Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Short communication: emerging technologies for biometeorology

Abstract

The first decade of the twenty-first century saw remarkable technological advancements for use in biometeorology. These emerging technologies have allowed for the collection of new data and have further emphasized the need for specific and/or changing systems for efficient data management, data processing, and advanced representations of new data through digital information management systems. This short communication provides an overview of new hardware and software technologies that support biometeorologists in representing and understanding the influence of atmospheric processes on living organisms.

This is a preview of subscription content, log in to check access.

References

  1. Abdelkader M, Shaqura M, Claudel CG, Gueaieb W (2013) A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, pp 25–34. doi: 10.1109/ICUAS.2013.6564670

  2. Allen MW, McKenzie RL (2010) Electronic UV dosimeters for research and education. In: NIWA UV Workshop, Queenstown, pp 7–9

  3. Alonso A, Muñoz-Carpena R, Kennedy RE, Murcia C (2016) Wetland landscape spatio-temporal degradation dynamics using the new Google Earth Engine cloud-based platform: opportunities for non-specialists in remote sensing. ASABE, St. Joseph. doi:10.13031/trans.59.11608

  4. Anagnostou MN, Kalogiros J, Nikolopoulos E, Derin Y, Anagnostou EN, Borga M (2017) Satellite rainfall error analysis with the use of high-resolution X-band dual-polarization radar observations over the Italian Alps. Springer, Cham, pp 279–286. doi:10.1007/978-3-319-35095-0_39

  5. Aursang SV, Dixit SK (2016) Automated environmental data acquisition system using Raspberry Pi. Int J Sci Eng Comput Technol Indian Association of Health, Research and Welfare, 6(7):282

  6. Baklanov A, Fritz S, Khachay M, Nurmukhametov O, Salk C, See L, Shchepashchenko D (2017) Vote aggregation techniques in the Geo-Wiki crowdsourcing game: a case study. Springer, Cham, pp 41–50. doi:10.1007/978-3-319-52920-2_4

  7. Bechtel B, Alexander PJ, Böhner J, Ching J, Conrad O, Feddema J, Mills G, See L, Stewart I (2015) Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS International Journal of Geo-Information 4(1):199–219. doi:10.3390/ijgi4010199

  8. Bernhard MC, Kent ST, Sloan ME, Evans MB, McClure LA, Gohlke JM (2015) Measuring personal heat exposure in an urban and rural environment. Environ Res 137:410–418. doi:10.1016/j.envres.2014.11.002

  9. Bik HM, Interactive P (2014) Phinch: an interactive, exploratory data visualization framework for–Omic datasets. bioRxiv, 009944. http://www.biorxiv.org/content/early/2014/10/03/009944

  10. Blumberg WG, Halbert KT, Supinie TA, Marsh PT, Thompson RL, Hart JA (2017) SHARPpy: an open source sounding analysis toolkit for the atmospheric sciences. Bull Am Meteorol Soc. doi: 10.1175/BAMS-D-15-00309.1

  11. Chemura, A., Mutanga, O. and Dube, T. (2016) Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions, Precision Agriculture. Springer US, pp. 1–23. doi: 10.1007/s11119-016-9495-0.

  12. Cope M, Mikhailova E, Post C, Schlautman M, McMillan P (2017) Developing an integrated cloud-based spatial-temporal system for monitoring phenology. Eco Inform 39:123–129

  13. Copeland P, Romano R, Zhang T, Hecht G, Zigmond D, Stefansen C (2013) Google disease trends: an update. Nature 457:1012–1014

  14. Cummings JN, Kiesler S (2005) Collaborative research across disciplinary and organizational boundaries. Soc Stud Sci Sage PublicationsSage CA, Thousand Oaks, 35(5):703–722. doi: 10.1177/0306312705055535.

  15. Darack E (2012) UAVs: the new frontier for weather research and prediction. Weatherwise Taylor & Francis Group, 65(2):20–27. doi: 10.1080/00431672.2012.653935

  16. Deville Cavellin L, Weichenthal S, Tack R, Ragettli MS, Smargiassi A, Hatzopoulou M (2016) Investigating the use of portable air pollution sensors to capture the spatial variability of traffic-related air pollution. Environ Sci Technol American Chemical Society, 50(1):313–320. doi: 10.1021/acs.est.5b04235

  17. Di Gennaro SF, Di Gennaro SF, Battiston E, Marco SD, Facini O, Matese A, Nocentini M, Palliotti A, Mugnai L (2016) Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathol Mediterr 55(2):262–275. doi:10.14601/Phytopathol_Mediterr-18312

  18. Dodge S, Bohrer G, Weinzierl R, Davidson SC, Kays R, Douglas D, Cruz S, Han J, Brandes D, Wikelski M (2013) The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data. Movement Ecology 1(1):3. doi:10.1186/2051-3933-1-3

