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Article

Effects of pH Adjustment on the Release of Carbon Source of Particulate Organic Matter (POM) in Domestic Sewage

by
Lei Zhu
1,2,*,
Jiahou Hao
3,
Houwei Lai
4,* and
Guibai Li
1
1
School of Environment, Harbin Institute of Technology, Harbin 150090, China
2
Jiangsu Yihuan Group Co., Ltd., Yixing 214206, China
3
School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, China
4
South China Institute of Environmental Science, Ministry of Ecology and Environment (MEE), Guangzhou 510655, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(13), 7746; https://doi.org/10.3390/su14137746
Submission received: 6 June 2022 / Revised: 20 June 2022 / Accepted: 22 June 2022 / Published: 24 June 2022
(This article belongs to the Special Issue Sustainable Advanced Water Treatment Technologies)
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Abstract

:
The use of anaerobic hydrolytic fermentation to develop more available carbon sources from domestic sewage influent particulate organic matter (POM) has received increasing attention. However, the slow hydrolysis rate of POM limits the application of this technology. This study aimed to improve the carbon source release efficiency of POM by pH adjustment and to reveal the hydrolysis mechanism. Results showed that adjusting the initial pH of POM to 3, 9, and 11 enhanced carbon source release in the anaerobic hydrolysis fermentation process of POM. The pretreatment under pH value of 11 contributed to the highest yield and productivity of carbon source, reaching the soluble chemical oxygen demand (SCOD) of 2782 mg/L at the 4th day. The pH 3 pretreatment was more beneficial for phosphorus resource recovery, which contributed to the highest release concentration of PO43−-P, reaching 48.2 mg/L at the 3rd day, accounting for 90% of TP. Microbial community structure analysis indicated that pH 11 preconditioning promoted the enrichment of proteolytic bacteria (Proteocatella and Proteiniclasticum) and polysaccharide hydrolytic bacteria (Trichococcus and Acinetobacter) and inhibited the growth of acetate-consuming methanogenic archaea, which contributed to the highest carbon release of POM in domestic sewage.

