AIMC Topic: Air Pollutants

Clear Filters Showing 131 to 140 of 367 articles

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology.

Ecotoxicology and environmental safety
BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine l...

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) ...

Enhancing indoor PM predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches.

Environmental pollution (Barking, Essex : 1987)
The presence of fine particulate matter (PM) indoors constitutes a significant component of overall PM exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, th...

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...

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems.

Journal of environmental quality
Soil nitrous oxide (NO) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at t...

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...