Importance of denitrification driven by the relative abundances of microbial communities in coastal wetlands

Environmental Pollution 244 (2019) 47e54

Contents lists available at ScienceDirect

Environmental Pollution journal homepage: www.elsevier.com/locate/envpol

Importance of denitrification driven by the relative abundances of microbial communities in coastal wetlands* Yan Zhang a, c, Guodong Ji a, *, Chen Wang a, Xuanrui Zhang a, Ming Xu b a

Key Laboratory of Water and Sediment Sciences, Ministry of Education, Department of Environmental Engineering, Peking University, Beijing, 100871, China b Department of Ecological, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, 08901, USA c State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 June 2018 Received in revised form 16 September 2018 Accepted 2 October 2018 Available online 9 October 2018

Excessive nitrogen (N) loadings from human activities have led to increased eutrophication and associated water quality impacts in China's coastal wetlands. Denitrification accounts for significant reduction of inorganic N to nitrous oxide (N2O) or dinitrogen gas (N2), and thereby curtails harmful effects of N pollution in coastal and marine ecosystems. However, the molecular drivers and limiting steps of denitrification in coastal wetlands are not well understood. Here, we quantified the abundances of functional genes involved in N cycling and determined denitrification rates using 15N paring technique in the coastal wetland sediments of Bohai Economic Rim in eastern China. Denitrification accounting for 80.7 ± 12.6% of N removal was the dominant pathway for N removal in the coastal wetlands. In comparison, anaerobic ammonium oxidation (ANAMMOX) removed up to 36.9 ± 7.3% of inorganic N. Structural equation modeling analysis indicated that the effects of ammonium on denitrification potential were mainly mediated by the relative abundances of nosZ/nirS, nirS/(narG þ napA) and amoA/nirK. Denitrification was limited by the relative strength of two steps, namely N2O reduction to N2 and nitrite (NO 2 ) reduction to nitric oxide (NO). Our results suggest that the relative abundances of functional genes which are more stable than sediment chemical compounds in the context of environmental changes are indictive of denitrification potential in coastal wetlands. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Coastal wetland Nitrogen cycle Denitrification Functional gene Eastern China

1. Introduction Nitrogen (N) fertilizers accounted for most of anthropogenic N fixing (Canfield et al., 2010). However, the low efficiency of fertilizer uses has leached a large amount of N into rivers, lakes and coastal areas. Cui et al. (2013) reported that nearly 20% of anthropogenic N were transported into coastal ecosystems by riverine discharges and atmospheric deposition, resulting in large contents of reactive N in China's coastal and marine ecosystems. Therefore, an improved understanding of the driving mechanisms for N transformations is required to protect the health of these coastal ecosystems. Coastal wetlands are crucial buffer strips for relieving pollution by trapping both natural and anthropogenic pollutants transferred

*

This paper has been recommended for acceptance by Joerg Rinklebe. * Corresponding author. E-mail address: [email protected] (G. Ji).

https://doi.org/10.1016/j.envpol.2018.10.016 0269-7491/© 2018 Elsevier Ltd. All rights reserved.

between the continents and open seas. (Canfield et al., 2010). The reduced condition in coastal sediments favors microbial respiratory processes that use alternate electron acceptors, such as denitrification and anaerobic ammonium oxidation (ANAMMOX), which contribute to N removal in a variety of environments (Fan et al., 2014; Zhang et al., 2015; Zhi et al., 2015; Lansdown et al., 2016). In the absence of oxygen, denitrifiers can sequentially respire ni trate (NO 3 ) to nitrite (NO2 ), nitric oxide (NO), nitrous oxide (N2O), and ultimately to dinitrogen gas (N2), while ANAMMOX bacteria  can consume ammonium (NHþ 4 ) and NO2 simultaneously during respiration. As a microbial process, denitrification is catalyzed by a series of enzymes encoded by different functional genes. The napA and narG genes encoding periplasmic and membrane-bound NO 3  reductases, respectively, are related to the reduction of NO 3 to NO2 ; nirS and nirK genes encoding cytochrome cd1 and copper NO 2 reductases, respectively, are related to the reduction of NO 2 to NO; qnorB gene encoding NO reductase is related to the reduction of NO to N2O; nosZ gene encoding N2O reductase is related to the reduction of N2O to N2. These functional genes have been

48

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

extensively used as molecular markers to investigate the dynamics of different denitrifier groups in coastal ecosystems (Li et al., 2011; Francis et al., 2013; Levy-Booth et al., 2014; Wang et al., 2014; Gao et al., 2016). Studies relating to the dynamics of microbial communities indicated that the characteristics (e.g., abundance, composition and diversity) of denitrifier communities were shaped by different environmental conditions (Francis et al., 2013; Fan et al., 2014; Gao et al., 2016). For instance, Fan et al. (2014) investigated the composition and diversity of nirS and nirK denitrifiers and found that the highest diversity of these two groups were observed at the  sites with higher NO 3 /NO2 concentrations, which was probably  due to the important role of NO 3 /NO2 in providing electron acceptors in denitrification. Although the characteristics of denitrifier communities might directly affect their metabolic rates (Burns et al., 2013), strong correlations between denitrification rates and a diverse of physicochemical factors have been reported in soil and sediment environments (Avrahami et al., 2002; Saleh-Lakha et al., 2009; Bru et al., 2010; Guo et al., 2013). Recently, few studies examined the integrated effects of soil characteristics and microbial abundances on denitrification potential in upland soils (e.g., forest, grassland and crop fields) and biofiltration systems (Attard et al., 2011; Petersen et al., 2012; Lammel et al., 2015; Zhang et al., 2015). However, these studies mainly focused on terrestrial systems. To date, little is known about the quantitatively causal relationships between sediment chemical and biological characteristics and denitrification potential in coastal wetlands. The molecular drivers and limiting steps of denitrification in coastal wetlands are not well understood. As a northern economic powerhouse, Bohai Economic Rim (BER) is one of the most densely populated and industrialized zones in China. However, as a semi-enclosed coastal region, BER was under severe environmental pollution risk. Dissolved inorganic N with the largest pollution area in this region was the most prevalent contaminant (Zhang et al., 2017). Over 40 rivers carrying approximate 65 billion m3 of freshwater flow throw the BER (Zhang et al., 2017), and discharge great amounts of N contaminants into the coastal areas. A study on the Haicheng River which is one of the major drainage systems in the BER reported that the river provided approximate 700 t of annual total N to the sea area of Liaodong Bay, contributing greatly to the eutrophication of local aquatic environments (Bu et al., 2011). In-situ determination of denitrification rates in nature remains as a great challenge partly because of the high atmospheric background of N2. Development in the use of isotopic 15N pairing technique provides an opportunity to estimate denitrification and ANAMMOX rates and to quantify their potential in N removal from natural environments (Thamdrup and Dalsgaard, 2002; Fan et al., 2014; Zhou et al., 2014). In this study, we investigated the abundances of functional genes involved in N cycling. By using the 15N paring technique, we determined the sediment denitrification and ANAMMOX rates and contributions to N removal in four types of coastal wetlands. The main objectives of this study were: (1) to quantify the causal relationships between sediment characteristics, functional gene abundances and denitrification contributions to N removal; and (2) to reveal the molecular drivers and limiting steps of denitrification in the BER coastal wetlands. 2. Materials and methods 2.1. Study area The BER is located between 34 23’- 43 29’N and 113 23’125 50’E and composed of three provinces (Liaoning, Hebei, Shandong) and two municipal cities (Beijing, Tianjin)

