遗传算法用于CMAQ模式污染源清单优化的研究

Optimizing Emission Inventory of CMAQ by Means of Genetic Algorithms

污染源清单的准确性是影响CMAQ模式模拟效果的一个重要因素.在Linux系统下建立了基于遗传算法的Models-3/CMAQ模型污染源清单非线性优化系统,包含污染源清单调整、种群初始化、遗传算法、CMAQ模式结果分析4个通用模块.选取1、4、7、10共4个代表月份的典型日,应用此系统优化了2002年北京市可吸入颗粒物污染源清单.优化后4个代表月的污染源排放速率分别提高60.3%、74.8%、72.3%、43.3%.优化前后的污染源清单分别输入CMAQ模式,进行空气质量的模拟,模拟误差分别降低2.6%、7.02%、14.07%、2.17%,表明遗传算法优化CMAQ模式污染源清单效果显著.

A models-3 community multi-scale air quality （CMAQ） modeling system is widely applied to air quality issues in recent years. Emission inventory is an important input data for the CMAQ model. A nonlinear optimizing system based on genetic algorithms （GAs）, which includes four modules： emission inventory adjusting, population initializing, GAs, and CMAQ result analyzing, is developed under the Linux system for optimizing the emission inventory of the CMAQ model. The system is used to optimize the emission inventory of Beijing in typical days. The improved emission inventory is applied to simulate Beijing＇ s PM10 concentrations of January, April, July, and October in 2002. The mean relative errors between the monitoring and the simulation values are reduced by 2.6% , 7.02% , 14. 07% and 2. 17% separately. This indicates that the nonlinear optimizing system based on genetic algorithms is an effective method to improve the emission inventory for the CMAQ modeling system.