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Using radiative transfer models in order to estimate PM10 concentration in south of Iran using MODIS images

  • M. HojatiEmail author
  • A. D. Boolorani
Original Paper
  • 120 Downloads

Abstract

Atmospheric particulate matters are the substances that are suspended in the atmosphere and consist of liquid and solid complex mixture. In recent decades, there have been a number of studies that focuses on the methods of estimation of PM10 concentrations using satellite images. Most of these modes use an index from those images, for example aerosol optical depth with a combination of meteorological parameters such as temperature and relative humidity. The main aim of this study is to use a satellite image index, extracted from MODIS images, in order to model PM10 concentration in Khuzestan province, Iran. In this study, a new index, aerosol reflectance, calculated from radiative transfer models is used beside some meteorological parameters. In order to model PM10 concentration, a regression-based mode and two artificial neural networks including multilayer perceptron as a feed-forward model and a radial basis function network are used. Results indicate that there is a correlation value of 0.432 between aerosol reflectance in band 3 (555 nm) and PM10 values. After comparing the models in order to predict PM10 concentration, a value of R2 = 0.765, with a root mean value of 79.01 is calculated for multilayer perceptron model which shows that multilayer perceptron networks results higher accuracy than others.

Keywords

Aerosol reflectance Aerosol optical depth MODIS PM10 Radiative transfer models 

Notes

Acknowledgements

The authors would like to acknowledge the Organization of Environment, Tehran, for their assistance during conducting the present study by providing the required dataset.

