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

With the increase in climate-change-related hazardous events alongside population
concentration in urban centres, it is important to provide resilient cities with tools for …

Anthropogenic sources of fine particulate matter (PM2. 5) threaten ecosystem security,
human health and sustainable development. The accuracy prediction of daily PM2. 5 …

Spatiotemporal (ST) learning has become a crucial technique for urban digitalization. Due to
expansions and dynamics of cities, current spatiotemporal models are inclined to suffer …

With urbanization and increasing consumption, there is a growing need to prioritize
sustainable development across various industries. Particularly, sustainable development is …

Air quality forecasting is very difficult in metropolitan areas due to emissions, high population
density, and uncertainty in defining meteorological areas. The use of incomplete information …

Clarifying the correlations between air pollutant concentrations and emissions is vital for
effective air pollution prevention and control. In this study, we established a coupling …

Intelligent traffic systems represent one of the crucial domains in today's world, aiming to
enhance traffic management efficiency and road safety. However, current intelligent traffic …

Currently, the world is facing the problem of climate change and other environmental issues
due to higher emissions of greenhouse gases. Saudi Arabia is not an exception due to the …

In order to survive, mankind needs air. The quality of life depends on the purity of the air we
breathe in. Hazardous pollutants are stirred up in our environment by various activities …