Search Results (32)

Biochar has significant potential for livestock microbiomes and crop agriculture regarding greenhouse gas emissions reduction. Therefore, a pilot study was designed to investigate the effect of biochar application on the surface of swine manure from an open lagoon and the associated microbial communities. Samples were collected from four different treatment groups: control (n = 4), coarse biochar (n = 4), fine biochar (n = 4), and ultra-fine biochar (n = 4). Additionally, aged manure in bulk was collected (n = 4) to assess alterations from the control group. The method of 16S rRNA amplicon sequencing along with microbial analyses was performed. Diversity was significantly different between aged manure in bulk samples and all treatment groups (Kruskal–Wallis; p < 0.05). Additionally, distinct community compositions were seen using both weighted and unweighted UniFrac distance matrices (PERMANOVA; p < 0.01). Differential abundance analysis revealed four distinct features within all treatment groups that were enriched (q < 0.001): Idiomarina spp., Geovibrio thiophilus, Parapusillimonas granuli, and an uncultured Gammaproteobacteria species. Similarly, Comamonas spp. and Brumimicrobium aurantiacum (q-value < 0.001) were significantly depleted by all the treatments. Stochastic and functional analyses revealed that biochar treatments were not deterministically altering assembly patterns, and functional redundancy was evident regardless of compositional shifts. Full article

Producing Class A biosolids that can be distributed or land-applied without restriction is a beneficial way to reuse wastewater treatment solids. For small water resource recovery facilities (WRRFs) in particular, low-cost, low-tech (LCLT) processes may be an appealing alternative to conventional technologies for producing Class A biosolids, such as processes to further reduce pathogens (PFRPs). Conventional Class A biosolids treatment processes tend to be energy-intensive and involve complex equipment and operations. However, a systematic comparison of the overall sustainability of conventional processes and LCLT alternatives for producing Class A biosolids to aid decision makers in selecting treatment processes is not readily available. Therefore, this study used life cycle assessments to compare five Class A biosolids treatment processes, including three conventional processes—Composting, Direct Heat Drying, and temperature-phased anaerobic digestion (TPAD)—and two LCLT processes—Air Drying, and long-term Lagoon Storage followed by Air Drying—on the basis of their environmental impacts. The environmental impacts were normalized to facilitate a comparison of the processes. The results indicate that Composting and Direct Heat Drying had the most significant environmental impacts, primarily from the biogenic emissions during Composting and the natural gas requirements for Direct Heat Drying. In comparison, TPAD and Air Drying had the lowest environmental impacts, and Lagoon Storage had intermediate impacts. Thus, LCLT processes may be more sustainable than some, but not all, conventional PFRPs. Full article

This paper presents a strategic planning model aimed at optimizing the economic and environmental impacts of palm oil mill effluent (POME) treatment systems. The model determines the optimal selection of POME treatment systems to minimize the environmental impact, specifically focusing on three systems: an anaerobic digester tank system (ADT), a covered lagoon system (CL) with biogas capture, and an open pond system (OP). The model incorporates constraints related to fresh fruit bunch (FFB) production, POME generation, the biological oxygen demand (BOD), the chemical oxygen demand (COD), and carbon dioxide (CO₂) emissions. The optimization framework, formulated as a mixed-integer linear programming (MILP) model, is solved using the GAMS 40.1.0 software. Integer decision variables are used to represent the choice of POME treatment system that minimizes the environmental impact. The study specifically considers the ADT, CL, and OP systems, with the results indicating that the ADT system is the most effective in reducing the BOD, COD, and CO₂-equivalent emissions, thereby highlighting its environmental benefits. The model selects the ADT treatment system, which exhibits the lowest COD, BOD, and CO₂e emissions. Specifically, the COD registered an 85% reduction, from 84,830 mg/L to 12,725 mg/L. The BOD level was reduced by 88%, resulting in a BOD level of 41,208 mg/L to 4945 mg/L. The minimum CO₂e emissions that could be achieved was about 3173 t CO₂e per annum. This model provides a valuable tool for governmental agencies and policymakers to guide the private sector in developing environmentally sustainable POME treatment strategies. Full article

Robinsoniella peoriensis is a Gram-positive, strictly anaerobic, spore-forming, rod-shaped bacterium belonging to the phylum Firmicutes and the family Lachnospiraceae. Until now, R. peoriensis is the only species of its genus. It was first isolated in 2003 during a study into the flora of lagoons and manure pits. Given the rarity of this microorganism and the sparse information in the literature about its way of transmission, the way to diagnose its infections and identify it in the microbiology laboratory, and its public health relevance, the present study aimed to identify all the published cases of Robinsoniella, describe the epidemiological, clinical, and microbiological characteristics, and provide information about its antimicrobial resistance, treatment, and outcomes. A narrative review was performed based on a Pubmed/Medline and Scopus databases search. In total, 14 studies provided data on 17 patients with infections by Robinsoniella. The median age of patients was 63 years and 47% were male. The most common types of infection were bone and joint infections, bacteremia, infective endocarditis, and peritonitis. The only isolated species was R. peoriensis, and antimicrobial resistance to clindamycin was 50%, but was 0% to the combination of piperacillin with tazobactam, aminopenicillin with a beta-lactamase inhibitor, and metronidazole which were the most commonly used antimicrobials for the treatment of these infections. The overall mortality depends on the type of infection and is notable only for bacteremia, while all other infections had an optimal outcome. Future studies should better assess these infections’ clinical and epidemiological characteristics and the mechanisms of the antimicrobial resistance of this microorganism from a mechanistic and genetic perspective. Full article

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water’s Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model’s accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model’s predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors. Full article

Livestock wastes can serve as the feedstock for biogas production (mainly methane) that could be used as an alternative energy source. The green energy derived from animal wastes is considered to be carbon neutral and offsetting the emissions generated from fossil fuels. In this study, an evaluation of methane production from anaerobic digesters utilizing different livestock residues (e.g., poultry rendering wastewater and dairy manure) was carried out. An anaerobic continuous flow system (15 million gallons, polyethylene-covered) subjected to natural conditions (i.e., high flow rate, seasonal temperatures, etc.) containing poultry rendering wastewater was set up to evaluate methane potential and energy production. A parallel pilot-scale plug-flow anaerobic digestion system (9 m³) was also set up to test different feedstocks and operating parameters. Biogas production was sampled and monitored by gas chromatography over several months of operation. The results showed that methane production increased as the temperature increased as well as depending on the type of feedstock utilized. The covered rendering wastewater lagoon achieved an upward of 80% (v/v) methane production. The rates of methane production were 0.0478 g per g of COD for the poultry rendering wastewater and 0.0141 g per g of COD for dairy manure as feedstock. Hence, a poultry processing plant with a rendering wastewater flow rate of about 4.5 million liters per day has the potential to capture about two million kilograms of methane for energy production per year from a waste retention pond, potentially reducing global warming potential by about 50,000 tons of CO₂ equivalent annually. Full article

