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Air Pollutants

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Soft sensing of NOx emission from waste incineration process based on data de-noising and bidirectional long short-term memory neural networks.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significa...

Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices.

Environmental pollution (Barking, Essex : 1987)
The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a global threat to human health. Therefore, this is the first research in Bangladesh to identify, and cha...

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

Environmental monitoring and assessment
This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory dise...

Characterizing sector-oriented roadside exposure to ultrafine particles (PM) via machine learning models: Implications of covariates influences on sectors variability.

Environmental pollution (Barking, Essex : 1987)
Ultrafine particles (UFPs; PM) possess intensified health risk due to their smaller size and unique spatial variability. One of major emission sources for UFPs is vehicle exhaust, which varies based on the traffic composition in each type of roadside...

Prediction of pollutant emission characteristics in ISO50001 energy management in the Americas: Uni and multivariate machine learning approach.

The Science of the total environment
The American continent is experiencing significant economic and industrial development driven by sustainability principles. In this context, discussions on improving energy consumption have become increasingly frequent and dynamic across various sect...

Application of triple-branch artificial neural network system for catalytic pellets combustion.

Journal of environmental management
On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to...

Accurately Predicting Spatiotemporal Variations of Near-Surface Nitrous Acid (HONO) Based on a Deep Learning Approach.

Environmental science & technology
Gaseous nitrous acid (HONO) is identified as a critical precursor of hydroxyl radicals (OH), influencing atmospheric oxidation capacity and the formation of secondary pollutants. However, large uncertainties persist regarding its formation and elimin...

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

The Science of the total environment
Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associa...

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

The Science of the total environment
Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O forecasting to mitigate these risks, focusing on ...

Prediction of developmental toxic effects of fine particulate matter (PM) water-soluble components via machine learning through observation of PM from diverse urban areas.

The Science of the total environment
The global health implications of fine particulate matter (PM) underscore the imperative need for research into its toxicity and chemical composition. In this study, zebrafish embryos exposed to the water-soluble components of PM from two cities (Har...