This study assessed the bacterial community composition of a drinking water system (DWS) serving a mid-sized city (120,000 inhabitants) in Brazil. Water samples, including raw and treated water, were collected at seven points throughout the DWS. DNA was extracted and analysed using high-throughput sequencing (Ion Torrent). Free chlorine and turbidity were measured in situ. Results showed that the highest relative abundance of 16S rRNA genes was from phyla Proteobacteria, followed by Bacteroidetes and Actinobacteria. The next most abundant phylum was Cyanobacteria, represented by Arthronema, Calothrix, and Synechococcus. An interesting observation was that the DNA-based analysis suggested a bacterial community change in the distribution network, with treated reservoir water being very different from the network samples. This suggests active microbiology within the distribution network and a tendency for bacterial diversity to decrease after chlorine disinfection but increase after pipeline distribution. In raw water, a predominance of Proteobacteria was observed with reduced Cyanobacteria, showing a negative correlation. In treated water, Proteobacteria were negatively correlated with Bacteroidetes. Finally, 16S rRNA genes from Firmicutes (especially Staphylococcus) had a high abundance in the chlorinated water, which may indicate the phylum's resistance to chlorine residuals. Opportunistic pathogens, e.g., Mycobacteria, Legionella, and Staphylococcus, were also observed.

  • Next-generation sequencing (NGS) revealed substantial changes in microbiota from a small drinking water system (DWS) after disinfection.

  • Proteobacteria, Bacteroidetes, Actinobacteria, and Cyanobacteria had high abundance.

  • Firmicutes in the chlorinated water may indicate resistance to chlorine residuals.

  • The chlorinated water sample presented opportunistic pathogens.

  • Environmental degradation may have led to bioremediation species’ growth.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Despite the Sanitation Law (11.445/2007), which obliges all Brazilian municipalities to provide water supply, sanitary sewage, urban cleaning, and solid waste management in ways appropriate to protect public health and the environment, recent data from the Institute Brazil and National Sanitation Information System (SNIS) show that, although sanitation has improved, half of Brazilians still have no access to sewerage (50.3%) and the coverage for water supply is only 83.3% (SNIS 2019). When proposing solutions that aim to comply with the Sanitation Law, managers and service providers often encounter factors that will limit the size of the system, which therefore will not include the entire population and generate social inequality in relation to the universal right to safe water and sanitation (Heller & Pádua 2016). Overcoming both non-structural and structural measures is important to realize the human right to safe water, especially among rural communities (Aleixo et al. 2019).

Another challenge is that the reduction in water availability in rivers and lakes has forced several water companies to use water from multiple sources to meet demand. This was the case for the city of Itabira, in the state of Minas Gerais (southeastern Brazil), which is the subject of the current study. Since 2014, the water providers for the city had to source approximately 25% of their abstraction from a river that suffers from unregulated urban development (ANA 2015; Batista et al. 2018).

In this context, academic research is often the only reliable source of information on the actual water quality, waterborne infectious diseases, and parasitic diseases in regions with poor enforcement of regulations (Marinho et al. 2016; Castro et al. 2020). Reports of microbial water quality during treatment and distribution are very few in Brazil (Batista et al. 2018), and most studies only focus on raw water quality (Köchling et al. 2017; Tessler et al. 2017). Bacterial diversity monitoring provides an additional tool in water safety assessment, especially in places where water availability is inadequate, either due to quality or quantity issues, such as is the case in most Brazilian cities (WHO 2017). Without proper maintenance, drinking water systems (DWSs) can compromise water safety after treatment and harbour risks for some waterborne diseases caused by opportunistic pathogens (Liu et al. 2019).

With the advent of modern molecular microbiology, more research on microbial communities in DWS has been done (Pinto et al. 2012, 2014; Wang et al. 2015; Han et al. 2020). Determining bacterial diversity and abundance in DWS has become an important topic for water resource management and public health. Knowing which bacterial communities are present in DWS and their response to major operational parameters can enable better water treatment, disinfection, and distribution strategies (Gomez-Alvarez et al. 2012; Chao et al. 2013; Huang et al. 2014; Pinto et al. 2014; Li et al. 2017; Liu et al. 2017a, 2017b, 2019; Han et al. 2020). The literature agrees that it is essential to better understand the dynamics of the DWS microbiome with the help of molecular microbiology methods, which also help identify the presence of microorganisms that pose a risk to consumers (Liu et al. 2017b).

In this study, water samples were repeatedly collected at different points for 6 months in a mid-sized DWS (serving a population of 120,000 inhabitants), and next-generation sequencing (NGS) was used to investigate the bacterial community composition in these water samples. The objectives of this study were to (a) assess the main bacterial taxa composition based on 16S rRNA gene sequencing; (b) investigate the relationships between the abundance of different types of bacteria and water quality parameters; and (c) determine spatiotemporal variation in bacterial diversity in the assessed DWS.

Sampling

The study area covered sampling points along a DWS for the mid-sized city of Itabira, Minas Gerais, Southeast Brazil. Over the study period, the Cardiopolis and Peixe rivers supplied 75 and 25% of the water to the city, respectively. The DWS has four single layers (1.8 m thickness) − rapid, upflow filters that are backwashed every 12 h with treated water and operate with an empty bed contact time of 7 min. The pipeline material is polyvinyl chloride (PVC). Free chlorine and fluoride are added to filtered water at concentrations of 1.5 mg Cl2 L−1 and 1.0 mg F L−1, respectively, with a contact time of approximately 30 min. Sampling points along the DWS included the inlet to the water treatment plant or (S1) raw water; (S2) filtered water; (S3) filter backwash; (S4) reservoir water (final treated and chlorinated water); and water from three points along the distribution network: one located on the lowest point in the DWS (near a municipal school, S5), one in an intermediate location (near a health centre, S6), and one far away from the water treatment plant (near a residence, S7; Figure 1). Sampling from all points was performed monthly from July to December 2015.
Figure 1

Illustration of the sampling points (S1–S7) on the DWS; raw water (S1), filtered (S2), filter backwash (S3), reservoir water (S4), municipal school (S5 – 19°38′2.83″S 43°13′50.16″W), health centre (S6 – 19°39′15.61″S 43°13′59.86″W), and residence (S7 – 19°39′14.12″S 43°12′34.11″W). Modified from information provided by the National Mining Agency.

Figure 1

Illustration of the sampling points (S1–S7) on the DWS; raw water (S1), filtered (S2), filter backwash (S3), reservoir water (S4), municipal school (S5 – 19°38′2.83″S 43°13′50.16″W), health centre (S6 – 19°39′15.61″S 43°13′59.86″W), and residence (S7 – 19°39′14.12″S 43°12′34.11″W). Modified from information provided by the National Mining Agency.

Close modal

Samples of treated water (S4, S5, S6, and S7) were kept in sterile glass bottles containing one tablet of 10% sodium thiosulphate to remove the free chlorine. The other samples (without chlorination – S1, S2, and S3) were kept in sterile glasses without sodium thiosulphate. All samples were filtered using cellulose acetate membrane filters (GF1 filter, 0.2 μm, Macherey and Nagel), and the final volume of filtrate ranged from 1 to 4 L. The filters were preserved at −20 °C for DNA extraction. Free chlorine concentrations were measured in situ using a 21055-colorimeter pocket Hach kit (Hach Lange, UK). Turbidity was measured using a 2100Q portable turbidity meter. In addition, 100 mL of each sample was analysed for coliforms and Escherichia coli using the ONPG-MUG (Colilert®) method.

DNA extraction and polymerase chain reaction amplification

DNA was extracted from membrane filters using a FastDNA Spin Kit for Soil (Qbiogene) according to the manufacturer's instructions. The concentration and purity of the DNA extracts were determined using a Nanodrop 1000 spectrophotometer (Thermo Scientific). Amplicon libraries were generated from each DNA sample by direct polymerase chain reaction (PCR) amplification, using the universal primers set (515F and 926R), targeting the V4 and most of the V5 region of the 16S rRNA gene, containing the Ion adapters A (50-CCATCTCATCCCTGCGTGTCTCCGACTCAG-30) and trP1 (50-CCTCTCTATGGGCAGTCGGTGAT-30) for forward and reverse primers, respectively (Caporaso et al. 2011; Quince et al. 2011; Parada et al. 2016). Samples were differentiated by adding unique 12 base pair barcodes to the forward primer through a ‘GAT’ space.

The PCR was carried out using the FastStart High Fidelity PCR System and the PCR Nucleotide Mix (Roche Diagnostics GmbH, Mannheim, Germany). This kit contains NHþ 4 buffer, MgCl2, dNTPs, and DNA polymerase. For the PCR, 1 μL of the 1:10 dilution for each of the DNA extract (a least 10 ng mL−1 of DNA template) was added to a mixture containing 5 μL FastStart High Fidelity Reaction Buffer, 1 μL dNTP, 1 μL of each primer (final primer concentration in 1× reaction: 0.2 μM), 0.5 μL of enzyme blend, and 40.5 μL of nuclease-free water to obtain a final reaction volume of 50 μL. The following PCR programme was used: initial denaturation at 95 °C for 4 min, followed by 25 cycles of 95 °C for 1 min, 55 °C (annealing) for 45 s, 72 °C (extension) for 1 min, and final elongation of 72 °C for 7 min. PCR products were checked by electrophoresis running at 100 V for 45 min using 5 μL of product/extract plus loading buffer on 2% agarose gels containing Nancy-520 DNA Gel Stain (Sigma-Aldrich) and in 1× Tris-acetate–EDTA buffer. Gels were visualized by UV illumination using a Bio-Rad Fluor-S Multi Imager (Bio-Rad, UK). Purification of PCR products was performed using the Agencourt AMPure XP PCR Purification system, which employs Agencourt's solid-phase paramagnetic bead technology for high-throughput purification of PCR amplicons. The resulting purified PCR product was used directly in the NGS workflow.

