Bioresource Technology 177 (2015) 217–223

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Assessing nitrogen transformation processes in a trickling filter under hydraulic loading rate constraints using nitrogen functional gene abundances Honglei Wang a, Guodong Ji a,⇑, Xueyuan Bai b, Chunguang He b a b

Key Laboratory of Water and Sediment Sciences, Ministry of Education, Department of Environmental Engineering, Peking University, Beijing 100871, China State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, China

h i g h l i g h t s  Hydraulic loading rate significantly

affected N removal processes.  N transformation processes were coupled at the molecular level (functional genes).  Functional gene groups were useful to assess N transformation process rates. +  NH4 -N transformation rate was jointly determined by five functional genes.

g r a p h i c a l a b s t r a c t NH4+-N

(a)

1.050

amoA/anammox

NH4+-N

-0.320

(c)

1.196

NO3--N

0.484

Article history: Received 6 October 2014 Received in revised form 15 November 2014 Accepted 21 November 2014 Available online 27 November 2014 Keywords: Trickling filter Hydraulic loading rate Functional gene group Nitrogen transformation process Path analysis

0.935

amoA/(narG + napA) 0.018

NO2--N -0.370

anammox/amoA -0.279

nxrA/(nirK + nirS) -0.208

-0.007

nosZ/(narG + napA + nirS + nirK + qnorB + nosZ)

-0.689

0.012

i n f o

-0.096

0.316 (nirS + nirK)/archaea

-0.028

a r t i c l e

(b)

Direct effect -0.004 0.183

Indirect effect

(napA + narG)/nxrA

a b s t r a c t A study was conducted of treatment performance and nitrogen transformation processes in a trickling filter (TF) used to treat micro-polluted source water under variable hydraulic loading rates (HLRs), ranging from 1.0 to 3.0 m3/m2 d. The TF achieved high and stable COD (97.7–99.3%) and NH+4-N (67.3–92.7%) removal efficiencies. Nitrification and anaerobic ammonium oxidation were the dominant nitrogen removal processes in the TF. Path analysis indicated that amoA/anammox and amoA/(narG + napA) were the two key functional gene groups driving the major processes for NH+4-N and NO2 -N, respectively. The analysis also revealed that anammox/amoA and nxrA/(nirK + nirS) were the two key functional gene groups affecting processes associated with the NO3 -N transformation rate. The direct and indirect effect of functional gene groups further confirmed that nitrogen transformation processes are coupled at the molecular level, resulting in a mutual contribution to nitrogen removal in the TF. Crown Copyright Ó 2014 Published by Elsevier Ltd. All rights reserved.

1. Introduction Environmental pollution from a variety of sources has been recognized as a serious and ongoing threat to the world’s drinking water supply (Vörösmarty et al., 2010). The problem of contaminants in water supplies posing significant human health risk has

⇑ Corresponding author. Tel.: +86 1062755914 87; fax: +86 1062756526. E-mail addresses: [email protected] (G. Ji), [email protected] (C. He). http://dx.doi.org/10.1016/j.biortech.2014.11.094 0960-8524/Crown Copyright Ó 2014 Published by Elsevier Ltd. All rights reserved.

developed into a subject of great concern in China (Yi et al., 2011). Economic development throughout China has resulted in the discharge into rivers of un- or under-treated sewage plant effluent with associated pollutants including excess nitrogen. Many of the rivers receiving sewage discharges also act as domestic water supplies, with low concentrations of ammonia (NH+4-N) and chemical oxygen demand (COD) being common primary pollutants. Low concentrations of NH+4-N and COD have been identified as significant drinking water contaminants because of their potential adverse effects on human health (Bhatnagara et al., 2011). It is