(Supplementary Fig. S1). This region has a temperate semi-humid monsoon climate with a 10-year-averaged annual temperature of 11.2  C and an annual precipitation of 687.5 mm (Lu et al., 2014). The BER receives a large amount of freshwater and sediments from inland rivers and has been considered as one of the most concentrated area of mudflats in China (Long et al., 2016). The coastal mudflats mainly locate in the Liao River and Yellow River deltas and play an important role in providing transit and breeding grounds for migratory birds in East Asia - Australia (Liu et al., 2017). The river estuaries with a total area of approximate 3.2  103 km2 are characterized by high loads of sediment transport from continents to the Bohai Sea (Wang et al., 2018). Reed (Phragmites australis) is the most representative emergent wetland plant in the BER. The reed swamps cover approximately 2.7  103 km2 and are mainly distributed in Jinzhou and Dongying districts which are important paper-making raw material bases (Jiang et al., 2015). As the main food crop in this region, rice paddy field with an area of approximate 4.8  103 km2 accounts for the most proportion of the area of constructed wetlands in the BER (Jiang et al., 2015). With the rapid economic development and urbanization, the coastal zone along the BER has become key areas of human exploitation and utilization (Sun et al., 2015), causing a large amount of domestic and industrial sewage discharges into the coastal wetlands. The great amounts of contaminant inputs and weak self-cleaning capacity have led to great degradation in the local coastal ecosystems. 2.2. Sampling A total of 12 sediment samples from four types of coastal wetlands (estuary, mudflat, paddy and reed) were collected from the BER in June 2012 (Supplementary Fig. S1). For the paddy sites, we collected samples after the harvest of rice. At each site, three replicate surface sediments (0e20 cm) were randomly collected using a grab sampler and then homogenized to form a composite sample. For each site, approximate 500 g of sediments were collected in a sterile plastic bag and stored at 4  C. Each sample was divided into three subsamples. One subsample was incubated to measure denitrification and ANAMMOX rates immediately after arriving at the laboratory. A second same was processed for analyses of chemical characteristics after sieving through a 2.0-mm mesh. The remainder was stored at 80  C for DNA extraction and molecular analyses. 2.3. Measurements of sediment characteristics Ten grams of the sediment sample for each site was extracted using 100 mL of 2 M KCl, and the suspension was passed through a quantitative filter paper with a pore size of 0.45 mm. The resulting suspension was used for analysis of nitrate nitrogen (NO 3 -N) and ammonium nitrogen (NHþ 4 -N) in a continuous flow analyzer (TRRACS, Bran þ Luebbe, Norderstedt, Germany). The pH was determined in the soil/water suspensions (1/2.5 w/v), and total organic carbon (TOC) was determined by an elemental analyzer (2400II CHNS/O, PerkinElmer, USA). 2.4. Determination of denitrification and ANAMMOX rates Sediment slurries were prepared with fresh samples and helium-purged overlying water from each site at a soil/water volume ratio of 1:5. The resulting slurries were transferred into a series of 12-mL glass vials (Exetainer, Labco, UK) under a helium atmosphere and pre-incubated for two weeks to eliminate the residual NOx and oxygen. Afterwards, one of the isotopic mixtures þ   15 14 15 (15NHþ 4 -N; NH4 -N þ NO3 -N; NO3 -N) was injected through the septa of each vial, resulting in a final N concentration of

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

approximate 100 mM. Time course incubations were carried out in triplicate for 0, 3, 6, 12 and 24 h at a temperature of 25 ± 1  C. The microbial activity was stopped by adding 200 mL of a 7 M ZnCl2 solution at each time point. Denitrification and ANAMMOX rates and contributions to N2 production were calculated from the 29N2 and 30N2 production, measured by continuous flow isotope ratio mass spectrometry (MAT253 with Gasbench II and autosampler [GC-PAL]; Bremen, Thermo Electron Corporation, Finnigan, Germany) according to Thamdrup and Dalsgaard (2002). The equations are as following:

Den ¼ P30  F 2 n

(1)

 i h  1  P30 Ana ¼ F 1 n  P29 þ 2  1  F n

(2)

rd ¼

Den  100% Den þ Ana

(3)

ra ¼

Ana  100% Den þ Ana

(4)