DNA library prepared for Ion Torrent sequencing

The individual amplicon libraries were quantified using a Qubit 2.0 Fluorometer (Invitrogen), a Qubit dsDNA HS reagent Assay kit, and Qubit™ assay tubes. A Qubit working solution was prepared for all samples by diluting the Qubit dsDNA HS reagent 1:200 in Qubit dsDNA HS buffer. Each sample tube required 199 μL of Qubit working solution and a 1 μL sample. The amplicon libraries were pooled in equimolar quantities into a unique solution for the downstream template preparation procedure for clonal amplification on Ion Spheres. The diluted library was freshly prepared before being used on the Ion One-Touch2 System.

Analysis of the bacterial communities by 16S rRNA gene amplicon sequencing (Ion Torrent platform)

Raw sequences generated by the Ion Torrent PGM were analysed using the QIIME (v 1.7.0) bioinformatics pipeline (Caporaso et al. 2010), and sequencing data were analysed using the SILVA ribosomal RNA gene database project (Quast et al. 2013). The final results were expressed as relative abundance (%) and were grouped into operational taxonomic units (OTUs) with 97% similarity.

Alpha- and beta-diversity indices were calculated using the R platform (R Development Core Team 2011) with the Phyloseq (McMurdie & Holmes 2013) and Vegan packages (Oksanen et al. 2019). A heatmap was constructed with the most abundant taxa identified from the sequencing data (≥1% in at least one of the samples). Resampling of the sequences was performed to calculate the diversity indices, so that all samples had the same number of sequences as the one with the lowest number of recovered sequences (October sample, S5 – 1,735 sequences).

A phylum-level heatmap was constructed to identify the most abundant phyla and cluster analysis was performed based on Ward's method and Euclidean distance measure using the software Microbiome Analyst (Dhariwal et al. 2017; Chong et al. 2020). The community profile was also analysed through core microbial composition (core microbiome analysis) to identify the main taxa present in the different sample groups based on their prevalence and relative abundance. Graphs with the relative abundances of the main phyla and genera were constructed using the same software.

Statistical methods

The similarities of the relative abundance of the OTUs detected from different sampling points were calculated by cluster analysis using Euclidean Distance based on the diversity of the more abundant orders (Proteobacteria, Actinobacteria, and Bacteroidetes) considering relative abundance values above 0.01%.

For the sampled variables from S1 to S7 (chlorine, turbidity, number of OTUs, and taxonomic phyla), Pearson's and Spearman's correlation coefficients were calculated for all sampled points (total correlation), and between points from S1 to S3 (raw water, excluding chlorine data) and S4 to S7 (treated water, excluding turbidity data). The correlation was calculated within the R programming environment (R Development Core Team 2011) from the algorithms of the ‘Openair’ (Carlaw & Ropkins 2012) package. The value of 70% was considered representative in the correlation matrices if the statistical significance was established (p < 0.05).

Dynamics and diversity of bacterial communities

Sequencing on the Ion Torrent platform generated a minimum of 1,735 to a maximum of 24,367 reads per sample. Table 1 summarizes the Shannon–Weaver index, Simpson's diversity index, dominance, and the number of reads and OTUs for each sample for the monitoring period. The rarefaction curves tended towards a plateau, indicating sufficient sample coverage (Supplementary Fig. A.1).

