The WIRC water colloquia series returns in 2021 with a webinar showcasing the research of FRESH doctoral student Franek Bydalek, and WISE CDT student Andy Barnes.
Due to the ongoing pandemic, this event will be held online via Microsoft Teams.
Make sure to register on EventBrite via the link below. A link to the event will be distributed to registered attendees via email.
The role of constructed wetlands in tackling water pollution problems and the spread of antimicrobial resistance
Constructed wetlands are man-made systems designed to enhance self-purification abilities of natural wetland ecosystems and improve water quality in both rural and urban areas. Constructed wetlands are a versatile technology that has been incorporated into water treatment schemes in both low- and high-income countries. Constructed wetlands were initially developed to meet basic treatment requirements focused on easily biodegradable organic pollution, suspended solids and nutrients. However, constructed wetland can be also used for the removal of other classes of pollutants, including those of emerging concern. Along with the increasing usage of antibiotics the spread of antimicrobial resistance (AMR) is a major global threat to public health, while wastewater is a main vector for AMR spread. Polishing constructed wetlands placed after conventional wastewater treatment facilities may serve as sink and buffer to AMR spread into the environment. However, currently little knowledge is available on the fate of AMR in those systems.
In collaboration with Wessex Water, we are investigating the operation of newly constructed 2-ha constructed wetland system receiving wastewater from a rural catchment of a population equivalent of 2000 people. The project aims to understand the fate of pathogens and microbial ecology in constructed wetlands with emphasis on their role as a barrier for AMR dissemination. We use high-throughput 16S rRNA amplicon sequencing to identify functional bacterial communities and assess their role in wastewater treatment process in the given operational conditions. To detect and quantify presence of antimicrobial resistance and related pathogen indicators we use real-time PCR (qPCR). Microbial source tracking methods are applied to indicate cross-contamination events attributed to avian activity. The early results show, promising performance in terms of reducing nutrient load and abundance of AMR genes. This might indicate that the system which has not be primarily designed to deal with pathogen and AMR removal, possess certain treatment capacity that could be further enhanced through target-specific design.
About Franek Bydalek
Franciszek (Franek) Bydalek is a 3rd year PhD student at the Department of Chemical Engineering at the University of Bath. He is member of NERC Center for Doctoral Training in Freshwater and Bioscience and Sustainability at Cardiff University. Trained as an environmental engineer (West Pomeranian University of Technology, Poland) Franek has been fascinated by constructed wetlands early on in his career. Franek conducts interdisciplinary research linking engineering knowledge with passion for environmental science. Franek research activities also include sustainable water management practices such as phosphorus recovery, catchment scale management and decentralized wastewater treatment.
Improving regional monthly rainfall forecasts using convolutional-neural networks
The ability to provide accurate monthly rainfall forecasts is key to operational planning in many sectors such as the agricultural industry who are reliant on sub-seasonal rainfall for crop yields and harvest periods. Traditional rainfall forecasting approaches utilize numerous numerical methods and empirical models to produce a gridded estimate of rainfall over a large area, the grid cells of which often span multiple regions and struggle to capture extreme events.
In recent years, advancements in new machine learning methods capable of mining information from images has opened a host of potential applications for rainfall forecasting. This talk presents a novel method of forecasting regional monthly rainfall by training a neural network capable of interpreting forecasted meteorological patterns. Forecasted mean sea-level pressure and 2m air temperature patterns are used to train a convolutional neural network against a benchmark rainfall dataset (CEH-GEAR). The network is then used to make predictions using an unseen set of patterns the results of which are evaluated against predictions made by the ECMWF SEAS5 service.
About Andy Barnes
Andy is currently a final year PhD student in the Department of Architecture and Civil Engineering at the University of Bath and is part of the Water, informatics, and science engineering centre for doctoral training (WISE CDT). Andy's research focus is on novel applications of deep machine learning methods for the analysis and prediction of extreme rainfall events in Great Britain. Prior to joining the WISE CDT Andrew held several software engineering positions after achieving a first-class honours degree in Computer Science from the University of Plymouth.
Since joining the University of Bath Andy has been involved with and has led several public engagement projects including the augmented reality sandbox as showcased at the Swindon Science Festival in 2020. Further to this, he has given a multitude of public facing talks on topics ranging from atmospheric rivers to the water cycle and engaged with members of the public of all ages regarding various aspects of his research.