Evaluation of common supermarket products as positive controls in biochemical methane potential (BMP) tests

Biochemical methane potential (BMP) tests are commonly applied to evaluate the recoverable amount of methane from a substrate. Standardized protocols require inclusion of a positive control with a known BMP to check the experimental setup and execution, as well as the performance of the inoculum. Only if the BMP of the positive control is within the expected range is the entire test validated. Besides ignorance of this requirement, limited availability of the standard positive control microcrystalline cellulose might be the main reason for neglecting a positive control. To address this limitation, eight widely available grocery store products have been tested as alternative positive controls (APC) to demonstrate their suitability. Among them, Tic Tacs and gummi bears were very promising, although they are dominated by easily degradable sugars and so do not test for hydrolytic performance. Coffee filters exhibited a similar performance to microcrystalline cellulose, while whole milk might be chosen when a more balanced carbohydrate:protein:lipid ratio is important. Overall, the approach of predicting the BMP of a substrate based on the nutritional composition provided on the product packaging worked surprisingly well: BMP of the eight tested products was 81-91% of theoretical maximum BMP based on nutritional information and generic chemical formulas for carbohydrates, proteins, and lipids

Power and limitations of biochemical methane potential (BMP) tests

As energy systems transition toward renewable sources, anaerobic digestion (AD), which can be used to recover energy from organic substrates, is receiving growing attention. AD research and practice both rely on biochemical methane potential (BMP) tests to determine the methane potential of sewage sludge, energy crops and organic wastes (Pearse et al., 2018). In contrast to continuous reactor experiments, BMP tests are batch, and can be conducted without a major investment of equipment, labor and time. However, this and other differences limit the applicability of results from a BMP test to full-scale plant operation. Yet even in the peer-reviewed literature, BMP test results are not always used appropriately. An example is the determination of synergistic or antagonistic effects during anaerobic co-digestion in substrate mixtures. A BMP test is a powerful and useful tool, but it is important to recognize the type of questions that can and cannot be answered with this experimental setup. Clarification of these issues is the objective of the present contribution

Impact of Storage Conditions on the Methanogenic Activity of Anaerobic Digestion Inocula

The impact of storage temperature (4, 22 and 37 ◦C) and storage time (7, 14 and 21 days) on anaerobic digestion inocula was investigated through specific methanogenic activity assays. Experimental results showed that methanogenic activity decreased over time with storage, regardless of storage temperature. However, the rate at which the methanogenic activity decreased was two and five times slower at 4 ◦C than at 22 and 37 ◦C, respectively. The inoculum stored at 4 ◦C and room temperature (22 ◦C) maintained methanogenic activity close to that of fresh inoculum for 14 days (<10% difference). However, a storage temperature of 4 ◦C is preferred because of the slower decrease in activity with lengthier storage time. From this research, it was concluded that inoculum storage time should generally be kept to a minimum, but that storage at 4 ◦C could help maintain methanogenic activity for longe

Development and Validation of a Low-Cost Gas Density Method for Measuring Biochemical Methane Potential (BMP)

Accurate determination of biochemical methane potential (BMP) is important for both biogas research and practice. However, access to laboratory equipment limits the capacity of small laboratories or biogas plants to conduct reliable BMP assays, especially in low- and middle-income countries. This paper describes the development and validation of a new gas density-based method for measuring BMP (GD-BMP). In the GD-BMP method, biogas composition is determined from biogas density. Biogas density is based on bottle mass loss and biogas volume, and these can be accurately measured using only a standard laboratory scale, inexpensive syringes, and a simple manometer. Results from four experiments carried out in three different laboratories showed that the GD-BMP method is both accurate (no significant bias compared to gravimetric or volumetric methods with biogas analysis by gas chromatography) and precise (<3% relative standard deviation is possible). BMP values from the GD-BMP method were also comparable to those measured for the same substrates with an industry standard automated system (AMPTS II) in two independent laboratories (maximum difference 10%). Additionally, the GD-BMP method was shown to be accurate even in the presence of leakage by excluding leakage from mass loss measurements. The proposed GD-BMP method represents a significant breakthrough for both biogas research and the industry. With it, accurate BMP measurement is possible with only a minimal investment in supplies and equipment

DATAMAN: A global database of nitrous oxide and ammonia emission factors for excreta deposited by livestock and land-applied manure

Nitrous oxide (N2 O), ammonia (NH3 ), and methane (CH4 ) emissions from the manure management chain of livestock production systems are important contributors to greenhouse gases (GHGs) and NH3 emitted by human activities. Several studies have evaluated manure-related emissions and associated key variables at regional, national, or continental scales. However, there have been few studies focusing on the drivers of these emissions using a global dataset. An international project was created (DATAMAN) to develop a global database on GHG and NH3 emissions from the manure management chain (housing, storage, and field) to identify key variables influencing emissions and ultimately to refine emission factors (EFs) for future national GHG inventories and NH3 emission reporting. This paper describes the "field" database that focuses on N2 O and NH3 EFs from land-applied manure and excreta deposited by grazing livestock. We collated relevant information (EFs, manure characteristics, soil properties, and climatic conditions) from published peer-reviewed research, conference papers, and existing databases. The database, containing 5,632 observations compiled from 184 studies, was relatively evenly split between N2 O and NH3 (56 and 44% of the EF values, respectively). The N2 O data were derived from studies conducted in 21 countries on five continents, with New Zealand, the United Kingdom, Kenya, and Brazil representing 86% of the data. The NH3 data originated from studies conducted in 17 countries on four continents, with the United Kingdom, Denmark, Canada, and The Netherlands representing 79% of the data. Wet temperate climates represented 90% of the total database. The DATAMAN field database is available at http://www.dataman.co.nz

Rekentool voor het bepalen van de effecten van voer- en management-maatregelen op de ammoniakemissie bij varkens: ontwikkeling en validatie

A calculation model was developed to determine the effect of housing, dietary and management measures on the ammonia emission from houses for growing-finishing pigs, weaned piglets and pregnant sows. On Pig Innovation Centre Sterksel measurements were done for further development and validation of the model

The ALFAM2 database on ammonia emission from field-applied manure: Description and illustrative analysis

peer-reviewedAmmonia (NH3) emission from animal manure contributes to air pollution and ecosystem degradation, and the loss of reactive nitrogen (N) from agricultural systems. Estimates of NH3 emission are necessary for national inventories and nutrient management, and NH3 emission from field-applied manure has been measured in many studies over the past few decades. In this work, we facilitate the use of these data by collecting and organizing them in the ALFAM2 database. In this paper we describe the development of the database and summarise its contents, quantify effects of application methods and other variables on emission using a data subset, and discuss challenges for data analysis and model development. The database contains measurements of emission, manure and soil properties, weather, application technique, and other variables for 1895 plots from 22 research institutes in 12 countries. Data on five manure types (cattle, pig, mink, poultry, mixed, as well as sludge and “other”) applied to three types of crops (grass, small grains, maize, as well as stubble and bare soil) are included. Application methods represented in the database include broadcast, trailing hose, trailing shoe (narrow band application), and open slot injection. Cattle manure application to grassland was the most common combination, and analysis of this subset (with dry matter (DM) limited to <15%) was carried out using mixed- and fixed-effects models in order to quantify effects of management and environment on ammonia emission, and to highlight challenges for use of the database. Measured emission in this subset ranged from <1% to 130% of applied ammonia after 48 h. Results showed clear, albeit variable, reductions in NH3 emission due to trailing hose, trailing shoe, and open slot injection of slurry compared to broadcast application. There was evidence of positive effects of air temperature and wind speed on NH3 emission, and limited evidence of effects of slurry DM. However, random-effects coefficients for differences among research institutes were among the largest model coefficients, and showed a deviation from the mean response by more than 100% in some cases. The source of these institute differences could not be determined with certainty, but there is some evidence that they are related to differences in soils, or differences in application or measurement methods. The ALFAM2 database should be useful for development and evaluation of both emission factors and emission models, but users need to recognize the limitations caused by confounding variables, imbalance in the dataset, and dependence among observations from the same institute. Variation among measurements and in reported variables highlights the importance of international agreement on how NH3 emission should be measured, along with necessary types of supporting data and standard protocols for their measurement. Both are needed in order to produce more accurate and useful ammonia emission measurements. Expansion of the ALFAM2 database will continue, and readers are invited to contact the corresponding author for information on data submission. The latest version of the database is available at http://www.alfam.dk

DataMan: A global dataset of nitrous oxide and ammonia emission factors for excreta deposited by livestock and land-applied manure

Nitrous oxide (N2O), ammonia (NH3) and methane (CH4) emissions from the manure management chain of livestock production systems are important contributors to greenhouse gases (GHG) and NH3 emitted by human activities. Several studies have evaluated manure-related emissions and associated key variables at regional, national or continental scales. However, there have been few studies focusing on these emissions using a global dataset. An international project was created (DataMan) to develop a global database on GHG and NH3 emissions from the manure management chain (housing, storage and field), to identify key variables influencing emissions, and ultimately to refine EFs for future national GHG inventories and NH3 emission reporting. This paper describes the “field” database that focuses on N2O and NH3 EFs from land-applied manure and excreta deposited by grazing livestock. We collated relevant information (EFs, manure characteristics, soil properties and climatic conditions) from published peer-reviewed research, theses, conference papers and existing databases. The database, containing 5,632 observations compiled from 184 studies, was relatively evenly split between N2O and NH3 (56% and 44% of the EF values, respectively). The N2O data were derived from studies conducted in 21 countries on five continents, with New Zealand, the UK, Kenya and Brazil representing 86% of the data. The NH3 data originated from studies conducted in 17 countries on four continents, with the UK, Denmark, Canada and the Netherlands representing 79% of the data. Wet temperate climates represented 90% of the total database. The DataMan field database is available online at http:// dataman.azurewebsites.net

Requirements for measurement and validation of biochemical methane potential (BMP).

This document presents the minimal requirements for measurement and validation of biochemical methane potential (also called biomethane potential) (BMP) in batch tests. It represents the consensus of more than 50 biogas researchers. The list of requirements is the same as in the open-access commentary by Holliger et al. [2021]. For details on development of these requirements see the open-access papers Holliger et al. [2016] and Hafner et al. [2020c]

Systematic error in manometric measurement of biochemical methane potential: sources and solutions

This work focused on identification and quantification of systematic sources of error in manometric measurement of biochemical methane potential (BMP). Error was determined by comparison to gravimetric measurements and direct measurement of leakage. One out of three types of septa leaked above 1 bar (gauge)headspace pressure, losing 25 to 30% of biogas produced. But manometric BMP showed a negative bias even in the absence of leakage. Maximum error was 24% from 160 mL bottles with 40 mL of headspace (headspace fraction of 0.25). Error decreased with increasing headspace fraction, and was small (3%)for a headspace fraction of 0.75, showing that a high headspace volume is the best approach for minimizing error. Relative error in CH production measurement increased with headspace pressure as well, but controlling pressure alone is not sufficient for minimizing error. Calculations showed that observed error may be due to volatilization of CH during venting as well as inaccurate headspace volume determination, although these sources do not completely explain the magnitude of error observed. Measurement of biogas composition before and after venting showed that CO volatilization can occur, but is probably a minor source of error. Calculations showed that error in estimation of ambient pressure or headspace temperature had only minor effects