Long-distance water transfer projects are important for water allocation. To enhance our understanding of biofilm growth and changes in water quality during raw water transfer, raw water through a long-distance non-full culvert at flow rates of 1.4−2.0 m/s was studied. The results revealed that: (1) the biofilm total cell count (TCC) and heterotrophic plate count (HPC) were the highest at a flow rate of 1.5 m/s, which were 3.7 × 104 cells/cm2 and 1.1 × 103 CFU/cm2, respectively; (2) Proteobacteria had the highest relative abundance (RA) among all samples, and the RA in biofilm (78.85%) was higher than that in water (48%−59%); (3) when the pollutants and biofilm were partially shed, the total phosphorus (TP), permanganate index (CODMn), and dissolved organic carbon (DOC) increased by 0.011, 0.36, and 0.5 mg/L at most, respectively; and (4) dissolved oxygen (DO) was sufficient during non-full flow water transport and nitrification occurred. The highest removal rates of ammonium nitrogen (NH+4-N) and nitrous nitrogen (-N) reached 27.16% and 66.76%, respectively. At the flow rates of 2.0 m/s, the efficiency decreased to 10.47% and 41.25%, respectively, due to the shedding of biofilm.
Raw water flow over a long-distance non-full culvert studied using an experimental model.
Flow rate influenced biofilm biomass, and resulted in different bacterial community structure in bulk water and biofilm.
Nitrification in the long-distance non-full culvert was only 0.54%–1.2% that in the simulation experiment.
With the development of society and the improvement of human living standards, global water consumption is rising. The uneven distribution of water resources is becoming increasingly prominent and has severely hindered the socioeconomic development of water-scarce areas. Water transfer projects realise the cross-regional allocation of water resources through pipelines, culverts or open channels and have become an effective measure for mitigating differences in the regional distribution of water resources.
Shaanxi Province, China, has the problem of large differences in the distribution of water resources, and the water resources are more abundant in the south than in the north. To optimise the allocation of water resources and promote regionally coordinated development, the Hanjiang-to-Weihe River Project was developed. The Hanjiang-to-Weihe River Project transports raw water from the Hanjiang River to the Weihe River basin through a long-distance culvert which has a total length of 98.3 km. It is designated for a flow rate of 70 m3/s, with a slope drop of 1/2,500 (Liu & Mao 2017).
Researchers have conducted numerous studies on the evolution of water quality and its driving factors in the process of water transportation. However, the existing water transportation studies have primarily focused on drinking water distribution systems, reuse water and sewage pipes, with a few studies on raw water transportation. The flow rate that was set in such studies was either low or fixed, the hydraulic residence time (HRT) was short, and the dissolved oxygen (DO) decreased gradually (Table S1, Supplementary Information). Many water distribution network studies have shown that a high flow velocity reduces biofilm microbial biomass (Manuel et al. 2009), affects microbial community structure (Douterelo et al. 2016a, 2016b), and causes changes in microbial gene expression and metabolic levels (Nauman et al. 2007; Crabbé et al. 2008). It is difficult to achieve the reoxygenation process using reactors such as biofilm annular reactors (BAR). The Hanjiang-to-Weihe River Project integrates the characteristics of high and variable flow, non-full flow, and substantial water delivery distance. When the water delivery flux is 10−70 m3/s, the flow rate in the culvert is 1.34−2.32 m/s (Ma 2018). The actual flow rate will fluctuate within this range. It is difficult to effectively evaluate the biofilm and water quality characteristics of the water transport process based on the results of the existing research.
To the best of our knowledge, no current research focuses on the characteristics of ultra-long distance, non-full flow, and continuously changing high flow rates in a simulated water transfer reactor. It is not clear whether biofilm formation and evolutions in water quality are in accordance with the conclusions of the existing studies. The objective of this study is to simulate raw water transport in a long-distance and non-full culvert as accurately as possible to obtain the growth law of biofilm and determine its impact on water quality. This is of great significance for ensuring water quality safety in the study region.
