dc.contributor.author | Macklin, MG | |
dc.contributor.author | Thomas, CJ | |
dc.contributor.author | Mudbhatkal, A | |
dc.contributor.author | Brewer, PA | |
dc.contributor.author | Hudson-Edwards, KA | |
dc.contributor.author | Lewin, J | |
dc.contributor.author | Scussolini, P | |
dc.contributor.author | Eilander, D | |
dc.contributor.author | Lechner, A | |
dc.contributor.author | Owen, J | |
dc.contributor.author | Bird, G | |
dc.contributor.author | Kemp, D | |
dc.contributor.author | Mangalaa, KR | |
dc.date.accessioned | 2023-12-01T14:28:27Z | |
dc.date.issued | 2023-09-21 | |
dc.date.updated | 2023-12-01T13:53:31Z | |
dc.description.abstract | An estimated 23 million people live on floodplains affected by potentially dangerous concentrations of toxic waste derived from past and present metal mining activity. We analyzed the global dimensions of this hazard, particularly in regard to lead, zinc, copper, and arsenic, using a georeferenced global database detailing all known metal mining sites and intact and failed tailings storage facilities. We then used process-based and empirically tested modeling to produce a global assessment of metal mining contamination in river systems and the numbers of human populations and livestock exposed. Worldwide, metal mines affect 479,200 kilometers of river channels and 164,000 square kilometers of floodplains. The number of people exposed to contamination sourced from long-term discharge of mining waste into rivers is almost 50 times greater than the number directly affected by tailings dam failures. | en_GB |
dc.description.sponsorship | University of Lincoln | en_GB |
dc.format.extent | 1345-1350 | |
dc.identifier.citation | Vol. 381, No. 6664, pp. 1345-1350 | en_GB |
dc.identifier.doi | https://doi.org/10.1126/science.adg6704 | |
dc.identifier.uri | http://hdl.handle.net/10871/134711 | |
dc.identifier | ORCID: 0000-0003-3965-2658 (Hudson-Edwards, KA) | |
dc.language.iso | en | en_GB |
dc.publisher | American Association for the Advancement of Science (AAAS) | en_GB |
dc.relation.url | https://mrdata.usgs.gov/mrds/ | |
dc.relation.url | https://www.bgs.ac.uk/datasets/britpits/ | |
dc.relation.url | https://www.spglobal.com/marketintelligence/en/campaigns/metals-mining | |
dc.relation.url | https://doi.org/10.5061/dryad.j3tx95xmg | |
dc.relation.url | https://tailing.grida.no/ | |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/37733841 | |
dc.rights | © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. | en_GB |
dc.title | Impacts of metal mining on river systems: a global assessment | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-12-01T14:28:27Z | |
dc.identifier.issn | 0036-8075 | |
exeter.place-of-publication | United States | |
dc.description | This is the author accepted manuscript. The final version is available from the American Association for the Advancement of Science via the DOI in this record | en_GB |
dc.description | Data and materials availability: The Water and Planetary Health Analytics (WAPHA) global metal mines database is divided into four components. Publicly available data on (i) active and (ii) inactive metal mines are available from the US Geological Survey Mineral Resources Data System [https://mrdata.usgs.gov/mrds/ (31)], the BritPits database of the British Geological Survey [https://www.bgs.ac.uk/datasets/britpits/ (32)], and the S&P Global Market Intelligence database [https://www.spglobal.com/marketintelligence/en/campaigns/metals-mining (33)]. In addition, data for ~100,000 additional active and inactive mines obtained from academic and gray literature are stored in the WAPHA database [https://doi.org/10.5061/dryad.j3tx95xmg (29)]. Publicly available data relating to (iii) TSFs and (iv) TDFs are available from ICOLD/UNEP [https://books.google.co.uk/books?id=8W0hAQAAIAAJ (34)], the World Information Service on Energy [https://wise-uranium.org/mdaf.html (35)], the World Mine Tailings Failures and Global Tailings Portal databases [https://tailing.grida.no/ (36)]. Additional TSF/TDF data obtained from academic and gray literature are stored in the WAPHA database [https://doi.org/10.5061/dryad.j3tx95xmg (29)]. Modeling was implemented procedurally in MATLAB v9.9.0 (R2020b) (37) with the open source TopoToolbox MATLAB program for the analysis of digital elevation models (https://topotoolbox.wordpress.com). The modeling workflow is presented in fig. S8 with example code available in the WAPHA database [https://doi.org/10.5061/dryad.j3tx95xmg (29)] | en_GB |
dc.identifier.eissn | 1095-9203 | |
dc.identifier.journal | Science | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2023-08-18 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2023-09-22 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-12-01T14:23:21Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2023-12-01T14:28:33Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2023-09-21 | |