Microbiological and Technological Insights on Anaerobic Digestion of Animal Manure: A Review

Anaerobic digestion of animal manure results in the production of renewable energy (biogas) and nutrient-rich biofertilizer. A further benefit of the technology is decreased greenhouse gas emissions that otherwise occur during manure storage. Since animal manure makes anaerobic digestion cost-efficient and further advance the technology for higher methane yields, it is of utmost importance to find strategies to improve bottlenecks such as the degradation of lignocellulose, e.g., in cattle manure, or to circumvent microbial inhibition by ammonia caused by the degradation of nitrogen compounds in, e.g., chicken, duck, or swine manure. This review summarizes the characteristics of different animal manures and provides insight into the underlying microbial mechanisms causing challenging problems with the anaerobic digestion process. A particular focus is put upon the retention time and organic loading rate in high-ammonia processes, which should be designed and optimized to support the microorganisms that tolerate high ammonia conditions, such as the syntrophic acetate oxidizing bacteria and the hydrogenotrophic methanogens. Furthermore, operating managements used to stabilize and increase the methane yield of animal manure, including supporting materials, the addition of trace elements, or the incorporation of ammonia removal technologies, are summarized. The review is finalized with a discussion of the research needed to outline conceivable operational methods for the anaerobic digestion process of animal manure to circumvent process instability and improve the process performance

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Quantitative Analysis of Major Metals in Agricultural Biochar Using Laser-Induced Breakdown Spectroscopy with an Adaboost Artificial Neural Network Algorithm

To promote the green development of agriculture by returning biochar to farmland, it is of great significance to simultaneously detect heavy and nutritional metals in agricultural biochar. This work aimed first to apply laser-induced breakdown spectroscopy (LIBS) for the determination of heavy (Pb, Cr) and nutritional (K, Na, Ca, Mg, Cu, and Zn) metals in agricultural biochar. Each batch of collected biochar was prepared to a standardized sample using the separating and milling method. Two types of univariate analysis model were developed using peak intensity and integration area of the sensitive emission lines, but the performance did not satisfy the requirements of practical application because of the poor correlations between the measured values and predicted values, as well as large relative standard deviation of the prediction (RSDP) values. An ensemble learning algorithm, adaboost backpropagation artificial neural network (BP-Adaboost), was then used to develop the multivariate analysis models, which had a more robust performance than traditional univariate analysis, partial least squares regression (PLSR), and backpropagation artificial neural network (BP-ANN). The optimized RSDP values for K, Ca, Mg, and Cu were less than 10%, while the RSDP values for Pb, Cr, Zn, and Na were in the range of 10–20%. Moreover, the pairwise t-test of its prediction set showed that there was no significant difference between the measurements of LIBS and ICP-MS. The promising results indicate that rapid and simultaneous detection of major heavy and nutritional metals in agricultural biochar can be achieved using LIBS and reasonable chemometric algorithms

Comparison and Evaluation of GHG Emissions during Simulated Thermophilic Composting of Different Municipal and Agricultural Feedstocks

Composting is widely used to recycle a variety of different organic wastes. In this study, dairy manure, chicken litter, biosolids, yard trimmings and food waste were selected as representative municipal and agricultural feedstocks and composted in simulated thermophilic composting reactors to compare and evaluate the GHG emissions. The results showed that the highest cumulative emissions of CO2, CH4 and N2O were observed during yard trimmings composting (659.14 g CO2 kg−1 DM), food waste composting (3308.85 mg CH4 kg−1 DM) and chicken litter composting (1203.92 mg N2O kg−1 DM), respectively. The majority of the carbon was lost in the form of CO2. The highest carbon loss by CO2 and CH4 emissions and the highest nitrogen loss by N2O emission occurred in dairy manure (41.41%), food waste (0.55%) and chicken litter composting (3.13%), respectively. The total GHG emission equivalent was highest during food waste composting (365.28 kg CO2-eq ton−1 DM) which generated the highest CH4 emission and second highest N2O emissions, followed by chicken litter composting (341.27 kg CO2-eq ton−1 DM), which had the highest N2O emissions. The results indicated that accounting for GHG emissions from composting processes when it is being considered as a sustainable waste management practice was of great importance

Comparison and Evaluation of GHG Emissions during Simulated Thermophilic Composting of Different Municipal and Agricultural Feedstocks

