Evaluation of consumer monitors to measure particulate matter

Sousan, Sinan; Koehler, Kirsten; Hallett, Laura; Peters, Thomas M.

doi:10.1016/j.jaerosci.2017.02.013

Cited by 106 publications

(88 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The AirBeam and AirVisual both had highly correlated and quantitative results for most of the large cooking and combustion sources, under‐reported the dust and humidifier sources, and missed sources with little mass above 0.3 μm particles. Sousan et al also found the AirBeam under‐reported road dust and welding aerosol. For ambient air measurements in May‐June 2015, the AQ‐SPEC evaluation reported a correlation of 0.66, slopes of 0.10‐0.18, and an offset of 10 μg m −3 to adjust the 3 tested AirBeam monitors to match the regulatory monitor.…”

Section: Resultsmentioning

confidence: 93%

“…The AQ‐SPEC evaluation reported poor correlation ( r 2 < 0.6) between the Foobot and reference monitor for ambient monitoring in July‐September 2016. Sousan et al reported linear correlations with quantitative agreement for salt (slope = 0.5) and road dust (slope = 0.7), but underreporting for welding aerosol (slope = 0.08).…”

Section: Resultsmentioning

confidence: 98%

“…Manikonda et al evaluated several consumer and research monitors measuring cigarette smoke and Arizona Test Dust in a room‐sized laboratory chamber. Sousan et al tested 3 consumer monitors for measuring salt, welding fumes, and Arizona Test Dust at levels relevant to occupational exposures. Holstius et al evaluated a custom‐built sensor‐based monitor against beta‐attenuation monitors at an ambient monitoring site in Oakland, CA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

2018

View full text Add to dashboard Cite

The ability to inexpensively monitor PM to identify sources and enable controls would advance residential indoor air quality (IAQ) management. Consumer IAQ monitors incorporating low-cost optical particle sensors and connections with smart home platforms could provide this service if they reliably detect PM in homes. In this study, particles from typical residential sources were generated in a 120 m laboratory and time-concentration profiles were measured with 7 consumer monitors (2-3 units each), 2 research monitors (Thermo pDR-1500, MetOne BT-645), a Grimm Mini Wide-Range Aerosol Spectrometer (GRM), and a Tapered Element Oscillating Microbalance with Filter Dynamic Measurement System (FDMS), a Federal Equivalent Method for PM . Sources included recreational combustion (candles, cigarettes, incense), cooking activities, an unfiltered ultrasonic humidifier, and dust. FDMS measurements, filter samples, and known densities were used to adjust the GRM to obtain time-resolved mass concentrations. Data from the research monitors and 4 of the consumer monitors-AirBeam, AirVisual, Foobot, Purple Air-were time correlated and within a factor of 2 of the estimated mass concentrations for most sources. All 7 of the consumer and both research monitors substantially under-reported or missed events for which the emitted mass was comprised of particles smaller than 0.3 μm diameter.

show abstract

Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

2018

View full text Add to dashboard Cite

show abstract

“…To date, only a few studies have attempted to compute parameters other than R 2 to gauge the overall performance of low-cost sensor technologies. They typically focus on the RMSE (Holstius et al, 2014;Cross et al, 2017;Zimmerman et al, 2018), the mean absolute error (MAE) and the mean bias error (MBE) (Cross et al, 2017;Zimmerman et al, 2018), and normalized residuals (Sousan et al, 2017;Kelly et al, 2017). In addition to the intercept, slope, and R 2 , we also used ratios of the calibrated PMS3003 PM 2.5 mass concentrations to reference monitor values to examine sensors' post-calibration performance.…”

Section: Sensor Performance Metricsmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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...

show abstract

“…These units could be assembled for a total cost of USD 50 and used in a distributed manner. In controlled laboratory tests, low-cost particle sensors have shown high linearity and stability in comparison with commercial instruments with a known particle size and composition (Wang et al, 2015;Manikonda et al, 2016;Sousan et al, 2016Sousan et al, , 2017Zikova et al, 2017). Several studies in the literature have reported the combination of low-cost particle sensors with "smart" home devices (e.g., temperature, humidity, carbon monoxide sensors, cameras) to provide more comfortable and energy-efficient homes and workplaces (Ivanov et al, 2002;Chung and Oh, 2006;Kim et al, 2010;Bhattacharya et al, 2012;Kim et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optical Characterization Studies of a Low-Cost Particle Sensor

Biswas

2017

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

Compact low-cost sensors for measuring particulate matter (PM) concentrations are receiving significant attention as they can be used in larger numbers and in a distributed manner. Most low-cost particle sensors work on optical scattering measurements from the aerosol. To ensure accurate and reliable determination of PM mass concentrations, a relationship of the scattering signal to mass concentration should be established. The scattering signal depends on the aerosol size distributions and particle refractive index. A systematic calibration of a low-cost particle sensor (Sharp GP2Y1010AU0F) was carried out by both experimental and computational studies. Sodium chloride, silica, and sucrose aerosols were used as test cases with size distributions measured using a scanning mobility particle sizer (SMPS). The mass concentration was estimated using the measured size distribution and density of the particles. Calculations of the scattered light intensity were done using these measured size distributions and known refractive index of the particles. The calculated scattered light intensity showed better linearity with the sensor signal compared to the mass concentration. To obtain a more accurate mass concentration estimation, a model was developed to determine a calibration factor (K). K is not universal for all aerosols, but depends on the size distribution and refractive index. To improve accuracy in estimation of mass concentration, an expression for K as a function of geometric mean diameter, geometric standard deviation, and refractive index is proposed. This approach not only provides a more accurate estimation of PM concentration, but also provides an estimate of the aerosol number concentration.

show abstract

Evaluation of consumer monitors to measure particulate matter

Cited by 106 publications

References 19 publications

Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

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

Optical Characterization Studies of a Low-Cost Particle Sensor

Contact Info

Product

Resources

About