ORIGINAL RESEARCH
Using an Urban Snow Cover Composition-Based Cluster Analysis to Zone Krasnoyarsk Town (Russia) by Pollution Level
Alexander Onuchin 1  
,   Gregory Kofman 1  
,   Olga Zubareva 1  
,   Irina Danilova 1  
 
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Sukachev Institute of Forest of the Siberian Branch of the Russian Academy of Sciences, Krasnoyarsk, Russian Federation
CORRESPONDING AUTHOR
Irina Danilova   

Sukachev Institute of Forest SB RAS, Arfdemgorodoc 50/28, 660036, Krasnoyarsk, Russia
Submission date: 2019-12-24
Final revision date: 2020-02-12
Acceptance date: 2020-02-12
Online publication date: 2020-06-01
Publication date: 2020-08-05
 
Pol. J. Environ. Stud. 2020;29(6):4257–4267
 
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ABSTRACT
Pollution level and distribution are among key indicators of townspeople’s life quality and accurate pollution estimation and town pollution zoning proceeding from these estimates are, therefore, challenging problems to be solved for big cities with highly developed industries. We carried out an integrated assessment of some pollutants for Krasnoyarsk Town using the 2018 snow cover chemical composition data and zoned the town by pollutant accumulation by cluster analysis. The resulting zoning based on the simultaneous use of pollutant-specific data was visualized using GIS and quantitatively confirmed the general public view of most districts of the town as being extremely adverse ecologically. Unlike most studies, where decisions on pollution zone boundaries are either intuitive, or made out of so-called “general considerations”, the cluster analysis-based methodology applied in this study enabled to approach this problem algorithmically, i.e. to avoid a priori assumptions. The analysis we carried out based on the results of our original experimental research approach that involved a uniform methodology of snow sampling and analysis in the lab combined with state-of-the-art methods of data processing and result visualization, revealed snow cover to be an informative recorder and effective tool to obtain a picture of integrated pollution of urban areas.
eISSN:2083-5906
ISSN:1230-1485