都市地區交通污染源之微粒肺部沉積表面積濃度評估

Abstract

肺部沉積表面積（Lung-deposited surface area, LDSA）是指微粒沉積於人體肺部的微粒總表面積。相較傳統以質量濃度為基礎的指標（如PM10或PM2.5），LDSA因同時考量了粒徑分布（Particle size distribution, PSD）與肺部沉積效率，使其更能反映出微粒對人體健康的影響。近年來，LDSA 已逐漸被廣泛應用於暴露風險與毒理研究中，尤其與直徑小於100奈米的超細懸浮微粒（Ultrafine particles, UFPs）所造成的健康效應具有較強的關聯性。 在都市地區，由於人口眾多，使得人為排放的空氣污染物也相對較多，而交通源為該地區最常見的UFPs污染源之一，再加上都市內複雜的建築群結構造成通風不良，使得污染物容易累積，此些原因對都市內的空氣品質和民眾健康皆產生負面的影響。然而，傳統監測方法往往難以掌握 UFPs的時空變異，因此需發展更準確、具健康相關性的暴露評估工具。本研究的主要目標為建立一套可評估都市環境中 LDSA 之時空分布及其相關健康風險的方法，聚焦於兩項核心挑戰：(1) 量化近道路環境中交通排放對LDSA的貢獻，以及 (2) 探討都市風場與建築群對UFPs傳輸與沉積行為的影響。 本研究於臺灣北部一高密度都市區內，整合現地監測、來源解析模型—正矩陣因子法（Positive Matrix Factorization, PMF）與計算流體力學模擬（Computational Fluid Dynamics, CFD）等分析技術，以評估由交通源所造成的LDSA之時空變化。首先，透過近道路監測站測得之PSD，結合PMF進行污染來源鑑別，最終可量化交通源所貢獻之LDSA濃度。CFD 模擬則以實際污染源的PSD為初始條件，模擬1.2公里 × 1.2公里範圍內的都市風場與粒徑介於10–1000 nm之微粒傳輸行為。模擬採用標準k-ε紊流模型（Standard k-ε turbulence model）與改良的對流-擴散模型（Modified convection–diffusion model），分析不同氣象條件與交通情境下 LDSA 的空間分布。此外，本研究亦以 LDSA 為暴露劑量，進一步評估不同年齡族群之超額終生致癌風險（Excess Lifetime Cancer Risk, ELCR）。 結果顯示，由PMF解析出交通相關的來源因子，其粒徑大多集中於 100 奈米以下，且其貢獻時序變化與車流量呈顯著相關（R2 > 0.77）。在量測期間，交通貢獻的LDSA平均濃度為 27.9 μm2/cm3，約佔總LDSA的59%，並且在通勤尖峰時段更可高達74%。CFD模擬結果顯示，LDSA呈現顯著的空間變異，高濃度區域主要集中在地面交通道路的街谷內。夏季的西南風將污染物傳輸至東北區域，再加上排放位置較高的交通源，使其污染影響的範圍較大，而冬季的東北風受到建築構形影響，污染物多集中於局部區域，反映出季節性風場變化對污染熱區的影響。本研究也與過往研究中的LDSA量測值進行比較，雖然模擬與實測 LDSA 在絕對數值上略有差異（主要因本研究僅考量交通源），但相對位置的濃度高低趨勢一致，此也進一步驗證模擬結果的準確性。而ELCR 結果顯示，9 歲兒童族群為最易受影響族群，因其肺部的微粒沉積效率較高，再加上呼吸率高與體重低，造成單位體重暴露劑量較高。 綜合而言，本研究建構一套結合監測數據、來源解析與 CFD 模擬之分析架構，可用於分析都市地區交通源相關LDSA之時空變化特性與健康風險。研究結果也表明，都市區域的LDSA空間濃度會有顯著差異，容易造成單一監測站量測代表性不足的情況。而本研究分析架構可作為都市規劃、空氣品質管理與監測站點選擇的重要參考依據，並促進都市環境中高解析度健康風險評估的發展。

Lung-deposited surface area (LDSA) represents the estimated surface area of airborne particles likely to deposit in the alveolar region of the human respiratory system. Unlike traditional mass-based indicators (e.g., PM10 or PM2.5), LDSA incorporates both the particle size distribution (PSD) and lung deposition efficiency, making it a more physiologically relevant metric for assessing the health risks of airborne particles. LDSA has gained increasing recognition in exposure and toxicological studies due to its stronger association with adverse health effects, particularly from ultrafine particles (UFPs) smaller than 100 nm in diameter. In urban areas, anthropogenic air pollutant emissions tend to be higher due to concentrated human activities, with traffic being one of the most common sources of UFPs. Additionally, the complex building structures in cities often result in poor ventilation, causing pollutants to accumulate. These factors negatively impact both air quality and public health in urban environments. However, traditional monitoring approaches often fail to capture the spatial and temporal variability of UFP exposure. This limitation underscores the need for a more accurate and health-relevant assessment tool. The primary objective of this study is to develop a methodology for evaluating the temporal and spatial variation of LDSA and its associated health risks in urban settings. Specifically, two key challenges are addressed: (1) quantifying traffic contributions to LDSA in near-road environments, and (2) understanding how urban airflow and morphology affect UFP transport and deposition. To address these objectives, this study was conducted in a highly urbanized district in northern Taiwan, characterized by complex urban morphology, high traffic volumes, and dense population. We integrated field measurements, source apportionment, and computational fluid dynamics (CFD) modeling. LDSA, PSD and black carbon concentrations were collected at a roadside site and analyzed using Positive Matrix Factorization (PMF) to resolve traffic-related sources. The CFD simulations were used to model wind flow and particle transport across a 1.2 km × 1.2 km urban domain. The simulations employed the standard k-ε turbulence flow model and a modified convection–diffusion model to simulate the transport of particles ranging from 10 to 1000 nm, enabling the estimation of LDSA spatial distributions under various meteorological and traffic scenarios. Additionally, excess lifetime cancer risk (ELCR) was assessed using LDSA-based exposure metrics and age-specific lung deposition models. The results show that traffic emissions contributed an average LDSA of 27.9 μm2/cm3, accounting for 59% of the total LDSA at the monitoring site during the campaign. Simulated traffic-related LDSA fields exhibited strong spatial heterogeneity, shaped by urban morphology and localized flow patterns. Comparison with measured data confirmed that the spatial trends were accurately captured. Spatial ELCR assessments revealed that the 9-year-old age group experienced the highest exposure risk due to their high lung deposition efficiency. By integrating measurements with CFD simulations, this study enables detailed, source- and spatiotemporal LDSA analysis, addressing the limitations of conventional exposure assessment methods and advancing high-resolution risk estimation in urban environments.