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 1.8 μg/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.
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