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Air Pollutants

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Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.

BMC medical research methodology
BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative...

Long short-term memory - Fully connected (LSTM-FC) neural network for PM concentration prediction.

Chemosphere
People have been suffering from air pollution for a decade in China, especially from PM (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-dr...

Machine vision analysis on abnormal respiratory conditions of mice inhaling particles containing cadmium.

Ecotoxicology and environmental safety
Inhalable environmental toxicants can induce pulmonary malfunction resulting abnormal respiratory conditions. The traditional methods currently available to detect the respiratory condition of animals rely on differential pressure transducers and sig...

Spatiotemporal continuous estimates of PM concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations.

Environment international
Ambient exposure to fine particulate matter (PM) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM concentrat...

Particulate Matter Exposure of Passengers at Bus Stations: A Review.

International journal of environmental research and public health
This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passengers waiting at bus stations. The usual factors affecting the characteristics and composition of PM...

Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks.

Ecotoxicology and environmental safety
Particulate matter with aerodynamic diameter less than 2.5 µm (PM) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective metho...

Space-time trends of PM constituents in the conterminous United States estimated by a machine learning approach, 2005-2015.

Environment international
Particulate matter with aerodynamic diameter less than 2.5 μm (PM) is a complex mixture of chemical constituents emitted from various emission sources or through secondary reactions/processes; however, PM is regulated mostly based on its total mass c...

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.

Environmental pollution (Barking, Essex : 1987)
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum ...

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM.

Environmental pollution (Barking, Essex : 1987)
Fine particulate matter (PM) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to m...

Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine.

Environmental pollution (Barking, Essex : 1987)
In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM...