3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece
3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece
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DOI：
10.1007/s11270-009-0179-5
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发表时间：
2010-06
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影响因子：
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通讯作者：
K. Moustris;I. Ziomas;A. Paliatsos
中科院分区：
文献类型：
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作者：
K. Moustris;I. Ziomas;A. Paliatsos
以合理的误差预测浓度趋势的困难仍然是一个悬而未决的问题。本文为此做了一些努力。人工神经网络用于预测欧洲区域污染指数的最大日值，以及一天中连续24至72小时内至少有一种污染物超过阈值浓度的小时数。该预测涉及希腊大雅典地区的七个不同地方。本研究中使用的气象和空气污染数据是由希腊环境、自然规划和公共工程部的网络在2001-2005年的5年期间记录的。雅典国家天文台记录了同一时期每小时的气压和全球太阳辐照度值。结果与实际监测数据非常吻合，p< 0.01的显著性水平。
The difficulty in forecasting concentration trends with a reasonable error is still an open problem. In this paper, an effort has been made to this purpose. Artificial Neural Networks are used in order to forecast the maximum daily value of the European Regional Pollution Index as well as the number of consecutive hours, during the day, with at least one of the pollutants above a threshold concentration, 24 to 72 h ahead. The prediction concerns seven different places within the Greater Athens Area, Greece. The meteorological and air pollution data used in this study have been recorded by the network of the Greek Ministry of the Environment, Physical Planning, and Public Works over a 5-year period, 2001–2005. The hourly values of air pressure and global solar irradiance for the same period have been recorded by the National Observatory of Athens. The results are in a very good agreement with the real-monitored data at a statistical significance level ofp< 0.01.