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| 基于遗传算法的大气污染总量控制新方法 | |
| 引用本文： | 刘品高,江南,余瑶,陈万隆.基于遗传算法的大气污染总量控制新方法[J].环境污染与防治,2007,29(3):233-237. |
| 作者姓名： | 刘品高 江南 余瑶 陈万隆 |
| 作者单位： | 1. 湖南省气象科学研究所,湖南,长沙,410007;湖南省气象局气象防灾减灾湖南省重点实验室,湖南,长沙,410007 2. 中国科学院南京地理与湖泊研究所,江苏,南京,210008 3. 南京林业大学森林资源与环境学院,江苏,南京,210037 4. 南京信息工程大学应用气象系,江苏,南京,210044 |
| 基金项目： | 南京大学环境学院李宗恺教授、南京大学大气科学系蒋维楣教授、中国气象局大气成分观测与服务中心徐大海研究员、中国环境科学研究院大气环境研究所赵德山研究员等对本项研究给予了热心指导,在此表示衷心的感谢! |
| 摘 要： | 提出一种利用遗传算法进行大气污染总量控制的新方法,并以柳州市SO2总量控制为例说明这种方法的具体运用.遗传算法具备自适应全局搜索寻优特点,从控制点浓度推算源强分布,该源强分布满足总量控制的根本要求.该方法的具体实现是:将区域内各污染源的排放量编码为染色体,让染色体群体在模拟的进化环境下按生物进化规律进行优胜劣汰的自然选择,经过若干代的进化,最终得到的最优个体即代表最佳的源强分布.研究结果表明,这种方法是有效的和可行的. |
| 关 键 词： | 遗传算法 大气污染总量控制 多源模拟 虚点源模式 |
Genetic algorithm based method of atmospheric pollutant total emission control |
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| Liu Pingao,Jiang Nan,Yu Yao,Chen Wanlong.Genetic algorithm based method of atmospheric pollutant total emission control[J].Environmental Pollution & Control,2007,29(3):233-237. | |
| Authors: | Liu Pingao Jiang Nan Yu Yao Chen Wanlong |
| Institution: | 1. Hunan Provincial Institute of Meteorological Sciences, Changsha Hunan 410007; 2. Key Lab of Hunan Province for Meteorological Disaster Prevention and Mitigation, Hunan Provincial Meteorological Bureau ,Changsha Hunan 410007;3. Nanjing Institute of Geography and Limnology ,CAS, Nanjing Jiangsu 210008;4. College of Forest Resources and Environment ,Nanjing Forestry University ,Nanjing Jiangsu 210037 ;5. Department of Applied Meteorology, Nanjing University of Information Science and Technology ,Nanjing Jiangsu 210044 |
| Abstract: | A new method of atmospheric pollutant total emission control (APTEC) based on the genetic algorithm (GA) was developed; a case of total emission control (TEC) of SO2 for Liuzhou City is presented to demonstrate its effectiveness. This method does not require the difficult-to-estimate permitted regional total emission amount (PRTEA) which is required by conventional methods.GA, an effective self-adapted global searching optimization algorithm, is employed for estimating the source strength distribution from the concentration distribution of key grid points of the study area. The calculated source strengths meet the demands of TEC. Using this method, the source strengths are first coded as chromosomes which are then placed in the simulated evolution environment for model simulation. After generations of natural selection, the best chromosome will carry the information of the optimal source strength distribution pattern. The GA-base APTEC method is feasible, simple and effective. |
| Keywords: | Genetic algorithm (GA)Atmospheric pollutant total emission control (APTEC)Multi-source modelingVirtual point source model |
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