Research Article
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Year 2020, , 48 - 55, 15.04.2020
https://doi.org/10.35860/iarej.672520

Abstract

References

  • 1. Christakos, G. A Bayesian maximum-entropy view to the spatial estimation problem. Mathematical Geology, 1990. 22(7), 763-777.
  • 2. Christakos, G. Some applications of the Bayesian, maximum entropy concept in Geostatistics. Fundamental Theories of Physics, 1991a. p.215-229. Kluwer Acad. Publ. Dordrecht, The Netherlands.
  • 3. Christakos, G. A theory of spatiotemporal random fields and its application to space-time data processing. IEEE Trans., Systems, Man & Cybernetics, 1991b. V. 21, No.4, p. 861-875.
  • 4. Christakos, G. Random Field Models in Earth Sciences. 1992, Academic Press, San Diego, CA.
  • 5. Christakos, G. Spatiotemporal information systems in soil and environmental sciences. Geoderma, 1998a. 85(2), 141-179.
  • 6. Christakos, G. Multi-point BME space/time mapping of environmental variables. Computational Methods in Water Resources XII 1998b, Computational Methods in Surface and Groundwater Transport. Computational Mechanics Publ., 1998b. Vol.2., p. 289-296, Southampton, UK.
  • 7. Serre M.L. and Christakos, G. Modern Geostatistics: Computational BME analysis in the light of uncertain physical knowledge-the Equus Beds study. Stochastic Environmental Research and Risk Assessment, 1999. 13(1-2), p.1-26.
  • 8. Serre M.L., Kolovos, A., Christakos, G., and Modis, K. An application of the holistochastic human exposure methodology to naturally occuring arsenic in bangladesh drinking water. Risk Analysis, 2003. 23(3), p.525-528.
  • 9. Bogaert, P., Christakos, G., Jerrett, M., and Yu, H.L. Spatiotemporal modelling of ozone distribution in the State of California. Atmospheric Environment, 2009. 43(15), p.2471-2480.
  • 10. Fan, L., Xiao, Q., Wen, J., Liu, Q., Jin, R., You, D., and Li, X. Mapping high-resolution soil moisture over heterogeneous cropland using multi-resource remote sensing and ground observations. Remote Sensing, 2015. 7, p.13273-13297.
  • 11. Shi, T., Yang, X., Christakos, G., Wang, J. and Li, L. Spatiotemporal interpolation of rainfall by combining bme theory and satellite rainfall estimates. Atmosphere, 2015. 6(9), p.1307-1326.
  • 12. Douaik, A., Van Meirvenne, M., Toth, T., and Serre, M. Space-time mapping of soil salinity using probabilistic bayesian maximum entropy. Stoch. Envir. Res. And Risk Ass., 2004. 18, p.219-227.
  • 13. Tang, S., Yang, X., Dong, D., and Li, Z. Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method. Front. Earth Sci., 2015. 9(4): p.722-731.
  • 14. Baydaroğlu, Ö., and Koçak, K. Spatiotemporal analysis of wind speed via the Bayesian Maximum Entropy. Environmental Earth Sciences, 2019. 78(1), 17.
  • 15. Alyuz, U., Alp, K. Emission inventory of primary air pollutants in 2010 from industrial processes in Turkey. Science of the Total Environment, 2014. 488, p.369-381.
  • 16. Kabatas, B., Unal, A., Pierce, R.B., Kindap, T., and Pozzoli, L. The contribution of Saharan dust in PM10 concentration levels in Anatolian Peninsula of Turkey. Science of the Total Environment, 2014. 488, p.413-421.
  • 17. Güler, N., and İşçi, Ö.G. The regional prediction model of PM10 concentrations for Turkey. Atmospheric Research, 2016. 180, p.64-77.
  • 18. Ozel, G., and Cakmakyapan, S. A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey. Atmospheric Pollution Research, 2015. 6(5), p.735-741.
  • 19. Im, U., Markakis, K., Unal, A., Kindap, T., Poupkou, A., Incecik, S., ..., and Mihalopoulos, N. Study of a winter PM episode in Istanbul using the high resolution WRF/CMAQ modeling system. Atmospheric Environment, 2010. 44(26), p.3085-3094.
  • 20. Şahin, Ü.A., Ucan, O.N., Bayat, C., and Tolluoglu, O. A new approach to prediction of SO2 and PM10 concentrations in Istanbul, Turkey: Cellular Neural Network (CNN). Environmental Forensics, 2011. 12(3), p.253-269.
  • 21. Karaca, F. Determination of air quality zones in Turkey. Journal of the Air & Waste Management Association, 2012. 62(4), p.408-419.
  • 22. Konovalov, I.B., Beekmann, M., Meleux, F., Dutot, A, and Foret, G. Combining deterministic and statistical approaches for PM10 forecasting in Europe. Atmospheric Environment, 2009. 43(40), p.6425-6434.
  • 23. Nonnemacher, M., Jakobs, H., Viehmann, A., Vanberg, I., Kessler, C., Moebus, S., ..., and Heinz Nixdorf Recall Study Investigative Group. Spatio-temporal modelling of residential exposure to particulate matter and gaseous pollutants for the Heinz Nixdorf Recall Cohort. Atmospheric Environment, 2014. 91, p.15-23.
  • 24. Djalalova, I., Wilczak, J., McKeen, S., Grell, G., Peckham, S., Pagowski, M., ..., and McHenry, J. Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM 2.5 during the TEXAQS-II experiment of 2006. Atmospheric Environment, 2010. 44(4), p.455-467.
  • 25. Debry, E., and Mallet, V. Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform. Atmospheric Environment, 2014. 91, p.71-84.
  • 26. Chen, Y., Shi, R., Shu, S., and Gao, W. Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis. Atmospheric Environment, 2013. 74, p.346-359.
  • 27. Kim, S.Y., and Song, I. National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea. Environmental Pollution, 2017. 226, p.21-29.
  • 28. Christakos, G. and Serre, M.L. BME analysis of spatiotemporal particulate matter distributions in North Carolina. Atmospheric Environment, 2000. 34(20), p.3393-3406.
  • 29. Puangthongthub, S., Wangwongwatana, S., Kamens, R.M., and Serre, M.L. Modeling the space/time distribution of particulate matter in Thailand and optimizing its monitoring network. Atmospheric Environment, 2007. 41(36), p.7788-7805.
  • 30. Yu, H.L., Chen, J.C., Christakos, G., and Jerrett, M. BME Estimation of Residential Exposure to Ambient PM10 and Ozone at Multiple Time Scales. Environmental Health Perspectives, 2009. 117(4), 537.
  • 31. Fernando, H.J., Mammarella, M.C., Grandoni, G., Fedele, P., Di Marco, R., Dimitrova, R., and Hyde, P. Forecasting PM 10 in metropolitan areas: efficacy of neural networks. Environmental Pollution, 2012. 163, p.62-67.
  • 32. Akita, Y., Chen, J.C., and Serre, M.L. The moving-window Bayesian maximum entropy framework: estimation of PM2. 5 yearly average concentration across the contiguous United States. Journal of Exposure Science and Environmental Epidemiology, 2012. 22(5), p.496-501.
  • 33. Beckerman, B.S., Jerrett, M., Serre, M., Martin, R.V., Lee, S.J., Van Donkelaar, A., ..., and Burnett, R.T. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environmental Science & Technology, 2013. 47(13), 7233.
  • 34. Bayesian Maximum Entropy Graphical Users Interface (BMEGUI), University of North Carolina. [cited 2015 May 1]. Available from: http://www.unc.edu/depts/case/BMEGUI/ BMEGUI3.0.1/BMEGUI3.0.1_WEB_2014. htm
  • 35. Christakos, G. Modern Spatiotemporal Geostatistics, 2000. Oxford University Press.
  • 36. Gao, S., Zhu, Z., Liu, S., Jin, R., Yang, G., and Tan, L. Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 2014. 32, p.54-66.
  • 37. Olea, R.A. Understanding Geostatistics. Course Notes, 1997. Civil Engineering Dept., Univ. of Kansas, Lawrence, KS.
  • 38. Bogaert, P., and Christakos, G. Spatiotemporal analysis and processing of thermometric data over Belgium. Journal of Geophysical Research-All Series-, 1997. 102, p.25-831.
  • 39. Şengün, T. and Kıranşan, K. Güneydoğu Anadolu Bölgesi’nde çöl tozlarının hava kalitesi üzerine etkisi. Türk Coğrafya Dergisi, 2012. (59) (in Turkish).
  • 40. Dolar, A. and Saraç, H.T.K. Türkiye’nin doğu illerindeki hava Kalitesinin PM10 açısından incelenmesi (In Turkish) Iğdır Univ. J. Inst. Sci. & Tech., 2015. 5(4): p.25-32.
  • 41. Baltaci, H. Spatial and Temporal Variation of the Extreme Saharan Dust Event over Turkey in March 2016. Atmosphere, 2017. 8(2), 41.
  • 42. Koçak, M., Mihalopoulos, N., and Kubilay, N. Contributions of natural sources to high PM10 and PM2.5 events in the eastern Mediterranean. Atmospheric Environment, 2007. 41(18), p. 3806-3818.

Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy

Year 2020, , 48 - 55, 15.04.2020
https://doi.org/10.35860/iarej.672520

Abstract

Spatial and temporal distribution of PM10 is modeled by Bayesian Maximum Entropy (BME) method. It is the spatiotemporal estimation method which combines exact measurements with the secondary information by considering local uncertainties. In this study, daily average PM10 data are used to generate spatial and temporal PM10 maps. Both annual and seasonal estimations have been realized.  This is the first study which concentrates on spatiotemporal distribution of PM10 for all regions of Turkey by using Bayesian Maximum Entropy method. Error variances are used as performance criteria in both seasonal and annual predictions. All prediction results stay within the limits of the confidence intervals. In addition, unknown PM10 values are estimated, including PM10 values over the seas. It is thought that the PM10 maps which show all regions of Turkey in detail are quite invaluable and informative. 

References

  • 1. Christakos, G. A Bayesian maximum-entropy view to the spatial estimation problem. Mathematical Geology, 1990. 22(7), 763-777.
  • 2. Christakos, G. Some applications of the Bayesian, maximum entropy concept in Geostatistics. Fundamental Theories of Physics, 1991a. p.215-229. Kluwer Acad. Publ. Dordrecht, The Netherlands.
  • 3. Christakos, G. A theory of spatiotemporal random fields and its application to space-time data processing. IEEE Trans., Systems, Man & Cybernetics, 1991b. V. 21, No.4, p. 861-875.
  • 4. Christakos, G. Random Field Models in Earth Sciences. 1992, Academic Press, San Diego, CA.
  • 5. Christakos, G. Spatiotemporal information systems in soil and environmental sciences. Geoderma, 1998a. 85(2), 141-179.
  • 6. Christakos, G. Multi-point BME space/time mapping of environmental variables. Computational Methods in Water Resources XII 1998b, Computational Methods in Surface and Groundwater Transport. Computational Mechanics Publ., 1998b. Vol.2., p. 289-296, Southampton, UK.
  • 7. Serre M.L. and Christakos, G. Modern Geostatistics: Computational BME analysis in the light of uncertain physical knowledge-the Equus Beds study. Stochastic Environmental Research and Risk Assessment, 1999. 13(1-2), p.1-26.
  • 8. Serre M.L., Kolovos, A., Christakos, G., and Modis, K. An application of the holistochastic human exposure methodology to naturally occuring arsenic in bangladesh drinking water. Risk Analysis, 2003. 23(3), p.525-528.
  • 9. Bogaert, P., Christakos, G., Jerrett, M., and Yu, H.L. Spatiotemporal modelling of ozone distribution in the State of California. Atmospheric Environment, 2009. 43(15), p.2471-2480.
  • 10. Fan, L., Xiao, Q., Wen, J., Liu, Q., Jin, R., You, D., and Li, X. Mapping high-resolution soil moisture over heterogeneous cropland using multi-resource remote sensing and ground observations. Remote Sensing, 2015. 7, p.13273-13297.
  • 11. Shi, T., Yang, X., Christakos, G., Wang, J. and Li, L. Spatiotemporal interpolation of rainfall by combining bme theory and satellite rainfall estimates. Atmosphere, 2015. 6(9), p.1307-1326.
  • 12. Douaik, A., Van Meirvenne, M., Toth, T., and Serre, M. Space-time mapping of soil salinity using probabilistic bayesian maximum entropy. Stoch. Envir. Res. And Risk Ass., 2004. 18, p.219-227.
  • 13. Tang, S., Yang, X., Dong, D., and Li, Z. Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method. Front. Earth Sci., 2015. 9(4): p.722-731.
  • 14. Baydaroğlu, Ö., and Koçak, K. Spatiotemporal analysis of wind speed via the Bayesian Maximum Entropy. Environmental Earth Sciences, 2019. 78(1), 17.
  • 15. Alyuz, U., Alp, K. Emission inventory of primary air pollutants in 2010 from industrial processes in Turkey. Science of the Total Environment, 2014. 488, p.369-381.
  • 16. Kabatas, B., Unal, A., Pierce, R.B., Kindap, T., and Pozzoli, L. The contribution of Saharan dust in PM10 concentration levels in Anatolian Peninsula of Turkey. Science of the Total Environment, 2014. 488, p.413-421.
  • 17. Güler, N., and İşçi, Ö.G. The regional prediction model of PM10 concentrations for Turkey. Atmospheric Research, 2016. 180, p.64-77.
  • 18. Ozel, G., and Cakmakyapan, S. A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey. Atmospheric Pollution Research, 2015. 6(5), p.735-741.
  • 19. Im, U., Markakis, K., Unal, A., Kindap, T., Poupkou, A., Incecik, S., ..., and Mihalopoulos, N. Study of a winter PM episode in Istanbul using the high resolution WRF/CMAQ modeling system. Atmospheric Environment, 2010. 44(26), p.3085-3094.
  • 20. Şahin, Ü.A., Ucan, O.N., Bayat, C., and Tolluoglu, O. A new approach to prediction of SO2 and PM10 concentrations in Istanbul, Turkey: Cellular Neural Network (CNN). Environmental Forensics, 2011. 12(3), p.253-269.
  • 21. Karaca, F. Determination of air quality zones in Turkey. Journal of the Air & Waste Management Association, 2012. 62(4), p.408-419.
  • 22. Konovalov, I.B., Beekmann, M., Meleux, F., Dutot, A, and Foret, G. Combining deterministic and statistical approaches for PM10 forecasting in Europe. Atmospheric Environment, 2009. 43(40), p.6425-6434.
  • 23. Nonnemacher, M., Jakobs, H., Viehmann, A., Vanberg, I., Kessler, C., Moebus, S., ..., and Heinz Nixdorf Recall Study Investigative Group. Spatio-temporal modelling of residential exposure to particulate matter and gaseous pollutants for the Heinz Nixdorf Recall Cohort. Atmospheric Environment, 2014. 91, p.15-23.
  • 24. Djalalova, I., Wilczak, J., McKeen, S., Grell, G., Peckham, S., Pagowski, M., ..., and McHenry, J. Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM 2.5 during the TEXAQS-II experiment of 2006. Atmospheric Environment, 2010. 44(4), p.455-467.
  • 25. Debry, E., and Mallet, V. Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform. Atmospheric Environment, 2014. 91, p.71-84.
  • 26. Chen, Y., Shi, R., Shu, S., and Gao, W. Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis. Atmospheric Environment, 2013. 74, p.346-359.
  • 27. Kim, S.Y., and Song, I. National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea. Environmental Pollution, 2017. 226, p.21-29.
  • 28. Christakos, G. and Serre, M.L. BME analysis of spatiotemporal particulate matter distributions in North Carolina. Atmospheric Environment, 2000. 34(20), p.3393-3406.
  • 29. Puangthongthub, S., Wangwongwatana, S., Kamens, R.M., and Serre, M.L. Modeling the space/time distribution of particulate matter in Thailand and optimizing its monitoring network. Atmospheric Environment, 2007. 41(36), p.7788-7805.
  • 30. Yu, H.L., Chen, J.C., Christakos, G., and Jerrett, M. BME Estimation of Residential Exposure to Ambient PM10 and Ozone at Multiple Time Scales. Environmental Health Perspectives, 2009. 117(4), 537.
  • 31. Fernando, H.J., Mammarella, M.C., Grandoni, G., Fedele, P., Di Marco, R., Dimitrova, R., and Hyde, P. Forecasting PM 10 in metropolitan areas: efficacy of neural networks. Environmental Pollution, 2012. 163, p.62-67.
  • 32. Akita, Y., Chen, J.C., and Serre, M.L. The moving-window Bayesian maximum entropy framework: estimation of PM2. 5 yearly average concentration across the contiguous United States. Journal of Exposure Science and Environmental Epidemiology, 2012. 22(5), p.496-501.
  • 33. Beckerman, B.S., Jerrett, M., Serre, M., Martin, R.V., Lee, S.J., Van Donkelaar, A., ..., and Burnett, R.T. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environmental Science & Technology, 2013. 47(13), 7233.
  • 34. Bayesian Maximum Entropy Graphical Users Interface (BMEGUI), University of North Carolina. [cited 2015 May 1]. Available from: http://www.unc.edu/depts/case/BMEGUI/ BMEGUI3.0.1/BMEGUI3.0.1_WEB_2014. htm
  • 35. Christakos, G. Modern Spatiotemporal Geostatistics, 2000. Oxford University Press.
  • 36. Gao, S., Zhu, Z., Liu, S., Jin, R., Yang, G., and Tan, L. Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 2014. 32, p.54-66.
  • 37. Olea, R.A. Understanding Geostatistics. Course Notes, 1997. Civil Engineering Dept., Univ. of Kansas, Lawrence, KS.
  • 38. Bogaert, P., and Christakos, G. Spatiotemporal analysis and processing of thermometric data over Belgium. Journal of Geophysical Research-All Series-, 1997. 102, p.25-831.
  • 39. Şengün, T. and Kıranşan, K. Güneydoğu Anadolu Bölgesi’nde çöl tozlarının hava kalitesi üzerine etkisi. Türk Coğrafya Dergisi, 2012. (59) (in Turkish).
  • 40. Dolar, A. and Saraç, H.T.K. Türkiye’nin doğu illerindeki hava Kalitesinin PM10 açısından incelenmesi (In Turkish) Iğdır Univ. J. Inst. Sci. & Tech., 2015. 5(4): p.25-32.
  • 41. Baltaci, H. Spatial and Temporal Variation of the Extreme Saharan Dust Event over Turkey in March 2016. Atmosphere, 2017. 8(2), 41.
  • 42. Koçak, M., Mihalopoulos, N., and Kubilay, N. Contributions of natural sources to high PM10 and PM2.5 events in the eastern Mediterranean. Atmospheric Environment, 2007. 41(18), p. 3806-3818.
There are 42 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Özlem Baydaroğlu Yeşilköy 0000-0003-2184-5785

Publication Date April 15, 2020
Submission Date January 9, 2020
Acceptance Date February 26, 2020
Published in Issue Year 2020

Cite

APA Baydaroğlu Yeşilköy, Ö. (2020). Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy. International Advanced Researches and Engineering Journal, 4(1), 48-55. https://doi.org/10.35860/iarej.672520
AMA Baydaroğlu Yeşilköy Ö. Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy. Int. Adv. Res. Eng. J. April 2020;4(1):48-55. doi:10.35860/iarej.672520
Chicago Baydaroğlu Yeşilköy, Özlem. “Estimation of PM10 Concentrations in Turkey Based on Bayesian Maximum Entropy”. International Advanced Researches and Engineering Journal 4, no. 1 (April 2020): 48-55. https://doi.org/10.35860/iarej.672520.
EndNote Baydaroğlu Yeşilköy Ö (April 1, 2020) Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy. International Advanced Researches and Engineering Journal 4 1 48–55.
IEEE Ö. Baydaroğlu Yeşilköy, “Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy”, Int. Adv. Res. Eng. J., vol. 4, no. 1, pp. 48–55, 2020, doi: 10.35860/iarej.672520.
ISNAD Baydaroğlu Yeşilköy, Özlem. “Estimation of PM10 Concentrations in Turkey Based on Bayesian Maximum Entropy”. International Advanced Researches and Engineering Journal 4/1 (April 2020), 48-55. https://doi.org/10.35860/iarej.672520.
JAMA Baydaroğlu Yeşilköy Ö. Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy. Int. Adv. Res. Eng. J. 2020;4:48–55.
MLA Baydaroğlu Yeşilköy, Özlem. “Estimation of PM10 Concentrations in Turkey Based on Bayesian Maximum Entropy”. International Advanced Researches and Engineering Journal, vol. 4, no. 1, 2020, pp. 48-55, doi:10.35860/iarej.672520.
Vancouver Baydaroğlu Yeşilköy Ö. Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy. Int. Adv. Res. Eng. J. 2020;4(1):48-55.



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