Topic Editors

School of Civil and Architectural Engineering, Beijing Jiaotong University, Beijing, China
Department of Building Science, Tsinghua University, Beijing 100084, China

Environmental Noise Prediction, Measurement and Control

Abstract submission deadline
closed (20 May 2024)
Manuscript submission deadline
20 July 2024
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Topic Information

Dear Colleagues,

Noise pollution impacts millions of people on a daily basis. Long periodic noise or excessive noise exposure can cause damage to people, both physically and psychologically. In addition, noise pollution can also contribute to the health threat to animals and wildlife, both on land and in the sea. Therefore, scholars around the world have been dedicated to minimizing noise to an acceptable level. 

Sound is intrinsically a mechanical wave transmitted in the atmosphere or underwater. In the past 10 decades, researchers have been devoted to sound prediction and measurement in open or enclosed environments and efficient sound-mitigating methods. 

The acoustic modelling and simulation methods include the analytical solution, finite/infinite element methods, statistical energy analysis and CFD simulations. Nowadays, as artificial intelligence is flourishing and booming in interdisciplinary research, the data-driven and physically-informed computing of acoustics can be envisioned as an emerging and promising direction. The sound measurement includes every type of sound tests, such as long-term or short-term monitoring. New techniques should be required to separate the mixed sound into individual sources through single/multi-channel blind source separation to identify and quantify the main source, which contributes most among all sources. Sound field reconstruction can be obtained through the sound-array test and beam-forming algorithm or acoustic holography. However, innovative sound visualization methods in open or enclosed spaces are worthy of attention. Currently, noise control technology can be divided into active noise cancellation and passive noise reduction. The active method is more dependent on algorithm and control logic, while the passive method relies more on the performance of the sound insulation/absorption material. The combination of active and passive control methods using fuzzy theory or optimization methods should be treated with equal attention.

This Topic aims to bring together first-class articles in the field of noise prediction, measurement and control. Both experimental and modeling of acoustic studies for enclosed (houses, buildings, schools, vehicles, subways, trains, etc.) and open environments are welcomed. This Topic is expected to attract widespread attention and will have an excellent impact on the field of environmental acoustics. The potential topics include (but are not limited to):

  • Reviews on environmental noise.
  • Computation or simulation of environmental acoustics.
  • Influence of noise on human or wildlife behavior.
  • Measurement techniques for sound location and identification.
  • Long-term monitoring solutions and key techniques of environmental noise.
  • Algorithm for active/passive noise control.
  • Artificial intelligence for environmental noise prediction and control.
  • Blind sound source separation.
  • Denoising techniques for background noise.

Dr. Bowen Hou
Dr. Jinhan Mo
Topic Editors


  • traffic noise
  • wheel–rail noise
  • airborne noise
  • underwater acoustics
  • sound source identification
  • sound source location
  • noise signal decoupling
  • sound wave reconstruction
  • noise mitigation
  • noise cancellation

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
2.1 3.7 2019 16.3 Days CHF 1600 Submit
3.7 5.7 2014 23.7 Days CHF 1800 Submit
Remote Sensing
5.0 8.3 2009 23 Days CHF 2700 Submit
3.9 7.3 2001 17 Days CHF 2600 Submit
2.2 4.1 2019 22.2 Days CHF 1600 Submit is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

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Published Papers (1 paper)

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24 pages, 7117 KiB  
Performance Evaluation of Nord2000, RTN-96 and CNOSSOS-EU against Noise Measurements in Central Jutland, Denmark
by Jibran Khan, Erik Thysell, Claus Backalarz, Per Finne, Ole Hertel and Steen Solvang Jensen
Acoustics 2023, 5(4), 1099-1122; - 21 Nov 2023
Cited by 1 | Viewed by 1989
This article aims to assess the performance of Nord2000, RTN-96, and CNOSSOS-EU, the Nordic and European noise prediction standards, in predicting daily LAeq24h and Lden levels (dBA), by comparing them with measurements gathered over 76 days from the E45 motorway in [...] Read more.
This article aims to assess the performance of Nord2000, RTN-96, and CNOSSOS-EU, the Nordic and European noise prediction standards, in predicting daily LAeq24h and Lden levels (dBA), by comparing them with measurements gathered over 76 days from the E45 motorway in Helsted, Central Jutland, Denmark. In addition, the article investigates the potential viability of utilizing Confidence-Weighting Average (CWA) for data fusion to enhance noise estimation accuracy. The results showed highly positive Spearman’s correlations (RS), reflecting strong agreements between observed and predicted data, Nord2000 = 0.85–0.98, CNOSSOS-EU = 0.79–0.92 and RTN-96 = 0.86–0.91. Model differences, RMSE = 0.4–3.3 dBA (Nord2000), 1.4 = 2.8 dBA (CNOSSOS) and 1.3–4.2 dBA (RTN-96), were mainly due to underlying model parametrization and uncertainties in model inputs. Overall, Nord2000 outperformed CNOSSOS and RTN-96 in reproducing observed noise levels. Moreover, CNOSSOS agreed well with the measured data and exhibited a high potential for noise mapping and health assessments. Likewise, the CWA is found to be a promising, forward-looking data fusion approach to improve noise estimates’ accuracy. More research is required to further evaluate the models in greater detail over a larger geographical area and across varied temporal scales (e.g., hourly, yearly). Full article
(This article belongs to the Topic Environmental Noise Prediction, Measurement and Control)
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