IDENTIFYING ANOMALIES AND FIXING THEM TO ENHANCE AIR QUALITY MONITORING

Air quality monitoring is a critical component in managing environmental health and public safety.However, the reliability of air quality data is often compromised by sensor anomalies caused by hardware malfunctions, environmental interferences, or data transmission issues.In this paper, we propose a robust framework for anomaly detection and data repairing to enhance the accuracy and consistency of air quality monitoring systems.The proposed approach integrates machine learning techniques for identifying both point anomalies (sudden spikes or drops) and contextual anomalies (unexpected values given environmental conditions).Following detection, a data repairing mechanism is employed using statistical imputation and predictive modeling to reconstruct plausible data values.Experimental evaluation on real-world air quality datasets demonstrates the effectiveness of our framework in improving data quality, reducing noise, and enhancing the performance of downstream predictive models.This work contributes to the development of more reliable environmental monitoring systems, ultimately aiding in informed decision-making for urban planning and public health.