綠地日益被視為關鍵的環境要素，能提升生態品質並促進人類健康，新興證據指出，接觸綠度可能減緩空氣與噪音污染、促進身體活動，並降低身心疾病風險，另有研究顯示，綠度可能透過複雜的生物與行為途徑，調節神經與免疫功能。
儘管綠地對健康的益處已有相當證據，仍存在重要的科學缺口。本文界定了兩個關鍵面向，以推進對綠地潛在健康支持作用的理解，其一，從環境流行病學的觀點，已有大量研究連結綠度暴露與身心健康結局的改善；然而，綠度對人體微生物群叢(microbiota)之潛在影響，人體微生物群叢為免疫功能、代謝過程與疾病易感性的關鍵調節者，然受到的關注相對有限，進一步而言，針對微生物群叢對綠度暴露的反應之研究，特別是在氣喘兒童等脆弱族群中，仍屬不足。
其二，從環境暴露評估的觀點，多數環境流行病學研究仰賴衛星衍生的植生指數量化綠度，該指數主要反映植被密度。此方法雖有助於評估整體綠覆，但可能無法涵蓋綠地其他具生物學意義的面向，尤其是植物所釋放、被認為可能具健康促進效果的芬多精(生物源揮發性有機化合物，biogenic volatile organic compounds, BVOCs)。不同於廣泛可得且標準化的植生指數，開放環境中芬多精濃度的資料仍極為有限。此外，現行暴露模式在空間解析度與方法學嚴謹性上常有不足，難以於複雜自然地景中準確估計此類化合物的分布與變異。
整體而言，這些缺口凸顯出一個整合式研究架構之必要：一方面釐清綠度與人體微生物群叢之間的潛在連結，另一方面發展更完善的方法以估算開放環境中的芬多精濃度。為回應此跨域課題，本文採取雙軌架構：(1)以環境流行病學方法檢驗周遭綠度與氣喘兒童微生物群叢組成之關聯(A部分)；(2)以環境建模方法估算並空間化開放環境、森林地景中的環境芬多精濃度(B部分)。此架構旨在從微生物學與生物源化合物暴露兩條途徑，較為全面地理解綠地對健康的貢獻。
在第一個架構(A部分)中，本研究採用環境流行病學方法，探討以衛星植生指數量化之個人居住綠度與氣喘兒童微生物群叢(鼻腔與腸道)組成的統計關聯 (IRB Number: A-BR106-069)。分析結果顯示，較高的綠度暴露與鼻腔及腸道微生物α多樣性增加呈顯著正相關。具體而言，1個月延遲的綠度平均值與鼻腔微生物多樣性呈正相關，包括觀測菌數(係數= 6.212；95% 信賴區間：2.059–10.370)與菌豐富度(係數= 8.764；95% 信賴區間：1.094–16.430)。腸道微生物多樣性亦呈現類似趨勢，包括觀測菌數(係數=8.311；95% 信賴區間：1.530–16.090)與菌豐富度(係數=15.414；95% 信賴區間：0.265–30.560)。此外，居住地 250 m 緩衝區內的綠度與鼻腔中潛在致病菌屬(如Streptococcus)之相對豐度呈負相關；1 km 緩衝區內的綠度則與更有利的腸道微生物組成相關，包括有益菌屬Bifidobacterium的相對豐度較高。
在第二個架構(B部分)中，本研究發展地理空間機器學習方法，以估算森林環境中環境芬多精的空間分布，並產製高解析度暴露地圖，以補強傳統監測技術在空間細節上的限制。於所測試的模型中，隨機森林(Random Forest, RF)、極限梯度提升(Extreme Gradient Boosting, XGB)與梯度提升(Gradient Boosting, GB)在camphene、α-pinene與α-thujene的預測表現較佳，其R²分別為0.833 (RMSE = 0.046 ppb)、0.984 (RMSE = 0.0001 ppb)與0.789 (RMSE = 0.0001 ppb)。芬多精的空間變異主要受植被組成與密度、局部微氣候與地形特徵影響。模型可解釋性分析進一步指出，優勢樹種與氣象變數為關鍵預測因子。
綜合而言，以上結果從微生物與化學性暴露兩個層面，為綠地的健康支持作用提供互補證據。本研究強調結合環境建模與族群健康研究的重要性，並為後續旨在支援循證都市設計與公共衛生策略的研究提供方法學基礎。

Green spaces are increasingly recognized as critical environmental features that contribute to enhanced ecological quality and human health. Emerging evidence has shown that exposure to greenness may mitigate air and noise pollution, encourage physical activity, and reduce the risk of both physical and psychological disorders. Moreover, greenness has been implicated in modulating neural and immune functions through complex biological and behavioral pathways.
Despite the well-documented benefit effects of green spaces for human health, important scientific gaps remain. This study identified two key areas that advance understanding of the potential health-supportive roles of green space. First, from an environmental epidemiology perspective, numerous studies have linked greenness exposure to improved physical and psychological health outcomes. However, the potential role of greenness in influencing the human microbiota, an essential regulator of immune function, metabolic processes, and disease susceptibility, has received limited attention. Furthermore, investigations into microbiota responses to greenness exposure, particularly in vulnerable populations such as children with asthma, remain underexplored.
Second, from an environmental exposure assessment perspective, most environmental epidemiology studies have relied on satellite-derived vegetation indices to quantify greenness, primarily reflecting vegetation density. While this method is valuable for assessing general green coverage, it may fail to capture other biologically meaningful dimensions of green space, particularly the emission of phytoncides, or biogenic volatile organic compounds (BVOCs), which are emitted by vegetation and are known for their potential health-promoting effects. Unlike vegetation indices, which are widely available and standardized, data on ambient phytoncide concentrations in open environments remain limit. Moreover, current exposure models often lack the spatial resolution and methodological rigor needed to accurately estimate the distribution and variability of these compounds across complex natural landscapes.
Together, these gaps underscore the need for an integrated research framework that can elucidate the potential links between greenness and the human microbiota, while also developing improved methods to estimate ambient phytoncide levels in open environments. To deal with these interdisciplinary challenges, this pilot study adopted a dual-framework approach: (1) an environmental epidemiological approach to examine associations between surrounding greenness and microbiota composition in pediatric asthma patients (Part A); and (2) an environmental modeling approach to estimate and spatially map ambient phytoncide concentrations in open environment, forested landscapes (Part B). This framework was designed to serve a more comprehensive knowledge of how green area contributes to health via both microbiological and exposure to biogenic compounds.
In the first framework (Part A), applied an environmental epidemiological approach to explore statistical associations between personalized residential greenness, measured using satellite-derived vegetation indices, and the microbiota composition of children with asthma, a population particularly vulnerable to environmental exposures (IRB Number: A-BR106-069). The analysis revealed that higher levels of greenness exposure were significantly associated with increased microbial α-diversity in both nasal and gut microbiota. Specifically, the 1-month lagged average of greenness was positively associated with nasal microbiota diversity, including observed bacterial counts (coefficient = 6.212; 95% CI: 2.059–10.370) and bacterial richness (coefficient = 8.764; 95% CI: 1.094–16.430). Similarly, positive associations were observed for gut microbiota diversity, including observed bacteria (coefficient = 8.311; 95% CI: 1.530–16.090) and bacterial richness (coefficient = 15.414; 95% CI: 0.265–30.560). In addition, residential greenness within a 250 m buffer was inversely associated with the relative abundance of potentially pathogenic nasal genera such as Streptococcus. Greenness within a 1 km buffer was also linked to more favorable gut microbiota profiles, including an increased abundance of Bifidobacterium, a genus widely recognized for its beneficial effects on gut health.
In the second framework (Part B), a geospatial machine learning approach was developed to estimate the spatial distribution of ambient forest phytoncides. High-resolution exposure maps were generated to address limitations in conventional monitoring techniques, providing spatially detailed estimates of phytoncide concentrations. Among the tested models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) demonstrated superior predictive performance for estimating camphene, α-pinene, and α-thujene, with R² values of 0.833 (RMSE = 0.046 ppb), 0.984 (RMSE = 0.0001 ppb), and 0.789 (RMSE = 0.0001 ppb), respectively. Spatial variability in phytoncides was primarily influenced by vegetation composition and density, local microclimate, and topographic features. Model interpretability was further enhanced through explainable artificial intelligence tools, which identified dominant tree species and meteorological variables as key predictors.
Together, these results serve complementary insights into the health-supportive roles of green spaces, emphasizing the relevance of both microbiome and chemically exposure processes. This work underscores the importance of integrating environmental modeling with population-based health research and offers a foundation for future studies aimed at informing evidence-based urban design and public health strategies.

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