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Int. J. Environ. Res. Public Health 2015, 12(12), 15233-15253; doi:10.3390/ijerph121214975

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

Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editors: Gary Adamkiewicz and M. Patricia Fabian
Received: 23 September 2015 / Revised: 8 November 2015 / Accepted: 25 November 2015 / Published: 1 December 2015
(This article belongs to the Special Issue Indoor Environmental Quality: Exposures and Occupant Health)
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Abstract

NO2 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 NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts. View Full-Text
Keywords: indoor/outdoor air quality; Geographical Information System (GIS) modelling; data mining; artificial neural networks; pollution; health impacts indoor/outdoor air quality; Geographical Information System (GIS) modelling; data mining; artificial neural networks; pollution; health impacts
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Challoner, A.; Pilla, F.; Gill, L. Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings. Int. J. Environ. Res. Public Health 2015, 12, 15233-15253.

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