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Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms

机译：使用自适应进化算法表征大气污染物源

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The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source.rnThe proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.
机译：使用基于地面浓度测量和高斯羽流模型的自适应进化策略（AES）方法，可以识别大气中未知排放源的特征。 AES方法选择了一组初始的源特性，包括位置，大小，质量排放率和风向，从中进行前向色散模拟。计算了来自试探性源的模拟浓度与观测到的地面测量值之间的误差。然后，AES算法规定了源特性的下一个暂定集合。迭代朝着最小误差的方向进行，对应于向真实源的收敛。rn所提出的方法用于确定草原草场色散实验中12种释放的源特征，每个大气稳定度类别有2种，范围从非常不稳定到稳定大气层。发现AES算法比用作基准的简单规范ES和蒙特卡洛（MC）方法具有优势。

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