ADAPTATION OF THE WEB-SERVICE OF AIR POLLUTION FORECASTING FOR OPERATION WITHIN CLOUD COMPUTING PLATFORM OF THE UKRAINIAN NATIONAL GRID INFRASTRUCTURE
DOI:
https://doi.org/10.15407/scine17.01.078Keywords:
Keywords: air pollution, atmospheric dispersion, web-systems, cloud computing.Abstract
Introduction. Air pollution modeling is a powerful tool that allows developing scientifically justified solutions to reduce the risks posed by atmospheric emissions of pollutants.
Problem Statement. Cloud computing infrastructures provide new opportunities for web-based air pollution forecasting systems. However the implementation of these capabilities requires changes in the architecture of the existing systems.
Purpose. The purpose is to adapt the web service of forecasting the atmospheric pollution in Ukraine to operate in the cloud computing platform of the Ukrainian National Grid infrastructure.
Materials and Methods. The web client – web server – cloud computing architecture was used. The calculation of the model is performed in the cloud infrastructure, while the client and server parts operate on separate computers.
Results. With the developed service the forecast of air pollution is possible for every point at the territory of Ukraine for more than thirty substances, including chlorine, ammonia, hydrogen sulfide and others. The forecast is performed using the data of the WRF-Ukraine numerical weather prediction system and visualized through a web interface. The capabilities of the developed system were demonstrated by the example of simulation of air pollution in part of Kyiv affected by the releases from the Energia incineration plant during pollution episode in September, 2019. The total releases of toluene gas from incineration plant and from the fire on spontaneous waste landfill, which is located a few km from Kyiv, were estimated and analyzed. For the considered period the fire could bring considerable additional amounts of pollutants to the studied region. The confidence interval for the maximum airborne concentration for the considered period is estimated from 0.7 to 2.1 mg·m-3 which is higher than the permissible value (0.6 mg· m-3).
Conclusions. The presented system could be used by institutions responsible for response to environmental accidents.
Keywords: air pollution, atmospheric dispersion, web-systems, cloud computing.
Introduction. Air pollution modeling is a powerful tool that allows developing scientifically justified solutions to reduce the risks posed by atmospheric emissions of pollutants.
Problem Statement. Cloud computing infrastructures provide new opportunities for web-based air pollution forecasting systems. However the implementation of these capabilities requires changes in the architecture of the existing systems.
Purpose. The purpose is to adapt the web service of forecasting the atmospheric pollution in Ukraine to operate in the cloud computing platform of the Ukrainian National Grid infrastructure.
Materials and Methods. The web client – web server – cloud computing architecture was used. The calculation of the model is performed in the cloud infrastructure, while the client and server parts operate on separate computers.
Results. With the developed service the forecast of air pollution is possible for every point at the territory of Ukraine for more than thirty substances, including chlorine, ammonia, hydrogen sulfide and others. The forecast is performed using the data of the WRF-Ukraine numerical weather prediction system and visualized through a web interface. The capabilities of the developed system were demonstrated by the example of simulation of air pollution in part of Kyiv affected by the releases from the Energia incineration plant during pollution episode in September, 2019. The total releases of toluene gas from incineration plant and from the fire on spontaneous waste landfill, which is located a few km from Kyiv, were estimated and analyzed. For the considered period the fire could bring considerable additional amounts of pollutants to the studied region. The confidence interval for the maximum airborne concentration for the considered period is estimated from 0.7 to 2.1 mg·m-3 which is higher than the permissible value (0.6 mg· m-3).
Conclusions. The presented system could be used by institutions responsible for response to environmental accidents.
References
Kadaverugu, R., Sharma, A., Matli, C., Biniwale, R. (2019). High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review. Asia-Pacific Journal of Atmospheric Sciences, 55, 539-556. https://doi.org/10.1007/s13143-019-00110-3
Shavrina, A. V., Sosonkin, M. G., Veles, A. A., Nochvay, V. I., Blum, O. B. (2008). Integrated Modeling Of Surface And Tropospheric Ozone For Kiev City. In Simulation and Assessment of Chemical Processes in a Multiphase Environment (Eds. I. Barnes, M. M. Kharytonov). NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. P. 345-356. https://doi.org/10.1007/978-1-4020-8846-9_26
Johnson, A. L., Abramson, M. J., Dennekamp, M., Williamson, G. J., Guo, Y. (2020). Particulate matter modelling techniques for epidemiological studies of open biomass fire smoke exposure: a review. Air Quality Atmosphere and Health, 13, 35-75. https://doi.org/10.1007/s11869-019-00771-z
Ma, S., Zhang, X., Gao, C., Tong, Q., Xiu, A., Zhao, H., Zhang, S. (2019). Simulating Performance of CHIMERE on a Late Autumnal Dust Storm over Northern China. Sustainability, 11, 1074. https://doi.org/10.3390/su11041074
Kim, G., Lee, M.-I., Lee, S., Choi, S.-D., Kim, S.-J., Song, C.-K. (2020). Numerical Modeling for the Accidental Dispersion of Hazardous Air Pollutants in the Urban Metropolitan Area. Atmosphere, 11, 477. https://doi.org/10.3390/atmos11050477
Kovalets, I. V., Romanenko, O., Synkevich, R. (2020). Adaptation of the RODOS system for analysis of possible sources of Ru-106 detected in 2017. Journal of Environmental Radioactivity, 220-221C, 106302. https://doi.org/10.1016/j.jenvrad.2020.106302
Rolph, G., Stein, A., Stunder, B. (2017). Real-time Environmental Applications and Display sYstem: READY. Environmental Modelling & Software, 95, 210-228. https://doi.org/10.1016/j.envsoft.2017.06.025
Kovalets, I. V., Maistrenko, S. Y., Khalchenkov, A. V., Zagreba, T. A., Khurtsilava, K. V., Anulich, S. N., Bespalov, V. P., Udovenko, O. I. (2017). Povitrya web-based software system for operational forecasting of atmospheric pollution after manmade accidents in Ukraine. Science and innovation, 13(6), 11-22. https://doi.org/10.15407/scin13.06.013
Scire, J. S., Strimaitis, D. G., Yamartino, R. J. (2000). A user's guide for the CALPUFF dispersion model (Version 5). USA, Concord: Earth Tech, Inc.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Xiang-Yu, H., Wang, W., Powers, J. G. (2008). A description of the advanced research WRF version 3. NCAR Technical Note NCAR/TN-475+STR. USA, Boulder: National Center for Atmospheric Research.
Kovalets, I. V., Khalchenkov, O. V., Polonsky, O. O. (2019). Using the WRF-Ukraine system for forecasting agrometeorological conditions. Mathematical Machines and Systems, 1, 36-48 [in Ukrainian].
Zynovyev, M., Svistunov, S., Sudakov, O., Boyko, Y. (2007). Ukrainian Grid Infrastructure: Practical Experience. 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. (6-8 Sept. 2007, Dortmund, Germany). Dortmund: IEEE. P. 165-169. https://doi.org/10.1109/IDAACS.2007.4488397
Mell, P., Grance, T. (2011). The NIST Definition of Cloud Computing. Special Publication, 800-145. USA, Gaithersburg: National Institute of Standards and Technology. URL: https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf (Last accessed: 28.05.2019).
Barclay, J. (2019). CALPUFF model, history and current status - part 1. Air Quality and Climate Change, 53(1), 8-9. URL: https://www.olores.org/en/techniques/dispersion-modelling/869-calpuff-model-history-and-current-status-part-1 (Last accessed: 28.05.2019).
SRTM3 Eurasia. URL: https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Eurasia/ (Last accessed: 28.05.2019).
Land-use USGS_LCI. URL: https://data.mint.isi.edu/files/raw-data/land-use/USGS_LCI/ (Last accessed: 28.05.2019).
Residents of the left-bank part of Kiev suffer from the stench at night. URL: http://kievvlast.com.ua/news/zhiteli-levoberezhnoj-chasti-kieva-zadyhayutsya-ot-voni-po-nocham (Last accessed: 28.05.2019).
Bezruk, Z. D., Porev, V. (2013). Study of the emission concentrations surface incinerator "Energy". Problems of Mechanical Engineering, 16(6), 37-42 [in Ukrainian].
Nammari, D. R., Hogland, W., Marques, M., Nimmermark, S., Moutavtchi, V. (2004). Emissions from a controlled fire in municipal solid waste bales. Waste Management, 24(1), 9-18. https://doi.org/10.1016/j.wasman.2003.08.003
Air Quality Guidelines for Europe (2nd Edition). WHO Regional Publications, European Series, No. 91. Copenhagen, Denmark: World Health Organization Regional Office for Europe, 2000.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Iван Ковалець, Світлана Майстренко , Олександр Халченков , Олександр Полонський , Тарас Донцов-Загреба , Констянтин Хурцилава , Олег Удовенко
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
