AIMC Topic: Air Pollution
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Air pollution forecasting based on wireless communications: review.
Environmental monitoring and assessment
Sep 5, 2023
The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and lo...
Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.
Journal of environmental sciences (China)
Aug 22, 2023
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on s...
Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts.
Environmental pollution (Barking, Essex : 1987)
Jul 30, 2023
Ambient ozone (O) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O formation processes. The emission-based ch...
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
Jul 22, 2023
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
Jun 5, 2023
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)
May 18, 2023
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
May 1, 2023
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
Apr 28, 2023
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...
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)
Mar 24, 2023
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)
Mar 24, 2023
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)...
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