AIMC Topic: Aerosols

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Unmasking the sky: high-resolution PM prediction in Texas using machine learning techniques.

Journal of exposure science & environmental epidemiology
BACKGROUND: Although PM (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.

Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.

Environmental science & technology
Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicabil...

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions.

Environmental pollution (Barking, Essex : 1987)
There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient gr...

Separating Daily 1 km PM Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Environmental science & technology
Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model si...

Modelling and optimization study to improve the filtration performance of fibrous filter.

Chemosphere
Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure d...

Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning.

ACS sensors
Exposure to bio-aerosols such as pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various types of pollen. To address this need, we present a mobile and...

Validation, analysis, and comparison of MISR V23 aerosol optical depth products with MODIS and AERONET observations.

The Science of the total environment
The latest Multi-angle Imaging Spectro Radiometer (MISR) Version (V) 23 aerosol optical depth (AOD) products were released, with an improved spatial resolution of 4.4 km, providing an unprecedented opportunity for the refined regional application. To...

Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy.

ACS sensors
Various volatile aerosols have been associated with adverse health effects; however, characterization of these aerosols is challenging due to their dynamic nature. Here, we present a method that directly measures the volatility of particulate matter ...

Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach.

Environment international
Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly devel...

New interpretable deep learning model to monitor real-time PM concentrations from satellite data.

Environment international
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM) is a key air quality parameter. A real-time knowledge of PM is highly valuable for lowering the risk of detrimental impacts on human health. To achieve th...