Methods and information systems for identification of sources of radioactive air pollution by inverse modeling

Abstract

The paper reviews the methods for identifying an unknown source of pollution by inverse mod-eling and information systems for air pollution forecasting and analysis. Several different for-eign and Ukrainian air pollution forecasting systems, such as the European Union's Nuclear Emergency Response System RODOS, have been developed on the basis of atmospheric transport models. However, the key data that determine the quality of forecasting in such sys-tems are the characteristics of the emission sources. In the case of detection of pollution from an unknown emission source, there should be performed inverse simulation. The use of the RODOS system, as well as other existing forecasting systems for such a task is possible but it requires multiple manual start of calculations of atmospheric transfer models in the reverse mode. Presented in the paper results of the application of inverse modeling methods during ra-diation incidents of the last decade demonstrate that modern methods of inverse modeling are sufficiently developed to set the task of automating inverse modeling in information systems for air pollution analysis and forecasting. Even though these methods not always can exactly identify the source of emissions due to the lack of measurements and poor conditioning of the inverse atmospheric transport problem, their application always leads to a significant reduction (by an order of magnitude or more) in the search for unknown sources compared to the detec-tion of pollutants. At present, in the existing forecasting systems the methods of inverse model-ing are only partially automated, namely for the case of known location and unknown emissions of the source of pollution. Therefore, this paper proposes the architecture of the future system for identifying unknown sources of emissions by inverse modeling.