### Abstract

Prediction of ozone (O3) and PM10 is very important as both these air pollutants affect human health, human activities and more. Short-term forecasting of air quality is needed as preventive measures and effective action can be taken. Therefore, if it is detected that the ozone data is of a chaotic dynamical systems, a model using the nonlinear dynamic from chaos theory data can be made and thus forecasts for the short term would be more accurate. This study uses two methods, namely the 0-1 Test and Lyapunov Exponent. In addition, the effect of noise reduction on the analysis of time series data will be seen by using two smoothing methods: Rectangular methods and Triangle methods. At the end of the study, recommendations were made to get better results in the future.

Original language | English |
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Title of host publication | 22nd National Symposium on Mathematical Sciences, SKSM 2014: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia |

Publisher | American Institute of Physics Inc. |

Volume | 1682 |

ISBN (Electronic) | 9780735413290 |

DOIs | |

Publication status | Published - 22 Oct 2015 |

Event | 22nd National Symposium on Mathematical Sciences: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia, SKSM 2014 - Selangor, Malaysia Duration: 24 Nov 2014 → 26 Nov 2014 |

### Other

Other | 22nd National Symposium on Mathematical Sciences: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia, SKSM 2014 |
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Country | Malaysia |

City | Selangor |

Period | 24/11/14 → 26/11/14 |

### Fingerprint

### Keywords

- 0-1 Test
- Chaos
- Dynamical Systems
- Lyapunov Exponents
- Ozone
- PM10

### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Cite this

*22nd National Symposium on Mathematical Sciences, SKSM 2014: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia*(Vol. 1682). [020043] American Institute of Physics Inc.. https://doi.org/10.1063/1.4932452

**The behaviour of PM10 and ozone in Malaysia through non-linear dynamical systems.** / Sapini, Muhamad Luqman; Rahim, Nurul Zahirah Binti Abd; Md. Noorani, Mohd. Salmi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*22nd National Symposium on Mathematical Sciences, SKSM 2014: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia.*vol. 1682, 020043, American Institute of Physics Inc., 22nd National Symposium on Mathematical Sciences: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia, SKSM 2014, Selangor, Malaysia, 24/11/14. https://doi.org/10.1063/1.4932452

}

TY - GEN

T1 - The behaviour of PM10 and ozone in Malaysia through non-linear dynamical systems

AU - Sapini, Muhamad Luqman

AU - Rahim, Nurul Zahirah Binti Abd

AU - Md. Noorani, Mohd. Salmi

PY - 2015/10/22

Y1 - 2015/10/22

N2 - Prediction of ozone (O3) and PM10 is very important as both these air pollutants affect human health, human activities and more. Short-term forecasting of air quality is needed as preventive measures and effective action can be taken. Therefore, if it is detected that the ozone data is of a chaotic dynamical systems, a model using the nonlinear dynamic from chaos theory data can be made and thus forecasts for the short term would be more accurate. This study uses two methods, namely the 0-1 Test and Lyapunov Exponent. In addition, the effect of noise reduction on the analysis of time series data will be seen by using two smoothing methods: Rectangular methods and Triangle methods. At the end of the study, recommendations were made to get better results in the future.

AB - Prediction of ozone (O3) and PM10 is very important as both these air pollutants affect human health, human activities and more. Short-term forecasting of air quality is needed as preventive measures and effective action can be taken. Therefore, if it is detected that the ozone data is of a chaotic dynamical systems, a model using the nonlinear dynamic from chaos theory data can be made and thus forecasts for the short term would be more accurate. This study uses two methods, namely the 0-1 Test and Lyapunov Exponent. In addition, the effect of noise reduction on the analysis of time series data will be seen by using two smoothing methods: Rectangular methods and Triangle methods. At the end of the study, recommendations were made to get better results in the future.

KW - 0-1 Test

KW - Chaos

KW - Dynamical Systems

KW - Lyapunov Exponents

KW - Ozone

KW - PM10

UR - http://www.scopus.com/inward/record.url?scp=84984578681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84984578681&partnerID=8YFLogxK

U2 - 10.1063/1.4932452

DO - 10.1063/1.4932452

M3 - Conference contribution

AN - SCOPUS:84984578681

VL - 1682

BT - 22nd National Symposium on Mathematical Sciences, SKSM 2014: Strengthening Research and Collaboration of Mathematical Sciences in Malaysia

PB - American Institute of Physics Inc.

ER -