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Modeling groundwater total dissolved solid from derived electromagnetic data using multiple linear regression analysis: a case study of groundwater contamination
The high concentration of total dissolved solids (TDS) and other physicochemical parameters in groundwater around dumpsites have been used to implicate contamination from decomposed waste materials. A simple multiple linear regression (MLR) TDS model that integrates the TDS data derived from borehol...
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Published in: | Modeling earth systems and environment 2020-09, Vol.6 (3), p.1863-1875 |
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description | The high concentration of total dissolved solids (TDS) and other physicochemical parameters in groundwater around dumpsites have been used to implicate contamination from decomposed waste materials. A simple multiple linear regression (MLR) TDS model that integrates the TDS data derived from boreholes and hand-dug wells to the geophysical parameters obtained from the frequency-domain electromagnetic (EM) data was developed in this research. This is with a view to efficiently monitor groundwater resources and exploration around the Olusosun dumpsite and its communities. With the aid of the MLR equation, the observed TDS concentration of water samples collected from boreholes and hand-dug wells, and the corresponding estimated ground conductivity data in the vertical dipole mode (VD 40) and horizontal dipole modes (HD 40 and HD 20), obtained from geophysical surveys were regressed in Microsoft Excel software to generate a MLR TDS model. The integrity of the derived TDS model was appraised to examine the possibility of deploying it to investigate the TDS content of groundwater around the study area. The EM data and the resistivity models obtained around the study area confirmed contamination going on around the dumpsite. The developed TDS model can be put to use with high confidence, for groundwater TDS prediction around the study area where there are only terrain conductivity data but with no boreholes parameters. Also, terrain conductivity data alone can be applied to the model to predict the concentration of TDS in groundwater where there are no boreholes and hand-dug wells, therefore reducing the cost and time of determining and monitoring both parameters independently. With the aid of the ArcGIS software, the TDS model was used to generate TDS estimate map for the area. The knowledge of the TDS variability in such a map could give a clue about the integrity of the underground water around the site. |
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A simple multiple linear regression (MLR) TDS model that integrates the TDS data derived from boreholes and hand-dug wells to the geophysical parameters obtained from the frequency-domain electromagnetic (EM) data was developed in this research. This is with a view to efficiently monitor groundwater resources and exploration around the Olusosun dumpsite and its communities. With the aid of the MLR equation, the observed TDS concentration of water samples collected from boreholes and hand-dug wells, and the corresponding estimated ground conductivity data in the vertical dipole mode (VD 40) and horizontal dipole modes (HD 40 and HD 20), obtained from geophysical surveys were regressed in Microsoft Excel software to generate a MLR TDS model. The integrity of the derived TDS model was appraised to examine the possibility of deploying it to investigate the TDS content of groundwater around the study area. The EM data and the resistivity models obtained around the study area confirmed contamination going on around the dumpsite. The developed TDS model can be put to use with high confidence, for groundwater TDS prediction around the study area where there are only terrain conductivity data but with no boreholes parameters. Also, terrain conductivity data alone can be applied to the model to predict the concentration of TDS in groundwater where there are no boreholes and hand-dug wells, therefore reducing the cost and time of determining and monitoring both parameters independently. With the aid of the ArcGIS software, the TDS model was used to generate TDS estimate map for the area. 