ENCYCLOPEDIC ENTRY

Noise pollution.

Noise pollution can cause health problems for people and wildlife, both on land and in the sea. From traffic noise to rock concerts, loud or inescapable sounds can cause hearing loss, stress, and high blood pressure. Noise from ships and human activities in the ocean is harmful to whales and dolphins that depend on echolocation to survive.

Anthropology, Sociology, Biology, Ecology, Conservation

Construction Noise Pollution

A man working with a jackhammer in a construction site. Noise pollution becomes and increasingly larger issue in big cities.

Photograph by Construction Photography/Avalon

A man working with a jackhammer in a construction site. Noise pollution becomes and increasingly larger issue in big cities.

Noise pollution is an invisible danger. It cannot be seen, but it is present nonetheless, both on land and under the sea. Noise pollution is considered to be any unwanted or disturbing sound that affects the health and well-being of humans and other organisms.

Sound is measured in decibels . There are many sounds in the environment, from rustling leaves (20 to 30 decibels ) to a thunderclap (120 decibels ) to the wail of a siren (120 to 140 decibels ). Sounds that reach 85 decibels or higher can harm a person’s ears. Sound sources that exceed this threshold include familiar things, such as power lawn mowers (90 decibels ), subway trains (90 to 115 decibels ), and loud rock concerts (110 to 120 decibels ).

Noise pollution impacts millions of people on a daily basis. The most common health problem it causes is Noise Induced Hearing Loss (NIHL). Exposure to loud noise can also cause high blood pressure, heart disease, sleep disturbances, and stress. These health problems can affect all age groups, especially children. Many children who live near noisy airports or streets have been found to suffer from stress and other problems, such as impairments in memory, attention level, and reading skill.

Noise pollution also impacts the health and well-being of wildlife. Studies have shown that loud noises can cause caterpillars' dorsal vessels (the insect equivalent of a heart) to beat faster, and cause bluebirds to have fewer chicks. Animals use sound for a variety of reasons, including to navigate, find food, attract mates, and avoid predators. Noise pollution makes it difficult for them to accomplish these tasks, which affects their ability survive.

Increasing noise is not only affecting animals on land, it is also a growing problem for those that live in the ocean. Ships, oil drills, sonar devices, and seismic tests have made the once tranquil marine environment loud and chaotic. Whales and dolphins are particularly impacted by noise pollution . These marine mammals rely on echolocation to communicate, navigate, feed, and find mates, and excess noise interferes with their ability to effectively echolocate.

Some of the loudest underwater noise comes from naval sonar devices. Sonar , like echolocation , works by sending pulses of sound down into the depths of the ocean to bounce off an object and return an echo to the ship, which indicates a location for object. Sonar sounds can be as loud as 235 decibels and travel hundreds of miles under water, interfering with whales’ ability to use echolocation . Research has shown that sonar can cause mass strandings of whales on beaches and alter the feeding behavior of endangered blue whales ( Balaenoptera musculus ). Environmental groups are urging the U.S. Navy to stop or reduce using sonar for military training.

Seismic surveys also produce loud blasts of sound within the ocean. Ships looking for deep-sea oil or gas deposits tow devices called air guns and shoot pulses of sound down to the ocean floor. The sound blasts can damage the ears of marine animals and cause serious injury. Scientists believe this noise may also be contributing to the altered behavior of whales.

Among those researching the effects of noise pollution is Michel Andre, a bioacoustics researcher in Spain who is recording ocean sounds using instruments called hydrophones . His project, LIDO (Listening to the Deep Ocean Environment), collects data at 22 different locations. Back in the lab, computers identify the sounds of human activities as well as 26 species of whales and dolphins. The analysis aims to determine the effects that underwater noise is having on these animals. Andre hopes his project will find ways to protect marine animals from the dangers of ocean noise.

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Article Contents

Introduction, supplementary data, ethics approval and consent to participate, data availability.

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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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Dzhambov AM , Dimitrova DD. Occupational noise and ischemic heart disease: a systematic review . Noise Health 2016 ; 18 : 167 – 77 .

