Analyzing the Impact of Air Quality on UV Index using Real-Time Weather Data from Major Cities Across the World

Ultraviolet radiation from sunlight has both beneficial and harmful effects on human health, with the UV index serving as a crucial metric for assessing potential harm. Excessive exposure can lead to adverse effects like skin cancers, cataracts, and immune suppression. Air quality influences the UV index by affecting the scattering and absorption of UV radiation, with particulate matter and pollutants altering the amount of radiation reaching the Earth's surface. Recognizing the dynamic relationship between air quality and the UV index is vital for understanding their combined impact on human health and the environment. This work utilizes the real-time weather information from prominent cities across the world, capturing diverse climatic conditions and air quality variations. With a particular emphasis on UV index prediction, the study focuses on employing a comparative analysis of various regression models, including linear regression, decision trees, support vector machines, logistic regression, random forest, XGBoost, LightGBM, gradient boosting and neural networks. The comparative analysis aims to identify the most proficient model for capturing the intricate relationships between chosen meteorological variables, including temperature, wind degree, precipitation, and others, in relation to the UV index. This paper explores the impact of air quality, temperature, wind degree, precipitation, and other relevant factors on UV index variations, offering insights into the complex interplay between meteorological parameters and ultraviolet radiation. The findings of this research hold potential implications for public health, as understanding the factors influencing UV index can contribute to enhanced sun exposure risk assessments. Furthermore, the comparative analysis of regression models provides valuable guidance for future studies aiming to predict UV index variations based on real-time weather data, thereby contributing to the development of more accurate and region-specific forecasting models.