地面气象观测资料在空气法规模型的标准化研究

以云量、风速、风向、温度3类地面气象观测数据的获取方法为研究对象,探讨如何规范地面气象观测数据在模型中的标准化应用.结合我国2008年颁布的《环境影响评价技术导则 大气环境》推荐的模型AERMOD对所需要的地面气象数据需求,并以内蒙古自治区正蓝旗上都电厂SO2实测数据为验证数据,在模型其他输入参数不变的情况下,利用试验站10min和1h地面气象数据,并分别以低云量和总云量代替蔽光云量,设置4种情景.其中,情景一使用试验站10min地面气象数据,并用低云量代替蔽光云量.与情景一相比,情景二使用总云量代替蔽光云量,情景三使用1h地面气象数据,情景四使用试验站1h地面气象数据,并用总云量代替蔽光云量.4种情景除了上述不同点,其他地面气象参数均相同.结果表明,在4种情景中,情景二、三、四的FB值均小于情景一,更靠近0.关于RHCR值,情景三和四更靠近于1,分别为1.33和1.41,表明在预测高端值时,情景三和四的效果更好.情景二的RHCR值为1.51,大于情景三,说明风相较于云对模型模拟结果影响更大.由FB值、RHCR值以及Q—Q图的综合分析得出,情景四的模拟值更接近实际监测值,其采用的地面气象数据全面符合本研究所推荐的数据标准化应用方法, 规范了模型数据标准化应用,提升了大气环境影响评价预测精度.

Standardization of meteorological observation data of surface in air regulatory model

In this study, the acquisition method of meteorological observation data of surface, such as cloud cover, wind speed, wind direction and temperature, was investigated to explore how to regulate the standardized application of meteorological observation data of surface in the model. Combined with the demand for meteorological data of AERMOD model, recommended in the “Guidelines for Environmental Impact Assessment-Atmospheric Environment” of HJ2.2-2008 promulgated by China, we set up four scenarios. We used measured SO2 data of Shangdu Power Plant as the verification data. In the case of other model input parameters constant, the four scenarios used 10minutes and 1hour meteorological data of surface of experiment station, and replaced opaque sky cover with total cloud amount and low cloud amount respectively. Scene One used 10minutes meteorological data of surface and replaced opaque sky cover with low cloud amount. Compared to Scene One, Scene Two replaced opaque sky cover with total cloud amount, Scene Three used 1hour meteorological data of surface, Scene Four used 1hour meteorological data of surface and replaced opaque sky cover with total cloud amount. In addition to the differences mentioned above, other meteorological data of surface were the same in the four scenarios. The results indicated that FB was closer to 0in Scene Two, Scene Three and Scene Four, less than that in Scene One. The RHCR in Scene Three and Scene Four was 1.33 and 1.41respectively, closer to 1, showing that Scene Three and Scene Four were better than other scenes in the prediction of high value. The RHCR of Scene Two exceeded Scene Three, indicating that wind had a greater effect on the simulation results in model than cloud. Composite analysis of FB and RHCR and Q-Q plots showed that the simulation of Scene Four was closer to the observation, the meteorological data were fully consistent with the standardized data application method recommanded in this study. This scene regulated standardized application of data in model and improved accuracy of the prediction of atmospheric environmental impact assessment.