Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district
The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a pr...
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Veröffentlicht in: | Ecotoxicology and environmental safety 2020-09, Vol.201, p.110833-110833, Article 110833 |
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creator | Jiménez-Oyola, Samantha García-Martínez, María-Jesús Ortega, Marcelo F. Bolonio, David Rodríguez, Clara Esbrí, José-María Llamas, Juan F. Higueras, Pablo |
description | The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a probabilistic human health risk assessment using Bayesian modeling to estimate the non-carcinogenic risk related to Hg through multiple exposure pathways. Samples of vegetables, wild mushrooms, fish, soil, water, and air were analyzed, and adult residents were randomly surveyed to adjust the risk models to the specific population data. On the one hand, the results for the non-carcinogenic risk based on Hazard Quotient (HQ) showed unacceptable risk levels through ingestion of Hg-contaminated vegetables and fish, with HQ values 20 and 3 times higher, respectively, than the safe exposure threshold of 1 for the 97.5th percentile. On the other hand, ingestion of mushrooms, dermal contact with soil, ingestion of water, dermal contact with water and inhalation of air, were below the safety limit for the 97.5th percentile, and did not represent a risk to the health of residents. In addition, the probabilistic approach was compared with the conservative deterministic approach, and similar results were obtained. This is the first study conducted in Almadén, which clearly reveals the high levels of human health risk to which the population is exposed due to the legacy of two millennia of Hg mining.
[Display omitted]
•Bayesian probabilistic approach was used to assess the human health risk.•Concentrations of Hg and on-site surveys were used to update the risk models.•The hazard quotient was quantified for nine exposure pathways.•The risk for the residents of Almadén exceeds the safe exposure threshold.•Ingestion of local crops accounts for 84% of the overall risk. |
doi_str_mv | 10.1016/j.ecoenv.2020.110833 |
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[Display omitted]
•Bayesian probabilistic approach was used to assess the human health risk.•Concentrations of Hg and on-site surveys were used to update the risk models.•The hazard quotient was quantified for nine exposure pathways.•The risk for the residents of Almadén exceeds the safe exposure threshold.•Ingestion of local crops accounts for 84% of the overall risk.</description><identifier>ISSN: 0147-6513</identifier><identifier>EISSN: 1090-2414</identifier><identifier>DOI: 10.1016/j.ecoenv.2020.110833</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Bayesian approach ; Hazard quotient ; Human health risk ; Mercury pollution ; Probabilistic risk</subject><ispartof>Ecotoxicology and environmental safety, 2020-09, Vol.201, p.110833-110833, Article 110833</ispartof><rights>2020 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-d7623a657b4d9ed5e63765f60802af2bd7e154e5f42810284401477ca45080513</citedby><cites>FETCH-LOGICAL-c339t-d7623a657b4d9ed5e63765f60802af2bd7e154e5f42810284401477ca45080513</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ecoenv.2020.110833$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3541,27915,27916,45986</link.rule.ids></links><search><creatorcontrib>Jiménez-Oyola, Samantha</creatorcontrib><creatorcontrib>García-Martínez, María-Jesús</creatorcontrib><creatorcontrib>Ortega, Marcelo F.</creatorcontrib><creatorcontrib>Bolonio, David</creatorcontrib><creatorcontrib>Rodríguez, Clara</creatorcontrib><creatorcontrib>Esbrí, José-María</creatorcontrib><creatorcontrib>Llamas, Juan F.</creatorcontrib><creatorcontrib>Higueras, Pablo</creatorcontrib><title>Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district</title><title>Ecotoxicology and environmental safety</title><description>The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a probabilistic human health risk assessment using Bayesian modeling to estimate the non-carcinogenic risk related to Hg through multiple exposure pathways. Samples of vegetables, wild mushrooms, fish, soil, water, and air were analyzed, and adult residents were randomly surveyed to adjust the risk models to the specific population data. On the one hand, the results for the non-carcinogenic risk based on Hazard Quotient (HQ) showed unacceptable risk levels through ingestion of Hg-contaminated vegetables and fish, with HQ values 20 and 3 times higher, respectively, than the safe exposure threshold of 1 for the 97.5th percentile. On the other hand, ingestion of mushrooms, dermal contact with soil, ingestion of water, dermal contact with water and inhalation of air, were below the safety limit for the 97.5th percentile, and did not represent a risk to the health of residents. In addition, the probabilistic approach was compared with the conservative deterministic approach, and similar results were obtained. This is the first study conducted in Almadén, which clearly reveals the high levels of human health risk to which the population is exposed due to the legacy of two millennia of Hg mining.
