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Channels and sewers are commonly used to collect sewage during extensively rural areas. The sewage and bacterial characteristics of rural sewage collection systems can influence their operation and maintenance performance which further affect appropriate system decision. In this study, eight rural sewage collection systems (four each of channels and sewers) were applied to evaluate the sewage quality, bacterial characteristics, and their differences of two kinds of collection systems. The results indicate that significantly distinction existed between the rural sewage collection systems of channels and sewers. Sewage in channels had higher suspended solid (SS) concentration but lower sulfide concentration than that in sewers. The SS, sulfate, and chemical oxygen demand (COD) removal capacity in channels was nearly 3.5, 4.0, and 0.6 times than those in sewers. At least 14 genera and 18 species of bacteria showed significantly distinction between channels and sewers even their main phylum, genus, and species of bacteria communities was Proteobacteria (∼70.3%), Acinetobacter (∼22.3%), and Pseudomonas fragi (∼13.8%), respectively. The structural characteristics and bacterial function caused the difference between channels and sewers. Overall, this study revealed the intrinsic and essential differences of channels and sewers, providing basic and meaningful data for rural sewage collection systems decision.

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  • Sewage quality and bacterial characteristics of channels and sewers were significantly different.

  • Channels had better SS and sulfate removal abilities but lower COD removal abilities.

  • Fourteen genera and 18 species of bacteria showed significant distinction between channels and sewers.

  • Structural characteristics and bacterial function caused the difference between channels and sewers.

Graphical Abstract

Graphical Abstract
Graphical Abstract
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Graphical Abstract
Graphical Abstract
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bacteria, channel, collection system, rural sewage, sewer
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Rural sewage is one of the most common domestic pollution sources which need to be effectively treated (Guo et al. 2014; Zheng et al. 2022). In general, the premise of sewage treatment is to collect sewage, so rural sewage collection systems play important roles in rural sewage governance. At present, there are two kinds of rural sewage collection systems, i.e. channels and sewers, globally, and both of them have widespread application (Ma et al. 2015; Gikas et al. 2017). Channels are easy to construct and operate as they have an open structure and lie on the ground. Sewers have more cost on construction, operation and maintenance (O&M) but do not affect the surface environment as they have an underground arrangement in comparison to the channels. To create a better surface environment and to achieve effective rain and sewage diversion, sewers are recommended instead of channels in many rural areas. However, the lack of construction funds and O&M capacities in rural areas extremely restrains the speed of the spread of sewers (Li et al. 2021). Most low-income rural areas still have channels as their main sewage collection systems.

Sewers have the ability to collect, transfer, and even pre-treat sewage in municipal and rural areas (Zhao et al. 2019; Zheng et al. 2020). With the inhabitance of bacteria in sewage, biofilms, and deposits, the biochemical process occurs frequently in sewers. Therefore, sewers can remove organics and generate microbial metabolites such as sulfide, causing operation and maintenance risk (Liu et al. 2016a; Li et al. 2019b). To better understand the rural sewage collection process, the sewage quality and bacterial communities of rural sewers have been studied and revealed by the previous research (Li et al. 2020a; Zheng et al. 2021). However, the sewage quality and bacterial characteristics of the most commonly used channels are still unclear and whether channels are nothing but cheap needs to be discussed based on the research data. Therefore, the distinction of rural sewers and channels need to be studied in-depth and provide further effective suggestions for rural sewage collection.

To investigate and evaluate the difference between drainage channels and sewers in rural areas, four villages that used channels and another four villages that used sewers, with similar characteristics, were selected and the sewage quality as well as bacterial characteristics were analyzed in this study. With the assistance of statistical methods and 16S rRNA gene whole region sequencing, the significant distinction between rural channels and sewers was observed and verified. This study provides a deeper understanding of rural channels and innovatively finds the differences between rural channels and sewers which may provide meaningful references for decision making.