Where Den and Ana are denitrification and ANAMMOX rates, respectively (nmol N g1 h1); P29 and P30 are 29N2 and 30N2 production rates, respectively (nmol N g1 h1); Fn is the fraction of 15N in the total nitrate pool. rd and ra are denitrification and ANAMMOX contributions to N2 production, respectively. 2.5. DNA extraction and pyrosequencing Using the FastDNA Spin Kit (MP Biomedicals, Santa Ana, CA, USA), DNA extraction was performed on 0.5 g of freeze-dried sediment samples. Three extractions were performed for each site. Using quantitative PCR (qPCR), 16S rRNA was used to assess the abundances of total bacteria and ANAMMOX bacteria (amx), and several functional genes were used to assess the abundances of different microbial communities involved in N cycling. The narG and napA genes were used to quantify NO 3 reducers; nirS, nirK and nosZ genes were used to quantify different groups of denitrifiers; amoA gene was used to quantify archaeal and bacterial NHþ 4 oxidizers (AOA and AOB). The forward and reverse primes and specific PCR procedures were given in Supplementary Table S1 and Table S2, respectively. Amplification was performed with an ABI PRISM 7300 (Applied Biosystems, Foster City, CA, USA) using SYBR green as the detection system in a reaction mixture of 10 mL Power SYBR Green Mixture, 10 mM of primer pairs, 1.0 mL DNA and sterile water to yield a total volume of 20 mL. Three replicate amplifications were performed for each sample. Amplicons were ligated to pEASY-T3 (TransGen Biotechnology, Beijing, China), and subsequently cloned into Trans1-T1 competent cells. The strains were then screened on ampicillin plates and incubated at 37  C overnight. The colony/stain was selected using a blue-white selection method. PCR and restriction endonuclease analyses were applied for plasmid identification, and the products were subsequently sent to Shanghai Biological Engineering Co. Ltd. for sequencing. The recombinant plasmids were extracted and purified using TIAN pure Mini plasmid kits. The plasmids containing targeted genes were used as standards in each run. Standard curves were generated from the mass concentrations of purified recombinant plasmids determined with a MyiQ2 nucleic acid protein quantity system (Bio-Rad, Hercules, CA, USA). Sterile water was used as a negative control to detect and exclude any possible contamination or carryover. Triplicate replications were performed to evaluate the standard curves from the qPCR functional genes. The R2 values for standard curves were 0.99

49

or higher for all runs. The qPCR efficiencies ranged from 93% to 99% for the quantitative assays. 2.6. Statistical analyses All statistical analyses were performed using R package (R Foundation for Statistical Computing, Vienna, Austria). Variables that were not normally distributed were transformed using BoxCox transformation (Bru et al., 2010). The relationships between sediment characteristics, functional gene abundances and denitrification contributions to N removal were assessed using ordinary least square regression and canonical correspondence analysis (CCA). To evaluate the effects of sediment biotic and abiotic factors on the denitrification contributions, we fit a stepwise multiple regression using the bidirectional variable elimination method (Petersen et al., 2012). During each step, predictors for the denitrification contributions were chosen at a significant level of 0.05. In addition, we fit a structure equation model (SEM) to explore possible causal relationships among sediment biotic and abiotic factors and to infer their relative importance on the denitrification contributions (Fox, 2006). We fit the SEM as a linear model and reported a standardized coefficient for each path. To determine whether further addition or removal of paths would improve the model accuracy, we used modification indices in the R package. The model fit was evaluated using the Chi-square goodness-of-fit (c2), Tucker-Lewis non-normed fit index (NNFI), akaike information criterion (AIC), and root mean square error of approximation (RMSEA) (Shipley, 2000). The SEM with a RMSEA less than 0.08 and NNFI greater than 0.9 was considered acceptable (Shipley, 2000). A non-significant P-value (P > 0.05) for the Chi-square statistic indicated that there was no significant difference in the covariance pattern predicted by the SEM and from the observed covariance, suggesting a good fit to the data (Shipley, 2000). 3. Results 3.1. Sediment characteristics The sediments were characterized by slightly high pH values (7.95e9.04) which showed no spatial difference across the sampling sites (P > 0.05, Fig. 1). The organic carbon content ranged from 9.53 mg kg1 at the estuary sites to 42.24 mg kg1 at the reed sites.  The NHþ 4 -N and NO3 -N concentrations ranged from 0.72 to 17.52 mg kg1 and from 0.69 to 3.37 mg kg1, respectively, with the

Fig. 1. Variations in sediment characteristics. The upper and lower boundaries indicate the 75th and 25th percentile, respectively. The mid-lines indicate the median of the distribution.

50

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

 1 highest values at the reed sites (NHþ 4 -N ¼ 15.37 mg kg , NO3 N ¼ 2.43 mg kg1, P < 0.05). The C/N ratios at the estuary (0.07) and reed (0.08) sites were lower than the ratios at the mudflat and paddy sites (P < 0.05).

3.2. Abundances of functional genes involved in nitrification, denitrification and ANAMMOX The abundance of bacterial 16S rRNA ranged from 1.26  109 to 1.43  1010 copies g1 and was significantly higher at the mudflat and reed sites (P < 0.05, Fig. 2a). The abundance of ANAMMOX 16S

