https://orcid.org/0009-0006-7150-1621
Accessible Data
Abstract
Municipal solid waste (MSW) landfills represent underexplored microbial ecosystems. Landfills contain variable amounts of antibiotic and construction and demolition (C&D) wastes, which have the potential to alter microbial metabolism due to biocidal or redox active components, and these effects are largely underexplored. To circumvent the challenge of MSW heterogeneity, we conducted a 65-day time series study on simulated MSW microcosms to assess microbiome changes using 16S rRNA sequencing in response to 1) Fe(OH)3 and 2) Na2SO4 to represent redox active components of C&D waste as well as 3) antibiotics. The addition of Fe(OH)3 altered the overall community composition and increased Shannon diversity and Chao1 richness. The addition of a mixture of seven antibiotics (1000 ng/L each) altered the community composition without affecting diversity metrics. Sulfate addition had little effect on microbial community composition or diversity. These results suggest that the microbial community composition in fresh MSW may be significantly impacted by influxes of iron waste and a single application of antibiotics.
Figures
Citation: Malas J, Khoury SC, Tanzillo M, Fischer GA, Bogner JE, Meyer-Dombard DR (2025) Impact of antibiotics, iron oxide, and sodium sulfate on microbial community composition in laboratory-built municipal solid waste microcosms. PLoS ONE 20(1): e0318351. https://doi.org/10.1371/journal.pone.0318351
Editor: Huan Li, Tsinghua University, CHINA
Received: October 2, 2024; Accepted: January 15, 2025; Published: January 28, 2025
Copyright: © 2025 Malas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All raw sequence data are available in the Sequence Read Archive (accession number PRJNA950411).
Funding: This work was supported by the Environmental Research and Education Foundation (EREF) [award number 086134.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Microbial communities within landfills mediate the decomposition of Municipal Solid Waste (MSW) and play an important role in global elemental cycles [1]. Landfills are significant contributors to the global carbon cycle and are an important source of anthropogenic methane emissions [2–4]. Conditions in landfills range in pH from 4.5 to neutral, temperatures from ambient up to 120°C, and depths from 15 m to 100 m [5, 6]. Fresh waste may be nutrient-dense due to food waste and oxygen availability, but buried waste is quickly depleted in oxygen and cut off from photosynthetic primary productivity. Due to the diversity of potential metabolic niches and environmental gradients, landfills also serve as valuable resources for biotechnology [7–9].
Studying the microbial communities within landfills presents a unique challenge due to the heterogeneity of landfill ecosystems. Previous studies have attempted to characterize the environmental drivers of microbial communities within landfills using internal liquids (leachate), gas, buried refuse, or intercalated soils [10–14]. While these studies are useful for comparing microbial communities across different climatic locations, ages, and depths, they can provide only a snapshot of specific locations within a given landfill at a given time. Landfills vary with regard to management strategies, inputs, moisture content, available nutrients, cover soil, and their local climate [11, 12, 15, 16]–all of which can impact the microbial community composition and thus present challenges for experimental reproducibility. Several studies have used homogenized landfill waste or fresh refuse in laboratory-based reactors to circumvent issues of heterogeneity [17–20]. However, information about waste input and waste age is lost in most cases.
Recognizing the challenges associated with studying drivers of microbial community composition and diversity in MSW landfills, we undertook a simplified model system to investigate the microbial dynamics of the MSW ecosystem and potential sources of disruption to typical microbial metabolic processes in MSW. Barlaz, Milke, and Ham (1989) observed four degradation stages under ideal conditions in laboratory bioreactors [18]: 1) the aerobic phase, wherein oxygen and nitrate were depleted, and sugars were converted to CO2 and water; 2) the anaerobic acid phase characterized by an accumulation of carboxylic acids and a decrease in pH; 3) the accelerated methane production phase accompanied by an increase in pH; and 4) the decelerated methane production phase. Improved understanding of the waste degradation process can facilitate effective landfill management, enhance prediction of greenhouse gas emissions, and support the implementation of environmentally sustainable practices such as biogas capture [21]. Here, we investigated the potential for different waste streams to alter the bacterial population expected during terminal phases of waste degradation and thus change the progression of waste degradation.
Many U.S. landfills accept large quantities of construction and demolition (C&D) waste as recycling rates are highly variable and, importantly, periodic disaster debris (i.e., from hurricanes) must be cleared quickly. In 2018, approximately 600 million tons of C&D debris were generated, 24% (144 million tons) of which were sent to U.S. landfills, including gypsum drywall (13.2 million tons) and total metals (1.1 million tons) [22]. While some landfill sites in the United States are designated industrial C&D landfills, MSW landfills can also accept C&D waste if it is non-hazardous. Both iron and sulfate are redox-active species present in C&D waste that can be used for various microbial metabolisms [1].
The addition of Fe2+ or Fe3+ has been shown to increase methane oxidation in digested sewage [23], as methane oxidation can be coupled to the reduction of Fe3+ [24]. In anoxic rice paddy soils, the addition of ferrihydrite (Fe3+) has been shown to suppress methanogenesis [e.g., 25]. Similarly, in anaerobic, wet soil environments, sulfate reduction may be coupled to organic carbon degradation, anaerobic methane oxidation, and hydrogen consumption–and therefore, sulfate reduction may compete with methanogenesis [26]. In addition to C&D waste, sulfate within landfills can come from in situ geology/soils at the site of landfill development (i.e., subaerial oxidation at former sulfide ore mine pit; marine-derived sulfate-rich coastal sediments) or sulfate-rich natural soils transported to site for use as cover materials. Historically, some landfill sites have used "fines" from recycled construction debris with relatively high concentrations of gypsum as an alternative daily cover soil [27]. This practice rapidly produced high levels of H2S in the recovered biogas, which also reduced relatively quickly due to the depletion of the fine-grained sulfate. Previous studies have differed with respect to the quantification of sulfate effects on methanogenesis or anaerobic methane oxidation in landfills [23, 28, 29], and few studies have investigated the influence of redox-active components of C&D waste on the MSW microbiome with modern sequencing technologies.
Landfills in the United States also contain at least 101 different prescription and non-prescription pharmaceuticals, industrial chemicals, household chemicals, steroid hormones, and animal and plant sterols [30]. Antibiotic consumption is increasing worldwide, and landfills are one of the primary routes for the disposal of these drugs [31]. Landfills have been found to be a major reservoir for antibiotics and antibiotic resistance genes (ARGs) [31–33]. Both antibiotics and ARGs have been found in high concentrations in environments surrounding landfills due to leaky liners or discharge of treated leachate effluent [31, 34–36]. The concentration of antibiotics within landfill leachates varies widely, from 0 ng/L up to over 250,000 ng/L, and 1000 ng/L is a commonly observed concentration in landfill leachates [31, 37]. Antibiotics present in the environment have been shown to alter microbial community composition, decrease microbial diversity, and contribute to the development of ARGs; however, the impact of antibiotics in different environments depends on several factors, including the type of antibiotic as well as the pH, organic carbon content, type of soil, and the resident microbial community [38–40]. Several studies have quantified the concentrations of antibiotics and ARGs in landfills [30, 41–44], and one report found that the addition of sulfamethazine to landfill refuse increased ARG levels tenfold after a 65-day incubation while reducing the functional capacity for denitrification [45]. Apart from the work by Wu et al. (2017), there is a relative dearth of experimental works investigating the influence of antibiotics on the MSW microbiome.
