Toward Massive Scale Air Quality Monitoring

Motlagh, Naser Hossein; Petäj̈ä, Tuukka; Kulmala, Markku; Trachoma, Sasu; Lagerspetz, Eemil; Nurmi, Petteri; Li, Xin; Varjonen, Samu; Mineraud, Julien; Siekkinen, Matti; Rebeiro‐Hargrave, Andrew; Hussein, Tareq

doi:10.1109/mcom.001.1900515

Cited by 75 publications

(51 citation statements)

References 14 publications

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“…However, data quality of the LCS remains a major issue that hinders the widespread of LCS implementation. Therefore, many research on LCS have dedicated their studies on developing in-field sensor calibration [7]. For example, LCS calibrations were carried out for improving the sensing variables of Carbon monoxide (CO) [8], CO 2 [9], Nitrogen dioxide (NO 2 ) [8], Sulfur dioxide (SO 2 ) [8], Ozone (O 3 ) [8], [9], particulate matter smaller than 10 µm (PM 10 ) and particulate matter smaller than 2.5 µm (PM 2.5 ) [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

et al. 2020

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This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

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Section: Introductionmentioning

confidence: 99%

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

et al. 2020

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“…Third parties, such as business entities and other researchers, can also supply or retrieve data and results through the Input and Exposure APIs. In the current research deployment data upload and access by third parties 4 , the Input and Exposure APIs ( Figure 1) are called Write and Read, respectively.…”

Section: B External Systemsmentioning

confidence: 99%

“…In response, many cities commit to a network of stations that monitor air quality in real-time. However, the high costs of installation and maintenance of these stations results in a sparse monitoring [4], satisfying the legislative requirements but not providing information about localized air pollution important to health protection [5].…”

Section: Introductionmentioning

confidence: 99%

MegaSense: Cyber-Physical System for Real-time Urban Air Quality Monitoring

Rebeiro‐Hargrave

Motlagh

Varjonen

et al. 2020

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacyaware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a batterypowered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hotspot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.

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“…Additionally, their sampling interval is rather long, at the cost of the data quality. Because of the above facts, low-cost light scattering PM sensor have been widely used for a practical alternative of the air monitoring station in dense sensor deployment [ 4 ]. Even though these sensors still have a major challenge on data quality, they have overwhelming advantages of less expensive price, more compact size, and faster update rate [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

Lee

Kang

Kim

et al. 2020

Sensors

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Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

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Toward Massive Scale Air Quality Monitoring

Cited by 75 publications

References 14 publications

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

MegaSense: Cyber-Physical System for Real-time Urban Air Quality Monitoring

Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

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