1. Introduction

At present, the municipal sewage discharge in China has exceeded 571.36 billion tons, many urban domestic sewage treatment plants often face the problem of insufficient carbon sources in the influent [1]. Although adding external carbon sources including methanol, sodium acetate, and ethanol can effectively improve the denitrification and phosphorus removal effect to ensure the stable discharge of wastewater treatment plants (WWTPs) [2], it greatly increases the operating costs of the WWTPs. Thus, it is not suitable for small-scale WWTPs. Improving the utilization efficiency of internal carbon sources from the influent is of great significance and has received increasing attention in recent years [2,3].
Recent studies have shown that particulate organic matter (POM), which is mainly composed of proteins, polysaccharides, and oils discharged from human activities, in the influent of WWTPs in China accounts for 40% to 60% of the total chemical oxygen demand (TCOD) [4,5,6]. The chemical composition and size distribution of POM will vary greatly with the source of sewage, living habits of residents, economic level, and sewage discharge methods. According to the different utilization rates of microorganisms, POM can be divided into readily biodegradable organic matter (RBOM), slowly biodegradable organic matter (SBOM), and inert organic matter. Among them, SBOM is abundant in actual sewage, accounting for 50% of the influent chemical oxygen demand (COD) in WWTPs [7,8]. The anaerobic digestion process mainly includes hydrolysis, acidification, hydrogen production, acetic acid production, and methane production. Among them, the hydrolysis of POM into soluble substrates is usually regarded as the rate-limiting stage, and more SBOM in POM can be converted into RBOM through microbial hydrolysis and acidification to improve the biodegradability of domestic sewage influent [9,10].
Previous studies have investigated the impact of operation parameters including pH, reaction time, organic loading rate, temperature, and substrate types on volatile fatty acids (VFAs) production. Among them, pH is an important parameter that can affect the hydrolytic fermentation of POM in various ways. Huijun Ma et al. [11] studied the effect of different pH values (7.0–10.0) on the anaerobic fermentation of raw sludge and thermal-alkali pretreated sludge, and found that the effect of acidogenic fermentation was the best when the pH was neutral. Alkaline conditions are beneficial to the hydrolysis of organic matter, but compared with pH conditions, the number of microorganisms under alkaline conditions is the lowest, which is not suitable for the growth and metabolism of acid-producing bacteria. Shikha Dahiya et al. [12] analyzed the effect of pH on acidogenic fermentation of food waste. The results showed that VFA production was better at pH = 10, followed by pH = 9, pH = 6, pH = 5, pH = 7, pH = 8, and pH = 11, and alkaline conditions were more conducive to the dissolution of carbohydrates, proteins, and fats and inhibited the growth of methanogens. Yanqing Duan et al. [13] used the solid organic matter retained by the microsieve as the substrate to study the effect of the initial alkaline adjustment on the fermentation and properties of cellulose components, and discovered that the VFA yield was the highest when the pH was 10.5, and alkaline treatment promoted polysaccharide release and inhibited methane production. Jian Wang et al. [6] studied the effect of different alkalis (NaOH, Na2CO3, and Ca(OH)2) on acid production from anaerobic fermentation of primary sludge, and found that all three types of alkalis can enhance the hydrolysis acidification effect and reduce methane production, but Na2CO3 can stably improve the yield of short-chain fatty acids (SCFAs) in a continuous reaction system.
Recent studies have shown that pH regulation not only affects the composition of fermentation products, but also affects the microbial community structure in the system. Yogananda Maspolim et al. [14] studied the characteristics of acid production by sludge fermentation from pH 4 to 10 and its effect on microbial community structure, the production effect of VFAs was the best when the pH was 8. Moreover, the abundance of microbial acidogenic bacteria (Tissierella, Petrimonas, Proteiniphilum, Levilinea, Proteiniborus, and Sedimentibacter) was increased at pH 8, resulting in better acid production. The abiotic effect was the main reason for the improved solubilization when the pH was higher, but pH ≥ 9 may inhibit biological activity and VFA production. Sijia Ma et al. [15] investigated the bacterial community characteristics in the alkaline fermentation of sludge. The results showed that the ATP synthase activity and unsaturated fatty acid (UFA) content in the system increased with the increase of pH. The predominant microorganisms in the operating stage of the reactor were inferred to be the key microorganisms affecting the production of VFAs. Xu Wang et al. [16] found that controlling pH = 11 for the first three days could effectively promote the chemical and biological hydrolysis of proteins in the sludge and provide a niche for Anaerobrancaceae sp. to convert the dissolved proteins into SCFAs. The total number of methanogenic archaea was reduced under these conditions, thereby reducing the consumption of short-chain VFAs by archaea. However, previous studies mainly focused on the influence of different pH conditions to optimize VFA production efficiency. To achieve the accurate control of hydrolysis and fermentation of granular organic matter, the correlation of different pH conditions with microbial community and maximum carbon source release efficiency needs to be elucidated.
The purpose of this work was to investigate the effects of pH on POM hydrolysis fermentation, protein and polysaccharide solubilization, soluble chemical oxygen demand (SCOD), nitrogen and phosphorus release, and microbial community characteristics. Batch assays were performed under 3, 5, 9, and 11 pH adjustment at 30 °C. A control experiment without pH adjustment was set up for comparison. The changes in pH, SCOD, NH4+-N, PO43−-P, and protein and polysaccharide production were further analyzed, and the relationship of pH adjustment and functional microorganisms was revealed through high-throughput sequencing.

2. Materials and Methods

2.1. POM Collection and Operation

After being filtered by a 60-mesh metal screen, the POM was collected from a small sewage treatment station in Wuxi (China). The inoculated sludge was taken from the anaerobic fermentation tank of another WWTPs in Wuxi (China), and heated in a water bath at 80 °C for 1 h before use. The basic physicochemical indexes of the mixed POM are shown in Table 1.
In the sequencing batch test, the substrate and seed sludge were mixed at a volume ratio of 9:1. Then, the pH was preconditioned to 3, 5, 9, and 11. The substrate and seed sludge were placed in four 250 mL fermentation flasks, and a control group was simultaneously set up. The reaction groups were cultured in an air-bath constant temperature oscillator with a temperature of 30 °C and speed of 150 rpm for 8 days. Ten milliliters of sludge samples were taken every day. After centrifugation, the samples were filtered with a 0.45-μm filter membrane to determine the concentrations of SCOD, NH4+-N, and PO43−-P in the filtrate. The fluorescent substance in the filtrate after filtration with a 0.22-μm filter was measured every 2 days. Sludge samples on the 4th day were taken for microbial community structure analysis.

2.2. Conventional Indicator Analysis

The concentrations of SCOD, PO43−-P, and NH4+-N were regularly measured according to the standard methods [17]. A portable multiparameter meter (HQ40d, Hach, Loveland, CO, USA) was used to test the pH. Protein and polysaccharide concentrations were measured by Dubois and Lowry methods, respectively [18,19]. After being filtered through a 0.22-μm filter membrane, the 3D fluorescence of the filtrate was measured by a Hitachi F-7000 spectrofluorometer (Tokyo, Japan) [20].

2.3. Microbial Community Structure

The mixed sludge samples at the 4th day were collected and centrifuged at 5000 rpm for 10 min, and the enriched sludge samples were extracted by a bacterial genomic DNA extraction kit (Omega Bio-tek, Norcross, GA, USA). The bacterial 16S rRNA was then subjected to PCR amplification using the bacterial fused primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), and the archaea fused primers 524F10extF (5′-TGYCAGCCGCCGCGGTAA-3′) and Arch958RmodR (5′-YCCGGCGTTGAVTCCAATT-3′). Finally, the purified PCR products were sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

3. Results and Discussions

3.1. Variation Characteristics of SCOD, NH4+-N, PO43−-P, and pH

The effect of pH preconditioning on SCOD concentrations is shown in Figure 1. The highest SCOD concentrations released by the POM were achieved on day 4, with concentrations of 2782 mg/L (pH 11), 1002 mg/L (pH 3), 904 mg/L (pH 9), and 791 mg/L (pH 5), which increased the SCOD concentration by 223.86%, 16.65%, 5.24%, and −11.62%, respectively, compared with the control. The results suggest that preconditioning the POM to pH > 7 or ≤3 was effective in increasing the release of SCOD from the POM and that preconditioning to pH 11 was more beneficial in promoting the release of carbon sources from the POM. This could be attributed to the following reasons: (1) different concentrations of soluble proteins and polysaccharides were released at different pH [21]; (2) the negative charge on the bacterial surface increases as the pH increases, and some substances adsorbed outside the microorganism can enter the liquid phase more easily under the effect of electrostatic repulsion [22].
The effect of pH preconditioning on NH4+-N concentration is shown in Figure 2, and the NH4+-N concentration increased with increasing reaction time under different pH preconditioning. This may be due to that alkaline conditions are conducive to protein hydrolysis and leaching of more NH4+-N, whereas other conditions of acid–base conditioning inhibit the release of protease from microorganisms, resulting in lower NH4+-N concentrations in the products released from the carbon source. As the reaction time increased, the number of bacteria with good carbon source releasing function gradually increased, and more protein was converted to NH4+-N. Over time, strong alkaline conditions resulted in more NH4+-N being released into the gas phase as NH3 due to the higher pH, leading to its relatively low concentration within the solution [23].
The effect of pH preconditioning on PO43−-P concentrations is shown in Figure 3, and PO43−-P concentrations were higher under strong acid preconditioning conditions, similar results also showed that it was easier to extract phosphorus from ash sludge under strong acid condition [24]. The order of magnitude of PO43−-P concentrations on day 3 was as follows: pH 3 (48.2 mg/L) > pH 11 (28.5 mg/L) > pH 9 (20.2 mg/L) > pH 7 (16.7 mg/L) > pH 5 (12.6 mg/L). The pH 3 preconditioning converted 90% of the phosphorus to PO43−-P for release into the liquid phase, indicating that strong acid preconditioning contributes to the release of phosphorus from the POM.

3.2. Variation of Protein, Polysaccharide and Fluorescent Substance Components

The effect of pH preconditioning on protein and polysaccharide concentrations is shown in Figure 4. The concentration of polysaccharides was higher under strong acidic and alkaline preconditioning, and the concentration of dissolved proteins was higher under alkaline preconditioning. Overall, strong alkaline conditioning was more favorable to the dissolution of proteins and polysaccharides. The polysaccharide leaching concentrations are presented as follows in descending order: pH 11 (176.70 mg/L) > pH 3 (58.32 mg/L) > pH 9 (39.25 mg/L) > pH 5 (38.70 mg/L) > pH 7 (29.36 mg/L). The protein leaching concentrations are presented as follows in descending order: pH 11 (52.50 mg/L) > pH 7 (4.94 mg/L) > pH 9 (4.75 mg/L) > pH 3 (3.30 mg/L) > pH 5 (2.07 mg/L). This may be due to the fact that lower pH only destroys the floc structure of the sludge, resulting in a lower release of polysaccharides. By contrast, higher pH not only destroys the floc structure of the particles, but also disrupts the cell structure and promotes the release of extracellular polymers from the sludge [25].
The effect of pH adjustment on the composition of fluorescent substances is shown in Figure 5 and Figure 6. When the preconditioned pH was 9, the higher fluorescence intensity was mainly for tryptophan-like proteins, tyrosine-like proteins, fulvic acid, microbial metabolites, and humic acids. When the preconditioned pH was 11, the higher fluorescence intensity was mainly for tryptophan-like proteins, tyrosine-like proteins, fulvic acid, microbial metabolites, and humic acids. Tryptophan proteins, tyrosine proteins, microbial metabolites and humic acids showed the highest fluorescence intensity under pretreatment conditions at pH 11.
Figure 6 shows that strong acid and alkaline conditioning increased the relative percentage of humic acid-like substances in the fluorescent lysate, suggesting that humic acids are probably mostly adsorbed on the extracellular polymeric substance (EPS) surface and that any disruption of the sludge structure would lead to cell release. A higher percentage of tyrosine in the fluorescent material was evident with strong alkaline conditioning (pH = 11), suggesting that alkaline conditioning facilitates the release of tyrosine proteins, probably because they are mostly internal to the sludge and require strong disruption of floc structure to facilitate release.

3.3. Effects of pH Adjustment on the Microbial Community Structure

3.3.1. Alpha Diversity Analysis

The results of alpha diversity analysis of bacteria and archaea in samples on day 4 after different pH preconditioning are shown in Table 2 and Table 3. By comparing the samples after different pH preconditioning, pH 3 pretreatment showed relatively high biodiversity according to the diversity indices (Shannon, Simpson). Compared with the control group, acid–base preconditioning facilitated to increase the bacterial diversity and richness, whereas alkaline preconditioning decreased the archaeal richness. Especially when the pH was pre-conditioned to 11, the diversity and richness of archaea in the samples were the lowest.
The effect of different pH preconditioning on the bacterial and archaeal community structure is shown in Figure 7a,b. As depicted in Figure 7a, the two principal axes together explained 64.91% of the variation in the bacterial community data, with principal axis 1 (PC1 axis) being the largest feature contributing 39.44% of the variation in the samples. The samples were clearly divided into three regions, with pH 5, control, and pH 9 clustered together to form a single cluster and pH 3 and pH 11 forming a separate cluster and differing significantly from the other samples. This finding suggests that the bacterial community structure was similar under the environmental factors, except for the strong acid (pH = 3) and alkaline (pH = 11) preconditioning conditions, which caused significant changes in the bacterial community structure. Figure 7b shows that the two main axes together explained 79.50% of the variation in the archaeal community data, with coordinate 1 (PC1 axis) being the largest feature causing variation in the samples, contributing 47.31%. The samples were clearly divided into three regions, with pH 5, pH 9, and pH 11 clustered together to form a single cluster and pH 3 and control forming a separate cluster and differing significantly from the other samples.

3.3.2. Effects of pH Adjustment on the Bacterial Community Structure

As shown in Figure 8a, there are significant differences in the phylum composition of different environmental factors at the phylum classification level, and the dominant phylum in each sample was Actinobacteriota, Firmicutes, Bacteroidota, Proteobacteria, Synergistota, Patescibacteria, Desulfobacterota, Chloroflexi, Spirochaetota, and Campilobacterota. Compared with the control group, with the increase of pH (alkaline pretreatment), the abundance of Bacteroidota, Proteobacteria, and Chloroflexi gradually decreased, whereas that of Firmicutes gradually increased; with the gradual decrease of pH (acid pretreatment), the abundance of Actinobacteriota and Chloroflexi gradually decreased. Under pH 3 pretreatment, the abundance of Bacteroidota, Proteobacteria, Desulfobacterota, Patescibacteria, Spirochaetota, and Campilobacterota was much higher than that in other samples, and the relative abundances were 19.58%, 12.73%, 4.27%, 2.19%, 1.22%, and 1.08%, respectively. Under pH 11 pretreatment, the abundance of Firmicutes was the highest at 41.32%, indicating that Firmicutes, which are closely related to carbon source release, were enriched under alkaline conditions.
As shown in Figure 8b, the sequencing results at the class level showed that the dominant class in each sample included Actinobacteria, Clostridia, Bacteroidia, Synergistia, Gammaproteobacteria, Alphaproteobacteria, Bacilli, Negativicutes, Saccharimonadia, Desulfobulbia, Desulfovibrionia, and Campylobacteria. Among them, Bacteroidia, Gammaproteobacteria, Negativicutes, Saccharimonadia, Desulfobulbia, Desulfovibrionia, and Campylobacteria had the highest abundance under pH 3 preconditioning, whereas Clostridia, Synergistia, and Bacilli had the highest abundance under pH 11 preconditioning.
To characterize the differences in functional bacterial compositions from the samples, microbial communities were analyzed at the genus level (Figure 8c). The abundance of Clostridium_sensu_stricto_1 spp. gradually increased and that of Gallicola spp. gradually decreased as the preconditioning pH decreased. Clostridium_sensu_stricto_1 is the main functional bacteriocin in hydrogen-producing fermentations, and it contains Clostridium butyricum and Clostridium paraputrificum, two common hydrogen-producing bacteria found in dark fermentations [26]. Gallicola is often found in hydrogen-producing fermentation systems and is an anaerobic bacterium with poor polysaccharides degradation capacity that occurs with the production of organic acids (acetic acid, butyric acid) and CO2 [27].
At pH 3 preconditioning, the abundance of Romboutsia, Clostridium_sensu_stricto_1, Megasphaera, Parabacteroides, Mycobacterium, Desulfobulbus, Thauera, Bacteroides, Escherichia-Shigella, Turicibacter, norank_f__Acidaminococcaceae, and Christensenellaceae_R-7_group were higher than that in other pH preconditioning conditions, with relative abundances of 7.87%, 5.58%, 3.02%, 2.80%, 1.74%, 1.54%, 1.37%, 1.15%, 1.11%, 1.08%, 1.05%, and 1.01%, respectively. Romboutsia was found to play an important role in the fermentation of glucose and use polysaccharides to produce gluconic acid and acetic acid [28]. Megasphaera can use glucose, citrate, glutamate, and lactate to produce hydrogen and organic acids (acetic acid, butyric acid) [29]. Parabacteroides can use polysaccharides to produce VFA, with the main fermentation products being acetate and succinate [30]. Desulfobulbus is able to adopt use propionate, lactate, pyruvate, and ethanol as electron donors and carbon sources and partially oxidize organic matter to acetate [31]. Thauera can achieve hydrogen oxidation autotrophic denitrification [32]. Bacteroides can employ polysaccharide fermentation to produce acetate, propionate, formic acid, and succinate [33].
Escherichia-Shigella can produce hydrogen from glycerol, sugar, and organic waste [34]. Turicibacter can produce lactic acid from carbohydrates under strictly anaerobic conditions [35]. norank_f__Acidaminococcaceae is a genus of homotypic acetic acid-producing bacteria that can produce acetic acid from H2 and CO2 [36]. Preconditioning at pH 11 favored the promotion of Petrimonas, Proteocatella, unclassified_o__Clostridiales, Trichococcus, Proteiniclasticum, Acinetobacter, and Gallicola spp. growth with relative abundances of 5.33%, 4.94%, 4.80%, 3.66%, 2.96%, 2.60%, and 1.59%, respectively. Studies have shown that Petrimonas can convert complex organic matter into acetate, hydrogen, and CO2 [37]. Proteocatella can degrade proteins, titin, and starch [38]. Trichococcus can break down carbohydrates into lactic acid, formic acid, acetic acid, ethanol and CO2 [39]. Proteiniclasticum is a strictly anaerobic protein-hydrolyzing bacterium that efficiently hydrolyses proteins in anaerobic fermentation [40]. Acinetobacter is an anaerobic or partly anaerobic bacterium with cellulose degradation capabilities that rapidly degrades organic matter such as cellulose [41]. Gallicola is often found in hydrogen-producing fermentation systems and is a non-sugar-degrading anaerobic bacterium that occurs with the production of organic acids such as acetic acid, butyric acid and CO2 [27].
The pH = 3 preconditioning is more helpful in promoting the growth of acid-producing bacteria, thus converting more POM into small molecules of organic matter and increasing the efficiency of carbon source release. The pH = 11 preconditioning facilitated to increase the abundance of protein-hydrolyzing functional bacterial spp. (Proteocatella and Proteiniclasticum) and polysaccharide-hydrolyzing functional bacterial spp. (Trichococcus and Acinetobacter), which allowed more POM to be released into the liquid phase as small molecules of polysaccharides and proteins, increasing the efficiency of carbon source release.

