IoT-enabled FMIND pipeline with chemical validation for microplastic contamination risk assessment in bottled water under varying storage conditions
Abstract
Microplastic contamination in bottled drinking water is an emerging environmental and public health concern, particularly when bottles are exposed to varying storage and thermal conditions. This study introduces FMIND (fuzzy microplastic inference for detection risk), an IoT-enabled fuzzy inference framework for rapid and low-cost microplastic contamination risk assessment. Bottled water stored in PET and stainless-steel containers under sunlight, shade, and freezer conditions was evaluated using IoT sensors measuring temperature, turbidity, and total dissolved solids (TDS) before and after 30 days of storage. Statistical analysis revealed strong correlations between sensor variations and contamination-related physicochemical indicators, including turbidity (r = 0.861), TDS (r = 0.793), and temperature (r = 0.565) (p < 0.001). The FMIND fuzzy model applied 12 Sugeno rules to generate a contamination risk score (0-100), while the HFIRM-GT enhanced configuration improved classification consistency within the experimental dataset, achieving an F1 score of 0.91. Laboratory validation using FTIR spectroscopy, SEM imaging, and EDAX elemental analysis on selected high-risk samples supported the presence of polymer-associated microplastic fragments in sunlight-exposed PET bottles. The proposed framework does not directly quantify microplastics through IoT sensors; instead, it estimates contamination risk using indirect physicochemical indicators supported by laboratory validation.…
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