Articles | Volume 8, issue 2
https://doi.org/10.5194/jsss-8-317-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/jsss-8-317-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regular research article
| 
05 Nov 2019
Regular research article |
| 05 Nov 2019
| 05 Nov 2019
Measure particulate matter by yourself: data-quality monitoring in a citizen science project
Aboubakr Benabbas
CORRESPONDING AUTHOR
Otto-Friedrich-Universität Bamberg, Bamberg, Germany
Martin Geißelbrecht
Otto-Friedrich-Universität Bamberg, Bamberg, Germany
Gabriel Martin Nikol
Otto-Friedrich-Universität Bamberg, Bamberg, Germany
Lukas Mahr
Hochschule Coburg, Coburg, Germany
Daniel Nähr
Hochschule Coburg, Coburg, Germany
Simon Steuer
Otto-Friedrich-Universität Bamberg, Bamberg, Germany
Gabriele Wiesemann
Transition Bamberg, Bamberg, Germany
Thomas Müller
Otto-Friedrich-Universität Bamberg, Bamberg, Germany
Daniela Nicklas
Otto-Friedrich-Universität Bamberg, Bamberg, Germany
Thomas Wieland
Hochschule Coburg, Coburg, Germany
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Nick Rüssmeier, Axel Hahn, Daniela Nicklas, and Oliver Zielinski
J. Sens. Sens. Syst., 6, 37–52, https://doi.org/10.5194/jsss-6-37-2017, https://doi.org/10.5194/jsss-6-37-2017, 2017
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Maritime study sites utilized as a physical experimental test bed for sensor data fusion, communication technology and data stream analysis tools can provide substantial frameworks for design and development of e-navigation technologies. Increasing safety by observation and monitoring of the maritime environment with new technologies meets forward-looking needs to facilitate situational awareness. The study highlights research potentials and foundations achieved by distributed optical sensors.
Related subject area
Applications: Environmental monitoring
The River Runner: a low-cost sensor prototype for continuous dissolved greenhouse gas measurements
Laboratory robustness validation of a humidity sensor system for the condition monitoring of grease-lubricated components for railway applications
An in-hive soft sensor based on phase space features for Varroa infestation level estimation and treatment need detection
A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images
An autonomous flame ionization detector for emission monitoring
Gas sensors for climate research
Metal ion binding and tolerance of bacteria cells in view of sensor applications
Highly sensitive benzene detection with metal oxide semiconductor gas sensors – an inter-laboratory comparison
Homogenous static magnetic field coils dedicated to portable nuclear magnetic resonance for agronomic studies
In situ measurements of O2 and CO eq. in cement kilns
Scanning method for indoor localization using the RSSI approach
High-temperature CO / HC gas sensors to optimize firewood combustion in low-power fireplaces
Atmospheric transmission coefficient modelling in the infrared for thermovision measurements
Partially integrated cantilever-based airborne nanoparticle detector for continuous carbon aerosol mass concentration monitoring
Luminescent determination of nitrite traces in water solutions using cellulose as sorbent
Catalytic metal-gate field effect transistors based on SiC for indoor air quality control
Characterization of ash particles with a microheater and gas-sensitive SiC field-effect transistors
Catalytic and thermal characterisations of nanosized PdPt / Al2O3 for hydrogen detection
Selective detection of hazardous VOCs for indoor air quality applications using a virtual gas sensor array
Aerosol-deposited BaFe0.7Ta0.3O3-δ for nitrogen monoxide and temperature-independent oxygen sensing
A novel horizontal to vertical spectral ratio approach in a wired structural health monitoring system
Sensing of gaseous malodors characteristic of landfills and waste treatment plants
Martin Dalvai Ragnoli and Gabriel Singer
J. Sens. Sens. Syst., 13, 41–61, https://doi.org/10.5194/jsss-13-41-2024, https://doi.org/10.5194/jsss-13-41-2024, 2024
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Greenhouse gas emissions from freshwaters are not well quantified on a global scale. Our prototype monitors aqueous concentrations of CH4 and CO2 at relevant timescales. Our low-cost design allows the application of replicated sensors to capture spatial heterogeneity and temporal variability in dissolved gases in highly dynamic ecosystems, thereby addressing a major bottleneck in the reliable estimation of emissions. We provide a detailed prototype description and share software code.
