{"version":"1.0","provider_name":"\u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb","provider_url":"http:\/\/www.geology.com.ua\/en","author_name":"\u0410\u0434\u043c\u0456\u043d\u0456\u0441\u0442\u0440\u0430\u0442\u043e\u0440","author_url":"http:\/\/www.geology.com.ua\/en\/blog\/author\/andriy\/","title":"Geoinformatika 2017; 1(61) : 42-50 - \u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"y99CemZFYe\"><a href=\"http:\/\/www.geology.com.ua\/en\/geoinformatika-2017-161-42-50\/\">Geoinformatika 2017; 1(61) : 42-50<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"http:\/\/www.geology.com.ua\/en\/geoinformatika-2017-161-42-50\/embed\/#?secret=y99CemZFYe\" width=\"600\" height=\"338\" title=\"&#8220;Geoinformatika 2017; 1(61) : 42-50&#8221; &#8212; \u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb\" data-secret=\"y99CemZFYe\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=http:\/\/www.geology.com.ua\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","description":"Geoinformatika 2017; 1(61) : 42-50\u00a0(in Ukrainian) DECOMPOSITION OF GEOGENIC RADON POTENTIAL BY LOGNORMAL KRIGING S. Vyzhva, O. Shabatura, V. Onyshchuk, D. Onyshchuk, I. Onyshchuk Institute of Geology, Taras Shevchenko National University of Kyiv, 90, Vasylkivska Str., Kyiv, 03022, Ukraine, e-mail: vsa@univ.kiev.ua, sand@univ.kiev.ua, vitus16@ukr.net, boenerges@ukr.net, oivan1@ukr.net Purpose. Based on the analysis of structural and functional factors of distribution of radon levels in soil air and groundwater, we have developed a geo-statistical model of a geogenic radon potential (GRP). The obtained results are in agreement with those of geologic-geophysical studies and geo-statistic techniques aimed at identifying the spatial structure of GRP and its probable risks in the areas with a moderate ecological risk. Design\/methodology\/approach. Firstly, we carried out a logarithmic transformation of radon measurements by grind\u00ading and determined the distribution of radon concentration by modeling a variogramm. Then we determined the distribution of the data by kriging simulation and performed geological interpretation of the decomposed components of GRP. This geo-statistical approach would be useful in making the required regulatory decisions on radon explora\u00adtion programs, including radon monitoring of equivalent equilibrium of radon and thoron activity in indoor air of dwellings and soil. Findings. Based on the analysis of factor-spatial components of GRP, we have discovered a linear and a few nested models. There are two large clusters of nested model having different spatial sizes. The first component of a nested model (the size of a cluster larger than 150 km) is probably related to a climatic factor; the second component (larger than 5 km) is likely to be linked with a certain type of soil. Small-sized spatial clusters of a nested model are described by the characteristics of the geomorphologic-landscape structure of the territory. We have also discovered some universality of the main model structure, which is determined by the averaged radium content in the bedrock and soil particles. Practical value\/implications. The main advantage of the geo-statistical evaluation with GRP is that it permits to make regional predications of a correct correlation level of the measured values of soil air radon and respective long-duration radon levels in indoor air of dwellings. The suggested method would make it possible to correct routine measurements of radon levels in indoor air of dwellings, to install a system of further observations in the regions with high-dose loadings, as well to plan radiological investigation and protective measures. Keywords: radon, geogenic radon potential, lognormal kriging, griding, radon risk. The full text of papers"}