Validate personal air-pollution sensors

Lewis, Alastair C.; Edwards, P. M.

doi:10.1038/535029a

Cited by 245 publications

(177 citation statements)

References 8 publications

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“…Reference instruments have high capital and on-going operational costs, and require secured sites with mains power. As a consequence, there has been considerable interest in the recent emergence of smaller, battery-operated instruments that can measure a range of air pollutants [6][7][8][9][10][11]. The lower capital cost, small size, and low power needs of these instruments have led to their deployment in large spatial networks [12][13][14] and in mobile and peripatetic (short periods of deployments at multiple sites) measurement designs [15][16][17][18][19], including in personal monitoring and 'citizen science' contexts [20][21][22][23].…”

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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“…However, air pollutant concentrations often exhibit significant spatial variability depending on local sources and features of the built environment (Marshall et al, 2008;Nazelle et al, 2009;Pugh et al, 2012;Tan et al, 2014), which may not be well captured by the existing monitoring networks. In the past sev-N. Zimmerman et al: A machine learning calibration model to improve sensor performance eral years, there has been a significant increase in the development and applications of low-cost sensor-based air quality monitoring technology (Lewis and Edwards, 2016;McKercher et al, 2017;Moltchanov et al, 2015;Snyder et al, 2013). The use of low-cost air quality sensors for monitoring ambient air pollution could enable much denser air quality monitoring networks at a comparable cost to the existing regime.…”

Section: Introductionmentioning

confidence: 99%

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

et al. 2018

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Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO 2 , O 3 , and CO 2 . We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO 2 , CO 2 , O 3 ) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO 2 (2 % relative error), 3.5 ppb for NO 2 (29 % relative error), and 3.4 ppb for O 3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO 2 and CO 2 . The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO 2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

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“…The low cost, small size, and low power consumption of these sensors offer the promise of distributed measurements over wide geographical areas, with potential applications for topics such as air quality (AQ) monitoring, source attribution, human exposure and epidemiology, and atmospheric chemistry. However, because of questions associated with their sensitivity, calibration, and long-term reliability, there is a critical need to establish a cohesive approach for evaluation and performance assessment of low-cost sensors prior to their large-scale adoption (Lewis and Edwards, 2016).…”

Section: Introductionmentioning

confidence: 99%

Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments

et al. 2018

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Abstract. The use of low-cost air quality sensors for air pollution research has outpaced our understanding of their capabilities and limitations under real-world conditions, and there is thus a critical need for understanding and optimizing the performance of such sensors in the field. Here we describe the deployment, calibration, and evaluation of electrochemical sensors on the island of Hawai'i, which is an ideal test bed for characterizing such sensors due to its large and variable sulfur dioxide (SO 2 ) levels and lack of other co-pollutants. Nine custom-built SO 2 sensors were colocated with two Hawaii Department of Health Air Quality stations over the course of 5 months, enabling comparison of sensor output with regulatory-grade instruments under a range of realistic environmental conditions. Calibration using a nonparametric algorithm (k nearest neighbors) was found to have excellent performance (RMSE < 7 ppb, MAE < 4 ppb, r 2 > 0.997) across a wide dynamic range in SO 2 (< 1 ppb, > 2 ppm). However, since nonparametric algorithms generally cannot extrapolate to conditions beyond those outside the training set, we introduce a new hybrid linear-nonparametric algorithm, enabling accurate measurements even when pollutant levels are higher than encountered during calibration. We find no significant change in instrument sensitivity toward SO 2 after 18 weeks and demonstrate that calibration accuracy remains high when a sensor is calibrated at one location and then moved to another. The performance of electrochemical SO 2 sensors is also strong at lower SO 2 mixing ratios (< 25 ppb), for which they exhibit an error of less than 2.5 ppb. While some specific results of this study (calibration accuracy, performance of the various algorithms, etc.) may differ for measurements of other pollutant species in other areas (e.g., polluted urban regions), the calibration and validation approaches described here should be widely applicable to a range of pollutants, sensors, and environments.

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Validate personal air-pollution sensors

Cited by 245 publications

References 8 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

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments

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