Bioresource Technology 241 (2017) 552–562

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Bioresource Technology

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Nitrate removal, spatiotemporal communities of denitrifiers and theimportance of their genetic potential for denitrification in noveldenitrifying bioreactors

http://dx.doi.org/10.1016/j.biortech.2017.05.2050960-8524/� 2017 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: wlmian@sina.com (L. Wang).

1 Equal contributions.

Yimin Zhang a,1, Longmian Wang a,⇑,1, Wei Han b, Xu Wang c, Zhaobing Guo d, Fuquan Peng a, Fei Yang a,Ming Kong a, Yuexiang Gao a, Jianying Chao a, Dan Wu a, Bin Xu a, Yueming Zhu a

aNanjing Institute of Environmental Sciences, Ministry of Environmental Protection, No. 8 Jiang Wang Miao Street, Nanjing 210042, PR Chinab Sino-Japan Friendship Center for Environmental Protection, No. 1 Yu Hui Nan Road, Chao Yang District, Beijing 100029, PR Chinac School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, PR ChinadNanjing University of Information Science & Technology, No. 219 Ningliu Road, Nanjing 210044, PR China

h i g h l i g h t s

� Higher-rate NO�3 -N removal is achieved in NUA–DNBF than in DAS–DNBF.� The potential N2O production rate was much lower in DAS–DNBF than NUA–DNBF.� Burkholderiales, Rhodocyclales and Rhizobiales were dominant in both substrates.� qnosZ and

Pqnir/qnosZ may serve as biological indicators for NO3-N removal in DNBF.

� The NO�3 -N removal rate in NUA increased linearly with the DEA.

a r t i c l e i n f o

Article history:Received 7 April 2017Received in revised form 27 May 2017Accepted 30 May 2017Available online 1 June 2017

Keywords:Denitrification enzyme activityDenitrifying biofilterDewatered alum sludgeFunctional geneNeutralized used acid

a b s t r a c t

Nitrate treatment performance and denitrification activity were compared between denitrifying biolog-ical filters (DNBFs) based on dewatered alum sludge (DAS) and neutralized used acid (NUA). The spa-tiotemporal distribution of denitrifying genes and the genetic potential associated with denitrificationactivity and nitrate removal in both DNBFs were also evaluated. The removal efficiency of NUA–DNBFincreased by 8% compared with that of DAS–DNBF, and the former NUA–DNBF emitted higher amountof N2O. Analysis of abundance and composition profiles showed that denitrifying gene patterns variedmore or less in two matrices with different depths at three sampling times. Burkholderiales,Rhodocyclales, and Rhizobiales were the most commonly detected in both media during stable periods.Denitrification was determined by the abundance of specific genes or their ratios as revealed by control-ling factors. The enhanced nitrate removal could be due to increasing qnosZ or decreasing

Pqnir/qnosZ.

Furthermore, NUA–DNBF solely reduced nitrate by increasing the denitrification enzyme activity.� 2017 Elsevier Ltd. All rights reserved.

1. Introduction

The impact of agricultural production on the nitrogen (N) cycleleads to N enrichment of surface and ground water, as well asincreased nitrous oxide (N2O) emissions (Wang et al., 2016a).Nitrate (NO�3 -N), which is an important component of N, causesagricultural runoff pollution vulnerably owing to its high watersolubility and mobility (Hua et al., 2016). High levels of NO�3 -N

from agricultural drainage to receiving waters can pose a risk tothe environment. Additionally, N2O is a greenhouse gas andozone-depleting substance emitted as a result of incomplete deni-trification that also leads to undesired effects on the atmosphereand ecosystem (Syakila and Kroeze, 2011). Thus, it is necessaryto remove excess NO�3 -N from agricultural fields and control N2Oproduction simultaneously to ensure the security of waterresources and human health.

Denitrifying bioreactors/biofilters (DNBFs) are a promisingapproach to reducing NO�3 -N loads from agriculture runoff dis-charged into waterways. These systems typically use media in con-tainers to convert NO�3 -N to N gas via microbial denitrification

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Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562 553

(Schipper et al., 2010). The medium acts as a carbon and energysource for denitrifying microorganisms, and is the key factor influ-encing nitrate removal and N2O emission. Neutralized used acid(NUA) and dewatered alum sludge (DAS) originating from indus-trial byproducts, which have demonstrated nitrate removal effi-ciencies ranging of 50–82%, have emerged as low-cost substratesof constructed wetlands (CWs) and laboratory columns to reduceNO�3 -N from synthetic wastewater and surface water (Wendlinget al., 2012; Hu et al., 2016). However, the studies mentionedabove only focused on reducing NO�3 -N using NUA or DAS-basedbiofilters for short-term treatment, while few studies have investi-gated the results of long-term operation or their usage for nitrateremoval in agricultural runoff treatment. In support of active den-itrification in bioreactors, elevated levels of denitrification enzymeactivity (DEA) have commonly been measured in DNBFs (Warnekeet al., 2011), but elevated DEA does not always mean that signifi-cant NO�3 -N removal is occurring (Schipper and McGill, 2008).Indeed, it is currently not known if NUA and DAS in DNBFs selectfor substrates capable of reducing NO�3 -N via increasing DEA. Inaddition, N2O production during denitrification is an importantissue that must be addressed when studying DNBFs. A study ofadverse effects in different substrates used in denitrification bedsbyWarneke et al. (2011) revealed that a combination of maize cobsand woodchips enhanced NO�3 -N removal while minimizing theadverse effects of N2O release, whereas Greenan et al. (2009) mea-sured negligible N2O emission of only 0.003–0.028% of the totalNO�3 -N removed in a woodchip column study. However, less atten-tion has been given to examining potential N2O production inDNBFs containing NUA or DAS.

