A semiparametric statistical approach for forecasting SO2 and NOx concentrations

作者：Lu Hongwei; Zhang Yimei; Wang Xiahui; He Li^*

来源：Environmental Science and Pollution Research, 2014, 21(13): 7985-7995.

DOI：10.1007/s11356-014-2748-4

• Air quality
• Forecasting
• Semiparametric statistical approach
• Multivariate regression
• Stepwise cluster
• Acid deposition

This study develops a new semiparametric statistical approach for urban air quality forecasting. Compared to conventional approaches, the semiparametric approach allows the model users to benefit from the positive aspects and alleviate the negative ones of parametric and nonparametric approaches. Two advantages of the approach lie in (1) the interpretation of the data set being easily decoded and used by the model and (2) its capability in dependence on prior assumption. To illustrate the performance of the proposed approach, three semiparametric regression models (i.e., linear-, quadratic-, and interactive-based semiparametric regression) are applied to an air quality forecasting problem in the city of Xiamen, China, and satisfactory training and prediction performance are obtained. The three models are also compared to three parametric and two nonparametric regression models. The results indicate that the predictive accuracy of semiparametric regression models is higher than those obtained from the parametric and stepwise cluster analysis models. However, the proposed three semiparametric regression models could be much favored, since they can be achieved more easily and rapidly than the artificial neural network model.

• 出版日期2014-7
• 单位环境保护部环境规划院; 华北电力大学（保定）; 华北电力大学