Environmental Modeling and Traffic Simulation: A multivariate approach to monitor urban air pollutant agents.
DOI:
https://doi.org/10.5753/jisa.2023.2378Keywords:
Environmental modeling, Vehicle Sensor Networks, Multivariate data analysisAbstract
This work presents an interdisciplinary assessment examining air quality tracking in urban environments. This application is well suited to be approached with wireless sensor networks' paradigm in their overall variations. The proposed modeling application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes in public transportation, addressing this study case with bus lines so that the mobiles spread the sampling activity through many different places visited during the route. Simultaneously, it alleviates power management restrictions, packaging dimensions (size and weight), and general maintenance issues. We perform environmental modeling based on real data considering temporal and spatial multivariate behavior on observed phenomena. We consider the city of São Paulo in our case study and parse the asserted data to create a multivariate map of samples, showing the behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Furthermore, the experiment considers a detailed description of roads, bus lines, vehicle itineraries, and general traffic information. The input data that has unformatted or missing information due to being sourced from real sensors is handled to create the map mentioned above. Our methodology addresses the following: 1) the mentioned environmental simulation, 2) the deployment of mobile sensor nodes and performing sensing process, 3) the implementation of network activity and delivery of collected data, 4) visualization of monitored environment based on gathered data using Voronoi Diagrams to fill blank data at non-reached areas. Finally, our VSN-based approach improved 126 times lower error and 11 times higher coverage compared to conventional monitoring with air quality stations.
Downloads
References
Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4):393-422. DOI: 10.1016/S1389-1286(01)00302-4.
Al-Ali, A.-R., Zualkernan, I., and Aloul, F. (2010). A mobile GPRS-sensors array for air pollution monitoring. IEEE Sensors Journal, 10(10):1666-1671. DOI: 10.1109/JSEN.2010.2045890.
Angelevska, B., Atanasova, V., and Andreevski, I. (2021). Urban air quality guidance based on measures categorization in road transport. Civil Engineering Journal-Tehran, 7(2):253-267. DOI: 10.28991/cej-2021-03091651.
Aquino, A., Junior, O., Frery, A., Albuquerque, E., and Mini, R. (2012). Musa: multivariate sampling algorithmfor wireless sensor networks. IEEE Transactions on Computers, 63(4):968-978. DOI: 10.1109/TC.2012.229.
Aurenhammer, F. (1991). Voronoi diagrams: A survey of a fundamental data structure. ACM Computing Surveys, 23:345-405. DOI: 10.1145/116873.116880.
Barthwal, A. and Acharya, D. (2022). Performance analysis of sensing-based extreme value models for urban air pollution peaks. Modeling Earth Systems And Environment, 8(3):4149-4163. DOI: 10.1007/s40808-022-01349-y.
Devarakonda, S., Sevusu, P., Liu, H., Liu, R., Iftode, L., and Nath, B. (2013). Real-time air quality monitoring through mobile sensing in metropolitan areas. In ACM International Workshop on Urban Computing. DOI: 10.1145/2505821.2505834.
Frery, A., Ramos, H., Alencar-Neto, J., Nakamura, E., and Loureiro, A. (2010). Data driven performance evaluation of wireless sensor networks. Sensors, 10(3):2150-2168. DOI: 10.3390/s100302150.
Hu, S.-C., Wang, Y.-C., Huang, C.-Y., and Tseng, Y.-C. (2009). A vehicular wireless sensor network for co² monitoring. In IEEE Sensors. DOI: 10.1109/ICSENS.2009.5398461.
Hu, S.-C., Wang, Y.-C., Huang, C.-Y., and Tseng, Y.-C. (2011). Measuring air quality in city areas by vehicular wireless sensor networks. Journal of Systems and Software, 84(11):2005-2012. DOI: 10.1016/j.jss.2011.06.043.
Kaivonen, S. and Ngai, E. (2020). Real-time air pollution monitoring with sensors on city bus. Digital Communications and Networks, 6(1):23-30. DOI: 10.1016/j.dcan.2019.03.003.
Khedo, K., Perseedoss, R., Mungur, A., et al. (2010). A wireless sensor network air pollution monitoring system. International Journal of Wireless & Mobile Networks, 2(2):31-45. DOI: 10.48550/arXiv.1005.1737.
