AIMC Topic: Air Pollution
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Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.
Environmental monitoring and assessment
Jul 24, 2024
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)
Jul 23, 2024
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
Jul 20, 2024
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...
Quantifying the multiple environmental, health, and economic co-benefits from the adoption of carbon capture technology in the power sector in southern Iraq, using a recurrent neural network-based health assessment approach.
Journal of environmental management
Jul 17, 2024
This study introduces a novel integrated quantitative modeling framework to assess the multiple environmental, health, and economic benefits from implementing carbon capture technology in the power sector of Basra province, Iraq. This province is str...
Accurately Predicting Spatiotemporal Variations of Near-Surface Nitrous Acid (HONO) Based on a Deep Learning Approach.
Environmental science & technology
Jul 9, 2024
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...
Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.
Scientific reports
Jul 9, 2024
Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among child...
Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.
The Science of the total environment
Jul 2, 2024
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
Jun 21, 2024
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 ...
Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950-2014.
Environmental pollution (Barking, Essex : 1987)
Jun 19, 2024
Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observation, reanalysis and model simulation data, XGBoost machine learning algorithm is used to correct the...
Predicting PM2.5 concentration with enhanced state-trend awareness and uncertainty analysis using bagging and LSTM neural networks.
Journal of environmental quality
Jun 19, 2024
Monitoring air pollutants, particularly PM2.5, which refers to fine particulate matter with a diameter of 2.5 µm or smaller, has become a focal point of environmental protection efforts worldwide. This study introduces the concept of state-trend awar...
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