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Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

Excessive nitrogen (N) discharge from agriculture causes widespread problems in aquatic ecosystems. Knowledge of spatiotemporal patterns and source attribution of N pollution is critical for nutrient management programs but is poorly studied in headwaters with various small water bodies and mini-point pollution sources. Taking a typical small watershed in the low mountains of Southeastern China as an example, N pollution and source attribution were studied for a multipond system around a village using the Hydrological Simulation Program-Fortran (HSPF) model. The results exhibited distinctive spatio-seasonal variations with an overall seriousness rank for the three indicators: total nitrogen (TN) > nitrate/nitrite nitrogen (NO x − -N) > ammonia nitrogen (NH 3 -N), according to the Chinese Surface Water Quality Standard. TN pollution was severe for the entire watershed, while NO x − -N pollution was significant for ponds and ditches far from the village, and the NH 3 -N concentrations were acceptable except for the ponds near the village in summer. Although food and cash crop production accounted for the largest source of N loads, we discovered that mini-point pollution sources, including animal feeding operations, rural residential sewage, and waste, together contributed as high as 47% of the TN and NH 3 -N loads in ponds and ditches. So, apart from eco-fertilizer programs and concentrated animal feeding operations, the importance of environmental awareness building for resource management is highlighted for small farmers in headwater agricultural watersheds. As a first attempt to incorporate multipond systems into the process-based modeling of nonpoint source (NPS) pollution, this work can inform other hydro-environmental studies on scattered and small water bodies. The results are also useful to water quality improvement for entire river basins.

A new MARINA-Nutrients model was developed to assess air and water pollution in Europe. Agriculture is responsible for 55% of N and sewage for 67% of P in rivers. Almost two-fifths of reactive N emissions to air are from animal housing and storage. Nearly a third of the basin area produces over half of N emissions to air and nutrients in rivers. Over 25% of river export of N ends up in the Atlantic Ocean and P in the Mediterranean Sea. Nutrient pollution of air and water is a persistent problem in Europe. However, the pollution sources are often analyzed separately, preventing the formulation of integrative solutions. This study aimed to quantify the contribution of agriculture to air, river and coastal water pollution by nutrients. A new MARINA-Nutrients model was developed for Europe to calculate inputs of nitrogen (N) and phosphorus (P) to land and rivers, N emissions to air, and nutrient export to seas by river basins. Under current practice, inputs of N and P to land were 34.4 and 1.8 Tg·yr–1, respectively. However, only 12% of N and 3% of P reached the rivers. Agriculture was responsible for 55% of N and sewage for 67% of P in rivers. Reactive N emissions to air from agriculture were calculated at 4.0 Tg·yr–1. Almost two-fifths of N emissions to air were from animal housing and storage. Nearly a third of the basin area was considered as pollution hotspots and generated over half of N emissions to air and nutrient pollution in rivers. Over 25% of river export of N ended up in the Atlantic Ocean and of P in the Mediterranean Sea. These results could support environmental policies to reduce both air and water pollution simultaneously, and avoid pollution swapping.

Salmonella enterica bacteria are a leading cause of foodborne illness in the United States; however, most Salmonella illnesses are not associated with known outbreaks, and predicting the source of sporadic illnesses remains a challenge. We used a supervised random forest model to determine the most likely sources responsible for human salmonellosis cases in the United States. We trained the model by using whole-genome multilocus sequence typing data from 18,661 Salmonella isolates from collected single food sources and used feature selection to determine the subset of loci most influential for prediction. The overall out-of-bag accuracy of the trained model was 91%; the highest prediction accuracy was for chicken (97%). We applied the trained model to 6,470 isolates from humans with unknown exposure to predict the source of infection. Our model predicted that >33% of the human-derived Salmonella isolates originated from chicken and 27% were from vegetables.

The provisional molecular approach, proposed by EFSA in 2013, for the pathogenicity assessment of Shiga toxin‐producing Escherichia coli (STEC) has been reviewed. Analysis of the confirmed reported human STEC infections in the EU/EEA (2012–2017) demonstrated that isolates positive for any of the reported Shiga toxin (Stx) subtypes (and encoding stx gene subtypes) may be associated with severe illness (defined as bloody diarrhoea (BD), haemolytic uraemic syndrome (HUS) and/or hospitalisation). Although strains positive for stx2a gene showed the highest rates, strains with all other stx subtypes, or combinations thereof, were also associated with at least one human case with a severe clinical outcome. Serogroup cannot be used as a predictor of clinical outcome and the presence of the intimin gene (eae) is not essential for severe illness. These findings are supported by the published literature, a review of which suggested there was no single or combination of virulence markers associated exclusively with severe illness. Based on available evidence, it was concluded that all STEC strains are pathogenic in humans, capable of causing at least diarrhoea and that all STEC subtypes may be associated with severe illness. Source attribution analysis, based on ‘strong evidence’ outbreak data in the EU/EEA (2012–2017), suggests that ‘bovine meat and products thereof’, ‘milk and dairy products’, ‘tap water including well water’ and ‘vegetables, fruit and products thereof’ are the main sources of STEC infections in the EU/EEA, but a ranking between these categories cannot be made as the data are insufficient. Other food commodities are also potentially associated with STEC infections but rank lower. Data gaps are identified, and are primarily caused by the lack of harmonisation in sampling strategies, sampling methods, detection and characterisation methods, data collation and reporting within the EU.

On the basis of the source attribution perspective of work–family conflict, this study aims to first test whether threat to the family role mediates the relationship between work-to-family conflict and job satisfaction. We then examine boundary conditions of the source attribution perspective by drawing on boundary management and gender role orientation theories to examine whether role segmentation enactment and gender role orientation moderate the relationship between work-to-family conflict and job satisfaction. Using a scenario-based experiment in Study 1, we find that threat to the family role mediates the relationship between work-to-family conflict and job satisfaction. This finding provides evidence supporting the appraisal process proposed by the perspective of source attribution. Using survey data collected from 216 Chinese managers and their spouses in Study 2, we find that work-to-family conflict has a negative relationship with job satisfaction only among people with high levels of role segmentation between work and home. In addition, for male managers, the negative moderating effect of role segmentation enactment on the relationship between work-to-family conflict and job satisfaction is stronger for those with a nontraditional gender role orientation, compared with those with a traditional gender role orientation. Theoretical and managerial implications are discussed.

Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution. Plain Language Summary Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification. Key Points A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring data The proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary location The deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model

The Foodborne Disease Burden Epidemiology Reference Group (FERG) was established in 2007 by the World Health Organization (WHO) to estimate the global burden of foodborne diseases (FBDs). This estimation is complicated because most of the hazards causing FBD are not transmitted solely by food; most have several potential exposure routes consisting of transmission from animals, by humans, and via environmental routes including water. This paper describes an expert elicitation study conducted by the FERG Source Attribution Task Force to estimate the relative contribution of food to the global burden of diseases commonly transmitted through the consumption of food. We applied structured expert judgment using Cooke's Classical Model to obtain estimates for 14 subregions for the relative contributions of different transmission pathways for eleven diarrheal diseases, seven other infectious diseases and one chemical (lead). Experts were identified through international networks followed by social network sampling. Final selection of experts was based on their experience including international working experience. Enrolled experts were scored on their ability to judge uncertainty accurately and informatively using a series of subject-matter specific 'seed' questions whose answers are unknown to the experts at the time they are interviewed. Trained facilitators elicited the 5th, and 50th and 95th percentile responses to seed questions through telephone interviews. Cooke's Classical Model uses responses to the seed questions to weigh and aggregate expert responses. After this interview, the experts were asked to provide 5th, 50th, and 95th percentile estimates for the 'target' questions regarding disease transmission routes. A total of 72 experts were enrolled in the study. Ten panels were global, meaning that the experts should provide estimates for all 14 subregions, whereas the nine panels were subregional, with experts providing estimates for one or more subregions, depending on their experience in the region. The size of the 19 hazard-specific panels ranged from 6 to 15 persons with several experts serving on more than one panel. Pathogens with animal reservoirs (e.g. non-typhoidal Salmonella spp. and Toxoplasma gondii) were in general assessed by the experts to have a higher proportion of illnesses attributable to food than pathogens with mainly a human reservoir, where human-to-human transmission (e.g. Shigella spp. and Norovirus) or waterborne transmission (e.g. Salmonella Typhi and Vibrio cholerae) were judged to dominate. For many pathogens, the foodborne route was assessed relatively more important in developed subregions than in developing subregions. The main exposure routes for lead varied across subregions, with the foodborne route being assessed most important only in two subregions of the European region. For the first time, we present worldwide estimates of the proportion of specific diseases attributable to food and other major transmission routes. These findings are essential for global burden of FBD estimates. While gaps exist, we believe the estimates presented here are the best current source of guidance to support decision makers when allocating resources for control and intervention, and for future research initiatives.

Aquatic ecosystems are being degraded by anthropogenic pollution on a global scale. Septic tank systems (STS), which are widely distributed in rural and peri-urban areas, are one potential source of water pollution. Although generally regarded as the most efficient method for onsite treatment of domestic wastewater, we question whether current regulation and management of these systems is sufficient to guarantee that they function effectively. Here, we present watershed-specific examples that illustrate some of the problems that arise when many years of inadequate regulation and management result in a legacy of failing STS that can become long-term, chronic sources of nutrient pollution. Our data suggest that more accurate accounting of the location, performance, and degree of failure of STS, and more research into their impacts on water quality, would improve source attribution of pollutants within rural watersheds. This would ensure that education of homeowners, mitigation, interdisciplinary research, and technological innovation could be targeted in a cost-effective way.

We quantify the source contributions to surface PM2.5 (fine particulate matter) pollution over North China from January 2013 to 2015 using the GEOS-Chem chemical transport model and its adjoint with improved model horizontal resolution (1 4° × 5 16°) and aqueous-phase chemistry for sulfate production. The adjoint method attributes the PM2.5 pollution to emissions from different source sectors and chemical species at the model resolution. Wintertime surface PM2.5 over Beijing is contributed by emissions of organic carbon (27% of the total source contribution), anthropogenic fine dust (27%), and SO2 (14%), which are mainly from residential and industrial sources, followed by NH3 (13%) primarily from agricultural activities. About half of the Beijing pollution originates from sources outside of the city municipality. Adjoint analyses for other cities in North China all show significant regional pollution transport, supporting a joint regional control policy for effectively mitigating the PM2.5 air pollution.