rRNA ranged from 2.84  105 to 8.37  105 copies g1 and showed no significant difference among the sites. Regarding to the functional genes encoding NHþ 4 oxidases, the abundance of AOB amoA gene was 2e38 times higher than the abundance of AOA amoA gene. The abundance of AOB amoA gene ranged from 2.84  105 to 3.42  107 copies g1 and was significantly higher at the reed sites (P < 0.05, Fig. 2b). In contrast, no significant difference among the sites was found for the AOA amoA genes. Regarding to the functional genes encoding NO 3 reductases, the abundance of napA gene ranged from 8.12  105 to 7.47  107 copies g1 and was higher at the mudflat sites (P < 0.05), while the abundance of narG gene ranged from 3.22  104 to 6.80  106 copies g1 and was higher at the reed sites (P < 0.05). Regarding to the functional genes encoding NO 2 reductases, the abundance of nirS gene which exhibited a strong correlation with the nirK gene was 2e3 times higher than the abundance of nirK gene across the sites (Fig. 2b, Supplementary Fig. S3). The abundance of nirS gene was higher at the reed sites and lower at the paddy sites (P < 0.05). This trend was also found for the nirK gene. The abundance of nosZ gene encoding N2O reductase was significantly lower (1e2 orders of magnitude) than the NO 2 reductase genes and fluctuated with no significant difference across the sites. The nir denitrifiers (nirS þ nirK) accounting for 38.71e85.40% of the total bacterial community were more abundant at the mudflat sites as compared to those at the paddy sites (P < 0.05, Fig. 2c). In contrast, the ANAMMOX bacteria accounting for 1.82e11.93% of the total bacterial community was most abundant at the paddy sites (P < 0.05). At the sites exhibiting denitrification and ANAMMOX, the relative abundances of nir were approximately 7e48 times higher than the relative abundance of ANAMMOX bacterial 16S rRNA across the sites (P < 0.05). This trend was found solely for the nirS and nirK genes which were more abundant than the ANAMMOX bacterial 16S rRNA (P < 0.05). The relative abundance of nirS gene was significantly higher at the mudflat sites compared with remaining sites (P < 0.05), while the relative abundance of nirK gene had no significant difference among the sites. 3.3. Denitrification and ANAMMOX rates and contributions to N removal Denitrification rates exhibited a great spatial variation, ranging from 2.58 to 13.40 nmol N g1 h1, and were about 1.7e40.9 times higher than the ANAMMOX rates across the sites (Supplementary Fig. S2). Denitrification contributed 62.80e98.64% to N removal, while the remainder were attributed to ANAMMOX (Supplementary Fig. S2). Mudflats with the highest denitrification rate average of 12.61 nmol N g1 h1 accounted for 97.05% of N removal, while ANAMMOX contributed less than 10% at the mudflat sites (Fig. 3). In contrast, denitrification rates averaged 6.22 nmol N g1 h1 at the reed sites and contributed the smallest proportion of 63.09% to N removal. 3.4. Drivers of denitrification contributions to N removal

Fig. 2. Variations in the abundances of functional genes in the sediments of BER coastal wetlands. a, ANAMMOX and bacterial 16S rRNA. b, functional genes involved in ammonium oxidation (AOA, AOB), nitrate reduction (napA, narG) and denitrification (nirK, nirS, nosZ). c, relative abundances of ANAMMOX and denitrifying genes. amx represent the abundance of ANAMMOX 16S rRNA. AOA and AOB represent the abundances of archaeal and bacterial amoA genes, respectively. nir represent the abundances of nirK and nirS genes. Data represent mean and standard deviation (error bar). Letters above bars denote significant difference (P < 0.05) among different types of coastal wetlands for each variable.

Results of the CCA showed that the sampling sites were distinctly grouped according to wetland types and the denitrification contributions to N removal were positively correlated with  NHþ 4 -N, TOC, C/N and NO3 -N, but negatively correlated with pH across the sites (Fig. 4a). In contrast to the absolute gene abundance (absolute Pearson coefficients: 0.10e0.53, P > 0.05. Supplementary Table S3), the denitrification contributions were strongly correlated with the relative abundances of nosZ/nirS, nirS/(narG þ napA) and AOB/nirK, which explained 85.2%, 81.8% and 83.3% of the variation in denitrification contributions, respectively (Fig. 4b). Results from the stepwise multiple regression used to determine the extent to

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

51

Table 1 Summary of the stepwise multiple regression for the effects of biotic and abiotic factors on denitrification contribution (rd). R2 ¼ 0.907, P < 0.05. Source

Estimate

SE

P-value

nosZ/nirS nirS/(narG þ napA) AOB/nirK napA/amx NHþ 4 -N

3.811 0.924 0.122 0.284 0.353

2.650 0.709 0.239 0.048 0.300

0.002 0.036 0.021 0.017 0.007

SE, standard error; AOB represent the abundance of bacterial amoA gene; amx represents the abundance of ANAMMOX bacterial 16S rRNA. The best-fit model can be expressed as: rd ¼ 1.336 þ 3.811  nosZ/nirS þ 0.924  nirS/ (narG þ napA) þ 0.122  amoA/nirK e 0.284  napA/amx þ 0.353  NHþ 4 -N.

Fig. 3. Variations in denitrification and ANAMMOX rates and contributions to N2 production. Data represent mean and standard deviation (error bar). Letters above bars denote significant difference (P < 0.05) among different types of coastal wetlands for each variable.

model that included NHþ 4 -N and the relative abundances of nosZ/ nirS, nirS/(narG þ napA), AOB/nirK and napA/amx, with nosZ/nirS being the most important explanatory variable.

Fig. 4. Effects of sediment biotic and abiotic factors on denitrification rates and contributions to N removal. a, Canonical correspondence analysis (CCA) between sediment physicochemical factors and denitrification rates (Den.) and contributions (rd) to N removal. Ana. and ra represent ANAMMOX rates and contributions to N removal, respectively. Ellipses represent 95% confidence interval for replicates. b, Relationships between denitrification contributions and relative abundances of functional genes. AOB represents the abundance of bacterial amoA gene. Red fitted lines are from ordinary least square regressions. c, Structural equation model of sediment biotic and abiotic factors as indicators of denitrification contribution. Standardized coefficients were used as the path effects. Solid black arrows represent positive effects and solid red arrows represent negative effects. Dotted grey arrows represent non-significant paths. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

which these biotic and abiotic factors influence the denitrification contributions were presented in Table 1. In total, 90.7% of the variation in denitrification contributions could be explained by a

The SEM with an explanation of 96.1% in the variation of denitrification contributions fit the observed data well according to the P ¼ 0.944, NNFI ¼ 0.998, model statistics (c2 ¼ 0.307,

