AIMC Topic: Air Pollution

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Machine learning helps reveal key factors affecting tire wear particulate matter emissions.

Environment international
Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wea...

Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.

The Science of the total environment
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...

Improving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.

Environment international
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2....

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

PloS one
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...

Emulating Wildfire Plume Injection Using Machine Learning Trained by Large Eddy Simulation (LES).

Environmental science & technology
Wildfires have a major influence on the Earth system, with costly impacts on society. Despite decades of research, wildfires are still challenging to represent in air quality and chemistry-climate models. Wildfire plume rise (injection) is one of tho...

Evaluating drivers of PM air pollution at urban scales using interpretable machine learning.

Waste management (New York, N.Y.)
Reducing urban fine particulate matter (PM) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM will enable the development of targeted strategies to reduce PM levels. This stud...

Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.

Environmental pollution (Barking, Essex : 1987)
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Environmental monitoring and assessment
Removing hydrogen sulfide (HS) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers of these industries. This problem involves different economic, environmental, and health issues. Var...

A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Environmental monitoring and assessment
Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in...

MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.

Environmental monitoring and assessment
The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual ...