 (41). The i represents the random error term in the regression model. The results of the regression diagnosis are described in the Supporting Information. The D was independently determined from the pond volume VL, and the flow rate Qr, as shown in eq 5. Therefore a t test can be used to test the hypothesis that De)D ()Qr/VL) VOL. 42, NO. 22, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. The concentration of chlorophyll-a in the treated and control ponds, the population of A. hemprichi in the bioreactor carrier and the removal ratio of chlorophyll-a. The solid line below the transverse axis shows when the bioreactor was operating.

FIGURE 1. Changes in phytoplankton biovolume in the experimental ponds. The solid line below the X axis shows when the bioreactor was operating. (42), where the D in the eq 6 is rewritten as the estimated parameter De. If the hypothesis that De)D is not rejected, then the reaction kinetics shown in the eq 1 do describe the process of algal biomass removal by the bioreactor, as predicted. All statistical calculations were performed using software R (ver. 2.4.1) (43).

Results and Discussion The proliferation of harmful cyanobacteria in eutrophic reservoirs, recreation and aquaculture ponds, or water storages for industrial reuse is a significant worldwide problem, which is reducing the value and amenity of these resources. This study was designed to evaluate the performance of a novel biological method for reducing the growth of the persistent toxigenic cyanobacterium, Microcystis sp. Before the operations of the bioreactor commenced, the fine sunny weather caused thermal stratification and slight warming of the bottom water in the experimental ponds until September 22 (see Figure S3 in Supporting Information). Thereafter, mixing in both ponds, driven either by diurnal cooling or by strong wind events (e.g., September 29th and October 23rd), obscured any additional mixing of the treated pond by the bioreactor pump. Microcystis comprised more than 96% of the phytoplankton biovolume in both ponds throughout the experiment, and the phytoplankton composition in the treated pond was not significantly altered by the bioreactor (Figure 1). The phytoplankton biovolume (which was essentially Microcystis) was highly correlated with the concentration of chlorophyll-a (Pearson test: P < 0.01; R ) 0.68). Thus chlorophyll-a can be considered a surrogate for the abundance of Microcystis and the chlorophyll-a concentration in the ponds can be converted to Microcystis cell density by the following equation, cell-density(cells · L-1) ) 2.2 × 106 · chlorophyll - a(µg · L-1) (10) After the ponds were inoculated with Microcystis, chlorophyll-a levels rose in synchrony, to maxima of 230 µg L-1 (treated pond) and 180 µg L-1 (control pond), in mid and late September (Figure 2). After the bioreactor commenced on October 2, the chlorophyll-a in the treated pond fell 8500

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progressively to 100 µg L-1. In contrast, the chlorophyll-a concentration of the control pond oscillated around a mean value of 150 µg L-1 throughout October. These chlorophyll data indicate that the bioreactor progressively removed Microcystis from the treated pond, whereas the Microcystis biomass in the control pond remained relatively constant. The performance of the bioreactor in removing Microcystis can be gauged from the removal ratio for its surrogate, chlorophyll-a. This is represented by the R value derived in eq 4, which is also plotted in Figure 2. The high R value (R ∼ 0.8) at the start of the bioreactor operations on October 9 was probably inflated by the mechanical removal of Microcystis, as colonies were trapped in pores on the bioreactor carriers. The removal rate stabilized at a relatively constant R ∼ 0.36 between October 13 and 20, which probably best represented the efficiency of the biological (predation) processes and coincidentally was the average R value over the entire period of the bioreactor operation (Table 1). We propose the biological component of the bioreactor performance in removing Microcystis colonies was due to predation by microfauna living on the bioreactor carriers. The oligochaete A. hemprichi successfully colonized the bioreactor after an inoculum was added at the beginning of the experiment (Figure 2). A second Microcystis predator, the rotifer Philodina erythrophthalma (35–37), was also observed living on the bioreactor carriers. However, the population dynamics of P. erythrophthalma and other carrier microfauna were not quantifiable, as these organisms did not left the carriers when placed in distilled water. On October 27, the bioreactor efficiency had fallen to R ) 0.2. At this time, colder water temperature (16 °C) would have reduced the feeding rates of the microfauna (44) thus lowering the efficiency. However, losses in bioreactor efficiency at water temperatures below 20 °C should be mitigated by reduced Microcystis growth rates (45). The bioreactor was not expected to directly affect the concentrations of dissolved nutrients in the continuous culture system. Nevertheless, faster cycling of nutrients could promote phytoplankton growth so the changes in dissolved nutrient concentration have been summarized in Table 1 and Figure 3. The batch additions of nitrate were washed out of the system as the reduction in nitrate concentration in both ponds approximated the flushing rate (0.10 day-1 initially, reduced to 0.05 day-1 after 23rd September). Orthophosphate behaved slightly differently. At the high flushing rate the orthophosphate was diluted in both ponds the but when the flushing rate was halved after 23rd September, the ortho-

