Enhanced Ambient Sensing Environment—A New Method for Calibrating Low-Cost Gas Sensors

Abstract

Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0–120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time.

Effect of Three-Dimensional-Printed Thermoplastics Used in Sensor Housings on Common Atmospheric Trace Gasses

“…This study has several limitations. First, the experimental design employed a constant gas flow setup with a residence time of approximately 75 s. Low-cost air quality sensing applications commonly employ a continuous flow setup, utilizing a fan to draw flow through a housing [ 12 , 63 , 64 ], but tend to have lower residence times than 75 s. However, in this study, the experimental residence time was set at a larger value to determine measurable changes in gas concentration.…”

Indoor air quality monitoring and source apportionment using low-cost sensors

“…There is currently no recognised standard procedure for the calibration of LCS (Russell et al 2022). The sensors can be calibrated in the laboratory, prior to deployment, or in the field by comparing their results to those from reference-grade instruments.…”

Development of Microclimate Data Recorder on Coffee-Pine Agroforestry Using LoRaWAN and IoT Technology

“…The calibration process is essential when discrepancies are observed in the readings of various parameters such as air temperature, soil temperature, air humidity, soil moisture, and light intensity, compared to standard measuring tools [74]. To enhance the accuracy and ensure the sensor node readings align more closely with those of the reference tools, calibration is performed using the linear regression method [75]. The linear regression formula, y = mx + c, where y represents the predicted value (reading from the reference instrument), m is the slope of the regression line, x is the independent value (sensor reading), and c is the intercept (yintercept), is employed to ascertain the optimal m and c values that best represent the relationship between the sensor readings and the readings from conventional tools [76].…”