Tracking and optimizing toxic chemical exposure pathways through food trade: A case study in SCCPs contaminated seafood in China

Abstract

Food safety is related to human health and sustainable development. International food trade poses food safety risks through the collateral transport of toxic chemicals that are detrimental to human health. Domestic interprovincial trade has similar effects within countries but has not been comprehensively investigated previously. Here, we assessed the effects of interprovincial trade on food safety and human dietary exposure to short-chain chlorinated paraffins (SCCPs), a group of emerging persistent toxic chemicals, in seafood across China by synthesizing data from field observation and various models. Our findings indicate that there is a higher level of SCCPs exposure risk in coastal provinces compared to inland provinces. Approximately, 70.3% of human exposure to SCCPs through seafood consumption in China was embodied in the interprovincial seafood trade in 2021. Specifically, the domestic trade led to a remarkable increase in SCCPs exposure in the coastal provinces in South China, attributable to low SCCPs pollution in these provinces and imported seafood from those provinces with high SCCPs pollution. In contrast, human exposure to SCCPs decreased in those coastal provinces in East China due to importing seafood from those provinces with low SCCPs concentrations. The interprovincial seafood trade routes were optimized by linear programming to minimize human exposure to SCCPs considering both shipping cost and health risk constraints. The optimized trade routes reduced the national per capita SCCPs exposure through seafood consumption by over 12%. This study highlights the importance of interprovincial food trade in the risk assessment of toxic chemicals.

Keywords: China; interprovincial food trade; risk assessment; seafood; short-chain chlorinated paraffins.

Fig. 1.

Comparison between simulated and measured SCCPs concentrations in seafood. Different species are highlighted by different colors. The solid black line indicates a 1:1 line. MB and normalized mean bias NMB are calculated as in Huang et al. (13).

Fig. 2.

A) EDI of SCCPs for an adult consumer induced by seafood intake in 2021 and B) simulated annual average concentrations in water (ng·L⁻¹) in 2021.

Fig. 3.

Transfer of EDI of SCCPs via interprovincial seafood trade across China in 2021. A) Net EDI of SCCPs export through interprovincial seafood trade and transfer of SCCPs EDI among 31 provinces. Positive value indicates regions of net EDI exports in the seafood trade, negative value indicates net EDI imports through interprovincial seafood trade. Arrow lines represent the pathway of EDI transfer embodied in the interprovincial seafood trade. B) Interprovincial EDI transfer embodied in the seafood trade is illustrated as a Circos plot. The width of each band represents the magnitude of EDI, and the band color represents the net inflow of EDI. The colors of the outer circular rings correspond to the provinces marked. C) Proportion of EDI (%) via locally produced and traded fish intake in each province. Each cell represents the EDI% from seafood consumption in each province indicated on the bottom x-axis to that produced in the region indicated by the left y-axis.

Fig. 4.

Dietary exposure of SCCPs was measured based on EDI via seafood consumption embodied by interprovincial seafood trade in 2021. A) EDI from no-trade simulation. B) Differences in EDI (EDI_DF) between trade and no-trade model runs. The difference is estimated as EDI_DF = EDI_T − EDI_NT, referred to as trade (EDI_T) and no-trade (EDI_NT) simulated EDI.

Fig. 5.

Optimized interprovincial seafood trade flows considering health risk impact on provincial EDI and cost from trade and logistics. A) Optimized interprovincial trade flows of seafood in 2021. B) Relative differences in provincial SCCPs EDI between optimized trade scenario considering health risk (EDI_optimized) and current trade scenario without considering health risk (EDI_current), calculated as EDI_RD = (EDI_optimized − EDI_current) × 100/EDI_current. C) Relative differences of the cost per unit trade seafood between optimized (Cost_optimized) and baseline trade scenario (Cost_base) estimated as Cost_DF = (Cost_optimized − Cost_base) × 100/Cost_base.