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Химическая безопасность
Article . 2020 . Peer-reviewed
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Application of Monte Carlo method in environmental chemistry

Применение метода Монте-Карло в решении задач экологической химии

Application of Monte Carlo method in environmental chemistry

Abstract

В статье рассматриваются возможности имитационного моделирования с применением метода Монте-Карло для задач прогнозирования последствий залповых выбросов загрязняющих веществ, их распространения и деградации в окружающей среде. Проведен обзор исторического развития данного класса методов моделирования. Рассмотрены предельные случаи режимов диффузии, дрейфа и химической деградации загрязняющего вещества, а также всевозможные их комбинации. Результаты модельных расчетов по Монте-Карло сопоставлены с полученными для идентичных условий результатами численного интегрирования дифференциального уравнения распространения загрязнителя. Показана полная адекватность имитационной модели и продемонстрированы ее преимущества, связанные с устойчивостью решения и меньшими затратами машинного времени. Показаны основные приемы для имплементации модели в среде VBA-Excel. Продемонстрированы возможности разработанного программного инструментария для применения метода Монте-Карло к решению задач пространственно-временной динамики пятна загрязнения в естественных условиях. Проанализированы и сформулированы эффективные приемы получения кинетических кривых по концентрации загрязнителя для выбранного квадрата на местности и построения профилей загрязнения для указанного момента времени. Проведена оценка необходимых параметров модели для получения качественных кинетических кривых и выданы рекомендации по их оптимизации. The article discusses the possibilities of simulations using the Monte Carlo method for predicting the effects of salvo emissions of pollutants, their spread and degradation in the environment. The historical evolution of this class of modeling methods has been reviewed. Limit cases of diffusion, drift and chemical degradation of the pollutant, as well as all their combinations, are considered. The results of the Monte Carlo model calculations are compared with the results of numerical integration of the differential equation for pollutant spreading under identical conditions. The full adequacy of the simulation model is shown and its advantages associated with the stability of the solution and less machine time are demonstrated. The basic knacks for implementing the model in the VBA-Excel environment are described. The possibilities of the developed software tools for applying the Monte Carlo method to solve problems of spatiotemporal dynamics of pollution spot in natural conditions have been demonstrated. Effective methods for obtaining kinetic curves of pollutant concentration for selected square on terrain and constructing contamination profiles for specified time point have been analyzed and formulated. The required parameters of the model have been evaluated to obtain qualitative kinetic curves and recommendations for their optimization are given.

Keywords

залповые выбросы, моделирование окружающей среды, пространственно-временная динамика, массоперенос, вычислительная эффективность, метод Монте-Карло

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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