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首页> 外文会议>International Conference on Energy, Environment and Sustainable Development >Spatial Distribution Characteristics and Hourly Forecast of PM_(2.5) Pollution in Summer in Beijing
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Spatial Distribution Characteristics and Hourly Forecast of PM_(2.5) Pollution in Summer in Beijing

机译：北京夏季夏季PM_（2.5）污染的空间分布特征及每小时预报

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The fine particulate matter (PM_(2.5)) has become the primary pollutant of atmospheric environment of Beijing now, therefore to know the spatial distribution characteristics and to make accurate and refined forecast of PM_(2.5) is very important. In this study, the spatial distribution characteristics of PM_(2.5) concentration in summer in Beijing was derived by using Kriging interpolation method based on the data from 30 air quality monitoring stations all over Beijing during June, July and August 2014. In general, the PM_(2.5) pollution is higher in the south and east, and is lower in the north and west, but when there is south or southeast wind blowing, the situation could be totally the opposite. Based on the hourly PM_(2.5) concentration data from one representative air quality monitoring station and the meteorological data from a nearby meteorological station during June, July and August 2014, the hourly PM_(2.5) concentration was forecasted up to 168 hours ahead by using BP (Error-back propagation) and RBF (Radial basis function) neural network methods. The results show RBF neural network method is more efficient, the curve trends of the forecasted values are similar with the curve trends of the monitored values and the forecasted values have significant linear relationship with the monitored values, which demonstrates the possibility of hourly forecast for PM_(2.5) pollution.
机译：细颗粒物质（PM_（2.5））已成为北京大气环境的主要污染物，因此要了解空间分布特性，并准确和精致的PM_（2.5）是非常重要的。在这项研究中，通过使用基于来自北京的30个空气质量监测站的数据，北京夏季夏季夏季夏季浓度的空间分布特征来自于北京，七月和2014年8月的30多个空气质量监测站。一般来说， PM_（2.5）污染在南部和东部较高，南部和西部较低，但当有南风或东南风吹时，情况可能完全相反。基于每小时PM_（2.5）浓度数据来自一个代表性的空气质量监测站和2014年6月的附近气象站的气象数据，每小时PM_（2.5）浓度预计通过使用预先提前168小时BP（错误反向传播）和RBF（径向基函数）神经网络方法。结果表明RBF神经网络方法更有效，预测值的曲线趋势与受监视值的曲线趋势类似，预测值与监测值具有显着的线性关系，这表明PM_的每小时预测的可能性（2.5）污染。

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