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Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings

Author

Listed:
  • Avril Challoner

    (Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland)

  • Francesco Pilla

    (Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland
    These authors contributed equally to this work.)

  • Laurence Gill

    (Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland
    These authors contributed equally to this work.)

Abstract

NO 2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO 2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM 2.5 concentrations. Hence, this approach could be used to determine NO 2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

Suggested Citation

  • Avril Challoner & Francesco Pilla & Laurence Gill, 2015. "Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings," IJERPH, MDPI, vol. 12(12), pages 1-21, December.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:12:p:14975-15253:d:59709
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    Citations

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    Cited by:

    1. Saleh M. Al-Sager & Saad S. Almady & Abdulrahman A. Al-Janobi & Abdulla M. Bukhari & Mahmoud Abdel-Sattar & Saad A. Al-Hamed & Abdulwahed M. Aboukarima, 2024. "Modelling of Carbon Monoxide and Suspended Particulate Matter Concentrations in a Rural Area Using Artificial Neural Networks," Sustainability, MDPI, vol. 16(22), pages 1-27, November.
    2. Ling-Tim Wong & Kwok-Wai Mui & Tsz-Wun Tsang, 2022. "Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models," IJERPH, MDPI, vol. 19(9), pages 1-23, May.
    3. Jierui Dong & Nigel Goodman & Priyadarsini Rajagopalan, 2023. "A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools," IJERPH, MDPI, vol. 20(15), pages 1-18, July.

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