Дисертації | Dissertations

The dissertation is devoted to developing methods, models and information technology for monitoring the state of environmental pollution based on trend forecasting models, statistical fractal estimation, etc. The developed methods, models and information technology can be used to improve the efficiency of environmental management in the region, in particular in large cities, based on monitoring the level of pollution, the stability of pollution in the dynamics, the cyclicality of emissions, forecasting pollution levels for future periods and trends in future pollution levels. Developing models of methods and information technology for monitoring environmental pollution is urgent. First, creating effective technologies for monitoring pollution is key to preserving citizens' health and quality of life, particularly in large cities. Monitoring the level of pollution and developing effective control strategies are critical to preserving the health of citizens. Effective pollution monitoring systems can also lead to economic benefits, including reduced disease treatment costs, improved quality of life, and promoting sustainable development. some types of pollution, such as greenhouse gas emissions, can lead to climate change, which has global impacts on ecosystems and human society. Monitoring and reducing these emissions is important to preserve the climate and reduce its negative impacts. Many countries have legislation regulating the level of environmental pollution. Developing and implementing effective monitoring systems helps ensure compliance with these regulations and standards. In these conditions, several unresolved issues arise. There are no sufficiently developed systems that would be focused not only on measuring the level of pollution by various indicators but also on making a qualitative forecast and assessing the state of the environment in a particular area. The structure of the time series of environmental pollution parameters can be a valuable source of information on the stability of pollution in the dynamics, the cyclical nature of harmful emissions, and helps to effectively predict pollution levels for future periods and trends in future pollution levels. Thus, the creation of methods, models and information technology for monitoring the state of environmental pollution based on trend forecasting models and statistical fractal estimation will practically improve the efficiency of environmental safety management and ensure a higher quality of life for citizens. The results obtained in this paper expand the theoretical and practical developments in this area. Thus, this thesis solves an important task, namely, methods, models and information technology for monitoring the state of environmental pollution based on trend forecasting models, statistical fractal analysis, etc. The developed methods and models are practically integrated into the relevant monitoring information technology. The object of research is the processes associated with monitoring and forecasting environmental pollution parameters for environmental safety management. The subject of the study is methods, models and information technology for monitoring environmental pollution parameters based on trend forecasting models. Research methods. The research is based on methods of knowledge representation and processing, monitoring and evaluation methods, time series forecasting methods, and statistical fractal analysis of information system design methods. The study aims to develop methods, models, and information technology for monitoring environmental pollution parameters for environmental safety management. Scientific novelty of the results: •For the first time, a method of monitoring environmental pollution parameters based on a comprehensive model for predicting time series of pollution parameters for decision-making on environmental safety management is described. •The model for predicting time series of environmental pollution is improved, considering the aggregation of various prediction models formed based on a predictive statistical analysis of pollution indicators. The model differs from the known models by providing the ability to adapt the model parameters to changes in the state of the environment, which is especially important when using such models in environmental monitoring systems. •An improved model for assessing the state of the environment in the monitoring system, which, unlike the known ones, takes into account the results of comprehensive forecasting of time series of changes in pollution and can be a tool for ensuring environmental safety. •The information technology for monitoring environmental pollution parameters was improved, which is distinguished by taking into account the results of analysis and forecasting of changes in pollution parameters and offers an assessment of the state of the environment, which provides opportunities for quantitative assessment of the environmental situation in the region. •The direction of developing an environmental index based on the developed methods of monitoring and forecasting time series of pollution and characterized by the consideration of prospective pollution indicators, which can be used in urban environmental monitoring and conditions of environmental uncertainty, was further developed. The first section describes the basic concepts and features of environmental monitoring. The necessity to increase the efficiency of monitoring and the main approaches to their solution through the improvement of methods and technologies are substantiated. The analysis of the properties of time series of pollutants shows that they can be classified into three classes: substances with a pronounced seasonal component, substances with a pronounced trend, and random variables. Such a classification allows for a better selection of forecasting and data transformation methods that can be used more effectively for each class of substances. The problem of environmental monitoring has been formalized in two formulations: point and plane. The main stages of environmental monitoring are highlighted. These are collecting data on the history of the state, monitoring the current state and predicting the state of environmental pollution in the future. Approaches and requirements for technical means at each stage are proposed. A review of known systems for monitoring air, water and soil pollution is made. The importance of the technical component is shown. Fundamental differences and new trends in the use of innovative technologies for monitoring environmental pollution parameters are identified. A scientific hypothesis defines the author's vision of an environmental monitoring organization by combining software and hardware systems and using trend models to predict environmental pollution parameters. By formalizing the problem of environmental monitoring, the structure of the information system for environmental monitoring is proposed. The information system should include the following subsystems: a subsystem for collecting information about the state of the environment, a subsystem for storing and accumulating data, forecasting the state of the environment, and a subsystem for user interaction. It is indicated that constructing an air pollution monitoring system is also essential for the whole and safe operation of some critical infrastructure facilities, including power plants, processing and chemical plants, airports, tunnels and subways, etc. The second section describes a comprehensive model for forecasting time series of environmental pollution indicators, considering the aggregation of various forecasting models formed based on a predictive statistical analysis of pollution indicators and having an adaptive nature. The model differs from the known models by providing the ability to adapt the model parameters to changes in the state of the environment, which is especially important when using such models in environmental monitoring systems. The fractal analysis method of time series is described, which allows finding the Hurst index for use in the developed forecasting models and determining the presence of long-term memory, cyclicity, etc., in the time series. The complex forecasting model includes higher-order exponential smoothing, Holt, Winters, moving average, weighted moving average, and autoregressive models. All the parameters set in these models are related to the Hurst index, which is calculated based on the predictive fractal statistical analysis of the time series. The corresponding descriptions and justifications are given. Using such a model as part of an econometric system will help to more effectively predict and respond to possible changes in the values of pollution parameters. In particular, the persistence of the time series of pollution parameters may mean a stable upward or downward trend in pollution. Suppose the time series becomes close to random or ergodic. In that case, this may mean an emergency or that additional non-permanent emissions have appeared in the region that need to be monitored. The third section describes a method for monitoring environmental pollution parameters based on a comprehensive model for predicting time series of pollution parameters with the use of statistical fractal analysis. The method takes into account the results of statistical fractal analysis to determine the direction of the time series trend, which may indicate whether the amount of pollution is increasing or decreasing in the short term. The method also determines the average cycle length based on the V statistic, which establishes the presence of long-term memory in the time series and determines the reliability of the trend forecast calculation. In addition, the Hurst index determines whether emissions of harmful substances, particularly into the air, are stable. That is, it is shown that if the Hurst index of a time series indicates that the time series is close to random, the environmental situation in the area is unstable, and excessive emissions are possible. This means local governments and environmental services should respond to this situation to ensure environmental safety. The model for assessing the state of the environment in the monitoring system has been improved, which, unlike the known ones, takes into account the results of comprehensive forecasting of time series of pollution changes and can be a tool for ensuring environmental safety. The model establishes a comprehensive assessment of the state of the environment based on the method of monitoring environmental pollution parameters. The direction of developing an index of the state of the environment, based on the developed methods of monitoring and forecasting time series of pollution and characterized by the consideration of prospective pollution indicators, which can be used in urban environmental monitoring and conditions of environmental uncertainty, has been further developed. The fourth section describes the information technology for monitoring environmental pollution parameters, which is distinguished by taking into account the results of analysis and forecasting changes in pollution parameters and offers an assessment of the state of the environment, which provides opportunities for quantitative assessment of the environmental situation in the region. Information technology includes methods for collecting information, a method for monitoring environmental pollution parameters, a model for assessing the state of the environment in the monitoring system, a method for calculating the environmental condition index, time series forecasting models, a method for statistical fractal analysis of time series, etc. All of these components allow for a qualitative analysis of the region's environmental situation and predict its future change. The information technology for monitoring pollution parameters based on a monitoring method that uses a comprehensive forecasting model, time series trend prediction, and statistical fractal analysis was verified. The verification was carried out on the example of a time series of environmental pollution parameters in different districts of Beijing, which were recorded from 2013 to 2017. The calculated errors in forecasting and assessing the state of the environment show the effectiveness of the development of such information technology and the relevance of this development for use by the city's environmental services and government agencies. Acts on implementing the results of work within the framework of research projects of Yancheng Polytechnic College (Appendix A). The practical significance of the results obtained is that the developed methods, models, and information monitoring of environmental pollution parameters will improve the efficiency of managing the state's environmental state. The resulting tool is important practically for ecological services and public authorities. In the long term, using the developed methods and models will positively impact the development of environmental policy in the state. The main provisions and results of the research were implemented and applied in the activities of Yancheng Polytechnic College. The results obtained, both in theoretical and practical terms, serve as a basis for further scientific and applied research to improve and enhance various aspects of the state's environmental management.