Kumar, G. J. R., Agbulu, G. P., Rahul, V, T., Natarajan, V, A., and Gokul, K. (2022). A cloud-assisted mesh sensor network solution for public zone air pollution real-time data acquisition. Journal Of Ambient Intelligence And Humanized Computing. DOI: 10.1007/s12652-022-03704-4.
Lima, P., Câmara, G., and Queiroz, G. (2002). Geobr: Syntactic and semantic interchange of spatial data (in portuguese). Available online [link].
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wiessner, E. (2018). Microscopic traffic simulation using sumo. In 21st IEEE International Conference on Intelligent Transportation Systems, pages 2575-2582. IEEE. DOI: 10.1109/ITSC.2018.8569938.
Ma, Y., Richards, M., Ghanem, M., Guo, Y., and Hassard, J. (2008). Air pollution monitoring and mining based on sensor grid in london. Sensors, 8(6):3601-3623. DOI: 10.3390/s80603601.
Pavani, M. and Rao, T. (2017). Urban air pollution monitoring using wireless sensor networks: a comprehensive review. International Journal of Communication Networks and Information Security, 9(3):439-449. DOI: 10.17762/ijcnis.v9i3.2708.
Pebesma, E. and Heuvelink, G. (2016). Spatio-temporal interpolation using gstat. RFID Journal, 8(1):204-218. DOI: 10.32614/RJ-2016-014.
Puiu, S., Udristioiu, M. T., and Velea, L. (2022). Air pollution management: A multivariate analysis of citizens' perspectives and their willingness to use greener forms of transportation. International Journal Of Environmental Research And Public Health, 19(21). DOI: 10.3390/ijerph192114613.
Qin, X., Do, T. H., Hofman, J., Bonet, E. R., La Manna, V. P., Deligiannis, N., and Philips, W. (2022). Fine-grained urban air quality mapping from sparse mobile air pollution measurements and dense traffic density. Remote Sesing, 14(11). DOI: 10.3390/rs14112613.
R Core Team (2014). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available online [link].
Rashid, B. and Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of network and computer applications, 60:192-219. DOI: 10.1016/j.jnca.2015.09.008.
Shakhov, V., Materukhin, A., Sokolova, O., and Koo, I. (2022). Optimizing urban air pollution detection systems. Sensors, 22(13). DOI: 10.3390/s22134767.
Shakhov, V. and Sokolova, O. (2021). On modeling air pollution detection with internet of vehicles. In 15th International Conference on Ubiquitous Information Management and Communication. DOI: 10.1109/IMCOM51814.2021.9377350.
U.S. Environmental Protection Agency (2016a). Basic information about carbon monoxide (co) outdoor air pollution. Available online [link].
U.S. Environmental Protection Agency (2016b). Basic information about no2. Available online [link].
U.S. Environmental Protection Agency (2016c). How mobile source pollution affects your health. Available online [link].
U.S. Environmental Protection Agency (2017). Basic information about lead air pollution. Available online [link].
U.S. Environmental Protection Agency (2018a). Ground-level ozone basics. Available online [link].
U.S. Environmental Protection Agency (2018b). Particulate matter (pm) basics. Available online [link].
U.S. Environmental Protection Agency (2018c). Technical assistance document for the reporting of daily air quality – the air quality index (aqi). Available online [link].
U.S. Environmental Protection Agency (2019). Sulfur dioxide basics. Available online [link].
Vasconcelos, I., Martins, I., Figueiredo, C., and Aquino, A. (2018). A data sample algorithm applied to wireless sensor network with disruptive connections. Computer Networks, 146:1-11.
Völgyesi, P., Nádas, A., Koutsoukos, X., and Lédeczi, 'A. (2008). Air quality monitoring with sensormap. In 2008 International Conference on Information Processing in Sensor Networks. DOI: 10.1109/IPSN.2008.50.
Wang, Y.-C. and Chen, G.-W. (2017). Efficient data gathering and estimation for metropolitan air quality monitoring by using vehicular sensor networks. IEEE Transactions on Vehicular Technology, 66(8):7234-7248. DOI: 10.1109/TVT.2017.2655084.
Yi, W., Lo, K., Mak, T., Leung, K. S., Leung, Y., and Meng, M. L. (2015). A survey of wireless sensor network based air pollution monitoring systems. Sensors, 15(12):31392-31427. DOI: 10.3390/s151229859.
