AIMC Topic: Ozone
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Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.
Environmental health perspectives
Jun 12, 2025
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal...
Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.
Nature communications
Mar 26, 2025
Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lip...
Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.
Environmental pollution (Barking, Essex : 1987)
Mar 6, 2025
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, tra...
Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.
Scientific reports
Feb 25, 2025
Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major ...
Constructing the 3D Spatial Distribution of the HCHO/NO Ratio via Satellite Observation and Machine Learning Model.
Environmental science & technology
Feb 20, 2025
The satellite-based tropospheric column ratio of HCHO to NO (FNR) is widely used to diagnose ozone formation sensitivity; however, its representation of surface conditions remains controversial. In this study, an approach to construct the 3D spatial ...
Forecasting O and NO concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach.
Environment international
Jan 2, 2025
Ozone (O) is a significant contributor to air pollution and the main constituent ofphotochemical smog that plagues China. Nitrogen dioxide (NO) is a significant air pollutant and a critical trace gas in the Earth's atmosphere. The presence of O and N...
Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.
Environmental pollution (Barking, Essex : 1987)
Nov 22, 2024
Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 ...
Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.
Chemosphere
Nov 16, 2024
Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O are secondary pollutants, and there remain significant challenges in accurately and efficiently pred...
High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.
Journal of environmental management
Nov 1, 2024
Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O). This study aims to elucidate global CO pollution in relation to healt...
3DVar sectoral emission inversion based on source apportionment and machine learning.
Environmental pollution (Barking, Essex : 1987)
Oct 18, 2024
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) ...
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