Model-based rendezvous calibration of mobile sensor networks for monitoring air quality

Arfire, Adrian; Marjovi, Ali; Martinoli, Alcherio

doi:10.1109/icsens.2015.7370258

Cited by 25 publications

(28 citation statements)

References 8 publications

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“…Tsujita et al [50] installed a low-cost NO 2 sensor in the city of Tokyo, Japan. They recognize that the major error source of their sensor appears to be baseline drift of the calibration [76] Linear Mobile Saukh et al [77] Linear Mobile Saukh et al [78] Linear Mobile Maag et al [79] Linear Mobile Budde et al [80] Linear Mobile Fu et al [81] Linear Mobile Markert et al [82] Linear Mobile Kizel et al [34] Linear Mobile Arfire et al [83] Non-Linear Mobile…”

Section: A Blind Calibrationmentioning

confidence: 99%

“…Whenever a mobile low-cost sensor is in a sensor rendezvous with a highly accurate sensor, e.g., from a governmental monitoring site, the low-cost sensor can use the reference measurement for calibration [78]. Arfire et al [83] apply a non-linear temperature correction for mobile electrochemical sensors in a collaborative fashion with a reference sensor. Hasenfratz et al [76] present three different calibration methods based on weighted least squares that also incorporate the age of measurement at the time of the calibration parameter calculations.…”

Section: B Collaborative Calibrationmentioning

confidence: 99%

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A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

Maag

Zhou

Thiele

2018

IEEE Internet Things J.

237

163

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Air pollution is a major concern for public health and urban environments. Conventional air pollution monitoring systems install a few highly accurate, expensive stations at representative locations. Their sparse coverage and low spatial resolution are insufficient to quantify urban air pollution and its impacts on human health and environment. Advances in lowcost portable air pollution sensors have enabled air pollution monitoring deployments at scale to measure air pollution at high spatiotemporal resolution. However, it is challenging to ensure the accuracy of these low-cost sensor deployments because the sensors are more error-prone than high-end sensing infrastructures and they are often deployed in harsh environments. Sensor calibration has proven to be effective to improve the data quality of low-cost sensors and maintain the reliability of longterm, distributed sensor deployments. In this article, we review the state-of-the-art low-cost air pollution sensors, identify their major error sources, and comprehensively survey calibration models as well as network re-calibration strategies suited for different sensor deployments. We also discuss limitations of exiting methods and conclude with open issues for future sensor calibration research.

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Section: A Blind Calibrationmentioning

confidence: 99%

Section: B Collaborative Calibrationmentioning

confidence: 99%

A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

Maag

Zhou

Thiele

2018

IEEE Internet Things J.

237

163

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“…This could be for two reasons: (i) the larger distance between tram-track and monitoring station, and (ii) the location of site SCH at a crossroad, where traffic flow is controlled by traffic lights and therefore, a larger short-term variability of NO 2 is expected. From a more general perspective, this is of importance when mobile sensor data is corrected based on data from nearby fixed reference sites, as suggested by Arfire et al (2015) and Saukh et al (2015). The parameters for data correction as determined from such an approach depend on the local concentration variability and the time response of the sensor and the reference instrument.…”

Section: Tram Based No 2 Measurementsmentioning

confidence: 99%

Mid-IR spectrometer for mobile, real-time urban NO<sub>2</sub> measurements

et al. 2018

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Abstract. Detailed knowledge about the urban NO2 concentration field is a key element for obtaining accurate pollution maps and individual exposure estimates. These are required for improving the understanding of the impact of ambient NO2 on human health and for related air quality measures. However, city-scale NO2 concentration maps with high spatio-temporal resolution are still lacking, mainly due to the difficulty of accurate measurement of NO2 at the required sub-ppb level precision. We contribute to close this gap through the development of a compact instrument based on mid-infrared laser absorption spectroscopy. Leveraging recent advances in infrared laser and detection technology and a novel circular absorption cell, we demonstrate the feasibility and robustness of this technique for demanding mobile applications. A fully autonomous quantum cascade laser absorption spectrometer (QCLAS) has been successfully deployed on a tram, performing long-term and real-time concentration measurements of NO2 in the city of Zurich (Switzerland). For ambient NO2 concentrations, the instrument demonstrated a precision of 0.23 ppb at one second time resolution and of 0.03 ppb after 200 s averaging. Whilst the combined uncertainty estimated for the retrieved spectroscopic values was less than 5 %, laboratory intercomparison measurements with standard CLD instruments revealed a systematic NO2 wall loss of about 10 % within the laser spectrometer. For the field campaign, the QCLAS has been referenced to a CLD using urban atmospheric air, despite the potential cross sensitivity of CLD to other nitrogen containing compounds. However, this approach allowed a direct comparison and continuous validation of the spectroscopic data to measurements at regulatory air quality monitoring (AQM) stations along the tram-line. The analysis of the recorded high-resolution time series allowed us to gain more detailed insights into the spatio-temporal concentration distribution of NO2 in an urban environment. Furthermore, our results demonstrate that for reliable city-scale concentration maps a larger data set and better spatial coverage is needed, e.g., by deploying more mobile and stationary instruments to account for mainly two shortcomings of the current approach: (i) limited residence time close to sources with large short-term NO2 variations, and (ii) insufficient representativeness of the tram tracks for the complex urban environment.

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“…When radiation changes after the coupling phase, the correction co-efficient does not fit the situation anymore and causes larger errors. However, as point (n) indicates, there is no clear difference between stations with open (Stations 1-5) and slightly obscuring surroundings (Stations [6][7][8][9][10][11][12][13][14][15][16]. In December, there are no significant differences between RMSE values at different station groups due to the low intensity of the SW radiation.…”

Section: Station Numbermentioning

confidence: 99%

“…Automatic calibration methods are thus used to eliminate the cumbersome and error-prone manual calibration [8]. One such automatic calibration method is the rendezvous model [9][10][11][12]. In the rendezvous model, observations by two or more sensors, mobile or stationary, are collected when the sensors are co-located, i.e.…”

Section: Introductionmentioning

confidence: 99%

Observing and forecasting road surface temperatures

Karsisto¹

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Wintertime weather conditions can be hazardous for road traffic. Icy roads and poor visibility caused by snowfall increase the accident risk. Accurate forecasting of road conditions is important, because reliable and precise forecasts help the road maintenance personnel to plan their operations accordingly. Well timed maintenance operations increase safety and enable economical savings as unnecessary actions can be avoided. Drivers can also adjust their route plan and driving behaviour appropriately when warnings of hazardous conditions are given well beforehand. Road conditions are forecasted in the Finnish Meteorological Institute (FMI) with specialized road weather model. Before executing the actual forecast, the model is first initialized by feeding it with observation data. The quality of this data is essential for forecast accuracy, as the forecast is greatly dependent of the initial model state. Road weather stations have traditionally been one of the main sources of information, but their density is sparse especially in rural areas. Road surface temperature can vary considerably across the road network, so observations should be done in dense enough spatial scale. Nowadays it is possible to gather real time information from vehicles. Mobile sources provide observations with high spatial density and thus facilitate detecting the road stretches most prone to freezing. However, the quality of mobile observations should be assessed before implementing them to the road weather forecasting systems. This dissertation aims to answer to two research questions. Firstly, it has been studied how to best use available surface temperature observations in the road weather model initialization. Secondly , it has been studied how differences in two road weather models' physics affect to the surface temperature forecast accuracy. A method called coupling was implemented to the FMI road weather model. The main idea of the method is to adjust the incoming radiation flux so that the modelled surface temperature fits to the last observed value. The results show that this method improves considerably the short range surface temperature forecasts. Mobile surface temperature observations done with Teconer RTS411 were compared to road weather station measurements to assess the mobile data quality. According to the results, the mobile observations were on average 0.62 ○ C warmer than the road weather station measurements at 0 ○ C and in dry conditions. It was found out that the difference between mobile observations and road weather station measurements was dependent on the road status. A calibration equation for mobile observations was developed using linear mixed models to get mobile observations more in line with road weather station measurements. The effect of the mobile observations to the road surface temperature forecast accuracy was studied. According to the results, using the mobile observations calibrated with the developed equation improved the accuracy of road surface temperature forecasts compared to a theoretical situ...

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Model-based rendezvous calibration of mobile sensor networks for monitoring air quality

Cited by 25 publications

References 8 publications

A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

Mid-IR spectrometer for mobile, real-time urban NO<sub>2</sub> measurements

Observing and forecasting road surface temperatures

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Model-based rendezvous calibration of mobile sensor networks for monitoring air quality

Cited by 25 publications

References 8 publications

A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

Mid-IR spectrometer for mobile, real-time urban NO&lt;sub&gt;2&lt;/sub&gt; measurements

Observing and forecasting road surface temperatures

Contact Info

Product

Resources

About

Mid-IR spectrometer for mobile, real-time urban NO<sub>2</sub> measurements