Seasonal Variation Characteristics and Forecasting Model of PM2.5 in Changsha, Central City in China

Abstract

This paper describe the seasonal variation characteristics of PM2.5, PM2.5/PM10 and established the multivariable linear regression model of PM2.5 based on the observation during the period of January 1, 2014 to December 31, 2014 in central city of China. It is found that the mean concentration of PM2.5 has obvious seasonal variation characteristics, the lowest value in summer and the higher value in winter and autumn. The daily average mass concentration of PM2.5 in January is 161.93 μg/m3 and the over standard rate is 90 percent which is the annual maximum; the annual minimum in august. The ratio of PM2.5 and PM10 is high ratio of autumn and winter, up to 2.9, the spring and summer is relatively stable trend, the ratio between 0.6 to 0.9. The two multivariable linear regression model of PM2.5: [PM2.5]=-39.241+0.394 × [PM10]+44.253 × [CO]+0.4 × [BPM2.5] and [PM2.5]=-43.979+0.462 × [PM10]+70.083 × [CO]. The former is suitable for short-term prediction, high accuracy, the latter is suitable for long-term forecast. Certain probability model can predict the trend of the PM2.5 to explore the pros and cons of air quality. But there are still many deficiencies, need to further improve.