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Evaluation of biochar amendment on heavy metal resistant bacteria abundance in biosolids compost
Bioresource Technology 306 (2020) 123114
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Evaluation of biochar amendment on heavy metal resistant bacteria abundance in biosolids compost Sanjeev Kumar Awasthi1, Tao Liu1, Mukesh Kumar Awasthi, Zengqiang Zhang
T
⁎
College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Bacterial diversity Heavy metal resistant bacteria Bioinformatic tools Thermophilic phase
The present investigation was design to evaluate the impact of different dosages of biochar on heavy metal resistant bacterial (HMRB) dynamic in biosolid (BS) compost. The bacterial abundance map was reveals that all the samples have 35 prominent genera and showed significant alteration of HMRB among the all biochar applied treatment. The main phyla identify in each treatments were Proteobacteria, Firmicutes and Chloroflexi, however, Pseudomonas, T78, Acinetobacter and Urebacillus were most abundant genera in all the treatment. The visualization of HMRB bio-diversity by bioinformatic tools and krona were clearly indicated a considerable difference in classification tree complexes and network analysis among the all biochar added treatments. Thus, in present study was found that HMRB like Paracoccus, Planomicrobium, Devosia and Agrobacterium viable hyper-thermo tolerant in BS compost. In addition, heat map analysis also confirmed that Proteobacteria, Firmicutes and Chloroflexi have significant correlation with physicochemical parameters.
1. Introduction All big-cities of China are facing major difficulty to recycle and discarding the produced biosolid (BS) by various anthropogenic activities. Until 2019, more than 40 million tons of BS was collected from distinctive wastewater treatment plants (WWTPs) of China, and its production is still increasing with growing population and innovation (NBSC, 2019). The ways of BS management including landfill, incineration and composting which are being used in the various cities but in few small cities, the BS is straightly landfilled onto the open land without any management which is causing serious health hazard problem in nearby residents (Qiu et al., 2019; Awasthi et al., 2016; Duan et al., 2019a). The unscientific disposal of BS creates lots of environmental threat such as emission of greenhouse gases and leakage of salt, heavy metals and persistent of other contaminants into the nearby water bodies and soils. Looking upon its serious effects on soil, human and animals, Chinese Government is seeking to disallow the use of untreated BS straight in agriculture (González et al., 2019; NBSC, 2019). Although, lots of work has been carried out to analyze the dynamics and composition of microbial population during composting of various kinds of organic waste (Tiquia, 2005; Awasthi et al., 2017a,b,c; Malinowski et al., 2019). Different traditional methods (culture
dependent) were used from decades to investigate the bacterial diversity in composting. However, this technique is not enough to give the heavy metal resistant bacteria (HMRB) detail structure and diversity of every obtainable native microbes during the composting; since it is difficult to cultivate all the bacteria by the using pure culture medium (Ogino et al., 2001; Du et al., 2019). Recently, researchers have developed various molecular biological technique to build up a detailed information linked to composition of microbial community during composting time, some of the techniques are clone library, DNA fingerprinting, qPCR or investigative microarrays (Hultman et al., 2010; Wu et al., 2017; Liu et al., 2019). In addition, 16S rRNA sequence based molecular technique is best methods to identify the specific microorganism in compost and soil samples (Wang et al., 2015; Fang et al., 2018; Gondek et al., 2018; Awasthi et al., 2020). Intensive research is required to understand the dynamics of microbes and overall loads of microorganism during different types of organic waste composting. The effect of biochar on characteristic and biodiversity of the BS aerobic composting is not been undertaken as there are very few reports are available. The addition of biochar at the time of BS composting may have several advantages over its normal composting as it will be an ecofriendly and decrease the loss of nutrients, gases emission and immobilization of heavy metals (Awasthi et al., 2017a, 2017b; González et al., 2019).
⁎
Corresponding author. E-mail address: [email protected] (Z. Zhang). 1 Equally contributing authors https://doi.org/10.1016/j.biortech.2020.123114 Received 27 January 2020; Received in revised form 22 February 2020; Accepted 28 February 2020 Available online 02 March 2020 0960-8524/ © 2020 Elsevier Ltd. All rights reserved.
