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Air Pollutants

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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
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...

Environmental odour management by artificial neural network - A review.

Environment international
Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are c...

Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

Neural networks : the official journal of the International Neural Network Society
In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the C...

A Novel Air Quality Early-Warning System Based on Artificial Intelligence.

International journal of environmental research and public health
The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and ...

Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.

Environment international
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...

Machine learning and statistical models for predicting indoor air quality.

Indoor air
Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mo...

Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China's Sulfur Dioxide Emissions.

International journal of environmental research and public health
Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the curr...

Machine learning models accurately predict ozone exposure during wildfire events.

Environmental pollution (Barking, Essex : 1987)
Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predic...

Determination of the physical domain for air quality monitoring stations using the ANP-OWA method in GIS.

Environmental monitoring and assessment
Air pollution is a major concern in some megacities of Iran. Specific cities in the country have reached an extremely harmful level of air pollution which poses a serious risk to the daily lives of Iranians. According to news reports, the air quality...