• Systematic Map
  • Open access
  • Published: 11 September 2020

Evidence of the impact of noise pollution on biodiversity: a systematic map

  • Romain Sordello 1 ,
  • Ophélie Ratel 1 ,
  • Frédérique Flamerie De Lachapelle 2 ,
  • Clément Leger 3 ,
  • Alexis Dambry 1 &
  • Sylvie Vanpeene 4  

Environmental Evidence volume  9 , Article number:  20 ( 2020 ) Cite this article

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A Systematic Map Protocol to this article was published on 12 February 2019

Ecological research now deals increasingly with the effects of noise pollution on biodiversity. Indeed, many studies have shown the impacts of anthropogenic noise and concluded that it is potentially a threat to the persistence of many species. The present work is a systematic map of the evidence of the impacts of all anthropogenic noises (industrial, urban, transportation, etc.) on biodiversity. This report describes the mapping process and the evidence base with summary figures and tables presenting the characteristics of the selected articles.

The method used was published in an a priori protocol. Searches included peer-reviewed and grey literature published in English and French. Two online databases were searched using English terms and search consistency was assessed with a test list. Supplementary searches were also performed (using search engines, a call for literature and searching relevant reviews). Articles were screened through three stages (titles, abstracts, full-texts). No geographical restrictions were applied. The subject population included all wild species (plants and animals excluding humans) and ecosystems. Exposures comprised all types of man-made sounds in terrestrial and aquatic media, including all contexts and sound origins (spontaneous or recorded sounds, in situ or laboratory studies, etc.). All relevant outcomes were considered (space use, reproduction, communication, etc.). Then, for each article selected after full-text screening, metadata were extracted on key variables of interest (species, types of sound, outcomes, etc.).

Review findings

Our main result is a database that includes all retrieved literature on the impacts of anthropogenic noise on species and ecosystems, coded with several markers (sources of noise, species concerned, types of impacts, etc.). Our search produced more than 29,000 articles and 1794 were selected after the three screening stages (1340 studies (i.e. primary research), 379 reviews, 16 meta-analyses). Some articles (n = 19) are written in French and all others are in English. This database is available as an additional file of this report. It provides an overview of the current state of knowledge. It can be used for primary research by identifying knowledge gaps or in view of further analysis, such as systematic reviews. It can also be helpful for scientists and researchers as well as for practitioners, such as managers of transportation infrastructure.

The systematic map reveals that the impacts of anthropogenic noises on species and ecosystems have been researched for many years. In particular, some taxonomic groups (mammals, birds, fishes), types of noise (transportation, industrial, abstract) and outcomes (behavioural, biophysiological, communication) have been studied more than others. Conversely, less knowledge is available on certain species (amphibians, reptiles, invertebrates), noises (recreational, military, urban) and impacts (space use, reproduction, ecosystems). The map does not assess the impacts of anthropogenic noise, but it can be the starting point for more thorough synthesis of evidence. After a critical appraisal, the included reviews and meta-analyses could be exploited, if reliable, to transfer the already synthesized knowledge into operational decisions to reduce noise pollution and protect biodiversity.

For decades, biodiversity has suffered massive losses worldwide. Species are disappearing [ 1 ], populations are collapsing [ 2 ], species’ ranges are changing (both shrinking and expanding) at unprecedented rates [ 3 ] and communities are being displaced by invasive alien species [ 4 ]. All of the above is caused by human activities and scientists regularly alert the international community to our responsibility [ 5 ]. In particular, urban growth is one of the major reasons for biodiversity loss [ 6 , 7 ] in that it destroys natural habitats, fragments the remaining ecosystems [ 8 ] and causes different types of pollution, for example, run-off, waste and artificial light impacting plants and animals [ 9 , 10 ]. Similarly, man-made sounds are omnipresent in cities, stemming from traffic and other activities (industrial, commercial, etc.) [ 11 ] and they can reach uninhabited places [ 12 ]. Anthropogenic noise can also be generated far from cities (e.g. tourism in a national park, military sonar in an ocean, civil aircraft in the sky).

Many studies have shown that such sounds may have considerable impact on animals. However, sound is not a problem in itself. A majority of species hear and emit sounds [ 13 ]. Sounds are often used to communicate between partners or conspecifics, or to detect prey or predators. The problem arises when sounds turn into “noise”, which depends on each species (sensitivity threshold) and on the type of impact generated (e.g. disturbances, avoidance, damage). In this case, we may speak of “noise pollution”. For instance, man-made sounds can mask and inhibit animal sounds and/or animal audition and it has been shown to affect communication [ 14 ], use of space [ 15 ] and reproduction [ 16 ]. This problem affects many biological groups such as birds [ 17 ], amphibians [ 18 ], reptiles [ 19 ], fishes [ 20 ], mammals [ 21 ] and invertebrates [ 22 ]. It spans several types of ecosystems including terrestrial [ 23 ], aquatic [ 24 ] and coastal ecosystems [ 25 ]. Many types of sounds produced by human activities can represent a form of noise pollution for biodiversity, including traffic [ 26 ], ships [ 27 ], aircraft [ 28 ] and industrial activities [ 29 ]. Noise pollution can also act in synergy with other disturbances, for example light pollution [ 30 ].

Despite this rich literature, a preliminary search did not identify any existing systematic maps pertaining to this issue. Some reviews or meta-analyses have been published, but most concern only one biological group, such as Morley et al. [ 31 ] on invertebrates, Patricelli and Blickley [ 32 ] on birds and Popper and Hastings [ 33 ] on fishes. Other syntheses are more general and resemble somewhat a systematic map, but their strategies seem to be incomplete. For instance, Shannon et al. [ 34 ] performed their literature search on only one database (ISI Web of Science within selected subject areas) and did not include grey literature. As another example, we can cite Rocca et al. in 2016, a meta-analysis that limited its population to birds and amphibians and its outcome to vocalization adjustment [ 35 ]. As a consequence, a more comprehensive map, covering all species and ecosystems, all sources of man-made sounds and all outcomes, and implementing a deeper search strategy (e.g. several databases, grey literature included) is needed to provide a complete overview for policy and practice.

This report presents a systematic map of evidence of the impact of noise pollution on biodiversity based on an a priori method published in a peer-reviewed protocol [ 36 ]. It describes the mapping process and the evidence base. It includes aggregate data and tables presenting the characteristics of the selected articles to highlight gaps in the literature concerning the issue. A database was produced in conjunction with this report, containing metadata for each selected article including key variables (species, types of sound, effects, etc.).

Stakeholder engagement

The current systematic map is managed by the UMS Patrimoine Naturel joint research unit funded by the French Biodiversity Agency (OFB), the National Scientific Research Center (CNRS) and the National Museum of Natural History (MNHN), in a partnership with INRAE. Our institutions act on behalf of the French Ecology Ministry and provide technical and scientific expertise to support public policies on biodiversity.

We identified noise pollution as an emergent threat for species and ecosystems that public authorities and practitioners will have to mitigate in the coming years. Indeed, for decades, noise regulations have focused primarily on the disturbances for humans, but we expect that public policies for biodiversity conservation will start to pay more attention to this threat. Already, in 1996, for the first time, the European Commission’s Green Paper on Future Noise Control Policy dealt with noise pollution from the point of view of environmental protection. Quiet areas are also recommended to guarantee the tranquility of fauna in Europe [ 37 ]. Since 2000 in France, an article in the Environmental Code (art. L571-1) has contained the terms “harms the environment” with respect to disturbances due to noise. To achieve these objectives, a knowledge transfer from research to stakeholders is needed for evidence-based decisions. We expect that concern for the impacts of noise pollution on biodiversity will develop along the same lines that it did for light pollution, which is now widely acknowledged by society. Anticipating this progress, we proposed to the French Ecology Ministry that we produce a systematic map of the impacts of noise on biodiversity in view of drafting a report on current knowledge and identifying sectors where research is needed to fill in knowledge gaps.

Objective of the review

The objective of the systematic map is to provide a comprehensive overview of the available knowledge on the impacts of noise pollution on species and ecosystems and to quantify the existing research in terms of the taxonomic groups, sources of noise and impact types studied.

The systematic map covers all species and ecosystems. In that we are currently not able to say exactly when a sound becomes a noise pollution for species (which is precisely why a systematic map and reviews are needed on this topic), this map covers all man-made sounds, regardless of their characteristics (e.g. frequency, speed, intensity), their origin (road traffic, industrial machines, boats, planes, etc.), their environment or media (terrestrial, aquatic, aerial) and their type (infrasound, ultrasound, white noise, etc.), and in most cases here uses the term “noise” or “noise pollution”. It does not include sounds made by other animals (e.g. chorus frogs) or natural events (e.g. thunder, waterfalls). The systematic map deals with all kinds of impacts, from biological to ecological impacts (use of space, reproduction, communication, abundance, etc.). It encompasses in situ studies as well as ex situ studies (aquariums, laboratories, cages, etc.). The components of the systematic map are detailed in Table  1 .

The primary question is: what is the evidence that man-made noise impacts biodiversity?

The secondary question is: which species, types of impacts and types of noise are most studied?

The method used to produce this map was published in an a priori peer-reviewed protocol by Sordello et al. [ 36 ]. Deviations are listed below. The method follows the Collaboration for Environmental Evidence (CEE) Guidelines and Standards for Evidence Synthesis in Environmental Management [ 38 ] unless noted otherwise, and this paper conforms to ROSES reporting standards [ 39 ] (see Additional file 1 ).

Deviation from the a priori protocol published by Sordello et al. [ 36 ]

Method enhancements.

We reinforced the search strategy with:

a search performed on both CORE and BASE, whereas the protocol was limited to a search on only one of these two search engines,

export of the first 1000 hits for each search string run on Google Scholar, whereas the protocol foresaw the export of the first 300 hits,

extraction of the entire bibliography of 37 key reviews selected from the previously provided corpus whereas the protocol did not foresee this option.

Method downgrades

Because of our resource limitations:

we could not extract the design comparator (e.g. CE, BAE, BACE),

we could not split each article included in the map into several entries (i.e. a book with several chapters, a proceeding with multiple abstracts, a study with several species, sources of noise or outcomes). Consequently, we coded the multiple aspects of these articles on one line in the map database.

Search for articles

Searches were performed using exclusively English search terms. The list of search terms is presented below (see “ Search string ”).

Only studies published in English and in French were included in this systematic map, due to limited resources and the languages understood by the map team.

Search string

The following search string was built (see Additional file 2 , section I for more details on this process):

((TI = (noise OR sound$) OR TS = (“masking auditory” OR “man-made noise” OR “anthropogenic noise” OR “man-made sound$” OR “music festival$” OR ((pollution OR transportation OR road$ OR highway$ OR motorway$ OR railway$ OR traffic OR urban OR city OR cities OR construction OR ship$ OR boat$ OR port$ OR aircraft$ OR airplane$ OR airport$ OR industr* OR machinery OR “gas extraction” OR mining OR drilling OR pile-driving OR “communication network$” OR “wind farm$” OR agric* OR farming OR military OR gun$ OR visitor$) AND noise))) AND TS = (ecolog* OR biodiversity OR ecosystem$ OR “natural habitat$” OR species OR vertebrate$ OR mammal$ OR reptile$ OR amphibian$ OR bird$ OR fish* OR invertebrate$ OR arthropod$ OR insect$ OR arachnid$ OR crustacean$ OR centipede$)).

Comprehensiveness of the search

A test list of 65 scientific articles was established (see Additional file 2 , section II) to assess the comprehensiveness of the search string. The test list was composed of the three groups listed below.

Forty relevant scientific articles identified by the map team prior to the review.

Eight key articles identified using three relevant reviews: Brumm, 2010 (two articles) [ 40 ], Cerema, 2007 (three articles) [ 41 ] and Dutilleux and Fontaine, 2015 (three articles) [ 42 ].

Seventeen studies not readily accessible or indexed by the most common academic databases, submitted by subject experts contacted prior to the review (29 subject experts were contacted, 7 responded).

Bibliographic databases

The two databases below were searched (see Additional file 2 , section III for more details on database selection):

“Web of Science Core Collection” on the Web of Science platform (Clarivate) using the access rights of the French National Museum of Natural History, using the search string described above. The search covered SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI and CCR-EXPANDED (see Additional file 2 , section III for the complete list of citation indexes). A first request was run on 14 December 2018, without any timespan restriction, and returned 7859 citations. Secondly, an update request, restricted to 2019, was performed, using the same search string and citation indexes, on 6 May 2020, to collect the documents published in 2019. 685 citations were exported.

Scopus (Elsevier). The search string described above was adapted to take into account differences in the search syntax (see Additional file 2 , section IV). A first search was run on 14 December 2018, without any timespan restriction, using the access rights of the University of Bordeaux and returned 11,186 citations. Secondly, a new request restricted to 2019 was performed on 6 May 2020, using the same search string, using the access rights of the CNRS, to collect the documents published in 2019. 859 citations were exported.

Web-based search engines

Additional searches were undertaken using the three following search engines (see Additional file 2 , section V for more details):

Google Scholar ( https://scholar.google.com/ ). Due to the limitations of Google Scholar, four search strings were constructed with English terms to translate the search string used for the bibliographic databases described above in a suitable form for Google Scholar. The first searches were performed on 11 June 2019 and the first 1000 citations (as a maximum, when available), sorted by citation frequency, were exported to a .csv file for each of the four search strings. Secondly, an update search was performed on 6 May 2020 with the same four search strings to collect the documents published in 2019; all hits (110) were exported;

BASE ( https://www.base-search.net ). Searches were performed on 12 April 2019. Given certain limitations of this search engine (maximum number of string characters), the search string built for the bibliographic databases described above was split into two search strings. Searches were performed on the titles of the articles, with no restriction to open access articles, on all types of documents and without any timespan restriction. The first 300 citations, sorted by relevance, were exported for each of the two search strings to a .csv file;

CORE ( https://core.ac.uk/ ). Searches were performed on 12 February 2019. The search engine allowed the use of the original search string used for the bibliographic databases. Searches were performed on the title of the articles and without any timespan restriction. The first 327 articles were manually downloaded, excepting the duplicates and the dead links.

Specialist websites

The following websites were manually searched for relevant articles, including grey literature:

Achieve QUieter Oceans by shipping noise footprint reduction website: http://www.aquo.eu/ .

Association for biodiversity conservation: http://www.objectifs-biodiversites.com .

Document portal of the French Ecology Ministry: http://www.portail.documentation.developpement-durable.gouv.fr/ .

Document database of the French General commission for sustainable development: http://temis.documentation.developpement-durable.gouv.fr/ .

European Commission websites: http://ec.europa.eu/ and http://publications.jrc.ec.europa.eu/ .

European parliament website: http://www.europarl.europa.eu/ .

French forum against noise: https://assises.bruit.fr/ .

Information and Documentation Center on Noise: http://www.bruit.fr .

We collected nine articles from these specialist websites that we included in the mapping process.

Supplementary searches

A call for literature was conducted via different channels from January 2019 to April 2019 to find supplementary literature, in particular non peer-reviewed articles, published in French or in English.

Specialized organizations were contacted via their networks, their web forums or their mailing lists:

the “IENE—Infra Eco Network Europe” ( http://www.iene.info/ ),

the French program on transportation infrastructure ITTECOP “Infrastructures de Transports Terrestres, ECOsystèmes et Paysages” ( http://www.ittecop.fr/ ),

the French national council for the protection of nature “Conseil national de protection de la nature (CNPN)”,

the Green and blue infrastructure policy, a French public policy ( http://www.trameverteetbleue.fr ),

the “Société Française d’Ecologie” ( https://www.sfecologie.org/ ),

the French national mailing list EvolFrance managed by INRAE on biological evolution and biodiversity ( https://www6.inra.fr/reid_eng/News/Evolfrance ).

The following social media were also used to alert the research community to the systematic map and to request non peer-reviewed articles: ResearchGate ( http://www.researchgate.net ), Twitter ( http://www.twitter.com ), LinkedIn ( http://www.linkedin.com ).

A total of 83 articles were sent to us in response to the call for literature.

Bibliographies from relevant reviews

After having collected the literature from the different sources described above, we selected 37 relevant reviews from our corpus. Then, we extracted all their bibliographic references, resulting in 4025 citations (see the list of the 37 reviews and their corresponding number of extracted citations in Additional File 3 ). Among these citations we excluded all duplicates (intra-duplicates and duplicates between these bibliographies and our previous literature collection). We screened the titles of the remaining citations, we retrieved the pdf file of the selected titles and then we screened their full-texts.

Testing the comprehensiveness of the search results

Among the 65 articles included in the test list, the number of articles retrieved from the main sources are (see Additional file 4 for more details on the comprehensiveness values): WOS CC 55, Scopus 56, Google Scholar 41, CORE 5, BASE 3, Relevant reviews 43.

The low comprehensiveness levels reached with CORE and BASE can be explained by the fact that these two search engines index mostly grey literature (they were included in the search strategy for this reason) such as reports, theses or books, whereas this type of literature is absent from the test list that mainly contains journal articles.

The overall comprehensiveness of the map search strategy is 95% (62 articles out of the 65 articles in the test list were retrieved by the different bibliographic sources, see in Additional file 4 the 3 unretrieved articles).

Manually added articles

Finally, some articles were added manually to the corpus:

the 3 articles included in the test list that were not retrieved by the search strategy,

36 relevant articles identified by the team that were found in other publications, but not retrieved by the search strategy. For example, these articles were detected in proceedings or books from which other articles had already been added to the map and that we discovered during the screening process or the full-text collection.

