Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece.
Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece.
Environ Pollut. 2019 Mar;246:89-98. doi: 10.1016/j.envpol.2018.11.080. Epub 2018 Nov 26.
In this study, data collected from an urban air quality monitoring network are being used for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations. The examined methods are the Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model. These schemes utilize daily PM and PM concentrations collected from five and three air quality monitoring sites respectively. In order to obtain the resulted estimations, the leave-one-out cross-validation methodology is used for all methods. The evaluation of their predictive ability is performed by using a combination of difference and correlation statistical measures, scatter plots and statistical tests. The results indicate the usefulness of FFNNs as they are found to be statistically significantly superior for modelling the particulate matter spatial variability. The model performance statistics show that in most cases the error values are considerably lower for the FFNN model. Additionally, the rank and Wilcoxon rank tests reveal that the null hypothesis for equal predictive accuracy is rejected for the majority of monitoring sites and schemes (values lower than the critical t-value). According to the comparison results, the FFNN model is selected for forecasting air quality limit exceedances set by the European Union and World Health Organization air quality standards. For two monitoring sites in which the largest number of exceedances occurred, the probability of detection is high while the probability of false detection is very low, further establishing the neural networks' predictive ability.
在本研究中，利用城市空气质量监测网络收集的数据，评估了在提出 PM 空间点估计有效且易于应用的方案背景下用于空间插值的各种方法。研究的方法是反距离加权、两个线性回归模型、多元线性回归和线性混合模型，以及前馈神经网络（FFNN）模型。这些方案分别利用从五个和三个空气质量监测站点收集的每日 PM 和 PM 浓度数据。为了获得结果估计，所有方法都使用留一交叉验证方法。通过使用差异和相关性统计度量、散点图和统计检验相结合的方法来评估它们的预测能力。结果表明，FFNN 非常有用，因为它们在模拟颗粒物空间变异性方面表现出统计学上的显著优势。模型性能统计数据表明，在大多数情况下，FFNN 模型的误差值明显较低。此外，秩和威尔科克森秩检验表明，对于大多数监测站点和方案（低于临界 t 值），相等预测精度的零假设被拒绝。根据比较结果，选择 FFNN 模型来预测欧盟和世界卫生组织空气质量标准规定的空气质量限值超标情况。对于超标次数最多的两个监测站点，检测概率较高，而误报概率非常低，进一步证明了神经网络的预测能力。
