Field Evaluation of Low-cost Particulate Matter Sensors in High and Low Concentration Environments
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 generally not well understood. In this study, we characterized the performance capabilities of a relatively new low-cost PM sensor model (Plantower PMS3003) of measuring PM2.5 at 1 min, 1 h, 6 h, 12 h and 24 h integration times. We tested the PMS3003s in both low concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM2.5 averaging 9 g m-3 (range: 0-62 μg m-3) and 10 g m-3 (range: 3-20 μg m-3) respectively and a high concentration urban location (Kanpur, India) with 1 h PM2.5 averaging 36 g m-3 (range: 0-127 μg m-3) and 116 g m-3 (range: 19-347 μg m-3) during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental -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 T640s and a SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease of 1 h mean errors of the calibrated sensors using univariate linear models from Durham (207%) to Kanpur monsoon (46%) and to post-monsoon (35%) season showed PMS3003 performance generally improved as ambient PM2.5 increased. The precision of reference instruments is critical in evaluating sensor performance and -attenuation-based monitors are unfavorable for testing PM sensors at low concentrations as were underscored by 1) the gentler gradient at which the mean errors reduced over averaging time in RTP (from 27% for 1 h to 9% for 24 h) than in Durham (from 201% to 15%); 2) the lower errors in RTP than Kanpur post-monsoon season (from 35% to 11%); 3) the higher precision of the T640 (1 h average: ±0.5 g m-3) than the SHARP (24 h average: ±2 g m-3, better than the E-BAM). A major RH influence was found in RTP (1 h mean RH = 64%, spanning 27-93%) that can explain up to ~30% of the variance in 1 min to 6 h PMS3003 PM2.5 measurements. The RH corrections can reduce errors from ~22-27% to ~10%. We observed that PMS3003s appeared to exhibit a non-linear response when ambient PM2.5 levels exceeded ~125 g m-3 and found that the quadratic and two-segment piecewise linear fits might be more appropriate than the univariate linear model to capture this non-linearity and can further reduce mean errors by up to 11% and 8%, respectively. Overall, our results have important implications for how variability in ambient PM2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance. These results can provide valuable guidance on future establishment of dense PM sensor networks with known and superior accuracy and precision.
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