使用微尺度的分散模型来评估城市热点的长期空气污染:在安特卫普的一项案例研究中,FAIRMODE联合对比实践
F Martín1, S Janssen2, V Rodrigues3
1CIEMAT, Research Center for Energy, Environment and Technology, Avenida Complutense 40, 28040 Madrid, Spain.
The Science of the total environment
|March 17, 2024
概括
计算流体动力学 (CFD) 和人工智能模型最好地估计复杂的城市地区的长期NO2度. 这些先进的模型在详细的空间空气质量评估中优于高斯模型,这对于欧洲指令至关重要.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 计算流体动力学的流体动力学.
背景情况:
- 欧洲空气质量建模论坛 (FAIRMODE) 进行了相互比较,以评估NO2度.
- 准确的NO2度数据对于欧洲环境空气质量指令至关重要.
研究的目的:
- 评估哪些建模系统最好地估计复杂的城市地区的长期平均NO2度.
- 为了比较各种建模方法的性能,包括CFD,拉格朗日,高斯和AI.
主要方法:
- 对比利时安特卫普的一个地区进行了高空间分辨率 (800x800 m2) 建模练习.
- 来自监测站和被动采样器的数据用于验证.
- 分析了来自9个团队的15个不同的建模应用程序,使用各种模型类型和场景.
主要成果:
- 所有模型都准确地估计了NO2度的每日波动.
- 高斯模型在没有特定的城市几何参数化的情况下努力提供详细的空间信息.
- CFD,拉格朗日和AI模型在捕捉城市天窗中NO2平均值的空间分布方面表现出卓越的表现.
结论:
- 结合复杂的城市几何形状 (CFD,拉格朗日,AI) 的模型提供了更准确的NO2空间分布估计.
- 稳定状态CFD-RANS模拟提供了与气象场景不稳定模拟相似的结果.
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