Table 1

Sample description, Ion Torrent sequencing data, and diversity indices

SamplesSequencesaOTUsbDominancecShannon–WeaverdSimpsoneEvennessf
S1 – Raw water 58,725 30,021 0.4259 0.9689 0.5741 0.8784 
S2 – Filtered 122,970 65,697 0.3807 1.023 0.6193 0.9271 
S3 – Filter backwash 119,933 57,925 0.4402 0.9427 0.5598 0.8556 
S4 – Reservoir water 53,997 33,761 0.4669 0.8874 0.5331 0.8096 
S5 – Municipal school 78,323 41,042 0.6328 0.6776 0.3672 0.6564 
S6 – Health centre 78,557 38,340 0.5362 0.8103 0.4638 0.7495 
S7 – Residence 100,443 52,559 0.4693 0.9067 0.5307 0.8254 
SamplesSequencesaOTUsbDominancecShannon–WeaverdSimpsoneEvennessf
S1 – Raw water 58,725 30,021 0.4259 0.9689 0.5741 0.8784 
S2 – Filtered 122,970 65,697 0.3807 1.023 0.6193 0.9271 
S3 – Filter backwash 119,933 57,925 0.4402 0.9427 0.5598 0.8556 
S4 – Reservoir water 53,997 33,761 0.4669 0.8874 0.5331 0.8096 
S5 – Municipal school 78,323 41,042 0.6328 0.6776 0.3672 0.6564 
S6 – Health centre 78,557 38,340 0.5362 0.8103 0.4638 0.7495 
S7 – Residence 100,443 52,559 0.4693 0.9067 0.5307 0.8254 

aNumber of short-read sequences.

bNumber of OTUs.

cSimpson's dominance (D);

dShannon–Wiener's diversity (H′).

eSimpson's diversity (1 − D).

fPielou's evennesss (J).

Increasing bacterial diversity was observed at the final points of water distribution (S5–S7) and in treated water (S3–S5), as shown in Table 1 and Supplementary Table A.1.

In general, the four most abundant phyla in all samples were Proteobacteria, Bacteroidetes, Actinobacteria, and Cyanobacteria (Supplementary Table A.2). Other bacteria are the species with very low total abundance (<0.01%) in the samples for each OTU (Supplementary Table A.3). When we compared the prevalence of phyla for each sample, the same phyla were detected (Figure 2).
Figure 2

Relative abundance (%) of the main phyla for each sampling point in the assessed DWS.

Figure 2

Relative abundance (%) of the main phyla for each sampling point in the assessed DWS.

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The presence of Cyanobacteria among the most abundant phyla (as shown in Figure 2) in the DWS after coagulation and flocculation steps could imply a risk to water quality, since Cyanobacteria continued to produce metabolites that may impact water quality, such as microcystin and geosmin, for approximately 10 days in sludge (Pestana et al. 2016). In the case study area, the supernatant from the sludge treatment facility is returned to the stream, and from there to the inlet of the plant. Cyanobacterial 16S rRNA genes affiliated with Synechococcus, Arthronema, and Calothrix were detected (Figure 3). This is of concern because the water treatment train does not have a final treatment for the removal of cyanotoxins such as activated carbon.
Figure 3

Relative abundance (%) of the main genus for each sampling point in the assessed DWS.

Figure 3

Relative abundance (%) of the main genus for each sampling point in the assessed DWS.

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Proteobacteria, Actinobacteria, and Bacteroidetes are commonly detected in freshwater environments (Berg et al. 2009; Goecke et al. 2013); however, inside a DWS, which is an oligotrophic environment, these microorganisms have less nutrition and are expected to be attached to the surfaces of the pipe and other particles forming a biofilm (Henne et al. 2012). Although the samples investigated in this study are not from biofilms, they may have contained bacteria detached from biofilms.

At all times, α-Proteobacteria followed by the β-, γ-, and δ-subdivisions represented the majority of sequences in raw water (S1) and distribution network samples from the municipal school (S5) and health centre (S6). In contrast, Bacteroidetes were more abundant in the filtered (S3) and reservoir water samples (S4). Actinobacteria were most abundant in the filter backwash (S3) and filtered water samples (S2). Cyanobacteria and Proteobacteria were at all times the most abundant phyla at the residence sampling point (S7) situated at the end of the distribution network.

Studies of water distribution systems reveal microbial communities dominated by the phyla Proteobacteria (i.e., α-, β -, and γ-Proteobacteria), Firmicutes, Nitrospirae, and Actinobacteria (Gomez-Alvarez et al. 2012; Chao et al. 2013; Huang et al. 2014; Pinto et al. 2014; Wang et al. 2014). Proteobacteria were also found to be dominant in raw water in the study of Zhu et al. (2020). Moreover, the authors observed that Actinobacteria was the most abundant class in treated water (after chlorination), suggesting the potential chlorine resistance of this bacterial group (Zhu et al. 2020). Hou et al. (2018) detected the groups Actinobacteria, Proteobacteria, and Firmicutes during the water treatment process. On the contrary, in treated water, the proportion of Actinobacteria decreased, whereas that of Proteobacteria and Firmicutes increased and predominated.

