We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Environmental Risk Assessment of the Harbin Section of the Songhua River Basin Based on Multi-Source Data Fusion
ClearAre we underestimating the driving factors and potential risks of freshwater microplastics from in situ and in silico perspective?
Researchers combined field sampling with machine learning predictions to assess microplastic contamination in rivers of China's Yangtze River Delta, incorporating land use, hydrology, and particle properties. The study found that conventional assessments may underestimate risk by overlooking smaller particle sizes and high-density polymers, and that textile manufacturing effluents are a major underrecognized source.
Water Quality Evaluation, Spatial Distribution Characteristics, and Source Analysis of Pollutants in Wanquan River, China
This paper is not about microplastics — it assesses water quality in a Chinese river basin, finding that agricultural runoff and domestic sewage are the main pollution sources, without examining plastic contamination.
Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Researchers used artificial neural network analysis to assess water quality and identify pollution causes in the Tuojiang River Basin in China, examining parameters including dissolved oxygen and ammonia-nitrogen to understand contamination patterns and risks in this waterway.
Spatial distribution and potential sources of microplastics in the Songhua River flowing through urban centers in Northeast China
Microplastics were sampled from river water and wastewater treatment plant effluents at five cities along the Songhua River in northeast China, finding polyethylene and polypropylene as dominant polymers and identifying urban WWTPs as the primary point sources of microplastic input to the river.
Microplastics in China’s surface water systems: Distribution, driving forces and ecological risk
Researchers compiled over 14,000 samples from across China to map microplastic pollution in surface water systems using machine learning models. They found that microplastic abundance varied enormously across regions, driven by a complex mix of human activities and natural conditions. The ecological risk assessment revealed that watersheds in nearly all Chinese provinces face high to extremely high contamination levels, underscoring the urgency of nationwide management efforts.
Distribution, Sources, and Ecological Risk Assessment of Microplastics in the Lower Minjiang River
Researchers characterized microplastic abundance, morphology, and polymer composition in surface water and sediments from the lower Minjiang River in China, then used pollution load indices and ecological risk assessments alongside socioeconomic data to identify likely pollution sources and ecological impacts.
Spatial–Temporal and Risk Assessment of Microplastics in the Surface Water of the Qinhuai River during Different Rainfall Seasons in Nanjing City, China
Researchers conducted a spatial-temporal analysis of microplastic contamination and risk in a river system across multiple seasons and sites, finding that concentrations varied significantly with location and time of year. Urban and industrial zones showed the highest microplastic loads and associated ecological risk.
Microplastic pollution in sophisticated urban river systems: Combined influence of land-use types and physicochemical characteristics
This study assessed microplastic pollution across an urban river network in China, finding that land-use type and water physicochemical properties jointly influence microplastic distribution, with industrial and residential areas contributing highest loads.
Evolving environmental awareness and shifts in management priorities: a socioeconomic lens on the min river basin, China
Not relevant to microplastics — this paper uses socioeconomic analysis and machine learning to study shifting environmental management priorities in China's Min River basin, focusing on water quality and land use.
Characteristics and sources of microplastic pollution in the water and sediments of the Jinjiang River Basin
Researchers characterized microplastic pollution across surface water, groundwater, and sediments throughout the Jinjiang River Basin in China, tracing sources via principal component analysis and documenting contamination from inland areas to the estuary.
Microplastic risk assessment in surface waters: A case study in the Changjiang Estuary, China
Researchers assessed microplastic risk in surface waters of the Changjiang Estuary, finding measurable contamination and identifying this major river delta as a significant source and pathway for microplastic transport into coastal marine environments.
Microplastic pollution in the Yangtze River: Characterization, influencing factors, and scenario-based predictions using machine learning method
Microplastic pollution in the Yangtze River was characterized across multiple sampling sites, documenting spatial patterns in particle abundance, polymer types, and size distributions. As one of the world's largest rivers, the Yangtze's microplastic burden has major implications for plastic delivery to the Pacific Ocean.
Evaluation of nitrate pollution sources in surface water across the typical rural-urban interface: a case study of Wen-Rui Tang River, China
Researchers identified the main sources of nitrate pollution in a rural-urban Chinese river, finding that human sewage and agricultural runoff were the primary contributors. While focused on nitrogen pollution, the study illustrates how mixed land use creates complex water quality challenges in rivers that also carry microplastics.
Machine learning models for forecasting microplastic dynamics in China’s coastal waters
Researchers used machine learning to analyze microplastic pollution patterns across China's four major coastal seas, drawing on over 1,100 data points from peer-reviewed studies. They found that urban centers and industrial activities are key drivers of contamination, with pollution levels varying significantly between marine, coastal, and estuary environments. The models project that economic development and education could reduce microplastic concentrations, while industrial expansion may increase them.
Microplastic pollution in Chinese Rivers: A detailed analysis of distribution, risk factors, and ecological impact
Researchers aggregated data from 2,474 microplastic samples across 165 publications to assess ecological risk in Chinese rivers, finding widespread contamination with average abundance varying substantially by watershed characteristics. A revised risk assessment accounting for particle morphology and polymer toxicity raised concern levels beyond previous estimates.
Correcting microplastic pollution and risk assessment in Chinese watersheds
Researchers compiled over 2,400 samples from 165 studies to create a national dataset of microplastic pollution across Chinese watersheds and developed a novel risk assessment framework. The study found that microplastic concentrations varied enormously across seven orders of magnitude, that population density and precipitation were key drivers of contamination, and that half of sampling sites fell into dangerous or extremely dangerous ecological risk categories.
Microplastics pollution in inland freshwaters of China: A case study in urban surface waters of Wuhan, China
Researchers characterized microplastic pollution in inland freshwaters across urban suburban areas of China, finding contamination that reflected land use intensity and population density in the surrounding catchments.
Basin-Scale Pollution Loads Analyzed Based on Coupled Empirical Models and Numerical Models
This study used a combination of field measurements and computer models to quantify pollutant loads from different sources across a Chinese river basin. Better tools for tracking pollution sources at basin scale can support efforts to reduce microplastic and other contaminant inputs to waterways.
[Microplastic Pollution Status and Ecological Risk Evaluation in Weihe River].
This Chinese study characterized microplastic abundance, shapes, sizes, colors, and polymer types in the Weihe River in northwest China. The findings document significant microplastic contamination in a major regional river that drains one of China's most densely populated agricultural areas, raising concerns about both ecosystem and human health.
[Occurrence Characteristics and Ecological Risk Assessment of Microplastic Pollution in the Yellow River Basin].
Researchers examined the spatial distribution, composition characteristics, and ecological risks of microplastic pollution across the Yellow River Basin in China, assessing contamination levels in the nation's historically significant waterway system.
Predicting microplastic quantities in Indonesian provincial rivers using machine learning models
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.
Environmental behavior of microplastic - heavy metal synergistic contamination in a typical urban-rural river network
Researchers investigated the seasonal co-occurrence of microplastics and heavy metals in urban and rural rivers in a Chinese inland city. They found that both pollutant types were present in all water samples and that microplastics can adsorb heavy metals, potentially increasing the combined environmental risk. The study reveals that river networks connecting urban and rural areas serve as pathways for spreading this dual contamination.
Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China
Researchers applied machine learning models — including random forest and support vector regression — to predict the spatial distribution of soil microplastic pollution in China's Taihu Lake region, finding that soil texture, population density, and proximity to known plastic sources were the dominant drivers, with nearly half of urban soils showing serious contamination.
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.