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Research region and object

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To compare the distinction of drainage channels and sewers in rural areas, eight villages in Ankang city of China were selected for investigation (32°40′6″–32°51′12″ N, 108°33′51″–109°6′1″ E). The eight villages had similar natural characteristics and social-economic characteristics. Four of them used drainage channels to collect rural sewage and the other four villages adopted sewers. The research drainage channels and sewers are made of concrete and in normal use. The research object was the sewage characteristic, including pollutants and environmental parameters, and bacterial communities of the liquid phase (i.e. sewage) and the solid phase (i.e. biofilms and deposits), shown in Figure 1. For ease of identification, CW, CB, and CD represents channel sewage, channel biofilms, and channel deposits, respectively, while SW, SB, and SD represents sewer sewage, sewer biofilms, and sewer deposits, respectively.
Figure 1
The research object of rural sewage collection systems.
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The research object of rural sewage collection systems.

Figure 1
The research object of rural sewage collection systems.
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The research object of rural sewage collection systems.

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Sample collection and analysis

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Sewage samples were collected through water sample spoons from the manhole in sewer systems and the free liquid surface in drainage channels and stored (at 4°C) in clean water sample bottles. The concentration of chemical oxygen demand (COD), ammonia nitrogen (AN), total nitrogen (TN), total phosphorus (TP), suspended solid (SS), turbidity (TU), sulfate and sulfide (S2−) in sewage were analyzed by the standard methods (APHA 2005). The dissolved oxygen (DO), pH, temperature (T) and oxidation reduction potential (ORP) were measured on site using DO meters (JPB-706A, INESA, China), pH meters (PHB-4, INESA, China), and ORP meters (ONB-116, NOBO, China), respectively. The electrical conductivity (EC) was analyzed by EC meters (LC-DDB-1M, LICHEN, China). The pollutant removal capacity was calculated by the ratio of pollutant concentration difference and channel/sewer length. Samples used in bacterial community detection were filtered with 0.22 μm sterile microfiltration membranes and stored at −20 °C (Li et al. 2022). Biofilms located on air-water interface of the channel and sewer inner surface were scraped off carefully with sterile swabs and placed in sterile centrifuge tubes (stored at −20 °C) for molecular biological analysis. Deposits in the channel and sewer were collected by scoops and also stored on sterile centrifuge tubes (stored at −20 °C). The number of effective sewage characteristic samples and bacteria samples (including sewage, biofilms, and deposits) was 40 and 48, respectively. Samples were taken in winter and were not combined for extraction. Correlation relationship analysis of sewage quality was conducted by IBM SPSS 25 statistics software with the Spearman correlation method, and the significant relationship (P < 0.05) was further visualized as the network with Gephi v0.9.1.

16s rRNA gene whole region sequencing and data processing

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The DNA from the samples for sequencing was extracted with the PowerSoil® DNA Isolation Kit (MOBIO, USA). The whole region of the 16S rRNA gene was amplified with 27F (5′-AGRGTTTGATYNTGGCTCAG-3′) and 1,492 R (5′-TASGGHTACCTTGTTASGACTT-3′) primers. The amplified procedure and sequencing data processing were described by the study (Zhu et al. 2022) and the products were sequenced on the PacBio platform by Biomarker Technologies Corporation (Beijing, China) to obtain the circular consensus sequencing (CCS) sequence from the valid data. Lima v1.7.0, UCHIME v4.2, and USEARCH v10.0 software were used to analyze the CCS data to divide operational taxonomic units (OTUs) at a similarity level of 97% (Miao et al. 2022). Raw sequence data were deposited in the NCBI Sequence Read Archive (SRA) database under the accession number of SRR21912686-SRR21912733. Further data processing methods were described by previous studies (Li et al. 2020b; Li et al. 2022). Linear discriminant analysis (LDA) effect size (LEfSe) was operated with a LDA threshold of 4 or 3 and P < 0.05 (Segata et al. 2011). Canonical correlation analysis (CCA) was performed using the Canoco v5.0 to indicate the relation of functional bacteria and environmental factors (Yang et al. 2019). PICRUSt2 analysis and BugBase method were conducted to predict bacterial function (Ward et al. 2017; Douglas et al. 2020) and the Wilcoxon signed-rank test (P < 0.05) was used as a non-parametric test for the predicted function.

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Current situation of the channel and sewer

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All of the studied channels and sewers were in the normal operation condition to collect and transfer rural sewage (Figure 2). The width and length of the studied channels was 40–80 mm and 15–52 m, respectively, whose slopes were 1.0% to 4.8%. The channels were completely exposed to the air and sunlight which had the ability to exchange substance and energy with the external environment. The diameter and length of the studied sewers was 30–70 mm and 17–58 m, respectively, which was similar with the channels and the slope of sewers was 0.7% to 2.6%. Contrary to the channels, the studied sewers were basically isolated from the outside environment and operated in darkness. The substance and energy exchange abilities in sewers were much lower than that in channels, which might lead to the distinction of these two kinds of sewage collection systems.
Figure 2
Photos of the studied channels and sewers.
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Photos of the studied channels and sewers.

Figure 2
Photos of the studied channels and sewers.
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Photos of the studied channels and sewers.

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Sewage qualities of the channel and sewer

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The sewage qualities of the channel and sewer are shown in Figure 3(a). The SS concentration in channels (528.1 ± 248.5 mg/L) was significantly higher than that in sewers (208.4 ± 189.0 mg/L) (P < 0.05) because of the entry of foreign particles. The COD concentration (406.6 ± 224.2 mg/L and 457.1 ± 239.1 mg/L) and pH (7.8 ± 0.3 and 7.8 ± 0.3) were similar between channels and sewers while AN, TN, and TP had differences. The average concentrations of AN (79.7 ± 59.6 mg/L), TN (109.0 ± 52.7 mg/L), and TP (8.9 ± 5.4 mg/L) in sewers were higher than in channels (50.6 ± 12.2 mg/L, 83.9 ± 43.9 mg/L, and 5.4 ± 2.2 mg/L, respectively) which might be caused by the drainage characteristics of residents and dilution of surface water in channels. The pollutant concentration in this study was generally higher than sewage in treatment facilities (Zhang et al. 2016; Cao et al. 2019). Sewage collection systems had the pretreatment ability so that upstream sewage usually contained more pollutant than the downstream (e.g. treatment facilities) (Liao et al. 2015; Jia et al. 2021). Based on Spearman correlation analysis (P < 0.05) (Figure 3(b)), conventional pollution parameters (e.g. COD, AN, TN, and TP) showed significantly positive relation with each other in channels and sewers. In channels, T was an important environmental parameter which had significant negative relations with COD and TN, relating to the higher water consumption and bacterial metabolic activity in higher T conditions (Hvitved-Jacobsen et al. 2013; Ji et al. 2021). The sulfide concentration (∼0.4 mg/L) did not show any significant relation with other parameters as the frequent exchange of substance, including sulfide, with the outside environment caused by the open channel structure. However, in sewers, the sulfide concentration (∼0.6 mg/L) showed significant relation with ORP and other conventional pollution parameters. In detailed, lower ORP caused suitable conditions for anaerobic bacteria including sulfate-reducing bacteria (SRB) which could further generate more sulfide (Hvitved-Jacobsen et al. 2013; Liu et al. 2016b). Additionally, DO concentration showed significantly negative relations with AN and TN which conformed to the basic law of the nitrification process (Ge et al. 2015; Wang et al. 2020). Interestingly, the SS concentration did not show any significant relation with other parameters either in channels or in sewers while TU plays important roles in both channel and sewer sewage quality relationship. Other research also observed that TU, not SS, had more significant relations with other pollutants (Li et al. 2022).
Figure 3
The sewage quality (a) and Spearman correlation relationship (P < 0.05) (b) of channels and sewers.
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The sewage quality (a) and Spearman correlation relationship (P < 0.05) (b) of channels and sewers.

