Daily PM10, periodicity and harmonic regression model: The case of London

Okkaoglu Y., AKDİ Y., Unlu K. D.

ATMOSPHERIC ENVIRONMENT, cilt.238, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 238
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.atmosenv.2020.117755
  • Dergi Adı: ATMOSPHERIC ENVIRONMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, Environment Index, Geobase, Greenfile, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Harmonic regression, Periodograms, PM10, London, Nonlinear time series analysis, Air pollution, NEURAL-NETWORK, TIME-SERIES, PM2.5, SO2, PREDICTION, NO2
  • Ankara Üniversitesi Adresli: Evet

Özet

One of the most important and distinguishable features of the climate driven data can be shown as the seasonality. Due to its nature air pollution data may have hourly, daily, weekly, monthly or even seasonal cycles. Many techniques such as non-linear time series analysis, machine learning algorithms and deterministic models, have been used to deal with this non-linear structure. Although, these models can capture the seasonality they can't identify the periodicity. Periodicity is beyond the seasonality, it is the hidden pattern of the time series. In this study, it is aimed to investigate the periodicity of daily Particulate Matter (PM10) of London between the periods 2014 and 2018. PM10 is the particulate matter of which aerodynamic diameter is less than 10 mu m. Firstly, periodogram based unit root test is used to check the stationarity of the investigated data. Afterwards, hidden periodic structure of the data is revealed. It is found that, it has five different cycle periods as 7 days, 25 days, 6 months, a year and 15 months. Lastly, it is shown that harmonic regression performs better in forecasting monthly and daily averages of the data.