A comparative study of mesophilic and thermophilic anaerobic digestion of municipal sludge with high-solids content: Reactor performance and microbial community

Journal Pre-proofs A comparative study of mesophilic and thermophilic anaerobic digestion of municipal sludge with high-solids content: Reactor performance and microbial community Zong-Lin Wu, Zhi Lin, Zhao-Yong Sun, Min Gou, Zi-Yuan Xia, Yue-Qin Tang PII: DOI: Reference:

S0960-8524(20)30120-6 https://doi.org/10.1016/j.biortech.2020.122851 BITE 122851

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Bioresource Technology

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Please cite this article as: Wu, Z-L., Lin, Z., Sun, Z-Y., Gou, M., Xia, Z-Y., Tang, Y-Q., A comparative study of mesophilic and thermophilic anaerobic digestion of municipal sludge with high-solids content: Reactor performance and microbial community, Bioresource Technology (2020), doi: https://doi.org/10.1016/j.biortech.2020.122851

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Title: A comparative study of mesophilic and thermophilic anaerobic digestion of municipal sludge with high-solids content: Reactor performance and microbial community

Author: Zong-Lin Wu1, Zhi Lin1, Zhao-Yong Sun*, Min Gou, Zi-Yuan Xia, Yue-Qin Tang Affiliation: College of Architecture and Environment, Sichuan University, No. 24 South Section 1 First Ring Road, Chengdu 610065, Sichuan, China

*Corresponding author: Zhao-Yong Sun College of Architecture and Environment, Sichuan University, No. 24 South Section 1 First Ring Road, Chengdu 610065, Sichuan, China Telephone: +86(28)85990936, Fax: +86(28)85990936, E-mail: [email protected] 1 These

authors contributed equally to this work.

Abstract: This study involved a comparison between mesophilic (MAD) and thermophilic anaerobic digestion (TAD) of municipal sludge with high (10%) solids content; the reactor performance and the response of total and active microbial communities to changes in sludge properties were monitored. Both TAD and MAD were stably maintained. TAD performed better than MAD in biogas production and volatile total solids reduction upon feeding sludge 1. TAD was slightly inhibited by ammonia, whereas the performance of MAD was improved when sludge 2 was used as the feedstock. Alpha- and beta-diversity analyses revealed significant differences in the microbial community based on DNA and RNA datasets, indicating that not all microbes function in AD. The active microbial community diversity and composition in MAD and TAD were also driven by sludge properties. Moreover, MAD showed significantly higher richness and diversity of the active microbial community compared with TAD, regardless of changes in sludge properties. Keywords: anaerobic digestion; municipal sludge; high solid; mesophilic; thermophilic

Abbreviations: AD: anaerobic digestion; MAD: mesophilic anaerobic digestion; TAD: thermophilic anaerobic digestion; TS: Total solids; VTS: Volatile total solids; VFAs: volatile fatty acids; TAN: total ammonium-nitrogen; FAN: free ammonia nitrogen; STOC; soluble total organic carbon; OLR: organic loading rate; OTU: operational taxonomic units; HRT: hydraulic retention time.

1. Introduction Municipal sludge is the main by-product eluted from wastewater treatment plants (WWTPs). Appropriate treatment and disposal of sludge is critical for reducing its environmental load and for attaining the sustainability of WWTPs. Anaerobic digestion (AD), a potential technology for sludge treatment, has advantages, such as energy recovery, volume reduction, and pathogen elimination (Nges and Liu, 2010). Additionally, high-solids AD has increasingly attracted attention compared to the conventional low-solids AD in recent years owing to its advantages of higher organic loading rate (OLR), lower energy requirements for heating and pumping because of a lower digester volume, and lower disposal volume (Peng et al., 2018). In Europe, high-solids AD is being widely applied in joint large scale biogas plants (e.g., in the field of municipal waste) and several novel high-solids AD reactor systems have been developed (Álvarez et al., 2018). However, commercial-scale application of highsolids AD in treating organic waste is limited in China. Because most of the municipal sludge is dewatered to about 80% moisture in China and the scale of WWTPs is too small for a large traditional digester, the development of high-solids AD is desired (Liao et al., 2014). AD is usually operated under mesophilic (30–40 °C) conditions, in which it is considered to have a stable performance, whereas thermophilic (50–60 °C) AD has recently attracted more attention because of its higher organic matter degradation efficiency, pathogen-free effluent, and lower viscosity (Hidaka et al., 2013). High-solids AD of sludge under mesophilic conditions has been investigated in several previous studies. Zhang et al. (2015) studied the influence of total solids (TS) content on the performance of AD, and indicated that 6% TS content was the boundary between low- and high-solids AD. Liao et al. (2014) reported that a longer treatment duration is required for mesophilic anaerobic digestion (MAD) of sludge with higher TS content (up to 15.67%), compared with low-solids AD. To

improve the efficiency of high-solids MAD of sludge, Liao and Li (2015) applied an improved agitation in AD reactor, and the biogas production was increased to 342.8 mL/g volatile total solids (VTS)added compared to that of conventional low-solids AD (185.3 mL/gVTSadded). Chen et al. (2018) pretreated the sludge before the AD process, resulting in an increase in biogas production from 350 to 510 mL/gVTSadded compared to using untreated sludge as feed. Using a completely stirred tank reactor (CSTR), Li et al. (2018) conducted a long-term MAD operation of dewatered sludge with 15%–20% TS content, while the levels of volatile fatty acids (VFAs) remained high, indicating an unstable performance. Moreover, while exploring the microbial mechanisms involved in the high-solids AD process, Liu et al. (2016a,b) found that the acetotrophic methanogenesis pathway was more active under MAD, whereas the relative abundance of hydrogenotrophic methanogens increased along with an increase in the TS content from 10% to 19%. Nevertheless, these high-solids AD processes were conducted under mesophilic conditions. Indeed, high-solids TAD of sludge is seldom investigated; Hidaka et al. (2013) compared the performance of MAD and TAD at different TS contents and found that MAD with 10% TS content performed well, whereas TAD with 7.5% TS content was unsuccessful because of inhibition by ammonia. Using a batch test, Wang et al. (2018) found that VTS degradation was higher in TAD than in MAD at 10% TS content, whereas specific biogas generation in the two reactors was similar, being approximately 300 mL/gVSadded. In a preliminary study, a long-term TAD operation of dewatered sludge was conducted in the authors’ lab; it was found that a fluctuation in physicochemical properties caused by sludge sources (e.g., sludge taken from different WWTPs) had a negative effect on the performance of the reactor with respect to digestion (data will be published elsewhere). Hidaka et al. (2018) reported that sludge from different WWTPs varies greatly, and indicated that the high-solids AD process of sludge collected from surrounding WWTPs is

complicated. Therefore, development of a robust AD process is necessary to treat sludge with high TS content. Prior to that, more research is required for a full understanding of the high-solids TAD process of sludge. AD is a complex biochemical process driven by various microbes in which specific populations of microbes play essential roles in material transformation. Unveiling the microbial dynamics of the AD process is essential to enhance the performance of the reactor. Recently, high-throughput sequencing has been widely used to explore the structure and diversity of the microbial community. Until now, most of the methods for sequencing of amplicons were based on 16S ribosomal RNA genes (16S rDNA), whereas microbial analysis based on the DNA levels, which includes growing, active, dormant, and dead microorganisms (Blazewicz et al., 2013), does not reflect the active microbes in the AD reactor. Several studies have demonstrated that analysis based on 16S ribosomal RNA (16S rRNA) is more accurate for explaining the microbial phenomenon. De Vrieze et al. (2018) reported that betadiversity analysis based on DNA and RNA data showed significantly different results. Moreover, these authors reported that RNA data-based analyses showed a faster response under salt perturbation during AD of waste active sludge. Furthermore, Ye et al. (2017) reported that distinct microbial diversity was revealed by DNA and RNA datasets during the treatment of wastewater from soy sauce production. However, the total and active microbial community involved in TAD of high-solids containing sludge has not been systematically investigated. In the present study, a comprehensive comparison was done between MAD and TAD of municipal sludge with a high solid content. To achieve this goal, the reactor performance and the response of the total and active microbial community composition to changes in sludge properties were monitored for 170 days. First, the reactor performance of high-solids MAD and TAD of sludge was compared in

terms of biogas production and other main physicochemical properties. Second, the microbial community was analyzed based on 16S rDNA and 16S rRNA sequencing to identify the total and active microbes under MAD and TAD of sludge throughout the process. 2. Materials and methods 2.1. Materials The feed sludge, namely sludge 1 and sludge 2, were taken from two municipal wastewater treatment plants of differing scales, located in Sichuan province, China. The anaerobic-anoxic-oxic (A2O), a typical wastewater treatment process in China, was applied to both the wastewater treatment plants. It is reported that the A2O process has prominent advantages for the efficiency of removal of N and P (Ye et al., 2017). The sludge was collected from the dewatering unit, and the TS contents of sludge 1 and 2 were 22.41% and 20.49%, with volatile total solids (VTS)/TS ratios of 44.85% and 51.24%, respectively. In addition, C/N ratio of Sludge 1 was slightly lower than that of Sludge 2. The sludge was stored at 4 °C in a refrigerated chamber before use. The main physicochemical properties of the two sludge materials are shown in Table 1. 2.2. AD reactor set-up and maintenance Figure 1 shows the schematic diagram of the AD reactor used in this study. Two identical, completely anaerobic, 10-L stirred-tank reactors (having a working volume of 7 L) (jar fermentor, MDL-10L; B.E. Marubishi, Chiba, Japan) were equipped with an improved universal mixer bracket. The reactors were operated under mesophilic (37±2 °C) and thermophilic conditions (53±2 °C), and the agitation speed was maintained at 85 rpm. The reactors were acclimated for more than 1 year by feeding the sludge material once daily using the draw-and-fill method at an OLR of 4 g/L/d based on the VTS content of the sludge. The TS content in the feed sludge was increased stepwise to 10%, and at