Duplicate removal

Duplicate removal was carried out throughout the mapping process using Excel (duplicate conditional formatting and visual identification line by line). Duplicates were removed from each corpus (e.g. intra Scopus duplicates) and between bibliographic sources (e.g. duplicates between Scopus and Google Scholar). The selected citation was systematically the one from Web of Science Core Collection because the metadata linked to the citations extracted from this database are more complete compared to the Scopus database and supplementary literature sources (BASE, CORE, Google Scholar, call for literature).

Article screening and study-eligibility criteria

Screening process.

Using the predefined inclusion/exclusion criteria detailed below, all articles were screened using Excel, first on titles, then on abstracts and finally on the full-texts.

When there was any doubt regarding the presence of a relevant inclusion criterion or if there was insufficient information to make an informed decision, articles were retained for assessment at a later stage. In particular, articles retained after title screening, but that did not have an abstract were immediately transferred to full-text screening. Given that titles and abstracts in grey literature do not conform to scientific standards, assessment of grey literature was performed during the full-text screening phase. Care was taken to ensure that reviewers never screened their own articles.

The three screening stages were conducted by three reviewers (RS, SV, AD). To assess the consistency of the inclusion/exclusion decisions, a Randolph’s Kappa coefficient was computed before screening the full search results. To that end, a set of articles was randomly selected (respectively composed of 200 articles for title screening, 20 articles for abstract screening and 15 articles for full-text screening) and screened by each reviewer independently. The process was repeated until reaching a Kappa coefficient value higher than 0.6. But even after reaching the necessary Kappa value, all disagreements were discussed and resolved before beginning the screening process.

During calibration of the map protocol, a scoping stage was conducted in the “Web of Science Core Collection” and the three stages of the screening process were tested by one reviewer (RS) in order to refine the eligibility criteria. For these articles, a second reviewer (SV) examined all the rejected articles. Disagreements were discussed and, in some cases, articles were re-included. At the title screening stage, 4692 titles rejected by RS were checked by SV and 156 (3%) were re-included. At the abstract screening stage, 180 abstracts rejected by RS were checked by SV and none were re-included. At the full-text screening stage, 95 full-texts rejected by RS were checked by SV and none were re-included.

Eligibility criteria

Article eligibility was based on the list of criteria detailed in Table  2 , with no deviation from the a priori protocol.

The language was considered as an eligibility criteria only at the full-text screening stage. This means that if an article had an abstract written in another language than French or English, it was not excluded for this reason and it was transferred to the full-text screening stage.

During the three screening stages, rejected articles were systematically classified into four categories (see Table  3 for examples). When an article topic obviously lay outside the scope of this map, it was marked “D” (for Diverse); otherwise it was marked P for irrelevant Population, E for irrelevant Exposure or O for irrelevant Outcome.

Study-validity assessment

No study validity assessment was performed because the intention of the map was not to examine the robustness of the study designs. Critical appraisals of study validity are usually conducted in the case of systematic reviews, not for systematic maps. Footnote 1

Data-coding strategy

All the articles passing the three screening stages were included in the mapping database, apart from those published in 2019 or 2020. This is because some literature searches did not cover 2019 and others covered only a part of it. Consequently, we decided not to include articles published in 2019 (or in 2020) to maintain consistency in the map statistics. Accepted full-texts published in 2019 or 2020 were not coded and were grouped in an additional file for a possible later update of the map.

Each article included in the map was coded based on the full-text using keywords and expanded comment fields describing various aspects. The key variables are:

Article description:

Article source (WOS research, Scopus research, Google Scholar research, etc.);

Basic bibliographic information (authors, title, article date, journal, DOI, etc.);

Language (English/French);

Article type (journal article, book, thesis, conference object, etc.);

Article content (four possibilities: study, review, meta-analysis, other). A study consists of an experiment or an observation, it can be field based (in situ or ex situ) or model based. A review is a collection of studies, based or not on a standardized method. A meta-analysis is a statistical analysis based on several previously published studies or data;

Article characteristics:

Type of population (taxonomic groups). First, we classified the articles according to four taxa: prokaryotes, vertebrates, invertebrates and plants. Then, for vertebrates and invertebrates, we classified the articles as concerning respectively amphibians/birds/fishes/mammals/reptiles/others or arachnids/crustaceans/insects/mollusks/others. This classification is based on different prior evidence syntheses on noise pollution [ 34 , 53 , 54 ], including more details concerning invertebrates. In addition, it is usual in biodiversity documentation and facilitates understanding by stakeholders;

Type of exposure (sources of noise, see Fig.  1 for more details);

figure 1

Categories to code the sources of noise (exposure)

Type of outcomes (types of impacts, see Fig.  2 for more details).

figure 2

Categories to code the impacts of noise (outcomes)

Here again, to categorize the exposure (sources of noise) and the outcomes (types of impacts), we used previously published evidence syntheses on noise pollution and biodiversity, in particular the review by Shannon et al. (2016) (see in this publication Table  2 , page 988 on the sources of noise and Table  3 , page 989 on the impacts of noise) [ 34 ].

For studies only:

Country where the study was conducted;

Type of habitat (terrestrial or aquatic);

Study context: in situ (field)/ex situ (laboratory, aquariums, etc.);

Experimental (causal)/observational (correlative) study;

Origin of noise (artificial, real, recorded).

These metadata were coded according to an a priori codebook (see Additional file 6 in Sordello et al. [ 36 ]) that was marginally adjusted. The final version of this codebook is included as a sheet in the provided database file (see below the corresponding Additional file 9 ).

As far as possible, controlled vocabularies were used to code the variables (e.g. article type, dates, country, etc.), using thesauri or ISO standards (e.g. ISO 639-1 for the language variable and the ISO 3166-1 alpha 3 code for the country).

Coding was performed by three coders (OR, AD and RS). Because of time and resource limitations in our project, we could not undertake double coding and not all the articles could be coded by a single coder. Coding was carried out by three persons who successively coded a part of the articles. RS began, AD continued and OR finished. One coder coded all variables for the articles included in his/her group of articles (i.e. an article was not coded by several coders). There was no overlap in article coding. To understand the coding rules, explanation was given by RS to AD and OR before they started to code their group of articles. Also, to better understand the coding rules, AD could use the articles previously coded by RS and OR could use the articles previously coded by RS and AD. The three coding steps were monitored by RS who discussed with the two other coders in case of doubt. Finally, when the three groups of articles had been coded, RS reviewed the entire database to identify any errors and homogenize the terminology.

Data-mapping method

By cross-tabulating key meta-data variables (e.g. population and outcomes), summary figures and tables of the article characteristics were produced for this map report to identify knowledge gaps (un- or under-represented subtopics that warrant further primary research) and knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review). Based on these results, recommendations were made on priorities for policy makers, practitioners and research.

Literature searches and screening stages

During the screening process, reviewers did not screen articles that they had authored themselves, except the protocol of this systematic map and it was excluded during the title-screening stage.

The ROSES flow diagram below (Fig.  3 ) provides an overview of the screening process and shows the volumes of articles at the different stages. Detailed screening results are explained in Additional file 5 and illustrated with a full flow diagram in Additional file 6 . The list of all collated and screened articles is provided as an Excel sheet attached to this map report (Additional file 7 ). It contains information on the three screening stages (names of screeners, date of screening, inclusion/exclusion decisions, reason for exclusion, etc.). This file was drafted according to a codebook that describes each variable and the available values and that is included as a sheet in the provided file. In a separate sheet, it also contains the list of excluded full-texts and the reason for exclusion.

figure 3

ROSES flow diagram of the systematic map process from the searching stage to the map database. Details are given in the Additional files 5 and 6

Among the 29,027 articles initially collected, 9482 were deleted because they were duplicates, 14,503 were excluded on titles, 947 on abstracts and 1262 on full-texts. A total of 1887 articles were definitively selected after the three screening stages. Among them, 1746 were included in the map to be coded (with 48 more articles manually added or coming from specialist websites) and 141 were grouped in a separate additional file because they were published in 2019–2020 (Additional file 8 ). The systematic-map database contains 1794 relevant articles on the impacts of anthropogenic noises on species and ecosystems (Additional file 9 ), of which 19 are written in French and 1775 in English.

General bibliometrics on the database

Article sources.

The systematic-map database is composed of 1794 articles that come (see Table  4 ):

mainly from bibliographic databases: 65% (48% from WOS CC and 17% from Scopus);

from the bibliography of relevant reviews in a significant proportion: 19%;

from web-based search engines: 12% (in particular 8% from Google Scholar).

Articles coming from the call for literature or the specialist websites and manually added articles represent less than 5% of the map.

Regarding the efficiency of the searches, the call for literature, CORE search engine and Web of Science CC database stand out as the most relevant sources of bibliography for this map (Table  4 ). For instance, 27% of the literature received from the call was included in the map as was 15% from CORE, however these two sources represent a very small part of the final map (1% and 3%, respectively). On the contrary, articles collected from Scopus represent 17% of the final map whereas only 3% of the total number of articles collected from this database were actually relevant. Concerning the key reviews from which citations were extracted, some of these reviews proved to be very useful for the map. For instance, 30% of the bibliography (47 articles) from Gomez et al. [ 55 ] were included in the map (see Additional file 3 for the percentage of extracted/included citations for each key review).

Article types and contents

Figure  4 a shows the distribution of article types. The systematic-map database is mainly composed of journal articles (1333, which represent more than 74%). The second highest proportions of article types in the map are book chapters and reports that each represent 8% of the map.

figure 4

Types ( a ) and contents ( b ) of articles included in the systematic-map database

Figure  4 b shows the distribution of article contents. The systematic-map database is mainly composed of studies (1340, which represent more than 75% of the map), then, reviews (379, 21%) and meta-analyses (16, 1% with one article that is a mixed review/meta-analysis).

Not surprisingly, the majority of studies (1096/1340, 82%) and meta-analyses (13/16, 81%) were published as journal articles. Reviews are more spread over the different types of bibliographic sources even if they are also mainly published as journal articles (186/379, 49%).

Chronological distribution

The systematic-map database contains articles from 1932 to 2018 included. Figure  5 shows that production truely started around 1970 and then strongly increased starting around 2000 (Fig.  5 ).

figure 5

Chronologic number of articles since 1950

Map characteristics on the population, exposure and outcomes

Taxonomic groups.

The systematic map contains articles almost exclusively on vertebrates (1641/1794, 91%). Invertebrates represent 9% of the map and plants and prokaryotes together form less than 1% (however, it should be noted here that our search string did not include “plant” nor “prokaryote” which may partly explain these results).

Mammals, birds and fishes are the three most studied taxonomic groups in the map (see Fig.  6 ), with respectively 778/1794 (43%), 524/1794 (29%) and 437/1794 documents (24%) (the sum of mammals, birds and fishes exceeds the number of vertebrates because one article counted as “vertebrates” can include several vertebrate sub-groups).

figure 6

Number of articles for each type of taxonomic group (population), with details for studies and reviews/meta-analyses

These observed patterns regarding the population for the whole map are the same for studies and for reviews/meta-analyses. Mammals, birds and fishes are also the three taxonomic groups most considered in the studies (respectively 40%, 28% and 22%) and in the reviews/meta-analyses (respectively 52%, 33%, 30%).

Among invertebrates, crustaceans represent the most examined group (4% of the map, 3% of the studies, 6% of the reviews/meta-analyses) followed closely by mollusks.

Sources of noise

For 69 articles (4%), we could not precisely code the source of noise in any exposure class. Indeed, these articles use imprecise expressions such as “anthropogenic noise”. Among the others, 619 articles (35% of the map, see Fig.  7 ) deal with transportation noise, followed by industrial noise (27%) and abstract noises (25%). Few articles deal with recreational noise (5% of the map).

figure 7

Number of articles for each source of noise (exposure) with details for studies and reviews/meta-analyses

Focusing on the 1340 studies, transportation noise (32%), abstract noise (30%) and industrial noise (23%) are also the three sources of noise most considered, but the ranking was different from that found for all articles. Regarding the reviews/meta-analyses, transportation (43%) and industry (40%) are the two first sources of noise most considered and military noise (27%) comes in as the third source instead of abstract noises.

Types of impacts

The articles included in the map mainly deal with behavioural impacts of noise (985/1794, 55% of the map, see Fig.  8 ). Biophysiology is also frequently considered in the articles (704/1794, 39%) and then communication (424/1794, 24%). For 19 articles (1% of the map) we could not code the outcome because it was not detailed by the authors.

figure 8

Number of articles for each type of impact (outcomes), with details for studies and reviews/meta-analyses

With a focus on the 1340 studies, impacts of noise on behaviour (51%), on biophysiology (34%) and on communication (22%) are the most considered, similar to the situation for reviews/meta-analyses (respectively 66%, 56% and 31%). On the contrary, space use is the least studied outcome.

Knowledge gaps and knowledge clusters

We combined the results (number of studies) between two of the three characteristics (population, exposure and outcome), resulting in Figs.  9 , 10 and 11 .

figure 9

Taxonomic groups (P) and sources of noise (E) in studies

figure 10

Taxonomic groups (P) and types of impacts (O) in studies

figure 11

Sources of noise (E) and types of impacts (O) in studies

For each of the three combinations of data, we extracted the top four results (those with the highest number of studies), resulting in 12 knowledge clusters presented in Table  5 . This analysis confirms the knowledge clusters previously noted in the results on population (in Fig.  6 , namely mammals, birds, fishes), exposure (in Fig.  7 , transportation, industrial, abstract noises) and outcomes (in Fig.  8 , behaviour, biophysiology and communication).

Concerning knowledge gaps, the analysis between population, exposure and outcomes reveals that many combinations have never been studied and it is difficult to identify any knowledge gaps in particular. We can refer to separate results on population, exposure and outcomes that show that few studies were conducted on amphibians (61), reptiles (18), all invertebrates (in particular arachnids: 3) and plants (8) in terms of population (see Fig.  6 ); recreational (57), military (106) and urban noises (131) in terms of exposure (see Fig.  7 ); space use (94), reproduction (149) and ecosystems (167) in terms of outcomes (see Fig.  8 ).

Study characteristics

Study location.

Almost one third of all studies (441/1340, 33%) were carried out in the USA (Fig.  12 ). A substantial proportion of the studies were also conducted in Canada (121/1340, 9%), Great Britain (84/1340, 6%), the Netherlands (70/1340, 5%) and even Australia (698/1340, 5%). The country is unknown in 135 studies (10%).

figure 12

Tree-map representation of the countries where at least 10 studies were included in the map. Values: USA: 441; CAN (Canada): 121; GBR (Great Britain): 84; NLD (Netherlands): 70; AUS (Australia): 69; DEU (Germany): 41; NOR (Norway): 37; FRA (France): 27; ITA (Italia): 27; BRA (Brazil): 26; ESP (Spain): 24; CHN (China): 22; DNK (Denmark): 20; SWE (Sweden): 17; NZL (New-Zealand): 15; MEX (Mexico): 14; POL (Poland): 11; RUS (Russia): 10

Noise source and media

Studies mainly deal with real noise (632/1340, 47%). Around a third of the studies (378/1340, 28%) are based on artificial noise and 16% of the studies (221/1340) use real recorded noise (Fig.  13 a top). The distribution between terrestrial or aquatic media through which noise is broadcast is virtually equivalent (see Fig.  13 b bottom, respectively 47% and 51%).

figure 13

Number of studies included in the map in terms of the noise generated (a; top) and noise media (b; bottom)

Study context and design

Figure  14 shows that 95% of studies (1274/1340) are field based whereas only 3% (40/1340) are model based and less than 1% (9/1340) are combined (field and model based studies). Among the 1283 studies that are totally or partially field based, 56% (720) are in situ whereas 42% (537) are ex situ (zoos, aquarium, cages, etc.) and 2% (26) are combined (Fig.  14 left). Also, a majority are experimental (856/1283, 67%), 32% (411/1283) are observational and less than 1% (12/1283) are combined (experimental and observational) (Fig.  14 right).

figure 14

Number of studies included in the map in terms of the context and design protocol

Reviews and meta-analyses

The high number of reviews included in the systematic map (379) can be explained by our methodology. Indeed, some articles were retrieved by our search strategy because they contain only one chapter or one paragraph that reviews the bibliography on impacts of anthropogenic noise on biodiversity. As a consequence, they were included in the map during the screening process even if the document as a whole does not deal with our map’s main issues. Nevertheless, the map does include many reviews that fully address the impacts of noise pollution on species and ecosystems. This means that, contrary to what was assumed beforehand, a huge amount of synthesis work has in fact already been invested in this topic. However, our results confirm that, for the moment, no prior systematic map—as broad and comprehensive as the present one—has been published yet, even if after the date of our literature search, a systematic-map protocol has been published on the impact of noise, focusing on acoustic communication in animals [ 56 ].

