Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

Nguyen, HAD; Le, TH; Ha, QP; Duc, H; Azzi, M

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

Nguyen, HAD Le, TH Ha, QP Duc, H Azzi, M

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Publisher:: EDP Sciences
Publication Type:: Journal Article
Citation:: E3S Web of Conferences, 2024, 496, pp. 04001
Issue Date:: 2024-03-12

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Field	Value	Language
dc.contributor.author	Nguyen, HAD
dc.contributor.author	Le, TH
dc.contributor.author	Ha, QP
dc.contributor.author	Duc, H
dc.contributor.author	Azzi, M
dc.contributor.editor	Truong, NV
dc.contributor.editor	Ha, Q https://orcid.org/0000-0003-0978-1758
dc.date.accessioned	2024-04-24T23:11:50Z
dc.date.available	2024-04-24T23:11:50Z
dc.date.issued	2024-03-12
dc.identifier.citation	E3S Web of Conferences, 2024, 496, pp. 04001
dc.identifier.issn	2555-0403
dc.identifier.issn	2267-1242
dc.identifier.uri	http://hdl.handle.net/10453/178350
dc.description.abstract	The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydneys suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.
dc.language	en
dc.publisher	EDP Sciences
dc.relation.ispartof	E3S Web of Conferences
dc.relation.isbasedon	10.1051/e3sconf/202449604001
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations
dc.type	Journal Article
utslib.citation.volume	496
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-24T23:11:46Z
pubs.publication-status	Published
pubs.volume	496

Abstract:

The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydneys suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.

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http://hdl.handle.net/10453/178350