Prevalence of sulfonamide resistance genes in bacterial isolates from manured agricultural soils and pig slurry in the United Kingdom

Prevalence of three sulfonamide resistance genes, sul1, sul2 and sul3 and sulfachloropyridazine (SCP) resistance was determined in bacteria isolated from UK manured agricultural clay soils and slurry samples, over a two year period. Slurry from tylosin-fed pigs amended with SCP and oxytetracycline (OTC) was used for manuring. Sul gene positive isolates were further screened for the presence of class 1 and 2 integrons. Phenotypic resistance to SCP was significantly higher in pig slurry and post application soil than in pre-application soil. Of 5isolates, 23 % carried sul1, 18 % sul2 and 9 % sul3 only. Two percent of isolates contained all three sul genes. Class 1 and class 2 integrons were identified in 5 % and 11.7 % of sul positive isolates. In previous reports, sul1 was linked to class 1 integrons, but in this study only 8 % of sul1 positive isolates carried the intI1 gene. Sulfonamide resistant pathogens were identified in slurry amended soil and soil leachate, including Shigella flexneri, Aerococcus spp. and Acinetobacter baumanni, suggesting a potential environmental reservoir. Sulfonamide resistance in Psychrobacter, Enterococcus and Bacillus spp. is reported for the first time, and this study also provides the first description of the genotype sul1, sul2 and sul3 outside the Enterobacteriacae, and in the soil environment

Integron prevalence and diversity in manured soil

Integron abundance and diversity were studied in soil amended with pig slurry. Real-time PCR illustrated a significant increase in class 1 integron prevalence post slurry-application with increased prevalence still evident at 10 months post-application. Culture dependent data revealed 10 genera, including putative human pathogens, carrying class 1 and 2 integrons

Decision-making tools to manage the microbiology of drinking water distribution systems

This paper uses a two-fold multi-criteria decision-making (MCDM) approach applied for the first time to the field of microbial management of drinking water distribution systems (DWDS). Specifically, the decision-making trial and evaluation laboratory (DEMATEL) was applied removing the need for reliance on expert judgement, and analysed interdependencies among water quality parameters and microbiological characteristics of DWDS composed of different pipe materials. In addition, the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) ranked the most common bacteria identified during trials in a DWDS according to their relative abundance while managing vagueness affecting the measurements. The novel integrated approach presented and proven here for an initial real world data set provides new insights in the interdependence of environmental conditions and microbial populations. Specifically, the application shows as the bacteria having associated the most significant microbial impact may not be the most abundant. This offers the potential for integrated management strategies to promote favourable microbial conditions to help safeguard drinking water quality

An in vitro method for determining the bioaccessibility of pharmaceuticals in wildlife

Wildlife can be exposed to human pharmaceuticals via prey that have accumulated the compounds from wastewater, surface water, sediment and soil. One factor affecting internal absorption of pharmaceuticals is bioaccessibility, the proportion of the compound that enters solution in the gastrointestinal tract. Currently, the bioaccessibility of most pharmaceuticals in prey remains unknown for most wildlife species. Here, we evaluate the potential of a two-compartment in vitro gastrointestinal tract model to compare the bioaccessibility of the antidepressant fluoxetine from invertebrate prey for birds and mammals. Samples of gizzard (or stomach) and intestinal phase digestive juices were obtained from the in vitro models along with the residual solid material. HPLC analysis revealed that the bioaccessibility of fluoxetine in the avian in vitro models (75.9% and 78.6%) was statistically significantly lower than in the mammalian models (88.2-89.6%) as a percentage of what was recovered; however there were no statistically or biologically significant inter-species difference in terms of the amount recovered per gram of 'food' inserted at the start of the simulation. Nevertheless, this in vitro model provides a useful method of comparing the bioaccessibility of pharmaceuticals in different prey for species with different gastrointestinal conditions. There may be merit for ecological risk assessments in further developing this in vitro approach to improve estimates of internal exposure for organics. This article is protected by copyright. All rights reserved

Recovery of metals from waste lithium ion battery leachates using biogenic hydrogen sulfide

Lithium ion battery (LIB) waste is increasing globally and contains an abundance of valuable metals that can be recovered for re-use. This study aimed to evaluate the recovery of metals from LIB waste leachate using hydrogen sulfide generated by a consortium of sulfate-reducing bacteria (SRB) in a lactate-fed fluidised bed reactor (FBR). The microbial community analysis showed Desulfovibrio as the most abundant genus in a dynamic and diverse bioreactor consortium. During periods of biogenic hydrogen sulfide production, the average dissolved sulfide concentration was 507 mg L−1 and the average volumetric sulfate reduction rate was 278 mg L−1 d−1. Over 99% precipitation efficiency was achieved for Al, Ni, Co, and Cu using biogenic sulfide and NaOH, accounting for 96% of the metal value contained in the LIB waste leachate. The purity indices of the precipitates were highest for Co, being above 0.7 for the precipitate at pH 10. However, the process was not selective for individual metals due to simultaneous precipitation and the complexity of the metal content of the LIB waste. Overall, the process facilitated the production of high value mixed metal precipitates, which could be purified further or used as feedstock for other processes, such as the production of steel

Forecasting bacteriological presence in treated drinking water using machine learning

A novel data-driven model for the prediction of bacteriological presence, in the form of total cell counts, in treated water exiting drinking water treatment plants is presented. The model was developed and validated using a year of hourly online flow cytometer data from an operational drinking water treatment plant. Various machine learning methods are compared (random forest, support vector machines, k-Nearest Neighbors, Feed-forward Artificial Neural Network, Long Short Term Memory and RusBoost) and different variables selection approaches are used to improve the model's accuracy. Results indicate that the model could accurately predict total cell counts 12 h ahead for both regression and classification-based forecasts—NSE = 0.96 for the best regression model, using the K-Nearest Neighbors algorithm, and Accuracy = 89.33% for the best classification model, using the combined random forest, K-neighbors and RusBoost algorithms. This forecasting horizon is sufficient to enable proactive operational interventions to improve the treatment processes, thereby helping to ensure safe drinking water

Pandora's email box? An exploratory study of Web-based email forgery detection and validation

Web based email systems may be a source of pristine digital evidence because of the perceived difficulty of client tampering with messages stored inside the email account. We demonstrate that such assumption is wrong in the case of Windows Live Hotmail®1. Windows Live Mail®1 synchronises message on client-side computers with the Hotmail® server, benefiting users wishing to synchronise their email accounts and personal devices. However, this synchronisation opens an exploit for wrongdoers to tamper with existing email messages and attachments as well as facilitating the insertion of fabricated messages. The exploit process enables persistent storage of tampered and fabricated messages on the Hotmail®1 server. The exploitation favours both account owners and wrongdoers who gain unauthorised access of others’ accounts. Even if tampering were suspected, we anticipate some difficulties in validating messages to determine their reliability and relevance. We predict, with trepidation, that the exploit process will become commonplace and pose greater challenges to the cyber forensics examiner and legal practitioner during investigations and legal proceedings. Regrettably, the exploit complements the existing arsenal of tools for email forgery. More ominously, it provides opportunity for traceless injection of illicit material/malware onto any machine synchronised with the Hotmail® account

A data-driven model for the prediction of chlorine losses in water distribution trunk mains

A data-driven model that uses 4 different machine learning (ML) algorithms (Feed forward artificial neural networks (ANN), Nonlinear autoregressive exogeneous (NARX) ANN, support vector machine and Random Forest) was designed for the prediction of chlorine loss events in water distribution trunk mains. The model, firstly, identifies past chlorine loss events and their associate flow or temperature events. Then, the detected past flow events and their associate past chlorine loss events are used to train the ML algorithms. The model was tested in 3 trunk mains of the same drinking water distribution system with similar diameter but with different characteristics, using each time a different combination of parameters (flow (input) - past chlorine losses (output) or flow, temperature, and chlorine (input) - past chlorine losses (output)) and machine learning algorithms. Results indicate that the model could predict a future chlorine loss event with a period between 2 to 10 hours depending on the parameter and ML algorithm used and the trunk mains’ hydraulic characteristics

Evaluation of a Novel Approach for Reducing Emissions of Pharmaceuticals to the Environment

Increased interest over the levels of pharmaceuticals detected in the environment has led to the need for new approaches to manage their emissions. Inappropriate disposal of unused and waste medicines and release from manufacturing plants are believed to be important pathways for pharmaceuticals entering the environment. In situ treatment technologies, which can be used on-site in pharmacies, hospitals, clinics, and at manufacturing plants, might provide a solution. In this study we explored the use of Pyropure, a microscale combined pyrolysis and gasification in situ treatment system for destroying pharmaceutical wastes. This involved selecting 17 pharmaceuticals, including 14 of the most thermally stable compounds currently in use and three of high environmental concern to determine the technology’s success in waste destruction. Treatment simulation studies were done on three different waste types and liquid, solid, and gaseous emissions from the process were analyzed for parent pharmaceutical and known active transformation products. Gaseous emissions were also analyzed for NOx, particulates, dioxins, furans, and metals. Results suggest that Pyropure is an effective treatment process for pharmaceutical wastes: over 99 % of each study pharmaceutical was destroyed by the system without known active transformation products being formed during the treatment process. Emissions of the other gaseous air pollutants were within acceptable levels. Future uptake of the system, or similar in situ treatment approaches, by clinics, pharmacists, and manufacturers could help to reduce the levels of pharmaceuticals in the environment and reduce the economic and environmental costs of current waste management practices

Climate change and food security: health impacts in developed countries.

BACKGROUND: Anthropogenic climate change will affect global food production, with uncertain consequences for human health in developed countries. OBJECTIVES: We investigated the potential impact of climate change on food security (nutrition and food safety) and the implications for human health in developed countries. METHODS: Expert input and structured literature searches were conducted and synthesized to produce overall assessments of the likely impacts of climate change on global food production and recommendations for future research and policy changes. RESULTS: Increasing food prices may lower the nutritional quality of dietary intakes, exacerbate obesity, and amplify health inequalities. Altered conditions for food production may result in emerging pathogens, new crop and livestock species, and altered use of pesticides and veterinary medicines, and affect the main transfer mechanisms through which contaminants move from the environment into food. All these have implications for food safety and the nutritional content of food. Climate change mitigation may increase consumption of foods whose production reduces greenhouse gas emissions. Impacts may include reduced red meat consumption (with positive effects on saturated fat, but negative impacts on zinc and iron intake) and reduced winter fruit and vegetable consumption. Developed countries have complex structures in place that may be used to adapt to the food safety consequences of climate change, although their effectiveness will vary between countries, and the ability to respond to nutritional challenges is less certain. CONCLUSIONS: Climate change will have notable impacts upon nutrition and food safety in developed countries, but further research is necessary to accurately quantify these impacts. Uncertainty about future impacts, coupled with evidence that climate change may lead to more variable food quality, emphasizes the need to maintain and strengthen existing structures and policies to regulate food production, monitor food quality and safety, and respond to nutritional and safety issues that arise