AIMC Topic: Environmental Monitoring

Clear Filters Showing 71 to 80 of 1335 articles

Utilizing data science to assess native Indian freshwater fish taxa and their conservation status.

The Science of the total environment
India ranks ninth globally in freshwater fish diversity but lacks updated checklists and data-driven fish diversity and conservation assessments, which are essential for better informed decision-making on conservation and species discovery efforts. T...

Unlocking flow-habitat relationships in mountain rivers of Epirus, Greece using object detection and hydrodynamic simulation.

The Science of the total environment
Human activities impact aquatic ecosystems by altering abiotic and biotic factors, which in turn affect habitat structure and biodiversity. Environmental flows, or the necessary water flow levels to sustain ecosystems, influence fish habitats, with f...

Machine Learning, Generalization, and Transfer Learning for Predicting the Exceedance of Fecal Indicator Bacteria Thresholds at Beaches.

Environmental science & technology
Beach water testing for fecal indicator bacteria (FIB) is a key element of public health protection for beachgoers. Because the process can be expensive and time-consuming, many beaches are infrequently monitored, putting the health of the public at ...

Post ferric-substitution detection method optimization for Ni(II)-organic complexes measurement: Simulation, experimentation, and modeling.

Environmental monitoring and assessment
Nickel (Ni(II)) complexes, especially those formed with strong ligands such as ethylenediaminetetraacetic acid (EDTA), are difficult to quantify due to their low environmental concentrations and weak ultraviolet (UV) absorbance. These characteristics...

Deep learning simulation and decision support system for groundwater salinity risk assessment in the lower Chao Phraya River Basin, Thailand.

Environmental monitoring and assessment
Groundwater salinization poses a critical threat to freshwater security in coastal regions, particularly under intensified extraction and evolving hydroclimatic conditions. This study examines the spatial and temporal evolution of salinity in the low...

Development of prediction models on the degradation kinetics parameters of antibiotics in aquatic environments with machine learning methods.

Environmental science. Processes & impacts
Antibiotics, as emerging contaminants, are increasingly detected in aquatic environments, raising significant concerns about their ecological risks. However, the lack of hydrolysis rate constants () and aqueous hydroxyl radical degradation rate const...

Machine learning-assisted multi-channel nanozyme sensor arrays for multiple pesticide tracking, tracing and metabolism analysis.

Biosensors & bioelectronics
To achieve precise pesticide residue detection and metabolic analysis, we innovatively proposed a machine learning-assisted multi-channel nanozyme sensor array. Five Cu-carboxylate nanozymes with outstanding laccase-like and peroxidase-like activitie...

Quantitative inversion of soil heavy metal pollution using a GA-BP neural network model.

Environmental monitoring and assessment
With the rapid development of industrialization in China, significant economic benefits have been accompanied by varying degrees of threat to the soil environment, particularly from heavy metal pollution. The rapid quantitative inversion of heavy met...

Surface water quality evaluation impacting drinking water sources and sanitation using water quality index, multivariate techniques, and interpretable machine learning models in Mahanadi River, Odisha (India).

Environmental geochemistry and health
Water quality and quantity affect crop productivity, with surface water quality having a significant impact. The amount of surface water being used for drinking is gradually rising. Thus, assessing surface water quality and related hydro-chemical cha...

Assessing future hydrological and sediment transport response of an urban watershed using a machine learning-based land cover change model.

Environmental monitoring and assessment
Assessing the impacts of land cover change (LCC) on hydrology and sediment load is essential for the sustainable management of urban watersheds. Modeling LCC using machine learning techniques enhances the ability to generate realistic future scenario...