• 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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Acknowledgements

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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Case study of New Delhi

Loudest city in india.

Back in 2011, a study by the Centre of Science and Environment (CSE) has confirmed that New Delhi is the loudest city in India. The level of noise in the streets can go above 100 decibels, which is several times louder than Singapore. The noise level has reached dangerous levels, beyond the recommended guidelines of 50-55 decibels for residential zones. Prolonged exposure to this level of noise has resulted in the increase of risk in hearing loss for the citizens. According to studies, the average age of citizens in New Delhi are 10 years older in terms of hearing, which means they are at greater risk of losing their hearing in their 50s or early 60s.

case study on noise pollution

A picture of a rush-hour traffic jam in the city of Delhi

The loud noise is often generated by the honking of cars, which means changes in attitude and behavior can reduce the main source of the noise. However, this is a hurdle as the habit of honking is ingrained into their daily routine. The streets of New Dehli are shared by vehicles, people, cyclists and more. Traffic is very heavy and the use of honk is essential to alert people walking on the street of an oncoming vehicle. As this concerns personal safety, the honking behavior will be a strong internal barrier as the drivers cannot simply stop honking.

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We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.

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Want to get in touch? Contact our London head office or media team here

Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing.

Home > Books > Noise Control

Impact of Noise Pollution during Covid-19: A Case Study of Balasore, Odisha

Submitted: 24 February 2022 Reviewed: 22 March 2022 Published: 13 May 2022

DOI: 10.5772/intechopen.104607

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Activities such as development of industrialisation, urbanisation is a part of our life in the present scenario. During this phase we face a lot of health issues due to noise pollution. Growing of vehicle traffic is one of the major causes towards noise pollution and it affects significantly on the environment. The impact of such pollution had been assessed in 20 major squares (Commercial, residential and silence area) of the Balasore town during and after lockdown imposition of Covid-19. During lockdown period, the noise level of the town was within the permissible limit set by CPCB while before and after lockdown period it was beyond the permissible limit. The demographics and psychophysiological (annoyance, sleeping problem, tiredness, headache, and depression) responses of the participants were collected using standard questionnaires. It was also observed that there were better health conditions among the public (150 participated in the questionnaire) during the lockdown period, then before and after the lockdown phase. It was revealed that socio-demographic factors have no effects on the annoyance level.

  • noise pollution
  • health issues
  • equivalent noise level

Author Information

Bijay kumar swain *.

  • DIET, India

Chidananda Prasad Das

  • Environmental Science Program, Department of Chemistry, ITER, S ‘O’ A Deemed to be University, India

Shreerup Goswami

  • PG Department of Geology, Utkal University, India

*Address all correspondence to: [email protected]

1. Introduction

One of the most common job-related occupational risks is noise and is a global problem. In urban areas it affects the health of people and also the environment. In many reports it has been reported how the people from different part of world are exposed and affected by noise pollution [ 1 , 2 , 3 , 4 ]. Many studies also reported that there is a corelation between noise and health problems like headache, irritability etc. [ 5 , 6 , 7 ]. The main source of noise pollution is vehicular traffic noise or road traffic noise, as reported by many studies [ 3 , 8 , 9 , 10 , 11 , 12 ]. Increased noise exposure is known to produce annoyance [ 5 , 13 , 14 ], headaches [ 15 , 16 , 17 , 18 ], diabetes [ 19 ], irritability [ 20 ], sleep disturbances [ 21 , 22 , 23 , 24 , 25 , 26 ], hypertension [ 27 , 28 , 29 , 30 ], and problem in blood pressure [ 31 ]. Presently, it is a global problem [ 32 ].

Again, in many studies, it was also reported about the noise pollution level and its impact on public in world-wide [ 33 , 34 , 35 , 36 ]. Similarly, in many parts of India, research has been going-on on noise pollution and its impact on human health. In most of the study, it also been reported that the noise levels on Indian road conditions was more than the prescribed noise level set by CPCB [ 37 ]. The noise levels of many towns of Odisha are also more than the prescribed limit [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Silence zones were the most affected by noise pollution, according to Kalawapudi et al. [ 53 ], followed by residential, business, and industrial zones. They went on to say that proper city design could help people avoid being exposed to growing noise pollution levels, in Mumbai Metropolitan region. Thakre et al. [ 54 ] also discovered a 4.4 and 5.2 dB increase in the morning and evening sessions, respectively, in Nagpur from 2012 to 2019 [ 54 ]. The impact noise on bus driver [ 9 ], public coming to the park for refreshment [ 10 ], Office [ 55 ], Bank [ 56 , 57 ], festivals [ 11 , 41 ], Industrial areas [ 58 , 59 ] and workers working in the stone crusher industry [ 60 , 61 ] has also been reported. Zambon et al. [ 62 ] reported about the comparison to the same period in 2019, noise levels in terms of both absolute noise levels (Lden) and hourly noise profiles (median across lockdown period) showed a substantial drop of nearly 6 dB [ 62 ], while it was 1–3 dB in Boston metropolitan areas of USA [ 63 ] and reduction of 5.1 dB in Ruhr area of Germany [ 64 ]. The highest sound levels were found along major roadways, with a logarithmic reduction as distance from the roads increased [ 63 ]. Significant outdoor noise fluctuations were discovered, and participants clearly perceived noise variations both in urban and indoor settings, claimed by Caniato et al. [ 65 ]. Alias and Alsina-Pages reported that there was a significant reduction in the harmful impact of noise on the population of Milan urban and Rome suburban areas [ 66 ].

Now, most of the Indian cities are going to face major threats in the form of noise pollution on public’s health. It can affect both physically and mentally on the public’s health. But the life changed after the spreading of COVID-19 in whole world. After its existence, first Janata Curfew was coming in to existence followed by the lock-down system. During this period the vehicular traffic noise has been reduced drastically in world-wide. But how much it was reduced is a concern. In this study, an attempt has been made to access the noise levels of the Balasore town before, during and after lockdown phase in different areas. The impact of such noise levels on public’s health was also accessed through questionnaire. Suggestive reduction procedures are also given in the present study.

2. Methodology

2.1 study area.

Balasore is one of the famous districts in the state Odisha and situated in the eastern part of the state. It is famous for its cultural heritage, vast sea-beach and many more. It is also famous for Chandipur Sea Beach. The study area is the district head-quarter. As per 2011 census of India, Balasore District has a population of 2,320,529 in 2011 but estimates as per aadhar uidai.gov.in Dec 2020 data as 2,645,403. But the population of the municipality/metropolitan areas was 1,77,751 and city had 1,18,162. The latitude and longitude of the district is 21 29 39 North, 86 55 54 East respectively ( Figure 1 ). The monitoring town has elevation of 16 m. the maximum and minimum temperatures are observed to be 31.8 and 21.9 respectively, with an average rainfall of 1706.1 mm, average relative humidity of 71% and speed of 11 km/h. The research area is about 194 km away from the state capital. Different rural roads are connected to this town. Thousands of vehicles along-with number of heavy vehicles are flowing on different roads of the town. The town has a very wide commercial areas and lot of people from different regions were depending on this market for their daily needs. The major road of the town also connected with the Chandipur beach, and other religious areas of the district. Thus, heavy rush in vehicle flow has been shown on the town. Every day, thousands of different cars enter and exit the city. The metropolitan environment has a diverse traffic flow. It is one of the busiest municipalities/towns of the state, with a variety of land-use patterns.

case study on noise pollution

Map of India showing the location area of the study area.

Nationwide lockdown (21 days) imposition in India was implemented between 25th March 2020 and 14th April 2020 as Phase 1 and between 15th April and 3rd May 2020 as Phase 2, Phase 3 from 4th May 2020 and 17th May 2020 and last phase (Phase 4) 18th May 2020 to 31st May 2020. Before this nation-wide voluntary public curfew was implemented on 22nd March 2020 for a time period of 14-hour. The same process of lockdown was also implemented in the Balasore town accordingly. Only essential good services are provided to the public. The Unlock phases was came into exist. The first unlock 1.0 came in to exist between 1st June to 30th June 2020. After the month of June 2020, the unlock phases was going on from unlock phase 1 to unlock 21 (1 February 2022 to 28 February 2022). In the present study, the noise levels recorded during unlock phase 1.0 and 2.0, i.e. 1st June 2020 to 30th June 2020 and 1st July 2021 to 31st July 2021. Similarly, the noise level also monitored during December 2019, January 2020 and February 2020 before imposition of the lockdown. During lockdown phase, the noise level had been accessed in the month of May 2020.

