Digital Visualization of Environmental Risk Indicators in the Territory of the Urban Industrial Zone

Ruslan Safarov, Zhanat Shomanova (), Yuriy Nossenko, Zhandos Mussayev and Ayana Shomanova ()
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Ruslan Safarov: Department of Chemistry, Faculty of Natural Sciences, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan
Zhanat Shomanova: Higher School of Natural Science, Margulan University, Pavlodar 140002, Kazakhstan
Yuriy Nossenko: Higher School of Natural Science, Margulan University, Pavlodar 140002, Kazakhstan
Zhandos Mussayev: Higher School of Natural Science, Margulan University, Pavlodar 140002, Kazakhstan
Ayana Shomanova: Higher School of Natural Science, Margulan University, Pavlodar 140002, Kazakhstan

Sustainability, 2024, vol. 16, issue 12, 1-40

Abstract: This study focused on predicting the spatial distribution of environmental risk indicators using mathematical modeling methods including machine learning. The northern industrial zone of Pavlodar City in Kazakhstan was used as a model territory for the case. Nine models based on the methods kNN, gradient boosting, artificial neural networks, Kriging, and multilevel b-spline interpolation were employed to analyze pollution data and assess their effectiveness in predicting pollution levels. Each model tackled the problem as a regression task, aiming to estimate the pollution load index (PLI) values for specific locations. It was revealed that the maximum PLI values were mainly located to the southwest of the TPPs over some distance from their territories according to the average wind rose for Pavlodar City. Another area of high PLI was located in the northern part of the studied region, near the Hg-accumulating ponds. The high PLI level is generally attributed to the high concentration of Hg. Each studied method of interpolation can be used for spatial distribution analysis; however, a comparison with the scientific literature revealed that Kriging and MLBS interpolation can be used without extra calculations to produce non-linear, empirically consistent, and smooth maps.

Keywords: urban industrial zone; sustainable city; pollution load index (PLI); machine learning methods; soil contamination (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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