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A Review on Air Quality Measurement Using an Unmanned Aerial Vehicle

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Abstract

In major cities, air quality is of significant concern because of its negative effect on the health of the region’s living conditions, climate, and economy. Recent studies show the significance of the data on microlevel pollution which includes severe air pollutants and their impacts on human. Conventional methods of measuring air quality need skilled personnel for accurate data measurement that are based on stationary and limited measuring station networks. However, it is costly to seize the spatio-temporal variability and to recognize pollution hotspots that are necessary to develop real-time exposure control strategies. Due to the restricted accessibility of information and the non-scalability of standard techniques for air pollution monitoring, a real-time system with both higher spatial and temporal resolution is crucial. In recent times, unmanned aerial vehicles (UAVs) mounted with various sensors have been implemented for on-site air quality surveillance as they can offer new methods and research possibilities in air pollution and emission tracking, as well as in the study of environmental developments. An extensive literature review has been conducted, and it was observed that there are types of UAVs and types of sensors that are used for air quality monitoring for the parameters like CO, SO2, NO2, O3, PM2.5, PM1.0, and black carbon. Low-cost wireless sensors have been using for monitoring purpose in the past studies, and when results obtained are validated with the stationary monitoring instruments, the coefficient of correlation (R2) is found to be varied from 0.3 to 0.9. The difficulties, however, are not just technical, but at present time, policies and laws, which vary from country to country, symbolize the major challenge to the extensive use of UAVs in air quality/monitoring studies.

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References

  • Aleixandre, M., & Gerboles, M. (2012). Review of small commercial sensors for indicative monitoring of ambient gas. Chemical Engineering Transactions, 30. https://doi.org/10.3303/CET1230029.

  • Altstädter, B., Platis, A., Wehner, B., Scholtz, A., Wildmann, N., Hermann, M., & Lampert, A. (2015). ALADINA–an unmanned research aircraft for observing vertical and horizontal distributions of ultrafine particles within the atmospheric boundary layer. Atmospheric Measurement Techniques, 8(4), 1627–1639. https://doi.org/10.5194/amt-8-1627-2015.

    Article  CAS  Google Scholar 

  • Alvarado, M., Gonzalez, F., Fletcher, A., & Doshi, A. (2015). Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open pit mine sites. Sensors, 15(8), 19667–19687. https://doi.org/10.3390/s150819667.

    Article  Google Scholar 

  • Alvarado, M., Gonzalez, F., Erskine, P., Cliff, D., & Heuff, D. (2017). A methodology to monitor airborne PM10 dust particles using a small unmanned aerial vehicle. Sensors, 17(2), 343. https://doi.org/10.3390/s17020343.

    Article  CAS  Google Scholar 

  • Alvear, O., Calafate, C. T., Hernández, E., Cano, J. C., & Manzoni, P. (2015). Mobile pollution data sensing using UAVs. In Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia (pp. 393–397). ACM. https://doi.org/10.1145/2837126.2843842.

  • Alvear, O., Zamora, W., Calafate, C., Cano, J. C., & Manzoni, P. (2016). An architecture offering mobile pollution sensing with high spatial resolution. Journal of Sensors, 2016. https://doi.org/10.1155/2016/1458147.

  • Alvear, O., Zema, N. R., Natalizio, E., & Calafate, C. T. (2017). Using UAV based systems to monitor air pollution in areas with poor accessibility. Journal of Advanced Transportation, 2017. https://doi.org/10.1155/2017/8204353.

  • Andaluz, V. H., Chicaiza, F. A., Cuzco, G., Carvajal, C. P., Ortiz, J. S., Morales, J., & Andaluz, G. M. (2019). Autonomous monitoring of air quality through an unmanned aerial vehicle. In International conference on industrial, engineering and other applications of applied intelligent systems. Cham: Springer, (146–157) https://doi.org/10.1007/978-3-030-22999-3_14.

    Chapter  Google Scholar 

  • Andersen, T., Scheeren, B., Peters, W., & Chen, H. (2018). A UAV based active AirCore system for measurements of greenhouse gases. Atmospheric Measurement Techniques, 11(5), 2683–2699. https://doi.org/10.5194/amt-11-2683-2018.

    Article  CAS  Google Scholar 

  • Anjomshoaa, A., Duarte, F., Rennings, D., Matarazzo, T. J., deSouza, P., & Ratti, C. (2018). City scanner: Building and scheduling a mobile sensing platform for smart city services. IEEE Internet of Things Journal, 5(6), 4567–4579. https://doi.org/10.1109/JIOT.2018.2839058.

    Article  Google Scholar 

  • Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C., & Hamburg, S. P. (2017). High resolution air pollution mapping with Google street view cars: Exploiting big data. Environmental Science & Technology, 51(12), 6999–7008. https://doi.org/10.1021/acs.est.7b00891.

    Article  CAS  Google Scholar 

  • Astuti, G., Giudice, G., Longo, D., Melita, C. D., Muscato, G., & Orlando, A. (2008). An overview of the “Volcan project”: An UAS for exploration of volcanic environments. In Unmanned aircraft systems. Dordrecht: Springer, (471–494). https://doi.org/10.1007/s10846-008-9275-9.

    Chapter  Google Scholar 

  • Aurell, J., Mitchell, W., Chirayath, V., Jonsson, J., Tabor, D., & Gullett, B. (2017). Field determination of multipollutant, open area combustion source emission factors with a hexacopter unmanned aerial vehicle. Atmospheric Environment, 166, 433–440. https://doi.org/10.1016/j.atmosenv.2017.07.046.

    Article  CAS  Google Scholar 

  • Australian Research Centre for Aerospace Automation (ARCAA) (n.d.). Enhanced Flight Assist System (eFAS) for automated Aerial Survey of Powerline Networks. Retrieved from http://www.arcaa.net/research/enhanced-flight-assist-system-efas-for-automated-aerial-survey-of-powerline-networks/. Accessed 10 Jan 2020.

  • Babaan, J. B., Ballori, J. P., Tamondong, A. M., Ramos, R. V., & Ostrea, P. M. (2018). Estimation of PM2.5 vertical distribution using customized UAV and Mobile sensors in Brgy. Up campus, Dil-iman, Quezon City. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(4/W9). https://doi.org/10.5194/isprs-archives-XLII-4-W9-89-2018.

  • Bates, T. S., Quinn, P. K., Johnson, J. E., Corless, A., Brechtel, F. J., Stalin, S. E., Meinig, C., & Burkhart, J. (2013). Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway, using unmanned aerial systems (UAS). Atmospheric Measurement Techniques, 6(8), 2115–2120. https://doi.org/10.5194/amt-6-2115-2013.