MATERIALS AND METHODS
Experimental equipment and operating conditions
The experimental equipment for long-distance water delivery is shown in Figure S1. It consists of stainless-steel pipes with a diameter of 40 cm, a circulating water pump, two water tanks, valves and a carbon steel bracket (Figure 1(a)). Biofilm coupons are mounted on stainless steel pipes for biofilm sampling (Figure 1(b) and 1(c)). The installation method of the biofilm coupons is described in S1 and Figure S2 (Supplementary Information).
The water used in this study was taken from a drinking water reservoir in northwest China. The major characteristics of the raw water are listed in Table S2. Based on engineering design parameters, five operating conditions were set to simulate the water transportation process of raw water flowing through a 250 km culvert at different flow rates (Table 1). Under each operating condition, the raw water circulates an HRT as a cycle, and the experimental water is replaced with fresh raw water after each cycle. Before the start of the experiment, the system was operated at a flow rate of 1.4 m/s for 15 days, and subsequently, it was operated for three cycles in each operating condition in turn from the beginning of operating condition I.
|Operating conditions .||Flow rate (m/s) .||Flow flux (m3/h) .||HRT (h) .||Slope (%) .||Fullness degree .||Shear force (Pa) .||Minimum DO (mg/L) .||Sampling point (km) .|
|Operating conditions .||Flow rate (m/s) .||Flow flux (m3/h) .||HRT (h) .||Slope (%) .||Fullness degree .||Shear force (Pa) .||Minimum DO (mg/L) .||Sampling point (km) .|
Collection and pre-processing of pipeline biofilms
Two biofilm coupons were removed before the start of the experiment and after the experiments with flow rates of 1.6 m/s and 2.0 m/s, where one was performed for the measurement of the biofilm's heterotrophic plate count (HPC) and total cell count (TCC) and the other was for biofilm observation using scanning electron microscopy. Two to three sterile cotton swabs were used to repeatedly wipe the surfaces of the biofilm coupon, after which the cotton swabs were placed quickly into a centrifuge tube containing 10 mL of sterile water. This was then ultrasonically vibrated at 40 kHz for 20 min to disperse the microorganisms in the water (Lu et al. 2005; Zhu et al. 2013).
Sample determination and analysis
Scanning electron microscopy (SEM) of biofilms
The coupons were rinsed with sterile water and fixed with 2% glutaraldehyde at 4 °C for 1 h. The samples were then dehydrated in ethanol solutions of different concentrations (30%, 50%, 70%, 90%, 95%, and 100%) in turn, with samples maintained in each concentration for 15 min. Finally, the ethanol was replaced with tert-butanol twice for 30 min each time, and subsequently, freeze-dried overnight before spraying with gold. A JSM-6510 (JEOL Ltd, Tokyo, Japan) scanning electron microscope was used for the analysis.
HPC and TCC
R2A agar was used for the HPC analysis; samples were diluted, spread on the medium, and subsequently incubated at 25 °C for 7 d before counting (Thayanukul et al. 2013). TCC was measured using the following steps. SYBR™ Green I (Thermo Fisher Scientific, Waltham, MA, USA) was diluted with DMSO (MP Biomedicals, Santa Ana, CA, USA) at a ratio of 1:100 to obtain the stain stock solution. Five microlitres of the stain stock solution was added to a 500 μL water sample, which had been diluted 50 times. The above samples were then dyed in the dark at 30 °C for 15 min before measuring TCC using a flow cytometer (Accuri C6, Becton, Dickinson and Company, Franklin Lakes, NJ, USA) (Berney et al. 2007; Hammes & Egli 2010).
Biological and chemical analysis of water quality
Water samples were collected at 0, 20, 40, 60, 80, 100, 150, 200, and 250 km per cycle as shown in Table 1. HPC and TCC in water samples were determined according to the method described above. The water quality, including total nitrogen (TN), dissolved total nitrogen (DTN), nitrate nitrogen (-N), NH+4-N, -N, total phosphorus (TP), dissolved total phosphorus (DTP) and permanganate index (CODMn), was assessed according to standard methods issued by the Chinese State Environmental Protection Agency (SEPA of China 2002). The dissolved organic carbon (DOC) of the bulk water was measured using a total organic carbon analyser (TOC-L, Shimadzu Corporation, Kyoto, Japan).
DNA extraction and quantification
In the first cycle of the experiment with a flow rate of 1.6 m/s, water samples at distances of 0, 100, and 250 km were collected, and biofilm samples were collected at the end of the cycle to analyse the difference in the bacterial community structure in water and in biofilm. DNA samples were extracted using E.Z.N.ATM Mag-Bind Soil DNA Kits (M5635-02; OMEGA Bio-Tek, Inc., Norcross, GA, USA), and DNA amplification was performed using a T100™ Thermal Cycle (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Universal primers 338F (5′-ACTCCTACGGGAGG-GAGC-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) in the V3–V4 region of the 16SRNA gene were used for PCR amplification. The polymerase chain reaction (PCR) reactions (30 μL) contained: 15 μL 2 × Hieff® Robust PCR Master Mix (10105ES03; Yeasen Biotech Co., Ltd, China), 1 μL 338F primer, 1 μL 806R primer, and 13 μL of Milli-Q water. Two PCR reactions were performed for each DNA extraction and each amplicon was quantified using a Qubit 3.0 DNA BR Assay Kit (Q10212; Thermo Fisher Scientific). The amplification reaction for the first reaction was as follows: 94 °C for 3 min followed by five cycles of 94 °C for 30 s, 45 °C for 20 s, and 65 °C for 30 s, followed by 20 cycles of 94 °C for 20 s, 55 °C for 20 s, 72 °C for 30 s, and an extension of 72 °C for 30 s; finally, an extension of 72 °C for 5 min was done. The conditions of PCR for the second reaction were as follows: 94 °C for 3 min followed by five cycles of 94 °C for 20 s, 55 °C for 20 s, 72 °C for 30 s, and a final extension at 72 °C for 5 min.
Illumina MiSeq analysis
MiSeq sequencing (Illumina, San Diego, CA, USA) of the DNA samples was performed by Sangon Biotech Co., Ltd (Shanghai, China). The sequences were pre-treated using Cutadapt (1.2.1) to remove chimeras and off-target sequences, and subsequently, Prinseq (0.20.4) was used for splicing sequence mass. After pre-treatment, 62,389, 64,938, 62,188, and 56,151 16S-RNA gene sequences were obtained for biofilm from the 0 km, 100 km, and 250 km samples, respectively. Next, Usearch (5.2.236) was used to perform operational taxonomic unit classification at a sequencing similarity threshold of 97%.
Calculation of the TCC of biofilm per unit length of pipeline
Calculation of per unit area of biofilm in contact water volume in per unit time
RESULTS AND DISCUSSION
Growth process and influencing factors of biofilm
Biomass of biofilm
The Hanjiang-to-Weihe River Project water transfer rate and flux are constantly changing. The TCC and HPC of the biofilm were measured to assess the impact of the flow rate on the microbial biomass of the biofilm (Figure 2). In contrast to previous steady-state experiments in which the biomass of the biofilm increased with the extension of culture time and eventually stabilised, the biomass in the present study initially increased and then decreased with the change in flow rate. When the flow rate was 1.5 m/s and the shear force was 3.43 Pa (the experimental equipment ran for about 28 days), the TCC and HPC reached peak values of 3.7 × 104 cells/cm2 and 1.1 × 103 CFU/cm2, respectively. At the end of the flow rate of 2 m/s, the TCC dropped to 9.5 × 103 cells/cm2, while the HPC dropped to 2.6 × 102 CFU/cm2. The HPC/TCC also decreased from 3.7% after the experiment with a flow of 1.5 m/s to 2% with the flow rate increased from 1.5 m/s to 2.0 m/s (Figure 2(a)). This ratio is smaller than the value of 5%–20% reported by Wang et al. (2019) and Manuel et al. (2007). In bulk water affected by biofilm shedding, the TCC and HPC increased slightly (Figures 2(b) and 2(c)).