2.2 Monitoring sites

At 20 separate locations throughout the town, the acoustic level was measured. All these monitoring stations are divided into three sections such as commercial zone, residential and silence zone. Seven locations from both commercial and residential zones are selected and six stations were selected for silence zone. Some of these locations are belong to the commercial zone, such as Cinema square, Fandi square, Motiganj Bazar, Station square, ITI square, Padhuanpada square, and Policeline square, while others are in silence areas, such as Hospital gate, Durganurshing home, FM college, Zilla school, Near Kendriya Vidyalay (KV), and Police High School and others are in residential areas, such as Mandal bagicha, Near ACPL apartment, Khaparapada New Colony, Rajabagicha, Angargadia, Santikanana and Swastik tower.

2.3 Sampling and data acquisition

The sound level metre Model HD2110L was used to collect acoustic data at each of the 20 sample stations in and around Balasore town. The calibration of the equipment was carried out according to the manufacturer’s instructions. The measurements were conducted on working days at street level in and around the chosen locations’ major road connections. The instrument was comfortably set in road sides, with the microphone aimed at the source of noise. The equipment was placed 2 m distant from the reflecting object, and the data was gathered while standing 1.5 m above ground level on the roadside. Within 10–20 m gaps, noise levels were measured based on road width. Each station’s noise levels were measured in the morning (8–10 a.m.), afternoon (3 p.m.–5 p.m.), and evening (7 p.m.–9 p.m.). The noise levels were measured in four different directions at each station, and one reading was taken every 2 min, for a total of five readings within a 10-minute time frame [ 67 , 68 , 69 , 70 ]. All of the information is saved on a computer for further study.

For noise level data analysis, noise indices such as Lmin, Lmax, and Leq were calculated. The maximum, minimum, and equivalent noise levels were calculated using all of the recorded data on an excel sheet. The minimal sound pressure level is Lmin, the maximum sound pressure level is Lmax, and the equivalent continuous sound level during that time period is Leq. Again, L10 and L90 refer to sound intensities that are greater than 10% and 90%, respectively.

2.4 Community response

The community reaction was gathered through the use of questionnaires distributed to members of the public going along the various route segments. During the month of March 2020, the public’s replies were gathered and recorded on a computer. The questionnaire sent to the participants through whatsapp and in some cases hard copies are also shared and the process was completed during the month of November 2020. One hundred fifty participants have been responded to the questionnaire. This questionnaire was filled out by individuals (those who agreed) who were 18 years old or older. There were two sections to the questionnaire. The first section of the questionnaire is about demographics, while the second section is about various health issues related to the town’s acoustic noise. The questionnaire in this study was designed in accordance with Vianna et al. [ 71 ], and the questionnaire was constructed appropriately. A total of 150 people from various age groups replied to the questionnaire in this study. The first section contains demographic data such as name, gender, age, educational attainment, and marital status. After minor adjustments by Vianna et al. [ 71 ], the second half of the questionnaire was separated into the following sections. The respondents completed the noise sensitivity scale created by Weinstein [ 72 ] and Eysenc’s personality Inventory (EPI) in this study [ 73 ]. Two items given under perception of noise such as aware of noise pollution and environmental noise asked the participants to answer in 5-point Likert scale. The question based on annoyance level and anxiety are also in 5-point Likert scale. Questions are given on hearing condition, sound quality of the environment, personality traits such as aggression, depression, stability, working ability, tiredness and drowsy, sensitivity, relaxation, developing symptoms, and on health risk. This part asks about how people perceive noise from things like road traffic and other sources, and the answer is either Yes or No. High, Moderate, and Little annoyance in regard to noise sources; Noise exposure effects (hearing loss, sleep disturbances, headaches, fatigue, drowsiness, and other illnesses); Hearing condition (Excellent, Good, Moderate, and Poor); environmental sound quality (Normal, Moderate, and Noisy); and environmental perception (Yes, No, and Undecided) [ 9 , 71 , 73 ]. The Chi-square test in SPSS 20.0 was used to look into the correlations between demographic characteristics and annoyance, and other environmental factors and the ANNOVA test was used to look into the association between noise exposure and the probable impacts of that noise on this community. At a significance threshold of 0.05, the relationship between individual and combination socio-demographic characteristics was examined. The datasets were analysed using SPSS software (20.0).

3. Results and discussion

3.1 studies related to zone specific noise.

The average noise levels of the 20 stations of different categories have been accessed and presented in Table 1 (Before Lockdown), Table 2 (during lockdown) and Table 3 (Unlock phases). The data collected during the month December 2019 and January and February 2020 are considered as pre-lockdown phase. 17th March 2020 to 31st May 2020 considered as lockdown period. After 1st June 2020 it is considered as unlock phases or after lockdown period. The comparative monthly variation of equivalent noise levels of these areas having different land use type is presented in Figure 2 . The figure clearly depicts that there is a sharp trend of noise levels of the town during three phases of the lockdown. It also demonstrates that the noise level during the lock down phases is very low than unlock and before lockdown phases. The monthly noise variation of all the stations is depicted in the Figures 2 – 10 . In each figure, first three belongs to the monthly noise level before lockdown period, while the fourth one belongs to the lockdown period and the last portions belong to the unlock phases.

Noise levels in dB at different traffic squares of Balasore town during different time interval (pre-lock down phase).

Noise levels in dB at different traffic squares of Balasore town during different time interval (during lock down phase).

Noise levels in dB at different traffic squares of Balasore town during different time interval (post-lock down phase).

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the morning hour of commercial zone.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the afternoon hour of commercial zone.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the evening hour of commercial zone.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the morning hour of silence zone.

The Table 1 clearly depicts that the noise levels for commercial zone ranged from 57.7 to 91.6 dB, 57.3 to 91.6 dB and 56.8 to 93.7 dB for morning, afternoon and evening hour respectively. Similarly, the noise level for silence zone ranged from 57.4 to 90.3 dB; 58.8 to 91.7 dB and 53.5 to 91.6 dB during the morning, afternoon and evening hour respectively and from 55.9 to 85.6 dB; 56.8 to 89.8 dB and 50.2 to 91.6 dB during the morning, afternoon and evening hour respectively for residential zone. It can be summarised that the noise for all zones before lockdown period had a ranged from 55.9 to 91.6 dB; 56.8 to 91.7 dB and 50.2 to 93.7 dB during the morning, afternoon and evening hour respectively. Table 1 also clearly depicted that for all time the noise level ranged from 50.2 to 91.7 dB during before lock-down phase ( Table 1 ).

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the afternoon hour of silence zone.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the evening hour of silence zone.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the morning hour of residential zone.

Similarly, Table 2 clearly demonstrated that all zones lie in the range of 38.3 to 81.4 dB during lockdown period ( Table 2 ) and then the range gradually increased to 45.7 to 92.5 dB during unlock period ( Table 3 ). The equivalent noise levels of all zones lie in the range of 65.8 to 81.7 dB ( Table 1 ); reduced to 43.2 to 60.7 dB during lock-down period ( Table 2 ) and the range then gradually increased from 56.8 to 73.1 dB during unlock period ( Table 3 ). The permitted limit for the said locations, as defined by the CPCB for Indian road conditions, is 65 dB during the day and 55 dB at night [ 37 ]. During the day time, the noise level exceeded the permitted limit [ 74 , 75 , 76 , 77 , 78 ]. The noise level during unlock phase and before imposing lockdown was beyond the permissible limit in the present study. It was reported that, if the exposure of noise level is more than 80 dB (A), then risk of hypertension will increase [ 34 ]. More research is needed to investigate the effect of such noise level on the public’s health in future study.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the afternoon hour of residential zone.

case study on noise pollution

Comparative equivalent noise level (first three are before lock-down, middle on during lock-down and the last twos are during unlock phase) for the evening hour of residential zone.

From the monthly variation it was demonstrated that the noise levels of residential areas during the morning hour are decreased by a noise level of 24.8 dB and then it increased up to 15.1 dB during unlock phase in Mandal Bagicha area. Similarly, the noise levels in other monitoring areas decreased by a noise level of 23.5, 23, 26.8, 23.3, 24.5 and 24 dB and then it was increased up to 14.7, 14, 15.5, 13, 14.7 and 19.95 dB for ACPL, Khaparapada, Rajabagicha, Angargadia, Santikanan and Swastik tower, respectively ( Table 1 ). During afternoon hour, the maximum reduction of noise level was noticed at Rajabagicha area (30.2 dB) and then it increased up to 17 dB during the unlock phase. From the Table 1 , it clearly depicts that maximum noise reduction between before lock-down phase and during unlock phase was observed at commercial zone (more than 25.5 dB) followed by residential zone (more than 25 dB) and silence zone (more than 22 dB). All these data are mentioned here are in an average data. Similarly, the maximum growing noise level between during lock-down and unlock phase was also noticed at commercial zone (more than 17.8 dB) and followed by silence zone (16.7 dB) and residential zone (14.1 dB). During evening hour and at Padhuanpada square maximum noise reduction i.e., 28.6 dB was noticed, while the lowest reduction was at 16.3 dB during morning hour at Durga Nursing home. Again, maximum increase of noise level was noticed at Cinema square (21.9 dB) during the evening hour, while the minimum increase noise was noticed at Hospital gate (8.7 dB) during the evening hour also.

Table 1 also clearly depicted that the equivalent noise level during before lock down phase ranged from 77.2 to 80.5 dB; 75.7 dB to 81.7 dB and 75.5 to 81.5 dB for morning, afternoon and evening hour respectively. But the noise level during the lock-down phase ranged from 53.9 to 56.2 dB; 52.8 to 54.4 dB and 51.1 to 53.4 dB during the morning, afternoon and evening hour respectively ( Table 2 ). Similarly, the noise level during the unlock phase ranged from 69.2 to 71.2 dB; 70.3 to 71.5 dB and 70.5 to 73.1 dB during the morning, afternoon and evening hour respectively ( Table 3 ). The noise level at silence zone ranged from 72.6 dB to 76.3 dB; 74 to 78.2 dB; 73.4 to 78.4 dB during before lock down phase; 51 to 58.3 dB; 49.6 to 56.7 dB and 50 to 60.7 dB during lock-down phases at morning, afternoon and evening hour and 65.7 to 72.9 dB; 66.9 to 72.7 dB and 68 to 69.5 dB during morning, afternoon and evening hour respectively. In the residential areas it ranged from 69.5 to 73.2 dB; 68.2 to 74.7 dB and 65.8 to 72.5 dB during before lock down phase; in lock-down phase the noise level ranged from 45.9 to 48.4 dB; 43.2 to 45.4 dB and 43.4 to 45.1 dB and in unlock phase it ranged from 60.5 to 61.9 dB; 57.3 to 61.5 dB and 56.8 to 58.5 dB in the morning, afternoon and evening hour respectively.

In location wise, Tables 1 – 3 clearly depicts the noise variation in all the monitoring stations. These Tables demonstrated that there is a reduction of 25.9 dB, 26.7 dB; 30.3 dB in three different monitoring hours for site 1 of commercial zone. Conversely, the reduction is almost 26, 26.8 and 27.2 dB for site 2, 23.4, 28.2, 26 dB for Site-3 and a similar trend was found in all other monitoring sites belong to commercial zone. In the commercial zone the minimum noise reduction ranged from 22.9 to 28.2 dB and 23.9 to 30.3 dB during afternoon and evening hour of the commercial zone. Again, the reduction of noise level ranged from 16.4 to 23.3 dB; 19.2 to 26.2 dB and 17.7 to 26.8 dB of silence zone and from 23 to 26.8 dB; 23.3 to 30.2 dB and 22.4 to 28.4 dB of residential zone during morning, afternoon and evening hour respectively. This result clearly depicted that there is almost same trend in the noise level reduction, both in commercial and residential zone of the town. The minimum noise level reduction was more than 15 dB and found at silence zone of the town and clearly depicted that due to the nationwide lock-down imposition, there was a sharp reduction in the noise level. It will impact the environment in a positive manner.

In comparison between Leq values of a particular sites during the lock-down period with unlock phase, there was sharp increase in the noise levels of each location. Noise levels from 13.9 to 17.7 dB was increased during the morning hour in between lockdown and unlock phases. Similarly, the noise levels increased by 17.1 to 18.1 dB and 19 to 21.9 dB in afternoon and evening hour of commercial zone. Again, the increased noise level ranged from 9.9 to 15.5 dB, 12.3 to 17.3 dB and 8.7 to 19 dB of silence zone and ranged from 13 to 15.5 dB, 13.3 to 17 dB and 13.3 to 13.6 dB of the residential zone during morning, afternoon and evening hour, respectively. Due to slight relaxation provided by the local administration, there was a sharp increase in noise level of the town. This trend was more commercial zone. In the present study it is also reported that there is no relation between the different monitoring hours and the situation i.e., before imposing lockdown and after imposing and lifting the lockdown phase of the town. But there is a good association between different areas such as residential, commercial and silence zone with unlock and before lock down phase of the town ( Table 4 ). In case of monthly noise level variation with different phases of the lockdown situation there is also good association between them and is presented in the Table 5 .

Two way ANNOVA analysis for equivalent noise levels during unlock and before lockdown phases with different areas.

R squared = 0.829 (Adjusted R squared = 0.823).

One way ANNOVA analysis for monthly equivalent noise levels with different areas.

In the present study, it was found that the noise level in the residential areas is growing on due to imposition of lockdown in the town. Due to lockdown, the commercial areas of the town and for such the people are selling different grocery items in the different parts of the residential areas. The open shops are instantly made on the roadside and there is slight gathering around such place. These shops are opened from 7 am to 7 pm during the unlock phase while it was opened from 7 am to 2 pm during the lockdown phase. Around the market or shop area there was gathering and due to which, the noise level during the unlock phase was raising. Again, during the unlock phase, the noise level suddenly increased due to immediate rush in different parts of the town, due to purchase of goods for their house. They creating a such situation unnecessarily by gathering around the temporary shops.

In case of silence zone, schools and hospitals were taken in the present study. All the monitoring stations were located along the main road. College and school squares are also along the road of different hospitals. Many private clinics and hospitals are also very close to the schools and colleges of the town. During lockdown, many shops, schools and colleges and other establishments were closed. All medicinal shops are opened throughout the day time. But vehicles are flowing on the road due to health matter. Continuous flowing of many vehicles including heavy vehicles on the road are controlled, but running of the two wheelers, ambulances and responsible for the noise levels of the town.

3.2 Community responses

During the month of March 2020, the public’s replies were gathered and recorded on a computer. The questionnaire was supplied to the participants both in online and offline mode. Those are expertise in the android mobile phones or in their PC or laptop they are responded to the questionnaire through online mode. Those are not comfortable in using these devices, asked the researcher to provide the such through offline mode and also provided to them as such. After getting their responses, it was then transferred in to MS Excel for its further analysis.

The questionnaire was completed by 150 people, as mentioned in the content and methods section. The average age of the responders was 37.8 years old, with a standard deviation of 9.4 years. Table 6 lists the various personal characteristics of the individuals. Table 6 clearly depicts that the majority of the participants are male respondent (59.3%), with 68.7% of the total completing their education at the graduation level. In the present study, majority of the participants are employed. Majority of the participants (78.7%) participated in this survey work are married. In the present study most of the young generation (48%) between the age of 18 to 30, responded to this questionnaire.

Personal characteristics of respondents (in percentage).

The Pearson Chi-square of noise discomfort to different demographic characters is shown in Table 7 . Table 7 clearly depicts that there is a good association between annoyance and gender of the present work. Again, there is no direct relationship between annoyance and other demographic characteristics, according to the data.

Relation between demographic character and annoyance.

In this study it was found that 36.7% individuals were extremely irritated, while 39.5% remain silent. In a study conducted by Alimohammadi et al. [ 73 ] on White-collar employees in Teharan, it was discovered that married people were more irritated than unmarried people. But in the present study it was contradicted that result ( p  = 0.217).

The participants’ perceptions on noise, health issues, hearing conditions, sound quality of the environment, environmental problems, opinion of participants on noise preventability, sensitivity to noise, annoyance, and the importance of controlling the town’s noise were all examined in the current study and presented in the Table 8 .

Participant’s perception towards different aspects of noise pollution.

On awareness towards road traffic noise pollution, majority of the participants (59.3%) were aware of it. More than 30% respondents were strongly aware about the noise pollution, which is also a good sign for the society. Regarding health issues majority respondents (36.7%) opined about a little impact of noise pollution on their health, while 24.6% respondents remain silent and only 18% viewed that they suffered moderately by the noise pollution. On hearing condition most of the participants (38%) were in moderate condition, while 30% responded as good in condition. Only 27.3% opined that their hearing condition was not so good or in bad condition. How much the hearing problem is affected is not studied in the present study. The researcher aimed to conduct the audiometry study of these respondents very soon to know their actual level of hearing in the next study. Noise induced hearing loss is also the most frequently recognised occupational disease in many countries [ 79 , 80 , 81 ].

