V International Conference on Environmental, Industrial and Applied Microbiology - BiomicroWorld 20113. Madrid, Spain, 2-4 October 2013.

1. Correlating exoenzyme activities, operational parameters,
cell viability and EPS in a membrane bioreactor treating
domestic wastewater
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I. Amorós1, C. Grañana1, L. Borrás2, E. Zuriaga-Agustí3, A. Zornoza1 and J.L. Alonso1
1Instituto
INTRODUCTION
Universitario de Ingeniería del Agua y Medio Ambiente, (IIAMA). Universitat Politècnica de València, Spain (e-mail: jalonso@ihdr.upv.es)
2Departamento de Ingeniería Química, Universitat de València, 46100 Burjassot, Valencia, Spain
3Instituto de Seguridad Industrial, Radiofísica y Medioambiental (ISIRYM), Universitat Politècnica de València, Spain
Membrane biorreactors (MBR )systems are based on the combination of the activated sludge system and membrane technologies to separate the particulate material from water,
avoiding the need of a secondary clarifier. The main operational problem in MBRs is the membrane fouling or biofouling.
Equalization Tank
CAS
Aeration Tank
Secondary clarifier
MBR
Effluent Tank
Effluent
Water
Wastewater
Equalization Tank
Effluent Tank
Effluent
Water
Wastewater
Activated Sludge
Activated Sludge
Figure 1. Scheme of Activated Sludge and MBR treatments
Bacteria present in the biomass of an activated sludge system produce sticky compounds
denominated extracellular polymeric substances (EPS) which result from active bacterial secretion,
shed from the cell surface or cell lysis and mediate their adhesion to surfaces as the submerged
membrane module. After long time filtration of wastewater, both accumulation of EPS and amount of
microbial populations reach to maximum, which also indicate the minimum permeating capacity of a
membrane module. EPS are mainly polysaccharides, proteins, nucleic acids and lipids. Recent
studies point to EPS as the main cause of membrane fouling. Relationships between them and
operational parameters as potential foulants have been determined. Enzymatic activity is involved in
the degradation of biopolymers and cell viability gives information about cells damaged.
MATERIALS AND METHODS
Figure 2. Biofouling
Sludge Samples from a MBR treating domestic wastewater (316 m3) have been analysed for Exoenzymatic activities (β-glucuronidase and phosphatase), protein, carbohydrates
and cell viability. After that statistical analyses were carried out by using Canonical correspondance analyses (CCA), a method of direct gradient analysis.
• β-glucuronidase and phosphatase: Determined by fluorogenic substrate (ELF 97) which yields and insoluble fluorescent precipitate upon cleavage the enzyme. The spatial
distribution of enzymatic activity in whole flocs are detected by epifluorescence microscopy and quantification by using an optimization of Matlab®.
• EPS: were extracted by chemical extraction with cation exchange resin (CER) and later analyses with BCA (proteins) and anthrone (carbohydrates).
• Cell viability: Kit Backlight and fluorescence microscopy.
Table 1. Operating parameters and influent characteristics
Operational parameters and physicho-chemicals parameters were supplied by the treatment plant.
RESULTS
Range
Standard
deviation
1004
180-2326
814
HRT
56.9
47.1-65.7
6.4
OLR
0.014
0.01-0.02
0.004
mg·L-1
MLSS
9853
8232-11648
1259
mg·L-1
MLVSS
7626
6127-8796
1006.407
°C
Tr
22.0
18.3-28.4
4.0409
%
%MLVSS
77.3
74.1-80.5
2.3
pH_inf
8.1
8-8.4
0.1
mg·L-1
BOD5_inf
234
180-290
37
Parameters
Units
Excess sludge
production
Hydraulic retention time
Kg·d-1
ESP
h
Kg BOD/kg
MLVSS day
Organic loading rate
Acronym Average
Mixed liquor suspended
solids
Mixed liquor volatile
suspended solids
Temperature in reactor
Percentage of Mixed
liquor volatile
suspended solids
pH in influent
Biological oxygen
demand in influent
Chemical oxygen
demand in influent
Conductivity in influent
Figure 5. Cell viability
Figure 4. Quantification of enzymatic activity
(Matlab)
Enzymatic Activity
mg·L-1
COD_inf
521
371-636
85
µS/cm
Cond_inf
2780
2680-2830
58
Total nitrogen in influent
Total bacteria
mg l-1
TN_inf
69.6
60.1-79.7
6.2
Figure 3. Enzymatic activity
50
5
40
Phosphatase
3
30
2
20
1
10
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Sample
Figure 6. EPS vs glucoronidase and phosphatase
activity
EPS (mg/g VSS)
% Activity
Carbohydrates
% Exoenzimatic Activity
Glucuronidase
4
Proteins
4
40
Glucoronidase
Phosphatase
30
3
20
2
10
1
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Sample
Figure 7. Proteins and carbohydrates vs
glucoronidase and phosphatase activity
Proteins (mg BSA/g SVS)
Carbohydrates (mg Glucose/gVSS)
EPS
0,8
0,6
0,6
0,4
0,4
Tr
0,2
0
OLR
-0,2
-0,4
0,2
Phosphatase
HRT
EPS Carbohydrates
Cell viability
MLSS
Proteins
MLVSS
Glucuronidase
%MLVSS
ESP
-0,6
TP_inf
TN_inf
cond_inf
F2 (14,00 %)
50
F2 (21,10 %)
5
EPS
Proteins
Phosphatase
Cell viability
pH_inf
carbohydrates
Glucuronidase
0
-0,2
-0,4
BOD5_inf
COD_inf
-0,6
-0,8
-1
-1,4 -1,2 -1 -0,8 -0,6 -0,4 -0,2
-0,8
0
0,2 0,4 0,6 0,8
1
1,2 1,4
-1
-0,8
-0,6
-0,4
-0,2
0,2
0,4
0,6
0,8
1
F1 (77,43 %)
F1 (73,60 %)
Figure 8. Biplot Cell viability, EPS,
exoenzimatic activities - Operational
parameters (axes F1 y F2: 94,71%)
0
Figure 9. Biplot Cell viability, EPS,
exoenzimatic activities – Influent pysicochemical parameters (axes F1 y F2: 91,43%)
Results showed that 94% of cells from the MBR were viable and only 6% of the cells were damaged. CCA results revealed the relationships between exoenzymatic activities, cell
viability and EPS with the operational parameters and wastewater influent characteristics. Phosphatase was correlated with high levels of hydraulic retention time (HRT), while βglucuronidase was related with excess sludge production (ESP). Bioreactor temperature (Tr) correlated negatively with β- gucuronidase and phosphatase in agreement with other
works. Results of biplot analysis of influent water showed an inverse relationship between the concentration of total phosphorus (TP) and the phosphatase and β-gucuronidase
exoenzimatic activities. The increase in the availability of C in the influent might have contribute negatively to the phosphatase activity.
CONCLUSIONS
 Studies on the interacting biological, chemical and physical phenomena in MBRs and innovative techniques to determine processes can contribute to a better knowledge of
biofouling and might lead to more robust MBRs operation.
ACKNOWLEDGEMENTS: This work was supported by grant no. ALI CGL2011-29231-C05-01 from the Spanish Ministry of Economy and Competitiveness.