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A high spatial resolution dataset for methylmercury exposure in Guangdong-Hong Kong-Macao Greater Bay Area.

Xiaoxin ZhangQiumeng ZhongWeicen ChangHui LiSai Liang
Published in: Scientific data (2023)
Dietary methylmercury (MeHg) exposure increases the risk of many human diseases. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is the world's most populous bay area and people there might suffer a high risk of dietary MeHg exposure. However, there lacks a time-series high spatial resolution dataset for dietary MeHg exposure in the GBA. This study constructs a high spatial resolution (1 km × 1 km) dataset for dietary MeHg exposure in the GBA during 2009-2019. It first constructs the dietary MeHg exposure inventory for each county/district of the GBA, based on MeHg concentrations of foods (i.e., rice and fish in this study) and per capita rice and fish intake. Subsequently, this study spatializes the dietary MeHg exposure inventory at 1 km × 1 km scale, using gridded data for food consumption expenditure as the proxy. This dataset can describe the spatially explicit hotspots, distribution patterns, and variation trend of dietary MeHg exposure in the GBA. This dataset can support spatially explicit evaluation of MeHg-related health risks in the GBA.
Keyphrases
  • machine learning
  • body mass index
  • risk assessment
  • artificial intelligence
  • climate change
  • electronic health record
  • weight loss
  • data analysis
  • weight gain