The sound quality of the town was not so good as per the response of the participants. Due to such issues, they face a lot of problems (56.7%) in their day-to-day life. According to the findings, 40.7% of the participants suffered illness, while most of them faced headache (54.7%) due to road traffic noise. How much it affects the public health and what are the possible symptoms are developed is to be investigated in the next phase of study. Majority of the respondents (57.3%) responded that they annoyed often. Running of vehicles (89.3%) is the major source of pollution, followed by railway (76.7%), two wheelers (75%), honking (70.7%) ( Table 8 ).

The acoustic quality of the area was described as noisy by the majority of the participants (60%). According to the study, majority of the of interviewees felt that road traffic noise was polluting the environment. When the participants’ knowledge was assessed, most of them said that road traffic noise poses a significant health risk. Noise pollution upset 67.3% of the participants, while 58.7% were sensitive to noise and 60% found it difficult to relax in these situations. More than 48% felt depressed, 82% were felt tired, 48.7% were not working in a stability manner. It may be due to the effect of the noise pollution.

The chi-square test was used to determine the relationship between age and annoyance in this study, and no link was found at p  = 0.01. However, there is a strong association between annoyance and gender ( p  = 0.004). There was also a link between work place noise levels and annoyance, according to Allomohammadi et al. [ 73 ]. But this result is similar to the present study. It can be said that occupation is not a good characteristic towards annoyance. According to reports, there is no correlation between age, education, or marital status and the town’s level of annoyance. The current study’s findings are comparable to those of Ohrstrom et al. [ 82 ], who found that age, sex, and other characteristics do not explain differences in annoyance between people and is very similar to the results of the present study. However, it has been reported in many research that annoyance is the most vulnerable consequence of traffic noise exposure [ 83 , 84 ], which contradicts the findings of the current study in many circumstances.

There is good association between gender and drowsiness of the public ( p  = 0.015) ( Table 9 ). Table 9 also demonstrates that there is an association between drowsy and qualification. Table 10 depicts that there is an association between relaxation and gender ( p  = 0.001) and age ( p  = 0.006). Most of the demographic characters have a good association with noise sensitivity ( Table 11 ). Noise sensitivity has a good association with gender ( p  = 0.001), age ( p  = 0.005), marital status ( p  = 0.001) and qualification ( p  = 0.038) of the present study ( Table 11 ). Table 12 reveals that both gender ( p  = 0.001) and marital status ( p  = 0.001) has an association with anxiety of the noise pollution ( Table 12 ). Gender is not a significant element in the influence of noise concern, according to certain studies [ 5 , 85 ]. Similar results also depicted in the present study. There was also a link between the individuals’ age and sleep problems ( p  = 0.046). It was also said that age is not a significant factor when it comes to the effects of noise exposure [ 5 , 80 ]. Increased parent-reported sleep issues were identified in the few studies that looked at the link between noise and child/adolescent sleep [ 23 , 82 ]. Sleep fragmentation, sleep continuity, and total sleep time have all been linked to noise [ 24 , 25 ]. There was no association between sleep duration and hourly minimum noise levels [ 86 ]. Again, it was also reported that there was no relation between sleep efficiency and mean noise levels, according to Missildine et al. [ 87 ]. But, the result of the present study contradicts it and it shows that there is an association between sleep problems and noise level of the town ( p  = 0.016).

Relation between demographic character and drowsy.

Relation between demographic character and relax.

Relation between demographic character and sensitive.

Relation between demographic character and Anexiety.

Table 13 depicts the results of ANNOVA analysis between noise annoyance and demographic characteristics. The table clearly depicts that there is an association between annoyance and gender of the study. However, there is no statistically significant link between other demographic factors and annoyance. There is a link between sex and anxiety ( p  = 0.033) as seen in Table 14 . There is no direct relation between sensitivity with the demographic characters except marital status ( Table 15 ). Table 16 reveals that there is an association between relaxation and age ( p  = 0.008) and sex ( p  = 0.001) of the participants of the present study. Table 17 shows the relation between annoyance and different environmental issues. This table clearly depicts that there is a strong association between relaxation, sensitivity, environmental noise, anxiety, irritation. Different vehicles are running on the main road of the town. During lock-down and unlock phases, ambulances are flowing from different areas of the town to the district hospital centre and also to the other clinics of the town. it has been reported that noise sensitivity—internal states that increase the chance of noise annoyance [ 88 ]—could alter the relationship between noise and health. Noise sensitivity has been linked to the beginning of depressed and psychological symptoms in adulthood. Higher morning saliva cortisol levels were linked to significant noise irritation and residing in high-noise locations in adolescents [ 89 ]. We did not have a way to gauge noise sensitivity or annoyance, so we could not assess its impact [ 90 ].

Analysis of ANNOVA between demographic characteristics and annoyance.

R squared = 0.074 (Adjusted R squared = 0.041).

Analysis of ANNOVA between demographic characteristics and anxiety.

R squared = 0.061 (Adjusted R squared = 0.028).

Analysis of ANNOVA between demographic characteristics and sensitivity.

R squared = 0.128 (Adjusted R squared = 0.098).

Analysis of ANNOVA between demographic characteristics and relax.

R squared = 0.118 (Adjusted R squared = 0.087).

Analysis of ANNOVA between annoyance and environmental factors.

R squared = 0.508 (Adjusted R squared = 0.476).

The current research clearly shows that persons in the study locations are sensitive to noise levels based on their age. Respondents are employed in a variety of sub-urban work sites. They are subjected to various types of noise. They are irritated by the noise levels in the vicinity as a result of this. It is impossible to say that the level of noise in their workplace is the sole source of their annoyance, although it could be one of them.

During unlock phases, different offices are also opened in a regular and controlled manner. The running of vehicles on the road also growing accordingly and that may affect the public health in anyway. Different construction works also going on in many parts of the town and it may cause problem to the public of the town. Heavy vehicles carrying various raw materials are also moving on this road due to road building in various portions of the road. Vehicles are driven at all hours of the day and night. People of all ages are directly exposed to these levels of noise. This activity may exacerbate their sleeping problems.

4. Conclusion

Our findings may have been influenced by the fact that the noise level decreased due to the imposition of the nationwide lockdown and it then increase sharply due to the incoming of unlock phases. Still, the reported noise level of the town was beyond the permissible limit except lockdown phases in residential and silence zone. It was reported in the present study that there is a good association between different areas such as residential, commercial and silence zone with unlock and before lock down phase of the town. In case of monthly noise level variation with different phases of the lockdown situation there is also good association between them and is presented in the Table 5 . Finally, studies have demonstrated that the relationship between noise and health differs depending on sex, health status, and other factors but we lacked the sample size to evaluate the relationship by subgroup. Longitudinal designs, enhanced exposure assessment, and objective sleep assessments of whether particular subgroups of teenagers are more susceptible to the potential negative effects of environmental noise, should be prioritised in future investigations. Direct regulation of noise sources as well as changes to the built environment are two public health techniques for reducing noise exposure [ 21 , 91 ]. We were unable to demonstrate a temporal relationship between exposure and outcome since the study was cross-sectional. Future research may want to utilise objective of audiometry test to test the exactness of the hearing quality of the respondents of the town.

Acknowledgments

The authors are very much thankful to Indrajit Patra and Pravat Kumar Mandal for their support in monitoring the noise levels.

Conflicts of interest

The authors declare that they have no conflicts of interest with regard to the content of this report.

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Wind farm noise and anuran diversity patterns: a case study in Brazilian seasonal dry tropical forest

Noise pollution contributes to the global biodiversity crisis, however the consequences of this pollution on anuran diversity patterns are poorly understood. This is especially true of less evident sources of noise like wind farms and highly exploited areas, as in the Brazilian semi-arid region. Here, we evaluated the influence of wind farm noise on anuran assemblages’ diversity at the Caatinga, a seasonal dry tropical forest in Brazil. We tested the hypothesis that wind farm noise negatively affects the diversity of anuran assemblages in terms of abundance, species richness and composition. Anurans were sampled in 19 temporary ponds along a noise gradient in two wind farms over a rainy season (March to August). A total of 2,047 individuals belonging to 20 species were recorded. Our results suggest that wind farm noise has a non-significant relationship with anuran diversity patterns (species richness, composition and abundance). To our knowledge, this is the first investigation into the effects of wind farm noise on anuran assemblages in the Caatinga dry forest. Despite our results suggesting that anuran diversity is insensitive to noise pollution caused by wind farms, identification of emerging threats is essential to mitigate impacts on anuran populations which are declining globally.

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Environmental Noise Pollution in the United States: Developing an Effective Public Health Response

Monica s. hammer.

1 The Network for Public Health Law—Mid-States Region, The University of Michigan School of Public Health, Ann Arbor, Michigan, USA

Tracy K. Swinburn

2 The Risk Science Center, The University of Michigan, Ann Arbor, Michigan, USA

Richard L. Neitzel

3 The Department of Environmental Health Sciences, The University of Michigan, Ann Arbor, Michigan, USA

Background: Tens of millions of Americans suffer from a range of adverse health outcomes due to noise exposure, including heart disease and hearing loss. Reducing environmental noise pollution is achievable and consistent with national prevention goals, yet there is no national plan to reduce environmental noise pollution.

Objectives: We aimed to describe some of the most serious health effects associated with noise, summarize exposures from several highly prevalent noise sources based on published estimates as well as extrapolations made using these estimates, and lay out proven mechanisms and strategies to reduce noise by incorporating scientific insight and technological innovations into existing public health infrastructure.

Discussion: We estimated that 104 million individuals had annual L EQ(24) levels > 70 dBA (equivalent to a continuous average exposure level of >70 dBA over 24 hr) in 2013 and were at risk of noise-induced hearing loss. Tens of millions more may be at risk of heart disease, and other noise-related health effects. Direct regulation, altering the informational environment, and altering the built environment are the least costly, most logistically feasible, and most effective noise reduction interventions.

Conclusion: Significant public health benefit can be achieved by integrating interventions that reduce environmental noise levels and exposures into the federal public health agenda.

Citation: Hammer MS, Swinburn TK, Neitzel RL. 2014. Environmental noise pollution in the United States: developing an effective public health response. Environ Health Perspect 122:115–119;  http://dx.doi.org/10.1289/ehp.1307272

Introduction

Noise, or unwanted sound, is one of the most common environmental exposures in the United States ( García 2001 ). In 1981, the U.S. Environmental Protection Agency (EPA) estimated that nearly 100 million people in the United States (about 50% of the population) had annual exposures to traffic noise that were high enough to be harmful to health ( Simpson and Bruce 1981 ). However, despite the widespread prevalence of exposure, noise has historically been treated differently than pollutants of a chemical or radiological nature, and especially air pollution. Congress has not seriously discussed environmental noise in > 30 years, although noise exposure is a large public concern. For example, in New York City noise is consistently the number one quality of life issue, and authorities there received > 40,000 noise complaints in 2012 ( Metcalfe 2013 ). Very few communities appear to consider the health risks of noise in their policy making ( Network for Public Health Law 2013 ) despite the fact that the health effects of noise have been explored over many decades, and the body of evidence linking noise to various health effects is, therefore, more extensive than for most other environmental hazards ( Goines and Hagler 2007 ; Passchier-Vermeer and Passchier 2000 ).

Even when cities and counties do address noise in their planning efforts, the results are disappointing. The Health Impacts Project (HIP) provides guidance for policy makers to identify the health consequences of potential projects by making public a national sample of health impact assessments ( HIP 2013 ). Dozens of recent health impact statements in the HIP database have incorporated noise, but none appeared to assess changes in sleep disturbance, learning, hypertension, or heart disease. Although HIP does not provide a complete picture of U.S. health impact assessments, it does indicate that decision makers lack the information they need to protect communities from noise-related health effects. Environmental impact statements that calculate changes in noise levels also do not necessarily provide information about adverse health impacts resulting from these changes ( U.S. Department of Transportation, Federal Highway Administration/Michigan Department of Transportation 2008 ).

In this commentary, we examine scientific and policy aspects of noise exposure. We first provide an overview of the relationship between high-impact health effects and noise. We then describe the most prevalent sources of noise and estimate prevalence of exposure. Finally, we explore policy approaches that can reduce the harmful effects of noise.

Chronic Noise: A Biopsychosocial Model of Disease

Chronic environmental noise causes a wide variety of adverse health effects, including sleep disturbance, annoyance, noise-induced hearing loss (NIHL), cardiovascular disease, endocrine effects, and increased incidence of diabetes ( Passchier-Vermeer and Passchier 2000 ; Sørensen et al. 2013 ). This commentary is not intended to provide a comprehensive review of all noise-related health effects, which is available elsewhere ( Goines and Hagler 2007 ). Rather, we focus on several highly prevalent health effects: sleep disruption and heart disease, stress, annoyance, and NIHL ( Figure 1 ). It is important to note that the levels of noise exposures associated with these health effects range widely; as a result, the prevention of different health effects involves specification of different exposure limits and metrics.

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Select effects of noise.

Sleep and heart disease . People in noisy environments experience a subjective habituation to noise, but their cardiovascular system does not habituate ( Muzet 2002 ) and still experiences activations of the sympathetic nervous system and changes from deep sleep to a lighter stage of sleep in response to noise. The body’s initial startle response to noise is activation of the sympathetic (fight or flight) part of the nervous system, similar to the preparations the body makes just before waking in the morning. Although blood pressure normally drops during sleep, people experiencing sleep fragmentation from noise have difficulty achieving a nadir for any length of time because blood pressure rises with noise transients and heart rate increases with noise level ( Haralabidis et al. 2008 ). Decreased quality and quantity of sleep elevates cardiovascular strain, which manifests as increased blood pressure and disruptions in cardiovascular circadian rhythms ( Sforza et al. 2004 ).

Disordered sleep is associated with increased levels of stress hormones ( Joo et al. 2012 ). Microarousals appear to be associated with increased lipids and cortisol levels, and feed into the same pathway of disordered sleep, even priming the neuroendocrine stress response in some individuals to be more at risk for disorders such as depression ( Meerlo et al. 2008 ). Increased blood lipid, heart rate, blood pressure, and stress levels from noise lead to atherosclerosis, which is causally related to heart disease ( Hoffman et al. 2013 ).

Stress . The effects of noise on conscious subjects are insidious and result at least in part from increased psychosocial stress and annoyance. Annoyance from continuous sound appears to vary substantially by individual ( Babisch et al. 2013 ; Stansfeld 1992 ), and there are a number of factors that may influence annoyance ( Babisch et al. 2012 ) and subsequent stress. Annoyance increases sympathetic tone, especially in noise-sensitive individuals ( Sandrock et al. 2009 ), and may be the non–sleep-mediated pathway that is present in individuals with high occupational noise exposures who subsequently develop heart disease ( Ha et al. 2011 ).

Environmental noise is not only a health risk to people who report being annoyed by noise, but these individuals are also at risk for additional health effects ( Sandrock et al. 2009 ). Children in noisy environments have poor school performance, which leads to stress and misbehavior ( Lercher et al. 2002 ). They also have decreased learning, lower reading comprehension, and concentration deficits ( Stansfeld et al. 2005 ).

NIHL . Long-term exposures to noise levels > 75 dBA ( U.S. EPA 1974 ) can cause metabolic changes in sensory hair cells within the cochlea, eventually leading to their demise ( Heinrich et al. 2006 ) and increasing inability to perceive sound (e.g., NIHL). Neuronal destruction may also occur; in such cases, the ability to perceive sound may remain undiminished, but the ability to understand the meaning of sound deteriorates ( Lin 2012 ). Extreme exposures can cause direct mechanical damage (acoustic trauma) to cochlear hair cells ( Newby and Popelka 1992 ). Noise exposure is also associated with tinnitus (ringing in the ears) and hyperacusis. NIHL has traditionally been associated with occupational noise, but there is increasing evidence that music may play an important role as well ( Lewis et al. 2013 ).

It is difficult to overstate the social cost of NIHL and its impact on quality of life. The additional effort required to process sound leads to fatigue, headaches, nervousness, depression, and anger ( Hetu et al. 1993 ). Functional limitations associated with a compromised ability to communicate restrict mobility, self-direction, self-care, work tolerance, and work skills and increase isolation. Assistive technologies can aid some individuals, but in no way represent a cure.

Children with NIHL suffer from decreased educational achievement and impaired social–emotional development, score significantly lower on basic skills, and exhibit behavioral problems and lower self-esteem ( Bess et al. 1998 ).

Exposure Limits and Sources of Noise

Exposure metrics and limits . Because of the array of health effects caused by noise, and the relative importance of exposure timing for some health effects, a variety of exposure metrics and limits are in use today. The U.S. EPA recommends an average 24-hr exposure limit of 55 A-weighted decibels (dBA) to protect the public from all adverse effects on health and welfare in residential areas ( U.S. EPA 1974 ). This limit is a day–night 24-hr average noise level (L DN ), with a 10-dBA penalty applied to nighttime levels between 2200 and 0700 hours to account for sleep disruption and no penalty applied to daytime levels.

The U.S. EPA recommends a second exposure limit of 70 dBA to prevent hearing loss ( U.S. EPA 1974 ). The limit is an equivalent continuous average exposure level over 24 hr [L EQ(24) ]. Unlike the 55-dBA L DN limit designed to protect against all long-term health effects, the 70-dBA limit considers daytime and nighttime exposures to be equally hazardous to hearing. This 24-hr limit is equivalent to a 75-dBA 8-hr workday exposure, with no noise exposure (i.e., noise < 70 dBA) during the remaining 16 hr.

