麻豆传媒

Dr Sheen Mclean Cabaneros is a Lecturer in Air Pollution at 麻豆传媒’s School of Civil Engineering and a Fellow of Advance HE. He specialises in air pollution modelling, with a particular focus on machine learning, and computer vision. He gained his PhD in Mechanical Engineering from the University of Strathclyde, Glasgow in 2020 and previously served as a Postdoctoral Research Associate and Lecturer at the University of Hull. His interdisciplinary work spans air pollution forecasting, smart manufacturing, and the detection of airborne microplastics using deep learning. He has contributed to notable projects including the NERC-funded “Development of the First Global Standard for Airborne Microplastic Monitoring” and Luxinar’s “Agile Laser Processing in Industrial Environments.” One of his publications was among the most downloaded and top-cited in Environmental Modelling and Software in 2021. His teaching portfolio includes modules on air pollution, machine learning, control theory, and engineering mathematics.

Qualifications

• PhD in Mechanical Engineering, "Spatiotemporal and temporal forecasting of ambient air pollution levels through data-intensive hybrid artificial neural network models", University of Strathclyde, Glasgow, 2020
• MSc in Applied Mathematics, "On Option Pricing using Fourier Inversion Methods", Ateneo de Manila University, 2014
• BSc in Mathematical Sciences, "Cancellation of Noise Artefacts by a Neural Network with Applications to Electrocardiogram (ECG) signals", University of Science and Technology of Southern Philippines, 2009

Prizes and Awards

• Recipient - Environmental Modelling & Software 2021 Most Downloaded Paper Awards, 2021
• Winner - ActInSpace Daresbury Hackathon 2018, European Space Agency Business Incubation Centre, 2018
• Winner - Best Undergraduate Thesis - Mathematical Sciences, University of Science and Technology of Southern Philippines, 2009

Research Interests

• Air Pollution Modelling
• Machine Learning
• Urban Environments
• Uncertainty Quantification

Research Funding

• Co-investigator - Development of the First Global Standard for Airborne Microplastic Monitoring, NERC, NE/X010201/1; Value: £80,532 
• Co-investigator - Agile Laser Processing in Industrial Environments, Luxinar Ltd.; Value: £40,000 

Academic Year 2025/2026:

• CEG8112 Air Pollution [Module Leader]
• CEG8529 Digitalisation and Artificial Intelligence [Module Leader]
• CEG8438 Stakeholder Engagement [Module Leader]

• Articles

  • Xie X, Kassem M, Cabaneros S, Pan HX. . Journal of Environmental Management 2026, 404, 129470.
  • Cabaneros SM, Chapman E, Hansen M, Williams B, Rotchell J. . Environmental Pollution 2025, 372, 125993.
  • Chapman E, Liddle C, Williams B, Hilmer E, Quick L, Garcia A, Suarez D, White D, Bunting J, Walker P, Cabaneros SM, Kinnnersley R, Shi H, Atherall C, Rotchell J. . Journal of Hazardous Materials 2024, 480, 136129.
  • Cabaneros, SM, Calautit, JK, Hughes, B. Spatial Estimation of Outdoor NO₂ Levels in Central London Using Deep Neural Networks and a Wavelet Decomposition Technique. Ecological Modelling 2020, 424, 109017.

• Conference Proceedings (inc. Abstracts)

  • Westcarr J, Gunturi VM, Cabaneros SM, Kureshi RR, Thakker D, Porter A. . In: IEEE International Conference on Omni-layer Intelligent Systems (COINS 2024). 2024, London: IEEE.
  • Cabaneros, SM, Calautit, JK, Hughes, B. Short- and long-term forecasting of ambient air pollution levels using wavelet-based non-linear autoregressive artificial neural networks with exogenous inputs. In: 27th International Conference on Modelling, Monitoring and Management of Air Pollution. 2020, International Journal of Environmental Impacts.
  • Cabaneros, SM, Calautit, JK, Hughes, B. Hybrid Artificial Neural Network Models for Effective Prediction and Mitigation of Urban Roadside NO₂ Pollution. In: Energy Procedia. 2018.

• Report

  • Hewitt, I, Cabaneros, SM, Danielli, F, Formetta, G, Gonzalez, R, Grinfield, M, Hankin, B, Johnstone, T, Kamilova, A, Kovacs, A, Kretzschmar, A, Kiradjiev, K, Pegler, S, Sander, G, Wong, C. JBA Trust Challenge: A Risk-based Analysis of Small Scale, Distributed,“Nature-based” Flood Risk Management Measures Deployed on River Networks. Turing Gateway to Mathematics, Cambridge, 2018. Report of Environmental Modelling in Industry Study Group, April 2017, of the UK EPSRC Living with Environmental Change Network “Maths Foresees”.

• Reviews

  • Cabaneros SM, Hughes B. . Environmental Modelling & Software 2022, 158, 105529.
  • Cabaneros, SM, Calautit, JK, Hughes, B. A Review of Artificial Neural Network Models for Ambient Air Pollution Prediction. Environmental Modelling & Software 2019, 119, 285-304.