Advancing Air Quality Prediction in Specific Cities Using Machine Learning
Keywords:
Accuracy, Air pollution, Detection, Machine learning, Prediction, RecommendationAbstract
The project aims to ensure optimal air quality in targeted urban areas by employing a sophisticated air quality monitoring system that collects data on contaminants from various locations. The release of hazardous gasses from industrial activities and the increasing emissions from vehicles have made air pollution a critical environmental and public health concern. Pollutants like particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and others, accumulate in the atmosphere, causing a deterioration in air quality and posing serious risks to both human health and the environment. The impact of air pollution is especially pronounced in major cities worldwide, where the concentration of industries and transportation systems worsens the problem. These urban areas often experience pollution levels that exceed the air quality standards set by governments, exposing residents to a harmful mixture of pollutants. By leveraging pre-collected data and employing the XG Boost algorithm, the ML technology calculates the Air Quality Index, thereby contributing to improved air quality management and its impact on public health.
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