Optimizing co-location calibration periods for low-cost sensors

Abstract

Abstract. Low-cost sensors are often co-located with reference instruments to assess their performance and establish calibration equations, but limited discussion has focused on whether the duration of this calibration period can be optimized. We placed a multipollutant monitor that contained sensors that measure particulate matter smaller than 2.5 mm (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and nitric oxide (NO) at a reference field site for one year. We developed calibration equations using randomly co-location subsets spanning 1 to 180 consecutive days out of the 1-year period and compared the potential root mean square errors (RMSE) and Pearson correlation coefficients (r). The co-located calibration period required to obtain consistent results varied by sensor type, and several factors increased the co-location duration required for accurate calibration, including the response of a sensor to environmental factors, such as temperature or relative humidity (RH), or cross-sensitivities to other pollutants. Using measurements from Baltimore, MD, where a broad range of environmental conditions may be observed over a given year, we found diminishing improvements in the median RMSE for calibration periods longer than about six weeks for all the sensors. The best performing calibration periods were the ones that contained a range of environmental conditions similar to those encountered during the evaluation period (i.e., all other days of the year not used in the calibration). With optimal, varying conditions it was possible to obtain an accurate calibration in as little as one week for all sensors, suggesting that co-location can be minimized if the period is strategically selected and monitored so that the calibration period is representative of the desired measurement setting.

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

“…Meanwhile the Surrey Field training period (EC nodes) is short and has a lower mean NO 2 level (7.7 ppb), with a lower NO 2 concentration range (0.50–29 ppb), followed by a validation period with a slightly greater mean NO 2 concentration (13 ppb). A lower mean concentration can result in a lower value regardless of the sensor being tested, and validating sensors using a concentration range greater than the training range is not optimal [ 34 , 55 , 56 ]. Half-hourly data is the maximum resolution available from HCAB, whereas the Surrey co-location data had a one-minute time resolution, using greater time-resolution generally results in lower performance statistics.…”

“…Field calibration is considered superior, particularly if calibration occurs in a similar geographical area to the actual measurement and in the same season of the year. However, it is time intensive, typically requiring several weeks to observe a comprehensive range of concentrations, particularly for more complex models, requires access to an official reference station or similar staging area, and leaves to chance whether or not the full range of pollutant concentrations is encountered [ 30 , 32 , 33 , 34 ]. Field calibration models may also not be transferable if moved between sites with different concentration profiles, co-pollutant matrices or prevailing meteorological conditions [ 22 , 35 , 36 ].…”

Spatially Adaptive Calibrations of Airbox PM2.5 Data

“…The calibration of low-cost sensors has been studied in the literature. For example, Datta et al (2020) and Zamora et al (2022) considered regression-calibration models with the reference variable as the response and low-cost variable and other covariates as regressors. Heffernan et al…”

“…(2020) and Zamora et al. (2022) considered regression‐calibration models with the reference variable as the response and low‐cost variable and other covariates as regressors. Heffernan et al.…”