季風及區外污染源對工業區空氣品質監測站網優選之影響分析

Abstract

為有效監控工業區對周圍環境的影響，應設置工業區空氣品質監測站網，以瞭解對周圍環境的影響以及評估污染防治設備之成效。過往設置工業區監測站網時，多考量整年的風向變化進行決策，可能導致某些季節性風向下並未設置監測站，因而無法得到該季節具代表性的監測資料。此外工業區以外污染源的影響，亦可能影響監測的代表性，降低數據的可靠度。因此本研究依據季節特徵風向，進行分季並以不同季節的風場分析結果決定出最適合的分季。並以ISCST3空氣品質模式模擬得到不同季節的污染濃度分佈，考量以季節性主要風向所佔之月份比例，判定監測站在不同風向下之權重分配，以確保季節性風向影響下的污染監測代表性。此外，本研究以ISCST3分別模擬區內污染源以及全區污染源下之濃度分布，建立一整數規劃模式，求取受區內污染源影響佔全區污染源比例最大化下之監測站網，以降低區外污染源之影響，提升監測代表性。本研究以兩個案例分別測試所發展之季節性測站優選策略以及考量區外污染源影響之監測站優選模式，並與過往決策模式比較及討論其差異性，以期改善所規劃監測站網之監測代表性。

For an industrial district, an air quality monitoring network (AQMN) is usually established to monitor potential pollution threats on the ambient air quality in areas surrounding the district and assess the effectiveness of pollution control facilities. In general, an AQMN is designed based on the prevailing wind direction. However, for an area with significant seasonal variation in wind directions, such an approach may lead to an AQMN without any station being placed at a location that can detect seasonal pollution impacts. In addition, the existence of external pollution sources can interfere the assessment and interpretation of monitored data. The study was thus initiated to develop models for designing an appropriate AQMN that can detect seasonal pollution and reduce the effect of external sources. A partition procedure was proposed to properly divide the whole year into several major seasons based on seasonal wind direction variations. The ISCST3 dispersion model was applied to simulate hourly pollution distribution. According to the season division and simulated pollution distribution, a previously developed model optimizing detection capability was utilized to determine an AQMN that can proper detect seasonal pollution distributions. The other model maximizing the detected ratio of pollution emitted from the industry district was developed to obtain an AQMN that is not significantly affected by external sources. Two case studies were implemented to evaluate the applicability of both models. As demonstrated by the obtained AQMNs, the ability for detecting seasonal pollution is improved and the interference from external sources is reduced.