Rice washing drainage (RWD) embedded in poly(vinyl alcohol)/sodium alginate as denitrification inoculum for high nitrate removal rate with low biodiversity

https://doi.org/10.1016/j.biortech.2022.127288Get rights and content

Highlights

  • RWD-PVA/SA gel beads was prepared for enhancing nitrate removal from water.

  • The highest denitrification rate of ∼ 600 mg/(L∙d) was obtained.

  • Ea of nitrate oxidoreductase was 28.64 kJ/mol for the RWD-PVA/SA gel beads.

  • RWD-PVA/SA gel beads denitrification has a simple microbial community structures.

Abstract

Immobilization technology with low maintenance is a promising alternative to enhance nitrate removal from water. In this study, washing rice drainage (RWD) was immobilized by poly(vinyl alcohol)/sodium alginate (PVA/SA) to obtain RWD-PVA/SA gel beads as inoculum for denitrification. When initial nitrate concentration was 50 mg N/L, nitrate was effectively removed at rates of 50–600 mg/(L∙d) using acetate as carbon source (C/N = 1.25). Arrhenius activation energy (Ea) of nitrate oxidoreductase was 28.64 kJ/mol for the RWD-PVA/SA gel beads. Temporal and spatial variation in microbial community structures were revealed along with RWD storage and in the reactors by Illumina high-throughput sequencing technology. RWD-PVA/SA gel beads has a simple (operational taxonomic units (OTUs) 〈1 0 0). Dechloromonas, Pseudomonas, Flavobacterium and Acidovorax were the most four dominant genera in the denitrification reactors inoculated with RWD-PVA/SA gel beads. This study provides an inoculum for denitrification with high nitrate removal performance and simple microbial community structures.

Introduction

Rice is the most important food crop, both worldwide and for China, and approximately 760 million tones of rice are annually produced in the world (FAO, 2018). Various food processing that uses rice as raw materials, such as production of noodles, snacks, sweeteners, thickeners, and alcoholic beverages, generate large amount of rice washing drainage (RWD) because of repeated washing before using (Jo et al., 2015). RWD is organic matter rich; thus, highly energy-intensive facilities are usually involved in RWD treatment. There are several technologies to treat the RWD, including filter presses, spray dryers (Watanabe et al., 2009) or upflow anaerobic filter (Jo et al., 2015). This will increase the cost of wastewater treatment. As the RWD contains organic matter and trace elements, which are conductive for microorganism growth. For resource reclamation, previous studies have proven that RWD can be an inoculum for denitrification system to enhance nitrate removal from water (He et al., 2016a, He et al., 2016b).

Denitrification can be conducted by bacteria, archaea and some eukaryotes (e.g., fungi), among which bacteria are the primary nitrate reduction microorganisms in natural and engineered ecosystems (Lu et al., 2014). Metabolic pathways varied with electron mediators in different microorganisms (Lu et al., 2014). Therefore, inoculum will directly affect the denitrification performance. In a previous study, RWD was continuous pumped in denitrification reactor, achieving complete nitrate removal even without inoculated sludge; thus, RWD was both inoculum and carbon source for denitrification (He et al., 2016b). RWD as denitrification inoculum showed a high denitrification rate (He et al., 2016a, He et al., 2016b). Meanwhile, dissolved organic matter and bacterial community structure in the RWD system were simpler compared to other denitrification systems (He et al., 2019). Therefore, RWD can be an inoculum for denitrification to simplify subsequent treatment for denitrified water. However, RWD is a kind of wastewater, which might be hard for reservation or transport.

Immobilization technology with low maintenance is a promising alternative to achieve RWD’s immobilization for improving its application. Microbial immobilization technologies have been widely used in nitrate-contaminated water treatment as its high biological density, strong resistance to toxicity and fast start-up process (Isaka et al., 2012, Dong et al., 2017, Gan et al., 2019). A variety of natural polymers (agar, agarose, alginate and chitosan) and synthetic polymers (polyacrylamide, polyurethane, poly(vinyl alcohol) (PVA) and poly(ethylene glycol) prepolymer) have been successfully applied for microorganisms immobilization (Song et al., 2005). Cross-linked PVA hydrogel, with the advantages of good biocompatibility and high mechanical strength, nontoxic to organisms and can be cheaply produced at industrial scale, has attracted great attention in wastewater treatment (Choi et al., 2018). In addition, sodium alginate (SA) is a type of natural anionic polymer that comprises linear polysaccharide, with advantages of biocompatibility, nontoxic and low cost (Ma et al., 2020). However, the SA gel has poor stability and low mechanical strength. Therefore, PVA and SA combination has been considered as a strategy to obtain gel beads for performance enhancement. The PVA/SA gel beads have several advantages, such as low cost, good durability, and high mechanical strength (Wang et al., 2021a). Recently, graphene oxide-modified PVA/SA gel beads were used to immobilize Pseudomonas fluorescens Z03 to enhance nitrogen removal efficiency levels at low temperatures (Tang et al., 2021). In addition, Chang et al. (2021) prepared sponge cube that immobilized Zoogloea Q7 bacterium using PVA/SA to bioremediate the water polluted by nitrate, manganese (Mn), and antibiotics. Thus, PVA and SA gel technique was considered to immobilize RWD in this study.

RWD-PVA/SA gel beads were prepared by entrapping RWD in PVA/SA to be an inoculum for enhancing nitrate removal from water. Meanwhile, acetate was selected as electron donor due to its low-molecular-weight and widely use as external carbon source in denitrification process (Zheng et al., 2019). Series continuous feed experiments were set up to evaluate the long-term stability of denitrification process inoculated with RWD-PVA/SA gel beads. Furthermore, side-by-side microcosm studies at different temperature were carried out to identify arrhenius activation energy (Ea) for nitrate oxidoreductase of the RWD-PVA/SA gel beads. Moreover, microbial community structure was analyzed in the gel beads to reveal the denitrifying consortia of PVA/SA gel beads in the bioreactor.

Section snippets

RWD entrapped in PVA/SA gel beads

RWD was simulated according to our previous study (He et al., 2016). Briefly, RWD was washed using deionized (DI) water at a ratio of 1 (g RWD):1 (mL DI water) for three times. In practical, tap water is used for rice washing in food processing plant. DI water was used in present study to minimize other impacts on denitrification performance. Considering that RWD generated from food processing might not be timely used in practice, fresh RWD (0 day, S0) and RWD stored for various periods were

Nitrate removal

Continuous experiments were carried out to investigate the denitrification performance of RWD-PVA/SA gel beads. Fig. 2 shows changes in nitrate, nitrite and ammonium concentrations in influent, effluent and middle sample port of the denitrification reactors, and nitrate removal efficiencies. Nitrate concentrations in the influent were around 52 mg N/L. PVA/SA gel beads entrapped by various RWD presented different denitrification performance. On the first day, nitrate concentrations in the S0,

Conclusions

In this study, immobilization microbial technology was used to prepare gel beads by entrapping RWD in PVA/SA. The gel beads used as inoculum for denitrification presented high denitrification rate of 603–606 mg/(L∙d) even with simple microbial community structures (OTUs 〈1 0 0). Illumina high-throughput sequencing analysis revealed Dechloromonas, Pseudomonas, Flavobacterium and Acidovorax were dominant denitrifying bacteria in the systems that used RWD-PVA/SA gel beads as inoculum and acetate as

CRediT authorship contribution statement

Qiaochong He: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Data curation, Funding acquisition, Writing – original draft. Yunpeng Shen: Investigation, Formal analysis, Visualization, Software, Funding acquisition, Writing – original draft. Rui Li: Formal analysis, Visualization, Writing – original draft. Tong Peng: Formal analysis, Visualization, Writing – original draft. Nan Chen: Methodology, Resources, Writing – review & editing. Zhenjun Wu: Formal analysis,

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research work was supported by Doctoral Scientific Fund Project of Henan University of Technology in China (2019BS039, 2019BS050), Educational Commission of Henan Province of China (21A610004), Natural Science Foundation of Henan in China (202300410107), the Innovative Funds Plan of Henan University of Technology in China (2021ZKCJ09) and Key Research and Development Project of Henan Province in China (212102310070). The author would like to express our gratitude to Jingwen Dai for her

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