Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering
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| Argitaratua izan da: | Atmosphere vol. 16, no. 5 (2025), p. 530 |
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| Egile nagusia: | |
| Beste egile batzuk: | |
| Argitaratua: |
MDPI AG
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3211859752 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos16050530 |2 doi | |
| 035 | |a 3211859752 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Ho-jun, Yoo |u Research Institute, RoadKorea Inc., Gyeonggido 18471, Republic of Korea | |
| 245 | 1 | |a Analytical Approach to Enhancing Efficiency of Silt Loading Collection in EPA Vacuum Sweep Method Using K-Means Clustering | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study explores the application of K-means clustering to optimize the selection of sampling locations for suspended silt loading (sL) on asphalt pavements, addressing the limitations of traditional random sampling methods in the EPA method. The objective was to identify reliable sampling points for road dust concentration measurement, with a focus on improving the accuracy of data collection using the vacuum sweep method. The elbow method was used to determine the optimal number of clusters, revealing that three clusters were ideal for 25 m intervals and five for 100 m intervals. The clustering analysis identified specific sampling locations within the 25 m and 100 m road sections, such as 1.5–4.5 m and 12–18 m, and 15–18 m, 39–42 m, 57 m, 69 m, and 87 m, respectively, which adequately captured sL characteristics. The silhouette score of 0.6247 confirmed the effectiveness of the clustering method in distinguishing distinct groups with similar sL characteristics. The comparison of clustered versus non-clustered sections across 15 pavement segments showed an error rate of approximately 6%. Properly selecting sampling points ensures more accurate dust concentration data, which is crucial for effective road maintenance and environmental management. The findings highlight that optimizing the sampling process can significantly enhance the precision of dust monitoring, especially in areas with varying sL characteristics. | |
| 610 | 4 | |a Environmental Protection Agency--EPA | |
| 651 | 4 | |a South Korea | |
| 651 | 4 | |a United States--US | |
| 653 | |a Accuracy | ||
| 653 | |a Dust | ||
| 653 | |a Silt | ||
| 653 | |a Data collection | ||
| 653 | |a Traffic | ||
| 653 | |a Environmental management | ||
| 653 | |a Emissions | ||
| 653 | |a Sampling methods | ||
| 653 | |a Silt load | ||
| 653 | |a Roads & highways | ||
| 653 | |a Laboratories | ||
| 653 | |a Asphalt pavements | ||
| 653 | |a Asphalt | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Random sampling | ||
| 653 | |a Elbow | ||
| 653 | |a Road maintenance | ||
| 653 | |a Intervals | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Clustering | ||
| 653 | |a Quality management | ||
| 653 | |a Statistical sampling | ||
| 653 | |a Optimization | ||
| 653 | |a Effectiveness | ||
| 653 | |a Atmospheric particulates | ||
| 653 | |a Methods | ||
| 653 | |a Vacuum | ||
| 653 | |a Vector quantization | ||
| 700 | 1 | |a Kim In-tai |u Department of Transportation Engineering, Myongji University, Gyeonggido 17058, Republic of Korea; kit1998@mju.ac.kr | |
| 773 | 0 | |t Atmosphere |g vol. 16, no. 5 (2025), p. 530 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211859752/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211859752/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211859752/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |