![]() ![]() Coverage extends to the use of monitoring in pollution assessment, and particular emphasis is given to the synthesis of monitoring data with toxicological, epidemiological and health data, as well as with pre-market screening results. The journal examines monitoring systems designed to estimate exposure both at the individual and population levels, and also focuses on the development of monitoring systems related to the management of various renewable natural resources in, for instance, agriculture, fisheries and forests. Bruce Wiersma, College of Natural Resources, Forestry, and Agriculture, University of Maine, USAĮnvironmental Monitoring and Assessment discusses technical developments and data arising from environmental monitoring and assessment, principles in the design of monitoring systems, and the use of monitoring data in assessing the consequences of natural resource management and pollution risks. Examines the synthesis of monitoring data with toxicological, epidemiological and health data.Describes methods and procedures for pollution risk assessment.Covers design and development of monitoring systems.If you are interested in this project and would like to learn more about the research you will be undertaking, please use the contact details on this page. How to applyĪll students can apply using the button below, following the Admissions Statement (PDF, 188kB).īefore applying, we recommend getting in touch with the project's supervisors. Microbiology experience is beneficial though not critical as training will be provided where necessary. You will have a broad interest in data science and artificial intelligence, and the application of these techniques to real-world problems. You will be based in the research group within the School of Geographical Sciences and be integrated into the Quantitative Spatial Science Lab within the same school. You will receive training in microbiology, state-of-the-art imaging techniques, convolutional neural network design, implementation and optimization, as well as several transferable skills in data handling and analysis, academic writing, and presentation. Whilst the overarching aims of the project are established, there is significant flexibility to allow the successful candidate to drive the project more toward the ecological or artificial intelligence disciplines depending on their preference. The successful candidate will work under the supervision of microbiologist Dr Chris Williamson, data scientist Dr Levi Wolf and in collaboration with a leading UK water company to produce robust CNNs capable of detecting, identifying and enumerating harmful microbial communities that dominate UK freshwater systems. Building on the recent success of the in the application of CNNs for the detection and identification of microbial communities from imaged lab and field samples, this project will expand and explore the capacity for CNNs to be used in real-time environmental monitoring of UK freshwater systems. Over the recent past, Artificial Intelligence has significantly reduced the gap between the capabilities of humans and machines, with deep learning convolutional neural networks (CNNs) allowing for rapid object detection and identification from image datasets. Traditional techniques for their identification and enumeration involves time-consuming microscopic analysis of water samples undertaken by highly trained individuals, and represents a significant bottleneck in current monitoring capabilities. Enteroccocus bacteria, harmful cyanobacterial blooms or waterborne protozoan parasites. The requirement for environmental monitoring through observation of key physical, chemical and biological properties is thus greater than ever, particularly for freshwater sources utilised for human consumption.īiological monitoring of freshwater resources involves regular characterization of microbial assemblages that pose potential human health risks, e.g. As the world’s population continues to increase, industrial development and agricultural practices continue to expand, as does their associated pollution. ![]() Environmental monitoring is critical to the protection of human health and the environment. ![]()
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