Municipal wastewater effluent influences dissolved organic matter quality and microbial community composition in an urbanized stream

STOTEN-135952; No of Pages 9 Science of the Total Environment xxx (xxxx) xxx

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Municipal wastewater effluent influences dissolved organic matter quality and microbial community composition in an urbanized stream Minda Yu a,c,1, Sijia Liu a,d,1, Guowen Li a,b, Hui Zhang a,b, Beidou Xi a,b, Zaifeng Tian e, Yuan Zhang f, Xiaosong He a,b,⁎ a

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Beijing 100012, China School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China d School of Environment and Energy, South China University of Technology, Guangzhou 510006, China e Hebei Provincial Academy of Environmental Science, Shijiazhuang 050030, China f Hebei Engineering Research Center for Geographic Information Application, Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050030, China b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• DOM in the effluent of WWTPs has a great influence on the river water quality. • Macromolecular humic acid is the main component of DOM in the river. • The number of bacteria is scarce, the community structure is unstable and the uniformity is good. • Eukaryotic community responds weakly to the discharge of WWTPs effluent. • The community structure of actinobacterial is simple, unstable and the uniformity is poor.

a r t i c l e

i n f o

Article history: Received 15 October 2019 Received in revised form 2 December 2019 Accepted 3 December 2019 Available online xxxx Editor: Frederic Coulon Keywords: Dissolved organic matter (DOM) Effluent-dominated stream Microbial community composition

a b s t r a c t Dissolved organic matter (DOM) from wastewater treatment plant (WWTP) effluent poses serious threats to the receiving aqueous ecosystems and their microbial communities. However, the correlation between effluentderived DOM and microbial community diversity in urbanized rivers is still poorly understood. In this study, the response relationship between the microbial community dynamics and the DOM evolution process in the effluent-dominated Xiaohe River was revealed. The results showed that macromolecular humic acids were the main components of DOM in this river with more carboxylic acid groups and humic-like acid substances found upstream and protein-like substances dominated downstream. The bacterial abundance in the upstream section of Xiaohe River was low, while its community structure was unstable but exhibited good uniformity, and the bacterial diversity in the downstream was rich. The response of bacterial and eukaryotic communities to WWTP effluent was weak, while that of Actinobacteria to WWTP effluent was more prominent. Furthermore, different microbial communities were affected by different compositions and structure of DOM in the effluent of WWTP. The protein-like components in DOM had the most profound impact on the microbial community, followed by polysaccharides and components rich in hydroxyl and amino functional groups. The study grasped

⁎ Corresponding author at: State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. E-mail address: [email protected] (X. He). 1 These authors contributed equally to this work.

https://doi.org/10.1016/j.scitotenv.2019.135952 0048-9697/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: M. Yu, S. Liu, G. Li, et al., Municipal wastewater effluent influences dissolved organic matter quality and microbial community co..., Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.135952

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the migration and evolution of DOM in rivers with unconventional water recharge, and revealed their diverse effects on microbial community in urbanized rivers. © 2019 Elsevier B.V. All rights reserved.

1. Introduction In many regions, uncontrolled urbanization and industrialization in urban catchments have resulted in limited water resources in terms of both quantity and quality. Due to the growing shortage of water resources, rivers in arid to semi-arid regions of the world receive a major portion of their base flow from municipal and industrial wastewater effluent (Rice et al., 2013). Although more stringent discharge limits have been implemented to obtain high effluent quality, it should be noted that effluent still contains large quantities of refractory dissolved organic matter (DOM) (Fauvel et al., 2016; Wang et al., 2019). Surface waters of river ecosystem that receive these DOM-rich effluent can experience detrimental effects, particularly for ecosystem function within the biosphere (Qiu et al., 2016). Microbial communities, which are a fundamental and highly variable component of river ecosystems, exhibit a comprehensive response to environmental pressure and disturbances. Furthermore, the source, concentration and quality of aquatic DOM may response to the strong link between microorganisms and biogeochemical cycles (Qiu et al., 2016). Riverine DOM is a complex mixture that can have different chemical composition, structure and biogeochemical reactivity. The DOM compounds can affect the transport, bioavailability and toxicity of inorganic and organic pollutants (Artifon et al., 2019; Yu et al., 2018). Additionally, DOM can be metabolized by microorganisms in the aquatic environment, as it acts as a carbon and nutrient source for heterotrophic bacteria and some algae (Shi et al., 2016). Previous studies indicate that the molecular weight, chemical composition and functional groups of DOM determine its bioavailability (Benner and Opsahl, 2001). Low molecular weight DOM fractions (urea, amino acids and nucleic acids) can be readily assimilated by microorganisms, whereas humic-type substances are more recalcitrant (Czerwionka, 2016; Fan et al., 2018). To some extent, the quality and composition of aquatic DOM is determined by its source, which plays a significant role in shaping river microbial communities (Qiu et al., 2016). A number of studies have reported the DOM quality or microbial diversity of water, mainly lakes (Nercessian et al., 2005), rivers (Qiu et al., 2016) and oceans (Bai et al., 2014); however, few studies have focused on the relationship between the quality of effluent DOM and microbial communities in effluent-dominated rivers. The Hai River catchment (3.18 × 105 km2), located in northern China, is one of the most developed and densely populated regions in China (Zhang et al., 2017). The catchment waters receive large quantities of municipal and industrial wastewater effluent (Gao and Cheng, 2012). Xiaohe River belongs to the Ziya River system in the Hai River catchment. It is a typical effluent-dominated rivers, which mainly receives direct discharge of effluent from the municipal sewage treatment plant and untreated domestic sewage along the river course (Yu et al., 2018). In a previous study, we characterized the speciation, distribution and composition of organic matter, subsequently revealing its impact on the water quality parameters of the Xiaohe River (Yu et al., 2018). Considering the significant role of DOM in shaping microbial communities, there have not been any detailed studies characterizing the diversity, composition and impact of DOM on the microbial community of the effluent-dominated river. Additionally, the interaction between aquatic microorganisms, specifically bacteria, eukaryotic and actinomycetes, and different optical properties of DOM in this typical effluentdominated rivers of northern China is unknown. The objective of this study was to provide further insights into the diversity, composition and distribution characteristics of the microbial

community and its function in the effluent-dominated rivers ecosystem. In the present study, we used polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) to identify the taxonomic diversity and community distribution of microorganisms in the Xiaohe River surface water. Multiple spectral analysis techniques were applied to discriminate and trace the changes in the DOM quantity and quality along the river course. Multivariate analysis, including parallel factor and canonical correspondence analyses, was used to investigate the linkage between the microbial community and various environmental factors (basic water quality parameters and DOM properties) in the Xiaohe River where effluent is the main factor influencing the dynamics of the aquatic environment. The purpose of this study was to provide an effective management strategy for water quality, control of eutrophication and ecological restoration of river basins. 2. Materials and methods 2.1. Study area and sample collection Xiaohe River is located in Hebei Province, Northern China, covering an area from 38°06′ to 37°52′ N and 114°38′ to 115°01′ E (Fig. 1). The river is approximately 86.5 km long, originating from the Wufengshan, and sewage effluent is its major water source. The river water ultimately flows into the Fuyang River where it merges with the Hai River, making the biggest basin in Northern China. Along the way, recharge water of Xiaohe River comes from industrial and domestic treated and untreated sewage in Luancheng County and Zhaoxian County. There are four wastewater treatment plants discharging tailing water into the Xiaohe River. The basic effluent characteristics of the four WWTPs are presented in the Supplementary Materials (Table 1). Ten sampling sections, numbered from S1 to S10, were set up along the main rivers of the Xiaohe River, and three parallel samples were taken from each sampling section (Fig. 1). Section S1 was downstream

Fig. 1. Map of Xiaohe River with sampling locations.

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M. Yu et al. / Science of the Total Environment xxx (xxxx) xxx Table 1 Physicochemical properties and absorption spectrum parameters of WWTPs effluent. Items

W1 (Qiaodong)

W2 (Qiaoxi)

W3 (Douyu)

W4 (Zhaoxian)

Treatment capacity (10 kt/d) pH DO (mg/L) NH+ 4 -N (mg/L) TN (mg/L) DOC (mg/L) TP (mg/L) COD (mg/L) a(355) E250/365 E253/203 S275–295 S350–400 A226–250 A260–400

60 5.31 5.22 1.33 32.7 35.8 0.87 120 0.28 10.3 0.008 0.031 0.026 15.8 14.5

30 5.53 6.31 0.69 18.2 25.2 1.88 61 0.42 17.1 0.008 0.082 0.049 16.8 13.2

3 5.96 4.51 34.0 50.7 89.2 3.06 232 7.42 2.97 0.041 0.018 0.014 8.35 7.81

10 5.32 2.92 62.9 84 68.1 1.96 159 7.56 4.32 0.082 0.015 0.017 9.56 6.75

Qiaodong WWTPs:60% domestic sewage +40% pharmaceutical enterprise sewage. Qiaoxi WWTPs:Municipal sewage. Douyu WWTPs:Chemical and manufacturing enterprise sewage. Zhaoxian WWTPs:70% domestic sewage +30% food manufacturing enterprise sewage.

