- 學位論文
臺北市空氣監測站間污染物濃度分布及特性分析
The Concentration Distribution and specification in air pollution from monitoring stations in Taipei City of Taiwan
摘要
空氣品質監測站站數及各監測站資料運用之代表性，直接影響各空品區污染改善評估及目標達成率之計算基準。因此，在確保良好監測品質之前提下，如何結合各單位監測資源，避免人力、物力之重覆浪費，對同一地區同性質監測站監測數據代表性加以分析探討，實有助於發揮空氣品質監測設施之最大效益及整合各單位監測資源。 本研究以臺北市各監測站所監測PM10、O3濃度變異分析及懸浮微粒中重金屬分布特性為研究重點，結合應用污染物濃度分布、因子分析法(Factor Analysis)、加強因子分析法(Enrichment Factor)等評估方法，推論造成某一污染事件日的影響因子，並探討環保署及地方環保局所屬各監測站監測資料於時間序列及空間分佈情形，以作為相關單位對於地區監測站網合併評估及全區污染改善資料引用之參考。 本研究結果顯示兩單位於懸浮微粒之濃度分布以單峰形狀韋伯分配及對數常態分配為主，而懸浮微粒於夏、秋兩季較具有趨勢性；臭氧除信義站春季外，其餘各季具有良好趨勢性。由污染事件日以因子分析及加強因子分析結果，各監測站空氣環境主要受焚化爐（Cu、K、Zn）、移動源（Al、Fe、Mg）及街道揚塵（Al、Fe、K、Ca、Mg）等三大污染來源影響因素較大。進一步建立兩單位時間序列最佳模式，其環保署測站懸浮微粒數據於時間數列模式及季節性周期顯現較為一致性，但兩者於時間序列的呈現上都具有季節性周期。臭氧時間序列模式於上述監測站均不具有明顯季節周期性，但各監測站於時間序列均具有相關趨勢性。以地理統計解析臭氧小時濃度值，經本研究設定條件所選取最適高斯模式的半變異圖所計算影響範圍（Effective Range）約41公里至98公里的範圍，影響範圍內的樣品點濃度值共變異性大，空間中兩位置間有空間相關性。由解析結果臺北市各監測站臭氧濃度值變化趨勢具有很高的相關性。因此以中尺度臭氧污染物監測站設置站數而言同類型監測站可考量予以整合合併，以有限資源達到最大效益。
並列摘要
The number of air quality monitoring stations and use of obtained data affects the pollution improvement evaluation and target attainment standard directly. Therefore, with guaranteed monitoring quality, incorporating monitoring resources from various departments, avoiding waste of manpower and materials. Analysis on the significance of data obtained from monitoring stations of the same kind in the same area can assist to maximize the efficiency of air quality monitoring facility and consolidate monitoring resources. This study focused on PM10, O3 concentration variation analysis and the heavy metal distribution in suspended particles obtained from monitoring stations in Taipei City. It combined with pollutant concentration distribution, Factor Analysis, Enrichment Factor Analysis, to deduce the factor affecting a pollution incident and discuss the time series and spatial distribution of data obtained by monitoring stations of EPA and local environment protection bureaus. The results are provided to related organizations as guidelines on consolidation of local monitoring stations and pollution improvement. The results showed that the concentration distribution of suspended particles was mostly single-peak Weibull distribution and lognormal distribution. The suspended particles showed trend in summer and autumn seasons. Ozone showed fine trend in all seasons, except for the data shown by Sinyi Station in spring season. Factor Analysis, Enrichment Factor Analysis were applied on the pollution incident, and showed that the main factors to the air quality were incinerator (Cu, K, Zn), traffic source (Al, Fe, Mg), and street dust (Al, Fe, K,Ca, Mg). The optimization model for two-unit time sequence showed that PM10 time series model and seasonal periodicity of EPA monitoring stations showed better consistency, of which both exhibited seasonal periodicity. The concentration of ozone showed no significant seasonal periodicity, but time series showed relative trend. Analyzing the per-hour ozone concentration with geostatistics, the most appropriate semi-variogram of Gaussian model calculated the effective range to be 41km to 98km. The variability of sample dot-concentration within the effective range was large, the space between the two points showed relativity. The analysis results showed high relativity of variation trend of ozone concentration. Therefore, based on the number of mid-scale ozone pollutant stations of the same kind, consolidation is feasible to maximize the limited resources.