In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, enabling the discovery of new insights and analyses. However, the presence of noise and artefacts, particularly unwanted natural features, poses significant challenges, and their removal requires the application of filtering techniques prior to conducting analysis. Unmanned aerial vehicle-based photogrammetry is used at Melbourne Water’s Western Treatment Plant as a cost-effective and efficient method of inspecting the floating covers on the anaerobic lagoons. The focus of interest is the elevation profile of the floating covers for these sewage-processing lagoons and its implications for sub-surface scum accumulation, which can compromise the structural integrity of the engineered assets. However, unwanted artefacts due to trapped rainwater, debris, dirt, and other irrelevant structures can significantly distort the elevation profile. In this study, a machine learning algorithm is utilised to group distinct features on the floating cover based on an image segmentation process. An unsupervised k-means clustering algorithm is employed, which operates on a stacked 4D array composed of the elevation of the DEM and the RGB channels of the associated orthomosaic. In the cluster validation process, seven cluster groups were considered optimal based on the Calinski–Harabasz criterion. Furthermore, by utilising the k-means method as a filtering technique, three clusters contain features related to the elevations associated with the floating cover membrane, collectively representing 84% of the asset, with each cluster contributing at least 19% of the asset. The artefact groups constitute less than 6% of the asset and exhibit significantly different features, colour characteristics, and statistical measurements from those of the membrane groups. The study found notable improvements using the k-means filtering method, including a 59.4% average reduction in outliers and a 36.3% decrease in standard deviation compared to raw data. Additionally, employing the proposed method in the scum hardness analysis improved correlation strength by 13.1%, removing approximately 16% of the artefacts in total assets, in contrast to a 3.6% improvement with the median filtering method. This improved imaging will lead to significant benefits when integrating imagery into deep learning models for structural health monitoring and asset performance. Full article

This paper presents results of an aquatic vegetation resource potential assessment. The study is aimed at assessing the perspective of biotechnological approaches to reducing the biogenic pollution of water bodies by the removal of aquatic vegetation. The article analyzes the dominant species of aquatic vegetation in the Curonian Lagoon, and their productivity and resource potential. It was established that the concentrations of protein and fat in the biomass of four dominant plants are extremely low, making it impossible to speak of their values in terms of biomass processing. Based on elemental composition, we can conclude that the biomass of Phragmites australis should have a high calorie content because it has a high carbon and hydrogen content (49.6% and 7.1%, respectively), resulting in a high energy value. Synchronous thermal analysis revealed that the maximum energy values of biomasses of Phragmites australis and Scirpus lacustris have a net calorific value of 12.62 and 12.55 MJ/kg, respectively. At the same time, the biomass of these plants has a low ash content (around 6.6–7.6%) and a low sulfur content (less than 0.41%). An analysis of the composition of aquatic vegetation biomass samples allowed us to establish that, given the permissible collection time (no earlier than September), the search for directions in the utilization of aquatic vegetation should focus on processing cellulose with the production of crystalline cellulose, biochar, or biofuel via anaerobic digestion. The removal of the excess biomass of aquatic vegetation (Phragmites australis) will allow the reduction of the nitrogen and phosphorus load in the water body by 140 kg/ha and 14 kg/ha, respectively. Full article

Wastewaters are mainly classified as domestic, industrial and agro-industrial based on their production source. Piggery wastewater (PWW) is a livestock wastewater characterized by its high concentrations of organic matter and ammonium, and by its odour nuisance. Traditionally, PWW has been treated in open anaerobic lagoons, anaerobic digesters and activated sludge systems, which exhibit high greenhouse gas emissions, a limited nutrients removal and a high energy consumption, respectively. Photosynthetic microorganisms can support a sustainable wastewater treatment in engineered photobioreactors at low operating costs and with an efficient recovery of carbon, nitrogen and phosphorous. These microorganisms are capable of absorbing solar irradiation through the photosynthesis process to obtain energy, which is used for their growth and associated carbon and nutrients assimilation. Purple phototrophic bacteria (PPB) represent the photosynthetic microorganisms with the most versatile metabolism in nature, whereas microalgae are the most-studied photosynthetic microorganisms in recent years. This review describes the fundamentals, symmetry and asymmetry of wastewater treatment using photosynthetic microorganisms such as PPB and microalgae. The main photobioreactor configurations along with the potential of PPB and microalgae biomass valorisation strategies are also discussed. Full article

California is the leading dairy state in the United States. The total sale of milk and its products represents about $6.3 billion annually out of the $50 billion generated from all agricultural production in the state. However, methane emissions from dairy manure and enteric fermentation represented nearly half of all annual methane emissions in California, with dairy manure accounting for 25%, and enteric fermentation for 20%. Methane emissions originating from manure are produced primarily from anaerobic settling basins and lagoons, which are the most common manure storage systems in the state. To achieve sustainability on dairy farms and to comply with state regulations for air and climate pollutants, dairy farms have implemented technologies such as anaerobic digestion and alternative manure management technologies. In addition, governmental incentive programs have been deployed to partially fund these technologies for eligible dairies in the state. The present article reviews the design and operations, effectiveness, and economics of the most common technologies employed in Californian dairies in reducing methane emissions. The technologies studied include anaerobic digesters, mechanical separators, compost-bedded pack barns, manure vacuuming followed by drying, and weeping walls. The current status and estimated effectiveness of government incentive programs are reviewed and recommendations for improvements presented. Finally, future trends and research needs for mitigating the emissions in Californian dairies are identified. Full article

Using palm oil mill effluent (POME) to produce biogas is an alternative and sustainable way to control POME GHG emissions while also providing economic benefits. The increasing area of oil palm plantations encourages an increase in palm oil production and the generation of POME in Indonesia. This could increase potential GHG emissions and global warming. In contrast, biogas power plants from POME are less attractive for economic investment in Indonesia. However, as the world’s largest palm oil producer, Indonesia still lacks techno-economic and environmental studies of biogas power generation from POME. This study aimed to evaluate the technical, economic, and environmental aspects of the biogas power generation from POME at the study site (Bangka Island, Indonesia). The result shows that the biogas plant at the study site can reduce COD levels of POME by up to 91% and produce biogas at 325,292 m³/month, with a 55% methane content. Biogas can be converted into electrical energy at 696,163 kWh/month. The operation of this biogas plant can reduce GHG emissions by 1131 tons CO₂-eq/month, with low profitability (NPV of IDR—1,281,136,274, IRR 6.75%, and a payback period of 10.8 years). This evaluation proves that the main problem in the factory is the POME used, which is insufficient, and which could be overcome by purchasing POME from other palm oil mills. Furthermore, using the mesophilic anaerobic degradation process at the study site is feasible. However, a technological shift from closed lagoons to more efficient bioreactors is urgently needed, to increase the process efficiency and economic benefits. Full article

Unmanned aerial vehicle (UAV) assisted photogrammetry has been used to perform a non-contact measurement of the covers on the anaerobic wastewater lagoons at Melbourne Water Corporation’s Western Treatment Plant (WTP). These floating covers are valuable assets that eliminate odour and greenhouse gas emissions and harvest the methane-rich biogas as a renewable resource to generate electricity. Hence, the state of deformation of the floating covers becomes an important engineering factor for structural integrity assessment. UAVs have been deployed to scan these covers and photogrammetry has been used to process the aerial images to construct the floating covers’ orthophoto and digital elevation model (DEM). This paper proposes to adopt the finite element formulation to improve the quantification of the in-plane movement of a floating cover. Distinguishable features on the floating cover are first identified and their (x, y and z) coordinates are recorded over time. The results show that the technique can be used to quantify the short-term and long-term relative global lateral movement of the floating covers at WTP. More importantly, the results not only highlight the usefulness of this analysis for the integrity management of the floating cover but also show the value of clearly defined markers on the floating cover to facilitate the calculation of the cover’s state of strain. Full article

