云量数据选用对AERMOD模式预测结果影响的差异性分析  

The Influence of the Cloud Cover Data Selection on the Prediction Results of AERMOD Model

作　　者：张瑞欣[1] 仝纪龙[1] 潘峰[1] 杜超[1] 

机构地区：[1]兰州大学大气科学学院,甘肃兰州730000

出　　处：《环境科技》2017年第4期46-51,57,共7页Environmental Science and Technology

基　　金：国家杰出青年科学基金项目(41225018)

摘　　要：采用单因子分析法,在源强参数和地形条件一定的情况下,设置不同预测情形,研究云量数据的选取对AERMOD模式点源和面源预测结果的影响。结果表明,不使用低云量数据时:点源在各计算点的SO_2和NO_2小时、日均、年均最大浓度占标率变化幅度基本在1%以内,符合度指数均大于0.95,预测结果相差不大;除个别点小时浓度外,面源在SO_2和NO_2在各个计算点的小时、日均、年均浓度最大值占标率的变化幅度均在3%以内,符合度指数均大于0.99,预测结果符合程度很好;从符合度指数整体的情况来看无低云量数据对面源预测结果的影响稍大于点源,但影响均不显著。因此,在使用总云量数据的前提下,低云量数据对AERMOD模式预测结果的影响不显著,可忽略不计。In this paper, the single factor analysis method has been used to set different predictive cases under the condition of strong source parameters and terrain conditions. The selection of research of cloud data of AERMOD model of point source and surface source were used to predict results. The prediction results showed that, without the use of low cloud data： the hourly, daily and annual mean concentrations of SO_2 and NO_2 are within 1% of the standard rate for point sources, and the coincidence index of the model predictions is above 0.95 for the two scenarios. For surface sources, SO_2 and NO_2 in the hourly,daily and annual mean concentrations of SO_2 and NO_2 in all the sensitive points were below 3%. According to the coincidence index, the coincidence exponent of model surface source prediction is above 0.99 in both cases. In addition, the influence of cloud data on surface source is slightly larger than that of point source, but the influence is not significant. Therefore, the impact of the low cloud data on the model prediction is not significant and could be neglected when the environmental impact forecast is carried out with the AERMOD model under the premise of ensuring the use of the total cloud data.

关 键 词：低云量 AERMOD 点源 面源 

分 类 号：X8[环境科学与工程—环境工程]