52

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

RMSEA ¼ 0.001, AIC ¼ 64.277) (Fig. 4c, Supplementary Table S4). In the SEM diagram, the direct effects refer to the direct influence of certain functional gene abundances or sediment characteristics on the denitrification contributions, while the indirect effects refer to their indirect influences through other gene fragments. The results indicated that the relative abundances of nosZ/nirS, nirS/ (narG þ napA) and AOB/nirK showed a decreasingly positive effect on the denitrification contributions (Supplementary Table S4). The nirS/(narG þ napA) affected the denitrification contributions with both a directly positive effect and an indirectly negative effect through nosZ/nirS. In addition, napA/amx had an indirectly negative effect on the denitrification contributions through nosZ/nirS and nirS/(narG þ napA). 4. Discussion This study quantified the relative importance of denitrification and ANAMMOX on N removal based on rate measurements in four types of coastal wetlands in BER. The highest denitrification rates observed at the mudflat sites were comparable with those from the floodplain soils of Coochin Creek catchment, southeast Queensland, Australia (20.5 ± 50.4 nmol N g1 h1) (Fellows et al., 2011). The denitrification rates at the reed sites were quite lower than the rates at other sites. However, these rate values exceeded those from the riverine soils of Beili River, Hebei, China (2.73 ± 0.09 nmol N g1 h1) (Zhu et al., 2018) and the freshwater sediments of Honghu Lake, Hubei, China (0.26 ± 0.07 nmol N g1 h1) (Yao et al., 2018), indicating a relatively high potential of denitrifiers in N removal at the sampling sites. Based on the dry sediment bulk density of 1.88 g cm3 reported by Hou et al. (2015), we estimated that N removal attributed to denitrification would be approximately (0.6e6.4)  105 t N annually, accounting for approximate 62.8e98.6% of total inorganic N. The slurry incubations used to measure isotopic N production probably underestimated the in-situ denitrification rates and contributions to N removal, since the labile organic carbon within the glass vials would be consumed and exhausted as the incubation proceeded. Additionally, as a microbial metabolic process that could produce N2O/N2 through the reduction of NO 2 by N compounds like azide, salicylhydroxamic acid and hydroxylamine (Spott et al., 2011), codenitrification might contributed to the 29N generation by reducing the 29N2O produced  15 15 from the utilization of 14NHþ NO 4 and 3 / NO2 during the slurry incubation experiment, resulting in relatively low contributions of denitrification to 30N2. Although the ANAMMOX rates were detected across the sediments investigated, ANAMMOX contributed little to the local N removal. The low ANAMMOX potential was probably because the relatively alkaline condition (pH ¼ 8.42 ± 0.35) in the sediments might inhibit the ANAMMOX process by interfering with the energy production within ANAMMOX bacteria (van der Star et al., 2010; Puyol et al., 2014). In order to better understand the variation in denitrification and ANAMMOX potential, further quantitative studies in estimating the in-situ metabolic rates are needed. The 16S rRNA has been commonly used as a molecular marker to detect the abundance of ANAMMOX bacteria in previous studies (Ligi et al., 2015; Waki et al., 2015). However, the ANAMMOX bacteria might contain more than one copy of 16S rRNA (Wang et al., 2016). Therefore, this study might overestimate the ANAMMOX bacterial abundance as we equated the bacteria number to the gene copy number. Additionally, the low abundance of ANAMMOX bacteria probably stemmed from the fact that ANAMMOX bacteria grow slowly and require more stable anaerobic conditions and NHþ 4 supply (Kuenen, 2008). In contrast, the BER sediment environments seemed more favorable to the nir denitrifiers as indicated by the higher proportion of nir abundance within the total bacterial

community. The dominance of the abundance of nirS gene over the abundance of nirK gene across the sites was consistent with the study of Francis et al. (2013) who recorded a higher abundance of nirS gene than nirK gene in the sediment of Chesapeake Bay, suggesting that the nirS denitrifiers were better adapted to the coastal environments. Despite the difference in the abundances of nirS and nirK denitrifiers at each site, the close relationships between these two groups indicated that their abundances appeared to covary with a relatively same magnitude across the sites. The relatively spatial covary of these two groups was probably a combined consequence of microbial interactions and different preferences in the structural components of corresponding reductases (cyto
et al., 2001), chrome cd1 for nirS and copper for nirK) (Cutruzzola which made their abundances vary in the same direction within a short sampling period. Although the relationships between absolute gene abundances and denitrification rates have been documented in previous studies (Saleh-Lakha et al., 2009; Morales et al., 2010; Guo et al., 2013), we found that the sediment denitrification was directly correlated with the relative abundances of several maker genes in the present study. One possible explanation for these close relationships was probably because the denitrification consists of an assemblage of different enzymatic steps and the distribution of different reductases varies among denitrifiers (Zumft, 1997). Therefore, denitrification might be better described as a molecular organization of different steps. The relative abundance of nosZ/nirS was found to be the variable that best explained the variation in denitrification contributions. To a certain extent, this ratio represented the proportion of microorganisms genetically capable of performing N2O to N2 reduction compared with those capable of performing NO 2 to NO reduction (Zumft, 1997; Yan et al., 2010). This result implied that the denitrification contributions were mainly limited by the relative strength of the second and last steps, namely NO 2 reduction to NO and N2O reduction to N2. As an important electron acceptor, the NO 2 in denitrification and ANAMMOX routed mainly from aerobic  NHþ 4 oxidation and anaerobic NO3 reduction (Peng and Zhu, 2006). Based on the abundances of marker genes for different microbial communities involved in the production and consumption of NO 2 (Dionisi et al., 2002; Bru et al., 2007; Yan et al., 2010), AOB/nirK denoted the level of NO 2 accumulation through the coupling of  bacterial NHþ 4 oxidation to NO2 (known as partial nitrification)  (Peng and Zhu, 2006) and NO2 reduction to NO; napA/amx denoted  the level of NO 2 accumulation through the coupling of NO3 reduction to NO 2 and ANAMMOX; nirS/(narG þ napA) denoted the  level of NO 2 consumption through the coupling of NO2 reduction  to NO and NO reduction to NO . The positive relationship between 3 2 AOB/nirK and the denitrification contributions indicated that denitrification coupled with partial nitrification driven by AOB community was a significant pathway for N removal, which has similarity to those reported in subtidal zones (Kessler et al., 2015; Marchant et al., 2016). Studies relating to the importance of AOA and AOB communities during nitrification in nature remains controversial (Petersen et al., 2012; Fan et al., 2015; Kim et al., 2016). In the present study, the higher abundance of AOB amoA gene than the AOA amoA gene indicated that AOB appeared to be more adapted to the local sediment environments. Nitrite has been regarded as a key limiting factor in ANAMMOX (Strous et al., 1998), especially in anoxic environments with abundant NHþ 4 . Compared   with NHþ 4 oxidizers, NO3 reducers are a major source of NO2 for ANAMMOX bacteria in sediments (Hou et al., 2013; Zhou et al., 2014). Accordingly, this could also be evidenced by the close relationship between the abundance of napA gene and the abundance of amx gene (r ¼ 0.793, P < 0.05, Supplementary Table S3) in the present study. The strong correlation of napA/amx with the denitrification contributions indicated an important role of the coupling

