| 英文摘要 |
This study is aimed to integrate the database of indoor air indicators and pollutants established by our research team over the past 15 years to evaluate the primary climatic impacts on IAQ in Taiwan. Since 1998, we have continuously collected monitoring data of IAQ in a total of 764 buildings in different indoor environments. We further integrated the profiles of ambient pollutants from EPA atmospheric stations according to the sampling area and specific time. Building characteristics and potential indoor and outdoor sources were summarized as well. Generalized Linear Model (GLM) or Generalized Estimating Equations (GEE), depending on the distribution of data format, were selected to evaluate the impacts of ambient pollutants and climate variations on IAQ. Results show that variations of ambient pollutants had significant impact on IAQ. Besides, these relationships are affected significantly by the change of ambient temperature. Taking into account the factor of “sampling year” in the examining models, current IAQ seems to be better than a decade ago since lower values of beta coefficients in each year are found compared to that in baseline year, except the pollutant of CO2. However, higher median value and beta of indoor O3 and fungi were found after the year of 2005 compared to the levels before 2004, demonstrating a worse situation of IAQ starts to occur. In conclusion, our study is the first to evidence a series of impacts of ambient pollutants and climate variations on indoor air pollutants by using a 15-years dataset of field investigations. We have clarified the factors contributing to the change of levels of indoor air pollutants and quantified the degrees of these impacts.
This study has also conducted a comparison between data of real-time monitor and standard sampling method for PM2.5 and PM10, and suggested that personal environment monitoring (PEM) instrument could be used to monitor the levels of PM2.5 in the fields. We also draft a document for “standard usage method for real-time monitors for PM2.5 and PM10”. Moreover, we investigated the characteristics and health effects of PM2.5 and PM10 in office spaces, and found that aluminum, calcium, iron and sodium elements were major compounds in study spaces. Road and uncover land were potential contributors for the elemental compositions of PM2.5 and PM10 by PCA, and total Cr has higher hazard risk by risk assessment. In the further, we need to investigate the characteristics and health risk of PM2.5 and PM10 in different kind of public spaces.
This study sampled the semi-volatile organic compounds (such as phthalates PAEs and naphthalene PAHs), gas, suspended particulate (PM2.5 and PM10), and floor dust, in seven domestic indoor public places. PAHs showed that the gas phase of PAHs in every sampling site is higher than the PAHs in PM2.5 and PM10, the gas phase of naphthalene concentration is similar to the indoor environmental concentration in the international research. In the aspects of suspended particles PM2.5 and PM10 of PAHs, phenanthrene was mostly detected; acenaphthylene and anthracene were also frequently detected. The result of PAEs, the total gas phase concentration is higher than the suspended particles PM2.5 and PM10. For PAEs in gas phase, molecular weight (<300 g/mol) were mostly detected, in which DBP was with the highest concentration; molecular weight (300-400 g/mol) such as DEHP were detected 100%; molecular weight over 400 g/mol such as DINP and DIDP in the gas phase mostly were all below the detection limits. For PAEs in suspended particles PM2.5 and PM10, The concentration of PAEs in PM10 is higher than PM2.5, mainly distribute on coarse, PM2.5-10. The analysis of floor dust shows that DIBP, DEHP, and DNOP reach the detection rate of 100%. In the dust concentration, the concentration of DEHP has the highest proportion; Followed by DNOP and DINP.
In order to assess the impacts of fungal exposure on human health, a systematic search of the Pubmed database was conducted (2009-2013). Seven full papers about the associations between dampness/visible mold/mold odor were selected for meta-analysis according to the selection criteria. The results of random effects model shows that dampness, visible mold, and mold odor can increase the risk of current asthma by 40.1% (95% CI=0.921-2.133), 60.1% (95% CI=1.264-2.027), and 79.9% (95% CI=1.268-2.553), respectively; however, the effect of dampness was not significant (p > 0.05). Moreover, this study also tried to evaluate the applicability of qPCR to airborne fungal monitoring by comparing the fungal exposure determined by culture assay and qPCR. In 10 tested spaces, the daily concentrations (median) of fungi and Aspergillus detected by culture assay were 456 and 55 CFU/m3, respectively, whereas qPCR quantified 9,233 sp-eq/m3 of fungi and 77 sp-eq/m3 of Aspergillus (median). Although there were no significant correlation between culture assay and qPCR in concentrations of fungi and Aspergillus both (p > 0.05), the positive correlation was showed after further combining the results detected from five spaces of a photonic fabrication factory (r= 0.408, p=0.043 for fungi). Such discordant findings may be because the characteristics of spaces would affect the metabolic activity of fungi, i.e. decrease or increase in the culturability of fungi. These findings also implied that the fungal exposure examined by culture assay and qPCR might link to different health impacts; however, this need a further study of investigating the associations of fungal exposure determined by the two methods and health outcomes.
Previous studies, in summary, have shown that temperature, humidity and organic material are important influence factors in microbiological growth, therefore controlling these factors is helpful for preventing biological contamination. This study had performed two of biological pollution improvement cases, and results of improvement were synthesized into 「A guide to control technology of indoor biological contamination」.
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