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Air Pollution, Indoor

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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...

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Environmental health perspectives
BACKGROUND: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon...

Effective detection of indoor fungal contamination through the identification of volatile organic compounds using mass spectrometry and machine learning.

Environmental pollution (Barking, Essex : 1987)
Indoor fungal contamination poses significant challenges to human health and indoor air quality. This study addresses an effective approach using mass spectrometry and machine learning to identify microbial volatile organic compounds (MVOCs) originat...

Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM in 22 cities in China.

Ecotoxicology and environmental safety
Many studies have confirmed that PM exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM in indoor environments is critical to population health. Large-population, long-term, continuous, and accura...

Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

Journal of environmental radioactivity
We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where measurements had not been made. These new predictions use a greater range of modelling techniques and larger variety of explanatory variables than ou...

Enhancing indoor PM predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches.

Environmental pollution (Barking, Essex : 1987)
The presence of fine particulate matter (PM) indoors constitutes a significant component of overall PM exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, th...

Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.

Environmental pollution (Barking, Essex : 1987)
Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilities. The risk of chronic exposure can be significantly reduced by accurately predicting airborne funga...

Identifying predictors of spatiotemporal variations in residential radon concentrations across North Carolina using machine learning analytics.

Environmental pollution (Barking, Essex : 1987)
Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations. This...

Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.

Chemosphere
Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learning environment. Traditional measurements methods pose challenges in cost, maintenance, and time. This study proposes a new approach using a deep lear...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Environment international
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...