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Earth Syst. Environ</addtitle><description>The high concentration of total dissolved solids (TDS) and other physicochemical parameters in groundwater around dumpsites have been used to implicate contamination from decomposed waste materials. A simple multiple linear regression (MLR) TDS model that integrates the TDS data derived from boreholes and hand-dug wells to the geophysical parameters obtained from the frequency-domain electromagnetic (EM) data was developed in this research. This is with a view to efficiently monitor groundwater resources and exploration around the Olusosun dumpsite and its communities. With the aid of the MLR equation, the observed TDS concentration of water samples collected from boreholes and hand-dug wells, and the corresponding estimated ground conductivity data in the vertical dipole mode (VD 40) and horizontal dipole modes (HD 40 and HD 20), obtained from geophysical surveys were regressed in Microsoft Excel software to generate a MLR TDS model. The integrity of the derived TDS model was appraised to examine the possibility of deploying it to investigate the TDS content of groundwater around the study area. The EM data and the resistivity models obtained around the study area confirmed contamination going on around the dumpsite. The developed TDS model can be put to use with high confidence, for groundwater TDS prediction around the study area where there are only terrain conductivity data but with no boreholes parameters. Also, terrain conductivity data alone can be applied to the model to predict the concentration of TDS in groundwater where there are no boreholes and hand-dug wells, therefore reducing the cost and time of determining and monitoring both parameters independently. With the aid of the ArcGIS software, the TDS model was used to generate TDS estimate map for the area. The knowledge of the TDS variability in such a map could give a clue about the integrity of the underground water around the site.</description><subject>Boreholes</subject><subject>Chemistry and Earth Sciences</subject><subject>Computer programs</subject><subject>Computer Science</subject><subject>Conductivity</subject><subject>Contamination</subject><subject>Data</subject><subject>Dipoles</subject><subject>Dug wells</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth System Sciences</subject><subject>Ecosystems</subject><subject>Environment</subject><subject>Geophysical surveys</subject><subject>Geophysics</subject><subject>Groundwater</subject><subject>Groundwater pollution</subject><subject>Groundwater studies</subject><subject>Integrity</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical Applications in the Physical Sciences</subject><subject>Original Article</subject><subject>Parameters</subject><subject>Physicochemical processes</subject><subject>Physicochemical properties</subject><subject>Physics</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Software</subject><subject>Statistics for Engineering</subject><subject>Terrain</subject><subject>Total dissolved solids</subject><subject>Waste materials</subject><subject>Water analysis</subject><subject>Water resources</subject><subject>Water sampling</subject><issn>2363-6203</issn><issn>2363-6211</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kc1q3DAUhU1ooUOaF-hKkLXTK9mW7e5CyB-kdJO9uJaujQaPNdWVW-Zx-qbRZEJLNl3dH853rsQpii8SriRA-5Vr6KArQUGZx16X_VmxUZWuSq2k_PC3h-pTccG8BQCpldZ9vyn-fA-OZr9MYophXdxvTBRFCgln4TxzmH-RE7l4J8YYdsJR9McVzWRTXuC0UPJWOEwoVj467dY5-f1MIvsSRhFpisTswyJwwfnAnr8JFBaZBKfVHUQY3523YUm48wumzHwuPo44M1281fPi-e72-eahfPpx_3hz_VTa_LtUdk2L0NuuHoBa3ZAD2aGqx8aRrgetO9kMiJ2VTStVC0ONtdLUVrYCZ8ehOi8uT7b7GH6uxMlswxrzc9moOkNKKtlmlTqpbAzMkUazj36H8WAkmGMY5hSGyWGY1zBMn6HqBHEWLxPFf9b_oV4AqCORRw</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Aduojo, Ameloko Anthony</creator><creator>Adebowole, Ayolabi Elijah</creator><creator>Uchegbulam, Okezie</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>7UA</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><orcidid>https://orcid.org/0000-0002-6204-4434</orcidid></search><sort><creationdate>20200901</creationdate><title>Modeling groundwater total dissolved solid from derived electromagnetic data using multiple linear regression analysis: a case study of groundwater contamination</title><author>Aduojo, Ameloko Anthony ; Adebowole, Ayolabi Elijah ; Uchegbulam, Okezie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-857a09c84b0e765ed018a24f5de64b66815baa8c1571270b4a426e73c30dcfb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Boreholes</topic><topic>Chemistry and Earth Sciences</topic><topic>Computer programs</topic><topic>Computer Science</topic><topic>Conductivity</topic><topic>Contamination</topic><topic>Data</topic><topic>Dipoles</topic><topic>Dug wells</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earth System Sciences</topic><topic>Ecosystems</topic><topic>Environment</topic><topic>Geophysical surveys</topic><topic>Geophysics</topic><topic>Groundwater</topic><topic>Groundwater pollution</topic><topic>Groundwater studies</topic><topic>Integrity</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical Applications in the Physical Sciences</topic><topic>Original Article</topic><topic>Parameters</topic><topic>Physicochemical processes</topic><topic>Physicochemical properties</topic><topic>Physics</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Software</topic><topic>Statistics for Engineering</topic><topic>Terrain</topic><topic>Total dissolved solids</topic><topic>Waste materials</topic><topic>Water analysis</topic><topic>Water resources</topic><topic>Water sampling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aduojo, Ameloko Anthony</creatorcontrib><creatorcontrib>Adebowole, Ayolabi Elijah</creatorcontrib><creatorcontrib>Uchegbulam, Okezie</creatorcontrib><collection>SpringerOpen</collection><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><jtitle>Modeling earth systems and environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aduojo, Ameloko Anthony</au><au>Adebowole, Ayolabi Elijah</au><au>Uchegbulam, Okezie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling groundwater total dissolved solid from derived electromagnetic data using multiple linear regression analysis: a case study of groundwater contamination</atitle><jtitle>Modeling earth systems and environment</jtitle><stitle>Model. Earth Syst. Environ</stitle><date>2020-09-01</date><risdate>2020</risdate><volume>6</volume><issue>3</issue><spage>1863</spage><epage>1875</epage><pages>1863-1875</pages><issn>2363-6203</issn><eissn>2363-6211</eissn><abstract>The high concentration of total dissolved solids (TDS) and other physicochemical parameters in groundwater around dumpsites have been used to implicate contamination from decomposed waste materials. A simple multiple linear regression (MLR) TDS model that integrates the TDS data derived from boreholes and hand-dug wells to the geophysical parameters obtained from the frequency-domain electromagnetic (EM) data was developed in this research. This is with a view to efficiently monitor groundwater resources and exploration around the Olusosun dumpsite and its communities. With the aid of the MLR equation, the observed TDS concentration of water samples collected from boreholes and hand-dug wells, and the corresponding estimated ground conductivity data in the vertical dipole mode (VD 40) and horizontal dipole modes (HD 40 and HD 20), obtained from geophysical surveys were regressed in Microsoft Excel software to generate a MLR TDS model. The integrity of the derived TDS model was appraised to examine the possibility of deploying it to investigate the TDS content of groundwater around the study area. The EM data and the resistivity models obtained around the study area confirmed contamination going on around the dumpsite. The developed TDS model can be put to use with high confidence, for groundwater TDS prediction around the study area where there are only terrain conductivity data but with no boreholes parameters. Also, terrain conductivity data alone can be applied to the model to predict the concentration of TDS in groundwater where there are no boreholes and hand-dug wells, therefore reducing the cost and time of determining and monitoring both parameters independently. With the aid of the ArcGIS software, the TDS model was used to generate TDS estimate map for the area. The knowledge of the TDS variability in such a map could give a clue about the integrity of the underground water around the site.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40808-020-00796-9</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-6204-4434</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Boreholes Chemistry and Earth Sciences Computer programs Computer Science Conductivity Contamination Data Dipoles Dug wells Earth and Environmental Science Earth Sciences Earth System Sciences Ecosystems Environment Geophysical surveys Geophysics Groundwater Groundwater pollution Groundwater studies Integrity Math. Appl. in Environmental Science Mathematical Applications in the Physical Sciences Original Article Parameters Physicochemical processes Physicochemical properties Physics Regression analysis Regression models Software Statistics for Engineering Terrain Total dissolved solids Waste materials Water analysis Water resources Water sampling |
title | Modeling groundwater total dissolved solid from derived electromagnetic data using multiple linear regression analysis: a case study of groundwater contamination |
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