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

International Journal of Environmental Research and Public Health

Avril Challoner²  Francesco Pilla¹  Laurence Gill¹  Gary Adamkiewicz² 

[1] Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland

关键词: indoor/outdoor air quality;    Geographical Information System (GIS) modelling;    data mining;    artificial neural networks;    pollution;    health impacts;   

DOI  :  10.3390/ijerph121214975

来源: mdpi

【 摘 要 】

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 PM_2.5 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.