  • Beijing MEMC. (2018) Retrieved from http://www.bjmemc.com.cn/. Accessed 20 Jan 2020.

  • Berman, E. S., Fladeland, M., Liem, J., Kolyer, R., & Gupta, M. (2012). Greenhouse gas analyzer for measurements of carbon dioxide, methane, and water vapor aboard an unmanned aerial vehicle. Sensors and Actuators B: Chemical, 169, 128–135. https://doi.org/10.1016/j.snb.2012.04.036.

    Article  CAS  Google Scholar 

  • Bing, L., Qing-Hao, M., Jia-Ying, W., Biao, S., & Ying, W. (2015). Three dimensional gas distribution mapping with a micro drone. In 2015 34th Chinese Control Conference (pp. 6011–6015). IEEE. https://doi.org/10.1109/ChiCC.2015.7260580.

  • Black, O., Chen, J., Scircle, A., Zhou, Y., & Cizdziel, J. V. (2018). Adaption and use of a quadcopter for targeted sampling of gaseous mercury in the atmosphere. Environmental Science and Pollution Research, 25(13), 13195–13202. https://doi.org/10.1007/s11356-018-1775-y.

    Article  CAS  Google Scholar 

  • Bolla, G. M., Casagrande, M., Comazzetto, A., Dal Moro, R., Destro, M., Fantin, E., Colombatti, G., Aboudan, A., & Lorenzini, E. C. (2018). ARIA: Air pollutants monitoring using UAVs. In 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) (pp. 225–229). IEEE. https://doi.org/10.1109/MetroAeroSpace.2018.8453584.

  • Bouzid, Y., & Zareb, E. M. (2019). Air quality monitoring by using UAV flight system: A review. https://doi.org/10.13140/RG.2.2.19293.56803.

  • Brady, J. M., Stokes, M. D., Bonnardel, J., & Bertram, T. H. (2016). Characterization of a quadrotor unmanned aircraft system for aerosol particle concentration measurements. Environmental Science & Technology, 50(3), 1376–1383. https://doi.org/10.1021/acs.est.5b05320.

    Article  CAS  Google Scholar 

  • Bretschneider, T., & Shetti, K. (2014). UAV-based gas pipeline leak detection. In 35th Asian Conference on Remote Sensing 2014. ACRS 2014: Sensing for Reintegration of Societies.

  • Buschmann, M., Bange, J., & Vörsmann, P. (2004). 6.7 MMAV—A Miniature Unmanned Aerial Vehicle (Mini UAV) for meteorological purposes.

  • Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4), 4026–4047. https://doi.org/10.3390/rs70404026.

    Article  Google Scholar 

  • Carrozzo, M., De Vito, S., Esposito, E., Formisano, F., Salvato, M., Massera, E., Di Francia, G., Veneri, P. D., Ladaresta, M., & Mennella, A. (2018). An UAV mounted intelligent monitoring system for impromptu air quality assessments. In Convegno Nazionale Sensori. Cham: Springer, (497–506). https://doi.org/10.1007/978-3-030-04324-7_60.

    Chapter  Google Scholar 

  • Casazza, M., Lega, M., Jannelli, E., Minutillo, M., Jaffe, D., Severino, V., & Ulgiati, S. (2019). 3D monitoring and modelling of air quality for sustainable urban port planning: Review and perspectives. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.05.257.

  • Chen, J., Scircle, A., Black, O., Cizdziel, J. V., Watson, N., Wevill, D., & Zhou, Y. (2018a). On the use of multicopters for sampling and analysis of volatile organic compounds in the air by adsorption/thermal desorption GCMS. Air Quality, Atmosphere & Health, 11(7), 835–842. https://doi.org/10.1007/s11869-018-0588-y.

  • Chen, L. Y., Huang, H. S., Wu, C. J., Tsai, Y. T., & Chang, Y. S. (2018b). A lora based air quality monitor on unmanned aerial vehicle for smart city. In 2018 International Conference on System Science and Engineering (ICSSE) (pp. 1–5). IEEE. https://doi.org/10.1109/ICSSE.2018.8519967.

  • Cheng, Y., Li, X., Li, Z., Jiang, S., Li, Y., Jia, J., & Jiang, X. (2014). AirCloud: A cloud based air quality monitoring system for everyone. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (pp. 251–265). ACM. https://doi.org/10.1145/2668332.2668346.

  • Chiang, C. W., Das, S. K., Chiang, H. W., Nee, J. B., Sun, S. H., Chen, S. W., Lin, P. H., Chu, J. C., Su, S., & Su, L. S. (2015). A new mobile and portable scanning lidar for profiling the lower troposphere. Geoscientific Instrumentation, Methods and Data Systems, 4(1), 35–44. https://doi.org/10.5194/gi-4-35-2015.

    Article  Google Scholar 

  • Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013.

    Article  Google Scholar 

  • Corrigan, C. E., Roberts, G. C., Ramana, M. V., Kim, D., & Ramanathan, V. (2008). Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles. Atmospheric Chemistry and Physics, 8(3), 737–747.

    Article  CAS  Google Scholar 

  • Curry, J. A., Maslanik, J., Holland, G., & Pinto, J. (2004). Applications of Aerosondes in the Arctic. Bulletin of the American Meteorological Society, 85(12), 1855–1862. https://doi.org/10.1175/BAMS-85-12-1855.

    Article  Google Scholar 

  • Dai, R., Fotedar, S., Radmanesh, M., & Kumar, M. (2018). Quality aware UAV coverage and path planning in geometrically complex environments. Ad Hoc Networks, 73, 95–105. https://doi.org/10.1016/j.adhoc.2018.02.008.

    Article  Google Scholar 

  • Danilov, A. S., Smirnov, U. D., & Pashkevich, M. A. (2015). The system of the ecological monitoring of environment which is based on the usage of UAV. Russian Journal of Ecology, 46(1), 14–19. https://doi.org/10.1134/S1067413615010038.

    Article  Google Scholar 

  • Dey, A. (2018). Semiconductor metal oxide gas sensors: A review. Materials Science and Engineering: B, 229, 206–217. https://doi.org/10.1016/j.mseb.2017.12.036.

