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 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 µ g m −3 , respectively, and a high-concentration urban
location (Kanpur, India) with 1 h PM 2.5 of 36±17 and 116±57 µ g 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 µ g m −3 for 1 h; SHARP: ±2 µ g 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 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 µ g 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 meteorological factors can
affect PMS3003 performance characterization.