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

Abstract

Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles’ properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors’ performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM’s mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.

Assessment of Aerosol Persistence in ICUs via Low-cost Sensor Network and Zonal Models

“…[52, 53] Here, original equipment manufacturer (OEM) calibration is used, as relative measurements are sufficient to construct a zonal map of the ICU environment. [42] The schematic and photograph of the monitor are shown in Supplementary Figure 1.…”

“…[30,[35][36][37][38][39][40][41] These reports show that low-cost sensors yield usable data when calibrated against research-grade reference instruments. [28,42,43] The sensor networks have the potential to provide high spatial and temporal resolution, identifying pollution sources and hotspots, which in turn can lead to the development of intervention strategies for exposure assessment and intervention strategies for susceptible individuals. [44] Data can be fitted to a regression model when analyzing large data sets from [45] An essential factor in the data analysis to simplify or condense the data is to aggregate data from sensors providing that the specifics of the data (such as the locations of the sensor nodes) are not left out.…”

“…The copyright holder for this preprint this version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271831 doi: medRxiv preprint construct a zonal map of the ICU environment. [42] The schematic and photograph of the monitor are shown in Supplementary Figure 1.…”

“…[30, 35–41] These reports show that low-cost sensors yield usable data when calibrated against research-grade reference instruments. [28, 42, 43]…”

“…[43,[48][49][50][51][52][53][54] These reports show that low-cost sensors yield usable data when calibrated against research-grade reference instruments. [41,55,56] The sensor networks have the potential to provide high spatial and temporal resolution, identifying pollution sources and hotspots, which in turn can lead to the development of intervention strategies for exposure assessment and intervention strategies for susceptible individuals. [57] Data can be fitted to a regression model when analyzing large data sets from such networks.…”