AIMC Topic: Particulate Matter

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Long-term PM exposure and the clinical application of machine learning for predicting incident atrial fibrillation.

Scientific reports Oct 1, 2020
Clinical impact of fine particulate matter (PM) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurem...
Female Risk Factors Aged Humans Machine Learning Republic of Korea Particle Size Particulate Matter Atrial Fibrillation Inhalation Exposure Male Incidence Regression Analysis
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Ensemble-based deep learning for estimating PM over California with multisource big data including wildfire smoke.

Environment international Sep 24, 2020
INTRODUCTION: Estimating PM concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in nat...
Air Pollution Particulate Matter Wildfires Big Data Environmental Monitoring Air Pollutants California Smoke Deep Learning
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New interpretable deep learning model to monitor real-time PM concentrations from satellite data.

Environment international Sep 10, 2020
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM) is a key air quality parameter. A real-time knowledge of PM is highly valuable for lowering the risk of detrimental impacts on human health. To achieve th...
Deep Learning China Cities Environmental Monitoring Humans Beijing Aerosols Air Pollutants Air Pollution Particulate Matter
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Machine Learning-Based Activity Pattern Classification Using Personal PM Exposure Information.

International journal of environmental research and public health Sep 9, 2020
The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM. However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to brin...
Machine Learning Temperature Child Particulate Matter Environmental Exposure Environmental Monitoring Air Pollutants Humans
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Heavy metals in submicronic particulate matter (PM) from a Chinese metropolitan city predicted by machine learning models.

Chemosphere Jul 8, 2020
The aim of this study was to establish a method for predicting heavy metal concentrations in PM (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) me...
Aerosols Metals, Heavy Meteorological Concepts Betacoronavirus Pneumonia, Viral Environmental Exposure Coronavirus Infections Seasons Air Pollutants Air Pollution Particulate Matter Computer Simulation Wind SARS-CoV-2 Environmental Monitoring Support Vector Machine China Humans Cities Neural Networks, Computer Pandemics COVID-19
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PM concentration estimation using convolutional neural network and gradient boosting machine.

Journal of environmental sciences (China) Jun 14, 2020
Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM) concentration. Here we explo...
Machine Learning China Neural Networks, Computer Particulate Matter Weather
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Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

Chemosphere May 27, 2020
The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were largely impeded in a complex system including multiple environmental stressors and health outcome of w...
Air Pollution Particulate Matter Seasons Nitrogen Dioxide China Incidence Humans Air Pollutants Cardiovascular Diseases Deep Learning
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Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables.

International journal of environmental research and public health Mar 19, 2020
Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO, NH, NO, NO, NO, O,...
Environmental Monitoring Air Pollutants Air Pollution Particulate Matter Neural Networks, Computer
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Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.

Environment international Jul 27, 2019
Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practic...
Air Pollutants Air Pollution Particulate Matter Mobile Applications Urban Population Environmental Monitoring Humans Linear Models Machine Learning Seoul Republic of Korea
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Using machine learning to understand the temporal morphology of the PM annual cycle in East Asia.

Environmental monitoring and assessment Jun 28, 2019
PM air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites have been established over the last decade to monitor real-time PM concentration. However, even thi...
Particulate Matter Linear Models Environmental Monitoring Humans Asia, Eastern Machine Learning Air Pollutants Algorithms Air Pollution
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