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Journals / Atmospheric Pollution Research / 2019 / Cilt: 10 - Sayı: 5

Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey

ÖZGÜR ZEYDAN
Yuhang WANG
Pages
1565–1576
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It was found thatTerra worked better than Aqua.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Satellite based particulate matter (PM) pollution monitoring on a regional basis is of importance due in part tothe adverse health effects of PM.
📊
Key Findings & Data:In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) derivedaerosol optical depth (AOD) data at 3 km and 10 km resolutions from both Terra and Aqua satellites were used,in conjunction with the surface in situ data, to improve the regional distribution of ground-level PM2.5 overTurkey.
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Conclusion & Impact:It was found thatTerra worked better than Aqua.
🔬Design:Modeling & Simulation
📍Scope:2011–2015 Verileri
⚙️Variables:Domain Parameters

Özet

Satellite based particulate matter (PM) pollution monitoring on a regional basis is of importance due in part tothe adverse health effects of PM. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) derivedaerosol optical depth (AOD) data at 3 km and 10 km resolutions from both Terra and Aqua satellites were used,in conjunction with the surface in situ data, to improve the regional distribution of ground-level PM2.5 overTurkey. Five years (2011–2015) of heating season's (15th October to 14th May) in situ PM2.5 measurements from7 monitoring stations in Ankara and 3 years (2013–2015) of the same data from 13 monitoring stations inMarmara Region were used. Linear and non-linear regression models were used to find the relationship betweenPM2.5 and AOD data. To improve the correlations between PM2.5 and AOD, the data points affected by freetropospheric long-range transport were removed from the analysis via back trajectory modeling analysis sincelong-range transport affects AOD more readily than surface PM2.5 data. Using non-linear models with the additionof meteorological parameters such as height of planetary boundary layer, surface temperature and surfacewind speed improved the correlations significantly. The best non-linear model can explain 61% (n=37,R2=0.61, p < 0.001, RMSE=0.337 μg/m3) of PM2.5 variations at the Edirne Keşan site. It was found thatTerra worked better than Aqua. Furthermore, 10-km aerosol products gave better correlations with PM2.5 ascompared to the 3-km products. With the aid of spatiotemporal model, PM2.5 distribution maps are created forthe first time for Turkey.

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Cite This Article

ZEYDAN, Ö., WANG, Y. (2019). Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. Atmospheric Pollution Research, Cilt: 10 - Sayı: 5, 1565–1576. https://ajindex.com/articles/351563

References (10)

  1. Agacayak, T., Kindap, T., Unal, A., Pozzoli, L., Mallet, M., Solmon, F., 2015. A case study for Saharan dust transport over Turkey via RegCM4.1 model. Atmos. Res. 153, 392–403. https://doi.org/10.1016/j.atmosres.2014.09.012.
  2. Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175. https://doi.org/10.1007/s13181-011-0203-1.
  3. Bayraktar, H., Turalioǧlu, F.S., Tuncel, G., 2010.
  4. Average mass concentrations of TSP, PM 10 and PM 2.5 in Erzurum urban atmosphere, Turkey. Stoch. Environ. Res. Risk Assess. 24, 57–65. https://doi.org/10.1007/s00477-008-0299-2.
  5. Bennouna, Y.S., Cachorro, V.E., Toledano, C., Berjón, A., Prats, N., Fuertes, D., Gonzalez, R., Rodrigo, R., Torres, B., de Frutos, A.M., 2011. Comparison of atmospheric aerosol climatologies over southwestern Spain derived from AERONET and MODIS. Remote Sens. Environ. 115, 1272–1284. https://doi.org/10.1016/j.rse.2011.01.011.
  6. Cheng, T., Chen, H., Gu, X., Yu, T., Guo, J., Guo, H., 2012.
  7. The inter-comparison of MODIS, MISR and GOCART aerosol products against AERONET data over China. J. Quant. Spectrosc. Radiat. Transf. 113, 2135–2145. https://doi.org/10.1016/j.jqsrt. 2012.06.016. 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 Sens. Environ. 114, 2485–2499. https://doi.org/10.1016/j.rse.2010.05.024.
  8. Floutsi, A.A., Korras-Carraca, M.B., Matsoukas, C., Hatzianastassiou, N., Biskos, G., 2016. Climatology and trends of aerosol optical depth over the Mediterranean basin during the last 12 years (2002-2014) based on Collection 006 MODIS-Aqua data. Sci. Total Environ. 551–552, 292–303. https://doi.org/10.1016/j.scitotenv.2016.01.192.
  9. Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier Van Der Gon, H., Facchini, M.C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J.G., Spracklen, D.V., Vignati, E., Wild, M., Williams, M., Gilardoni, S., 2015. Particulate matter, air quality and climate: lessons learned and future needs. Atmos. Chem. Phys. 15, 8217–8299. https://doi. org/10.5194/acp-15-8217-2015.
  10. Gaga, E.O., Arı, A., Akyol, N., Üzmez, Ö.Ö., Kara, M., Chow, J.C., Watson, J.G., Özel, E., Döğeroğlu, T., Odabasi, M., 2018. Determination of real-world emi