AIMC Topic: Air Pollution

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Air quality index prediction with optimisation enabled deep learning model in IoT application.

Environmental technology
The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over the years. The measurement of the air quality index (AQI) depends on various environmental situatio...

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

Environmental science and pollution research international
In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and...

Imaging pollen using a Raspberry Pi and LED with deep learning.

The Science of the total environment
The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigation of hay fever symptoms. We demonstrate the use of a white light LED (light emitting diode) to ill...

3DVar sectoral emission inversion based on source apportionment and machine learning.

Environmental pollution (Barking, Essex : 1987)
Air quality models are increasingly important in air pollution forecasting and control. Sectoral emissions significantly impact the accuracy of air quality models and source apportionment. This paper studied the 3DVar (three-dimensional variational) ...

PM concentration prediction algorithm integrating traffic congestion index.

Journal of environmental sciences (China)
In this study, a strategy is proposed to use the congestion index as a new input feature. This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter (PM) concentrations. To assess the effectiven...

Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China.

Environmental pollution (Barking, Essex : 1987)
Atmospheric ozone (O) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O. However, traditional experimenta...

Hourly PM concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model.

Journal of environmental management
Accurate prediction of PM concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several chal...

Long-term Evaluation of Machine Learning Based Methods for Air Emission Monitoring.

Environmental management
Machine learning (ML) techniques have been researched and used in various environmental monitoring applications. Few studies have reported the long-term evaluation of such applications. Discussions regarding the risks and regulatory frameworks of ML ...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

Journal of environmental management
This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air...

Forecasting carbon dioxide emissions in Chongming: a novel hybrid forecasting model coupling gray correlation analysis and deep learning method.

Environmental monitoring and assessment
Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shan...