    Article  CAS  Google Scholar 

  • Diaz, J. A., Pieri, D., Wright, K., Sorensen, P., Kline-Shoder, R., Arkin, C. R., Fladelend, M., Bland, G., Buongiorno, M. F., Ramirez, C., Corrales, E., Alan, A., Algeria, O., Diaz, D., & Linick, J. (2015). Unmanned aerial mass spectrometer systems for in-situ volcanic plume analysis. Journal of the American Society for Mass Spectrometry, 26(2), 292–304. https://doi.org/10.1007/s13361-014-1058-x.

    Article  CAS  Google Scholar 

  • Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman, Y. J., Eck, T. F., & Slutsker, I. (2000). Accuracy assessments of aerosol optical properties retrieved from aerosol robotic network (AERONET) Sun and sky radiance measurements. Journal of Geophysical Research: Atmospheres, 105(D8), 9791–9806. https://doi.org/10.1029/2000JD900040.

    Article  CAS  Google Scholar 

  • Dunnington, L., & Nakagawa, M. (2017). Fast and safe gas detection from underground coal fire by drone fly over. Environmental Pollution, 229, 139–145. https://doi.org/10.1016/j.envpol.2017.05.063.

    Article  CAS  Google Scholar 

  • Evangelatos, O., & Rolim, J. (2015). An airborne wireless sensor system for near real time air pollution monitoring. Sensors & Transducers, 189(6), 12.

    Google Scholar 

  • Everaerts, J. (2008). The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. The international archives of the photogrammetry. Remote Sensing and Spatial Information Sciences, 37(2008), 1187–1192.

    Google Scholar 

  • Fahlstrom, P., & Gleason, T. (2012). Introduction to UAV systems. John Wiley & Sons.

  • Falabella, A. D., Wallin, D. O., & Lund, J. A. (2018). Application of a customizable sensor platform to detection of atmospheric gases by UAS. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 883–890). IEEE. https://doi.org/10.1109/ICUAS.2018.8453480.

  • Fladeland, M., Sumich, M., Lobitz, B., Kolyer, R., Herlth, D., Berthold, R., McKinnon, D., Monforton, L., Brass, J., & Bland, G. (2011). The NASA SIERRA science demonstration programme and the role of small–medium unmanned aircraft for earth science investigations. Geocarto International, 26(2), 157–163. https://doi.org/10.1080/10106049.2010.537375.

    Article  Google Scholar 

  • Fuertes, W., Carrera, D., Villacís, C., Toulkeridis, T., Galárraga, F., Torres, E., & Aules, H. (2015). Distributed system as internet of things for a new low cost, air pollution wireless monitoring on real time. In Proceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications (pp. 58–67). IEEE Press. https://doi.org/10.1109/DS-RT.2015.28.

  • Gao, Y., Dong, W., Guo, K., Liu, X., Chen, Y., Liu, X., Bu, J., & Chen, C. (2016). Mosaic: A low cost mobile sensing system for urban air quality monitoring. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (pp. 1–9). IEEE. https://doi.org/10.1109/INFOCOM.2016.7524478.

  • Girish, S. V., Prakash, R., & Ganesh, A. B. (2016). Real time remote monitoring of indoor air quality using internet of things (IoT) and GSM connectivity. In Artificial intelligence and evolutionary computations in engineering systems. New Delhi: Springer, (527–533). https://doi.org/10.1007/978-81-322-2656-7_48.

    Chapter  Google Scholar 

  • Gu, Q., & Jia, C. (2019). A Consumer UAV based air quality monitoring system for smart cities. In 2019 IEEE International Conference on Consumer Electronics (ICCE), IEEE, 1-6. https://doi.org/10.1109/ICCE.2019.8662050.

  • Gu, Q., Michanowicz, R., & D., & Jia, C. (2018). Developing a modular unmanned aerial vehicle (UAV) platform for air pollution profiling. Sensors, 18(12), 4363. https://doi.org/10.3390/s18124363.

    Article  Google Scholar 

  • Haas, P. Y., Balistreri, C., Pontelandolfo, P., Triscone, G., Pekoz, H., & Pignatiello, A. (2014). Development of an unmanned aerial vehicle UAV for air quality measurement in urban areas. In 32nd AIAA Applied Aerodynamics Conference (Vol. 2272). https://doi.org/10.2514/6.2014-2272.

  • Hansen, J., & Nazarenko, L. (2004). Soot climate forcing via snow and ice albedos. Proceedings of the National Academy of Sciences, 101(2), 423–428. https://doi.org/10.1073/pnas.2237157100.

    Article  CAS  Google Scholar 

  • Hardin, P. J., & Hardin, T. J. (2010). Small-scale remotely piloted vehicles in environmental research. Geography Compass, 4(9), 1297–1311. https://doi.org/10.1111/j.1749-8198.2010.00381.x.

  • Harrison, W. A., Lary, D. J., Nathan, B. J., & Moore, A. G. (2015). Using remote control aerial vehicles to study variability of airborne particulates. Air, soil and water research, 8, ASWR-S30774. 10.4137%2FASWR.S30774. https://doi.org/10.4137/ASWR.S30774.

  • Hasenfratz, D., Saukh, O., Sturzenegger, S., & Thiele, L. (2012). Participatory air pollution monitoring using smartphones. Mobile Sensing, 1, 1–5.

    Google Scholar 

  • Hasenfratz, D., Saukh, O., Walser, C., Hueglin, C., Fierz, M., Arn, T., Beutel, J., & Thiele, L. (2015). Deriving high resolution urban air pollution maps using mobile sensor nodes. Pervasive and Mobile Computing, 16, 268–285. https://doi.org/10.1016/j.pmcj.2014.11.008.

    Article  Google Scholar 

  • Hernández-Vega, J. I., Varela, E. R., Romero, N. H., Hernández-Santos, C., Cuevas, J. L. S., & Gorham, D. G. P. (2018). Internet of things (IoT) for monitoring air pollutants with an unmanned aerial vehicle (UAV) in a Smart City. In Smart technology. Cham: Springer,(108–120). https://doi.org/10.1007/978-3-319-73323-4_11.

    Chapter  Google Scholar 

  • Holland, G. J., McGeer, T., & Youngren, H. (1992). Autonomous aerosondes for economical atmospheric soundings anywhere on the globe. Bulletin of the American Meteorological Society, 73(12), 1987–1998. https://doi.org/10.1175/1520-0477(1992)073%3C1987:AAFEAS%3E2.0.CO;2.

    Article  Google Scholar 

  • Houston, A. L., Argrow, B., Elston, J., Lahowetz, J., Frew, E. W., & Kennedy, P. C. (2012). The collaborative Colorado–Nebraska unmanned aircraft system experiment. Bulletin of the American Meteorological Society, 93(1), 39–54. https://doi.org/10.1175/2011BAMS3073.1.

