Ambient and laboratory evaluation of a low-cost particulate matter sensor

Abstract

Low-cost, light-scattering-based particulate matter (PM) sensors are becoming more widely available and are being increasingly deployed in ambient and indoor environments because of their low cost and ability to provide high spatial and temporal resolution PM information. Researchers have begun to evaluate some of these sensors under laboratory and environmental conditions. In this study, a low-cost, particulate matter sensor (Plantower PMS 1003/3003) used by a community air-quality network is evaluated in a controlled wind-tunnel environment and in the ambient environment during several winter-time, cold-pool events that are associated with high ambient levels of PM. In the wind-tunnel, the PMS sensor performance is compared to two research-grade, light-scattering instruments, and in the ambient tests, the sensor performance is compared to two federal equivalent (one tapered element oscillating microbalance and one beta attenuation monitor) and gravimetric federal reference methods (FEMs/FRMs) as well as one research-grade instrument (GRIMM). The PMS sensor response correlates well with research-grade instruments in the wind-tunnel tests, and its response is linear over the concentration range tested (200-850 μg/m³). In the ambient tests, this PM sensor correlates better with gravimetric methods than previous studies with correlation coefficients of 0.88. However additional measurements under a variety of ambient conditions are needed. Although the PMS sensor correlated as well as the research-grade instrument to the FRM/FEMs in ambient conditions, its response varies with particle properties to a much greater degree than the research-grade instrument. In addition, the PMS sensors overestimate ambient PM concentrations and begin to exhibit a non-linear response when PM_2.5 concentrations exceed 40 μg/m³. These results have important implications for communicating results from low-cost sensor networks, and they highlight the importance of using an appropriate correction factor for the target environmental conditions if the user wants to compare the results to FEM/FRMs.

Keywords: Air quality; Cold-air pool; Low-cost sensors; Particulate matter.

Fig. 1.

(a) Plantower PMS 3003 (b) schematic of the Plantower PMS 3003 sensor, and (c) housing for the outdoor PMS sensor.

Fig. 2.

Comparison of co-located hourly ${\text{PM}}_{2.5}$ (μg/m³) concentrations from the Utah Division of air quality monitors (DAQ TEOM, DAQ Sharp), a research grade monitor (GRIMM), and the PMS sensors from January 11, 2016 to February 17, 2016. Note the PMS sensors went offline between January 20 and January 27th because of power-supply problems. This caused the PMS1003–1 and PMS1003–2 data to be 81% and 74% complete, respectively.

Fig. 3.

Scatter plots and correlation coefficients for ${\text{PM}}_{2.5}$ (μg/m³) concentrations (PMS 1003–1/2) with FEMs (TEOM and Sharp), research-grade monitor (GRIMM), temperature and RH. No correlation was seen between ${\text{PM}}_{2.5}$ concentration measured between any of the devices and wind speed (R² of 0.03–0.04), results not shown.

Fig. 4.

Daily average particle counts for (a) the two PMS sensors, (b) the GRIMM and (c) the APS during ambient measurements at the Hawthorne monitoring station. Note that the x and y axes are log scale.

Fig. 5.

Exponential and linear fit for the PMS sensor ${\text{PM}}_{2.5}$ concentrations.

Fig. 6.

Ten-minute average ${\text{PM}}_{2.5}$ concentration and lo-occupancy percentage (Shinyei) versus DustTrack mass-adjusted ${\text{PM}}_{2.5}$ concentration with standard errors shown. Each point is the summary of four average measurements at each wind-tunnel condition. Fig. 6a shows the PMS 1003 sensor response with a housing, and Fig. 6b shows a subsequent experiment with the housing removed from the PMS 1003s. In Fig. 6b, the PMS 3003 sensor readings are combined because they do not overlap.