this level, the hydraulic retention time (HRT) was shortened to 14 days. When the sludge was used for anaerobic digestion, Ni2+ (NiCl2·6H2O) and Co2+ (CoCl2·6H2O) ions were added at 0.05 mg/g VTS to enhance the methane producing reaction (Kida et al., 2001; Sun et al., 2014). After the biogas was cooled and dried, the amount of gas produced was recorded using an electromagnetic flow meter (KOFLOC3760, Kojima Instruments Inc., Kyoto, Japan). It should be noted that the two aforementioned reactors used the same initial inoculum for anaerobic digestion; the inoculum used was sludge from a mesophilic anaerobic digester treating piggery wastewater. 2.3. Analytical methods and calculation The TS content was determined by oven drying the samples at 105 °C until there was no further loss of mass. The VTS content was determined by combusting the oven-dried samples in a muffle furnace (TMF 5 T; THOMAS, Tokyo, Japan) at 600 °C for 2 h. The VTS degradation efficiency was calculated according to Li et al. (2018), assuming that the inorganic mass of the sludge was kept constant. The elemental content of the sludge was determined by a CHN element analyzer (Vario EL cube, Langenselbold, Elementar, Germany). The pH was measured by a pH meter (REX, Shanghai, China). Soluble total organic carbon (STOC) was measured by a TOC auto analyzer (TOC-VE, Shimadzu, Japan). The concentration of NH4+ was analyzed with an ion chromatography system (Dionex ICS-1100, Thermo Scientific, Waltham, MA, USA) equipped with a CS-12A column for the analysis and a CG-12A guard column. The concentration of free ammonia-nitrogen (FAN) was calculated according to Tao et al. (2017). Volatile fatty acids, including lactic, acetic, propionic, and nbutyric acids were analyzed using a post-label method with an HPLC (Shimadzu, Kyoto, Japan), equipped with a UV detector (at 450 nm) and Shimpack SCR-101H column, as previously described (Kida et al., 1993). The methane (CH4) content of the biogas was measured by gas chromatography

(GC 350B; GL Sciences, Japan) using a thermal conductivity detector (TCD), equipped with a packed column (Porapack Q, GL Science). The theoretical biogas and methane production were calculated based on Buswell equation (1) at standard temperature and pressure (STP, equations (2) and (3)) (Buswell and Mueller, 1952).

Ca HbOc 

4a - b - 2c 4a  b - 2c 4a - b  2c CO2 CH4  H 2O  4 8 8

VBiogas (25C )  VCH 4 ( STP )  VCO2 ( STP )  

VCH 4 (25C )  VCH 4 ( STP) 

273  25 273

273  25 273

(1) (2) (3)

In equation (2), the production of biogas, CO2, and CH4 are defined as VBiogas, VCO2, and VCH4, respectively. The volume of biogas was converted by (273+25)/273 at STP to room temperature (25 °C) and atmospheric pressure (101 kPa). 　 2.4. Microbial community analysis Samples taken from mesophilic and thermophilic reactors on days 43, 84, 125, and 160 were used for DNA and RNA extraction. Prior to DNA extraction, samples were washed with phosphate-buffered saline (PBS) and centrifuged at 10,000 rpm for 5 min, and the pellets were used for DNA extraction. The samples for RNA extraction were prepared using RNA-Be-Locker A Reagent (Sangon Biotech, Shanghai, China), according to the manufacturer’s instructions. The samples were stored at -80 °C before DNA or RNA extraction. The hexadecyltrimethylammonium bromide (CTAB) protocol was used for DNA and RNA extraction, according to Griffiths et al. (2000). RNA was reverse transcribed with random hexamer primers using the PrimeScript RT Reagent Kit (TaKaRa Bio, Dalian, China), according to the manufacturer’s instructions. The extracted DNA and reverse-transcribed cDNA were sequenced by Majorbio Co. Ltd. (Shanghai, China). Barcode-attached bacterial/archaeal universal primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′)/909R (5′-CCCCGYCAATTCMTTTRAGT-3′)

were used to amplify the V4-V5 hypervariable region of the 16S rRNA gene, and the amplicon libraries were sequenced on the Illumina Miseq platform. The raw data were processed based on the isanger cloud platform (www.i-sanger.com). Briefly, the reads were merged by FLASH, operational taxonomic units (OTU) were clustered after quality control by Usearch (sequence similarity ≥ 97%), and a Bayesian algorithm-based ribosomal database project classifier was used for taxonomic analysis, according to the Silva 132 database. The original sequencing data are available at the National Center for Biotechnology Information (accession no. PRJNA577521). 2.5. Statistical analysis The data were processed by EXCEL (Microsoft, USA); origin 8.5 (Originlab, USA) was used for plotting the data. The alpha-diversity index was calculated using the software mothur (The University of Michigan, USA). Principal coordinate analysis (PCoA) based on the Bray–Curtis distance, which was conducted using the R-package vegan, was used to visualize the distinction. Permutational multivariate analysis of variance (PERMANOVA) was used to test the significance between samples based on the Bray–Curtis distance using R. Besides, Spearman’s correlation coefficient was calculated using R. A p-value < 0.05 was considered as statistically significant. 3. Results and discussion 3.1. Reactor performance The changes in the main parameters during MAD and TAD of sludge are shown in Figure 2, and the average parameter values under stable conditions are summarized in Table 2. The entire process of each reactor can be divided into two phases, according to the different sludge substrate, i.e., phase 1 (feeding sludge 1 on days 1–84) and phase 2 (feeding sludge 2 on days 85–166); each phase was operated for about six hydraulic retention times (HRTs).

