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

Özgür ZEYDAN, (Zonguldak Bülent Ecevit Üniversitesi, Çevre Mühendisliği Bölümü, Zonguldak, Türkiye)
Yuhang WANG (School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA)
Atmospheric Pollution Research
20 0
Yıl: 2019 Cilt: 10 Sayı: 5 Sayfa Aralığı: 1565 - 1576 Metin Dili: İngilizce DOI: 10.1016/j.apr.2019.05.005 İndeks Tarihi: 21-12-2020

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

Öz:
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.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Bibliyografik
0
0
0
  • 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.
  • 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.
  • Bayraktar, H., Turalioǧlu, F.S., Tuncel, G., 2010. 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.
  • 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.
  • Cheng, T., Chen, H., Gu, X., Yu, T., Guo, J., Guo, H., 2012. 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.
  • 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.
  • 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.
  • 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 emission factors of trace metals, EC, OC, BTEX, and semivolatile organic compounds (PAHs, PCBs and PCNs) in a rural tunnel in Bilecik, Turkey. Sci. Total Environ. 643, 1285–1296. https://doi.org/10.1016/j.scitotenv.2018.06.227.
  • Grantz, D.A., Garner, J.H.B., Johnson, D.W., 2003. Ecological effects of particulate matter. Environ. Int. 29, 213–239. https://doi.org/10.1016/S0160-4120(02) 00181-2.
  • Guo, J., Xia, F., Zhang, Y., Liu, H., Li, J., Lou, M., He, J., Yan, Y., Wang, F., Min, M., Zhai, P., 2017. Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: implications for PM2.5 remote sensing. Environ. Pollut. 221, 94–104. https://doi.org/10.1016/j.envpol.2016.11.043.
  • Guo, J.P., Zhang, X.Y., Che, H.Z., Gong, S.L., An, X., Cao, C.X., Guang, J., Zhang, H., Wang, Y.Q., Zhang, X.C., Xue, M., Li, X.W., 2009. Correlation between PM concentrations and aerosol optical depth in eastern China. Atmos. Environ. 43, 5876–5886. https://doi.org/10.1016/j.atmosenv.2009.08.026.
  • Gupta, P., Christopher, S.A., 2008. Seven year particulate matter air quality assessment from surface and satellite measurements. Atmos. Chem. Phys. 8, 3311–3324. https:// doi.org/10.5194/acp-8-3311-2008.
  • He, Q., Huang, B., 2018. Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling. Remote Sens. Environ. 206, 72–83. https://doi.org/10.1016/j.rse.2017.12.018.
  • He, Q., Zhang, M., Huang, B., Tong, X., 2017. MODIS 3 km and 10 km aerosol optical depth for China: evaluation and comparison. Atmos. Environ. 153, 150–162. https:// doi.org/10.1016/j.atmosenv.2017.01.023.
  • Hu, X., Belle, J.H., Meng, X., Wildani, A., Waller, L.A., Strickland, M.J., Liu, Y., 2017. Estimating PM2.5 concentrations in the conterminous United States using the random forest approach. Environ. Sci. Technol. 51, 6936–6944. https://doi.org/10.1021/acs. est.7b01210.
  • Hu, X., Waller, L.A., Lyapustin, A., Wang, Y., Al-Hamdan, M.Z., Crosson, W.L., Estes, M.G., Estes, S.M., Quattrochi, D.A., Puttaswamy, S.J., Liu, Y., 2014. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sens. Environ. 140, 220–232. https:// doi.org/10.1016/j.rse.2013.08.032.
  • JASP, 2018. JASP Statistical Software. [WWW Document]. https://jasp-stats.org/. Kabatas, B., Unal, A., Pierce, R.B., Kindap, T., Pozzoli, L., 2014. The contribution of Saharan dust in PM10 concentration levels in Anatolian Peninsula of Turkey. Sci. Total Environ. 488–489, 413–421. https://doi.org/10.1016/j.scitotenv.2013.12.045.
  • Kahya, C., Bekta Balcik, F., Oztaner, Y.B., Ozcomak, D., Seker, D.Z., 2017. Spatio-temporal analysis of pm2 . 5 over marmara region , Turkey. Fresenius Environ. Bull. 26, 310–317.
  • Kara, M., Hopke, P.K., Dumanoglu, Y., Altiok, H., Elbir, T., Odabasi, M., Bayram, A., 2015. Characterization of PM using multiple site data in a heavily industrialized region of Turkey. Aerosol Air Qual. Res. 15, 11–27. https://doi.org/10.4209/aaqr. 2014.02.0039.
  • Karaca, F., Alagha, O., Ertürk, F., 2005. Statistical characterization of atmospheric PM10 and PM 2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. Chemosphere 59, 1183–1190. https://doi.org/10.1016/j.chemosphere.2004.11.062.
  • Karaca, F., Anil, I., Alagha, O., 2009. Long-range potential source contributions of episodic aerosol events to PM10 profile of a megacity. Atmos. Environ. 43, 5713–5722. https://doi.org/10.1016/j.atmosenv.2009.08.005.
  • Kendall, M., Pala, K., Ucakli, S., Gucer, S., 2011. Airborne particulate matter (PM2.5 and PM10) and associated metals in urban Turkey. Air Qual. Atmos. Health. 4, 235–242. https://doi.org/10.1007/s11869-010-0129-9.
  • Kim, E., Park, H., Park, E.A., Hong, Y.-C., Ha, M., Kim, H.-C., Ha, E.-H., 2016. Particulate matter and early childhood body weight. Environ. Int. 1–9. https://doi.org/10.1016/ j.envint.2016.06.021.
  • Kim, K.-H., Kabir, E., Kabir, S., 2015. A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143. https://doi.org/10.1016/j.envint. 2014.10.005.
  • Kindap, T., Unal, A., Chen, S., Hu, Y., Odman, M.T., Karaca, M., 2006. Long-range aerosol transport from Europe to istanbul , Turkey. Atmos. Environ. 40, 3536–3547. https:// doi.org/10.1016/j.atmosenv.2006.01.055.
  • Kloog, I., Chudnovsky, A.A., Just, A.C., Nordio, F., Koutrakis, P., Coull, B.A., Lyapustin, A., Wang, Y., Schwartz, J., 2014. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data. Atmos. Environ. 95, 581–590. https://doi.org/10.1016/j. atmosenv.2014.07.014.
  • Kloog, I., Koutrakis, P., Coull, B.A., Lee, H.J., Schwartz, J., 2011. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos. Environ. 45, 6267–6275. https://doi. org/10.1016/j.atmosenv.2011.08.066.
  • Kloog, I., Sorek-Hamer, M., Lyapustin, A., Coull, B., Wang, Y., Just, A.C., Schwartz, J., Broday, D.M., 2015. Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data. Atmos. Environ. 122, 409–416. https://doi.org/10.1016/j.atmosenv.2015.10.004.
  • Koçak, M., Mihalopoulos, N., Kubilay, N., 2007. Contributions of natural sources to high PM10 and PM2.5 events in the eastern Mediterranean. Atmos. Environ. 41, 3806–3818. https://doi.org/10.1016/j.atmosenv.2007.01.009.
  • Koukouli, M.E., Kazadzis, S., Amiridis, V., Ichoku, C., Balis, D.S., Bais, A.F., 2010. Signs of a negative trend in the MODIS aerosol optical depth over the Southern Balkans. Atmos. Environ. 44, 1219–1228. https://doi.org/10.1016/j.atmosenv.2009.11.024.
  • Kumar, N., Chu, A., Foster, A., 2007. An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. Atmos. Environ. 41, 4492–4503. https:// doi.org/10.1016/j.atmosenv.2007.01.046.
  • 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. Atmos. Chem. Phys. 10, 10399–10420. https://doi.org/10.5194/acp-10-10399-2010.
  • Li, C., Hsu, N.C., Tsay, S.-C., 2011. A study on the potential applications of satellite data in air quality monitoring and forecasting. Atmos. Environ. 45, 3663–3675. https:// doi.org/10.1016/j.atmosenv.2011.04.032.
  • Liang, S., Zhong, B., Fang, H., 2006. Improved estimation of aerosol optical depth from MODIS imagery over land surfaces. Handb. Environ. Chem. 5, 416–425. https://doi. org/10.1016/j.rse.2006.05.016. Water Pollut. 104.
  • Lin, C., Li, Y., Yuan, Z., Lau, A.K.H., Li, C., Fung, J.C.H., 2015. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5. Remote Sens. Environ. 156, 117–128. https://doi.org/10.1016/j.rse.2014.09.015.
  • Liu, Y., Franklin, M., Kahn, R., Koutrakis, P., 2007. Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: a comparison between MISR and MODIS. Remote Sens. Environ. 107, 33–44. https://doi.org/10. 1016/j.rse.2006.05.022.
  • Liu, Y., Paciorek, C.J., Koutrakis, P., 2009. Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environ. Health Perspect. 117, 886–892. https://doi.org/10.1289/ehp. 0800123.
  • Ma, Z., Hu, X., Huang ei, L., Bi un, J., Liu, Y., 2014. Estimating ground-level PM2.5 in China using satellite remote sensing. Environ. Sci. Technol. 48, 7436–7444. https:// doi.org/10.1021/es5009399.
  • Ma, Z., Liu, Y., Zhao, Q., Liu, M., Zhou, Y., Bi, J., 2016. Title: satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model. Atmos. Environ. 133, 156–164. https://doi.org/ 10.1016/j.atmosenv.2016.03.040.
  • Martin, R.V., 2008. Satellite remote sensing of surface air quality. Atmos. Environ. 42, 7823–7843. https://doi.org/10.1016/j.atmosenv.2008.07.018.
  • Michaelides, S., Karacostas, T., Sánchez, J.L., Retalis, A., Pytharoulis, I., Homar, V., Romero, R., Zanis, P., Giannakopoulos, C., Bühl, J., Ansmann, A., Merino, A., Melcón, P., Lagouvardos, K., Kotroni, V., Bruggeman, A., López-Moreno, J.I., Berthet, C., Katragkou, E., Tymvios, F., Hadjimitsis, D.G., Mamouri, R.E., Nisantzi, A., 2018. Reviews and perspectives of high impact atmospheric processes in the Mediterranean. Atmos. Res. 208, 4–44. https://doi.org/10.1016/j.atmosres.2017.11. 022.
  • NAQMS, 2016. Ministry of Environment and Urbanisation/National Air Quality Monitoring Website. [WWW Document]. http://www.havaizleme.gov.tr/STN/STN_ Report/StationDataDownload.
  • NASA, 2016. Level-1 and Atmosphere Archive and Distribution System. [WWW Document]. https://ladsweb.modaps.eosdis.nasa.gov/search/.
  • NCEP/NCAR, 2016. NCEP/NCAR Reanalysis Data Archive. [WWW Document]. ftp:// arlftp.arlhq.noaa.gov/pub/archives/reanalysis/.
  • NCEP, 2016. NCEP Climate Forecast System Reanalysis (CFSR). [WWW Document]. https://rda.ucar.edu/pub/cfsr.html.
  • Onat, B., Sahin, U.A., Akyuz, T., 2013. Elemental characterization of PM2.5 and PM1 in dense traffic area in Istanbul, Turkey. Atmos. Pollut. Res. 4, 101–105. https://doi. org/10.5094/Apr.2013.010.
  • Onat, B., Şahin, Ü.A., Uzun, B., Akın, Ö., Özkaya, F., Ayvaz, C., 2019. Determinants of exposure to ultrafine particulate matter, black carbon, and PM2.5 in common travel modes in Istanbul. Atmos. Environ. 206, 258–270. https://doi.org/10.1016/j. atmosenv.2019.02.015.
  • Özşahin, E., Eroğlu, I., Pektekel, H., 2016. Air pollution in Keşan (Edirne). J. Inst. Soc. Sci. Selçuk Univ. 36, 83–100.
  • Öztaner, Y.B., Güney, B., Özçomak, D., Kahya, C., Bektaş Balçık, F., Çakır, S., Kalkan, K., 2015. An investigation of the relationship between modis AOD and PM2.5 over marmara region. In: VII. Atmospheric Science Symposium, pp. 170–180.
  • Papadimas, C.D., Hatzianastassiou, N., Mihalopoulos, N., Kanakidou, M., Katsoulis, B.D., Vardavas, I., 2009. Assessment of the MODIS Collections C005 and C004 aerosol optical depth products over the Mediterranean basin. Atmos. Chem. Phys. 9, 2987–2999. https://doi.org/10.5194/acpd-8-16891-2008.
  • Pekey, B., Bozkurt, Z.B., Pekey, H., Doĝan, G., Zararsiz, A., Efe, N., Tuncel, G., 2010. Indoor/outdoor concentrations and elemental composition of PM10/PM2.5 in urban/ industrial areas of Kocaeli City, Turkey. Indoor Air 20, 112–125. https://doi.org/10. 1111/j.1600-0668.2009.00628.x.
  • Polichetti, G., Cocco, S., Spinali, A., Trimarco, V., Nunziata, A., 2009. Effects of particulate matter (PM10, PM2.5 and PM1) on the cardiovascular system. Toxicology 261, 1–8. https://doi.org/10.1016/j.tox.2009.04.035.
  • Remer, L.A., Mattoo, S., Levy, R.C., Munchak, L.A., 2013. MODIS 3 km aerosol product: algorithm and global perspective. Atmos. Meas. Tech. 6, 1829–1844. https://doi.org/ 10.5194/amt-6-1829-2013.
  • Sahin, S., Kerem Cigizoglu, H., 2012. The sub-climate regions and the sub-precipitation regime regions in Turkey. J. Hydrol. 450–451, 180–189. https://doi.org/10.1016/j. jhydrol.2012.04.062.
  • Sathe, Y., Kulkarni, S., Gupta, P., Kaginalkar, A., Islam, S., Gargava, P., 2019. Application of moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) and weather research forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM 2.5 ) over India. Atmos. Pollut. Res. 10, 418–434. https://doi.org/10.1016/j.apr.2018.08.016.
  • Song, Y.Z., Yang, H.L., Peng, J.H., Song, Y.R., Sun, Q., Li, Y., 2015. Estimating PM2.5 concentrations in Xi’an City using a generalized additive model with multi-source monitoring data. PLoS One 10, 1–22. https://doi.org/10.1371/journal.pone. 0142149.
  • Sorek-hamer, M., Kloog, I., Koutrakis, P., Strawa, A.W., Chat, R., Cohen, A., Ridgway, W.L., Broday, D.M., 2015. Assessment of PM 2 . 5 concentrations over bright surfaces using MODIS satellite observations. Remote Sens. Environ. 163, 180–185. https:// doi.org/10.1016/j.rse.2015.03.014.
  • Streets, D.G., Canty, T., Carmichael, G.R., De Foy, B., Dickerson, R.R., Duncan, B.N., Edwards, D.P., Haynes, J.A., Henze, D.K., Houyoux, M.R., Jacob, D.J., Krotkov, N.A., Lamsal, L.N., Liu, Y., Lu, Z., Martin, R.V., Pfister, G.G., Pinder, R.W., Salawitch, R.J., Wecht, K.J., 2013. Emissions estimation from satellite retrievals: a review of current capability. Atmos. Environ. 77, 1011–1042. https://doi.org/10.1016/j.atmosenv. 2013.05.051 Review.
  • Szigeti, T., Mihucz, V.G., Óvári, M., Baysal, A., Atilgan, S., Akman, S., Záray, G., 2013. Chemical characterization of PM2.5 fractions of urban aerosol collected in Budapest and Istanbul. Microchem. J. 107, 86–94. https://doi.org/10.1016/j.microc.2012.05.
  • Tecer, L.H., Alagha, O., Karaca, F., Tuncel, G., Eldes, N., 2008. Particulate matter (PM2.5, PM10-2.5, and PM10) and children's hospital admissions for asthma and respiratory diseases: a bidirectional case-crossover study. J. Toxicol. Environ. Health 71, 512–520. https://doi.org/10.1080/15287390801907459.
  • Tsai, T.C., Jeng, Y.J., Chu, D.A., Chen, J.P., Chang, S.C., 2011. Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmos. Environ. 45, 4777–4788. https://doi.org/10.1016/j.atmosenv.2009.10.006.
  • Wang, C., Liu, Q., Ying, N., Wang, X., Ma, J., 2013a. Air quality evaluation on an urban scale based on MODIS satellite images. Atmos. Res. 132–133, 22–34. https://doi.org/ 10.1016/j.atmosres.2013.04.011.
  • Wang, Y.Q., Zhang, X.Y., Draxler, R.R., 2009. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from longterm air pollution measurement data. Environ. Model. Softw 24, 938–939. https:// doi.org/10.1016/j.envsoft.2009.01.004.
  • Wang, Z., Liu, Y., Hu, M., Pan, X., Shi, J., Chen, F., He, K., Koutrakis, P., Christiani, D.C., 2013b. Acute health impacts of airborne particles estimated from satellite remote sensing. Environ. Int. 51, 150–159. https://doi.org/10.1016/j.envint.2012.10.011.
  • Wei, Y., Zhang, J.J., Li, Z., Gow, A., Chung, K.F., Hu, M., Sun, Z., Zeng, L., Zhu, T., Jia, G., Li, X., Duarte, M., Tang, X., 2016. Chronic exposure to air pollution particles increases the risk of obesity and metabolic syndrome: findings from a natural experiment in Beijing. FASEB J. 30, 1–8. https://doi.org/10.1096/fj.201500142.
  • Xie, Y., Wang, Y., Zhang, K., Dong, W., Lv, B., Bai, Y., 2015. Daily estimation of groundlevel PM2.5 concentrations over beijing using 3 km resolution MODIS AOD. Environ. Sci. Technol. 49, 12280–12288. https://doi.org/10.1021/acs.est.5b01413.
  • Xin, J., Zhang, Q., Wang, L., Gong, C., Wang, Y., Liu, Z., Gao, W., 2014. The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011. Atmos. Res. 138, 179–188. https:// doi.org/10.1016/j.atmosres.2013.11.001.
  • Yatkin, S., Bayram, A., 2008. Source apportionment of PM10 and PM2.5 using positive matrix factorization and chemical mass balance in Izmir, Turkey. Sci. Total Environ. 390, 109–123. https://doi.org/10.1016/j.scitotenv.2007.08.059.
  • You, W., Zang, Z., Zhang, L., Zhang, M., Pan, X., Li, Y., 2016. A nonlinear model for estimating ground-level PM10 concentration in Xi’an using MODIS aerosol optical depth retrieval. Atmos. Res. 168, 169–179. https://doi.org/10.1016/j.atmosres. 2015.09.008.
  • Zhai, L., Li, S., Zou, B., Sang, H., Fang, X., Xu, S., 2018. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas. Atmos. Environ. 181, 145–154. https://doi.org/10.1016/j.atmosenv.2018.03.017.
  • Zhang, T., Liu, G., Zhu, Z., Gong, W., Ji, Y., Huang, Y., 2016. Real-time estimation of satellite-derived PM2.5 based on a semi-physical geographically weighted regression model. Int. J. Environ. Res. Public Health 13, 974. https://doi.org/10.3390/ ijerph13100974.
  • Zhang, Y., Li, Z., 2015. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sens. Environ. 160, 252–262.
APA Zeydan Ö, WANG Y (2019). Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. , 1565 - 1576. 10.1016/j.apr.2019.05.005
Chicago Zeydan Özgür,WANG Yuhang Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. (2019): 1565 - 1576. 10.1016/j.apr.2019.05.005
MLA Zeydan Özgür,WANG Yuhang Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. , 2019, ss.1565 - 1576. 10.1016/j.apr.2019.05.005
AMA Zeydan Ö,WANG Y Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. . 2019; 1565 - 1576. 10.1016/j.apr.2019.05.005
Vancouver Zeydan Ö,WANG Y Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. . 2019; 1565 - 1576. 10.1016/j.apr.2019.05.005
IEEE Zeydan Ö,WANG Y "Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey." , ss.1565 - 1576, 2019. 10.1016/j.apr.2019.05.005
ISNAD Zeydan, Özgür - WANG, Yuhang. "Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey". (2019), 1565-1576. https://doi.org/10.1016/j.apr.2019.05.005
APA Zeydan Ö, WANG Y (2019). Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. Atmospheric Pollution Research, 10(5), 1565 - 1576. 10.1016/j.apr.2019.05.005
Chicago Zeydan Özgür,WANG Yuhang Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. Atmospheric Pollution Research 10, no.5 (2019): 1565 - 1576. 10.1016/j.apr.2019.05.005
MLA Zeydan Özgür,WANG Yuhang Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. Atmospheric Pollution Research, vol.10, no.5, 2019, ss.1565 - 1576. 10.1016/j.apr.2019.05.005
AMA Zeydan Ö,WANG Y Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. Atmospheric Pollution Research. 2019; 10(5): 1565 - 1576. 10.1016/j.apr.2019.05.005
Vancouver Zeydan Ö,WANG Y Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey. Atmospheric Pollution Research. 2019; 10(5): 1565 - 1576. 10.1016/j.apr.2019.05.005
IEEE Zeydan Ö,WANG Y "Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey." Atmospheric Pollution Research, 10, ss.1565 - 1576, 2019. 10.1016/j.apr.2019.05.005
ISNAD Zeydan, Özgür - WANG, Yuhang. "Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey". Atmospheric Pollution Research 10/5 (2019), 1565-1576. https://doi.org/10.1016/j.apr.2019.05.005
Sayfa Oluşturulma Tarihi
23-04-2024 16:9

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları