AIMC Topic: Particulate Matter

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Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Analytical chemistry
Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ mann...

PM2.5 concentration modeling and prediction by using temperature-based deep belief network.

Neural networks : the official journal of the International Neural Network Society
Air quality prediction is a global hot issue, and PM is an important factor affecting air quality. Due to complicated causes of formation, PM prediction is a thorny and challenging task. In this paper, a novel deep learning model named temperature-ba...

Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations.

Environmental research
Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO) concentrations using approximately 20,000...

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

Scientific reports
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...

Ensemble-based deep learning for estimating PM over California with multisource big data including wildfire smoke.

Environment international
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...

New interpretable deep learning model to monitor real-time PM concentrations from satellite data.

Environment international
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...

Machine Learning-Based Activity Pattern Classification Using Personal PM Exposure Information.

International journal of environmental research and public health
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...

Heavy metals in submicronic particulate matter (PM) from a Chinese metropolitan city predicted by machine learning models.

Chemosphere
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...

PM concentration estimation using convolutional neural network and gradient boosting machine.

Journal of environmental sciences (China)
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...

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

Chemosphere
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...