We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Prediction of Microplastic Emissions in River Basins Based on Mathematical Models
Summary
Researchers developed a mathematical model integrating multivariate linear regression and stepwise regression to predict microplastic emissions in the Guangzhou section of the Pearl River Basin, using historical abundance data to forecast 2025 annual average concentrations in this urban river system.
Microplastics have become a globally concerning persistent pollutant in aquatic environments, with rivers acting as critical pathways for their transport from land to oceans. This study developed a mathematical model to predict microplastic emissions in the Guangzhou section of the Pearl River Basina typical urban river area in the Pearl River Delta. The model integrated multivariate linear regression and stepwise regression. The annual average microplastic abundance (item/L) served as the dependent variable. The updated model was used to predict the 2025 annual average microplastic abundance in the study area, yielding a value of 2730.64 item/L. This study provides a cost-effective, reliable tool for predicting riverine microplastic emissions and offers scientific support for microplastic pollution prevention and control in the Pearl River Basin and similar urban river systems.
Sign in to start a discussion.
More Papers Like This
Microplastic abundance, distribution and composition in the Pearl River along Guangzhou city and Pearl River estuary, China
Microplastic abundance and distribution were surveyed in the Pearl River urban section through Guangzhou and the Pearl River estuary, finding average concentrations of 19,860 items/m³ in the urban section and 8,902 items/m³ in the estuary, with over 80% of particles smaller than 0.5 mm. The study documents exceptionally high microplastic concentrations in an urban Chinese river and identifies wastewater effluent and urban tributaries as major sources.
Riverine Microplastic Pollution in the Pearl River Delta, China: Are Modeled Estimates Accurate?
Researchers compared field measurements of microplastic loads in Pearl River Delta tributaries with model-based estimates, finding significant discrepancies that suggest current models either underestimate or overestimate emissions depending on assumptions used. Improving model accuracy requires better local emission data and validation against diverse river systems.
Occurrence and distribution of microplastics in an urban river: A case study in the Pearl River along Guangzhou City, China
Microplastics were measured in surface water and sediments at 14 sites along the urban Pearl River in Guangzhou, finding 379–7,924 items/m³ in water and 80–9,597 items/kg in sediment with polyethylene and polypropylene as dominant types, and a significant relationship between population density and microplastic abundance. The study documents extremely high microplastic contamination in an urban Chinese river and quantifies the influence of urbanization on microplastic loading.
Study on the Migration and Distribution Characteristics of Microplastics in the Pearl River Estuary under Changing Environmental Conditions
Researchers investigated the migration and spatial distribution of microplastics in the Pearl River Estuary under changing environmental conditions, modeling the dynamic transport of the estimated 66 tons of microplastics discharged annually from this estuary into the South China Sea.
Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.