Vol. 15 No. 2 (2024): (Issue in progress)
Research Article

Citizens as Environmental Sensors: Noise Mapping and Assessment on Lemnos Island, Greece, Using VGI and Web Technologies

Sofianos Sofianopoulos
Department of Geography, Harokopio University, Athens, Greece
Stefanos Stigas
Department of Geography, Harokopio University, Athens, Greece
Efstathios Stratakos
Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece
Konstantinos Tserpes
Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece
Antigoni Faka
Department of Geography, Harokopio University, Athens, Greece
Christos Chalkias
Department of Geography, Harokopio University, Athens, Greece
Cartographic background according to the measurements we received from the mobile devices: (a) All Day. (b) Day 7:00 a.m – 7:00 p.m. (c) Evening 07:00 p.m – 12:00 p.m. (d) Night 12:00 p.m. – 7:00 a.m.

Published 2024-06-14

Keywords

  • noise pollution,
  • VGI,
  • perception,
  • smartphones,
  • calibration,
  • system
  • ...More
    Less

How to Cite

Sofianopoulos, Sofianos, Stefanos Stigas, Efstathios Stratakos, Konstantinos Tserpes, Antigoni Faka, and Christos Chalkias. 2024. “Citizens As Environmental Sensors: Noise Mapping and Assessment on Lemnos Island, Greece, Using VGI and Web Technologies”. European Journal of Geography 15 (2):106-19. https://doi.org/10.48088/ejg.s.sof.15.2.106.119.
Received 2024-03-04
Accepted 2024-06-05
Published 2024-06-14

Abstract

Noise caused by industrial activities, urban life and traffic has a negative impact on people's health and quality of life. Environmental noise mapping is an important tool to study and address this problem. Through specialized measurement and analysis techniques, it is possible to identify areas at high risk of noise and understand the extent of the problem. This information can be used to develop strategies to protect the population exposed to high noise levels. In this paper, a method for collecting, processing, managing and mapping spatial noise data based on Volunteered Geographic Information (VGI) and web mapping technologies is presented. The proposed method uses noise pollution data collected from citizens using mobile devices. This includes both on-site measurements and information on users' subjective perceptions, providing a comprehensive picture of the problem. This enables authorities to take into account the individual perceptions and needs of citizens when planning noise mitigation measures, which has not been the case in the inter-national literature to date. To achieve this goal, a prototype of a free and open-source tool called "Noise Pollution" was developed. The proposed methodology and the resulting system are intended to help improve the quality of life in noisy environments and protect the health of citizens. They provide a practical tool for collecting and analyzing noise data and enable the development of noise mitigation strategies for the benefit of the population exposed to high noise levels.

Highlights:

  • Introduction of a novel method to collect, process, manage, and visualize noise pollution data using Volunteered Geographic Information (VGI) and web mapping technologies.
  • Highlighting the unique approach of combining objective noise measurements with citizens' subjective perceptions to gain a comprehensive understanding of noise exposure.
  • Presentation of the practical application of the developed mobile application on the island of Lemnos, Greece, to demonstrate its potential to improve environmental noise management and enhance citizens' quality of life.

Downloads

Download data is not yet available.