    Article  Google Scholar 

  • Hu, Y., Dai, G., Fan, J., Wu, Y., & Zhang, H. (2016). BlueAer: A fine grained urban PM2.5 3D monitoring system using mobile sensing. In IEEE INFOCOM 2016 The 35th Annual IEEE International Conference on Computer Communications (pp. 1–9). IEEE. https://doi.org/10.1109/INFOCOM.2016.7524479.

  • Hu, Z., Bai, Z., Yang, Y., Zheng, Z., Bian, K., & Song, L. (2019). UAV aided aerial ground IoT for air quality sensing in Smart City: Architecture, technologies, and implementation. IEEE Network, 33(2), 14–22. https://doi.org/10.1109/MNET.2019.1800214.

    Article  Google Scholar 

  • Inoue, Y., Morinaga, S., & Tomita, A. (2000). A blimp based remote sensing system for low altitude monitoring of plant variables: A preliminary experiment for agricultural and ecological applications. International Journal of Remote Sensing, 21(2), 379–385. https://doi.org/10.1080/014311600210894.

    Article  Google Scholar 

  • Inzelt, G. (2018). Conducting polymers: Past, present, future. Journal of Electrochemical Science and Engineering, 8(1), 3–37. https://doi.org/10.5599/jese.448.

    Article  CAS  Google Scholar 

  • Ippolito, C., Fladeland, M., & Yeh, Y. H. (2009). Applications of payload directed flight. In 2009 IEEE Aerospace conference (pp. 1–15). IEEE. https://doi.org/10.1109/AERO.2009.4839612.

  • Jacobson, M. Z., & Kaufman, Y. J. (2006). Wind reduction by aerosol particles. Geophysical Research Letters, 33(24). https://doi.org/10.1029/2006GL027838.

  • Jensen, T., Apan, A., Young, F., & Zeller, L. (2007). Detecting the attributes of a wheat crop using digital imagery acquired from a low altitude platform. Computers and Electronics in Agriculture, 59(1–2), 66–77. https://doi.org/10.1016/j.compag.2007.05.004.

    Article  Google Scholar 

  • Jones, K. H., & Gross, J. N. (2014). Reducing size, weight, and power (SWaP) of perception systems in small autonomous aerial systems. In 14th AIAA Aviation Technology, Integration, and Operations Conference (Vol. 2705). https://doi.org/10.2514/6.2014-2705.

  • Karion, A., Sweeney, C., Wolter, S., Newberger, T., Chen, H., Andrews, A., Kofler, J., Neff, D., & Tans, P. (2013). Long term greenhouse gas measurements from aircraft. Atmospheric Measurement Techniques, 6(3), 511–526. https://doi.org/10.5194/amt-6-511-2013.

    Article  Google Scholar 

  • Kersnovski, T., Gonzalez, F., & Morton, K. (2017). A UAV system for autonomous target detection and gas sensing. In 2017 IEEE Aerospace Conference (pp. 1–12). IEEE. https://doi.org/10.1109/AERO.2017.7943675.

  • Khan, A., Schaefer, D., Tao, L., Miller, D. J., Sun, K., Zondlo, M. A., Harrison, W. A., Roscoe, B., & Lary, D. J. (2012). Low power greenhouse gas sensors for unmanned aerial vehicles. Remote Sensing, 4(5), 1355–1368. https://doi.org/10.3390/rs4051355.

    Article  Google Scholar 

  • Kim, J. W., Jeong, Y. K., & Lee, I. W. (2012). Automatic sensor arrangement system for building energy and environmental management. Energy Procedia, 14, 265–270. https://doi.org/10.1016/j.egypro.2011.12.928.

    Article  Google Scholar 

  • Kodu, M., Berholts, A., Kahro, T., Eriksson, J., Yakimova, R., Avarmaa, T., Renge, I., Alles, H., & Jaaniso, R. (2019). Graphene based Ammonia sensors functionalised with sub monolayer V2O5: A comparative study of chemical vapour deposited and epitaxial Graphene. Sensors, 19(4), 951. https://doi.org/10.3390/s19040951.

    Article  CAS  Google Scholar 

  • Koppmann, R. (Ed.). (2008). Volatile organic compounds in the atmosphere. John Wiley & Sons.

  • Krishna, S. M., Gangadhar, M., & Rao, C. V. K. (2018). Ambient air quality monitoring PM2.5 with quadcopter in rajam town of srikakulam district of andhra pradesh. International Journal of Mechanical Engineering and Technology, 9, 780–785.

  • Kuantama, E., Tarca, R., Dzitac, S., Dzitac, I., Vesselenyi, T., & Tarca, I. (2019). The design and experimental development of air scanning using a sniffer Quadcopter. Sensors, 19(18), 3849. https://doi.org/10.3390/s19183849.

    Article  CAS  Google Scholar 

  • Kunz, M., Lavric, J. V., Gerbig, C., Tans, P., Neff, D., Hummelgård, C., Martin, H., Rödjegård, H., Wrenger, B., & Heimann, M. (2018). COCAP: A carbon dioxide analyser for small unmanned aircraft systems. Atmospheric Measurement Techniques, 11(3), 1833–1849. https://doi.org/10.5194/amt-11-1833-2018.

    Article  CAS  Google Scholar 

  • Lawrence, D. A., & Balsley, B. B. (2013). High resolution atmospheric sensing of multiple atmospheric variables using the DataHawk small airborne measurement system. Journal of Atmospheric and Oceanic Technology, 30(10), 2352–2366. https://doi.org/10.1175/JTECH-D-12-00089.1.

    Article  Google Scholar 

  • Lelieveld, J., Berresheim, H., Borrmann, S., Crutzen, P. J., Dentener, F. J., Fischer, H., Flatau, P. J., Heland, J., Holzinger, R., & Korrmann, R. (2002). Global air pollution crossroads over the Mediterranean. Science, 298(5594), 794–799. https://doi.org/10.1126/science.1075457.

    Article  CAS  Google Scholar 

  • Levy, R. C., Remer, L. A., & Kaufman, Y. J. (2004). Effects of neglecting polarization on the MODIS aerosol retrieval over land. IEEE Transactions on Geoscience and Remote Sensing, 42(11), 2576–2583. https://doi.org/10.1109/TGRS.2004.837336.

    Article  Google Scholar 

  • Li, J., Fu, Q., Huo, J., Wang, D., Yang, W., Bian, Q., Duan, Y., Zhang, Y., Pan, J., Lin, Y., Huang, K., Bai, Z., Wang, S. H., Fu, J. S., & Louie, P. K. K. (2015). Tethered balloon based black carbon profiles within the lower troposphere of Shanghai in the 2013 East China smog. Atmospheric Environment, 123, 327–338. https://doi.org/10.1016/j.atmosenv.2015.08.096.

    Article  CAS  Google Scholar 

  • Li, S., Xi, T., Tian, Y., & Wang, W. (2017a). Inferring fine grained PM2. 5 with bayesian based kernel method for crowdsourcing system. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1–6). IEEE. https://doi.org/10.1109/GLOCOM.2017.8254421.

  • Li, X. B., Wang, D. S., Lu, Q. C., Peng, Z. R., Lu, S. J., Li, B., & Li, C. (2017b). Three dimensional investi-gation of ozone pollution in the lower troposphere using an unmanned aerial vehicle platform. Environmental Pollution, 224, 107–116. https://doi.org/10.1016/j.envpol.2017.01.064.

    Article  CAS  Google Scholar 

  • Li, X. B., Wang, D. S., Lu, Q. C., Peng, Z. R., & Wang, Z. Y. (2018). Investigating vertical distribution patterns of lower tropospheric PM2.5 using unmanned aerial vehicle measurements. Atmospheric Environment, 173, 62–71. https://doi.org/10.1016/j.atmosenv.2017.11.009.

    Article  CAS  Google Scholar 

  • Li, B., Cao, R., Wang, Z., Song, R. F., Peng, Z. R., Xiu, G., & Fu, Q. (2019). Use of multi rotor unmanned aerial vehicles for fine grained roadside air pollution monitoring. Transportation research record, 0361198119847991, 10.1177%2F0361198119847991.

    Google Scholar 

  • Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair Rohani, H., AIMazroa, M. A., Amann, M., Anderson, H. R., Andrews, K. G., & Aryee, M. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859), 2224–2260. https://doi.org/10.1016/S0140-6736(12)61766-8.

    Article  Google Scholar 

  • Liu, C., Huang, J., Wang, Y., Tao, X., Hu, C., Deng, L., Xu, J., Xiao, H. W., Luo, L., Xiao, H. Y., & Xiao, W. (2019). Vertical distribution of PM2.5 and interactions with the atmospheric boundary layer during the development stage of a heavy haze pollution event. Science of The Total Environment, 135329. https://doi.org/10.1016/j.scitotenv.2019.135329.

  • Liu, B., Wu, C., Ma, N., Chen, Q., Li, Y., Ye, J., Martin, S. T., & Li, Y. J. (2020). Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China. Science of the Total Environment, 709, 136109. https://doi.org/10.1016/j.scitotenv.2019.136109.

    Article  CAS  Google Scholar 

  • Lopez-Peña, F., Varela, G., Paz-Lopez, A., Duro, R. J., & González-Castaño, F. J. (2010). Public transportation based dynamic urban pollution monitoring system. Sensors & Transducers, 8, 13.

    Google Scholar 

  • Malaver Rojas, J., Gonzalez, F., Motta, N., Depari, A., & Corke, P. (2012). Towards the development of a gas sensor system for monitoring pollutant gases in the low troposphere using small unmanned aerial vehicles. 2012 Workshop on Robotics for Environmental Monitoring, 1–3.

  • Malaver, A., Motta, N., Corke, P., & Gonzalez, F. (2015). Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases. Sensors, 15(2), 4072–4096. https://doi.org/10.3390/s150204072.

    Article  CAS  Google Scholar 

  • Marshall, D. M., Barnhart, R. K., Hottman, S. B., Shappee, E., & Most, M. T. (2016). Introduction to unmanned aircraft systems. CRC Press.

  • Martin, S., Bange, J., & Beyrich, F. (2011). Meteorological profiling of the lower troposphere using the research UAV" M2AV Carolo". Atmospheric Measurement Techniques, 4(4), 705–716. https://doi.org/10.5194/amtd-3-5179-2010.

    Article  Google Scholar 

  • Mayuga, G. P., Favila, C., Oppus, C., Macatulad, E., & Lim, L. H. (2018). Airborne particulate matter monitoring using uavs for smart cities and urban areas. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1398–1402). IEEE. https://doi.org/10.1109/TENCON.2018.8650293.

  • Mazeh, H., Saied, M., & Francis, C. (2018). Development of a multirotor based system for air quality monitoring. In In 2018 Third International Conference on Electrical and Biomedical Engineering, Clean Energy and Green Computing (EBECEGC), IEEE (pp. 23–28). https://doi.org/10.1109/EBECEGC.2018.8357127.

  • McKinney, K. A., Wang, D., Ye, J., de Fouchier, J. B., Guimarães, P. C., Batista, C. E., Souza, R. A. F., Alves, E. G., Gu, D., Guenther, A. B., & Martin, S. T. (2019). A sampler for atmospheric volatile organic compounds by copter unmanned aerial vehicles. Atmospheric Measurement Techniques, 12(6), 3123–3135. https://doi.org/10.5194/amt-12-3123-2019.

    Article  CAS  Google Scholar 

  • Melnyk, R., Schrage, D., Volovoi, V., & Jimenez, H. (2014). Sense and avoid requirements for unmanned aircraft systems using a target level of safety approach. Risk Analysis, 34(10), 1894–1906. https://doi.org/10.1111/risa.12200.

    Article  Google Scholar 

  • Messier, K. P., Chambliss, S. E., Gani, S., Alvarez, R., Brauer, M., Choi, J. J., Hamburg, J. J., Kerckhoffs, J., LaFranchi, B., Lunden, M. M., & Marshall, J. D. (2018). Mapping air pollution with Google street view cars: Efficient approaches with mobile monitoring and land use regression. Environmental Science & Technology, 52(21), 12563–12572. https://doi.org/10.1021/acs.est.8b03395.

    Article  CAS  Google Scholar 

  • Miller, D. J., Sun, K., Zondlo, M. A., Kanter, D., Dubovik, O., Welton, E. J., Winker, D. M., & Ginoux, P. (2011). Assessing boreal forest fire smoke aerosol impacts on US air quality: A case study using multiple data sets. Journal of Geophysical Research: Atmospheres, 116(D22). https://doi.org/10.1029/2011JD016170.

  • Miller, D. R., Akbar, S. A., & Morris, P. A. (2014). Nanoscale metal oxide based heterojunctions for gas sensing: A review. Sensors and Actuators B: Chemical, 204, 250–272. https://doi.org/10.1016/j.snb.2014.07.074.

    Article  CAS  Google Scholar 

  • Monks, P. S., Granier, C., Fuzzi, S., Stohl, A., Williams, M. L., Akimoto, H., Amann, H., Baklanov, A., Baltensperger, U., Bey, I., & Blake, N. (2009). Atmospheric composition change–global and regional air quality. Atmospheric Environment, 43(33), 5268–5350. https://doi.org/10.1016/j.atmosenv.2009.08.021.

    Article  CAS  Google Scholar 

  • Moseley, P. T. (2017). Progress in the development of semiconducting metal oxide gas sensors: A review. Measurement Science and Technology, 28(8), 082001. https://doi.org/10.1088/1361-6501/aa7443.

    Article  CAS  Google Scholar 

  • Neumann, P. P., Hernandez Bennetts, V., Lilienthal, A. J., Bartholmai, M., & Schiller, J. H. (2013). Gas source localization with a micro drone using bio inspired and particle filter based algorithms. Advanced Robotics, 27(9), 725–738. https://doi.org/10.1080/01691864.2013.779052.

    Article  Google Scholar 

  • Nolan, P., Pinto, J., González Rocha, J., Jensen, A., Vezzi, C., Bailey, S., Boer, G., Diehl, C., Laurence III, R., Powers, C., Foroutan, H., Ross, S., & Schmale III, D. (2018). Coordinated unmanned aircraft system (UAS) and ground based weather measurements to predict lagrangian coherent structures (LCSs). Sensors, 18(12), 4448. https://doi.org/10.3390/s18124448.

    Article  CAS  Google Scholar 

  • Novikov, S., Lebedeva, N., Satrapinski, A., Walden, J., Davydov, V., & Lebedev, A. (2016). Graphene based sensor for environmental monitoring of NO2. Sensors and Actuators B: Chemical, 236, 1054–1060. https://doi.org/10.1016/j.snb.2016.05.114.

    Article  CAS  Google Scholar 

  • Ozdemir, U., Aktas, Y. O., Vuruskan, A., Dereli, Y., Tarhan, A. F., Demirbag, K., Erdem, A., Kalaycioglu, G. D., Ozkol, I., & Inalhan, G. (2014). Design of a commercial hybrid VTOL UAV system. Journal of Intelligent & Robotic Systems, 74(1–2), 371–393. https://doi.org/10.1007/s10846-013-9900-0.

    Article  Google Scholar 

  • Padró-Martínez, L. T., Patton, A. P., Trull, J. B., Zamore, W., Brugge, D., & Durant, J. L. (2012). Mobile monitoring of particle number concentration and other traffic related air pollutants in a near highway neighborhood over the course of a year. Atmospheric Environment, 61, 253–264. https://doi.org/10.1016/j.atmosenv.2012.06.088.

    Article  CAS  Google Scholar 

  • Pan, Z., Yu, H., Miao, C., & Leung, C. (2017). Crowdsensing air quality with camera enabled mobile devices. In 2017 Twenty Ninth Conference on Association for the Advancement of Artificial Intelligence (AAAI), 4728–4733.

  • Peng, C. C., & Hsu, C. Y. (2015). Integration of an unmanned vehicle and its application to real-time gas detection and monitoring. In 2015 IEEE international conference on consumer electronics-Taiwan (pp. 320–321). IEEE. https://doi.org/10.1109/ICCE-TW.2015.7216921.

  • Peng, C. C., & Hsu, C. Y. (2015). Integration of an unmanned vehicle and its application to real-time gas detection and monitoring. In 2015 IEEE International Conference on Consumer Electronics-Taiwan (pp. 320–321). IEEE. https://doi.org/10.1109/ICCE-TW.2015.7216921.

  • Penza, M. (2020). Low cost sensors for outdoor air quality monitoring (pp. 235–288). In Advanced Nanomaterials for Inexpensive Gas Microsensors: Elsevier. https://doi.org/10.1016/B978-0-12-814827-3.00012-8.

  • Penza, M., Suriano, D., Pfister, V., Prato, M., & Cassano, G. (2017). Urban air quality monitoring with networked low cost sensor systems. In Multidisciplinary Digital Publishing Institute Proceedings, 1(4), 573. https://doi.org/10.3390/proceedings1040573.

  • Peters, J., Van den Bossche, J., Reggente, M., Van Poppel, M., De Baets, B., & Theunis, J. (2014). Cyclist exposure to UFP and BC on urban routes in Antwerp, Belgium. Atmospheric Environment, 92, 31–43. https://doi.org/10.1016/j.atmosenv.2014.03.039.

    Article  CAS  Google Scholar 

  • Pöllänen, R., Toivonen, H., Peräjärvi, K., Karhunen, T., Smolander, P., Ilander, T., Rintala, K., Katajainen, T., Nimela, J., Juusela, M., & Palos, T. (2009). Performance of an air sampler and a gamma ray detector in a small unmanned aerial vehicle. Journal of Radioanalytical and Nuclear Chemistry, 282(2), 433. https://doi.org/10.1007/s10967-009-0284-3.

    Article  CAS  Google Scholar 

  • Qiu, S., Chen, B., Wang, R., Zhu, Z., Wang, Y., & Qiu, X. (2017). Estimating contaminant source in chemical industry park using UAV based monitoring platform, artificial neural network and atmospheric dispersion simulation. RSC Advances, 7(63), 39726–39738. https://doi.org/10.1039/c7ra05637k.

    Article  CAS  Google Scholar 

  • Ramanathan, V., Crutzen, P. J., Lelieveld, J., Mitra, A. P., Althausen, D., Anderson, J., Andreae, M. O., Cantrell, W., Cass, G. R., Chung, C. E., & Clarke, A. D. (2001). Indian Ocean experiment: An integrated analysis of the climate forcing and effects of the great indo-Asian haze. Journal of Geophysical Research: Atmospheres, 106(D22), 28371–28398. https://doi.org/10.1029/2001JD900133.

    Article  CAS  Google Scholar 

  • Reuder, J., Jonassen, M. O., & Ólafsson, H. (2012). The small unmanned meteorological observer SUMO: Recent developments and applications of a micro-UAS for atmospheric boundary layer research. Acta Geophysica, 60(5), 1454–1473. https://doi.org/10.2478/s11600-012-0042-8.

    Article  Google Scholar 

  • Richards, M., Ghanem, M., Osmond, M., Guo, Y., & Hassard, J. (2006). Grid based analysis of air pollution data. Ecological Modelling, 194(1–3), 274–286. https://doi.org/10.1016/j.ecolmodel.2005.10.042.

