Quantification of nitrate contamination sources in groundwater from the Anayari catchment using major ions, stable isotopes, and Bayesian mixing model, Ghana
Nitrate (NO 3 − ) contamination threatens the quality of groundwater in many agricultural areas, and excessive NO 3 − consumption may cause adverse human health implications. Determination of NO 3 − occurrence and its source of contamination is essential for the effective management of groundwater q...
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| Veröffentlicht in: | Environmental earth sciences 2023-08, Vol.82 (16), p.381, Article 381 |
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| creator | Zakaria, Nafisatu Gibrilla, Abass Owusu-Nimo, Frederick Adomako, Dickson Anornu, Geophrey K. Fianko, Joseph R. Gyamfi, Charles |
| description | Nitrate (NO
3
−
) contamination threatens the quality of groundwater in many agricultural areas, and excessive NO
3
−
consumption may cause adverse human health implications. Determination of NO
3
−
occurrence and its source of contamination is essential for the effective management of groundwater quality. The characteristics of NO
3
−
in groundwater from the Anayari Catchment were analysed using hydrogeochemical and stable isotope data. Concentrations of major ions and the isotopic values of δ
2
H, δ
18
O, δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of groundwater and δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of potential NO
3
−
source materials (precipitation (P), chemical fertilisers (CF), soil nitrogen (SN), and sewage or manure (S/M)) in the area were determined and used to identify groundwater NO
3
−
sources. The Bayesian mixing model was applied to estimate the proportional contributions of NO
3
−
load in boreholes (BH) and hand–dug wells (HDW) in the Catchment. The NO
3
−
levels were mostly lower than the WHO guideline limit. Correlation analyses indicate that the source of NO
3
−
was mostly livestock and septic waste. The results from δ
15
N–NO
3
−
and δ
18
O–NO
3
−
showed that the contribution of NO
3
−
from potential sources is in the order of S/M > SN > CF > P, with denitrification influencing the NO
3
−
concentration in some of the groundwater sources. The Bayesian mixing model demonstrated that by proportion, SN and S/M were the major NO
3
−
contributors to BH and HDW, respectively. There is a need to institute and enforce proper manure application management strategies to reduce the risk of groundwater contamination from manure nutrients. |
| doi_str_mv | 10.1007/s12665-023-11068-x |
| format | Article |
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3
−
) contamination threatens the quality of groundwater in many agricultural areas, and excessive NO
3
−
consumption may cause adverse human health implications. Determination of NO
3
−
occurrence and its source of contamination is essential for the effective management of groundwater quality. The characteristics of NO
3
−
in groundwater from the Anayari Catchment were analysed using hydrogeochemical and stable isotope data. Concentrations of major ions and the isotopic values of δ
2
H, δ
18
O, δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of groundwater and δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of potential NO
3
−
source materials (precipitation (P), chemical fertilisers (CF), soil nitrogen (SN), and sewage or manure (S/M)) in the area were determined and used to identify groundwater NO
3
−
sources. The Bayesian mixing model was applied to estimate the proportional contributions of NO
3
−
load in boreholes (BH) and hand–dug wells (HDW) in the Catchment. The NO
3
−
levels were mostly lower than the WHO guideline limit. Correlation analyses indicate that the source of NO
3
−
was mostly livestock and septic waste. The results from δ
15
N–NO
3
−
and δ
18
O–NO
3
−
showed that the contribution of NO
3
−
from potential sources is in the order of S/M > SN > CF > P, with denitrification influencing the NO
3
−
concentration in some of the groundwater sources. The Bayesian mixing model demonstrated that by proportion, SN and S/M were the major NO
3
−
contributors to BH and HDW, respectively. There is a need to institute and enforce proper manure application management strategies to reduce the risk of groundwater contamination from manure nutrients.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-023-11068-x</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agrochemicals ; Bayesian analysis ; Bayesian theory ; Biogeosciences ; Boreholes ; Catchment area ; Catchments ; Contamination ; Correlation analysis ; Denitrification ; Dug wells ; Earth and Environmental Science ; Earth Sciences ; Environmental Science and Engineering ; Farmyard manure ; Geochemistry ; Geology ; Groundwater ; Groundwater pollution ; Groundwater quality ; Groundwater sources ; Hydrogeochemistry ; Hydrology/Water Resources ; Ions ; Isotopes ; Livestock ; Manures ; Nitrates ; Nutrients ; Original Article ; Probability theory ; Risk reduction ; Sewage ; Soil chemistry ; Soil contamination ; Stable isotopes ; Terrestrial Pollution ; Water quality</subject><ispartof>Environmental earth sciences, 2023-08, Vol.82 (16), p.381, Article 381</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-bebab8b157a1b3f693a3bca9a18898d7bfdd52bd52ac60265811295b09227ce73</citedby><cites>FETCH-LOGICAL-a342t-bebab8b157a1b3f693a3bca9a18898d7bfdd52bd52ac60265811295b09227ce73</cites><orcidid>0000-0002-1417-0858</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12665-023-11068-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12665-023-11068-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Zakaria, Nafisatu</creatorcontrib><creatorcontrib>Gibrilla, Abass</creatorcontrib><creatorcontrib>Owusu-Nimo, Frederick</creatorcontrib><creatorcontrib>Adomako, Dickson</creatorcontrib><creatorcontrib>Anornu, Geophrey K.</creatorcontrib><creatorcontrib>Fianko, Joseph R.</creatorcontrib><creatorcontrib>Gyamfi, Charles</creatorcontrib><title>Quantification of nitrate contamination sources in groundwater from the Anayari catchment using major ions, stable isotopes, and Bayesian mixing model, Ghana</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>Nitrate (NO
3
−
) contamination threatens the quality of groundwater in many agricultural areas, and excessive NO
3
−
consumption may cause adverse human health implications. Determination of NO
3
−
occurrence and its source of contamination is essential for the effective management of groundwater quality. The characteristics of NO
3
−
in groundwater from the Anayari Catchment were analysed using hydrogeochemical and stable isotope data. Concentrations of major ions and the isotopic values of δ
2
H, δ
18
O, δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of groundwater and δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of potential NO
3
−
source materials (precipitation (P), chemical fertilisers (CF), soil nitrogen (SN), and sewage or manure (S/M)) in the area were determined and used to identify groundwater NO
3
−
sources. The Bayesian mixing model was applied to estimate the proportional contributions of NO
3
−
load in boreholes (BH) and hand–dug wells (HDW) in the Catchment. The NO
3
−
levels were mostly lower than the WHO guideline limit. Correlation analyses indicate that the source of NO
3
−
was mostly livestock and septic waste. The results from δ
15
N–NO
3
−
and δ
18
O–NO
3
−
showed that the contribution of NO
3
−
from potential sources is in the order of S/M > SN > CF > P, with denitrification influencing the NO
3
−
concentration in some of the groundwater sources. The Bayesian mixing model demonstrated that by proportion, SN and S/M were the major NO
3
−
contributors to BH and HDW, respectively. There is a need to institute and enforce proper manure application management strategies to reduce the risk of groundwater contamination from manure nutrients.</description><subject>Agrochemicals</subject><subject>Bayesian analysis</subject><subject>Bayesian theory</subject><subject>Biogeosciences</subject><subject>Boreholes</subject><subject>Catchment area</subject><subject>Catchments</subject><subject>Contamination</subject><subject>Correlation analysis</subject><subject>Denitrification</subject><subject>Dug wells</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental Science and Engineering</subject><subject>Farmyard manure</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Groundwater</subject><subject>Groundwater pollution</subject><subject>Groundwater quality</subject><subject>Groundwater sources</subject><subject>Hydrogeochemistry</subject><subject>Hydrology/Water Resources</subject><subject>Ions</subject><subject>Isotopes</subject><subject>Livestock</subject><subject>Manures</subject><subject>Nitrates</subject><subject>Nutrients</subject><subject>Original Article</subject><subject>Probability theory</subject><subject>Risk reduction</subject><subject>Sewage</subject><subject>Soil chemistry</subject><subject>Soil contamination</subject><subject>Stable isotopes</subject><subject>Terrestrial Pollution</subject><subject>Water quality</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9UdtKxDAQLaKgqD_g04CvVnPZpumjijcQRNDnMGnT3SzbZE1S3P0Y_9VoRd8cGGaYOecMwymKE0rOKSH1RaRMiKokjJeUEiHLzU5xQKUQpWBNs_vbS7JfHMe4JDk45Q0RB8XH84gu2d62mKx34HtwNgVMBlrvEg7WTYvox9CaCNbBPPjRde8ZE6APfoC0MHDpcIvBQtZpF4NxCcZo3RwGXPoAWSGeQUyoVwZs9MmvTR6g6-AKtyZadDDYzTfBd2Z1BncLdHhU7PW4iub4px4Wr7c3L9f35ePT3cP15WOJfMZSqY1GLTWtaqSa96LhyHWLDVIpG9nVuu-6iumc2ArCRCUpZU2lScNY3ZqaHxank-46-LfRxKSW-V2XTyomZ3wmaUVlRrEJ1QYfYzC9Wgc7YNgqStSXE2pyQmUn1LcTapNJfCLFDHZzE_6k_2F9Agnwj8w</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Zakaria, Nafisatu</creator><creator>Gibrilla, Abass</creator><creator>Owusu-Nimo, Frederick</creator><creator>Adomako, Dickson</creator><creator>Anornu, Geophrey K.</creator><creator>Fianko, Joseph R.