基于微区域PM2.5浓度卡尔曼插值预测模型的研究  

Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration

作　　者：王伟[1] 郑斌[1] 陈彬林 安耀明 姜小明 李章勇[1] 

机构地区：[1]重庆邮电大学生物医学工程研究中心,重庆400065

出　　处：《生物医学工程学杂志》2018年第1期64-69,共6页Journal of Biomedical Engineering

基　　金：重庆市科委(物联网专项)项目(cstc2015zdcy-ztzx120002);重庆市科委共性关键技术项目(cstc2015shmsztzx0098);重庆市教委科学技术研究项目(KJ1704073)

摘　　要：目前颗粒物(尤其是PM2.5)污染问题日趋严重,人们对其关注度越来越高。本文提出一种结合三次样条插值方法的卡尔曼预测模型并将其应用于微区域校园环境PM2.5浓度的预测,以及实现PM2.5浓度的插值模拟图,模拟PM2.5的空间分布。本文实验基于实验室已搭建的环境信息监测系统服务器数据,其PM2.5浓度数据预测值和实际值通过Wilcoxon带符号秩检验后,双侧渐进显著性概率为0.527,远大于显著性水平α=0.05。同时,与神经网络模型预测方法(BP预测)和支持向量机预测方法(SVM预测)对比,卡尔曼预测模型的结果更理想,其日均值PM2.5浓度数据预测值和监测值的平均绝对误差(MEA)为1.8μg/m3,平均相对误差(MER)为6%,相关系数R为0.87。实验结果表明:卡尔曼预测模型能有效地用于PM2.5浓度预测,结合样条插值方法可以较好地模拟PM2.5的空间分布及局部污染特征。In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people＇s attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error （MEA） of Kalman prediction model was 1.8 μg/m3, the average relative error （MER） was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation （BP） prediction and support vector machine （SVM） prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

关 键 词：PM2.5浓度 卡尔曼预测 三次样条插值 微区域 

分 类 号：X513[环境科学与工程—环境工程]