AIMC Topic: Air Pollution
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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)
May 16, 2024
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
May 10, 2024
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...
Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach.
Frontiers in public health
May 7, 2024
Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are mos...
Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.
Environmental science and pollution research international
Apr 30, 2024
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 ...
Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.
Environmental pollution (Barking, Essex : 1987)
Apr 27, 2024
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)
Apr 25, 2024
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)
Apr 12, 2024
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
Apr 9, 2024
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...
Unmasking the sky: high-resolution PM prediction in Texas using machine learning techniques.
Journal of exposure science & environmental epidemiology
Apr 1, 2024
BACKGROUND: Although PM (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.
BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.
Environmental monitoring and assessment
Mar 4, 2024
Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders...
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