Analisis Hubungan Karbon Monoksida dengan Variabel Lingkungan Menggunakan Google Earth Engine
Abstract
As the center of government of Central Java Province, Semarang City is experiencing many changes and developments, and various human activities occur in this city. This can affect air quality, namely an increase in air pollution, one of which is carbon monoxide. This pollutant can cause serious problems because it adversely affects public health and the environment. So, efforts need to be made to overcome it, such as monitoring the condition of carbon monoxide and environmental factors that have the potential to influence its increase, such as vegetation density, surface temperature, and rainfall. This research aims to monitor carbon monoxide, as well as these environmental factors using Google Earth Engine and satellite data. In addition, it aims to understand the relationship between carbon monoxide and some of these environmental factors. The results show that the northern region of Semarang City has higher carbon monoxide levels. Also, areas with low vegetation density and rainfall, as well as high surface temperatures, tend to have higher carbon monoxide levels.
Keywords: Google Earth Engine; Carbon Monoxide; NDVI; LST; Rainfall
Abstrak
Sebagai pusat pemerintahan Provinsi Jawa Tengah, Kota Semarang mengalami banyak perubahan dan perkembangan, serta berbagai aktivitas manusia terjadi di kota ini. Hal ini dapat berpengaruh terhadap kualitas udara, yaitu adanya peningkatan polusi udara, salah satunya karbon monoksida. Polutan ini dapat menimbulkan permasalahan serius karena berdampak buruk bagi kesehatan masyarakat dan lingkungan. Sehingga, perlu dilakukan upaya untuk mengatasinya, seperti melakukan pemantauan terhadap kondisi karbon monoksida dan faktor lingkungan yang berpotensi mempengaruhi peningkatannya, seperti kerapatan vegetasi, suhu permukaan, dan curah hujan. Penelitian ini bertujuan untuk memantau karbon monoksida, serta faktor-faktor lingkungan tersebut menggunakan Google Earth Engine dan data satelit. Selain itu, bertujuan untuk memahami hubungan antara karbon monoksida dengan beberapa faktor lingkungan tersebut. Hasil penelitian menunjukkan bahwa wilayah utara Kota Semarang memiliki kadar karbon monoksida lebih tinggi. Juga, daerah dengan kerapatan vegetasi dan curah hujan rendah, serta suhu permukaan yang tinggi, cenderung memiliki kadar karbon monoksida yang lebih tinggi.
Keywords
References
E. Nidianti, Y. T. Lukiyono, and H. A. P. Ningrum, “Pendampingan Kesehatan dari Bahaya Paparan Gas Karbon Monoksida di Desa Simo Angin-angin, Kecamatan Wonoayu, Kabupaten Sidoarjo,” J. Mandala Pengabdi. Masy., vol. 4, no. 2, pp. 408–415, Dec. 2023, doi: 10.35311/jmpm.v4i2.282.
Kementerian Lingkungan Hidup dan Kehutanan, “Stasiun Pemantauan Kualitas Udara,” Indeks Standar Pencemaran Udara. Accessed: Jun. 28, 2024. [Online]. Available: https://ispu.menlhk.go.id/
S. Tavakkoli Piralilou et al., “A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions,” Remote Sens., vol. 14, no. 3, p. 672, Jan. 2022, doi: 10.3390/rs14030672.
A. Shelestov, M. Lavreniuk, N. Kussul, A. Novikov, and S. Skakun, “Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping,” Front. Earth Sci., vol. 5, Feb. 2017, doi: 10.3389/feart.2017.00017.
A. S. Giananti, B. Hanggadireksa, M. F. Rohman, R. Syahputra, and A. H. Saputra, “Analisis Variabilitas Emisi Karbon Monoksida Selama Penyelenggaraan MotoGP di Kabupaten Lombok Tengah,” Geosfera J. Penelit. Geogr., vol. 2, no. 2, pp. 71–77, Dec. 2023, doi: 10.37905/geojpg.v2i2.23014.
S. Garai et al., “Assessing Correlation Between Rainfall, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) in Eastern India,” Saf. Extrem. Environ., vol. 4, no. 2, pp. 119–127, Aug. 2022, doi: 10.1007/s42797-022-00056-2.
Donni Kurnia Saputra and H. Nugroho, “Monitoring Peningkatan Kadar Co Berdasarkan Indeks Vegetasi Dan Suhu Permukaan Menggunakan,” Semin. Nas. dan Disem. Tugas Akhir 2023, pp. 1071–1077, 2023.
BPS Kota Semarang, Kota Semarang Dalam Angka 2023. Kota Semarang: BPS Kota Semarang, 2023.
Badan Informasi Geospasial, “Unduh RBI.” Geospasial untuk Negeri. Accessed: Mar. 19, 2024. [Online]. Available: https://tanahair.indonesia.go.id
J. van Geffen et al., “Sentinel-5P TROPOMI NO2 Retrieval: Impact of Version V2.2 Improvements and Comparisons with OMI and Ground-Based Data,” Atmos. Meas. Tech., vol. 15, no. 7, pp. 2037–2060, Apr. 2022, doi: 10.5194/amt-15-2037-2022.
D. G. Loyola et al., “The Operational Cloud Retrieval Algorithms from TROPOMI on Board Sentinel-5 Precursor,” Atmos. Meas. Tech., vol. 11, no. 1, pp. 409–427, Jan. 2018, doi: 10.5194/amt-11-409-2018.
M. Kazemi Garajeh, G. Laneve, H. Rezaei, M. Sadeghnejad, N. Mohamadzadeh, and B. Salmani, “Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine,” Pollutants, vol. 3, no. 2, pp. 255–279, May 2023, doi: 10.3390/pollutants3020019.
M. S. Dhillon, T. Dahms, C. Kübert-Flock, I. Steffan-Dewenter, J. Zhang, and T. Ullmann, “Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria,” Remote Sens., vol. 14, no. 3, p. 677, Jan. 2022, doi: 10.3390/rs14030677.
J. Hsu, W.-R. Huang, P.-Y. Liu, and X. Li, “Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales,” Remote Sens., vol. 13, no. 2, p. 254, Jan. 2021, doi: 10.3390/rs13020254.
QGIS, “QGIS.” Accessed: Jun. 04, 2024. [Online]. Available: https://www.qgis.org/
D. Makowski, M. Ben-Shachar, I. Patil, and D. Lüdecke, “Methods and Algorithms for Correlation Analysis in R,” J. Open Source Softw., vol. 5, no. 51, p. 2306, Jul. 2020, doi: 10.21105/joss.02306.
A. Kamel and M. R. Abonazel, “A Simple Introduction to Regression Modeling using R,” Comput. J. Math. Stat. Sci., vol. 2, no. 1, pp. 52–79, Apr. 2023, doi: 10.21608/cjmss.2023.189834.1002.
F. M. Giorgi, C. Ceraolo, and D. Mercatelli, “The R Language: An Engine for Bioinformatics and Data Science,” Life, vol. 12, no. 5, p. 648, Apr. 2022, doi: 10.3390/life12050648.
S. Janhäll, “Review on Urban Vegetation and Particle Air Pollution – Deposition and Dispersion,” Atmos. Environ., vol. 105, pp. 130–137, Mar. 2015, doi: 10.1016/j.atmosenv.2015.01.052.
D. J. Nowak, S. Hirabayashi, A. Bodine, and E. Greenfield, “Tree and Forest Effects on Air Quality and Human Health in The United States,” Environ. Pollut., vol. 193, pp. 119–129, Oct. 2014, doi: 10.1016/j.envpol.2014.05.028.
B. Gao, W. Ouyang, H. Cheng, Y. Xu, C. Lin, and J. Chen, “Interactions Between Rainfall and Fine Particulate Matter Investigated by Simultaneous Chemical Composition Measurements in Downtown Beijing,” Atmos. Environ., vol. 218, p. 117000, Dec. 2019, doi: 10.1016/j.atmosenv.2019.117000.
M. Santamouris et al., “Progress in Urban Greeny Mitigation Science – Assessment Methodologies Advanced Technologies and Impact on Cities,” J. Civ. Eng. Manag., vol. 24, no. 8, pp. 638–671, Dec. 2018, doi: 10.3846/jcem.2018.6604.
How To Cite This :
Refbacks
- There are currently no refbacks.