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becoming increasingly important, therefore, that polluted waters receive pre-treatment to significantly reduce the excess nitrogen. Trickling filter (TF) technology has been successfully applied to remove both nitrogen and COD from ground and surface water (Ji et al., 2013; Van den Akker et al., 2011, 2008). TF systems provide several advantages over other treatment alternatives including low construction and operational costs, no required aeration and high purification efficiency. However, under variable hydraulic loading rates (HLRs), nitrogen transformation process rates in TFs exhibit substantial fluctuations and treatment results are often unsatisfactory. An in-depth understanding of the nitrogen transformations processes in TFs and key nitrogen functional genes that affect nitrogen removal is imperative for designing, adjusting and maintaining TFs in order to optimize wastewater treatment efficiency. Nitrogen removal in a TF primarily involves several microbiological processes, including nitrification, denitrification and anaerobic ammonium oxidation (anammox) (Ji et al., 2013; Satoh et al., 2004). These various nitrogen processes involve several functional genes, including ammonia monooxygenase (amoA), nitrite oxidoreductase (nxrA), periplasmic nitrate reductase (napA) and membrane-bound nitrate reductase (narG), nitrite reductase (nirK/nirS), nitric oxide reductase (qnorB), nitrous oxide reductase (nosZ), archaeal 16S rRNA (archaeal) and anaerobic ammonium oxidation (anammox) (Galloway et al., 2008; Ji et al., 2013). The amoA gene and nxrA gene are two functional genes involved in the nitrification process. Six other genes, narG, napA, nirK, nirS, qnorB and nosZ, are six functional genes associated with denitrification (Ji et al., 2013). Anammox and archaeal are two functional genes involved in ammonium oxidation processes (Zhi and Ji, 2014). Previous studies have demonstrated that the HLR significantly influences nitrogen transformation and removal processes in TFs (Ji et al., 2011; Tan and Ji, 2010). Li et al. (2011) reported that levels of NH+4-N and COD in effluent rose with increasing HLR. Specifically, microbial ammonia oxidation, which is the first and rate-limiting step for subsequent nitrogen transformation and removal, is impaired by a higher HLR. In a later study, Ji et al. (2013) investigated the association of the nitrogen transformation functional gene community using correlational analyzes in multimedia biofilters. The results indicated that anammox, napA, qnorB and nosZ genes exhibited partially or mutually beneficial cooperation in the nitrogen transformation process. The nxrA and nirK genes demonstrated protocooperation, while amoA and narG genes exhibited partially beneficial cooperation. Zhi and Ji (2014) investigated the quantitative response relationships between nitrogen transformation rates and nitrogen functional genes using stepwise regression analysis, showing that different nitrogen transformation processes were coupled at the molecular level (functional genes), and collaboratively contributed to nitrogen removal. To date, there have been no reports that quantitatively estimate the dynamics of nitrogen transformation process rates in a trickling filter under hydraulic loading rate constraints using nitrogen functional gene abundances. The goals of this study were to assess the rates at which nitrogen transformation processes occur in TFs at the molecular level under different HLRs and quantify the relative contribution of various functional genes to nitrogen removal. Specifically, the purpose of this study was to: (i) evaluate TF treatment performance under varying HLRs, (ii) quantify the absolute abundance of bacterial 16S rRNA, archaeal 16S rRNA, and anammox bacterial 16S rRNA, in addition to known nitrogen transformation functional genes (amoA, nxrA, napA, narG, nirK, nirS, qnorB and nosZ), using real-time PCR, (iii) quantify the relationships between the nitrogen transformation rates and functional genes under HLR constraints, (iv) identify key functional genes that determine the nitrogen

transformation process in TFs, and (v) analyze the direct and indirect effect of functional genes on nitrogen removal process rates. The results obtained from this study are expected to contribute to improving the understanding of TF processes and enhancing the efficiency of TF systems under varying HLRs. 2. Methods 2.1. Trickling filter setup and operation A single laboratory scale trickling filter (TF), constructed with a metal framework and PVC board, had the following dimensions: 40 cm length  30 cm width  240 cm height (working volume of 144 L). The TF was used to treat micro-polluted source water. The TF consisted of four functional layers. The dimensions of a single functional layer were 40  30  30 cm (L  H  W, respectively). The distance between two functional layers was 20 cm. A sieve tray was installed between each layer. From top to bottom, the four functional layers were each filled with sponge (polyurethane foaming plastic) of the following aperture size: 5–8, 2–3, 5–8 and 2–3 mm, respectively. For the material roles, operation and performance of the sponge, please see the literatures (Guo et al., 2010; Moe and Irvine, 2000). Synthetic wastewater was added to the top of the TF, and was allowed to flow via gravity through the four functional layers. Treated wastewater was discharged and collected from the bottom of the TF. The experiment was initiated on December 28, 2012, and was divided into six stages over the 228 d experimental period: (1) start-up Stage (HLR = 3.0 m3/m2 d), from December 28, 2012 to February 23, 2013; (2) Stage A (HLR = 1.0 m3/m2 d), from February 24 to March 29, 2013; (3) Stage B (HLR = 1.5 m3/m2 d), from March 30 to May 3, 2013; (4) Stage C (HLR = 2.0 m3/m2 d), from May 4 to 7 June, 2013; (5) Stage D (HLR = 2.5 m3/m2 d), from June 8 to July 12, 2013; and (6) Stage E (HLR = 2.5 m3/m2 d), from July 13 to August 16, 2013. Beijing groundwater (4.9–5.9 mg/L of NO3 -N) was used to make the synthetic wastewater, which was prepared daily in a feeding tank and pumped into the TF through flumes in the distribution layers. The synthetic wastewater contained glucose as a carbon source and NH4Cl as a nitrogen source (ammonium–nitrogen, NH+4-N). The influent chemical oxygen demand (COD) concentration was 30 mg/L, and the influent NH+4-N concentration was 1.5 mg/L. KH2PO4, FeCl3
7H2O, CaCl2, NaHCO3 and MgSO4
7H2O were added at concentrations of 4.0, 3.0, 2.0, 23, 12 and 100 mg/L, respectively. The pH of the water was 7.0–7.4. The TF was located indoors; the temperature range of the influents and effluents was 15.1–27.0 °C. 2.2. Sample collection and determination Water samples were collected from the inlet and outlet of the TF at least three times during each operational stage. The samples were immediately analyzed for COD, NH+4-N, NO2 -N and NO3 -N at the Key Laboratory of Water and Sediment Sciences, Peking University, China. COD was determined with a HACH DR2800 (HACH, USA). The three nitrogenous compounds were measured using a UV-1800 spectrophotometer (SHIMADZU, Japan). All variables were analyzed according to standard analytical procedures (Zhi and Ji, 2014). Microorganism samples were collected from the four functional layers one time at the end of Stages A to E. For this sampling, four or five microorganism samples were taken from each functional layer and mixed well (approximately 5 g). After on-site collection, the samples were stored in an incubator with ice and immediately transported to the Laboratory of Environmental Engineering, Peking University, for total DNA extraction.

