Spatio-Temporal Analysis Polutan Karbon Monoksida (CO) Jakarta Selama Pandemi Menggunakan Sentinel-5P TROPOMI
DOI:
https://doi.org/10.52661/j_ict.v4i2.101Keywords:
Kualitas Udara, Karbon Monoksida, Sentinel-5P, Google Earth Engine, Climate ChangeAbstract
Memahami kualitas udara secara jangka panjang merupakan salah satu tantangan besar. Saat ini DKI Jakarta termasuk kota dengan dengan tingkat mobilisasi dan polusi yang paling tinggi. Namun tingkat polusi menurun sejak Covid-19 menyebar di Indonesia. Karbon dioksida (CO) sebagai salah satu polutan yang banyak dihasilkan dari sisa pembakaran kendaraan bermotor diteliti untuk melihat pengaruh adanya pembatasan kegiatan masyarakat selama pandemi dengan tingginya konsentrasi CO. Data citra satelit Sentinel-5P Tropomi diolah menggunakan Google Earth Engine untuk memonitoring kadar ambien CO. Spatio-temporal analysis dilakukan sebagai pendekatan dalam menganalisis data time series. Pola spatio temporal menunjukkan nilai konsentrasi vertical CO yang menurun jika dibandingkan sebelum terjadinya pandemi hingga 30%, dengan nilai koefisien regresi y = -0,2415x + 35,961 dan R² = 0,1456.
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[2] G. Kaplan, Yigit Avdan, Zehra et.al., 2019, Spaceborne Nitrogen Dioxide observations from the Sentinel-5P TROPOMI Over Turkey. Proceedings. 18. 10.3390/ECRS-3-06181.
[3] L. Wu, & R. Wang, 2005, Carbon Monoxide: Endogenous Production, Physiological Functions, and Pharmacological Applications, Pharmacological Reviews, 57(4). doi.org/10.1124/pr.57.4.3
[4] Sheffiera I., Lucky H., Dewantoro, et.al., 2021, Dinamika Konsentrasi Emisi Gas Karbon Monoksida (Co) Selama Periode Psbb Menggunakan Komputasi Berbasis Cloud Pada Google Earth Engine Studi Kasus di Provinsi DKI Jakarta, Indonesia (Dynamics of Gas Emission Concentration of Monoxide (CO) during PSBB Period Using Cloud Computing Based on Google Earth Engine, Case Study of DKI Jakarta Province, Indonesia), Majalah Ilmiah Globe. 23. 35 - 42. 10.24895/MIG.2021.23-1.1258Onisimo M., and L. Kumar, 2019, Google Earth Engine Applications, Reprinted from: Remote Sens. 2019, 11, 591, doi:10.3390/rs11050591
[5] Peraturan Menteri Lingkungan Hidup Dan Kehutanan Republik Indonesia, 2020, Nomor P.14/Menlhk/Setjen/Kum.1/7/2020 Tentang Indeks Standar Pencemar Udara
[6] V. Huijnen, H.J. Eskes, A. Poupkou, et al., 2010, Comparison of OMI NO2 tropospheric columns with an ensemble of global and European regional air quality models. Atmos. Chem. Phys., 10(7), 3273–3296.
[7] Liu et.al., 2009, A Remote Sensing Image Process Method of Supervised Classification under Grid Environment, IEEE.
[8] R. Khatami, G. Mountrakis, S.V. Stehman, 2016, A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment 177 (2016) 89–100. Elsevier. doi: 10.1016/j.rse.2016.02.028
[9] Hertanto S., 2017, Analisis Multivariat Performa Principal Polar Spectral Indices Pada Eo-1 Hyperion: Studi Kasus Pemetaan Lahan Padi, Magister Thesis, Universitas Brawijaya, http://repository.ub.ac.id/id/eprint/1840/
[10] L. Kumar, and Onisimo M., 2018, Google Earth Engine Applications Since Inception: Usage, Trends, and Potential, Reprinted from: Remote Sens. 2018, 10, 1509, doi:10.3390/rs10101509
[11] Onisimo M., and L.Kumar, 2019, Google Earth Engine Applications, Reprinted from: Remote Sens. 2019, 11, 591, doi:10.3390/rs11050591
[12] Mohamed M., 2021, Monitoring the Impacts of COVID-19 pandemic on Climate Change and the Environment on Egypt Using Sentinel-5P Images, and the Carbon Footprint Methodology, The Egyptian Journal of Remote Sensing and Space Sciences (2021), doi: 10.1016/j.ejrs. 2021.07.003
[13] Shams A., Masoud G, 2022, Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown, Journal of Environmental Management, Volume 312, 114902, ISSN 0301-4797, doi.org/10.1016/j.jenvman.2022.114902.
[14] J. de Vries, R. Voorsa, Barend O., et.al, 2016, TROPOMI on ESA’s Sentinel 5p ready for launch and use. Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), Proc. of SPIE Vol. 9688, 96880B. doi: 10.1117/12.2240839
[15] P. Schneider, P.D. Hamer, A. Kylling, et.al., 2021, Spatiotemporal Patterns in Data Availability of the Sentinel-5P NO2 Product over Urban Areas in Norway. Remote Sens. 2021, 13, 2095. https://doi.org/10.3390/rs13112095
[16] J. Veefkind, et al., 2012, TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens. Environ. 70–83.
[17] Van Weele, Levelt, Aben, I., et al., 2008, Science requirements document for TROPOMI, volume I. Mission and science objectives and observational requirements. KNMI.
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Published
2023-01-13
How to Cite
Suryoprayogo, H. (2023). Spatio-Temporal Analysis Polutan Karbon Monoksida (CO) Jakarta Selama Pandemi Menggunakan Sentinel-5P TROPOMI. Journal of Informatics and Communication Technology (JICT), 4(2), 47–54. https://doi.org/10.52661/j_ict.v4i2.101
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