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Prediction of PM_(2.5) concentration based on the similarity in air quality monitoring network

机译：基于空气质量监测网络相似度的PM_（2.5）浓度预测

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摘要

Recently particulate matter pollution has becoming more and more serious in China and plenty of equipment has been purchased to detect it in air quality monitoring network. But it is inevitable to make the government to bear a significant financial burden because of expensive equipment. With this consideration, we attempt to explore some practicable methods to estimate the pollutant concentration with available data at surrounding stations instead of measurement. In light of this, the Spearman correlation analysis and cluster analysis are utilized to reveal the similar behavior in Shanghai PM2.5 monitoring network respectively. They coincidentally demonstrate that there exists redundant equipment in monitoring network. Then based on it, the linear method of stepwise regression and the nonlinear method of support vector regression are applied to predict PM2.5 concentration at target station in term of the values at surrounding stations. Both of them show good performance and they are recognized to be practicable to estimate the values measured by redundant equipment. Obviously, these findings give rise to the possibility to remove some equipment in monitoring network. Hence, in order to remove it reasonably, two removing criteria for redundant equipment are suggested finally. It makes use of the similarity in air quality monitoring network and guarantees that the missed values caused by removed equipment can be replaced successfully through prediction, which are advantage for monitoring network advisors to make informed decisions as to whether a redundant equipment must be removed or relocated.
机译：最近，在中国，颗粒物污染变得越来越严重，并且已经购买了许多设备在空气质量监测网络中进行检测。但是不可避免的是，由于设备昂贵，政府不得不承担沉重的财政负担。考虑到这一点，我们尝试探索一些可行的方法来估计周围环境的污染物浓度，而不是进行测量。有鉴于此，利用Spearman相关分析和聚类分析分别揭示了上海PM2.5监测网络中的相似行为。他们恰巧证明了监控网络中存在冗余设备。然后在此基础上，采用逐步回归的线性方法和支持向量回归的非线性方法，根据周围站点的值预测目标站点的PM2.5浓度。它们都表现出良好的性能，并且被认为可以估计由冗余设备测得的值。显然，这些发现引起了拆除监控网络中某些设备的可能性。因此，为了合理地去除它，最后提出了两个冗余设备的去除标准。它利用了空气质量监测网络中的相似性，并保证通过预测可以成功地替换掉因设备故障而导致的遗漏值，这对于监测网络顾问就是否必须拆卸或重新布置冗余设备做出明智的决定是有利的。 。

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