Long Chen, Sai Liang*, Maodian Liu, Yujun Yi, Zhifu Mi*, Yanxu Zhang, Yumeng Li, Jianchuan Qi, Jing Meng, Xi Tang, Haoran Zhang, Yindong Tong, Wei Zhang, Xuejun Wang, Jiong Shu* & Zhifeng Yang*
Nature Communications volume 10, Article number: 1484 (2019)
Framework of the CMSTM model
1. Emission inventory
1.1 Production-based emissions
Atmospheric Hg is mainly emitted from human production activities, such as fuel consumption, nonferrous metal smelting, production of building materials, primary Hg mining, and waste incineration. As a common practice, emissions can be calculated by multiplying the energy usage or product yields by the corresponding emission factors. We compiled a Chinese production-based Hg emission inventory (see Dataset S3 in Chen et al. 2018), which is used as the satellite account of a MRIO model in this study.
1.2 Consumption-based emissions and Income-based emissions
Treating production-based Hg emissions of sectors as the satellite account of a MRIO table, an environmentally-extended MRIO (EE-MRIO) model can be constructed. Consumption-based emissions and income-based emissions can be evaluated based on the EE-MRIO model. We use the MRIO table developed by Liu et al. (2014) to evaluate the consumption-based and income-based Hg emissions for China in 2010.
The Leontief inverse matrix and Ghosh inverse matrix are used in the evaluation_._The Leontief inverse matrix can capture the effect of product supply chains by describing both direct and indirect inputs from various sectors to satisfy the unitary final demand of specific sectors, and the Ghosh inverse matrix can capture the effect of product sale chains by describing both direct and indirect outputs from various sectors enabled by the unitary primary input of specific sectors.
The results of the region-specific production-based, consumption-based, and income-based Hg emissions are shown in Figure S5 in Chen et al. (2019).
/Dataset S3 in Chen et al. (2018)/
/Figure S5 in Chen et al. (2019/
2. Atmospheric transport and deposition
The GEOS-Chem chemical transport model (version 9-02) is used to simulate the atmospheric Hg transport and deposition over China. The model tracks three Hg species in the atmosphere (i.e., elemental Hg0, divalent HgII, and particulate HgP) and nearly all of the Hg is in the inorganic form. Meteorology in the model is driven by the Goddard Earth Observing System (GEOS-5) assimilated fields taken from the NASA Global Modeling and Assimilation Office (GMAO). Global simulations are conducted at a 4°×5° horizontal resolution with 47 vertical levels from the surface to 0.01 hPa. These simulations serve as boundary conditions for a nested, higher-resolution simulation at a horizontal resolution of 1/2°×2/3° over China.
In Chen et al (2019), we run the nested model for the year 2010 with an initial spin-up of three months in 2009. Global simulations from 2008-2010 serve as boundary conditions. We sum the deposition over all GEOS-Chem grid boxes for each of the 30 Chinese provinces and 4 seas (i.e., the Bohai Sea, Yellow Sea, East China Sea, and South China Sea) to evaluate the changes in Hg deposition and consequent Hg concentrations in food products in each specific region.
/Website of the GEOS-Chem model/
3. Changes in food MeHg resulting from atmospheric deposition
Ten categories of Hg-containing food products are selected as the main intake pathways of total Hg and MeHg for the Chinese population in this study: rice, wheat, beans, vegetables, pork, poultry, milk, eggs, marine fish, and freshwater fish. Here, marine fish and freshwater fish include major fish species in China (e.g., grass carp, crucian carp, common carp, catfish, silver pomfret, and yellow croaker) and other aquatic products (e.g., shrimp, crab, and shellfish).
At present, we apply an assumption of positive linear correlation between Hg in all food products and atmospheric Hg deposition. Changes in MeHg concentrations in food products that are caused by changes in the atmospheric Hg deposition in a specific region are calculated using the following equation:
where represents the changes in atmospheric deposition over region I when the emissions from geographical source j or sectoral source k are changed; represents the gross deposition over region I; denotes the changes in MeHg concentration in a specific food product harvested in region I when the emissions from geographical source j or sectoral source k are changed; and denotes the total MeHg concentration in a specific food product harvested in region I.
4. Intake inventory of MeHg
The CMSTM model compiles an intake inventory of MeHg for the Chinese population at the provincial scale. The method for compiling the inventory includes three steps: compiling MeHg concentrations in food products, modelling the trade of food products, and evaluating estimated daily intake (EDI).
4.1 Compiling MeHg concentrations in food products
The model compiles MeHg concentrations in food products harvested in each province. Concentrations of THg and MeHg in food products harvested in specific provinces are collected from previous studies conducted by authoritative scientific research institutions in China and published in peer-reviewed journals (see Data 1).
Compared to THg, data for MeHg concentrations are scarce in China. The CMSTM model establishes the best linear correlation between THg and MeHg concentrations for certain food products whose published data are abundant, such as rice, vegetables, marine fish, and freshwater fish. For food products with limited data (i.e., beans, pork, poultry, and eggs), the model averages the ratios of MeHg to THg concentration using the limited paired data (see Data 1). The figures of the linear correlation between MeHg concentrations and THg concentrations and associated equations are _Figure S6_in Chen et al. (2019).
4.2 Modelling the trade of food products
The CMSTM model uses a MRIO model to simulate the interprovincial trade of food products in this study. The MRIO table for China is in 2010 and is developed by Liu et al. (2014). The final demand data for the Farming, Forestry, Animal Husbandry and Fishery sector from the MRIO table are used for the simulation of food trade.
A ratio of its output value to gross agricultural output is introduced to extract the final demand data for the specific food product from the total final demand data of the Farming, Forestry, Animal Husbandry and Fishery sector in the MRIO table for each province. The output values of food products and gross agricultural output are from the China Agriculture Yearbook 2011. The detailed data for output values of each type of food products are provided in Data 2. Moreover, for marine fish, the source contributions of four coastal seas to the coastal provinces are estimated based on statistics from the China Fisheries Yearbook 2011 in the model.
The simulated trade of each type of food products among Chinese provinces is shown in Figure S7 in Chen et al. (2019).
4.3 Evaluating estimated daily intake (EDI)
By combining the MeHg concentrations in food products harvested in specific provinces and interprovincial trade of the food products, the MeHg concentrations in food products consumed in specific provinces can be estimated. Subsequently, the EDI of MeHg is evaluated by multiplying the MeHg concentrations in consumed food products by the intake rate of each type of food products for the population in each province.
The model obtains the intake rate of food products from the data of per-capita consumption. The provincial data of per-capita consumption for rural and urban populations are taken from the China Statistical Yearbook 2011and provincial statistical yearbooks in 2011 (see Data 3). The per-capita consumption amount for the rural population is taken directly from these yearbooks. For urban population, the per-capita consumption amount in each province can be estimated from the data on per-capita consumption expenditures in each province and total national consumption amounts, which can be directly obtained from these yearbooks (see Data 3). The total national intake rates of food products from the China Health and Nutrition Survey (CHNS) are used to adjust the per-capita consumption for the population in each province. The equations for the calculation of EDI are provided below:
where indicates per-capita intake rate of food I by the population in province j. BW represents average body weights of Chinese adult males and females. is the MeHg concentration in food I harvested in province k, and represents the EDI of MeHg by the population in province j.
/Figure S6 in Chen et al. (2019)/
/Figure S7 in Chen et al. (2019)/
5. Human health impacts
IQ decrements in foetuses and fatal heart attacks in adults due to Hg exposure are evaluated. For IQ decrements, the associated coefficients for the relationship were characterized by epidemiologic studies simultaneously. Following previous studies (Rice et al., 2010; Giang and Selin, 2016), the model applies the linear dose-response relationship without thresholds between maternal intake of MeHg and foetal IQ decrements to assess the IQ effects caused by MeHg in China. The assessment of the IQ effects is shown below:
The descriptions and values for the coefficients are shown in _Table S4_in Chen et al. (2019).For fatal heart attacks, the associated coefficients for the relationship were suggested by an epidemiological study called the European Community Multicenter Study of Antioxidants, Myocardial Infarction and Breast Cancer (EURAMIC). Following previous studies (Rice et al., 2010; Giang and Selin, 2016), the model applies log-linear relationship for Hg-related fatal heart attacks in China. The dose-response relationship is shown bellow:
The descriptions and values for the coefficients are shown in _Table S4_in Chen et al. (2019), and the population data for Pg and are provided in Data 4 and _Table S5_in Chen et al. (2019), respectively.
/Table S4 in Chen et al. (2019)/
/Table S5 in Chen et al. (2019)/
The contents in this page are retrieved from Chen et al. (2019). Please refer to the paper for more details. When using the CMSTM model, the citation of Chen et al. (2019) is needed. The computer codes for CMSTM model are available from the corresponding authors of the paper upon reasonable request.
Chen, L.; Liang, S.; Liu, M.; Yi, Y.; Mi, Z.; Zhang, Y.; Li, Y.; Qi, J.; Meng, J.; Tang, X.; Zhang, H.; Tong, Y.; Zhang, W.; Wang, X.; Shu, J.; Yang, Z. Trans-provincial health impacts of atmospheric mercury emissions in China. Nature Communications 2019, 10 (1), 1484.
Chen, L. et al. Trade-induced atmospheric mercury deposition over China and implications for demand-side controls. Environ. Sci. Technol. 52, 2036–2045 (2018).
Liu, W. D., Tang, Z. P., Chen, J. & Yang, B. China’s Interregional Input−Output Table for 30 Regions in 2010 (China Statistics Press, Beijing, 2014).
Rice, G. E., Hammitt, J. K. & Evans, J. S. A probabilistic characterization of the health benefits of reducing methylmercury intake in the United States. Environ. Sci. Technol. 44, 5216–5224 (2010).
Giang, A. & Selin, N. E. Benefits of mercury controls for the United States. Proc. Natl. Acad. Sci. USA 113, 286–291 (2016).