At the class level, β-Proteobacteria (Albidiferax, Dechloromonas, Polynucleobacter, Ottowia, Ralstonia, Acidovorax, Polaromonas, and Hydrogenophaga), which include ammonia-oxidizing and arsenic-resistant soil bacteria such as Polynucleobacter (Heckmann & Schmidt 1987), are frequently an abundant fraction of freshwater bacterioplankton. Polynucleobacter has some species that are living in association with ciliates as an obligate symbiont, namely Polynucleobacter necessarius (Newton et al. 2011).

The clade α-Proteobacteria contains the genera Nitratireductor, Novosphingobium, Ochrobactrum, and Sphingomonas and was found in the DWS (Figure 4).
Figure 4

Heatmap with major clades found for all sampling points in the assessed DWS.

Figure 4

Heatmap with major clades found for all sampling points in the assessed DWS.

Close modal

There was an increase in the relative abundance of versatile degraders of complex organic substrates such as autotrophic perchlorate-degrading bacteria (Dechloromonas) – Figure 3 (Nov-15 sample) – in S5 and S7. The anthropogenic impacts such as fishing, lack of riparian forests, loss of habitats, and agricultural activities all have contributed in recent years to the environmental degradation in this river (Carneiro et al. 2019).

Sha et al. (2020) also observed an increase in taxa from the Betaproteobacteria as well as Bacteroidetes, Actinobacteria, and Saccharibacteria in microbial communities exposed to a high concentration of perchlorate. In contrast, there was a decrease in the relative abundance of taxa such as Chloroflexi, Verrucomicrobia, and Gammaproteobacteria (Sha et al. 2020). The regrowth of this genus Dechloromonas in DWS points to a possible selection of species more resistant to chlorination. Wang et al. (2013) warn that breakdown of bacterial cells by chlorination releases precursors for the formation of disinfection by-products (trihalomethanes, haloacetonitriles, chloral hydrate, chloropicrin, and 1,1,1-trichloro-2-propanone).

Pinto et al. (2012) observed that Alphaproteobacteria increased from approximately 6% in the source water to 38% in the filter and 23% in the DWS. Conversely, the relative abundance of Betaproteobacteria ranged from 34% in the source water to 43% in the DWS.

The predominant OTUs (>0.01% relative abundance) at the genus level were Pseudarcicella, Polynucleobacter, Sediminibacterium, and Candidatus-Panktophila in S1–S4, corresponding to the raw water until the end of the treatment. From the beginning of the water distribution network (S5), there was a change in the composition of the bacterial communities in relation to the OTUs observed between the months; except for S6, where the same OTUs were always present, i.e., Pseudarcicella, Polynocleobacter, Sediminibacterium, and Candidatus (Figure 3). These taxa with tolerance for a broad pH spectrum further emphasize differences in habitat-specific adaptations. For example, Sediminibacterium (family Chitinophagaceae, phylum Bacteroidetes) was first described by Qu & Yuan (2008). This genus has only two species that were isolated from reservoir sediment samples, Sediminibacterium salmoneum, and from soil, Sediminibacterium ginsengisoli (Kim et al. 2013).

At S5, there was a significant change in the composition of the community with a greater presence of the genera Novosphingobium, Synechococcus, Staphylococcus (October sample, S5) and Burkholderia followed by Candidatus-Flaviluna, Sphingomonas, and Calothrix (October sample, S5). The same phenomenon was observed in S7, with notable occurrence in September and November (S7) for the OTUs Arthronema, Sphingomonas, and Calothrix, and from the phylum Firmicutes the genera Chlorobi, Spirochaetae, Chloroflexi, and Nitrospire (Figure 4).

Bacterial community composition

Bacterial diversity decreased after disinfection with chlorine (S4) but increased along the distribution networks (S5–S7, Supplementary Fig. A.1), where bacteria might include the microbial community of the filter (Pinto et al. 2012), loose deposits, and detachment of pipeline biofilms (Liu et al. 2017a, 2017b).

Active microbiology within the distribution network has been linked to the dispersal of biofilm-associated microbes during passage through the water distribution pipes (Huang et al. 2014), in which the material of the pipes may have influenced. The PVC material of pipes can serve as a support for microorganisms, in addition to serving as a carbon source. This condition is aggravated by the age and maintenance of the pipes, which is quite deficient in developing countries, increasing the possibility of pipe fractures and adverse pressure events in the DWS (Liu et al. 2017b).

Deterioration of water quality along the network can be caused by long retention times or ‘water age’, as observed worldwide (Liu et al. 2017b). Long retention times have been associated with microbial communities dominated by the phyla Proteobacteria, Actinobacteria, and Bacteroidetes (Gomez-Alvarez et al. 2012; Chao et al. 2013; Huang et al. 2014), regardless of the water treatment plant and disinfection type (Table 2). In addition, because removing all microorganisms from source water is impossible, microbial regrowth may occur in DWSs (Reynolds et al. 2008), even after the advanced treatment processes (Cui et al. 2020).

