AIMC Topic: Air Pollutants

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Performance assessment of gas-phase toluene removal in one- and two-liquid phase biotrickling filters using artificial neural networks.

Chemosphere
The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase biotrickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 d in the presence of ...

Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks.

Environmental research
We paired existing land use regression (LUR) models for ambient ultrafine particles in Montreal and Toronto, Canada with satellite images and deep convolutional neural networks as a means of extending the spatial coverage of these models. Our finding...

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Environmental pollution (Barking, Essex : 1987)
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather R...

Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN).

Computational intelligence and neuroscience
Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precauti...

Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions.

Environment international
BACKGROUND: Accurate estimation of nitrogen dioxide (NO) and nitrogen oxide (NO) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acut...

Machine-learned modeling of PM exposures in rural Lao PDR.

The Science of the total environment
This study presents a machine-learning-enhanced method of modeling PM personal exposures in a data-scarce, rural, solid fuel use context. Data collected during a cookstove (Africa Clean Energy (ACE)-1 solar-battery-powered stove) intervention program...

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.

International journal of environmental health research
This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk facto...

Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.

BMC medical research methodology
BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative...

Long short-term memory - Fully connected (LSTM-FC) neural network for PM concentration prediction.

Chemosphere
People have been suffering from air pollution for a decade in China, especially from PM (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-dr...

Machine vision analysis on abnormal respiratory conditions of mice inhaling particles containing cadmium.

Ecotoxicology and environmental safety
Inhalable environmental toxicants can induce pulmonary malfunction resulting abnormal respiratory conditions. The traditional methods currently available to detect the respiratory condition of animals rely on differential pressure transducers and sig...