Mapping Air Quality in IoT Cities: Cloud Calibration and Air Quality Inference of Sensor Data

Hofman, Jelle; Nikolaou, Martha E.; Huu, Tien; Qin, Xuening; Rodrigo, Esther; Philips, Wilfried; Deligiannis, Nikos; Manna, Valerio Panzica La

doi:10.1109/sensors47125.2020.9278941

Cited by 12 publications

(8 citation statements)

References 27 publications

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“…51 Calibration approaches (for a single LCS or a network of LCS units), recently reviewed, 61 comprise several techniques. 19,21,47,62 Here, we explore ordinary least squares to build a linear relationship between the PurpleAir uncalibrated output and the research-grade instrument. The slope and the intercept of the linear functions are obtained using the LCS uncalibrated concentrations as independent variables and the coincident research-grade concentrations as dependent variables.…”

Section: Instruments and Datamentioning

confidence: 99%

“…A synergy between traditional monitoring networks, low-cost sensors, modelling and satellite-based measurements could overcome the limitations of the present monitoring schemes (Mead et al 2013, Munir et al 2019, Li et al 2019, Datta et al 2020, Li et al 2020a. For example, current networks may increase their capacities through the deployment of distributed networks of sensors (Mead et al 2013, Gao et al, 2015, Kim et al 2018, Caubel et al 2019, Lu et al, 2021, static and mobile (de Nazelle et al 2013, Van den Bossche et al 2016, Hofman et al 2020, Santana et al 2021 with high time resolution capacities to help identify hot spots and short-term emissions spikes (Kumar et al 2018, Zikova et al, 2017, Rickenbacker et al, 2019, Horsburgh et al 2019, Subramanian R., 2020, Qiao et al 2021, of great relevance for, e.g., human exposure assessment (Jerrett et al 2017, Li et al 2019, Zuidema et al 2021, or to develop real-time strategies (Kumar et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…reviewed byGiordano et al (2021), comprise several techniques(Badura et al 2019, Zaidan et al 2020, Hofman et al 2020, Lu et al 2021. Here, we explore the ordinaryleast squares to build a linear relationship between the PurpleAir uncalibrated output and the research-grade instrument.…”

mentioning

confidence: 99%

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Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative

Caseiro

Schmitz

Villena

et al. 2022

Environ. Sci.: Atmos.

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Section: Instruments and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative

Caseiro

Schmitz

Villena

et al. 2022

Environ. Sci.: Atmos.

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“…For areas not measured within a city, spatial pollution distribution maps, incorporating urban topography, have been developed using these high-frequency, spatially distributed measurements. One notable example is the method developed [45] as a result of a mobile sensing project in Antwerp (https://www.imeccityofthings.be/en/projects/dencitymore-sensors-in-the-city (accessed on 02 May 2021)) in which 20 postal vehicles were equipped with LCS and combined with the measurements from 15 fixed LCS locations.…”

Section: The Smart Air Quality Network Designmentioning

confidence: 99%

High-Resolution Assessment of Air Quality in Urban Areas—A Business Model Perspective

Schäfer¹,

Lande

Grimm³

et al. 2021

Atmosphere

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The increasing availability of low-cost air quality sensors has led to novel sensing approaches. Distributed networks of low-cost sensors, together with data fusion and analytics, have enabled unprecedented, spatiotemporal resolution when observing the urban atmosphere. Several projects have demonstrated the potential of different approaches for high-resolution measurement networks ranging from static, low-cost sensor networks over vehicular and airborne sensing to crowdsourced measurements as well as ranging from a research-based operation to citizen science. Yet, sustaining the operation of such low-cost air quality sensor networks remains challenging because of the lack of regulatory support and the lack of an organizational framework linking these measurements to the official air quality network. This paper discusses the logical inclusion of lower-cost air quality sensors into the existing air quality network via a dynamic field calibration process, the resulting sustainable business models, and how this expansion can be self-funded.

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“…These sensors are known to be less accurate than the reference grade monitors, and hence, in-field calibration is required to obtain a reasonable sensor readings. Two settings are commonly used, (i) calibrating the sensors against a co-deployed reference monitor, and (ii) collating the spatio-temporal measurements in a network of sensors [1], [7]. In this paper, we focus on the first setting.…”

Section: Introductionmentioning

confidence: 99%

Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning

Kalpit¹,

Arora²,

Kumar³

et al. 2021

Preprint

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Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic-Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.

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Mapping Air Quality in IoT Cities: Cloud Calibration and Air Quality Inference of Sensor Data

Cited by 12 publications

References 27 publications

Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative

Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative

High-Resolution Assessment of Air Quality in Urban Areas—A Business Model Perspective

Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning

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