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Particulate Matter

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A machine learning method to estimate PM concentrations across China with remote sensing, meteorological and land use information.

The Science of the total environment
BACKGROUND: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at...

Maternal exposure to ambient PM during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models.

The Science of the total environment
Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM) on CHDs are inconsistent. We used ...

Assessing the impact of PM on respiratory disease using artificial neural networks.

Environmental pollution (Barking, Essex : 1987)
Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression mo...

Sludge reflects intra-amniotic inflammation with or without microorganisms.

American journal of reproductive immunology (New York, N.Y. : 1989)
PROBLEM: To investigate whether amniotic fluid (AF) "sludge" in patients with preterm labor (PTL) with intact membranes is related to intra-amniotic infection or inflammation.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

Environmental pollution (Barking, Essex : 1987)
Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model lon...

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States.

Environmental research
Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, h...

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

Computational intelligence and neuroscience
Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form Jan...

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

Environmental pollution (Barking, Essex : 1987)
Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air ...

Epithelial-mesenchymal transition effect of fine particulate matter from the Yangtze River Delta region in China on human bronchial epithelial cells.

Journal of environmental sciences (China)
Epidemiological studies have demonstrated that fine particulate matter (PM) exposure causes airway inflammation, which may lead to lung cancer. The activation of epithelial-mesenchymal transition (EMT) is assumed to be a crucial step in lung tumor me...