Temporal variation of bacterial population and response to physical and chemical parameters along a petrochemical industry wastewater treatment plant

Abstract

Abstract: The petrochemical industry has played a considerable role in generation and release of waste in the environment. Activated sludge and facultative lagoons are commonly used for domestic and industrial wastewater treatment due to their low-cost and minimal need for operational requirements. Microorganisms present in wastewater treatment plant (WWTP) are responsible for most nutrient removal. In this study, microbiological and physicochemical parameters were used to estimate changes in bacterial community in a petrochemical industrial WWTP. The activated sludge was the place with higher heterotrophic bacterial quantification. Denitrifying bacteria was reduced at least 5.3 times throughout all collections samples. We observe a decrease in the total Kjeldahl nitrogen, oxygen demand and phosphate throughout the WWTP. In this work, we also use Matrix-Assisted Laser Desorption Ionisation-Time-of-Flight Mass Spectrometry (MALDI-TOF MS) for bacteria isolates identification comparing with 16S rDNA sequencing. The MALDI-TOF MS allowed the identification of 93% of the isolates and only 5% show different results from 16S rDNA sequencing showing that the MALDI-TOF MS can be a tool for identifying environmental bacteria. The observation of microbial community dynamics in the WWTP is important in order to understand the functioning of the ecological structure formed in a specific environment.

Key words
bacteria; diversity; wastewater treatment; facultative lagoon; activated sludge

INTRODUCTION

Petrochemical industry processing is responsible for the generation of large volumes of effluents characterized by contaminants such as iron, suspended solids, oils and fats, and hydrocarbons, among other inorganic ions (Wang et al. 2014). According to the National Water Agency from Brazil - ANA (Agencia Nacional das Águas 2013), the industrial sector consumes approximately 18% of the total water intended for population uses. In view of their high toxicity, effective methods of effluent treatment should be investigated in order to protect human health and prevent damage to water resources (Piekutin and Skoczko 2016).

Biological treatment processes in the form of wastewater treatment plants (WWTPs) are the most widely used approach for treating municipal and industrial wastewater due to the high efficiency for organic matter removal and the low operational cost (Ye and Zhang 2013). Studies of seasonal variation are an alternative way to correlate environmental factors with bacterial community and function (Foster et al. 2003, Ju et al. 2014).

Fundamental knowledge regarding the microbial structure of effluent treatment is essential for understanding and improving the process and operation of those systems that use biological treatment. Although gene sequencing is the most frequently used method for identification, it is still an expensive and time-consuming method to be applied to wide populations and, therefore, alternative methods are necessary for the reliable identification of these bacteria, thereby shortening the time needed to achieve this process.

MALDI-TOF MS has gained popularity as a microbial biotyping tool due to its speed, low-cost, simplicity, and applicability for a wide range of microbes. It is becoming an increasingly essential technique for microbial characterization and identification in environmental microbiology and microbial diversity studies (Patil et al. 2015, Kopcakova et al. 2014, Busquets et al. 2014). MALDI-TOF MS has been used to identify environmental bacteria isolated from sludge (Ruelle et al. 2004) and industrial waste disposal (Kopcakova et al. 2014). Compared to other techniques, MALDI-TOF MS showed a high potential for the screening and discrimination of different environmental bacterial (Koubek et al. 2012).

Here, we aimed to evaluate the microbial community present in the wastewater treatment plant of Polo Petroquímico do Sul, Triunfo, Rio Grande do Sul, Brazil which consists of activated sludge and facultative lagoons. To evaluate diversity and changes in the microbial community we analyze microbiological, molecular and physicochemical parameters. Microbiological analyses aimed to estimate the amount of heterotrophic, denitrifying, sulfate-reducing, and nitrate-reducing bacteria in the WWTP and molecular analyses aimed to use MALDI-TOF MS techniques as a tool for identification of cultivable environmental microorganisms.

MATERIALS AND METHODS

SAMPLING SITE

The wastewater treatment plant is located at Triunfo city, Rio Grande do Sul, Brazil and uses activated sludge with mechanical aeration and eight facultative lagoons which operate in series (Fig. 1). The liquid effluent is mainly from industrial activity but also receives domestic wastewater. The wastewater effluent constitutes two streams: organic and inorganic. The organic requires a more complex treatment through physical-chemical and biological processes (biological treatment by prolonged aeration). The inorganic is cleaner and only needs a final polish. Organic liquid effluents are treated in three stages: in the primary stage, floating and coarse materials, separated oil and sand are eliminated and the liquid stream is equalized; in the secondary stage, the organic matter is eliminated through the biological process by activated sludge in the aeration tanks. Thereafter, this liquid stream passes through decantation. In the tertiary step, the remaining solids are removed by filtration. Finally, the treated effluent joins the inorganic and is conducted to the stabilization lagoons for final polishing. The effluent is finally disposed on the ground by sprinklers and perforated tubing. The excess of sludge, generated by the process, is disposed in the soil at a site called “Mud Farm”.

Figure 1
Wastewater treatment plant pathway. () effluent pathway. The gray boxes represent emergency tanks for effluent and activated sludge overloads. *Collection points.

For this study, the active sludge tank (AS) and four of the facultative lagoons (FL1, FL3, FL6, and FL8) were selected for microbial assays. For the physicochemical parameters, the sampling sites were the effluent input (API oil/water separators) and the effluent output (Fig. 1). The main characteristics of the sampling sites are summarised in Table I.

TABLE I
Sampling sites points and their main characteristics.

Sampling sites/Coordinates	AS	FL1	FL3	FL6	FL8
Size (m3)	50.000	150.000	406.000	269.000	103.000
Treatment	activated sludge	facultative lagoon	facultative lagoon	facultative lagoon	facultative lagoon
Hydraulic retention time	78 hours	6 days	21 days	14 days	5 days

• AS: activated sludge tank. FL1, FL3, FL6, and FL8: facultative lagoons.

