AIMC Topic: Particulate Matter
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Enhancing PM Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model.
Sensors (Basel, Switzerland)
Jun 11, 2022
In a world where humanity's interests come first, the environment is flooded with pollutants produced by humans' urgent need for expansion. Air pollution and climate change are side effects of humans' inconsiderate intervention. Particulate matter of...
In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.
International journal of environmental research and public health
May 24, 2022
The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have a...
Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model.
Chemosphere
May 14, 2022
Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and ...
An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.
The Science of the total environment
Apr 19, 2022
This study proposes a new model for the spatiotemporal prediction of PM concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU)...
Self-feedback LSTM regression model for real-time particle source apportionment.
Journal of environmental sciences (China)
Jan 16, 2022
Atmospheric particulate matter pollution has attracted much wider attention globally. In recent years, the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques. Such ...
Deciphering urban traffic impacts on air quality by deep learning and emission inventory.
Journal of environmental sciences (China)
Jan 4, 2022
Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep lea...
Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.
Epidemiology and infection
Nov 16, 2021
This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], w...
PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning.
IEEE transactions on neural networks and learning systems
Oct 5, 2021
This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time dela...
Air quality prediction using CNN+LSTM-based hybrid deep learning architecture.
Environmental science and pollution research international
Sep 23, 2021
Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships between these variables by sop...
Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China.
Environmental research
Jul 28, 2021
Epidemiological studies have revealed the associations of air pollutants and meteorological factors with a range of mental health conditions. However, little is known about local explanations and global understanding on the importance and effect of i...
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