Conventional heterotrophic denitrification, which is the domi-nant mechanism of NO�3 -N removal in DNBFs, consists of consecu-tive reaction steps (Schipper et al., 2010). Nitrite reductase genes(nirS and nirK), which catalyze the reduction of nitrite to nitric-oxide, are considered to be the key functional genes involved indenitrification. Additionally, the last step of denitrification (thereduction of N2O to N2) is catalyzed by the nitrous oxide reductase(nosZ) gene (Huang et al., 2013). These denitrifying genes areresponsible for N transformations resulting in NO�3 -N removaland N2O alleviation. Although the denitrifying genes described todate have been used as molecular markers for quantitative andconstituent studies of denitrifying bacteria in the media of biofil-ters and CWs (Warneke et al., 2011; Hu et al., 2016; Wang et al.,2016a), the spatiotemporal abundance and distribution hetero-geneity of denitrifying genes at NUA and DAS in DNBFs isunknown. Many studies have shown that differences in the com-munity structure patterns and abundance of denitrifying bacterialgenes were correlated with a variety of physical and biogeochem-ical conditions (Ruiz-Rueda et al., 2009; Chen et al., 2014; Liu et al.,2016; Zhang et al., 2016). However, the abundance of denitrifyingbacteria in these NUA or DAS-based DNBFs in relation to the efflu-ent quality, DEA and potential N2O production rate (Pot N2O) underconstant environmental conditions are currently not wellunderstood.

Hence, this study was conducted to compare the removal effi-ciency (total organic carbon (TOC) and NO�3 -N removal) usingDNBFs for synthetic agricultural runoff treatment with media ofNUA and DAS. The primary focus was the NO�3 -N removal mecha-nism in these bioreactors, which included exploring the DEA andPot N2O, as well as the abundance of nirS, nirK and nosZ at differentdepths of NUA and DAS over time, and analyzing the compositionsof these bacteria in the systems. Furthermore, the mechanisms bywhich the abundance of denitrification bacteria influences denitri-fying activity (DEA and Pot N2O) and effluent concentrations, aswell as the correlation between DEA and NO�3 -N removalefficiencies were examined to determine the main genetic factors

influencing NO�3 -N removal and N2O emission. Therefore, the TOCvariation, N removal, and microbial community shift presented inthis study might provide insights into strategies that fine-tunethe operational parameters of biological processes in agriculturalrunoff treatment by using DNBFs.

2. Materials and methods

2.1. Properties of NUA and DAS

Table S1 summarizes the characteristics of the filter materialsused for this study. NUA and DAS were collected from a rutile plantand waterworks in Jiangsu Province, respectively. Following collec-tion, these industrial byproducts were washed using distilledwater to remove dirt and floating fine particles, after which theywere naturally air dried, ground and sieved (particle size 4–10 mm). Bulk density and porosity were determined using stan-dard soil science methods (Liu, 1996). The pH of the substratewas measured in a 10% (w/v) aqueous solution using a digital pHmeter. The specific surface area of the NUA and DAS was measuredby the Brunauer-Emmett-Teller method with N using a NOVA3000e surface area and pore size analyzer (Quantachrome Instru-ments, USA).

2.2. Experimental design and operation

The laboratory-scale experimental downward-flow DNBFs arepresented in Fig. S1. The system consisted of three parts: NUAand DAS reaction columns, a raw water feeding subsystem, and abackwashing subsystem. Two identical cylinder-shaped Plexiglascolumns (110 cm in height and 40 cm in diameter) were designed(from bottom to top) with a supporting layer of 10 cm of cobble-stones (diameter 4–10 cm), 90 cm of reaction layers and a 10 cmwater distribution layer. NUA and DAS were added to each con-tainer as the media at the reaction layers after preprocessing. EachDNBF had three sampling ports distributed at different depths inthe two columns for collection of filter materials. Three outletswere located at the base of the columns (30 cm, effluent 1;60 cm, effluent 2; 90 cm, effluent 3) to collect samples fromdifferent-layer effluents.

Simulated agricultural runoff was prepared according to themethod described by Hua et al. (2016), and glucose and KNO3 dis-solved in water were respectively used as carbon and NO�3 -Nsources. Synthetic wastewater was pumped through a water dis-tributor to ensure that the influent was uniformly distributed.Afterward, this wastewater infiltrated the NUA or DAS beds andwas finally discharged from the outlets. The characteristics of thesynthetic raw water are shown in Table S2. The overall experimentwas composed of a start-up phase and operation periods. TheDNBFs were inoculated with the activated sludge during start-upphase. The start-up process included the following three stages:(i) the first stage (days 0–20), in which there was a 0.1 m3 m�2 d�

hydraulic loading rate. During this phase, synthetic waterremained in the columns for 3 h after the influent reached the totalreaction volume. (ii) The second stage (days 21–40), in which therewas a 0.15 m3 m�2 d� hydraulic loading rate under conditions ofhydraulic retention time (HRT) = 2 h. The raw water quality inthe second stage was the same as that used in the first stage. (iii)The third stage (days 41–60), in which there was identical rawwater with a 0.2 m3 m�2 d� hydraulic loading rate under aHRT = 2 h. When the NO�3 -N concentration of effluent 3 was below4.0 mg L�1, the start-up of the DNBFs was deemed complete. Sub-sequently, the operation experiments were performed under thesame operational conditions in the third stage of start-up for150 days. During the start-up period, backwashing was not

