AIMC Topic: Air Pollution

Clear Filters Showing 211 to 220 of 305 articles

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...

A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.

Ecotoxicology and environmental safety
Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level oz...

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...

An ensemble learning based hybrid model and framework for air pollution forecasting.

Environmental science and pollution research international
As advance of economy and industry, the impact of air pollution has gradually gained attention. In order to predict air quality, there were many studies that exploited various machine learning techniques to build predictive model for pollutant concen...

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Environmental science & technology
Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with acc...

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...

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

Sensors (Basel, Switzerland)
The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in ...

Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables.

International journal of environmental research and public health
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,...

An artificial neural network ensemble approach to generate air pollution maps.

Environmental monitoring and assessment
The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO concentration at unsampled locations. Spatial interpolation methods and linear regression models with regularization have been...