AIMC Topic: Environmental Monitoring

Clear Filters Showing 121 to 130 of 1335 articles

Sensitivity-driven control strategy and analysis of operating parameter MLSS in the stacking total nitrogen prediction model.

Environmental monitoring and assessment
The operation of wastewater treatment plants (WWTPs) is frequently characterized by complexity, largely attributable to the properties of the influent and the nonlinear fluctuations that occur throughout the wastewater treatment process. Accurate mod...

Comparative evaluation of machine learning algorithms for greenhouse gas emission forecasting: a case study of Turkey (2012-2021).

Environmental monitoring and assessment
Accurate forecasting of greenhouse gas (GHG) emissions is essential for assessing climate change dynamics and developing evidence-based environmental policies. This study aims to comparatively evaluate the prediction performance of various machine le...

Machine learning-based prediction of drinking water quality index in Western Tehran using KAN, MLP, and traditional models.

Environmental monitoring and assessment
In this study, the water quality index (WQI) was calculated using multivariate statistics, incorporating physical, chemical, and microbiological analysis of water samples taken from water supply networks in the western district of Tehran from 2021 to...

New insights into soil bacteria communities in Beijing urban greenspace based on urbanization gradient.

The Science of the total environment
Research on urban soils has traditionally neglected two significant dimensions: the spatial heterogeneity emerging within megacity resulting from varying urbanization rates, and the dynamic responses of soil microbial communities to ongoing urban exp...

A data-intensive framework for evaluating ecological and human health impacts of soil potentially toxic elements (PTEs) in the mining-endemic region of Singida, Tanzania.

Environmental geochemistry and health
Uncontrolled soil contamination by potentially toxic elements (PTEs) poses serious threats to environmental and public health in mining-intensive regions. Against this background, this study assessed the distribution, sources, ecological impact, and ...

Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation.

The Science of the total environment
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial dis...

From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems.

The Science of the total environment
Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roo...

Refining Air Pollution Exposure Estimates: A Comparison of Citywide and Neighborhood Land Use Regression Models in Toronto.

Environmental science & technology
Land use regression (LUR) models assess air pollution exposure but often struggle with transferability (predicting concentrations in areas without measurements) and generalizability (capturing spatial patterns across neighborhoods). This study evalua...

River water quality forecasting: a novel LSTM-Transformer approach enhanced by multi-source data.

Environmental monitoring and assessment
Water quality prediction holds crucial importance as a fundamental technical support for efficient water resource management and strong ecological protection. In this study, aiming to meet the pressing requirement for eutrophication prevention and co...

Time series forecasting of chlorophyll-a concentrations in the Chesapeake Bay.

Scientific reports
Declining water quality poses serious environmental and public health risks, with chlorophyll-a serving as a key biological indicator of harmful algal blooms. This study evaluates the use of a Long Short-Term Memory (LSTM) neural network to forecast ...