Land use regression (LUR) models are widely used in epidemiological and environmental
studies to estimate humans' exposure to air pollution within urban areas. However, the early …

Abstract According to the World Health Organization (WHO), air pollution kills seven million
people every year. Outdoor air pollution is a major environmental health problem affecting …

Prediction of air pollution index may help in traffic routing and identifying serious pollutants.
Modeling of the complex relationships between these variables by sophisticated methods in …

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting
models and the shortcomings in the exploitation of meteorological data, this research …

This book presents chapters that discuss contemporary issues related to the digital
economy, mainly in relation to the challenges and opportunities by artificial intelligence and …

This paper proposes a combination of hybrid models like Input Variable Selection (IVS),
Machine Learning (ML), and regression method to predict, model, and forecast the daily …

Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by
various factors. We developed prediction models for short-term UFP exposures using street …

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a
large drag on the sustainable developments of future metropolises. Here, combined with the …

Many studies use machine learning to predict atmospheric pollutant levels, prioritizing
accuracy over interpretability. This systematic review will focus on reviewing studies that …

The continuously growing human activity in large and densely populated cities pollutes air
and consequently puts public health in danger. This is why air quality monitoring is …