Abstract: Air pollution has become a major concern due to its harmful impact on human health and the surrounding environment. Most existing air quality monitoring systems focus on city-level Ambient Air Quality Index (AQI) values, which often fail to reflect pollution differences within smaller regions of an urban area. As a result, sudden pollution events and localized emission sources may remain undetected. This project presents a Context-Based Small Area Air Quality Index Prediction and Air Pollution Event Detection System (CM-SAAQIDS) designed to overcome these limitations. The proposed system uses historical air quality data along with environmental factors such as temperature, humidity, and wind patterns to estimate AQI values for individual micro-zones. It also identifies real-time pollution spikes and analyses possible causes, including traffic density, industrial activity, and unfavourable weather conditions. To ensure reliable results, the system incorporates methods to manage missing or inconsistent sensor data. By offering localized air quality forecasts and early warning alerts, the proposed approach supports timely decision-making by citizens and authorities, contributing to improved air pollution control and public health management.

Keywords: Cognitive Behavioral Therapy, Emotion Recognition, Cognitive Distortions, Mental‑Health Chatbot, Deep Learning, Natural Language Processing (NLP).

Downloads: | DOI: 10.17148/IARJSET.2026.13417