Follow us on LinkedIn for more updates here.
Tuesday 15th July, UK Exposure Science 2025 UKHSA Training Centre, Didcot, OX11 0RQ, To register your place click here.
You can submit an abstract for an oral or poster presentation up to 16th May 2025, please follow the instructions here to do that.
2025 Meeting
Dr Nicola Carslaw, University of York.
‘The INGENIOUS Project: Understanding air pollution in homes‘.
Abstract: This presentation will provide an overview of the INGENIOUS (UnderstandING the sourcEs, traNsformations and fates of IndOor air pollUtantS) project, aiming to better understand air pollution in homes. Our homes are the microenvironment in which we spend most of our time, but we still know relatively little about the sources, transformation processes and fates of indoor air pollutants within them. INGENIOUS aims to address this knowledge gap by delivering: an indoor emissions inventory for UK homes; comprehensive air pollutant measurements in 310 homes in Bradford using a combination of low cost-sensors and more advanced air quality instrumentation; an analysis of the impact of indoor air pollution on outdoor air quality and vice versa using mobile measurements; insight into future indoor air quality using detailed air pollution models; identification of indoor air pollutants that warrant further toxicological study; and better understanding of the barriers and facilitators for behaviour that drives improved indoor air quality. The presentation will cover some preliminary findings from the study and their implications.
Dr David Topping, University of Manchester.
‘The growing use of AI in enabling search and discovery.’
Abstract: Artificial Intelligence (AI), particularly Large Language Models (LLMs), is driving a step change in data discovery by enabling more intuitive, context-aware search capabilities. Beyond simple keyword matching, LLMs leverage their semantic understanding to uncover hidden relationships between disparate datasets, enhancing data lineage and integration. A key example of this is Retrieval-Augmented Generation (RAG), which combines LLMs with external knowledge retrieval, ensuring that search and discovery are both contextually rich and constrained by expert derived literature. RAG has seen widespread adoption in scientific research and forms the basis of many apps now on the market, adding an increasing level of explainable workflows in data linkages. This potential is also being used in air pollution exposure research, where linking data assets including environmental monitoring, health records, and mobility data, could yield new insights into public health impacts. A case study presented here involves connecting data assets across UK Research and Innovation (UKRI) data repositories, where LLM-powered discovery tools facilitate cross-domain data integration. However, this progress brings challenges, including scalable hosting solutions, API standardization for seamless access, and robust privacy mechanisms to ensure compliance with regulatory frameworks. Addressing these challenges will be critical in realising the full potential of AI-driven search and discovery for scientific and policy-driven advancements.