Abstract <p>The prediction of the level of air pollution by gaseous and aerosol constituents in cities becomes increasingly significant in view of their serious negative impact on public health and growing ecological risks. The article presents an approach to estimating and adjusting the emission power of anthropogenic sources based on direct and inverse modeling. The WRF-Chem model was used as a direct simulation tool, and the IMDAF system developed by the authors was used for inverse simulation. The results of direct simulation provided data on meteorological fields and the distribution of admixtures necessary for solving adjoint problems. The use of the adjoint problem method made it possible to calculate a correction factor that determines how much the power of sources that fall into the sensitivity zone should be changed to achieve the best agreement with measurements. Our approach can be used to improve the prediction of air quality, refine the inventories of anthropogenic emissions, and develop the strategies for reducing the ecological risks on global and regional scales.</p>

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Adjustment of the Power of Model Emissions of Anthropogenic Atmospheric Pollution Sources Based on Measurement Data and Adjoint Problem Methods

  • P. N. Antokhin,
  • A. V. Penenko,
  • M. Yu. Arshinov,
  • B. D. Belan,
  • A. V. Gochakov

摘要

Abstract

The prediction of the level of air pollution by gaseous and aerosol constituents in cities becomes increasingly significant in view of their serious negative impact on public health and growing ecological risks. The article presents an approach to estimating and adjusting the emission power of anthropogenic sources based on direct and inverse modeling. The WRF-Chem model was used as a direct simulation tool, and the IMDAF system developed by the authors was used for inverse simulation. The results of direct simulation provided data on meteorological fields and the distribution of admixtures necessary for solving adjoint problems. The use of the adjoint problem method made it possible to calculate a correction factor that determines how much the power of sources that fall into the sensitivity zone should be changed to achieve the best agreement with measurements. Our approach can be used to improve the prediction of air quality, refine the inventories of anthropogenic emissions, and develop the strategies for reducing the ecological risks on global and regional scales.