AIMC Topic: Particulate Matter

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Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations.

Chemosphere
Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters...

Extraction of multi-scale features enhances the deep learning-based daily PM forecasting in cities.

Chemosphere
Characterising the daily PM2.5 concentration is crucial for air quality control. To govern the status of the atmospheric environment, a novel hybrid model for PM2.5 forecasting was proposed by introducing a two-stage decomposition technology of compl...

Generating a long-term (2003-2020) hourly 0.25° global PM dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS).

The Science of the total environment
Generating a long-term high-spatiotemporal resolution global PM dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Coper...

Enhancing PM Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model.

Sensors (Basel, Switzerland)
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
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
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
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)
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)
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
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...