</creator><creator>Gyamfi, Charles</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</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>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-1417-0858</orcidid></search><sort><creationdate>20230801</creationdate><title>Quantification of nitrate contamination sources in groundwater from the Anayari catchment using major ions, stable isotopes, and Bayesian mixing model, Ghana</title><author>Zakaria, Nafisatu ; Gibrilla, Abass ; Owusu-Nimo, Frederick ; Adomako, Dickson ; Anornu, Geophrey K. ; Fianko, Joseph R. ; Gyamfi, Charles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-bebab8b157a1b3f693a3bca9a18898d7bfdd52bd52ac60265811295b09227ce73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agrochemicals</topic><topic>Bayesian analysis</topic><topic>Bayesian theory</topic><topic>Biogeosciences</topic><topic>Boreholes</topic><topic>Catchment area</topic><topic>Catchments</topic><topic>Contamination</topic><topic>Correlation analysis</topic><topic>Denitrification</topic><topic>Dug wells</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental Science and Engineering</topic><topic>Farmyard manure</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Groundwater</topic><topic>Groundwater pollution</topic><topic>Groundwater quality</topic><topic>Groundwater sources</topic><topic>Hydrogeochemistry</topic><topic>Hydrology/Water Resources</topic><topic>Ions</topic><topic>Isotopes</topic><topic>Livestock</topic><topic>Manures</topic><topic>Nitrates</topic><topic>Nutrients</topic><topic>Original Article</topic><topic>Probability theory</topic><topic>Risk reduction</topic><topic>Sewage</topic><topic>Soil chemistry</topic><topic>Soil contamination</topic><topic>Stable isotopes</topic><topic>Terrestrial Pollution</topic><topic>Water quality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zakaria, Nafisatu</creatorcontrib><creatorcontrib>Gibrilla, Abass</creatorcontrib><creatorcontrib>Owusu-Nimo, Frederick</creatorcontrib><creatorcontrib>Adomako, Dickson</creatorcontrib><creatorcontrib>Anornu, Geophrey K.</creatorcontrib><creatorcontrib>Fianko, Joseph R.</creatorcontrib><creatorcontrib>Gyamfi, Charles</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>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 Korea</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>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</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><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Environmental earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zakaria, Nafisatu</au><au>Gibrilla, Abass</au><au>Owusu-Nimo, Frederick</au><au>Adomako, Dickson</au><au>Anornu, Geophrey K.</au><au>Fianko, Joseph R.</au><au>Gyamfi, Charles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantification of nitrate contamination sources in groundwater from the Anayari catchment using major ions, stable isotopes, and Bayesian mixing model, Ghana</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2023-08-01</date><risdate>2023</risdate><volume>82</volume><issue>16</issue><spage>381</spage><pages>381-</pages><artnum>381</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Nitrate (NO
3
−
) contamination threatens the quality of groundwater in many agricultural areas, and excessive NO
3
−
consumption may cause adverse human health implications. Determination of NO
3
−
occurrence and its source of contamination is essential for the effective management of groundwater quality. The characteristics of NO
3
−
in groundwater from the Anayari Catchment were analysed using hydrogeochemical and stable isotope data. Concentrations of major ions and the isotopic values of δ
2
H, δ
18
O, δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of groundwater and δ
15
N–NO
3
−
and δ
18
O–NO
3
−
of potential NO
3
−
source materials (precipitation (P), chemical fertilisers (CF), soil nitrogen (SN), and sewage or manure (S/M)) in the area were determined and used to identify groundwater NO
3
−
sources. The Bayesian mixing model was applied to estimate the proportional contributions of NO
3
−
load in boreholes (BH) and hand–dug wells (HDW) in the Catchment. The NO
3
−
levels were mostly lower than the WHO guideline limit. Correlation analyses indicate that the source of NO
3
−
was mostly livestock and septic waste. The results from δ
15
N–NO
3
−
and δ
18
O–NO
3
−
showed that the contribution of NO
3
−
from potential sources is in the order of S/M > SN > CF > P, with denitrification influencing the NO
3
−
concentration in some of the groundwater sources. The Bayesian mixing model demonstrated that by proportion, SN and S/M were the major NO
3
−
contributors to BH and HDW, respectively. There is a need to institute and enforce proper manure application management strategies to reduce the risk of groundwater contamination from manure nutrients.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-023-11068-x</doi><orcidid>https://orcid.org/0000-0002-1417-0858</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1866-6280 |
| ispartof | Environmental earth sciences, 2023-08, Vol.82 (16), p.381, Article 381 |
| issn | 1866-6280 1866-6299 |
| language | eng |
| recordid | cdi_proquest_journals_2843481518 |
| source | SpringerNature Journals |
| subjects | Agrochemicals Bayesian analysis Bayesian theory Biogeosciences Boreholes Catchment area Catchments Contamination Correlation analysis Denitrification Dug wells Earth and Environmental Science Earth Sciences Environmental Science and Engineering Farmyard manure Geochemistry Geology Groundwater Groundwater pollution Groundwater quality Groundwater sources Hydrogeochemistry Hydrology/Water Resources Ions Isotopes Livestock Manures Nitrates Nutrients Original Article Probability theory Risk reduction Sewage Soil chemistry Soil contamination Stable isotopes Terrestrial Pollution Water quality |
| title | Quantification of nitrate contamination sources in groundwater from the Anayari catchment using major ions, stable isotopes, and Bayesian mixing model, Ghana |
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