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
Proteobacteria, Firmicutes, Chloroflexi, Bacteroidetes, Planctomycetes and Actinobacteria which accounted 92–94% of the total HMRB. All these compost samples, Proteobacteria was identify most abundant HMRB phyla in T3 and T6, and it was raised by 20.32% and 35.51%, respectively as biochar concentration increased, while this phyla decreased by 8.81% in the 18%B blended compost or T7 treatment. However, T2 and T3 treatments were higher in Firmicutes abundance, and then biochar dosage increasing drastically decreased, as in treatment T7 moderate RAs of Firmicutes was investigated. During the biosolids composting, it seems that the key player of organic waste degradation was performed by Proteobacteria (Awasthi et al., 2017c), as T5 and T6 blended higher biochar than T2, T3 and T4, where the lowest RA of Proteobacteria among the all other treatments. The higher percentage of biochar application was considerably decreased the RAs HMRB like Proteobacteria, Firmicutes, and Acidobacteria in the thermophilic phases of all the treatments, however, relatively greater abundance of HMRB was recorded in control treatments (Figs. 1 and 3).
Thus, the effect of biochar on HMRB abundance during the biosolids composting is essential to identify the mechanism of composting to improve the sanitation value of compost. In addition, evaluate the correlation of heavy metals/HMRB percentage with physicochemical variation and potential environmental risk. 2. Materials and methods 2.1. Collection of raw materials and its processing The BS and wheat straw (WS) were used as a raw material for this investigation. The BS and WS were obtained from Yangling wastewater treatment plant and University agricultural organic farm. The characteristics of raw materials are similar to the study of Awasthi et al. (2017a). The biochar was prepared from wheat straw biomass via slow and dry pyrolysis at a temperature of 500–600 °C at atmospheric pressure for 24 h, which was initiated by the pyrolysis of feedstock from the bottom of the kiln as per Awasthi et al. (2017a).
3.2. Krona visualization of heavy metal resistant bacteria in compost
2.2. Experiment design and compost sample collection
The distribution of operational taxonomic units (OTUs) and bacteria RAs were identify through krona taxonomy visualization among the all compost samples (Fig. 2a−f). The krona visually showed the analysis result of species annotation, in which circles from inner to exterior attributed for different level of classification and proportion of distinctive OTUs annotation for each treatment were observed in the area of sector. However, the present investigation showed the utility of krona for metagenomic and opposed to other toolkits i.e. Metagenomic rapid annotations using subsystems technology and MEGAN which could be normally used for metagenomic visualizations. But in this study reported that petals figure and krona were provided clear picture of different metagenomic datasets of bacterial diversity among the all treatments compost and its compatibility through popular metagenomic analysis. Simultaneously, the analysis of RAs of taxa across multiple level of hierarchy can be also carried out by krona as per metagenomic point of view. Awasthi et al. (2017a) and Duan et al. (2019b) also performed the metagenomic study with the use of krona web browser to identify the RAs of bacteria and their functional groups in pig and chicken manure compost. Therefore, this approach is beneficial for direct comparison and evaluation of many compost samples blended with six different percentage of biochar which is normally very difficult with radial charts. Based upon our previous findings and present literature survey krona is the first bioinformatics tool that serves as an expression of power of emerging web technology to examine the RAs and easy recognition of HMRB diversity visualization tool (Ogino et al., 2001; Yin et al., 2016; Awasthi et al., 2017a, 2020).
The polyvinyl chloride composting reactors dimension and functional action were employed for the current study as per Awasthi et al. (2016). The experiment consists of seven treatments in triplicate and applied different amount of biochar in BS composting and compared with control. In this study, we evaluated the effect of biochar on HMRB diversity and identify the optimum biochar dosage for rapid composting. Each treatment has ~100-L of (biosolids + wheat straw mixed with biochar) mixture and composted for 56 days in a 130-L reactor. The compost sample collection and further physicochemical analysis were followed according to Awasthi et al. (2017a). The fresh compost samples of each treatment were used for DNA extraction and the procedure has been described in Sun et al. (2019). The nano-spectrophotometer was used to quantify the nucleotide concentrations of the polymerase chain reaction products before being sequenced with the help of Novogene, Beijing, China as details methodology previously described in Awasthi et al. (2017a). The statistical analysis of the physic-chemical parameters, sequence and their correlation were performed according to the study of Awasthi et al. (2017a). 3. Results and discussion 3.1. Heavy metal resistant bacteria diversity in compost The relative abundance (RAs) of HMRB at phylum and genus levels among the all treatments is shown in Fig. 1. The results revealed that each compost treatments have six most dominant phyla i.e.
Fig 1. Basic information of the microbial communities in different treatments: the relative abundance of the dominant bacterial taxonomic groups separated using total 16S rDNA sequences. Bacterial phyla level (top 10) and clustering tree based on the Weighted Unifrac distance assessed based on their relative abundance across all samples and dominant groups were chosen for each having grater relative abundance. Relative abundance was estimated based on frequency of occurrence of sequence classified to each taxonomic group.
2
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
Fig. 2. Krona is taxonomy web visualization; circles from inside to outside stand for different classification levels, and the area of sector means respective proportion of different OTUs annotation results.
Fig. 3. The relative abundance of each class based on 16S rDNA sequence analysis. The relative abundance is expressed in percentage and classification tree of complex samples. Different color of circle fan means different sample; the size of the fan means the relative abundance of proportional size on classification level of samples; the numbers below the classification name stands for the average percentage of relative abundance on this classification level in all samples. There were two numbers, the former one means the percentage of all species, the latter one means the percentage of selected species. 3
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
Fig. 4. Network and variation analysis.
Fig. 5. Heat maps showing correlations between abundance of sequences in compost amended with biochar assigned to each phylum, gaseous emission and environmental factors temperature and pH, as well as concentrations of nitrate and ammonium. Pearson’s correlation coefficients (r) are given, with r < 0 indicating a negative correlation (green), r = 0 indicating no correlation (white) and r greater than 0 indicating a positive correlation (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
and gaseous emission. Moreover, negative relation of total RAs of HMRB was found in germination index (GI), NH4 +-N, TKN, TP, DON, AP, nitrite, WK, WNa, and bioavailable heavy metals (Cu and Zn). The heat map analysis also reveals that among all the treatments, a total of 32.89% to 45.39% variation of HMRB abundance could be appraised by important analyzed parameters. However, this phylum level bacterial RAs variation cannot be noticed and higher dosage of biochar considerably effect the HMRB abundance. The present study also suggest that biochar has big surface area and high sorption capacity to created
3.3. Correlation analysis The correlation heat map analysis of among the top phyla Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria as well as physicochemical parameters is shown in Fig. 5. In higher dosage of biochar applied treatments have greater significant positive correlation with the top 4 HMRB phyla, total organic carbon (TOC), dissolve organic carbon (DOC), and C/N ratio as compared with control treatment. There was no considerable correlation observed between all HMRB, compost pH 4
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
research staff members is greatly acknowledged for their constructive advice and help.
favorable condition to immobilized ionic molecules and thus serving to reduce the environmental risk (Gondek et al., 2018; Du et al., 2019). The genera Paracoccus, Planomicrobium, Devosia and Agrobacterium were shows positive correlation with TOC, DOC, and C/N ratio and negative relation with GI and NH4+-N, while Pseudomonas, T78, Acinetobacter and Urebacillus also showed negative correlation with GI and NH4+-N but registered significant positive correlation with TOC, DOC, and C/N ratio in T7 treatment (Fig. 5b). Interestingly, these genera were also found in T5 and T6 treatments to be positively or negatively correlated with TKN, TP, DON, AP, nitrite, WK, WNa, and bioavailable heavy metals as observed in T7 treatment but they did not show any significant correlation with GI and NH4+-N (Fig. 4a). Only genus Pseudomonas and Urebacillus was noticed to be positively correlated with TN in T4 treatment. This study also confirmed the report of Yin et al. (2016) who also observed that copper resistant and ammonia oxidizer bacteria have a positive significant correlation with GI and NH4+-N during cattle manure composting. These results similar with the report of Awasthi et al. (2017b), who noticed HMRB diversity during pig manure composting. However, Acinetobacter showed no significant correlation with TKN, nitrate, GI and NH4+-N among the all treatments, this result opposite to identify by Duan et al. (2019b) and Sun et al. (2019), who reported that TKN, nitrate, GI and NH4+-N to be notably associated with Pseudomonas during cow manure composting. Overall, some HMRB genera can be identify as possible biomarkers of a thermophilic phase of the composting (Tortosa et al., 2017; Awasthi et al., 2017c).
References Awasthi, M.K., Duana, Y., Awasthi, S.K., Liua, T., Chena, H., Pandey, A., Zhanga, Z., Taherzadeh, M.J., 2020. Emerging applications of biochar: improving pig manure composting and attenuation of heavy metal mobility in mature compost. J. Hazard. Mater. 389, 122116. Awasthi, M.K., Wang, M., Chen, H., Wang, Q., Zhao, J., Rena, X., Li, D.S., Awasthi, S.K., Shen, F., Li, R., Zhang, Z., 2017a. Heterogeneity of biochar amendment to improve the carbon and nitrogen sequestration through reduce the greenhouse gases emissions during sewage sludge composting. Bioresour. Technol. 224, 428–438. Awasthi, M.K., Wang, Q., Chen, H., Wang, M., Rena, X., Zhao, J., Li, J., Guo, D., Li, D.S., Awasthi, S.K., Sun, X., Zhang, Z., 2017b. Evaluation of biochar amended biosolids cocomposting to improve the nutrient transformation and its correlation as a function for the production of nutrient-rich compost. Bioresour. Technol. 237, 156–166. Awasthi, M.K., Wang, Q., Huang, H., Li, R., Shen, F., Lahori, A.H., Wang, P., Guo, D., Guo, Z., Jiang, S., Zhang, Z., 2016. Effect of biochar amendment on greenhouse gas emission and bio-availability of heavy metals during sewage sludge co-composting. J. Clean. Prod. 135, 829–835. Awasthi, M.K., Zhang, Z., Wang, Q., Shen, F., Li, R., Li, D.S., Ren, X., Wang, M., Chen, H., Zhao, J., 2017c. New insight with the effects of biochar amendment on bacterial diversity as indicators of biomarkers support the thermophilic phase during sewage sludge composting. Bioresour. Technol. 238, 589–601. Du, J., Zhang, Y., Qu, M., Yin, Y., Fan, K., Hu, B., Zhang, H., Wei, M., Ma, C., 2019. Effects of biochar on the microbial activity and community structure during sewage sludge composting. Bioresour. Technol. 272, 171–179. Duan, Y., Awasthi, S.K., Liu, T., Zhang, Z., Awasthi, M.K., 2019a. Response of bamboo biochar amendment on volatile fatty acids accumulation reduction and humification during chicken manure composting. Bioresour. Technol. 291, 121845. Duan, Y., Awasthi, S.K., Liu, T., Verma, S., Wang, Q., Chen, H., Ren, X., Zhang, Z.Q., Awasthi, M.K., 2019b. Positive impact of biochar alone and combined with bacterial consortium amendment on improvement of bacterial community during cow manure composting. Bioresour. Technol. 280, 79–87. Fang, H., Han, L., Zhang, H., Long, Z., Cai, L., Yu, Y., 2018. Dissemination of antibiotic resistance genes and human pathogenic bacteria from a pig feedlot to the surrounding stream and agricultural soils. J. Hazard. Mater. 357, 53–62. Gondek, K., Mierzwa-Hersztek, M., Kopeć, M., 2018. Mobility of heavy metals in sandy soil after application of composts produced from maize straw, sewage sludge and biochar. J. Environ. Manage. 210, 87–95. González, D., Colón, J., Gabriel, D., Sánchez, A., 2019. The effect of the composting time on the gaseous emissions and the compost stability in a full-scale sewage sludge composting plant. Sci. Total Environ. 654, 311–323. Hultman, J., Kurola, J., Rainisalo, A., Kontro, M., Romantschuk, M., 2010. Utility of molecular tools in monitoring large scale composting. In: Insam, H. (Ed.), Microbes at Work. Springer-Verlag, Berlin Heidelberg, pp. 135–151. Liu, H., Wang, L., Lei, M., 2019. Positive impact of biochar amendment on thermal balance during swine manure composting at relatively low ambient temperature. Bioresour. Technol. 273, 25–33. Malinowski, M., Wolny-Koładka, K., Vaverková, M.D., 2019. Effect of biochar addition on the OFMSW composting process under real conditions. Waste Manage. 84, 364–372. NBSC (National Bureau of Statistics of China), 2019. China Statistical Yearbook. China Statistics Press, Beijing. Ogino, A., Koshikawa, H., Nakahara, T., Uchiyama, H., 2001. Succession of microbial communities during a bio-stimulation process as evaluated by DGGE and clone library analyses. J. Appl. Microbiol. 91, 625–635. Qiu, X., Zhou, G., Zhang, J., Wang, W., 2019. Microbial community responses to biochar addition when a green waste and manure mix are composted: a molecular ecological network analysis. Bioresour. Technol. 