AIMC Topic: Nitrogen Dioxide

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The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

Journal of research in health sciences
BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronav...

Identifying Driving Factors of Atmospheric NO with Machine Learning.

Environmental science & technology
Dinitrogen pentoxide (NO) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of NO formation remains ...

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

Environmental monitoring and assessment
This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is a...

Deep learning bias correction of GEMS tropospheric NO: A comparative validation of NO from GEMS and TROPOMI using Pandora observations.

Environment international
Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric colum...

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Journal of environmental management
Nitrogen dioxide (NO) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO levels thro...

BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.

Environmental monitoring and assessment
Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders...

A hybrid deep learning model for regional O and NO concentrations prediction based on spatiotemporal dependencies in air quality monitoring network.

Environmental pollution (Barking, Essex : 1987)
Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring netw...

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6).

Scientific reports
Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollut...

IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO.

Sensors (Basel, Switzerland)
This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on ...

Asthma-prone areas modeling using a machine learning model.

Scientific reports
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran conside...