ENVIRONMENTAL POLLUTION ANALYSIS AND PREDICTION OF INFLUENTIAL FACTORS: A DATA-DRIVEN INVESTIGATION

Abstract

Environmental pollution is a pressing concern affecting humanity and other Earth species today. The escalation of air pollution due to heavy traffic density and industrial effluents in urban areas poses a significant threat to human life and the environment. The Air Quality Index (AQI) is employed as a crucial metric to gauge the extent of air pollution in a city. Apart from industrial and traffic emissions, meteorological factors, including wind speed, wind direction, temperature, humidity, and total precipitation, play a pivotal role in shaping a citys pollution levels. Mitigating the harm caused by air pollution is contingent on the ability to predict the factors influencing air pollutant levels. This predictive capability enables issuing advanced warnings to citizens or implementing precautionary measures for their protection. This paper focuses on forecasting temperature, humidity, wind speed, wind direction, and total precipitation in the town of Basel, Switzerland, on specific future dates, utilizing historical data maintained by local authorities. The methodology leverages Hive tools and MapReduce frameworks and employs machine learning techniques to predict future values of desired parameters. Various regression techniques, including Lasso, Linear Regression, Ridge Regression, and Polynomial Regression, were evaluated for their performance in predicting air quality factors. Polynomial Regression emerged as the preferred choice due to its superior performance. To enhance user accessibility, a user-friendly graphical interface (GUI) has been developed to input data and visualize predicted results. This study presents a comprehensive approach to address the critical issue of air pollution by providing accurate predictions of meteorological factors, thus enabling proactive measures to safeguard public health and the environment.