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    <responseDate>2026-10-12T19:07:47Z</responseDate>
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    <identifier>10.57760/sciencedb.14091</identifier>
    <datestamp>2026-05-22T16:28:14Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2026-05-22</dc:date>
  <dc:title>Global predicted data and risk assessment of PFAS concentrations in water and fish</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.14091</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Per- and poly-fluoroalkyl substances (PFAS), known as &amp;quot;forever chemicals,&amp;quot; are pervasive globally and present a significant risk to ecosystems and human health. The database comprised 161,568 records of the PFAS concentration in water and fish globally. Subsequently, machine learning was employed to generate the inaugural global maps of PFAS concentrations in water and fish from 2003 to 2020 at a resolution of 1&amp;deg;. Setting different scenarios to predict the distribution of PFAS in 2025&amp;ndash;2050. Ultimately, based on the model data, a global-scale assessment of PFAS-associated health risks was conducted. These findings offer invaluable insights into the distribution and risk of PFAS worldwide, contributing to strategies for PFAS control.</dc:description>
  <dc:subject>PFAS ; water; fish; risk assessment</dc:subject>
  <dc:creator>Yangyu Wu</dc:creator>
  <dc:creator>Qiansheng Huang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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