AIMC Topic: Air Pollutants

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Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms.

Journal of hazardous materials
Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly un...

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Environmental science & technology
Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominan...

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.

Environmental research
Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of gr...

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Environmental research
Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value ...

Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO emissions.

Environmental research
Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In th...

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Environmental monitoring and assessment
Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change ...

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction.

Environmental pollution (Barking, Essex : 1987)
Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine le...

Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China.

Journal of environmental management
As surface ozone (O) gains increasing attention, there is an urgent need for high temporal resolution and accurate O monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based...

Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind.

Environmental pollution (Barking, Essex : 1987)
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effe...

Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

Respiratory research
BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and i...