| Summary: | 碩士 === 嘉南藥理大學 === 環境工程與科學系 === 104 === Fine particulate matter (PM2.5) causes much more severe health damage than coarse particles. The Environmental Protection Administration (EPA) of Taiwan published PM2.5 air quality standards on May 14, 2012, 35 µg/m³ is the target value of 24-hour average, and 15 µg/m³ for annual limit value. To ensure the protection of the citizen's health, the EPA set up air quality monitoring network, the monitoring methods for suspended particulate matter are divided into "Manual monitoring" and "Automatic monitoring", and the adjustment principle of automatic monitoring was also announced on May 2014. Referring the data from manual monitoring to adjust the automatic monitoring data, so that these two data convergence would be announced immediately, and provide early warning capabilities and accurate information.
In this research, firstable, to collect the PM2.5 data from two air quality monitoring stations- Tainan station and Xinying station, and analysis the PM2.5 data which both were monitored from manual method and automatic method from November 2013 to October 2015. Then, the auto-monitoring data of automatic air quality monitoring station as independent variables (including PM2.5 automatic measured values, PM10 automatic measured values, temperature, humidity, wind speed, SO2, NOX, CO and O3), and the manual-monitoring data of PM2.5 as dependent variables in the multiple regression analysis. In the end, to include the data from other two PM2.5 monitoring stations- Shanhua station and Annan station located in Tainan which don’t have manual PM2.5 monitoring, and to discuss the effectiveness of the final correction data, then to establish the indicators of determined from the mean absolute percentage error (MAPE).
The result shows that the majority of automatic measured values are more than the manual measured values no matter in Tainan station or Xinying station. Although it will reduce the mean absolute error by using linear regression equation, but it usually leads to the underestimation of the automatic measured values. With the multiple regression, not only revise downward measured values but also adjust upward measured value if needed, so that the automatic measured value would be closer to the manual measured value, the MAPE indicator will reach the level of "Good forecast".
Shanhua station and Annan station adopted the manual-monitoring data of PM2.5 from Tainan station to be the multiple regression analysis, even have the good forecast, but their prediction capability was poor compared to Tainan station. In order to achieve better predictive capability, Shanhua station and Annan station should be adopted the manual-monitoring data of PM2.5 from themselves.
Application of multiple linear regression equation In Tainan station and Xinying station from January to April 2016, and made 16 times PM2.5 manual monitoring in Shanhua station from January to March 2016, the results showed that the multiple regression have better predictive ability than announcement of linear regression equation.The MAPE value of Tainan station and Shanhua stationcan reach the level of "Good forecast", and Xinying stationcan reach the level of "highly accurate forecast". But overall, Shanhua station adopted the manual-monitoring data of PM2.5 from Tainan station to be the multipleregression, it’s prediction capability was poor compared to Tainan station and Xingying station.
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