Ranking hazards pertaining to human health concerns from land application of anaerobic digestate

peer-reviewedAnaerobic digestion (AD) has been identified as one of the cleanest producers of green energy. AD typically uses organic materials as feedstock and, through a series of biological processes, produces methane. Farmyard manure and slurry (FYM&S) are important AD feedstock and are typically mixed with agricultural waste, grass and/or food wastes. The feedstock may contain many different pathogens which can survive the AD process and hence also possibly be present in the final digestate. In this study, a semi-quantitative screening tool was developed to rank pathogens of potential health concern emerging from AD digestate. A scoring system was used to categorise likely inactivation during AD, hazard pathways and finally, severity as determined from reported human mortality rates, number of global human-deaths and infections per 100,000 populations. Five different conditions including mesophilic and thermophilic AD and three different pasteurisation conditions were assessed in terms of specific pathogen inactivation. In addition, a number of scenarios were assessed to consider foodborne incidence data from Ireland and Europe and to investigate the impact of raw FYM&S application (without AD and pasteurisation). A sensitivity analysis revealed that the score for the mortality rate (S3) was the most sensitive parameter (rank coefficient 0.49) to influence the final score S; followed by thermal inactivation score (S1, 0.25) and potential contamination pathways (S2, 0.16). Across all the scenarios considered, the screening tool prioritised Cryptosporidium parvum, Salmonella spp., norovirus, Streptococcus pyogenes, enteropathogenic E. coli (EPEC), Mycobacterium spp., Salmonella typhi (followed by S. paratyphi), Clostridium spp., Listeria monocytogenes and Campylobacter coli as the highest-ranking pathogens of human health concern resulting from AD digestate in Ireland. This tool prioritises potentially harmful pathogens which can emerge from AD digestate and highlights where regulation and intervention may be required

A Bayesian inference approach to quantify average pathogen loads in farmyard manure and slurry using open-source Irish datasets

peer-reviewedFarm-to-fork quantitative microbial risk assessments (QMRA) typically start with a preliminary estimate of initial concentration (Cinitial) of microorganism loading at farm level, consisting of an initial estimate of prevalence (P) and the resulting pathogen levels in animal faeces. An average estimation of the initial concentration of pathogens can be achieved by combining P estimates in animal populations and the levels of pathogens in colonised animals' faeces and resulting cumulative levels in herd farmyard manure and slurry (FYM&S). In the present study, 14 years of data were collated and assessed using a Bayesian inference loop to assess the likely P of pathogens. In this regard, historical and current survey data exists on P estimates for a number of pathogens, including Cryptosporidium parvum, Mycobacterium avium subspecies paratuberculosis (MAP), Salmonella spp., Clostridium spp., Campylobacter spp., pathogenic E. coli, and Listeria monocytogenes in several species (cattle, pigs, and sheep) in Ireland. The results revealed that Cryptosporidium spp. has potentially the highest mean P (Pmean) (25.93%), followed by MAP (15.68%) and Campylobacter spp. (8.80%) for cattle. The Pmean of E. coli is highest (7.42%) in pigs, while the Pmean of Clostridium spp. in sheep was estimated to be 7.94%. Cinitial for Cryptosporidium spp., MAP., Salmonella spp., Clostridium spp., and Campylobacter spp. in cattle faeces were derived with an average of 2.69, 4.38, 4.24, 3.46, and 3.84 log10 MPN g −1, respectively. Average Cinitial of Cryptosporidium spp., Salmonella spp., Clostridium spp., and E. coli in pig slurry was estimated as 1.27, 3.12, 3.02, and 4.48 log10 MPN g −1, respectively. It was only possible to calculate the average Cinitial of Listeria monocytogenes in sheep manure as 1.86 log10 MPN g −1. This study creates a basis for future farm-to-fork risk assessment models to base initial pathogen loading values for animal faeces and enhance risk assessment efforts

Anaerobic digestion of agricultural manure and biomass – Critical indicators of risk and knowledge gaps

peer-reviewedAnaerobic digestion (AD) has been identified as a potential green technology to treat food and municipal waste, agricultural residues, including farmyard manure and slurry (FYM&S), to produce biogas. FYM&S and digestate can act as soil conditioners and provide valuable nutrients to plants; however, it may also contain harmful pathogens. This study looks at the critical indicators in determining the microbial inactivation potential of AD and the possible implications for human and environmental health of spreading the resulting digestate on agricultural land. In addition, available strategies for risk assessment in the context of EU and Irish legislation are assessed. Storage time and process parameters (including temperature, pH, organic loading rate, hydraulic retention time), feedstock recipe (carbon-nitrogen ratio) to the AD plant (both mesophilic and thermophilic) were all assessed to significantly influence pathogen inactivation. However, complete inactivation of all pathogens is unlikely. There are limited studies evaluating risks from FYM&S as a feedstock in AD and the spreading of resulting digestate. The lack of process standardisation and varying feedstocks between AD farms means risk must be evaluated on a case by case basis and calls for a more unified risk assessment methodology. In addition, there is a need for the enhancement of AD farm-based modelling techniques and datasets to help in advancing knowledge in this area.Department of Agriculture, Food and the Marin

Quantitative microbial risk assessment associated with ready-to-eat salads following the application of farmyard manure and slurry or anaerobic digestate to arable lands

peer-reviewedFarmyard manure and slurry (FYM&S) and anaerobic digestate are potentially valuable soil conditioners providing important nutrients for plant development and growth. However, these organic fertilisers may pose a microbial health risk to humans. A quantitative microbial risk assessment (QMRA) model was developed to investigate the potential human exposure to pathogens following the application of FYM&S and digestate to agricultural land. The farm-to-fork probabilistic model investigated the fate of microbial indicators (total coliforms and enterococci) and foodborne pathogens in the soil with potential contamination of ready-to-eat salads (RTEs) at the point of human consumption. The processes examined included pathogen inactivation during mesophilic anaerobic digestion (M-AD), post-AD pasteurisation, storage, dilution while spreading, decay in soil, post-harvest washing processes, and finally, the potential growth of the pathogen during refrigeration/storage at the retail level in the Irish context. The QMRA highlighted a very low annual probability of risk (Pannual) due to Clostridium perfringens, norovirus, and Salmonella Newport across all scenarios. Mycobacterium avium may result in a very high mean Pannual for the application of raw FYM&S, while Cryptosporidium parvum and pathogenic E. coli showed high Pannual, and Listeria monocytogenes displayed moderate Pannual for raw FYM&S application. The use of AD reduces this risk; however, pasteurisation reduces the Pannual to an even greater extent posing a very low risk. An overall sensitivity analysis revealed that mesophilic-AD's inactivation effect is the most sensitive parameter of the QMRA, followed by storage and the decay on the field (all negatively correlated to risk estimate). The information generated from this model can help to inform guidelines for policymakers on the maximum permissible indicator or pathogen contamination levels in the digestate. The QMRA can also provide the AD industry with a safety assessment of pathogenic organisms resulting from the digestion of FYM&S

Comparing the immune response to a novel intranasal nanoparticle PLGA vaccine and a commercial BPI3V vaccine in dairy calves

peer-reviewedBackground There is a need to improve vaccination against respiratory pathogens in calves by stimulation of local immunity at the site of pathogen entry at an early stage in life. Ideally such a vaccine preparation would not be inhibited by the maternally derived antibodies. Additionally, localized immune response at the site of infection is also crucial to control infection at the site of entry of virus. The present study investigated the response to an intranasal bovine parainfluenza 3 virus (BPI3V) antigen preparation encapsulated in PLGA (poly dl-lactic-co-glycolide) nanoparticles in the presence of pre-existing anti-BPI3V antibodies in young calves and comparing it to a commercially available BPI3V respiratory vaccine. Results There was a significant (P < 0.05) increase in BPI3V-specific IgA in the nasal mucus of the BPI3V nanoparticle vaccine group alone. Following administration of the nanoparticle vaccine an early immune response was induced that continued to grow until the end of study and was not observed in the other treatment groups. Virus specific serum IgG response to both the nanoparticle vaccine and commercial live attenuated vaccine showed a significant (P < 0.05) rise over the period of study. However, the cell mediated immune response observed didn’t show any significant rise in any of the treatment groups. Conclusion Calves administered the intranasal nanoparticle vaccine induced significantly greater mucosal IgA responses, compared to the other treatment groups. This suggests an enhanced, sustained mucosal-based immunological response to the BPI3V nanoparticle vaccine in the face of pre-existing antibodies to BPI3V, which are encouraging and potentially useful characteristics of a candidate vaccine. However, ability of nanoparticle vaccine in eliciting cell mediated immune response needs further investigation. More sustained local mucosal immunity induced by nanoparticle vaccine has obvious potential if it translates into enhanced protective immunity in the face of virus outbreak

Quantitative microbial human exposure model for faecal indicator bacteria and risk assessment of pathogenic Escherichia coli in surface runoff following application of dairy cattle slurry and co-digestate to grassland

peer-reviewedAnimal waste contains high numbers of microorganisms and therefore can present a potential biological threat to human health. During episodic rainfall events resulting in runoff, microorganisms in the waste and soil may migrate into surface runoff, contaminating surface water resources. A probabilistic human exposure (HE) model was created to determine exposure to faecal indicator bacteria (FIB): total coliforms (TC), E. coli and enterococci following application of bio-based fertiliser (dairy cattle slurry, digestate) to grassland; using a combination of experimental field results and literature-based data. This step was followed by a quantitative microbial risk assessment (QMRA) model for pathogenic E. coli based on a literature-based dose-response model. The results showed that the maximum daily HE (HEdaily) is associated with E. coli for unprocessed slurry (treatment T1) on day 1, the worst-case scenario where the simulated mean HEdaily was calculated as 2.84 CFU day −1. The results indicate that the overall annual probability of risk (Pannual) of illness from E. coli is very low or low based on the WHO safe-limit of Pannual as 10 −6. In the worst-case scenario, a moderate risk was estimated with simulated mean Pannual as 1.0 × 10 −5. Unpasteurised digestate application showed low risk on day 1 and 2 (1.651 × 10 −6, 1.167 × 10 −6, respectively). Pasteurised digestate showed very low risk in all scenarios. These results support the restriction imposed on applying bio-based fertiliser if there is any rain forecast within 48 h from the application time. This study proposes a future extension of the probabilistic model to include time, intensity, discharge, and distance-dependant dilution factor. The information generated from this model can help policymakers ensure the safety of surface water sources through the quality monitoring of FIB levels in bio-based fertiliser