Some of the collected reviews are general syntheses and provide an overview of the impacts of anthropogenic noise on species (i.e. Kight and Swaddle [ 57 ]; Dufour [ 58 ]). However, most of reviews are focused on one or more population(s), exposure(s) and outcomes(s) or even a combination of these three parameters. For instance:

concerning taxonomic groups (population): some reviews deal with specific taxa—such as fishes [ 59 ], marine mammals [ 60 ] or crustaceans [ 61 ]—or with wider groups—such as invertebrates [ 31 ] or even terrestrial organisms [ 62 ];

concerning types of noise (exposure): Pepper et al. [ 63 ] address aircraft noise, Patricelli and Blickley [ 32 ] urban noise and Larkin [ 64 ] military noise;

concerning types of impacts (outcomes): De Soto et al. [ 65 ] (which is a proceeding) focus on physiological effects, Brumm and Slabbekoorn [ 66 ] target communication and Tidau and Briffa [ 67 ] (which is also a proceeding) deal with behavioural impacts.

Five reviews are presented as “systematic reviews” by their authors. One of them is Shannon et al. [ 34 ], which is indeed a wide synthesis of the effects of noise on wildlife. Another is dedicated to behavioural responses of wild marine mammals and includes a meta-analysis (quantitative synthesis) [ 55 ]. Two other systematic reviews include noise effects in a wider investigation of the impacts of some human activities, respectively seismic surveys [ 68 ] and wind energy [ 69 ]. The fifth is more specific and deals with the impact of prenatal music and noise exposure on post-natal auditory cortex development for several animals such as chickens, rats, mice, monkeys, cats and pigs [ 70 ]. Two other reviews—Radford [ 54 ] and Williams et al. [ 71 ]—could be qualified as “systematic” because their method is standardized (e.g. search string, screening process), but their authors have not done so.

Among the meta-analyses included in the map, we can cite in particular Cox et al. [ 72 , 73 ] on fishes, Roca et al. [ 35 ] on birds and anurans and Gomez et al. [ 55 ] on marine mammals. Birds are particularly considered since two more meta-analyses deal with this taxonomic group [ 74 , 75 ]. We can also note Cardoso et al. [ 76 ] on the impact of urban noise on several species.

Finally, regarding books, five of them are particularly relevant to the map topic, chronologically:

“Effects of Noise on Wildlife” [ 77 ];

“Marine Mammals and Noise” [ 78 ];

“Animal Communication and Noise” [ 79 ];

“The Effects of Noise on Aquatic Life” (Popper and Hawkins), published in two volumes 2012 and 2016 [ 80 , 81 ];

“Effects of Anthropogenic Noise on Animals” [ 82 ] which is the newest book on noise pollution and wildlife with syntheses for taxonomic groups such as fishes [ 83 ], reptiles and amphibians [ 84 ], birds [ 85 ] and marine mammals [ 86 ].

Some other books can be very general in discussing noise pollution, for instance “Railway ecology” [ 87 ]. Lastly, some other books can contain entire chapters specifically on noise pollution, e.g. “Avian Urban Ecology: Behavioural and Physiological Adaptations” [ 88 , 89 ] or “The Handbook of Road Ecology” [ 90 , 91 ]. We can also cite the “Ornithological Monographs” N°74 which is dedicated to noise pollution and contains one review [ 92 ] and several studies that are all included in the map [ 93 , 94 ].

Recently, some relevant syntheses were published in 2019 (not included in the map; see Additional file 8 ). A meta-analysis was performed on the effects of anthropogenic noise on animals [ 53 ] and a systematic review was published on intraspecific variation in animal responses to anthropogenic noise [ 95 ]. In addition, one review on the impact of ship noise on marine mammals includes a systematic literature search [ 96 ]. Two non-systematic reviews can also be cited, one about invertebrates [ 97 ] and the other about fishes [ 98 ].

Among all these bibliographic syntheses (including those from 2019), we selected those whose literature collection is based on a standardized approach (e.g. search string, database request, screening process)—which includes meta-analyses and systematic reviews/maps or similar—and whose topic is as close as possible to our systematic map (e.g. focused on noise and not on wider human pressures). We summarized the main features (topic delimitation, search strategy, number of citations) for the 12 selected evidence syntheses in Table  6 with more details in Additional file 10 .

In most cases, these reviews and meta-analyses contain far fewer articles than what we collected, which can be explained by their topic restrictions (P, E, O) as well as their search strategy (e.g. number of databases, complementary searches or not, screening criteria). In terms of topics, Shannon et al. [ 34 ] would appear to be the only standardized evidence synthesis as wide as ours (all wildlife, all sources of noise, all impacts), but the authors gathered 242 articles from 1990 to 2013. The synthesis published by Radford [ 54 ]—which, as a report, is grey literature—also provides an overview of the state of knowledge with descriptive statistics, according to a standardized method, although it focuses on non-marine organisms and it is based on 86 articles. In 2019, Kunc and Schmidt published a meta-analysis that covers all impacts of noise on animals and they collected 108 articles [ 53 ].

General comments

This map reveals that the literature on the impact of anthropogenic noise on species and ecosystems is already extensive, in that 1794 relevant articles were collected, including 1340 studies, 379 reviews and 16 meta-analyses. Studies are mainly located in North America, in particular in the United States and Canada. In Europe, the United Kingdom and the Netherlands have produced the largest numbers of articles. Australia is also active in this field.

This high volume of bibliography highlights the fact that this issue is already widely studied by scientists. The production on this topic started many years ago, around 1970, and has surged considerably since 2000. More than one hundred articles a year since 2012 are listed in our map.

This chronological pattern is quite usual and can be encountered for other topics such as light pollution [ 99 ]. It can be due to practical reasons such as better dissemination and accessibility of articles (e.g. database development), but it also certainly reflects a real increase in research activity on the topic of “noise pollution” in response to social concern for environmental issues.

The articles are mainly provided through academic sources (i.e. journal articles), but grey literature is also substantial. 461 articles included in the map (i.e. around a fourth of the map) can be grouped as ‘‘grey literature’’ (books and book chapters, reports, theses, conference objects). In particular, 36 theses from all over the world address this issue.

Regarding the population, the systematic map confirms that a very broad range of species is the topic of literature on the effects of noise pollution. Indeed, all of the 11 population classes of our coding strategy contain articles. Nevertheless, a high proportion of the map concerns mammals and, to a lesser extent birds and fishes. Among the 778 articles targeting mammals, many infrataxa are concerned (e.g. Cetacea [ 100 ], Carnivora [ 101 ], Cervidae [ 102 ], Chiroptera [ 103 ], Rodentia [ 104 ]), but the highest proportion of the articles on mammals deals with aquatic noise (500/778, 64%), which suggests that many may concern Cetacea (e.g. dolphins, whales, beluga).

The other taxonomic groups receive far less attention. Amphibians, crustaceans, mollusks, insects, reptiles and arachnids each represent 5% or less of the whole map. However, comparing these knowledge gaps to contemporary biodiversity issues, we can say, for instance, that amphibians, reptiles and invertebrates are highly threatened species [ 105 , 106 ] and noise pollution around the world is probably part of the threats [ 31 , 84 ]. These taxonomic groups are likely impacted by noise depending on the sense used. In particular, amphibians communicate extensively using sounds (i.e. chorus frogs) [ 107 ], insects demonstrate hyperacuity in directional hearing [ 108 ], reptiles (in particular snakes) and spiders can feel vibrations [ 109 , 110 , 111 , 112 ].

In terms of exposure, the map confirms that a very wide variety of anthropogenic activities generate noise and that the effects of these emissions have already been studied.

Transportation (that includes terrestrial infrastructure as well as civil aircraft and boats) is the source of noise most considered. It is closely followed by industrial sources among which high diversity is observed (e.g. pile-driving [ 113 ], seismic surveys [ 114 ], wind turbines [ 115 ], mining [ 116 ], constructions [ 117 ]). Abstract noises are in third position. This category does not necessary correspond to any precise human activities but comprises a large set of computer or machinery sounds (e.g. alarms [ 118 ], pingers [ 119 ], tones [ 120 ], pulses [ 121 ], bells [ 122 ]). Often, articles in this category do not contain many details about the source of noise. Military noise is especially studied for mammals and urban noise is significantly considered for birds (but not otherwise). Recreational noise is the least studied, however a certain diversity of sources is observable (e.g. zoo visitors [ 123 ], music festivals [ 124 ], sporst activities [ 125 ], tourists in natural habitats [ 126 ], Formula one Grand Prix racing [ 127 ], whale-watching [ 128 ]). However, urban and recreational sources of noise are important and will increase in the future because, on the one hand, urbanization is spreading all over the word and, on the other, human presence in natural habitats is also becoming more and more frequent (e.g. recreational activities in nature). For example, the expansion of Unmanned Aircraft could be a serious threat for biodiversity [ 129 ].

In terms of outcomes, the map also confirms a very wide range of impacts of noise on species and ecosystems. The most studied are the behavioural impacts involving measurements on movement [ 130 ], foraging [ 131 ], hunting [ 132 ], social behaviour [ 133 ], aversive reaction [ 134 ], etc. Biophysiology and communication are also well covered, especially the impacts on the biophysiology of mammals and fishes and on the communication birds. Biophysiological outcomes can be very diverse (e.g. hormonal response [ 135 ], heart rate [ 136 ], blood parameters [ 137 ], organ development [ 138 ]). On the other hand, the lack of literature on ecosystems, reproduction and space use is of concern. Ecosystems are a very significant aspect of biodiversity and will be increasingly integrated in public policies and scientific research, notably concerning ecosystem services in the context of global changes [ 139 , 140 ]. Reproduction and mobility of species are essential for the sustainability of their population and we already know that noise can impair them [ 141 , 142 ].

Concerning the systematic map, at the moment, we are not able to conclude whether this very rich literature provides strong evidence on impacts of anthropogenic noise on animals. Indeed, we do not know if the studies and other articles confirm or invalidate such impacts and if the studies are sufficiently robust for that purpose. However, our database highlights that a majority of studies are experimental field-based studies. This is a very good point in planning further meta-analyses or systematic reviews with the prospect of quantifying the level of impacts because these studies would probably be selected following critical analysis. For future systematic reviews/meta-analyses, we identified that the three outcomes comprising the highest number of experimental studies (which are the type of content that systematic reviews or meta-analyses would use) are: behaviour (453), biophysiology (391), communication (145).

Given the scope of our map resulting in a high number of population (P), exposure (E) and outcome (O) classes, there is a wide range of possible PEO combinations. Therefore, it is difficult to go further in this report in terms of identifying knowledge gaps and clusters and possible specific questions for future systematic reviews. At the same time, this large number of PEO combinations offers stakeholders (e.g. researchers, practitioners, decision-makers) an opportunity to gain information on the combination of interest to them.

Comparison to other evidence syntheses

It is interesting to check whether other evidence syntheses previously published have arrived at the same results, knowledge clusters and knowledge gaps as those highlighted by our map. However, given the differences in terms of methodology, topic delimitation and volume of the existing reviews, exposed in the results section, it is difficult to make such comparisons for all reviews. But we can compare our results to those from two other reviews, namely Shannon et al. [ 34 ] and Radford [ 54 ] (see Fig.  15 ).

figure 15

Comparison between our map results (SM) and two other standardized reviews [ 34 , 54 ] on population ( a ; top) and exposure ( b ; bottom). A = Transportation; B = Industrial; C = Military; D: Recreational

Concerning population (Fig.  15 a), mammals are the most studied species in Shannon et al. [ 34 ] (39%) as they are in our map (40%). In Radford [ 54 ], birds greatly surpass mammals (65% vs. 9%), but that can be explained by the exclusion of marine species (among which there are many mammals) in the synthesis. Fishes are more represented in our map (22%) than in the two other reviews (Shannon et al.: 15%, Radford: 10%).

Regarding exposure (Fig.  15 b), transportation is the greatest source of noise in Shannon et al. [ 34 ] for terrestrial activities (30%), similar to our map (15%). For aquatic activities, industrial noise is the exposure most frequent in our map (20%) as in Shannon et al. [ 34 ] (28%). In Radford [ 54 ], transportation noise is by far the foremost exposure (more than 75% exclusively for road and aircraft noise). These results seem to be quite consistent.

Concerning outcomes, in Shannon et al. [ 34 ], vocalization is the most frequent for terrestrial studies (44%) whereas behavioural outcomes come first in our map (19%). Behavioural is the most frequent outcome for aquatic studies in Shannon et al. [ 34 ] (more than 40%) whereas biophysiology comes first in our map (24%). Here, our results are more consistent with Radford [ 54 ], where behavioural outcomes are the most frequent (approximately 65%, compared to approximately 54% in our database).

Limitations of the systematic map

Search strategy.

We are aware that two academic databases (WOS CC and Scopus) in our search strategy is a minimum according to the CEE guidelines [ 38 ]. Nevertheless, WOS CC is the most used database in Ecology and Scopus is probably the second. Furthermore, our overall strategy includes eight bibliographic sources (see Table  4 ) and in particular three search engines. In addition, a large number of hits were exported from each of the search engines (e.g. 1000 citations for each search string on Google Scholar instead of the 300 initially expected). We also completed our search strategy with the extraction of all the bibliographic references from 37 relevant reviews. Finally, when a reference was a part of a more comprehensive article (i.e. a meeting abstract inside a proceeding with multiple abstracts), we checked whether other parts of the article could be also interesting for the map (i.e. other meeting abstracts from the same conference proceeding). We could not check systematically due to our limited resources but, nevertheless, this verification produced 36 articles that were added manually to the map.

In conclusion, although our search strategy is robust for journal articles/studies, we may have missed some relevant articles in other formats (e.g. conference papers, books, chapters). That being said, studies are the most important documentation for conducting further systematic reviews.

In addition, in light of the considerations exposed in “ Results ” and “ Discussion ” sections), our systematic map would seem to be wide-ranging and complete because it does not restrict the population, the exposure or the outcomes, contrary to the majority of reviews included in the map. The number of articles collected in the 12 systematic reviews/meta-analyses described in Table  6 shows that our map (1794 articles) constitute a very important dataset.

Full-text searching

In order to facilitate a possible additional full-text research, we have compiled a list of the unretrieved full-text texts in a dedicated Additional file 11 (Sheet 1). We could retrieve 90% of the searched full-texts which means that we had to exclude 376 articles from the map process because we could not get their full-texts. We are aware that this volume of unretrievable full-texts is not a satisfactory result, however there is no standard minimum in the CEE guidelines [ 38 ] and we did everything we could to find the full-texts. First, we benefited from different institutional accesses thanks to our map team (MNHN, CNRS, INRAE). We even performed an additional search during the Covid period when some publishers suspended their paywall. Secondly, we also asked for French and even international interlibrary loans and, when necessary, we went to the libraries to collect them. We also asked for the missing full-texts on ResearchGate. A large number of unretrieved full-texts come from the extracted relevant reviews, from Scopus and from Google Scholar (see Additional file 11 , Sheet 2 for more details on retrieved/not retrieved full-texts depending on the bibliographic sources). In the end, we could obtain some explanations for a majority of the unretrieved full-texts, i.e. 25 (7%) are available online but behind an embargo, a paywall or another access restriction, 124 (33%) are not accessible to the map team (unpublished thesis or report, unlocatable conference proceedings, only available in a print journal, etc.), 47 (13%) would be excluded during screening because of their language (according to Scopus information), 19 (5%) were requested on ResearchGate without any response.

Languages accepted at full-text screening stage

We are aware that we accepted only two languages, English and French. Nevertheless, among the 3219 screened pdf files, only 54 articles were rejected at the full-text stage because of their language. This represents less than 2%. In the end, to facilitate a possible additional screening of these full-texts, we listed them in Additional file 12 . It should also be noted that when a title or an abstract was not in English or in French, it was not rejected for this reason during the title/abstract screening, it was sent directly to abstract and/or full-text screening to check its effective language.

Coding strategy

Due to resource limitations, we were not able to perform double coding of each article by two reviewers, as requested by the CEE guidelines. We are aware that this is not a totally rigorous approach, but we anticipated it in our a priori protocol [ 36 ] because we knew that time and resources would be limited. We think that our approach did not affect coding consistency because the three coders (RS, AD, OR) followed the same coding rules and one person (RS) was present throughout the coding process to explain the rules to the other coders and to help them if necessary. In addition, at the end of the coding procedure, RS reviewed the entire map for analysis purposes.

Regarding the coding strategy, we are aware that our classification (in particular for exposure and outcome classes) is not perfect, but it is difficult to achieve a perfect solution. We decided to use published reviews such as Shannon et al. [ 34 ] or Radford [ 54 ], but different strategies exist. For example, Radford [ 54 ] split the transportation sources of noise (e.g. road, rail, boat), whereas Shannon et al. [ 34 ] grouped them in a “transportation” class. Such classes may appear too broad, but this strategy produces an initial overview of the available literature, which is certainly one of the objectives of a systematic map. As another example, the outcome class “Reproduction” was also difficult to delimit because it can include reproduction in the strictest sense (e.g. number of eggs) as well as other impacts that can influence reproduction (e.g. physiological impacts on adults in a breeding colony). In such cases, we coded the article for the different outcomes (i.e. biophysiology/reproduction).

This systematic map collated and catalogued literature dealing with the impacts of anthropogenic noise on species (excluding humans) and ecosystems. It resulted in a database composed of 1794 articles, including 1340 studies, 379 reviews and 16 meta-analyses published worldwide. Some systematic reviews and meta-analyses have already been published and were collected, however, no systematic map has yet been produced with so few topic restrictions (all wildlife, all sources of noise, all kinds of impacts) and using such a large search strategy (two databases, three search engines, etc.).

This map can be used to inform policy, provide the evidence for systematic reviews and demonstrate where more primary research is needed. It confirms that a broad range of anthropogenic activities can generate noises which may produce highly diverse impacts on a wide array of taxa. To date, some taxonomic groups (mammals, birds, fishes), types of noise (transportation, industrial, abstract) and outcomes (behavioural, biophysiological, communication) have undergone greater studies than others. Less knowledge is available on certain species (invertebrates, reptiles, amphibians), noises (recreational, urban, military) and impacts (space use, reproduction, ecosystems). Currently, this map cannot be used to determine whether the included studies demonstrate that noise does indeed produce impacts. However, it can be the starting point for more thorough syntheses of evidence. Included reviews and meta-analyses should be exploited to transfer this synthesized knowledge into operational decisions to reduce noise pollution and protect biodiversity.

Implications for policy/management

Given the volume of bibliographic data, we obviously do not face to a totally unexplored topic. But surprisingly, this rich literature on the impacts of noise pollution on biodiversity does not seem to be exploited by practitioners and decision-makers. Indeed, to date, noise pollution has been considered in terms of impacts on human health, but very little or no consideration has been given to impacts on other species and ecosystems. Two key implications emerge from this map.

First, the high volume of reviews and meta-analyses collected in this map can facilitate the immediate integration of these evidence syntheses into public policies on the national and international levels. Some reviews and the meta-analyses have quantified the level of impacts concerning the species, sources of noise and outcomes they considered. A strategy should be defined to assess the quality of these syntheses (critical appraisal) and, if reliable, transfer this already synthesized knowledge to institutional texts (e.g. regulations, guidelines, frameworks). Thanks to the exposure categorization undertaken in this map, many stakeholders and practitioners (urban planners, transport infrastructure owners, airlines and airports, military authorities, tour operators, manufacturing companies, etc.) will be able to directly identify the articles that concern their activities/structures. Such knowledge may also be useful for the European Commission, which intends to produce indicators to monitor the reduction of submarine noise pollution, as part of a new strategy for biodiversity [ 143 ].

Secondly, several knowledge clusters identified in this map may be used for new systematic reviews and meta-analyses to assess the evidence of impacts. Resources should be invested in evidence syntheses capable of exploiting the full range of the mapped literature. In particular, these analyses could determine sensitivity thresholds for guilds of species representing several natural habitats. These thresholds are essential in taking noise pollution into account for green and blue infrastructures in view of preserving and restoring quiet ecological networks. Practitioners (e.g. nature reserves and local governments) in France have started to implement this type of environmental policy and this will increase in the future [ 144 ].

Implications for research

New research programs should initiate studies on knowledge gaps, using robust experimental protocols (such as CE—Control/Exposure, BAE—Before/After/Exposure, B(D)ACE—Before(/During)/After/Control/Exposure) [ 145 , 146 , 147 , 148 ] and taking into account different types of bias [ 149 , 150 , 151 ]. In particular, studies should be started on some taxonomic groups (amphibians, reptiles and invertebrates), on certain sources of noise (recreational, military and urban) and to assess particular impacts (space use, reproduction, ecosystems) because these populations, exposures and outcomes have received little study to date. Many PEO combinations have never been studied. In addition, the findings of the current map show that research is not evenly spread worldwide, with main areas of research being in North America (United States, Canada). This finding may have an operational impact because some results may not be transposable to other contexts. Articles on further studies could also be more detailed by the authors. Indeed, some meta-data were unavailable in a significant percentage of the mapped literature. For example, the study location was unknown for 10% of the studies and approximately 1% of the articles did not indicate the source of noise or the outcome that they studied.

The map findings show that research in ecology has already addressed the issue of noise pollution. Deeper analysis is needed to assess the validity of the literature collected in this map, whether primary studies or reviews, in order to produce new syntheses and to transfer this knowledge to the applied field.

Availability of data and materials

All data, generated or analyzed during this study, are included in this published article and its addition information files.

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The map team thanks:

Dakis-Yaoba Ouédraogo (MNHN) and Yorick Reyjol (OFB) for providing comments on earlier versions of the manuscript;

Marc Morvan, Magali Morvan and Benoît Pichet from the library of the National Museum of natural History for their help during the pdf search;

All the institutions that transmitted full-texts to us during the pdf search, namely the library of the “Arts-et-Métiers” (Isabelle FERAL), the library of the “Ecole de Médecine” (Isabelle Beaulande), the library of the “Maison des Sciences de l’Homme” (Amélie Saint-Marc), the library of the “École Polytechnique” (Claire Vandermeersch), the library of “Sorbonne Université” (Isabelle Russo and Peggy Bassié), the library of “Paris 13 Villetaneuse”, ZeFactory ARTELIA (Magalie Rambaudi);

all the organizations that relayed our call for literature through their websites or mailing lists, namely the “Centre de ressources Trame verte et bleue”, the IENE, the ITTECOP;

everyone who transmitted literature to us during the call, namely Vital Azambourg (MNHN), Ludivine Boursier (FRB), Fabien Claireau (MNHN), Patricia Detry (CEREMA), Cindy Fournier (MNHN), Philippe Goulletquer (IFREMER), Aurelie Goutte, Anne Guerrero (SNCF Réseau), Eric Guinard (CEREMA), Heinrich Reck, Antonin Le Bougnec (PNR Morbihan), Barbara Livoreil (FRB), Sylvain Moulherat (TerrOïko), Dakis-Yaoba Ouédraogo (MNHN), Marc Thauront (Ecosphère), Dennis Wansink (BUWA);

Barbara Livoreil (FRB) for her help with the protocol of this map;

Cary Bartsch for his proofreading and corrections concerning the English language.

This research was undertaken as current work of UMS Patrimoine Naturel, a joint research unit funded by the French Biodiversity Agency (OFB), the National Scientific Research Center (CNRS) and the National Museum of Natural History (MNHN), on behalf of the French Ecology Ministry.

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RS originated the idea of the systematic map and was the scientific coordinator of the map. RS conducted the first scoping stage. FF participated in the search strategy. RS, SV and AD screened the articles. RS searched the full-texts with help from FF and CL. OR, AD and RS extracted the metadata. RS analysed, interpreted and discussed the results, helped by the rest of the team. RS wrote the draft of the manuscript and the rest of the team contributed to it. All authors read and approved the final manuscript.

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Supplementary information

Additional file 1..

ROSES form.

Additional file 2.

Search strategy.

Additional file 3.

Key reviews from which bibliographic references were extracted.

Additional file 4.

Comprehensiveness of databases and search engines.

Additional file 5.

Detailed screening process.

Additional file 6.

Full flow diagram.

Additional file 7.

Inclusion/exclusion decisions during the three screening stages and extraction of rejected full-texts.

Additional file 8.

Accepted full-texts published in 2019–2020.

Additional file 9.

Systematic map database.

Additional file 10.

Information on standardized evidence syntheses.

Additional file 11.

List and statistics on missing full-texts.

Additional file 12.

Rejected full-texts (language exclusion).

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Sordello, R., Ratel, O., Flamerie De Lachapelle, F. et al. Evidence of the impact of noise pollution on biodiversity: a systematic map. Environ Evid 9 , 20 (2020). https://doi.org/10.1186/s13750-020-00202-y

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Environmental noise exposure and health outcomes: an umbrella review of systematic reviews and meta-analysis

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Xia Chen, Mingliang Liu, Lei Zuo, Xiaoyi Wu, Mengshi Chen, Xingli Li, Ting An, Li Chen, Wenbin Xu, Shuang Peng, Haiyan Chen, Xiaohua Liang, Guang Hao, Environmental noise exposure and health outcomes: an umbrella review of systematic reviews and meta-analysis, European Journal of Public Health , Volume 33, Issue 4, August 2023, Pages 725–731, https://doi.org/10.1093/eurpub/ckad044

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Environmental noise is becoming increasingly recognized as an urgent public health problem, but the quality of current studies needs to be assessed. To evaluate the significance, validity and potential biases of the associations between environmental noise exposure and health outcomes.

We conducted an umbrella review of the evidence across meta-analyses of environmental noise exposure and any health outcomes. A systematic search was done until November 2021. PubMed, Cochrane, Scopus, Web of Science, Embase and references of eligible studies were searched. Quality was assessed by AMSTAR and Grading of Recommendations, Assessment, Development and Evaluation (GRADE).

Of the 31 unique health outcomes identified in 23 systematic reviews and meta-analyses, environmental noise exposure was more likely to result in a series of adverse outcomes. Five percent were moderate in methodology quality, the rest were low to very low and the majority of GRADE evidence was graded as low or even lower. The group with occupational noise exposure had the largest risk increment of speech frequency [relative risk (RR): 6.68; 95% confidence interval (CI): 3.41–13.07] and high-frequency (RR: 4.46; 95% CI: 2.80–7.11) noise-induced hearing loss. High noise exposure from different sources was associated with an increased risk of cardiovascular disease (34%) and its mortality (12%), elevated blood pressure (58–72%), diabetes (23%) and adverse reproductive outcomes (22–43%). In addition, the dose–response relationship revealed that the risk of diabetes, ischemic heart disease (IHD), cardiovascular (CV) mortality, stroke, anxiety and depression increases with increasing noise exposure.

Adverse associations were found for CV disease and mortality, diabetes, hearing impairment, neurological disorders and adverse reproductive outcomes with environmental noise exposure in humans, especially occupational noise. The studies mostly showed low quality and more high-quality longitudinal study designs are needed for further validation in the future.

Environmental noise, an overlooked pollutant, is becoming increasingly recognized as an urgent public health problem in modern society. 1 , 2 Noise pollution from transportation (roads, railways and aircraft), occupations and communities has a wide range of impacts on health and involves a large number of people. 2–6 It is reported that environmental noise exposure may affect human health by influencing hemodynamics, hemostasis, oxidative stress, inflammation, vascular function and autonomic tone. 7–11 Prolonged noise exposure can cause dysregulation of sleep rhythms and lead to adverse psychological and physiological changes in the human body such as distress response, behavioral manifestations, cardiovascular (CV) disease and mortality, etc. 12–19 It is reported that environmental noise is second only to air pollution as a major factor in disability-adjusted life years (DALYs) lost in Europe. 20

There have been many epidemiological studies and systematic reviews assessing the effects of environmental noise on health, but the quality of the evidence included in these reviews varies due to subjective or inconsistent evaluation criteria. Therefore, it is hard to contextualize the magnitude of the associations across health outcomes according to current reviews. To comprehensively assess the significance, validity and potential biases of existing evidence for any health outcomes associated with environmental noise, we performed an umbrella review of systematic reviews and meta-analyses. 21 The results may provide evidence for decision-makers in clinical and public health practice.

Search strategy

The umbrella review search followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 22 We searched systematic reviews and meta-analyses of observational or interventional studies studying the relationship between noise exposure and any health outcome from PubMed, Cochrane, Scopus, Web of Science and Embase databases to November 2021 ( Supplementary tables S1 and S2 ). Pre-defined search strategy as follows: noise AND (systematic review* or meta-analysis*). Two researchers (X.C. and M.L.) independently screened qualified literature, and we also manually searched the references of qualified articles. Any discrepancies were resolved by a third investigator for the final decision (L.Z.).

Inclusion and exclusion criteria

Researches meeting the following criteria have been included: (1) Systematic reviews and/or meta-analyses of observational studies (cohort, case–control and cross-sectional studies) or interventional studies [randomized controlled trials (RCTs) and quasi-experimental studies]. (2) The exposure or intervention of meta-analysis and/or systematic reviews is ‘noise’. We ruled out the following research: (1) Outcome is not a health outcome, such as students’ examination scores. (2) Meta-analysis and/or systematic reviews only evaluated the combined effects of noise exposure and other risk factors on health outcomes and it is not possible to extract the separate effect of noise.

Data extraction

Four researchers (X.C., M.L., L.Z. and X.W.) independently extracted data from each eligible systematic review or meta-analysis. We extracted the following data from original articles: name of the first author; publication time; research population; type of noise and measurement method(s); the dose of noise exposure; study types (RCTs, cohort, case–control studies or cross-sectional); the number of studies included in the meta-analysis; the number of total participants included in each meta-analysis; the number of cases included in each meta-analysis; estimated summary effect (OR, odds ratio; RR, relative risk; HR, hazard ratio), with the 95% confidence intervals (CIs). We also extracted the type of effect model, publication bias by Egger’s test, dose–response analyses, I 2 , information on funding and conflict of interest. Any disagreement in the process of data extraction was settled through group discussion.

Quality of systematic review and strength of evidence

AMSTAR 2 is a measurement tool to assess the methodological quality of systematic reviews by 16 items. 23 The quality of the method was divided into four grades: ‘high’, ‘moderate’, ‘low’ and ‘very low’.

For the quality of evidence for each outcome included in the umbrella review, we adopted the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) to make recommendations and to classify the quality of evidence. 24 The baseline quality of evidence is determined by the research design. The quality of evidence decreases when there is a risk of bias, inconsistency, indirectness, imprecision or publication bias in the article, while it can be elevated when there is the presence of magnitude of effect, plausible confounding and dose–response gradient. 25 The quality of evidence can also be divided into four levels: ‘high’, ‘medium’, ‘low’ or ‘very low’.

Data analysis

Noise exposure was divided into six types: (1) transportation noise (combined road, railway or aircraft noise); (2) road noise; (3) railway noise; (4) aircraft noise; (5) occupational noise and (6) combined noise (two or more kinds of noise above or wind turbine noise, etc.). We divided the results into: (1) mortality; (2) CV outcome; (3) metabolic disorders; (4) neurological outcomes; (5) hearing disorder; (6) neonatal/infant/child-related outcomes; (7) pregnancy-related diseases and (8) others. When a systematic review and/or meta-analysis includes different exposures or outcomes, we extracted the data for each of the different types of exposure and health outcomes, respectively. When two or more systematic reviews and/or meta-analyses had the same exposure and health results, we selected the recently published research with the largest number of studies included.

The associations across studies were commonly measured with RR (or OR and HR). We recalculated the adjusted pooled effect values and corresponding 95% CIs by using the random-effects model by DerSimonian and Laird, 26 which takes into account heterogeneity both within and between studies. And all results were reported by RRs for simplicity in our study.

Based on I 2 statistics and the Cochrane Q test, we evaluated the heterogeneity of each study. 27 Due to I 2 being dependent on the study size, we therefore also calculated τ 2 , which is independent of study size and describes variability between studies concerning the risk estimates. 28 Publication bias was estimated by Egger’s test. 29 Pooled effects were also reanalyzed in articles that included only cohort studies in the sensitivity analysis.

Patient and public involvement

No patients contributed to this research.

Features of meta-analysis

Our initial systematic retrieve recognized 5617 studies from PubMed, EMBASE, Web of Science, Cochrane and Scopus. The search finally yielded 64 meta-analyses of observational research in 23 articles with 31 unique outcomes after excluding duplicates or irrelevant articles, 30– 52 and no interventional study was identified. Figure 1 shows the flow diagram of the literature search and study selection. The distribution of health outcomes from noise exposure is displayed in Supplementary figure S1 . Most meta-analyses focused on road noise (16 meta-analyses) and the incidence of CV events (18 meta-analyses).

Study flowchart

Study flowchart

Most of the findings presented were expressed in terms of highest to lowest noise exposure, and statistically significant associations of noise exposure were identified with CV mortality and incidence of diabetes, elevated blood pressure (BP), CV disease, speech-frequency noise-induced hearing loss (SFNIHL), high-frequency noise-induced hearing loss (HFNIHL), work-related injuries, metabolic syndrome, elevated blood glucose, fetal malformations, small for gestational age, acoustic disturbance and acoustic neuroma. The associations of environmental noise exposure with the incidence of other outcomes [angina pectoris, myocardial infarction, ischemic heart disease (IHD), elevated triglyceride, obesity, low high-density lipoprotein cholesterol, perinatal death, preterm birth, gestational hypertension, spontaneous abortion and preeclampsia] were not statistically significant. Similarly, in dose–response analysis, statistical significance was achieved for harmful associations with CV mortality, stroke mortality, IHD mortality, non-accidental mortality and incidence of IHD, diabetes, anxiety, elevated BP, stroke, depression, work-related injuries, low birth weight, small for gestational age and preterm birth, whereas other outcomes were not significant.

Transportation noise

We identified four studies on transportation noise and health. 32 , 34 , 39 , 48 Transportation noise exposure might increase the risk of developing CV outcomes, metabolic disorders and neurological outcomes. Compared with individuals who had the lowest exposure to transportation noise, those with the highest exposure had a higher risk of diabetes (RR: 1.23; 95% CI: 1.10–1.38). 32 Dose–response analysis showed that an increase of 5 dB was associated with a 25% increase in diabetes risk. 39 When the noise exposure from transportation was per 10 dB increment, the risks of developing IHD 34 and anxiety 48 increased by 6% and 7%, respectively ( Supplementary figure S2 ).

Associations between road noise exposure and health outcomes. Co: cohort; CC: case control; CS: cross-sectional and NP: not provide

Associations between road noise exposure and health outcomes. Co: cohort; CC: case control; CS: cross-sectional and NP: not provide

Eight studies focused on the associations between road noise and health. 30 , 35 , 38 , 39 , 43 , 46 , 47 , 50 The highest exposure to road noise, compared with the lowest exposure, was associated with increased risks of developing CV outcomes, including angina pectoris (RR: 1.23; 95% CI: 0.80–1.89), 30 myocardial infarction (RR: 1.06; 95% CI: 0.96–1.16), 47 CV disease (RR: 1.06; 95% CI: 0.96–1.18), 30 and IHD (RR: 1.00; 95% CI: 0.79–1.27). 30 In the analysis of the dose–response relationship, the risk of incidence of diabetes increased by 7% for every 5 dB increase of road noise (RR: 1.07; 95% CI: 1.02–1.12). 39 Every 10 dB road noise increment could increase by 2–8% risk of mortality and incidence of diseases (including CV outcomes, neurological outcomes and neonatal-related outcomes), although the results did not reach statistical significance. The most significant harmful association was shown for stroke mortality (5%) 50 in mortalities, for elevated BP (2%) 35 , 38 in CV outcomes, for depression (2%) 46 in neurological outcomes and for low birth weight (8%) 43 in neonatal-related outcomes, but the estimates did not reach significance ( figure 2 ).

Railway noise

Three studies focused on railway noise 39 , 46 , 50 and the results did not show a significant association with any health outcome ( figure 3 ).

Associations between railway noise exposure and health outcomes. Co: cohort; CC: case control; CS: cross-sectional and NP: not provide

Associations between railway noise exposure and health outcomes. Co: cohort; CC: case control; CS: cross-sectional and NP: not provide

Aircraft noise

Six studies focused on aircraft noise and health. 30 , 33 , 39 , 44 , 46 , 50 Current evidence showed that aircraft noise exposure was associated with the risk of CV mortality, and incidence of elevated BP, stroke, diabetes and neurological outcomes. People exposed to aircraft noise had an elevated BP (RR: 1.63; 95% CI: 1.14–2.33), compared with those non-exposed. 33 A dose–response analysis demonstrated that stroke risk increased by 1% for every 10 dB increase of aircraft noise. The risk of diabetes increased by 17% for every 5 dB increase of aircraft noise (RR: 1.17; 95% CI: 1.06–1.29). 39 With every 10 dB increase in noise, the risk of anxiety 50 and depression 46 increased by 22% and 14%, respectively. We did not find a significant association of aircraft noise exposure with other CV outcomes ( figure 4 ).

Associations between aircraft noise exposure and health outcome. Co: cohort; CC: case control; CS: cross-sectional and NP: not provide

Associations between aircraft noise exposure and health outcome. Co: cohort; CC: case control; CS: cross-sectional and NP: not provide

Occupational noise

Eight studies focused on occupational noise, 32 , 36 , 37 , 42 , 45 , 49 , 52 , 53 and the study population of occupational noise exposure mainly came from workers in manufacturing, metals, transportation and mining. Occupational noise exposure increases the risk of mortality, and incidence of CV outcomes, hearing disorders and other diseases. The risk of SFNIHL was greatly attributed to occupational noise exposure (RR: 6.68; 95% CI: 3.41–13.07). 53 Similarly, those exposed to occupational noise showed an increased risk of CV disease (RR: 1.34; 95% CI: 1.15–1.56), 36 HFNIHL (RR: 4.46; 95% CI: 2.80–7.11), 53 and acoustic neuroma (RR: 1.26; 95% CI: 0.78–2.00), 42 compared with the non-exposed group. In addition, the highest exposed group had an increased risk of CV mortality (RR: 1.12; 95% CI: 1.02–1.24), 36 elevated BP (RR: 1.72; 95% CI: 1.46–2.01) 45 and work-related injuries (RR: 2.40; 95% CI: 1.89–3.04). 37 The risk of work-related injuries increased by 22% for every 5 dB increase in occupational noise (RR: 1.22; 95% CI: 1.15–1.29) 37 ( Supplementary figure S3 ).

Combined noise

We identified six studies that combined various noise sources. 31 , 39–41 , 51 , 52 The findings suggested that combined noise or other noise might increase the risk of developing CV disease, metabolic disorders, neonatal-related disease, pregnancy-related and hearing disorders. Hearing impairment was statistically different between the exposed and non-exposed groups. 41 , 42 Compared with the lowest exposure group, the most harmful association was shown for metabolic syndrome (27%) 51 in metabolic disorders, fetal malformations (43%) 31 in neonatal-related outcomes and gestational hypertension (27%) 31 in pregnancy-related outcomes. Dose–response analysis showed that an increase of 5 dB was associated with a 6% increase in diabetes risk. 39 ( Supplementary figure S4 ).

Sensitivity analysis

In the sensitivity analyses of cohort studies, the summary results of recalculating the associations between transportation, road, railway and occupational noise with multiple health outcomes remained similar ( Supplementary table S3 ).

Heterogeneity and publication bias

Heterogeneities across 62 meta-analyses were reanalyzed, of which 15 meta-analyses appeared high heterogeneity, 29 with low heterogeneity and 2 were not able to calculate heterogeneity due to a limited number of individual studies.

Most meta-analyses did not report significant publication bias or a statistical test for publication bias did not publish due to a limited number of studies included, except for the bias found in meta-analyses examining occupational noise and elevated BP.

AMSTAR and GRADE classification

Of the 64 meta-analyses, about 5% were rated as medium quality, 9% as low quality and the rest were graded as extremely low evidence, which was likely rooted in their failure to state that the review methods were established before the review or lack of explanation for publication deviation. The AMSTAR 2 details for every outcome are outlined in Supplementary table S4 . In terms of evidence quality, the majority (69%) were classified as extremely low-quality evidence due to the presence of risk of bias, inconsistency and publication bias or lack of statistical tests for publication bias ( Supplementary tables S5–S7 ).

Main findings and interpretation

Our umbrella review provides a comprehensive overview of associations between environmental noise and health outcomes by incorporating evidence from systematic reviews and meta-analyses. We identified 23 articles with 64 meta-analyses and 31 health outcomes, and no interventional study was identified. We found significant associations of environmental noise with all-cause mortality, and incidence of CV outcomes, diabetes, hearing disorders, neurological and adverse reproductive outcomes, whereas environmental noise was not associated with the beneficial effect of any health outcome.

Occupational noise is harmful to CV morbidity and mortality, and similar results were found for road noise, railway noise, aircraft noise, transportation noise and combined noise, but the former two did not reach statistical significance. It is worth mentioning that we found that most of the studies reported a harmful association of noise with elevated BP. 54 , 55 Noise can cause elevated BP and a range of CV-related diseases by activating the hypothalamic–pituitary–adrenal (HPA) axis and sympathetic nervous system, 56 , 57 or by causing elevated stress hormones such as cortisol and catecholamines through sleep deprivation, 8 leading to vascular endothelial damage. 58 It has also been found that environmental noise, by inducing oxidative stress, 59 can also lead to CV dysfunction. 11 In line with current results, the following large cohort studies also reported that occupational and transportation noises were significantly associated with CV morbidity and mortality. 60–62

When analyzing the research on noise exposure and diabetes, we found that environmental noise was harmful to diabetes, except for occupational and railway noises. Quality assessments of studies with aircraft, road, traffic and combined noise exposure showed extremely low-quality levels. 32 , 39 Environmental noise is related to the stress response of human beings and animals, 63 and several studies have confirmed that impaired metabolic function is associated with chronic stress. 64 , 65 Furthermore, long-term exposure to noise increases the production of glucagon. 66 , 67 The following studies also found a null association between occupational noise 68 , 69 or railway noise with diabetes. 70 The non-significant results for railway noise exposure may be due partly to the limited studies and the low level of railway traffic noise compared with other traffic sources. 70 Different types of noise produced varying levels of annoyance, with aircraft noise being reported as the most annoying type of noise. 71 , 72 Protective equipment use, higher physical activity and healthy worker effects in occupationally exposed populations may account for our findings of invalidity in occupational noise exposure. This hypothesis is further supported by a 10-year prospective study that found that among people with occupational noise, those with high levels of physical activity had a lower risk of developing diabetes. 73 However, recent large cohort studies reported that occupational 74 and railway 75 noise exposure could increase the risk of diabetes by 35% and 2%, respectively.

There is little evidence of the influence of road or railway noise exposure on hearing loss. Noise exposure from occupation increases the risk of hearing disorders, especially occupational noise exposure was observed in our umbrella review. The occupational groups studied mainly come from workers in manufacturing, metals, transportation and mining. It is common for them to be even exposed to more than 85 dB of noise. 3 Some biological mechanisms can explain the damage caused by occupational noise exposure. Occupational noise exposure caused by mechanical injury may damage the hair cells of cortical organs and the eighth Cranial Nerve. 76 , 77 A series of experiments have demonstrated that exposure to high-intensity noise causes substantial neuronal damage, which in turn causes hearing loss. 78–83 Noise exposure may cause DNA errors in cell division by affecting mechanical damage repair, ultimately leading to cell proliferation disorders. 84 Meanwhile, some animal studies have shown that after noise exposure, free radicals that can cause DNA damage were found in vestibular ganglion cells. 85 , 86

The associations of noise exposure with adverse reproductive outcomes such as preeclampsia, preterm birth, perinatal death and spontaneous abortion are still inconclusive. Our analysis found that combined noise exposure significantly increased the risk of birth malformations, small gestational age and gestational hypertension. This is biologically plausible, dysregulation of the HPA axis due to psychological stress 87,88 induced by noise exposure has been shown to impair cortisol rhythms, 89 , 90 and corticosteroids across the placental barrier stimulate the secretion of adrenotropin-releasing hormone by the placenta, which is toxic to the embryo and leads to adverse reproductive outcomes. 91 , 92 However, the quality of evidence from studies on the relationship between the two was assessed as extremely low, the association of road noise with neonatal outcomes was not examined in our review. Danish national birth cohort reported that road traffic exposure was not associated with a higher risk of birth defects. 93 A systematic review found associations between road traffic noise and preterm birth, low birth weight and small gestational age, but the quality of evidence was low. 94

Although most of the current studies showed low quality, current evidence suggested a wide array of harmful effects of environmental noise on human health. Strategies such as limiting vehicle speed, reducing engine noise, building a sound barrier and reducing friction between the air and the ground could be adopted to reduce traffic noise. 11 For occupational noise, it is necessary to educate and train employees to recognize the awareness of noise hazards, equip them with hearing protection devices and monitor the noise exposure level in real-time. 95 , 96 A study summarizing the latest innovative approaches to noise management in smart cities found dynamic noise mapping, smart sensors for environmental noise monitoring and smartphones and soundscape studies to be the most interesting and promising examples to mitigate environmental noise. 97

Strengths and limitations

We systematically summarized the current evidence of noise exposure and multiple health outcomes from all published meta-analyses. We conducted a comprehensive search of five scientific literature databases, which ensures the integrity of literature search results. Two researchers screened the literature independently, then four researchers performed the data extraction. We used AMSTAR 2 as a measurement tool to assess the methodological quality of systematic reviews and the GRADE tool to evaluate the quality of evidence. 23 , 25

There are some limitations in our umbrella reviews. All meta-analyses included in our umbrella reviews were observational studies, which led to lower evidence quality scores. The studies on occupational and railway noise exposure with some health outcomes were limited. In meta-analyses that we were unable to disentangle the noise types, the presented results were from the combined estimates of all included studies, so these results should be explained cautiously. The dose–response associations of environmental noise exposure with health outcomes should be further investigated.

In a nutshell, the umbrella review suggested that environmental noise has harmful effects on CV mortality and incidence of CV disease, diabetes, hearing impairment, neurological disorders and adverse reproductive outcomes. The results of railway noise are not yet fully defined. More high-quality cohort studies are needed to further clarify the effects of environmental noise in the future.

Supplementary data are available at EURPUB online.

This work was financially supported by the Hunan Provincial Key Laboratory of Clinical Epidemiology [grant number 2021ZNDXLCL002] and Program for Youth Innovation in Future Medicine, Chongqing Medical University [No. W0088].

Not applicable.

The data that support the findings of this study are available in the Supplementary Material of this article.

Conflicts of interest : None declared.

The first umbrella meta-analysis of the relationship between noise and multiple health.

Environmental noise has harmful associations for a range of health outcome.

The impact of railway noise on health outcomes is inconclusive.

Most of the current studies showed low methodological and evidence quality.

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The remaining references are listed in the Supplementary Reference .

Author notes

  • cardiovascular diseases
  • cerebrovascular accident
  • ischemic stroke
  • diabetes mellitus, type 2
  • depressive disorders
  • noise, occupational
  • pregnancy outcome
  • arterial pressure, increased
  • hearing loss
  • health outcomes
  • noise exposure

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  • Published: 26 January 2024

Noise and mental health: evidence, mechanisms, and consequences

  • Omar Hahad 1 , 2   na1 ,
  • Marin Kuntic 1 , 2   na1 ,
  • Sadeer Al-Kindi 3 ,
  • Ivana Kuntic 1 ,
  • Donya Gilan 4 , 5 ,
  • Katja Petrowski 6 ,
  • Andreas Daiber 1 , 2 &
  • Thomas Münzel 1 , 2  

Journal of Exposure Science & Environmental Epidemiology ( 2024 ) Cite this article

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The recognition of noise exposure as a prominent environmental determinant of public health has grown substantially. While recent years have yielded a wealth of evidence linking environmental noise exposure primarily to cardiovascular ailments, our understanding of the detrimental effects of noise on the brain and mental health outcomes remains limited. Despite being a nascent research area, an increasing body of compelling research and conclusive findings confirms that exposure to noise, particularly from sources such as traffic, can potentially impact the central nervous system. These harms of noise increase the susceptibility to mental health conditions such as depression, anxiety, suicide, and behavioral problems in children and adolescents. From a mechanistic perspective, several investigations propose direct adverse phenotypic changes in brain tissue by noise (e.g. neuroinflammation, cerebral oxidative stress), in addition to feedback signaling by remote organ damage, dysregulated immune cells, and impaired circadian rhythms, which may collectively contribute to noise-dependent impairment of mental health. This concise review linking noise exposure to mental health outcomes seeks to fill research gaps by assessing current findings from studies involving both humans and animals.

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Noise as a public health challenge and trigger of chronic non-communicable diseases.

Noise is one of the most ubiquitous environmental pollutants, as suggested by reports from the World Health Organization (WHO) and the European Environment Agency (EEA) that noise exposure is a major public health threat affecting both physical and mental health [ 1 , 2 ]. In the European Union alone, estimates indicate that at least 20% of the urban population are affected by the harmful effects of road traffic noise. Consequently, long-term transportation noise levels result in at least 18 million people being highly noise annoyed and further 5 million suffering from high sleep disturbances [ 2 ]. In addition, the WHO reported a loss of more than 1.6 million healthy life years annually due to environmental noise exposure in Western European countries [ 1 ]. Importantly, annoyance and sleep disturbance are proposed as key drivers of noise-associated non-communicable disease (NCD) onset and progression (Fig.  1 ) including both physical and mental health conditions [ 3 ]. Indeed, noise exposure has been implicated in a wide range of major NCDs including cardiovascular disease, metabolic disease, cancer, and respiratory disease (Fig.  2 provides an overview). We recently reviewed the cerebral consequences of environmental noise exposure in detail, suggesting that noise exposure could be an important but largely unrecognized risk factor for neuropsychiatric outcomes [ 4 ]. However, in contrast to the well-established effects of noise exposure on major NCDs, and particularly on cardiovascular disease, its effects on mental health have not been mapped in detail. This is also reflected by the omission of the quantitative details of the harms of noise on mental health consequences in reports by the WHO or the EEA. This is of concern as mental health disorders may contribute substantially to the burden of disease in the population exposed to noise. Thus, this compact review on mental health identifies some areas of future research by evaluating recent findings from human and animal studies.

figure 1

One DALY equals to the loss of 1 year of healthy life attributed to morbidity, mortality, or both. The most important contributors to the total burden of disease of environmental noise are annoyance and sleep disturbance because of the large number of people affected. Adapted from [ 70 ]. DALYs disability-adjusted life years.

figure 2

Noise from different sources was previously shown to likely affect different organ systems and promote a wide variety of diseases. Detrimental effects of noise can also play a prominent role in onset and progression of many aspects of mental health, like anxiety and depression. Data derived from the following studies: [ 49 , 50 , 51 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ].

