Anomaly Detection in Urban Air Quality Sensor Networks

D. Lestari; M. Gunawan

doi:10.5555/fji-demo-006

Open Access DOI: 10.5555/fji-demo-006 How to use a DOI?

Anomaly Detection in Urban Air Quality Sensor Networks

D. Lestari^*¹, M. Gunawan²

^*Corresponding author: D. Lestari

¹FJI Press
²Institute of Technology Nusantara

Review Article · Pages 1–22 · In: Machine Learning for Environmental Monitoring · Published 2026-06-15 · 164 views · 70 downloads

Article history: Received 2026-04-10 · Accepted 2026-05-22 · Available online 2026-06-15

Abstract

This chapter surveys anomaly detection methods for low-cost urban air quality sensors, covering statistical, machine-learning and hybrid approaches. We compare methods on a public dataset of 120 sensors and give practical guidance on choosing a method under sensor drift and missing data.

Keywords: air quality; anomaly detection; sensor networks; survey

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D. Lestari, M. Gunawan (2026). Anomaly Detection in Urban Air Quality Sensor Networks. Machine Learning for Environmental Monitoring,, 1–22. https://doi.org/10.5555/fji-demo-006

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This is an open access article distributed under the CC BY-NC 4.0 license. CC BY-NC 4.0

DOI: 10.5555/fji-demo-006
Article type: Review Article
Pages: 1–22
Published: 2026-06-15
Metrics: 164 views · 70 downloads