554 Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562

conducted to prevent the loss of biomass. At steady state, theDNBFs were regularly backwashed every 7 days to prevent clog-ging of the filter bed. Additionally, the system was fed daily, oneprocess of introducing water was performed once a day throughoutthe period, and the temperature was maintained at 21.0 �C ± 1.5 �C.

2.3. Water quality analysis

The influent and effluent samples were collected and analyzedevery 5 days during the first 30 days and every 10 days in the fol-lowing 180 days. The concentrations of NHþ4 -N; NO

�3 -N; NO

�2 -N,

total N and pH were determined according to the standard meth-ods (APHA, 2002). TOC was determined by the combustion oxida-tion nondispersive infrared absorption method using a TOCanalyzer (TOC-L CPH, Shimadzu, Japan). The dissolved oxygen(DO) was measured in situ with a DO meter (YSI Model no. 550A,USA). All samples, including the controls, were analyzed intriplicate.

2.4. Denitrification activity measurements

DEA was determined in media using an acetylene inhibitiontechnique as previously described (Ryden and Dawson, 1982).Samples of initial NUA and DAS (N0 and D0), NUA and DAS col-lected on day 60 from 0–30 cm (N11 and D11), 30–60 cm (N12and D12) and 60–90 cm (N13 and D13), and NUA and DAS col-lected on day 180 from depths of 0–30 cm (N21 and D21), 30–60 cm (N22 and D22) and 60–90 cm (N23 and D23) were obtainedfrom the columns. One type of media was prepared by mixing 25 gof sample and 25 mL of a solution containing 1 mM glucose, 1 mMKNO3 and 1 g L�1 chloramphenicol in a 125 mL glass bottle. Chlo-ramphenicol in a bottle was used as a supplementary nitrificationinhibitor because acetylene diffused slowly in viscous media. Theheadspace was evacuated and flushed for 10 min with He, afterwhich 10 mL of acetylene were added. The samples were shakenat 25 �C, after which the concentration of N2O was measured inthe headspace after 30, 40, 50, and 60 min of incubation by gaschromatography as previously described (Warneke et al., 2011).N2O increased during incubation in the presence of acetylene andthus inhibited the nitrification and reduction of N2O to N2, whichprovided a basis for the application of this technique to calculateDEA indirectly by utilizing Bunsen coefficient for N2O dissolvedin water. Pot N2O was determined by incubating parallel sampleswithout acetylene.

2.5. Quantification of the functional genes

Total genomic DNA from microbial samples (0.25 g) collected atdifferent times and depths was first extracted and purified using anUltra Clean Soil DNA Isolation Kit (MO BIO Laboratories, Loker AveWest, USA), then analyzed by 1% agarose gel electrophoresis andstored at �20 �C until use. The 16S rRNA and main bacteriainvolved in the denitrification processes were quantified usingreal-time polymerase chain reaction (PCR). Primers and thermalcycling conditions used for each reaction are summarized inTable S3. All quantitative PCR (qPCR) reactions were performedon an ABI PRISM7500 Real-Time PCR System (Applied Biosystems,CA, USA) in a total volume of 25 mL containing 12.5 mL SYBR PremixEx TaqTM (Takara), 2 mL template DNA, 1 mL of each primer(5 mmol L�1), 0.5 mL ROX Reference Dye II (50�) and 8 mL RNase-free water. All samples were run in triplicate. Standard curves wereobtained by serial dilution from 103 to 108 copies of linearizedplasmids containing the respective functional genes. The R2 valuefor each standard curve exceeded 0.99 and the amplification effi-ciencies were 85–110%.

2.6. PCR–denaturing gradient gel electrophoresis (DGGE), sequencingand phylogenetic analysis

DNA extracted from different filter materials was subjected tofunctional PCR amplification targeting specific genes involved indenitrification. The primers and PCR-DGGE reaction conditionsare shown in Table S4. A detailed description of the PCR/DGGE set-tings used is available in Wang et al. (2016b).

The numbered dominant bands from DGGE gels were excised,washed, and dissolved in sterile water. The eluted DNA was sub-sequently reamplified as templates using the primer sets shownin Table S4, but without the GC clamp. Amplified DNA was thenpurified and ligated with the pTG19-T PCR Product Cloning Kit(Generay, Shanghai, China) according to the manufacturer’s pro-tocols, after which the clones were used as templates forsequencing by Biolinker Inc. (Shanghai, China). The retrievedsequences were then compared with those available in the Gen-Bank database using the Basic Local Alignment Search Tool. Theobtained sequences have been deposited in GenBank underaccession numbers KX000671 to KX000684 for nirS, KX000652to KX000670 for nirK and KX000685 to KX000704 for nosZ. Phy-logenetic analysis was performed using Molecular EvolutionaryGenetics Analysis version 4.0, and neighbor-joining trees wereconstructed using the p-distance model with a bootstrap of1000 replications.