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2.3. Quantitative polymerase chain reaction (qPCR) 2.3.1. DNA extraction OMEGA soil DNA kits D5625-01 (Omega, USA) were used to extract and purify the total genomic DNA from the samples. Extracted genomic DNA was detected by 1% agarose gel electrophoresis and stored at 20 °C until use. 2.3.2. Primer design Quantitative analysis was performed on the 16S rRNA fragment of anammox bacteria (anammox 16S rRNA) and the target fragments of the following functional genes: amoA, nxrA, narG, napA, nirK, nirS, qnorB and nosZ. All primers summarized in Supplementary Table S1 were synthesized by Shanghai Invitrogen Biotechnology Co. Ltd. (China). Each primer concentration was 10 pmol/lL. 2.3.3. qPCR qPCR was performed on a MyiQ2 Real-Time PCR Detection System (Bio-Rad, USA) in final 20 mL volume reaction mixtures containing the following components: 10 mL SYBR Green I PCR master mix (Applied Biosystems, USA), 1 mL template DNA (sample DNA or plasmid DNA for standard curves), forward and reverse primers (Table S2), and sterile water (Millipore, USA). qPCR was performed in a three-step thermal cycling procedure, and the protocol and parameters for each target gene are presented in Supplementary Table S2. Each qPCR amplification was performed in 40 cycles and followed by a melting curve analysis. Sterile water was used as a negative control and the data obtained from the qPCR were normalized to copies per gram of biological carrier in the TF.

219

the three paths are: P10 (direct effect), R12  P20 (indirect effect via X2), and R13  P30 (indirect effect via X3); For variable X2, the three paths are: P20 (direct effect), R21  P10 (indirect effect via X1), and R23  P30 (indirect effect via X3); For variable X3, the three paths are: P30 (direct effect), R31  P10 (indirect effect via X1), and R32  P20 (indirect effect via X2).

3. Results and discussion 3.1. Overall performance of the TF 3.1.1. COD and NH+4-N removal efficiencies The efficiency at which certain pollutants were removed in the TF varied greatly with different HLRs. As the HLR increased from 1.0 to 3.0 m3/m2 d, the effluent COD decreased from 0.7 to 0.2 mg/L, accompanied by an increase in COD removal efficiency from 97.7% to 99.3% (Fig. 1a). As HLR increased from 1.0 to 3.0 m3/m2 d, the concentration of NH+4-N in the effluent shifted, although not monotonically, dropping first to a low of 0.2 mg/L and then rising to 0.5 mg/L at the highest HLR. NH+4-N removal efficiency also varied, with an initial value of approximately 77.3% at a HLR of 1.0 m3/m2 d and both rising and falling over the course of the study to reach a final low value of 67.3% at a HLR of 3.0 m3/m2 d (Fig. 1b).

2.3.4. Standard curves The plasmids containing specific bacteria (bacterial 16S rRNA), archaea (archaeal 16S rRNA) and anammox bacteria (anammox bacterial 16S rRNA), in addition to various functional genes (i.e., amoA, nxrA, napA, narG, nirK, nirS, qnorB and nosZ) were manufactured by the Majorbio BioPharm Technology Company (Shanghai, China). The standard samples were diluted to yield a series of 10-fold concentrations, and were subsequently used for qPCR standard curves. The coefficient of determination (R2) for each standard curve exceeded 0.99, indicating robust linear relationships over the concentration ranges used in this study. 2.4. Data analysis Influent and effluent concentrations and HLRs were used to calculate the removal efficiencies and transformation or accumulation rates of COD, NH+4-N, NO2 -N and NO3 -N. Path analysis was used to determine the direct and indirect effects of functional genes on nitrogen transformation process. Direct effects (path coefficients) can be obtained by the simultaneous solution of the normal equations for multiple linear regression in standard measure (Alwin and Hauser, 1975). In this study, Stepwise regression models were built to determine the standardization multiple linear regression equation between nitrogen transformation rates and functional genes using SPSS Statistics 20 (IBM, USA). Indirect effects can be derived from simple correlation coefficients between functional genes using SPSS Statistics 20 (IBM, USA). According to regression analysis and path coefficient analysis with three independent variable (i.e. X1, X2, X3) and one dependent variable (Y0), the methodology of path analysis is briefly explained as follows. The standardization multiple linear regression equation was formulated (Y = P1X1 + P2X2 + P3X3). P1 (X1 ? Y0), P2 (X2 ? Y0), and P3 (X3 ? Y0) represent path coefficients. The simple correlation coefficients between X1, X2, and X3 were R12, R13, and R23, respectively. There are three different paths from an independent variable to the dependent variable. For variable X1,

