Approaches to optimizing radioecological survey of territories with various elements of agrolandscapes contaminated by Cs-137

The paper justifies the necessity of developing approaches to the radioecological assessment of Cs-137-contaminated agricultural lands with pronounced landscape features. The main goal is to balance the statistical validity of the results while reducing the material and time costs of field and desk work. A set of methodological principles have been formulated to serve as the foundation for such assessment. They include the application of a probabilistic approach, identification of conditionally homogeneous areas, recognition of the universality of the log-normal law, and a strong focus on optimizing the estimation of probability distribution parameters. The prerequisites for formulating these principles are the patterns of Cs-137 distribution in the soil, established in the course of analysing the consequences of radiation accidents, and the relationship between the in situ measured ambient dose equivalent rate and the "labor-intensive" indicator – the Cs-137 content in the soil. The paper presents the stages of radiological assessment of agricultural lands with different elements of agrolandscapes during the long-term post-accident period. The implementation of the developed procedure requires several key components. These include a GIS project that integrates agroecological data, methods for gathering experimental information and conducting statistical analysis of empirical samples, as well as techniques for estimating the parameters of log-normal distributions of Cs-137 content in conditionally homogeneous soil areas. The final stage involves calculating the risks of exceeding the standards for Cs-137 content in agricultural products, based on the characteristics of the distributions of this radionuclide in soil across different elements of agrolandscapes. It is recommended to use a set of statistical models as a calculation tool to solve three interrelated tasks: assessing radioecological risks, determining the maximum soil contamination density, and estimating the time for self-remediation of agricultural land.