| Summary: | 碩士 === 國立臺北科技大學 === 環境工程與管理研究所 === 96 === Taiwan is located in a special geographical environment with its humidity and temperature not only higher than those in most American-European countries but also more compatible with the growth of microorganisms. This particular environmental feature has triggered a steady increase in the number of the patients suffering from SBS (Sick Building Syndrome) related diseases and allergies every year in Taiwan and it indicates directly the damages of indoor environment upon human health. At present, measurement of bio-aerosol can be complicated and both time- and cost-consuming. Our study accordingly strives to develop an empirical prediction model to measure indoor bio-aerosol concentrations as a replacement of the traditional bio-aerosol concentration test for saving both time and cost. The study uses portable direct-reading devices to measure indoor air quality of office buildings and hospitals and analyze the collected data. Multiple Regression analysis is adopted to examine the indoor and outdoor measurements as well as the indoor/outdoor ratio so as to identify the variables influencing indoor bio-aerosol concentrations. The variables are then used to develop the empirical model for predicting indoor bio-aerosol concentrations. Accuracy of the model is represented by MAPE (Mean Absolute Percentage Error), and the prediction results indicate that the developed empirical model fails to achieve a satisfactory accuracy in calculating the concentration of indoor bio-aerosol based on the combined data of office buildings and hospitals.
In the case of office buildings, the MAPE of the empirical model falls in the range between 58~62 % of Indoor Bacteria Concentration; the MAPE appeared to be 16 % (2~36 %) of the Multiple Regression analysis empirical model of Indoor Bacteria Concentration in a single office building; Moreover, the MAPE of the empirical model falls in the range between 2~208 % of Indoor Fungi Concentration in office buildings. The MAPE appeared to be 2 % (0~5 %) of the Multiple Regression analysis empirical model of Indoor Fungi Concentration in a single office building.
In the case of hospitals, the MAPE of the empirical model falls in the range between 29~59 % of Indoor Bacteria Concentration; the MAPE appeared to be 29 % (0~84 %) of the exponent empirical model of Indoor Bacteria Concentration in a single hospital. The MAPE of the empirical model falls in the range between 1~69 % of Indoor Fungi Concentration in hospitals; the MAPE appeared to be 1 % (0~3 %) of the Multiple Regression analysis empirical model of Indoor Fungi Concentration in a single hospital.
Based on the analysis results with the application of the empirical prediction model, the degree of accuracy in calculating the concentration of indoor air bacteria of in a single office building and hospital is better than all the hospitals of Taiwan and all the offices of Taipei city and county. The empirical prediction model proposed by in this research can be applied to perform instant calculation of the indoor bio-aerosol concentration in office buildings and hospitals and to serve as an assessment tool to confirm the improvement in indoor air quality.
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