Several microbes and chemicals have been considered as potential tracers to identify fecal sources in the environment. However, to date, no one approach has been shown to accurately identify the origins of fecal pollution in aquatic environments. In this multilaboratory study, different microbial and chemical indicators were analyzed in order to distinguish human fecal sources from nonhuman fecal sources using wastewaters and slurries from diverse geographical areas within Europe. Twenty-six parameters, which were later combined to form derived variables for statistical analyses, were obtained by performing methods that were achievable in all the participant laboratories: enumeration of fecal coliform bacteria, enterococci, clostridia, somatic coliphages, F-specific RNA phages, bacteriophages infecting Bacteroides fragilis RYC2056 and Bacteroides thetaiotaomicron GA17, and total and sorbitol-fermenting bifidobacteria; genotyping of F-specific RNA phages; biochemical phenotyping of fecal coliform bacteria and enterococci using miniaturized tests; specific detection of Bifidobacterium adolescentis and Bifidobacterium dentium ; and measurement of four fecal sterols. A number of potentially useful source indicators were detected (bacteriophages infecting B. thetaiotaomicron , certain genotypes of F-specific bacteriophages, sorbitol-fermenting bifidobacteria, 24-ethylcoprostanol, and epycoprostanol), although no one source identifier alone provided 100% correct classification of the fecal source. Subsequently, 38 variables (both single and derived) were defined from the measured microbial and chemical parameters in order to find the best subset of variables to develop predictive models using the lowest possible number of measured parameters. To this end, several statistical or machine learning methods were evaluated and provided two successful predictive models based on just two variables, giving 100% correct classification: the ratio of the densities of somatic coliphages and phages infecting Bacteroides thetaiotaomicron to the density of somatic coliphages and the ratio of the densities of fecal coliform bacteria and phages infecting Bacteroides thetaiotaomicron to the density of fecal coliform bacteria. Other models with high rates of correct classification were developed, but in these cases, higher numbers of variables were required.

Up to 80% of the plastics in the oceans are believed to have been transferred from river networks. Microplastic contamination of river sediments has been found to be pervasive at the global scale and responsive to periods of flooding. However, the physical controls governing the storage, remobilization and pathways of transfer in fluvial sediments are unknown. This means it is not currently possible to determine the risks posed by microplastics retained within the world’s river systems. This problem will be further exacerbated in the future given projected changes to global flood risk and an increased likelihood of fluvial flooding. Using controlled flume experiments we show that the evolution of the sediment bed surface and the flood wave characteristics controls the transition from rivers being ‘sinks’ to ‘sources’ of microplastics under flood conditions. By linking bed surface evolution with microplastic transport characteristics we show that similarities exist between granular transport phenomena and the behavior, and hence predictability, of microplastic entrainment during floods. Our findings are significant as they suggest that microplastic release from sediment beds can be managed by altering the timing and magnitude of releases in flow managed systems. As such it may be possible to remediate or remove legacy microplastics in future.

Numerous bacterial genetic markers are available for the molecular detection of human sources of fecal pollution in environmental waters. However, widespread application is hindered by a lack of knowledge regarding geographical stability, limiting implementation to a small number of well-characterized regions. This study investigates the geographic distribution of five human-associated genetic markers (HF183/BFDrev, HF183/BacR287, BacHum-UCD, BacH, and Lachno2) in municipal wastewaters (raw and treated) from 29 urban and rural wastewater treatment plants (750 – 4,400,000 population equivalents) from 13 countries spanning six continents. In addition, genetic markers were tested against 280 human and non-human fecal samples from domesticated, agricultural and wild animal sources. Findings revealed that all genetic markers are present in consistently high concentrations in raw (median log10 7.2 - 8.0 marker equivalents (ME) 100 ml-1) and biologically treated wastewater samples (median log10 4.6 - 6.0 ME 100 ml-1) regardless of location and population. The false positive rates of the various markers in non-human fecal samples ranged from 5% to 47%. Results suggest that several genetic markers have considerable potential for measuring human-associated contamination in polluted environmental waters. This will be helpful in water quality management and pollution modeling and health risk assessment across the globe (as demonstrated by QMRAcatch).