Fig. 1. Influent concentrations, effluent concentrations and removal efficiencies of COD (a) and NH+4-N (b) and nitrogen transformation rates of NH+4-N, NO3 -N, and NO2 -N (c) under different hydraulic loading ra. Note: the positive values of NH+4-N in the above figure indicated that they were transformed (reduced) in the system, while the negative values of NO2 -N and NO3 -N indicated that they were accumulated (increased) in the system.

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3.1.2. Nitrogen transformation rates The NH+4-N, NO3 -N and NO2 -N transformation rates (or ‘‘accumulation rates’’ if effluent N concentration was increased) were notably different from each other over the course of the study (Fig. 1c). The NH+4-N transformation rate steadily increased from 7.6 g/m3 d at a HLR of 1.0 m3/m2 d to a peak of 23.5 at a HLR of 2.5 m3/m2 d, and decreased somewhat to 20.5 g/m3 d at a HLR of 3.0 m3/m2 d. The NO2 -N accumulation rate was 2.1 g/m3 d at a HLR of 1.0 m3/m2 d and shifted to 0.8 g/m3 d at a HLR of 1.5 m3/m2 d, but then remained relatively stable at between 1.1 and 1.7 g/m3 d when HLR increased from 2.0 to 3.0 m3/m2 d. The rate for NO3 -N transformation was 2.9 g/m3 d at a HLR of 1.0 m3/m2 d but dropped sharply as the study progressed, reaching a final value of 10.8 g/m3 d when HLR was 3.0 m3/m2 d. When HLR increased from 1.5 to 3.0 m3/m2 d, full nitrification was evident in the TF, as the NO3 -N concentration (5.1–6.5 mg/L) was 1.1–1.2-fold greater in effluent than the average influent concentration (range of 4.7–5.6 mg/L). Previous studies have demonstrated that the low HLR of TF leads to the formation of channels through the media. This phenomenon accelerates wastewater movement, which, in turn, reduces treatment efficiency, causing the microbes to be undernourished (Ji et al., 2012). During the present study, channels did not form in the TF, even when the HLR was low, because of the tower-structure design of the media. Improved hydraulic flow and oxygenation of the TF facilitates promotes robust growth of nitrobacteria in aerobic zones, which enhances nitrification (Ji et al., 2011). As HLR increased from 1.0 to 3.0 m3/m2 d, nitrification was primarily accomplished by aerobic nitrobacteria since increased HLR supports the liquid phase and mass transfer of biofilm in the TF (Ji et al., 2012). Between a HLR of 1.5 and 3.0 m3/ m2 d, denitrification is primarily performed by heterotrophic bacteria, because organic loading rises in response to the higher HLR. The growth rate of heterotrophic bacteria, therefore, may also increase, leading to competition with nitrobacteria for oxygen and nutrients (Srinandan et al., 2012), and potentially causing a reduction in NH+4-N transformation (Ji et al., 2011). 3.2. Effect of HLR on functional genes and nitrogen transformation processes The absolute abundance of bacterial 16S rRNA decreased from 2.1  107 copies/g at a HLR of 1.0 m3/m2 d to 1.3  107 copies/g at a HLR of 3.0 m3/m2 d (Fig. 2a). Archaeal 16S rRNA decreased from 2.7  103 copies/g at a HLR of 1.0 m3/m2 d to 1.3  103 copies/g at a HLR of 3.0 m3/m2 d (Fig. 2a), showing low initial abundance. Archaea were not dominant in the microbial community (relative to bacteria) and dropped proportionally as HLR increased. This result is consistent with previous studies where archaea was found to potentially play a vital role in nitrogen transformation and removal (Angnes et al., 2013; Zhi and Ji, 2014). The absolute abundance of amoA, nxrA and anammox, which are the three functional genes involved in NH+4 -N transformation, showed very similar patterns relative to changing HLRs (Fig. 2b). The oxidation of ammonia to hydroxylamine, catalyzed by the ammonia monooxygenase coding gene amoA, is often used as a marker of aerobic ammonia oxidation, in which NH+4-N is oxidized to NO2 -N (Dionisi et al., 2002). The amoA gene absolute abundance was 1.4  copies/g at a HLR of 1.0 m3/m2 d, reached a peak of 5.4  102 copies/g at a HLR of 1.5 m3/m2 d, and then declined somewhat but remained relatively stable at 3.5  102 copies/g, at HLRs of 2.0–3.0 m3/m2 d. Published studies have demonstrated that NH+4-N concentrations have an important effect on the physiological activity and community structure of ammonia-oxidizing bacteria (Dionisi et al., 2002; Okano et al., 2004). The NH+4-N concentration (1.5 mg/L) was very low, and under those circumstances

