ABSTRACT
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.
ABSTRACT
China is the largest atmospheric mercury (Hg) emitter globally, which has been substantially investigated. However, the estimation of national or regional Hg emissions in China is insufficient in supporting emission control, as the location of the sources may have significant impacts on the effects of Hg emissions. In this concern, high-spatial-resolution datasets of China's Hg emissions are necessary for in-depth and accurate Hg-related studies and policymaking. Existing gridded datasets are constructed using population distribution as the proxy, which is limited as Hg emissions are closely related to energy consumption and economic processes. This study constructs a dataset of anthropogenic atmospheric Hg emissions in China gridded to a 1 km resolution during 1998-2014. This dataset is produced based on data of land uses, individual enterprises, roadmaps, and population, uncovering Hg emissions in agriculture, industries, services, and residents. This dataset can promote the reliability of Hg-related studies at a high spatial resolution. Moreover, this dataset can support spatially explicit Hg reduction of economic sectors.
