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Microplastics assessment in the lower stretch of the Ganga River sediment from East Indian region: Influence of land use and rainfall patterns

Chemosphere 2025 3 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.
Varsha Varsha, Rajeev Ranjan, Avinash Dass, Sushil Kumar Singh, Tanuja Singh

Summary

This study investigated microplastic pollution in sediments along the lower Ganga River in East India, finding that land use type and seasonal rainfall patterns significantly influence the abundance and distribution of microplastics across different stretches of the river.

Study Type Environmental

Microplastic (MP) pollution is increasingly viewed as a serious threat to waterways. However, little is known about the effects of land use and rainfall patterns on the occurrence and distribution of MPs in the river sediments. Herein, the MP pollution in different land uses (Settlement, waterbodies, vegetation, agriculture, fallow land, and open spaces) along the lower stretch of the Ganga River, in the East Indian region, was investigated. This study performed sediment sampling for MPs at different monitoring locations in the Patna region, Bihar, India along the Ganga River basin. The Land Use Land Cover (LULC) across the Patna district was classified using Random Forest (RF) and a machine learning classifier on Google Earth Engine (GEE). The study observed a steady rise in MP concentrations across all sampled locations. There is a positive relationship between the amount of MP and both urban density (r = 0.52, p < 0.05) and population density (r = 0.42, p < 0.05) within a 2 km area. Additionally, rainfall exhibited a strong and significant relationship with MP levels, with a correlation coefficient of 0.90 (p < 0.05) across the study area. Using the RF algorithm for classifying LULC resulted in very high accuracy, with an overall accuracy of 98.3 % and a kappa coefficient of 0.97. The findings from this research provide valuable insights that can assist in designing effective policies and strategies to mitigate MP pollution and safeguard water quality in river ecosystems.

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