Pollutant Time Series Analysis for Improving Air-Quality in Smart Cities

  1. Raúl López-Blanco 1
  2. Miguel Chaveinte García 2
  3. Ricardo S. Alonso 1
  4. Javier Prieto 1
  5. Juan M. Corchado 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 AIR Institute
    info

    AIR Institute

    Carbajosa de la Sagrada, España

Revue:
IJIMAI

ISSN: 1989-1660

Année de publication: 2023

Volumen: 8

Número: 3

Pages: 98-112

Type: Article

DOI: 10.9781/IJIMAI.2023.08.005 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

D'autres publications dans: IJIMAI

Résumé

The evolution towards Smart Cities is the process that many urban centers are following in their quest for efficiency, resource optimization and sustainable growth. This step forward in the continuous improvement of cities is closely linked to the quality of life they want to offer their citizens. One of the key issues that can have the greatest impact on the quality of life of all city dwellers is the quality of the air they breathe, which can lead to illnesses caused by pollutants in the air. The application of new technologies, such as the Internet of Things, Big Data and Artificial Intelligence, makes it possible to obtain increasingly abundant and accurate data on what is happening in cities, providing more information to take informed action based on scientific data. This article studies the evolution of pollutants in the main cities of Castilla y León, using Generative Additive Models (GAM), which have proven to be the most efficient for making predictions with detailed historical data and which have very strong seasonalities. The results of this study conclude that during the COVID-19 pandemic containment period, there was an overall reduction in the concentration of pollutants.

Références bibliographiques

  • [1] Bank, 2021. [Online]. Available: https://data.worldbank.org/indicator/ SP.RUR.TOTL.ZS.
  • [2] G. Duranton, D. Puga, “The growth of cities,” Handbook of economic growth, vol. 2, pp. 781–853, 2014.
  • [3] E. Commission, “Sustainable urban mobility in the eu: No substantial improvement is possible without member states’ commitment,” Jun 2020. [Online]. Available: https://op.europa.eu/webpub/eca/special- reports/ urban-mobility-6-2020/en/.
  • [4] W. H. Organization, 2016. [Online]. Available: https://www.euro.who. int/ data/assets/pdf_file/ 0005/321971/Urban-green-spaces-and-healthreview- evidence.pdf.
  • [5] W. H. Organization, “Air pollution,” 2023. [Online]. Available: https:// www.who.int/health-topics/air- pollution.
  • [6] Y. Zhu, J. Xie, F. Huang, L. Cao, “Association between short-term exposure to air pollution and covid-19 infection: Evidence from china,” Science of the total environment, vol. 727, p. 138704, 2020.
  • [7] P. Chamoso, A. González-Briones, S. Rodríguez, J. M. Corchado, “Tendencies of technologies and platforms in smart cities: a state-of-the-art review,” Wireless Communications and Mobile Computing, vol. 2018, 2018.
  • [8] T. Yigitcanlar, R. Mehmood, J. M. Corchado, “Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures,” Sustainability, vol. 13, no. 16, p. 8952, 2021.
  • [9] V. Giannico, G. Spano, M. Elia, M. D’Este, G. Sanesi, R. Lafortezza, “Green spaces, quality of life, and citizen perception in european cities,” Environmental Research, vol. 196, p. 110922, 2021.
  • [10] R. Casado-Vara, P. Novais, A. B. Gil, J. Prieto, J. M. Corchado, “Distributed continuous-time fault estimation control for multiple devices in iot networks,” IEEE Access, vol. 7, pp. 11972–11984, 2019.
  • [11] P. Pihkala, “Eco-anxiety and environmental education,” Sustainability, vol. 12, no. 23, p. 10149, 2020.
  • [12] R. Casado-Vara, A. Martín del Rey, R. S. Alonso, S. Trabelsi, J. M. Corchado, “A new stability criterion for iot systems in smart buildings: Temperature case study,” Mathematics, vol. 8, no. 9, p. 1412, 2020.
  • [13] T. Yigitcanlar, N. Kankanamge, M. Regona, A. Ruiz Maldonado, B. Rowan, A. Ryu, K. C. Desouza, J. M. Corchado, R. Mehmood, R. Y. M. Li, “Artificial intelligence technologies and related urban planning and development concepts: How are they perceived and utilized in australia?,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 6, no. 4, p. 187, 2020.
  • [14] J. M. Corchado, F. Pinto-Santos, O. Aghmou, S. Trabelsi, “Intelligent development of smart cities: Deepint. net case studies,” in Sustainable Smart Cities and Territories, 2022, pp. 211–225, Springer.
  • [15] Y. Mezquita, A. González-Briones, R. Casado-Vara, P. Wolf, F. de la Prieta, A.-B. Gil-González, “Review of privacy preservation with blockchain technology in the context of smart cities,” in Sustainable Smart Cities and Territories, 2022, pp. 68–77, Springer.
  • [16] Y.-S. Chang, H.-T. Chiao, S. Abimannan, Y.-P. Huang, Y.-T. Tsai, K.-M. Lin, “An lstm-based aggregated model for air pollution forecasting,” Atmospheric Pollution Research, vol. 11, no. 8, pp. 1451–1463, 2020.
  • [17] J. M. Corchado, “Technologies for sustainable consumption - researchgate. net,” Apr 2021.
  • [18] R. López-Blanco, R. S. Alonso, J. Prieto, S. Trabelsi, “Automating the implementation of unsupervised machine learning processes in smart cities scenarios,” in Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference, 2023, pp. 71–80, Springer.
  • [19] T. Dimri, S. Ahmad, M. Sharif, “Time series analysis of climate variables using seasonal arima approach,” Journal of Earth System Science, vol. 129, no. 1, pp. 1–16, 2020.
  • [20] R. López-Blanco, J. H. Martín, R. S. Alonso, J. Prieto, “Time series forecasting for improving quality of life and ecosystem services in smart cities,” in Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence, 2023, pp. 74–85, Springer.
  • [21] A. Hasnain, Y. Sheng, M. Z. Hashmi, U. A. Bhatti, A. Hussain, M. Hameed, S. Marjan, S. U. Bazai, M. A. Hossain, M. Sahabuddin, et al., “Time series analysis and forecasting of air pollutants based on prophet forecasting model in jiangsu province, china,” Frontiers in Environmental Science, p. 1044, 2022.
  • [22] J. Shen, D. Valagolam, S. McCalla, “Prophet forecasting model: A machine learning approach to predict the concentration of air pollutants (pm2. 5, pm10, o3, no2, so2, co) in seoul, south korea,” PeerJ, vol. 8, p. e9961, 2020.
  • [23] G. Swamy, S. Nagendra, U. Schlink, “Impact of urban heat island on meteorology and air quality at microenvironments,” Journal of the Air & Waste Management Association, vol. 70, no. 9, pp. 876–891, 2020.
  • [24] J. Ngarambe, S. J. Joen, C.-H. Han, G. Y. Yun, “Exploring the relationship between particulate matter, co, so2, no2, o3 and urban heat island in seoul, korea,” Journal of Hazardous Materials, vol. 403, p. 123615, 2021.
  • [25] G. Miskell, W. Pattinson, L. Weissert, D. Williams, “Forecasting shortterm peak concentrations from a network of air quality instruments measuring pm2. 5 using boosted gradient machine models,” Journal of environmental management, vol. 242, pp. 56–64, 2019.
  • [26] W.-W. Li, R. Orquiz, J. H. Garcia, T. T. Espino, N. E. Pingitore, J. GardeaTorresdey, J. Chow, J. G. Watson, “Analysis of temporal and spatial dichotomous pm air samples in the el paso-cd. juarez air quality basin,” Journal of the Air & Waste Management Association, vol. 51, no. 11, pp. 1551–1560, 2001.
  • [27] S. Fei, R. A. Wagan, A. Hasnain, A. Hussain, U. A. Bhatti, E. Elahi, “Spatiotemporal impact of the covid-19 pandemic lockdown on air quality pattern in nanjing, china,” Frontiers in Environmental Science, p. 1548, 2022.
  • [28] Y. Wang, S. Zhu, C. Li, “Research on multistep time series prediction based on lstm,” 10 2019, pp. 1155–1159.
  • [29] M. C. Turner, Z. J. Andersen, A. Baccarelli, W. R. Diver, S. M. Gapstur, C. A. Pope III, D. Prada, J. Samet, G. Thurston, A. Cohen, “Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations,” CA: a cancer journal for clinicians, vol. 70, no. 6, pp. 460–479, 2020.
  • [30] “Datos abiertos de castilla y león. calidad del aire (por días).” https:// datosabiertos.jcyl.es/web/jcyl/set/es/medio-ambiente/calidad_aire_ historico/ 1284212629698. Accessed: 2023-07-07.
  • [31] W. Robson, “The math of prophet,” Jun 2019. [Online]. Available: https:// medium.com/future-vision/the- math-of-prophet-46864fa9c55a.
  • [32] S. J. Taylor, B. Letham, “Forecasting at scale,” 09 2017, doi: 10.7287/peerj. preprints.3190v2.
  • [33] AEMET, “Opendata aemet.” [Online]. Available: https://opendata.aemet.es.
  • [34] S. Abdullah, A. A. Mansor, N. N. L. M. Napi, W. N. W. Mansor, A. N. Ahmed, M. Ismail, Z. T. A. Ramly, “Air quality status during 2020 malaysia movement control order (mco) due to 2019 novel coronavirus (2019-ncov) pandemic,” Science of The Total Environment, vol. 729, p. 139022, 2020, doi: https://doi.org/10.1016/j.scitotenv.2020.139022.
  • [35] R. Bao, A. Zhang, “Does lockdown reduce air pollution? evidence from 44 cities in northern china,” Science of The Total Environment, vol. 731, p. 139052, 2020, doi: https://doi.org/10.1016/j.scitotenv.2020.139052.
  • [36] E. Bontempi, C. Carnevale, A. Cornelio, M. Volta, A. Zanoletti, “Analysis of the lockdown effects due to the covid-19 on air pollution in brescia (lombardy),” Environmental Research, vol. 212, p. 113193, 2022, doi: https://doi.org/10.1016/j.envres.2022.113193.
La source de données: Dialnet