Anomaly Detection in Air Quality Monitoring Networks

EasyChair Preprint 12817

Abstract

Air quality monitoring is imperative for safeguarding human health and environmental integrity, especially in the face of escalating pollution levels and climate change. Anomaly detection emerges as a pivotal technique within this domain, enabling the timely identification and mitigation of irregularities in air quality data. This abstract presents a comprehensive overview of anomaly detection in air quality monitoring, highlighting its significance, methodologies, and applications. Leveraging advanced statistical analysis, machine learning algorithms, and sensor technology, anomaly detection systems can effectively detect deviations from expected patterns or norms, including sudden spikes, unusual trends, or unexpected fluctuations in pollutant concentrations. Such anomalies often signal potential environmental hazards, equipment malfunctions, or emerging pollution sources, necessitating prompt intervention. By integrating real-time anomaly detection capabilities into air quality monitoring networks, stakeholders can enhance their responsiveness and ability to mitigate risks proactively. This abstract underscores the critical role of anomaly detection in advancing environmental health initiatives, promoting sustainable development, and ensuring the well-being of communities worldwide.Air quality monitoring is imperative for safeguarding human health and environmental integrity, especially in the face of escalating pollution levels and climate change. Anomaly detection emerges as a pivotal technique within this domain, enabling the timely identification and mitigation of irregularities in air quality data. This abstract presents a comprehensive overview of anomaly detection in air quality monitoring, highlighting its significance, methodologies, and applications.

Keyphrases: Environmental, air quality, anomaly detection

@booklet{EasyChair:12817,
  author    = {Adhikari Durga Venkata Madhav and Amanchi Sravan Kumar and Addanki Gargeya and Ambati Vinod and V Ragavarthini},
  title     = {Anomaly Detection in Air Quality Monitoring Networks},
  howpublished = {EasyChair Preprint 12817},
  year      = {EasyChair, 2024}}