    Article  CAS  Google Scholar 

  • Rionda, A., Marin, I., Martinez, D., Aparicio, F., Alija, A., Allende, A. G., Minambres, M., & Pañeda, X. G. (2013). UrVAMM—A full service for environmental urban and driving monitoring of professional fleets. In 2013 International Conference on New Concepts in Smart Cities: Fostering Public and Private Alliances (SmartMILE) (pp. 1–6). IEEE. https://doi.org/10.1109/SmartMILE.2013.6708173.

  • Rohi, G., Ejofodomi, O., & Ofualagba, G. (2020). Autonomous monitoring, analysis, and countering of air pollution using environmental drones. Heliyon, 6, e03252. https://doi.org/10.1016/j.heliyon.2020.e03252.

    Article  Google Scholar 

  • Roldán, J., Joossen, G., Sanz, D., del Cerro, J., & Barrientos, A. (2015). Mini UAV based sensory system for measuring environmental variables in greenhouses. Sensors, 15(2), 3334–3350. https://doi.org/10.3390/s150203334.

    Article  Google Scholar 

  • Rossi, M., Brunelli, D., Adami, A., Lorenzelli, L., Menna, F., & Remondino, F. (2014). Gas drone: Portable gas sensing system on UAVs for gas leakage localization. In SENSORS, 2014 IEEE (pp. 1431–1434). https://doi.org/10.1109/ICSENS.2014.6985282.

  • Rutkauskas, M., Asenov, M., Ramamoorthy, S., & Reid, D. T. (2019). Autonomous multispecies environmental gas sensing using drone based Fourier transform infrared spectroscopy. Optics Express, 27(7), 9578–9587. https://doi.org/10.1364/OE.27.009578.

    Article  CAS  Google Scholar 

  • Schuyler, T. J., Bailey, S. C., & Guzman, M. I. (2019). Monitoring tropospheric gases with small unmanned aerial systems (sUAS) during the second CLOUDMAP flight campaign. Atmosphere, 10(8), 434. https://doi.org/10.3390/atmos10080434.

    Article  CAS  Google Scholar 

  • Seiber, C., Nowlin, D., Landowski, B., & Tolentino, M. E. (2018). Tracking hazardous aerial plumes using IoT enabled drone swarms. In 2018 IEEE 4th World Forum on Internet of Things (WFIoT) (pp. 377–382). IEEE. https://doi.org/10.1109/WFIoT.2018.8355118.

  • Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: from air pollution to climate change. John Wiley & Sons.

  • Singh, E., Meyyappan, M., & Nalwa, H. S. (2017). Flexible graphene based wearable gas and chemical sensors. ACS Applied Materials & Interfaces, 9(40), 34544–34586. https://doi.org/10.1021/acsami.7b07063.

    Article  CAS  Google Scholar 

  • Smith, K. W. (2015). Drone technology: benefits, risks, and legal considerations. Seattle Journal of Environmental Law, 5(1).

  • Steinle, S., Reis, S., Sabel, C. E., Semple, S., Twigg, M. M., Braban, C. F., Leeson, S. R., Heal, M. R., Harrison, D., Lin, C., & Wu, H. (2015). Personal exposure monitoring of PM2. 5 in indoor and outdoor microenvironments. Science of the Total Environment, 508, 383–394. https://doi.org/10.1016/j.scitotenv.2014.12.003.

    Article  CAS  Google Scholar 

  • Tang, N., Jiang, Y., Qu, H., & Duan, X. (2017). Conductive polymer nanowire gas sensor fabricated by nanoscale soft lithography. Nanotechnology, 28(48), 485301. https://doi.org/10.1088/1361-6528/aa905b.

    Article  CAS  Google Scholar 

  • Toscano, P., Gioli, B., Dugheri, S., Salvini, A., Matese, A., Bonacchi, A., Zaldei, A., Cupelli, V., & Miglietta, F. (2011). Locating industrial VOC sources with aircraft observations. Environmental Pollution, 159(5), 1174–1182. https://doi.org/10.1016/j.envpol.2011.02.013.

    Article  CAS  Google Scholar 

  • U.S. Department of Energy (2010) Energy Efficiency and Renewable Energy, Industrial Technologies Program, “Wireless Sensor Technology”. Retrieved from www.eere.energy.gov/informationcenter. Accessed 20 Jan 2020.

  • Valavanis, K. P., & Vachtsevanos, G. J. (Eds.). (2015). Handbook of unmanned aerial vehicles (2993–3009). Dordrecht, The Netherlands: Springer. https://doi.org/10.1007/978-90-481-9707-1.

    Book  Google Scholar 

  • Van den Bossche, J., Peters, J., Verwaeren, J., Botteldooren, D., Theunis, J., & De Baets, B. (2015). Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset. Atmospheric Environment, 105, 148–161. https://doi.org/10.1016/j.atmosenv.2015.01.017.

    Article  CAS  Google Scholar 

  • Van den Kroonenberg, A., Martin, T., Buschmann, M., Bange, J., & Vörsmann, P. (2008). Measuring the wind vector using the autonomous mini aerial vehicle M2AV. Journal of Atmospheric and Oceanic Technology, 25(11), 1969–1982. https://doi.org/10.1175/2008JTECHA1114.1.

    Article  Google Scholar 

  • Velasco, A., Ferrero, R., Gandino, F., Montrucchio, B., & Rebaudengo, M. (2016). A mobile and low cost system for environmental monitoring: A case study. Sensors, 16(5), 710. https://doi.org/10.3390/s16050710.

    Article  Google Scholar 

  • Villa, T., Gonzalez, F., Miljievic, B., Ristovski, Z., & Morawska, L. (2016). An overview of small unmanned aerial vehicles for air quality measurements: Present applications and future prospectives. Sensors, 16(7), 1072. https://doi.org/10.3390/s16071072.

    Article  Google Scholar 

  • Villa, T. F., Jayaratne, E. R., Gonzalez, L. F., & Morawska, L. (2017). Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle. Environmental Pollution, 230, 134–142. https://doi.org/10.1016/j.envpol.2017.06.033.

    Article  CAS  Google Scholar 

  • Völgyesi, P., Nádas, A., Koutsoukos, X., & Lédeczi, Á. (2008). Air quality monitoring with sensormap. In Proceedings of the 7th international conference on Information processing in sensor networks (pp. 529–530). IEEE Computer Society. https://doi.org/10.1109/IPSN.2008.50.

