A Review: Identifying and Analyzing Air Pollution Hotspots Using Machine Learning and Remote Sensing Techniques

Abstract

Air pollution has drawn significant scholarly attention over the past two decades due to its widespread environmental and health impacts. It refers to the presence of harmful substances in the atmosphere that degrade air quality and negatively affect the physical environment and human health. This literature review aims to identify the sources and hotspots of air pollution, examine its spatial and temporal patterns, evaluate control mechanisms, and predict future air quality scenarios. The review draws on a range of literature sourced online and organized around these objectives. Findings confirm that air pollution is a critical global issue, originating from both natural and anthropogenic sources such as fossil fuel combustion, industrial emissions, vehicular exhaust, household pollutants, and agricultural activities. These sources significantly degrade air quality and contribute to respiratory and cardiovascular diseases in humans. Globally, air pollution ranks as the fourth highest health risk, accounting for an estimated 6.7 million deaths annually. Even at low levels, pollutants cause lasting damage to ecosystems. Air pollutants are typically categorized into primary and secondary types: primary pollutants are emitted directly from sources, while secondary pollutants are formed through atmospheric chemical reactions. The methodological approach of this study includes the identification of air pollution hotspots and the analysis of spatial and temporal trends. Additionally, the study evaluates the effectiveness of current mitigation strategies. The reviewed literature underscores the urgent need for effective control measures and continued monitoring to mitigate the growing threat of air pollution.