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

Abstract

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 PM 2.5 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 PM 2.5 (mean ± SD) of 9 ± 9 and 10 ± 3 µg m −3 , respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM 2.5 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 PM 2.5 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 PM 2.5 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 (R 2 ≥ 0.63) than SHARP-PMS3003 (R 2 ≥ 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 PM 2.5 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 PM 2.5 concentrations within ∼ 10 % of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM 2.5 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 PM 2.5 concentrations, reference monitor types, and meteorologi...

Examining the functional range of commercially available low‐cost airborne particle sensors and consequences for monitoring of indoor air quality in residences

“…We see that apparent limit of quantification is a strong function of averaging time at averaging times less than approximately 10 min. Similar phenomena were measured by others . This ten‐minute threshold was consistent for all sensors tested.…”

“…Similar phenomena were measured by others. 21,22 This ten-minute threshold was consistent for all sensors tested. For this reason, we adopt the convention of averaging data over ten minutes for the remainder of this work.…”

Strategies of method selection for fine-scale PM<sub>2.5</sub> mapping in an intra-urban area using crowdsourced monitoring

“…S. Xu et al: Strategies of method selection for fine-scale PM 2.5 mapping sion conditions (Kumar et al, 2015;Zou et al, 2015;Apte et al, 2017). Spatial mapping methods, including air dispersion modelling, spatial interpolation, satellite remote sensing (RS), and empirical models, have been increasingly employed to estimate concentrations of PM 2.5 in unobserved locations over the past 2 decades (Jerrett et al, 2005;Henderson et al, 2007;El-Harbawi, 2013;Saraswat et al, 2013;Kim et al, 2014;Rice et al, 2015;Fang et al, 2016;Zou et al, 2017;Zhai et al, 2018;Xu et al, 2018;Liu et al, 2018).…”

“…The outputs of a dispersion model considerably depend on detailed emission inventories and meteorological information, which are not usually available for many cities. The coarse spatial resolution (≥ 1-10 km) of satellite instruments and the missing data problem due to the cloud cover prohibit the widespread use of RS in PM 2.5 concentration mapping in urban environments (Zou et al, 2015;Apte et al, 2017).…”

“…Some studies have improved the estimating accuracy by combining these two technologies (Mercer et al, 2011;De Hoogh et al, 2018). While they have been successfully applied to map the spatial variability of PM 2.5 concentrations in various geographic areas, their accuracy varies as the concentration levels and sample sizes change Mercer et al, 2011;Lee et al, 2014;Zou et al, 2015;Gillespie et al, 2016;Choi et al, 2017;De Hoogh et al, 2018).…”