Keywords
approximation, self-organization, environmental contamination
Start Date
1-7-2004 12:00 AM
Abstract
We propose new methodology for the treatment of highly noised statistical data that are measured and defined on a strongly irregular grid of observations. Using this methodology, we have processed and analyzed statistical data on congenital heart diseases and oncological diseases over three Ukrainian regions which were influenced by the Chernobyl disaster. The data appeared to be strongly inhomogeneous. We have built the maps of the people morbidity spatial distribution and the maps of the man caused contaminations in these regions. We have used Cs-137 and Sr-90 radioactive isotopes’ and pesticide pollutions as the factors that characterize the rate of environmental contamination over these regions of Ukraine. 81 points have presented the information about each kind of disease and pollution. Geometrically, these points correspond to the location of regional capital cities. On the base of these results, we have built the maps of correlation coefficients of morbidity versus pollution spatial distribution.
The Method of Self-Organisation of Mathematical Models and its Application to the Population Analysis and Environmental Monitoring
We propose new methodology for the treatment of highly noised statistical data that are measured and defined on a strongly irregular grid of observations. Using this methodology, we have processed and analyzed statistical data on congenital heart diseases and oncological diseases over three Ukrainian regions which were influenced by the Chernobyl disaster. The data appeared to be strongly inhomogeneous. We have built the maps of the people morbidity spatial distribution and the maps of the man caused contaminations in these regions. We have used Cs-137 and Sr-90 radioactive isotopes’ and pesticide pollutions as the factors that characterize the rate of environmental contamination over these regions of Ukraine. 81 points have presented the information about each kind of disease and pollution. Geometrically, these points correspond to the location of regional capital cities. On the base of these results, we have built the maps of correlation coefficients of morbidity versus pollution spatial distribution.