Sampling was performed in May (collection 1 - C1), August (collection 2 - C2), and December (collection 3 - C3) 2015, and March 2016 (collection 4 - C4). At each facultative lagoon (FL1, FL3, FL6, and FL8), 250 mL of water was collected from eight sampling points (1-8) of superficial water using a collection bucket with a three meters rope. For each lagoon, a pool of sampling was performed with a total volume of two liters of water per lagoon. At the activated sludge tank (AS), one liter of water was collected directly from the aeration tank using a collection bucket. Transportation of the samples to the laboratory was performed in cooler boxes.

PHYSICOCHEMICAL CHARACTERIZATION OF WATER

The physicochemical parameters were analysed according to the Standard Methods for the Examination of Water and Wastewater (SMEWW): total organic carbon (TOC) (SMEWW 22^nd 5310); chemical oxygen demand (COD) (SMEWW 22^nd 5220 B); total phosphate/orthophosphate (SMEWW 22^nd 4500); ammoniacal nitrogen (SMEWW 22^nd 4500-NH3 B, C and F); total Kjeldahl nitrogen (NTK) (SMEWW 22^nd 4500-NH3 B, C and F), dissolved oxygen (DO) (SMEWW 22^nd 4500-O-G); dissolved solids (SD) (SMEWW 22^nd 2540 C); suspended solids (SS) (SMEWW 22^nd 2540 D); and total suspended solids (TSS) (SMEWW 22^nd 2540 B). Temperature and pH were measured locally.

MICROBIOLOGICAL ANALYSIS

Samples were prepared according to the Standard Methods for the Examination of Water and Wastewater - APHA (2005).

BACTERIAL QUANTIFICATION AND ISOLATION

Mesophilic heterotrophic bacteria were determined on Plate Count Agar (PCA: tryptone 5.0 g/L, yeast extract 2.5 g/L, dextrose 1.0 g/L, agar 15 g/L) medium by the pour plate method. Samples were diluted in saline sodium chloride 0.9% solution. For the sludge samples, dilutions of 10-² to 10-⁶ were performed, while the dilutions were 10-¹ to 10-³ for the lagoon samples. After, 0.1 mL of each dilution was inoculated in plates containing de PCA medium. After, the plates were incubated at 25°C for 24–72 hours. Bacterial colonies were chosen randomly for genomic DNA identification using MALDI-TOF MS and 16S rDNA sequence analysis.

NITRATE-REDUCING BACTERIA

The number of nitrate-reducing bacteria was estimated using the multiple-tube method describe by APHA (2005). To determine the most probable number (MPN) of nitrate-reducing bacteria, a series of 15 tubes distributed 5 out of 5, containing each 10 mL of nitrate broth culture medium (peptic digest of animal tissue 5.0 g/L, meat extract 3.0 g/L potassium nitrate 1.0 g/L, sodium chloride 30 g/L). In the first 5 tubes, 10 mL of the water sample to be examined was inoculated in each tube (dilution 1:1). In the remaining 10 tubes, 5 tubes were inoculated with 1 mL of the sample (dilution 1:10) and the last 5 tubes with 0.1 mL of the sample (dilution 1:100). The tubes were incubated at 30°C for 18-24 hours. After the incubation period, five drops of each reagent A (8 g of sulphanilic acid in one liter of acetic acid) and B (5 g of alpha-naphthylamine in one-liter acetic acid) were added in each tube. The appearance of red or pink color indicates the reduction of nitrate. The estimation of bacteria nitrate reducers was obtained through the Most Probable Number tables (MPN) (APHA 2005).

DENITRIFYING BACTERIA

The determination of the MPN of denitrifying bacteria was performed through the multiple tubes technique using 8.0 g/L of nutrient medium (peptone 5.0 g/L, sodium chloride 5.0 g/L, HM peptone B# 1.5 g/L, yeast extract 1.5 g/L, agar 15 g/L) and 0.428 g/L of sodium nitrate (NaNO₃ - Himidea), accordingly Mendonça (2002). In the first 5 tubes 10 mL of the water sample to be examined was inoculated in each tube (dilution 1:1). In the remaining 10 tubes, 5 were inoculated with 1 mL of the sample (dilution 1:10) and 5 tubes inoculated with 0.1 mL of the sample (dilution 1:100). The tubes were incubated at 30°C for 15 days. After the tubes incubation period, drops of a solution of diphenylamine 2.0 g [(C₆H5)₂ NH] and 1 liter of concentrated sulfuric acid (H₂SO₄), was added to the tubes to check for the presence or absence of nitrate. The absence of staining after the reaction indicates the absence of nitrate and the possible presence of denitrifying bacteria (positive result), and blue staining indicates remaining nitrate, i.e. no denitrification (negative result). The estimation of denitrifying bacteria was obtained using the MPN tables (APHA 2005).

SULPHATE-REDUCING BACTERIA

The number of sulphate-reducing bacteria was determined by the multiple-tube method using SIM medium (tryptone 20 g/L, peptone 6.1g/L, peptonized iron 0.2 g/L, sodium thiosulphate 0.2 g/L, agar 3.0 g/L) (Atlas 2005). In the first 5 tubes, 10 mL of the water 180 sample to be examined was inoculated in each tube (dilution 1:1). In the remaining 10 181 tubes, 5 tubes were inoculated with 1 mL of the sample (dilution 1:10) and in the last 5 182 tubes 0.1 mL of the sample into each tube (dilution 1:100). The tubes were incubated at 30°C for 24-48 hours. The number of sulphate-reducing bacteria was estimated using an MPN table (APHA 2005).

DNA EXTRACTION

DNA was extracted following the instructions of the Wizard® Genomic DNA Purification Kit Protocol (Promega), with modifications. Cells were grown in brain heart infusion broth medium (brain heart infusion powder 17.5 g/L; proteose peptone 10g/L; dextrose 2g/L; sodium chloride 5g/L; disodium phosphate 2.5 g/L) at 25°C for 24–48 hours under agitation of 100 rpm. After that, cultures were centrifuged in an Eppendorf centrifuge (MiniSpin, Eppendorf AG, Hamburg, Germany) at 10.000 rpm and cells were resuspended in 50 mM EDTA (Kasvi), pH 8.0, with lysozyme (Merk), at a final concentration of 1 mg/mL and incubated in a water bath at 37°C for 45 min. Purity and concentration of the DNA were analyzed using the Nanodrop Spectrophotometer (K23-0002, KASVI).