Figure 3
The sewage quality (a) and Spearman correlation relationship (P < 0.05) (b) of channels and sewers.
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The sewage quality (a) and Spearman correlation relationship (P < 0.05) (b) of channels and sewers.

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Bacterial characteristics of the channel and sewer

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Based on 16S rRNA gene whole region sequencing, 339,538 sequences were obtained from 48 samples including sewage, biofilms, and deposits samples, assigned to 2922 OTUs. The alpha-diversity index of samples is given in Figure 4(a). Deposits in channels and sewers had higher ACE and Shannon index compared by sewage and biofilms, indicating that bacteria in deposits had more abundant and diverse communities. The principal component analysis (PCA) (Figure 4(b)) indicated that bacterial communities deposits were similar with biofilms to some extent but showed difference with sewage bacteria communities. Compared with the distinction caused by different phases (i.e. sewage, biofilms, and deposits), the difference caused by collection types (i.e. channels and sewers) was relatively lower.
Figure 4
Alpha diversity index (a) and principal component analysis (b) of bacterial communities. The black and red vertical bars of (a) are standard deviations, n = 8.
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Alpha diversity index (a) and principal component analysis (b) of bacterial communities. The black and red vertical bars of (a) are standard deviations, n = 8.

Figure 4
Alpha diversity index (a) and principal component analysis (b) of bacterial communities. The black and red vertical bars of (a) are standard deviations, n = 8.
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Alpha diversity index (a) and principal component analysis (b) of bacterial communities. The black and red vertical bars of (a) are standard deviations, n = 8.

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The phylum presented in the highest relative abundance in rural sewage collection systems was Proteobacteria (70.3% ± 21.0%), followed by Firmicutes (12.1% ± 11.0%) (Figure 5(a)), similar to previous research (Meng et al. 2019). Acinetobacter (∼22.3%), Pseudomonas (∼18.6%), and Psychrobacter (11.8%) were the abundant bacterial genus in rural sewage collection systems (Figure 5(b)) which were also founded in other sewers (Zhang et al. 2022). As for the species level, obvious differences occurred in the 48 samples. From a holistic point of view, Pseudomonas fragi (∼13.8%), Acinetobacter harbinensis (∼11.8%), and Psychrobacter cibarius (∼8.7%) were abundant and the relative abundance of Acinetobacter bohemicus (∼5.1%) and Acinetobacter albensis (∼4.3%) was also relatively high (Figure 5(c)). Additionally, some species of human intestinal bacteria or potential pathogens such as Escherichia coli (∼0.1%), Klebsiella pneumoniae (∼0.1%), and Enterococcus faecium (<0.01%) were detected in this study which was consistent with the characteristics of domestic sewage. Based on LEfSe analysis (Figure 6), bacterial communities (including sewage, biofilms, and deposits) in channels and sewers showed significant distinction. The relative abundance of 12 genera of bacteria in channels, e.g. Acidovorax, Acinetobacter, Brevundimonas, and Flavobacterium, was significantly higher than sewers while two genus of bacteria, i.e. Dokdonella and Syntrophorhabdus, were opposite. At the species level, there were 14 species of bacteria (e.g. Acidovorax defluvii, Acinetobacter harbinensis, Brevundimonas bullata, and Flavobacterium tegetincola) had significantly higher abundance in channels than in sewers while four species of bacteria (i.e. Amaricoccus macauensis, Dokdonella immobilis, Pseudomonas putida, and Pseudomonas viridiflava) were opposite. Acidovorax defluvii and Acinetobacter harbinensis can inhabit in activated sludge and river water, respectively (Schulze et al. 1999; Li et al. 2014). Amaricoccus macauensis and Dokdonella immobilis can inhabit in activated sludge (Maszenan et al. 1997; Liu et al. 2013). The structure and operation distinction of sewers and channels caused significant influences on their bacterial communities of sewage, biofilms, and deposits.
Figure 5
Bacteria characteristics at the phylum (a), genus (b), and species (c) level.
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Bacteria characteristics at the phylum (a), genus (b), and species (c) level.

Figure 5
Bacteria characteristics at the phylum (a), genus (b), and species (c) level.
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Bacteria characteristics at the phylum (a), genus (b), and species (c) level.

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Figure 6
LEfSe analysis of bacteria in channels and sewers.
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LEfSe analysis of bacteria in channels and sewers.

Figure 6
LEfSe analysis of bacteria in channels and sewers.
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LEfSe analysis of bacteria in channels and sewers.

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Biochemical process difference between the channel and sewer

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The main function of sewage collection systems, e.g. channels and sewers, is to collect and transfer sewage to the wastewater treatment plants. The biochemical process caused by bacteria in biofilms and deposits could increase the pollutant removal effect. Figure 7(a) indicates that rural sewage collection systems effectively removed COD and SS in sewage. The pollutant removal capacity could reach 3.1 mg COD·L−1·m−1 and 6.3 mg SS·L−1·m−1 in channels while 5.5 mg COD·L−1·m−1 and 1.8 mg SS·L−1·m−1 respectively in sewers. TP in sewage were basically not removed during the channels or sewers. This was caused by the low abundance of polyphosphate accumulating organisms (PAOs) in this study. Only one genus of PAOs (i.e. Tetrasphaera) (Nielsen et al. 2019) was detected in this study and its relative abundance was much lower than 0.1%. In this study, sewers had lower SS removal capacity but higher COD removal ability than channels. The shear stress, related to the slope and hydraulic radius of gravity sewers or channels (Park et al. 2014; Li et al. 2019a), was the main influencing factor of sedimentation process which was related to the SS removal. Sewers had better hydraulic radius than channels as the distinction of structure so that sewers had higher shear stress and reduced the sedimentation process which led to lower SS removal. Sewers and channels had sulfate and nitrogen removal ability as the existence of sulfate-reducing bacteria (SRB), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB), shown in Figure 8. Additionally, Figure 7 showed that channels had better sulfate removal ability (2.4 mg·L−1·m−1) and lower AN removal ability compared with sewers (0.6 mg SO42−·L−1·m−1) which was also demonstrated by the abundance of SRB and AOB in Figure 8. In this study, SRB, AOB, and NOB preferred to live in deposits and their distribution was significantly influenced by environmental factors, e.g. COD, AN, TU, SO42-, and ORP, etc. Figure 7(b) indicates that abundance of SRB mainly had a positive relation with conventional pollutants, such as COD, AN, and TP, while had a negative relation with ORP, consistent with its growth characteristics (Muyzer & Stams 2008; Dong et al. 2017). In this study, AOB and NOB had positive relations with DO and ORP, also consistent with their characteristics (Shen et al. 2012; Srithep et al. 2015).
Figure 7
The pollutant removal capacity (a) and the canonical correlation analysis (b) of bacteria in channels and sewers. The black vertical bars of (a) are standard deviations, n = 4.
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The pollutant removal capacity (a) and the canonical correlation analysis (b) of bacteria in channels and sewers. The black vertical bars of (a) are standard deviations, n = 4.

Figure 7
The pollutant removal capacity (a) and the canonical correlation analysis (b) of bacteria in channels and sewers. The black vertical bars of (a) are standard deviations, n = 4.
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The pollutant removal capacity (a) and the canonical correlation analysis (b) of bacteria in channels and sewers. The black vertical bars of (a) are standard deviations, n = 4.

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Figure 8
The relative abundance of sulfate-reducing bacteria (SRB), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB).
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The relative abundance of sulfate-reducing bacteria (SRB), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB).

