| URN | etd-0720110-142633 | Statistics | This thesis had been viewed 680 times. Download 5 times. |
| Author | Shui-Huei Chang | ||
| Author's Email Address | No Public. | ||
| Department | hazards | ||
| Year | 2009 | Semester | 2 |
| Degree | Master | Type of Document | Master's Thesis |
| Language | zh-TW.Big5 Chinese | Page Count | 133 |
| Title | A Study of Air Quality in Taipei County from 2000 to 2009 |
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| Abstract | In this study, we used the recorded database of 5 automatic air quality monitoring stations, which set up by the environmental protection administration, by year from 2000 to 2009 in Taipei County to analysis the relationship between the main air pollutant ozone and other pollutants and meteorological factors. The results showed that the air quality have great improvement of primary air pollutants such as nitrogen oxide, carbon monoxide and sulphur oxide in last ten years, it means the air pollution control of stationary and mobile pollution sources working well. According to the statistics of 10-year Taiwan's Pollutant Standards Index (PSI), the major index pollutant of poor air quality days (Pollution Standards Index＞100) was ozone especially at Xindian station. From pollutants correlation analysis within each monitoring station, CO has strong positive correlation with NOx (the correlation coefficient r＞0.85) and middle positive correlation with PM10 (r =0.55±0.05), this inferred the domination of combustion sources. According to the correlation of one pollutant between stations, PM10 and O3 were high and CO and NOx were middle correlation, r value ＞0.8 and between 0.5 and 0.8, respectively, revealing that PM10 and O3 were large-scale and CO and NOx were regional pollutants. Humidity presents middle negative correlation with O3 that means humidity restrains the generation of O3. Ozone also has negative correlation with its precursors such as benzene, toluene, ethylbenzene and NMHC because of the titration effect. Wind velocity has positive correlation with O3 implying that increased wind speed would increase the interactive reaction of molecules. The results of one-way ANOVA (analysis of variance) of 5 stations database for recent 3 years indicated that there were significant difference between monitoring stations. The hourly average concentration of ozone were 25~30 ppb being the least difference and SO2 had the most difference between each stations, as the hourly average concentration was 6.3 ppb on Sinjhuang station and 3.0 ppb on Xindian station. This may be caused by quantities of stationary sources locating in Sinjhuang. According to the result of linear regression analysis of O3 with other parameters, without intercept was much better than with intercept regression, the r2 were in the range of 0.80~0.91 and 0.44~0.58, respectively. Besides, using the high-ozone-hour data such as 2pm and 3pm were slightly better than whole-day-hour data. Therefore, we suggested that one should take the high ozone concentration data with other parameters to do without-intercept linear regression to predict O3’s trend. |
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| Advisor Committee | |||
| Files | indicate in-campus access only | ||
| Date of Defense | 2010-06-21 | Date of Submission | 2010-07-20 |