Calibrating low-cost air quality sensors using multiple arrays of sensors

Abstract

The remarkable advances in sensing and communication technologies have introduced increasingly low-cost, smart and portable sensors that can be embedded everywhere and play an important role in environmental sensing applications such as air quality monitoring. These user-friendly wireless sensor platforms enable assessment of human exposure to air pollution through observations at high spatial resolution in near-realtime, thus providing new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. However, data quality from such platforms is a concern since sensing hardware of such devices is generally characterized by a reduced accuracy, precision, and reliability. Achieving good data quality and maintaining error free measurements during the whole system lifetime is challenging. Over time, sensors become subject to several sources of unknown and uncontrollable faulty data which comprise the accuracy of the measurements and yield observations far from the expected values. This paper investigates calibration of lowcost air quality sensors in a real sensor network deployment. The approach leverages on the availability of sensor arrays in a wireless node to estimate parameters that minimize the calibration error using fusion of data from multiple sensors. The obtained results were encouraging and show the effectiveness of the approach compared to a single sensor calibration.

Self-calibration methods for uncontrolled environments in sensor networks: A reference survey

“…Gas sensors, such as CO, O 3 , and NO 2 sensors, are examples [15,16,27,28,29,30,31,32,33,34] of sensors that follow 4 The input parameters are called predictors, features, independent variables, or variables in machine learning and statistical learning terminology. 5 The output is also called response or dependent variable in machine learning and statistical learning terminology.…”

“…(iii) The coefficient of determination (R 2 ) measures the proportion of variability in Y that can be explained using X, and it is bounded between 0 and 1. A value of R 2 close to 1 [33] Multiple linear regression Root mean squared error Real O 3 [55] Multiple linear regression Mean error Real Photosynthetically active radiation (PAR) [56] Maximum likelihood Relative bias error Synthetic Air pollution indicates that a large proportion of the variability in the response has been explained by the regression.…”

“…[35] X X X X X X X X Light [36] X X X X X X X Motion (accelerometer) [41] X X X X X X X Light [43] X X X X X X X Temperature [44] X X X X X X X Temperature [45] X X X X X X X X Temp. Hum., Light [46,47,48] X X X X X X X Temperature [49] X X X X X X X Vibration (Water Flow) [50] X X X X X X X Temp., Hum., Sound [51] X X X X X X X Temperature Attributes used in localization, synchronization and target detection applications [5] X X X X X X X Localization [17] X X X X X X X X X Target Detection [37] X X X X X X X Target Detection [38] X X X X X X X Localization [39,40] X X X X X X X Localization [52] X X X X X X X Localization [53,10] X X X X X X X Synchronization [54] X X X X X X X Synchronization Attributes used in air pollution and water chemistry applications [12] X X X X X X X Water Chemistry [15] X X X X X X X X X O 3 [16,27] X X X X X X X O 3 ,CO,CO 2 ,NO,NO 2 [28] X X X X X X X CO,CH 4 ,C 3 H 8 ,CeO 2 ,NiO [29] X X X X X X X O 3 ,CO [30] X X X X X X X O 3 ,CO,NO 2 [31] X X X X X X X O 3 , NO, NO 2 [32] X X X X X X X X X O 3 , CO, NO 2 [33] X X X X X X X O 3 [55] X X X X X X X Photosynthetically Active Radiation (PAR) [56] X X X X X X X Air pollution CaliBree [8] is a model-based calibration network in which ground-truth calibrated nodes inform non-calibrated nodes, using a beacon protocol, that they will participate in the calibration process. These nodes, then, use a discrete consensus distributed algorithm to calibrate the uncalibrated node by computing the degree of disagreement between the reference nodes and t...…”

Citizen-Based Air Quality Monitoring: The Impact on Individual Citizen Scientists and How to Leverage the Benefits to Affect Whole Regions

“…Analyses of these interviews, together with onsite observations from the research team, are presented in this paper. Insights on the technical aspects of the low-cost ozone measurement are presented in Ripoll et al (2019) and Barcelo-Ordinas et al (2018).…”

A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms

“…For this reason, much research has focused on the interaction of environmental conditions such as temperature and relative humidity [3], [4], [7], [8], [9] or on the interactions of other pollutants [10], [11] with respect to one pollutant sensor. In addition, there is recently a greater interest in comparing and studying [11], [12], [13], [14], [15] how signal processing techniques behave for calibrating different air pollution lowcost sensors in IoT platforms. Many of these investigations focus on comparing what is the error obtained using several linear and non-linear machine learning algorithms.…”