All Outputs (3)

Airborne microplastic monitoring: developing a simplified outdoor sampling approach using pollen monitoring equipment (2024)
Journal Article
Chapman, E., Liddle, C. R., Williams, B., Hilmer, E., Quick, L. J., Garcia, A. G., Suárez, D. C., White, D., Bunting, M. J., Walker, P., Cabaneros, S. M. S., Kinnersley, R., Hansen, M. F., Atherall, C. A., & Rotchell, J. M. (2024). Airborne microplastic monitoring: developing a simplified outdoor sampling approach using pollen monitoring equipment. Journal of hazardous materials, 480, Article 136129. https://doi.org/10.1016/j.jhazmat.2024.136129

A novel, yet simple, airborne microplastic (MP) sampling approach using global pollen monitoring equipment was applied to identify, characterise and quantify outdoor airborne MPs for the first time. Modification of Burkard spore trap tape adhesive pr... Read More about Airborne microplastic monitoring: developing a simplified outdoor sampling approach using pollen monitoring equipment.

Devising a Responsible Framework for Air Quality Sensor Placement (2024)
Presentation / Conference Contribution
Westcarr, J., Gunturi, V. M. V., Cabaneros, S. M., Raja, R., Thakker, D., & Porter, A. (2024, July). Devising a Responsible Framework for Air Quality Sensor Placement. Presented at 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS), London, United Kingdom

A major challenge faced when developing smart, sustainable urban environments is the reduction of air pollutants that adversely impact citizens' health. The UK has implemented strategies such as clean air zones (CAZs) coupled with the use of sensor t... Read More about Devising a Responsible Framework for Air Quality Sensor Placement.

Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting (2022)
Journal Article
Cabaneros, S. M., & Hughes, B. (2022). Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting. Environmental modelling & software : with environment data news, 158, Article 105529. https://doi.org/10.1016/j.envsoft.2022.105529

The use of data-driven techniques such as artificial neural network (ANN) models for outdoor air pollution forecasting has been popular in the past two decades. However, research activity on the uncertainty surrounding the development of ANN models h... Read More about Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting.