AIMC Topic: Environmental Monitoring

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Surveillance of ship emissions and fuel sulfur content based on imaging detection and multi-task deep learning.

Environmental pollution (Barking, Essex : 1987)
Shipping makes up the major proportion of global transportation and results in an increasing emission of air pollutants. It accounts for 3.1%, 13%, and 15% of the annual global emissions of CO, SO, and NO, respectively. Hence, effective regulatory me...

Evaluation of external contamination on the vial surfaces of some hazardous drugs that commonly used in Chinese hospitals and comparison between environmental contamination generated during robotic compounding by IV: Dispensing robot vs. manual compounding in biological safety cabinet.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
OBJECTIVES: The aims of the study were to evaluate the external contamination of hazardous drug vials used in Chinese hospitals and to compare environmental contamination generated by a robotic intelligent dispensing system (WEINAS) and a manual comp...

Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models.

The Science of the total environment
Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complex...

Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Environmental pollution (Barking, Essex : 1987)
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying t...

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.

Journal of hazardous materials
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome ...

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

Environmental science and pollution research international
A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithm...

Neural networks for increased accuracy of allergenic pollen monitoring.

Scientific reports
Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in imag...

Application of artificial intelligence deep learning in numerical simulation of seawater intrusion.

Environmental science and pollution research international
Seawater intrusion not only causes fresh water shortages in coastal areas, but also has a negative impact on regional economic and social development. Global climate change will affect precipitation, sea level, and many other factors, which will in t...

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Environmental monitoring and assessment
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accurac...

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.

PloS one
Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become...