scholarly journals Environmental quality assessment in Kirovsk (Murmansk Region) based on the state of Siberian spruce needles

2020 ◽  
Vol 9 (3) ◽  
pp. 10-14
Author(s):  
Evgenia Y. Aleksandrova ◽  
Alla A. Trotsenko ◽  
Lyudmila S. Kalinovskaya

The paper presents data on environmental quality assessment of the state of Siberian spruce needles (Picea obovata) in Kirovsk, Murmansk Region for the period autumn 2019. It is confirmed that the methods of bioindication of the environment using coniferous plants are based primarily on the study of their morphological and structural changes. It was found that the condition of Siberian spruce needles in the study area (Kirovsk, Murmansk Region) is assessed as satisfactory. The average percentage of the area of damaged plants at different sampling points ranges from 1,96 to 2,4%. With the height of the tree, the needles become more susceptible to drying out, which may be due to an increase in the age (aging) of shoots and needles, as well as the action of abiotic factors (wind, precipitation). The dependence of the average percentage of damage on height was not revealed. The main conclusion is that the state of the environment in Kirovsk, Murmansk Region is rated as good. Indicators for assessing the state of the environment in the study areas differ slightly. The obtained data can be used for monitoring the environment of various districts of the Murmansk Region and other areas of Northern latitudes, for making a plan of environmental measures and environmental monitoring of various industrial enterprises of the Murmansk Region.

2011 ◽  
Vol 38 (6) ◽  
pp. 6805-6813 ◽  
Author(s):  
Jorge Garcia-Gutierrez ◽  
Luis Gonçalves-Seco ◽  
Jose C. Riquelme-Santos

2019 ◽  
Vol 7 (3) ◽  
pp. 184-205
Author(s):  
Snejana Dineva ◽  
Zlatin Zlatev

In this paper, an analysis of the potential use of the surface and geometric characteristics of mulberry leaves as parameters for environmental quality assessment is made. Methods have been used to reduce the amount of data of latent variables, linear and kernel variants of principal components. It has been found that a kernel variant of the principal components, combined with nonlinear separating functions of discriminant analysis and a method of support vector machines, are an appropriate methods for distinguishing the degree of air pollution from the mulberry leaf data. The results obtained could be used as preliminary baseline data for future evaluations and studies related to remote monitoring of urban air quality.


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