Xianyang City Atmospheric Dust Analysis in the Spring of 2008

Shi, Xuhua; Jin, Ningning

doi:10.1109/iceet.2009.502

Cited by 1 publication

(2 citation statements)

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“…The accuracy of the developed correlations was evaluated by sensitivity (true positive) and specificity (true negative) using the threshold dust concentration (µ d ) 15 mg/m 3 As shown in Figure 9, more than 90% of the data from both correlations fall in quadrant 1, meaning when the dust concentrations are less than the threshold, the segmentation can make an accurate prediction. If a stricter dust threshold is considered, for example, µ d = 5 mg/m 3 , most of the data in both correlations would still fall in quadrant 1.…”

Section: Results and Validationmentioning

confidence: 99%

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Beyond Conventional Monitoring: A Semantic Segmentation Approach to Quantifying Traffic-Induced Dust on Unsealed Roads

de Silva,

Ranasinghe,

Sounthararajah

et al. 2024

Sensors

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Road dust is a mixture of fine and coarse particles released into the air due to an external force, such as tire–ground friction or wind, which is harmful to human health when inhaled. Continuous dust emission from the road surfaces is detrimental to the road itself and the road users. Due to this, multiple dust monitoring and control techniques are currently adopted in the world. The current dust monitoring methods require expensive equipment and expertise. This study introduces a novel pragmatic and robust approach to quantifying traffic-induced road dust using a deep learning method called semantic segmentation. Based on the authors’ previous works, the best-performing semantic segmentation machine learning models were selected and used to identify dust in an image pixel-wise. The total number of dust pixels was then correlated with real-world dust measurements obtained from a research-grade dust monitor. Our method shows that semantic segmentation can be adopted to quantify traffic-induced dust reasonably. Over 90% of the predictions from both correlations fall in true positive quadrant, indicating that when dust concentrations are below the threshold, the segmentation can accurately predict them. The results were validated and extended for real-time application. Our code implementation is publicly available.

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Section: Results and Validationmentioning

confidence: 99%

“…Due to the different sizes of PM causing different types of health issues, this study focuses on five particle sizes, PM 30 , PM 10 , PM 4 , PM 2.5 , and PM 1 . Notably, the inhalable (PM 10 ) and respirable (PM 2.5 ) PM can induce adverse health effects [ 2 ], thus are often focused on more in dust-related studies [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%