Figure 8
The relative abundance of sulfate-reducing bacteria (SRB), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB).
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The relative abundance of sulfate-reducing bacteria (SRB), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB).

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Based on the BugBase method, deposits in channels and sewers had more anaerobic bacteria while the biofilms contained more aerobic bacteria (Figure 9), consisting with their living environment. Carbon, nitrogen, and sulfur metabolism function were generally distributed in all samples, predicted by the PICRUST2 method, and sewers had significantly higher carbon metabolism ability but lower sulfur metabolism ability compared with channels (Figure 9). This phenomenon could well explain the distinction of pollutant removal ability (COD and sulfate) in sewers and channels (Figure 7(a)) from the perspective of microbial function. Therefore, except for the structural difference in rural sewage collection systems, the significantly difference of bacterial communities and their functions also lead to the difference of pollutant removal in channels and sewers. Rural sewage collection channels had essential structural and functional differences with rural sewage collection sewers.
Figure 9
The predicted function of the bacteria in channels and sewers. C, channels; S, sewers. The black vertical bars of column chart are standard deviations, n = 8.
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The predicted function of the bacteria in channels and sewers. C, channels; S, sewers. The black vertical bars of column chart are standard deviations, n = 8.

Figure 9
The predicted function of the bacteria in channels and sewers. C, channels; S, sewers. The black vertical bars of column chart are standard deviations, n = 8.
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The predicted function of the bacteria in channels and sewers. C, channels; S, sewers. The black vertical bars of column chart are standard deviations, n = 8.

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Inspiration, limitations and future perspectives

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In the current study, the difference of sewage quality, bacterial communities, and their metabolism function in rural sewage collection channels and sewers were compared and the intrinsic and essential differences were found. Generally speaking, sewers are most likely to be recommended to replace channels in rural areas as sewers have a better appearance and can avoid odor effusion. However, based on the results of this study, using channels or sewers to collect rural sewage need to be considered according to the treatment process and operational characteristics. Without the substance and energy exchange with the outside environment, sewage in sewers contained more pollutant, except for the SS, and more sulfide which might increase the load of terminal treatment facilities and cause operation and maintenance risk. Additionally, the high COD removal effect in sewers might cause insufficient carbon sources for denitrification in treatment facilities, and further deteriorating operating conditions of rural sewage systems. The desilting process to remove deposits in both channels and sewers is necessary because of the high abundance of SRB in deposits which can cause sulfide generation. However, there was still a limitation of this study. All of the data were obtained in the actual rural sewage systems, leading to the difficulty to control variables precisely and further becoming the sources of errors in this study. Future research is supposed to establish lab scale channels and sewers to precisely control the variable and test the results of this study.

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The sewage quality and bacterial characteristics of eight rural sewage collection systems, including four channels and four sewers, were investigated and the distinction between channels and sewers was analyzed. The open structure of channels caused significantly higher SS but lower sulfide concentration compared with sewers. T and ORP were the important environmental factors in channels and sewers, respectively. Proteobacteria, Acinetobacter, and Pseudomonas fragi were the most abundant bacteria in rural sewage collection systems at the phylum, genus, and species level, respectively. The bacterial communities showed significant differences (at least 14 genera and 18 species detected by LEfSe method) between the channels and sewers. Channels had higher SS and sulfate removal abilities but lower COD removal abilities compared with sewers and the causes of distinction were explained by the structural characteristics and bacterial function analysis.

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The authors gratefully acknowledge the financial support of the Housing and Urban-Rural Construction Science and Technology Planning Project of Shaanxi Province (Program No. 2021-K46), the Innovation Capability Support Program of Shaanxi (Program No. 2022KJXX-14), and the Qin Chuang Yuan Cited High-level Innovation and Entrepreneurship Talents Project (Program No. QCYRCXM-2022-73).

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Data cannot be made publicly available; readers should contact the corresponding author for details.

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The authors declare there is no conflict.

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