ACHIEVEMENTS AND CHALLENGES OF CLOUD TECHNOLOGIES FOR AIR QUALITY MONITORING

Abstract

Air pollution is a critical environmental problem affecting both developed and developing countries. It has far-reaching consequences, contributing to global warming and having a direct adverse effect on human health. The need for clean air is not only an environmental problem, but also a public health imperative. As the level of air pollution continues to increase, especially in urban areas with high population density, it becomes very important to accurately monitor and mitigate its effects.

Governments and regulatory bodies around the world play an important role in improving air quality. Effective measurement of the impact of policies and measures on air pollution requires accurate and timely data on the spatial and temporal distribution of air pollutants. Traditionally, this requirement was met by deploying a network of air quality monitoring stations. Although these stations provide highly accurate data, their high cost and operational complexity limit their number and, therefore, the degree of detail they can collect.

The paper reviews the latest achievements in the field of cloud and IoT air quality monitoring systems, highlighting the integration of inexpensive sensor technologies, artificial intelligence, and machine learning for advanced data analysis and prediction. Various approaches to air quality monitoring are analyzed, including the use of cellular, Wi-Fi, and LPWA protocols for data transmission, and the advantages and challenges of deploying scalable and cost-effective solutions in urban and remote areas are explored. A comprehensive overview of current trends and challenges in the field of air quality monitoring is presented, along with insights into future research and development directions in this area.