北京市冬季PM2.5浓度变化特征及估算模型研究

Study on variation characteristics and estimation model of PM2.5 concentration in Beijing in winter

  • 摘要: 基于北京市2017年1月的每小时空气质量监测数据,探讨了北京市PM2.5的小时浓度与区域浓度变化特征,利用多元逐步回归构建了12个监测点的PM2.5与PM10及主要气态污染物浓度的估算模型。结果表明:北京市PM2.5浓度的小时变化趋势为晚间高(18:00—次日07:00),白天低(08:00—17:00)。怀柔镇、定陵及昌平镇3个监测点的PM2.5月平均浓度较低,为78~94 μg∕m 3,其余监测点为106~128 μg∕m 3。PM10及主要气态污染物与PM2.5浓度的相关性为PM10>CO>NO2>O3>SO2,12个监测点的PM2.5浓度估算模型调整系数( R adj 2 )均达到0.96以上,标准误差(SE)为13.6~24.5,利用各监测点估算模型预测该监测点的PM2.5浓度效果较好,而估算其他监测点的PM2.5浓度效果一般。

     

    Abstract: Based on the hourly air quality monitoring data in Beijing in January 2017, the hourly and regional changes of the concentration of PM2.5 in Beijing were discussed, and the estimation models of PM2.5, PM10 and major gaseous pollutants of 12 sites were constructed by multiple stepwise regression. The results showed that the hourly concentration change trend of PM2.5 concentration in Beijing was high at night (18:00-07:00) and low in the daytime (08:00-17:00), the monthly averaged PM2.5 concentrations of 3 sites in Huairou Town, Changping Town and Dingling were relatively low (78-94 μg∕m 3), while that of other sites were between 106-128 μg∕m 3. The correlation between PM10, 4 kinds of gaseous pollutants and PM2.5 was in the order of PM10>CO>NO2>O3>SO2. The adjustment coefficient R adj 2 of the PM2.5 concentration estimation model of the 12 sites was all more than 0.96, and the standard error SE was between 13.6-24.5. It is more effective to estimate the PM2.5 concentration of the site by using the estimation model of the site itself, and the effect of estimating the PM2.5 concentration was poorer by using the data of other sites.

     

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