of the Qiaodong WWTP; section S2 was downstream of the Qiaoxi WWTP; section S3 was downstream of the Douyu WWTP; and section S8 was downstream of Zhaoxian WWTPs. All water samples were stored in brown bottles at room temperature and immediately filtered with a 0.45 μm Millipore polycarbonate filter membrane. The filtrate was stored in cold storage and shielded from the light. All samples were tested within 2–3 days of collection. 2.2. Physicochemical analyses The water filtrate was brought back to the laboratory and tested for basic water quality indexes. Ammonia nitrogen (NH+ 4 -N) was determined by Nessler's reagent method; nitrate nitrogen (NO− 3 -N) was determined by phenol disulfonic acid spectrophotometry; nitrite nitrogen (NO− 2 -N) was determined by ion chromatography (ICS-2000, Dionex USA); and total nitrogen (TN) was digested by alkaline potassium persulfate and measured by ultraviolet spectrophotometry. Chemical oxygen demand (COD) was titrated with potassium dichromate and total phosphorus (TP) was determined by ammonium molybdate spectrophotometry (Chinese EPA, 2002). 2.3. Spectroscopic analysis Prior to fluorescence spectroscopy and ultraviolet-visible (UV–Vis) analysis, all filtered samples were diluted to 10 mg/L DOC using MilliQ water. The fluorescence spectra of the water samples were obtained with a Hitachi F-7000 fluorescence photometer (Hitachi F-7000, shanghai) using the following scanning parameters. A 150 W xenon arc lamp was selected as the excitation light source of the photometer. The voltage of photomultiplier tube was 700 V, SNR N 110; the excitation wavelength range was 200–450 nm; the emission wavelength was 280–550 nm; the scanning distance was 5 nm; the scanning speed was 12,000 nm·min−1; the slit width of the excitation and emission monochromatic was 5 nm; and the response time was automatic. Three-dimensional fluorescence spectrum scanning was then performed (He et al., 2014). With pure water as the blank, the measured three-dimensional fluorescence value was subtracted from the blank value. Rayleigh scattering and Raman scattering were removed by the interpolation method and then parallel factor analysis was carried out. The fluorescence data of different components of the water samples were derived and the three-dimensional data matrix was analyzed using the DOMFluor toolbox in MATLAB 7.0 (Mathworks, Natick, MA). The score was expressed by the Fmax value and the relative content of

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different components was obtained by EEM-PARAFAC analysis of the changing Fmax values (He et al., 2011; Xiao et al., 2019). The UV–Vis was measured with a Shimadzu UV-1700 ultraviolet spectrophotometer. The scanning wavelength range of the UV spectrum was 200–700 nm and the scanning distance was 1 nm. The DOM absorbance was determined at 204 nm, 210 nm, 250 nm, 254 nm and 365 nm. The absorption coefficient ratios of E210/E254, E254/E204 and E250/E365 were calculated. The ratio SR was obtained by calculating the slope of the straight line fitted to the natural logarithm of absorbance at 275–295 nm and 350–400 nm, denoted as S275–295 and S350–400, respectively (He et al., 2011; Wang et al., 2009). The operational parameters of the American Nicolet 5DX Fourier infrared spectrometer were as follows: scanning range, 4000–400 cm−1; resolution 4 cm−1; and scanning times, 16. A suitable amount of filtrate sample was filtered with a 0.45 μm acetate cellulose membrane, frozen at −20 °C and then vacuum freeze-dried at −54 °C. The solid powder samples were mixed with KBr (spectral purity) at a mass ratio of 1:300, ground under infrared light and then pressed at 50–100 MPa. After measuring the infrared spectrum, the absorption peak areas of the different bands were integrated, and the relative contents of the different functional groups in the effluent-dominated rivers were semiquantitatively characterized using the percentage of absorption peak areas of each functional group. 2.4. Microbial community analysis 2.4.1. DNA extraction and PCR amplification of 16S rRNA Total DNA was extracted from the samples using the TIANGEN soil genome extraction kit. After purification of the extracted DNA, the V3 hypervariable region of the 16S rRNA gene was amplified using the universal bacterial primers 357F/517R and the universal Actinobacteria primers F243/R513. The 18S rDNA gene was amplified using the universal eukaryotic primers NS1/Fung. The PCR mixture contained 1 μL 10 × Tag Buffer 5 μL, 4 μL dNTP solution, 4 μL MgCl2, 1 μL of each primer (10 μmol L−1), 5 U μL−1 Taq DNA polymerase, 0.5 μL of template DNA (20–50 ng) and sterile Milli-Q water to 50 μL. The amplification conditions for the 16S rRNA gene were as follows: initial denaturation at 94 °C for 5 min; followed by 30 cycles of denaturing at 94 °C for 1 min, annealing at 48 °C for 1 min and extension at 72 °C for 1 min; and a final extension at 72 °C for 10 min. The amplification conditions for the 18S rRNA gene as follows: initial denaturation step at 94 °C for 5 min; followed by 10 cycles of denaturation at 94 °C for 30 s, annealing at 60–50 °C for 30 s and elongation at 72 °C for 1 min; and a final elongation step at 72 °C for 10 min. The amplification conditions for the Actinobacteria 16S rRNA gene were as follows: initial denaturation at 95 °C for 10 min; denaturing at 95 °C for 30 s, annealing at 60 °C for 45 s and extension at 72 °C for 1 min; and a final extension at 72 °C for 10 min. The PCR products were then extracted from a 1.5% agarose gel. 2.4.2. DGGE and 16S rRNA sequences PCR samples (100 μL) containing approximately equal amounts of amplicons were loaded onto 1-mm thick 6%–12% (w/v) polyacrylamide gels in 1 TAE buffer using a denaturing gradient ranging from 40% to 60% for the bacterial and Actinobacteria amplicons and 30–50% for the eukaryotic amplicons. The DGGE was conducted by running the gene mutation detection system at 65 V for 16 h at 60 °C. The gels were stained with SYBY-Green I for 30 min and then scanned with a gel imaging system. Bands used for sequencing were cut from the DGGE gels with a sterile scalpel on a 345 nm UV transilluminator and put into tubes with 40 μL buffer solution. The DNA was placed at 4 °C for 24 h and then extracted. The DGGE bands were re-amplified using a forward primer without a GC clamp. The microbial diversity and richness of the DGGE fingerprints were analyzed by Quantity One software. The Shannon index (H′) was calculated using the formula H′ = −Σ Pi ln Pi; the

