AIMC Topic: Air Pollutants
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A study on the impact of meteorological and emission factors on PM concentrations based on machine learning.
Journal of environmental management
Feb 15, 2025
PM pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accurately identifying these factors and their contributions is essential for effective pollution manageme...
Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networks.
PloS one
Feb 11, 2025
In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the t...
A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors.
Sensors (Basel, Switzerland)
Feb 9, 2025
Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, ...
Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data.
Journal of environmental management
Feb 5, 2025
The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. This study employed machine learning (ML) and deep learning (DL) techniques to examine the relationsh...
Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning.
Environmental science & technology
Feb 4, 2025
To further reduce atmospheric particulate matter concentrations, there is a need for a more precise identification of their sources. The SEM-EDS technology (scanning electron microscopy and energy-dispersive X-ray spectroscopy) can provide high-resol...
Trustworthy and Human Centric neural network approaches for prediction of landfill methane emission and sustainable waste management practices.
Waste management (New York, N.Y.)
Jan 30, 2025
Landfills rank third among the anthropogenic sources of methane gas in the atmosphere, hence there is a need for greater emphasis on the quantification of landfill methane emission for mitigating environmental degradation. However, the estimation and...
Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy.
Environmental science & technology
Jan 29, 2025
Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify ...
XIS-PM: A daily spatiotemporal machine-learning model for PM in the contiguous United States.
Environmental research
Jan 27, 2025
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a ...
A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.
Scientific reports
Jan 18, 2025
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. a...
Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.
Environmental pollution (Barking, Essex : 1987)
Jan 16, 2025
Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilities. The risk of chronic exposure can be significantly reduced by accurately predicting airborne funga...
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