Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments

Abstract

Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of the new generation of low-cost PM sensors under field conditions is not well understood. In this study, we characterized the performance capabilities of a new low-cost PM sensor model (Plantower model PMS3003) for measuring PM2.5 at 1 min, 1 h, 6 h, 12 h, and 24 h integration times. We tested the PMS3003 sensors in both low-concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM2.5 (mean ± SD) of 9±9 and 10±3 µg m−3, respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM2.5 of 36±17 and 116±57 µg m−3 during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental β attenuation monitor, E-BAM) to determine how these sensors perform across a range of PM2.5 concentrations and meteorological factors (e.g., temperature and relative humidity, RH). In RTP, the sensors were compared to three Federal Equivalent Methods (FEMs) including two Teledyne model T640s and a Thermo Scientific model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease in 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201 %) to Kanpur monsoon (46 %) and post-monsoon (35 %) seasons showed that PMS3003 performance generally improved as ambient PM2.5 increased. The precision of reference instruments (T640: ±0.5 µg m−3 for 1 h; SHARP: ±2 µg m−3 for 24 h, better than the E-BAM) is critical in evaluating sensor performance, and β-attenuation-based monitors may not be ideal for testing PM sensors at low concentrations, as underscored by (1) the less dramatic error reduction over averaging times in RTP against optically based T640 (from 27 % for 1 h to 9 % for 24 h) than in Durham (from 201 % to 15 %); (2) the lower errors in RTP than the Kanpur post-monsoon season (from 35 % to 11 %); and (3) the higher T640–PMS3003 correlations (R2 ≥ 0.63) than SHARP–PMS3003 (R2 ≥ 0.25). A major RH influence was found in RTP (1 h RH  = 64±22 %) due to the relatively high precision of the T640 measurements that can explain up to ∼ 30 % of the variance in 1 min to 6 h PMS3003 PM2.5 measurements. When proper RH corrections are made by empirical nonlinear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003 sensors can measure PM2.5 concentrations within ∼ 10 % of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM2.5 exceeded ∼ 125 µg m−3 and found that the quadratic fit is more appropriate than the univariate linear model to capture this nonlinearity and can further reduce errors by up to 11 %. Our results have substantial implications for how variability in ambient PM2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance characterization.