Establishment and Application of Air Quality Statistical Forecasting Model-- Taking Air Quality Data from City A as an Example

2018 International Conference on Air Pollution and Environmental Engineering

生态环境科学

Rao, Xinzhi^1

University of Colorado Boulder, United States^1

关键词: Air quality forecasts;    Automatic monitoring;    Average concentration;    Fine particulate matter (PM2.5);    Statistical forecasting;    Statistical prediction model;    Stepwise regression;    Stepwise regression method;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/208/1/012008/pdf
DOI  :  10.1088/1755-1315/208/1/012008

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

By using the atmosphere automatic monitoring data and meteorological data of January and February 2017 in city A, 19 forecasting factors are selected and the statistical prediction model of winter air quality in city A is established by using the stepwise regression method. Forecast items include fine particulate matter (PM2.5), inhalable particle (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) average daily concentration and ozone (O3) maximum 8h average concentration daily. From November 2017 to January 2018, the model was applied and revised in combination with human experience to carry out the environmental air quality forecast in city A. The comparison between the forecast results and the measured results showed that the level accuracy rate of the environmental air forecast results was 79.1%, and the accuracy rate of the primary pollutant was 73.6%.