Introduction
Long-term monitoring of the protozoa and metazoa community inhabiting activated sludge has already been conducted by scientists in several countries in the world: in Spain by Salvadó and Gracia 1993, Martin-Cereceda et al. (1996), Zornoza (2017), in Germany by Ettl (2001) and in China by Zhou et al. (2006, 2008), Liu et al. (2008), Hu et al. (2013a,b). As was mentioned in introduction, results from previous studies on protozoa community in conventional activated sludge plants cannot be directly extrapolated to new treatment plants designed for biological nutrient removal and new research in bio-indicators is needed. (Perez-Uz et al. 2010, Dubber & Gray 2011a).
The results from four Polish wastewater treatment plants presented in this chapter fit very well to current demand in this research field and are intended to broaden the existing data collection.
During monitoring period there was investigated if changes in protozoa and metazoa community composition could be connected with changes in operational or environmental parameters. Moreover, determination of the values of protozoa and metazoa as bioindicators of the effectiveness of the suspended solids (SS), biological oxygen demand (BOD5), chemical oxygen demand (COD), nitrogen (Ntotal) removal process was checked.
Materials and Methods
The 81 samples of activated sludge from 300 to 1000 ml volume each were taken from the WWTPs aeration tanks. Samples were collected from four treatment plants operating in the Małopolska voivodship, southern Poland. Details on the all investigated treatment plants were gathered in Table 6.1.
More detailed information about each investigated plants is provided below in the text.
Table 6.1. Main characteristics of investigated WWTP’s.
WWTP
88 In technical description of treatment plants only biological stage from each treatment plant was described.
SK WWTP
The SK plant is the biggest investigated object; their real size is 100 569 people equivalent (PE).
SK working based on anaerobic/anoxic/oxic (A2/O) technology and is obligated to eliminate N and P compounds. Plant has buffer tank and in biological stage two working reactors with activated sludge.
The influent volume is divided into two reactors: 15% of influent flow through first bioreactor and 85%
through second.
First biological reactor has built in secondary clarifier and consist of:
• Denitrification chamber V= 1488 m3
• Nitrification chamber V= 2210 m3
• Secondary clarifier V= 678 m3
Second biological reactor has in total 6196 m3 working volume and consist of (respectively):
• pre-denitrification chamber
• phosphate removal chamber V= 2923 m3
• denitrification chamber
• nitrification chamber V= 3273 m3
Secondary clarifier has working volume V= 2600 m3
Return sludge flows from secondary clarifier into pre-denitrification chamber, activated sludge is recycled from nitrification chamber to denitrification chamber. PIX chemicals are used depending on sludge sedimentation properties and effluent quality. Approximately 30% of sewage flows from industry.
During our study samples were collected from second reactor.
NP WWTP
The NP plant is medium size object 82 047 PE. NP works based on anaerobic/anoxic/oxic (A2/O) technology and is obligated to eliminate N and P compounds. Plant has not buffer tank and biological stage consist of two working reactors with activated sludge. One biological reactor has in total 4998 m3 working volume and consist of (respectively):
• phosphate removal chamber V= 712 m3
• denitrification chamber V= 712 m3
• nitrification chamber V= 3574 m3
This plant has separation zone built in nitrification chamber instead of a separate secondary clarifier.
The PIX chemicals are used when total P concentration in effluent exceed 1 mg/l. The 35−40% of sewage flows from industry.
89 CH WWTP
The CH plant is medium size object 73 915 PE which has working based on Modified Ludzack-Ettinger process (MLE). CH is obligated to eliminate N and P compounds. Plant has buffer tank and three bioreactors with activated sludge, but only two of them work. One biological reactor has in total 2650 m3 working volume and consists of (respectively):
• denitrification chamber V= 833 m3
• nitrification chamber V= 1817 m3 One secondary clarifier V= 3940 m3
Return sludge flows from secondary clarifier into denitrification chamber, activated sludge is recycled from nitrification chamber to denitrification chamber. PIX and PAX chemicals are used depending on sludge sedimentation properties and effluent quality. 15−20% of sewage flows from industry.