References

  1. Afzali A, Rashid M, Sabariah B, Ramli M (2014) PM10 Pollution: its prediction and meteorological influence in Pasir Gudang, Johor. In: IOP conference series: earth and environmental science, 18, 12100. https://doi.org/10.1088/1755-1315/18/1/012100
  2. Ångström A (1930) On the atmospheric transmission of sun radiation. II. Geografiska Annaler, 12, 130–159 CR–Copyright © 1930 Swedish Societ. https://doi.org/10.2307/519561
  3. Antanasijević DZ, Pocajt VV, Povrenović DS, Ristić MĐ, Perić-Grujić AA (2013) PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. doi: 10.1016/j.scitotenv.2012.10.110 CrossRefGoogle Scholar
  4. Ashish D, Hoogenboom G, McClendon RW (2004) Land-use classification of gray-scale aerial images using probabilistic neural networks. Trans ASAE 47(5):1813–1819. doi: 10.13031/2013.17598 CrossRefGoogle Scholar
  5. Azmi S, Latif M, Ismail A, Juneng L, Jemain A (2010) Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Qual Atmos Health 3(1):53–64. doi: 10.1007/s11869-009-0051-1 CrossRefGoogle Scholar
  6. Bilal M (2013) Monitoring of fine particulates in Hong Kong and Muhammad Bilal Ph.D. The Hong Kong Polytechnic University. The Hong Kong Polytechnic University DepartmentGoogle Scholar
  7. Bilal M, Nichol JE, Bleiweiss MP, Dubois D (2013) A Simplified high resolution MODIS aerosol retrieval algorithm (SARA) for use over mixed surfaces. Remote Sens Environ 136:135–145. doi: 10.1016/j.rse.2013.04.014 CrossRefGoogle Scholar
  8. Bilal M, Nichol JE, Chan PW (2014a) Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms. Remote Sens Environ 153:50–60CrossRefGoogle Scholar
  9. Bilal M, Nichol JE, Chan PW (2014b) Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms. Remote Sens Environ 153:50–60. doi: 10.1016/j.rse.2014.07.015 CrossRefGoogle Scholar
  10. Bird RE, Riordan C (1986) Simple solar spectral model for direct and diffuse irradiance on horizontal and tilted planes at the earth’s surface for cloudless atmospheres. J Clim Appl Meteorol 25:87–97. doi: 10.1175/1520-0450(1986)025<0087:SSSMFD>2.0.CO;2 CrossRefGoogle Scholar
  11. Bucholtz A (1995) Rayleigh-scattering calculations for the terrestrial atmosphere. Appl Opt 34(15):2765–2773. doi: 10.1364/AO.34.002765 CrossRefGoogle Scholar
  12. Chitranshi S, Sharma SP, Dey S (2014) Satellite-based estimates of outdoor particulate pollution (PM 10) for Agra City in northern India. Air Qual Atmos Health. doi: 10.1007/s11869-014-0271-x CrossRefGoogle Scholar
  13. Council Directive (1999) Council Directive 99/30/EC of 22 April 1999 relating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen in the air. EU Publication NrGoogle Scholar
  14. Demir G, Ozdemir H, Ozcan HK, Ucan ON, Bayat C (2010) An artificial neural network-based model for short-term predictions of daily mean PM10 concentrations. J Environ Prot Ecol 11(3):1163–1171Google Scholar
  15. Draxler RR, Gillette DA, Kirkpatrick JS, Heller J (2001) Estimating PM10 air concentrations from dust storms in Iraq, Kuwait and Saudi Arabia. Atmos Environ 35:4315–4330. doi: 10.1016/S1352-2310(01)00159-5 CrossRefGoogle Scholar
  16. 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(11):2485–2499. doi: 10.1016/j.rse.2010.05.024 CrossRefGoogle Scholar
  17. Foody GM (1995) Land cover classification by an artificial neural network with ancillary information. Int J Geogr Inf Syst 9(August):527–542. doi: 10.1080/02693799508902054 CrossRefGoogle Scholar
  18. Fröhlich C, Shaw GE (1980) New determination of Rayleigh scattering in the terrestrial atmosphere. Appl Opt 19(11):1773–1775. doi: 10.1364/AO.19.001773 CrossRefGoogle Scholar
  19. Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32(14–15):2627–2636. doi: 10.1016/S1352-2310(97)00447-0 CrossRefGoogle Scholar
  20. Grgurić S, Križan J, Gašparac G, Antonić O, Špirić Z, Mamouri R, Hadjimitsis D (2014) Relationship between MODIS based aerosol optical depth and PM10 over croatia. Open Geosci 6(1):2–16. doi: 10.2478/s13533-012-0135-6 CrossRefGoogle Scholar
  21. Grivas G, Chaloulakou A (2006) Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos Environ 40(7):1216–1229. doi: 10.1016/j.atmosenv.2005.10.036 CrossRefGoogle Scholar
  22. Guo Y, Feng N, Christopher SA, Kang P, Zhan FB, Hong S (2014) Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS. Int J Remote Sens 35:6522–6544. doi: 10.1080/01431161.2014.958245 CrossRefGoogle Scholar
  23. Gvozdić V, Kovač-Andrić E, Brana J (2011) Influence of meteorological factors NO2, SO2, CO and PM10 on the concentration of O3 in the urban atmosphere of Eastern Croatia. Environ Model Assess 16(5):491–501. doi: 10.1007/s10666-011-9256-4 CrossRefGoogle Scholar
  24. Hooyberghs J, Mensink C, Dumont G, Fierens F, Brasseur O (2005) A neural network forecast for daily average PM10 concentrations in Belgium. Atmos Environ 39(18):3279–3289. doi: 10.1016/j.atmosenv.2005.01.050 CrossRefGoogle Scholar
  25. Hörmann S, Pfeiler B, Stadlober E (2005) Analysis and prediction of particulate matter PM10 for the winter season in Graz. Aust J Stat 34(4):307–326CrossRefGoogle Scholar
  26. Hrdličková Z, Michálek J, Kolář M, Veselý V (2008) Identification of factors affecting air pollution by dust aerosol PM10 in Brno City, Czech Republic. Atmos Environ 42(37):8661–8673. doi: 10.1016/j.atmosenv.2008.08.017 CrossRefGoogle Scholar
  27. Ichoku C, Kaufman YJ, Remer LA, Levy R (2004) Global aerosol remote sensing from MODIS. Adv Space Res 34:820–827. doi: 10.1016/j.asr.2003.07.071 CrossRefGoogle Scholar
  28. Kaufman YJ, Tanré D, Remer LA, Vermote EF, Chu A, Holben BN (1997) Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J Geophys Res 102:17051. doi: 10.1029/96JD03988 CrossRefGoogle Scholar
  29. Kokhanovsky AA, de Leeuw G (2009) Satellite aerosol remote sensing over land. doi: 10.1017/CBO9781107415324.004
  30. Kokhanovsky A, Leeuw G (2009) Satellite aerosol remote sensing over land. Vasa. doi: 10.1007/978-3-540-69397-0
  31. Kokhanovsky AA, Breon FM, Cacciari A, Carboni E, Diner D, Di Nicolantonio W, von Hoyningen-Huene W (2007) Aerosol remote sensing over land: a comparison of satellite retrievals using different algorithms and instruments. Atmos Res 85:372–394. doi: 10.1016/j.atmosres.2007.02.008 CrossRefGoogle Scholar
  32. Kong S, Ji Y, Lu B, Chen L, Han B, Li Z, Bai Z (2011) Characterization of PM10 source profiles for fugitive dust in Fushun-a city famous for coal. Atmos Environ 45(30):5351–5365. doi: 10.1016/j.atmosenv.2011.06.050 CrossRefGoogle Scholar
  33. Lenoble J, Remer LA, Tanré D (2013) Aerosol remote sensing. Springer, London. doi: 10.1007/978-3-642-17725-5 CrossRefGoogle Scholar
  34. Levy RC, Kaufman YJ, Remer LA, Mattoo S (2004) A new, more physically based algorithm, for retrieving aerosol properties over land from MODISGoogle Scholar
  35. Levy R, Remer L, Tanré D (2009) Algorithm for remote sensing of tropospheric aerosol over dark targets from MODIS: collections 005 and 051: revision 2; Feb 2009. Download from Http://…, 1–96. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.386.980&rep=rep1&type=pdf
  36. 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(1–2):33–44. doi: 10.1016/j.rse.2006.05.022 CrossRefGoogle Scholar
  37. Ludwig J, Schmitt M, Lichtenberg-Fraté H (2009) Saccharomyces cerevisiae as biosensor for cyto- and genotoxic activity. In: Kim PDYJ, Platt PDU, Gu DMB, Iwahashi DH (eds) Atmospheric and biological environmental monitoring. Springer, Berlin, pp 251–259. doi: 10.1007/978-1-4020-9674-7_17
  38. Martin RV (2008) Satellite remote sensing of surface air quality. Atmos Environ 42(34):7823–7843. doi: 10.1016/j.atmosenv.2008.07.018 CrossRefGoogle Scholar
  39. Mattis I (2004) Multiyear aerosol observations with dual-wavelength Raman lidar in the framework of EARLINET. J Geophys Res. doi: 10.1029/2004JD004600 CrossRefGoogle Scholar
  40. Mei L, Xue Y, Kokhanovsky AA, von Hoyningen-Huene W, de Leeuw G, Burrows JP (2013) Retrieval of aerosol optical depth over land surfaces from AVHRR data. Atmos Meas Tech Discuss 6(1):2227–2251. doi: 10.5194/amtd-6-2227-2013 CrossRefGoogle Scholar
  41. Nishihama M, Wolfe RE, Solomon D, Patt F (1997) MODIS level 1A earth location: algorithm theoretical basis. SDST-092, MODIS Science, 147Google Scholar
  42. Perez P, Reyes J (2006) An integrated neural network model for PM10 forecasting. Atmos Environ 40(16):2845–2851. doi: 10.1016/j.atmosenv.2006.01.010 CrossRefGoogle Scholar
  43. Querol X, Alastuey A, Rodriguez S, Plana F, Ruiz CR, Cots N, Puig O (2001) PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain. Atmos Environ 35(36):6407–6419. doi: 10.1016/S1352-2310(01)00361-2 CrossRefGoogle Scholar
  44. Remer LA, Kaufman YJ, Tanré D, Mattoo S, Chu DA, Martins JV, Holben BN (2005) The MODIS aerosol algorithm, products, and validation. J Atmos Sci 62(4):947–973. doi: 10.1175/JAS3385.1 CrossRefGoogle Scholar
  45. Rodríguez S, Querol X, Alastuey A, de la Rosa J, de la Rosa J (2007) Atmospheric particulate matter and air quality in the mediterranean: a review. Environ Chem Lett 5(1):1–7. doi: 10.1007/s10311-006-0071-0 CrossRefGoogle Scholar
  46. Rumpf DL (2004) Statistics for Dummies. Technometrics 46(3):366–367. doi: 10.1198/tech.2004.s204 CrossRefGoogle Scholar
  47. Schmidhuber J (2015) Deep Learning in neural networks: an overview. Neural Netw. doi: 10.1016/j.neunet.2014.09.003 CrossRefGoogle Scholar
  48. Shafer G (1986) Probability judgment in artificial intelligence. In: Kanal LN, Lemmer JF (eds) Uncertainty in artificial intelligence. North-Holland, Amsterdam, pp 127–135CrossRefGoogle Scholar
  49. Tanré D, Kaufman YJ, Herman M, Mattoo S (1997) Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. J Geophys Res Atmos 102(D14):16971–16988. doi: 10.1029/96JD03437
  50. van de Hulst HC (1948) Scattering in a planetary atmosphere. \apj, 107, 220. https://doi.org/10.1086/145005
  51. van de Kassteele J, Koelemeijer RBA, Dekkers ALM, Schaap M, Homan CD, Stein A (2006) Statistical mapping of PM10 concentrations over Western Europe using secondary information from dispersion modeling and MODIS satellite observations. Stoch Environ Res Risk Assess 21(2):183–194. doi: 10.1007/s00477-006-0055-4 CrossRefGoogle Scholar
  52. Vermote EF, Vermeulen A (1999) Atmospheric correction algorithm: spectral reflectances (MOD09). ATBD Version, 4, 1–107Google Scholar
  53. von Hoyningen-Huene W, Kokhanovsky A, Burrows JP (2008) Retrieval of particulate matter from MERIS observations. Adv Environ Monit. doi: 10.1007/978-1-4020-6364-0_15 CrossRefGoogle Scholar
  54. Von Hoyningen-Huene W, Yoon J, Vountas M, Istomina LG, Rohen G, Dinter T, Burrows JP (2011) Retrieval of spectral aerosol optical thickness over land using ocean color sensors MERIS and SeaWiFS. Atmos Meas Tech 4:151–171. doi: 10.5194/amt-4-151-2011 CrossRefGoogle Scholar
  55. Voukantsis D, Karatzas K, Kukkonen J, Räsänen T, Karppinen A, Kolehmainen M (2011) Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci Total Environ 409(7):1266–1276. doi: 10.1016/j.scitotenv.2010.12.039 CrossRefGoogle Scholar
  56. Who (2005) WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide: Global update 2005. Genebra. 20p, 1–21. https://doi.org/10.1016/0004-6981(88)90109-6
  57. Wilderson WD (1991) Dust and sand forecasting in Irag and adjoining countries. Air weather service scott Afb Il. USAF Environmental Technical Applications Center. http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA247588
  58. Wong MS, Nichol JE, Lee KH (2011) An operational MODIS aerosol retrieval algorithm at high spatial resolution, and its application over a complex urban region. Atmos Res 99(3–4):579–589. doi: 10.1016/j.atmosres.2010.12.015 CrossRefGoogle Scholar
  59. World Health Organization (2015) Climate and Health Country Profiles—2015: Iran. Climate and Health Country Profile—2015, 24. http://www.who.int/globalchange/resources/PHE-country-profile-China.pdf?ua=1
  60. Young AT (1980) Revised depolarization corrections for atmospheric extinction. Appl Opt 19(20):3427–3428. doi: 10.1364/AO.19.003427 CrossRefGoogle Scholar
  61. Young AT (1981) On the rayleigh-scattering optical depth of the atmosphere. J Appl Meteorol 20(3):328–330. doi: 10.1175/1520-0450(1981)020<0328:OTRSOD>2.0.CO;2 CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2017

Authors and Affiliations

  1. 1.Department of Remote Sensing and GIS, Faculty of GeographyUniversity of TehranTehranIran

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