Though research on the microbial dynamics of landfill systems has increased significantly in the last 5 years, more experimental work is needed to determine the influence of bioactive wastes on the MSW microbial community. Here, microcosms were constructed for laboratory incubations of fresh refuse from predetermined amounts of raw material (e.g., paper, plastic, food waste) and incubated for 65 days at 45°C. We used this model system to test the redox active components of construction and demolition (C&D) waste and antibiotics. The treatments, each introduced separately, were 1) Fe(OH)3, 2) Na2SO4, and 3) antibiotics. Changes to the microbial community were monitored via 16S rRNA sequencing before and after adding the three "contaminant" treatments. These inputs were selected as they could change microbial metabolism by offering alternative energy sources in the case of Fe(OH)3 or Na2SO4 or eradicating groups of organisms in the case of antibiotics. The Fe(OH)3 was added as an insoluble substrate to resemble the addition of solid metal construction waste that has rusted after landfilling. The sulfate addition was intended to mimic the addition of gypsum wallboard waste.
2. Materials and methods
2.1. Lab-built MSW microcosms
MSW microcosms were constructed in 100 mL wide-mouth, flat-bottom glass media bottles. Screw caps included a silicon septum to allow the release of produced gas. An artificial MSW formulation was developed that was made up of small pieces of waste components: 2 g shredded paper (length 2 cm ± 1, width 0.75 cm ± 0.3), 0.8 g aluminum (length 1.7 cm ± 0.6, width 1.5 cm ± 0.4), 2 g plastic (length 2.2 cm ±1, width 1 cm ± 0.5), and 2.5 g dry leaves (length 2.5 cm ± 1, width 1.5 ± 0.6). As well as 10.8 g food waste (fruit, vegetables, grains, peanut butter), 2 g sawdust, 2.4 g soil, and 4 g sand. The total weight of solid waste inputs was 26.5 g. These components were based roughly on Environmental Protection Agency statistics for waste generation [46]; however, the relative proportion of paper waste had to be decreased due to volumetric constraints. The food waste was homogenized in a commercial blender to maintain consistency in individual microcosms, as previous work has suggested that food waste may be the primary contributor to the microbial composition in fresh refuse [47]. The non-food inputs were not finely ground.
In order to collect samples for downstream DNA extraction in a reproducible manner, four perforated, 0.6 mL centrifuge tubes filled with sterile 3 mm spherical Soda-lime glass beads (~75% SiO2; Fisher Scientific catalog # 11-312A) were inserted into the microcosms at the time of construction and embedded fully in the solid waste (Fig 1). The tubes were distributed randomly within the solid waste. The microorganisms on the beads represent those transferred through the microcosm in the fluid that then colonized the glass beads. Glass beads were used because they are a non-reactive surface chemically abundant in soils (SiO2), typically an average of 72 weight percent of soil, that would not directly impact the metabolic choices of the microorganisms [48]. Each 0.6 mL centrifuge tube fits 16 glass beads, for a total of 64 glass beads per microcosm. In addition, 70 mL of sterile water was added to the bottles to cover the waste contents (reaching the 80 mL fill line on the bottles). The water ensured that the organisms were not transport-limited within the waste, provided a solvent for even distribution of contaminant treatments, and served as simulated leachate for spectrophotometric detection of dissolved ions. Glass bottles, deionized water, glass beads, and 0.6 mL tubes were autoclaved at 121°C for 55 min prior to assembling the microcosms.
A) Experimental setup and timeline. Each circle represents one microcosm. Microcosm bottles are destroyed during simulated leachate chemistry analyses. T1, T2, T3, T4, T5, and T6 correspond to time points when DNA was sampled during the experiment. B) Depiction of assembled microcosm in 100 mL bottle. C) Centrifuge tube with glass beads, which are inserted into the microcosm during assembly. Arrows point to perforations in the tubes through which microcosm liquid can flow to the beads.
Microcosms were incubated in water baths at 45°C to keep temperatures constant for 65 days to capture the first three phases of waste degradation previously observed in laboratory-based reactors under similar incubation temperatures [18]. A temperature of 45°C was selected as it is in the typical operating range for landfills, though specific temperatures within landfills are dependent on local climate [49].
2.2. Contaminant treatments
Microcosms were set up in triplicate, with one set of triplicate bottles for each experimental treatment, as well as six control group bottles, for a total of 15 microcosms (Fig 1). The microcosms were maintained untreated for 44 days. This incubation time reflects the approximate onset of the production of methane [18], the terminal product of waste degradation. The addition of contaminants at this phase was intended to determine whether bioactive contaminants alter the progression of waste degradation. After 44 days, three bottles each were amended with the following treatments: 0.5 g Fe(OH)3, 0.21 g Na2SO4 (~2000 mg/L sulfate), or a mixture of each of seven antibiotics to a final concentration of 1000 ng/L total per antibiotic. The antibiotics used were Amoxicillin, Cephalexin hydrate, Amoxicillin trihydrate: Potassium Clavalante (4:1 penicillin combination), Azithromycin, Sulfamethoxazole, Bacitracin, and Tetracycline. All antibiotics were purchased from Sigma-Aldrich. Solutions of antibiotics were made with autoclaved DI water, and the solutions were filter sterilized with a 0.2 micron Whatman Puradisc syringe filter before incorporation into the microcosms. The Fe(OH)3 and a solution of Na2SO4 were autoclaved at 121°C for 55 min prior to addition. The concentrations selected for antibiotic and sulfate additions were based on previous findings in landfill leachate [12, 41].
2.3. Microcosm simulated leachate chemistry
After 40 days (T4), three control microcosms were destructively sampled to analyze sulfide, sulfate, total iron, and ferrous iron from unfiltered microcosm liquids (simulated leachate). The remaining three control and nine treated bottles were sampled for chemistry at the end of the 65-day period (T6). Sulfide, sulfate, total iron, ferrous iron (Fe2+), and pH were measured in all microcosms at T6 after 65 days of incubation. Paper strips were used for pH measurements to preserve simulated leachate for remaining chemical analyses. Data were obtained using a portable field HACH spectrophotometer DR2700, as described previously [50].
2.4. DNA sampling, extraction, and sequencing
DNA was sampled at six timesteps after 6 (T1), 20 (T2), 29 (T3), 40 (T4), 50 (T5), and 65 (T6) days (Fig 1). A DNA sample was not collected at the start of the incubation as the beads were sterilized prior to microcosm assembly. Sterilized forceps were used to extract a perforated tube from the microcosm, and six glass beads were collected from the microcosm per sample and deposited directly into a PowerBead tube from the QIAGEN DNeasy PowerMax Soil Kit, which was used to extract the DNA according to the manufacturer’s instructions. Additional sterilized deionized water was added to the 80 mL fill line to keep the waste submerged if water was lost during sampling. The DNA sampling was conducted in an anaerobic chamber so as not to introduce oxygen after initial microcosm construction to mimic the conditions of buried waste. PowerBead tubes with glass beads were stored at -20°C until DNA extraction.
