| Abstract |
Fine suspended particulates (PM2.5) are the suspended particulate with particle size less than 2.5 μ m/m^3. They go deep into the lungs easily and through alveolar capillaries enter the blood circulation, then harm the human body. The epidemiology study has discussed that fine suspended particulates have influence to incidence and death rate of lungs disease. The Environmental Protection Administration (EPA) in Taiwan has monitored the PM2.5 for eight years and has setup air quality monitoring network. This study is aimed at investigating the trend of PM2.5 and the relationship among all stations through analyzing the monitoring data.
Besides PM2.5, the air quality monitoring network has also been monitoring other variables, such as: PM10, SO2, NO2, O3, CO, NO, NOx, temperature, rainfall, humidity, direction and velocity of wind. In order to investigate the correlation between PM2.5 and other variables, we refer to PM2.5 as the response variable and the other variables as the predictor or regressor variables. We use the factor analysis to maximize the variance of a linear combination of the regressor variables, and then try to fit a linear regression model between PM2.5 and the factor scores which obtained from the results of factor analysis. Furthermore, for the purpose of controlling quality of PM2.5, we select five more representative stations from fifteen stations in the southern Taiwan to draw the quality control charts. Later the fitted time series model is used to forecast the future trend, which may be useful to the EPA,
as reference to the environment control quality of PM2.5. |
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