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Air Pollutants

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Is replacing missing values of PM constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

Environmental pollution (Barking, Essex : 1987)
East Asian countries have been conducting source apportionment of fine particulate matter (PM) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Ko...

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

PloS one
Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-ho...

Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.

Environmental science and pollution research international
Carbon dioxide (CO) emissions result from human activities like burning fossil fuels. CO is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO emissions include transitioning to renewable energy. Monitoring and ...

Application of a generalized hybrid machine learning model for the prediction of HS and VOCs removal in a compact trickle bed bioreactor (CTBB).

Chemosphere
This study presents a generalized hybrid model for predicting HS and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odo...

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

Environmental pollution (Barking, Essex : 1987)
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such a...

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning.

Environmental pollution (Barking, Essex : 1987)
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there r...

Explainable geospatial-artificial intelligence models for the estimation of PM concentration variation during commuting rush hours in Taiwan.

Environmental pollution (Barking, Essex : 1987)
PM concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM concentration and its spatial distribution during rush hours using machine learning mo...

Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction.

Environmental science and pollution research international
The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accu...

Development of an automated photolysis rates prediction system based on machine learning.

Journal of environmental sciences (China)
Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of OD, NO, HONO, HO, HCHO...

The association between PM components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

Environmental research
BACKGROUND: PM is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develo...