KORELASI PM10 TERHADAP PARAMETER CUACA MENGGUNAKAN MODEL REGRESI MELALUI PENDEKATAN DERET FOURIER
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Date
2022-01
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Elfitra
Abstract
Air pollution, especially PM10 in Pekanbaru City is a familiar thing. The large population over tim e
causes an increase in pollution that can cause diseases in humans. Along with the development of
technology, research on the relationship between PM10 and weather parameters using a Fourier series
regression approach has been conducted. In this approach Matlab-based Fast Fourier Transform
algorithm that converts signals in the time domain to frequency signal was utilized. This study aims to
determine the PM10 cycle (frequency and period), pattern as a function of month. The relationship or
correlation of PM10 to weather parameters was determined using Fourier series regression approach for
the first 2 and 8 terms. The results show that the frequency of monthly PM10 cycles in Pekanbaru City
from 2014 to 2019 using the FFT algorithm method, for the three selected frequencies, namely 0.0169,
0.0508 and 0.0847 cycles/month, correspond to the period of 59.00, 19,67 and 11.80 months/cycle,
respectively. PM10 correlation with weather parameters such as rainfall, humidity and temperature is
periodic, so this research can use Fourier series. The 8-term Fourier series regression is much better
than that for 2-term Fourier series regression. The next research is more accurate in calculating the
PM10 cycle and it’s correlation with weather parameters using more extensive data until the end of the
last year.
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Keywords
PM10, signal, Fast Fourier Transform (FFT)
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