Composting is widely used to recycle a variety of different organic wastes. In this study, dairy manure, chicken litter, biosolids, yard trimmings and food waste were selected as representative municipal and agricultural feedstocks and composted in simulated thermophilic composting reactors to compare and evaluate the GHG emissions. The results showed that the highest cumulative emissions of CO2, CH4 and N2O were observed during yard trimmings composting (659.14 g CO2 kg−1 DM), food waste composting (3308.85 mg CH4 kg−1 DM) and chicken litter composting (1203.92 mg N2O kg−1 DM), respectively. The majority of the carbon was lost in the form of CO2. The highest carbon loss by CO2 and CH4 emissions and the highest nitrogen loss by N2O emission occurred in dairy manure (41.41%), food waste (0.55%) and chicken litter composting (3.13%), respectively. The total GHG emission equivalent was highest during food waste composting (365.28 kg CO2-eq ton−1 DM) which generated the highest CH4 emission and second highest N2O emissions, followed by chicken litter composting (341.27 kg CO2-eq ton−1 DM), which had the highest N2O emissions. The results indicated that accounting for GHG emissions from composting processes when it is being considered as a sustainable waste management practice was of great importance

Large Semi-Membrane Covered Composting System Improves the Spatial Homogeneity and Efficiency of Fermentation

Homogenous spatial distribution of fermentation characteristics, local anaerobic conditions, and large amounts of greenhouse gas (GHGs) emissions are common problems in large-scale aerobic composting systems. The aim of this study was to examine the effects of a semi-membrane covering on the spatial homogeneity and efficiency of fermentation in aerobic composting systems. In the covered group, the pile was covered with a semi-membrane, while in the non-covered group (control group), the pile was uncovered. The covered group entered the high-temperature period earlier and the spatial gradient difference in the group was smaller compared with the non-covered group. The moisture content loss ratio (5.91%) in the covered group was slower than that in the non-covered group (10.78%), and the covered group had a more homogeneous spatial distribution of water. The degradation rate of organic matter in the non-covered group (11.39%) was faster than that in the covered group (10.21%). The final germination index in the covered group (85.82%) was higher than that of the non-covered group (82.79%) and the spatial gradient difference in the covered group was smaller. Compared with the non-covered group, the oxygen consumption rate in the covered group was higher. The GHG emissions (by 30.36%) and power consumption in the covered group were reduced more significantly. The spatial microbial diversity of the non-covered group was greater compared with the covered group. This work shows that aerobic compost covered with a semi-membrane can improve the space homogeneity and efficiency of fermentation

Hemolymph Metabolism Analysis of Honey Bee (<i>Apis mellifera</i> L.) Response to Different Bee Pollens

Pollen is essential to the development of honey bees. The nutrients in bee pollen vary greatly among plant species. Here, we analyzed the differences in the amino acid compositions of pear (Pyrus bretschneideri), rape (Brassica napus), and apricot (Armeniaca sibirica) pollens and investigated the variation in hemolymph metabolites and metabolic pathways through untargeted metabolomics in caged adult bees at days 7 and 14. The results showed that the levels of five essential amino acids (isoleucine, phenylalanine, lysine, methionine, and histidine) were the highest in pear pollen, and the levels of four amino acids (isoleucine: 50.75 ± 1.93 mg/kg, phenylalanine: 87.25 ± 2.66 mg/kg, methionine: 16.00 ± 0.71 mg/kg and histidine: 647.50 ± 24.80 mg/kg) were significantly higher in pear pollen than in the other two kinds of bee pollen (p p = 0.0091)”, “tryptophan metabolism (p = 0.0245)”, and “cysteine and methionine metabolism (p = 0.0277)”, were significantly affected on day 7. There was no meaningful pathway enrichment on day 14. In conclusion, pear pollen had higher nutritional value among the three bee pollens in terms of amino acid level, followed by rape and apricot pollen, and the difference in amino acid composition among bee pollens was reflected in the lipid and amino acid metabolism pathways of early adult honey bee hemolymph. This study provides new insights into the physiological and metabolic functions of different bee pollens in bees

Evaluation of Controlled Release Urea on the Dynamics of Nitrate, Ammonium, and Its Nitrogen Release in Black Soils of Northeast China

Controlled release urea (CRU) is considered to enhance crop yields while alleviating negative environmental problems caused by the hazardous gas emissions that are associated with high concentrations of ammonium (NH4+) and nitrate (NO3−) in black soils. Short-term effects of sulfur-coated urea (SCU) and polyurethane-coated urea (PCU), compared with conventional urea, on NO3− and NH4+ in black soils were studied through the buried bag experiment conducted in an artificial climate chamber. We also investigated nitrogen (N) release kinetics of CRU and correlations between the cumulative N release rate and concentrations of NO3− and NH4+. CRU can reduce concentrations of NO3− and NH4+, and PCU was more effective in maintaining lower soil NO3−/NH4+ ratios than SCU and U. Parabolic equation could describe the kinetics of NO3− and NH4+ treated with PCU. The Elovich equation could describe the kinetics of NO3− and NH4+ treated with SCU. The binary linear regression model was established to predict N release from PCU because of significant correlations between the cumulative N release rate and concentrations of NO3− and NH4+. These results provided a methodology and data support for characterizing and predicting the N release from PCU in black soils