Eutrophication determines algal blooms and the subsequent accumulation of organic matter in sediments, which, in turn, results in the dominance of anaerobic respiratory processes that release toxic gases. Dystrophy is a final dissipative moment that reduces the organic load in the sediment. A case of dystrophy occurring in the Burano lagoon (Tuscany, Italy) in 2021 is reported. The study examined the weather, physico-chemistry of the water, submerged vegetation and sediment labile organic matter. In spring, dissolved oxygen (DO) and pH showed high values, in an abundance of submerged vegetation, while low values had ammonium, nitrate and orthophosphate. In mid-August, as warm and moist sea breezes prevailed, hydrogen sulfide releases were produced, preceded by a sharp rise in ammonium and orthophosphate concentrations, which remained high until November. During dystrophy, DO varied between anoxia and oversaturation, the latter in Cyanobacteria blooms. Dystrophic waters evolved gradually due to microphytes blooms, which changed from Cyanobacteria, in August, to the Dinophyta Alexandrium tamarense, in September, and Bacillariophyta, in November. Sediment labile organic matter varied between 3% and 7%. Ruppia spiralis meadows suffered the total detachment of fronds and stems during the dystrophy and proved to be areas of accumulation of organic detritus, themselves sources of dystrophic phenomena. Full article

Increased demand for animal protein is met by increased food animal production resulting in large quantities of manure. Animal producers, therefore, need sustainable agricultural practices to protect environmental health. Large quantities of antimicrobials are used in commercial food animal production. Consequently, antimicrobial-resistant bacteria and the resistance genes emerge and are excreted through feces. Manure management is essential for the safe disposal of animal waste. Lagoons, with or without covers, and anaerobic digesters, with the primary purpose of methane production, and composting, with the primary purpose of producing organic fertilizer, are widely used methods of manure treatment. We reviewed manure management practices and their impact on tetracycline resistance genes. Lagoons are maintained at ambient temperatures; especially uncovered lagoons are the least effective in removing tetracycline resistance genes. However, some modifications can improve the performance of lagoons: sequential use of uncovered lagoons and the use of covered lagoons resulted in a one-log reduction, while post-treatments such as biofiltration following covered lagoon treatment resulted in 3.4 log reduction. Mesophilic digestion of animal manure did not have any significant effect; only a 0.7 log reduction in tet(A) was observed in one study. While thermophilic anaerobic digesters are effective, if properly operated, they are expensive for animal producers. Aerobic thermophilic composting is a promising technology if optimized with its economic benefits. Composting of raw animal manure can result in up to a 2.5 log reduction, and postdigestion composting can reduce tetracycline resistance gene concentration by >80%. In general, manure management was not designed to mitigate antimicrobial resistance; future research is needed to optimize the economic benefits of biogas or organic fertilizer on the one hand and for the mitigation of foodborne pathogens and antimicrobial resistance on the other. Full article

Hydrogen sulfide (H₂S) from hog operations contributes to noxious odors in the surrounding environment and can be life-threatening. There is, however, limited understanding of what influences H₂S emissions from these farms. Emissions of H₂S were measured periodically over the course of two years at hog finisher farms in humid mesothermal (North Carolina, NC, USA) and semi-arid (Oklahoma, OK, USA) climates. Emissions were determined using an inverse dispersion backward Lagrangian stochastic model in conjunction with line-sampled H₂S concentrations and measured turbulence. Daily emissions at the two lagoons were characterized by low emissions on most days with occasional days of high emissions. Mean annual area-specific emissions were much lower for the NC lagoon (1.32 µg H₂S m⁻² s⁻¹ ± 0.07 µg H₂S m⁻² s⁻¹) than the OK lagoon (6.88 µg H₂S m⁻² s⁻¹ ± 0.13 µg H₂S m⁻² s⁻¹). Mean annual hog-specific emissions for the NC lagoon were 0.75 g H₂S hd⁻¹ d⁻¹ while those for the OK lagoon were 1.92 g H₂S hd⁻¹ d⁻¹. Emissions tended to be higher during the afternoon, likely due to higher mean winds. Daily H₂S emissions from both lagoons were greatest during the first half of the year and decreased as the year progressed and a reddish color (indicating high populations of purple sulfur bacteria (PSB)) appeared in the lagoon. The generally low emissions at the NC lagoon and higher emissions at the OK lagoon were likely a result of the influence of wind on mixing the lagoon and not the presence of PSB. Full article