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Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

机译：基于多源数据的北京市PM2.5浓度相关性分析和短期预测

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摘要

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods. (C) 2016 Published by Elsevier Ltd.
机译：PM2.5问题被证明是重大的公共危机，并且引起了公众的极大关注，需要紧急应对。由于PM2.5形成和发展的复杂性，从大气动力学理论的角度来看有关PM2.5的信息和预测仍然受到限制。在本文中，我们尝试使用多源数据挖掘来实现中国北京PM2.5浓度的相关性分析和短期预测。 PM2.5与物理数据（气象数据，包括区域平均降雨量，日平均温度，平均相对湿度，平均风速，最大风速以及其他污染物浓度数据，包括CO，NO2，SO2，PM10）的相关性分析模型），并基于多元统计分析方法提出了社交媒体数据（微博数据）。研究发现，在这些因素的影响下，平均风速的值，CO，NO2，PM10的浓度以及关键词“北京；北京；空气污染与PM2.5浓度具有高度的数学相关性。基于大数据的机器学习模型-反向传播神经网络（以下简称BPNN）模型，进一步研究了相关性分析。发现BPNN方法在相关挖掘中表现更好。最后，本文采用自回归综合移动平均线（以下简称ARIMA）时间序列模型，探索短期时间序列中PM2.5的预测。预测结果与实测数据吻合良好。这项研究不仅有助于实现PM2.5的实时监控，分析和预警，而且还有助于扩大大数据的应用范围和多源数据挖掘方法。 （C）2016由Elsevier Ltd.出版

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