Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments
Yadlpalli Maheshwar krithik
Department Of Electronic and Communication
Engineering Panimalar Institute of Technology
Chennai, India
maheshwarkrithik687@gmail.com
Guru Pandi D
Department Of Electronic and
Communication Engineering Panimalar
Institute of Technology Chennai, India
Gurupandi85@gmail.com
Santhosh.R
Department Of Electronic and Communication
Engineering Panimalar Institute of Technology
Chennai, India
lifeuse143@gmail.com
Sathiya Priya S
Department Of Electronic and Communication
Engineering Panimalar Institute of Technology Chennai, India
priya.anbunathan@gmail.com
Thirunavukarasu.P
Department Of Electronic and Communication Engineering
Panimalar Institute of Technology Chennai, India
thirunavukarasu528@gmail.com
Jeya Ramya V
Department Of Electronic and Communication Engineering
Panimalar Institute of Technology Chennai, India
jeyaramyav@gmail.com
ABSTRACT:
Abstract—Low-cost light-scattering particulate matter sensors are often advocated for dense monitoring networks. Recent liter- ature has focused on evaluating their performance. Nonetheless, low-cost sensors are also considered unreliable and imprecise. Consequently, exploring techniques for anomaly detection, re- silient calibration, and improvement of data quality should be more discussed. In this study, we analyze a year-long acquisition campaign by positioning 56 low-cost light-scattering sensors near the inlet of an official particulate matter monitoring station. We use the collected measurements to design and test a data processing pipeline composed of different stages, including fault detection, filtering, outlier removal, and calibration. These can be used in large-scale deployment scenarios where the quantity of sensors’ data can be too high to be analyzed manually. Our framework also exploits sensor redundancy to improve reliability and accuracy. Our results show that the proposed data processing framework produces more reliable measurements, reduces errors, and increases the correlation with the official reference.
Index Terms—Light-scattering sensor, sensor calibration, par- ticulate matter, air quality, air monitoring.
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