Using Vehicles’ Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring

Abstract

This study examines the feasibility of the in situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicle measurements. Two vehicles with identical makes and models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2 h in total during winter in Seattle, Washington. To identify an optimal intervehicle distance for rendezvous calibration, 6 different buffers within 0−140 m for UFP monitors and 5 different buffers within 0−90 m for BC monitors were chosen, and the results of calibration were compared against a reference scenario, which consisted of mobile colocation measurements with both sets of the UFP and BC monitors deployed in one of the vehicles. Results indicate that the optimal distances for rendezvous calibration are 10−80 m for UFP monitors and 0− 30 m for BC monitors. In comparison with the mobile colocation calibration, the rendezvous calibration shows a normalized root mean squared deviation of 6−14% and a normalized mean absolute deviation of 4−8% for these monitors. Criteria for applying a rendezvous calibration approach are presented, and an extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.

Distinct Ultrafine Particle Profiles Associated with Aircraft and Roadway Traffic

“…The full calibration process is described in detail elsewhere. 47 The Li-850 CO 2 analyzer was calibrated for zero and span in the lab with certified standard CO 2 gas. We conducted mobile colocation with all sets of UFPs and/or BC monitors deployed in one vehicle, using the average reading of all instruments as a reference.…”

Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements

“…After identifying the optimal models using BIC and estimating model coefficients, the model was then applied to the pre-calibrated 1-minute PMS measurement to produce the post-calibrated concentrations for model evaluation. Calibration performance was assessed using the normalized mean absolute error (NMAE), which was calculated using the following equation [ 53 ]: where C PMS_post-cal is the post-calibrated 1-minute averaged PMS concentration and C APS is the 1-minute averaged APS concentration. The linear models were fitted using the lm function in R. All the analyses were conducted using R version 3.6.3.…”

“…After identifying the optimal models using BIC, and estimating model coefficients, the model was then applied to the pre-calibrated 1-minute PMS measurement to produce the post-calibrated concentrations for model evaluation. Calibration performance was assessed using the normalized mean absolute error (NMAE), which was calculated using the following equation [53]:…”

Residential cooking-related PM2.5: Spatial-temporal variations under various intervention scenarios

“…US Environmental Protection Agency has approved an updated version of the Grimm monitor (Grimm EDM 180) as a federal equivalent method (FEM) [31]. The normalized root mean squared errors (NRMSE) [32] of the postcalibrated monitors were 6-7%, indicating reasonably accurate measurements (see more details of the calibration process in Appendix Fig. A2 and Table A1).…”