SI WWTP
The SI plant is the smallest from investigated objects 7 462 PE. SI works based on anaerobic/anoxic/oxic (A2/O) technology and is not obligated to reduce N and P compounds to meet the requirements outlined in the Water Law Act. Plant has buffer tank and two bioreactors with activated sludge, but only one works. Biological reactor has in total 1940 m3 working volume and consists of (respectively):
phosphate removal chamber V= 150 m3
denitrification chamber V= 660 m3
nitrification chamber V= 1130 m3 One secondary clarifier V= 242 m3
Return sludge flows from secondary clarifier into denitrification chamber, activated sludge is recycled from denitrification chamber to phosphate removal chamber. PIX and PAX chemicals are used depending on sludge sedimentation properties and effluent quality.
Influent, effluent quality and process parameters
Chemical analysis of influent and effluent parameters: SS, COD, BOD5, Ntotal, Ptotal was carried out by treatment plants laboratory (SK, CH, SI) or by accredited laboratory of MPWiK Kraków for NP plant. The plants staff provided information about operating parameters such as: mixed liquor suspended solids (MLSS), hydraulic retention time (HRT), sludge retention time (SRT), sludge load (F/M) and temperature (T) and the sludge volume index (SVI) of the mixed liquor.
All investigated objects are municipal waste water treatment plants. Plant SK, NP, CH additionally purify variable volume of industrial sewage inflow. In all WWTP phosphorousis reduced mainly by PIX so its values are not included into the data analysis.
90 Microscopic observation
Density of protozoa and metazoa community was determined based on analysis of two or three 25 μl subsamples taken from a well-mixed activated sludge sample examined immediately after delivery to the laboratory. Small flagellates were counted along the diagonal in the Fuchs-Rosenthal chamber.
Microscopic analysis was conducted using the Nikon Eclipse 80i and Olympus IX 71 microscopes with 200x and 400x magnification. The microbial density was averaged and converted into 1 ml volume of activated sludge. Ciliates species determination was done based on Foissner et al. (1991, 1992, 1994, 1995, 1996) identification keys.
For the data analysis protozoa and metazoa species were assigned to eight functional groups:
Crawling ciliates
Attached ciliates
Swimming ciliates
Predatory ciliates
Testate amoebas
Naked amoebas
Metazoa
Flagellates
Sludge Biotic Index calculations
Calculations of the SBI were undertaken in accordance with Madoni’s (1994) guidelines.
91 Results
The investigated WWTPs differed between themselves in values of process parameters such like SVI, temperature, HRT and sludge load (Table 6.2). Additionally, influent parameters also differed between monitored WWTPs (Table 6.3). The highest differences were observed between WWTP in Ntotal and Ptotal reduction rate.
Table 6.2. Process parameters in investigated WWTPs. (Mean ± SD).
WWTP SVI MLSS [g/L] T [oC] HRT [days] F/M [gBOD5/gMLSS/d]
Strong linear relationship between mean species number and sum of all biological tanks volume was noticed: R2 = 0.84, F(1,2) = 16.72, p = 0.055 (Figure 6.1). In WWTP with higher volume of all biological tanks on average more protozoa and metazoa species was found.
92 Figure 6.1. Relationship between mean species number and biological tank volume. Each dot
represents one WWTP.
Table 6.4. Protozoa and metazoa community found in the mixed liquor from activated-sludge plants.
Species WWTP
93
Generally high fluctuations in protozoa and metazoa density and community composition in time was noticed in all WWTPs. The higher fluctuation in protozoa and metazoa density was observed in bigger WWTP: SK and NP than in CH and SI. Each WWTP during monitoring period differed in the most stable functional group (with the lowest coefficient of variation value). In SK crawling ciliates was the most stable functional group, in NP most stable were predatory ciliates, in CH flagellates and in SI attached ciliates. In all WWTPs during monitoring period flagellates were the most dominant functional group (Figure 6.2, 6.3, 6.4, 6.5). In each WWTP the peaks of flagellates density was observed: in SK at 23.03.2016 and 20.10.2016, in NP at 24.02.2016 and 24.11.2016, in CH at 05.04.2016 and 24.08.2016, in SI at 21.04.2016, 14.09.2016 and 08.11.2016 (Figure 6.2, 6.3, 6.4, 6.5). Generally higher density of flagellates and testate amoebas were observed in bigger facilities: SK and NP in comparison to smaller WWTP: CH and SI. Extremely high testate amoebas density was noticed in SK in January and February 2017. Crawling and attached ciliates were the most numerous functional groups out of ciliates
94 population in all WWTPs. Swimming ciliates was the least numerous group of all functional groups in all treatment plants.
Figure 6.2. Annual variations of the density of functional groups in SK WWTP.
Figure 6.3. Annual variations of the density of functional groups in NP WWTP.
0
95 Figure 6.4. Annual variations of the density of functional groups in CH WWTP.
Figure 6.5. Annual variations of the density of functional groups in S WWTP.
0
96 Functional group composition in investigated WWTP
Figure 6.6. PCA analysis of activated sludge functional group composition of all data received during the monitoring. Dots represent WWTP: black – SK, red – NP, green – CH, yellow – SI. First axis explained 33.33% and second axis 23.74% of the variance in activated sludge functional group community.
The PCA biplot (Figure 6.6) showed that none of the monitored WWTPs significantly differed from others due to the composition of functional groups. Only three samples from SK and three samples from SI differed strongly from the remaining samples due to functional group composition.
97 Species composition in investigated WWTP
Figure 6.7. PCA analysis of activated sludge biocenosis composition of all data received during the monitoring. Arrows represent best fitted 15 protozoa and metazoa representatives. Dots represent WWTP: black – SK, red – NP, green – CH, yellow – SI. First axis explained 14.23% and second axis 11.99% of the variance in protozoa and metazoa activated sludge community.
PCA analysis showed that activated sludge in all four WWTPs had similar protozoa and metazoa species composition, because sets of samples from WWTP overlapped each other (Figure 6.7).
Protozoa and metazoa community from NP WWTP (red dots) was the most different from CH WWTP (green dots). The species composition in SK WWTP was similar to species composition in other wastewater treatment plants. Based on results of this analysis any specific/unique protozoa and metazoa community composition for individual WWTP could not be defined. It should be taken into account that
98 PCA diagram (Figure 6.7) explained 26.22% of the variance in protozoa and metazoa species composition.
Species composition in seasons
Figure 6.8. PCA analysis of activated sludge biocenosis composition of all data received during the monitoring. Arrows represent best fitted 15 protozoa and metazoa representatives. Dots represent seasons: green – spring, yellow – summer, orange – autumn, blue – winter. First axis explained 14.23%
and second axis 11.99% of the variance in protozoa and metazoa activated sludge community.
PCA analysis showed that seasons did not differentiate strongly protozoa and metazoa species composition in investigated WWTP, as sets of samples from WWTP overlapped each other (Figure 6.8).
99 Protozoa and metazoa community was the most variable in winter (blue dots). The most stable protozoa and metazoa community was in summer (yellow dots). Based on results of this analysis any specific/unique protozoa and metazoa community composition for individual season could not be defined.
Species composition explained by WWTP configuration and year season
Figure 6.9. Biplot diagram from RDA analysis summarizing the effects of interaction of WWTP and seasons upon protozoa and metazoa communities in activated sludge samples. Arrows represent best fitted 15 protozoa and metazoa representatives. Tringles represent interaction between WWTP and seasons. First axis explained 10.89% and second axis 9.60% of the variance in protozoa and metazoa community composition.
100 Figure 6.10. Biplot diagram from RDA analysis summarizing the effects of interaction of WWTP and seasons upon activated sludge samples. Tringles represent interaction between WWTP and seasons.
Dots represent samples from WWTP: black – SK, red – NP, green – CH, yellow – SI. First axis explained 10.89% and second axis 9.60% of the variance in protozoa and metazoa community composition.
RDA diagram (Figure 6.9) showed that protozoa and metazoa community composition depends on WWTP individual configuration and season. The effect of WWTP and seasons on protozoa and metazoa community were significant (p = 0.002) and both ordination axis explained 20.49% of the variance in mentioned above groups composition.
