AIMC Topic: Rivers

Clear Filters Showing 231 to 240 of 258 articles

Multi-scale transformation and evolutionary factors of ecological security patterns in the Yangtze River Economic Belt.

Journal of environmental management
Ecological security is vital for ecosystem sustainability and varies across scales. Macro-scale assessments often miss local details, whereas micro-scale evaluations may overlook broader patterns. Multi-scale analysis of ecological security patterns ...

Profiling of mangrove forest dynamics in the Fly River delta, Papua New Guinea.

Marine pollution bulletin
Mangrove forests (MFs), as vital ecosystems in tropical and subtropical coastal regions, play a significant role in the global carbon cycle. However, MFs are currently facing unprecedented risks of degradation due to both natural and anthropogenic fa...

A robust black carbon prediction model derived from observational datasets in the Yangtze River Delta region, China.

Environmental pollution (Barking, Essex : 1987)
Black carbon (BC) is a short-lived pollutant with significant environment and human health impacts. Monitoring BC is important, but its spatial coverage is limited. Therefore, predicting BC concentration is crucial in densely populated regions like t...

Integrating Regression and Boosting Techniques for Enhanced River Water Quality Monitoring in the Cauvery Basin: A Seasonal and Sustainable Approach.

Water environment research : a research publication of the Water Environment Federation
This study addresses a critical research gap in water quality monitoring, specifically within the Cauvery River basin, where substantial contamination poses significant risks to both human health and aquatic ecosystems. The paper introduces an effect...

Long-term water quality simulation and driving factors identification within the watershed scale using machine learning.

Journal of contaminant hydrology
Understanding long-term trends and analyzing their driving factors are essential to effectively enhance water quality in watersheds. In China, although the overall quality of surface water continues to improve, significant issues remain in certain re...

Microbial degradation potential of microplastics in urban river sediments: Assessing and predicting the enrichment of PE/PP-degrading bacteria using SourceTracker and machine learning.

Journal of environmental management
Microplastic mitigation strategies that adapt to various actual aquatic environments require the ability to predict their microbial degradation potential. However, the sources and enrichment characteristics of the degrading bacteria in the plastisphe...

Mapping Regional Meteorological Processes to Ozone Variability in the North China Plain and the Yangtze River Delta, China.

Environmental science & technology
High-concentration ozone threatens human health and ecosystems, modulated by dynamic, multiscale meteorological processes. Existing machine learning studies for ozone prediction rarely incorporate the spatiotemporal evolution of regional meteorologic...

Satellite Remote Sensing-Implemented Nontargeted Screening of Emerging Contaminant Fingerprints in a River-to-Ocean Continuum through Interpretable Machine Learning: The Pivotal Intermediary Role of Dissolved Organic Matter.

Environmental science & technology
Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satelli...

Comparison and prediction of shallow groundwater nitrate in Shaying River basin based on urban distribution using multiple machine learning approaches.

Water environment research : a research publication of the Water Environment Federation
Groundwater, a pivotal water resource in numerous regions worldwide, confronts formidable challenges posed by severe nitrate pollution. Traditional research methodologies aimed at addressing groundwater nitrate contamination frequently struggle to ac...

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Water environment research : a research publication of the Water Environment Federation
Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed mo...