Genomic DNA was PCR amplified with primers CS1_515 (GTGCCAGCMGCCGCGGTAA) and CS2_806R (GGACTACHVGGGTWTCTAAT) (Caporaso et al. 2011); region-specific primers used to amplify the V4 region of the 16S rRNA gene. The primers contained 5’ common sequence tags known as common sequence 1 (CS1) and 2 (CS2), as described previously [51]. Amplicons were generated using a two-stage PCR amplification protocol [52]. During the second PCR amplification, each sample received a unique 10-base barcode obtained from the Access Array Barcode Library for Illumina (Fluidigm, South San Francisco, CA; Item# 100–4876). These AccessArray primers contained the CS1 and CS2 linkers at the 3’ ends of the oligonucleotides. Libraries were then pooled in equal volumes, and the pool was purified using an AMPure XP cleanup protocol (0.6X, vol/vol; Agencourt, Beckmann-Coulter) to remove fragments smaller than 300 bp. The pooled libraries, with a 15% PhiX sequencing control spike-in, were loaded onto an Illumina MiniSeq mid-output flow cell (2x153 paired-end reads). Based on the distribution of reads per barcode, the amplicons (before purification) were re-pooled to generate a more balanced distribution of reads. The re-pooled libraries, with a 15% PhiX spike-in, were loaded onto a Miniseq flow cell and sequenced (2x153 paired-end reads). Fluidigm sequencing primers targeting the CS1 and CS2 linker regions were used to initiate sequencing. De-multiplexing of reads was performed on the instrument. Library preparation, pooling, and sequencing were performed at the Genome Research Core within the Research Resources Center at the University of Illinois at Chicago. Raw data files were deposited to the Sequence Read Archive (SRA) and are available under BioProject accession number PRJNA950411.
2.5. Data analyses
Paired-end reads were merged with PEAR (v.0.9.11; [53]). After merging, the reads were quality processed to remove sequences with low-quality scores and binned into amplicon sequence variants (ASVs) using DADA2 (v.1.22.0; [54]), according to the pipeline outlined by Callahan et al. (2016b) with minor adjustments to account for differences in read length [55]. Quality-filtered reads were used to construct an amplicon sequence variant (ASV) table using the SILVA database v.138. Taxonomic composition analyses were performed using Phyloseq (v.1.38.0; [56]) and visualized using ggplot2 (v3.4.4; [57]) in R (v 4.1.3).
Beta diversity of the samples was calculated using both the Bray-Curtis and Euclidean distance metrics. As rarefying sequence data (cutting all sample libraries to the same read number) has been advised against [58], the Bray-Curtis distance metric is a popular metric recommended for untransformed data [59]. However, this method does not account for potential bias due to differences in library sizes. Therefore, as an alternative to rarefaction, Euclidean distances were also calculated after applying a variance stabilizing transformation to account for the differences in number of reads per sample in DESeq2 (v 1.34.0) using code published by Lee et al. (2019) [58, 60, 61]. A Non-metric Multidimensional Scaling (NMDS) plot or a Principal coordinates analysis (PCoA) plot was made for the Bray-Curtis and Euclidean distances, respectively. To assess statistical significance, a permutational multivariate analysis of variance (PERMANOVA) test was conducted using both distance metrics within the Vegan package (v2.6–4) in R [62]. PERMANOVA tests were calculated based on 10,000 permutations and p values were adjusted for multiple testing using the Bonferroni method. A multivariate homogeneity of group dispersions (PERMDISP2) test was conducted in conjunction with each PERMANOVA to ensure that the variance within groups did not exceed the variance between groups.
Alpha diversity metrics were calculated within the Microbiome package (v1.16.0;[63]). A Wilcoxon rank-sum test was used to assess the statistical significance of alpha diversity between groups. DA testing was conducted with Analysis of Compositional Microbiomes with Bias Correction (ANCOM-BC) (v.2.0.3; [64]). ASVs were aggregated at the Genus level and P values were adjusted for multiple testing using the Holm method for the ANCOM-BC analyses.
3. Results
3.1. Simulated leachate chemistry
Sulfide, sulfate, total iron, ferrous iron (Fe2+), and pH were measured in the untreated microcosms after 40 days of incubation. Subsequently, these measurements were conducted in all microcosms after 65 days of incubation (Table 1). The pH of all microcosms, regardless of treatment, was found to be ~5.5 at 40 days as well as at the end of the incubation period. Sulfate was only detected in the sulfate-treated microcosms, likely sourced from the added treatment, with an average of 2000 mg/L. This concentration was roughly equivalent to the ~2000 mg/L sulfate added to each microcosm. In contrast, dissolved total iron in the Fe(OH)3 treatments was found to be less than the control microcosms at 65 days, suggesting that the addition of iron triggered precipitation of iron from solution or chelation / complex-formation of the iron.
Data are reported in mg/L for all chemical species. N.d. = not detected.
3.2. Microbial community composition
A total of 82 DNA samples passed quality control. Two samples had to be removed either because they did not amplify by PCR prior to sequencing or due to low read counts (< 850 reads per sample). A total of 1564 ASVs were detected. Taxonomic classification with the SILVA database v.138 was possible to the genus level, however, many ASVs were only classified to the phylum or order level.
The microbial community composition of the microcosms at the start of the incubation (T1; day 6 of incubation) is distinct from all other sampling time points (Fig 2). The microbial community composition was largely established by T2, as the composition of the untreated microcosms did not exhibit significant differences during the other sampled time points (Table 2). The NMDS and PCoA plots show T1 samples clustering separately from the rest of the samples (Fig 2). Pairwise PERMANOVA testing on Euclidean distances shows that the difference (beta-diversity) between T1 and other sample time groups is significant (p < 0.05), aside from T6 (Table 2). Though the PERMANOVA indicates T6 is not significantly different from T1, this result may be due to the untreated control outlier sample in T6, which can be seen in the NMDS/PCoA plots (Fig 2).
A) PCoA of Euclidean distances. B) NMDS of Bray-Curtis distances.
P values were adjusted for multiple testing using the Bonferroni method. Pairwise comparisons between sample times (i.e. T1-T6) include only untreated microcosms. Post-treatment groups include both T5 and T6.
Contaminant treatments produced a shift in the microbial community, particularly in the Fe(OH)3 and antibiotic treatments compared to the untreated control. The Fe(OH)3 treatment had the greatest influence on the microbial community, reflected in the highest R2 of the three treatment groups relative to the untreated microcosms (R2 = 0.31; Table 2) and a significant increase in Shannon and Chao1 indexes (Fig 3). The antibiotic treatment significantly influenced the microbial community composition to a lesser degree than the iron treatment (Fig 2; Table 2). Shannon and Chao1 alpha diversity metrics of the untreated control and the antibiotic group were not significantly different (Fig 3). This indicates that while some taxa may have been substituted due to antibiotic treatment, the overall alpha diversity within the microcosms did not increase or decrease significantly due to treatment. The Na2SO4 treatment did not significantly impact the community or diversity (Table 2; Fig 3). The statistical significance of treatment on community composition were consistent when using either the Bray-Curtis or Euclidean distance metric (Tables 2 and 3).
A) Chao1 B) Shannon-Weaver alpha diversity metrics. Wilcoxon-rank sum testing was conducted to test for significant differences in diversity between the untreated control and each treatment. P <0.01 is indicated by "**", not significant is indicated "ns".
Results include both T5 and T6. P values were adjusted for multiple testing using the Bonferroni method.
3.3. Important taxa
As shown in Fig 4A, Bacteria dominated the sequenced taxa in all microcosms, with the archaeal phylum Crenarchaeota detected in negligibly small amounts (mean of 0.001%). Taxa of the phylum Firmicutes were most abundant in all microcosms, followed by Proteobacteria and Actinobacteriota with a mean of 79%, 2.98%, and 0.78%, respectively across all microcosms (Fig 4A). The genus Caproiciproducens within the phylum Firmicutes was a dominant taxon of nearly all the microcosms from the beginning (T1, 6 days) to the end (T6, 65 days) of the incubation period (Fig 4B). There was a higher proportion of low abundance (<0.5%) genera in the microcosms at T5 and T6 than at the start of the incubation at T1 and T2 (Fig 4B), suggesting that lower abundance taxa were driving the compositional differences between treatment groups. This may have been the case in the Fe(OH)3 treatment, as the Chao1 richness, which is sensitive to the presence of rare species, was significantly higher in these treatments than in the untreated control (Fig 3B).
A) shows Phylum level classification and B) shows Genus level classification. Each stacked bar represents the average relative abundance for microcosms replicates at each sampling time. Treatments were added at after T4.
Differential abundance testing was conducted to determine which taxa increased or decreased after the treatments. An ANCOM-BC test was conducted on the treated microcosms compared to the untreated control microcosms, both groups pooled at T5 and T6. Taxa were aggregated at the genus level, and a total of 103 genera were tested for differential abundance. Taxa that changed by natural log fold change of greater than | 1 | were considered differentially abundant, and any aggregated taxa that were not classified at the genus level were filtered from the results. Consistent with the PERMANOVA results, the Fe(OH)3 treatment resulted in the largest number of differentially abundant taxa post-treatment compared to the untreated control, followed by the antibiotic treatment (Fig 5). The genus Aeribacillus displayed the greatest log fold change of 2.86 after iron addition, corresponding to approximately an order of magnitude increase in abundance (ln(2.86) = 1.05). The sulfate treatment did not result in significantly differentially expressed taxa according to the ANCOM-BC test.
Differentially abundant taxa as determined by ANCOM-BC testing for a) antibiotic treatment and B) Fe(OH)3 treatment compared to untreated control microcosm. Natural log fold change reported on the Y axes. P = Phylum, G = Genus.
Differentially abundant taxa that were classified at the Genus level were further investigated by performing a Wilcoxon test on the log10 abundance in the different treatment groups to validate the ANCOM results. Wilcox testing on the log 10 abundance confirmed that taxa of the following genera were differentially abundant (p < 0.05). Taxa of the genera Gaiella, Pseudoclostridium, Aliidiomarina, Syntrophaceticus, Clostridium sensu stricto 10, Moorella, and Aeribacillus were significantly differentially abundant in the iron-treated microcosms (Fig 5B). Taxa of the genera Streptococcus and Sarcina were significantly differentially abundant in the antibiotic-treated microcosms (Fig 5A), and the genus Mahella and Clostridium sensu stricto 10 was significant in both treatment groups.
4. Discussion
Previous work has found that sampling site, sampling depth, landfill age, and geographic context/local climate all influence the microbial community structure within landfills [12, 65–67], which makes assessing the influence of a single variable particularly challenging. Here, we have attempted to circumvent this challenge by utilizing lab-built microcosms simulating fresh MSW. The advantage of this simulated MSW degradation study with lab-built microcosms is that waste inputs can be controlled rather than inferred and DNA can be collected in a reproducible manner across time and across samples. In this study, we have used microcosms that simulated fresh MSW degradation to study the response of the solid-particle-associated microbial community to selected antibiotics and redox-active components of C&D waste.
We observed a higher degree of heterogeneity in the lab-built microcosms at the start of the incubation (T1), however, the community began to stabilize and converge after just 15 days of incubation at T2 (Fig 2; Table 2). Across microcosms, we identified several taxa that are commonly observed in MSW. The bacterial taxa belonging to the phyla Firmicutes, Proteobacteria, Acidobacteriota (Acidobacteria), Actinobacteriota, Fusobacteriota, and Bacteroidota are consistently found in municipal solid waste landfills (reviewed in, [68]; Fig 4A). Bacteria within these phyla are known to be responsible for the hydrolytic and fermentative processes of waste degradation [68]. At the genus level, several higher abundance genera (> 0.5%) identified in the microcosms of this study have previously been identified in landfill systems (Fig 4B), including Acinetobacter [13], Brevibacillus [69], Lactobacillus [70], Caproiciproducens [71], and Halomonas [13]. Low abundance genera in the microcosms have also been previously detected in landfills, for example, Gelria [66], Brevundimonas [72], and Pseudomonas [73]. Several other genera in the microcosms have been detected in anaerobic digestors, wastewater, agriculture, and other industrial environments such as the anaerobic, acetogenic taxon Oxobacter [74], which was observed in high relative abundance in the live microcosms at the end of the experimental period (Fig 4B).
It is worth noting that while extracting DNA from glass beads within the microcosms enhances the reproducibility of the technique, it may not capture all microorganisms present if they were not transferred via the microcosm fluid. In a microcosm study investigating the interactions between soil minerals and microorganisms in the rhizosphere, it was found that the community composition of quartz minerals was different from the surrounding soil, but that this difference was driven largely by dispersal limitation (individuals cannot move between communities easily), rather than by variable selective pressure (abiotic or biotic pressures selecting for different characteristics) [75]. The authors suggested that the expression of flagella or other motility factors, as well as sufficient soil moisture would support bacterial dispersion [75]. In the current work, the waste components in the microcosms were fully submerged in water which would support the dispersal of microorganisms into/out of the bead tubes, however, some dispersal limitation may still be influencing the resultant microbial community. Dependent on the goals of future research, it may be worthwhile to sample organisms from individual waste components for greater spatial resolution.
