The air quality in Kaohsiung and Ping-Dong district is the worst in Taiwan. The air pollution episodes in Kaohsiung are attributed to high concentrations of PM10 and O3. Among them, over half of the episodes result from PM10. In addition to Pollutant Standards Index (PSI), atmospheric visibility is also an indicator of ambient air quality. Citizens always complain about the impairment of visibility because it can be visualized directly. Visibility is closely correlated to both air pollutants and meteorological condition. Extinction of visible light by fine particles is the major reason for visibility impairment.
In this study, an artificial neural network was applied to predict the concentration of PM10 and atmospheric visibility. The objectives of this study were to investigate the effects of meteorological factor and air pollutants on visibility and to apply artificial neural network to predict the concentration of PM10 and atmospheric visibility.
The measured PM10 data were divided into two parts (i.e. summer and winter, ) to understand whether different season affect the prediction of PM10 concentration. The modeling results showed that the optimum input variables included the PM10 concentration, atmospheric pressure, surface radiation, relative humidity, atmospheric temperature, and cloud condition. The network outputs showed high correlation with measured PM10 concentration (R=0.876) in the whole-year set. Furthermore, the prediction of summer set also showed high correlation with measured PM10 concentration (R=0.753). The winter set demonstrated the worse prediction among three sets, and showed medium correlation with measured PM10 concentration (R=0.553).
The visibility network test was conducted by two stages. The first stage (set-1~set-3) showed that relative humidity, atmospheric temperature, and cloud condition were the most important meteorological factors, while PM10, O3, and NO3 were the most important air pollutants on the prediction of atmospheric visibility. The prediction of set-1 considering only meteorological factors was the worst (R=0.586), while set-3 was the best and showed medium correlation with measured atmospheric visibility (R=0.633). The second stage (set-4 and set-5) increased the hidden neuron numbers and input variables, and added atmospheric visibility in the input variables. Although the correlation coefficients between predicted and measured data did not increase, the prediction of atmospheric visibility had significant improvement.
Finally, a short-term prediction of PM10 and atmospheric visibility was conducted and validated by the level of PSI values and atmospheric visibility. Prediction results showed that the accuracy of PM10 prediction was 76.9%, while the prediction of atmospheric visibility by set-3 network demonstrated an accuracy of 76.9%. Moreover, no significant difference of prediction was detected by using either three-level or five-level visibility systems.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0730102-185048 |
| Date | 30 July 2002 |
| Creators | Lin, Yat-Chen |
| Contributors | none, none, none, none |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730102-185048 |
| Rights | unrestricted, Copyright information available at source archive |
Page generated in 0.0024 seconds