AIMC Topic: Nitrogen Dioxide
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IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO.
Sensors (Basel, Switzerland)
Jul 28, 2022
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
Jan 21, 2021
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...
Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations.
Environmental research
Oct 28, 2020
Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO) concentrations using approximately 20,000...
Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.
Chemosphere
May 27, 2020
The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were largely impeded in a complex system including multiple environmental stressors and health outcome of w...
A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide.
Environment international
Jun 20, 2019
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms hav...
Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions.
Environment international
May 8, 2019
BACKGROUND: Accurate estimation of nitrogen dioxide (NO) and nitrogen oxide (NO) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acut...
Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.
BMC medical research methodology
Mar 29, 2019
BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative...
Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.
Journal of environmental management
Dec 17, 2015
Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defin...
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...
Low-cost sensors for atmospheric NO measurement: A review.
Environmental pollution (Barking, Essex : 1987)
Jul 15, 2025
Nitrogen dioxide (NO) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among these, the chemiluminescence method stands out as the most commonly used and is widely regarded as a ref...
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