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 inversi...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.展开更多
基金supported by the National Key R&D Program of China[grant numbers 2017YFC1501803 and 2017YFC1502102].
文摘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.
