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A geospatial investigation of microplastics leaching in Ubon Ratchathani province, Thailand: fuzzy logic-based analysis

Environmental Monitoring and Assessment 2025 4 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 48 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Thammarat Koottatep Eklabya Sharma, Thammarat Koottatep, Thammarat Koottatep, Thammarat Koottatep, Thammarat Koottatep K.C. Surendra, Thammarat Koottatep, Thammarat Koottatep Thammarat Koottatep Dan Tran Thanh, Dan Tran Thanh, Thammarat Koottatep, Thammarat Koottatep

Summary

Researchers applied GIS combined with fuzzy logic analysis in Ubon Ratchathani province, Thailand to map microplastic leakage sources and predict pollution transport through river networks, demonstrating that this spatial modeling approach can identify priority catchment areas for microplastic management.

Study Type Environmental

Microplastics pollution poses significant environmental challenge, with river networks serving as major pathways for transport to oceans. Effectively managing microplastics requires identification of their sources and pathways into river networks, yet there is a lack of understanding, hindering successful mitigation efforts. This study demonstrates the novel use of fuzzy logic-based tools in geographic information system (GIS) for the precise identification of microplastics leakage sources in Ubon Ratchathani province, situated in Northeastern Thailand. A leakage density map was developed by applying fuzzy logic to variables responsible for microplastics production in the environment, using available geospatial datasets. A fuzzy overlay was performed, merging the density map and drainage networks of the province, creating a comprehensive microplastics leakage sources map. This leakage sources map illustrated the flow of microplastics from leakage-dense areas towards the susceptible river network in the province. It identified key sources of microplastic leakage, such as road networks, facilities, and industries contaminating urban waterways. Field-based microplastic data verified the map's accuracy. A comparative analysis between identified polluted rivers and those not flagged revealed that microplastic accumulation is influenced not only by source proximity but also by river characteristics such as flow rate, hydrology, and seasonal variations. The study underscores the effectiveness and reliability of fuzzy logic-based GIS tools in identifying microplastics source hotspots within a specific region. Furthermore, it provides a valuable approach for advancing Sustainable Development Goal 14 (Life Below Water) by managing microplastics in river networks to prevent their accumulation in marine environments.

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