The City of Amman, Jordan, has been subjected to persistent increase in road traffic due to overall increase in prosperity, fast development and expansion of economy, travel and tourism. This study investigates traffic noise pollution in Amman. Road traffic noise index L10(1 h) was measured at 28 locations that cover most of the City of Amman. Noise measurements were carried out at these 28 locations two times a day for a period of one hour during the early morning and early evening rush hours, in the presence and absence of a barrier. The Calculation of Road Traffic Noise (CRTN) prediction model was employed to predict noise levels at the locations chosen for the study. Data required for the model include traffic volume, speed, percentage of heavy vehicles, road surface, gradient, obstructions, distance, noise path, intervening ground, effect of shielding, and angle of view. The results of the investigation showed that the minimum and the maximum noise levels are 46 dB(A) and 81 dB(A) during day-time and 58 dB(A) and 71 dB(A) during night-time. The measured noise level exceeded the 62 dB(A) acceptable limit at most of the locations. The CTRN prediction model was successful in predicting noise levels at most of the locations chosen for this investigation, with more accurate predictions for night-time measurements.
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http://dx.doi.org/10.1007/s10661-005-9077-5 | DOI Listing |
The explosive growth of mobile data traffic and the demands of 6 G networks for ultra-high data rates and low latency necessitate advanced infrastructure solutions. One promising approach is the implementation of radio-over-fiber (RoF)-based distributed antenna systems (DAS), which can efficiently transmit radio frequency signals over optical fiber, especially in dense indoor environments. However, analog RoF systems face challenges, including noise, nonlinearities, and power fading caused by chromatic dispersion.
View Article and Find Full Text PDFPLoS One
January 2025
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
This study tried to focus on the older drivers' group and explore the impact factors of injury severity involving older drivers from geo-spatial analysis. To reach the goal, a spatial analysis was proposed employing geographic information systems (GIS) with a case study application to two counties in Nevada. First, crash clusters were explored using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach to investigate the spatial crash pattern for older drivers, and determine high risk locations of injury severity.
View Article and Find Full Text PDFAntioxidants (Basel)
January 2025
Laboratory of Molecular Cardiology, Department of Cardiology 1, University Medical Center of the Johannes Gutenberg-University, 55131 Mainz, Germany.
Noise pollution is a known health risk factor and evidence for cardiovascular diseases associated with traffic noise is growing. At least 20% of the European Union's population lives in noise-polluted areas with exposure levels exceeding the recommended limits of the World Health Organization, which is considered unhealthy by the European Environment Agency. This results in the annual loss of 1.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Civil Engineering, National Institute of Technology, Mizoram, India.
Chronic exposure to traffic noise is associated with increased stress and sleep disruptions. Research on the health consequences of environmental noise, specifically traffic noise, has primarily been conducted in high-income countries (HICs), which have guided the development of noise regulations. The relevance of these findings to policy frameworks in low- and middle-income countries (LMICs) remains uncertain.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Since traffic flow has not been generated, a traffic noise prediction model based on actual traffic state data cannot be directly applied to the planned road network. Therefore, a regional traffic noise prediction method is proposed to find the upper limit of network noise emission based on design elements. The model is developed with noise predictions of the basic road section, interrupted/continuous intersections, and regional network.
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