AIMC Topic: Air Pollution

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Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction.

Environmental science and pollution research international
The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accu...

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.

BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.

Environmental monitoring and assessment
Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders...

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.

Environmental research
Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of gr...

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Environmental research
Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value ...

Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China.

Journal of environmental management
As surface ozone (O) gains increasing attention, there is an urgent need for high temporal resolution and accurate O monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based...

Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

Respiratory research
BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and i...

Large-scale spatiotemporal deep learning predicting urban residential indoor PM concentration.

Environment international
Indoor PM pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-u...

Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies.

Environmental science and pollution research international
High-resolution urban surface information, e.g., the fraction of impervious/pervious surface, is pivotal in studies of local thermal/wind environments and air pollution. In this study, we introduced and validated a domain adaptive land cover classifi...

Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review.

Environmental monitoring and assessment
Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the adv...