4.1. Impact of contaminant treatments
Landfills have been identified as a reservoir of antibiotic-resistant genes (ARG), which could pose issues when/if they seep into the surrounding environment [31, 34]. The results of the antibiotic treatment indicated a significant difference in microbial community composition as compared to the untreated control microcosms (Tables 2 and 3). The alpha diversity of the microbial community, as determined by the Shannon diversity and Chao1 metrics, was not significantly decreased compared to the untreated control (Fig 3). These results are concordant with previous work that showed antibiotic exposure did not substantially decrease the diversity but did affect the community structure of the total bacteria in a laboratory incubation of refuse samples collected from an MSW landfill [45]. Wu et al. (2017) found that the addition of sulfamethazine to landfill refuse increased ARG levels by tenfold after 65 days of exposure to antibiotics while reducing the functional capacity for denitrification, however, the concentrations of antibiotics utilized were several-fold higher than the concentration used here at 25 mg/L [45]. Though the presence of ARGs was not directly assessed here, the significant change in microbial community structure despite stable microbial diversity raises potential concern for the development of antibiotic-resistant genes at the dosages used in this study. The long-term impact of repeated antibiotic exposure at various dosages on the microbial community and the development of ARGs should be investigated in future studies. Though landfilling medical waste is generally considered a safer alternative to disposal directly into wastewater via flushing, there are still important safety considerations to consider when landfilling bioactive medical wastes as they can have a potentially negative influence on the surrounding environment and on nearby human populations [31, 76]. Advanced treatment and containment processes to remove and/or prevent the spread of antibiotic waste and ARGs from landfills have been suggested to overcome these challenges [31, 76].
The addition of iron treatment also impacted the microbial community composition as well as increased microbial diversity in comparison to the control microcosms (Table 2; Fig 5).Total soluble iron at the end of the incubation in the Fe(OH)3 treated microcosms was found to be less than the control at 65 days, indicating that the addition of oxidized iron stimulates the removal of iron from solution or chelation / complex-formation of the iron (Table 1). This removal of iron after the addition of Fe(OH)3 could be a direct result of biomineralization. Alternatively, the Fe3+ could have been converted to Fe2+ abiotically and subsequently removed from solution by iron-oxidizing bacteria. Indeed, the majority of iron in our landfill bottles was as Fe2+ (Table 1), as expected since Fe2+ is more soluble and stable at circumneutral pH (pH 5.5–7.2) [77]. All microcosms measured pH 5.5 regardless of treatment or sample time (Table 1). Biological iron redox can be superimposed against abiotic reactions, and oxidation and reduction of iron may occur simultaneously, which makes parsing the contribution of each extremely difficult [78].
Several genera increased significantly after iron addition including Gaiella, Pseudoclostridium, Aliidiomarina, Syntrophaceticus, Clostridium sensu stricto 10, Mahella, Moorella, and Aeribacillus (Fig 5B). Most of the strains from these genera are capable of fermentation, aside from Gaiella and Aliidiomarina, which are only capable of respiration [79, 80]. In landfills, acetate is an important intermediate to methanogenesis; acetoclastic methanogens can use acetate directly or, in the syntrophic pathway, acetate is degraded by acetate oxidizing bacteria and the produced hydrogen is then consumed by a methanogen. Moorella is an acetogenic and hydrogenogenic bacterium, capable of utilizing insoluble poorly crystalline Fe(III) oxide to oxidize lactate into acetate [81], and therefore potentially contributes important intermediates for methanogenesis. The acetate-oxidizing genus Syntrophaceticus also increased after iron addition; species of this genus have been identified as syntrophic partners to methanogens [82]. It has been reported that zero valence iron, iron oxides, and conductive iron oxides can improve the efficiency of waste degradation and increase methane yield in anaerobic digester systems [83–85]. Hao et al. (2017) used waste iron scraps to enhance degradation efficiency of sludge in a mesophilic fermenter and found that iron scraps stimulated acetogenic and homoacetogenic bacteria, which in turn provided substrates for methanogens [86]. Though our study was limited due to the absence of methanogenic archaea (discussed below), these results support previous findings that the addition of iron stimulates fermentative bacteria that are beneficial to waste degradation efficiency.
The addition of sulfate simulated little change in the overall microbial community (Table 2; Fig 5). Sulfate concentrations measured at the end of the incubation period in the sulfate-treated microcosms is 2000 ± 100 mg/L sulfate, which was equivalent to the amount of sulfate added. This is reflected in the results of the PERMANOVA test, which showed sulfate addition did not influence microbial community composition (Table 2). These results indicate addition of sulfate did not produce changes in the overall microbial community; however, this does not necessarily indicate that sulfur-related metabolism was not active during the incubations. The genus Desulfotomaculum was found in low abundance (~0.001%) in several microcosms at the start of the incubation but no other common sulfate reducing bacteria (SRB) were detected later in the incubation period (data not shown), however, some species of the more abundant genera Acinetobacter and Caldalkalibacillus have been found to be capable of sulfate reduction [87, 88]. Caldalkalibacillus was also most abundant at the start of the incubation (Fig 4B), along with the organosulfur oxidizing bacterium Brevibacillus [89]. Common household waste such as paper products and packaging materials can provide a source of sulfate, and sulfate was detected in the simulated leachate prior to treatment addition (Table 1), so SRB may have been active early in the experiment and later outcompeted by fermentative bacteria for carbon substrates. Sulfate reducers have been detected in landfill systems, and laboratory incubations have shown competition between methanogens and sulfate reducers [1, 26]. In addition, sulfate has been shown to stimulate AOM when added to landfill cover soils [90]. Therefore, future work should investigate the addition of sulfate at different times during the incubation period, as well as different sources and/or concentrations of sulfate to better understand the influence of sulfate on MSW degradation.
The absence of methanogens in our microcosms warrants potential adjustments to the experimental design such as adding a mechanism for microcosm liquid recycling and neutralization or the addition of a seed of anaerobic bacteria and/or methanogenic archaea [18, 91]. Previous work has found that methane production rates are highly variable in a laboratory setting, and leachate recycling and neutralization is an effective method to stimulate methane production [18, 92]. The leachate recycling and neutralization technique was not used here. The pH at the end of the experimental period of 65 days was 5.5, which would have inhibited the onset of methanogenesis in the absence of acid-tolerant methanogens [6]. This result indicates a potential limitation of this experimental design, as the 100 mL glass bottles were not set up to redistribute liquid collected at the bottom of the microcosms. This should be a consideration for future studies implementing a lab-built waste design.
Our results show that microcosms built with simulated fresh MSW may be an effective tool for observing microbial dynamics to single variables in a heterogenous system over time, with the suggested adjustments to the experimental design to ensure the detection of methanogens. Here, we have tested a small subset of the potential variables that may affect landfill microbiology and waste degradation and found that insoluble iron oxide increased microbial diversity and stimulated fermentative bacteria, while antibiotics shifted the microbial community without influencing microbial diversity. Testing various concentrations and species of sulfur/iron/antibiotics may also be of value as the concentrations of these different waste streams vary in space and time in landfill ecosystems. Future microcosm work would benefit from the incorporation of metagenomic analysis or functional gene testing, as well as produced gas composition analysis to connect changes in the microbial community to metabolic functional changes that would impact waste degradation trajectory.
5. Conclusions
Landfill ecosystems are inherently heterogeneous, and while this may present challenges for reproducibility, our results show that microcosms built with simulated fresh MSW can be used to track changes to the landfill microbiome in response to contaminants of concern. Both the antibiotics and iron treatment additions altered the microbial community composition of the microcosms. Some resident microbial taxa within fresh MSW may be replaced with more resistant taxa following a single antibiotic dose. Further work investigating the long-term impact of exposure to antibiotics and various antibiotic dosages on the development of antibiotic-resistance genes and their influence on waste-degrading microorganisms is needed. The influence of iron-bearing metal contamination significantly increased microbial diversity and stimulated fermentative bacteria within the microcosms. Metagenomic or metatranscriptomic sequencing to confirm the metabolic genes present/and or utilized within MSW would be of great value to understanding the dynamics and waste degradation potential of landfill microbial communities.
Acknowledgments
The authors would like to thank Ian D. Patete for his contributions in the laboratory. The authors would also like to thank F. Kenig, A.J. Dombard, R.E. Plotnick, and M.J. Malaska for their helpful input and discussions about the manuscript. This is EDGElab contribution #11.
References
- 1. Meyer-Dombard DR, Bogner JE, Malas J. A Review of Landfill Microbiology and Ecology: A Call for Modernization With ’Next Generation’ Technology. Front Microbiol. 2020;11:1127. pmid:32582086
- 2. Bogner JE, Spokas KA, Chanton JP. Seasonal Greenhouse Gas Emissions (Methane, Carbon Dioxide, Nitrous Oxide) from Engineered Landfills: Daily, Intermediate, and Final California Cover Soils. J Environ Qual. 2011;40(3):1010–20. pmid:21546687
- 3.
National Academies of Sciences, Engineering, and Medicine. Improving Characterization of Anthropogenic Methane Emissions in the United States. Washington, DC: The National Academies Press; 2018.
- 4. Spokas KA, Bogner J, Corcoran M. Modeling landfill CH4 emissions: CALMIM international field validation, using CALMIM to simulate management strategies, current and future climate scenarios. Elementa. 2021;9(1):00050.
- 5. Christensen TH, Kjeldsen P, Bjerg PL, Jensen DL, Christensen JB, Baun A, et al. Biogeochemistry of landfill leachate plumes. Appl Geochem. 2001;16(7):659–718.
- 6. Staley BF, de Los Reyes FL, 3rd, Barlaz MA. Effect of spatial differences in microbial activity, pH, and substrate levels on methanogenesis initiation in refuse. Appl Environ Microbiol. 2011;77(7):2381–91. pmid:21296940
- 7. Ransom-Jones E, McCarthy AJ, Haldenby S, Doonan J, McDonald JE. Lignocellulose-Degrading Microbial Communities in Landfill Sites Represent a Repository of Unexplored Biomass-Degrading Diversity. mSphere. 2017;2(4). pmid:28776044
- 8. Remmas N, Roukouni C, Ntougias S. Bacterial community structure and prevalence of Pusillimonas-like bacteria in aged landfill leachate. Environ Sci Pollut Res Int. 2017;24(7):6757–69. pmid:28091993
- 9. Sharma P, Nadda AK, Kumar S. Microbial community profiling in bio-stimulated municipal solid waste for effective removal of organic pollutants containing endocrine disrupting chemicals. Microbiol Res. 2023;267:127273. pmid:36481500
- 10. Henneberger R, Chiri E, Blees J, Niemann H, Lehmann MF, Schroth MH. Field-scale labelling and activity quantification of methane-oxidizing bacteria in a landfill-cover soil. FEMS Microbiol Ecol. 2013;83(2):392–401. pmid:22928887
- 11. Song L, Wang Y, Tang W, Lei Y. Archaeal community diversity in municipal waste landfill sites. Appl Microbiol Biotechnol. 2015;99(14):6125–37. pmid:25758957
- 12. Stamps BW, Lyles CN, Suflita JM, Masoner JR, Cozzarelli IM, Kolpin DW, et al. Municipal Solid Waste Landfills Harbor Distinct Microbiomes. Front Microbiol. 2016;7:534. pmid:27148222
- 13. Hou F, Du J, Yuan Y, Wu X, Zhao S. Analysis of Microbial Communities in Aged Refuse Based on 16S Sequencing. Sustainability. 2021;13(8).
- 14. Hu L, Zhang D, Qian Y, Nie Z, Long Y, Shen D, et al. Microbes drive changes in arsenic species distribution during the landfill process. Environ Pollut. 2022;292(Pt A):118322. pmid:34634411
- 15.
Hoornweg D, Bhada-Tata P. What a Waste: A Global Review of Solid Waste Management. Washington, DC: World Bank, 2012.
- 16. Henneberger R, Chiri E, Bodelier PE, Frenzel P, Luke C, Schroth MH. Field-scale tracking of active methane-oxidizing communities in a landfill cover soil reveals spatial and seasonal variability. Environ Microbiol. 2015;17(5):1721–37. pmid:25186436
- 17. Farquhar GJ, Rovers FA. Gas production during refuse decomposition. Water Air Soil Pollut. 1973;2(4):483–95.
- 18. Barlaz MA, Schaefer DM, Ham RK. Bacterial Population Development and Chemical Characteristics of Refuse Decomposition in a Simulated Sanitary Landfill. Appl Environ Microbiol. 1989;55(1):55–65. pmid:16347835
- 19. Bogner J, Spokas K. Landfill CH4: Rates, fates, and role in global carbon cycle. Chemosphere. 1993;26(1):369–86.
- 20. Staley BF, de Los Reyes FL, 3rd, Wang L, Barlaz MA. Microbial ecological succession during municipal solid waste decomposition. Appl Microbiol Biotechnol. 2018;102(13):5731–40. pmid:29705953
- 21. Themelis N, Bourtsalas A. Methane Generation and Capture of U.S. Landfills. J Environ Sci Eng A. 2021;10.
- 22.
Advancing Sustainable Materials Management: 2018 Fact Sheet. United States Environmental Protection Agency, Office of Land and Emergency Management; 2020 Dec. Report No.: EPA 530-F-20-009.
- 23. Zehnder AJB, Brock TD. Anaerobic Methane Oxidation: Occurrence and Ecology. Appl Environ Microbiol. 1980;39(1):194–204. pmid:16345488
- 24. Beal EJ, House CH, Orphan VJ. Manganese- and Iron-Dependent Marine Methane Oxidation. Science. 2009;325(5937):184–7. pmid:19589998
- 25. Hori T, Müller A, Igarashi Y, Conrad R, Friedrich MW. Identification of iron-reducing microorganisms in anoxic rice paddy soil by 13C-acetate probing. ISME J. 2010;4(2):267–78. pmid:19776769
- 26. Gurijala KR, Suflita JM. Environmental Factors Influencing Methanogenesis from refuse in landfill samples. Environ Sci Tech. 1993;27:1176–81.
- 27.
Tolaymat T, Carson D. Best Management Practices to Prevent and Control Hydrogen Sulfide and Reduced Sulfur Compound Emissions at Landfills That Dispose of Gypsum Drywall. United States Environmental Protection Agency; 2015. Report No. EPA/600/R-14/039.
- 28. Fairweather RJ, Barlaz MA. Hydrogen Sulfide Production during decomposition of Landfill Inputs. J Environ Eng. 1998;124(4).
