Abstract:Siltation of spawning gravels in upland rivers appears to be an increasing hindrance to salmonids' spawning success. River managers seek an eective and non-labour intensive means of loosening gravel and reducing ®ne material, so improving spawning success; this study compared three practical gravel cleaning techniques, applied at realistic (rather than intensive) levels, by assessing survival to hatching of buried brown trout, Salmo trutta L., ova at ®ve sites on four rivers with gravel substrate in southern England. Each site consisted of six reaches, of which three were cleaned by tractor rotovating, high pressure jet washing and pump washing; these were compared with adjacent, untreated reaches. Brown trout ova were buried in both ®ne mesh and coarse mesh boxes in each reach.Signi®cant improvements (at P 5 0Á05) in survival (number of live alevins) were found in three of the ®ve pump washed reaches, two of the ®ve tractor rotovated reaches and one pressure washed reach when the data were analysed by site. When data from all ®ve sites were analysed together, all treated reaches showed a signi®cant improvement (at P 5 0Á05) in egg survival to hatching compared with control reaches for ®ne mesh egg boxes; for coarse mesh boxes only pump washed reaches showed such an improvement.We feel that pump-washing provides the most eective, inexpensive and suitably non labour-intensive means of improving gravel, although ultimately it may be better to reduce the silt load of rivers. Freeze core bed samples taken before and immediately after cleaning were analysed for silt content; pump washing and high pressure washing may have reduced the amount of ®ne material.
Monitoring and regulating discharges of wastewater pollution in water bodies in England is the duty of the Environment Agency. Identification and reporting of pollution events from wastewater treatment plants is the duty of operators. Nevertheless, in 2018, over 400 sewage pollution incidents in England were reported by the public. We present novel pollution event reporting methodologies to identify likely untreated sewage spills from wastewater treatment plants. Daily effluent flow patterns at two wastewater treatment plants were supplemented by operator-reported incidents of untreated sewage discharges. Using machine learning, known spill events served as training data. The probability of correctly classifying a randomly selected pair of ‘spill’ and ‘no-spill’ effluent patterns was above 96%. Of 7160 days without operator-reported spills, 926 were classified as involving a ‘spill’. The analysis also suggests that both wastewater treatment plants made non-compliant discharges of untreated sewage between 2009 and 2020. This proof-of-principle use of machine learning to detect untreated wastewater discharges can help water companies identify malfunctioning treatment plants and inform agencies of unsatisfactory regulatory oversight. Real-time, open access flow and alarm data and analytical approaches will empower professional and citizen scientific scrutiny of the frequency and impact of untreated wastewater discharges, particularly those unreported by operators.
A Correction to this paper has been published: https://doi.org/10.1038/s41545-021-00116-3
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