AIMC Topic: Ozone

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Evaluating the transferability of low-cost sensor calibration using ANFIS: a field study in Putrajaya, Malaysia.

Environmental monitoring and assessment
This study evaluates the robustness of a previously developed calibration model for low-cost ozone sensors, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). The model was deployed at a different site without retraining. It was tested in Pu...

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

Environmental health perspectives
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...

Spatiotemporal evolutions and drivers of ground-level ozone in China (2015-2020): A GTWR-Kriging approach.

Environmental research
Ground-level ozone pollution has emerged as a primary environmental challenge in China. An accurate and high-resolution analysis of ground-level ozone concentrations is crucial for effectively mitigating pollution and achieving sustainability goals. ...

Evaluating degradation efficiency of pesticides by persulfate, Fenton, and ozonation oxidation processes with machine learning.

Environmental research
Quantifying organic properties is pivotal for enhancing the precision and interpretability of degradation predictive machine learning (ML) models. This study used Binary Morgan Fingerprints (B-MF) and Count-Based Morgan Fingerprints (C-MF) to quantif...

A framework predicting removal efficacy of antibiotic resistance genes during disinfection processes with machine learning.

Journal of hazardous materials
Disinfection has been applied widely for the removal of antibiotic resistance genes (ARGs) to curb the spread of antibiotic resistance. Quantitative polymerase chain reaction (qPCR) is the most used method to quantify the damage of DNA thus calculati...

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Nature communications
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)
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
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
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
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...