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Air Pollutants

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High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.

Environmental pollution (Barking, Essex : 1987)
Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional f...

Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.

Chemosphere
Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O are secondary pollutants, and there remain significant challenges in accurately and efficiently pred...

Improving the construction and prediction strategy of the Air Quality Health Index (AQHI) using machine learning: A case study in Guangzhou, China.

Ecotoxicology and environmental safety
Effectively capturing the risk of air pollution and informing residents is vital to public health. The widely used Air Quality Index (AQI) has been criticized for failing to accurately represent the non-threshold linear relationship between air pollu...

Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission.

Journal of theoretical biology
A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the mechanism of air pollution and its effect on the spread of diseases. The key is to characterize the in...

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...

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.

Journal of environmental management
Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O). This study aims to elucidate global CO pollution in relation to healt...

Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning.

The Science of the total environment
PM bound mercury (PBM) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when converted to methylmercury. Despite its importance, comprehensive macroanalyses of PBM on large sca...

Air quality index prediction with optimisation enabled deep learning model in IoT application.

Environmental technology
The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over the years. The measurement of the air quality index (AQI) depends on various environmental situatio...

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...

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

Environmental science and pollution research international
In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and...