Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

Williams, David E.

doi:10.1021/acssensors.9b01455

Cited by 63 publications

(41 citation statements)

References 70 publications

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“…Subsequently, we use an analysis of differences between measured and modelled concentrations to identify local urban conditions that are poorly captured by the static land use model. We show that these deviations have reasonable explanations which in turn reinforces confidence in the original dataset (Williams, 2019).…”

Section: Introductionsupporting

confidence: 80%

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Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Weissert

Alberti²,

Miles³

et al. 2020

Atmospheric Environment

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The development of low-cost sensors and novel calibration algorithms offer new opportunities to supplement existing regulatory networks to measure air pollutants at a high spatial resolution and at hourly and sub-hourly timescales. We use a random forest model on data from a network of low-cost sensors to describe the effect of land use features on local-scale air quality, extend this model to describe the hourly-scale variation of air quality at high spatial resolution, and show that deviations from the model can be used to identify particular conditions and locations

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Section: Introductionsupporting

confidence: 80%

“…Such attempts to fuse land-use and sensor network data face the challenge of demonstrating plausibility of data from low-cost sensor networks (Williams, 2019). A considerable amount of research has focused on sensor performance.…”

Section: Introductionmentioning

confidence: 99%

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Weissert

Alberti²,

Miles³

et al. 2020

Atmospheric Environment

Self Cite

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“…For instance, in EC sensors, reagents are spent over time and have a typical lifespan of 1 to 2 years (Masson et al, 2015;Jiao et al, 2016). Thus, there is a need for the reliable calibration of LCAQ sensors to satisfy performance demands of end-use applications (De Vito et al, 2018;Akasiadis et al, 2019;Williams, 2019).…”

Section: Challenges In Low-cost Sensor Calibrationmentioning

confidence: 99%

Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

et al. 2021

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Abstract. Low-cost sensors offer an attractive solution to the challenge of establishing affordable and dense spatio-temporal air quality monitoring networks with greater mobility and lower maintenance costs. These low-cost sensors offer reasonably consistent measurements but require in-field calibration to improve agreement with regulatory instruments. In this paper, we report the results of a deployment and calibration study on a network of six air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed in two phases over a period of 3 months at sites situated within two megacities with diverse geographical, meteorological and air quality parameters. A unique feature of our deployment is a swap-out experiment wherein three of these sensors were relocated to different sites in the two phases. This gives us a unique opportunity to study the effect of seasonal, as well as geographical, variations on calibration performance. We report an extensive study of more than a dozen parametric and non-parametric calibration algorithms. We propose a novel local non-parametric calibration algorithm based on metric learning that offers, across deployment sites and phases, an R2 coefficient of up to 0.923 with respect to reference values for O3 calibration and up to 0.819 for NO2 calibration. This represents a 4–20 percentage point increase in terms of R2 values offered by classical non-parametric methods. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature into calibration models; (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature-weighing or metric-learning technique; (3) using local calibration techniques such as k nearest neighbors (KNN); (4) performing temporal smoothing over raw time series data but being careful not to do so too aggressively; and (5) making all efforts to ensure that data with enough diversity are demonstrated in the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors and offer an encouraging opportunity to use them to supplement and densify compliance regulatory monitoring networks.

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“…The study of the use of low-cost sensors for the estimation of pollution concentrations is an important field of research [2], [5], [15], [16]. The main challenge lies in the calibration and accuracy of these low-cost sensors [17]. Since the lowcost sensors have been calibrated on specific chambers or have not been directly calibrated by the manufacturer, they have to be calibrated in-situ with reference stations in the deployment field [3], [4].…”

Section: Related Workmentioning

confidence: 99%

Graph Learning Techniques Using Structured Data for IoT Air Pollution Monitoring Platforms

Ferrer-Cid

Barceló-Ordinas

García-Vidal

2021

IEEE Internet Things J.

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Existing air pollution monitoring networks use reference stations as the main nodes. The addition of low-cost sensors calibrated in-situ with machine learning techniques allows the creation of heterogeneous air pollution monitoring networks. However, current monitoring networks or calibration techniques have limitations in estimating missing data, adding virtual sensors or recalibrating sensors. The use of graphs to represent structured data is an emerging area of research that allows the use of powerful techniques to process and analyze data for air pollution monitoring networks. In this paper, we compare two techniques that rely on structured data, one based on statistical methods and the other on signal smoothness, with a baseline technique based on the distance between nodes and that does not rely on the measured signal data. To compare these techniques, the sensor signal is reconstructed with a supervised method based on linear regression and a semi-supervised method based on Laplacian interpolation, which allows reconstruction even when data is missing. The results, on data sets measuring O3, NO2 and PM10, show that the signal smoothness-based technique behaves better than the other two, and used together with the Laplacian interpolation is near-optimal with respect to the linear regression method. Moreover, in the case of heterogeneous networks, the results show a reconstruction accuracy similar to the in-situ calibrated sensors. Thus, the use of the network data increases the robustness of the network against possible sensor failures.

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Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

Cited by 63 publications

References 70 publications

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

Graph Learning Techniques Using Structured Data for IoT Air Pollution Monitoring Platforms

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Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

Cited by 63 publications

References 70 publications

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O&lt;sub&gt;3&lt;/sub&gt; and NO&lt;sub&gt;2&lt;/sub&gt; concentrations in diverse environments

Graph Learning Techniques Using Structured Data for IoT Air Pollution Monitoring Platforms

Contact Info

Product

Resources

About

Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments