以微型感測器探究鄰里尺度懸浮微粒濃度之時空變異：以台灣大學校總區與其附近大學里為例

Abstract

近年來，許多研究藉由分析土地利用類型和城市結構的方法，來探討懸浮微粒的時空分佈。然而，較少研究著重在小尺度高空間解析度（校園或社區尺度）的土地利用特徵與PM濃度變化之間的關係。校園與社區就相當於城市的縮小版，擁有許多不同的土地利用情況、不同的地理環境和人類活動。這些因素都不可避免地會影響到懸浮微粒的變化。此外，了解校園與社區尺度的空氣品質狀況，不僅可以觀察到環境與PM濃度之間更細微的關係外，還可以進一步分析結果，以提高未來教師、學生和附近居民的環境舒適度，確保健康的生活環境。 本研究與台灣大學的「台大系統舒適度+」計畫（SCplus）團隊合作，共同製作微型感測器「NTU4AQ」，此感測器整合了多元的感測元件，可量測溫度、濕度、風速、噪音、太陽輻射與空氣品質。並將其設置在台灣大學校園的14個站點與大學里8個站點以進行監測行動，使用了2021年4月和2021年11月所量測的數據進行探討。為了保持數據的品質，本研究更通過排除這些時段間發生降雨和境外污染事件發生的日期來減少其他外部因素的影響。另外，為了解建成環境與人為活動對於空氣污染物累積與消散的影響。本研究以「PM濃度變化量」作為分析指標，藉由觀察不同站點PM濃度在長期與短期的變化，分析時空差異和變化量之間的關係。最後，本研究以2021年4月的案例分析，將數個特定站點的PM變化量和空間參數（如：天空開闊度）進行K-means分群分類分析，以觀察數據的分群是如何呈現以及造成其結果的可能原因。 由結果可發現觀測期間4月的平均PM濃度較11月高的，但在濃度值與變化量值的趨勢，不同月份會有不一樣的結果。空間分佈的結果，當時間在台北的早上尖峰時段（07:00~09:00），4月與11月變化量都呈現正值（污染物累積）。然而，11月下午時段的變化卻與4月不同。此外，根據視覺化分析可以發現各個站點的變化量雖然略有不同，但差別並沒有太大。最後案例分析的結果，也可以看到在一天中，也會因為早上、晚上時段的不同而有不一樣的情形產生。在早上尖峰時段，較靠近交通要道的站點「外教中心」累積量達12.66 μg〖 m〗^(-3) h^(-1)，這個場域是較為不空曠的區域。但在晚上的尖峰時段，卻變成「台大體育場」的累積量較高，數值高達13.04 μg〖 m〗^(-3) h^(-1)，此場域卻是空曠的。因此，本研究認為PM濃度變化的時間影響程度大於空間。本研究可以歸納出不同時間人為活動強度對於PM濃度的影響最大外，距離交通要道的遠近也是其主要原因。在校園與社區的尺度中，污染源會較直接的影響各個測站的結果，導致校園與社區內不同的土地利用之影響較不明顯。為了在空間上的分析中更佳能釐清影響原因，未來勢必要使用更多的控制變量來進行討論。可以再加上更多的可代表土地利用型態的參數，或是透過建模的方式去模擬當時的環境加以分析真實的情況。

In the recent years, many studies have explored the spatiotemporal distribution of PM by analyzing land-use types and urban structures. However, fewer studies have focused on the relationship between small-scale high spatial resolution (campus or community scale) land-use characteristics and changes in PM concentrations. The campus and the community are miniature versions of cities, with many different land-use types and human activities. These factors will inevitably affect the change of PM. In addition, understanding air quality conditions at the campus and community scales not only allows for the observation of more nuanced relationships between the environment and PM concentrations but also allows for further analysis of the results to improve environmental comfort for researchers, nearby residents, and decision-makers in the future, ensuring health and wellness living environment. This study used data measured at 14 sites at the National Taiwan University (NTU) campus in April 2021 and 9 sites in the DaXue Vil. （大學里） next to NTU campusin November 2021. To maintain the quality of the data, this study reduces the influence of other external factors by excluding the dates of rainfall and pollution events during these periods. In addition, to understand the impact of the built environment and human activities on the accumulation and dissipation of air pollutants, the “PM change rate” was used as the analysis index, and by observing the long-term and short-term changes of PM concentration at different sites, the relationship between spatiotemporal differences and changes was analyzed. Finally, this study used a case study in April 2021 to perform the K-means cluster classification analysis on PM variation and spatial parameters (e.g., sky openness) at several specific sites to observe how the data clusters are presented and possible reasons for its results. From the results, it can be found that the average PM concentration in April was higher than that in November during the observation period, but the trend of the concentration value and the variation value will differ in different months. As a result of the spatial distribution, when the time is in the morning rush hour (07:00~09:00), the variation in April and November both showed positive values (pollutant accumulation). However, the changes in the afternoon period in November were different from those in April. In addition, according to the visual analysis, it can be found that the changes of each site are slightly different. The results of the final case analysis show that in a day, there will be different situations due to different morning and evening periods. During the morning rush hour, the accumulated amount closer to the main traffic road reached 12.66 μg〖 m〗^(-3) h^(-1), and this field is a less empty area. However, during the rush hour period at night, it turned into an open field with a high accumulation, with a value as high as 13.04 μg〖 m〗^(-3) h^(-1). Therefore, this study considers that the temporal influence of PM concentration changes is greater than that of space. In this study, it can be concluded that the intensity of human activities at different times has the greatest impact on PM concentration, and the distance from the main road is also the main reason. On the scale of campus and community, pollution sources will directly affect the results of each station, resulting in a less obvious impact of different land use on campus and community. To better clarify the reasons for the impact in the spatial analysis, it is necessary to use more control variables for discussion in the future. More parameters representing land use patterns can be added, or the real situation can be analyzed by simulating the current environment through modeling.