ArticleRSC advances2026
Spatial patterns and associations between microplastics and heavy metals in wastewater effluents of Pakistan's twin cities: insights from structural equation modeling.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Microplastics (MPs) have become ubiquitous environmental pollutants. Wastewater treatment plants (WWTPs), which have been considered barriers to contamination, are now being understood as significant pathways for releasing MPs and heavy metals (HMs) in aquatic systems. The study has investigated the presence, nature, and association between MPs and HMs in effluents of twelve urban and industrial WWTPs in the twin cities of Pakistan. Microscopic studies were used to quantify MPs and characterize their size, shape, and color, while polymer identification was done by FTIR analyses based on characteristic functional groups. HM (Cu, Pb, Cd, Zn, Fe, and Mn) concentrations were analyzed by Atomic Absorption Spectroscopy (AAS). Pollution Load Index (PLI) and Ecological Risk Index (RI) were estimated to assess the possibility of environmental risks. Moreover, Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to explain connections between MP size fractions, types of polymers, HM association, and ecological risk indicators. The outcome showed that MP contamination was prevalent in all WWTP effluents, with concentrations ranging from 477 to 1354 MPs per L. Small-sized microplastics (<300 µm), which were primarily fragments and fibers, dominated the MP assemblage. The polymers that were identified were PE, PP, PET, PS, PVC, nylon, and cellulose. Despite the relatively low ecological risk (6.67-36.13) associated with the overall HM pollution levels, considerable amounts of MP-HM association were reported. Polymers, particularly PVC and PE, exhibited strong statistical associations with Cd site concentrations, highlighting clear field co-occurrence trends between specific polymer profiles and target heavy metals. These exploratory co-variation pathways underscore the utility of multi-variate modeling in identifying structural distribution patterns within complex wastewater environments, thereby presenting microplastics as potentially significant co-occurring environmental vectors. Ultimately, these results underscore the need for polymer-focused wastewater management strategies to mitigate downstream ecological risks.
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