3.3.3. Effects of pH Adjustment on the Archaeal Community Structure

As shown in Figure 9a, the main phyla include Halobacterota, Euryarchaeota, Crenarchaeota, and Thermoplasmatota. Compared with the control group, the abundance of Halobacterota gradually decreased with the increase of pH (pH > 7). As illustrated in Figure 9b, the main classes include Methanosarcinia, Methanobacteria, Methanomicrobia, Thermoplasmata, and Thermococci. Compared with the control group (pH 7), with the increase of pH (pH > 7), the abundance of Methanosarcinia gradually decreased, and that of Methanobacteria gradually increased. It can be seen from Figure 9c that the main genera of Archaea under different environmental factors are Methanosaeta, Methanobacterium, Methanosarcina, Methanobrevibacter, Methanospirillum, Methanosphaera, and Candidatus_Methanofastidiosum.
Methanosaeta can use acetate to produce methane [42]. Methanobacterium, Methanobrevibacter, and Methanospirillum are typical hydrogenotrophic methanogens [43,44,45]. Methanosarcina is able to use H2 and acetic acid for methanogenesis, which has been proved by many researchers to be a co-trophic methanogenesis based on DIET [46]. Candidatus_Methanofastidiosum was confirmed to have a high hydrogen consumption capacity [47]. The abundance of Methanosaeta (from low to high) is as follows: 19.81% (pH = 11) < 20.69% (pH = 3) < 29.01% (pH = 5) < 53.92% (pH = 9) < 66.16% (control), indicating that the preconditioning conditions of pH = 3 and pH = 11 have the most obvious inhibition on Methanosaeta and that acid–base preconditioning can inhibit the growth of Methanosaeta, which is consistent with the results described by Yogananda Maspolim et al. However, the inhibition of Methanosaeta by pH 3 and pH 11 preconditioning could effectively reduce the consumption of acetic acid carbon source, therefore, the cumulative concentration of SCOD was higher than other conditions.

3.4. Pathway of Carbon Source Release

The results show that pH 3 pretreatment and alkaline pretreatment were all beneficial to promote the release of carbon sources from POM. The release concentration of carbon source was the highest at pH 11 pretreatment, and the release concentration of phosphate was the highest at pH 3 pretreatment. Combining reactor experimental results and microbiological analysis, a putative pathway of carbon source release from POM via pH adjustment was proposed (Figure 10). The pH 11 preconditioning is feasible for alleviating the shortage of carbon in the influent of small-scale WWTPs due to its ease of operation, short reaction time, and high carbon release efficiency.

4. Conclusions

The carbon source release concentration was optimized at pH 11, where proteins, polysaccharides, fulvic acid, soluble microbial metabolites, humic acids, and other organic matter formed the organic compositions. The concentration of SCOD released by the hydrolysis fermentation of POM was the highest on day 4. SCOD reached the highest concentration of 2782 mg/L at pH 11 pretreatment, and the concentration was 2.24 times higher than that of the control. The hydrolysis of POM facilities the utilization of carbon source and good biological nitrogen and phosphorus removal could be subsequently achieved. The best phosphorus release effect was obtained under pH 3 preconditioning, and 86.07% of TP was converted into PO43−-P on the 3rd day, which is beneficial to the recovery or removal of phosphorus from the fermentation liquor by phosphorus precipitation or struvite formation. The high-throughput sequencing results revealed that pH 3 and pH 11 had the most obvious inhibition on Methanosaeta, which could effectively reduce the consumption of acetic acid carbon source. The pH 3 preconditioning is more helpful in promoting the growth of acid-producing bacteria, whereas the pH 11 preconditioning is more conducive to promoting the growth of hydrolytic functional genera Proteocatella, Proteiniclasticum, Trichococcus, and Acinetobacter.

Author Contributions

Conceptualization, L.Z.; methodology, L.Z. and H.L.; investigation, L.Z. and J.H.; writing—original draft preparation, L.Z. and J.H.; writing—review and editing, L.Z. and H.L.; supervision, G.L.; project administration, H.L. and L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2019YFC1906504).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Effect of pH adjustment on SCOD concentration.
Figure 1. Effect of pH adjustment on SCOD concentration.
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Figure 2. Effect of pH adjustment on NH4+-N concentration.
Figure 2. Effect of pH adjustment on NH4+-N concentration.
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Figure 3. Effect of pH adjustment on PO43−-P concentration.
Figure 3. Effect of pH adjustment on PO43−-P concentration.
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Figure 4. Effect of pH adjustment on protein and polysaccharide concentrations.
Figure 4. Effect of pH adjustment on protein and polysaccharide concentrations.
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Figure 5. Variations of 3D-EEM at different pH adjustment.
Figure 5. Variations of 3D-EEM at different pH adjustment.
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Figure 6. Percentage of sludge liquid phase fluorescence response at different pH adjustment.
Figure 6. Percentage of sludge liquid phase fluorescence response at different pH adjustment.
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Figure 7. (a) Principal component analysis (PCA) to compare the bacterial community structures of each sample under different environmental factors. (b) PCA to compare the archaeal community structures of each sample under different environmental factors.
Figure 7. (a) Principal component analysis (PCA) to compare the bacterial community structures of each sample under different environmental factors. (b) PCA to compare the archaeal community structures of each sample under different environmental factors.
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Figure 8. (a). Bacterial community phylum-level relative abundance. (b). Bacterial community class-level relative abundance. (c). Bacterial community genus-level relative abundance.
Figure 8. (a). Bacterial community phylum-level relative abundance. (b). Bacterial community class-level relative abundance. (c). Bacterial community genus-level relative abundance.
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Figure 9. (a). Phylum-level relative abundance of archaeal communities. (b). Class-level relative abundance of archaeal communities. (c). Genus-level relative abundance of archaeal communities.
Figure 9. (a). Phylum-level relative abundance of archaeal communities. (b). Class-level relative abundance of archaeal communities. (c). Genus-level relative abundance of archaeal communities.
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Figure 10. Proposed pathway of carbon source release from POM via pH adjustment.
Figure 10. Proposed pathway of carbon source release from POM via pH adjustment.
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Table
Table 1. Physicochemical indexes of mixed POM.
Table 1. Physicochemical indexes of mixed POM.
ParameterSCOD
mg/L		NH4+-N
mg/L		TP
mg/L		PO43−-P
mg/L		SS g/LpH
Concentration450 ± 366.5 ± 0.253.5 ± 27.8 ± 0.138.5 ± 17.08 ± 0.01
Table
Table 2. Bacterial alpha diversity index table.
Table 2. Bacterial alpha diversity index table.
SampleChaoCoverageASVShannonSimpson
pH 31715117155.7980.012
pH 51505115055.2380.021
control93219325.1350.023
pH 91356113565.4220.015
pH 111338113385.1600.016
Table
Table 3. Archaeal alpha diversity index table.
Table 3. Archaeal alpha diversity index table.
SampleChaoCoverageASVShannonSimpson
pH 347914793.720.059
pH 527112712.620.177
control60616062.280.318
pH 917111712.240.226
pH 1116611662.240.276
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Zhu, L.; Hao, J.; Lai, H.; Li, G. Effects of pH Adjustment on the Release of Carbon Source of Particulate Organic Matter (POM) in Domestic Sewage. Sustainability 2022, 14, 7746. https://doi.org/10.3390/su14137746

AMA Style

Zhu L, Hao J, Lai H, Li G. Effects of pH Adjustment on the Release of Carbon Source of Particulate Organic Matter (POM) in Domestic Sewage. Sustainability. 2022; 14(13):7746. https://doi.org/10.3390/su14137746

Chicago/Turabian Style

Zhu, Lei, Jiahou Hao, Houwei Lai, and Guibai Li. 2022. "Effects of pH Adjustment on the Release of Carbon Source of Particulate Organic Matter (POM) in Domestic Sewage" Sustainability 14, no. 13: 7746. https://doi.org/10.3390/su14137746

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