  • Wang, M., Zhu, T., Zheng, J., Zhang, R. Y., Zhang, S. Q., Xie, X. X., & Li, Y. (2009). Use of a mobile laboratory to evaluate changes in on road air pollutants during the Beijing 2008 summer Olympics. Atmospheric Chemistry and Physics, 9(21), 8247–8263.

    Article  CAS  Google Scholar 

  • Wang, D., Wang, Z., & Peng, Z. R. (2019). Using unmanned aerial vehicle to investigate the vertical distribution of fine particulate matter. International Journal of Environmental Science and Technology, 1–12. https://doi.org/10.1007/s13762-019-02449-6.

  • Watai, T., Machida, T., Ishizaki, N., & Inoue, G. (2006). A lightweight observation system for atmospheric carbon dioxide concentration using a small unmanned aerial vehicle. Journal of Atmospheric and Oceanic Technology, 23(5), 700–710. https://doi.org/10.1175/JTECH1866.1.

    Article  Google Scholar 

  • Watts, A. C., Ambrosia, V. G., & Hinkley, E. A. (2012). Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sensing, 4(6), 1671–1692. https://doi.org/10.3390/rs4061671.

    Article  Google Scholar 

  • Weber, K., Heweling, G., Fischer, C., & Lange, M. (2017). The use of an octocopter UAV for the determination of air pollutants–a case study of the traffic induced pollution plume around a river bridge in Duesseldorf, Germany. International Journal of Environmental Science, 2, 63–68.

    Google Scholar 

  • Wich, S., & Koh, L. P. (2012). Conservation drones. GIM International, 26, 29–33.

    Google Scholar 

  • Wivou, J., Udawatta, L., Alshehhi, A., Alzaabi, E., Albeloshi, A., & Alfalasi, S. (2016). Air quality monitoring for sustainable systems via drone based technology. In 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICIAFS.2016.7946542.

  • Wong, C. J., MatJafri, M. Z., Abdullah, K., Lim, H. S., & Low, K. L. (2007). Temporal air quality monitoring using surveillance camera. In 2007 IEEE International Geoscience and Remote Sensing Symposium (pp. 2864–2868). IEEE. https://doi.org/10.1109/IGARSS.2007.4423441.

  • Woolley, C. C. A., et al. (2013). U.S. Patent No. 8,584,985. Washington, DC: U.S. Patent and Trademark Office.

  • Wyllie, T. (2001). Parachute recovery for UAV systems. Aircraft Engineering and Aerospace Technology, 73(6), 542–551. https://doi.org/10.1108/00022660110696696.

    Article  Google Scholar 

  • Yang, Y., Zheng, Z., Bian, K., Jiang, Y., Song, L., & Han, Z. (2017a). Arms: a finegrained 3D AQI realtime monitoring system by UAV. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1–6). IEEE. https://doi.org/10.1109/GLOCOM.2017.8253968.

  • Yang, Y., Zheng, Z., Bian, K., Song, L., & Han, Z. (2017b). Real time profiling of fine grained air quality index distribution using UAV sensing. IEEE Internet of Things Journal, 5(1), 186–198. https://doi.org/10.1109/JIOT.2017.2777820.

    Article  Google Scholar 

  • Yang, Y., Bai, Z., Hu, Z., Zheng, Z., Bian, K., & Song, L. (2018a). AQNet: finegrained 3D spatio-temporal air quality monitoring by aerial ground WSN. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1–2). IEEE. https://doi.org/10.1109/INFCOMW.2018.8406985.

  • Yang, Y., Zheng, Z., Bian, K., Song, L., & Han, Z. (2018b). Sensor deployment recommendation for 3D fine grained air quality monitoring using semi supervised learning. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICC.2018.8422656.

  • Yang, Y., Hu, Z., Bian, K., & Song, L. (2019). ImgSensingNet: UAV vision guided aerial-ground air quality sensing system. In IEEE INFOCOM 2019-IEEE conference on computer communications. IEEE, (1207–1215). https://doi.org/10.1109/INFOCOM.2019.8737374.

  • Yao, W. (2014). In H. C. Liu & W. P. Sung (Eds.), Information technology and computer application engineering. CRC Press.

  • Yi, W. Y., Lo, K. M., Mak, T., Leung, K. S., Leung, Y., & Meng, M. L. (2015). A survey of wireless sensor network based air pollution monitoring systems. Sensors, 15(12), 31392–31427. https://doi.org/10.3390/s151229859.

    Article  Google Scholar 

  • Yungaicela-Naula, N. M., Zhang, Y., Garza-Castañon, L. E., & Minchala, L. I. (2018). UAV-based air pollutant source localization using gradient and probabilistic methods. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 702–707). IEEE. https://doi.org/10.1109/ICUAS.2018.8453430.

  • Zang, W., Lin, J., Wang, Y., & Tao, H. (2012). Investigating small scale water pollution with UAV remote sensing technology. In World Automation Congress 2012 (pp. 1–4). IEEE.

  • Zappa, D., Galstyan, V., Kaur, N., Arachchige, H. M. M., Sisman, O., & Comini, E. (2018). “Metal oxide based heterostructures for gas sensors” A Review. Analytica chimica acta. https://doi.org/10.1016/j.aca.2018.09.020.

  • Zappi, P., Bales, E., Park, J. H., Griswold, W., & Rosing, T. Š. (2012). The citisense air quality monitoring mobile sensor node. In Proceedings of the 11th ACM/IEEE Conference on Information Processing in Sensor Networks. Beijing: China.

  • Zhao, P., Yang, Y., Zhang, Y., Bian, K., Song, L., Qiao, P., & Li, Z. (2018). Optimal trajectory planning of drones for 3d mobile sensing. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE. https://doi.org/10.1109/GLOCOM.2018.8647796.

  • Zhu, Y., Wu, Z., Park, Y., Fan, X., Bai, D., Zong, P., QIN, B., Cai, X., & Ahn, K. H. (2019). Measurements of atmospheric aerosol vertical distribution above North China plain using hexacopter. Science of the Total Environment, 665, 1095–1102. https://doi.org/10.1016/j.scitotenv.2019.02.100.

  • Zirnig, W., Ulbricht, M., Fix, A., & Klingenberg, H. (2004). Helicopter-borne laser methane detection system-a new tool for efficient gas pipeline inspection. In 2004 Vancouver International Gas Research Conference (IGRC).

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Lambey, V., Prasad, A.D. A Review on Air Quality Measurement Using an Unmanned Aerial Vehicle. Water Air Soil Pollut 232, 109 (2021). https://doi.org/10.1007/s11270-020-04973-5

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