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Table 2 Water quality parameters of surface water in the Xiaohe River. Items

COD (mg/L)

TP (mg/L)

TN (mg/L)

NH+ 4 -N (mg/L)

NO− 3 -N (mg/L)

NO− 2 -N (mg/L)

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 Maximum value Minimum value Average value

67 69 85 43 52 89 57.5 170 84 69.5 170

3.32 1.49 0.92 1.60 2.11 1.75 1.91 1.64 1.80 1.17 3.32

20.1 17.3 11.1 20.9 20.9 19.6 20.9 21.5 19.3 19.1 21.5

0.95 0.58 6.32 0.53 0.62 0.58 1.02 2.95 1.22 0.75 6.32

10.2 9.53 8.64 2.99 11.5 10.9 11.0 10.9 9.94 10.4 11.5

0.33 0.25 0.44 0.17 0.50 0.74 0.96 1.02 1.02 1.26 1.26

43

0.92

11.1

0.53

2.99

0.17

78.7

1.81

19.1

1.60

9.60

0.70

Simpson index (D) was calculated using the formula D = 1-Σ (Pi)2; and the evenness Pielou index (J′ ) was calculated using the formula J′ = H′/ ln S. Pi represents the relative signal intensity of bands in the lane and S represents the number of sample bands, that is, species abundance. After sequencing, the results were compared with GenBank from the National Center of Biotechnology Information (NCBI) using BLAST. 3. Results and discussion 3.1. Environmental parameters There were some differences in the conventional water quality parameters from the upstream to the downstream samples of the effluent-dominated rivers in the basin (Table 2). The COD ranged from 43 to 170.5 mg/L, with an average value of 78.7 mg/L, and was highest in the S3, S6 and S8 sections. This may be due to the fact that these sections are located downstream of the sewage treatment plant, so the effluent contains a large amount of organic matter. The nitrate and phosphate indexes are used to evaluate water quality. In general, the water samples exhibited high TN (11.06–21.51 mg/L), NH+ 4 -N (0.53–6.32 mg/L), NO− (2.99–11.50 mg/L) and NO− 3 -N 2 -N (0.17–1.26 mg/L) but relatively lower TP (0.92–3.32 mg/L). It is worth noting that the sections with heavy pollution indexes (COD and nitrogen species) were downstream of the sewage outlet. The above results indicate that the WWTP effluent has a great influence on the water quality of the receiving water. Our previous research shows that organic matter is the most important component of the effluent from municipal WWTPs around Xiaohe River and that DOM is one of the main causes of pollution in the receiving water bodies (Yu et al., 2017). However, the composition of DOM is complex and has a serious impact on the received water quality; therefore, it is necessary to further reveal the composition of DOM in combination with a series of spectral analysis methods. 3.2. DOM variability along the transect The PARAFAC model was used to analyze the three-dimensional fluorescence spectra of DOM in the river. Using residual and half-tohalf analyses, five fluorescence components were determined (Fig. 2). Five maximum excitation/emission (Ex/Em) wavelength pairs were identified as (250, 335)/400 nm, (220, 285, 355)/425 nm, (265, 325, 370)/455 nm, (230, 280)/345 nm and (225, 270)/320 nm. The fluorescence peak of component 1 (C1) is mainly derived from fulvic-like substances in biotransformation products of terrestrial or authigenic

organic matter. Component 2 (C2) may also be attributed to fulvic-like substances. The spectral characteristics of component 3 (C3) can be classified as terrestrial humic-like substances (Kowalczuk et al., 2005). Component 4 (C4) has been shown to be strongly associated with bioavailable and biodegradable organic substrates, as well as biodegradable peptide substances. Component 5 (C5) is similar to protein-like substances (Mounier et al., 1999). The Fmax values of the five components were plotted (Fig. 3a). The contents of C1, C2 and C3 showed a downward trend from the upstream to the downstream areas, while the contents of C4 and C5 showed the opposite trend. C1 and C2 were dominant in the upstream, whereas C4 and C5 were dominant in the downstream. The results show that humic-like substances were dominant in the upstream, and protein-like substances were dominant in the downstream of Xiaohe River. From the upstream S1 section to the downstream S10 section, the contents of protein-like substances and tryptophan-like substances sharply increased, indicating that the changes in the composition of these substances were the most obvious. Tryptophan-like substances have been used as an indicator of organic pollution resulting from human activities. These results show that there were great changes in DOM in the urbanized rivers water, which has a great impact on the confluence of WWTP effluent and human activities. Absorption ratios and spectral slope have been suggested as tools to infer the aromaticity, hydrophobicity and molecular weight distribution of DOM (Li and Hur, 2017； Yuan et al., 2018). According to this study, the value of E210/E254 is considered to reflect the aromaticity of organic matter (Fig. 3b). E210/E254 showed a sharp downward trend in the upstream and a relatively stable decline in the middle and lower sections, indicating that DOM in effluent-dominated rivers contains abundant aromatic carbon, phenolic structures and conjugated double bonds. The SR value is positively proportional to the molecular weight of DOM, while E250/E365 is inversely proportional to the molecular weight and aromaticity of organic matter (Giancoli et al., 2003). The SR value increased overall, and the value of E250/E365 was above 5.6 (Fig. 3b and Fig. S1), which indicates that the molecular weight of DOM in the stream gradually increased. The value of E254/E204 reflects the hydrophobicity of organic matter. Its value increased in the upstream and decreased in individual sampling sites downstream, but showed an upward trend overall, indicating that DOM in the effluent-dominated rivers is mainly composed of hydrophobic substances. Hydrophobic organic compounds are mainly composed of macromolecular humic-like acids, whereas hydrophilic organic compounds are mainly composed of low molecular weight substances, polysaccharides, proteins and amino acids (Zularisam et al., 2007). Thus, the DOM in the urbanized rivers is mainly composed of macromolecular humic-like acids. This result is consistent with the above molecular weight analysis. Fourier transform infrared spectroscopy (FTIR) was used to determine the structure of DOM in the effluent-dominated rivers (Fig. 3c). Because urbanized rivers mainly rely on sewage supply, the molecular structure and functional groups of DOM in urbanized rivers were very different. Significant differences were observed in the regions 3400–3300 cm−1 (FTIR5), 1690–1600 cm−1 (FTIR4), −1 −1 1420–1400 cm (FTIR3), 1120–950 cm (FTIR2) and 870–610 cm−1 (FTIR1). As shown in Fig. 3d, the region of 3400–3300 cm−1 represents the O\\H stretching peak of a hydroxyl group and the N\\H vibration absorption peak of an amino group (Ndegwa et al., 2008). The maximum value of the peak was observed in the upstream section S1, and the intensity of the peak gradually decreased with the input of wastewater from the Qiaodong and Douyu WWTPs. This indicates that the confluence of WWTP effluent has an effect on the composition of DOM, as well as the content of functional groups in the effluent-dominated rivers. The 1690–1600 cm−1 region represents the C_C vibration of the amide I band, the C_C skeleton vibration of aromatics and the stretching vibration peaks of

Fig. 2. EEM contours, excitation and emission loadings of the five components identified by DOMFlour PARAFAC analysis.

