Publications by authors named "Shanheng Huang"
Human activities exacerbate river network degrading in the Qinhuai River basin.
Sci Total Environ
November 2024
Rivers are undergoing significant changes under the pressures of natural processes and human activities. However, characterizing and understanding these changes over the long term and from a spatial perspective have proven challenging. This paper presents a novel framework featuring twelve indicators that combine geometric and spatial structures for evaluating changes in river network patterns.
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- Reticular river networks are vital for ecosystems but face connectivity assessment challenges due to their complex structures and human-made barriers, calling for improved methods to analyze flow changes.
- The Hydraulic Capacity Connectivity Index (HCCI) employs complex network theory to evaluate river connectivity by calculating factors like maximum flow and resistance distance between nodes.
- HCCI has been validated in both virtual and real river networks, showing a strong correlation with river discharge, which indicates its effectiveness in analyzing spatiotemporal flow dynamics and connectivity in intricate river systems.
Article Synopsis
- Significant discharge of perfluoroalkyl acids (PFAAs) into waterways is linked to dense populations and industries near developed inland waterways, with unclear effects of ship navigation on PFAA release.
- A field study in China's Wangyu River revealed that PFAA levels initially spiked after ships passed, followed by a complex pattern of fluctuations instead of a steady decline over time.
- The study demonstrated that the ship's jet scouring velocity, disturbance duration, and draught significantly influenced PFAA concentrations, with rapid and slow release processes affecting overall pollution levels in the water column.
Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018-2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups.
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