The MAD and TAD reactors maintained the stability after the initial three HRTs in phase 1 (Figure 2). As shown in Table 2, VFAs in the two reactors were not detected during the stable time, and the STOC of MAD and TAD remained at lower levels of 741.9 mg/L and 1441.9 mg/L, respectively. The VTS degradation efficiency and biogas yield of TAD (33.12% and 295.6 mL/gVTSadded, respectively) were higher than that of MAD (27.31% and 225.8 mL/gVTSadded, respectively). Thus, TAD showed higher performance than MAD during phase 1. Additionally, by comparing with the results reported in some previous studies on the MAD and TAD of sludge, the biogas yield and VTS degradation efficiency obtained in the present work were comparable to those of previous studies (E-Supplementary data). After operating the phase 1 for six HRTs, sludge 2 was fed instead of sludge 1. Phase 2 also remained stable after three HRTs, and VFAs were not detected in MAD; the VFAs value of TAD was below 305.7 mg/L, and it mainly comprised of acetic and propionic acids. However, STOC was doubled in both the reactors, which suggested that sludge 2 was more degradable than sludge 1. In the AD process, the VTS in sludge was converted to biogas, biomass, and dissolved inorganic and organic matter in the reactor. Thus, to some extent, the concentration of STOC in the reactor could be an important indicator for sludge biodegradability. Additionally, NH4+ of the MAD and TAD was increased from 2253.4 to 2856.6 mg/L and from 2612.5 to 3135.3 mg/L, respectively. This was also probably attributed to the higher biodegradability of sludge 2. As shown in Table 2, an increased VTS degradation efficiency was observed after changing the sludge material, although the C/N ratio of sludge 2 was little higher than that of sludge 1. Therefore, more ammonia was released when sludge 2 was fed. Duan et al. (2012) reported that the reactor performance was not inhibited with 1000–2500 mg/L total ammonium-nitrogen (TAN) and less than 250 mg/L free ammonia-nitrogen (FAN) under

high-solids MAD conditions. In this study, MAD was not inhibited with FAN at 126.4 mg/L (phase 1) and 233.8 mg/L (phase 2). Because FAN was positively correlated with temperature and pH (Yenigün and Demirel, 2013), the FAN of TAD was increased from 464.6 to 701.3 mg/L after changing the sludge; the emergence of VFAs also suggested that the TAD was slightly inhibited by ammonia. Moreover, this inhibition was also reflected by the production of biogas. The biogas yield of MAD was increased to 241.5 mL/gVTSadded, and the VTS degradation efficiency increased to 30.53%; however, the yields of TAD were decreased to 258.6 mL/gVTSadded and 31.99%, respectively. Correspondingly, the variation in methane content was consistent with the biogas yield and VTS degradation efficiency; the methane content remained within the narrow ranges of about 60.1%–62.1% and 61.3%–62.1% in MAD and TAD, respectively. In addition, the pH of the two reactors was slightly increased, probably because of the increased ammonium concentration. As a result, the two reactors showed differing responses to the change in the sludge. The performance of TAD declined due to slight inhibition by ammonia, whereas MAD showed a slight increase in biogas production because of more biodegradable sludge feed (i.e., sludge 2). 3.2. Microbial community analysis 3.2.1. Alpha- and beta- diversity of microbial community Figure 3 shows the Chao index of the microbial community in MAD and TAD reactors as revealed by the DNA and RNA datasets. The Chao index indicated that the MAD microbial community had higher richness than that of TAD at both DNA and RNA levels (P<0.05). In a previous study, Sun et al. (2015) also reported that the Chao index of observed OTUs decreased as temperature increased for bacteria and archaea in anaerobic co-digestion of straw and cow manure. Therefore, temperature was the main factor to drive the difference in microbial alpha-diversity between MAD and TAD.

Furthermore, the DNA sample exhibited higher richness than the RNA sample (P<0.05). De Vrieze et al. (2016) also reported that DNA had significantly higher richness than RNA in MAD; it is reasonable because the DNA pool extracted from environmental samples usually consists of nucleic acids derived from living, dormant, and dead microbial cells, whereas the RNA pool characterizes the growing or metabolically active microbes. This phenomenon indicated that not all microbes in the anaerobic reactors exhibited activity and function. Regarding the effect of sludge properties, the Chao index declined at the RNA level after changing sludge in MAD and TAD, indicating that the active microbial community was dynamically altered because of the change in sludge. However, the Chao index of the DNA level changed slightly, indicating a stable total microbial community even after changing the sludge feed. As discussed in section 3.1, the performance of reactor also changed after changing of sludge. Therefore, the RNA dataset-based result was probably more accurate to reflect the reactor performance. The rarefaction curve of the DNA and RNA samples suggests that the sequencing revealed the majority of the microbial community (E-supplementary data). PCoA (Figure 4) was conducted to elucidate the distinction of microbial communities among the samples based on the relative abundance of OTUs using the Bray–Curtis distance. The MAD and TAD samples were significantly distinct at both DNA and RNA levels (P<0.05), which was consistent with previous studies. Carballa et al. (2011) reported that samples taken from MAD and TAD were distinguished by cluster analysis based on denaturing gradient gel electrophoresis and terminalrestriction fragment length polymorphism data. Lin et al. (2016) also indicated that the samples taken at different temperatures were distinct, based on either the DNA or RNA datasets (PCoA, Bray–Curtis). In addition, PCoA indicated that DNA or RNA samples (at the OTU level) taken from the same reactor were clustered together but were separated from each other. In a previous study, De Vrieze et al. (2018)