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intermolecular or intramolecular hydrogen-bonded carboxylic acid (C_O) (Larry et al., 2001). The maximum value of the peak was observed in the upstream section S1, and the peak weakened after the confluence of WWTP effluent, indicating that organic matter, such as lipids and proteins in the effluent-dominated rivers, gradually degraded. The peaks in the 1420–1400 cm−1 region are derived from the absorption of aromatic rings or the stretching vibration of symmetric carboxylate anions. The maximum value of this peak was also in the S1 section, and its intensity gradually increased in section S3 with input from the Douyu WWTP. The intensity of the upstream peak was higher than that of the downstream peak, indicating that there are more carboxylate ions upstream of the effluent-dominated rivers. C\\O stretching peaks (1120–950 cm−1) representing carbohydrates and esters were observed (Larry et al., 2001). The maximum value of the peak was detected in section S9, which may be caused by the inflow of sewage from the Zhaoxian WWTP. The region 870–610 cm−1 represents C\\N, N\\H and C\\H out-of-plane flexural vibration peaks of peptides and proteins (Dignac et al., 2000). This characteristic peak gradually weakened in the effluent-dominated rivers, indicating that the contents of carbohydrates, peptides and proteins decreased in the lower areas. In general, the concentration of DOM and functional groups in the effluent-dominated rivers were affected by the confluence of the WWTP effluent. There were more carboxylate ions in the upstream sections, while the contents of carbohydrates, peptides and proteins in the downstream sections decreased. However, the relative content of structurally complex organic matter tended to increase. Furthermore, DOM affects aquatic ecosystems through its bioavailability, which is closely related to the DOM composition and structure as well as the type of functional groups therein. There are great differences in microbial degradation and utilization of substances that are composed of different functional groups; therefore, it is necessary to further study the diversity and distribution of microbial communities and their functional role in aquatic ecosystems. 3.3. Dynamics and diversity of microbial communities The microbial community composition at different sampling sites was compared using the PCR-DGGE technique. The intensity and brightness of the electrophoretic bands in each lane of the PCR-DGGE fingerprint represent the specific microorganisms and their relative abundance in the microbial community, and more bands indicate a higher species richness. The DGGE fingerprint of the whole bacterial community is shown in Fig. S2a. For this community, 20 bands were obtained, which indicates that the bacterial community was abundant in the river water. For the microbial eukaryotic community, 10 bands were obtained (Fig. S2 b). The most bands were observed in sections S6, S7 and S8, while the least were observed in S1. There were 16 Actinobacteria bands (Fig. S2c), with the lowest number observed in S3 and S7. These results indicate that the microbial communities in the urbanized rivers were rich and different. The Shannon index (H′) mainly reflects the species diversity of a given environment. The evenness Pielou index (J′) is used to analyze the distribution of strains in the community (Zahedi et al., 2019). The larger the value, the closer the content of bacteria in the community. The H′ index of the bacterial, eukaryotic and Actinobacteria communities ranged between 2.50 and 2.89; 1.38 and 1.93; and 2.19 and 2.54, respectively (Table 3). This indicates that the microbial diversity in the water of Xiaohe River is rich. The highest H′ value for the bacterial and eukaryotic communities was observed in the S7 section in the middle and lower reaches, and the minimum value was observed in the upstream S1 section. The J′ values for bacteria in the S1, S2 and S3 sections of the river were relatively low, which indicates that the distribution of bacteria in the upper reaches of the river was less and the community structure was unstable, but the evenness was good. The J′ value of microbial eukaryotic community was relatively low in the S3 section of the river, but it was large and similar in the other sections of the