ammonia-oxidizing bacteria will reproduce at a low rate, which is why the absolute abundance of ammonia-oxidizing bacteria was lower in the TF. NH+4-N and NO2 -N can be converted to N2 by anammox bacteria under anoxic conditions (Stramma et al., 2008). The 16S rRNA of the anammox bacteria can be exploited as a marker of anaerobic ammonium oxidation (Bae et al., 2010). The anammox gene absolute abundance ranged from 1.4  105 to 4.9  105 copies/g during the operational period. As HLR increased from 1.0 to 3.0 m3/m2 d, the absolute abundance of anammox was about 750 that of amoA, suggesting that anaerobic ammonium oxidation was the dominant NH+4-N removal process in the TF. The nitrite oxidase coding gene nxrA is often used as the functional marker for nitrite oxidation (NO2 -N ? NO3 -N) (Poly et al., 2008). As HLR increased from 1.0 to 3.0 m3/m2 d, the absolute abundance of the nxrA gene ranged from 1.2  102 to 2.3  102 copies/g, in a trend that resembled the temporal variation of the amoA gene. The close similarity of these patterns was likely due to similar environmental adaptations and ecological interactions between ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) (Ji et al., 2012). Both AOB and NOB are autotrophic and require aerobic conditions (Kaelin et al., 2009). In addition, AOB performs NH+4-N to NO2 -N oxidation, providing substrate to NOB for the nitrite oxidation of NO2 -N to NO3 -N. This might also explain the corresponding lower abundance of the nxrA gene compared to the amoA gene. Absolute abundances of napA, narG, nirK, nirS, qnorB and nosZ genes, which are the six functional genes involved in denitrification processes, are shown in Fig. 2c. NO3 -N to NO2 -N reduction, the first reaction step in denitrification, is catalyzed by the key gene narG, encoding the membranebound nitrate reductase (NAR) enzyme, and the key gene napA, encoding the periplasmic nitrate reductase (NAP) enzyme. Both narG and napA genes are often used as nitrate reduction markers to study the denitrifying bacterial community (Bru et al., 2007; Ji et al., 2013). As HLR increased from 1.0 to 3.0 m3/m2 d, both the narG gene and the napA gene displayed similar temporal trends. The absolute abundance of the narG gene ranged from 5.1  103 to 9.5  105 copies/g, the latter peak value occurring at a HLR of 1.5 m3/m2 d. The absolute abundance of the napA gene ranged from 6.2  102 to 1.1  104 copies/g, the latter peak value also occurring at a HLR of 1.5 m3/m2 d. These results agree with previous research which found that the napA and narG genes of different functional layers in biofilters show mutual inhibition (Ji et al., 2013). This phenomenon may also explain the corresponding lower abundance of the napA gene compared to the narG gene. NO2 -N to NO reduction, the second reaction step in denitrification, is catalyzed by two equivalent types of nitrite reductases (Nir), either a cytochrome cd1 encoded by nirS, or a Cu-containing enzyme encoded by nirK (Kandeler et al., 2006). Both nirS and nirK genes are often used as nitrite reduction markers to study the denitrifying bacterial community (Braker et al., 1998; García-Lledó et al., 2011). With increasing HLR, the absolute abundance of nirK and nirS genes varied greatly. The nirS gene was more abundant than nirK during the entire operational period, a situation that was identified in other studies where the composition of the nirS genotypes demonstrated substantial variation, with the nirK communities being more stable than the nirS communities (Hallin et al., 2006). NO to N2O reduction, the third step in denitrification, is catalyzed by nitric oxide reductase (qnorB) which can be used as a marker of NO transformation by bacteria (Fujiwara and Fukumori, 1996). Results of the current evaluation showed that the absolute abundance of qnorB increased gradually from 7.4  102 to 1.2  104 copies/g when HLR was 1.5 m3/m2 d, which was followed by a general decrease in qnorB abundance as HLR rose, and a final value of 2.5  103 copies/g. The reaction utilizes NO as the substrate, and the migration of NO gas released by aerobic denitrifiers may be one of the

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Fig. 2. Absolute abundance of microbial communities and functional genes: bacterial and archaeal 16S rRNA (a); amoA, nxrA, and anammox (b); narG, napA, nirS, nirK, qnorB, and nosZ (c).

reasons that qnorB was relatively enriched in the functional layers (Ji et al., 2013, 2012). N2O to N2 reduction, catalyzed by nitrous oxide reductase, is the final reaction step in the denitrification process and, by decreasing the concentration of N2O, is important in controlling greenhouse gas emissions (Ji et al., 2012). The nosZ gene has been used as a marker for complete denitrification (Stres et al., 2004). In the current study, slight fluctuations in nosZ gene abundance were observed with increasing HLR. Overall, the absolute abundance of the six genes discussed previously, which are intimately involved in the denitrification process, tended to gradually decline during the operational period when HLR rose. These data suggest higher HLR negatively affects the denitrifying community. In conclusion, nitrification and anaerobic ammonium oxidation represented the dominant nitrogen removal pathways in the TF. Archaea may potentially play vital roles in nitrogen removal processes. 3.3. Relative importance of functional gene groups 3.3.1. Functional gene groups determining nitrogen transformation process rates Following our descriptive characterization of the roles of the functional genes involved in nitrogen transformation processes, the next objective was to identify key functional genes that determine nitrogen transformation process rates in the TF. Thus, a series of stepwise regression models were built to provide a linear

quantitative measure of functional genes association with nitrogen transformation rates. While valuable information can be derived from stepwise regression analysis that uses the absolute abundance of single functional genes as input variables, utilization of functional gene groups (ratio or summation of different functional genes) may provide additional explanatory information that is beneficial in characterizing the dynamics of nitrogen transformation processes (Ji et al., 2009; Philippot, 2002; Zhi and Ji, 2014). A series of reasonable variables (functional gene groups) were introduced into stepwise regression analysis. All three NH+4-N, NO2 -N and NO3 -N regression equations were successfully established (Table 1). The results indicate that amoA/anammox and (nirS + nirK)/ archaea are the key functional gene groups determining NH+4-N transformation process rates (Table 1). The first variable, amoA/ anammox, in the NH+4-N equation denoted as NO2 -N accumulation showed a positive relationship with NH+4-N transformation, which is not unexpected given that both amoA and anammox were primarily involved in NH+4-N conversion, as previously discussed. The second variable, (nirS + nirK)/archaea, in the NH+4-N equation denoted as NO2 -N consumption was positively related to the NH+4-N transformation rate. Both nirS and nirK genes contributed to NO2 -N consumption in the second step of the denitrification process, and the archaea gene was primarily involved in NO2 -N production. Therefore, the consumption to production ratio symbolized the extent or level of NO2 -N consumption. Greater NO2 -N consumption translates to higher NH+4-N transformation. Low concentrations of NO2 -N are preferable in TFs or any biologically-dependent treatment system since environmental toxicity can result from NO2 -N accumulation which may reduce microorganism abundance and/or efficiency (Fujiwara and Fukumori, 1996). The current study demonstrated that the TF may achieve high NH+4-N and COD removal, in addition to reducing the costs of maintaining wastewater aeration and/or constructing alternative or enhanced treatment units. As shown by the data in Table 1, amoA/(narG + napA) and nosZ/ (narG + napA + nirS + nirK + qnorB + nosZ) are the key functional gene groups determining NO2 -N accumulation rates. The first variable, amoA/(narG + napA), was positively associated with NO2 -N accumulation rates. Both amoA and narG/napA genes were primarily involved in NO2 -N production, supporting the analysis in Section 3.2. The negative relationship between nosZ/(narG + napA + nirS + nirK + qnorB + nosZ) and the NO2 -N accumulation rate suggests that increased N2O reduction, which is catalyzed by nitrous oxide reductase encoded by the nosZ gene, will decrease NO2 -N accumulation by enhancing NO2 -N reduction (nirK/nirS) (Zhi and Ji, 2014). The data gathered in this study indicate that anammox/amoA, nxrA/(nirK + nirS), and (napA + narG)/nxrA are the key functional gene groups determining NO3 -N accumulation rates (see Table 1). The first variable, anammox/amoA, in the NO3 -N equation denoted as NO2 -N consumption, showed a positive relationship with the NO3 -N accumulation process. There are two processes associated with NO2 -N production: (i) the first step of nitrification (amoA, NH+4-N ? NO2 -N) and (ii) the first step of denitrification (napA

Table 1 Stepwise regression models with nitrogen transformation rates as dependent variables and functional gene groups as independent variables (n = 5). Standardization regression equations

R2

P

NH+4-N

1.000 0.995

0.000 0.002

1.000

0.005

= 1.050 amoA/anammox + 0.316 (nirS + nirK)/archaea NO2 -N = 0.935 amoA/(narG + napA) 0.370 nosZ/ (narG + napA + nirS + nirK + qnorB + nosZ) NO3 -N = 1.196 anammox/amoA + 0.484 nxrA/ (nirK + nirS) 0.028 (napA + narG)/nxrA

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and narG, NO3 -N ? NO2 -N). In this study, the absolute abundance of anammox was about 750 that of amoA (Fig. 2b), suggesting that the first step of nitrification was not a unique process providing substrate (NO2 -N) for anammox. In conclusion, both ammoniaoxidizing and denitrifying microorganisms are involved in, and contribute to, NH+4-N transformation, in addition to simultaneous nitrification, anammox and denitrification (SNAD) processes, all of which were identified at the molecular level (i.e., the functional gene level) in the TF. The co-existence of the SNAD processes may assist in the simultaneous removal of nitrogen and organic carbon in a single system, rather than requiring a sequential chain of treatments (Lan et al., 2011). Instead the two processes together contribute to substrate provision (NO2 -N) for anammox (NO2 -N + NH+4-N ? N2). Therefore, the consumption and production ratio (anammox/amoA) symbolizes the extent or level of NO2 -N consumption; greater NO2 -N consumption leads to more NO3 -N transformation. The first variable also indicates that functional gene groups have a substantial, though indirect, influence on NO3 -N transformation. In addition, nxrA/(nirK + nirS) in the NO3 -N equation denoted as NO2 -N consumption was positively related to the NO3 -N accumulation rate. The nxrA gene was the main process contributing to NO3 -N production (NO2 -N ? NO3 -N), while nirS and nirK genes consumed NO2 -N (NO2 -N ? NO), therefore, the consumption ratio symbolized the extent or level of NO3 -N accumulation; the more NO2 -N consumption, the more NO3 -N accumulation. The third variable (napA + narG)/nxrA in the NO3 -N equation denoted as NO3 -N consumption showed a negative relationship with NO3 -N accumulation. The napA and narG genes were involved in NO3 -N transformation (NO3 -N ? NO2 -N), while the nxrA gene was involved in NO3 -N production (NO2 -N ? NO3 -N). Thus, the transformation and production ratio symbolized the extent or level of NO3 -N consumption.

aforementioned indirect impact. These results indicate that amoA/anammox is the better predictive variable (key functional gene group) and a major determining factor for NH+4-N transformation rate. The results also indicate that increasing (nirS + nirK)/ archaea can result in a declining NH+4-N transformation rate, which corroborate the results presented in Section 3.3.1. The direct effect of amoA/(narG + napA) on NO2 -N accumulation rate (0.935) was positive and relatively high (Fig. 3b). The direct effect of nosZ/(narG + napA + nirS + nirK + qnorB + nosZ) on NO2 -N accumulation rate was negative ( 0.370) and was also lower in strength than the positive effect of amoA/(narG + napA). Regarding the indirect effects of the functional groups on NO2 -N accumulation rate, neither pathway was particularly robust. The indirect effect of amoA/(narG + napA) via nosZ/(narG + napA + nirS + nirK + qnorB + nosZ) was negative ( 0.007) while the indirect effect of nosZ/(narG + napA + nirS + nirK + qnorB + nosZ) via amoA/(narG + napA) was positive (0.018) (Fig. 3b). The results show that amoA/ (narG + napA) is the better predictive variable and a major determining element affecting NO2 -N transformation rate, as previously discussed. The results also indicate that increasing nosZ/(narG + napA + nirS + nirK + qnorB + nosZ) can decrease the NO2 -N accumulation rate duo to the higher direct negative effect ( 0.370), supporting the results presented in Section 3.3.1. Anammox/amoA had the highest direct positive effect on NO3 -N accumulation rate (1.196). The indirect negative effects of amoA/ (narG + napA) via nxrA/(nirK + nirS) and (napA + narG)/nxrA on NO3 -N accumulation rate were 0.279 and 0.004, respectively (Fig. 3c). The direct positive effect from nxrA/(nirK + nirS)/amoA (0.484) on NO3 -N accumulation rate was relatively high, while the direct negative effect of (napA + narG)/nxrA on NO3 -N accumulation rate ( 0.028) was the lowest calculated during this study. The indirect negative effect of nxrA/(nirK + nirS)/amoA via anammox/amoA on NO3 -N accumulation rate was 0.689 (Fig. 3c). The indirect negative effects of (napA + narG)/nxrA via nxrA/(nirK + nirS) and anammox/amoA on NO3 -N accumulation rate were 0.208 and 0.183, respectively (Fig. 3c). The results indicate that anammox/amoA is the best predictive variable and a major contributor to the determination of the NO3 -N transformation rate. The results also suggest that increasing nxrA/(nirK + nirS) can markedly decrease the NO3 -N accumulation rate, as evidenced by the high direct negative effect ( 0.689). The results presented here strongly indicate that key functional gene groups can serve as integrative variables to characterize nitrogen transformation (accumulation) rates. Gene copy numbers (absolute abundance) are not useful for the characterization of dynamic processes of nitrogen (NH+4-N, NO2 -N, and NO3 -N) transformation (accumulation), because this processes perform

