以土地利用回歸模式評估細懸浮微粒與二氧化氮短期暴露量與心血管疾病相關性

Abstract

前言: 空氣汙染物已在許多研究中證實與心血管疾病的發生與惡化有關，尤其是交通汙染源所產生的汙染物(例如:細懸浮微粒與二氧化氮)。過去進行短期效應的暴露評估時，大多使用空氣品質監測站數據作為居民空氣汙染物暴露程度的指標。本研究在臺北地區發展短期的土地利用回歸模式，探討土地利用與交通排放對於二氧化氮與細懸浮微粒濃度空間分布的貢獻，並將之應用於急性心血管效應與不同暴露評估方法比較。 方法: 本研究於2013年在臺北地區選擇了117位在某金融大樓工作的員工,並在其辦公地點的大樓進行心血管的健康檢查與室內空氣品質監測。在空氣汙染暴露的推估方面使用了三種不同的暴露評估設計:(1)使用土地利用模式去預測受試者住家的室外空氣品質(2)利用土地利用模式結合辦公大樓的室內空氣品質監測(3)最近測站法。在心血管檢查的部分，進行了一般檢查、血樣抽樣以及非侵入性的血管彈性量測。最後我們所選取的心血管效應指標為: 臂踝脈波傳播速率(baPWV) 、踝肱指數(ABI) 以及高敏感度C-反應蛋白(hsCRP)。 結果: 本研究在暴露評估上的方法比較上，顯示當土地利用模式結合辦公大樓的室內空氣品質監測，相較於單純用土地利用模式，對於心血管效應指標的探討有著更顯著的結果，表示室內空氣品質監測對於暴露評估上的應用，有一定的重要性。在交通汙染物與急性心血管效應的關聯上，我們發現PM2.5 以及 NO2和 baPWV有顯著的正相關，而與ABI並沒有顯著的相關。在與hsCRP的關係中，僅發現NO2 與其有顯著的正相關。 結論: 本研究在臺北地區建立了短期的細懸浮微粒與二氧化氮的土地利用模式，進行交通產生的汙染物與急性心血管效應的探討。並在此研究中反映出交通汙染源產生的汙染物會造成急性心血管效應的惡化。

Background: The study was designed to combine air quality monitoring data with land use data to build land use regression (LUR) models in Taipei Metropolis to predict individualized traffic-related air pollutants exposure levels and linked with cardiovascular endpoints to discuss the association between short-term traffic-related air pollution and acute cardiovascular effects. Method: We selected 117 subjects working at a bank to have health examination in February, June, and September in 2013. The health examinations included general medical examination and cardiovascular screening. Additionally, we monitored air quality at the subjects’ workplaces over the period of health examination. We also collected information on the subjects’ home addresses and time-activity patterns to predict individualized PM2.5 and NO2 exposures at the subjects’ home addresses by land use regression models. Three exposure assessment methods were developed to represent personal exposure: (1) LUR models, (2) LUR models combine with indoor air monitoring, and (3) Nearest station. For cardiovascular markers, we used the inflammation maker (High-sensitivity CRP, hsCRP) and the markers for the arterial stiffness (baPWV and ABI) as our health endpoints. Results: With regard to the different exposure assessment methods, we found that using LUR models combining with indoor air monitoring data had a better explanation on the relationship with acute cardiovascular effects. With regard to the association between traffic-related air pollutants and cardiovascular endpoints, we found that both PM2.5 and NO2 was significantly associated with baPWV, and NO2 was significantly associated with hsCRP. However, ABI was not found to be associated with traffic-related air pollutants. Conclusion: We were able to develop land use regression models by combining air-quality monitoring data with geographic variables to predict personal exposure in Taipei Metropolis. These LUR models were applied to link with the subjects’ health data. It was found that acute cardiovascular effects were significantly associated with short-term traffic-related air pollution.