Paper Info
Title
Automated data scanning for dense networks of low-cost air quality instruments: Detection and differentiation of instrumental error and local to regional scale environmental abnormalities.
Abstract
Recent improvements in low-cost air quality instrumentation make deployment of dense networks of sensors possible. However, the shear volume of data from these networks means that traditional methods for data quality control and data analysis are no longer viable. We propose a real-time data scanning routine that detects local and regional variability within the data sets. This can be used to differentiate errors resulting from instrument malfunction or calibration drifts from natural (environmentally driven) regional changes in ambient concentrations. Our case study considered hourly-averaged ozone data from Texas and from two networks in Vancouver. We used 7 and 28 days of data for the algorithm initialisation with simulated and real instrumental changes. The algorithm output can be used as part of a limited resource maintenance schedule for sensor networks, and to improve understanding of air quality processes and their relation to environmental and public health data.
Year
DOI
Venue
2018
10.1016/j.envsoft.2017.12.002
Environmental Modelling & Software
Keywords
Field
DocType
Change-point detection,Linear multi-regression,Sensor networks,Data reliability
Data set,Data quality,Software deployment,Computer science,Real-time computing,Air quality index,Wireless sensor network,Management science,Calibration,Instrumentation
Journal
Volume
ISSN
Citations 
101
1364-8152
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Maryam Alavi-Shoshtari100.34
Jennifer Ann Salmond200.34
Ciprian Doru Giurcaneanu34312.44
Georgia Miskell400.34
Lena Weissert500.34
David E. Williams601.35