基于偏相关与逐步回归方法的AQI影响因素分析及预测

Abstract

空气质量指数(AQI)是定量描述空气质量状况的无量纲指数，可用于评价大气环境质量以及控制污染。本文以北京、长沙、海口AQI及PM2.5等6种大气污染指标的日均数据为研究对象。一方面，分别对三个城市的各个指标进行偏相关分析，结果表明影响北京空气质量的主要因素是PM10、PM2.5、NO2和O3；影响长沙空气质量的主要指标是PM10、PM2.5、NO2和CO；而影响海口空气质量的主要因素是PM10、PM2.5和SO2。北京、长沙对AQI影响最大的是PM2.5，而海口的PM10与AQI的相关关系最为显著。另一方面，应用逐步回归分析得到了影响上述三城市AQI的最显著的空气污染指标，结果与偏相关分析的结果相吻合。此外，通过偏相关分析和逐步回归分析，获得AQI的最优线性回归预测模型，模型的拟合效果显著。模型检验表明建立的预测方程能有效用于AQI的短期预测。最后针对分析结果提出一些解决对策，为空气质量改善提供建议。 Air quality index (AQI) is a dimensionless index which describes air condition quantitatively. It can be used to evaluate the quality of atmospheric environment as well as control contamination. In this paper, to investigate the influence of atmospheric pollution indexes on AQI, three cities of China, namely, Beijing, Changsha and Haikou, are chosen for your consideration. Six kinds of at-mospheric pollution index contains PM2.5 of daily date are used as observation. On the one hand, partial correlation analysis is employed for each index of the three cities respectively to deter-mine the main pollutants affecting AQI. It shows the main factors affecting the air quality in Beijing, Changsha and Haikou are PM10, PM2.5, NO2 and O3; PM10, PM2.5, NO2 and CO; PM10, PM2.5 and SO2, respectively. Moreover, the most significant correlation between the six indicators and AQI is PM2.5 for both Beijing and Changsha, but that is PM10 for Haikou. On the other hand, we also use stepwise regression method to access the primary factors affecting the AQI, which is consistent with the conclusion obtained from partial correlation analysis. In addition, we obtain the AQI optimal linear regression prediction model through partial correlation analysis and stepwise regression analysis. Besides, by the model tests, we find the proposed models for the three cities are workable, which can be used to forecast short-term AQI. Finally, some suggestions are provided for improving the air quality according to the results.