The noise/stress concept

The association between noise exposure and adverse mental health outcomes involves a complex interplay of psychological and behavioral mechanisms. In accordance with the noise/stress concept developed by Wolfgang Babisch [ 5 ], there are two main pathways by which noise exposure causes adverse health effects. The so-called “direct pathway” , i.e. exposure to extreme high decibel levels (>100 dB(A)) causing direct ear organ damage, and the so-called “indirect pathway” related to the exposure to lower decibel levels in the range of 50–70 dB(A) that impairs daily activities, sleep, and communication. Sleep disturbance is strongly linked to mental health problems, including anxiety and depression [ 6 ]. This lower decibel noise leads to sympathetic and endocrine activation and several cognitive and emotional stress reactions, including annoyance, depressive-like states, and mental stress characterized by elevated stress hormone levels and activation of the sympathetic nervous system (Fig.  3 ). Noise annoyance, characterized by feelings of displeasure and discomfort, can contribute to increased stress levels and the development or exacerbation of mental health issues [ 3 ]. This noise-induced pathophysiological cascade favors not only the development and progression of mental health conditions but also of cardiovascular risk factors and cardiovascular disease [ 3 ]. Importantly, chronic mental stress per se is a well-known risk factor for both physical and mental health [ 7 ]. Even acute nighttime aircraft noise exposure induces takotsubo cardiomyopathy, also known as broken-heart syndrome, a condition triggered by emotional stress and excessive release of stress hormones [ 8 ]. In general, chronic noise annoyance/stress may impair adaptation and increase stress vulnerability, leading to decreased stress resistance and coping capacity [ 3 ]. In addition, noise exposure may promote maladaptive coping styles as indicated by recent studies demonstrating that traffic noise exposure is associated with increases in smoking, alcohol consumption, and sedentary behavior, all of which can increase the vulnerability to mental health conditions [ 9 , 10 , 11 ]. Learned helplessness, characterized by passive resignation due to a perceived lack of control, often arises from chronic exposure to uncontrollable stressors. These exposures trigger a sustained stress response, impacting cognitive processes and leading to a belief that a stress situation is unchangeable, which may increase the vulnerability to mental health problems. Recent research suggests an involvement of learned helplessness when it comes the adverse mental health effects of noise exposure [ 12 ].

figure 3

Noise induces the stress response through either direct (hearing loss and inner ear damage) pathway or indirect (annoyance and sleep disturbance) pathway. The stress response results in the activation of the hypothalamic–pituitary–adrenal (HPA) axis and an increase in systemic inflammation that becomes neuroinflammation, resulting in the fear and anxiety response. Prolonged exposure to a high stress response leads to maladaptive coping strategies, such as smoking or alcohol consumption. CRH (corticotropin-releasing hormone), ACTH (adrenocorticotropic hormone), NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells), SNS (sympathetic nervous system), dAAC (dorsal anterior cingulate cortex), mPFC (medial prefrontal cortex), TNFα (tumor necrosis factor alpha), IL-6/1β (interleukin 6/1β). Adapted from [ 27 ].

Mechanisms of noise-induced mental health consequences—insights from animal models

Several studies in animal models reported that environmental noise can influence inflammatory and oxidative stress pathways in the brain, leading to anxiety and depression-like behavior. A study in mice indicated that traffic noise caused hyperactivity of the hypothalamic–pituitary–adrenal (HPA) axis, leading to lower performance in all cognitive and motor tasks, a reduction of size in the hippocampal formation, medial prefrontal cortex (mPFC), and amygdala, and a reduced neuronal density in the mPFC and dentate gyrus (DG) [ 13 ]. Although the results are indicative of cognitive decline, the authors point out that the behavior of mice is suggestive of anxiety-like behavior, providing the connection to mental health decline. The same group also observed increases in anxiety-like behavior, reduced time spent exploring new object/environment even when mice were exposed to a 3000 Hz synthetic sound tone [ 14 ]. Neuroinflammation, as shown by increases in IL-1β IL-6 and TNFα in the hippocampus and prefrontal cortex, was observed in mice exposed to a synthetic noise stimulus of 80 dB [ 15 ]. These authors also observed depression-like behaviors, envisaged by a decrease in sucrose preference and reduction in times of crossings in the open-field test and the times of rearings (standing on hind legs) in the open-field test. Another study in mice showed that chronic noise exposure caused an increase in malondialdehyde (MDA) levels in the brain, together with a decrease in superoxide dismutase (SOD) and glutathione peroxidase (GPx) activity [ 16 ]. These increases in oxidative stress markers were also accompanied by greater circulating cortisol levels and impaired social interactions. A 30-day noise exposure study in rats showed that elevated plasma corticosterone levels are linked to impairment in spatial memory [ 17 ]. This was also accompanied by decreases in catalase and glutathione peroxidase activity in the medial prefrontal cortex and hippocampus, suggesting increased oxidative stress. Another study showed that plasma levels of corticosterone, adrenaline, noradrenaline, endothelin-1, nitric oxide and malondialdehyde were increased in rats chronically exposed to intermittent noise, while superoxide dismutase expression was decreased [ 18 ]. A study in spontaneously hypertensive rats showed that noise stress resulted in exaggerated glutamatergic responses in the amygdala, pointing to the activation of this important pathway [ 19 ].

Our studies in mouse models show that 4-day of exposure to aircraft noise increased levels of pro-inflammatory cytokines IL-6, inducible nitric oxide synthase (iNOS) and cluster of differentiation 68 (CD68) in mouse brains [ 20 ]. Down-regulated catalase and neuronal nitric oxide synthase (nNOS) were also observed as key factors of cerebral/neuronal damage in mice exposed to noise. These negative effects were ameliorated by the genetic deletion of the subunit of phagocytic NADPH oxidase (gp91phox), pointing to the important role of immune cell-derived oxidative stress. Interestingly, the effects were more pronounced when noise was applied during the sleeping phase of mice, which correlates well with the impairment of circadian rhythms by sleep fragmentation and deprivation [ 20 ]. Dysregulation of circadian rhythms seems to represent a hallmark of noise-induced pathomechanisms as it is clear that nighttime noise exposure is much more detrimental for humans than daytime noise [ 21 , 22 , 23 ]. We also observed increases in levels of circulating catecholamines (adrenaline and noradrenaline) in a mouse model of 3-day aircraft noise exposure [ 24 ]. These experimental data point to a biological state associated with anxiety- and depression-like symptoms, but more preclinical research is needed to draw a strong correlation. Mechanistic findings from animal models have been used to produce a stress response pathway that enables us to better understand the implications of noise exposure on human mental health.

Mechanisms of noise-induced mental health consequences—stress response pathways

It is generally challenging to identify biochemical correlates of mental health, as mental health is not a single disease, but a collection of complex psychological states with overlapping signs and symptoms. However, anxiety, depression and general mental stress have been associated with activation of certain neurological and endocrine pathways. Anxiety and depression are both correlated with fear and stress via the autonomic nervous system [ 25 ]. Noise-induced stress responses activate the hypothalamic–pituitary–adrenal (HPA) axis and the sympathetic nervous system (SNS) [ 26 ]. The stress response is triggered when the hypothalamus releases corticotropin-releasing hormone (CRH) and arginine vasopressin (AVP) into the pituitary gland, further stimulating the release of adrenocorticotropic hormone (ACTH) into the circulation. ACTH then signals the adrenal cortex to release glucocorticoids and the SNS signals the adrenal medulla to release catecholamines. The overstimulation of the SNS suppresses the ability of glucocorticoids to modulate the inflammatory response, resulting in the release of pro-inflammatory cytokines [ 27 , 28 ]. Likewise, chronic stress and the overproduction of glucocorticoids leads to down-regulation of their receptors in immune cells, with a subsequent loss of the ability of glucocorticoids to suppress the activation of inflammatory pathways, e.g. cytokine release, a condition called “cortisol resistance” [ 29 ]. The release of pro-inflammatory cytokines is mostly modulated by the activation of the transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) [ 30 ]. The inflammatory state can contribute to the maintenance of the fear and stress response by modulating the activity of the brain regions implicated in anxiety, like the amygdala, hippocampus, insula, prefrontal cortex (mPFC), and the anterior cingulate cortex (dACC) [ 31 ]. This systemic inflammatory response can in turn exacerbate neuroinflammation [ 32 ]. Pro-inflammatory cytokines, such as interleukins 1β/1α/6 (IL-1β, IL-1α, IL-6) and tumor necrosis factor alpha (TNFα), cannot penetrate the blood brain barrier, but can induce inflammatory responses in the circumventricular organs [ 33 ]. Microglia and astrocytes become activated and propagate neuroinflammation further by releasing of pro-inflammatory cytokines [ 34 ]. Activated immune cells in the brain can disrupt the blood brain barrier and lead to further influx of circulating pro-inflammatory cytokines into the brain [ 35 ].

Another important brain region associated with anxiety and depression is the amygdala [ 36 , 37 ]. During conditions of external stress, the amygdala can become hyperactivated, increasing the sensitivity to environmental stimuli [ 38 ]. The increase in amygdala activity is both a source of neuroinflammation while also being susceptible to systemic inflammation [ 39 , 40 ]. Oxidative stress and inflammation are almost inseparable in a diseased state, as neuroinflammation is accompanied by oxidative stress in the brain tissue [ 41 , 42 ]. The release of reactive oxygen species (ROS) is a ubiquitous defense mechanism for any resident immune cells. Neuronal tissue is more susceptible to oxidative stress as neurons have membranes rich in polyunsaturated fats, making them prone to lipid oxidation [ 43 ]. In addition, dopamine, norepinephrine, and serotonin are prone to auto-oxidation, impairing synaptic signaling [ 44 ]. Nervous tissue also lacks many antioxidant defense mechanisms available to other tissues [ 45 ]. The mechanisms of noise-induced stress response are presented in Fig.  3 .

Epidemiological evidence

Depression and anxiety.

A meta-analysis by Dzhambov and Lercher reported that road traffic noise exposure was associated with 4% higher odds of depression (odds ratio (OR) 1.04, 95% CI 1.03–1.11) as well as 12% higher odds of anxiety (OR 1.12, 95% CI 1.04–1.30 both per 10 dB(A) increase in L den ). However, it is important to acknowledge that most of the studies in the meta-analysis were cross-sectional and of lower quality [ 46 ]. In agreement, the meta-analysis by Hegewald et al. provided data supporting an association between traffic noise exposure and depression and anxiety [ 47 ]. The authors demonstrated a 12% increase in risk of depression (effect size 1.12, 95% CI 1.02–1.23 per 10 dB increase in L den ) in response to aircraft noise exposure, while weaker risk increases of 2–3% (not statistically significant) were obtained for road traffic and railway noise exposure. A meta-analysis of nine studies indicated a 9% higher odds of anxiety (OR 1.09, 95% CI 0.97–1.23 per 10 dB increase in L den ) due to traffic noise exposure [ 48 ]. Higher traffic noise levels were associated with depressive (OR 1.17, 95% CI 1.03–1.32) and anxiety disorders (OR 1.22, 95% CI 1.09–1.38 both per 3.21 dB increase in L den ) in the Netherlands Study of Depression and Anxiety ( N  = 2980) [ 49 ]. A German case-controlled study investigated depression risk by aircraft, road traffic, and railway noise exposure [ 50 ]. For road traffic noise, a linear exposure-risk relationship was determined (OR 1.17, 95% CI 1.10–1.25 for L pAeq,24h  ≥ 70 dB vs. <40 dB). The highest risk increases were shown for aircraft noise ranging at L pAeq,24h  = 50–55 dB (OR of 1.23, 95% CI 1.19-1.28 for comparison < 40 dB) and for railway noise ranging at L pAeq,24h  = 60–65 dB (OR 1.15, 95% CI 1.08–1.22 for comparison <40 dB). Interestingly, combining all three exposures (above 50 dB L pAeq,24h ) resulted in the most excessive risk increase of an OR of 1.42 (95% CI 1.33–1.52 with the reference group being no exposure of 40 dB or more to traffic noise of any source). In the UK Biobank, a positive association between symptoms of nerves, anxiety, tension or depression (OR 1.04, 95% CI 1.01–1.07 for ≥57.8 dB) and bipolar disorder (OR: 1.54, 95% CI 1.21–1.97 for ≥57.8 dB) and road traffic noise exposure was found, while an inverse association occurred for major depression (OR 0.95, 95% CI 0.90-1.00 for 52.1-54.9 dB) [ 51 ]. The incidence of depression due to road traffic, railway, and aircraft noise exposure (L den ) as well as noise annoyance was examined in the Swiss cohort study on air pollution and lung and heart diseases in adults (SAPALDIA) [ 52 ]. For road traffic (RR 1.06, 95% CI 0.93–1.22) and aircraft noise exposure (RR 1.19, 95% CI 0.93–1.53 both per 10 dB L den ) suggestive positive evidence was found for harm, while the effect of noise annoyance was more robust (RR 1.05, 95% CI 1.02–1.08 per point increase). The association between residential noise exposure during pregnancy and later depression hospitalization was examined in sample of 140,456 Canadian women [ 53 ]. Herein, strongest risk increases were found for nighttime noise exposure (hazard ratio (HR) 1.68, 95% CI 1.05–2.67 for 70 vs. 50 dB(A) L night ). Evidence from a Korean study ( N  = 45,241) suggested self-reported exposure to occupational noise and vibration elevated the odds of anxiety in both men (OR 2.25, 95% CI 1.77–2.87) and women (OR 2.17, 95% CI 1.79–2.61 both vs. no occupational exposure to noise and vibration) [ 54 ]. Interestingly, in 2,745 subjects from the Heinz Nixdorf recall study from Germany, there was a pronounced decrease in cognitive function in response to traffic noise when comparing depressed vs. non-depressed subjects, suggesting that those with existing mental health conditions may be more vulnerable to the adverse consequences of noise exposure [ 55 ]. Suggestive evidence for an association between the use of psychotropic drugs including sleep medication, anxiolytics, and antidepressants and levels of traffic noise, noise annoyance, and sensitivity was shown by a Finnish study including 7321 subjects [ 56 ]. Results from the German Gutenberg Health Study ( N  = 11,905) indicated an association between noise annoyance due to various sources and the incidence of depression, anxiety, and sleep disturbance [ 57 ]. While data from 4508 US adolescents from an urban area indicated an association between living in a high-noise area and later bedtimes, a weaker association for depression and anxiety was found [ 58 ]. In a cohort of 2,398 men from the UK, road traffic noise exposure (OR 1.82, 95% CI 1.07–3.07 for 56–60 dB(A)), high noise annoyance (OR 2.47, 95% CI 1.00-6.13), and high noise sensitivity (OR 1.65, 95% CI 1.09-2.50) were associated with incident psychological ill-health, which was determined by a questionnaire that predominantly measures depression and anxiety [ 59 ].

The Swiss National Cohort examined the association between source-specific transportation noise and suicide [ 60 ]. The authors demonstrated that road traffic and railway noise was associated with total suicides (HR 1.040, 95% CI 1.015–1.065 and HR 1.022, 95% CI 1.004–1.041, respectively per 10 dB L den ). In contrast, this association was weaker for aircraft noise as observed risk increases starting from 50 dB were masked by an inverse association in the very low exposure range from 30 to 40 dB (Fig.  4 ). In the city of Madrid, short-term exposure to traffic noise was associated with emergency hospital admissions due to anxiety, dementia, and suicides [ 61 ]. Higher nighttime noise exposure was associated with elevated risks of suicide death in younger adults (HR 1.32, 95% CI 1.02–1.70), older adults (HR 1.43, 95% CI 1.01-2.02), and adults with mental illness (HR 1.55, 95% CI 1.10–2.19 all per interquartile range increase) in a Korean study ( N  = 155,492) [ 62 ].

figure 4

A Association (hazard ratios and 95% confidence interval) between transportation noise source (L den ) and mortality from all intentional self-harm (ICD-10: X60–84, excl. ICD-10 ×61.8, X61.9, X81–82) after multivariable adjustment including PM 2.5 exposure. B Exposure-response relationships for the association between transportation noise source (L den ) and mortality from intentional self-harm (ICD-10: X60–84, excl. ICD-10 ×61.8, X61.9, X81–82). Vertical dashed red lines show source-specific WHO guideline levels: road traffic = 53 dB, railway = 54 dB, aircraft = 45 dB. Adapted from [ 60 ] with permission.

Behavioral problems in children and adolescents

In the Danish National Birth Cohort study ( N  = 46,940), a 10 dB increase in road traffic noise exposure from birth to 7 years of age was associated with a 7% increase (95% CI 1.00–1.14) in abnormal versus normal total difficulties scores, 5% (95% CI 1.00–1.10) and 9% (95% CI 1.03–1.18) increases in borderline and abnormal hyperactivity/inattention subscale scores, respectively, and 5% (95% CI 0.98–1.14) and 6% (95% CI 0.99–1.12) increases in abnormal conduct problem and peer relationship problem subscale scores, respectively (assessed by the parent-reported Strengths and Difficulties Questionnaire) [ 63 ]. Likewise, among schoolchildren in China, residential road traffic noise exposure was associated with increases in total/abnormal difficulties score, emotional problems, and behavioral concerns [ 64 ]. In a cohort of 886 adolescents in Switzerland aged 10–17, cross-sectionally analyzed peer relationship problems increased by 0.15 units (95% CI 0.02–0.27) per 10 dB increase in road traffic noise exposure [ 65 ]. However, this relationship was absent in longitudinal analysis. In preschool children in the city of São Paulo ( N  = 3385 children at 3 years of age and N  = 1546 children at 6 years of age), community noise exposure above L den of 70 dB and L night of 60 dB was associated with impaired behavioral and cognitive development [ 66 ]. In contrast, no association was observed between prenatal or childhood road traffic or total noise exposure and emotional, aggressive, and attention-deficit/hyperactivity disorder-related symptoms in children from two European (Spain and Netherlands) birth cohorts [ 67 ]. A positive association between noise exposure at school and attention-deficit/hyperactivity disorder-related symptoms was found in a study of children aged 7–11 years in the city of Barcelona [ 68 ].

Future research needs and conclusions

Noise exposure likely has effects on mental health since the brain represents the primary target organ of noise-mediated effects. While the effects may seem minor when examining human studies, the public health implications are significant. This is evident in reports from the WHO and the EEA, which highlight that environmental stressors such as noise have substantial and continuous impacts on large segments of the population [ 1 , 2 ]. Some direct adverse phenotypic changes in brain tissue by noise (e.g. neuroinflammation, cerebral oxidative stress), feedback signaling by remote organ damage, dysregulated immune cells, and impaired circadian clock may also play important roles in noise-dependent impairment of mental health. Based on the mechanistic findings on noise research, it is evident that there is a substantial pathomechanistic overlap with mental health conditions, such as depression, that are all linked to cerebral oxidative stress and inflammation. By sharing pathomechanisms, noise can either promote the development of mental health problems or increase their severity in a bonfire fashion.

