Global Environmental Health Assessment: Analyzing Air Quality and Water Pollution Data

Abstract

Abstract: This study employs machine learning techniques to assess global environmental health, focusing on air quality and water pollution. Through extensive data collection and preprocessing, including feature engineering, insights are extracted from diverse datasets encompassing pollutant concentrations, meteorological conditions, and socio-economic indicators. Machine learning algorithms, including LSTM models, are employed to analyze temporal dependencies and predict pollution levels. Additionally, clustering, regression analysis, and spatial analysis techniques aid in identifying pollution hotspots and trends. The proposed system integrates IoT technology for real-time data collection and Apache Spark for efficient processing. Evaluation metrics such as Mean Absolute Error and Root Mean Square Error assess model performance. The dataset comprises hourly averaged responses from chemical sensors deployed in a polluted area, complemented by ground truth data from a reference analyzer. This research contributes to informed decision-making for environmental management and sustainable development in smart city environments