101 Process and operational parameters
Figure 6.11. PCA analysis of process parameters and SVI of all data received during the monitoring.
Arrows represent process parameters Dots represent WWTP: black – SK, red – NP, green – CH, yellow – SI. First axis explained 40.83% and second axis 27.23% of the variance in process parameters.
PCA analysis showed that monitored WWTPs formed three groups due to the analyzed process parameters and SVI: one group − SI WWTP, second group − SK with NP WWTP, third group – CH WWTP (Figure 6.11). SI WWTP clearly differs from the others facilities due to its process parameters:
HRT, temperature, sludge load and SVI. PCA diagram (Figure 6.11) explained 68.05% of the variance in process parameters values.
102 Protozoa and metazoa community composition explained by process and operational parameters
Figure 6.12. Biplot diagram from RDA analysis summarizing the effects of process parameters and SVI descriptors upon protozoa and metazoa communities in activated sludge. 15 best-fitting protozoa and metazoa representatives are shown. 8.13% (first axis) and 5.47% (second axis) of the variance in microbial community composition were explained by process parameters descriptors.
On RDA bipolt diagram Figure 6.12 the first ordination axis is correlated mainly with the activated sludge temperature and with SVI. The testate amoeba − Arcella sp. and ciliates species − A.cicada, Chiolodonella sp. tend to had larger abundance at higher temperature. On the other hand, Vorticella sp.
V. convallaria and H. discolor had higher probability of occurrence at lower temperatures. Calyptotricha sp. tend to had larger abundance in higher value of SVI. Both group of rotifers: Monogononta and Bdelloidea had higher probability of occurrence at lower value of SVI. The second ordination axis was
103 more correlated with HRT. The ciliates species like M. pusillus tend to had higher density at higher HRT value. Testate amoeba Cochlipodium sp. and attached ciliates Opercularia spp. tend to had higher probability of occurrence at smaller HRT values. The effect of temperature, sludge load, SVI and HRT on protozoa and metazoa community were significant (p = 0.002) and both ordination axis explained 13.6% of the variance in protozoa and metazoa community composition.
As was mentioned earlier process parameters were highly correlated with groups of WWTP (Figure 6.10) so it should be taken into the account that the effects of process parameters descriptors upon protozoa and metazoa communities were also correlated with individual treatment plant traits (Figure 6.13)
Figure 6.13. Biplot diagram from RDA analysis summarizing the effects of process parameters descriptors upon protozoa and metazoa communities in activated sludge. Dots represent WWTP: black – SK, red – NP, green – CH, yellow – SI and 15 best-fitting protozoa and metazoa representatives are
104 shown. 8.13% (first axis) and 5.47% (second axis) of the variance in microbial community composition were explained by process parameters descriptors.
Functional groups composition explained by process and operational parameters
Figure 6.14. Biplot diagram from RDA analysis summarizing the effects of process parameters descriptors upon functional/ecological groups in activated sludge. 10.44% (first axis) and 5.52% (second axis) of the variance in functional groups composition were explained by process parameters descriptors.
On RDA biplot diagram Figure 6.14 the first ordination axis is correlated mainly with the activated sludge temperature and with SVI. Swimming ciliates tend to had larger abundance at higher temperature and simultaneously attached ciliates tend to had larger abundance at lower temperature. Testate amoebas, large naked amoebas, flagellates, metazoa and crawling ciliates had lower probability of
105 occurrence at higher values of SVI. The second ordination axis was more correlated with HRT.
Predatory ciliates tend to had lower density at higher HRT value. The effect of temperature, sludge load, SVI and HRT on functional groups community were significant (p = 0.002) and both ordination axis explained 15.96% of the variance in functional groups composition.
Protozoa and metazoa community composition explained by reduction rate of some pollution measures
Figure 6.15. Biplot diagram from RDA analysis summarizing the effects of reduction rate of pollution descriptors upon protozoa and metazoa communities in activated sludge. 15 best-fitting protozoa and metazoa representatives are shown. 4.54% (first axis) and 3.23% (second axis) of the variance in microbial community composition were explained by reduction rate of pollution measures.
106 On RDA biplot diagram Figure 6.15 the first ordination axis is slightly correlated with suspended solids reduction rate. Ciliates: Thuricola sp., Metacystis sp., Plagiocampa rouxi and tardigrades tend to had higher density at higher suspended solids reduction rate. Testate Arcella sp. was highly correlated with BOD5 reduction rate and predatory ciliates H. discolor was highly negatively correlated with BOD5 and total nitrogen reduction rate. Suctoria and A. cicada tend to had larger abundance at higher BOD5 and total nitrogen reduction rate. The second ordination axis seems to be weakly correlated with COD and total nitrogen reduction rate. Attached ciliates Vorticella sp. had lower probability of occurrence at higher values of COD reduction rate. Analyzed reduction rates had significant effect on protozoa and metazoa community (p = 0.002) and both ordination axis explained 7.77% of the variance in protozoa and metazoa community composition.
107 Functional groups composition explained by reduction rate of some pollution measures
Figure 6.16. Biplot diagram from RDA analysis summarizing the effects of reduction rate of pollution descriptors upon functional/ecological groups in activated sludge. 4.60% (first axis) and 3.35% (second axis) of the variance in functional groups composition were explained by reduction rate of pollution descriptors.
The RDA biplot diagram Figure 6.16 presented the effect of SS, BOD5, COD, Ntotal reduction rate on functional groups community composition. However, analyzed predictors had insignificant (p = 0.086) effect on functional groups composition.
108 Sludge biotic index
All investigated activated sludges had SBI values between 8–10 which corresponding to first sludge quality class and indicating a well-colonized and stable sludge, with excellent biological activity and high purification efficiency.
109 Discussion
Our results similarly to Zornoza (2017) research suggested that distribution (density and species composition) of protozoa and metazoa in activated sludge depend on two main factors: bioreactor configuration (spatial factor) and year seasons (temporal factor).
The temperature in bioreactors were the most relevant factor explained changes in microbial community.
This result was also consisted with Zornoza (2017) analysis. According to Zornoza (2017) the temperature had more direct
effect than sludge age on variability in microbial community. But on the other hand temperature is closely related to sludge age, because it is common practice to increase the sludge age with the decrease of the nitrifying activity at the low temperatures in the bioreactors (Dymaczewski 2011).
Additionally, in research conducted by Hu and co-workers (2013b) influent temperature and SVI showed the highest factorial loads in the first component axis in PCA analysis explored changes in protozoan and metazoa community. In our RDA analysis (Figure 6.12) similar results were obtained, but temperature in bioreactor was applied instead of influent temperature. It should be pointed out that PCA is exploratory/descriptive analysis without significance test unlike RDA. Our study showed that the effect of temperature and SVI on protozoa and metazoa community were significant but first ordination axis explained only 8.13% of the variance in protozoa and metazoa community composition.
It should be mentioned that temperature, SVI and metazoa especially rotifers are closely related to each other. Fiałkowska and Pajdak-Stós (2008) showed that rotifers Lecane inermis were able to consumed and reduced the number of filamentous bacteria in activated sludge and are therefore able to reduce SVI value. These relations strongly depend on temperature. With temperature decreases rotifers density also decreases and thus the predator pressure on filamentous bacteria decreases and as a consequence SVI values increases (Pajdak-Stós & Fiałkowska 2012). Ettl (2001) also found significant correlation between individual protozoa taxa and the temperature of influent, effluent and activated sludge in aeration tanks. Strong temperature effect on protozoa community structure could be explained by correlation of environmental temperature with metabolic activity of ciliates (Laybourn & Finlay 1976).
Weisse and co-authors (2002) described an interaction between temperature and food concentration on growth and production of planktonic protozoa. These results showed that factors independent of the treatment plant operators have the strongest effect on protozoa community.
Weisse and co-authors (2002) described an interaction between temperature and food concentration on growth and production of planktonic protozoa. These results showed that factors independent of the treatment plant operators have the strongest effect on protozoa community.