273, 666–671. Sun, Y., Men, M., Xu, B., Meng, Q., Bello, A., Xu, X., Huang, X., 2019. Assessing key microbial communities determining nitrogen transformation in composting of cow manure using illumina high-throughput sequencing. Waste Manag. 92, 59–67. Tiquia, S.M., 2005. Microbial community dynamics in manure com- posts basedon16S and 18S rDNA T-RFLP profiles. Environ. Technol. 26, 1101–1113. Tortosa, G., Castellano-Hinojosa, A., Correa-Galeote, D., Bedmar, E.J., 2017. Evolution of bacterial diversity during two-phase olive mill waste (“alperujo”) composting by 16S rRNA gene pyrosequencing. Bioresour. Technol. 224, 101–111. Wang, X., Cui, H., Shi, J., Zhao, X., Zhao, Y., Wei, Z., 2015. Relationship between bacterial diversity and environmental parameters during composting of different raw materials. Bioresour. Technol. 198, 395–402. Wu, S., He, H., Inthapanya, X., Yang, C., Lu, L., Zeng, G., Han, Z., 2017. Role of biochar on composting of organic wastes and remediation of contaminated soils - a review. Environ Sci Pollut. Res Int. 24 (20), 16560–16577. Yin, Y., Song, W., Gu, J., Zhang, K., Qian, X., Zhang, X., Li, Y., 2016. Effects of copper on the abundance and diversity of ammonia oxidizers during dairy cattle manure composting. Bioresour. Technol. 221, 181–187.
4. Conclusion Biochar addition could significantly reduce the mobality of HMRB diversity in biosolids compost. The dominant phyla are Proteobacteria, Firmicutes and Chloroflexi, while the genera are Paracoccus, Planomicrobium, Devosia and Agrobacterium in each treatment. In addition, results revealed that physicochemical parameters have more significant correlations with HMRB in higher dosage of biochar applied treatments, while negatively correlated with control. Further study needed to precisely unveil key function of HMRB and their associated environmental risk by the application of different kinds of organic waste converted compost. CRediT authorship contribution statement Sanjeev Kumar Awasthi: Data curation, Formal analysis, Investigation, Writing - original draft. Tao Liu: Data curation, Formal analysis. Mukesh Kumar Awasthi: Conceptualization, Supervision, Writing - original draft. Zengqiang Zhang: Conceptualization, Supervision, Writing - original draft. 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. Acknowledgement The authors are grateful to the Shaanxi Introduced Talent Research Funding (A279021901), China and The Introduction of Talent Research Start-up fund (No. Z101021904), College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China for the financial support from Research Fund for International Young Scientists. The support from all the colleagues and
5
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Evaluation of biochar amendment on heavy metal resistant bacteria abundance in biosolids compost Sanjeev Kumar Awasthi1, Tao Liu1, Mukesh Kumar Awasthi, Zengqiang Zhang
T
⁎
College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Bacterial diversity Heavy metal resistant bacteria Bioinformatic tools Thermophilic phase
The present investigation was design to evaluate the impact of different dosages of biochar on heavy metal resistant bacterial (HMRB) dynamic in biosolid (BS) compost. The bacterial abundance map was reveals that all the samples have 35 prominent genera and showed significant alteration of HMRB among the all biochar applied treatment. The main phyla identify in each treatments were Proteobacteria, Firmicutes and Chloroflexi, however, Pseudomonas, T78, Acinetobacter and Urebacillus were most abundant genera in all the treatment. The visualization of HMRB bio-diversity by bioinformatic tools and krona were clearly indicated a considerable difference in classification tree complexes and network analysis among the all biochar added treatments. Thus, in present study was found that HMRB like Paracoccus, Planomicrobium, Devosia and Agrobacterium viable hyper-thermo tolerant in BS compost. In addition, heat map analysis also confirmed that Proteobacteria, Firmicutes and Chloroflexi have significant correlation with physicochemical parameters.
1. Introduction All big-cities of China are facing major difficulty to recycle and discarding the produced biosolid (BS) by various anthropogenic activities. Until 2019, more than 40 million tons of BS was collected from distinctive wastewater treatment plants (WWTPs) of China, and its production is still increasing with growing population and innovation (NBSC, 2019). The ways of BS management including landfill, incineration and composting which are being used in the various cities but in few small cities, the BS is straightly landfilled onto the open land without any management which is causing serious health hazard problem in nearby residents (Qiu et al., 2019; Awasthi et al., 2016; Duan et al., 2019a). The unscientific disposal of BS creates lots of environmental threat such as emission of greenhouse gases and leakage of salt, heavy metals and persistent of other contaminants into the nearby water bodies and soils. Looking upon its serious effects on soil, human and animals, Chinese Government is seeking to disallow the use of untreated BS straight in agriculture (González et al., 2019; NBSC, 2019). Although, lots of work has been carried out to analyze the dynamics and composition of microbial population during composting of various kinds of organic waste (Tiquia, 2005; Awasthi et al., 2017a,b,c; Malinowski et al., 2019). Different traditional methods (culture
dependent) were used from decades to investigate the bacterial diversity in composting. However, this technique is not enough to give the heavy metal resistant bacteria (HMRB) detail structure and diversity of every obtainable native microbes during the composting; since it is difficult to cultivate all the bacteria by the using pure culture medium (Ogino et al., 2001; Du et al., 2019). Recently, researchers have developed various molecular biological technique to build up a detailed information linked to composition of microbial community during composting time, some of the techniques are clone library, DNA fingerprinting, qPCR or investigative microarrays (Hultman et al., 2010; Wu et al., 2017; Liu et al., 2019). In addition, 16S rRNA sequence based molecular technique is best methods to identify the specific microorganism in compost and soil samples (Wang et al., 2015; Fang et al., 2018; Gondek et al., 2018; Awasthi et al., 2020). Intensive research is required to understand the dynamics of microbes and overall loads of microorganism during different types of organic waste composting. The effect of biochar on characteristic and biodiversity of the BS aerobic composting is not been undertaken as there are very few reports are available. The addition of biochar at the time of BS composting may have several advantages over its normal composting as it will be an ecofriendly and decrease the loss of nutrients, gases emission and immobilization of heavy metals (Awasthi et al., 2017a, 2017b; González et al., 2019).
⁎
Corresponding author. E-mail address: [email protected] (Z. Zhang). 1 Equally contributing authors https://doi.org/10.1016/j.biortech.2020.123114 Received 27 January 2020; Received in revised form 22 February 2020; Accepted 28 February 2020 Available online 02 March 2020 0960-8524/ © 2020 Elsevier Ltd. All rights reserved.
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
Proteobacteria, Firmicutes, Chloroflexi, Bacteroidetes, Planctomycetes and Actinobacteria which accounted 92–94% of the total HMRB. All these compost samples, Proteobacteria was identify most abundant HMRB phyla in T3 and T6, and it was raised by 20.32% and 35.51%, respectively as biochar concentration increased, while this phyla decreased by 8.81% in the 18%B blended compost or T7 treatment. However, T2 and T3 treatments were higher in Firmicutes abundance, and then biochar dosage increasing drastically decreased, as in treatment T7 moderate RAs of Firmicutes was investigated. During the biosolids composting, it seems that the key player of organic waste degradation was performed by Proteobacteria (Awasthi et al., 2017c), as T5 and T6 blended higher biochar than T2, T3 and T4, where the lowest RA of Proteobacteria among the all other treatments. The higher percentage of biochar application was considerably decreased the RAs HMRB like Proteobacteria, Firmicutes, and Acidobacteria in the thermophilic phases of all the treatments, however, relatively greater abundance of HMRB was recorded in control treatments (Figs. 1 and 3).
Thus, the effect of biochar on HMRB abundance during the biosolids composting is essential to identify the mechanism of composting to improve the sanitation value of compost. In addition, evaluate the correlation of heavy metals/HMRB percentage with physicochemical variation and potential environmental risk. 2. Materials and methods 2.1. Collection of raw materials and its processing The BS and wheat straw (WS) were used as a raw material for this investigation. The BS and WS were obtained from Yangling wastewater treatment plant and University agricultural organic farm. The characteristics of raw materials are similar to the study of Awasthi et al. (2017a). The biochar was prepared from wheat straw biomass via slow and dry pyrolysis at a temperature of 500–600 °C at atmospheric pressure for 24 h, which was initiated by the pyrolysis of feedstock from the bottom of the kiln as per Awasthi et al. (2017a).
3.2. Krona visualization of heavy metal resistant bacteria in compost
2.2. Experiment design and compost sample collection
The distribution of operational taxonomic units (OTUs) and bacteria RAs were identify through krona taxonomy visualization among the all compost samples (Fig. 2a−f). The krona visually showed the analysis result of species annotation, in which circles from inner to exterior attributed for different level of classification and proportion of distinctive OTUs annotation for each treatment were observed in the area of sector. However, the present investigation showed the utility of krona for metagenomic and opposed to other toolkits i.e. Metagenomic rapid annotations using subsystems technology and MEGAN which could be normally used for metagenomic visualizations. But in this study reported that petals figure and krona were provided clear picture of different metagenomic datasets of bacterial diversity among the all treatments compost and its compatibility through popular metagenomic analysis. Simultaneously, the analysis of RAs of taxa across multiple level of hierarchy can be also carried out by krona as per metagenomic point of view. Awasthi et al. (2017a) and Duan et al. (2019b) also performed the metagenomic study with the use of krona web browser to identify the RAs of bacteria and their functional groups in pig and chicken manure compost. Therefore, this approach is beneficial for direct comparison and evaluation of many compost samples blended with six different percentage of biochar which is normally very difficult with radial charts. Based upon our previous findings and present literature survey krona is the first bioinformatics tool that serves as an expression of power of emerging web technology to examine the RAs and easy recognition of HMRB diversity visualization tool (Ogino et al., 2001; Yin et al., 2016; Awasthi et al., 2017a, 2020).
The polyvinyl chloride composting reactors dimension and functional action were employed for the current study as per Awasthi et al. (2016). The experiment consists of seven treatments in triplicate and applied different amount of biochar in BS composting and compared with control. In this study, we evaluated the effect of biochar on HMRB diversity and identify the optimum biochar dosage for rapid composting. Each treatment has ~100-L of (biosolids + wheat straw mixed with biochar) mixture and composted for 56 days in a 130-L reactor. The compost sample collection and further physicochemical analysis were followed according to Awasthi et al. (2017a). The fresh compost samples of each treatment were used for DNA extraction and the procedure has been described in Sun et al. (2019). The nano-spectrophotometer was used to quantify the nucleotide concentrations of the polymerase chain reaction products before being sequenced with the help of Novogene, Beijing, China as details methodology previously described in Awasthi et al. (2017a). The statistical analysis of the physic-chemical parameters, sequence and their correlation were performed according to the study of Awasthi et al. (2017a). 3. Results and discussion 3.1. Heavy metal resistant bacteria diversity in compost The relative abundance (RAs) of HMRB at phylum and genus levels among the all treatments is shown in Fig. 1. The results revealed that each compost treatments have six most dominant phyla i.e.
Fig 1. Basic information of the microbial communities in different treatments: the relative abundance of the dominant bacterial taxonomic groups separated using total 16S rDNA sequences. Bacterial phyla level (top 10) and clustering tree based on the Weighted Unifrac distance assessed based on their relative abundance across all samples and dominant groups were chosen for each having grater relative abundance. Relative abundance was estimated based on frequency of occurrence of sequence classified to each taxonomic group.
2
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
Fig. 2. Krona is taxonomy web visualization; circles from inside to outside stand for different classification levels, and the area of sector means respective proportion of different OTUs annotation results.
Fig. 3. The relative abundance of each class based on 16S rDNA sequence analysis. The relative abundance is expressed in percentage and classification tree of complex samples. Different color of circle fan means different sample; the size of the fan means the relative abundance of proportional size on classification level of samples; the numbers below the classification name stands for the average percentage of relative abundance on this classification level in all samples. There were two numbers, the former one means the percentage of all species, the latter one means the percentage of selected species. 3
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
Fig. 4. Network and variation analysis.
