AIMC Topic: Air Pollutants

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Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.

Environment international
BACKGROUND: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

Machine and deep learning for modelling heat-health relationships.

The Science of the total environment
Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between tempe...

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions.

Environmental pollution (Barking, Essex : 1987)
There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient gr...

Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.

Chemosphere
Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), ...

Separating Daily 1 km PM Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Environmental science & technology
Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model si...

Synthesis of δ-MnO via ozonation routine for low temperature formaldehyde removal.

Journal of environmental sciences (China)
Nowadays, it is still a challenge to prepared high efficiency and low cost formaldehyde (HCHO) removal catalysts in order to tackle the long-living indoor air pollution. Herein, δ-MnO is successfully synthesized by a facile ozonation strategy, where ...

Cooperative simultaneous inversion of satellite-based real-time PM and ozone levels using an improved deep learning model with attention mechanism.

Environmental pollution (Barking, Essex : 1987)
Ground-level fine particulate matter (PM) and ozone (O) are air pollutants that can pose severe health risks. Surface PM and O concentrations can be monitored from satellites, but most retrieval methods retrieve PM or O separately and disregard the s...

Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States.

Environmental pollution (Barking, Essex : 1987)
The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL)...

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.

Scientific reports
China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framew...

A hybrid deep learning model for regional O and NO concentrations prediction based on spatiotemporal dependencies in air quality monitoring network.

Environmental pollution (Barking, Essex : 1987)
Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring netw...