[Display omitted]
•Bayesian probabilistic approach was used to assess the human health risk.•Concentrations of Hg and on-site surveys were used to update the risk models.•The hazard quotient was quantified for nine exposure pathways.•The risk for the residents of Almadén exceeds the safe exposure threshold.•Ingestion of local crops accounts for 84% of the overall risk.</description><subject>Bayesian approach</subject><subject>Hazard quotient</subject><subject>Human health risk</subject><subject>Mercury pollution</subject><subject>Probabilistic risk</subject><issn>0147-6513</issn><issn>1090-2414</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kMtu2zAQRYmiBeq6_YMuuMxGLt-yNwEaI0kDOMgmXRM0NbLp6uFyKNf6-1JQ1lkNMDh3MPcQ8p2zFWfc_DitwPfQXVaCibzibC3lB7LgbMMKobj6SBaMq7IwmsvP5AviiTEmmdYLcn0emhSKs0vHf26kx6F1HYXrucchAo0B_1CHCIgtdIkOGLoDvXMjYMhc21fQTBuXaDoCPQZMfQzeNc1IGxcPgIm2EP0QR9qGbkKrzGQkfSWfatcgfHubS_L74f51-6vYvTw-bX_uCi_lJhVVaYR0Rpd7VW2g0mBkaXRt2JoJV4t9VQLXCnStxJozsVZqalp6p3RGct8luZnvnmP_d8gP2Tagh6ZxHfQD2skP48JIk1E1oz72iBFqe46hdXG0nNlJtD3ZWbSdRNtZdI7dzjHINS4BokUfoPNQhQg-2aoP7x_4DyEOikw</recordid><startdate>20200915</startdate><enddate>20200915</enddate><creator>Jiménez-Oyola, Samantha</creator><creator>García-Martínez, María-Jesús</creator><creator>Ortega, Marcelo F.</creator><creator>Bolonio, David</creator><creator>Rodríguez, Clara</creator><creator>Esbrí, José-María</creator><creator>Llamas, Juan F.</creator><creator>Higueras, Pablo</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20200915</creationdate><title>Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district</title><author>Jiménez-Oyola, Samantha ; García-Martínez, María-Jesús ; Ortega, Marcelo F. ; Bolonio, David ; Rodríguez, Clara ; Esbrí, José-María ; Llamas, Juan F. ; Higueras, Pablo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c339t-d7623a657b4d9ed5e63765f60802af2bd7e154e5f42810284401477ca45080513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Bayesian approach</topic><topic>Hazard quotient</topic><topic>Human health risk</topic><topic>Mercury pollution</topic><topic>Probabilistic risk</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiménez-Oyola, Samantha</creatorcontrib><creatorcontrib>García-Martínez, María-Jesús</creatorcontrib><creatorcontrib>Ortega, Marcelo F.</creatorcontrib><creatorcontrib>Bolonio, David</creatorcontrib><creatorcontrib>Rodríguez, Clara</creatorcontrib><creatorcontrib>Esbrí, José-María</creatorcontrib><creatorcontrib>Llamas, Juan F.</creatorcontrib><creatorcontrib>Higueras, Pablo</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Ecotoxicology and environmental safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiménez-Oyola, Samantha</au><au>García-Martínez, María-Jesús</au><au>Ortega, Marcelo F.</au><au>Bolonio, David</au><au>Rodríguez, Clara</au><au>Esbrí, José-María</au><au>Llamas, Juan F.</au><au>Higueras, Pablo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district</atitle><jtitle>Ecotoxicology and environmental safety</jtitle><date>2020-09-15</date><risdate>2020</risdate><volume>201</volume><spage>110833</spage><epage>110833</epage><pages>110833-110833</pages><artnum>110833</artnum><issn>0147-6513</issn><eissn>1090-2414</eissn><abstract>The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a probabilistic human health risk assessment using Bayesian modeling to estimate the non-carcinogenic risk related to Hg through multiple exposure pathways. Samples of vegetables, wild mushrooms, fish, soil, water, and air were analyzed, and adult residents were randomly surveyed to adjust the risk models to the specific population data. On the one hand, the results for the non-carcinogenic risk based on Hazard Quotient (HQ) showed unacceptable risk levels through ingestion of Hg-contaminated vegetables and fish, with HQ values 20 and 3 times higher, respectively, than the safe exposure threshold of 1 for the 97.5th percentile. On the other hand, ingestion of mushrooms, dermal contact with soil, ingestion of water, dermal contact with water and inhalation of air, were below the safety limit for the 97.5th percentile, and did not represent a risk to the health of residents. In addition, the probabilistic approach was compared with the conservative deterministic approach, and similar results were obtained. This is the first study conducted in Almadén, which clearly reveals the high levels of human health risk to which the population is exposed due to the legacy of two millennia of Hg mining.
[Display omitted]
•Bayesian probabilistic approach was used to assess the human health risk.•Concentrations of Hg and on-site surveys were used to update the risk models.•The hazard quotient was quantified for nine exposure pathways.•The risk for the residents of Almadén exceeds the safe exposure threshold.•Ingestion of local crops accounts for 84% of the overall risk.</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.ecoenv.2020.110833</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Bayesian approach Hazard quotient Human health risk Mercury pollution Probabilistic risk |
title | Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district |
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