AIMC Topic: Particulate Matter
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A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States.
Environmental research
Sep 18, 2017
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
Aug 28, 2017
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)
Jul 18, 2017
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 ...
Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution.
Revista de saude publica
Jun 22, 2017
OBJECTIVE: Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State.
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)
May 9, 2017
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...
Evaluating the predictability of PM grades in Seoul, Korea using a neural network model based on synoptic patterns.
Environmental pollution (Barking, Essex : 1987)
Sep 6, 2016
As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM) classified into four grades: low (PM ≤ 30 μg m), moderate (30 < PM ≤ 80 μg m), high (80 < PM ≤ 150 ...
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Computational intelligence and neuroscience
May 18, 2016
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each me...
Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.
Environmental science and pollution research international
Apr 4, 2016
Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM10) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM10, whic...
Use of a Robotic Sampler (PIPER) for Evaluation of Particulate Matter Exposure and Eczema in Preschoolers.
International journal of environmental research and public health
Feb 19, 2016
While the association of eczema with asthma is well recognized, little research has focused on the potential role of inhalable exposures and eczema. While indoor air quality is important in the development of respiratory disease as children in the U....
A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.
Environmental research
Dec 22, 2015
Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure ...
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