2016~2018年昌吉市空气污染预报及效果检验

The Forecast and Effect Text of Air Pollution in Changji from 2016 to 2018 Years

摘要: 基于2016年1月至2018年12月昌吉市逐日空气质量浓度与同期高空、地面气象要素进行了相关性分析，利用SPSS软件建立了多元线性回归方程，并对预报效果进行检验，具体结论如下：1) 各类污染物变化基本呈V型，冬季是各类污染物质量浓度剧增的季节；其中臭氧的变化趋势为倒V型，夏季是质量浓度最高的季节；PM10、PM2.5的质量浓度在冬季增加明显；2) PM10作为首要污染物在春季出现的次数最多，夏季中臭氧的作为首要污染物出现次数最多；秋季中PM10再次转为首要污染物；冬季PM2.5出现频次最多，四季中SO₂未作为首要污染物出现；3) 春季、夏季和秋季空气质量主要以二级(良)为主；4) 预报效果检验来看，预报值偏高，夏季PM10浓度预报值误差较大，其他类污染物为略偏高；预报结果较实况略偏高，夏半年的预报效果优于冬半年，PM10、PM2.5预报效果较差，SO₂、CO和NO₂的预报效果较好；针对严重污染的空气质量预报效果较差。

Abstract: Based on the correlation analysis between daily air quality concentration and corresponding period of the meteorological factors at high altitude and ground in Changji City from January 2016 to December 2018, the multiple linear regression equation was established by SPSS software, and the prediction effect was tested. The concrete conclusions were as follows: 1) The change of con-centration of various pollutants was basically V-shaped, and winter was the season in which the mass concentration of pollutants increased sharply; the change trend of ozone was “inverted V” type, and summer was the season with the highest mass concentration; the mass concentration of PM10 and PM2.5 increased obviously in winter; 2) PM10 as the primary pollutant appeared most frequently in spring, while the ozone as the primary pollutant appeared most frequent pollutant in summer, PM10 as the primary pollutant was appeared in autumn again, the most high frequency of PM2.5 was in winter, and SO₂ was not as a primary pollutant in four seasons; 3) The air quality in spring, summer and autumn was mainly secondary; 4) According to the test of forecast effect, the forecast value was higher, the forecast error of PM10 concentration in summer was bigger, and the other pollutants were slightly higher; the forecast result was slightly higher than the actual situation, the forecast effect of summer half year was better than winter half year, the forecast effect of PM10, PM2.5 was worse, the forecast effect of SO₂, CO and NO₂ was better; the prediction effect for air quality with serious pollution was poor.