通过模拟较小的道路交通量来改进与交通有关的空气污染估计
Miguel Alvarado-Molina1, Ariadna Curto2, Amanda J Wheeler3
1Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Environmental pollution (Barking, Essex : 1987)
|October 9, 2023
概括
估计小路上的交通是空气污染暴露的关键. 一个线性模型的年平均每日流量 (AADT) 在小路显示出更好的准确性比固定值的方法,当与交通和污染数据验证.
科学领域:
- 环境科学 环境科学
- 运输工程 运输工程
- 公共卫生 公共卫生
背景情况:
- 准确估计小路的年度平均每日交通量 (AADT) 对于评估与交通有关的空气污染 (TRAP) 暴露至关重要,特别是在住宅区.
- 小型道路占城市道路网络的很大一部分,但往往缺乏全面的AADT数据.
研究的目的:
- 评估三种用于估计澳大利亚墨尔本小路AADT的方法的直接和间接外部有效性.
- 使用各种预测变量来比较固定值方法,线性模型和负二项式 (NB) 模型.
主要方法:
- 在小路上的AADT估计使用固定值方法 (每天600辆车),线性模型和NB模型,结合道路类型,人口密度和道路密度等因素.
- 直接验证使用了外部流量计数,而间接验证使用了201个地点的黑碳 (BC) 和超细颗粒 (UFP) 测量.
- 使用Akaike信息标准 (AIC),直接验证的一致性相关系数 (CCC) 和间接验证的Spearman相关系数来比较模型性能.
主要成果:
- 较小的道路占墨尔本88.5%的道路,但只有18.9%的道路记录了AADT.
- 线性和NB模型显示了可比的性能 (AIC:1,023,686与1,058,502).
- 在住宅区,线性模型 (CCC:0.47) 和NB模型 (CCC:0.29) 对交通计数进行了更好的直接验证,而不是固定值方法 (CCC: -0.001). 对UFP的间接验证显示,与固定值方法 (斯皮尔曼:0.34) 相比,对线性模型的相关性更高 (斯皮尔曼:0.50).
结论:
- 在对交通和TRAP测量进行验证时,线性模型表现出优于固定值方法的性能.
- 开发的方法适用于具有不完整小路AADT数据的其他地区.
- 在次要道路上改进的AADT估计可以提高TRAP暴露评估的准确性.
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