A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists.

A review of large-scale climate indicators (LSCI) and their environmental and health implications in the Mediterranean region.

Large-scale climate indicators (LSCI) refer to the intricate connections between the atmosphere, oceans, and continents in specific regions. To comprehend the relationship between these vital indicators and atmospheric and climate variability, it is crucial to explore them in detail. The objective of the present study is to gather and review relevant research on LSCI in the Mediterranean area to gain a better understanding of their impacts on atmospheric variability, climate, air quality, ecosystems, and health in the region.

A Review on Climate, Air Pollution, and Health in North Africa.

Purpose Of Review: The aim of this review is to summarize and provide clear insights into studies that evaluate the interaction between air pollution, climate, and health in North Africa.

Recent Findings: Few studies have estimated the effects of climate and air pollution on health in North Africa. Most of the studies highlighted the evidence of the link between climate and air pollution as driving factors and increased mortality and morbidity as health outcomes.