PCA-BP模型下皖北城市群PM2.5浓度分析

为探究皖北城市群大气污染物在不同地域、不同时间下的分布规律以及影响空气中PM2.5浓度的相关变量,结合2018-2021年安徽省生态环境厅统计数据及相关地域资料,采用主成分分析(PCA)法按时间段长短及季节性变化分别选取月度数据与年度数据对空气质量的影响因子做相关性分析,对比分析不同季节下空气污染物PM2.5、PM10的浓度及其它空气污染物的变化,构建基于PCA算法的反向传播神经网络 (BP),建立PCA-BP模型并采用交叉-验证法提高模型精度,对大气中的污染物PM2.5浓度做短期预测。实验结果表明:PM2.5浓度的主要影响因子为PM10、CO、NO2、SO2；皖北地区PM2.5含量整体在冬季偏高；预测模型的精度在夏季与秋季较高,冬季较低,四季的预测精度R2分别达到0.924、0.958、0.935、0.794。

PM2.5 concentration analysis in northern Anhui urban agglomeration under PCA-BP model

In order to explore the distribution of air pollutants in different regions and at different times in the northern Anhui urban agglomeration and the relevant variables affecting the concentration of PM2.5 in the air, combined with the statistical data of Anhui Provincial Department of Ecology and Environment from 2018 to 2021 and relevant regional data, The principal component analysis (PCA) method was used to analyze the correlation between the monthly data and the annual data on the influencing factors of air quality respectively according to the time period and seasonal changes. The changes in the concentration of air pollutants PM2.5 and PM10 and other air pollutants in different seasons were compared, and the backpropagation neural network (BP) based on PCA algorithm was constructed. PCA-BP model was established and cross-validation method was used to improve the accuracy of the model, and short-term prediction of PM2.5 concentration in the atmosphere was made. The experimental results show that the main influencing factors of PM2.5 concentration are PM10, CO, NO2 and SO2. PM2.5 content in northern Anhui is higher in winter. The accuracy of the prediction model was higher in summer and autumn, but lower in winter. The prediction accuracy R2 of the four seasons reached 0.924, 0.958, 0.935 and 0.794, respectively.