water. This indicates that the eukaryotic strain content in the whole river is similar and that the response to the confluence of WWTP effluent was not large. The highest value of the Actinobacteria diversity index was in the upstream S2 section, while the J′ value and H′ index of Actinobacteria in the S1 section of Xiaohe River were the smallest. This indicates that the community structure of Actinobacteria in the S1 section of Xiaohe River was unstable and the evenness was not good. In the middle and lower reaches of the Xiaohe River, the J′ value was relatively large, as were the fluctuations of this value, indicating that the content of Actinobacteria in the middle and lower reaches of the river was unstable. When considering the position of the WWTP effluent, it can be concluded that the response of Actinobacteria to the confluence of WWTP effluent is large. The dominant bands in the DGGE profiles of the samples were sequenced and compared to sequences in GenBank of the NCBI database. The results show that the similarity between the bacterial sequences obtained in this study were 97%–100% similar to those in GenBank. The taxa were classified as Bacillariophyta, Proteobacteria, Actinobacteria, Chloroflexi, Bacteroidete and Firmicute, as well as groups of bacteria whose taxonomy could not be determined (Table S1). Bacillariophyta decreased in the S1-S3 section and fluctuated in the middle and lower reaches, but the maximum value was observed in the S8 section (Fig. 4a). Proteobacteria and Actinobacteria fluctuated from the upstream to the downstream sections of the urbanized rivers, but showed an increasing trend overall. Chloroflexi showed a downward trend from the upstream to the downstream. Bacteroidete and Firmicute fluctuated greatly in the upstream, but were relatively stable in the middle and lower reaches. The sequence similarity between the eukaryotic sequences in this study and GenBank data was 99%–100% (Table S2). The microbial eukaryotic community were classified into five groups, namely Ciliophora, Eukaryotes, Mastigomycotina, Basidiomycota and Platyhelminthes. Ciliophora was generally inverted saddle shape. The variation of Eukaryotes fluctuated greatly with no obvious regularity, while Mastigomycotin tended to decrease overall and was highest in the S2 and S3 sections (Fig. 4b). The maximum value of Basidiomycota appeared in section S3 and then decreased, while Platyhelminthes appeared only in section S8. The similarity between the Actinobacteria sequences in this study and GenBank data was 99%–100% (Table S3). These sequences were classified as Rubrobacteridae and Actinobacteridae. Both Rubrobacteridae and Actinobacteridae fluctuated, but Rubrobacteridae tended to increase overall, while Actinobacteridae exhibited the opposite trend (Fig. 4c). In summary, the environment of the urbanized rivers is complex and variable, making the river microbial diversity and species richness. However, the existence of microbial species is affected by environmental factors; therefore, further analysis is required in order to determine this relationship. 3.4. Relationship between microorganisms and water quality parameters The response relationship between the distribution of microorganisms and environmental factors of the water in the effluentdominated rivers was analyzed using CANOCO software. The DNA band distribution and environmental factors were first analyzed by detrended correspondence analysis (DCA). The results show that the maximum gradient length values were b3. Therefore, redundancy analysis (RDA) was used to analyze the correlation between the microbial community composition and the water quality parameters in the effluent-dominated rivers. This analysis helped to determine which factors were most influential and to what extent the various environmental parameters affected the variation in microbial species. The main microbial populations in water were bacteria, eukaryotic and Actinobacteria, so the correlation of the three microorganisms was analyzed. Actinobacteria (B13, B14 and B19) were greatly affected by C1, C2, C4, SR and FTIR5 (Fig. 5a). This indicates that the distribution of Actinobacteria is greatly affected by fulvic-like acid and protein-like

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Fig. 3. Spectroscopic analysis of DOM characteristics in Xiaohe River water: (a) The Fmax value of the five fluorescence components identified by parallel factor analysis; (b) UV–Vis absorption coefficient of Xiaohe River DOM; (c) Changes of FTIR Spectra and (d) relative contents of DOM functional groups.

substances, and is related to the molecular weight, and the hydroxyl and amino groups of organic matter. The response of Chloroflexi (B9) to E250/ E365 was obvious, indicating that this taxon is related to the humification degree, molecular weight and aromaticity of organic matter. Proteobacteria (B11 and B12) was affected by FTIR2 and E254/E204, which indicates that they are affected by polysaccharides, carbohydrates and the hydrophilicity of the effluent-dominated rivers. Ciliophora (F1 and F8) were greatly affected by C4 and SR (Fig. 5b), indicating that they are mainly affected by the molecular weight of proteinlike substances and organic matter. Eukaryotes (F2, F3 and F5) were mainly related to FTIR2, FTIR5 and E254/E204, indicating that its distribution is mainly affected by the content of polysaccharides, carbohydrates, hydroxyl groups and amino groups in the water. Mastigomycotina (F10) was affected by E250/E365, which indicates that it is related to the molecular weight and aromaticity of organic matter. Basidiomycota (F4) was greatly affected by C1 and C2, indicating that it is more responsive to fulvic-like acid substances. Rubrobacteridae (A6 and A10) was affected by C4, FTIR2 and E250/E365 (Fig. 5c), indicating that its distribution is greatly affected by the molecular weight and aromaticity of proteins, polysaccharides, carbohydrates and organic matter. Finally,

Actinobacteridae (A11, A12, A13, A14 and A15) was affected by SR, E254/E204, C1, C2 and FTIR5, indicating that this taxon was more responsive to the molecular weight and hydrophobicity of organic matter, fulvic-like acid substances and hydroxyl and amino groups. 3.5. Environmental implications Our results demonstrated that effluent DOM altered downstream aquatic DOM composition and microbial communities as WWTPs effluent discharged into receiving waterbodies, which has important implications for water quality management and ecosystem protection. Above all, better management of the effluent quality of the inflow to the river is required. Hence, advanced oxidation processes such as ozonation, photo-Fenton, and electrochemical advanced oxidation, may be employed to improve the efficiency of WWTPs in the treatment of effluent DOM (Hofman-Caris et al., 2017; Garcia-Segura et al., 2018; SorianoMolina et al., 2019). Moreover, buffer zones, such as wetlands around confluence areas, may be constructed to improve river water quality. This is to be supplemented with reasonable waste disposal and implementation of a water diversion plan in the watershed.

Table 3 Microbial diversity of Xiaohe River. Items

Index

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

Bacteria

H′ D J' S H′ D J' S H′ D J' S

2.5 0.91 0.98 13 1.38 0.75 0.99 4 2.19 0.83 0.88 12

2.63 0.92 0.97 15 1.58 0.79 0.98 5 2.54 0.92 0.99 13

2.5 0.91 0.97 13 1.49 0.75 0.93 5 2.22 0.88 0.96 10

2.68 0.93 0.99 15 1.60 0.80 0.99 5 2.46 0.91 0.99 12

2.66 0.93 0.98 15 1.76 0.82 0.98 6 2.36 0.90 0.98 11

2.86 0.94 0.99 18 1.93 0.85 0.99 7 2.45 0.91 0.99 12

2.89 0.94 0.98 19 1.93 0.85 0.99 7 2.29 0.89 0.99 10

2.68 0.93 0.99 15 1.90 0.84 0.98 7 2.47 0.91 0.99 12

2.61 0.92 0.99 14 1.77 0.83 0.99 6 2.37 0.91 0.99 11

2.66 0.93 0.98 15 1.60 0.80 0.99 5 2.37 0.90 0.98 11

Eukaryote

Actinobacterial

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Fig. 4. Relative percentages of bacterial (a), eukaryotic (b) and actinobacterial (c) community structures.

The discharge of effluent exert significance effects on receiving water DOM quantity and quality, and then influence the microbial communities' structure and function. The above pathways are ubiquitous in aquatic ecosystems, but the extent of their impact will be determined by local environmental conditions, seasons and the flowrate of effluents discharged form each WWTP. Further studies are required to elucidate

Fig. 5. Redundancy analysis of the relationship between bacterial community (a), eukaryotic community (b), actinobacterial community (c) and environmental factors.

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the impact degrees and the generality of our finding across different WWTP flowrate, seasons and geographical types. 4. Conclusion WWTP effluent has an important impact on the composition and functional groups of DOM in the urbanized rivers, resulting in more carboxylate groups in the upstream region of the urbanized rivers and lower contents of carbohydrates, peptides and proteins in the downstream region. However, the relative content of structurally complex organic matter showed an increasing trend. The composition and functional groups of DOM in the urbanized rivers will affect the diversity of microbial communities. The main factors affecting the change of bacterial community were fulvic-like acid and protein-like substances, hydroxyl groups, amino groups, polysaccharide-like substances, carbohydrates and hydrophobicity in DOM. The distribution of microbial eukaryotic community was mainly affected by protein-like substances, molecular weight of organic matter, fulvic acid-like substances, polysaccharides, carbohydrates, hydroxyl groups and amino groups. Protein-like substances, polysaccharides, carbohydrates, molecular weight of organic matter, fulvic acid-like substances, hydroxyl groups and amino groups were the main factors affecting Actinobacteria community. Declaration of competing interest The authors declared that they have no any actual or potential conflict of interest to this work, including any financial, personal or other relationships with other people or organizations. Acknowledgements This study was supported by the National Water Pollution Control and Management Technology Major Project of China (2018ZX07109001) and Beijing Natural Science Foundation (No. 8182057) for the support of this work. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.135952. References Artifon, V., Zanardi-Lamardo, E., Fillmann, G., 2019. Aquatic organic matter: classification and interaction with organic microcontaminants. Sci. Total Environ. 649 (1), 1620–1635. Bai, J., Liu, X.S., He, R., et al., 2014. Community structure and influencing factors of bacterioplankton in the southern South China Sea. China Environ. Sci. 34 (11), 2950–2957. Benner, R., Opsahl, S., 2001. Molecular indicators of the sources and transformations of dissolved organic matter in the Mississippi River plume. Org. Geochem. 32 (4), 597–611. Chinese EPA, 2002. Methods for the Examination of Water and Wastewater. 4th edn. Chinese Environmental Science Press, Beijing. Czerwionka, K., 2016. Influence of dissolved organic nitrogen on surface waters. Oceanologia 58 (1), 39–45. Dignac, M.F., Ginestet, P., Rybacki, D., et al., 2000. Fate of wastewater organic pollution during activated sludge treatment: nature of residual organic matter. Water Res. 34 (17), 4185–4194. Fan, L., Brett, M.T., Li, B., et al., 2018. The bioavailability of different dissolved organic nitrogen compounds for the freshwater algae Raphidocelis subcapitata. Sci. Total Environ. 618 (15), 479–486.

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