AMPLIFICATION, SEQUENCING, AND ANALYSIS OF 16S rDNA GENES

The isolates were identified by sequencing of the 16S rDNA region using the primers described in Table II (Edwards et al. 1989, Stackbrandt and Liesack 1993). PCR was performed in an Life Technology - Applied Biosystems™ ProFlex™ PCR System in 25 µL reaction volume containing 1x PCR buffer (Invitrogen), 1.5 mM MgCl₂ (Invitrogen), 0.3 mM dNTPs (Invitrogen), 0.2 µM of each primer (IDT), 1U de Taq polymerase (Invitrogen), 1µL of bacterial DNA (50ng/µL). The PCR reaction was as follow: initial denaturation at 94°C for 5 min, followed by 35 cycles of 94°C for 1 min, 59°C for 1 min, 72°C for 2 min, final extension at 72°C for 10 min, accordingly to Garbeva et al. (2003). The amplified products (~1500 bp) were sequenced by Ludwig Biotec using an ABI-Prism 3500 Genetic Analyser (Applied Biosystems). The sequences obtained were analyzed and aligned with sequences available in the GenBank database using BLAST software (http://www.ncbi.nlm.nih.gov).

TABLE II
Primers used for the sequence of 16S rDNA of isolates.

Name	Sequence (5’-3’)	Position a	References
BacPaeF (pAb)	AGAGTTTGATCCTGGCTCAG	8–28	Stackebrandt and Liesack (1993)
Bac1542R (1542Rb)	AGAAAGGAGGTGATCCAGCC	1525–1542	Edwards et al. (1989)

• ^a position in E. coli, ^b reference citation.

MALDI-TOF MS ANALYSIS

Maldi-Tof MS (mdt) sample preparation

For MALDI-TOF MS analysis, samples were grown in TSA medium (Himedia) for 24 h. The extraction followed by the manufacturer MALDI Biotyper 4.0 (Bruker Daltonics, Germany). Bacteria were resuspended in 300 µL of MiliQ water and 900 µL of absolute ethanol was added. The mixture was centrifuged at 10.000 rpm and the supernatant discarded. Residual ethanol was removed, and the pellet allowed drying at room temperature. Subsequently, 50 µL of formic acid (70%) (Merk) was added and mixed with the pellet by vortexing. Next, 50 µL of acetonitrile (100%) (Merk) was added and mixed. The solution was centrifuged at 13.000 rpm and 1 µL of the supernatant was spotted on the MALDI target plate in two replicates. After drying, 1 µL of the matrix solution (a-cyano- 4-hydroxycinnamic acid (HCCA, Bruker Daltonics, Germany) in 50% acetonitrile and 2.5% trifluoroacetic acid) was added to each spot and allowed to air dry. Samples were then processed in the MALDI-TOF MS spectrometer (MALDI Biotyper 4.0; Bruker Daltonics) with flex control software. Each spectrum was obtained by averaging 500 laser shots acquired in the automatic mode at the minimum laser power necessary for ionization of the samples. The spectra were analyzed in a m/z ratio of 2–20 kDa. Data analysis was performed using the MALDI Biotyper Real-time Classification (MBT RTC) software (Bruker®). MALDI-TOF MS spectrometer identifications were classified using score values proposed by the manufacturer. The score cut-offs recommended by the manufacturer (≥ 2.000 for species-level, 1.700 to 1.999 for genus level and <1.700 no reliable identification were considered for identification.

STATISTICAL ANALYSES

Statistical analyses Student’s T-test and ANOVA were performed by Past software (Hammer et al. 2001) were applied in the quantification of mesophilic heterotrophic bacteria between facultative lagoons at each period of sample collection (C1, C2, C3 and C4).

RESULTS AND DISCUSSION

PHYSICOCHEMICAL PARAMETERS AND MICROBIOLOGICAL ANALYSIS

Heterotrophic bacteria are microorganisms that degrade organic matter, mainly carbonaceous material, resulting in the reduction of BOD (biochemical demand of oxygen) and COD from the system. The COD is related to the amount of oxygen that the aerobic heterotrophic bacteria need to oxidize the organic matter present in the environment. In this study, the highest values for COD were observed in the collection C1 (399.5 mg/L) and C3 (637 mg/L) (Table III). These collections also showed the higher quantification for heterotrophic bacteria 1.64 × 10⁶ CFU/mL and 6.40× 10⁵CFU/mL respectively (Table IV). A decrease in the COD was observed together with an increase in oxygenation during the effluent treatment. The metabolic activity of heterotrophic bacteria is closely linked to COD since the degradation of organic matter results in the reduction of the COD from the system. Related to this, throughout the treatment system, DO increased in all performed collections together with a decrease of heterotrophic bacterial counts. Accordingly to Von Sperling (2002), the concentration of microorganisms’ biomass is limited by the amount of substrate available, so it is expected a reduction of the microbial count when the organic matter, that is easily degradable, is no longer available.

TABLE III
Physicochemical parameters observed in samples collected from the Wastewater Treatment Plant.

Site	C1a	C1b	C2a	C2b	C3a	C3b	C4a	C4b
TOC	115.0	23.82	20	4.6	100	22	91	17
COD	399.5	92.2	13.33	57.2	637	255.6	83.6	25
DO	2.1	6.6	2.02	8.6	0.7	7.0	0.5	8.9
TSS	1286	1273	788	730	1477	767	1625	1092
SS	48	32	22	12	56	37	53	71
SD	1241	1227	678	708	1428	733	1508	1016
FFO	12.90	2.26	12.84	2.14	10.52	2.15	18.2	3.9
FFa	12.74	1.91	9.23	1. 15	10.41	0.4	11.1	1.61
NTK	67. 67	21.85	13.08	4.36	25.54	4.09	27.53	4.11
AN	8.74	20	4.32	1.16	11.49	1.53	8.44	0.42

• mg.L-¹. (a) input; (b) output. C1 (May 2015); C2 (August 2015); C3 (December 2015); C4 (March 2016).
  Total Organic Carbon (TOC); Chemical Oxygen Demand (COD); Dissolved Oxygen (DO); Total suspended solids (TSS);
  Solids suspended (SS); Solid Dissolved (SD); Total phosphorus (FFO); Phosphate (FFA); Total Kjeldahl Nitrogen (NTK);
  Ammoniacal Nitrogen (AN).

