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Air Pollutants

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Forecasting carbon dioxide emissions in Chongming: a novel hybrid forecasting model coupling gray correlation analysis and deep learning method.

Environmental monitoring and assessment
Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shan...

Co-firing characteristic prediction of solid waste and coal for supercritical CO power cycle based on CFD simulation and machine learning algorithm.

Waste management (New York, N.Y.)
The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO (S-CO) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, a...

Ultralow-resistance and self-sterilization biodegradable nanofibrous membranes for efficient PM removal and machine learning-assisted health management.

Journal of hazardous materials
The development of multifunctional nanofibrous membranes (NFMs) that enable anti-viral protection during air purification and respiratory disease diagnosis for health management is of increasing importance. Herein, we unraveled a heterostructure-enha...

Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework.

The Science of the total environment
Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage...

Urban road BC emissions of LDGVs: Machine learning models using OBD/PEMS data.

Chemosphere
Urban Black Carbon (BC) emissions from light-duty gasoline vehicles (LDGVs) are challenging to quantify in real-world settings. This study employed a Portable Emission Measurement System (PEMS) to assess BC emissions from five LDGVs on urban roads. W...

Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.

Scientific reports
This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes...

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...

Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment.

Environment international
BACKGROUND: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monit...

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Environmental monitoring and assessment
Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is o...

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Environmental monitoring and assessment
Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid m...