AI-Based Predictive Modeling For Air Quality Assessment And Environmental Risk Forecasting In Urban Ecosystems

Abstract

The exponential growth of urbanization has intensified environmental degradation, particularly in terms of air pollution, posing severe health and ecological risks to urban populations. Traditional air quality monitoring systems, while effective in data collection, often fall short in predictive capability and real-time responsiveness. In this context, artificial intelligence (AI)-driven predictive modeling emerges as a transformative tool in environmental risk assessment, offering advanced analytical techniques that can not only evaluate current air quality conditions but also forecast future pollution levels with remarkable accuracy. This study proposes a comprehensive AI-based framework for predictive modeling aimed at assessing air quality and anticipating environmental risks in urban ecosystems. The proposed model integrates multiple data streams—including meteorological parameters, vehicular emissions, industrial discharge data, and real-time pollutant concentrations (e.g., PM2.5, NO₂, CO, O₃)—using machine learning algorithms such as Long Short-Term Memory (LSTM) networks, Random Forests, and Gradient Boosting Machines. The methodology emphasizes data preprocessing techniques, including normalization, missing value imputation, and outlier detection, to enhance model reliability. The spatial-temporal analysis is conducted to identify pollution hotspots and patterns across different urban zones. A novel feature of this study is the implementation of ensemble modeling, which combines the strengths of various AI algorithms to improve predictive accuracy and minimize error margins. The model was trained and validated using historical air quality datasets from metropolitan regions across different climatic zones. Results indicate a significant improvement in predictive performance when compared to traditional statistical models, with forecasting accuracy exceeding 90% in several cases. Moreover, the system demonstrates the capacity to issue early warnings related to critical pollution thresholds, enabling proactive interventions by urban planners and public health authorities. Beyond prediction, the model offers risk classification and prioritization by estimating the probable health impact indices based on demographic vulnerability and pollutant toxicity. The interpretability of model outputs is enhanced using SHAP (Shapley Additive exPlanations) values to ensure transparency and stakeholder trust. Furthermore, a user-friendly dashboard was developed to present real-time visualizations and alerts for municipal decision-makers. In conclusion, this research underscores the transformative role of AI in environmental governance, illustrating how predictive modeling can elevate urban sustainability by fostering data-driven policy-making. The proposed AI framework holds vast potential for scalability and adaptation across global urban centers striving for resilience against the escalating challenge of air pollution.