A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

Abstract

Air pollution is a major concern for public health and urban environments. Conventional air pollution monitoring systems install a few highly accurate, expensive stations at representative locations. Their sparse coverage and low spatial resolution are insufficient to quantify urban air pollution and its impacts on human health and environment. Advances in lowcost portable air pollution sensors have enabled air pollution monitoring deployments at scale to measure air pollution at high spatiotemporal resolution. However, it is challenging to ensure the accuracy of these low-cost sensor deployments because the sensors are more error-prone than high-end sensing infrastructures and they are often deployed in harsh environments. Sensor calibration has proven to be effective to improve the data quality of low-cost sensors and maintain the reliability of longterm, distributed sensor deployments. In this article, we review the state-of-the-art low-cost air pollution sensors, identify their major error sources, and comprehensively survey calibration models as well as network re-calibration strategies suited for different sensor deployments. We also discuss limitations of exiting methods and conclude with open issues for future sensor calibration research.

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

“…Several authors [10], [17], [18] have shown that in order to calibrate air pollution sensors such as CO, NO 2 , CH 4 , O 3 , CeO 2 or C 3 H 8 it is needed an array of sensors. The idea of sensor array calibration consists in measuring all the cross-sensitivities to compensate for all interfering pollutants and environmental conditions [5], [6]. For example, to calibrate a NO 2 sensor, NO 2 , O 3 , temperature, and relative humidity are measured.…”

“…Many of the low-cost sensors in IoT platforms are not calibrated by the manufacturers or if they are calibrated by them, the calibration has been done in laboratory chambers and not in the environmental conditions of the place where the nodes are deployed [3], [4]. In this case, the sensors of the IoT platform is calibrated during network deployment in an uncontrolled environment without laboratory instruments [5], [6].…”

A City-Wide Real-Time Traffic Management System: Enabling Crowdsensing in Social Internet of Vehicles

“…, where the set of parameters θ are estimated during the calibration process. Given that the IoT deployed in urban area can be heterogeneous in terms of operation & maintenance (O&M), the parameters θ m might have different dispersion across the network [9]. Therefore, each data y m t is associated with an uncertainty measure (e.g., variance) in order to be weighted properly in the later process of information fusion.…”

Development of a low-cost outdoor carbon monoxide analyzer applied to the city of Oran, Algeria.

“…These sensors are generally small, consume little energy, cost between 10 and 1,000 US Dollars, and measure concentrations of all major air pollutants. Compared to large, high-end solutions costing more than 100,000 US Dollars, low-cost sensors are particularly useful for large-scale static and mobile deployments [9,10,11].…”