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Spatiotemporal Patterns of Aquatic Product Risks in China Based on Entropy-Weighted TOPSIS.

Guangcan Tao^1,2, Guoyan Li¹, Dingfang Pu¹

¹School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 561113, China.

Foods (Basel, Switzerland)

|December 30, 2025

Summary

This study identified top hazardous substances in Chinese aquatic products, including cadmium and enrofloxacin. Risks are highest in summer/autumn, necessitating targeted, seasonal regulations for food safety.

Keywords:

Pareto principle hazardous substances risk classification spatial autocorrelation spatial–temporal distribution characteristics

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Area of Science:

Food Science
Environmental Science
Risk Assessment

Background:

Hazardous substance contamination in aquatic products poses risks to public health.
Understanding the spatiotemporal distribution of these contaminants is crucial for effective regulation.

Purpose of the Study:

To classify risks of hazardous substances in Chinese aquatic products.
To analyze the spatiotemporal evolution patterns of these risks.
To provide a scientific basis for regulatory strategies.

Main Methods:

Entropy-weighted TOPSIS method for risk ranking and classification.
Spatial autocorrelation analysis for distribution patterns.
Risk-adjustment factors for dynamic spatiotemporal analysis.

Main Results:

Cadmium, enrofloxacin, and total volatile basic nitrogen were the top three hazardous substances.
Significant spatial autocorrelation was found for cadmium, enrofloxacin, furazolidone metabolites, and chloramphenicol.
Aquatic product risks were highest in summer and autumn, reaching a maximum value of 0.92.

Conclusions:

The integrated methodology offers a novel approach to risk assessment in aquatic products.
Findings support targeted regulation of high-risk substances and seasonal-regional control strategies.
Recommendations provided for adjusting regulatory measures to enhance aquatic product safety supervision.