Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network

Williams, David E.; Henshaw, Geoff S.; Bart, M.; Laing, Greer; Wagner, J.K.; Naisbitt, Simon Carl; Salmond, Jennifer

doi:10.1088/0957-0233/24/6/065803

Cited by 80 publications

(116 citation statements)

References 61 publications

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“…We compare the performance of our RF models to these studies in Table 3. While several low-cost sensor calibration studies have investigated calibration models within laboratory environments (Masson et al, 2015a;Mead et al, 2013;Piedrahita et al, 2014;Williams et al, 2013), we have elected to limit our comparison to field data.…”

Section: Comparison Of Results To Other Published Studiesmentioning

confidence: 99%

“…Additionally, NO has also been observed to interfere with NO 2 , and CO sensors have exhibited some cross-sensitivity to molecular hydrogen in urban environments (Mead et al, 2013). Furthermore, low-cost sensors can be affected by meteorology (Masson et al, 2015b;Moltchanov et al, 2015;Pang et al, 2017;Williams et al, 2013). Most electrochemical sensors are configured such that the reactions are diffusion-limited, and the diffusion coefficient can be affected by temperature (Hitchman et al, 1997); Masson et al (2015b) have shown that at relative humidity (RH) exceeding 75 % there is significant error, possibly due to condensation on potentiostat electronics.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Comparison Of Results To Other Published Studiesmentioning

confidence: 99%

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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“…In 2013, Williams and coworkers quantified a tungstic oxide ozone sensor in the lab while addressing some of the main drawbacks associated with Metal oxide (MOx) ozone sensors 20 (i.e. drift/long term stability, material degradation and sensitivity fluctuations) (Williams et al, 2013). Researchers also deployed these gas semiconductor sensors in British Columbia over roughly 10,000 km 2 for three months finding low errors (3 +/-2 ppb) between hourly averaged sensor and reference instruments while documenting the challenges of using, in this instance, wireless sensor networks (Bart et al, 2014).…”

mentioning

confidence: 99%

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

Sadighi

Coffey

Polidori

et al. 2017

Preprint

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Abstract. Sensor networks are being more widely used to characterize and understand compounds in the atmosphere such as ozone and carbon dioxide. This study employs a measurement tool, called the U-Pod, constructed at the University of Colorado Boulder, to investigate spatial and temporal variability of O3 and CO2 in a 314 km 2 area of Riverside County near Los Angeles, 10California. This tool provides 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 O3 and CO2 measurements to minute resolution reference observations resulted in R-squared and root mean squared errors (RMSE) of 0.95 -0.97 and 4.4 -15 7.2 ppbv for O3 and 0.79 and 15 ppmv CO2, respectively. Using the deployment data, ozone and carbon dioxide concentrations were observed to vary on this small spatial scale. In the analysis based on hourly binned data, the median R-squared values between all possible U-Pod 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 ppb for the ozone deployment. For carbon dioxide, distributions 20 of all measurements vary from 413 -425 ppm during the calibration (collocation) and 406 -472 during the deployment. Since median differences between U-Pod concentrations during deployment are larger than the respective root mean square error values for ozone and carbon dioxide, we can conclude that there is spatial variability in these pollutants across the study area. This is important because it means that citizens may be exposed to more ozone than they would assume based on current regulatory monitoring. 25Atmos. Meas. Tech. Discuss., https://doi

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“…There are a number of empirical functions that can be used to calibrate the sensor. In [5], we illustrated a function derived from a theoretical model of the sensor response, (P):…”

Section: Drift Characteristicsmentioning

confidence: 99%

“…A critical issue is confirming the integrity and calibration of the sensor over time. Common issues in sensors can be effects of ambient conditions (e.g., sublimation of sensor material from high humidity [4], increasing baseline measurements [5], drifting calibration, and site impacts (e.g., dirt in inlets or power variations). Identifying changes in baseline or calibration so that action can be taken would improve management of such networks.…”

Section: Introductionmentioning

confidence: 99%

Reliable Long-Term Data from Low-Cost Gas Sensor Networks in the Environment

Miskell

Salmond

Grange

et al. 2017

Proceedings of Eurosensors 2017, Paris, France, 3–6 September 2017

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This poster examines long-term performance of low-cost instruments in environmental networks using a semiconducting oxide sensor measuring ground-level ozone (O3). Sensors were placed outside in networks and automated methodologies based on knowledge of sensor and environment characteristics were successfully applied to confirm sensor stability. These networks demonstrated how environmental data could be collected and confirmed using networks of low-cost sensors, which supplemented observations from the existing regulatory network. This work is a critical step in the development of networks of low-cost environmental sensors when considering the delivery of reliable data.

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Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network

Cited by 80 publications

References 61 publications

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

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

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

Reliable Long-Term Data from Low-Cost Gas Sensor Networks in the Environment

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