Modeling Microplastic Impacts on Soil Quality in Sri Lanka Using Data Analytics and Graph Data Science

Abstract

Plastic and microplastic pollution has emerged as a critical environmental challenge, with profound implications for soil ecosystems. In Sri Lanka, rapid urbanization and inadequate waste management exacerbate soil contamination, yet the specific impacts on soil quality remain underexplored. This study leverages graph data science and computational modeling to assess how plastic and microplastic pollution affects soil health, focusing on soil fertility, microbial activity, and contaminant dispersion patterns. Using a PRISMA-based systematic literature review, we identify a critical research gap: the lack of integrated, data-driven approaches to quantify microplastic soil interactions in tropical agricultural contexts like Sri Lanka. Our methodology employs graph-based networks to model pollutant interactions and computational simulations to predict long-term soil degradation. The analysis incorporates six graphical representations and five data tables to illustrate pollutant distribution, soil property changes, and microbial disruptions. Findings suggest that microplastics significantly alter soil structure and nutrient cycling, with implications for agricultural productivity. This research provides actionable insights for policymakers and environmental practitioners in Sri Lanka to mitigate soil pollution through targeted interventions.