Department of Chemical Engineering and Mary Kay O'Connor Process Safety Center, Texas A&M University at Qatar, Education City, Doha, PO Box 23874, Qatar.
Qatar Biobank for Medical Research, Doha, 5825, Qatar.
Sci Rep. 2018 Apr 4;8(1):5596. doi: 10.1038/s41598-018-23767-8.
The intentional or accidental release of airborne toxics poses great risk to the public health. During these incidents, the greatest factor of uncertainty is related to the location and rate of released substance, therefore, an information of high importance for emergency preparedness and response plans. A novel computational algorithm is proposed to estimate, efficiently, the location and release rate of an airborne toxic substance source based on health effects observations; data that can be readily available, in a real accident, contrary to actual measurements. The algorithm is demonstrated by deploying a semi-empirical dispersion model and Monte Carlo sampling on a simplified scenario. Input data are collected at varying receptor points for toxics concentrations (C; standard approach) and two new types: toxic load (TL) and health effects (HE; four levels). Estimated source characteristics are compared with scenario values. The use of TL required the least number of receptor points to estimate the release rate, and demonstrated the highest probability (>90%). HE required more receptor points, than C, but with lesser deviations while probability was comparable, if not better. Finally, the algorithm assessed very accurately the source location when using C and TL with comparable confidence, but HE demonstrated significantly lower confidence.
空气中有毒物质的有意或无意释放对公众健康构成巨大风险。在这些事件中，最大的不确定性因素与释放物质的位置和速度有关，因此，这是应急准备和响应计划的重要信息。提出了一种新的计算算法，该算法基于健康影响观测来有效地估计空气传播有毒物质源的位置和释放速率；在实际事故中，可以很容易地获得数据，而不是实际测量。该算法通过在简化场景中部署半经验扩散模型和蒙特卡罗抽样进行了演示。在不同的受体点收集了有毒物质浓度（C；标准方法）和两种新型数据：有毒负荷（TL）和健康影响（HE；四个级别）。将估计的源特征与场景值进行了比较。TL 的使用需要最少数量的受体点来估计释放率，并且具有最高的概率（>90％）。HE 需要比 C 更多的受体点，但偏差较小，而概率相当，如果不是更好的话。最后，该算法在使用 C 和 TL 时非常准确地评估了源位置，具有可比的置信度，但 HE 显示出明显较低的置信度。