- 29. Lovley DR, Dwyer DF, Klug MJ. Kinetic Analysis of Competition Between Sulfate Reducers and Methanogens for Hydrogen in Sediments. Appl Environ Microbiol. 1982;43(6):1373–9. pmid:16346033
- 30. Masoner JR, Kolpin DW, Furlong ET, Cozzarelli IM, Gray JL. Landfill leachate as a mirror of today’s disposable society: Pharmaceuticals and other contaminants of emerging concern in final leachate from landfills in the conterminous United States. Environ Toxicol Chem. 2016;35(4):906–18. pmid:26562222
- 31. Zhang R, Yang S, An Y, Wang Y, Lei Y, Song L. Antibiotics and antibiotic resistance genes in landfills: A review. Sci Total Environ. 2022;806:150647. pmid:34597560
- 32. Wang P, Wu D, You X, Su Y, Xie B. Antibiotic and metal resistance genes are closely linked with nitrogen-processing functions in municipal solid waste landfills. J Hazard Mater. 2021;403:123689. pmid:32835993
- 33. Song L, Li L, Yang S, Lan J, He H, McElmurry SP, et al. Sulfamethoxazole, tetracycline and oxytetracycline and related antibiotic resistance genes in a large-scale landfill, China. Sci Total Environ. 2016;551–552:9–15. pmid:26874755
- 34. Chen QL, Li H, Zhou XY, Zhao Y, Su JQ, Zhang X, et al. An underappreciated hotspot of antibiotic resistance: The groundwater near the municipal solid waste landfill. Sci Total Environ. 2017;609:966–73. pmid:28783909
- 35. Czatzkowska M, Rolbiecki D, Zaborowska M, Bernat K, Korzeniewska E, Harnisz M. The influence of combined treatment of municipal wastewater and landfill leachate on the spread of antibiotic resistance in the environment–A preliminary case study. J Environ Manage. 2023;347:119053. pmid:37748295
- 36. Wang J-Y, An X-L, Huang F-Y, Su J-Q. Antibiotic resistome in a landfill leachate treatment plant and effluent-receiving river. Chemosphere. 2020;242:125207. pmid:31675591
- 37. Song L, Yang S, Gong Z, Wang J, Shi X, Wang Y, et al. Antibiotics and antibiotic-resistant genes in municipal solid waste landfills: Current situation and perspective. Curr Opin Environ Sci Health. 2023;31:100421.
- 38. Polianciuc SI, Gurzău AE, Kiss B, Ştefan MG, Loghin F. Antibiotics in the environment: causes and consequences. Med Pharm Rep. 2020;93(3):231–40. pmid:32832887
- 39. Ding C, He J. Effect of antibiotics in the environment on microbial populations. Appl Microbiol Biotechnol. 2010;87(3):925–41. pmid:20508933
- 40. Zhao R, Feng J, Liu J, Fu W, Li X, Li B. Deciphering of microbial community and antibiotic resistance genes in activated sludge reactors under high selective pressure of different antibiotics. Water Res. 2019;151:388–402. pmid:30616051
- 41. Masoner JR, Kolpin DW, Furlong ET, Cozzarelli IM, Gray JL, Schwab EA. Contaminants of emerging concern in fresh leachate from landfills in the conterminous United States. Environ Sci Process Impacts. 2014;16(10):2335–54. pmid:25111596
- 42. Zhao W, Wang B, Yu G. Antibiotic resistance genes in China: occurrence, risk, and correlation among different parameters. Environ Sci Pollut Res. 2018;25(22):21467–82. pmid:29948704
- 43. Wu D, Wang L, Su Y, Dolfing J, Xie B. Associations between human bacterial pathogens and ARGs are magnified in leachates as landfill ages. Chemosphere. 2021;264(Pt 1):128446. Epub 2020/10/11. pmid:33038756
- 44. Liu H, Li H, Qiu L, Chen B, Wang H, Fang C, et al. The panorama of antibiotics and the related antibiotic resistance genes (ARGs) in landfill leachate. Waste Manag. 2022;144:19–28. pmid:35303504
- 45. Wu D, Chen G, Zhang X, Yang K, Xie B. Change in microbial community in landfill refuse contaminated with antibiotics facilitates denitrification more than the increase in ARG over long-term. Sci Rep. 2017;7:41230. pmid:28120869
- 46.
Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2012. United States Environmental Protection Agency; 2014.
- 47. Staley BF, de los Reyes FL, 3rd, Barlaz MA. Comparison of Bacteria and Archaea communities in municipal solid waste, individual refuse components, and leachate. FEMS Microbiol Ecol. 2012;79(2):465–73. pmid:22092358
- 48. Monger HC, Kelly EF. Silica Minerals. Soil Mineralogy with Environmental Applications. In: Dixon JB, Schulze DG, editors. Soil Science Society of America Book Series No.7; 2002. p. 611–36.
- 49. Hanson James L, Yeşiller N, Oettle Nicolas K. Spatial and Temporal Temperature Distributions in Municipal Solid Waste Landfills. J Environ Eng. 2010;136(8):804–14.
- 50. Cardace D, Meyer-Dombard DR, Woycheese KM, Arcilla CA. Feasible metabolisms in high pH springs of the Philippines. Front Microbiol. 2015;6:10. pmid:25713561
- 51. Moonsamy PV, Williams T, Bonella P, Holcomb CL, Höglund BN, Hillman G, et al. High throughput HLA genotyping using 454 sequencing and the Fluidigm Access Array™ system for simplified amplicon library preparation. Tissue Antigens. 2013;81(3):141–9. pmid:23398507
- 52. Naqib A, Poggi S, Wang W, Hyde M, Kunstman K, Green SJ. Making and Sequencing Heavily Multiplexed, High-Throughput 16S Ribosomal RNA Gene Amplicon Libraries Using a Flexible, Two-Stage PCR Protocol. Methods Mol Biol. 2018;1783:149–69. pmid:29767361
- 53. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinform. 2014;30(5):614–20. pmid:24142950
- 54. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. pmid:27214047
- 55. Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP. Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000Res. 2016;5:1492. pmid:27508062
- 56. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. pmid:23630581
- 57.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. Cham: Springer International Publishing: Imprint: Springer; 2016.
- 58. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10(4):e1003531. Epub 2014/04/05. pmid:24699258
- 59. Kers JG, Saccenti E. The Power of Microbiome Studies: Some Considerations on Which Alpha and Beta Metrics to Use and How to Report Results. Front Microbiol. 2022;12. pmid:35310396
- 60. Lee M. Happy Belly Bioinformatics: an open-source resource dedicated to helping biologists utilize bioinformatics. J Open Source Educ 2 (19). 2019.
- 61. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology. 2010;11(10):R106. pmid:20979621
- 62. Anderson MJ. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online2017. p. 1–15.
- 63. Lahti L, Shetty S. Microbiome package. v1.16.0 ed: Bioconductor 2017.