The U.S. EPA recommendations—adopted in 1974 and mirrored by the World Health Organization (WHO) ( Berglund et al. 1999 )—may be considered a truly “safe” level for protection against hearing loss. In contrast, the U.S. Occupational Safety and Health Administration’s 8-hr workplace regulation of 90 dBA may result in a 25% excess risk of hearing impairment among workers exposed over a working lifetime [ National Institute of Occupational Safety and Health (NIOSH) 1998 ].

Other limits may be needed or appropriate for preventing additional health effects not described here or for emerging sources of noise (e.g., wind turbines) that are substantially different from historical noise sources. For example, the WHO recently adopted a set of health-based guidelines for nighttime noise exposure that are much lower than previously recommended levels ( WHO 2009 ).

Sources of noise . Primary sources of noise in the United States include road and rail traffic, air transportation, and occupational and industrial activities [ National Academy of Engineering (NAE) 2010 ]. Additional individual-level exposures include amplified music, recreational activities (including concerts and sporting events), and firearms. Personal music player use appears to be common among adolescents ( Kim et al. 2009 ; Vogel et al. 2011 ) and may involve potentially harmful sound levels ( Breinbauer et al. 2012 ). Exposures from recreational activities and music are not “noise” in the sense of being unwanted sound, but adverse health effects are possible even from desirable sounds.

Prevalence of Harmful Noise Exposure

Data on the prevalence of noise exposures in the United States are dated and inadequate. The most recent national surveys of community and occupational noise exposures occurred in the early 1980s ( NIOSH 1988 ; Simpson and Bruce 1981 ). Current estimates of workers exposed to “hazardous” levels of workplace noise (an 8-hr L EQ of ≥ 85 dBA) range from 22 to 30 million ( NIOSH 2001 ; Tak et al. 2009 ). This wide range in estimates for the working population, which is more closely tracked than the general public, should give some indication as to the tremendous uncertainty in community estimates.

The limited data available suggest that a substantial portion of the U.S. population may be at risk of noise-related health effects and that modern 24-hr societies are increasingly encroaching on “quiet” periods (e.g., night). An annual level of 55- to 60-dBA L DN may increase risk of hypertension ( van Kempen and Babisch 2012 ). In 1981, Simpson and Bruce (1981) estimated that at least 92.4 million people (46.2% of the U.S. population) were exposed at or above this level. Applying the 1981 U.S. EPA estimate of exposure prevalence to the current U.S. population (315 million in March 2013) ( U.S. Census Bureau 2010 ), and assuming noise levels have not changed since then, we estimate that at least 145.5 million people were at potential risk of hypertension due to noise in 2013. Lower levels (e.g., 50–55 dBA, to which a larger fraction of the population is exposed) may increase risk of myocardial infarction ( Willich et al. 2006 ).

Recent studies of individuals’ noise exposures ( Flamme et al. 2012 ) indicate that a substantial fraction of U.S. adults may be exposed to noise levels above the U.S. EPA 70-dBA L EQ(24) limit. Neitzel et al. (2012) sampled > 4,500 adults in New York City and estimated that 9 of 10 exceeded the recommended U.S. EPA limit. The Neitzel et al. (2012) study is the most comprehensive quantitative estimate of annual noise exposures in a large sample of U.S. residents in decades, and it represents a basis for developing contemporary estimates of urban U.S. noise exposures.

There are 16 metropolitan statistical areas in the United States with a population of > 4 million for which the New York City estimates might be considered representative. These areas comprised a total population of 80,621,123 in 2012 ( U.S. Census Bureau 2010 ), or 25.6% of the U.S. population. By applying the New York City exposure prevalence estimates of Neitzel et al. (2012) to these 16 largest urban agglomerations, we estimate that at least 72.6 million urban U.S. residents were exposed to annual L EQ(24) levels of > 70 dBA in 2010. By comparison, the U.S. EPA estimated in 1981 that 66 million people, or 33% of the U.S. population (not just urban dwellers), were exposed above the recommended limit ( Simpson and Bruce 1981 ). Applying the 1981 U.S. EPA estimate to 2013 census data, and again assuming no change in noise levels over that time, we estimate that 104 million individuals had annual L EQ(24) levels of > 70 dBA in 2013 and were at risk of NIHL and possibly other noise-related health effects. Unfortunately, given the lack of assessment of noise exposure in health surveillance programs in the United States, it is difficult to evaluate these estimated health impacts against observed health effects, and for some health effects metrics other than the L EQ(24) (e.g., the L DN ) are likely more appropriate.

Health Protection Policy

Given the substantial exposures to noise in the United States, the severity of associated health consequences, and the limited power of the public to protect themselves, there is a clear need for policy aimed at reducing noise exposures. Because noise is expected to rise with increasing urbanization ( García 2001 ), policy leaders need to explore the use of law as a practical tool to manage and reduce noise exposures. Here we highlight the interventions we believe hold the most promise for policy leaders. We first explain how noise can be integrated into the federal public health agenda and then explore the ways state and local governments may use the law to respond to and reduce noise.

The federal public health agenda . The United States National Prevention Strategy (NPS) can provide leadership by putting noise on the national health policy agenda. The NPS brings together 17 federal agencies (including the Departments of Transportation, Health and Human Services, Education, and Labor as well as the U.S. EPA) to provide a foundation for the nation’s prevention goal delineated under the Affordable Care Act: to increase the number of Americans who are healthy at every stage of life through focus on wellness and prevention ( National Prevention Council 2011 ). Two of NPS’s priorities are a ) to promote healthy and safe community settings that prevent injury, and b ) to empower people in ways that support positive physical and mental health. In addition, some of the objectives of the Department of Health and Human Services (DHHS), as articulated in their Healthy People 2020 goals, are to decrease the proportion of adolescents who have NIHL, reduce new cases of work-related noise-induced hearing loss ( DHHS 2013a ), increase cardiovascular health, and reduce coronary heart disease deaths ( DHHS 2013b ). These federal objectives, designed to encourage collaboration and improve decision making, can also be used to coordinate and measure the impact of prevention strategies set forth below. Although there is a large range of options for addressing noise exposures in the United States ( NAE 2010 ), we believe that direct regulation and altering the informational environment are the least costly, most logistically feasible, and most effective federal-level noise reduction interventions.

Source control through direct regulation. Direct regulation that sets maximum emission level for noise sources is the only intervention that guarantees population-level exposure reductions. The NPS supports proven strategies, and source reduction is the most cost-effective intervention to protect health ( García 2001 ). There is already evidence of the great potential for this approach in the United States: annual U.S. air transport noise exposures > 65 dBA L DN have seen a remarkable 90% reduction since 1981 (from affecting 4% of the population in 1981 to 0.015% in 2007) despite a sixfold increase in number of person-miles travelled by air. This reduction can be attributed in large part to direct federal regulation, and subsequent technological improvements of jet engines ( Waitz et al. 2007 ).

The regulatory scheme for direct source regulation is straightforward. Congress gave power to the U.S. EPA to regulate noise emitted from construction equipment, transportation equipment, any motor or engine, and electrical or electronic equipment in the Noise Control Act (NCA) of 1972 ( NCA 1972a ). Between 1972 and 1981 the U.S. EPA Office of Noise Abatement and Control (ONAC) led efforts which resulted in noise emission limits on air compressors, motorcycles, medium and heavy trucks, and truck-mounted waste compactors. An attempt to regulate lawn mowers was not well received ( Shapiro 1991 ), and the agency lost funding in 1981, when the ONAC budget was $12.7 million ($32.5 million in 2013 dollars) ( U.S. EPA 1982 ).

The U.S. EPA could resume noise control work with support from Congress and the NPS. The majority of the U.S. EPA’s funding ($7.1 billion in 2012) consists of discretionary appropriations from Congress, which means that the U.S. EPA can exercise the full scope of its regulatory authority under the NCA at any time. However, U.S. EPA funding in real dollars adjusted for inflation peaked in 1978 ( Congressional Research Service 2012 ), so it is likely that the U.S. EPA will resume activity on noise control only when Congress and the NPS support their efforts.

Altering the informational environment. The NPS seeks to empower individual decision making by addressing barriers to the dissemination and use of reliable health information. Altering the informational environment enables informed choice in partnership with direct regulation. Without source control, changing the informational environment can only offer limited reductions in noise because individuals often lack control over significant noise sources. However, several interventions have the potential to drastically alter the informational environment.

