Dataset Information

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

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.

SUBMITTER: Zhang X

PROVIDER: S-EPMC10582186 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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

Zhang Xiaoxin X Zhong Qiumeng Q Chang Weicen W Li Hui H Liang Sai S

Scientific data 20231017 1

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 dietar ...[more]

PMID: 37848476

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Project description:The Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration is an urban agglomeration with some of the most intensive urbanization since 1980s. A large amount of cultivated land, forest land, water bodies and other land types in the region has been occupied by construction land, resulting in fragmented ecological landscapes and biodiversity in the region and causing many other ecological problems. Based on this, this paper takes the Guangdong-Hong Kong-Macao Greater Bay Area as a case study, constructs an ecological network of the dispersion scale of five species from 1990 to 2020 based on a morphological spatial pattern analysis (MSPA) method, identifies the ecological groups in the network and uses the core node-based community evolution path tracking algorithm to analyze the ecological groups in order to explore the changes of ecological network connectivity at different scales in the region and to reveal the overall and local characteristics and changes of the migratory space of terrestrial mammals with different dispersion capabilities. The research results show that: (1) From 1990 to 2020, the area of construction land in the Guangdong-Hong Kong-Macao Greater Bay Area increased sharply, with good connectivity in the northwest, southwest and eastern regions and poor connectivity in the central region. (2) There are obvious differences between the overall and local changes in the connectivity trends of multi-scale regional ecological networks. On the whole, the overall ecological connectivity of the ecological network at each scale showed a gradual upward trend, and the overall connectivity index IIC and the possible connectivity index PC gradually increased with the increase of the maximum dispersal distance of species. From the perspective of local patches, the larger the species dispersion scale, the larger the value of the revised betweenness centrality index and the patch possible connectivity index. (3) The distribution of ecological groups at different species dispersion scales is different, and the smaller the dispersal scale of the species, the greater the distribution of ecological groups. Small-scale species are limited by the maximum dispersal distance, and the range of their ecological groups is generally small. Small-scale (3 km), mesoscale (10 km) and large-scale (30 km) core nodes of ecological groups show a gradual increase trend, and the overall connectivity of ecological groups has improved. However, the core nodes of the extra-large-scale (60 km) and ultra-large-scale (100 km) ecological groups show a trend of decreasing fluctuations, and the overall connectivity within the ecological group has declined. This study is helpful to clarify the structural characteristics of regional ecological space and provide a theoretical basis for regional ecological planning.

Project description:The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China's transformation from a manufacturing to an innovation cluster. However, due to rapid economic expansion and rapid urbanization, the Great Bay Area still struggles with low energy efficiency and environmental degradation, which has slowed down the pace of development. Therefore, in order to alleviate energy pressure, promote the country's sustainable development and gain a competitive advantage in the global market, researching energy efficiency and improving energy utilization efficiency is crucial. In this study, macro-level energy efficiency indicators are constructed using energy consumption data from various cities in the Greater Bay Area for the period from 2000 to 2020, and the spatio-temporal evolution of energy efficiency is analysed. The results show that all cities in the Greater Bay Area experienced an increasing trend in energy efficiency from 2000 to 2019, with significant variation in growth rates and magnitudes between cities. Compared to the nine cities in Guangdong province, Hong Kong and Macao exhibited significantly superior energy efficiency, with Foshan recording the highest growth rate of 14%. In 2020, most cities experienced a decline in energy efficiency due to the COVID-19 pandemic, with Macao experiencing the greatest decrease at 57%. Hong Kong and Macao are both in the "low consumption and high efficiency" target region, while Guangzhou, Shenzhen and Zhuhai are consistently in the "both high" region. Changes in the industrial upgrading index correspond significantly with changes in energy efficiency trajectories, with the transition from primary to secondary and tertiary industries playing a more substantial role. There is no significant association found between the strength of environmental regulation and changes in energy efficiency. The study's findings indicate that the most effective way to achieve economic transformation in the majority of China's regions is to combine adequate environmental legislation with industrial structural adjustment.

Dataset Information

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

Publications

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

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