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Embedding citizens within airborne microplastic and microfibre research (2023)
Journal Article
Williams, B., De Vito, L., Margarida Sardo, A., Pringle, K., Hansen, M., Taylor, M., …Hayes, E. (2023). Embedding citizens within airborne microplastic and microfibre research. Cambridge Prisms: Plastics, 1(e11), 1-5. https://doi.org/10.1017/plc.2023.11

Microplastics are ubiquitous in our environment but their presence in air is less well understood. Homes are likely a key source of airborne microplastics and microfibres to the environment owing to the frequent use and storage of plastics and textil... Read More about Embedding citizens within airborne microplastic and microfibre research.

Deep learning-based multi-target regression for traffic-related air pollution forecasting (2023)
Journal Article
Akinosho, T. D., Bilal, M., Hayes, E. T., Ajayi, A., Ahmed, A., & Khan, Z. (2023). Deep learning-based multi-target regression for traffic-related air pollution forecasting. Machine Learning with Applications, 12, Article 100474. https://doi.org/10.1016/j.mlwa.2023.100474

Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air qua... Read More about Deep learning-based multi-target regression for traffic-related air pollution forecasting.