Abstract
Vessel noise pollution in inland waterways can have negative impacts on human health in urban areas. Thus, it is important to evaluate the vessel noise exposure level, in order to take effective measures to protect the urban environment and human health. The aims of this research were (a) to summarize current application tools on modeling and estimating exposure levels for traffic noise pollution in inland waterways; (b) to compare the existing inland waterway traffic noise prediction and evaluation models in terms of their associated strengths and weaknesses, influence factors, simulation accuracy, and application scope. The research reviews the literature with regard to the regression models for estimating traffic noise exposure in inland waterways, including the modified FHWA, RLS 90, Schall 03, CoRTN and Nord 2000 models. The sections providing these models are independent, with establishment principles, fundamental equations, and noise attenuation influencing factors included. The factors that may affect the accuracy of these models—source emission, propagation path, channel characteristics, and other factors are analyzed. Water surface absorption and embankment shielding, especially, are recognized as key factors influencing the attenuation of vessel traffic noise. The simulation accuracy and reliability of the models are then discussed. It was found that all these models produce good agreement with the surveyed results. Special attention is paid to comparing vessel noise prediction and evaluation tools, and to informal alternative schemes for the reduction of the negative influences of inland waterway vessel noise on human beings and the urban environment.
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Acknowledgements
This work was supported by the Jiangsu Key Research and Development (R&D) Projects (Social Development, BE2020772), Huaian Natural Science Research Program (HAB202157) and Natural Science Major Foundation of the Jiangsu Higher Education Institutions of China (22KJA610002).
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Dai, B.L., Sheng, N., Huang, J. et al. Inland waterway traffic noise prediction model: a comparison. Int. J. Environ. Sci. Technol. 21, 2007–2016 (2024). https://doi.org/10.1007/s13762-023-05009-1
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DOI: https://doi.org/10.1007/s13762-023-05009-1