AIMC Topic: Air Pollutants

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Air pollution macro-regions identification using machine learning and spatio-temporal analysis.

PloS one
Air pollution caused by suspended particulate matter (PM) remains one of the key environmental challenges in Poland, particularly in the context of public health and spatial planning. This study presents a spatio-temporal analysis based on data from ...

Spatial-temporal distribution and variation of atmospheric NO dry deposition in the Yellow River Basin from 2015 to 2023.

Environmental monitoring and assessment
Nitrogen dioxide (NO) is a major atmospheric pollutant that threatens human health and environmental quality amid rapid urbanization and industrialization. The Yellow River Basin is a heavily populated and economically significant area that is essent...

Quantifying key drivers of atmospheric methane across Pakistan using a machine learning approach.

Environmental monitoring and assessment
Atmospheric methane (CH), a potent greenhouse gas, has shown a consistent rise since the Industrial Revolution, contributing significantly to global warming and climate change. Understanding the temporal and spatial variability of methane concentrati...

Divergent Ozone Predictions in China Under Carbon Neutrality: Why Chemical Mechanisms Disagree.

Environmental science & technology
Uncertainty in air quality models can lead to divergent assessments of emission control policies. Here, we investigate why two widely used chemical mechanisms in the Weather Research and Forecasting model with Chemistry (WRF-Chem) predict inconsisten...

Ambient PM Exposure Modeling in LMICs: An Example from Peru.

Current environmental health reports
PURPOSE OF REVIEW: Fine particulate matter (PM) poses a public health risk, disproportionately impacting low- and middle-income countries (LMICs). In Peru, where ambient concentrations in urban areas significantly exceed the World Health Organization...

Merged methods of artificial neural networks and statistical techniques in forecasting air quality in the northern region of Peninsular Malaysia.

Environmental monitoring and assessment
Artificial neural networks (ANNs) are widely applied in air quality modelling because they can capture nonlinear interactions among pollutants and support reliable air pollutant index (API) forecasting. This study aims to identify the pollutants that...

The micro- and nanoplastics exposome feedback loop drives synergistic air-lung toxicity.

The Science of the total environment
Airborne micro- and nanoplastics (MNPs) are now recognized as persistent components of the atmospheric exposome. While their presence is established, their toxicological role remains incompletely understood. In this perspective, we propose the MNP Ex...

A Transformer-Based Deep Learning Approach to Predicting Air Organic Pollutant-Human Protein Interactions.

Environmental science & technology
Air pollution poses a critical global public health challenge. Molecular-level initiating events, such as pollutant-protein interactions, can trigger cascades of biological responses that may contribute to adverse health effects. However, current met...

Multi-scale dynamic graph neural network for PM2.5 concentration prediction in regional station cluster.

PloS one
Accurate prediction of PM2.5 concentrations is crucial for public health and environmental management. However, effectively capturing complex spatiotemporal dependencies across multiple time scales remains a persistent challenge for existing methods,...

Unveiling and interpreting the relationships among multi-pollutant emission factors in municipal solid waste incineration by machine learning.

Waste management (New York, N.Y.)
Effective control of key parameters is critical for regulating pollutant emissions in municipal solid waste incineration (MSWI), but existing research on these parameters remains limited and lacks comprehensiveness. This study used over 600,000 indus...