TABLE 1. The Averaged Removal Ratio and Daily Removal Amount for Nutrients and Chlorophyll-a by the Bioreactora water quality parameters NH4-N NO3-N inorg.-N T-N PO4-P T-P chlorophylla Microcystis cells

average removal ratio R¯

daily average removal amount ∆X¯(g day-1)

0.42 -0.16 -0.13 0.39 0.029 0.18 0.43 (0.36)

0.18 -1.06 -0.88 6.28 0.10 0.61 0.23 (0.19) 5.2 × 1011 cells day-1 (4.3 × 1011 cells day-1)

a The values in the table were calculated using the equations listed below the table. A negative value indicates the concentration in the effluent was higher than in the influent. The removal of Microcystis cells was estimated using eq 10. b The average removal ratio was given by \mathop R¯) (Xin-Xout)/Xinand the daily average removal amount was given by ∆X¯ ) (Xin - Xout) /(T1 - T0) where Xin ) ∫0t1 Qr · xin(t)dt, Xout ) ∫oti Qr · xout(t)dt. Here, xin(t) represents a concentration of water quality parameter “x” flowing into the bioreactor and xout(t) represents the concentration of water quality parameter “x” flowing out from the bioreactor. Qr () 3.57 m3 day-1) represents the flow rate to the bioreactor (see Supporting Information). The Xin and Xout were calculated by the trapezoidal rule (40). The integration interval was from October 9 (T0) to October 20 (T1), except for the values in parentheses integrated from October 6 (T0) to October 27 (T1).

FIGURE 4. Actual h calculated by eq 11 versus predicted h using the multiple regression model equation (20) and the residuals. The arrow shows the data point from October 9. The errors (residuals) were uncorrelated (Durbin-Watson test, p > 0.99). There is no colinearity between regressor r and t (42), because the points (0) of the r-t plot are not on a line that passes the origin but approximately on a zigzag line. is also abundant in pond water (e.g., resuspended detritus, microbes, bacteria, and protozoans (49, 50). The weighted regression method was used because the data analysis procedure cannot assume constant variance for residuals in the regression, as shown in Figure S4 in the Supporting Information. In addition, the regression diagnostics have also been presented in the Supporting Information. The response variables for each pond hi (i ) 0...n) were calculated from the chlorophyll-a (as algal biomass) data using eq 7, for the period from September 4 until October 27. The obtained regression equation is as follows: h ) 0.1054 + 0.0081t - 0.1072r

