### Abstract

This study highlights the advantage of network analysis in assessing an air pollutant called particulate matters of size less the 10 micrometer, PM10. Aim of the study is to develop a network model based on the concentration data of PM10 and the location of the air quality stations. Then, the behavior of the model was studied using certain network measurements. The network model, G ω(V,E) consists of a set of nodes, V representing air quality monitoring stations and a set of edges, E representing the correlation value of the combined data. Threshold and simulation methods have been implemented to determine the appropriate interval correlation on the network. In this study, five network measurements were considered to analyze the network, which are degree centrality, closeness centrality, betweenness centrality, local clustering coefficient and local assortativity. Network analysis was performed to identify the important nodes (hubs) in the network. The network model of air quality based on the concentration of PM10 and the exact location of air quality monitoring stations were developed using the threshold value of 0.664. The results show that the number of air quality monitoring stations in Malaysia successfully optimized.

Original language | English |
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Title of host publication | Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015 |

Publisher | American Institute of Physics Inc. |

Volume | 1750 |

ISBN (Electronic) | 9780735414075 |

DOIs | |

Publication status | Published - 21 Jun 2016 |

Event | 23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015 - Johor Bahru, Malaysia Duration: 24 Nov 2015 → 26 Nov 2015 |

### Other

Other | 23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015 |
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Country | Malaysia |

City | Johor Bahru |

Period | 24/11/15 → 26/11/15 |

### Fingerprint

### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Cite this

*Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015*(Vol. 1750). [030024] American Institute of Physics Inc.. https://doi.org/10.1063/1.4954560

**Modelling the correlation of PM10 concentration and location of air quality monitoring stations in Malaysia using network method.** / Rasidi, Norsuhaili Mahamed; Abu Bakar, Sakhinah; Abdul Razak, Fatimah.

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

*Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015.*vol. 1750, 030024, American Institute of Physics Inc., 23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015, Johor Bahru, Malaysia, 24/11/15. https://doi.org/10.1063/1.4954560

}

TY - GEN

T1 - Modelling the correlation of PM10 concentration and location of air quality monitoring stations in Malaysia using network method

AU - Rasidi, Norsuhaili Mahamed

AU - Abu Bakar, Sakhinah

AU - Abdul Razak, Fatimah

PY - 2016/6/21

Y1 - 2016/6/21

N2 - This study highlights the advantage of network analysis in assessing an air pollutant called particulate matters of size less the 10 micrometer, PM10. Aim of the study is to develop a network model based on the concentration data of PM10 and the location of the air quality stations. Then, the behavior of the model was studied using certain network measurements. The network model, G ω(V,E) consists of a set of nodes, V representing air quality monitoring stations and a set of edges, E representing the correlation value of the combined data. Threshold and simulation methods have been implemented to determine the appropriate interval correlation on the network. In this study, five network measurements were considered to analyze the network, which are degree centrality, closeness centrality, betweenness centrality, local clustering coefficient and local assortativity. Network analysis was performed to identify the important nodes (hubs) in the network. The network model of air quality based on the concentration of PM10 and the exact location of air quality monitoring stations were developed using the threshold value of 0.664. The results show that the number of air quality monitoring stations in Malaysia successfully optimized.

AB - This study highlights the advantage of network analysis in assessing an air pollutant called particulate matters of size less the 10 micrometer, PM10. Aim of the study is to develop a network model based on the concentration data of PM10 and the location of the air quality stations. Then, the behavior of the model was studied using certain network measurements. The network model, G ω(V,E) consists of a set of nodes, V representing air quality monitoring stations and a set of edges, E representing the correlation value of the combined data. Threshold and simulation methods have been implemented to determine the appropriate interval correlation on the network. In this study, five network measurements were considered to analyze the network, which are degree centrality, closeness centrality, betweenness centrality, local clustering coefficient and local assortativity. Network analysis was performed to identify the important nodes (hubs) in the network. The network model of air quality based on the concentration of PM10 and the exact location of air quality monitoring stations were developed using the threshold value of 0.664. The results show that the number of air quality monitoring stations in Malaysia successfully optimized.

UR - http://www.scopus.com/inward/record.url?scp=84984577969&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84984577969&partnerID=8YFLogxK

U2 - 10.1063/1.4954560

DO - 10.1063/1.4954560

M3 - Conference contribution

VL - 1750

BT - Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015

PB - American Institute of Physics Inc.

ER -