To understand the changes of biofilm biomass more deeply, the biofilm coupons before the 1.4 m/s flow rate experiment and after the experiments with flow rates of 1.6 m/s and 2.0 m/s were observed using SEM (Figure S3). Before the experiment began, few turning marks caused by processing and no apparent microbial community enrichment were observed. After the experiment with flow rate of 1.6 m/s, the surface of the coupon appeared rougher, while the clean area observed previously was covered by irregular microbial communities. After the experiment with the flow rate of 2.0 m/s, the biofilm that had formed previously on the biofilm coupon partially peeled off, exposing more smooth surfaces.
It can be observed from the above results that the biofilm biomass was always at a low level, which is only half or even lower than that of steady-state water transport at low flow rates (Lehtola et al. 2004; Gonzalez et al. 2016; Luo 2016), and was related to flow rate, shear force on the pipe wall, physical and chemical stability of the pipe, water quality, and biofilm cultivation time (Niquette et al. 2000; Douterelo et al. 2014). In this experiment, the flow rate in the pipe gradually increased from 1.4 m/s to 2 m/s, and the shear force on the biofilm was at least 3.01 Pa and at most 5.97 Pa, which was different from the experiment using a single flow rate. At a constant flow rate, the biofilm biomass was higher and more stable, and generally, took place over one to several months. At the same time, the wall surface of the stainless-steel pipe was relatively smooth, and the physical and chemical properties were stable. Compared with cast iron and concrete pipes, it is difficult for microorganisms to attach to stainless steel. The limitation of HPC is that only a few kinds of bacteria can form colonies on the culture medium and that changes in habitat such as water temperature, pH, nutrient depletion, and oxidative stress may cause bacteria to become viable but non-cultivable (VBNC) cells (Ayrapetyan et al. 2015). The slight drop in HPC/TCC after the experiment with a flow rate of 1.5 m/s may result from the following reasons: (1) some of the bacteria cultured in the R2A medium are not suitable for high flow rates and high shear environments, hence they detach from the biofilm; (2) high flow rates may induce bacteria to become VBNC.
Based on the results of this study, it may be concluded that the biofilm in the actual project adheres to the following rule: when the flow rate is lower than 1.5 m/s, it will enter a short period of rapid growth. Subsequently, it gets washed into bulk water before reaching maturity due to the increased flow rate. After the flow rate decreases, it will increase again, and repeat the process in cycles. This reveals that in the actual water transport culvert, the biofilm does not exist in an absolutely stable state, but instead, undergoes a dynamic changing process with low biomass.
Pipes lined with brick cement mortar with a roughness of about 0.0145 were adopted for the water transport culverts in the Hanjiang–Weihe Project. Compared with stainless steel pipes, pipes lined with brick cement mortar provide a better environment for the attachment of pollutants and microorganisms; it has a higher biofilm biomass and pollutant removal rate than that revealed in this study under the same flux and flow rates.
Bacterial communities in biofilm and bulk water
The species with relative abundance (RA) >1% at the phylum level and the species with RA >3% at the genus level were plotted (Figure 3). It can be observed that the RA of Proteobacteria in all the samples, except raw water, exceeded 50%, and the proportion in the biofilm was as high as 78.85%. Actinobacteria in water decreased from 38.41% to 21.47% after 250 km of transport, with only 3.96% in the biofilm. In addition, Nitrospirae and Verrucomicrobia in water increased from 0.68% and 0.13% to 1.83% and 1.24%, respectively, while Firmicutes, which is 0.24% in raw water, accounted for 2.96% of the biofilm samples.