  19. Dong J, Xiao X, Menarguez MA, Zhang G, Qin Y, Thau D, Biradar C, III BM (2016) Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens Environ 185:142–154. doi:10.1016/j.rse.2016.02.016

  20. Dutta P, Aoki PM, Kumar N, Mainwaring A, Myers C, Willett W, Woodruff A (2009) Common sense. In: Proceedings of the 7th ACM conference on embedded networked sensor systems - sensys ‘09, ACM Press, New York, p 349. doi: 10.1145/1644038.1644095

  21. Ebi KL, McGregor G, Burton I (2009) The status and prospects for biometeorology. In: Biometeorology for adaptation to climate variability and change, Springer Netherlands, Dordrecht, pp 269–278. doi: 10.1007/978-1-4020-8921-3_12

  22. Filippa G, Cremonese E, Migliavacca M, Galvagno M, Forkel M, Wingate L, Tomelleri E, Morra di Cella U, Richardson AD (2016) Phenopix: a R package for image-based vegetation phenology. Agric For Meteorol 220:141–150. doi:10.1016/j.agrformet.2016.01.006

  23. Filippis TD, Rocchi L, Vignaroli P, Bacci M, Tarchiani V, Rapisardi E (2016) Open source geoprocessing tools and meteorological satellite data for crop risk zones monitoring in Sub-Saharan Africa. PeerJ Inc. doi:10.7287/PEERJ.PREPRINTS.2265V2

  24. Förster K, Hanzer F, Winter B, Marke T, Strasser U (2016) An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0. 1.1). Geosci Model Dev Copernicus GmbH, 9(7):2315–2333

  25. Freifeld CC, Mandl KD, Reis BY, Brownstein JS (2008) HealthMap: global infectious disease monitoring through automated classification and visualization of internet media reports. J Am Med Inform Assoc Oxford University Press, 15(2):150–157. doi: 10.1197/jamia.M2544

  26. Gerrett N, Redortier B, Voelcker T, Havenith G (2013) A comparison of galvanic skin conductance and skin wettedness as indicators of thermal discomfort during moderate and high metabolic rates. J Therm Biol 38(8):530–538. doi:10.1016/j.jtherbio.2013.09.003

  27. Gharesifard M, Wehn U, van der Zaag P (2017) Towards benchmarking citizen observatories: features and functioning of online amateur weather networks. J Environ Manag. doi:10.1016/j.jenvman.2017.02.003

  28. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature Nature Publishing Group, 457(7232):1012–1014. doi: 10.1038/nature07634

  29. Gozzi F, Della Ventura G, Marcelli A (2016) Mobile monitoring of particulate matter: state of art and perspectives. Atmospheric Pollution Research 7(2):228–234. doi:10.1016/j.apr.2015.09.007

  30. Guo X (2016) Application of meteorological big data. In: 2016 16th International Symposium on Communications and Information Technologies (ISCIT). IEEE, pp 273–279. doi: 10.1109/ISCIT.2016.7751635

  31. Haj-Omar A, Thompson WL, Kim Y-S, Glick P, Tolley M, Coleman TP (2016) Stretchable and flexible adhesive-integrated antenna for biomedical applications. In: 2016 I.E. International Symposium on Antennas and Propagation (APSURSI), IEEE, pp 459–460. doi: 10.1109/APS.2016.7695938

  32. Hu K, Yang X, Zhong J, Fei F, Qi J (2017) Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data. Environ Sci Technol American Chemical Society, 51(3):1498–1507. doi: 10.1021/acs.est.6b04355

  33. Hulley GC, Duren RM, Hopkins FM, Hook SJ, Vance N, Guillevic P, Johnson WR, Eng BT, Mihaly JM, Jovanovic VM, Chazanoff SL, Staniszewski ZK, Kuai L, Worden J, Frankenberg C, Rivera G, Aubrey AD, Miller CE, Malakar NK, Sánchez Tomás JM, Holmes KT (2016) High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES). Atmos Meas Tech 9:2393–2408. doi:10.5194/amt-9-2393-2016

  34. Hwang K, Park S-K (2016) Experimental study of real-time comprehensive indoor air quality. Springer, Singapore, pp 151–155. doi:10.1007/978-981-10-1536-6_20

  35. Inoue T, Nagai S, Yamashita S, Fadaei H, Ishii R, Okabe K, Taki H, Honda Y, Kajiwara K, Suzuki R (2014) Unmanned aerial survey of fallen trees in a deciduous broadleaved forest in eastern Japan. PLoS one 9(10):e109881