- 64. Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11(1):3514. pmid:32665548
- 65. Sekhohola-Dlamini L, Selvarajan R, Ogola HJO, Tekere M. Community diversity metrics, interactions, and metabolic functions of bacteria associated with municipal solid waste landfills at different maturation stages. Microbiologyopen. 2021;10(1):e1118. pmid:33314739
- 66. Xu S, Lu W, Liu Y, Ming Z, Liu Y, Meng R, et al. Structure and diversity of bacterial communities in two large sanitary landfills in China as revealed by high-throughput sequencing (MiSeq). Waste Manag. 2017;63:41–8. pmid:27515184
- 67. Zainun MY, Simarani K. Metagenomics profiling for assessing microbial diversity in both active and closed landfills. Sci Total Environ. 2018;616–617:269–78. pmid:29117585
- 68. Sekhohola-Dlamini L, Tekere M. Microbiology of municipal solid waste landfills: a review of microbial dynamics and ecological influences in waste bioprocessing. Biodegradation. 2020;31(1–2):1–21. pmid:31512011
- 69. Jain R, Majumdar D, Devi S. Microbial Treatment of Raw and Primary Treated Sanitary Landfill Leachate by Indigenous Strain Brevibacillus agri. Appl Biochem Biotechnol. 2023;195(4):2317–31.
- 70. Mwaikono KS, Maina S, Sebastian A, Schilling M, Kapur V, Gwakisa P. High-throughput sequencing of 16S rRNA Gene Reveals Substantial Bacterial Diversity on the Municipal Dumpsite. BMC Microbiol. 2016;16(1):145. pmid:27400733
- 71. Ci M, Yang W, Jin H, Hu L, Fang C, Shen D, et al. Evolution of sulfate reduction behavior in leachate saturated zones in landfills. Waste Manag. 2022;141:52–62. pmid:35093856
- 72. Song L, Yang S, Liu H, Xu J. Geographic and environmental sources of variation in bacterial community composition in a large-scale municipal landfill site in China. Appl Microbiol Biotechnol. 2017;101(2):761–9. pmid:27770177
- 73. Wang X, Cao A, Zhao G, Zhou C, Xu R. Microbial community structure and diversity in a municipal solid waste landfill. Waste Manag. 2017;66:79–87. pmid:28442259
- 74. Bengelsdorf FR, Poehlein A, Schiel-Bengelsdorf B, Daniel R, Dürre P. Genome Sequence of the Acetogenic Bacterium Oxobacter pfennigii DSM 3222T. Genome Announc. 2015;3(6). pmid:26634756
- 75. Whitman T, Neurath R, Perera A, Chu-Jacoby I, Ning D, Zhou J, et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ Microbiol. 2018;20(12):4444–60. pmid:30047192
- 76. Anand U, Reddy B, Singh VK, Singh AK, Kesari KK, Tripathi P, et al. Potential Environmental and Human Health Risks Caused by Antibiotic-Resistant Bacteria (ARB), Antibiotic Resistance Genes (ARGs) and Emerging Contaminants (ECs) from Municipal Solid Waste (MSW) Landfill. Antibiotics [Internet]. 2021; 10(4). pmid:33915892
- 77. Melton ED, Swanner ED, Behrens S, Schmidt C, Kappler A. The interplay of microbially mediated and abiotic reactions in the biogeochemical Fe cycle. Nat Rev Microbiol. 2014;12(12):797–808. pmid:25329406
- 78. Kappler A, Bryce C, Mansor M, Lueder U, Byrne JM, Swanner ED. An evolving view on biogeochemical cycling of iron. Nature Rev Microbiol. 2021;19(6):360–74. pmid:33526911
- 79. Yang M, Xue Q, Zuo Z, Zhou J, Zhang S, Li M, et al. Aliidiomarina halalkaliphila sp. nov., a haloalkaliphilic bacterium isolated from a soda lake in Inner Mongolia Autonomous Region, China. IJSEM. 2022;72(3). pmid:35244531
- 80. Severino R, Froufe HJC, Barroso C, Albuquerque L, Lobo-da-Cunha A, da Costa MS, et al. High-quality draft genome sequence of Gaiella occulta isolated from a 150 meter deep mineral water borehole and comparison with the genome sequences of other deep-branching lineages of the phylum Actinobacteria. MicrobiologyOpen. 2019;8(9):e00840. pmid:30977302
- 81. Jiang B, Henstra A-M, Paulo PL, Balk M, van Doesburg W, Stams AJM. Atypical one-carbon metabolism of an acetogenic and hydrogenogenic Moorella thermoacetica strain. Arch. Microbiol. 2009;191(2):123–31. pmid:18854977
- 82. Westerholm M, Liu T, Schnurer A. Comparative study of industrial-scale high-solid biogas production from food waste: Process operation and microbiology. Bioresour Technol. 2020;304:122981. pmid:32088624
- 83. Cheng J, Zhu C, Zhu J, Jing X, Kong F, Zhang C. Effects of waste rusted iron shavings on enhancing anaerobic digestion of food wastes and municipal sludge. J. Clean. Prod. 2020;242:118195.
- 84. Yamada C, Kato S, Ueno Y, Ishii M, Igarashi Y. Conductive iron oxides accelerate thermophilic methanogenesis from acetate and propionate. JBB. 2015;119(6):678–82. pmid:25488041
- 85. Suanon F, Sun Q, Li M, Cai X, Zhang Y, Yan Y, et al. Application of nanoscale zero valent iron and iron powder during sludge anaerobic digestion: Impact on methane yield and pharmaceutical and personal care products degradation. J Hazard Mater. 2017;321:47–53. pmid:27607932
- 86. Hao X, Wei J, van Loosdrecht MCM, Cao D. Analysing the mechanisms of sludge digestion enhanced by iron. Water Res. 2017;117:58–67. pmid:28390236
- 87. Gao X, Xu Z, Zhang L, Li G, Nghiem LD, Luo W. Assimilatory sulphate reduction by acidogenesis: The key to prevent H2S formation during food and green waste composting for sustainable urbanization. Chem Eng J. 2024;499:156149.
- 88. Han Z, Zhao Y, Yan H, Zhao H, Han M, Sun B, et al. Struvite precipitation induced by a novel sulfate-reducing bacterium Acinetobacter calcoaceticus SRB4 isolated from river sediment. Geomicrobiol J. 2015;32(10):868–77.
- 89. She Y, Wu W, Hang C, Jiang X, Chai L, Yu G, et al. Genome sequence of Brevibacillus agri strain 5–2, isolated from the formation water of petroleum reservoir. Mar Genomics. 2014;18:123–5. pmid:25194923
- 90. Parsaeifard N, Sattler M, Nasirian B, Chen VCP. Enhancing anaerobic oxidation of methane in municipal solid waste landfill cover soil. Waste Manag. 2020;106:44–54. pmid:32182561
- 91. Barlaz MA, Milke MW, Ham RK. Gas production parameters in sanitary landfill simulators. Waste Manag Res. 1987;5(1):27–39.
- 92. Barlaz MA, Schaefer DM, Ham RK. Inhibition of Methane Formation from Municipal Refuse in Laboratory Scale Lysimeters. Appl Biochem Biotechnol. 1989;20:181–205.