Krisztián Dubek, Christoph Schneidhofer, Nicole Dörr, and Ulrich Schmid
J. Sens. Sens. Syst., 13, 9–23, https://doi.org/10.5194/jsss-13-9-2024, https://doi.org/10.5194/jsss-13-9-2024, 2024
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A new method for water detection in lubricated rail components is presented. It is based on a robust humidity sensor combined with robust data evaluation to determine the water content of greases. Based on a laboratory evaluation in the relevant environment, the presented approach offers an online monitoring tool to predict the water content of grease-lubricated rail parts, thereby enhancing the reliability and safety while reducing the maintenance costs and downtime of railway wagons.
Andreas König
J. Sens. Sens. Syst., 11, 29–40, https://doi.org/10.5194/jsss-11-29-2022, https://doi.org/10.5194/jsss-11-29-2022, 2022
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Bees play a major role in our ecosystem and the human food supply chain. Numerous threats, from pesticides to parasites, endanger bees and possibly cause bee colony collapse. The Varroa mite is one major parasite, and its timely detection and treatment is a key task for beekeepers. Contemporary sensors, electronics, and AI/PR allow vital parameters of bee hives to be monitored. Recent gas sensors (e.g., SGP30 or BME680) allow continuous in-hive parameter and Varroa population monitoring.
Anton V. Kvasnov and Vyacheslav P. Shkodyrev
J. Sens. Sens. Syst., 10, 127–134, https://doi.org/10.5194/jsss-10-127-2021, https://doi.org/10.5194/jsss-10-127-2021, 2021
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We have prepared an article that demonstrates one of the ways to recognize objects on the ground surface. This paper is a result of experimental data that were collected with unnamed aerial vehicles (UAVs) with synthetic aperture radar. Although UAV radar has a small monitoring area, we noted that such pictures can contain the steady features of the mutual arrangement between detected objects. We have constructed an artificial neural network that solves the tasks of group object recognition.
Jan Förster, Winfred Kuipers, Christian Lenz, Steffen Ziesche, and Franz Bechtold
J. Sens. Sens. Syst., 8, 67–73, https://doi.org/10.5194/jsss-8-67-2019, https://doi.org/10.5194/jsss-8-67-2019, 2019
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Reliable detection of hydrocarbons can be achieved with a flame ionization detector (FID). However, these devices have not been implemented as true field devices yet. Miniaturization by using ceramic multilayer technology leads to a strong reduction of gas consumption and allows autonomous operation of the FID with gas supply by electrolysis. Thus, this research enables the use of the FID in the field. Characterization of this new FID reveals a performance comparable to conventional FIDs.
Louisa Scholz, Alvaro Ortiz Perez, Benedikt Bierer, Jürgen Wöllenstein, and Stefan Palzer
J. Sens. Sens. Syst., 7, 535–541, https://doi.org/10.5194/jsss-7-535-2018, https://doi.org/10.5194/jsss-7-535-2018, 2018
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The availability of datasets providing information on the spatial and temporal evolution of greenhouse gas concentrations is of high relevance for the development of reliable climate simulations. Here we present a novel, non-dispersive infrared absorption spectroscopy (NDIR) device that can possibly act as a central building block of a sensor node to provide high-quality data of carbon dioxide (CO2) concentrations under field conditions at a high measurement rate.
Jonas Jung, Anja Blüher, Mathias Lakatos, and Gianaurelio Cuniberti
J. Sens. Sens. Syst., 7, 433–441, https://doi.org/10.5194/jsss-7-433-2018, https://doi.org/10.5194/jsss-7-433-2018, 2018
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We assessed the applicability of bacterial surface layer proteins of Lysinibacillus sphaericus JG-B53 and Sporosarcina ureae ATCC 13881 for the detection of metal ions in water. Based on the interactions of the cell components with metal complexes, two potential sensor systems, one colorimetric with functionalized gold nanoparticles and the other using a regenerative sensor layer, were developed. The systems' detection limits of YCl3 in water were 1.67 x 10−5 and 1 x 10−4 mol L−1, respectively.
Tilman Sauerwald, Tobias Baur, Martin Leidinger, Wolfhard Reimringer, Laurent Spinelle, Michel Gerboles, Gertjan Kok, and Andreas Schütze
J. Sens. Sens. Syst., 7, 235–243, https://doi.org/10.5194/jsss-7-235-2018, https://doi.org/10.5194/jsss-7-235-2018, 2018
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For detection of benzene, a multichannel gas sensor system was tested in two different laboratories at the concentration range from 0.5 ppb up to 10 ppb. A model is used to extract the channels and multilinear regression is done to compensate cross interference to other gases. Depending on the measurement conditions, the quantification accuracy is between ±0.2 ppb and ±2 ppb. Regression models for one laboratory were transferable between the labs under comparable measurement conditions.
Rahima Sidi-Boulenouar, Ariston Reis, Eric Nativel, Simon Buy, Pauline de Pellegars, Pan Liu, Michel Zanca, Christophe Goze-Bac, Jérome Barbat, Eric Alibert, Jean-Luc Verdeil, Frédéric Gatineau, Nadia Bertin, Atma Anand, and Christophe Coillot
J. Sens. Sens. Syst., 7, 227–234, https://doi.org/10.5194/jsss-7-227-2018, https://doi.org/10.5194/jsss-7-227-2018, 2018
Olga Driesner, Fred Gumprecht, and Ulrich Guth
J. Sens. Sens. Syst., 6, 327–330, https://doi.org/10.5194/jsss-6-327-2017, https://doi.org/10.5194/jsss-6-327-2017, 2017
Ahmad Warda, Bojana Petković, and Hannes Toepfer
J. Sens. Sens. Syst., 6, 247–251, https://doi.org/10.5194/jsss-6-247-2017, https://doi.org/10.5194/jsss-6-247-2017, 2017
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We studied the problem of wireless indoor mobile robot localization and tracking using noise-free data and data with additive white Gaussian noise at three receiver positions. We proposed a new scanning method to overcome the drawbacks of fingerprint, which includes time-consuming construction of a database and its need for rebuilding every time a significant change in the environment occurs.
Binayak Ojha, Navas Illyaskutty, Jens Knoblauch, Muthu Raman Balachandran, and Heinz Kohler
J. Sens. Sens. Syst., 6, 237–246, https://doi.org/10.5194/jsss-6-237-2017, https://doi.org/10.5194/jsss-6-237-2017, 2017
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A novel combustion airstream control concept has been developed based on in situ sensors for combustion temperature, residual oxygen concentration and residual un-combusted CO / HC components. The implementation of this control concept allows for a large reduction in toxic gas emissions by up to 80 % compared to hand-operated furnaces. A stable long-term CO / HC sensor for such an application is not available; thus, the long-term sensor signal stability of different CO / HC sensors is studied.
W. Minkina and D. Klecha
J. Sens. Sens. Syst., 5, 17–23, https://doi.org/10.5194/jsss-5-17-2016, https://doi.org/10.5194/jsss-5-17-2016, 2016
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The aim of this paper is to discuss different models that describe atmospheric transmission in the infrared. They were compared in order to choose the most appropriate one for certain atmospheric conditions. Universal models and different inaccuracies connected with them were analysed in this paper. There have been models analysed from the literature, and these are used in infrared cameras.
H. S. Wasisto, S. Merzsch, E. Uhde, A. Waag, and E. Peiner
J. Sens. Sens. Syst., 4, 111–123, https://doi.org/10.5194/jsss-4-111-2015, https://doi.org/10.5194/jsss-4-111-2015, 2015
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The performance of a low-cost partially integrated cantilever-based airborne nanoparticle (NP) detector (CANTOR-1) is evaluated in terms of its real-time measurement and robustness. The device is used for direct reading of exposure to airborne carbon engineered nanoparticles (ENPs) in indoor workplaces.
S. G. Nedilko, S. L. Revo, V. P. Chornii, V. P. Scherbatskyi, and M. S. Nedielko
J. Sens. Sens. Syst., 4, 31–36, https://doi.org/10.5194/jsss-4-31-2015, https://doi.org/10.5194/jsss-4-31-2015, 2015
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Microcrystalline cellulose, microcrystalline nitrite powders of common formulae MNO2, (M = Na, K) and two-component materials (cellulose + nitrite) have been prepared and characterized by means of optical microscopy and luminescence spectroscopy.The method of determining the nitrite compound traces via their sorption by cellulose using luminescent properties of the NO2- molecular ion has been developed and the low limit of NaNO2 determination in water solution was evaluated as 0.035 mg/l.
D. Puglisi, J. Eriksson, C. Bur, A. Schuetze, A. Lloyd Spetz, and M. Andersson
J. Sens. Sens. Syst., 4, 1–8, https://doi.org/10.5194/jsss-4-1-2015, https://doi.org/10.5194/jsss-4-1-2015, 2015
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This study aims at the development of high-performance and cost-efficient gas sensors for sensitive detection of three specific hazardous gases, i.e., formaldehyde, naphthalene, and benzene, commonly present in indoor environments in concentrations of health concern. We used silicon carbide field effect transistors to investigate the sensor performance and characteristics under different levels of relative humidity up to 60%, demonstrating excellent detection limits in the sub-ppb range.
C. Bur, M. Bastuck, A. Schütze, J. Juuti, A. Lloyd Spetz, and M. Andersson
J. Sens. Sens. Syst., 3, 305–313, https://doi.org/10.5194/jsss-3-305-2014, https://doi.org/10.5194/jsss-3-305-2014, 2014
T. Mazingue, M. Lomello-Tafin, M. Passard, C. Hernandez-Rodriguez, L. Goujon, J.-L. Rousset, F. Morfin, and J.-F. Laithier
J. Sens. Sens. Syst., 3, 273–280, https://doi.org/10.5194/jsss-3-273-2014, https://doi.org/10.5194/jsss-3-273-2014, 2014
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In this article, we propose detecting hydrogen (H2) traces at room temperature with nanostructured PdPt/Al2O3 catalysts. We measure the temperature rise during the exothermic oxidation of H2 by the catalyst. An appropriate formulation of about 1 mg of PdPt/Al2O3 leads to reversible thermal responses of 3°C in only 5 s. We show that this active material is a promising candidate for autonomous and reversible passive transducers for H2 sensors working at room temperature in explosive atmospheres.
M. Leidinger, T. Sauerwald, W. Reimringer, G. Ventura, and A. Schütze
J. Sens. Sens. Syst., 3, 253–263, https://doi.org/10.5194/jsss-3-253-2014, https://doi.org/10.5194/jsss-3-253-2014, 2014
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An approach for detecting hazardous volatile organic compounds (VOCs) in ppb and sub-ppb concentrations is presented. Using metal oxide semiconductor (MOS) gas sensors in temperature cycled operation, VOCs in trace concentrations are successfully identified against a varying ethanol background of up to 2 ppm. For signal processing, linear discriminant analysis is applied to single sensor data and sensor fusion data. Integrated gas sensor systems using the same MOS sensors were characterized.
M. Bektas, D. Hanft, D. Schönauer-Kamin, T. Stöcker, G. Hagen, and R. Moos
J. Sens. Sens. Syst., 3, 223–229, https://doi.org/10.5194/jsss-3-223-2014, https://doi.org/10.5194/jsss-3-223-2014, 2014
F. P. Pentaris
J. Sens. Sens. Syst., 3, 145–165, https://doi.org/10.5194/jsss-3-145-2014, https://doi.org/10.5194/jsss-3-145-2014, 2014
B. Fabbri, S. Gherardi, A. Giberti, V. Guidi, and C. Malagù
J. Sens. Sens. Syst., 3, 61–67, https://doi.org/10.5194/jsss-3-61-2014, https://doi.org/10.5194/jsss-3-61-2014, 2014
Cited articles
Adelantado, F., Vilajosana, X., Tuset-Peiro, P., Martinez, B., Melia-Segui, J., and Watteyne, T.: Understanding the limits of LoRaWAN, IEEE Commun.
Mag., 55, 34–40, 2017. a
Boubel, R. W., Vallero, D., Fox, D. L., Turner, B., and Stern, A. C.:
Fundamentals of air pollution, Elsevier, USA, 2013. a
Budde, M., Riedel, T., Beigl, M., Schäfer, K., Emeis, S., Cyrys, J.,
Schnelle-Kreis, J., Philipp, A., Ziegler, V., Grimm, H., and Gratza, T.:
SmartAQnet: remote and in-situ sensing of urban air quality, in: Proc. SPIE 10424, Remote Sensing of Clouds and the Atmosphere XXII, 104240C, 6 October 2017, Warsaw, Poland, https://doi.org/10.1117/12.2282698, 2017a. a
Budde, M., Schankin, A., Hoffmann, J., Danz, M., Riedel, T., and Beigl, M.:
Participatory Sensing or Participatory Nonsense? Mitigating the Effect of
Human Error on Data Quality in Citizen Science, Proc. ACM Interact. Mob.
Wearable Ubiquit. Technol., 1, 39:1–39:23, https://doi.org/10.1145/3131900, 2017b. a
Calbimonte, J.-P., Eberle, J., and Aberer, K.: Toward Self-monitoring Smart
Cities: the OpenSense2 Approach, Informatik-Spektrum, 40, 75–87,
https://doi.org/10.1007/s00287-016-1009-y, 2017. a
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B.,
Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms
contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, 293–302, 2017. a
Compton, M., Barnaghi, P., Bermudez, L., García-Castro, R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., Huang, V., Janowicz, K., Kelsey, W. D., Le Phuoc, D., Lefort, L., Leggieri, M., Neuhaus, H., Nikolov, A., Page, K., Passant, A., Sheth, A., and Taylor, K.: The SSN ontology of the W3C semantic sensor network incubator group, Web Semantics, 17, 25–32, https://doi.org/10.1016/j.websem.2012.05.003, 2012. a
Crilley, L. R., Shaw, M., Pound, R., Kramer, L. J., Price, R., Young, S., Lewis, A. C., and Pope, F. D.: Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring, Atmos. Meas. Tech., 11, 709–720, https://doi.org/10.5194/amt-11-709-2018, 2018. a
Garg, B. D., Cadle, S. H., Mulawa, P. A., Groblicki, P. J., Laroo, C., and
Parr, G. A.: Brake Wear Particulate Matter Emissions, Environ. Sci. Technol., 34, 4463–4469, 2000. a
Geisler, S., Quix, C., Weber, S., and Jarke, M.: Ontology-Based Data Quality
Management for Data Streams, J. Data Inform. Qual., 7, 18:1–18:34, https://doi.org/10.1145/2968332, 2016. a
Giechaskiel, B., Maricq, M., Ntziachristos, L., Dardiotis, C., Wang, X.,
Axmann, H., Bergmann, A., and Schindler, W.: Review of motor vehicle
particulate emissions sampling and measurement: From smoke and filter mass to
particle number, J. Aerosol Sci., 67, 48–86, https://doi.org/10.1016/j.jaerosci.2013.09.003, 2014. a, b
Hagemann, R., Corsmeier, U., Kottmeier, C., Rinke, R., Wieser, A., and Vogel,
B.: Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory `AERO-TRAM', Atmos. Environ., 94, 341–352,
https://doi.org/10.1016/j.atmosenv.2014.05.051, 2014. a
Heimann, I., Bright, V., McLeod, M., Mead, M., Popoola, O., Stewart, G., and
Jones, R.: Source attribution of air pollution by spatial scale separation
using high spatial density networks of low cost air quality sensors, Atmos. Environ., 113, 10–19, 2015. a
Heinrich, J., Hoelscher, B., Frye, C., Meyer, I., Pitz, M., Cyrys, J., Wjst,
M., Neas, L., and Wichmann, H.-E.: Improved Air Quality in Reunified Germany
and Decreases in Respiratory Symptoms, Epidemiology, 13, 394–401, 2002. a
Jovaševic-Stojanovic, M., Bartonova, A., Topalovic, D., Lazovic, I., Pokric, B., and Ristovski, Z.: On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter, Environ. Poll. (Barking, Essex: 1987), 206, 696–704,
https://doi.org/10.1016/j.envpol.2015.08.035, 2015. a
Kallweit, D. and Wintermeyer, D.: Berechnung der gesundheitlichen Belastung
der Bevölkerung in Deutschland durch Feinstaub (PM10), UMID
Umw. Mensch-Informationsdienst, 4, 18–24, 2013. a
Kollia, I., Glimm, B., and Horrocks, I.: SPARQL Query Answering over OWL Ontologies, in: The Semantic Web: Research and Applications, edited by: Grigoris, A., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D.,
De Leenheer, P, and Pan, J., Springer, Berlin, Heidelberg, 382–396, 2011. a
Kuka, C.: Qualitaetissensitive Datenstromverarbeitung zur Erstellung von
dynamischen Kontextmodellen, PhD thesis, University of Oldenburg, Oldenburg, Germany, 2014. a
Kuka, C. and Nicklas, D.: Supporting Quality-aware Pervasive Applications by
Probabilistic Data Stream Management, in: Proceedings of the 8th ACM
International Conference on Distributed Event-Based Systems, 26–29 May 2014, Mumbai, India, 330–333, https://doi.org/10.1145/2611286.2611319, 2014. a
Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino,
S., Bell, M., Norford, L., and Britter, R.: The rise of low-cost sensing for
managing air pollution in cities, Environ. Int.l, 75, 199–205, 2015. a
Laquai, B.: Kompensation des Feuchte-Effekts bei Low-Cost
Feinstaubsensoren-Sensoren nach dem Streulichtverfahren, available at:
http://opengeiger.de/Feinstaub/FeuchteKompensation.pdf (last access: 28 October 2019), 2018. a
McBride, B.: Jena: a semantic Web toolkit, IEEE Internet Comput., 6, 55–59,
https://doi.org/10.1109/MIC.2002.1067737, 2002. a
Mellon, D., King, S. J., Kim, J., Reid, J. P., and Orr-Ewing, J. A.: Measurements of Extinction by Aerosol Particles in the Near-Infrared Using Continuous Wave Cavity Ring-Down Spectroscopy, J. Phys. Chem. A, 115, 774–783, https://doi.org/10.1021/jp109894x, 2011.
a
Nussbaumer, T., Czasch, C., Klippel, N., Bafver, L., and Tullin, C.:
Particulate Emissions from Biomass Combustion in IEA Countries Survey on
Measurements and Emission Factors, Bioenergy Task, International Energy Agency (IEA), in: 16th European Biomass Conference and Exhibition, 2–6 June 2008, Valencia, Spain, Oral Pre-sentation OA 9.2, 32 pp., 2008. a
Penkov, S., Taneva, A., Petrov, M., and Kalkov, V.: Industrial network design using Low-Power Wide-Area Network, in: 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, 40–44, 2017. a
Penza, M., Suriano, D., Villani, M. G., Spinelle, L., and Gerboles, M.: Towards air quality indices in smart cities by calibrated low-cost sensors applied to networks, in: SENSORS, 2014 IEEE, Valencia, 2012–2017,
https://doi.org/10.1109/ICSENS.2014.6985429, 2014. a
Petäjäjärvi, J., Mikhaylov, K., Yasmin, R., Hämäläinen, M., and Iinatti, J.: Evaluation of LoRa LPWAN Technology for Indoor Remote Health and Wellbeing Monitoring, Int. J. Wireless Inform. Netw., 24, 153–165, 2017. a
Steuer, S., Beetz, F., Geisselbrecht, M., Kaiser, F., Nicklas, D., Nikol, G.,
Ostrowski, T., Wieland, T., Wiesemann, G., and Zschiegner, R.: Feinstaub
selbst messen: Datenqualität in einem Citizen-Science-Projekt, in:
ITG-Fb. 281: Sensoren und Messsysteme, VDE, 19. ITG/GMA-Conference, 26–27 June 2018, Nürnberg, 2018. a, b
Truong, H. L. and Stanford-Clark, A.: MQTT For Sensor Networks (MQTT-SN)
Protocol Specification Version 1.2, available at: http://www.mqtt.org/new/wp-content/uploads/2009/06/MQTT-SN_spec_v1.2.pdf
(last access: 28 October 2019), 2013. a
van Donkelaar, A., Martin, R. V., Brauer, M., and Boys, B. L.: Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter, Environ. Health Perspect., 123, 135–143, https://doi.org/10.1289/ehp.1408646, 2015. a
Vincent, J. H.: Aerosol Sampling, in: Science, Standards, Instrumentation and Applications, Direct-reading Aerosol Sampling Instruments, Wiley, England, 489–513, https://doi.org/10.1002/9780470060230.ch10, 2007. a, b
Wang, Z., Chen, L., Tao, J., Zhang, Y., and Su, L.: Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method, Remote Sens. Environ., 114, 50–63,
https://doi.org/10.1016/j.rse.2009.08.009, 2010. a
WHO: Burden of disease from household air pollution for 2016, available at:
https://www.who.int/airpollution/data/HAP_BoD_results_May2018_final.pdf (last access: 28 October 2019), 2018. a
Wixted, A. J., Kinnaird, P., Larijani, H., Tait, A., Ahmadinia, A., and
Strachan, N.: Evaluation of LoRa and LoRaWAN for wireless sensor networks, in: 2016 IEEE SENSORS, Orlando, FL, 1–3., https://doi.org/10.1109/ICSENS.2016.7808712, 2016. a, b
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