TABLE IV
Quantification of mesophilic heterotrophic bacteria in active sludge and facultative lagoons.

	AS	FL1	FL3	FL6	FL8
C1	1.48E+06	1.19E+05*	1.81E+04	1.15E+04	1.62E+04
C2	2.12E+05	1.17E+05	1.15E+05	1.35E+05	1.80E+04
C3	2.77E+05	2.00E+05*	1.51E+05	1.08E+04	1.29E+04
C4	1.09E+05	2.23E+04*	1.91E+04	1.28E+03	1.36E+03

• Unit: CFU mL-¹ . *Tukey test (p=0.05) applied between facultative lagoons at each period of sample collection (C1, C2, C3 and C4) . Underline numbers are the lowest counts. (AS- active sludge; FL1- lagoon 1; FL3 – lagoon 3; FL6 – lagoon 6; FL8 – lagoon 8; C1 – collection 1; C2 – collection 2; C3 – collection 3; C4 – collection 4).

Studies of biological processes involved in the bioconversion of pollutants present in the wastewater allow the observation of the relationship between the rate of oxygen consumption and the amount of substrate added (Svensson et al. 2015).

According to Van Haandel and Marais (1999), for heterotrophs, the proportion ratio for this is 1:1, that is, 1 unit of biodegradable soluble organic matter that will be oxidized will require 1 unit of oxygen for this oxidation. Experimental results indicate that the production of bacterial mass in oxidative metabolism is in the range of 0.35 g to 0.52 g of suspended volatile solids (VSS) per gram of metabolized COD, with 0.45 g of VSS per gram of metabolized COD being frequently used as mean value (Van Haandel and Marais 1999). That is, only one-third (33%) of the organic matter tends to be directed to catabolic metabolism; the remainder (67%) leading to anabolism. Such a ratio between the microbial mass synthesized and the metabolized COD mass is called the cellular synthesis coefficient, growth coefficient or the coefficient of sludge yield.

In the treatment system studied here, there are two processes: activated sludge followed by stabilization lagoons. The second stage of the treatment might have contributed to increasing dissolved oxygen in the effluent due to the presence of cyanobacteria and algae in stabilization lagoons. In facultative lagoons, the biological process occurs with the coexistence of cyanobacteria and heterotrophic bacteria. Oxygen released by cyanobacteria through photosynthesis is consumed by heterotrophic bacteria that degrade organic matter, and these, in turn, release the carbonic gas that is used during cyanobacterial metabolism (Uehara and Vidal 1989, Morales et al. 2014). This positive balance of oxygen allows the occurrence of aerobic degradation processes of carbonaceous matter.

During the sampling of the stabilization lagoons, the mean pH was 8.5, and the temperature ranged between 24°C and 29°C, which favor the presence of cyanobacteria. According to Calijuri et al. (2006), environmental conditions for cyanobacteria are freshwater environments, a pH of 6.0 to 9.0, and temperatures varying between 15 and 30ºC, with high concentrations of nutrients.

Among the physical-chemical parameters that must be determined in a waste treatment system are the organic and inorganic nutrients. Phosphorus and nitrogen are essential nutrients for the construction of the protein system. Data about their quantity are needed to evaluate the potential of a biological treatment, such as effluent treatment (Metcalf and Eddy 2003). On the other hand, the excess of nutrients in an aquatic environment, particularly nitrogen and phosphorus, can lead to the excessive growth of photosynthetic organisms causing eutrophication. Effluents from chemical industries and domestic sewage tend to have high concentrations of phosphorus (Von Sperling 2002) and, according to the Water Pollution Control Federation (1991), the effluents from the production fertilizers, protein product processing, petroleum refineries and chemical industries contain high concentrations of nitrogen.

Biological wastewater treatment plants based in activated sludge have been in charge of treating both domestic and industrial wastes and have constituted an essential instrument in environmental protection due to the removal of organic matter, suspended soils, nutrients (N, P) and pathogens (Bitton 2005).

Several populations of fast-growing ordinary heterotrophic organisms have been identified in active sludge. Genera like Pseudomonas, Flavobacterium and Acinetobacter (Amorim et al. 2014) are supposed to form extracellular polymeric substances matrices that embed microcolonies of slower growing heterotrophic populations of slow-growing polyphosphate- (PAOs), glycogen-accumulating organisms (GAOs), and autotrophic populations of nitrifiers and anammox bacteria (Weissbrodt et al. 2013, Winkler et al. 2012).

Bacillus sp. can metabolize a variety of compounds including carbohydrates, proteins, peptides and amino acids for growth and energy. Some of the major products produced from carbon sources such as sucrose or glucose during anaerobic respiration include L-lactate, acetate, formate, succinate, ethanol, and carbon dioxide (Mols et al. 2007).

Alcaligenes sp. are well-known aerobic denitrifiers genera involved denitrification function in active sludge (Tian et al. 2015) and also are related to biodegradation of volatile organic sulfur compounds (Yiming et al. 2016)

Some microorganisms are commonly found in the gastrointestinal tract of humans such as Enterobacteriaceae (Lawrence 2014) and Enterococcus sp. (Nachtigall et al. 2013). The input of domestic sewage together with industrial effluent may have caused the presence of these microorganisms.

In this study, the correlation between phosphate and DO was -0.94, showing a high relation between these two parameters for this type of treatment. We observed a reduction in the phosphorus and total nitrogen values and an increase in DO during the treatment of the effluent. Algae metabolism in the facultative lagoon may have contributed to ammoniacal nitrogen consumption since these organisms use ammonia as a nutritional source for their cellular metabolism (Valero et al. 2010). Thus, nitrifying bacteria might also have helped in the nitrogen reduction, since in the nitrification phase, under aerobic conditions, the ammonium is oxidized in two phases: in the first, ammonia-oxidizing bacteria (AOB) oxidate ammonium to nitrite, and subsequently, nitrite-oxidizing bacteria (NOB) oxidate nitrite to nitrate (Chang et al. 2011, Muñoz et al. 2009).

Nitrifying bacteria are sensitive to different environmental and operational factors that can directly influence their metabolism in ways that interfere with their growth. The main parameters of this process are temperature, pH, dissolved oxygen content, the presence of ammoniacal nitrogen, and toxic or inhibitory substances (Grunditz and Dalhammar 2001). In this study, a reduction of nitrifying bacteria from 1600 MPN/mL to 220 MPN/mL was observed throughout the treatment plant (Table V). The highest total quantification of nitrifiers occurred at collection C1. The coexistence of nitrifying and heterotrophic bacteria in the same ecosystem leads, in most cases, to compete for the available oxygen. When heterotrophic predominance occurs, nitrogen removal rates are directly affected. This heterotrophic and nitrifying ratio may have interfered with the total nitrogen removal index since, among the samples collected, C1 presented the second-lowest rate of removal. Collection C3 and C4 correspond to the samples with the highest rates of removal of ammoniacal nitrogen (86.86% and 95.02%) and total nitrogen (83.99% and 85.07%), and also presented the lowest quantification of heterotrophic bacteria. Another factor that may have influenced the rate of nitrogen removal is related to pH. In this study, we recorded pH values of ≅ 6.6 (activated sludge) and ≅ 8.0 (mean of stabilization lagoons) for samples collection C1 and C2 respectively and ≅ 7.1 (activated sludge) and ≅ 9.0 (mean of the stabilization lagoons) for C3 and C4 respectively. The nitrification rate has an optimum pH range of 7.2 to 8.0; considerably decreasing at pH below 6.0. According to Metcalf and Eddy (2003), for pH values of the effluent close to neutrality (6.0 to 8.0), almost all ammoniacal nitrogen (more than 90%) is in the form of ammonium ion (NH₄+), favoring the nitrification process. In this sense, Jeschke et al. (2013) stated that the accumulation of ammonia observed in natural environments or treatment systems is due to the inhibition of the process caused by acidic pH values in the reaction medium.

TABLE V
Quantification of sulfate-reducing, nitrifying, and denitrifying bacteria collected from active sludge and facultative lagoons.bacteriabacteria

Test	Site	C1	C2	C3	C4
Sulfate-reducing	AS to FL8	>1600	>1600	>1600	>1600
Nitrifyingbacteria	AS	>1600	>1600	>1600	>1600
	FL1	>1600	280	1600	900
	FL3	>1600	220	900	280
	FL6	>1600	2	280	220
	FL8	>1600	2	220	2
Denitrifyingbacteria	AS	1600	1600	1600	1600
	FL1	170	350	350	1600
	FL3	170	280	350	300
	FL6	170	220	210	300
	FL8	170	140	110	300

• MPN.100 mL-¹. AS- active sludge; FL1- lagoon 1; FL3 – lagoon 3; FL6 – lagoon 6; FL8 – lagoon 8; C1 – collection 1; C2 – collection 2; C3 – collection 3; C4 – collection 4.

Another important factor to be considered is the concentration of dissolved oxygen in the effluent. In the, C3 and C4 samples, the values observed were 0.7 and 0.5 mg/L respectively, close to the critical concentration rate of 0.2 mg/L recommended by Von Sperling (2002) for the nitrification process to occur. On the other hand, the concentration observed at collection C1 was 2.1 mg/L. Higher values should be maintained in the aeration tank so that, in all regions, the concentration of dissolved oxygen is higher than the critical concentration. According to Hidaka et al. (2002), oxygen is one of the most relevant parameters in nitrification, since concentrations lower than 2 mg O₂/L may partially or totally limit the activity of nitrifying bacteria.

In stabilization lagoons, nitrogen removal occurs mainly through algae absorption, settling, and soil adsorption (Pirsaheb et al. 2014). Pirsaheb et al. (2014) and Soares et al. (1996) have shown that stabilization lagoons systems have low nitrogen removal efficiency and require complementary processes to improve the final effluent.

Traditionally, denitrification occurs only under anaerobic or anoxic conditions, thus restricting the occurrence of denitrification for nitrification in different zones (Zhu et al. 2008). However, new aerobic denitrifying bacteria have been reported (Guo et al. 2013) compared to conventional nitrogen removal systems. These bacteria can oxidize ammonia to nitrite and simultaneously reduce nitrite and nitrate to gaseous nitrogen through aerobic denitrification (Guo et al. 2013). In the present work, the presence of denitrifying bacteria was observed in the activated sludge and along the stabilization lagoons. In the activated sludge system, the presence of dead zones in which aeration is not sufficient and the formation of micro niches with a low concentration of oxygen may have favored the metabolism of the denitrifying bacteria. We observe no difference along the MPN counting for facultative lagoon in C1 along all the collections. This result may have been caused by insufficient dilution of the sample.

During the collection, DO input values were between 0.5 and 2.1 mg/L and the low oxygen concentration may have favored the conversion of nitrate to gaseous nitrogen, nitrous oxide or both. Denitrification processes have also been observed in biofilms (Kadlec and Knigth 1996) and in systems where there is a low concentration of DO. Tallec et al. (2008) observed the occurrence of denitrification under low oxygen concentration in a municipal sewage treatment system. In this study, there was a decrease in the denitrifiers counts during the serial treatment of the stabilization lagoons. This result can be related to the increase of oxygen concentration resulting from the photosynthetic process of the algae present in this environment, thus disfavoring the denitrification process.

As nitrification, denitrification has as factors influencing the process: the dissolved oxygen concentration, temperature, pH, and the quality and quantity of carbon sources (Tonetti et al. 2013). According to Metcalf and Eddy (2003) and Surampalli et al. (1997), the ideal temperature range for the denitrification process is between 10°C and 30°C, and denitrification increases with temperature elevation until the optimum value of 40°C (Van Haandel and Marais 1999). In relation to pH the optimum range shows a variation according to different authors (Henze et al. 1994, Metcalf and Eddy 2003, Glass and Silverstein 1998) and when the pH is less than 6.0 there is a great reduction of denitrifying activity leading to the accumulation of nitrite or nitrate in the system.

An essential goal of wastewater treatment of domestic and industrial waste is the targeted removal of nitrogen, which is present at high levels for bacterial growth requirements with the available carbon. Alternating nitrification and denitrification is the overall process responsible for nitrogen removal from biological nutrient removal in activated sludge wastewater treatment plants (McIlroy et. al. 2016).

Studies in WWTPs suggested a higher diversity of active denitrifiers, including uncharacterized members of the Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria (Osaka et al. 2006, Hagman et al. 2008, Morgan-Sagastume et al. 2008).

Certain groups of heterotrophic nitrification–aerobic denitrification microorganisms, such as Alcaligenes faecalis, Pseudomonas stutzeri and Bacillus sp. have been isolated from soils and wastewater treatment systems (Joo et al. 2005, Kim et al. 2008, Yang et al. 2011). These heterotrophic nitrifying–aerobic denitrifying microorganisms capable of nitrification and denitrification simultaneously under aerobic presented advantages as applied for nitrogen removal: (i) methodology simplicity, nitrification, and denitrification can take place simultaneously; (ii) less acclimation problems; (iii) lesser buffer quantity needed because alkalinity generated during denitrification can partly compensate for the alkalinity consumption in nitrification (Zhao et al. 2010, Yao et al. 2013).

In the present work, the presence of sulfate reducing bacteria (SRB) was observed in all points of collection. Van den Brand et al. (2015) detected the presence of SRB in nine activated sludge wastewater treatment plants by analyzing the population diversity of SBR and the effect on SBR metabolism at prolonged oxygen exposure. The authors also observed a seasonal (winter and summer) impact on the SRB population and the most common and dominant species were Desulfobacter postgatei, Desulfovibrio desulfuricans, and Desulfovibrio intestinalis. The occurrence of SBR in WWT has also been reported by Manz et al. (1998) using in situ probes in different stages of the activated sludge process, where the presence of Desulfobacteriaceae and Desulfovibrionaceae was observed in anaerobic, anoxic, and aerobic zones. Van den Brand et al. (2015) advocates that SRB grow in granules in order to protect them from exposure to oxygen, so they are naturally present in the aerobic WWTP due to the formation of these elements.

PERFORMANCE OF MALDI-TOF MS COMPARED TO 16S rDNA SEQUENCE

The MALDI-TOF MS technique (MDT) allowed the identification of 68 (93%) of the 73 isolates at the genus or species level. Considering the total of the isolates identified (n=68), 19% (n=14) were identified at the species level (score ≥ 2.000) and 74% (n=54) at the genus level (score of 1.700–1.999). Five (7%) of the isolates scored below 1.700 no reliable identification (NRI) and could not be identified by the MDT due to the absence of peaks correlated to the species or genus in the Biotyper 2.0 database.

Identification using 16S rDNA allowed the identification of 41% (n=30) isolates at genus level and 59% (n=43) at the species level (similarity ≥ 97). No divergence was found between the results obtained by the two techniques, MALDI-TOF MS and 16S rDNA, for the isolates that were possible to perform identification at the genus or species level.

In some cases, the MDT method was able to identify a high taxonomic level than 16S rDNA, isolate 26 (Microbacterium oxidans), isolate 38 (Alcaligenes faecalis) and isolates 7, 29, 72 (Acinetobacter pitii.) the MDT method was able to identify samples at the species level while 16S rDNA was only at the genus level (Table VI).

The MALDI-TOF MS is becoming increasingly essential for the characterization and identification of microorganisms in environmental microbiology and microbial diversity studies (Kopcakova et al. 2014, Busquets et al. 2014).

TABLE VI
Isolates identification using MALDI-TOF MS and 16SrDNA.

MALDI-TOF MS				16S rDNA
Collection	Isolate	Identification	Score	Identification	Similarity	GenBak
C1	7	Acinetobacter pittii	2.3	Acinetobacter sp.	98%	KX639781.1
C1	14	Acinetobacter pittii	2.2	Acinetobacter calcoaceticus	96%	KR262850.1
C1	9	Alcaligenes faecalis	2.0	Alcaligenes sp.	98%	KY476152.1
C1	19	Arthrobacter creatinolyticus	1.8	Arthrobacter creatinolyticus	96%	KC456596.1
C1	12	Bacillus cereus	2.3	Bacillus cereus	98%	JQ660597.1
C1	18	Bacillus cereus	2.0	Bacillus sp.	98%	KY476301.1
C1	1A	Bacillus pumilus	1.8	Bacillus pumilus	99%	HQ202554.1
C1	2	Bacillus pumilus	2.0	Bacillu spumilus	99%	KR152300.1
C1	17	Bacillus pumilus	2.0	Bacillus pumilus	99%	JX083967.1
C1	15	Bacillus subtilis	2.1	Bacillus sp.	98%	KF746888
C1	8	Lelliottia amnigena	1.7	Lelliottia amnigena	95%	MF581413.1
C1	11	Microbacterium hydrocarbonoxydans	1.9	Microbacterium phyllosphaerae	98%	FJ613556.1
C1	5	Microbacterium oxydans	2.2	Microbacterium oxydans	97%	KF358264.1
C1	10	Microbacterium oxidans	2.0	Microbacterium sp.	96%	JQ012995.1
C1	1	Micrococcus luteus	2.3	Micrococcus luteus	97%	KY007582.1
C1	6	Providencia rettgeri	2.5	Providencia rettgeri	98%	CP017671
C1	3	NRI	1.3	Myroides phaeus	97%	NR117475.1
C1	4	NRI	1.3	Myroides phaeus	97%	NR117475.1
C1	13	NRI	1.3	Gordonia amicalis	97%	KU904410.1
C1	16	NRI	1.3	Myroides phaeus	97%	NR117475
C2	29	Acinetobacter pittii	2.3	Acinetobacter sp.	98%	KX639781.1
C2	38	Alcaligenes faecalis	2.3	Alcaligenes sp.	98%	KF735809.1
C2	28	Aneurinibacillus aneurinilyticus	2.8	Aneurinibacillus aneurinilyticus	98%	KY744681.1
C2	41	Aneurinibacillus aneurinilyticus	1.9	Aneurinibacillus aneurinilyticus	98%	JX290554.1
C2	31	Bacillus cereus	2.2	Bacillus cereus	98%	JQ660597.1
C2	25	Bacillus pumilus	2.0	Bacillus pumilus	99%	JX083967.1
C2	35	Bacillus pumilus	1.9	Bacillu sp.	98%	KJ184909.1
C2	43	Bacillus pumilus	1.7	Bacillus pumilus	98%	KX832718
C2	33	Bacillus safensis	1.7	Bacillus sp.	99%	KY625521
C2	20	Enterobacter asburiae	2.0	Enterobacter asburiae	97%	GU003846.1
C2	22	Microbacterium oxidans	2.2	Microbacterium sp.	96%	JQ012995.1
C2	26	Microbacterium oxidans	2.3	Microbacterium sp.	96%	JQ012995.1
C2	24	Sthapylococcus warneri	2.1	Staphylococcus pasteuri	97%	KX387355.1
C2	27	Pseudomonas mendocina	1.9	Pseudomonas sp.	98%	KC294058.1
C2	32	Pseudomonas mendocina	2.0	Pseudomonas sp.	98%	KC294058.1
C2	39	Pseudomonas mendocina	2.1	Pseudomonas sp.	98%	KC294058.1
C2	23	Pseudomonas oleovorans	2.0	Pseudomonas sp.	98%	KC294058.1
C2	37	NRI	1.3	Acinetobacter calcoaceticus	98%	KR262850.1
C3	45	Bacillus sp.	1.7	Bacillus sp.	98%	KU986673
C3	52	Bacillus altitudinis	1.8	Bacillus sp.	99%	KU644503.1
C3	56	Brevibacillus agri	2.0	Brevibacillus agri	98%	KY818990
C3	49	Bacillus cereus	2.2	Bacillus cereus	98%	KU573976.1
C3	51	Bacillus marisflavi	1.8	Bacillus sp.	99%	KF746888
C3	48	Bacillus pumilus	1.7	Bacillus sp.	98%	KJ184909.1
C3	58	Bacillus pumilus	1.8	Bacillus pumilus	99%	JX083967.1
C3	59	Bacillus pumilus	1.9	Bacillus pumilus	98%	KX832718
C3	61	Bacillus pumilus	1.8	Bacillus sp.	97%	GU377066
C3	65	Bacillus pumilus	1.8	Bacillus pumilus	99%	JX083967.1
C3	44	Brevibacillus borstelensis	2.3	Brevibacillus sp.	98%	KP795806.1
C3	60	Enterobacter asburiae	2.3	Enterobacter asburiae	97%	GU003846.1
C3	63	Enterococcus faecium	2.3	Enterococcus faecium	99%	KM495940
C3	62	Lysinibacillus boronitolerans	2.2	Lysinibacillus sp.	99%	KY576798
C3	64	Lysinibacillus boronitolerans	2.0	Lysinibacillus sphaericus	98%	KX908032.1
C3	50	Lysinibacillus fusiformis	1.8	Lysinibacillus sphaericus	98%	CP015224.1
C3	57	Lysinibacillus fusiformis	2.2	Lysinibacillus sphaericus	98%	CP015224
C3	46	Pantoea ananatis	2.1	Pantoea ananatis	98%	KM884897.1
C3	47	Pseudomonas putida	1.7	Pseudomonas putida	99%	KT380508
C4	75	Alcaligenes faecalis	2.1	Alcaligenes faecalis	96%	KT013264.1
C4	72	Acinetobacter pittii	2.3	Acinetobacter sp.	98%	CR543861.1
C4	70	Bacillus cereus	1.9	Bacillus cereus	99%	JQ660597.1
C4	66	Bacillus cereus	2.1	Bacillus sp.	99%	KY379943.1
C4	67	Bacillus pumilus	1.9	Bacillus pumilus	99%	JX083967.1
C4	68	Bacillus pumilus	1.8	Bacillus sp.	98%	KJ184909.1
C4	69	Bacillus pumilus	1.7	Bacillus pumilus	99%	JX083967.1
C4	71	Bacillus pumilus	2.0	Bacillus pumilus	99%	JX083967.1
C4	81	Bacillus pumilus	1.8	Bacillus pumilus	99%	HQ202554.1
C4	77	Bacillus safensis	1.7	Bacillus safensis	99%	KT758564.1
C4	79	Bacillus safensis	1.8	Bacillus safensis	99%	KT758564.1
C4	73	Kosakonia cowanii	2.0	Kosakonia cowanii	95%	CP019447.1
C4	74	Lysinibacillus boronitolerans	1.9	Lysinibacilluss phaericus	98%	KX908032.1
C4	78	Lysinibacillus boronitolerans	2.3	Lysinibacillus sp.	98%	KY576798
C4	76	Micrococcus luteus	2.0	Micrococcus luteus	98%	KY007582.1
C4	80	Providencia rettgeri	2.5	Providencia rettgeri	99%	MF100122.1
C4	82	Providencia rettgeri	1.8	Providencia rettgeri	98%	KU052647.1

Kopcakova et al. (2014) identified less than 20% of the bacterial isolates from industrial waste at the species level, and 43% of the isolates were not identified by the MDT technique. Urwyler and Glaubitz (2016) evaluated the performance MALDI-TOF MS (Bruker and Vitek) phenotyping on industrial isolates in comparison with biochemical-based phenotyping and genotyping. At the genus level, both MALDI-TOF MS based systems showed the lowest number of false (4%) identifications and approximately 60% correct identifications.

On the other hand, the limited number of spectra in the database of MALDI-TOF MS includes few varieties of non-clinical origin and presents a low percentage of identification (43–65%) for microorganisms isolated from soil, water, and other environments (Rahi et al. 2016). In addition, there is a need to increase the existing MALDI-TOF MS databases with more diverse microorganism spectra. Isolates identified as Myroides phaeus (3, 4 and 16) and Gordonia amicalis (13) by 16S rDNA sequencing have no spectra in the MALDI-TOF MS database. The development of an open, online and universal access to databases would allow in-house data exchange between research groups. Therefore, it is expected that the availability of updated and robust databases, together with optimized methods and protocols, will allow the extensive use of this technique in the field of environmental microbiology, microbial ecology, and taxonomy.

Other factors that affect the identification by MDT are the inability of the spectra to differentiate between very similar species (Bizzini et al. 2011) and the fact that some taxonomic groups are still an identification challenge for both MD and 16S rDNA, such as the Bacillus cereus complex, Burkholderia cepacia complex, Escherichia coli, and Shigella group, Enterobacter cloacae complex, and Pseudomonas putida complex ( Pavlovic et al. 2012, Khot and Fisher 2013, Almuzara et al. 2015). Some Bacillus isolates (15, 18, 22, 35, 48, 51, 52, 61, 66 and 68) had species-level identification only using the MDT technique. The identification of species in the genus Bacillusby classical methods is often difficult, due to similarities between closely related species that share a pattern of morphological, biochemical, and genetic characteristics. These unusual similarities are particularly evident among members of theB.cereus group showing almost identical 16S rDNA gene sequences and a high level of chromosomal synteny (Rasko et al. 2005).

Acinetobacter sp. also presents difficulties in identification at the species level. This genus comprises more than 50 species, with four that are closely related and difficult to distinguish by either phenotypic or genotypic methods, known as the Acinetobacter calcoaceticus-baumannii complex (ACB). MDT has been employed in the identification of species in the ACB complex (Sedo et al. 2013), showing reproducible detection of subtle mass spectral differences between strains belonging to the same species, but is not reliable for strain typing. Some Acinetobacter isolates (7, 29, 72) had species-level identification only using the MDT technique.

Sequencing obtained through the 16S rDNA classified 56% of the identified isolates belong to Firmicutes, 25% to Proteobacteria, 8% to Other, and 11% to Actinobacteria. For the MDT analysis, 55% of the isolates were identified as Firmicutes, 22% to Proteobacteria, 8% to Actinobacteria, and 7% as NRI (no reliable identification) (Fig. 2).

Figure 2
Comparison of the bacteria identification methods 16S rRNA and MALDI-TOF MS.

Based on the identifications by two techniques, MDT, and 16S rDNA, we realized that Bacillus sp. was present in all collections and was the most abundant genus. C1 presents 30% of isolates belonging to Bacillus sp., C2 had 27%, C3 had 47% and the major percentage occurred in C4, with 53%. This genus is commonly found in industrial wastewater treatment plants (Poleto et al. 2016, Busi et al. 2017), showing its ecological importance in this kind of effluent treatment. Some microorganisms had higher abundance in certain collections, Pseudomonas sp. isolates appears most in C2 (22%), Lysinibacillus sp. composes 21% of C3 isolates (Table VI). Another isolates appear transiently and are present only in one of the collections, Arthrobacter creatinolyticus (C1), Aneurinibacillus aneurinilyticus (C2), Pantoea ananatis (C3) and Kosakonia cowanii (C4).

CONCLUSIONS

With the exception of sulphate-reducing bacteria, activated sludge was the site with higher quantification of the analyzed microorganisms. In facultative lagoon, the presence of cyanobacteria and algae might have contributed to increasing dissolved oxygen in the effluent and the biological process occurs with the coexistence of cyanobacteria and heterotrophic bacteria. In activated sludge, the presence of dead zones may have favored the metabolism of the denitrifying bacteria. In the present work, the presence of sulfate reducing bacteria (SRB) was observed in all points of collection. In physical-chemical parameters, we observed a reduction in the phosphorus, total nitrogen values and an increase in DO during the treatment of the effluent.

Environmental microbiology is a research area where the use of MALDI-TOF MS remains to be comprehensively explored. Even numbers of ecological studies (sewage, soil, water) have previously applied this technique, each environment presents its particularities according to their location and environmental characteristics. Nevertheless, environmental microorganisms are more diverse and so their identification and characterization pose a significant challenge as the field grows.

In our study, MALDI-TOF MS analysis allowed the identification of 68 (93%) of the 73 isolates at the genus or species level. Comparing with 16S rDNA sequencing, the protein profile was able to identify a high taxonomy level identification for Microbacterium sp., Alcaligenes sp., Acinetobacter sp. and Bacillus sp. There was no divergence between the results obtained by the two techniques. Based on this results, we suggest that the MALDI-TOF MS technique can be used as a tool for screening for the identification of bacteria isolated from environmental samples, requiring, in some cases, complementary 16S rDNA sequencing.

ACKNOWLEGMENTS

We would like to thank Sistema Integrado de Tratamento de Efluentes Líquidos do Polo Petroquímico (SITEL-CORSAN) for authorizing the sample collection. Special thanks go to Angelo Augustinho Massing for accompanying all collections and to Amanda Muliterno Domingues Lourenço de Lima for her technical help with the MDT. Thanks also to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico (CNPq), and Fundação de Amparo à Pesquisa no Estado do Rio Grande do Sul (FAPERGS) for their financial support and scholarships grants.

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Publication Dates

• Publication in this collection
  01 July 2019
• Date of issue
  2019

History

• Received
  23 Apr 2018
• Accepted
  24 Sept 2018