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Tytuł artykułu

Distribution Patterns of Ambient Air Quality Pre- and During Pandemic in the Urban Area of Yogyakarta, Indonesia

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study identified the spatial distribution pattern of the ambient air quality in the Yogyakarta Urban Area. It was performed to determine the distribution pattern of SO2, CO, and NO2 concentrations for 2016–2019 (pre-pandemic) and 2020 (during pandemic). Furthermore, the spatial analysis was performed using the Inverse Distance Weighting interpolation method. This study proved that spatial modeling using this method has good accuracy, and it is easier to map the distribution pattern of ambient air quality. In 2020, most of the locations met the quality standard (62.64%). As a result, the SO2 and CO parameters immediately showed that most conditions are satisfactory. In 2016, the SO2 parameters met the quality standards at 74.24% of locations. In 2020, the number increased to 85.71%. In addition, the CO parameter reached the quality standard at 81.82% of locations in 2016 and a perfect level of 100% in 2020. This occurred due to the effects of the COVID-19 pandemic because most human and business activities decreased drastically. Therefore, all studies can be used as the basis for air quality modeling and post-COVID-19 predictions. This study is also important as a policy material in the monitoring and management system of ambient air quality in urban areas.
Rocznik
Strony
116--128
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Department of Environmental Engineering, Universitas Islam Indonesia, Kaliurang Str. km 14.5, Yogyakarta, 55584, Indonesia
  • Department of Environmental Engineering, Universitas Islam Indonesia, Kaliurang Str. km 14.5, Yogyakarta, 55584, Indonesia
  • Department of Environmental Engineering, Universitas Islam Indonesia, Kaliurang Str. km 14.5, Yogyakarta, 55584, Indonesia
autor
  • Department of Environmental Engineering, Universitas Islam Indonesia, Kaliurang Str. km 14.5, Yogyakarta, 55584, Indonesia
Bibliografia
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  • 4. Brontowiyono, W. 2021. Ecological Mitigation and Earth Restoration Strategies in the COVID-19 Post-Pandemic Era. Endless: International Journal of future studies, 4(2), 298–309.
  • 5. Cooper, C.D., Alley, F.C. 2011. Air Pollution Control: A Design Approach. Fourth Edition. Long Grove, IL: Wavelan Press, Inc.
  • 6. Elmina, E. 2016. Analisis Kualitas Udara dan Keluhan Kesehatan Yang Berkaitan Dengan Saluran pernapasan pada pemulung di tempat pembuangan akhir sampah (TPA) terjun kecamatan medan marlan, Medan: Universitas Sumatera Utara. http://repositori.usu.ac.id/handle/123456789/3029
  • 7. Fanelli, R.M. 2020. The Spatial and Temporal Variability of the Effects of Agricultural Practices on the Environment. Environments, 7(4). https://doi.org/10.3390/environments7040033
  • 8. Fitriana, D. 2019. Gambaran Kualitas Udara SO2 dan NO2, Faktor Individu, Penggunaan Masker Dan Keluhan Sesak Napas Pemulung (Studi Kasus di TPA Blondo Kabupaten Semarang). Semarang: FIK UNDIP. http://lib.unnes.ac.id/39728/1/6411414111%20_Optimized.pdf
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  • 16. Pahrudin, P., Chen, C.T., Liu, L.W. 2021. A modified theory of planned behavioral: A case of tourist intention to visita destination post pandemic Covid-19 in Indonesia. Heliyon, 7(10). https://doi.org/10.1016/j.heliyon.2021.e08230
  • 17. Ratnawati, H. 2010. Hubungan antara Kadar Karbon Monoksida (CO) Udara dan Tingkat Kewaspadaan Petugas Parkir di Tiga Jenis Tempat Parkir. Bandung: Fakultas Kedokteran Universitas Kristen Maranatha. https://media.neliti.com/media/publications/150760-ID-hubungan-antara-kadar-karbon-monoksida-c.pdf
  • 18. Rita, E., Chizoo, E., Cyril, U.S. 2021. Sustaining COVID-19 pandemic lockdown era air pollution impact through utilization of more renewable energy resources. Heliyon, 7(7). https://doi.org/10.1016/j.heliyon.2021.e07455
  • 19. Rohani, A., Morteza, T., Masoumeh, A. 2017. A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renewable Energy, 115, 411–422. DOI: 10.1016/j.renene.2017.08.061
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  • 23. Sihayuardhi, E.R., Brontowiyono, W., Maziya, FB., Hakim, L. 2021. The Effect of the COVID-19 Pandemic on Ambient Air Quality in Yogyakarta Urban Area Parameters SO2, CO and, NO2 with Inverse Distance Weighting (IDW). IOP Conference Series: Earth and Environmental Science, 933(1), 012013. DOI: 10.1088/1755–1315/933/1/012013
  • 24. Teodoro, A., Santos, P., Marques, J.E., Ribeiro, J., Mansilha, C., Melo, A., Duarte, L., Rodrigues de Almeida, C., Deolinda Flores, D. 2021. An Integrated Multi-Approach to Environmental Monitoring of a Self-Burning Coal Waste Pile: The São Pedro da Cova Mine (Porto, Portugal) Study Case. Environments, 8(6), 48. https://doi.org/10.3390/environments8060048
  • 25. Thomas, R., Khan, U.T., Valeo, C., Talebzadeh, F. 2021. An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data. Environments, 8(6), 50. https://doi.org/10.3390/environments8060050
  • 26. Wardhana,W.A. 2004. Dampak Pencemaran Lingkungan. Cetakan Keempat. Yogyakarta. Penerbit ANDI.
  • 27. Wen, L., Yang, C., Liao, X., Zhang, Y., Chai, X., Gao, W., Guo, S., Bi, Y., Tsang, S., Chen, Z., Qi, Z., Cai, Z. 2022. Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China. Journal of Environmental Sciences, 115, 443–452. https://doi.org/10.1016/j.jes.2021.07.009
  • 28. Yasrebi, J., Saffari, M., Fathi, H., Karimian, N., Moazallahi, M., Gazni, R. 2009. Evaluation and Comparison of Ordinary Kriging and Inverse Distance Weighting Method For Prediction Of Spatial Variability Of Some Soil Chemical Parameters. Research Journal of Biological Science, 4(1), 93–102. https://medwelljournals.com/abstract/?doi=rjbsci.2009.93.102
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fdcbad39-116e-4bc1-b69f-e3def267e56e
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