大氣環境中PM2.5是影響健康的主要危險因子之一，為了減輕人群健康負擔，適當的暴露評估(EA)至關重要。本論文整合了三種常見的EA方法-移動監測系統(MMS)、混合土地利用迴歸模式(h_LUR)和空氣品質監測站(AQMS)數據，以城市尺度的時空地面暴露視角來呈現居民的暴露結果，並比較由此得出的相關健康負擔差異。
我們以台灣台中市的沙鹿地區做為研究區域，該區域已安裝有AQMS收集PM2.5數據，同時我們透過已發表的h_LUR模型獲得了1年的PM2.5估計數據，也自行設計MMS進行地面PM2.5採樣。為了評估地面居民的暴露程度，我們藉由GPS信息，結合了在相同時段與地區範圍內的MMS和h_LUR小時數據，並收集了其他共變量來構建地面預測模型。我們利用GPS特意將研究區域劃分全區為3個區域(工業區、城區和港區)來比較水平空間PM2.5差異。對於長期暴露估計，透過將9年(2013-2021年)AQMS數據(即AQMS9yr)，合併到h_LUR模型和地面預測模型(即h_LUR9yr，MMS9yr)中獲得整個區域和3個區域的9年長時間數據集。
在1年的MMS採樣、h_LUR模式和AQMS對應的每小時PM2.5濃度中，h_LUR和MMS可以呈現水平區域空間差異(在PM2.5水準上，城區=工業區>港區)，而AQMS則無法呈現。另外，通過多元線性迴歸分析建立了合適全區和3個區域的地面預測模型(R2=0.61)。本論文應用地面預測模型，獲得地面9年的年度PM2.5幾何平均值(GM)後，我們發現AQMS年均數據逐年持續下降(從22至11ug/m3)，但低於h_LUR(29.7~ 14.9ug/m3)和MMS(32~18.1ug/m3)。這說明了使用AQMS跟h_LUR都可能低估地面居民的暴露。以2021年的MMS年均PM2.5數據來看，將此暴露評估結果應用於HIA時，使用AQMS監測數據作為人群暴露估計會導致疾病負擔(5種特定疾病)的低估(低估26%~42%)，而使用LUR模型則會低估少一些(11%~18%)。我們還發現，以MMS年均數據而言，每減少一個單位PM2.5(ug/m3)，就能減少疾病負擔歸因分數AF(%)(LRI：1.24，IHD：0.76，LC：0.75，COPD：0.65，stroke：0.76)。控制PM2.5進而對疾病負擔的降低，有益於採行PM2.5控制與預防對策的權衡。
本論文結果顯示，有效的人群地面EA方法值得開發和研究，以免低估PM2.5對健康負擔的貢獻。

(Objectives) Ambient PM2.5 is a significant health risk factor. To understand the actual adverse health effects on ground residents, it is imperative to conduct a comprehensive ground exposure assessment (EA). This study applies three common ground EA methods, Mobile Monitoring System (MMS), Hybrid Land Use Regression (h_LUR), and Air Quality Monitoring Stations (AQMS), to obtain distinct datasets to characterize a city-scale ground exposure and associated health burdens.
(Methods) In the study area, Shalu area in Taichung City, a government-operated AQMS was strategically installed. 1-year PM2.5 datasets collected from the three EA methods were utilized to compare each other and establish ground predictive models (by multiple linear regression). To facilitate comparison of horizontal spatial PM2.5 differences, we employed GPS coordinates to divide the study area into three distinct regions (i.e., industrial, urban and harbor). For long-term ground exposure estimation and subsequent health impact assessment (HIA), we obtained temporal city area and regional MMS data (referred to as MMS9y) using predictive models based on a nine-year dataset spanning from 2013 to 2021. This dataset was derived from AQMS data covering the same nine-year period (AQMS9yr) and the corresponding h_LUR model (h_LUR9yr).
(Results) In both the h_LUR and MMS datasets, higher levels of PM2.5 levels were observed in urban and industrial regions followed by the harbor region. In contrast, AQMS data did not and cannot exhibit these regional differences. The ground predictive models demonstrated reasonable performance with R-squared values (R2= 0.61). When the prediction model was applied to obtain the nine-year annual geometric mean (GM) of the PM2.5 data, a notable trend emerged. AQMS data showed a consistent annual decrease, reducing from 22 to 11 µg/m³, which was lower compared to the h_LUR model (ranging from 29.7 to 14.9 µg/m³) and MMS (ranging from 32 to 18.1 µg/m³). Both EA methods of AQMS and h_LUR underestimated residents’ exposure.
When extrapolating these results to HIA for the year 2021 and comparing with MMS data, it became evident that utilizing AQMS monitoring data as population exposure led to a substantial underestimation of disease burdens, with underestimations ranging from 26% to 42%. The use of the h_LUR model exhibited slightly lower underestimations, ranging from 11% to 18%. Furthermore, our analysis revealed that a reduction in per unit PM2.5 (µg/m³) measured by MMS corresponded to reductions in fractions attributed to disease burden (AF%) as follows: Lower Respiratory Infections (LRI): 1.24, Ischemic Heart Disease (IHD): 0.76, Lung Cancer (LC): 0.75, Chronic Obstructive Pulmonary Disease (COPD): 0.65, and stroke: 0.76.
(Conclusion) Our study emphasizes the critical significance of carefully selecting appropriate EA methods for the adequate evaluation of spatiotemporal ground-level PM2.5. Moreover, it sheds light on the consequential implications of these methodological choices in assessing population health risks associated with air quality of PM2.5.

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