Product Disclosure

Labels that disclose the noise emitted from products promote informed consumer choice. Mandatory labeling of noise emissions is required for certain products in China, Argentina, Brazil, and the European Union ( NAE 2010 ). Disclosure will inform consumer choice only if the consumer understands the implications of what the label discloses, so we discuss product disclosures with the assumption that they will be accompanied by education.

The NCA requires that the U.S. EPA adopt regulations that label products that emit noise capable of adversely affecting the public health or welfare ( NCA 1972b ). The U.S. EPA implemented this mandate only for portable air compressors, even though there are many other, more noisy products, including children’s toys ( Hawks 1998 ). Individuals without access to education may still experience some benefit from product disclosures that are easily understood, such as warnings based on red, yellow, and green colors. The U.S. EPA could resume its work mandating disclosures with NPS leadership and Congressional funding.

Geographic noise maps alter the informational environment and are one way to ensure that noise control policy is based on objective and accurate information. The NPS seeks to expand and increase access to information technology and integrated data systems. Governments in the European Union have already prepared noise maps of roads, railways, and airports ( Commission to the European Parliament and the Council 2011 ). Although the U.S. government does not map noise levels to protect the public, the National Oceanic and Atmospheric Administration (2012) has created a noise map of the world’s oceans to investigate the impact of noise on marine species. Cities such as San Francisco have mapped traffic noise, but most cities and states would need federal support and guidance to initiate comprehensive mapping. Measurement and mapping of noise levels—following the example of the CDC’s air and water quality databases—would identify priorities for additional evaluation and help inform protective measures. Congress can appropriate funding to the U.S. EPA, ONAC, or CDC to support this work. However, mapping efforts will require a substantially increased and ongoing noise monitoring effort.

State and local action . The NPS addresses the complex interactions between federal, state, tribal, local, and territorial policies addressing community environments. The NCA was first enacted at the behest of industry trade groups that argued that national standards would protect manufacturers from the imposition of disparate and inconsistent state and local standards. However, after it was enacted, industry groups asked for a defunding of the NCA by asserting that it was best to control noise at the local level ( Shapiro 1991 ).

State and local governments can enact regulations on sources of noise not already regulated by the U.S. EPA or another federal agency. Theoretically, a mixed system where federal and state jurisdiction overlap increases functionality. In the case of noise control, however, few states and localities attempt direct regulations because they do not have sufficient market power and resources and because of preemption challenges from other law ( Air Transport Association of America v. Crotti 1975 ). Municipal regulation evolved into noise ordinances that regulate the timing and intensity of noise, are expensive and difficult to enforce, and have not proven to be effective at reducing noise ( Dunlap 2006 ).

Given these considerations, we believe that the most cost-effective legal interventions at the state and local levels are through a ) spending and procurement, and b ) altering the built environment.

Spending and procurement. A number of municipal noise sources, including emergency sirens, transit vehicles, garbage and street maintenance equipment, and construction equipment ( Bronzaft and Van Ryzin 2007 ), may be reduced through careful purchasing and contractual agreements. Some countries go so far as to require contractors to pay for temporary relocation of citizens seeking relief from construction noise ( BSM 2012 ). Adoption of procurement policies intended to reduce community noise is an opportunity for government to lead by example ( Perdue et al. 2003 ).

Altering the built environment. The NPS recommends that governments take steps to ensure safe and healthy housing because health suffers when people live in poorly designed physical environments ( Perdue et al. 2003 ). Although altering the built environment can influence individual noise exposures, it often does not reduce noise source levels. In addition, it can be construed as inherently inequitable because the recipients of noise bear the burden of exposure reduction, and those creating the noise continue to have no incentive to reduce emissions. Therefore, this intervention requires thorough analysis and careful planning.

Sustainable building design programs, such as Leadership in Energy and Environmental Design (LEED), offer the possibility of achieving noise reductions through good acoustical design ( U.S. Green Building Council 2013 ). LEED standards incorporate American National Standards Institute recommendations regarding background noise and encourage sound-absorptive finishes to limit reverberation in schools ( U.S. Green Building Council 2010 ). Improvements in construction materials, siting considerations (e.g., siting sensitive structures such as homes and schools well away from noise sources such as high traffic roads and hospitals), and design can have a dramatic impact on noise levels inside buildings—and improve the occupants’ quality of life in the process.

Although the Federal Highway Administration does not currently provide federal funding for low-noise pavement ( NAE 2010 ), such pavement can reduce noise by up to 6 dB in areas where vehicles travel at speeds > 35 miles/hr. For slower traffic, planning can reduce high noise from delivery trucks within city limits by encouraging adoption of smaller electric delivery vehicles. This scheme has already been implemented in several other countries ( Allen et al. 2012 ) and also has the potential to reduce air pollution and traffic fatalities.

We have identified a number of opportunities to lower noise exposures and ultimately improve public health while additional research is being conducted. Updated national-level estimates of individual noise exposures are needed; our use of 1981 U.S. EPA data introduces a substantial amount of uncertainty into our estimates and highlights the need for an updated national survey of noise exposures in the United States. Although prevention of different health effects will require additional research to identify appropriate exposure limits, once informed and supported by ongoing research, federal leaders can focus on lowering noise at its source, and states can prioritize altering the built environment. Meanwhile, local government can adjust their procurement policies and encourage building approaches that reduce community noise.

In the manuscript originally published online, the reported annual noise level that may increase risk for hypertension, the reported estimate of the number of people exposed at or above the annual noise level, and the authors’ estimate of the number of people at potential risk of hypertension due to noise in 2013 were incorrect in the second paragraph of the “Prevalence of Harmful Noise Exposure” section. They have been corrected here.

Acknowledgments

We gratefully acknowledge the assistance of L.A. Schwankl and S.C. Betzler in preparing this manuscript.

This work was made possible by the Robert Wood Johnson Foundation Public Health Law Attorney Fellow Program (N015293), the Network for Public Health Law, and resources from the University of Michigan Risk Science Center.

The authors declare they have no actual or potential competing financial interests.

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Noise pollution in Mumbai Metropolitan Region (MMR): An emerging environmental threat

  • Published: 30 January 2020
  • Volume 192 , article number  152 , ( 2020 )

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  • Komal Kalawapudi 1 ,
  • Taruna Singh 1 ,
  • Jaydip Dey 1 ,
  • Ritesh Vijay   ORCID: orcid.org/0000-0001-5731-560X 1 &
  • Rakesh Kumar 1  

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Noise pollution in urban areas is an emerging environmental threat which local agencies and state authorities must consider in planning and development. Excessive noise is becoming a significant problem adversely affecting the physiological and psychological health of the citizens. Present study was carried out to assess and quantitatively evaluate ambient noise levels in Mumbai Metropolitan Region (MMR) consisting of 9 cities namely Bhiwandi-Nizampur, Kalyan-Dombivli, Mira-Bhayandar, Mumbai, Navi Mumbai, Panvel, Thane, Ulhasnagar and Vasai-Virar. The noise environment was assessed on the basis of equivalent continuous sound pressure levels (L eq ), day-night noise levels (L DN ) and noise limit exceedance factor (NEF) during day and night time of working and non-working days in four different area categories, viz. industrial, commercial, residential and silence zones. Present study shows that silence zones have been the worst affected areas where noise pollution levels and NEF indicate excessive violation of permissible noise limits due to unplanned, congested and unruly spaces for developmental and commercial activities, followed closely by residential and commercial zones. Cities with separate industrial and commercial zones showed less noisy surroundings in comparison with those cities where land use pattern of industrial and commercial zones is around or overlapping each other. It can thus be concluded that appropriate demarcation and planned use of city space is important to avoid exposure to rising noise pollution levels. Based on the noise pollution in (MMR), various control measures are suggested including awareness campaign and strict compliance of the rules and regulations.

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The authors are also thankful to Maharashtra Pollution Control Board (MPCB) for providing financial support to carry out this study.

