| 引用本文: | 汪宇,彭晓武,沈劲,嵇萍,邓滢,谢敏.基于气象因子的PM2.5回归预测模型研究[J].环境监控与预警,2018,10(4):8-11 |
| ANG Yu, PENG Xiao wu, SHEN Jin, JI Ping, DENG Ying, XIE Min.Research on the Regression Model of PM2.5 Concentration Based on Meteorological Parameters[J].Environmental Monitoring and Forewarning,2018,10(4):8-11 |
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| 摘要: |
| 用Pearson相关系数分析了2013—2016年3大典型城市北京、南京和广州的ρ（PM2.5）与各气象因子的关系。结果表明，3个城市ρ（PM2.5）与各风速因子最大的相关系数依次为-0.44，-0.29和-0.37，与各气温因子最大的相关系数依次为-0.44，-0.33和-0.37，气压与南京和广州的ρ（PM2.5）正相关，气压因子最大的相关系数分别为0.25和0.34，湿度与北京ρ（PM2.5）正相关，与广州ρ（PM2.5）负相关，湿度因子最大的相关系数分别为0.49和-0.36，日照时数与北京ρ（PM2.5）相关系数为-0.46，降水量与南京和广州ρ（PM2.5）相关系数分别为-0.20和-0.24；采用逐步线性回归方法建立城市次日ρ（PM2.5）与气象因子的预测模型，复合相关系数分别为0.722 8，0.770 6和0.809 9。模型预测3个城市2016年PM2.5年均值分别偏高4，5和3μg/m3，日均值平均相对误差为±45.6%，±32.9%和±26.0%，模型对高ρ（PM2.5）普遍低估。 |
| 关键词: 细颗粒物 气象因子 相关性 线性回归 北京 南京 广州 |
| DOI： |
| 分类号:X513 |
| 文献标识码:B |
| 基金项目:国家科技支撑计划基金资助项目（2014BAC21B04）;国家自然科学基金面上资助项目（21477045） |
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| Research on the Regression Model of PM2.5 Concentration Based on Meteorological Parameters |
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ANG Yu, PENG Xiaowu, SHEN Jin, JI Ping, DENG Ying, XIE Min1,2
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1.Guangdong Environmental Monitoring Center, Guangzhou, Guangdong 510308, China;2. South China Institute of Environmental Sciences, Guangzhou, Guangdong 510655, China
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| Abstract: |
| Using Pearson correlation coefficient, relationship between PM2.5 concentration ［ρ（PM2.5］and meteorological factors in three typical cities Beijing, Nanjing and Guangzhou were analyzed during 2013 to 2016. The results showed that the maximun correlation coefficient between ρ（PM2.5） and wind speed factor in the three cities was -0.44, -0.29 and -0.37 in turn, and the maximun correlation coefficient was -0.44, -0.33 and -0.37 for the temperature factor. Atmospheric pressure was positively correlated with ρ（PM2.5） in Nanjing and Guangzhou, and the maximum correlation coefficient of the pressure factor was 0.25 and 0.34, respectively. Humidity was positively correlated with ρ（PM2.5） in Beijing but negatively correlated with ρ（PM2.5）in Guangzhou, with the maximum correlation coefficient of humidity factor 0.49 and -0.36, respectively. The correlation coefficient between sunshine hours and ρ（PM2.5） of Beijing was -0.46. The correlation coefficient between precipitation and ρ（PM2.5） in Nanjing and Guangzhou was -0.20 and -0.24, respectively. The prediction model of next day ρ（PM2.5） and meteorological factors was established by the stepwise linear regression method, with the composite correlation coefficients being 0.722 8, 0.770 6 and 0.809 9 respectively. Annual average ρ（PM2.5） values in 2016 are overestimated by 4， 5 and 3 μg/m3, while the average relative errors for daily mean are ± 45.6%, ± 32.9% and ± 26.0%, respectively The model generally underestimated the high value of ρ（PM2.5）. |
| Key words: PM2.5 Meteorological factor Correlation Linear regression Beijing Nanjing Guangzhou |
