Inversion method for multi-point source pollution identification: Sensitivity analysis and application to European Tracer Experiment data

Fast and accurate identification of unknown pollution sources plays a crucial role in the emergency response and source control of air pollution. In this work, the applicability of a previously proposed two-step inversion method is investigated with sensitivity experiments and real data from the first release of the European Tracer Experiment (ETEX-1). The two-step inversion method is based on the principle of least squares and carries out additional model correction through the residual iterative process. To evaluate its performance, its retrieval results are compared with those of two other existing algorithms. It is shown that for those cases with richer measurements, all three methods are less sensitive to errors, while for cases where measurements are sparse, their retrieval accuracy will rapidly decrease as errors increase. From the results of sensitivity experiments, the new method provides higher estimation accuracy and a more stable performance than the other two methods. The new method presents the smallest maximum location error of 18.20 km when the amplitude of the measurement error increases to 100%, and 22.67 km when errors in the wind fields increase to 200%. Moreover, when applied to ETEX-1 data, the new method also exhibits good performance, with a location error of 4.71 km, which is the best estimation with respect to source location.摘要快速并且准确地识别未知污染源, 在大气污染应急响应和源头控制过程中起着至关重要的作用. 本文利用敏感性试验及欧洲示踪物测场试验(ETEX-1)数据研究了新提出的两步反演算法的实用性, 并将其反演结果与现有的两种算法进行了对比分析. 敏感试验表明, 在观测数据较为丰富的情况下, 三种算法对观测误差和风场误差的敏感性均较低; 而当观测数据较为稀疏时, 所有算法的估计精度都将随着误差的增加而下降, 但与其他两种算法相比, 两步反演算法具有更高的估计精度以及更稳定的估计性能. 此外, 欧洲示踪物测场试验的源项估计结果也表明, 在三个算法中, 两步反演算法具有最小的位置估计误差.