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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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  • Systematic Map Protocol
  • Open access
  • Published: 12 February 2019

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

  • Romain Sordello 1 ,
  • Frédérique Flamerie De Lachapelle 2 ,
  • Barbara Livoreil 3 &
  • Sylvie Vanpeene 4  

Environmental Evidence volume  8 , Article number:  8 ( 2019 ) Cite this article

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A Systematic Map to this article was published on 11 September 2020

For decades, biodiversity has suffered massive losses worldwide. Urbanization is one of the major drivers of extinction because it leads to the physical fragmentation and loss of natural habitats and it is associated with related effects, e.g. pollution and in particular noise pollution given that many man-made sounds are generated in cities (e.g. industrial and traffic noise, etc.). However, all human activities generate sounds, even far from any human habitation (e.g. motor boats on lakes, aircraft in the air, etc.). Ecological research now deals increasingly with the effects of noise pollution on biodiversity. Many studies have shown the impacts of anthropogenic noise and concluded that it is potentially a threat to life on Earth. The present work describes a protocol to systematically map evidence of the environmental impact of noise pollution on biodiversity. The resulting map will inform on the species most studied and on the demonstrated impacts. This will be useful for further primary research by identifying knowledge gaps and in view of further analysis, such as systematic reviews.

Searches will include peer-reviewed and grey literature published in English and French. Two online databases will be searched using English terms and search consistency will be assessed with a test list. No geographical restrictions will be applied. The subject population will include all species. Exposures will include all types of man-made sounds (industrial, traffic, etc.) in all types of environments (or media) (terrestrial, aerial, aquatic), including all contexts and sound origins (spontaneous or recorded sounds, in situ or laboratory studies, etc.). All relevant outcomes will be considered (space use, reproduction, communication, abundance, etc.). An open-access database will be produced with all relevant studies selected during the three screening stages. For each study, the database will contain metadata on key variables of interest (species, types of sound, outcomes, etc.). This database will be available in conjunction with a map report describing the mapping process and the evidence base with summary figures and tables of the study characteristics.

For decades, biodiversity has suffered massive losses worldwide. Species are disappearing (e.g. [ 36 ]), populations are collapsing (e.g. [ 15 ]), species’ ranges are changing (both shrinking and expanding) at unprecedented rates (e.g. [ 7 ]) and communities are being displaced by invasive alien species (e.g. [ 24 ]). All the above are caused by human activities and scientists regularly alert the international community concerning our responsibility [ 30 ]. In particular, urban growth is one of the major reasons for biodiversity loss [ 21 , 29 ] in that it destroys natural habitats, fragments the remaining ecosystems (e.g. [ 40 ]) and also has other impacts, such as pollution. For example, cities produce artificial light at night that disturbs circadian rhythms, impacting plants and animals [ 2 , 13 ]. Similarly, many man-made sounds are generated in cities, by traffic and numerous human activities (industrial, commercial, etc.) [ 39 ]. In fact, anthropogenic noise is omnipresent and ranges beyond cities. All human activities generate noise, even far from cities (e.g. motor boats on lakes, aircraft in the sky, etc.) and those sounds can reach wild, uninhabited places [ 16 ].

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 use, hear and emit sounds (e.g. Romer and Bailey 1990 [ 32 ]). Sounds are often used to communicate between partners or conspecifics, or to detect prey or predators. The problem arises when sounds turn into “noise”, i.e. a disturbance or even a form of pollution. In this case, man-made sounds can mask and inhibit animal sounds and/or animal audition and it has been shown to affect communication [ 37 ], use of space [ 10 ] or reproduction [ 3 ]. This problem affects many biological groups such as birds [ 19 ], amphibians [ 9 ], reptiles [ 22 ], fish [ 1 ], mammals [ 34 , 35 ] and invertebrates [ 6 ]. It spans several types of ecosystems including terrestrial [ 18 ], aquatic [ 17 ] and coastal ecosystems [ 33 ]. Many types of sounds produced by human activities would seem to be a form of noise pollution affecting biodiversity, including traffic [ 20 ], ships [ 38 ], aircraft [ 4 ] and industrial activities [ 23 ]. Noise pollution can also act in synergy with other disturbances, for example light pollution [ 26 ].

For decades, noise regulations have focused on human disorders but recently, public policies in biodiversity conservation have started to pay more attention to noise pollution. 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. Today in Europe, quiet areas are recommended to guarantee the tranquility of fauna [ 12 ]. Since 2000 in France, an article in “Code de l’environnement” (art. L571-1) has contained the terms “harms the environment” with respect to disturbances due to noise. To further mitigate the effects of noise pollution on biodiversity, the French Ecology Ministry wants to obtain more information on the impacts of noise on biodiversity in order to initiate policies focused on species which are known to be highly exposed. The Ministry is also interested in the types of impacts that have been effectively demonstrated and in the types of noise that have been proven to affect wildlife. We proposed to produce a systematic map of the literature dealing with this issue to provide the Ministry with a report on current knowledge and to identify sectors (sources, types of impact, etc.) where research is needed to fill in knowledge gaps.

