A5035095594- Geochemistry and Geologic Mapping
- Mining and Gasification Technologies
- Engineering and Environmental Studies
- Radioactivity and Radon Measurements
- Impact of Light on Environment and Health
- Heavy metals in environment
- Iron and Steelmaking Processes
- Air Quality and Health Impacts
- Economic and Technological Systems Analysis
- Industrial Engineering and Technologies
- Environmental Sustainability and Technology
- COVID-19 impact on air quality
S. Toraighyrov Pavlodar State University
2024-2025
Pavlodar State Pedagogical University
2019-2023
This study assesses heavy metal (HM) contamination in soils of an urban industrial zone using statistical and spatial analysis methods. Concentrations 12 key HMs, including Zn, Pb, Cu, Ni, were measured X-ray fluorescence (XRF), with values exceeding background levels several times certain areas. Pollution indices such as the Load Index (PLI) Total Indicator (Zc) revealed moderate to high levels, PLI ranging from 1.05 3.38 Zc between 0.67 51.34. Health risk assessments indicated that hazard...
This study focused on predicting the spatial distribution of environmental risk indicators using mathematical modeling methods including machine learning. The northern industrial zone Pavlodar City in Kazakhstan was used as a model territory for case. Nine models based kNN, gradient boosting, artificial neural networks, Kriging, and multilevel b-spline interpolation were employed to analyze pollution data assess their effectiveness levels. Each tackled problem regression task, aiming...
The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban pollution Pavlodar, a city with significant industrial presence promising touristic potential. Using mobile sensors for detailed spatial data collection, aims quantify concentrations of particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur (SO2), ground-level ozone (O3); assess their distribution; identify key...
The utilization or secondary use of technogenic waste is a relevant problem for the current economy. To assess environmental influence and economic potential, it necessary to study elemental content objects reveal tendencies spatial distribution elements, components, indices such as pollution coefficient. In this study, we performed analysis, calculation indicators: average gross content, hazard quotients, concentration coefficients metals, total ground samples taken from ash-slag storage...
On the base of samples taken from ash-sludge collector Pavlodar Aluminum Plant we have created neural network for making forecasts concentration distributions different elements compounding production waste plant. For every analyzed element separate was created. Levenberg-Marquardt algorithm chosen training. Architecture includes 5 layers, where one layer is input, – output and three between them are hidden layers. Neural demonstrates high accuracy on all data obtained by means partitioning...
The effective utilization and secondary application of technogenic waste pose significant challenges within the present-day economy.In order to evaluate environmental impact economic viability, it is imperative examine elemental composition materials unveil spatial distribution patterns elements, components, indices, such as pollution coefficient.In this research endeavor, we conducted an analysis calculated various parameters, including average overall content, hazard quotients, metal...