Table 2

Community patterns observed in different studies of water distribution systems

Treatment processesDisinfectionMain phylaPlatformReference
Coagulation, flocculation with pH adjustment followed by sand filtration Free chlorine Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria Ion Torrent Present study 
Lime-softening compounds, sedimentation, ozonation, and dual medium filtration Free chlorine and ammonia to produce chloramine prior to distribution Proteobacteria (Betaproteobacteria and Alphaproteobacteria) 454 GS-FLX Pinto et al. (2014)  
Flocculation and settling with pH adjustment followed by sand filtration and granular activated carbon Free chlorine Proteobacteria, Firmicutes, Nitrospirae, and Actinobacteria GS-FLX Titanium Gomez-Alvarez et al. (2012)  
Flocculation, sedimentation, and sand filtration Free chlorine Proteobacteria (Beta, Gamma, and Alpha) HTS Chao et al. (2013)  
Sand filtration Free chlorine Proteobacteria, Firmicutes, Nitrospirae, and Actinobacteria Illumina MiSeq Huang et al. (2014)  
Biofiltration Free chlorine Acidobacteria and Gemmatimonadetes 454 FLX Titanium El-Chakhtoura et al. (2015)  
Preozonation, flocculation, sedimentation, sand filtration, post-ozonation and biological activated carbon filtration Free chlorine Proteobacteria and Bacteroidetes Illumina MiSeq Li et al. (2017)  
Aeration, rapid sand filtration, softening, and activated carbon filtration UV Proteobacteria (Beta) and Bacteroidetes Illumina MiSeq Liu et al. (2017a)  
Coagulation, sedimentation, dual-media filtration Free chlorine Actinobacteria, Proteobacteria, and Firmicutes Illumina MiSeq Hou et al. (2018)  
Annular reactors (ARs, Model 1320LJ, BioSurface Technologies Co., USA), inoculated with groundwater, simulating the DWS UV and free chlorine Opportunistic pathogenic bacteria from Proteobacteria and Bacteroidetes Illumina MiSeq Liu et al. (2019)  
Flocculation, coagulation, sedimentation, and filtration Chlorine Proteobacteria Illumina MiSeq Han et al. (2020)  
Annular reactors (ARs, Model 1320LJ, BioSurface Technologies Co., USA), Cl2 disinfection No. of combinations of UV, free chlorine, and NaClO Proteobacteria, Planctomycitecia, and Sphingobacteria GS-FLX Titanium Zhu et al. (2020)  
Pre-ozone, coagulation, sedimentation, and sand filtration Ozone-biological activated carbon (O3-BAC) Proteobacteria (Pseudomonas spp.) Ion S5™ XL Bian et al. (2021)  
The facility operates two trains of water treatment processes in parallel (Train I uses horizontal flow sedimentation and a V-filter, while Train II uses Multiflo-sedimentation and a TGV filter). Both trains contain coagulation–sedimentation, filtration, and O3-BAC Chlorine disinfection Proteobacteria, Actinobacteria, Cyanobacteria, Firmicute, Bacteroides, Acidobacteria, Planctomycete, and Patescibacteria Illumina MiSeq Tang et al. (2022)  
Treatment processesDisinfectionMain phylaPlatformReference
Coagulation, flocculation with pH adjustment followed by sand filtration Free chlorine Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria Ion Torrent Present study 
Lime-softening compounds, sedimentation, ozonation, and dual medium filtration Free chlorine and ammonia to produce chloramine prior to distribution Proteobacteria (Betaproteobacteria and Alphaproteobacteria) 454 GS-FLX Pinto et al. (2014)  
Flocculation and settling with pH adjustment followed by sand filtration and granular activated carbon Free chlorine Proteobacteria, Firmicutes, Nitrospirae, and Actinobacteria GS-FLX Titanium Gomez-Alvarez et al. (2012)  
Flocculation, sedimentation, and sand filtration Free chlorine Proteobacteria (Beta, Gamma, and Alpha) HTS Chao et al. (2013)  
Sand filtration Free chlorine Proteobacteria, Firmicutes, Nitrospirae, and Actinobacteria Illumina MiSeq Huang et al. (2014)  
Biofiltration Free chlorine Acidobacteria and Gemmatimonadetes 454 FLX Titanium El-Chakhtoura et al. (2015)  
Preozonation, flocculation, sedimentation, sand filtration, post-ozonation and biological activated carbon filtration Free chlorine Proteobacteria and Bacteroidetes Illumina MiSeq Li et al. (2017)  
Aeration, rapid sand filtration, softening, and activated carbon filtration UV Proteobacteria (Beta) and Bacteroidetes Illumina MiSeq Liu et al. (2017a)  
Coagulation, sedimentation, dual-media filtration Free chlorine Actinobacteria, Proteobacteria, and Firmicutes Illumina MiSeq Hou et al. (2018)  
Annular reactors (ARs, Model 1320LJ, BioSurface Technologies Co., USA), inoculated with groundwater, simulating the DWS UV and free chlorine Opportunistic pathogenic bacteria from Proteobacteria and Bacteroidetes Illumina MiSeq Liu et al. (2019)  
Flocculation, coagulation, sedimentation, and filtration Chlorine Proteobacteria Illumina MiSeq Han et al. (2020)  
Annular reactors (ARs, Model 1320LJ, BioSurface Technologies Co., USA), Cl2 disinfection No. of combinations of UV, free chlorine, and NaClO Proteobacteria, Planctomycitecia, and Sphingobacteria GS-FLX Titanium Zhu et al. (2020)  
Pre-ozone, coagulation, sedimentation, and sand filtration Ozone-biological activated carbon (O3-BAC) Proteobacteria (Pseudomonas spp.) Ion S5™ XL Bian et al. (2021)  
The facility operates two trains of water treatment processes in parallel (Train I uses horizontal flow sedimentation and a V-filter, while Train II uses Multiflo-sedimentation and a TGV filter). Both trains contain coagulation–sedimentation, filtration, and O3-BAC Chlorine disinfection Proteobacteria, Actinobacteria, Cyanobacteria, Firmicute, Bacteroides, Acidobacteria, Planctomycete, and Patescibacteria Illumina MiSeq Tang et al. (2022)  