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Kalawapudi, K., Singh, T., Dey, J. et al. Noise pollution in Mumbai Metropolitan Region (MMR): An emerging environmental threat. Environ Monit Assess 192 , 152 (2020). https://doi.org/10.1007/s10661-020-8121-9

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Nexus between residential air pollution and physiological stress is moderated by greenness

  • Ka Yan Lai   ORCID: orcid.org/0000-0002-0764-244X 1 , 2 ,
  • Sarika Kumari   ORCID: orcid.org/0000-0001-8492-8508 1 ,
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  • Chinmoy Sarkar   ORCID: orcid.org/0000-0001-5374-217X 1 , 2 , 3  

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Urban living is synonymous with a higher exposure to environmental stressors such as air pollution and associated physiological stress; however, the modifying role of greenness has been understudied. We included 190,200 participants from a UK-wide cohort to examine the modifying role of residential greenness on associations between air pollutants and composite physiological stress (CPS) constructed from 13 biomarkers of three physiological functions and two organs. We found that living in areas with higher air pollutants was associated with higher CPS, whereas higher residential greenness was inversely associated with CPS. Relative to participants exposed to low air pollution and high greenness (least-impacted group), those exposed to high air pollution and low greenness (double-impacted group) had higher odds of their CPS being in the highest quartile (22% (95% confidence interval (CI): 12–31%) for PM 2.5 , 18% (95% CI: 9–28%) for PM 10 , 17% (95% CI: 7–27%) for PM 2.5–10 and 13% (95% CI: 4–23%) for NO x ), with evidence of synergistic interactions between the pollutants PM 10 , PM 2.5–10 and NO x and greenness exposures on the risk of high CPS. Considerable between-city variability was observed. The evidence points to the need for nature-based interventions, such as optimizing urban greenness for healthy cities with lower stress levels and related health burdens.

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Data availability

UK Biobank data, including linked environmental exposure metrics, are available from the UK Biobank at https://www.ukbiobank.ac.uk/ for researchers who meet the criteria for access to de-identified data. The built environment metrics of UKBUMP were developed by the authoring team based at The University of Hong Kong and linked to the UK Biobank. Several spatial databases were used in its creation, which were obtained upon request. The Bluesky color infrared data were obtained from LandMap Services of Manchester Information and Associated Services (MIMAS) at the University of Manchester. The urbanicity metrics were created using AddressBase Premium data and Integrated Transport Network Layers, which were obtained from UK Ordnance Survey GB. The greenspace typologies data 50 used to generate Extended Data Fig. 5 were obtained by accessing the Green and Blue Infrastructure (England) database under the Open Government Licence at https://www.data.gov.uk/dataset/f335ab3a-f670-467f-bedd-80bdd8f1ace6/green-and-blue-infrastructure-england . The air pollution data 43 used to generate Supplementary Fig. 1 were obtained by accessing the Modelled Background Pollution Data under the Open Government Licence at https://uk-air.defra.gov.uk/data/pcm-data .

Code availability

The study was performed as a part of a project approved by UK Biobank under a restricted Material Transfer Agreement, and thus computer codes are not publicly available. Analysis was performed using custom-made scripts in Stata v.17. The codes for exposure metrics used in the models can be requested from the corresponding author upon reasonable request.

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Acknowledgements

The research used the UK Biobank resource (approved application number: 11730). C.S. acknowledges a fellowship in Global Health Leadership from the National Academy of Medicine, Washington DC and the University of Hong Kong. J.G. acknowledges funding from the Medical Research Council: MR/T0333771 award for the Dementias Platform UK. The built environment metrics of UKBUMP used in this study were supported by a seed grant from the UK Biobank and the UK Economic and Social Research Council’s Transformative Research grant (ES/L003201/1).

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Ka Yan Lai, Sarika Kumari, Chris Webster & Chinmoy Sarkar

Department of Urban Planning & Design, Faculty of Architecture, The University of Hong Kong, Hong Kong, China

Ka Yan Lai, Chris Webster & Chinmoy Sarkar

Department of Psychiatry, University of Oxford, Oxford, UK

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Contributions

C.S., K.Y.L., C.W., J.G. and S.K. contributed to concept and design of the study. K.Y.L. and S.K. contributed to the data cleaning. K.Y.L. and C.S. contributed to the data analyses and drafted the manuscript. All authors participated in interpretation of the data. All authors contributed to critical revision of the manuscript. C.S. supervised the study. All authors read and approved the final paper.

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Correspondence to Chinmoy Sarkar .

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Extended data

Extended data fig. 1 flowchart for participant selection..

Abbreviation : NDVI, Normalized Difference Vegetation Index.

Extended Data Fig. 2 Associations of NDVI greenness with composite physiological stress allowing for non-linear associations (N = 190,200).

Models were fitted for air pollutants using restricted cubic splines with Harrell’s knots placed at 10 th , 50 th and 90 th percentiles, adjusting for sex, age, education attainment, employment status, household income, healthy lifestyle score (including smoking, alcohol consumption and dietary factors), sleeping duration, sound level of noise pollution, urbanicity, stressful life events, biological age (measured as residual of leucocyte telomere length, adjusted for chronological age and co-morbidities of cancer, cardiovascular diseases, hypertension, psychiatric disorders and respiratory diseases) and PM 2.5 . The continuous line shows the estimated composite physiological stress and the shaded regions show the corresponding 95% confidence intervals. Abbreviations : NDVI, Normalized Difference Vegetation Index.

Extended Data Fig. 3 Joint associations of air pollutants and NDVI greenness with odds of allostatic load in the 4th quartile (N = 190,990).

The joint associations of PM 2.5 and NDVI greenness ( a ), PM 10 and NDVI greenness ( b ), PM 2.5-10 and NDVI greenness ( c ), and NO x and NDVI greenness ( d ) with odds of allostatic load in the 4 th quartile using logistic regression models. Participants were stratified into 9 groups by air pollutants (Q1, Q2-Q4, Q5) and NDVI greenness (Q1, Q2-Q4, Q5) categories, with participants exposed to low air pollution (Q1) and high NDVI greenness (Q5) (least-impacted group) acting as the reference group. RERI was used to examine additive interaction between air pollution (high (Q5) vs low (Q1)) and NDVI greenness (low (Q1) vs high (Q5)), and additive interaction was statistically significant when confidence intervals did not include 0. P-interaction indicates significance of multiplicative interaction between categories of NDVI greenness and air pollutants. Models adjusted for sex, age, education attainment, employment status, household income, healthy lifestyle score (including smoking, alcohol consumption and dietary factors), sleeping duration, sound level of noise pollution, urbanicity, stressful life events and biological age (measured as residual of leucocyte telomere length, adjusted for chronological age and co-morbidities of cancer, cardiovascular diseases, hypertension, psychiatric disorders and respiratory diseases). The vertical bars show the odds ratios and the error bars show the corresponding 95% confidence intervals. The asterisks represent statistically significant (two-sided p < 0.05) point estimates. The index of allostatic load comprises nine biomarkers of three physiological functions (cardiovascular, metabolic and inflammatory functions). Abbreviations : NDVI, Normalized Difference Vegetation Index; Q, Quintile; RERI, relative excess risk due to interaction.

Extended Data Fig. 4 Joint associations of air pollutants and greenspace categories (by use) with odds of composite physiological stress in the 4 th quartile (N = 228,154).

The joint associations of PM 2.5 and total green area ( a ), PM 10 and total green area, ( b ), PM 2.5-10 and total green area ( c ), NO x and total green area ( d ), PM 2.5 and outdoor sports ( e ), PM 10 and outdoor sports ( f ), PM 2.5-10 and outdoor sports ( g ), NO x and outdoor sports ( h ), PM 2.5 and natural greenspace ( i ), PM 10 and natural greenspace ( j ), PM 2.5-10 and natural greenspace ( k ), and NO x and natural greenspace ( l ) with odds of composite physiological stress in the 4 th quartile using logistic regression models. Participants were stratified into 9 groups by air pollutants (Q1, Q2-Q4, Q5) and NDVI greenness (Q1, Q2-Q4, Q5) categories, with participants exposed to low air pollution (Q1) and high NDVI greenness (Q5) (least-impacted group) acting as the reference group. Models adjusted for sex, age, education attainment, employment status, household income, healthy lifestyle score (including smoking, alcohol consumption and dietary factors), sleeping duration, sound level of noise pollution, urbanicity, stressful life events and biological age (measured as residual of leucocyte telomere length, adjusted for chronological age and co-morbidities of cancer, cardiovascular diseases, hypertension, psychiatric disorders and respiratory diseases). The vertical bars show the odds ratios and the error bars show the corresponding 95% confidence intervals. The asterisks represent statistically significant (two-sided p < 0.05) point estimates. Abbreviations : Q, Quintile.

Extended Data Fig. 5 Map of England showing greenspace categories (by use).

The English Green and Blue Infrastructure Database was accessed from https://www.data.gov.uk/dataset/f335ab3a-f670-467f-bedd-80bdd8f1ace6/green-and-blue-infrastructure-england . Adapted from ref. 50 under an Open Government Licence v3.0 . UK Crown ©2023.

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Lai, K.Y., Kumari, S., Gallacher, J. et al. Nexus between residential air pollution and physiological stress is moderated by greenness. Nat Cities (2024). https://doi.org/10.1038/s44284-024-00036-6

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