FIGURE 3. The concentration of nutrients in the treated and the control ponds. The solid line shows when the reactor was operating. Nutrient additions are marked by down arrows. phosphate gradually approached an asymptote at 0.6 mg-P L-1, probably as the internal loading from the pond sediment matched the rate of dilution by flushing. There concentrations of nitrate and orthophosphate were not significantly affected by the bioreactor operations. However, ammoniacal nitrogen was significantly reduced during by the reactor operations (Table 1 and Figure 3). The reduction in ammoniacal N was attributed predominantly to nitrification to nitrate by bacteria within the bioreactor. Sparging would have removed about 10% of the ammoniacal N, as gaseous ammonia at pH 8.0-8.5 (46). The bioreactor effectiveness in removing particulate material was determined by the difference between the mass of the total and dissolved nutrients removed (Table 1). The bioreactor removed approximately 0.5 g day-1 of particulate organic P (POP), 7.2 g day-1 of PON and 0.23 g day-1 of chlorophyll-a. The removal ratio of chlorophyll-a to POP was about 1:2 by mass. This contrasts with the ratio of chlorophyll-a to TP of approximately 1:1 in particulate matter from the open water of lakes dominated by phytoplankton, where ortho-P is limiting (47, 48). Perhaps the bioreactor also removed an equal quantity of the nonchlorophyllous organic matter that

(11)

Figure 4 shows the predicted values of h by the eq 11, and the summary of the regressions are shown in Table S1 in the Supporting Information. The residual distribution was normal by the Q-Q plot (See Figure S5 in the Supporting Information), and the residuals were uncorrelated (Durbin-Watson test, p > 0.99) (42). The results of the regression diagnostics showed the regression was statistically reliable (42). Although the residual on October 9 was quite different from the predicted value (see Figure 4), even this large residual value was within the deviation of the normal distribution. The estimated difference in the mean rate of change of chlorophyll-a (∆µ) between the Treated and the control pond was +0.0081 day-1, indicating that chlorophyll-a production in the treated pond was slightly higher than in the control pond. One contribution to ∆µ could be a difference in the flushing rate of the ponds. The error in setting the flow rate in each pond was < ( 2%. However, this could not contribute to the ∆µ, because it was a random error with the same mean and deviation and the flow rates were adjusted regularly. On the other hand, the difference in the capacity of the ponds was estimated as ( 0.4% and this difference could contribute to ∆µ as it would produce a systematic error in the flushing rate. Therefore the maximum contribution to ∆µ was estimated as 0.0008 day-1 due to the capacity error, when the HRT was 10 days. The remaining contributors to ∆µ could not be quantitatively determined. The small positive ∆µ clearly shows that the chlorophyll-a in the treated pond increased at a slightly higher rate than in the control pond before operation of the bioreactor. Our analysis has assumed that this tendency continued during the operation of the bioreactor. The measure of the circulation rate parameter D, estimated from the regression eq 6 was De ) 0.11 day-1. The alternative value for D calculated directly from eq 5 was of D ) 0.13 day-1. As the calculated (D) and estimated (De) values of D were not significantly different (t test, p < 0.05), VOL. 42, NO. 22, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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the simple mathematical relationship described in eq 1 does describe the removal of Microcystis cells by the bioreactor. The average kinetic parameter k)0.040 day-1 for the decrease in chlorophyll-a due to the bioreactor only, was estimated from the equation k ) DR, where D was estimated as De ) 0.11 day-1 and the average removal ratio R )0.36 (Table 1). The rate of decrease in chlorophyll-a in the treated pond when it was treated by the bioreactor was k ) 0.040 day-1 was much greater than the potential decrease (0.0026 day-1) due to variation in dilution rate between both ponds at that time (see experimental section). Therefore, the significant decrease in chlorophyll-a in the treated pond, while the chlorophyll-a level was maintained in the neighboring control pond was attributed to the operation of the bioreactor in removing Microcystis cells from the pond water. These results show the bioreactor successfully reduced the Microcystis concentration a small experimental pond. We expect this type of bioreactor system could be used to treat Microcystis blooms in small water bodies with high economic or aesthetic value, such as urban ponds in city parks, storage ponds for water recycled in water industrial processes, or aquaculture ponds. This work is a first step in demonstrating that effective bioreactor systems can be designed to manage such high value applications at a reasonable cost.

Acknowledgments We especially thank Dr. Peter Ross Hawkins for his important discussions and suggestions to improve the English.

Supporting Information Available The design of the experimental pond system, the structure of the bioreactor, the derivation of the regression analysis formula, the regression diagnostics, and the pond water temperature during the experiment. This material is available free of charge via the Internet at http://pubs.acs.org.

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