Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors

Heimann, Ines; Bright, V. B.; McLeod, M.W.; Mead, Mohammed Iqbal; Popoola, Olalekan; Stewart, GB; Jones, R. L.

doi:10.1016/j.atmosenv.2015.04.057

Cited by 119 publications

(80 citation statements)

References 10 publications

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“…For example, CO, NO, and NO 2 have been measured in an urban sensor network with additional analysis demonstrating the ability to differentiate local emissions from regional trends Heimann et al, 2015). In another example, researchers demonstrated the feasibility of collecting personal CO, NO 2 , O 3 , and CO 2 exposure data with uncertainty estimations using a portable, wearable system (Piedrahita et al, 2014).…”

Section: Previous Sensor Researchmentioning

confidence: 99%

“…Given the variety of low-cost sensors available, using the Figaro TGS 2600 sensors in a sensor array could provide additional signals at each deployment site facilitating more reliable data. Including multiple sensor signals in a neural network calibration approach may also improve the accuracy of the calibrated data (Zimmerman et al, 2018;De Vito et al, 2008;Huyberechts et al, 1997). Future analysis of the data collected in Los Angeles and continued use of this sensor in areas with complex mixtures will require carbon monoxide and non-methane hydrocarbon impacts be considered.…”

Section: Sensor Cross Sensitivitiesmentioning

confidence: 99%

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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: Previous Sensor Researchmentioning

confidence: 99%

Section: Sensor Cross Sensitivitiesmentioning

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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“…Hand-held monitors usually have lower capital costs than automatic analysers, meaning that more could be made available for deployment, potentially increasing the spatial resolution of measurement networks with high temporal resolution concentration measurements [6][7][8][9]. The use of such instruments in mobile and personal monitoring has been reported [8,10].…”

Section: Introductionmentioning

confidence: 99%

Temporal changes in field calibration relationships for Aeroqual S500 O3 and NO2 sensor-based monitors

Masey

Gillespie

Ezani

et al. 2018

Sensors and Actuators B: Chemical

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This version is available at https://strathprints.strath.ac.uk/64922/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. sensor output, and discrepancies between the 2 Aeroqual NO 2 sensors) resulted in relatively inaccurate concentrations estimates (cf. reference concentrations) from calibration equations derived in the first training period and applied to subsequent test deployments (e.g. NO 2 RMSE = 47.2 μgm −3 (n = 286) for a dataset of all test periods combined, for one of the two monitor pairs). Substantial improvements in accuracy of estimated concentrations were achieved by combination of repeated intermittent training data into a single calibration dataset (NO 2 RMSE = 8.5 μgm −3 for same test dataset described above). This latter approach to field calibration is recommended.

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“…They offer flexibility and opportunity to explore hypotheses previously too difficult to test [2]. The numerous benefits of this technological development has led to a growing body of work; however, the delivery of good quality data is key, and in the literature trade-offs are often mentioned [3]. A critical issue is confirming the integrity and calibration of the sensor over time.…”

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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Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors

Cited by 119 publications

References 10 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

Temporal changes in field calibration relationships for Aeroqual S500 O3 and NO2 sensor-based monitors

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

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