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Environmental Monitoring

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Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning.

Environmental monitoring and assessment
Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial lo...

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Environmental monitoring and assessment
Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid m...

Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Environmental science and pollution research international
Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) m...

Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning.

Environmental science & technology
Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well...

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics.

Environmental geochemistry and health
The contribution of mica mining activities to fluoride (F) contamination in groundwater has been chased in this study. For the purpose, groundwater samples (n = 40, replicated thrice) were collected during the post-monsoons (September-October) from a...

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The Science of the total environment
The traditional prediction of the Cd content in grains (Cd) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cd) and pH, neglecting inter-factorial interactions and nonlinear causal links between external e...

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The Science of the total environment
The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate ...

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks.

Chemosphere
The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the...

Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization.

Environmental science and pollution research international
The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guide...

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach.

Water research
Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distin...