2.7. Statistical analysis

The intensity of the bands identified from the DGGE images wasmeasured using the Quantity One image analysis software (Version4.0, BioRad, USA), after which principal component analysis (PCA)was conducted based on similarity in relative band intensity andposition of DGGE profiles using the CANOCO software 4.5 (Micro-computer Power, Ithaca, USA).

The data presentation and treatment was accomplished usingthe SPSS software (Version 16.0). The removal rates of TOC andNO�3 -N were calculated as the differences between the influentand effluent concentrations divided by the influent concentration.Differences in DEA and Pot N2O were tested for the effects of NUAvs. DAS substrates at different depths at the same sampling time byone-way ANOVA and Duncan’s multiple range test. The Pearsoncorrelations among the abundances of 16S rRNA and denitrifyingfunction genes, gene ratios, DEA and Pot N2O of substrates andthe concentrations of wastewater from the effluent were deter-mined. Linear regression mode was used to predict the relationshipbetween DEA and NO�3 -N removal efficiencies in NUA- and DAS-based DNBF. The significance level for all tests was 0.05.

3. Results

3.1. TOC and NO�3 -N removal performance

The average removal efficiencies of TOC by NUA- and DAS-based DNBF during the stable stage were 60.3% and 70.1%, respec-tively (Fig. S2a). The main form of nitrogen in effluents was NO�3 -N,and low NHþ4 -N and NO

�2 -N concentrations of 0.01–0.5 mg L

�1

were obtained (data not shown). As shown in Table S5, theNO�3 -N level decreased gradually from influent to effluent whileflowing through the filter material layers. After the start-up period,the NO�3 -N removal rates were 74.8–92.2% and 73.5–85.6% for theNUA and DAS columns, respectively (Fig. S2b), with the averageremoval efficiency of the NUA–DNBF being 8% higher than that ofthe DAS–DNBF.

Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562 555

3.2. Denitrification enzyme activity and potential N2O production rate

The DEA of DAS and NUA increased gradually over time(Fig. 1a). The DEA of substrate sampled in the same depth at iden-tical time was significantly higher in NUA than DAS (P < 0.05),except between the initial media and D21 and N21. The DEA forboth substrates at various depths did not differ significantly onday 60 and 180 (P > 0.05), except for that between N21 and N23(P < 0.05).

When compared to the untreated media, the Pot N2O of NUAand DAS increased significantly for both sampling times (Fig. 1b).As shown in Fig. 1b, the average Pot N2O of DAS was reduced by135% and 74% compared to that of NUA for day 60 and 180, respec-tively. Similar to the DEA, the Pot N2O of DAS did not change signif-icantly with depth, but that of N12 and N13 was significantlyenhanced relative to N11, and the highest value was observed inN23 on day 180 (Fig. 1b).

Fig. 1. DEA (a) and Pot N2O (b) of different substrates during operation period inboth systems. Columns with the same letter for the identical sampling time do notdiffer significantly (ANOVA; Duncan’s multiple range test, P < 0.05).

3.3. Copies of 16S rRNA and denitrification genes (nirS, nirK and nosZ)

In Fig. 2a–d, low copy numbers of 16S rRNA and denitrificationgenes were found in the initial media, and the 16S rRNA, nirS, nirKand nosZ in filter materials in the stable stage were present at 109–1010, 108–109, 107–108 and 106–107 copies g�1 substrate, respec-tively. For comparison, the ratios of nirS/nirK and

Pnir/nosZ of

these substrates ranged from 0.01 to 31.4 and 83.7 to 886.7,respectively. Overall,

Pnir genes were in the range of 0.3–20.4%

of the estimated 16S rRNA copies.As shown in Fig. 2a–e, the gene copies of nirK, nosZ and 16S

rRNA in NUA were much higher than those at the same depth inDAS on day 60 and 180, while the nirS and

Pnir numbers varied

slightly between the two substrates. The gene copy numbers ofnirK, nosZ and 16S rRNA increased in both media as time pro-gressed, but nirS and

Pnir remained nearly unchanged from day

60 to 180. Moreover, the top layer of media had relatively lowergene copy numbers of most genes than the middle and lower sub-strate. However, nirS and

Pnir of DAS decreased slightly on day

180 throughout the depth gradient.As shown in Fig. 2f–h, there were no major differences in any of

the gene ratios spatially in both filter materials on day 60 and 180.Moreover, the nirS/nirK in DAS and

Pnir/16s rRNA in both sub-

strates decreased dramatically from day 60 to 180, while no obvi-ous changes in nirS/nirK in NUA and

Pnir/nosZ in either media

were observed throughout the stable sampling period.

3.4. Community composition and phylogenetic analysis of denitrifiers

Comprehensive DGGE analysis of denitrifying genes from allsamples showed distinct patterns for each time and location(Figs. 3a–5a). No obvious differences in nirS community structurewere detected between DAS on day 60 and NUA (except N23) onday 60 and 180 (Fig. S3). Moreover, with the exception of N23,the structures of nirS at different layers did not shift significantlyat the same sampling time in either substrate. PCA of the nirSDGGE profiles also indicated good separation for DAS, but not forNUA among different stable operation periods. In the phylogenetictree of nirS (Fig. 3b), sequences from the both substrates were sep-arated into twomain groups. For the initial media, band 3 belongedto Group I.a at D0 and no bands were observed at N0. When thetwo operation times were considered as a whole, sequences fromthe various depths of NUA and DAS did not separate from eachother clearly, and they included all of subgroup I and Group II. Spe-cially, Ideonella (band 5) belonged to Group I.a and undeterminedbacteria (band 2) in Group II were only found in D21–D23, butbands 3 and 10 clustered into Group I.a were not seen in N23.

PCA revealed clear variations in the nirK community structurefor both substrates from the initial time to day 60 and 180(Fig. S4). However, no temporal or spatial pattern of significantlychanging community structures between NUA and DAS (exceptD21, N11, N21) was observed from 60 day to 180. Additionally,the nirK community from N11 and N21, D21, and other substrateswere distinctly clustered into three groups during the operationperiod, and DAS rather than NUA at the top layer followed atime-dependent shift in nirK. Phylogenetic analysis showed thatsequences present in Group I.a, I.c, II.a and II.c were the major typesin all samples, whereas Group I.b was more heterogeneous anddominated by band 5 retrieved from D21, N11 and N21. As shownin Fig. 4b, nirK compositions in both the initial and top-layer sub-strates (except D11) were different from those of the other media.Specifically, only 2 bands in D0, 3 bands in N0 and 4 bands in N11and N21 were grouped into Group I.a, while no bands in D21belonged to Group II.b. Therefore, nirK compositions from the ini-tial media, D21, N11 and N21 were separated from the othermedia.

The PCA results of the DGGE banding pattern from DNBFs atNUA and DAS (except D23) revealed differences in nosZ commu-nity structures (Fig. S5). During the stable operation period, a tem-poral pattern of significantly changing nosZ community structurefor DAS (except the middle-layer medium) was seen, but notime-dependent shift was observed for NUA. In the spatial patternof the nosZ community, no depth-dependent variation at NUA wasfound. The structure of nosZ at DAS shifted significantly along thedepth gradient on day 180, but no distinct variation existed amongDAS on 60 day. In Fig. 5b, 11 of 16 and 5 of 16 bands belonged to

Fig. 2. Number of copies of nirS (a), nirK (b), nosZ (c), bacterial 16S rRNA (d),P

nir (e), and ratios of gene copies of nirS/nirK (f), ratios of gene copies ofP

nir/16S rRNA (g) andgene copies of

Pnir/nosZ (h).

556 Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562

Group I.a and II.b, respectively, both of which were generally foundin all substrates except N0. The distinct bands, 7 and 17, whichwere clustered within Group I.a and I.b, respectively, were foundin all NUA and D23 samples, but not in other substrates. The nosZcompositions of D0 and D21 were different from those of othermedia since Group II.a and I.b were not observed in DO and D21,respectively.

3.5. Factors controlling denitrification

As shown in Table 1, the concentrations of TOC and NO3-N inthe effluents had a significant negative and positive relationshipwith qnosZ and

Pqnir/qnosZ, respectively, but no other correla-

tions between biological gene indicators and effluents were found.Moreover, a significant positive correlation existed between the

Group .c

Group .b

Group

Group .a

(a)

(b)

Band 2Band 3

Band 1

Band 4Band 5

Band 6

Band 7Band 8

Band 9

Band 10Band 11

Band 12Band 14

Band 13

D0 D11 D12 D13 D21 D22 D23 N23 N22 N21 N13 N12 N11 N0

Fig. 3. DGGE profiles of nirS gene fragments from different spatial-temporal NUA and DAS (a), and neighbor-joining phylogenetic tree of nirS sequences retrieved from thenumbered DGGE bands (b). Arrows indicate the bands excised (1–14) for sequencing. The sequences are 425 bp. The numbers at branch points are bootstrap values. Scale barindicates 5 changes per 100 nucleotide positions.

Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562 557

abundance of functional genes (qnirK, qnosZ, q16S rRNA) and DEA,and between qnirS, qnirK,

Pqnir and Pot N2O. There were also neg-

ative relationships between qnirS/qnirK and DEA, and between

Pqnir/qnosZ and DEA. As shown in Fig. S6, the NO�3 -N removal rate

in NUA–DNBF increased linearly with the DEA of NUA, but that inDAS–DNBF was not linearly correlated with the DEA of DAS.

Group .c

Group .b

Group .c

Group .b

Group .a

Group .a

(a)

(b)

D0 D11 D12 D13 D21 D22 D23 N23 N22 N21 N13 N12 N11 N0

Band 1

Band 2

Band 3

Band 4

Band 5

Band 6

Band 8

Band 7

Band 9Band 10Band 11

Band 12Band 14Band 15Band 13Band 16

Band 17Band 18

Band 19

Fig. 4. DGGE profiles of nirK gene fragments from different spatial-temporal NUA and DAS (a), and neighbor-joining phylogenetic tree of nirK sequences retrieved from thenumbered DGGE bands (b). Arrows indicate the bands excised (1–19) for sequencing. The sequences are 473 bp. The numbers at branch points are bootstrap values. Scale barindicates 5 changes per 100 nucleotide positions.

558 Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562

4. Discussion

4.1. Effective NO�3 -N reduction

Both NUA- and DAS-based DNBFs can substantially removeNO�3 -N from wastewater. It is well known that efficient NO

�3 -N

removal rates based on suitable porosity and high surface area ofmedia at the bioreactor are expressed in wetlands or other terres-

trial ecosystems (Schipper et al., 2010). These substrates werefound to have effective porosity values and adequate specific sur-face areas (Table S1), which is helpful to biofilm growth in thepores of the media. Denitrifying bacteria in the attached biofilmcan use NO�3 -N as the electron acceptor via conventional microbialdenitrification, which in turn reduces the NO�3 -N concentration insewage. The removal rate of NO�3 -N by NUA–DNBF was higher thanthat by DAS–DNBF possibly because of different fluctuations of TOC

Group .b

Group .b

Group .a

Group .a

D0 D11 D12 D13 D21 D22 D23 N23 N22 N21 N13 N12 N11 N0

Band 1

Band 2Band 3

Band 4

Band 5

Band 6

Band 7

Band 8Band 9

Band 10

Band 11

Band 12

Band 13

Band 17Band 14Band 15

Band 16

Band 18

Band 19Band 20

(a)

(b)

Fig. 5. DGGE profiles of nosZ gene fragments from different spatial-temporal NUA and DAS (a), and neighbor-joining phylogenetic tree of nosZ sequences retrieved from thenumbered DGGE bands (b). Arrows indicate the excised bands (1–20) for sequencing. The sequences are 453 bp. The numbers at the branch points are bootstrap values. Scalebar indicates 5 changes per 100 nucleotide positions.

Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562 559

contents between these DNBFs. The relatively slow depletion ofavailable carbon in NUA–DNBF but not in DAS–DNBF enhancedNO�3 -N removal efficiencies through denitrification by usingorganic carbon from the feed water in the NUA column. It has beenconfirmed that high TOC release is usually coupled with highNO�3 -N removal rates (Warneke et al., 2011).

4.2. Denitrification activity and its relationship with the abundance oftotal and denitrifying bacterial communities

In general, denitrification activity is primarily controlled by var-ious environmental factors, including carbon availability, moisture,properties of filter materials, influent qualities, and denitrifying

Table 1Pearson correlation coefficients (R) between denitrification functional genes, denitrification enzyme activity, potential N2O production rate and the concentrations of wastewaterfrom the effluent.

Eff TOC Eff NO�3 -N Eff DO DEA Pot N2O

qnirS �0.210NS �0.240NS �0.411NS 0.237NS 0.620*qnirK �0.539NS �0.374NS 0.063NS 0.954** 0.631*qnosZ �0.718** �0.708* �0.229NS 0.881** 0.533NSq16S rRNA �0.449NS �0.410NS 0.124NS 0.768** 0.209NSP

qnir �0.301NS �0.290NS �0.376NS 0.416NS 0.717**qnirS/qnirK 0.401NS 0.210NS �0.213NS �0.799** �0.373NSP

qnir/16SrRNA bacteria 0.271NS 0.229NS �0.236NS �0.559NS 0.002NSP

qnir/qnosZ 0.652* 0.615* 0.132NS �0.720** �0.300NS

Eff: Effluent wastewater. DEA: denitrification enzyme activity. Pot N2O: Potential N2O production rate.*and** indicate significant (2-tailed) effects at P < 0.05 and P < 0.01, respectively. The bold values represent significant Pearson correlation.

560 Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562

bacteria (Correa-Galeote et al., 2013). The abundance of denitrifiercommunities is often considered the dominant factor influencingDEA (Lyautey et al., 2013). Both DEA of DAS and NUA showed acommon temporal pattern, with the lowest value being observedin the initial samples and the highest on day 180. This patternwas markedly different from that of the spatial variation, whichshowed no significant differences in DEA among major different-depth media. These findings were supported by the significant pos-itive correlation between DEA and the numbers of functional bac-teria (qnirK, qnosZ, q16S rRNA), which suggests that the peak ofDEA occurred on day 180, when the functional bacterial abun-dances were highest, and that similar values were present in depthgradients in which most bacteria levels (qnirK, qnosZ, q16S rRNA)are alike. Similar results were reported in the sediments of CWsand biofilms of rivers (Correa-Galeote et al., 2013; Lyautey et al.,2013). Specifically, these studies revealed that the DEA of sedi-ments was positively affected by q16S rRNA and qnirK, and thatthis positive correlation was observed between the DEA of biofilmsand the qnosZ. However, previous reports also found that qnirS butnot qnosZ, or neither qnirS nor qnirK, imposed significant controlon DEA (Correa-Galeote et al., 2013; Lyautey et al., 2013). Theseinverse results might account for the spatiotemporal distributionof denitrifying genes abundances among different media. In thisstudy, the qnirS of both media changed slightly with the depth ofDNBFs from day 60 to 180, but qnirK and qnosZ changed substan-tially. These situations caused distinct relationships between DEAand denitrifying genes. Furthermore, higher DEA at NUA wascaused by the increased abundance of q16S rRNA, nirK and qnosZin this medium. The absence of differences in DEA between D21and N21 may partly be explained by the similar rich nutrients(especially NO�3 -N) in the upper layers of NUA- and DAS–DNBF.Tiedje (1988) also suggested that nitrate rich environments sup-port higher DEA. Indeed, as the bacterial community matures onday 180, limited oxygen diffusion to the deeper layers favors thepresence of bacterial functional groups with low oxygen require-ments at the bottom layers (Lyautey et al., 2005), which wasadvantageous to elevated DEA. Thus, the discrepancies of DEAbetween N21 and N23 were partially due to a slight decrease inDO at the outlet.

Denitrification is considered to be the major source of N2Ounder most conditions (Huang et al., 2013). The spatial-temporalpatterns of Pot N2O in DAS most likely depended on

Pqnir. We

found a strong positive relationship betweenP

qnir and Pot N2O.The slight changes in abundance of

Pnir during operation periods

as depth increased explain the lack of obvious differences in PotN2O from DAS, regardless of temporal and spatial variations. Whencompared to DAS, the spatiotemporal distribution of Pot N2O fromNUA was heterogeneous and complex. NirS/

Pqnir and nirK abun-

dances showed different spatiotemporal distributions, with nocharacteristic time or depth dependence being observed for PotN2O at NUA, but a positive correlation in Pot N2O value. Similarly,

the genes involved in the denitrification processes were not dis-tributed according to the spatial patterns of Pot N2O, but a few cor-relations were observed between denitrification variables (PotN2O) and gene (nirS, nirK) abundances (Correa-Galeote et al.,2013). Although in this study higher qnosZ had the ability toreduce N2O to N2 in NUA, the Pot N2O of NUA was clearly muchhigher than that of DAS. The marginal contribution of nosZ toN2O reduction may have been due to

Pnir and nosZ having very

different abundance ranges (P

nir was two orders of magnitudemore abundant than nosZ), which suggested the relatively smallqnosZ brought about a low activity of nosZ. Šimek et al. (2004)demonstrated that a low pH increases N2O production from deni-trification. The faintly acid property of NUA likely caused N2O pro-duction to become prominent through accelerated denitrification.Therefore, these findings configure a scenario of complex relation-ships between biogeochemical properties of the substrates, nutri-ent contents, genes and spatiotemporal distributions of DEA andPot N2O, which were dominated by correlations with specific geneabundances.

4.3. Structure and compositions of denitrifying bacterial communities

DGGE analysis of the denitrifying genes from substrates showeda quickly well-adapted denitrifier community attached to the NUA,regardless of how space and time changed, and a complicated den-itrifier community along the depth gradient attached to DAS dur-ing different operation periods. With the exception of very lowdiversity or of the absence of denitrifying genes in N0, communitystructures of nirS, nirK and nosZ in most of the NUA tended to bestable, suggesting that NUA directly influenced the micro-environment conditions and provided a suitable ecological nichefor denitrifying bacterium growth and propagation. Thus, specificphysiochemical characteristics of NUA (such as sorptive, high sur-face area) seemingly have significant impacts on certain denitrify-ing bacterial community structures and compositions relative toother environmental variables. The study has also shown that spe-cial material geochemical properties were strongly related to bac-terial community diversity and structure (Despland et al., 2014).The temporal differences in DAS suggested that all nirS and mostnosZ show a shift in the community’s structures from a circum-stance of rapid colonization with a high rate of reproduction to astabilized environment colonized by specific species. These find-ings are in accordance with those of previous studies that revealedchanges in the community structure of nirS and nosZ over time inthe CW (Ruiz-Rueda et al., 2009). However, most nirK structureswere time-independent in this study, which differed from generalvariations in nirK structures that have been reported with time insoil (Yoshida et al., 2009). This apparent preference of microorgan-isms harboring nirS at DAS for certain environments might rule outvariations in the structure and compositions of nirK at the samedepth throughout the operation period. Ruiz-Rueda et al. (2007)

Y. Zhang et al. / Bioresource Technology 241 (2017) 552–562 561

also observed inversely related diversity indices for nirK and nirS.Intriguingly, most structures of denitrifying genes were space-independent. Although the differences in denitrifying communitystructures were usually related to the depth gradient and differ-ences in HRT among various depths (Truu et al., 2009), the similarHRT in the DNBFs evaluated in this study likely contributed to thestability with experimental depth. Moreover, some individuals (i.e.,nirS of N23) separated from the main group, temporal-independentnosZ for DAS in middle layer, and depth-dependent nosZ for DAS onday 180, did not comply with the prevailing trends of denitrifyingcommunity structure variations. These discrepancies could proba-bly be ascribed at least in part to the roles of N contents or DO dif-fusion gradients in DNBFs. It has been verified that nutrientssupply rates and oxygen depletion influenced denitrifier commu-nity structures (Despland et al., 2014; Iribar et al., 2015); however,the occurrence of these phenomena in the present study are stilldifficult to explain.

Phylogenetic analysis showed that no unique sequences dif-fered between the DAS- and NUA-based DNBF, and that denitrifierscomprised a diverse microbial community. Most sequences in bothsubstrates were closely related to Burkholderiales (Group I.a in nirS,I.b in nirK and II in nosZ), Rhodocyclales (Group II in nirK) and Rhi-zobiales (Groups I.a, II.a and II.c in nirK, I.a in nosZ), which areknown to dominate the denitrification process in wastewatertreatment systems and grasslands (Chon et al., 2010; Pan et al.,2016). In addition to being commonly detected in all samples,Burkholderiales, Rhodocyclales, and Rhizobiales were more adaptedto exist in nirS-nosZ, nirS, and nirK-nosZ, respectively, as indicatedin Figs. 3b–5b Some minor genera (i.e., Pseudomonas containingboth nirS-harboring and nosZ-harboring denitrifiers belonging toGammaproteobacteria in Group I.b at nirS and nosZ, Arthrobacterbelonging to Actinobacteria in Group I.c at nirS, Staphylococcusbelonging to Bacilli in Group II.b at nirK) were also observed in bothDNBFs. Despite only a few sequences showing homology to theaforementioned genera, those possessing denitrification functionswere universally obtained from environmental samples, such assediments, soils and activated sludge (Faulwetter et al., 2009;Harbi et al., 2010; Mulec et al., 2015). In contrast, the nirS and nirKcommunities were more diverse than the nosZ communities. Fur-thermore, some of the nirK sequences in Group I.c were not clus-tered with known denitrifying populations, suggesting novel andunique nirK communities in these DNBFs.

4.4. Genetic factors controlling denitrification and N2O production

Microbial denitrification might be the mechanism of NO�3 -Nremoval in the experimental DNBFs because qnosZ and

Pqnir/

qnosZ ratio were identified as the main factors limiting NO�3 -N con-centrations in effluents. Changes in q16S rRNA or

Pqnir/16SrRNA

could not be used to infer NO�3 -N variations because most bacteriapossess at least a few copies of 16S rRNA (Zhang et al., 2016).Nitrite reductase genes, such as nirS and nirK, which were rela-tively more abundant than nosZ in this study, were responsiblefor the second step of denitrification and were indirectly involvedin NO�3 -N transformation. The principle of these function genesmight reveal that NO�3 -N removal was not associated with qnirS,qnirK,

Pqnir, and qnirS/qnirK. Conversely, a previous study showed

that Eff NO�3 -N decreased linearly with theP

qnir, but not qnosZ, inCWs (Chen et al., 2014). In the present study, although no correla-tions were detected between Eff DO and the abundance of anyfunctional genes, the similar DO concentrations among differenttimes at both DNBFs led to considerable distribution variations inqnosZ rather than

Pqnir. These findings suggested that increasing

qnosZ or decreasingP

qnir/qnosZ could facilitate the removal ofNO�3 -N in DNBF. The positive relationship between DEA and Pot

N2O and the specific abundances of functional genes conformedthe characteristic distributions of these gene quantities, whichhave been mentioned above. Furthermore, the results shown inTable 1 indicated that the ratios of qnirS/qnirK and

Pqnir/qnosZ

would have a negative impact on enhancement of DEA via thechanging relative abundances of the corresponding genes.

The NO�3 -N removal rate in NUA–DNBF was significantly posi-tively correlated with DEA, which demonstrated that DEA couldbe used to estimate NO�3 -N removal from this system. However,there is currently debate regarding whether or not increasingDEA is conductive to denitrification (Schipper et al., 2010). The dis-crepancy in DAS–DNBFs might be explained by the concurrent Nimmobilization, dissimilatory nitrate to ammonium, or Anammox(Burgin and Hamilton, 2007), all of which could alter NO�3 -N fates.

5. Conclusion

The combination of NUA and DAS in DNBFs may enhance nitrateremoval and minimize the adverse effects of N2O during agricul-tural runoff treatment. Phylogenetic analysis confirmed that thedominance of denitrifying bacterial community at both substrateswas the same during stable and prolonged operation. Analysis ofgenetic factors indicated that the absolute or relative abundanceof specific functional genes primarily contributed to denitrification.Therefore, the qnosZ and ratio of

Pqnir/qnosZ may serve as biolog-

ical indicators for nitrate removal at DNBFs. DEA could be used topredict nitrate removal in NUA rather than DAS.

Acknowledgements

This study was supported by the National Natural ScienceFoundation of China (No. 51308247) and the Foundation ResearchProject of Jiangsu Province (No. BK20161100).

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.biortech.2017.05.205.

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Nitrate removal, spatiotemporal communities of denitrifiers and the importance of their genetic potential for denitrification in novel denitrifying bioreactors1 Introduction2 Materials and methods2.1 Properties of NUA and DAS2.2 Experimental design and operation2.3 Water quality analysis2.4 Denitrification activity measurements2.5 Quantification of the functional genes2.6 PCR–denaturing gradient gel electrophoresis (DGGE), sequencing and phylogenetic analysis2.7 Statistical analysis

3 Results3.1 TOC and [$] {{\rm NO}}_{3}^{-} {\rm {\hyphen}N} [$] removal performance3.2 Denitrification enzyme activity and potential N2O production rate3.3 Copies of 16S rRNA and denitrification genes (nirS, nirK and nosZ)3.4 Community composition and phylogenetic analysis of denitrifiers3.5 Factors controlling denitrification

4 Discussion4.1 Effective [$] {{\rm NO}}_{3}^{-} {\rm {\hyphen}N} [$] reduction4.2 Denitrification activity and its relationship with the abundance of total and denitrifying bacterial communities4.3 Structure and compositions of denitrifying bacterial communities4.4 Genetic factors controlling denitrification and N2O production

5 ConclusionAcknowledgementsAppendix A Supplementary dataReferences