Department of Physics, City University of Hong Kong, Hong Kong SAR, China.
Int J Environ Res Public Health. 2023 Jan 29;20(3):2412. doi: 10.3390/ijerph20032412.
Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM measurements. When comparing the diurnal hourly measured PM variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application.
由于城市空气污染对健康的不良影响，人们越来越关注城市空气污染。因此，能够快速准确地预测城市空气质量的模型非常重要，这将有助于智慧城市的发展。然而，只有计算流体动力学（CFD）模型才能更好地解析城市峡谷层内的扩散行为。本研究采用人工神经网络（ANN）方法构建了一个机器学习（ML）模型，用于研究在紧凑的高层建筑群环境中车辆衍生空气颗粒物（PM）的扩散情况。各种实测气象参数和 PM 浓度被用作模型输入，以训练 ANN 模型。还建立了相同环境设置下的基于计算流体力学的模型（CFD），以比较其模型性能与 ANN 模型。结果表明，当比较 >1000 小时的 PM 测量值时，ANN 模型表现出良好的性能（r=0.82，分数偏差=0.002）。在比较晴天的逐时实测 PM 变化时，ANN 和 CFD 模型都表现良好（r>0.8）。CFD 模型的良好性能依赖于现场逐时交通剖面的知识、合适的移动源排放因子（例如，来自 MOBILE 6 和 COPERT4）的采用以及城市热力和动力学变量的使用，以捕捉中性和不稳定大气条件下的 PM 变化。这些要求/限制使得它在日常运行中不切实际。相反，这里采用的 ML（ANN）模型没有这些限制，而且速度很快（相对于 CFD 模型，计算时间不到 0.1%）。这些结果表明，ANN 模型是智慧城市应用的一个较好选择。