Future research needs include: preclinical noise research should deepen the mechanistic understanding of noise-induced mental health problems, allowing for drug-based interventions at different levels that target the detrimental neuronal signaling cascade. In addition, biomarkers of noise-triggered mental health harms should be identified using validated animal models in order to allow early diagnosis of vulnerable groups at higher risk of noise-inflicted mental disease. Clinical noise research should further extend the evidence base of exposure-mediated mental health effects and also investigate non-pharmacological mitigation strategies (e.g. coping mechanisms for improved resilience) such as exercise, meditation, green space availability, co-exposures, and mental health training [ 69 ]. Additional research is also needed on the benefits of technology to reduce noise (e.g noise cancellation headphones, active noise cancellation home kits, etc).

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TM is a principal investigator and MK, OH as well as AD are (Young) Scientists of the DZHK (German Center for Cardiovascular Research), Partner Site Rhine-Main, Mainz, Germany.

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These authors contributed equally: Omar Hahad, Marin Kuntic.

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Department of Cardiology—Cardiology I, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

Omar Hahad, Marin Kuntic, Ivana Kuntic, Andreas Daiber & Thomas Münzel

German Center for Cardiovascular Research (DZHK), partner site Rhine-Main, Mainz, Germany

Omar Hahad, Marin Kuntic, Andreas Daiber & Thomas Münzel

Cardiovascular Prevention and Wellness, DeBakey Heart and Vascular Center, Houston Methodist, Houston, TX, USA

Sadeer Al-Kindi

Leibniz Institute for Resilience Research (LIR), Mainz, Germany

Donya Gilan

Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

Medical Psychology & Medical Sociology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

Katja Petrowski

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OH, MK, SA-K, IK, DG, KP, AD, and TM contributed to the conception of the research, acquisition of data, drafting, and revision of the manuscript.

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Hahad, O., Kuntic, M., Al-Kindi, S. et al. Noise and mental health: evidence, mechanisms, and consequences. J Expo Sci Environ Epidemiol (2024). https://doi.org/10.1038/s41370-024-00642-5

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DOI : https://doi.org/10.1038/s41370-024-00642-5

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research paper on noise pollution

Environmental Noise in India: a Review

  • Noise Pollution (PH Zannin, Section Editor)
  • Published: 24 June 2017
  • Volume 3 , pages 220–229, ( 2017 )

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research paper on noise pollution

  • Shreerup Goswami 1 &
  • Bijay K. Swain 2  

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Purpose of Review

This article reviews the literature on research carried out during the last two decades on noise impacts in India to demonstrate the current status of noise pollution research in India and gaps in studies. It also summarizes future perspectives of acoustic research.

Recent Findings

The noise pollution studies over the years have focused on the monitoring, recording, modeling, geospatial mapping, and exposure-effect relationship. The review of papers demonstrated that road traffic noise is the predominant cause for annoyance among the respondents. The evidence comes mostly from studies focusing on health impacts. Only 10% of articles enumerated zone-specific noise pollution. 44.89% of articles reported details of subjective response data with the help of a questionnaire tool, while 14.3% of articles reported details about the noise in workplaces of different areas of India. Ten percent of articles attributed to the harmful effect of festive noise. Studies in relation to the physiological and sleep disturbances in Indian condition are negligible.

Noise pollution limits are being breached in almost all Indian cities. Violations are the worst in urban areas. The laws should be properly implemented in India to control this ever-growing menace. The government is now working on devising new noise pollution standards. City-wise noise pollution mitigation strategies should be worked out at all levels. It is concluded that coordinated and long-term integrated noise pollution research (comprising assessment of noise descriptors, noise mapping, prediction by noise modeling, and experimental studies to demonstrate exposure-effect relationship, advanced study on acoustic absorption material) is the need of the hour.

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Goswami, S., Swain, B.K. Environmental Noise in India: a Review. Curr Pollution Rep 3 , 220–229 (2017). https://doi.org/10.1007/s40726-017-0062-8

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Published : 24 June 2017

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DOI : https://doi.org/10.1007/s40726-017-0062-8

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Navy jet noise could mean long-term health impacts for Whidbey Island

Elise Takahama

More than 74,000 people on Whidbey Island could face long-term health effects from the U.S. Navy jet noise that has blasted over residents several days a week for over a decade , new research shows. 

A study from the University of Washington, published last week in the Journal of Exposure Science and Environmental Epidemiology, reports the noise from the Boeing EA-18G Growlers and their training drills present a “substantial risk” to two-thirds of Island County residents.

Everyone living in Oak Harbor and Coupeville, and 85% of the Swinomish Reservation were exposed to potentially harmful levels of noise, said lead author Giordano Jacuzzi, a graduate student in the UW College of Environment. Every monitoring station on the island measured noise over 100 decibels — as loud as a rock concert, he added.

The effects could expose communities to high levels of sleep disturbance, hearing impairment, increased risk of cardiovascular disease and delays in childhood learning, as well as annoyance and stress, the study says. The jets are based at Naval Air Station Whidbey Island, whose noise has sparked various community and legal debates over the past decade.

“There is very little literature and comparatively few scientific studies that look at impacts of military aircraft noise,” Jacuzzi said in an interview this week. “This is not an Alaska Airlines jet coming in every 10 minutes into Sea-Tac [International Airport]. These are sporadic events that can happen at any hour of the day or night.”

A spokesperson for the Naval Air Station Whidbey Island declined to comment on the UW report, though Jacuzzi confirmed his team has been in communication with the air station throughout the research process.

The spokesperson instead cited Navy policy not to respond to matters pending litigation, as the station is involved in an ongoing lawsuit filed by state Attorney General Bob Ferguson and the Citizens of Ebey’s Reserve in 2019, after the station increased the number of jet flights by about 33%. A U.S. District Court judge ruled last September the station could keep its number of flights, though because it failed to accurately quantify overall noise impacts , it had to redo its environmental impact survey — which remains ongoing.

In the past, the noise from the island’s air station has shown to be so loud it travels underwater and can disrupt the habitats and lives of endangered southern resident orcas.

The latest UW study again draws attention to the flight racket, this time homing in on its serious risks for public health, Jacuzzi said. 

According to the paper, about 74,300 people were at risk of adverse health effects, including annoyance and stress. Of those, researchers estimated about 41,000 experienced “high levels of sleep disturbance,” while about 8,000, most of whom lived near the aircraft landing strips, were at risk of hearing impairment over time, the study showed.

The researchers measured potential effects by analyzing four weeks of acoustic and flight operations data collected by the Navy in 2020 and 2021, in addition to data collected by a private acoustics company and the National Park Service. The team then mapped noise exposure across the region to estimate how much noise specific communities were exposed to in an average year, and later brought in exposure-response models recommended by the World Health Organization to predict health outcomes, Jacuzzi said.

“Our bodies produce a lot of stress hormone response to noise in general, it doesn’t matter what kind of noise it is,” co-author Edmund Seto, a UW professor of environmental and occupational health sciences, said in a post . “But particularly if it’s this repeated acute noise, you might expect that stress hormone response to be exacerbated.”

What was really interesting, he said, was that researchers measured noise exposure levels that are “actually harmful for hearing.” 

“Usually I only think of hearing in the context of working in factories or other really, really loud occupational settings,” Seto said. “But here, we’re reaching those levels for the community.”  

In a video posted by UW, Whidbey Island resident Bob Wilbur describes the noise reverberating through the trees and shaking the windows in this home. 

“It interrupts your day,” Wilbur, current chair of the Citizens of Ebey’s Reserve group, said in the post. “You’re unable to have a pleasant evening at home. You can’t communicate. You constantly try to organize your day around being gone when the jets are flying.” 

Another island resident, Jane Monson, described in the video the noise feeling “like you’re in a war, like you’re about to get bombed.”

Despite years of disputes over the noise, the trainings at the station — which has been around since 1942 and received the Navy’s first Growler production aircraft in 2008 — are crucial, Navy spokesperson Michael Welding has said in the past.   

As of 2020, the military embarked on about 2,300 flights per year over Olympic or 6.3 flights per day when averaged over a full year, Welding confirmed this week, noting flights generally happen during the workweek, not the weekend. 

Since then, the Navy has launched a new transit route between the air station and the Olympic training areas, which Welding said is outside the boundaries of Olympic National Park and has reduced the level of military aircraft noise over it.

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It’s still too loud, UW researchers contended in their report and a recent op-ed in The Seattle Times. In the op-ed, Jacuzzi and other study authors aimed to draw more attention to the sound of military aviation in general, which is “unlike any other source of noise,” they wrote. 

In a separate UW paper from 2020, researchers found military flights were the largest cause of noise pollution on the Olympic Peninsula, also affected by the increase in training flights at the Whidbey Island station. 

“It’s intense,” Jacuzzi said. “It has this rumbling, low-frequency energy, and that is a kind of sound that penetrates windows and shakes walls.”

In the op-ed, Jacuzzi and study co-authors urged the Navy to consider changes to its training operations and schedules — and by doing so, it could “demonstrate that the interests of national security need not come at the expense of protecting the public at home.”

“This isn’t a zero-sum game,” Jacuzzi said. “There’s a range of solutions to these public health impacts — that could be anything from altered flight paths to more strategic operational schedules to potentially avoiding sensitive periods or locations, like school hours and sleeping hours.”

Past research has shown noise pollution is a growing concern among environmental and public health experts, contributing to both accelerating effects of climate change and a range of worsening health impacts.

According to the World Health Association , consistent evidence has confirmed noise exposure can harm children’s cognitive performance and long-term academic achievement. One 2013 paper concluded noise can have worse long-term impacts on children than adults when it comes to speech perception, listening comprehension, short-term memory, reading and writing. 

More research has emerged that also links exposure to traffic noise to higher risks of cardiovascular disease, such as ischemic heart disease and heart failure — largely due to the increase in stress hormone levels and blood pressure.

Study co-authors plan to continue the research this year with a follow-up report that includes surveys of Whidbey Island residents to collect details from their personal experiences, Jacuzzi said.

“We hope that the Navy will take the next step in building on top of the science and engaging in open conversations with the communities that are affected, and use those two things together to be able to devise effective mitigation actions moving forward,” Jacuzzi said.

The opinions expressed in reader comments are those of the author only and do not reflect the opinions of The Seattle Times.

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The Effect of Noise Exposure on Cognitive Performance and Brain Activity Patterns

Mohammad javad jafari.

1 Environmental and Occupational Hazards Control Research Center, School Of Public Health And Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Reza Khosrowabadi

3 Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran

Soheila Khodakarim

4 Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Farough Mohammadian


It seems qualitative measurements of subjective reactions are not appropriate indicators to assess the effect of noise on cognitive performance.

In this study, quantitative and combined indicators were applied to study the effect of noise on cognitive performance.


A total of 54 young subjects were included in this experimental study. The participants’ mental workload and attention were evaluated under different levels of noise exposure including, background noise, 75, 85 and 95 dBA noise levels. The study subject’s EEG signals were recorded for 10 minutes while they were performing the IVA test. The EEG signals were used to estimate the relative power of their brain frequency bands.

Results revealed that mental workload and visual/auditory attention is significantly reduced when the participants are exposed to noise at 95 dBA level (P < 0.05). Results also showed that with the rise in noise levels, the relative power of the Alpha band increases while the relative power of the Beta band decreases as compared to background noise. The most prominent change in the relative power of the Alpha and Beta bands occurs in the occipital and frontal regions of the brain respectively.


The application of new indicators, including brain signal analysis and power spectral density analysis, is strongly recommended in the assessment of cognitive performance during noise exposure. Further studies are suggested regarding the effects of other psychoacoustic parameters such as tonality, noise pitch (treble or bass) at extended exposure levels.


The influence of noise on human cognitive performance and brain activity has been often neglected [ 1 ]. Noise has different negative effects ranging from interference with cognitive processing to damaging mental and physical health [ 2 ]. The non-auditory effects of noise exposure include perceived disturbance, annoyance, cognitive impairment, cardiovascular disorders and sleep disturbance [ 1 ]. Noise exposure is a problem in many occupational and non-occupational environments. It is estimated that 22 million workers in the United States are exposed to hazardous noise [ 3 ]. It is also reported that 100 million people are exposed to dangerous environmental noise due to traffic, personal listening devices and other sources [ 4 ]. The World Health Organization (WHO) estimates that at least 1 million healthy life-years (disability-adjusted life-years) are lost annually as a result of environmental noise in high income western European nations (with a population of around 340 million) [ 1 ]. In any vital industry, optimising human performance is a key factor in accident prevention. Noise is one aspect of the work environment that affects workplace safety. Workers in vital occupational roles require high levels of cognitive skill and they need to maintain effective performance while exposed to higher levels of noise than Threshold Limit Values (TLV). Studies show that noise causes cognitive impairment and oxidative stress in the brain [ 5 ]. According to Wang et al., with further urbanisation and industrialisation, noise pollution has become a risk factor for depression, cognitive impairment and neurodegenerative disorders [ 5 ]. It has been observed that exposure to noise influences the central nervous system leading to emotional stress, anxiety, cognitive and memory defects [ 6 ]. Previous studies have suggested that the Limbic system in the brain is involved in emotional activities, The Amygdala and the Hippocampus are two of the main parts within the Limbic system that receives sensory information directly and indirectly from the central auditory system. Auditory stimulation itself can directly or indirectly affect these areas.

The active process of cognitive selection is called “attention” [ 7 ]. Attention plays a significant role in daily activities such as physical movements, emotional responses and perceptual and cognitive functions. When quantifiable information processing is limited, the attention system directs human behaviour based on geographic and temporal characteristics. Noise can affect performance either by impairing information processing or causing changes in strategic responses. In particular, noise increases the level of general alertness or activation and attention. Noise can also reduce performance accuracy and working memory performance, but does not seem to affect performance speed. The scope of cognitive and mental function is diverse, encompassing reaction time, attention, memory, intelligence and concentration, to name a few. Altered cognitive function leads to human error and subsequently increases accidents. This can ultimately lead to reduced performance and productivity. Some studies have shown that noise, improves performance, especially in sleep-deprived workers, mainly due to increased arousal. Certain individuals may be sensitive to noise even when it is lower than TLV. Sensitivity to noise which is referred to as environmental intolerance influence attention and recognition. There are conflicting reports regarding the effect of noise on cognitive performance in the relevant literature. The review study by Gawron regarding the effects of noise on cognitive performance revealed that among 58 studies, 29 reported a negative effect, 7 reported a positive effect and 22 reported no effect of noise on cognitive performance [ 8 ]. Noise as a sensory stimulus increases arousal which is believed to cause a reduction in the breadth of attention. In other words, loud noise causes alterations in the performance of attentional functions.

Smith believes that noise characteristics to be one of the influential parameters regarding the effect of noise on cognitive performance [ 9 ]. A study by Hockey showed that loud noise at 100 dBA (compared to 70 dBA) increased central visual stimuli processing but reduced peripheral stimulus processing [ 10 ]. Exposure to noise above 85 dBA intensity leads to many adverse auditory and non-auditory effects. The non-auditory effects of noise exposure depend on exposure duration, type of task, gender, age and sensitivity to noise. Physiological signals are comprised of: a) signals related to the peripheral nervous system, including heartbeat and Electromyogram and b) signals related to the central nervous system including electroencephalography (EEG). In recent years, interesting results have been obtained from the first group of signals, however, few studies have used EEG signals as a valuable tool for cognitive performance evaluation [ 11 ]. Cognitive theory suggests that the brain is highly involved in emotions. Basic emotions use specific cortical and subcortical systems within the brain and are different from the brain’s electrical and metabolic activities. Therefore, EEG is one of the most effective and common methods of brain imaging used for Brain activity processing relating to human stress including noise [ 12 ]. EEG signals measure all fluctuations in the electrical fields resulting from nerve activity in millisecond resolutions. EEG signals are usually evaluated in multiple frequency bands to determine their relationship with stresses. These bands include the Alpha (8-12.5 Hz), Theta (4-8 Hz), Delta (1-4 Hz) and Beta (12.5-30 Hz) bands. Humphreys and Reveille suggest that fluctuations in the Alpha and Beta bands, in particular, are an indication of cognitive function. Increases in the Alpha frequency band along with decreases in the Beta frequency band causes increased cognitive function [ 13 ]. A reduction in the power of the Alpha band along with a rise in the power of the Theta and Beta bands is an indicator of neurological disorders. Marshal et al., have shown a reverse relationship in the prefrontal cortex between the Alpha power rhythm in an EEG and suffering from stressful conditions, meaning that the Alpha rhythm goes down with stress [ 14 ]. Choi demonstrated a positive relationship between the Beta power rhythm in an EEG and suffering from stressful conditions in the temporal lobe [ 12 ]. Other studies have shown a reduction in the relative power of the Alpha band when attention is reduced. Compared to other imaging techniques, Electroencephalography has certain advantages which include being non-invasive, low cost, comfortable, safe, mobile, and having high time resolution. Therefore, EEG can be a great tool not just for detecting stressors in the environment but also for predicting the negative effects of noise exposure.

