臭氧是台灣地區光化學污染的主要產物，由前驅物質(主要為NOx和VOC)經大氣化學反應所形成的二次污染物質，其生成是一複雜的非線性反應，許多氣象因子如地表溫度、日照強度、雲量、風速、風向、相對溼度等均扮演重要的角色，而且隨著季節變化，不同地區的天氣型態所造成的臭氧事件日也有所不同，所以本研究期望藉由類神經網路來建立臭氧預測模式，以作為臭氧濃度預報系統。 研究中以傳統統計預報及完美預報來進行次日最大小時臭氧濃度及八小時平均臭氧濃度的預報工作，並將預報結果和實測值比較，結果顯現出以完美預報所得的結果比傳統統計預報為佳，此外，以完美預報所預測的八小時平均臭氧濃度與實測值之相關係數均能達到高度相關，然而所有預測均出現高估低濃度值、低估高濃度值的現象。 The ground-level ozone produced by photochemical air pollution is a serious environmental problem in Taiwan. It is a secondary pollutant generated by precursors (mainly NOx and VOC) through serial complicated reactions with other chemical species in the atmosphere. The forming of ozone is a complicated and non-linear reaction. Other than the chemical reaction, the weather condition, such as surface temperature, sunlight strength, cloudiness, wind speed, wind direction and humidity, also play an important role in the photochemistry pollution. This study focuses on building a model that predicts the next day’s maximun ozone concentration with artificial neural network. In the study, two forecasts, i.e., classical statistical and perfect prog forecast, were used to predict the next day’s maximum hourly and eight hour average ozone concentration prediction. The study found that the results obtained from perfect prog forecast are more accurate than that obtained from classical statistical forecast. The results of all forecasts are very reasonable. High correlations between the predictions of perfect prog forecast and measured 8h ozone concentrations are noted. However, all forecasts tend to overpredict in the low concentrations and underpredicted in the high concentrations.
