AIMC Topic: Ozone
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Machine learning models accurately predict ozone exposure during wildfire events.
Environmental pollution (Barking, Essex : 1987)
Jul 5, 2019
Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predic...
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)
May 27, 2019
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
May 23, 2019
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...
Low-Concentration Oxygen/Ozone Treatment Attenuated Radiculitis and Mechanical Allodynia via PDE2A-cAMP/cGMP-NF-B/p65 Signaling in Chronic Radiculitis Rats.
Pain research & management
Dec 13, 2018
BACKGROUND: Oxygen/ozone therapy is a minimally invasive technique for the treatment of radiculitis from lumbar disc herniation. This study aimed at investigating whether intrathecal administration of low-concentration oxygen/ozone could attenuate ch...
Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.
Environmental pollution (Barking, Essex : 1987)
Oct 13, 2018
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum ...
Forecasting air quality time series using deep learning.
Journal of the Air & Waste Management Association (1995)
May 24, 2018
UNLABELLED: This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of ide...
Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution.
Revista de saude publica
Jun 22, 2017
OBJECTIVE: Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State.
Ozone decreases sperm quality in systemic lupus erythematosus patients.
Revista brasileira de reumatologia
Nov 23, 2015
OBJECTIVE: To investigate the deleterious effects of air pollutants exposure in the Sao Paulo metropolitan region on semen quality in systemic lupus erythematosus (SLE).
Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments.
Environmental monitoring and assessment
Jul 1, 2015
The paper presents the screening of various feedforward neural networks (FANN) and wavelet-feedforward neural networks (WFANN) applied to time series of ground-level ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10 and PM2.5 fractions...
Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies.
Environment international
Jul 1, 2025
In the context of climate change, various countries/regions across East Asia have witnessed severe ground-level ozone (O) pollution, which poses potential health risks to the public. The complex relationships between O and its drivers, including the ...
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