Cilt 3 Sayı 3 Makale 2

Uydulardan Elde Edilebilen Aerosol Optik Derinlik Verilerini Kullanarak Zemin Seviyesi İnce Partikül Konsantrasyonlarını Tahmin Etmek İçin Doğrusal Olmayan Bir Model Geliştirilmesi

Yazar(lar): Talha Kemal Koçak 1, Farshad Ebrahi̇mi̇ 2,

İnce partiküllerin (PM2.5) kardiyovasküler mortalite ve morbidite ile ilişkili olduğu gösterilmiştir. Bu nedenle, halk sağlığını korumak ve düzenleyici kurumların taleplerini karşılamak için PM2.5 konsantrasyonunun izlenmesi gerekmektedir. Bununla birlikte, hakkında kapsamlı veri elde etmek için bu kadar büyük gözlem ağlarının korunması oldukça maliyetlidir (özellikle de daha az ayrıcalıklı topluluklar için). Uyduların ortaya çıkışı, belirli bir zaman periyodunda herhangi bir yerde veri toplamak için daha ucuz yollar açmış ve bu da hava kalitesi izleme çalışmaları için yeni fırsatlar doğurmuştur. Bu çalışmada uydulardan elde edilebilen aerosol optik derinlik (AOD) verisini ve rüzgar ile sıcaklık gibi meteorolojik parametreleri PM2.5 ile ilişkilendiren doğrusal olmayan bir model geliştirdik. Çalışma bir aylık (Kasım 2016) zaman dilimini kapsamaktadır ve çalışma alanı Hillsborough Vilayeti'dir (Florida). Modelin R kare değeri 0,53 olarak bulundu ve üç parametrenin (rüzgar, sıcaklık ve AOD) model sonuçlarını iyileştirdiği görüldü. Beklenildiği gibi AOD için regresyon katsayısı pozitifti ve rüzgar için ise negatifti. Ancak, sıcaklık için regresyon katsayısı negatifti. Bunun sebebi numunenin azlığından veya sıcaklığın, PM2.5’daki sülfat içeriğinin organik karbona oranını arttırmasından olabilir. Bu çalışma ampirik modellerin kalitesini değerlendirmek ve uydu verilerinin ne ölçüde güvenilir sonuçlar verebileceğini belirlemek için yapılan uygulamalara bir örnek oluşturmaktadır.



Anahtar Kelime(ler): Aerosol Optical Depth, Air Quality, Fine Particles,

Fine particles (PM2.5) are shown to be associated with cardiovascular mortality and morbidity. Therefore, there is a need to monitor PM2.5 concentrations to protect public health as well as to meet the demands of regulatory agencies. However, maintaining such big observational networks to achieve comprehensive data on PM2.5 is very costly, especially for the less-privileged communities. The emergence of satellites has opened cheaper ways to gather data in any place over several years and this, in turn, has opened new ways of air quality monitoring. In the current study, we developed a non-linear model that correlates aerosol optical depth (AOD) data (obtained from a satellite) and meteorological parameters (wind and temperature) to PM2.5. The study was conducted for one month (November 2016) in Hillsborough County, Florida. The R-squared of the model was 0.53. All three parameters (wind, temperature, and AOD) were found to improve model results. The regression coefficient for the AOD was positive while it was negative for the wind as expected. However, the regression coefficient for the temperature was negative. It could be due to the small size of the sample or the fact that temperature increases the ratio of the sulfate content to the organic carbon of PM2.5. This study is an example of applications to evaluate the quality of empirical models and to determine to what extent satellite data can yield reliable results.



