Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations
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 e...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/26/e3sconf_eier2024_04001.pdf |
| _version_ | 1797249037316915200 |
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| author | Nguyen Huynh A. D. Le Trung H. Ha Quang P. Duc Hiep Azzi Merched |
| author_facet | Nguyen Huynh A. D. Le Trung H. Ha Quang P. Duc Hiep Azzi Merched |
| author_sort | Nguyen Huynh A. D. |
| collection | DOAJ |
| description | 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 Sydney’s 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. |
| first_indexed | 2024-04-24T20:24:07Z |
| format | Article |
| id | doaj.art-c1f5bf55488c4e04a95b58d95f220542 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T20:24:07Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c1f5bf55488c4e04a95b58d95f2205422024-03-22T07:54:10ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014960400110.1051/e3sconf/202449604001e3sconf_eier2024_04001Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring StationsNguyen Huynh A. D.0Le Trung H.1Ha Quang P.2Duc Hiep3Azzi Merched4Faculty of Engineering and IT, University of Technology SydneyFaculty of Engineering and IT, University of Technology SydneyFaculty of Engineering and IT, University of Technology SydneyDepartment of Planning and Environment of New South WalesDepartment of Planning and Environment of New South WalesThe 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 Sydney’s 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/26/e3sconf_eier2024_04001.pdfparticulate mattermonitoringclusteringlow-cost sensorsair-quality stations |
| spellingShingle | Nguyen Huynh A. D. Le Trung H. Ha Quang P. Duc Hiep Azzi Merched Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations E3S Web of Conferences particulate matter monitoring clustering low-cost sensors air-quality stations |
| title | Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations |
| title_full | Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations |
| title_fullStr | Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations |
| title_full_unstemmed | Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations |
| title_short | Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations |
| title_sort | particulate matter monitoring and forecast with integrated low cost sensor networks and air quality monitoring stations |
| topic | particulate matter monitoring clustering low-cost sensors air-quality stations |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/26/e3sconf_eier2024_04001.pdf |
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