Abstract
ABSTRACTHumans spend a considerable amount of time indoors, and indoor biological airborne pollutants may harm human health. Active bioaerosol samplers and conventional microbiological culture methods, which are widely applied in studies of airborne microbial contamination, are not only unable to perform continuous monitoring over long periods, but are also time-consuming and expensive. In order to rapid assess indoor airborne microbial contamination, multiple linear regression models were constructed by statistically analyzing the measured bioaerosol samples and the real-time measured mass and number concentrations of airborne particles using a direct reading instrument from 43 air-conditioned public spaces. There were significant positive correlations of indoor airborne bacterial and fungal concentrations with indoor size-segregated particle mass and number concentrations. The predictive power of the model was sufficient for predicting indoor bacterial concentrations from the indoor and outdoor size-segregated particle number concentrations as independent variables. Particle number concentration outperforms particle mass concentration as an independent variable in predicting indoor bioaerosol concentrations. The prediction model for indoor bacterial bioaerosol levels constructed in this study could facilitate a rapid assessment of potential airborne bacterial contamination via the simple and feasible measurement of particle number concentration, thus helping to improve the management and maintenance of indoor air quality.
Highlights
Social patterns and lifestyles change over time
The results indicate that both models could reasonably predict indoor airborne bacterial concentrations, and that the Case II-1 model obtained via multiple linear regression (MLR) is the most favorable model
Numerous studies have investigated the correlation between indoor bioaerosol concentrations with various indoor and outdoor air and environmental parameters, but those studies used multiple and complex parameters to construct linear and non-linear regression models for the prediction of indoor bioaerosol concentrations
Summary
Social patterns and lifestyles change over time. People spend nearly 90% of their time indoors, and indoor air quality (IAQ) has become an important environmental issue of concern to all (Sundell, 2004). Long-term exposure to indoor environments with inadequate air exchange and poor air quality may cause sick building syndrome (SBS), allergic reactions, respiratory tract infection, and lung cancer (Dales et al, 2008; Joshi, 2008; Sidra et al, 2015). Indoor airborne biological pollutants have numerous sources, including outdoor air, human bodies, wallpaper, carpet, resuspended particles, air conditioning systems, and animal waste (Lindemann et al, 1982; Pastuszka et al, 2000; Chao et al, 2002; Hargreaves et al, 2003; Kalogerakis et al, 2005; Tseng et al, 2011; Hospodsky et al, 2012; Xu et al, 2017).
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