Probabilistic statistical modeling of air pollution from vehicles

Adikanova, Saltanat; Malgazhdarov, Yerzhan; Madiyarov, M.N.; Temirbekov, Nurlan

doi:10.1063/1.5000671

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…The control results are clearly nonlinear, which makes it impossible to predict the control results without the use of a mathematical apparatusThe quantitative assessment of control errors is determined not so much by the absolute values of σφ, σs, sn, sv, but to a greater extent by the ratios: σφ / σs; sn / σs; sv / σs. In these relations, as follows from Figure 2, the control errors are dominated by the normative value [23,24]. The presentation of the simulation results in 3D form allows you to reveal hidden, invisible in 2D constructions, patterns, for example, extreme areas of the minimum, as follows from Figure 2.…”

Section: Resultsmentioning

confidence: 95%

Stochastic Programmable Paradigm of Quality Control Management in Multi-Agent Systems

Alibekkyzy

2022

IJMSIT

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The article aims to develop a methodology for quantitative assessment and forecasting of the quality of decision-making in organizational and technical systems under the conditions of uncertainty of control agents. A stochastic model for predicting the reliability of control results and decision-making risks under the uncertainty of model agents was developed. The paper proposes a method for aggregating system structural uncertainties of the control and measurement process on the example of robust multi-aspect. The proposed mathematical application implements a multi-agent approach to solving the general problem of evaluating the robustness of control according to the criteria of «producer risk» and «consumer risk». For the purposes of modeling, such branches of mathematics and methods as probability theory and mathematical statistics, regression and correlation analysis, expert evaluation methods, simulation and structural-functional modeling, and agent-based approach are used.A probabilistic model has been developed to assess and predict the reliability of control and decision-making risks under the uncertainty of system agents. The novelty of the proposed model consists in taking into account the statistical nature of normative values. The proposed mathematical application implements a dual method for solving the general problem of assessing the quality of the control process by the magnitude of risks in the decision-making system. In the first case, the problem of quantitative risk assessment is solved for given statistical characteristics of control agents, and in the second case, the necessary measurement accuracy is determined for given uncertainties and risk levels in the control system.Process, model, probability, decision making, statistics, modeling, distribution law, pressure.

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Section: Resultsmentioning

confidence: 95%

Stochastic Programmable Paradigm of Quality Control Management in Multi-Agent Systems

Alibekkyzy

2022

IJMSIT

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“…A probabilistic statistical model proposed in [27,28] provides an alternative approach for modeling the spread of harmful impurities in the atmosphere. This method significantly reduces the number of calculations required without compromising accuracy.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Data on Air Pollutants in the City by Machine-Intelligent Methods Considering Climatic and Geographical Features

Temirbekov

Kasenov

Berkinbayev³

et al. 2023

Atmosphere

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In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora.

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“…Worldwide, including in Kazakhstan, numerous scientific publications address various methods for modeling pollutant dispersion in the atmosphere, as works of [11]- [28]. Recent advancements in computer technology have also led to the utilization of deep machine learning and artificial neural networks for atmospheric air quality monitoring and forecasting [29]- [33].…”

Section: Introductionmentioning

confidence: 99%

Mathematical and computer modeling of atmospheric air pollutants transformation with input data refinement

Temirbekov,

Malgazhdarov,

Tamabay

et al. 2023

IJEECS

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<p>This article addresses the critical issue of harmful impurity dispersion in industrial facility atmospheres, considering point sources and factoring in photochemical changes. Conjugate equations are employed to assimilate pollutant data into the transfer equations' right side. Boundary conditions derive from global models like weather research and forecasting (WRF) and system for integrated modeling of atmospheric composition (SILAM), customized for the unique characteristics of an industrial city's pollutants. To encompass anthropogenic heat sources and surface heterogeneity, the model incorporates differential schemes for the atmospheric boundary layer, transport equations, and impurity transformation equations. Parameters for photochemical transformations, varying with weather and time of day, are derived from Ust-Kamenogorsk. A cloud-based geoinformation system (GIS) is developed for monitoring and forecasting air pollution. It assimilates data sources and accounts for photochemical transformations, enabling visualization of diverse weather and environmental scenarios. The article presents numerical modeling results of impurity spread and transformation influenced by mesometeorological processes, topography, and water resources within a specific city.</p>

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Probabilistic statistical modeling of air pollution from vehicles

Cited by 4 publications

References 2 publications

Stochastic Programmable Paradigm of Quality Control Management in Multi-Agent Systems

Stochastic Programmable Paradigm of Quality Control Management in Multi-Agent Systems

Analysis of Data on Air Pollutants in the City by Machine-Intelligent Methods Considering Climatic and Geographical Features

Mathematical and computer modeling of atmospheric air pollutants transformation with input data refinement

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