Fig. 5. Heat maps showing correlations between abundance of sequences in compost amended with biochar assigned to each phylum, gaseous emission and environmental factors temperature and pH, as well as concentrations of nitrate and ammonium. Pearson’s correlation coefficients (r) are given, with r < 0 indicating a negative correlation (green), r = 0 indicating no correlation (white) and r greater than 0 indicating a positive correlation (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
and gaseous emission. Moreover, negative relation of total RAs of HMRB was found in germination index (GI), NH4 +-N, TKN, TP, DON, AP, nitrite, WK, WNa, and bioavailable heavy metals (Cu and Zn). The heat map analysis also reveals that among all the treatments, a total of 32.89% to 45.39% variation of HMRB abundance could be appraised by important analyzed parameters. However, this phylum level bacterial RAs variation cannot be noticed and higher dosage of biochar considerably effect the HMRB abundance. The present study also suggest that biochar has big surface area and high sorption capacity to created
3.3. Correlation analysis The correlation heat map analysis of among the top phyla Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria as well as physicochemical parameters is shown in Fig. 5. In higher dosage of biochar applied treatments have greater significant positive correlation with the top 4 HMRB phyla, total organic carbon (TOC), dissolve organic carbon (DOC), and C/N ratio as compared with control treatment. There was no considerable correlation observed between all HMRB, compost pH 4
Bioresource Technology 306 (2020) 123114
S.K. Awasthi, et al.
research staff members is greatly acknowledged for their constructive advice and help.
favorable condition to immobilized ionic molecules and thus serving to reduce the environmental risk (Gondek et al., 2018; Du et al., 2019). The genera Paracoccus, Planomicrobium, Devosia and Agrobacterium were shows positive correlation with TOC, DOC, and C/N ratio and negative relation with GI and NH4+-N, while Pseudomonas, T78, Acinetobacter and Urebacillus also showed negative correlation with GI and NH4+-N but registered significant positive correlation with TOC, DOC, and C/N ratio in T7 treatment (Fig. 5b). Interestingly, these genera were also found in T5 and T6 treatments to be positively or negatively correlated with TKN, TP, DON, AP, nitrite, WK, WNa, and bioavailable heavy metals as observed in T7 treatment but they did not show any significant correlation with GI and NH4+-N (Fig. 4a). Only genus Pseudomonas and Urebacillus was noticed to be positively correlated with TN in T4 treatment. This study also confirmed the report of Yin et al. (2016) who also observed that copper resistant and ammonia oxidizer bacteria have a positive significant correlation with GI and NH4+-N during cattle manure composting. These results similar with the report of Awasthi et al. (2017b), who noticed HMRB diversity during pig manure composting. However, Acinetobacter showed no significant correlation with TKN, nitrate, GI and NH4+-N among the all treatments, this result opposite to identify by Duan et al. (2019b) and Sun et al. (2019), who reported that TKN, nitrate, GI and NH4+-N to be notably associated with Pseudomonas during cow manure composting. Overall, some HMRB genera can be identify as possible biomarkers of a thermophilic phase of the composting (Tortosa et al., 2017; Awasthi et al., 2017c).
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4. Conclusion Biochar addition could significantly reduce the mobality of HMRB diversity in biosolids compost. The dominant phyla are Proteobacteria, Firmicutes and Chloroflexi, while the genera are Paracoccus, Planomicrobium, Devosia and Agrobacterium in each treatment. In addition, results revealed that physicochemical parameters have more significant correlations with HMRB in higher dosage of biochar applied treatments, while negatively correlated with control. Further study needed to precisely unveil key function of HMRB and their associated environmental risk by the application of different kinds of organic waste converted compost. CRediT authorship contribution statement Sanjeev Kumar Awasthi: Data curation, Formal analysis, Investigation, Writing - original draft. Tao Liu: Data curation, Formal analysis. Mukesh Kumar Awasthi: Conceptualization, Supervision, Writing - original draft. Zengqiang Zhang: Conceptualization, Supervision, Writing - original draft. 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. Acknowledgement The authors are grateful to the Shaanxi Introduced Talent Research Funding (A279021901), China and The Introduction of Talent Research Start-up fund (No. Z101021904), College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China for the financial support from Research Fund for International Young Scientists. The support from all the colleagues and
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