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Year 2022, Volume: 10 Issue: 4, 397 - 401, 19.10.2022

Damla Mengüş Bihter Daş

https://doi.org/10.17694/bajece.1180676

Abstract

References

  • [1] B. Li, Z. Qiu, J. Zheng Impacts of noise barriers on near-viaduct air quality in a city: a case study in Xi’an Build. Environ., 196 (2021), Article 107751, 10.1016/j.buildenv.2021.107751
  • [2] K.F. Lu, H.D. He, H.W. Wang, X.B. Li, Z.R. Peng Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas Build. Environ., 172 (2020), Article 106678, 10.1016/j.buildenv.2020.106678
  • [3] H.D. He, H.O. Gao Particulate matter exposure at a densely populated urban traffic intersection and crosswalk Environ. Pollut., 268 (2021), Article 115931, 10.1016/j.envpol.2020.115931
  • [4] A. Lak, M. Ramezani, R. Aghamolae Reviving the lost spaces under urban highways and bridges: an empirical study J. Place Manag. Dev., 12 (2019), pp. 469-484, 10.1108/JPMD-12-2018-0101
  • [5] A. Sharma, D.D. Massey, A. Taneja A study of horizontal distribution pattern of particulate and gaseous pollutants based on ambient monitoring near a busy highway Urban Clim., 24 (2018), pp. 643-656, 10.1016/j.uclim.2017.08.003
  • [6] K.F. Lu, H.D. He, H.W. Wang, X.B. Li, Z.R. Peng Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas Build. Environ., 172 (2020), 10.1016/j.buildenv.2020.106678
  • [7] T. Sheng, J. Pan, Y.S. Duan, Q.Z. Liu, Q.Y. Fu Study on characteristics of typical traffic environment air pollution in shanghai China Environ. Sci., 39 (8) (2019), pp. 3193-3200 http://www.zghjkx.com.cn/CN/Y2019/V39/I8/3193
  • [8] C. Wu, H. He, R. Song, Z. Peng Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method Build. Environ., 207 (2022), Article 107436, 10.1016/j.buildenv.2021.108436
  • [9] G. Kurnaz, A.S. Demir Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: case of industrial city Sakarya Urban Clim., 41 (2021), Article 101051, 10.1016/j.uclim.2021.101051
  • [10] P. Perez, C. Menares, C. Ramírez PM2.5 forecasting in Coyhaique, the most polluted city in the Americas Urban Clim., 32 (2020), p. 100608, 10.1016/j.uclim.2020.100608
  • [11] A. Aggarwal, D. Toshniwal A hybrid deep learning framework for urban air quality forecasting, J. Clean. Prod., 329 (2021), Article 129660, 10.1016/j.jclepro.2021.129660
  • [12] Bachechi, Chiara, vd. “Big Data Analytics and Visualization in Traffic Monitoring”. Big Data Research, c. 27, Şubat 2022, s. 100292. ScienceDirect, https://doi.org/10.1016/j.bdr.2021.100292.
  • [13] von Brömssen, Claudia, vd. “A Toolbox for Visualizing Trends in Large-Scale Environmental Data”. Environmental Modelling & Software, c. 136, Şubat 2021, s. 104949. ScienceDirect, https://doi.org/10.1016/j.envsoft.2020.104949.
  • [14] Huang, Wenke, vd. “A License Plate Recognition Data to Estimate and Visualise the Restriction Policy for Diesel Vehicles on Urban Air Quality: A Case Study of Shenzhen”. Journal of Cleaner Production, c. 338, Mart 2022, s. 130401. ScienceDirect, https://doi.org/10.1016/j.jclepro.2022.130401.
  • [15] Carro, Gustavo, vd. “Exploring Actionable Visualizations for Environmental Data: Air Quality Assessment of Two Belgian Locations”. Environmental Modelling & Software, c. 147, Ocak 2022, s. 105230. ScienceDirect, https://doi.org/10.1016/j.envsoft.2021.105230.
  • [16] Pérez-Campuzano, Darío, vd. “Visualizing the Historical COVID-19 Shock in the US Airline Industry: A Data Mining Approach for Dynamic Market Surveillance”. Journal of Air Transport Management, c. 101, Haziran 2022, s. 102194. ScienceDirect, https://doi.org/10.1016/j.jairtraman.2022.102194.
  • [17] Prasad, K. Rajendra, vd. “A Novel Data Visualization Method for the Effective Assessment of Cluster Tendency through the Dark Blocks Image Pattern Analysis”. Microprocessors and Microsystems, c. 93, Eylül 2022, s. 104625. ScienceDirect, https://doi.org/10.1016/j.micpro.2022.104625.
  • [18] A. Eldawy, M. Mokbel, A. Alharthi, A. Azaidy, K. Tarek, S. Ghani SHAHED: a MapReduce-based system for querying and visualizing spatio-temporal satellite data IEEE 31st International Conference on Data Engineering (2015), pp. 1585-1596, 10.1109/ICDE.2015.7113427
  • [19] G. Van der Snickt, S. Legrand, J. Caen, F. Vanmeert, M. Alfeld, K. Jansses Chemical imaging of stained-glass windows by means of macro X-ray fluorescence (MA-XRF) scanning.Microchem. J. (2016), pp. 615-622
  • [20] A. Syed, N. Gupta, G. Nayak, R. Lenka Big Data Visualization: Tools and Challenges IEEE 2nd International Conference on Contemporary Computing and Informatics (2016), pp. 656-660, 10.1109/IC3I.2016.7918044
  • [21] G. Carro, O. Schalm, W. Jacobs, and S. Demeyer, “Exploring actionable visualizations for environmental data: Air quality assessment of two Belgian locations,” Environmental Modelling & Software, vol. 147, p. 105230, Jan. 2022, doi: 10.1016/j.envsoft.2021.105230.
  • [22] SIM Air Quality - Station Data Download Continuous Monitoring Center https://sim.csb.gov.tr/STN/STN_Report/StationDataDownloadNew (2020) (accessed 17 June 2022)

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Year 2022, Volume: 10 Issue: 4, 397 - 401, 19.10.2022

Damla Mengüş Bihter Daş

https://doi.org/10.17694/bajece.1180676

Abstract

Air pollution is increasing day by day due to the increasing population, urbanization, and industrial development. In our country, the amounts of pollutants in the air are recorded every day at different points. These recorded data continue to be collected in an increasing amount day by day. Information overload, which renders the data meaningless, complicates the interpretation of these data. One of the ways to solve this problem is to visualize curves and trends in measured pollution concentrations over time. In this study, using the data provided by the continuous monitoring center of the Turkey Ministry of Environment, Urbanization and Climate Change, visualization of different pollutants in the air was provided. Scatter plots, line scatter plots, and bar plots were used as data visualization. Data visualization makes it easy for non-experts to estimate air quality information from the concentration profiles displayed.

Keywords

Data visualization air pollution line scatter point scatter bar plots air quality assessment visual analytics

References

  • [1] B. Li, Z. Qiu, J. Zheng Impacts of noise barriers on near-viaduct air quality in a city: a case study in Xi’an Build. Environ., 196 (2021), Article 107751, 10.1016/j.buildenv.2021.107751
  • [2] K.F. Lu, H.D. He, H.W. Wang, X.B. Li, Z.R. Peng Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas Build. Environ., 172 (2020), Article 106678, 10.1016/j.buildenv.2020.106678
  • [3] H.D. He, H.O. Gao Particulate matter exposure at a densely populated urban traffic intersection and crosswalk Environ. Pollut., 268 (2021), Article 115931, 10.1016/j.envpol.2020.115931
  • [4] A. Lak, M. Ramezani, R. Aghamolae Reviving the lost spaces under urban highways and bridges: an empirical study J. Place Manag. Dev., 12 (2019), pp. 469-484, 10.1108/JPMD-12-2018-0101
  • [5] A. Sharma, D.D. Massey, A. Taneja A study of horizontal distribution pattern of particulate and gaseous pollutants based on ambient monitoring near a busy highway Urban Clim., 24 (2018), pp. 643-656, 10.1016/j.uclim.2017.08.003
  • [6] K.F. Lu, H.D. He, H.W. Wang, X.B. Li, Z.R. Peng Characterizing temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in urban residential areas Build. Environ., 172 (2020), 10.1016/j.buildenv.2020.106678
  • [7] T. Sheng, J. Pan, Y.S. Duan, Q.Z. Liu, Q.Y. Fu Study on characteristics of typical traffic environment air pollution in shanghai China Environ. Sci., 39 (8) (2019), pp. 3193-3200 http://www.zghjkx.com.cn/CN/Y2019/V39/I8/3193
  • [8] C. Wu, H. He, R. Song, Z. Peng Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method Build. Environ., 207 (2022), Article 107436, 10.1016/j.buildenv.2021.108436
  • [9] G. Kurnaz, A.S. Demir Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: case of industrial city Sakarya Urban Clim., 41 (2021), Article 101051, 10.1016/j.uclim.2021.101051
  • [10] P. Perez, C. Menares, C. Ramírez PM2.5 forecasting in Coyhaique, the most polluted city in the Americas Urban Clim., 32 (2020), p. 100608, 10.1016/j.uclim.2020.100608
  • [11] A. Aggarwal, D. Toshniwal A hybrid deep learning framework for urban air quality forecasting, J. Clean. Prod., 329 (2021), Article 129660, 10.1016/j.jclepro.2021.129660
  • [12] Bachechi, Chiara, vd. “Big Data Analytics and Visualization in Traffic Monitoring”. Big Data Research, c. 27, Şubat 2022, s. 100292. ScienceDirect, https://doi.org/10.1016/j.bdr.2021.100292.
  • [13] von Brömssen, Claudia, vd. “A Toolbox for Visualizing Trends in Large-Scale Environmental Data”. Environmental Modelling & Software, c. 136, Şubat 2021, s. 104949. ScienceDirect, https://doi.org/10.1016/j.envsoft.2020.104949.
  • [14] Huang, Wenke, vd. “A License Plate Recognition Data to Estimate and Visualise the Restriction Policy for Diesel Vehicles on Urban Air Quality: A Case Study of Shenzhen”. Journal of Cleaner Production, c. 338, Mart 2022, s. 130401. ScienceDirect, https://doi.org/10.1016/j.jclepro.2022.130401.
  • [15] Carro, Gustavo, vd. “Exploring Actionable Visualizations for Environmental Data: Air Quality Assessment of Two Belgian Locations”. Environmental Modelling & Software, c. 147, Ocak 2022, s. 105230. ScienceDirect, https://doi.org/10.1016/j.envsoft.2021.105230.
  • [16] Pérez-Campuzano, Darío, vd. “Visualizing the Historical COVID-19 Shock in the US Airline Industry: A Data Mining Approach for Dynamic Market Surveillance”. Journal of Air Transport Management, c. 101, Haziran 2022, s. 102194. ScienceDirect, https://doi.org/10.1016/j.jairtraman.2022.102194.
  • [17] Prasad, K. Rajendra, vd. “A Novel Data Visualization Method for the Effective Assessment of Cluster Tendency through the Dark Blocks Image Pattern Analysis”. Microprocessors and Microsystems, c. 93, Eylül 2022, s. 104625. ScienceDirect, https://doi.org/10.1016/j.micpro.2022.104625.
  • [18] A. Eldawy, M. Mokbel, A. Alharthi, A. Azaidy, K. Tarek, S. Ghani SHAHED: a MapReduce-based system for querying and visualizing spatio-temporal satellite data IEEE 31st International Conference on Data Engineering (2015), pp. 1585-1596, 10.1109/ICDE.2015.7113427
  • [19] G. Van der Snickt, S. Legrand, J. Caen, F. Vanmeert, M. Alfeld, K. Jansses Chemical imaging of stained-glass windows by means of macro X-ray fluorescence (MA-XRF) scanning.Microchem. J. (2016), pp. 615-622
  • [20] A. Syed, N. Gupta, G. Nayak, R. Lenka Big Data Visualization: Tools and Challenges IEEE 2nd International Conference on Contemporary Computing and Informatics (2016), pp. 656-660, 10.1109/IC3I.2016.7918044
  • [21] G. Carro, O. Schalm, W. Jacobs, and S. Demeyer, “Exploring actionable visualizations for environmental data: Air quality assessment of two Belgian locations,” Environmental Modelling & Software, vol. 147, p. 105230, Jan. 2022, doi: 10.1016/j.envsoft.2021.105230.
  • [22] SIM Air Quality - Station Data Download Continuous Monitoring Center https://sim.csb.gov.tr/STN/STN_Report/StationDataDownloadNew (2020) (accessed 17 June 2022)
There are 22 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Araştırma Articlessi
Authors

Damla Mengüş This is me 0000-0002-6706-0230

Bihter Daş 0000-0002-2498-3297

Publication Date October 19, 2022
Published in Issue Year 2022 Volume: 10 Issue: 4

Cite

APA Mengüş, D., & Daş, B. (2022). Actionable Data Visualization for Air Quality Data in the Istanbul Location. Balkan Journal of Electrical and Computer Engineering, 10(4), 397-401. https://doi.org/10.17694/bajece.1180676
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