analyzed the samples taken from different reactors with different temperatures based on DNA and RNA levels, and the Bray–Curtis dissimilarity showed significant differences in bacteria and archaea based on DNA and RNA levels. Moreover, the samples taken from phases 1 and 2, where sludge 1 and 2 were respectively used as feed, were also distinct. These results indicated that changes in the source and/or properties of sludge probably could be an important factor affecting the reactor performance. As discussed above, different microbial communities were reflected by the DNA and RNA datasets. Because functional prediction of microbes is more relevant during an AD process, the differences in the active microbial communities revealed by the RNA dataset in MAD and TAD are discussed and compared with the DNA dataset in the following sections. 3.2.2. Taxonomic composition of the active microbial community The composition of the microbial community comprising bacteria and archaea at the phylum level (relative abundance > 1%) is shown in Figure 5A. For bacteria, the dominant phyla were Firmicutes, Bacteroidetes, and Proteobacteria, which occupied approximately 80% of the entire community at both DNA and RNA levels in MAD. The members of the three phyla were associated with the hydrolysis, fermentation, and acetogenesis step in the AD process (Chen et al., 2018). However, the relative abundance of these three phyla decreased in TAD to 67.96%±2.71% and to 27.45%–60.20% at DNA and RNA levels, respectively. Compared to MAD, the phyla Thermotogae, Synergistetes, and Coprothermobacteraeota were enriched in TAD, even becoming dominant at the RNA level. Moreover, the phylum Thermotogae decreased as the phyla Synergistetes and Coprothermobacteraeota increased in TAD at both DNA and RNA levels after changing the sludge. For archaea, the phylum Euryarchaeota (mainly methanogens) had relative abundances of 4.64%–9.02% and 5.35%–5.96% at DNA and RNA levels in MAD, respectively. Although the abundance of methanogens decreased at the

DNA level after changing the sludge material in MAD, no obvious difference was observed at the RNA level. For methanogens in TAD, no major difference in abundance was observed when using sludge 1 compared with MAD. However, the abundance of methanogens decreased at both DNA and RNA levels after changing the sludge material, probably because of inhibition by methanogen. It is reported that, compared to bacteria, methanogens are more sensitive to higher ammonia concentrations (Chen et al., 2008). 3.2.2.1. Bacteria Figure 5B shows the genus-level bacterial community composition (relative abundance>0.5%) in MAD and TAD reactors. The phylum Firmicutes showed abundance in all the RNA samples, and its relative abundance in MAD was higher than that in TAD. Among this phylum, the class Clostridia was the most abundant, and Clostridia-affiliated genera presented various metabolic pathways with functions including hydrolysis, fermentation, and some with syntrophic organic acid oxidation (Ziganshin et al., 2013). The identified genera included Clostridia_DTU014, Clostridia_MBA03, Tepidimicrobium, Lutispora, Syntrophomonas, and other unclassified Clostridia. Clostridia_DTU014 had a quite high abundance of 50.88%–54.71% in phase 1 at the RNA level in MAD, and the relative abundance decreased to 16.29%–27.97% in phase 2 after changing the sludge. However, this genus was seldom found in TAD at either RNA or DNA levels. It was suggested in a previous study that Clostridia_DTU014 was probably a propionate- and butyrate-oxidizing bacterium (Lee et al., 2019). Clostridia_MBA03 was observed in both MAD and TAD; its abundance remained stable in MAD, whereas its relative abundance in TAD declined from 4.17%–13.99% to 0.15%–0.3% at the RNA level after changing the sludge. This genus is suspected to perform hydrolysis or fermentation of cellulosebased substrates (FitzGerald et al., 2019). Tepidimicrobium had a higher relative abundance in TAD

compared to that in MAD, and the relative abundance of this genus at the RNA-level was higher than that at the DNA level (6.28%–6.63% vs. 1.86%–2.36% in phase 1, respectively). Members of the genus Tepidimicrobium can utilize carbohydrates and proteinaceous compounds to produce VFAs (Niu et al., 2009). Lutispora was found in both the reactors, and its relative abundance in MAD was higher than that in TAD; this genus can utilize proteins, but not carbohydrates (Shiratori et al., 2008). Syntrophomonas was observed in both the reactors but had lower RNA-level relative abundance, i.e., 0.79%–0.92% in MAD and 0.38%–0.79% in TAD. The members of this genus have the ability to syntrophically oxidize butyrate and other long-chain fatty acids (Sousa et al., 2007). Another dominant phylum revealed by RNA datasets was Bacteroidetes, which varied in abundance from 7.69% to 33.05%, similar to that revealed by DNA datasets (14.14%–42.11%). The genera Lentimicrobiaceae_norank, Proteiniphilum, and Paludibacteraceae_norank were detected to be abundant. Among these, the genus Lentimicrobiaceae_norank had the highest abundance, and no differences were found between MAD (18.59±8.77%) and TAD (18.52±6.52%) throughout the process at DNA and RNA levels. Members of this genus can ferment a range of complex carbohydrates (Sun et al., 2016). Proteiniphilum showed higher relative abundance in TAD compared to that in MAD; this genus is proteolytic and can use pyruvate (Chen and Dong, 2005). Paludibacteraceae_norank was only found in MAD and showed higher RNA-level relative abundance. Some members in the family Paludibacteraceae can produce propionate (Ueki et al., 2006). For phylum Proteobacteria, class Gammaproteobacteria was mainly detected, including Pseudomonas, Acinetobacter, Hydrogenophilaceae_ norank, and Burkholderiaceae_unclassified. The genus Pseudomonas showed a higher abundance in both MAD (1.23%–3.29%) and TAD (4.77%– 13.76%) as revealed by RNA datasets, although the DNA-level relative abundance was low. This result

was consistent with previous studies (De Vrieze et al., 2018; Lin et al., 2016) in that the RNA-level relative abundance of Pseudomonas was higher than that at the DNA level. Zheng et al. (2013) reported that the genus Pseudomonas could secrete proteases, which was beneficial for protein hydrolysis. The genus Acinetobacter is a typical pathogen; it showed a higher relative abundance in MAD, but not in TAD, indicating that TAD had a higher pathogen-killing capability. Hydrogenophilaceae_norank and Burkholderiaceae_unclassified showed lower RNA-level relative abundance in both the reactors. The genera Defluviitoga, Acetomicrobium, and Coprothermobacter, which are affiliated with the phyla Thermotogae, Synergistetes, and Coprothermobacteraeota, respectively, were more abundant in TAD than in MAD. Defluviitoga was decreased at the RNA level from 21.34%–21.57% to 3.02%– 8.99% after changing the sludge in TAD. This genus can utilize microcrystalline cellulose, xylan, and other low-molecular saccharides, but not organic acids or peptone, and the end products of glucose fermentation were acetate, H2, and CO2 (Ben Hania et al., 2012). Acetomicrobium, known for acetogenesis, can ferment organic acids and carbohydrates to produce acetate, H2, and CO2 (Menes and Muxí, 2002); its abundance was increased from 0.62%–10.59% to 19.47%–23.06% at the RNA level after changing the sludge in TAD. Coprothermobacter was increased at the RNA level from 5.85%– 12.84% to 17.37%–45.49% after changing the sludge in TAD. The genus Coprothermobacter contains proteolytic anaerobic thermophilic microbes, and acetate, H2, and CO2 were the major products of its glucose fermentation (Etchebehere et al., 1998). Additionally, this genus can establish syntrophy with hydrogenotrophic methanogens, such as Methanothermobacter thermautotrophicus (Gagliano et al., 2015). The increase in this genus was probably correlated with the NH4+ concentration as a result of protein hydrolysis. Similar results were reported in a previous study (Pervin et al., 2013). The changes

in relative abundances of the main microbes may be caused by changes in physicochemical properties in the sludge substrate. For example, sludge 2 had a lower C/N ratio and contained more protein and relatively fewer carbohydrates; consequently, the microbial community was altered. 3.2.2.2. Archaea Figure 5C shows the genus-level relative abundance of the archaea (relative abundance > 0.5%). The genus Methanosarcina was the predominant archaea detected in all the samples at DNA or RNA levels, with abundances ranging from 60.05% to 98.98%. This genus can utilize acetate, methylamine, and H2 plus CO2, and it can function as either hydrogenotrophic or acetoclastic methanogen (Kern et al., 2016). In the MAD reactor, the relative abundance of Methanosarcina was further increased from 89.85%–97.06% to 97.69%–98.98% at the RNA level after changing the sludge. The other genera detected in MAD at the RNA level had much lower abundance. These results indicated a major functional role of Methanosarcina in methane production in MAD of high-solids containing sludge. However, more diverse methanogen genera were detected in TAD, even in the RNA dataset. The genus Methanoculleus was the second most abundant methanogen in phase 1 in TAD, as revealed by the RNA dataset; this genus is known as a hydrogenotrophic methanogen (Zellner et al., 1998). The other methanogens, such as Methanomassiliicoccus and Methanothermobacter, were also detected during phase 1 in the TAD rector. The effect of changing the sludge material on the methanogen composition in TAD differed from that in MAD. Methanosarcina decreased from 89.41%–91.26% to 60.05%– 81.22% in TAD at the RNA level. Interestingly, the dominant hydrogenotrophic methanogen Methanoculleus was replaced by Methanothermobacter. Hori et al. (2006) reported that VFA accumulation aids in the growth of Methanothermobacter, although a low VFA concentration was detected in TAD. Because Methanoculleus had a higher H2 affinity compared to Methanothermobacter

(needs higher H2 partial pressure), the increase in H2-producing bacteria, such as Acetomicrobium and Coprothermobacter, in phase 2 compared to phase 1 in TAD may induce a high H2 partial pressure, which also may support the increase in Methanothermobacter. The dominant methanogens in the AD process were influenced by many factors, such as temperature, different substrates, low or high-solids conditions, and the organic loading rate. Furthermore, various physicochemical properties (VFAs, NH4+ concentration, H2 partial pressure, or concentrations of other inhibitors) caused by different conditions may induce a different archaeal community. In previous studies, Chen et al. (2018) and Li et al. (2018) reported that Methanosarcina was the dominant archaea found in the high-solids MAD of municipal sludge, which was consistent with the results obtained in the present work. Moreover, Cho et al. (2013) showed that Methanosarcina was also the dominant archaea under AD of food waste. On the contrary, Li et al. (2015) reported that hydrogenotrophic methanogens were the dominant archaea in solid-state AD of corn stover, and genera Methanoculleus and Methanothermobacter were dominant in MAD and TAD, respectively. In total, the different substrate sources may have an important effect on the dominant methanogens. 3.3. Relationship between dominant active microbes and environmental factors Figure 6 shows the heatmap of the Spearman’s correlation coefficient between the active microbial community and environmental factors. Among the top 30 genera, 14 were significantly +

correlated to environmental factors, including temperature, pH, VTS, STOC, NH4 , and FAN (p<0.05). Moreover, 10 genera were correlated to temperature, indicating that temperature was the main factor to drive the community dynamics. For bacteria, genus Clostridiales_Family_XI_unclassified, Tepidimicrobium, and Pseudomonas showed significantly positive correlation to temperature (p<0.05). As discussed above (see section 3.2), Clostridiales_Family_XI_unclassified and Tepidimicrobium can

ferment carbohydrates and protein to produce VFAs by employing various metabolic pathways; the genus Pseudomonas was beneficial to protein hydrolysis. Based on the results, these bacteria populations likely play an important role in substrate fermentation, and consequently contribute to enhanced performance in TAD than in MAD. In contrast, genera Longilinea, Anaerolinea, Proteiniborus, Acinetobacter, Paludibacteraceae_norank, Clostridia_DTU014 showed significantly negative correlation to temperature (p<0.05). These populations are probably specialized microorganisms involved in the MAD reactor. In addition, the genus Coprothermobacter, members of which can ferment proteinaceous compounds, showed significantly positive correlation with pH and FAN (p<0.05). The result was consistent with those of a previous study (Pervin et al., 2013), wherein it was reported that Coprothermobacter coincided with increased ammonium concentration. Because the TAD reactor was slightly inhibited by ammonium after changing the sludge material, FAN and pH are likely to have an important effect on the change in microbial community structure. For archaea, the genus Methanothermobacter was closely positively correlated to temperature, pH, and FAN (p<0.05). It showed higher relative abundance in TAD, especially at phase 2. This indicated that Methanothermobacter was more resistant to ammonia than other methanogens. Controlling these factors could be an effective means of optimizing the active microbial communities to improve the performance of high-solids AD reactor. 3.4. Possible guidance for the practical engineering project Municipal sludge is dewatered to a moisture ~80% in most WWTPs in China, and thus, a highsolids AD process of sludge is a good choice. Although MAD is usually applied in commercial market, TAD of sludge with high solid content has benefits, such as higher organic matter degradation efficiency, pathogen-free effluent, and lower viscosity (Hidaka et al., 2013). However, it is a big

challenge for TAD operation of sludge with high solid content, and the digestion performance and its influencing mechanism has been rarely described quantitatively. These results are the basis for developing a robust process, treating sludge with high solid. From the present study, it confirmed that TAD showed better performance than MAD under stable conditions, as reflected by enhanced biogas production and VTS degradation efficiency. Thermophilic condition was indeed an important factor, which screened specialized microorganisms in TAD, and consequently contributes to enhanced digestion of the sludge. Compare to MAD, the limitation of TAD is that it is more sensitive to environmental factors (i.e., sludge properties) and ammonia inhibition is the main risk of high-solids TAD of sludge. To apply high-solids TAD for sludge treatment, a highly efficient method for decreasing ammonia inhibition must be developed. For instance, to treat proteinrich waste, a previous study proposed a novel method to reduce ammonia inhibition during TAD via recirculating water-washed biogas into the reactor. Consequently, the NH4+ concentration in the reactor was controlled at a lower level of approximately 2400 mg/L, and a simultaneous decrease in ammonia inhibition during TAD was achieved (Sun et al., 2014). Additionally, co-digestion with material with higher C/N ratio (e.g., agricultural straw) may also be a promising alternative strategy (Sun et al., 2015; Ziganshin et al., 2013). 4. Conclusions This study demonstrates the advantages and limitations of a TAD process for treating sludge with high TS content by comparing a MAD process. TAD showed better performance in biogas production and VTS reduction than MAD under stable conditions; however, TAD was more sensitive to changes in sludge properties, and ammonia inhibition is the main risk for TAD. Strategies for ammonia inhibition alleviation during high solid TAD of sludge is necessary. Microbial analysis based on RNA

datasets more accurately reflected the AD process, and the active microbial community diversity and composition had a close relationship with environmental factors. E-supplementary data for this work can be found in e-version of this paper online. Acknowledgements This work was supported by the National Science Foundation of China (51808361), the China Postdoctoral Science Foundation (2018M640936), Sichuan Science and Technology Program (2018JY0536), and Chengdu Science and Technology Program (2016-HM01-00291-SF).

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Figure legends Figure 1. Schematic diagram of AD reactor used in this study. Figure 2. Changes in the main parameters during high-solids MAD (A) and TAD (B) of sludge. Figure 3. Chao index of the microbial community in MAD and TAD reactors as revealed by DNA and RNA datasets. Letters C and D represent the RNA and DNA levels, respectively; M and T refer to mesophilic and thermophilic AD, respectively. Figure 4. PCoA analysis based on Bray–Curtis distance at the OTU level. Ellipse indicates a confidence level of 0.68. Letters C and D represent the RNA and DNA levels, respectively; M and T refer to mesophilic and thermophilic AD, respectively; and numbers 1–4 represent samples taken from days 43 (phase 1), 84 (phase 1), 125 (phase 2), and 160 (phase 2), respectively. Figure. 5. Microbial community structure in MAD and TAD of sludge. (A) Microbes at the phylum level, (B) Bacterial community structure at the genus level and (C) Archaeal community structure at the genus level. Letters C and D represent the RNA and DNA levels, respectively; M and T refer to mesophilic and thermophilic AD, respectively; and numbers 1–4 represent samples taken from days 43 (phase 1), 84 (phase 1), 125 (phase 2), and 160 (phase 2), respectively. Figure. 6. Heatmap of the Spearman’s correlation coefficient between active microbial community and environmental factors.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Table 1 The main physicochemical properties of the two sludge materials Parameters\Materials

Sludge 1

Sludge 2

TS (%)

22.91±0.70

20.56±0.11

VTS (%)

9.74±0.21

10.20±0.21

VTS/TS (%)

44.48±0.98

49.61±1.10

pH (-)

6.92±0.11

6.93±0.16

STOC (mg/L)

ND

132.2±9.4

NH4 (mg/L)

15.66±2.35

ND

Lactic acid (mg/L)

ND

ND

Acetic acid (mg/L)

ND

ND

Propionic acid (mg/L)

ND

ND

Butyric acid (mg/L)

ND

ND

C/N

5.72±0.06

5.89±0.05

C/H

6.06±0.01

5.67±0.02

Theoretical molecular formula

C1.81H3.59O0.98N0.27

C2.08H4.40O1.17N0.30

988.0±8.3

971.3±4.1

55.6±0.3

56.9±0.1

+

(based on VTS) Theoretical biogas yield (mL/gVTS) Theoretical methane content (%) ND, not detected.

Table 2 The average values of main parameters under stable conditions in MAD and TAD MAD Parameters\Source

TAD

Sludge 1

Sludge 2

Sludge 1

Sludge 2

6322.7±469.6

6760.9±537.9

8276.4±485.8

7241.2±499.6

Biogas yield (mL/gVTS)

225.8±16.8

241.5±19.2

295.6±17.4

258.6±17.8

Biogas yield efficiency

22.9±1.7

24.8±2.0

29.9±1.8

26.6±1.8

pH (-)

7.76±0.18

7.82±0.13

7.88±0.14

7.92±0.09

TS (%)

8.73±0.36

8.49±0.19

8.53±0.34

8.49±0.13

VTS (%)

3.24±0.15

3.47±0.15

2.99±0.15

3.40±0.12

VTS degradation

27.31±3.57

30.53±3.17

33.12±3.87

31.99±2.72

STOC (mg/L)

741.9±177.0

1514.2±113.6

1441.9±196.1

2746.1±201.0

NH4 (mg/L)

2253.4±209.1

2856.6±89.3

2612.5±184.1

3135.3±70.2

FAN (mg/L)

126.4±47.1

233.8±59.9

464.6±47.2

701.3±87.9

Methane content (%)

60.1±1.1

62.1±0.4

62.1±2.2

61.3±2.6

VFAs (mg/L)

ND

ND

ND

< 305.7

Materials Biogas production (mL/d)

(%)

efficiency (%) +

ND, not detected.

Declaration of interest

None.

Highlights 1.

MAD and TAD of high-solids containing sludge were comprehensively compared.

2.

The response of microbial community composition to sludge properties was monitored.

3.

TAD performed better than MAD in biogas production and VTS reduction.

4.

Ammonia inhibition was the main risk for high-solids TAD of sludge.

5.

Microbial analysis based on RNA datasets more accurately reflected the AD process.

38

Author contributions： Zong-Lin Wu: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft. Zhi Lin: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft. Zhao-Yong Sun: Resources, Writing - Review & Editing, Supervision, Data Curation. Min Gou: Validation, Formal analysis, Visualization, Software. Zi-Yuan Xia: Validation, Formal analysis, Visualization. Yue-Qin Tang: Writing- Review & Editing.

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