Abstract Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NOx, CO and PM2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NOx, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

中文翻译：

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RLINE 扩散模型在交通相关空气污染物研究中的操作评估


摘要 接触交通相关空气污染物 (TRAP) 仍然是一个关键的公共卫生问题，需要改进接触措施来支持健康影响和流行病学研究，并为监管应对措施提供信息。最近开发的研究 LINE 源模型 (RLINE) 是一种高斯线源扩散模型，已用于 TRAP 暴露的多项流行病学研究，但在此类应用中对 RLINE 性能的评估受到限制。本研究提供了 RLINE 的运行评估，其中将 NOx、CO 和 PM2.5 的预测与位于密歇根州底特律高交通道路附近的空气质量监测站的观测结果进行比较。对于 CO 和 NOx，模型性能在靠近主要道路的地点、顺风条件下、工作日和某些季节期间表现最佳。对于 PM2.5，由于背景水平高、监测网络稀疏以及某些过程（例如二次气溶胶的形成）和非- 移动来源（例如，区域、逃逸）。总体而言，RLINE 在近道路环境中的性能表明其对于估计空间和时间分辨曝光的有用性。该研究强调了与健康影响和流行病学应用相关的考虑因素，包括选择适当污染物、使用适当监测方法、在研究设计过程中考虑盛行风向以及考虑不确定性的重要性。