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Spectral Analysis of Air Pollution in Tehran

Zare Shahneh, Maryam | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43796 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Arhami, Mohammad
  7. Abstract:
  8. Tehran possesses various environmental crises due to excessive population growth, a huge increase of vehicles and heavy concentrated industries. One of the most important concern is air pollution. Spectral Analysis by discrete Fourier transform are described and applied to harmonic analysis of time series for detecting Present periodicities.
    The current work proposes an approach for the determine the contribution of different frequencies to the data variance using air quality measured data. In this research, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. Because of The air quality data in Tehran have irregular sampling periods and missing data that preclude the straightforward application of the fast Fourier transform (FFT), The Lomb- Scargle periodogram is used to Identification of frequencies for unevenly sampled data. Also, we used KZ filter as moving average and low pass filter to extract the temporal component of air quality time series.
    The Lomb- Scargle periodogram and KZ filter are described and applied to harmonic analysis of two typical data sets, one criteria air pollutants time series and one meteorological variables time series. The air quality data is a 8 year series in active stations in Tehran. The meteorological variables is 8 year series of 3 hour average dry bulb temperature, wet bulb temperature, relative humidity, intensity and direction of wind at Mehrabad station. This methodology was applied to estimate the most important frequencies of criteria pollutants and meteorological variables. With using this methods, The daily cycle identified for O3 and CO data. The long-term cycle is identified in PM10, NO2 and SO2. and The daily and yearly cycle is identified in meteorological variables. The KZ filter result shows O3, NO2, SO2, dry bulb temperature, intensity and direction of wind have increased at recent year
  9. Keywords:
  10. Spectral Analysis ; Time Series ; Fourier Transform ; Air Pollution ; KZ Filter

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