北京市冬季PM<sub>2.5</sub>浓度变化特征及估算模型研究

熊俊丽; 李彩艳

doi:10.3969/j.issn.1674-991X.2018.05.070

北京市冬季PM_2.5浓度变化特征及估算模型研究

doi: 10.3969/j.issn.1674-991X.2018.05.070

湖南葆华环保有限公司,湖南长沙 410004

详细信息

作者简介:
熊俊丽(1983—),女,工程师,主要研究方向为生态类环境影响评价,

中图分类号: X701
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- 文章访问数: 612
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- 被引次数: 0
出版历程
- 收稿日期: 2018-03-22
- 刊出日期: 2018-09-20

Study on variation characteristics and estimation model of PM_2.5 concentration in Beijing in winter

Hunan Baohua Environmental Protection Co., Ltd., Changsha 410004, China

摘要

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PM_2.5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PM_2.5与PM₁₀及主要气态污染物浓度的估算模型。结果表明:北京市PM_2.5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PM_2.5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PM_2.5浓度的相关性为PM₁₀>CO>NO₂>O₃>SO₂,12个监测点的PM_2.5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PM_2.5浓度效果较好,而估算其他监测点的PM_2.5浓度效果一般。
- PM_2.5 /
- 气态污染物 /
- 估算模型 /
- 多元逐步回归 /
- 北京市
Abstract: Based on the hourly air quality monitoring data in Beijing in January 2017, the hourly and regional changes of the concentration of PM_2.5 in Beijing were discussed, and the estimation models of PM_2.5, PM₁₀ and major gaseous pollutants of 12 sites were constructed by multiple stepwise regression. The results showed that the hourly concentration change trend of PM_2.5 concentration in Beijing was high at night (18:00-07:00) and low in the daytime (08:00-17:00), the monthly averaged PM_2.5 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 PM_2.5 was in the order of PM₁₀>CO>NO₂>O₃>SO₂. The adjustment coefficient $R adj 2$ of the PM_2.5 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 PM_2.5 concentration of the site by using the estimation model of the site itself, and the effect of estimating the PM_2.5 concentration was poorer by using the data of other sites.
- PM_2.5 /
- gaseous pollutants /
- estimation model /
- multiple stepwise regression /
- Beijing

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