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 Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China
ClearIdentification 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.
Environmental Risk Assessment of the Harbin Section of the Songhua River Basin Based on Multi-Source Data Fusion
This paper is not about microplastic pollution. It evaluates environmental risks to water quality in the Harbin section of the Songhua River Basin in China, using neural networks and multi-source data to assess pollution from agricultural, industrial, and domestic sources across different districts.
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.
The Distribution Characteristics and Ecological Risks of Alkylphenols and the Relationships between Alkylphenols and Different Types of Land Use
Researchers analyzed the spatial distribution of nine alkylphenols in the Yongding and Beiyun Rivers in China using principal component analysis, examining links between alkylphenol concentrations and surrounding land use categories. The study found widespread alkylphenol contamination with greater pollution risks in the Yongding River, and identified land use type as a key factor shaping spatial distribution patterns.
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.
Characteristics and Influencing Factors of Spatial Differentiation of Urban Black and Odorous Waters in China
This Chinese study analyzed the geographic distribution of urban black and odorous water bodies — severely polluted urban waterways — and the factors driving their spatial patterns. Urban waterways are major pathways for microplastics from cities to receiving water bodies and coastal zones.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
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.
Assessing spatial variability and source identification of heavy metals in agricultural soils: A geostatistical and multivariate analysis of coastal eastern Zhejiang, China
Researchers used geostatistical and multivariate analysis techniques to assess the spatial variability and sources of five heavy metals in agricultural soils along the coast of eastern Zhejiang, China. While focused primarily on heavy metals rather than microplastics, the study provides methodology relevant to understanding pollutant distribution in coastal agricultural areas. The findings identified industrial emissions, agricultural practices, and natural geological processes as key contamination sources.
A First Step towards Developing a Decision Support System Based on the Integration of Environmental Monitoring Activities for Regional Water Resource Protection
Researchers developed a decision support system integrating environmental monitoring data on diffuse pollutants - including nitrates, pesticides, metals, and plastics - to characterise the degradation state of water resources at the municipal level. The open-source system identifies areas sharing similar environmental characteristics and pollution pressure values, providing policymakers with tools for more accurate land management decisions.
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.
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.
A geospatial investigation of microplastics leaching in Ubon Ratchathani province, Thailand: fuzzy logic-based analysis
Researchers applied GIS combined with fuzzy logic analysis in Ubon Ratchathani province, Thailand to map microplastic leakage sources and predict pollution transport through river networks, demonstrating that this spatial modeling approach can identify priority catchment areas for microplastic management.
Identification Sources and High-Risk Areas of Sediment Heavy Metals in the Yellow River by Geographical Detector Method
Scientists measured heavy metal contamination in river sediments of the Yellow River in Inner Mongolia, identifying industrial emissions and agricultural activities as the main sources. While focused on heavy metals, the research is relevant because microplastics frequently co-occur with and transport heavy metal pollutants in river systems.
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.
Spatiotemporal Variations of Water Eutrophication and Non-Point Source Pollution Prevention and Control in the Main Stream of the Yellow River in Henan Province from 2012 to 2021
This is not a microplastics study; it tracks water quality and eutrophication in the Yellow River in Henan Province over a decade, finding improvements linked to pollution control policies and identifying agricultural runoff as the dominant non-point source of contamination outside the flood season.
Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model
Researchers applied a Random Forest Regression model to predict water quality indicators in Northwest China's urban wastewater treatment systems, achieving near-perfect accuracy (R² > 0.999) for key pollutants like nitrogen and phosphorus. The model offers a powerful tool for optimizing wastewater treatment and managing water resources in rapidly urbanizing regions.
Spatial analysis of the influence on “microplastic communities” in the water at a medium scale
Spatial analysis of microplastic communities in Hubei Province, China found that microplastics were more abundant in rivers than lakes (average 1.74 items/L), negatively correlated with distance from residential areas, and that anthropogenic land cover increased abundance while natural vegetation decreased it.
Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China
Researchers combined dual stable isotope analysis with statistical modeling to trace nitrogen pollution sources in two coastal rivers flowing into Hangzhou Bay, finding that soil runoff and domestic wastewater together contributed roughly two-thirds of total nitrogen, with aquaculture tailwater as the second-largest source.
Analysis of the Spatial Distribution Characteristics of Emerging Pollutants in China
Researchers mapped the spatial distribution of four types of emerging pollutants in China's water environment, including microplastics, endocrine disruptors, brominated flame retardants, and perfluorinated compounds. They found that pollution levels correlate with regional economic development, with the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei region showing significantly higher contamination. The study provides a reference framework for emerging pollutant prevention and control across China.
Identification of Hydrochemical Characteristics, Spatial Evolution, and Driving Forces of River Water in Jinjiang Watershed, China
This paper is not about microplastic pollution. It analyzes the hydrochemical characteristics of river water in the Jinjiang Watershed in China, identifying rock weathering, mining, agricultural runoff, and domestic pollution as the main factors influencing water chemistry.
Combination of multivariate data analysis and mixing modelling to assess tracer potential of contaminants of emerging concern in aquifers
Researchers combined principal component analysis, self-organizing maps, and mixing models to identify which pharmaceutical and industrial contaminants of emerging concern best trace pollution sources in a complex mixed aquifer in Barcelona, finding that lamotrigine, carbamazepine, and two related compounds outperformed conventional chemical tracers.
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.
Coupled effects of urbanization level and dam on microplastics in surface waters in a coastal watershed of Southeast China
Researchers analyzed the distribution of microplastics across 17 sampling sites in the Minjiang River Watershed in southeast China, finding that microplastic concentrations were positively correlated with urbanization indicators and that dams influenced spatial distribution patterns.