Modeling spatiotemporal patterns of microplastic pollution in the lupit river using multilinear regression

Area of Science:

• Environmental Science
• Water Quality Monitoring
• Pollution Studies

Background:

• Microplastic contamination is a growing global concern, yet understanding its sources and influencing factors remains limited.
• Rivers act as significant pathways for microplastics entering larger water bodies and oceans.
• Assessing microplastic abundance requires robust methodologies, especially in data-scarce regions.

Purpose of the Study:

• To investigate microplastic pollution levels and distribution in the Lupit River across different zones and seasons.
• To develop and validate a predictive model for microplastic concentration using environmental and anthropogenic factors.
• To provide a cost-effective tool for monitoring microplastic pollution and informing waste management strategies.

Main Methods:

• Surface water samples were collected seasonally from rural, residential, informal settlement, and commercial zones.
• Microplastic concentrations (particles/m²) were quantified and modeled using multiple linear regression.
• Predictors included population density, seasonality, macroplastic frequency, and volumetric flow rate; multicollinear variables were removed for model stability.

Main Results:

• Widespread microplastic pollution was detected, with significantly higher concentrations during the dry season compared to the wet season.
• The predictive model demonstrated strong explanatory power (R² = 0.690), identifying population and seasonality as key significant predictors.
• Contrary to expectations, population density showed a negative correlation, potentially indicating better waste management in densely populated areas.

Conclusions:

• The developed predictive model offers a valuable, low-cost tool for estimating microplastic levels in data-limited river systems.
• Seasonal variations significantly impact microplastic concentrations, with accumulation occurring during dry periods.
• The findings support the need for targeted waste management policies and highlight the utility of predictive modeling for pollution assessment.