Melas D, Kioutsioukis I, Ziomas I C
Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece.
J Air Waste Manag Assoc. 2000 Apr;50(4):495-501. doi: 10.1080/10473289.2000.10464039.
In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O3 and NO2 from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.
本文尝试使用神经网络模型对光化学污染物水平进行24小时预测。为此，开发了一个将污染物峰值浓度与气象和排放变量及指标相关联的模型。该分析基于雅典市的臭氧（O3）和二氧化氮（NO2）测量数据。选择气象变量以涵盖决定空气中污染物归宿的大气过程，同时特别注意确保从常规观测或预报中获取所需的输入数据。模型预测与实际观测之间的比较显示出良好的一致性。此外，进行了一系列敏感性测试，以评估模型对气象变量不确定性的敏感性。模型预报通常对大多数输入气象数据的小扰动不太敏感，而对风速和风向的变化相对更敏感。