3.3.2. Direct and indirect effect of functional gene groups on nitrogen transformation (Path analysis) In order to further explore the relationships between nitrogen transformation process rates and the key functional gene groups, path analysis was utilized to evaluate the direct and indirect effects occurring in the TF. Path analysis revealed that amoA/anammox had a direct positive effect on NH+4-N transformation rate (1.050) (Fig. 3a). Compared with amoA/anammox, (nirS + nirK)/archaea had a lower direct positive effect on NH+4-N transformation rate (0.316). The indirect negative effect of amoA/anammox via (nirS + nirK)/archaea on NH+4-N transformation was only 0.096. The indirect effect of (nirS + nirK)/archaea via amoA/anammox on NH+4-N transformation rate was also negative ( 0.320) but was greater than the

(a)

1.050

amoA/anammox

NH4+-N

-0.320

-0.096

(b)

NO2--N

0.316 (nirS + nirK)/archaea

(c)

1.196

0.484

0.018

-0.370

-0.279

nxrA/(nirK + nirS) 0.012

-0.028

amoA/(narG + napA) -0.007

nosZ/(narG + napA + nirS + nirK + qnorB + nosZ)

anammox/amoA -0.689

NO3--N

0.935

-0.208

-0.004

0.183

Direct effect Indirect effect

(napA + narG)/nxrA

Fig. 3. Path diagrams estimating the direct and indirect effects of functional gene groups on NH+4-N transformation rate (a), NO3 -N transformation rate (b), and NO2 -N transformation rate (c). Arrows designate the direction of causality; numbers adjacent to arrows represent the degree of direct and indirect effects; positive and negative numbers represent positive and negative effects, respectively.

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simultaneously and influence one another (Ji et al., 2013). The direct and indirect effect of functional gene groups further confirmed that the NH+4-N, NO3 -N, and NO2 -N transformation processes are coupled at the molecular level (i.e., the functional gene level), resulting in their jointly contributing to nitrogen removal in the TF. 4. Conclusions A TF was constructed and operated under variable HLRs, ranging from 1.0 to 3.0 m3/m2 d. The TF achieved high and stable COD (97.7–99.3%) and NH+4-N (67.3–92.7%) removal efficiencies. Nitrification and anaerobic ammonium oxidation were the dominant nitrogen removal pathways in the TF. The amoA/anammox, amoA/(narG + napA), and anammox/amoA were three key functional gene groups and major determining processes for NH+4-N, NO2 -N and NO3 -N transformation rates, respectively. Functional gene groups were useful in characterizing the dynamic processes of nitrogen transformation. Acknowledgements The National Key Technology R&D Program of China (No. 2012BAJ25B01), the National Natural Science Foundation of China (No. 51179001), and Jilin Province Transportation Science and Technology Projects (No. 2011-1-7) provided support for this study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biortech.2014. 11.094. References Alwin, D.F., Hauser, R.M., 1975. The decomposition of effects in path analysis. Am. Sociol. Rev. 40, 37–47. Angnes, G., Nicoloso, R.S., Silva, M.L.B.D., de Oliveira, P.A.V., Higarashi, M.M., Mezzari, M.P., Miller, P.R.M., 2013. Correlating denitrifying catabolic genes with N2O and N2 emissions from swine slurry composting. Bioresour. Technol. 140, 368–375. Bae, H., Park, K., Chung, Y., Jung, J., 2010. Distribution of anammox bacteria in domestic WWTPs and their enrichments evaluated by real-time quantitative PCR. Process Biochem. 45, 323–334. Bhatnagara, A., Kumara, E., Sillanpääb, M., 2011. Fluoride removal from water by adsorption – a review. Chem. Eng. J. 171, 811–840. Braker, G., Fesefeldt, A., Witzel, K.P., 1998. Development of PCR primer systems for amplification of nitrite reductase genes (nirK and nirS) to detect denitrifying bacteria in environmental samples. Appl. Environ. Microbiol. 64, 3769–3775. Bru, D., Sarr, A., Philippot, L., 2007. Relative abundances of proteobacterial membrane-bound and periplasmic nitrate reductases in selected environments. Appl. Environ. Microb. 73, 5971–5974. 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. Microb. 68, 245–253. Fujiwara, T., Fukumori, Y., 1996. Cytochrome cb-type nitric oxide reductase with cytochrome c oxidase activity from Paracoccus denitrificans ATCC 35512. J. Bacteriol. 178, 1866–1871. Galloway, J.N., Townsend, A.R., Erisman, J.W., Bekunda, M., Cai, Z., Freney, J.R., Martinelli, L.A., Seitzinger, S.P., Sutton, M.A., 2008. Transformation of the

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