Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants Ӏ. Application of incomplete adjoint operator
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The ultimate solution to anthropogenic air pollution depends on an adjustment and upgrade of industrial and energy structures. Before this process can be completed, reducing the anthropogenic pollutant emissions is an effective measure. This is a problem belonging to “Natural Cybernetics”, i.e., the problem of air pollution control should be solved together with the weather prediction; however, this is very complicated. Considering that heavy air pollution usually occurs in stable weather conditions and that the feedbacks between air pollutants and meteorological changes are insufficient, we propose a simplified natural cybernetics method. Here, an off-line air pollution evolution equation is first solved with data from a given anthropogenic emission inventory under the predicted weather conditions, and then, a related “incomplete adjoint problem” is solved to obtain the optimal reduction of anthropogenic emissions. Usually, such solution is sufficient for satisfying the air quality and economical/ social requirements. However, a better solution can be obtained by iteration after updating the emission inventory with the reduced anthropogenic emissions. Then, this paper discusses the retrieval of the pollutant emission source with a known spatio-temporal distribution of the pollutant concentrations, and a feasible mathematical method to achieve this is proposed. The retrieval of emission source would also help control air pollution.
KeywordsAir pollution Optimal control Source retrieval Incomplete adjoint operator
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The authors thank Dr. Zhang Peng in the National Satellite Meteorological Center for providing the satellite monitoring data of air pollutions and many useful discussions, and Prof. Zhu Jiang for the helpful discussions. Besides, we wish to thank the anonymous reviewers whose suggestions improved the paper. This work was supported by the National Natural Science Foundation of China (Grant No. 41630530) and the National Key Research and Development Program of China (Grant No. 2016YFC0209000).
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