Keyword(s):Aerosol Optical Depth, Air Quality, Fine Particles,

  • [1] Pope, C., & Dockery, D. (1999). Chapter 31. Epidemiology of particle effects. In S. T. Holgate, H. S. Koren, J. M. Samet, & R. L. Maynard (Eds.), Air pollution and health (pp. 673−705).
  • [2] Dehghani, Mansooreh & Kashtgar, Laila & Reza Javaheri, Mohammad & Derakhshan, Zahra & Gea, Oliveri Conti & Zuccarello, Pietro & Ferrante, Margherita. (2017). The effects of air pollutants on the mortality rate of lung cancer and leukemia. Molecular Medicine Reports. 15: 3390-3397
  • [3] Kaufman, Y.J., Tanre, D. and Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419, 215-223.
  • [4] Duncan BN, AI Prados, LN Lamsal, Y Liu, DG Streets, P Gupta, et al. 2014. Satellite data of atmospheric pollution for U.S. air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid. Atmospheric Environment 94: 647 –662.
  • [5] Liu, Yang & Franklin, Meredith & Kahn, Ralph & Koutrakis, Petros. (2007). Using aerosol optical thickness to predict ground-level PM2. 5 concentrations in the St. Louis area: A comparison between MISR and MODIS. Remote Sensing of Environment, 107 (2007) 33-44.
  • [6] Davis, S. M., Landgrebe, D. A., Phillips, T. L., Swain, P. H., Hoffer, R. M., Lindenlaub, J. C., & Silva, L. F. (1978). Remote sensing: the quantitative approach. New York, McGraw-Hill International Book Co., 1978. 405 p.
  • [7] Wang, J., & Christopher, S. A. (2003). Intercomparison between satellite‐derived aerosol optical thickness and PM2. 5 mass: implications for air quality studies. Geophysical research letters, 30(21).
  • [8] Ritchie, J. C., Zimba, P. V., & Everitt, J. H. (2003). Remote sensing techniques to assess water quality. Photogrammetric Engineering & Remote Sensing, 69(6), 695-704.
  • [9] Jha, M. K., Chowdhury, A., Chowdary, V. M., & Peiffer, S. (2007). Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resources Management, 21(2), 427-467.
  • [10] Al-Hanbali, A., Alsaaideh, B., & Kondoh, A. (2011). Using GIS-based weighted linear combination analysis and remote sensing techniques to select optimum solid waste disposal sites within Mafraq City, Jordan. Journal of Geographic Information System, 3(04), 267.
  • [11] Bolch, T. (2007). Climate change and glacier retreat in northern Tien Shan (Kazakhstan/Kyrgyzstan) using remote sensing data. Global and Planetary Change, 56(1), 1-12.
  • [12] Sifakis, N., & Deschamps, P. Y. (1992). Mapping of Air Pollution Using SPOT Satellite. Photogrammetric Engineering & Remote Sensing, 5, 4.
  • [13] Martin, R. V. (2008). Satellite remote sensing of surface air quality. Atmospheric Environment, 42(34), 7823-7843.
  • [14] Kloog, I., Koutrakis, P., Coull, B. A., Lee, H. J., & Schwartz, J. (2011). Assessing temporally and spatially resolved PM 2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmospheric environment, 45(35), 6267-6275.
  • [15] Hu, Z., & Baker, E. (2017). Geographical Analysis of Lung Cancer Mortality Rate and PM2.5 Using Global Annual Average PM2. 5 Grids from MODIS and MISR Aerosol Optical Depth. Journal of Geoscience and Environment Protection, 5(06), 183.
  • [16] Wang, B. (2017). Retrieval of Aerosol Optical Depth and Correlation Analysis of PM2.5 Based on GF-1 Wide Field of View Images. World Academy of Science, Engineering and Technology, International Journal of Geological and Environmental Engineering, 4(12).
  • [17] Chu, D., Kaufman, Y., Zibordi, G., Chern, J., Mao, J., Li, C., et al. (2003). Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS). Journal of Geophysical Research, 108(D21) (Art. No. 4661).
  • [18] Vidot, J., Santer, R., & Ramon, D. (2007). Atmospheric particulate matter (PM) estimation from SeaWiFS imagery. Remote sensing of environment, 111(1), 1-10.
  • [19] Gupta, P., and S. A. Christopher (2009), Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach, J. Geophys. Res., 114, D14205.
  • [20] Barnes, W. L., & Salomonson, V. V. (1992, November). MODIS: A global imaging spectroradiometer for the Earth Observing System. In Optical Technologies for Aerospace Sensing: A Critical Review (Vol. 10269, p. 102690G). International Society for Optics and Photonics.
  • [21] Remer, L. A., Tanre, D., Kaufman, Y. J., Ichoku, C., Mattoo, S., Levy, R. & Martins, J. V. (2002). Validation of MODIS aerosol retrieval over ocean. Geophysical research letters, 29(12).
  • [22] King, M. D., Menzel, W. P., Kaufman, Y. J., Tanré, D., Gao, B. C., Platnick, S., ... & Hubanks, P. A. (2003). Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 442-458.
  • [23] Wan, Z., Wang, P., & Li, X. (2004). Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25(1), 61-72.
  • [24] Qin, J., Yang, K., Lu, N., Chen, Y., Zhao, L., & Han, M. (2013). Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sensing of Environment, 138, 1-9.
  • [25] Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C., Gao, F., ... & Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote sensing of environment, 84(3), 471-475.
  • [26] Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C. & Moore, B. (2005). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote sensing of environment, 95(4), 480-492.
  • [27] Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34(4), 710-714.
  • [28] Emili, E., Popp, C., Petitta, M., Riffler, M., Wunderle, S., & Zebisch, M. (2010). PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region. Remote sensing of environment, 114(11), 2485-2499.
  • [29] Ruiz-Arias, J. A., Dudhia, J., Gueymard, C. A., & Pozo-Vázquez, D. (2013). Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling. Atmospheric Chemistry and Physics, 13(2), 675-692.
  • [30] National Aeronautics and Space Administration (n.d.). Giovanni. Retrieved June 21, 2018, from https://giovanni.gsfc.nasa.gov/giovanni/
  • [31] Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., & Eck, T. F. (2010). Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21), 10399-10420.
  • [32] Noble, C. A., Vanderpool, R. W., Peters, T. M., McElroy, F. F., Gemmill, D. B., & Wiener, R. W. (2001). Federal reference and equivalent methods for measuring fine particulate matter. Aerosol Science & Technology, 34(5), 457-464.
  • [33] Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., ... & Nakajima, T. (2002). Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and Sun photometer measurements. Journal of the atmospheric sciences, 59(3), 461-483.

KAYNAK GÖSTER

Atıf tipi: APATalha Kemal Koçak, Farshad Ebrahi̇mi̇. (2020). Uydulardan Elde Edilebilen Aerosol Optik Derinlik Verilerini Kullanarak Zemin Seviyesi İnce Partikül Konsantrasyonlarını Tahmin Etmek İçin Doğrusal Olmayan Bir Model Geliştirilmesi. Ulusal Çevre Bilimleri Araştırma Dergisi, 3 ( 3 ) , 119-127. http://ucbad.com/volume-3/issue-3/article-2/
Atıf tipi: BibTex@article{2020, title={Uydulardan Elde Edilebilen Aerosol Optik Derinlik Verilerini Kullanarak Zemin Seviyesi İnce Partikül Konsantrasyonlarını Tahmin Etmek İçin Doğrusal Olmayan Bir Model Geliştirilmesi}, volume={3}, number={3}, publisher={International Journal of Environmental Pollution and Environmental Modelling}, author={Talha Kemal Koçak, Farshad Ebrahi̇mi̇}, year={2020}, pages={119-127} }
Atıf tipi: MLATalha Kemal Koçak, Farshad Ebrahi̇mi̇. Uydulardan Elde Edilebilen Aerosol Optik Derinlik Verilerini Kullanarak Zemin Seviyesi İnce Partikül Konsantrasyonlarını Tahmin Etmek İçin Doğrusal Olmayan Bir Model Geliştirilmesi. no. 3 International Journal of Environmental Pollution and Environmental Modelling, (2020), pp. 119-127.