Because noise level is one of the influencing factors regarding the effects of noise on cognitive function and brain signals, this study focused on 75, 85 and 95 dBA levels. Also, due to the conflicting results in other studies regarding cognitive function and its importance in many tasks and the few studies on the effects of various noise levels on brain activity patterns, this study was designed in two parts. The first part investigates the effects of various noise levels on mental workload and auditory/visual attention. The second part investigates the effects of noise on the relative power of brain frequency bands and their relationship with visual/auditory attention.

Material and Methods

Study subjects and selection criteria.

Study subjects were selected from university student volunteers. The including criteria was 23-33 years of age, normal hearing, no prior cardiovascular disorders, no alcohol and caffeine consumption 12 hours before testing, a BMI index of 18-28, no hypersensitivity to noise and no sleep disorders. After finalising the selection, testing procedures were trained to the study subjects. All participants had to complete ethical consent forms, General Health questionnaires (GHQ) and Weinstein’s Noise Sensitivity questionnaires. The validity and reliability of the Persian version of these questionnaires had been approved in other studies [ 15 ].

Experimental Design

This experimental study was conducted in an acoustically insulated, climate-controlled room (H = 3 m, L = 3.5 m and W = 2.5 m). A total of 54 participants, including 27 males and 27 females, took part in this study. Study subjects were divided into 3 groups, each with 9 males and 9 females. All study groups were exposed to background noise (45 dBA), and three different noise levels (including 75, 85 and 95 dBA). Table 1 shows the experimental design in detail.

The study protocol for each subject included a 10-minute relaxing phase before testing, followed by the Integrated Visual and Auditory Continuous Performance (IVA) test which was accompanied by background noise while EEG signals were being recorded. After a 30-minute rest, the subject was exposed to noise for 15 minutes, and at the 16 th -minute mark, while the subject was being exposed to various noise levels, the IVA test was initiated, and EEG signals were once again recorded ( Figure 1 ).

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Study protocols timing

Noise Source and Presentation

In this study, the used noise was recorded in a household appliance factory using a B and K PULSE Multi-Analyzer System Type 3560. The recorded noise was then analysed using a B & K Sound Level Meter Type 2238. To modify the noise and obtain steady noise at 75, 85 and 95 dBA levels, the Gold Wave software version 4.26 was used. Finally, the noise was replayed using two Genius HF-2020 speakers situated on either side of the test table.

NASA-Task Load Index (NASA-TLX) Questionnaire

A NASA-TLX questionnaire is a well-known tool for evaluating subjective mental workload (as perceived by the subject). This multi-dimensional method assigns an overall score for mental load based on average weights obtained from six scales including mental demand, physical demand, temporal demand, effort, performance, and frustration. Every part of the task is assigned to a 100-point rating score. The mental load evaluation process using this indicator is comprised of three stages. In the first stage, the six scales are self-assessed by the study subject. In the second stage, after weighing the load of each scale, it is given a score by the subject. Finally, the score and the weight of the load are obtained, and the total mental load score is determined. The validity and reliability of this questionnaire have been approved by Mohammadi in Iran, and its Cronbach alpha score was 0.83 [ 16 ].

Integrated Visual and Auditory Continuous Performance Test

Integrated Visual and Auditory test, which was designed by Stanford et al., is part of the Continuous Performance Tests (CPTs) and used to evaluate auditory/visual attention [ 17 ]. It consists of a 13-minute continuous auditory and visual test that evaluates two factors of response control and attention. The task involves responding or not responding (response prevention) to 500 test stimuli. Each stimulus is presented for 1.5 seconds. The subject is asked to click once if he/she detects a 1 and not to respond if detecting a 2. This test has an appropriate sensitivity of 92% and a predictive power of 90%. The Persian version of this test has a validity index of 53% to 93% [ 18 ].

EEG Recording and Analysis

The EEG signals were recorded from 16 Ag/AgCl electrodes mounted in an elastic cap with the amplifier bandpass set to 1 – 40 Hz at a sampling rate of 250 Hz. The electrodes were placed at the frontal (Fp1, Fp2, F3, F4, F7 and F8), temporal (T3 and T4), central (Cz, C3 and C4), parietal (Pz, P3 and P4) and occipital (O1 and O2) regions. This is according to the international 10-20 system of electrode placement ( Figure 2 ). The reference electrode was the left mastoid (A1 in Figure 2 ). Impedance was maintained at below 10 KΩ during the experiment. Both in the background noise condition and during exposure to noise levels of 75, 85 and 95 dBA, while the subject was performing the IVA + Plus test, EEG signals were recorded for 10 minutes with the subject’s eyes open. First, the EEG data was pre-processed using an EEGLAB 2013a toolbox [ 19 ]. Then, using Independent Component Analysis (ICA) on each electrode, artefacts about blinking, eye movements or small body movements were eliminated.

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Electrode placement

In order to measure relative power, the filtered signals were separated into various frequency bands (Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12.5 Hz), Beta (12.5-30 Hz) and Gamma (30 Hz upwards)) based on their power spectral density using the MATLAB software version 2017b. To calculate the relative power of the frequency bands, the following equations were used:

Let x i (n) denote the n th element of i th EEG channel after preprocessing and X = [x 1 , x 2 … x nc ] where NC denotes the number of EEG channel. The Power spectrum of the EEG signal was calculated using Fast Fourier Transform (FFT) which transforms the EEG signal X from the time domain to the frequency domain Z. The FFT of each EEG channel was calculated separately given by the following:

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Where f denotes the frequency, N is the sample size; I is the channel number and J is the imaginary unit. Then absolute power spectrum (PSD) of EEG was calculated using the following:

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Where k 1 and k 2 denote the frequency range of the selected band. The relative power of the selected band was then calculated by the following:

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Statistical analysis of the mental workload and attention data was carried out using the SPSS 22 software solution. Before performing t-tests, data distribution norms were checked using the Kolmogorov–Smirnov test. A p -value of less than 0.05 was considered statistically significant. The Generalized Estimating Equations (GEE) statistical method was applied for data analysis.

Demographic Characteristics of Participants

Table 2 displays the study subjects’ demographic characteristics. A total of 56 individuals, 27 males and 27 females, were enrolled in the study. Average and standard deviation of age and Body Mass Index (BMI) was 26.56 ± 2.45 and 23.81 ± 1.43, respectively.

Study subjects’ demographic characteristics (N = 54)

Effect of Noise levels on Mental Workload

Figure 3 illustrates the effects of various noise levels on average overall mental workload compared to background noise (45 dBA) for study subjects. The results show that 75 and 85 dBA noise levels, as compared to just background noise, does not follow a particular trend and does not cause a considerable change in the average mental workload (P > 0.05). At 70 dBA level, compared to just background noise, the mental workload had decreased while at 85 dBA it had increased. At 95 dBA level, compared to just background noise, the increase in mental workload was statistically significant (P = 0.03).

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The effect of noise levels on mental workload. Background noise = 45dB (A)

The Effect of Noise levels on Visual and Auditory Attention

Figure 4 presents the average and standard deviation for the visual and auditory attention score at various levels of noise compared to background noise (45 dBA). The results show that the changes in visual and auditory attention under exposure to various noise levels are very similar in pattern. At 85 dBA levels, average attention scores are reduced, as compared to just background noise, but this is not statistically significant (P > 0.05). But at 95 dBA levels, average attention scores are reduced considerably compared to background noise; this was statistically significant (P < 0.05).

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The effect of noise levels on visual and auditory attention

The Effect of Noise levels on EEG Fluctuations

The Kolmogorov – Smirnov test results indicated that the data were distributed normally. Therefore, the t-test was used in this part. The relative power of the intended brain frequency bands was used to analyse brain signals during exposure to various noise levels relative to background noise (45 dBA). The considered frequency bands include the Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12.5 Hz), Beta (12.5-30 Hz) and Gamma (30 Hz upwards) bands.

The results show that among the mentioned frequency bands, the Alpha and Beta bands undergo considerable changes, as relative to just background noise, and are being affected by noise. Based on Table 3 , going from 75 dBA to 95 dBA noise level causes a statistically significant average variation in the relative power of the Alpha band for the Fp 1 , F 4 , P 3 , O 1 and O 2 regions of the brain (P< 0 .05). Again, based on Table 3 , at 95 dBA, the largest variation in the relative power of the Alpha band is observed for the O 1 region of the brain (P < 0.001).

Average variation in the relative power of the Alpha band (μV^2) during exposure to noise relative to background noise (45 dBA)

A significant reduction in the relative power of the Alpha band was only observed for the F 3 region (P<0.05), though a slight reduction was observed for the C 4 , F 7 and F 3 regions of the brain also. The most affected areas of the brain when exposed to noise seems to be the Occipital, Prefrontal, Frontal and Parietal regions of the brain. Figure 5A shows the Scalp Topographical mapping.

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Topographical mapping of frequency bands’ relative power during exposure to noise as relative to background noise (45 dBA)

Table 4 demonstrates average variation in the relative power of the Beta band during exposure to various noise levels relative to background noise. The results show a reduction in the relative power of the Beta band in all channels as a result of exposure to 75, 85 and 95 dBA noise, although this reduction was most prominent at 95 dBA. Based on table 4 , this reduction is statistically significant (P < 0.05) and the order by which it occurs, and the affected areas are as follows: F8-T3-C4-Cz-O2-Fp1-T4-F3-C3. No significant effect was observed in the other areas of the brain under study (P > 0.05). Also, based on figure 5b , the reduction in the relative power of the beta band as a result of the increase in the level of noise occurs in the Frontal, Temporal, Occipital and Central lobes.

Average variation in the relative power of the Beta band (μV^2) during exposure to noise as relative to background noise

The results of this study showed that as a stressor, noise affects cognitive performance and brain signals. Also, noise pressure level is an important factor regarding impairment of cognitive function and power spectral density of the brain, meaning that low levels noise is not as effective compared to high levels of noise. It can be said that the results of this study are in agreement with the proposal that a relationship exists between low performance and high levels noise [ 20 ]. Previous studies have neglected to investigate cognitive performance during exposure to noise [ 21 ], [ 22 ]. Some studies have used qualitative measurements including subjective responses for the evaluation of the effects of noise exposure on cognitive function. In this study, however, quantitative indicators were used in combination, including the evaluation of mental workload, evaluation of auditory/visual attention and brain signals (power spectral density) analysis.

In a study by Yoorim Choi, EEG signals were used as a new method for environmental stressor analysis. This method is suggested to overcome the limitations in physiological evaluation techniques [ 12 ]. Share et al., also suggest that to improve cognitive and mental stress evaluation, a combination of these tools should be used [ 23 ]. Sabine et al. revealed that Stroop and mental arithmetic performance increased when exposed to 50 dBA levels noise compared to 70 dBA levels noise. Melamed et al. stated exposure to higher than 85 dBA intensity noise causes irritability, fatigue and stress which is consistent with the present study [ 24 ]. In previous studies, the effects of noise exposure on heartbeat and blood pressure at 95 dBA were compared to 75 and 85 dBA [ 25 ]. Elmenhorst et al. demonstrated that noise exposure causes increased reaction times and errors in field and laboratories study [ 26 ]. The result obtained by Patricia Tassi et al. indicated that noise exposure reduces attention in subjects which is also consistent with the present study [ 27 ]. The effects of high levels of noise exposure on cognitive performance can be amended to the Poulton arousal model which states that noise exposure increases cognitive performance at first. The reason for this is an increase in arousal to reduce the effect of noise on cognitive function. But gradually, the effect of arousal wears off, and the negative effects of noise exposure on cognitive function begin to show [ 28 ]. The results in the present study can be explainable using arousal theory. This theory states that the level of central nervous system activity (which alternates between being asleep and awake) regulates human response to stimuli. There is no overall consensus on the validity of this theory at present, and some have suggested that it cannot be used to describe the relationship between noise exposure and cognitive performance. In any case, considering this theory, it can be said that when arousal is high or low, or in other words, in both low stress and high-stress situations, performance is reduced [ 29 ].

There were conflicting results regarding the effects of noise on cognitive function in previous studies. Some studies determined that noise had improved cognitive function [ 30 ]. While others had concluded that noise had reduced cognitive function [ 31 ]. This is part of the reason why, in this study, quantitative measurements were used in combination. The results of the present study reveal that the reduction of cognitive function and brain signals was only significant when exposed to noise at 95 dB level and not at 75 or 85 dBA. This could be due to other psychoacoustic factors such as noise pitch, tonality, exposure duration, and noise type. The importance of noise pitch and its effects on cognitive function and brain activity has been emphasised in other studies. The results of the study by Kazempour et al., showed that “base” noise (low frequency) reduces computational accuracy and performance [ 32 ]. Pawlaczyk et al. observed a higher sensitivity to “base” noise that caused reduced cognitive function as compared to reference noise [ 33 ]. Naserpour et al., also exhibited that “base” noise at 500 Hz caused longer reaction times as compared to “treble” noise at 800 Hz [ 34 ]. The study by Allahverdy and Jafari showed the complexity of brain activity increases at midrange frequencies, showing the effects of the change in frequency on brain activity [ 35 ].

Another effective parameter regarding noise and performance is noise tonality. In the study by Joonhee et al., it was observed that performance was reduced with increasing noise tone strengths [ 36 ]. Type of noise is also important when evaluating the effects of noise on cognitive function. Studies have shown that the effect of fluctuating noise on cognitive function is higher than steady noise [ 37 ]. Steady noise was the only type used in our study. Also, exposure times used were rather short, which may result in a reduced effect of noise on performance and brain signals when exposed to lower than TLV noise. The lesser effect of lower than TLV noise (45, 75 and 85 dBA) on performance and brain activity may also be due to non-psychoacoustic parameters as well. For instance, scope and diversity are influential in the methods used for cognitive function evaluation [ 38 ]. Simplicity or complexity of the task is another example as a complex task cause a greater cognitive dysfunction when compared to simple tasks. Personal characteristics may also be a factor when subjects are exposed to noise. As some may experience reduced cognitive function while others may not, and some may even show increased cognitive function [ 38 ]. These factors may not be as influential in the present study as the subjects were prescreened for mental disorders, cardiovascular disorders and behavioural abnormalities before selection. Many aspects of brain function and behaviour can only be discussed in terms of neurons communicating with each other. All cognitive processes in the brain are carried out through neuronal activity such as synapses and spikes. Orientation and executive function which are involved in the processing of attention are specifically undermined to enable information processing. The disruption of attention likely occurs in subjects whenever there is a need for sustained attention.

Here, Brain signal analysis disclosed that the Alpha and Beta frequency bands were affected by noise. With an increase in noise levels, the relative power of the Alpha and increased while the relative power of the Beta band decreased. Topographical mapping of the scalp shows that all four lobes of the brain are usually affected by noise, but this is more pronounced in the frontal and occipital lobes, which is consistent with the results of other studies [ 39 ]. Other conclusions can be made from this study regarding the relationship between visual / auditory attention and the relative power of the Alpha and Beta bands. In this regard, it can be said that with increasing noise levels, participants’ auditory / visual attention score went down while the relative power of the Alpha and Beta bands increased and decreased respectively. Topographical mapping of the scalp indicates that the area responsible for attention processing is located in the frontal, temporal and occipital regions of the brain which is consistent with the results of Liz et al., [ 40 ]. Therefore, the results of this study suggest that when one is exposed to various noise levels, mental workload, visual / auditory attention and the relative power of the frequency bands follow a similar trend. In studies that pertain to brain signals and cognitive performance, attention to artifacts such as eye and body movement, electrical interference, impedance fluctuations, sleep disorders, personality characteristics, age, sex and race are all important, and this has been reiterated in various studies [ 41 ]. The benefits of using the NASA TLX and IVA +Plus tests along with EEG signal recording in the psychological and neurophysiological evaluation include the ease of administration, non-invasiveness, short evaluation times and low cost. It is suggested that in future studies on the evaluation of the effects of noise, other psychoacoustic parameters such as noise pitch, tonality and also extended periods of exposure be considered. It is also suggested that more than 16 channels be used for the EEG recordings for better and more detailed evaluations of the various brain regions.

In conclusion, noise levels seem not to have the appropriate sensitivity at levels below 85 dBA on cognitive performance. Therefore, other psychoacoustic parameters that influence cognitive function, including noise pitch and tonality are suggested as candidates for future research. Scalp topographic mapping indicates that the frontal and occipital regions along with the Alpha and Beta frequency bands are most affected by exposure to noise considering the influence of task complexity, personality characteristics, the effects of other psychoacoustic parameters on cognitive and neuro-physiological functions, applying new methods such as the use of brain biosignals along with power spectral density in the evaluation of environmental and occupational stress, especially in the case of noise exposure is suggested. It can thus be concluded that the evaluation of mental workload, auditory / visual attention and brain signals (power spectral density) in combination can be considered as a useful indicator for the assessment of the effects of noise exposure on cognitive performance.


This research was conducted as a PhD thesis supported by Shahid Beheshti University of Medical Sciences. The researchers thank the authorities of Shahid Beheshti University of Medical Sciences and all participants who kindly helped us to conduct the study.

Funding: This research did not receive any financial support

Competing Interests: The authors have declared that no competing interests exist

Ethics Approval

The Research and Ethics Committee approved the study proposal of Shahid Beheshti University of Medical Sciences (Ethical code. IR. SBMU. PHNS.1396, 63). Written consent was obtained from the participants after the explanation of the purpose and benefits of research.



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