References

  1. ACOUSTICS BULLETIN. (2018). Institute of Acoustics, 43(4). https://www.ioa.org.uk/sites/default/files/Acoustics%20Bulletin%20July-August%202018.pdf
  2. Aghaei, S. (2012). Evolution of the World Wide Web : From Web 1.0 to Web 4.0. International Journal of Web & Semantic Technology, 3, 1–10. https://doi.org/10.5121/ijwest.2012.3101
  3. Alashaikh, A. S., & Alhazemi, F. M. (2022). Efficient Mobile Crowdsourcing for Environmental Noise Monitoring. IEEE Access, 10, 77251–77262. https://doi.org/10.1109/ACCESS.2022.3191780
  4. Alvares-Sanches, T., Osborne, P. E., & White, P. R. (2021). Mobile surveys and machine learning can improve urban noise mapping: Beyond a weighted measurements of exposure. Science of the Total Environment, 775, 145600. https://doi.org/10.1016/j.scitotenv.2021.145600
  5. Aram, S., Troiano, A., & Pasero, E. (2012). Environment Sensing using Smartphone. IEEE Sensors Applications Symposium Proceedings, pp. 1-4. https://doi.org/10.1109/SAS.2012.6166275
  6. Aumond, P., Can, A., Mallet, V., Gauvreau, B., & Guillaume, G. (2021). Global sensitivity analysis for road traffic noise modelling. Applied Acous-tics, 176. https://doi.org/10.1016/j.apacoust.2020.107899
  7. Awan, F. M., Minerva, R., & Crespi, N. (2021). Using Noise Pollution Data for Traffic Prediction in Smart Cities: Experiments Based on LSTM Recur-rent Neural Networks. IEEE Sensors Journal, 21(18), 20722–20729. https://doi.org/10.1109/jsen.2021.3100324
  8. Barry, T. M., Reagan, J. A., & null. (1978, January 1). FHWA highway traffic noise prediction model (United States. Federal Highway Administra-tion, Ed.). ROSA P. https://rosap.ntl.bts.gov/view/dot/30259
  9. Bescond, L. (2022). NoiseModelling Documentation Release 4.0.2. https://noisemodelling.readthedocs.io/_/downloads/en/latest/pdf/
  10. Biały, W., Bołoz, Ł., & Sitko, J. (2021). Mechanical Processing of Hard Coal as a Source of Noise Pollution. Case Study in Poland. Energies, 14(5), 1332. https://doi.org/10.3390/en14051332
  11. Bocher, E., Petit, G., Fortin, N., Picaut, J., Guillaume, G., & Palominos, S. (2016). OnoM@p: A Spatial Data Infrastructure dedicated to noise moni-toring based on volunteers measurements. PeerJ Preprints 4:e2273v2. https://peerj.com/preprints/2273/
  12. Boumchich, A., Picaut, J., & Bocher, E. (2022). Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Data-base for Environmental Noise Assessment. Sensors, 22, 8832. https://doi.org/10.3390/s22228832
  13. Chauhan, R., Shrestha, A., & Khanal, D. (2021). Noise pollution and effectiveness of policy interventions for its control in Kathmandu, Nepal. Environmental Science and Pollution Research, 28(27), 35678–35689. https://doi.org/10.1007/s11356-021-13236-7
  14. Cooper, A., Coetzee, S., & Kourie, D. (2017). Volunteered geographical information, crowdsourcing, citizen science and neogeography are not the same. In Proceedings of the ICA (Vol. 1). https://doi.org/10.5194/icaproc11312018
  15. Dubey, R., Bharadwaj, S., & Biswas, D. S. (2020). Intelligent noise mapping using smart phone on web platform. International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC), pp.69–74. https://doi.org/10.1109/ICSIDEMPC49020.2020.9299597
  16. Dubey, R., Bharadwaj, S., Zafar, M. I., Bhushan Sharma, V., & Biswas, S. (2020). Collaborative noise mapping using smartphone. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B4-2020, 253–260. https://doi.org/10.5194/isprs-archives-xliii-b4-2020-253-2020
  17. European Commission (2021). EUR-Lex - 52021DC0400 - EN - EUR-Lex “COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PAR-LIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS Pathway to a Healthy Planet for All EU Action Plan: ‘Towards Zero Pollution for Air, Water and Soil’ COM/2021/400 final.” Europa.eu. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021DC0400
  18. European Environment Agency (2020). Environmental noise in Europe — 2020. European Environment Agency. https://www.eea.europa.eu/publications/environmental-noise-in-europe
  19. European Parliament (2002). EUR-Lex - 32002L0049 - EN - EUR-Lex “Directive 2002/49/EC of the European Parliament and of the Council of 25 June 2002 relating to the assessment and management of environmental noise - Declaration by the Commission in the Conciliation Commit-tee on the Directive relating to the assessment and management of environmental noise.” Europa.eu. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32002L0049
  20. Givargis, S., & Mahmoud, M. (2008). Converting the UK calculation of road traffic noise (CORTN) to a model capable of calculating LAeq,L1h for the Tehran’s roads. Applied Acoustics, 69, 1108–1113. https://doi.org/10.1016/j.apacoust.2007.08.003
  21. Goodchild, M. F., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3(3), 231–241. https://doi.org/10.1080/17538941003759255
  22. Gössling, S., Humpe, A., Litman, T., & Metzler, D. (2019). Effects of Perceived Traffic Risks, Noise, and Exhaust Smells on Bicyclist Behaviour: An Economic Evaluation. Sustainability, 11(2), 408. https://doi.org/10.3390/su11020408
  23. Green, M., & Murphy, D. (2020). Environmental sound monitoring using machine learning on mobile devices. Applied Acoustics, 159, 107041. https://doi.org/10.1016/j.apacoust.2019.107041
  24. Hoffmann, B., Moebus, S., Stang, A., Beck, E.-M. ., Dragano, N., Mohlenkamp, S., Schmermund, A., Memmesheimer, M., Mann, K., Erbel, R., & Jockel, K.-H. . (2006). Residence close to high traffic and prevalence of coronary heart disease. European Heart Journal, 27(22), 2696–2702. https://doi.org/10.1093/eurheartj/ehl278
  25. ISO/TC 43/SC 1. (2018) Acoustics Soundscape Part 2: Data collection and reporting requirements. ISO. https://www.iso.org/standard/75267.html
  26. ISO/TS 12913-3:2019 (2019). Acoustics Soundscape Part 3: Data analysis. ISO. https://www.iso.org/standard/69864.html
  27. Koprowska, K., Łaszkiewicz, E., Kronenberg, J., & Marcińczak, S. (2018). Subjective perception of noise exposure in relation to urban green space availability. Urban Forestry & Urban Greening, 31, 93–102. https://doi.org/10.1016/j.ufug.2018.01.018
  28. Kurakula, V., Stoter, J. E., & Kluijver, H. de. (2007). 3D noise models: a methodology to improve noise modelling and 3D visualisation of noise in urban areas. Coordinates, 3(12), 24–29. https://research.utwente.nl/en/publications/3d-noise-models-a-methodology-to-improve-noise-modelling-and-3d-v
  29. Lee, H., Garg, S., & Lim, K. (2020). Crowdsourcing of environmental noise map using calibrated smartphones. Applied Acoustics. 160, 107130. https://doi.org/10.1016/j.apacoust.2019.107130
  30. Maisonneuve, N., Stevens, M., Niessen, M. E., & Steels, L. (2009). NoiseTube: Measuring and mapping noise pollution with mobile phones. In-formation Technologies in Environmental Engineering, 215–228. https://doi.org/10.1007/978-3-540-88351-7_16
  31. Markou, D. (2022). Exploring spatial patterns of environmental noise and perceived sound source dominance in urban areas. Case study: the city of Athens, Greece. European Journal of Geography, 13(4), 60–78. https://doi.org/10.48088/ejg.d.mar.13.2.060.078
  32. Marques, G., & Pitarma, R. (2019). Noise Mapping Through Mobile Crowdsourcing for Enhanced Living Environments. Lecture Notes in Computer Science, 670–679. https://doi.org/10.1007/978-3-030-22744-9_52
  33. Mesene, M., Meskele, M., & Mengistu, T. (2022). The proliferation of noise pollution as an urban social problem in Wolaita Sodo city, Wolaita zone, Ethiopia. Cogent Social Sciences, 8(1). https://doi.org/10.1080/23311886.2022.2103280
  34. Münzel, T., Schmidt, F. P., Steven, S., Herzog, J., Daiber, A., & Sørensen, M. (2018). Environmental Noise and the Cardiovascular System. Journal of the American College of Cardiology, 71(6), 688–697. https://doi.org/10.1016/j.jacc.2017.12.015
  35. Murphy, E., & King, E. A. (2014). Chapter 2 - principles of environmental noise (E. Murphy & E. A. King, Eds.; pp. 9–49). Elsevier. https://doi.org/10.1016/B978-0-12-411595-8.00002-1
  36. Murphy, E., & King, E. A. (2016). Smartphone-based noise mapping: Integrating sound level meter app data into the strategic noise mapping process. Science of the Total Environment, 562, 852–859. https://doi.org/10.1016/j.scitotenv.2016.04.076
  37. Neumann, A. (2008). Web Mapping and Web Cartography. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_1485
  38. Nivala, A.-M., Brewster, S., & Sarjakoski, L. (2008). Usability evaluation of web mapping sites. The Cartographic Journal, 45. https://doi.org/10.1179/174327708X305120
  39. Okokon, E., Turunen, A., UngLanki, S., Vartiainen, A., Tiittanen, P., & Lanki, T. (2015). RoadTraffic Noise: Annoyance, Risk Perception, and Noise Sensitivity in the Finnish Adult Population. International Journal of Environmental Research and Public Health, 12, 5712–5734. https://doi.org/10.3390/ijerph120605712
  40. Owoyemi, J., Falemara, B., & Owoyemi, A. (2016). Noise Pollution and Control in Wood Mechanical Processing Wood Industries. Sciprints. https://doi.org/10.20944/preprints201608.0236.v1
  41. Paiva, K. M., Cardoso, M. R. A., & Zannin, P. H. T. (2019). Exposure to road traffic noise: Annoyance, perception and associated factors among Brazil’s adult population. Science of the Total Environment, 650, 978–986. https://doi.org/10.1016/j.scitotenv.2018.09.041
  42. Paiva, K., Cardoso, M., & Rodrigues, R. (2015). Noise pollution and annoyance: An urban soundscapes study. Noise & Health, 17, 125–133. https://doi.org/10.4103/14631741.155833
  43. Picaut, J., Can, A., Fortin, N., Ardouin, J., & Lagrange, M. (2020). Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review. Sensors, 20(8), 2256. https://doi.org/10.3390/s20082256
  44. Picaut, J., Fortin, N., Bocher, E., Petit, G., Aumond, P., & Guillaume, G. (2019). An open-science crowdsourcing approach for producing communi-ty noise maps using smartphones. Building and Environment, 148, 20–33. https://doi.org/10.1016/j.buildenv.2018.10.049
  45. Radicchi, A. (2017). The HUSH CITY app. In press. https://opensourcesoundscapes.org/wp-content/uploads/2017/09/Radicchi_2017_Hush-City-app.pdf
  46. Radicchi, A., Henckel, D., & Memmel, M. (2018). Citizens as smart, active sensors for a quiet and just city. The case of the “open source sound-scapes” approach to identify, assess and plan “everyday quiet areas” in cities. Noise Mapping, 5, 1–20. https://doi.org/10.1515/noise20180001
  47. Ranpise, R., Tandel, B. & Singh, V. (2021). Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network. Noise Mapping, 8(1), 172-184. https://doi.org/10.1515/noise-2021-0013
  48. Rey Gozalo, G., Aumond, P., & Can, A. (2020). Variability in sound power levels: Implications for static and dynamic traffic models. Transportation Research Part D: Transport and Environment, 84, 102339. https://doi.org/10.1016/j.trd.2020.102339
  49. Sternad, D. (2018). It’s not (only) the mean that matters: variability, noise and exploration in skill learning. Current Opinion in Behavioral Sciences, 20, 183–195. https://doi.org/10.1016/j.cobeha.2018.01.004
  50. Technical Chamber of Greece. (2008). The problem of urban noise pollution – The importance of prevention techniques at the source, during propagation, at the receiver, and the role of the consumer. Technical Chamber of Greece. http://library.tee.gr/digital/m2301/m2301_hatziliberis.pdf
  51. The European Commission. (2015). Commission Directive (EU) 2015/996 of 19 May 2015 establishing common noise assessment methods ac-cording to Directive 2002/49/EC of the European Parliament and of the Council (Text with EEA relevance). Legislation.gov.uk. https://www.legislation.gov.uk/eudr/2015/996/introduction/adopted
  52. Veenendaal, B., Brovelli, M. A., & Li, S. (2017). Review of Web Mapping: Eras, Trends and Directions. ISPRS International Journal of Geo-Information, 6(10), 317. https://doi.org/10.3390/ijgi6100317
  53. World Health Organization. (2019). Environmental noise guidelines for the European Region. https://www.who.int/europe/publications/i/item/9789289053563
  54. Yang, W., He, J., He, C., & Cai, M. (2020). Evaluation of urban traffic noise pollution based on noise maps. Transportation Research Part D: Transport and Environment, 87(0). https://trid.trb.org/view/1729798
  55. Zhang, X., Kuehnelt, H., & De Roeck, W. (2021). Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network. Applied Sciences, 11(6), 2714. https://doi.org/10.3390/app11062714