AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP025150111
Submit to AJWEP
Cite this article
6
Download
57
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
REVIEW ARTICLE

Technical methods for identifying and assessing residential environmental damage caused by noise

Minxuan Chen1,2 ,  Xiaofei Chen1,2* ,  Feng Huang1,2* ,  Fanyao Meng1,2 ,  Junqi Yu1,2
Show Less
1 Judicial Appraisal Center of Ecological and Environmental Damage, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan, Hubei, China
2 Hubei Key Laboratory of Pollutant Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan, Hubei, China
AJWEP, 025150111 https://doi.org/10.36922/AJWEP025150111
Received: 11 April 2025 | Revised: 29 November 2025 | Accepted: 10 December 2025 | Published online: 28 January 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

The impact of noise on residential environments has become increasingly prominent—a trend reflected in noise disturbance complaints, which rank first among all environmental pollution-related grievances. However, no authoritative guidance documents have yet been developed to standardize evaluation methods for residential environmental damage caused by noise, and damage assessment standards remain a key contention in noise-related tort disputes. Based on existing Chinese and international noise-related standards (e.g., International Organization for Standardization 1996-1 and World Health Organization guidelines) and practical experience, this review systematically summarizes the challenges in identifying and assessing residential environmental damage caused by noise, focusing specifically on the technical approaches in three key areas: damage identification, causality analysis, and damage quantification. This research establishes a replicable framework for practitioners engaged in noise damage assessment, effectively ensuring the standardization of assessment procedures and the reliability of outcomes.

Graphical abstract
Keywords
Noise
Living environment
Damage identification and assessment
Technical methods
Funding
This work was financially supported by the Research Project on Judicial Appraisal Industry in Hubei Province for 2023–2024 (Project No. 27).
Conflict of interest
The authors declare that they have no conflicts of interest.
References
  1. Guski R, Schreckenberg D, Schuemer R. WHO environmental noise guidelines for the European region: A systematic review on environmental noise and annoyance. Int J Environ Res Public Health. 2017;14(12):1539. doi: 10.3390/ijerph14121539

 

  1. Ministry of Ecology and Environment of the People’s Republic of China, Central Civilization Office, Ministry of Education, et al. 2023 Annual Report on Prevention and Control of Noise Pollution in China. Beijing: Ministry of Ecology and Environment of the People’s Republic of China; 2023. Available from: https://www.mee.gov.cn/ hjzl/sthjzk/hjzywr/202307/W020230728374728553582. pdf [Last accessed on 2025 May 12].

 

  1. Zhang Q, Hong H, Chen M, Deng J, Pei Y, Bao M. Sheng tai huan jing sun hai jian ding ping gu biao zhun hua ti xi wen ti fen xi ji dui ce yan jiu [Problems and countermeasures of the standardization system for ecological environment damage identification and assessment]. Stand Sci. 2024;47(11):101-106. doi: 10.3969/j.issn.1674-5698.2024.11.018

 

  1. International Organization for Standardization. Acoustics - Description, Measurement and Assessment of Environmental Noise - Part 1. Geneva: ISO; 2016. Available from: https://www.iso.org/standard/59765. html [Last accessed on 2025 Nov 12].

 

  1. World Health Organization. Regional Office for Europe. Environmental Noise Guidelines for the European Region. WHO. Regional Office for Europe; 2018. Available from: https://iris.who.int/handle/10665/279952 [Last accessed on 2025 Nov 12].

 

  1. Zhang Q, Chen M, Bao M. Zao sheng huan jing sun hai jian ding biao zhun hua cheng xu yu fang fa gou jian [Construction of standardized procedures and methods for noise environmental damage identification]. Stand Sci. 2025;48(3):85-91. doi: 10.3969/j.issn.1674-5698.2025.03.013

 

  1. Liao WT. Zao sheng wu ran sun hai jian ding [Identification of noise pollution damage]. Legal Syst Expo. 2015;(33):112-113.

 

  1. Yang C, Ming L. Zhu zhai dian ti zao sheng pai fang xian zhi que ding de shi yong yan jiu [Practical research on the determination of noise emission standards for residential elevators]. Legal Syst Expo. 2022;(8):69-71. doi: 10.3969/j.issn.2095-4379.2022.08.022

 

  1. Zhang QL, Cao GP, Lu QZ, Zuo YZ. Huan jing sun hai si fa jian ding zhong di pin zao sheng de ce liang yu ping jia tan tao [Discussion on the measurement and evaluation of low-frequency noise in the forensic identification of environmental damage]. Res Ecol Environ Damage. 2025;(2):28-37.

 

  1. Song LY, Zhang A, Kwan MP. Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model. Environ Impact Assess Rev. 2026;108110. doi: 10.1016/j.eiar.2025.108100

 

  1. Han GQ, Liu X, Shi HK, Yan WH. Zao sheng di tu zai hu he hao te sheng huan jing guan li zhong de ying yong yan jiu [Research on the application of noise maps in acoustic environment management in Hohhot]. Sci Technol Innov. 2025;(22):64-67.

 

  1. Hu Y, Li FC, Shao W, Meng LL, Zhou JW. Gong cheng ji xie zao sheng yuan shi bie ji shu yan jiu jin zhan [Research progress in noise source identification technology for construction machinery]. Noise Vib Control. 2015;35(5):1-15.

 

  1. Liu YX. Bei Dong Sheng Mu Biao Shi Bie Li Lun Yan Jiu [Theoretical research on Passive Acoustic Target Recognition] [Dissertation]. Nanjing: Nanjing University of Science and Technology; 2011. Available from: https://kns.cnki.net/kcms2/article/abstract?v= dzw7IdLhHkFyx4AsAmofgLqlruY9iv8wcbYwWB 1pz3-fxqzihqa_97gvrd6py_pmmm2ytkopfwvzsqnw fcfgzl4ejekyk1zpfyakcuif88jozjnezzmj2odbvtan wcblrekjozkdrvebtamduwou9qgyto7wohvtw_- z4o_lhxdmdozsjlzqxa==&uniplatform=nzkp t&language=chs [Last accessed on 2025 May 22].

 

  1. Li Q, Zhang B, Xie Z, Wang Y, Tang J, Chen Y. Wu li - shu ju hun he qu dong de che liao zhi xin ce pian jiao gu ji [Physics-data hybrid driven estimation of vehicle side slip angle]. Automotive Eng. 2025;47(4):714-723. doi: 10.19562/j.chinasae.qcgc.2025.04.012

 

  1. Yu P, Zhai GQ, Huang YF, et al. Cheng shi ju zhu qu she bei zao sheng pin lv te xing fen xi [Frequency characteristics analysis of equipment noise in urban residential areas]. China Environ Sci. 2006;26(4):491-495.

 

  1. Jin Y. Ji Yu Xiao Bo Bian Huan He Du Li Fen Liang Fen Xi De Nei Ran Ji Zhen Dong Sheng Xue Te Xing Yan Jiu [Research on Vibro-Acoustic Characteristics of Internal Combustion Engines Based on Wavelet Transform and Independent Component Analysis] [Dissertation]. Hangzhou: Zhejiang University; 2007. Available from: https://kns.cnki.net/kcms2/article/ abstract?v=dzw7idlhhkee27hhouynkenwmuvfymgi m w 3 i 3 z 0 z 6 k a x x o y x q 3 d _ t s _ g _ n 1 x j f b c h n o 0 8 w g b 8 b g _ 3 x f f l v q w q f u 1 3 i l v 1 q a 4 d h b d 7 v z - i x k e w 9 e i m r 6 s 3 o a q j r 2 w a 5 o x q e k c v1w5fjzzeiwdsc5cbsik_-a1msamghojfej0surdm02y-gjc5g==&unip latform=nzkpt&language=chs [Last accessed 2025 May 23].

 

  1. Du XF. Di Zao Sheng Chai You Ji Ji Ti Zhen Dong Sheng Xue Xin Hao Te Zheng Ti Qu Ji Jie Gou Gai Jin Yan Jiu [Research on Feature Extraction of Vibro-Acoustic Signals and Structural Improvement for Low-Noise Diesel Engine Bodies] [Dissertation]. Tianjin: Tianjin University; 2012. Available from: https://kns.cnki.net/kcms2/ article/abstract?v=dzw7idlhhkeabbteqtin-yguebmwy-seeqtvgqxk9ubx0lu2co-4kifqz5iykjwdbmnfdt-jc3z_ ac5yw2ghcjpq_t0c-nj3ixj9fwg04ma-nzniofeygmtlvkm qutyucpjo655ie0gjwktzqa8zapxeo5ymbs2ot0oamttxjs wvsogjzkw6mg==& uniplatform=nzkpt&language=chs [Last accessed on 2025 May 23].

 

  1. Liu DX. Mou Xing Wa Jue Ji Jia Shi Shi Zao Sheng Yuan Shi Bie Yu Kong Zhi Shi Yan Yan Jiu [Experimental Study on Noise Source Identification and Control in the Cab of a Certain Type of Excavator] [Dissertation]. Hefei: Hefei University Technolpgy; 2013. Available from: https://kns.cnki.net/kcms2/article/ abstract?v=dzw7idlhhkg4-ifmyoa-w9fehauuil4gaowsl7 wwqncmsyefhfhrjcfd7z8xlfrlvnlayiuzktzlsgaydrdykicwi cj3kcjtu4s7hmlmuck2kt1dd-xjrnrgcbmwqfxkryxmonlx-idki8yjr3adb3doslyfvkqq5tz25mxjpv-ydhsck6z9zsav _k4adezc&uniplatform=nzkpt&language=chs

 

  1. Zolghadr E, Furht B. Scene classification using external knowledge source. In: IEEE International Symposium on Multimedia. New York: IEEE; 2015. p. 535-540. doi: 10.1109/ISM.2015.85

 

  1. Ozer I, Ozer Z, Findik O. Noise robust sound event classification with convolutional neural network. Neurocomputing. 2017;272:505-512. doi: 10.1016/j.neucom.2017.07.021

 

  1. Han Y, Lee K. Convolutional neural network with multiple-width frequency-delta data augmentation for acoustic scene classification. IEEE AASP Challenge Detect Classif Acoust Scenes Events; 2016. Available from: https://dcase.community/documents/ challenge2016/technical_reports/DCASE2016_ Lee_1034.pdf [Last accessed on 2025 Oct 25].

 

  1. Salamon J, Bello JP. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett. 2017;24(3):279-283. doi: 10.1109/LSP.2017.2657381

 

  1. Murota Y, Kitamura D, Koyama S, Saruwatari H, Nakamura S. Statistical modeling of binaural signal and its application to binaural source separation. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). South Brisbane, QLD, Australia: IEEE; 2015. p. 494-498. doi: 10.1109/ICASSP.2015.7178018

 

  1. Çakir E, Virtanen T. End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input. In: 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil: IEEE; 2018. p. 1-7. doi: 10.1109/IJCNN.2018.8489470

 

  1. Sawhney N, Maes P. Situational Awareness from Environmental Sounds. Project Rep. Vol. 13. Pattie Maes; 1997. p. 1-7. Available from: https://www.researchgate. net/publication/2796654_situational_awareness_from_ environmental_sounds [Last accessed on 2025 Oct 30].

 

  1. Oikarinen T, Srinivasan K, Meisner O. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. J Acoust Soc Am. 2019;145(2):654-662. doi: 10.1121/1.5087827

 

  1. Fastl H, Zwicker E. Psychoacoustics: Facts and Models. 3rd ed. Berlin: Springer-Verlag; 2007. doi: 10.1007/978-3-540-68888-4

 

  1. Li H.Cheng shi jian she gong cheng zuan kong shi gong zao sheng wu ran kong zhi fang fa yan jiu [Study on noise pollution control methods for drilling in urban construction projects]. Environ Sci Manag. 2022;47(8):96-100. doi: 10.3969/j.issn.1673-1212.2022.08.021

 

  1. Yu F, Zhang ZH, Sun Q, Zhang Y. Huan jing sun hai ping gu fang fa xue jian li [Establishment of methodologies for environmental damage assessment]. Environ Prot. 2020;48(24):16-21.

 

  1. Merchan CI, Diaz-Balteiro L, Soliño M. Noise pollution in national parks: Soundscape and economic valuation. Landsc Urban Plan. 2014;123:1-9. doi: 10.1016/j.landurbplan.2013.11.006

 

  1. Ma H, Wen M, Xu L, Zhang Z. Contingent valuation of road traffic noise: A case study in China. Transp Res Part D Transp Environ. 2021;93:102765. doi: 10.1016/j.trd.2021.102765

 

  1. Cai LX. Fu za Huan Jing Xia Jing Zhun mu Biao Zao Sheng Ti Qufen Li Ji Ding Liang ping Gu Fang Fa Yan Jiu [Research on Precise Target Noise Extraction, Separation and Quantitative Assessment Methods in Complex Environments] [Doctoral Dissertation]. Guangzhou: Guangzhou University; 2025. doi: 10.27040/d.cnki.ggzdu.2025.001046
Next article in this issue
Share
Back to top
Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept