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| DOI | 10.1016/j.atmosenv.2020.117755 |
| Daily PM10, periodicity and harmonic regression model: The case of London | |
| Okkaoğlu Y.; Akdi Y.; Ünlü K.D. | |
| 发表日期 | 2020 |
| ISSN | 1352-2310 |
| 卷号 | 238 |
| 英文摘要 | 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 μm. 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. © 2020 Elsevier Ltd |
| 关键词 | Air pollutionHarmonic regressionLondonNonlinear time series analysisPeriodogramsPM10 |
| 语种 | 英语 |
| scopus关键词 | Learning algorithms; Machine learning; Particles (particulate matter); Regression analysis; Aerodynamic diameters; Deterministic models; Harmonic regression; Hidden patterns; Nonlinear structure; Nonlinear time-series analysis; Particulate Matter; Unit root tests; Time series analysis; algorithm; atmospheric pollution; harmonic analysis; machine learning; particulate matter; periodicity; regression analysis; seasonal variation; seasonality; air pollution; article; England; forecasting; particulate matter; periodicity; seasonal variation; time series analysis; London |
| 来源期刊 | ATMOSPHERIC ENVIRONMENT
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/249016 |
| 作者单位 | University of Southampton, Department of Mathematical Sciences, Southampton, SO17 1BJ, United Kingdom; Ankara University, Department of Statistics, Ankara, 06100, Turkey; Atilim University, Department of Mathematics, Ankara, 06830, Turkey |
| 推荐引用方式 GB/T 7714 | Okkaoğlu Y.,Akdi Y.,Ünlü K.D.. Daily PM10, periodicity and harmonic regression model: The case of London[J],2020,238. |
| APA | Okkaoğlu Y.,Akdi Y.,&Ünlü K.D..(2020).Daily PM10, periodicity and harmonic regression model: The case of London.ATMOSPHERIC ENVIRONMENT,238. |
| MLA | Okkaoğlu Y.,et al."Daily PM10, periodicity and harmonic regression model: The case of London".ATMOSPHERIC ENVIRONMENT 238(2020). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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