Toward a Unified Terminology of Processing Levels for Low-Cost Air-Quality Sensors

Schneider, Philipp; Bartoňová, Alena; Castell, Núria; Dauge, Franck René; Gerboles, Michel; Hagler, Gayle; Hüglin, Christoph; Jones, R. L.; Khan, Sean; Lewis, Alastair C.; Mijling, Bas; Müller, Michael; Penza, M.; Spinelle, Laurent; Stacey, B.J.; Vogt, Matthias; Wesseling, J.; Rt, Williams

doi:10.1021/acs.est.9b03950

Cited by 34 publications

(31 citation statements)

References 4 publications

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“…The recent emergence of low-cost sensors has enabled new possibilities in traditional air quality monitoring (Kumar et al, 2015;Morawska et al, 2018;Snyder et al, 2013). As a result of low unit costs and compact size, sensors can be deployed to the field in much higher quantities than before, thus enabling higher-resolution spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

et al. 2020

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Abstract. Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ∼0.55 to 8.4 µm) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.

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Section: Introductionmentioning

confidence: 99%

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

et al. 2020

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“…Initial PM 2.5 calibration (using the manufacturer reported PM 2.5 output) results showed a piecewise continuous response that split at ~10 µg m −3 , which has been observed in other studies. 62 A RH correction to the sensor PM 2.5 data (level 2A correction) 63 was employed, 58 which provided an estimate of dry PM 2.5 from the LCSs. Calibrations lasted 2 days and were conducted every 2 weeks during the study period to account for any drifts that occur.…”

Section: Methodsmentioning

confidence: 99%

Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City

Lal

Das

Fan

et al. 2020

Environ�Health�Insights

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Cities in the United States have announced initiatives to become more sustainable, healthy, resilient, livable, and environmentally friendly. However, indicators for measuring all outcomes related to these targets and the synergies between them have not been well defined or studied. One such relationship is the linkage between air quality with emotional well-being (EWB) and neighborhood infrastructure. Here, regulatory monitoring, low-cost sensors (LCSs), and air quality modeling were combined to assess exposures to PM2.5 and traffic-related NOx in 6 Minneapolis, MN, neighborhoods of varying infrastructure parameters (median household income, urban vs suburban, and access to light rail). Residents of the study neighborhoods concurrently took real-time EWB assessments using a smart phone application, Daynamica, to gauge happiness, tiredness, stress, sadness, and pain. Both LCS PM2.5 observations and mobile-source-simulated NOx were calibrated using regulatory observations in Minneapolis. No statistically significant (α = 0.05) PM2.5 differences were found between urban poor and urban middle-income neighborhoods, but average mobile-source NOx was statistically significantly (α = 0.05) higher in the 4 urban neighborhoods than in the 2 suburban neighborhoods. Close proximity to light rail had no observable impact on average observed PM2.5 or simulated mobile-source NOx. Home-based exposure assessments found that PM2.5 was negatively correlated with positive emotions such as happiness and to net affect (the sum of positive and negative emotion scores) and positively correlated (ie, a higher PM2.5 concentration led to higher scores) for negative emotions such as tiredness, stress, sadness, and pain. Simulated mobile-source NOx, assessed from both home-based exposures and in situ exposures, had a near-zero relationship with all EWB indicators. This was attributed to low NOx levels throughout the study neighborhoods and at locations were the EWB-assessed activities took place, both owing to low on-road mobile-source NOx impacts. Although none of the air quality and EWB responses were determined to be statistically significant (α = 0.05), due in part to the relatively small sample size, the results are suggestive of linkages between air quality and a variety of EWB outcomes.

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“…The presented LP8 sensor model corresponds to level-2B in the terminology presented by Schneider et al (2019). This means that, related to the sensor unit, internal and external information is employed but is limited to parameters that are appropriate for artefact correction and directly related to the measurement principle.…”

Section: Lp8 Sensormentioning

confidence: 99%

“…Some approaches also employ information in the model that is only valid in a statistical manner, such as similar pollutant 3816 M. Müller et al: Integration and calibration of NDIR CO 2 low-cost sensors concentrations at the sensor location and at the closest reference site during selected time periods (Mueller et al, 2017;Kim et al, 2018). The use of a standardized terminology for processing levels, as was recently proposed by Schneider et al (2019), is recommended to clearly define the type of information a sensor model is based on.…”

Section: Introductionmentioning

confidence: 99%

Integration and calibration of non-dispersive infrared (NDIR) CO<sub>2</sub> low-cost sensors and their operation in a sensor network covering Switzerland

Müller

Graf

Meyer³

et al. 2020

Atmos. Meas. Tech.

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Abstract. More than 300 non-dispersive infrared (NDIR) CO2 low-cost sensors labelled as LP8 were integrated into sensor units and evaluated for the purpose of long-term operation in the Carbosense CO2 sensor network in Switzerland. Prior to deployment, all sensors were calibrated in a pressure and climate chamber and in ambient conditions co-located with a reference instrument. To investigate their long-term performance and to test different data processing strategies, 18 sensors were deployed at five locations equipped with a reference instrument after calibration. Their accuracy during 19 to 25 months deployment was between 8 and 12 ppm. This level of accuracy requires careful sensor calibration prior to deployment, continuous monitoring of the sensors, efficient data filtering, and a procedure to correct drifts and jumps in the sensor signal during operation. High relative humidity (> ∼85 %) impairs the LP8 measurements, and corresponding data filtering results in a significant loss during humid conditions. The LP8 sensors are not suitable for the detection of small regional gradients and long-term trends. However, with careful data processing, the sensors are able to resolve CO2 changes and differences with a magnitude larger than about 30 ppm. Thereby, the sensor can resolve the site-specific CO2 signal at most locations in Switzerland. A low-power network (LPN) using LoRaWAN allowed for reliable data transmission with low energy consumption and proved to be a key element of the Carbosense low-cost sensor network.

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Toward a Unified Terminology of Processing Levels for Low-Cost Air-Quality Sensors

Cited by 34 publications

References 4 publications

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City

Integration and calibration of non-dispersive infrared (NDIR) CO<sub>2</sub> low-cost sensors and their operation in a sensor network covering Switzerland

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Toward a Unified Terminology of Processing Levels for Low-Cost Air-Quality Sensors

Cited by 34 publications

References 4 publications

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City

Integration and calibration of non-dispersive infrared (NDIR) CO&lt;sub&gt;2&lt;/sub&gt; low-cost sensors and their operation in a sensor network covering Switzerland

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Product

Resources

About

Integration and calibration of non-dispersive infrared (NDIR) CO<sub>2</sub> low-cost sensors and their operation in a sensor network covering Switzerland