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Assessing microplastics risk in marine habitats of Haizhou Bay based on an integrated approach.
Summary
Scientists studied tiny plastic bits (microplastics) in a Chinese bay where popular seafood species live, and found that while older methods suggested almost the entire bay was "high risk," a more precise approach—one that accounts for where fish actually live and feed—showed the real risk was much lower than initially feared. This matters because it means simple "plastic count" measurements can overestimate danger, and better tools are needed to figure out which seafood-producing waters truly need cleanup priority versus which ones just look scary on paper. For consumers, this suggests that not all microplastic detection in fish habitats translates to meaningful health risk
Current risk assessments often overlook the spatial overlap between microplastics (MPs) exposure and species-specific habitat distributions, which may bias ecological risk estimates. To address this gap, we developed an integrated approach combining MPs distribution maps with habitat suitability models for ten key fishery species in Haizhou Bay, China. Traditional risk quotients (RQ) were compared with a probabilistic method based on joint probability curves (JPC) to estimate overall risk probability (ORP). Mean MPs abundances were 2.34 ± 2.32 items/L in spring and 1.89 ± 2.38 items/L in autumn, with the highest concentrations found in the southern nearshore area. Random Forest identified dissolved iron, temperature, and velocity as the main factors influencing MPs distribution in both seasons. RQ-based assessment classified over 90% of Haizhou Bay as potential risk zones, and more than half of the suitable habitat for all ten species overlapped with these areas. By contrast, ORP values from JPC analysis remained approximately 1.0 % across the entire bay and within individual habitats. This divergence suggests that concentrations above safety thresholds do not necessarily imply high ecological risk. By integrating spatial predictions, habitat modeling, and probabilistic risk estimation, the proposed framework can identify priority management areas and quantify overall risk probability and is adaptable to other coastal systems facing MPs pollution.