{"id":19583,"date":"2022-03-21T14:51:30","date_gmt":"2022-03-21T14:51:30","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=19583"},"modified":"2022-03-21T14:51:30","modified_gmt":"2022-03-21T14:51:30","slug":"novel-technology-identify-toxic-materials-in-water","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/novel-technology-identify-toxic-materials-in-water\/19583\/","title":{"rendered":"Novel technology developed to identify toxic materials in water"},"content":{"rendered":"<h2>A research team from the <a href=\"https:\/\/ok.ubc.ca\/\" target=\"_blank\" rel=\"noopener\">University of British Columbia, Okanagan Campus<\/a> (UBCO), has employed machine learning technology in order to identify toxic materials in water.<\/h2>\n<h3>What impact do these materials have?<\/h3>\n<p>The toxic materials in water, produced from oil sands extraction, that are stored in tailings ponds can pose a risk to the natural habitat and neighbouring communities when they leach into groundwater and surface <a href=\"https:\/\/www.innovationnewsnetwork.com\/the-density-snowshoe-hares-reveals-information-about-forest-ecosystem\/19169\/\">ecosystems<\/a>.<\/p>\n<p>Until now, an effective analysis of toxic waste materials has posed a challenge for the oil sands industry to achieve without complex and lengthy testing. Scientists have noted that the complexity has contributed towards a backlog: \u201cFor example, in Alberta alone, there are an estimated 1.4 billion cubic metres of fluid tailings,\u201d explained Nicol\u00e1s Peleato, an Assistant Professor of Civil Engineering at UBCO.<\/p>\n<h3>How does this new technology identify the toxic materials in water?<\/h3>\n<p><span class=\"TextRun SCXW126114275 BCX9\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW126114275 BCX9\">R<\/span><span class=\"NormalTextRun SCXW126114275 BCX9\">esearchers at UBCO\u2019s\u202fSchool of Engineering\u202fha<\/span><span class=\"NormalTextRun SCXW126114275 BCX9\">ve<\/span><span class=\"NormalTextRun SCXW126114275 BCX9\"> uncovered a new, faster<\/span><span class=\"NormalTextRun SCXW126114275 BCX9\">,<\/span><span class=\"NormalTextRun SCXW126114275 BCX9\"> and more reliable, method of analysing these samples. <\/span><span class=\"NormalTextRun SCXW126114275 BCX9\">Scientists have noted that this is simply the first step, <\/span><span class=\"NormalTextRun SCXW126114275 BCX9\">yet<\/span><span class=\"NormalTextRun SCXW126114275 BCX9\"> it is considered very promising.<\/span><\/span><span class=\"EOP SCXW126114275 BCX9\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559738&quot;:75,&quot;335559739&quot;:225,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p>\u201cCurrent methods require the use of expensive equipment, and it can take days or weeks to get results,\u201d added Peleato. \u201cThere is a need for a low-cost method to monitor these waters more frequently as a way to protect public and aquatic ecosystems.\u201d<\/p>\n<p>Scientists utilised fluorescence spectroscopy to swiftly detect key toxic materials in water. They also ran the results through a modelling programme that accurately predicts the composition of the water.<\/p>\n<p>\u201cThe composition can be used as a benchmark for further testing of other samples,\u201d Rinc\u00f3n said. The researchers are using a convolutional neural network that processes data in a grid-like topology, such as an image. \u201cIt is similar to the type of modelling used for classifying hard to identify fingerprints, facial recognition, and even self-driving cars.<\/p>\n<p>\u201cThe modelling takes into account variability in the background of the water quality and can separate hard to detect signals, and as a result it can achieve highly accurate results.\u201d<\/p>\n<p>The research considered a combination of organic compounds that are toxic waste materials, including naphthenic acids\u2014which can be found in many petroleum sources. By utilising high-dimensional fluorescence, scientists can identify most types of organic matter.<\/p>\n<p>\u201cThe modelling method searches for key materials, and maps out the sample\u2019s composition,\u201d explained Peleato.\u00a0\u201cThe results of the initial sample analysis are then processed through powerful image processing models to accurately determine comprehensive results.\u201d<\/p>\n<h3>What does this mean for the future of this technology?<\/h3>\n<p><span class=\"TextRun SCXW151430298 BCX9\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW151430298 BCX9\">While <\/span><span class=\"NormalTextRun SCXW151430298 BCX9\">scientists recognise that the results<\/span><span class=\"NormalTextRun SCXW151430298 BCX9\"> are encouraging, both Rinc\u00f3n and Dr <\/span><span class=\"NormalTextRun SCXW151430298 BCX9\">Peleato<\/span><span class=\"NormalTextRun SCXW151430298 BCX9\"> warn<\/span><span class=\"NormalTextRun SCXW151430298 BCX9\"> that<\/span><span class=\"NormalTextRun SCXW151430298 BCX9\"> the technique <\/span><span class=\"NormalTextRun SCXW151430298 BCX9\">requires a further evaluation <\/span><span class=\"NormalTextRun SCXW151430298 BCX9\">at a larger scale\u2014at which point there may be potential to incorporate screening of additional toxins.<\/span><\/span><span class=\"EOP SCXW151430298 BCX9\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559738&quot;:75,&quot;335559739&quot;:225,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p>This potential screening tool is the first step, but it does have some limitations since not all toxins or naphthenic acids can be detected\u2014only those that are fluorescent. And the technology will have to be scaled up for future, more in-depth testing.<\/p>\n<p>\u201cWhile it will not replace current analytical methods that are more accurate, this approach will allow the oil sands industry to accurately screen and treat its waste materials,\u201d concluded Peleato. \u201cThis is a necessary step to continue to meet the Canadian Council of Ministers of the Environment standards and guidelines.\u201d<\/p>\n<p>The research appears in the\u00a0<em>Journal of Hazardous Materials<\/em> and is funded by the Natural Sciences and Engineering Research Council of Canada Discovery Grant programme.<\/p>\n<p>To keep up to date with our content,\u00a0<a href=\"https:\/\/www.innovationnewsnetwork.com\/subscribe\/\">subscribe for updates<\/a>\u00a0on our digital publication and newsletter.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A research team from the University of British Columbia, Okanagan Campus (UBCO), has employed machine learning technology in order to identify toxic materials in water. What impact do these materials have? The toxic materials in water, produced from oil sands extraction, that are stored in tailings ponds can pose a risk to the natural habitat [&hellip;]<\/p>\n","protected":false},"author":21,"featured_media":19585,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[830],"tags":[745,24128,3365],"acf":[],"yoast_head":"\n<title>Novel technology developed to identify toxic materials in water<\/title>\n<meta name=\"description\" content=\"A research team the University of British Columbia has employed machine learning technology in order to identify toxic materials in water.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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