POTENTIAL USE OF ARTIFICIAL INTELLIGENCE AND GEOSPATIAL ANALYSIS IN ENVIRONMENTAL MONITORING: Air quality in a large city
DOI:
https://doi.org/10.56177/11icmie2023.31Keywords:
Artificial Intelligence, Geospatial Analysis, Environmental Monitoring, Machine Learning, Air QualityAbstract
Environmental monitoring of air quality is essential to understanding the impact of pollution on human health and the environment. In urban areas, air quality is a significant concern due to high levels of pollutants emitted by transportation, industry, and energy production. Traditional air quality monitoring methods are often limited in their spatial and temporal resolution and can be expensive to maintain. Recent advances in artificial intelligence (AI) and geospatial analysis provide an opportunity to revolutionize the way we monitor air quality in urban areas. This paper explores the potential use of AI and geospatial analysis in air quality monitoring in the city of Bucharest. We present an overview of a specific supervised AI algorithm and its application in air quality monitoring. We also discuss the use of geospatial data, such as satellite imagery and GIS data, to enhance the accuracy and effectiveness of air quality monitoring. To demonstrate the potential of AI and geospatial analysis in air quality monitoring, this paper presents a case study of air quality monitoring in City of Bucharest. We use machine learning algorithms to analyze data collected from air quality sensors (state-owned and private) and geospatial data, such as traffic density and land use. Our results demonstrate the effectiveness of AI and geospatial analysis in predicting air quality parameters, such as particulate matter and nitrogen oxides, with a high degree of accuracy. Overall, the study highlights the potential of AI and geospatial analysis in air quality monitoring in urban areas. By integrating these technologies into air quality monitoring programs, we can improve our understanding of the sources and impact of pollutants on air quality in cities. This, in turn, can help policymakers and urban planners make better-informed decisions to reduce pollution and protect public health.
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