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深圳市大气能见度与细粒子浓度统计模型
Statistical Model of the Relationship Between Atmospheric Visibility and PM2.5 in Shenzhen
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摘要: 利用深圳市2007年全年逐时能见度、PM2.5质量浓度和相对湿度观测数据, 在分析大气消光机理及其影响因素的基础上确立了能见度与PM2.5之间的基本模型关系, 着重讨论分析了相对湿度对颗粒物消光影响的常见修正方式, 并通过线性和非线性回归分析筛选相对湿度影响修正因子fRH的表达形式和确定模型参数, 最终建立起适合于深圳本地情况的能见度与PM2.5之间的最优统计模型 (R2=0.43, n=8024)。进一步利用能见度与PM2.5的日平均值进行了多元回归分析, 模型拟合值与实测值之间的相关系数 (R2) 高达0.73(n=350), 而且预测偏差范围小, 较好地反映了深圳市大气能见度与PM2.5之间的定量相关关系。Abstract: With the development of economy, the level of air quality in main cities of China has experienced a continuously deteriorating process. Pearl River Delta Region are confronting with the disturbance of more and more haze weather especially. As one of the cities with the most serious haze problem in the region, Shenzhen experiences 231 haze days in 2007 according to the definition of haze that visual range is lower than ten kilometers and relative humidity is not higher than 80% at the same time. The degradation of atmospheric visibility is mainly caused by the extinction effect of aerosol particles, especially of fine particles, including scattering by inorganic components and absorption by black carbon. The average concentration of fine particle (particle whose aerodynamic diameter is lower than 2.5 μm) in Shenzhen is as high as 53.4±37.3 μg·m-3 in 2007, which is a little lower than other main cities in China but two times higher than American national standard enacted by USEPA. Accordingly, the average visibility is as low as 13.4±9.3 km, and shows the same seasonal variation as fine particle concentration. However, few studies on the quantitative relationship between visibility and fine particles in Chinese cities are reported in the literature. Based on the analysis of the extinction mechanisms and relevant influential factors, statistic models are developed for describing the relationship between visibility and fine particles in the urban air of Shenzhen using multiple regression techniques. The data includes visual range acquired by visual observation, fine particles concentration generated by TEOM 1400a (an online instrument for monitoring the concentration of fine particles) and relative humidity (RH). All the data are monitored simultaneously in the year of 2007, and abnormal values are excluded before regression analysis. fRH is used to eliminate the light extinction of humidity to particles, and four usual forms of it are discussed too. Multiple linear and nonlinear regression methods are used for regression analysis and the initial values of parameters come from literatures for nonlinear regression. Finally, a power function form of fRH containing underlying physical mechanism of particles' extinction is selected to reflect humidity's effect due to good agreement and the compact form. The complete model expression is given at the same time. The correlative coefficient between the observed visibility values and the reconstructed visibility values using the best model is 0.40 for the 1-hour average data. The model for 24-hour average data is also established in the same form. The correlative coefficient reaches as high as 0.73, and the deviation of the reconstructed values is small, so the model can properly reflect a good relationship between visibility and fine particles concentration. In addition, the expression of extinction efficiency changing with relative humidity demonstrates the similar increasing patterns in existing study, and reasonably describes the relationship between extinction efficiency and relative humidity in Shenzhen.
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图 1 能见度模型拟合效果(1 h平均)
Fig. 1 Relationship between measured and reconstructed vsibility for 1-hour average data
图 2 能见度拟合模型(24 h平均)
Fig. 2 Relationship between measured and reconstructed vsibility for 24-hour average data
图 3 消光效率随相对湿度增长曲线
Fig. 3 The curve of extinction efficiency depending on relative humidity
表 1 不同fRH形式的模型拟合结果
Table 1 Regression models with different fRH forms
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