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

of NO 3 reduction and ANAMMOX during denitrification and the negative effect of napA/amx was probably due to the competition for elector acceptors between denitrifiers and ANAMMOX bacteria (Zhou et al., 2014). Compared with the abundances of functional genes which are more stable in the context of environmental changes (Philippot et al., 2009), the sediment NHþ 4 -N seemed less important in predicting the denitrification contributions, but showed greatly indirect effects through the relative abundances of napA/amx, nosZ/nirS and AOB/nirK. The indirectly negative effect of NHþ 4 -N on the denitrification contributions through napA/amx might be partly because the increased NHþ 4 -N concentration would promote the growth of ANAMMOX bacteria (Kuenen, 2008), which would respire more NO 2 -N in the sediments. The indirectly positive effects of NHþ 4 -N on the denitrification contributions through nosZ/ nirS and AOB/nirK were probably because the supply of oxidized nitrogen provided electron acceptors for denitrifiers (Avrahami et al., 2002). These results are in agreement with previous studies which reported close relationships between microbial abundances and different physicochemical factors in soils and sediments (Langer and Rinklebe, 2009; Petersen et al., 2012; Moche et al., 2015; Kim et al., 2016), indicating that the variation in denitrification potential might result from genetic fluctuations caused by ammonium-induced dynamics of microbial communities in the coastal wetlands. 5. Conclusion Based on an integration of laboratory experiments and statistical models, the present study quantitatively linked a macrocharacterization with microscopic molecular mechanisms for N removal in coastal wetlands. Denitrification was the dominant mechanism for N removal in the BER coastal wetlands and the major determinant was found to be nosZ/nirS. The relative abundances of functional genes could reflect quantitative relationships with denitrification potential and provide information about the interactive impacts of microbial communities and sediment chemical characteristics on denitrification. Results of this study are valuable to guide and facilitate protection and restoration practices for coastal wetlands by incorporating multiple parameters of functional genes into N management strategies. Acknowledgements The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 51721006), the National Natural Science Foundation of China (No. 51679001) and the Collaborative Innovation Center for Regional Environmental Quality provided support for this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2018.10.016. References Attard, E., Recous, S., Chabbi, A., De Berranger, C., Guillaumaud, N., Labreuche, J., Philippot, L., Schmid, B., Le Roux, X., 2011. Soil environmental conditions rather than denitrifier abundance and diversity drive potential denitrification after changes in land uses. Global Change Biol. 17, 1975e1989. Avrahami, S., Conrad, R., Braker, G., 2002. Effect of soil ammonium concentration on N2O release and on the community structure of ammonia oxidizers and denitrifiers. Appl. Environ. Microbiol. 68, 5685e5692. Bru, D., Ramette, A., Saby, N.P.A., Dequiedt, S., Ranjard, L., Jolivet, C., Arrouays, D., Philippot, L., 2010. Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale. ISME J. 5, 532e542.

53

Bru, D., Sarr, A., Philippot, L., 2007. Relative abundances of proteobacterial membrane-bound and periplasmic nitrate reductases in selected environments. Appl. Environ. Microbiol. 73, 5971e5974. Bu, H., Meng, W., Zhang, Y., 2011. Nitrogen pollution and source identification in the Haicheng River basin in Northeast China. Sci. Total Environ. 409, 3394e3402. Burns, R.G., DeForest, J.L., Marxsen, J., Sinsabaugh, R.L., Stromberger, M.E., Wallenstein, M.D., Weintraub, M.N., Zoppini, A., 2013. Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol. Biochem. 58, 216e234. Canfield, D.E., Glazer, A.N., Falkowski, P.G., 2010. The evolution and future of Earth's nitrogen cycle. Science 330, 192e196. Cui, S., Shi, Y., Groffman, P.M., Schlesinger, W.H., Zhu, Y., 2013. Centennial-scale analysis of the creation and fate of reactive nitrogen in China (1910-2010). Proc. Natl. Acad. Sci. U.S.A. 110, 2052e2057.
, F., Brown, K., Wilson, E.K., Bellelli, A., Arese, M., Tegoni, M., Cambillau, C., Cutruzzola Brunori, M., 2001. The nitrite reductase from pseudomonas aeruginosa: essential role of two active-site histidines in the catalytic and structural properties. Proc. Natl. Acad. Sci. U.S.A. 98, 2232e2237. Dionisi, H.M., Layton, A.C., Harms, G., Gregory, I.R., Robinson, K.G., Sayler, G.S., 2002. Quantification of Nitrosomonas oligotropha-like ammonia-oxidizing bacteria and Nitrospira spp. from full-scale wastewater treatment plants by competitive PCR. Appl. Environ. Microbiol. 68, 245e253. Fan, H., Bolhuis, H., Lucas, J.S., 2014. Denitrification and the denitrifier community in coastal microbial mats. FEMS Microbiol. Ecol. 91, 206e213. Fan, H., Bolhuis, H., Stal, L.J., 2015. Nitrification and nitrifying bacteria in a coastal microbial mat. Front. Microbiol. 6, 1367. Fellows, C.S., Hunter, H.M., Eccleston, C.E.A., Hayr, R.W.D., Rassam, D.W., Beard, N.J., Bloesch, P.M., 2011. Denitrification potential of intermittently saturated floodplain soils from a subtropical perennial stream and an ephemeral tributary. Soil Biol. Biochem. 43, 324e332. Fox, J., 2006. Structural equation modeling with the sem package in R. Struct. Equ. Model. 13, 465e486. Francis, C.A., O'Mullan, G.D., Cornwell, J.C., Ward, B.B., 2013. Transitions in nirS-type denitrifier diversity, community composition, and biogeochemical activity along the Chesapeake Bay estuary. Front. Microbiol. 4, 237. Gao, J., Hou, L., Zheng, Y., Liu, M., Yin, G., Li, X., Lin, X., Yu, C., Wang, R., Jiang, X., Sun, X., 2016. nirS-Encoding denitrifier community composition, distribution, and abundance along the coastal wetlands of China. Appl. Environ. Microbiol. 100, 8573e8582. Guo, G.X., Deng, H., Qiao, M., Yao, H.Y., Zhu, Y.G., 2013. Effect of long-term wastewater irrigation on potential denitrification and denitrifying communities in soils at the watershed scale. Environ. Sci. Technol. 47, 3105e3113. Hou, L., Zheng, Y., Liu, M., Gong, J., Zhang, X., Yin, G., You, L., 2013. Anaerobic ammonium oxidation (anammox) bacterial diversity, abundance, and activity in marsh sediments of the Yangtze Estuary. J. Geo. Res. Biogeosci. 118, 1237e1246. Hou, L., Zheng, Y., Liu, M., Li, X., Lin, X., Yin, G., Gao, J., Deng, F., Chen, F., Jiang, X., 2015. Anaerobic ammonium oxidation and its contribution to nitrogen removal in China's coastal wetlands. Sci. Rep. 5, 15621. Jiang, T., Pan, J., Pu, X., Wang, B., Pan, J., 2015. Current status of coastal wetlands in China: degradation, restoration, and future management. Estuar. Coast Shelf Sci. 164, 265e275. Kessler, A.J., Glud, R.N., Cardenas, M.B., Cook, P.L., 2015. Transport zonation limits coupled nitrification-denitrification in permeable sediments. Environ. Sci. Technol. 47, 13404e13411. Kim, H., Bae, H.-S., Reddy, K.R., Ogram, A., 2016. Distributions, abundances and activities of microbes associated with the nitrogen cycle in riparian and stream sediments of a river tributary. Water Res. 106, 51e61. Kuenen, J.G., 2008. Anammox bacteria: from discovery to application. Nat. Rev. Microbiol. 6, 320e326. Lammel, D.R., Feigl, B.J., Cerri, C.C., Nüsslein, K., 2015. Specific microbial gene abundances and soil parameters contribute to C, N, and greenhouse gas process rates after land use change in Southern Amazonian Soils. Front. Microbiol. 6, 1057. Langer, U., Rinklebe, J., 2009. Lipid biomarkers for assessment of microbial communities in floodplain soils of the Elbe River (Germany). Wetlands 29, 353e362. Lansdown, K., McKew, B.A., Whitby, C., Heppell, C.M., Dumbrell, A.J., Binley, A., Olde, L., Trimmer, M., 2016. Importance and controls of anaerobic ammonium oxidation influenced by riverbed geology. Nat. Geosci. 9, 357e360. Levy-Booth, D.J., Prescott, C.E., Grayston, S.J., 2014. Microbial functional genes involved in nitrogen fixation, nitrification and denitrification in forest ecosystems. Soil Biol. Biochem. 75, 11e25. Li, M., Cao, H., Hong, Y.G., Gu, J.D., 2011. Seasonal dynamics of anammox bacteria in estuarial sediment of the Mai Po Nature Reserve revealed by analyzing the 16S rRNA and hydrazine oxidoreductase (hzo) genes. Microb. Environ. 26, 15e22. ~ lvak, H., Mander, Ü., Mitsch, W.J., Truu, J., 2015. The Ligi, T., Truu, M., Oopkaup, K., No genetic potential of N2 emission via denitrification and ANAMMOX from the soils and sediments of a created riverine treatment wetland complex. Ecol. Eng. 80, 181e190. Liu, X., Liu, L., Peng, Y., 2017. Ecological zoning for regional sustainable development using an integrated modeling approach in the Bohai Rim, China. Ecol. Model. 353, 158e166. Long, X., Liu, L., Shao, T., Shao, H., Liu, Z., 2016. Developing and sustainably utilize the coastal mudflat areas in China. Sci. Total Environ. 569e570, 1077e1086. Lu, Q.S., Gao, Z.Q., Zhao, Z.P., Ning, J.C., Bi, X.L., 2014. Dynamics of wetlands and their effects on carbon emissions in China coastal region e case study in Bohai

54

Y. Zhang et al. / Environmental Pollution 244 (2019) 47e54

Economic Rim. Ocean Coast Manag. 87, 61e67. Marchant, H.K., Holtappels, M., Lavik, G., Ahmerkamp, S., Winter, C., Kuypers, M.M.M., 2016. Coupled nitrification-denitrification leads to extensive N loss in subtidal permeable sediments. Limnol. Oceanogr. 61, 1033e1048. Moche, M., Gutknecht, J., Schulz, E., Langer, U., Rinklebe, J., 2015. Monthly dynamics of microbial community structure and their controlling factors in three floodplain soils. Soil Biol. Biochem. 90, 169e178. Morales, S.E., Cosart, T., Holben, W.E., 2010. Bacterial gene abundances as indicators of greenhouse gas emission in soils. ISME J. 4, 799e808. Peng, Y., Zhu, G., 2006. Biological nitrogen removal with nitrification and denitrification via nitrite pathway. Appl. Environ. Microbiol. 73, 15e26. Petersen, D.G., Steven, J.B., Mary, F., Herman, D., J.H, Merritt, T., Mark, W., 2012. Abundance of microbial genes associated with nitrogen cycling as indices of biogeochemical process rates across a vegetation gradient in Alaska. Environ. Microbiol. 14, 993e1008.  Saby, N.P.A., Che
neby, D., Chron  , A., Bru, D., Arrouays, D., Philippot, L., Uhel, J.C.,  Martin-Laurent, F., Simek, M., 2009. Mapping field-scale spatial patterns of size and activity of the denitrifier community. Environ. Microbiol. 11, 1518e1526. Puyol, D., Carvajal-Arroyo, J.M., Li, G.B., Dougless, A., Fuentes-Velasco, M., SierraAlvarez, R., Field, J.A., 2014. High pH (and not free ammonia) is responsible for Anammox inhibition in mildly alkaline solutions with excess of ammonium. Biotechnol. Lett. 36, 1981e1986. Saleh-Lakha, S., Shannon, K., Henderson, S., Goyer, C., Trevors, J., Zebarth, B., Burton, D., 2009. Effect of pH and temperature on denitrification gene expression and activity in Pseudomonas mandelii. Appl. Environ. Microbiol. 75, 3903e3907. Shipley, B., 2000. Cause and correlation in biology: a user's guide to path analysis, structural equations, and causal inference. Struct. Equ. Model. 82, 646e649. Spott, O., Russow, R., Stange, C.F., 2011. Formation of hybrid N2O and hybrid N2 due to codenitrification: first review of a barely considered process of microbially mediated N-nitrosation. Soil Biol. Biochem. 43, 1995e2011. Strous, M., Heijnen, J.J., Kuenen, J.G., Jetten, M.S.M., 1998. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Appl. Environ. Microbiol. 50, 589e596. Sun, C., Yang, Y., Zhao, L., 2015. Economic spillover effects in the Bohai Rim region of China: is the economic growth of coastal counties beneficial for the whole area. China Econ. Rev. 33, 123e136. Thamdrup, B., Dalsgaard, T., 2002. Production of N2 through anaerobic ammonium oxidation coupled to nitrate reduction in marine sediments. Appl. Environ. Microbiol. 68, 1312e1318.

van der Star, W.R.L., Dijkema, C., de Waard, P., Picioreanu, C., Strous, M., van Loosdrecht, M.C.M., 2010. An intracellular pH gradient in the anammox bacterium Kuenenia stuttgartiensis as evaluated by 31P NMR. Appl. Microbiol. Biotechnol. 86, 311e317. Waki, M., Yasuda, T., Suzuki, K., Komada, M., Abe, K., 2015. Distribution of anammox bacteria in a free-water-surface constructed wetland with wild rice (Zizania latifolia). Ecol. Eng. 81, 165e172. Wang, C., Lv, Y., Li, Y., 2018. Riverine input of organic carbon and nitrogen in watersediment system from the Yellow River estuary reach to the coastal zone of Bohai Sea, China. Continent. Shelf Res. 157, 1e9. Wang, L., Zheng, B., Nan, B., Hu, P., 2014. Diversity of bacterial community and detection of nirS- and nirK-encoding denitrifying bacteria in sandy intertidal sediments along Laizhou Bay of Bohai Sea, China. Mar. Pollut. Bull. 88, 215e223. Wang, Z., Ni, S., Zhang, J., Zhu, T., Ma, Y., Liu, X., Kong, Q., Miao, M., 2016. Gene expression and biomarker discovery of anammox bacteria in different reactors. Biochem. Eng. J. 115, 108e114. Yan, T., Fields, M.W., Wu, L., Zu, Y., Tiedje, J.M., Zhou, J., 2010. Molecular diversity and characterization of nitrite reductase gene fragments (nirK and nirS) from nitrate- and uranium-contaminated groundwater. Environ. Microbiol. 5, 13e24. Yao, L., Chen, C., Liu, G., Liu, W., 2018. Sediment nitrogen cycling rates and microbial abundance along a submerged vegetation gradient in a eutrophic lake. Sci. Total Environ. 616, 899e907. Zhang, M., Shi, Y., Lu, Y., Johnson, A.C., Sarvajayakesavalu, S., Liu, Z., Su, C., Zhang, Y., Juergens, M.D., Jin, X., 2017. The relative risk and its distribution of endocrine disrupting chemicals, pharmaceuticals and personal care products to freshwater organisms in the Bohai Rim, China. Sci. Total Environ. 590e591, 633e642. Zhang, Y., Ji, G.D., Wang, R.J., 2015. Genetic associations as indices of nitrogen cycling rates in an aerobic denitrification biofilter used for groundwater remediation. Bioresour. Technol. 194, 49e56. Zhi, W., Yuan, L., Ji, G.D., He, C.G., 2015. Enhanced long-term nitrogen removal and its quantitative molecular mechanism in tidal flow constructed wetlands. Environ. Sci. Technol. 49, 4575e4583. Zhou, S., Borjigin, S., Riya, S., Terada, A., Hosomi, M., 2014. The relationship between anammox and denitrification in the sediment of an inland river. Sci. Total Environ. 490, 1029e1036. Zhu, G., Wang, S., Li, Y., Zhuang, L., Zhao, S., Wang, C., Kuypers, M.M.M., Jetten, M.S.M., Zhu, Y., 2018. Microbial pathways for nitrogen loss in an upland soil. Environ. Microbiol. 20, 1723e1738. Zumft, W.G., 1997. Cell biology and molecular basis of denitrification. Microbiol. Mol. Biol. Rev. 61, 533e616.