We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Modeling spatiotemporal patterns of microplastic pollution in the lupit river using multilinear regression
AI summary Read the abstract
Researchers modelled spatiotemporal patterns of microplastic pollution in the Lupit River using multiple linear regression with four predictors — population, seasonality, macroplastic frequency, and volumetric flow rate — finding widespread contamination across rural, residential, informal settlement, and commercial zones.
Despite increasing awareness of microplastics as contaminants, their sources and abundance factors remain poorly understood. This study found widespread microplastic pollution in the Lupit River. Microplastic concentrations were determined from surface water samples once per season. Samples were analyzed across rural, residential, informal settlement, and commercial zones during both seasons. Microplastic concentration (particles/m) was modeled using multiple linear regression with four predictors: population, seasonality, macroplastic frequency (items/m-hr), and volumetric flow rate (m/s). To enhance model stability, multicollinear variables (velocity, width, and depth) were removed. Concentrations were lower in the wet season due to dilution and flushing and higher in the dry season likely from accumulation and weathering. The final model showed strong explanatory power (R = 0.690, adjusted R = 0.643), with population and seasonality as significant predictors (p < .001). Seasonality had a negative effect (β = - 621.90) due to dilution. Surprisingly, population correlated negatively (β = -0.0217), suggesting better waste infrastructure in denser areas. Macroplastic abundance and flow rate were not statistically significant due to slow weathering and local microplastic accumulation. The model's standard error (301.6 particles/m) accounted for 16.9% of the mean concentration (1,780 particles/m), signifying good prediction accuracy. The developed predictive model provides a low-cost tool for estimating microplastic levels in data-scarce areas which can assist agencies in tracking pollution trends over time and to assess the effectiveness of waste management policies. Future research should validate the model across different catchments and temporal scales to enhance its utility in long-term monitoring.
More Papers Like This
Identifying Environmental Factors Influencing the Concentration, Fluxes and Spatial Distribution of Microplastic Pollution in an Urban River: A Case Study of the Klip River, South Africa
AI summary Read the abstract
Environmental factors such as precipitation, temperature, and land cover were analyzed to identify which variables most strongly control microplastic concentrations in a study ecosystem. Pinpointing these drivers helps predict spatial and seasonal patterns of microplastic pollution for monitoring and management purposes.
Predicting microplastic quantities in Indonesian provincial rivers using machine learning models
AI summary Read the abstract
This study used machine learning models to predict microplastic levels in rivers across 24 Indonesian provinces based on environmental and economic data. Temperature, economic output, and population density were the strongest predictors of microplastic pollution. The approach could help environmental agencies monitor and manage microplastic contamination in freshwater systems more efficiently.
Abundance, Distribution and Drivers of Microplastic Contaminant in Urban River Environments
AI summary Read the abstract
Researchers surveyed microplastic distribution in urban river environments and identified key drivers of accumulation hotspots, finding that land use, hydrology, and infrastructure factors concentrated microplastics at predictable locations that could inform targeted management interventions.
Microplastic contamination on urban river under different precipitation rate and land use gradient
AI summary Read the abstract
Researchers sampled water, sediment, and fish across nine sites in a Javanese urban stream through wet and dry seasons, finding that microplastic abundance in water was higher during dry conditions when developed land use concentrated runoff, while seasonal flushing during the wet season obscured these land-use patterns and fish accumulation depended more on species behavior than pollution levels.
Integrating land cover, point source pollution, and watershed hydrologic processes data to understand the distribution of microplastics in riverbed sediments
AI summary Read the abstract
Researchers sampled riverbed sediments across the Meramec River watershed in Missouri and applied hydrological modelling to assess which factors best predict benthic microplastic distribution, finding that land cover and point source pollution variables outperformed discharge and sediment load in explaining spatial patterns, highlighting the dominance of anthropogenic sources over transport dynamics.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.