AIMC Topic: Particulate Matter
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A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide.
Environment international
Jun 20, 2019
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms hav...
Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks.
Environmental research
May 30, 2019
We paired existing land use regression (LUR) models for ambient ultrafine particles in Montreal and Toronto, Canada with satellite images and deep convolutional neural networks as a means of extending the spatial coverage of these models. Our finding...
A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants.
ISA transactions
May 21, 2019
The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applicatio...
Machine-learned modeling of PM exposures in rural Lao PDR.
The Science of the total environment
Apr 18, 2019
This study presents a machine-learning-enhanced method of modeling PM personal exposures in a data-scarce, rural, solid fuel use context. Data collected during a cookstove (Africa Clean Energy (ACE)-1 solar-battery-powered stove) intervention program...
Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.
International journal of environmental health research
Mar 31, 2019
This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk facto...
Particulate Matter
Environmental Exposure
Weather
Sex Factors
Age Factors
Acute Disease
Air Pollutants
Beijing
Respiratory Tract Diseases
Infant
Deep Learning
China
Adolescent
Incidence
Child, Preschool
Child
Young Adult
Adult
Male
Middle Aged
Risk Factors
Aged
Aged, 80 and over
Humans
Female
Infant, Newborn
Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model.
International journal of environmental research and public health
Feb 4, 2019
Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for e...
An ensemble long short-term memory neural network for hourly PM concentration forecasting.
Chemosphere
Jan 25, 2019
To protect public health by providing an early warning, PM concentration forecasting is an essential and effective work. In this paper, an ensemble long short-term memory neural network (E-LSTM) is proposed for hourly PM concentration forecasting. Th...
Long short-term memory - Fully connected (LSTM-FC) neural network for PM concentration prediction.
Chemosphere
Dec 21, 2018
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
Dec 18, 2018
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
Dec 18, 2018
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...
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