Disinfection could select resistant bacterial populations. Differential resistance of drinking water bacterial communities was observed when Legionella, Escherichia, Geobacter (in a lab-scale system), and Mycobacterium, Sphingomonas, and Coxiella (in a full-scale system) increased in their relative abundance during monochloramine treatment (Chao et al. 2013). Furthermore, the detachment of biofilms from pipes promoted by disinfectants can affect general bacterial resistance to antibiotics in tap water (Zhang et al. 2018).

Enterobacteriales (Escherichia) were detected via NGS analyses in smaller quantities at several points inside the DWS, e.g., at the health centre (S6, August), municipal school (S5, September and December), residence (S7, July and November), and in raw water (S1, November) – Figure 3 – which could indicate some faecal contamination entering the DWS. However, the routine method ONPG-MUG (Colilert®) detected E. coli only in the non-treated samples (S1–S3) (Supplementary Table A.4). The regrowth of these microorganisms due to inadequate operational practices can affect the microbiological safety of the DWS. We also observed that some bacterial genera containing opportunistic pathogens, such as Legionella, Klebsiella, and Shigella showed a higher abundance in treated water than in raw water (Figure 3, and more details in Batista et al. 2018). Liu et al. (2017a) observed that loose deposits in PVC pipes constitute a hotspot for adenosine triphosphate (ATP) and bacteria from the genera Aeromonas, Mycobacterium, and Legionella. Opportunistic pathogens can be very resistant to the disinfection residuals in drinking water when adhered to biofilm, corrosion products, and loose deposits, which may render them tolerant to disinfection with UV and chlorine (Liu et al. 2019).

Of every 100 L of water collected and treated in Brazil, only 63 L are consumed. In addition, 37% of water in the country is lost because of leaks, theft or clandestine connections, lack of measurement, or incorrect measurements of water consumption. In the state of Minas Gerais, the loss is 36% (SNIS 2020). This situation favours the introduction of contaminants external to the distribution system, such as faecal contaminants (LeChevallier et al. 2003).

Association between chorine and turbidity versus OTUs detected

Pearson's correlation matrices for the raw (S1–S3) and treated water sample points (S4–S7) are presented in Figure 5. For total correlation (S1–S7), it was observed that turbidity showed a high correlation with Acidobacteria (70%, p-value = 0.000591) and with Planctomycetes (71%, p-value = 0.0007769). Acidobacteria is prevalent in soils and has been linked to resistance to pollutants such as petroleum derivatives and nitrophenols. This resistance, in general, has been attributed to their exopolysaccharide (EPS) production capacity (Kielak et al. 2016). Thus, it is suggested that suspended particles that contribute to turbidity may carry microorganisms from this phylum and Planctomycetes since EPS facilitates the formation of aggregates. Li et al. (2017) evaluated a water treatment plant in China and observed that Planctomycetes outcompete Actinobacteria and Verrucomicrobia when turbidity is quite high.
Figure 5

(a) Pearson's correlation for all sampling points (S1–S7); (b) Spearman's correlation for all sampling points (S1–S7); (c) Pearson's correlation for all sampling points (S1–S3); and (d) Pearson's correlation for all sampling points (S4–S7).

Figure 5

(a) Pearson's correlation for all sampling points (S1–S7); (b) Spearman's correlation for all sampling points (S1–S7); (c) Pearson's correlation for all sampling points (S1–S3); and (d) Pearson's correlation for all sampling points (S4–S7).

Close modal

When only samples S1–S3 (raw water) were considered, in addition to the high correlation of turbidity with the phyla Planctomycetes (75%, p-value = 0.0008502) and Acidobacteria (70%, p-value = 0.002742), Acidobacteria and Planctomycetes correlated positively (86%, p-value = 0.0007602). It should also be noted that the increase in Proteobacteria implied a reduction in Cyanobacteria, with a negative correlation of 72% (p-value = 0.0007655). Li et al. (2019) also showed a strong negative correlation between Cyanobacteria and Proteobacteria.

High correlations were not observed in the samples from S4 to S7 (treated water); therefore, the correlation matrix of 50% was considered. Thus, it was observed that the increase in Cyanobacteria implies an increase in Acidobacteria (r = 55, p-value = 0.005745). The rise in Proteobacteria was negatively correlated by 60% (p-value = 0.001924) with Bacteroidetes. Finally, Firmicutes seem to better tolerate the increase in chlorine concentrations since the increase in chlorine implied a rise in this phylum (r = 59%, p-value = 0.002281). A significant increase of the relative abundance after chlorination in the DWS, mainly in the residence point (S7) (Figures 2 and 5) at the end of the DWS, was observed for Firmicutes (Staphylococcus), which tend to predominate in chlorinated and unchlorinated biofilms (Mi et al. 2015), and together with Proteobacteria can predominate in treated water (Hou et al. 2018). Several studies on DWS observed that disinfection with chlorine promoted the predominance of Firmicutes (Hou et al. 2018; Zhang et al. 2019), and disinfection strategy could have a strong influence on the abundance of Firmicutes in the bacterial community present within these systems (Mi et al. 2015).

Seasonal distribution of the microbiota

Cluster analyses showed in the dry season (Figure 6(a)) that the relative abundance of some samples from network distribution (S6 and S7) was similar to the relative abundance detected in raw water (S1). However, a more significant similarity was observed in the wet season (Figure 6(b)) between the samples of the backwash filter (S3) and some points of the distribution network (S5 and S7).
Figure 6

Cluster analysis from (a) dry and (b) wet seasons to the monitoring points (distance represents similarity between the samples).

Figure 6

Cluster analysis from (a) dry and (b) wet seasons to the monitoring points (distance represents similarity between the samples).

Close modal

After the rains began (in mid-October 2015), there was an apparent change in microbial structure dynamics, which was determined more by backwash (S3) than by raw water (S1). With precipitation, it is expected to increase the suspended solid concentrations. So, there is a natural increase in the load of microorganisms adhered to these particles that tend to be carried to the filters and consequently colonize the sludge filters, since this could be an ecological survival strategy for bacterial communities in DWSs (Pinto et al. 2012). As a result, the recirculation of this water with a higher load of particles and microorganisms will influence the quality of the water treated and be distributed to the population. Water sampling at the tap water of the treatment outlet (S4) presented the greatest dissimilarity in relation to the relative abundance of the orders observed in the other points, both in dry and wet seasons, therefore indicating that there is a spatial variation of the community dynamics after the treatment.

This study investigated the bacterial community of water samples collected in the DWS for 6 months in Itabira, Brazil, based on the Ion Torrent sequence analysis of 16S rRNA gene amplicons. Predominant phyla and changes in microbial dynamics over space and time were identified from raw water to supplied water samples.

The most abundant phyla detected were Proteobacteria, Bacteroidetes, and Actinobacteria, which are usually reported in water samples, especially in samples of freshwater environments. However, we observe a change in the microbial community structure in the assessed DWS after disinfection, with a dominance of resistance taxa (Firmicutes), versatile degraders of complex organic matter, and some opportunistic pathogens. Moreover, Cyanobacteria have also been detected and pose a potential health risk, since no cyanotoxin removal technology is used in the DWS.

The authors acknowledge the support from FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais), CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), Brazil, and the Project Global Innovation Partnership to Investigate, Restore, and Protect the Urban Water Environment, funded by the British Council.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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