AIMC Topic: Air Pollution

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Elevating hourly PM forecasting in Istanbul, Türkiye: Leveraging ERA5 reanalysis and genetic algorithms in a comparative machine learning model analysis.

Chemosphere
Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA...

Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review.

Environmental research
BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient ni...

Prediction of PM concentration based on a CNN-LSTM neural network algorithm.

PeerJ
Fine particulate matter (PM) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM, pollutant sources can be better traced, allowing measures to protect human health to b...

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models.

The Science of the total environment
This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data in...

Green workspace and urban health: exploring the impacts of industrial robotics in pollution emissions and public health.

Frontiers in public health
INTRODUCTION: This study addresses a critical gap in understanding how technological advancements, specifically industrial robots, influence urban pollution emissions and public health. The rapid evolution of technology and changing working condition...

The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

Journal of research in health sciences
BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronav...

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

Environmental monitoring and assessment
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)
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
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
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...