The Solution of Semi-empirical Equation of Turbulent Diffusion in Problems of Polluting Impurity Transfer by Gauss Approach

Duisebekova, Kulanda; Serbin, V. V.; Kuandykov, A.A.; Duysebekov, Turar; Alimanova, Madina; Orazbekov, Sayatbek; Kozhamzharova, Dinara; Alimzhanova, Laura

doi:10.1016/j.procs.2016.08.057

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Assuming that the wind direction was from site B to site C, the diffusion process from site B to site C is indicated by the arrow. In the practical work of an atmospheric environmental impact assessment, Gaussian diffusion is typically used for the atmospheric diffusion calculation [48][49][50]. The Gaussian diffusion model is a point source diffusion model that is suitable for uniform atmospheric conditions and an area of wide and flat ground [51].…”

Section: The Gaussian Diffusion Model and The Candlestick Chartmentioning

confidence: 99%

A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model

Liu

Wan

et al. 2021

Atmosphere

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Currently, the continuous change prediction of PM2.5 concentration is an air pollution research hotspot. Combining physical methods and deep learning models to divide the pollution process of PM2.5 into effective multiple types is necessary to achieve a reliable prediction of the PM2.5 value. Therefore, a candlestick chart sample generator was designed to generate the candlestick chart from the online PM2.5 continuous monitoring data of the Guilin monitoring station site. After these generated candlestick charts were analyzed through the Gaussian diffusion model, it was found that the characteristics of the physical transmission process of PM2.5 pollutants can be reflected. Based on a set three-day period, using the time linear convolution method, 2188 sets of candlestick chart data were obtained from the 2013–2018 PM2.5 concentration data. There existed 16 categories generated by unsupervised classification that met the established classification judgment standards. After the statistical analysis, it was found that the accuracy rate of the change trend of these classifications reached 99.68% during the next period. Using the candlestick chart data as the training dataset, the Visual Geometry Group (VGG) model, an improved convolutional neural network model, was used for the classification. The experimental results showed that the overall accuracy (OA) value of the candlestick chart combination classification was 96.19%, and the Kappa coefficient was 0.960. IN the VGG model, the overall accuracy was improved by 1.93%, on average, compared with the support vector machines (SVM), LeNet, and AlexNet models. According to the experimental results, using the VGG classification method to classify continuous pollution data in the form of candlestick charts can more comprehensively retain the characteristics of the physical pollution process and provide a classification basis for accurately predicting PM2.5 values. At the same time, the statistical feasibility of this method has been proved.

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Section: The Gaussian Diffusion Model and The Candlestick Chartmentioning

confidence: 99%

A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model

Liu

Wan

et al. 2021

Atmosphere

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“…Therefore, according to Kazhydromet data, there are traces of active chemical compounds of hazard class 3, 4 (nitrogen oxide, carbon, sulfur) in the air, less often compounds of hazard class 1-2 [4].…”

Section: Ecological Factorsmentioning

confidence: 99%

Determination of the Most Polluted Atmospheric Air Pollution Category of Almaty City

Sarsenova¹,

Saimassayeva²,

Smaiyl³

2020

Scientific Journal of Astana IT University

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This article discusses the impact of suspended particles on human health, by providing small definitions of PM2.5, including how they appear, what particles they consist of, and how they harm the respiratory and circulatory systems. In addition, the analysis of the pollution level of the city of Almaty for the last three years from March 22, 2017, to October 6, 2020, with categorical intermediate values of suspended particles was carried out. Careful work was done with the SCV file such as data was skipped in the cells, that is, there were empty values; translation to a single data type; filling in empty cells. It also considers making a decision on the six categories provided to identify the average meeting categories. The authors identified specific categories based on digital readings of values received from sensors, where each category has its own verbal values that are understandable for each person. The indicator displays with weights as a graph for a specific Seifullin-Dulatov sensor location with categorical and without categorical division. Then for each intersection or location of the sensors is shown in a table. It is also revealed which level or category is the rarest among the others and the most common category as well.

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Analysis and processing of environmental monitoring system

Pyagay

Sarsenova

Duisebekova

et al. 2020

Procedia Computer Science

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The Solution of Semi-empirical Equation of Turbulent Diffusion in Problems of Polluting Impurity Transfer by Gauss Approach

Cited by 5 publications

References 5 publications

A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model

A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model

Determination of the Most Polluted Atmospheric Air Pollution Category of Almaty City

Analysis and processing of environmental monitoring system

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