Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

Wan, Jieqing; Hagler, Gayle; Williams, Ronald; Sharpe, Robert J.; Brown, Ryan Andrew; Garver, Daniel; Judge, Robert; Caudill, Motria; Rickard, Joshua; Davis, Michael J.; Weinstock, Lewis; Zimmer-Dauphinee, Susan; Buckley, K.

doi:10.5194/amt-2016-131

Cited by 29 publications

(40 citation statements)

References 2 publications

(2 reference statements)

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“…Whilst quantitative performance of portable monitors can be rigorously evaluated under controlled laboratory conditions [22,[24][25][26], it is essential that monitor performance is also evaluated in ambient deployment [11,25,27]. Most monitor evaluation studies report data from a single comparison [12,13,20,[28][29][30][31][32], including those that are conducted under the auspices of agency evaluations [27,[33][34][35], although some studies first divide the co-location data into training and test datasets [36,37].…”

Section: Introductionmentioning

confidence: 99%

Practical Field Calibration of Portable Monitors for Mobile Measurements of Multiple Air Pollutants

Lin

Masey

et al. 2017

Atmosphere

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Abstract:To reduce inaccuracies in the measurement of air pollutants by portable monitors it is necessary to establish quantitative calibration relationships against their respective reference analyser. This is usually done under controlled laboratory conditions or one-off static co-location alongside a reference analyser in the field, neither of which may adequately represent the extended use of portable monitors in exposure assessment research. To address this, we investigated ways of establishing and evaluating portable monitor calibration relationships from repeated intermittent deployment cycles over an extended period involving stationary deployment at a reference site, mobile monitoring, and completely switched off. We evaluated four types of portable monitors: Aeroqual Ltd. (Auckland, New Zealand) S500 O 3 metal oxide and S500 NO 2 electrochemical; RTI (Berkeley, CA, USA) MicroPEM PM 2.5 ; and, AethLabs (San Francisco, CA, USA) AE51 black carbon (BC). Innovations in our study included: (i) comparison of calibrations derived from the individual co-locations of a portable monitor against its reference analyser or from all the co-location periods combined into a single dataset; and, (ii) evaluation of calibrated monitor estimates during transient measurements with the portable monitor close to its reference analyser at separate times from the stationary co-location calibration periods. Within the~7 month duration of the study, 'combined' calibration relationships for O 3 , PM 2.5 , and BC monitors from all co-locations agreed more closely on average with reference measurements than 'individual' calibration relationships from co-location deployment nearest in time to transient deployment periods. 'Individual' calibrations relationships were sometimes substantially unrepresentative of the 'combined' relationships. Reduced quantitative consistency in field calibration relationships for the PM 2.5 monitors may have resulted from generally low PM 2.5 concentrations that were encountered in this study. Aeroqual NO 2 monitors were sensitive to both NO 2 and O 3 and unresolved biases. Overall, however, we observed that with the 'combined' approach, 'indicative' measurement accuracy (±30% for O 3 , and ±50% for BC and PM 2.5 ) for 1 h time averaging could be maintained over the 7-month period for the monitors evaluated here.

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Section: Introductionmentioning

confidence: 99%

Practical Field Calibration of Portable Monitors for Mobile Measurements of Multiple Air Pollutants

Lin

Masey

et al. 2017

Atmosphere

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“…Metrics such as the intercept, slope, and coefficient of determination (R 2 ) obtained from OLS models of sensor outputs with reference instrument measurements are widely used to evaluate sensor performance (Holstius et al, 2014;Gao et al, 2015;Wang et al, 2015;Jiao et al, 2016;Cross et al, 2017;Kelly et al, 2017;Zimmerman et al, 2018). In this study, all the R 2 values in figures represent regression coefficients of the (calibration) equations while all the R 2 values in tables represent regression coefficients between the calibrated sensor and reference measurements.…”

Section: Sensor Performance Metricsmentioning

confidence: 99%

“…All these observations seem to imply that beta-attenuation-based monitors might be unfavorable for low-cost particle sensor evaluation at the low concentrations typically present in the US. U.S. EPA FEMs are valid for 24 h PM 2.5 measurements rather than for 1 h measurements (Jiao et al, 2016). An inappropriate selection of reference monitors might prejudice the overall performance of low-cost sensors, particularly for time resolutions finer than 24 h.…”

Section: Pms3003 Performance Characteristics On Various Timescales Prmentioning

confidence: 99%

“…These models include Shinyei PPD20V (Johnson et al, 2018), Shinyei PPD42NS (Holstius et al, 2014;Gao et al, 2015), Shinyei PPD60PV (SCAQMD, 2015a;Jiao et al, 2016;Mukherjee et al, 2017;Johnson et al, 2018), Alphasense OPC-N2 (SCAQMD, 2015b;Mukherjee et al, 2017;Crilley et al, 2018), Plantower PMS1003 (Kelly et al, 2017;SCAQMD, 2017b), Plantower PMS3003 (SCAQMD, 2017a), and Plantower PMS5003 (SCAQMD, 2017c). Currently, all Plantower particulate matter sensor (PMS) models have only been tested at low to moderately high ambient PM 2.5 concentrations in the US.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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...

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“…However, in the same hours and later, the R 2 values between all U-Pods decrease over time and remain low during the night, indicating that U-Pods are more different from each other than during the afternoon. Some studies have assumed negligible ozone precursor spatial differences in the first hours of the day and therefore spatial ozone homogeneity during the early morning hours (Moltchanov et al, 2015;Jiao et al, 2016). Figure 9 shows that the range of spatial absolute differences in O 3 is smallest at night.…”

Section: Deployment Datamentioning

confidence: 99%

Intra-urban spatial variability of surface ozone in Riverside, CA: viability and validation of low-cost sensors

et al. 2018

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Abstract. Sensor networks are being more widely used to characterize and understand compounds in the atmosphere like ozone (O 3 ). This study employs a measurement tool, called the U-Pod, constructed at the University of Colorado Boulder, to investigate spatial and temporal variability of O 3 in a 200 km 2 area of Riverside County near Los Angeles, California. This tool contains low-cost sensors to collect ambient data at non-permanent locations. The U-Pods were calibrated using a pre-deployment field calibration technique; all the U-Pods were collocated with regulatory monitors. After collocation, the U-Pods were deployed in the area mentioned. A subset of pods was deployed at two local regulatory air quality monitoring stations providing validation for the collocation calibration method. Field validation of sensor O 3 measurements to minute-resolution reference observations resulted in R 2 and root mean squared errors (RMSEs) of 0.95-0.97 and 4.4-5.9 ppbv, respectively. Using the deployment data, ozone concentrations were observed to vary on this small spatial scale. In the analysis based on hourly binned data, the median R 2 values between all possible UPod pairs varied from 0.52 to 0.86 for ozone during the deployment. The medians of absolute differences were calculated between all possible pod pairs, 21 pairs total. The median values of those median absolute differences for each hour of the day varied between 2.2 and 9.3 ppbv for the ozone deployment. Since median differences between U-Pod concentrations during deployment are larger than the respective root mean square error values, we can conclude that there is spatial variability in this criteria pollutant across the study area. This is important because it means that citizens may be exposed to more, or less, ozone than they would assume based on current regulatory monitoring.

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Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

Cited by 29 publications

References 2 publications

Practical Field Calibration of Portable Monitors for Mobile Measurements of Multiple Air Pollutants

Practical Field Calibration of Portable Monitors for Mobile Measurements of Multiple Air Pollutants

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

Intra-urban spatial variability of surface ozone in Riverside, CA: viability and validation of low-cost sensors

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