Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network

Abstract

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations. The models for all five pollutants produced high Pearson correlation coefficients (r > 0.85), and the hourly averaged calibrated sensor and reference concentrations from the evaluation period were within 3–12%. Each sensor required a distinct set of predictors to achieve the lowest possible root-mean-square error (RMSE). All five sensors responded to environmental factors, and three sensors exhibited cross-sensitives to another air pollutant. We compared the RMSE from models (NO2, O3, and NO) that used colocated regulatory instruments and colocated sensors as predictors to address the cross-sensitivities to another gas, and the corresponding model RMSEs for the three gas models were all within 0.5 ppb. This indicates that low-cost sensor networks can yield useable data if the monitoring package is designed to comeasure key predictors. This is key for the utilization of low-cost sensors by diverse audiences since this does not require continual access to regulatory grade instruments.

Identifying optimal co-location calibration periods for low-cost sensors

“…The Essex site (lat 39.310833 • , long −76.474444 • ) is about 11 km east of the Oldtown site (lat 39.298056 • , long −76.604722 • ). Additional details about the sensors in the multipollutant monitor have been described in detail by Buehler et al (2021) and Levy Zamora et al (2022). Co-location data from 1 February 2019 to 1 February 2020 were used in the PM 2.5 analysis, and co-location data from 1 February to 20 December 2019 were used in the CO, NO, NO 2 , and O 3 sensor analyses.…”

“…Sensor data from the calibration period were used to determine the coefficients for multiple linear regression (MLR) models based on previously identified known environmen-tal factors influencing concentration for each sensor (Levy Zamora, 2022). A generic MLR model is given by Reference…”

“…In the case of low-cost air pollution sensors, the raw output is often a voltage or resistance instead of a concentration, so a calibration curve is needed to convert the raw output into practical units. Crosssensitivities to environmental conditions or other pollutants, nonlinear responses, and variability between sensor units are common difficulties that must be considered when working with low-cost sensor data (Van Zoest et al, 2019;Levy Zamora, 2022;Li et al, 2021;Spinelle et al, 2015;Ripoll et al, 2019). Several methodologies have been used to derive the calibration equations needed to convert the raw data into useable concentrations, such as exposing the sensors to known concentrations in a laboratory setting and co-locating the sensors with a reference instrument, often in a similar setting to which the sensor is to be used (Taylor, 2016;Zimmerman et al, 2018;Mead et al, 2013;Ikram et al, 2012;Hagler et al, 2018;Cross et al, 2017;Holstius et al, 2014;Mukherjee et al, 2019;Gao et al, 2015;Heimann et al, 2015;Air Quality Sensor Performance Evaluation Center, 2016a, b, 2017Levy Zamora et al, 2018a).…”

Optimizing co-location calibration periods for low-cost sensors

“…Sensor data from the calibration period was used to determine the coefficients for multiple linear regression (MLR) models based on previously identified known environmental factors influencing concentration for each sensor (Levy Zamora, 2022). A generic MLR model is given by:…”

“…To further clarify the influence of coverage on calibration outcomes, the median RMSEs as a function of temperature and RH coverages when the PM2.5 concentration coverage was greater than 50% are shown in Table 3. RH strongly influences the sensor's raw output, particularly compared to The NO2 sensor exhibits cross-sensitivities to O3 and NO in addition to responding to temperature and RH (Li et al, 2021;Levy Zamora, 2022), so an adequate calibration period should cover an adequate range for all four parameters. The reference NO2 concentrations ranged between 1 and 58 ppb, with a median concentration of 5 ppb.…”