AIMC Topic: Nitrogen Dioxide

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Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO via machine learning.

Environmental pollution (Barking, Essex : 1987)
The Geostationary Environment Monitoring Spectrometer (GEMS) has revolutionized air quality monitoring with hourly resolution from geostationary Earth orbit (GEO). However, satellite-derived air quality data often face limitations and biases due to m...

Assessing the impact of traffic restriction interventions on school air quality: a citizen science-based modelling study.

Environmental research
Air pollution poses a significant threat to human health, especially for the vulnerable groups such as children. Given that schools are central to their daily lives, ensuring good air quality in these environments is crucial. This study evaluates the...

Multigas Identification by Temperature-Modulated Operation of a Single Anodic Aluminum Oxide Gas Sensor Platform and Deep Learning Algorithm.

ACS sensors
Semiconductor metal oxide (SMO) gas sensors are gaining prominence owing to their high sensitivity, rapid response, and cost-effectiveness. These sensors detect changes in resistance resulting from oxidation-reduction reactions with target gases, res...

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...

Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.

Environmental geochemistry and health
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological condi...

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO Concentration from Satellite and Ground Monitors.

Environmental science & technology
Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a no...

Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning.

ACS sensors
Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to ...

Performance analysis of machine learning models for AQI prediction in Gorakhpur City: a critical study.

Environmental monitoring and assessment
Air pollution and climate change are two complementary forces that directly or indirectly affect the environment's physical, chemical, and biological processes. The air quality index is a parameter defined to cope with this effect of air pollution. T...

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan.

Ecotoxicology and environmental safety
Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used m...

Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review.

Environmental research
BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient ni...