  36. Jiang Q, Kresin F, Bregt AK, Kooistra L, Pareschi E, van Putten E, Volten H, Wesseling J (2016) Citizen sensing for improved urban environmental monitoring. J Sens Hindawi Publishing Corporation, 2016:1–9. doi: 10.1155/2016/5656245

  37. Jovašević-Stojanović M, Bartonova A, Topalović D, Lazović I, Pokrić B, Ristovski Z (2015) On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter. Environ Pollut 206:696–704. doi:10.1016/j.envpol.2015.08.035

  38. Kalluri S, Gundy J, Haman B, Paullin A, Van Rompay P, Vititoe D, Weiner A (2015) A high performance remote sensing product generation system based on a service oriented architecture for the next generation of geostationary operational environmental satellites. Remote Sens Multidisciplinary Digital Publishing Institute, 7(8):10385–10399. doi: 10.3390/rs70810385

  39. Kim DH, Lu N, Ma R, Kim YS, Kim RH, Wang S, Wu J, Won SM, Tao H, Islam A, Yu KJ, Kim TI, Chowdhury R, Ying M, Xu L, Li M, Chung HJ, Keum H, McCormick M, Liu P, Zhang YW, Omenetto FG, Huang Y, Coleman T, Rogers JA (2011) Epidermal electronics. Science, 333(6044):838–843

  40. Køster B, Søndergaard J, Nielsen JB, Allen M, Bjerregaard M, Olsen A, Bentzen J (2015) Feasibility of smartphone diaries and personal dosimeters to quantitatively study exposure to ultraviolet radiation in a small national sample. Photodermatology, Photoimmunology & Photomedicine 31(5):252–260. doi:10.1111/phpp.12179

  41. Kuras ER, Hondula DM, Brown-Saracino J (2015) Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: insights from a new approach to measuring heat exposure. Int J Biometeorol Springer Berlin Heidelberg, 59(10):1363–1372. doi: 10.1007/s00484-014-0946-x

  42. Kuras E, Bernhard M, Calkins M, Ebi K, Hess J, Kintziger K, Jagger M, Middel A, Scott AA, Spector J, Uejio C, Vanos J, Zaitchik B, Gohlke J, Hondula D (2017) Opportunities and challenges for personal heat exposure research. Environ Health Perspect. doi:10.1289/EHP556

  43. Matese A, Di Gennaro SF (2015) Technology in precision viticulture: a state of the art review. Int J Wine Res Dove Press, 7:69. doi: 10.2147/IJWR.S69405

  44. McDonough MacKenzie C, Murray G, Primack R, Weihrauch D (2016) Lessons from citizen science: assessing volunteer-collected plant phenology data with Mountain Watch. Biol Conserv. doi:10.1016/j.biocon.2016.07.027

  45. McKercher GR, Vanos JK (2017) Low-cost mobile air pollution monitoring in urban environments: a pilot study in Lubbock, Texas. Environ Technol 1–10. doi: 10.1080/09593330.2017.1332106

  46. McKercher GR, Salmond JA, Vanos JK (2017) Characteristics and applications of small, portable gaseous air pollution monitors. Environ Pollut 223:102–110. doi:10.1016/j.envpol.2016.12.045

  47. McKinley DC, Miller-Rushing AJ, Ballard HL, Bonney R, Brown H, Cook-Patton SC, Evans DM, French RA, Parrish JK, Phillips TB, Ryan SF, Shanley LA, Shirk JL, Stepenuck KF, Weltzin JF, Wiggins A, Boyle OD, Briggs RD, Chapin SF, Hewitt DA, Preuss PW, Soukup MA (2016) Citizen science can improve conservation science, natural resource management, and environmental protection. Biol Conserv. doi:10.1016/j.biocon.2016.05.015

  48. Mujlid HM (2016) Real-time monitoring of sand and dust storm winds using wireless sensor technology. Available at https://repository.lib.fit.edu/handle/11141/1073. Accessed 13 Mar 2017

  49. Neethirajan S (2017) Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research 12:15–29. doi:10.1016/j.sbsr.2016.11.004

  50. Ouwehand L, European Space Agency, Living Planet Symposium (Prague) (2016) Living planet symposium 2016: proceedings. ESA Communications. Available at http://adsabs.harvard.edu/abs/2016ESASP.740E.200L. Accessed 9 Mar 2017

  51. Pagels P, Wester U, Söderström M, Lindelöf B, Boldemann C (2016) Suberythemal sun exposures at Swedish schools depend on sky views of the outdoor environments—possible implications for pupils health. Photochem Photobiol 92(1):201–207. doi:10.1111/php.12540

  52. Palamuttam R, Mogrovejo RM, Mattmann C, Wilson B, Whitehall K, Verma R, McGibbney L, Ramirez P (2015) SciSpark: applying in-memory distributed computing to weather event detection and tracking. In: 2015 I.E. International Conference on Big Data (Big Data), IEEE, pp 2020–2026. doi: 10.1109/BigData.2015.7363983

  53. Raffa F, Ludeno G, Patti B, Soldovieri F, Mazzola S, Serafino F, Raffa F, Ludeno G, Patti B, Soldovieri F, Mazzola S, Serafino F (2017) X-band wave radar for coastal upwelling detection off the southern coast of Sicily. J Atmos Ocean Technol 34(1):21–31. doi:10.1175/JTECH-D-16-0049.1

  54. Rosemartin AH, Denny EG, Weltzin JF, Lee Marsh R, Wilson BE, Mehdipoor H, Zurita-Milla R, Schwartz MD (2015) Lilac and honeysuckle phenology data 1956–2014. Sci Data Nature Publishing Group, 2:150038. doi: 10.1038/sdata.2015.38

  55. Saini H, Thakur A, Ahuja S, Sabharwal N, Kumar N (2016) Arduino based automatic wireless weather station with remote graphical application and alerts. In: 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, pp 605–609. doi: 10.1109/SPIN.2016.7566768

  56. Sargent F (1963) The nature and nurture of biometeorology. AIBS Bull 13(3):20. doi:10.2307/1293081

  57. Schmit TJ, Gunshor MM, Menzel WP, Gurka JJ, Li J, Bachmeier AS, Schmit TJ, Gunshor MM, Menzel WP, Gurka JJ, Li J, Bachmeier AS (2005) Introducing the next-generation Advanced Baseline Imager of GOES-R. Bull Am Meteorol Soc 86(8):1079–1096. doi:10.1175/BAMS-86-8-1079

  58. Scott AA, Zaitchik B, Waugh DW, O’Meara K, Scott AA, Zaitchik B, Waugh DW, O’Meara K (2017) Intraurban temperature variability in Baltimore. J Appl Meteorol Climatol 56(1):159–171. doi:10.1175/JAMC-D-16-0232.1

  59. See L, Fritz S, Dias E, Hendriks E, Mijling B, Snik F, Stammes P, Vescovi FD, Zeug G, Mathieu P-P, et al (2016) Supporting earth-observation calibration and validation: a new generation of tools for crowdsourcing and citizen science. IEEE Geosci Remote Sens Mag IEEE, 4(3):38–50

  60. Sherwood RJ, Greenhalgh DMS (1960) A personal air sampler. Ann Occup Hyg Lond 2(2):127–132

  61. Stadler P, Farnleitner AH, Zessner M (2017) Development and evaluation of a self-cleaning custom-built auto sampler controlled by a low-cost RaspberryPi microcomputer for online enzymatic activity measurements. Talanta 162:390–397. doi:10.1016/j.talanta.2016.10.031

  62. Vanos JK, McKercher GR, Naughton K, Lochbaum M (2017) Schoolyard shade and sun exposure: assessment of personal monitoring during children’s physical activity. Photochem Photobiol. doi:10.1111/php.12721

  63. Wang YQ (2014) MeteoInfo: GIS software for meteorological data visualization and analysis. Meteorol. Appl John Wiley & Sons, Ltd, 21(2):360–368. doi: 10.1002/met.1345

  64. Wilson B, Palamuttam R, Whitehall K, Mattmann C, Goodman A, Boustani M, Shah S, Zimdars P, Ramirez P (2016) SciSpark: highly interactive in-memory science data analytics. In: 2016 I.E. International Conference on Big Data (Big Data), IEEE, pp 2964–2973. doi: 10.1109/BigData.2016.7840948

  65. Xie J, Yang C, Zhou B, Huang Q (2010) High-performance computing for the simulation of dust storms. Comput Environ Urban Syst 34(4):278–290. doi:10.1016/j.compenvurbsys.2009.08.002

  66. Zhao N, Cao G, Vanos JK, Vecellio DJ (2017) The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance. Int J Biometeorol Springer Berlin Heidelberg, pp 1–16. doi: 10.1007/s00484-017-1306-4

  67. Zheng Y, Chen X, Jin Q, Chen Y, Qu X, Liu X, Chang E, Ma W-Y, Rui Y, Sun W (2014) A cloud-based knowledge discovery system for monitoring fine-grained air quality. MSR-TR-2014--40, Tech Rep

Download references

Author information

Correspondence to Jennifer K. Vanos.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mehdipoor, H., Vanos, J.K., Zurita-Milla, R. et al. Short communication: emerging technologies for biometeorology. Int J Biometeorol 61, 81–88 (2017). https://doi.org/10.1007/s00484-017-1399-9

Download citation

Keywords

  • Hardware technology
  • Biometeorology
  • Data acquisition
  • Sensors
  • Software technology
  • Biometeorological data processing