AIMC Topic: Ozone
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Deep learning-based forecasting of daily maximum ozone levels and assessment of socioeconomic and health impacts in South Korea.
The Science of the total environment
Jun 25, 2025
Accurate forecasting of ground-level ozone (O) is essential for assessing its public health and socioeconomic impacts. This study evaluates the performance of three deep learning models-Deep Convolutional Neural Networks (Deep-CNN), Long Short-Term M...
Mapping Regional Meteorological Processes to Ozone Variability in the North China Plain and the Yangtze River Delta, China.
Environmental science & technology
May 20, 2025
High-concentration ozone threatens human health and ecosystems, modulated by dynamic, multiscale meteorological processes. Existing machine learning studies for ozone prediction rarely incorporate the spatiotemporal evolution of regional meteorologic...
Distribution characteristics of volatile organic compounds and its multidimensional impact on ozone formation in arid regions based on machine learning algorithms.
Environmental pollution (Barking, Essex : 1987)
May 15, 2025
Volatile Organic Compounds (VOCs) are key components of atmospheric pollution and play a critical role in ozone (O) formation. Understanding their distribution and pollution sources is essential to grasping the multifaceted impact of VOCs on O produc...
Amazon's climate crossroads: analyzing air pollution and health impacts under machine learning-based temperature increase scenarios in Northern Mato Grosso, Brazil.
Environmental geochemistry and health
May 14, 2025
Air pollution has long been a public health concern in South America, now increasingly linked to climate change. In Brazil, this issue is particularly acute in smaller cities with limited monitoring infrastructure. Sinop, located in the Amazon biome ...
Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation.
Environmental science & technology
May 13, 2025
Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and charact...
Development of an automated photolysis rates prediction system based on machine learning.
Journal of environmental sciences (China)
May 1, 2025
Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of OD, NO, HONO, HO, HCHO...
A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.
Journal of mass spectrometry : JMS
Sep 1, 2024
Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to...
Improve the biodegradability of post-hydrothermal liquefaction wastewater with ozone: conversion of phenols and N-heterocyclic compounds.
Water science and technology : a journal of the International Association on Water Pollution Research
Apr 1, 2017
Hydrothermal liquefaction is a promising technology to convert wet biomass into bio-oil. However, post-hydrothermal liquefaction wastewater (PHWW) is also produced during the process. This wastewater contains a high concentration of organic compounds...
Ozone therapy as an adjuvant for endondontic protocols: microbiological - ex vivo study and citotoxicity analyses.
Journal of applied oral science : revista FOB
Jan 1, 2016
OBJECTIVES: This study evaluated the antimicrobial efficacy of ozone therapy in teeth contaminated with Pseudomonas aeruginosa, Enterococcus faecalis, and Staphylococcus aureus using a mono-species biofilm model. Parallel to this, the study aimed to ...
Application of central composite design and artificial neural network in modeling of reactive blue 21 dye removal by photo-ozonation process.
Water science and technology : a journal of the International Association on Water Pollution Research
Jan 1, 2016
The present study deals with use of central composite design (CCD) and artificial neural network (ANN) in modeling and optimization of reactive blue 21 (RB21) removal from aqueous media under photo-ozonation process. Four effective operational parame...
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