{"id":170,"date":"2016-03-10T00:17:07","date_gmt":"2016-03-09T21:17:07","guid":{"rendered":"http:\/\/home.dgrechka.net\/blog\/?p=170"},"modified":"2016-03-19T12:49:07","modified_gmt":"2016-03-19T09:49:07","slug":"background-radiation-level","status":"publish","type":"post","link":"https:\/\/grechka.family\/dmitry\/blog\/2016\/03\/background-radiation-level\/","title":{"rendered":"Background radiation level and elevation"},"content":{"rendered":"<p>Today I check whether the background radiation level correlates with the elevation from sea surface.<br \/>\nI show that the correlation between background radiation level and elevation is about 0.03 (uSv\/h per km) and this slope explains only 3% of radiation level variation.<\/p>\n<p><!--more--><\/p>\n<p>As a data source I use <a href=\"http:\/\/radioactiveathome.org\/\" target=\"_blank\">radioactiveathome.org<\/a> publicly available data.<br \/>\nI use the January 2016 data records for this research. For this single month the data set contains 2,359,151 total records from 299 active sensors all over the world.<\/p>\n<h2>Step 1. Obtaining\u00a0raw data<\/h2>\n<p>Each record in the data set contains the following fields: <strong>pulses<\/strong>,<strong>timespan<\/strong>, <strong>lat<\/strong>, <strong>lon<\/strong>, <strong>sensorID<\/strong>, <strong>measurementStartTime<\/strong><\/p>\n<p>Each record corresponds to the short-time (usually about four minutes) measurement by single sensor.<\/p>\n<p><strong>pulses<\/strong> is the number of pulses regestered with Geiger-Muller counter, <strong>timespan<\/strong> is the pulses registration time period, <strong>lat<\/strong> is the latitude of the sensor, <strong>lon<\/strong> is the longitude of the sensor, sensorID is unique numeric identifier of the sensor and the <strong>measurementStartTime<\/strong> identifies the record in time so we can distinguish several measurements of the same sensor.<\/p>\n<p>I get the dataset as a CSV file using the following query<\/p>\n<pre>select \r\n    max(pulsesCount ) as pulses,\r\n    measureTimespanMin as timespan,\r\n    lat,\r\n    lon,\r\n    HostID as sensorID,\r\n    timestampUTC as measurementStartTime\r\n       INTO OUTFILE '\/tmp\/radac_jan_2016_qc.csv'\r\n       FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\"'\r\n       LINES TERMINATED BY '\\n'\r\n    from SamplesExt\r\n    where timestampUTC&gt;'2016-01-01' AND timestampUTC&lt; '2016-02-01'\r\n       AND isExperiment=0 AND quality=0\r\n<\/pre>\n<p>The result is this file. [wpfilebase tag=file id=12 tpl=download-button \/]<\/p>\n<h2>Step 2. Calculating background radiation level for each sensor<\/h2>\n<p>We assume that the background radiation level for single sensor is the median value of the dose rates across all measurements.<br \/>\nSince there is no median aggregation function available in MySQL, it is calculated in R as well as the rest of the research.<\/p>\n<p>First we load the data<\/p>\n<pre>data &lt;- read.csv('radac_jan_2016_qc.csv',sep=',',header = F)\r\ncolnames(data) &lt;- c('pulses','timespan','lat','lon','sensorID','mTime') \r\n<\/pre>\n<p>We calculate the dose rate based on the pulses count and the length of measurement time interval.<br \/>\n(as discussed at <a href=\"http:\/\/radioactiveathome.org\/boinc\/forum_thread.php?id=60&amp;nowrap=true#716\">radioactive at home forum<\/a>)<\/p>\n<pre>data$doseRate &lt;- (data$pulses\/data$timespan)\/171.232876\r\n<\/pre>\n<p>Then, we group the data by the sensors, so we have one record for each sensor. For each sensor we aggregate all records in two ways: dose rate median and number of measurements count.<\/p>\n<pre>median_doseRates &lt; - aggregate(doseRate ~ sensorID, data = data , FUN= median)\r\nmeasurements_counts &lt;- aggregate(pulses ~ sensorID, data = data , FUN= length)\r\ncolnames(measurements_counts) &lt;- c('sensorID','measurements')\r\n\r\nsensors &lt;- unique(data.frame(data$sensorID,data$lat,data$lon))\r\ncolnames(sensors) &lt;- c('sensorID','lat','lon')\r\n\r\nsensors &lt;- merge(sensors,median_doseRates)\r\nsensors &lt;- merge(sensors,measurements_counts)\r\n<\/pre>\n<p>dose rate median is considered to be background radiation level as it eliminates outlier values.<\/p>\n<p>We filter out the sensors that reported too few measurements comparing with others. Thus the median will be only for sensors with lots of measurements.<\/p>\n<pre>sensors &lt;- subset(sensors, measurements &gt; 1000)\r\n<\/pre>\n<h2>Step 3. Obtaining elevations for the locations<\/h2>\n<p>We use <a href=\"http:\/\/fetchclimate2.cloudapp.net\" target=\"_blank\">FetchClimate service<\/a> to obtain the elevations for the locations. In particular,<a href=\"https:\/\/github.com\/dgrechka\/Rfc\" target=\"_blank\"> my R script<\/a> that extracts the data from it.<\/p>\n<pre>source('FetchClimate.R')\r\nfetched &lt;- fcTimeSeriesYearly(variable =  \"elev\",\r\n    latitude = sensors$lat,\r\n    longitude = sensors$lon,\r\n    firstYear = 2000,lastYear = 2000)\r\nsensors$elev &lt;- fetched$values<\/pre>\n<p>Good. Now we have quality controlled data ready for analysis.<\/p>\n<h2>Step 4. Analysing the data<\/h2>\n<p>Plotting elevation \/ dose rate scatter plot reveals that there is one station with much higher radiation dose level.<br \/>\n<a href=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/rad_outlier.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-182 size-full\" src=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/rad_outlier.png\" alt=\"outlier\" width=\"570\" height=\"544\" srcset=\"https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/rad_outlier.png 570w, https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/rad_outlier-300x286.png 300w\" sizes=\"auto, (max-width: 570px) 85vw, 570px\" \/><\/a><\/p>\n<p>This is the sensor with ID 12450 located in in Hong Kong owned by the user philip-in-hongkong.<br \/>\nIt has 2767 total measurements during the January with the median dose rate level of 1.402946 uSv\/h.<br \/>\n<a href=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/philip-in-hongkong.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-183 size-full\" src=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/philip-in-hongkong.png\" alt=\"philip-in-hongkong sensor\" width=\"781\" height=\"636\" srcset=\"https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/philip-in-hongkong.png 781w, https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/philip-in-hongkong-300x244.png 300w, https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/philip-in-hongkong-768x625.png 768w\" sizes=\"auto, (max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 61vw, (max-width: 1362px) 45vw, 600px\" \/><\/a><br \/>\nThis is clearly not the background radiation level whatever causes it! So, let&#8217;s remove the station from our data.<\/p>\n<pre>sensors &lt;- subset(sensors,sensorID != 12450)\r\n<\/pre>\n<p>After this we get the following histogram of background radiation level across all sensors all over the world.<br \/>\n<a href=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/rad_hist.png\" rel=\"attachment wp-att-185\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-185 size-full\" src=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/rad_hist.png\" alt=\"histagram of radiation dose rate\" width=\"570\" height=\"544\" srcset=\"https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/rad_hist.png 570w, https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/rad_hist-300x286.png 300w\" sizes=\"auto, (max-width: 570px) 85vw, 570px\" \/><\/a><br \/>\nAnd the doseRate\/elev scatter plot is<br \/>\n<a href=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/rad_qc.png\" rel=\"attachment wp-att-184\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-184 size-full\" src=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/rad_qc.png\" alt=\"background radiation level to elevation scatter plot\" width=\"570\" height=\"544\" srcset=\"https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/rad_qc.png 570w, https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/rad_qc-300x286.png 300w\" sizes=\"auto, (max-width: 570px) 85vw, 570px\" \/><\/a><\/p>\n<p><em>Linear regression gives that for every kilometer of elevation gain, the dose rate grows for 0.02892 uSv\/h<br \/>\nHowever only 3% of the data variance is explained with linear regression.<\/em><\/p>\n<p><a href=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/radac_lm-1.png\" rel=\"attachment wp-att-198\"><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/home.dgrechka.net\/blog\/wp-content\/uploads\/2016\/03\/radac_lm-1.png\" alt=\"background radiation level to elevation correlation\" width=\"580\" height=\"538\" class=\"aligncenter size-full wp-image-198\" srcset=\"https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/radac_lm-1.png 580w, https:\/\/grechka.family\/dmitry\/blog\/wp-content\/uploads\/2016\/03\/radac_lm-1-300x278.png 300w\" sizes=\"auto, (max-width: 580px) 85vw, 580px\" \/><\/a><\/p>\n<pre>lm(formula = doseRate ~ elev, data = sensors)\r\n\r\nResiduals:\r\n      Min        1Q    Median        3Q       Max \r\n-0.061040 -0.018112 -0.004224  0.014123  0.100310 \r\n\r\nCoefficients:\r\n             Estimate Std. Error t value Pr(&gt;|t|)    \r\n(Intercept) 1.104e-01  2.357e-03  46.849  &lt; 2e-16 ***\r\nelev        2.892e-05  8.671e-06   3.335 0.000968 ***\r\n---\r\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\r\n\r\nResidual standard error: 0.02461 on 277 degrees of freedom\r\nMultiple R-squared:  0.03861,\tAdjusted R-squared:  0.03514 \r\nF-statistic: 11.12 on 1 and 277 DF,  p-value: 0.000968\r\n<\/pre>\n<p>The R script with analysis is here.<\/p>\n<p>[wpfilebase tag=file id=14 tpl=download-button \/]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today I check whether the background radiation level correlates with the elevation from sea surface. I show that the correlation between background radiation level and elevation is about 0.03 (uSv\/h per km) and this slope explains only 3% of radiation level variation.<\/p>\n","protected":false},"author":1,"featured_media":198,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[27,5],"tags":[8,42,43,44,13],"class_list":["post-170","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-environment","category-study-research","tag-dataset","tag-environment","tag-radiation-level","tag-radioactivehome","tag-sensor"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Background radiation level and elevation - Dmitry A. 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