摘要
根据2005年、2006年采暖期RegCM 3模式输出产品和郑州市环境监测中心逐日监测资料,利用逐步回归方法建立了PM10、SO2、NO2等污染物质量浓度预报方程。该方法在2007年采暖期的试报中效果不理想,预报准确率明显低于历史拟合率。为了提高预报准确率,针对目前采用的统计方法中存在的不足,即在选择预报因子时没有考虑预报因子之间的相关性,挑选的预报因子由于非正交,使回归计算的结果不稳定。将自然正交分解和多元回归分析结合起来,以采暖期各污染物的日均质量浓度为预报对象,建立预报模型。结果表明,采用新方法制作的空气质量预报准确率有一定程度提高。
Based on the output data of model RegCM3 during the heating period of 2005 and 2006,and the daily monitoring data of Zhengzhou Municipal Environmental Monitoring Center,a prognostic equation is constructed to forecast the mass concentrations of air pollutants PM10,SO2 and NO2 by using the stepwise regression method.But the application of the method to forecasting tests of the pollutants＇ mass concentrations in the heating period of 2007 is not satisfactory,and the forecasting accuracy is significantly lower than the historical fitting rate.In order to improve the forecasting accuracy,according to deficiency of the statistical method（the correlations among forecasting factors are not considered when selecting them,so the nonorthogonalities among them result in regression instability and more errors）,a new forecasting model is proposed by using the empirical orthogonal function（EOF） combined with the multiple linear regression analysis,and the daily mean mass concentrations of pollutants are chosen as the forecasting object during the heating period.Results show that the new method can improve the forecasting accuracy of air quality in a certain degree.
出处
《南京气象学院学报》
CSCD
北大核心
2009年第2期314-320,共7页
Journal of Nanjing Institute of Meteorology
基金
公益性行业(气象)科研专项(GYHY(QX)200706026)
教育部留学回国人员科研启动基金项目
江苏省"六大人才高峰"计划资助项目