The results of genus-level classification show that the total proportion of all genera with RA >3% is 26.23%–38.32%, reflecting the high diversity of bacterial communities. Meanwhile, except for Pseudomonas (15.2%) in the biofilm, species with RA >10% were not found in the other samples. Changes in flow state and nutrient concentration caused continuous changes in the bacterial communities in the water. Specifically, Pseudomonas dropped from 6.15% to 0.12%, Flavobacterium dropped from 4.54% to 0.66%, and Ilumatobacter decreased from 3.3% to 1.28%. In addition, some of the bacterial RA gradually increased after adapting to the non-full flow environment, including Methylophilus and Perlucidibaca, which increased from 0.36% and 0.07% to 5.38% and 5.71%, respectively. It can be observed that there are differences in communities between bulk water at different transportation distances and between bulk water and biofilm.
Proteobacteria, which have the highest RA in all samples, have diverse metabolic patterns; many of them are functional bacteria (such as Pseudomonas and Burkella) that can remove organic matter and nitrogen (Feng et al. 2017). In addition, Firmicutes can produce spores to resist harsh environments. Proteobacteria, especially β-proteobacteria, can secrete a large quantity of extracellular polymers (Nascimento & Martins 2011). Both of them had a higher RA in biofilm than in water. Actinomycetes, which has a relatively high abundance in raw water, accounts for only 3.96% of the biofilm due to its poor resistance to scour. Although there are many factors affecting the bacterial community structure, the influence of a high flow rate and high shear force on the community structure may be much more significant than other factors, which is consistent with the views of Holinger et al. (2014) and Douterelo et al. (2016a, 2016b). In the biofilm, we also found Pantoea (4.55%), which is often found in soil, sediments, wetlands, and activated sludge, and has a strong ability to gather phosphorus and remove nitrogen.
Variations in water quality during transportation
Variations in organic matter
CODMn and DOC monitoring in the experiment revealed that concentrations of organic matter vary under different flow states. The maximum reduction in CODMn and DOC was 0.42 mg/L and 0.17 mg/L, respectively, when the flow rate was 1.5 m/s (Figure 4). In the later stages of the experiment, a part of the biofilm and loose deposits was desorbed due to the increase in the flow rate, such that CODMn and DOC increased by 0.36 mg/L and 0.49 mg/L at most, respectively. The reduction in CODMn was an expected result of the oxidation of organic matter and reduction of substances by microbial utilisation, which is also why CODMn drops more than DOC. Overall, the organic index of raw water decreased when the flow rate was lower than 1.5 m/s and increased when the flow rate was higher than 1.5 m/s, with a fluctuation range of ±0.5 mg/L.
Variations in phosphorus
Throughout the experiment, the concentration of TP in raw water was 0.048 ± 0.005 mg/L, and due to the high turbidity of raw water ranging from 1.4 m/s to 1.6 m/s, the particulate phosphorus accounted for 31.6%–46.95% of the TP. Both TP and DTP showed a significant decrease, with the decrease in TP being slightly greater than that of DTP (Figure 4). The decrease in TP was caused by the assimilation of the biofilm and the suspended bacteria; however, part of the particulate phosphorus settled on the biofilm during water transport, thereby becoming part of the biofilm as a pollutant crosslinked with microorganisms.
The increase in the flow rate caused the biofilm microorganisms to fall off, and at the same time, caused resuspension of the particle phosphorus in the pipe wall sediments. Therefore, DTP, especially TP, showed a fluctuating upward trend in the later stage of the experiment, with a maximum increase of 0.012 mg/L. If the flow rate were to be lowered again, the results would mimic those at the flow rates ranging from 1.4 m/s to 1.6 m/s.
Nitrification in the pipeline
Researchers have found that nitrification of biofilm is common in drinking water and raw water transportation. In this experiment, the concentrations of NH+4-N and -N decreased by different degrees at different flow rates (Figure 5). The change in the removal rate of NH+4-N and -N was similar to the change in the biofilm biomass, reaching a peak value of 27.16% and 66.76%, respectively, followed by a gradual decrease. Consistent with the findings of a previous study (Xu et al. 2018), -N continues to rise with a decrease in NH+4-N and -N; the stronger the nitrification, the higher the increase. However, the nitrification rate in the present study was found to be much lower than that reported in similar studies with shorter HRT (Luo 2016; Xu et al. 2018). When the flow rate was less than 1.5 m/s, TN and DTN showed a slight decrease caused mainly by the assimilation of biofilm.
Comparison of experiment and culvert conditions
Although the pipe roughness used in the simulation experiment is different from that of the actual project, the hydrodynamic simulation is similar, so the changes of organic matter and phosphorus in the actual water delivery process are similar to the simulation results. However, nitrification is affected by the microbial growth environment, biomass, and concentration of the substrate. This experiment simulates the reoxygenation process, such that the DO is maintained at 8 mg/L. At the same time, the experimental water was taken from a source water reservoir in northwest China, ensuring that the substrate concentration was similar. The flow rate became an important factor affecting nitrification by altering the TCC of the biofilm per unit length. Therefore, we calculated the total number of bacteria per unit length of the pipeline at different flow rates according to Equation (1), and the results are listed in Table S3.
As can be deduced from Table S3, even though the fullness and wetted perimeter increased with the flow rate, the total number of biofilm bacteria per unit length decreased gradually after the flow rate of 1.5 m/s. The slope of the first-order reaction equation also shows the same result. The absolute value of the slope is the highest at 1.5 m/s and the nitrification reaction is the fastest. When the flow rate exceeds 1.5 m/s, the time required for the water to flow through the unit length of the pipeline is shortened, and the TCC of the biofilm per unit length of the pipeline decreases. Both lead to a decrease in the removal rate.
It cannot be ignored that the amount of water contacts with the biofilm per unit area per unit time also affected the water purification effect, which was defined as μ. The smaller the μ, the smaller the amount of water contact with the biofilm per unit area per unit time, and the stronger the nitrification. According to Equation (2), μ is closely related to the diameter of the pipeline, but the size of the pipeline used in the experiment is different from the actual water culvert.
As shown in Table S4, in the experiment, the range of μ is 0.0005–0.0008 m/h, while the value of the actual culvert is 0.0421–0.1428 m/h at the corresponding flow rate. It is found that the μ of the simulated pipeline is only 0.54%–1.20% of the actual water delivery culvert, which indicates that the water purification effect of the biofilm in the actual water conveyance process may be far less than that observed in the simulation experiment. Combined with the experimental results, the Hanjiang-to-Weihe River Project has very limited effects on water quality improvement.
In this study, the influence of constantly changing high flow rate (1.4−2.0 m/s) on the development of biofilm and the evolution of water quality in a long-distance, non-full culvert was examined, which has been rarely evaluated in previous experiments. According to the experimental results, alterations in biofilm biomass and community composition were detected, and their effects on water quality were investigated. Finally, the relationship between the simulation results and the actual water transfer process is established by the calculation of the μ value. The primary conclusions are drawn as follows:
Unlike previous studies, under changing high flow rates, the TCC and HPC of the biofilm are consistently low and in continuous fluctuation, with a range of 1 × 103−1 × 104 cells/cm2 and 1 × 102−1 × 103 CFU/cm2, respectively.
There is a difference in the structure of the bacterial community in the biofilm and bulk water. Proteobacteria had the highest relative abundance (RA) in all samples. Subject to changing high flow rates, Firmicutes and Proteobacteria with a strong resistance to harsh environments have higher RA in biofilms. Pantoea may have been found for the first time in the biofilm of the raw water transport pipeline and is found to have a positive effect on water purification in water transportation.
Nitrification occurring in the long-distance, non-full flow water conveyance process has a certain purification effect; however, due to the large water volume and the low biofilm biomass, the removal rates of NH+4-N and -N were only 10.87%/100 km and 26.7%/100 km, respectively, which were lower than the values reported in similar studies. Furthermore, the calculation of μ showed that the purification effect was limited in the actual culvert.
Scouring causes the resuspension of the biofilm and the particulate pollutants; however, to a certain extent, it reduces the engineering maintenance problems caused by the deposition of pollutants in the pipeline. To reduce the maintenance costs of the project, we suggest that high flow flux and flow rates should be adopted in the long-distance water transport culvert.
This work was funded by Joint Foundation of Shaanxi (NO. 2019JML-61).
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.