Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary)

Clements, Andrea L.; Griswold, William G.; Rs, Abhijit; Johnston, Jill; Herting, Megan M.; Thorson, Jacob; Collier-Oxandale, Ashley; Hannigan, Michael P.

doi:10.3390/s17112478

Cited by 175 publications

(158 citation statements)

References 45 publications

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“…A recent workshop for low-cost sensors outlined some of the concerns shared throughout the research community including deployment logistics, data formatting and sharing, and communication of uncertainty (Clements et al, 2017). With our datasets, we investigated three issues related to the development of best practices: the length of a co-location for a field normalization, additional dataset-specific filtering based on environmental parameters, and cross sensitivities to non-methane pollutants.…”

Section: Further Sensor Quantification Considerationsmentioning

confidence: 99%

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

et al. 2018

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Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a 1-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesburg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing and explore cross sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area -highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

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Section: Further Sensor Quantification Considerationsmentioning

confidence: 99%

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

et al. 2018

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“…These technologies, in virtually all applications, still depend on reference-grade measurements or standards in order to fulfil most research objectives. As such, many view these tools not as replacements of regulatory measurements but rather a supplement to them (Clements et al, 2017). Detecting pollutant variability between the regulatory AQMS supports the idea that more detailed information can be obtained by increased monitoring between existing stations.…”

Section: Introductionmentioning

confidence: 92%

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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“…A recent workshop for low-cost sensors outlined some of the concerns shared throughout the research community including deployment logistics, data formatting and sharing, communication of uncertainty, etc. (Clements et al, 2017). With our datasets, we 10 investigated three questions related to the development of best practices: the length of a co-location for a field normalization, additional dataset-specific filtering based on environmental parameters, and cross-sensitivities to non-methane pollutants.…”

Section: Further Sensor Quantification Considerationsmentioning

confidence: 99%

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

Collier-Oxandale¹,

Hannigan²,

Casey³

et al. 2018

Preprint

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Abstract.Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first, a one-month deployment along the Colorado Front Range includes sites near active oil and gas operations in the Denver-15Julesberg basin. The second deployment in an urban Los Angeles neighborhood, an subject to complex mixture of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results 20 illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area -highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

show abstract

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary)

Cited by 175 publications

References 45 publications

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

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

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

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