A preliminary search did not identify any existing systematic maps or reviews, however a few reviews of the literature have been published. Most of them concern only one biological group, such as Morley et al. [ 25 ] on invertebrates, Patricelli and Blickley [ 27 ] on birds and Popper and Hastings [ 28 ] on fish. A synthesis published by Shannon et al. in 2016 [ 34 ] is more general and comes closer to a systematic map, but the search strategy would seem to be incomplete. The literature search was performed on only one database (ISI Web of Science within selected subject areas) and the review did not include grey literature. Finally, a meta-analysis was performed by Roca et al. [ 31 ], but it dealt exclusively with birds and amphibians and the authors were interested in only one effect (vocalization adjustment).

This report describes the protocol used to develop a systematic map of noise pollution and biodiversity. The systematic map will provide further information on the knowledge currently available on this issue. It will include all the relevant studies (with grey literature) collected after three screening stages. An open-access database will be produced, containing metadata for each study on key variables of interest (species, types of sound, effects, etc.). This database will be available in conjunction with a map report describing the mapping process and the evidence base. It will include aggregate data and tables of the study characteristics to highlight any gaps in the research evidence concerning the issue.

Objective of the map

The objective of the systematic map is to assess the biological and ecological impacts of noise pollution. Noise pollution is considered here as anthropogenic noise. It does not include noise made by other animals (e.g. chorus frogs) or natural events (e.g. thunder, waterfalls). The systematic map will address all man-made noise whatever its origin (road traffic, industrial machines, boats, planes, etc.), its environment or media (terrestrial, aquatic, aerial) or its type (infrasound, ultrasound, white noise, etc.). The goal is to provide a comprehensive image of the available knowledge on this topic and to quantify the literature by taxonomic groups, types of impacts and even types of studies. For this reason, the systematic map will cover all species. It will deal with all kinds of impacts, from biological to ecological (use of space, reproduction, communication, abundance, etc.). It will encompass in situ studies as well as ex situ studies (aquariums, laboratories, cages, etc.).

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

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

The components of the systematic map are detailed in Table  1 .

Searching for articles

Searches will be performed using exclusively English search terms.

Only studies published in English and in French will be included in this systematic map, due to limited resources and the languages understood by the map team. The list of search terms is presented below (see “ Search string ” section).

Search string

A scoping exercise was conducted on the “Web of Science Core Collection” database to build-up the search strings. Terms describing the exposure (noise pollution) and the population (biodiversity) were combined in an iterative manner until best performance was obtained. Terms describing effects (outcomes) were not included because the aim of the map is to document the available literature without any a priori restrictions on the types of effects measured in the articles.

The search string that produced the highest efficiency (number of hits compared to the test list) is presented below (see Additional file 1 for more details on the process to build the search string).

((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 ) and used 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 review team prior to the review.

Eight key articles identified using three relevant reviews:

Brumm [ 5 ] (two articles);

Cerema [ 8 ] (three articles);

Dutilleux and Fontaine [ 11 ] (three articles).

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).

Online publication databases

We first listed the databases to which the members of our review team had access, databases that covered ecology and that guaranteed reproducibility (accessibility by researchers from all over the world, advanced search functions, etc.). The resource limitations weighing on the project did not allow us to cover more than two databases given the number of articles obtained during the scoping exercise.

On the basis of the criteria listed above, the two databases below were selected:

“Web of Science Core Collection” on the Web of Science platform (Clarivate). See Additional file 3 for citation indexes included in the “Web of Science Core Collection” to which the review team had access via the team members’ institutions. As explained above, the scoping exercise was conducted using this database. It returned 7859 articles (the search was run on the 14 December 2018 and covered SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI and CCR-EXPANDED, without any timespan restrictions). The search comprehensiveness value was 92% (60 articles in the test list were referenced in the WOS CC and 55 were retrieved by the string).

Scopus (Elsevier). The search string described above was adapted to take into account differences in search syntax. It returned 11,186 articles (a preliminary search was run on 14 December 2018, without any timespan restrictions). The comprehensiveness value was 92% (61 articles in the test list were indexed in Scopus and 56 were retrieved by the string).

Approximately 6000 articles were listed in both databases. One of the articles not indexed in the WOS CC was indexed in Scopus and was retrieved by the search string. Consequently, combining the two databases, the global comprehensiveness value was 93% (61 articles indexed and 57 articles retrieved by the search string). See Additional file 4 for more details on the comprehensiveness values.

Internet searches

Searches will be performed using the search engines:

Google Scholar ( https://scholar.google.com/ );

BASE ( https://www.base-search.net ) and/or CORE ( https://core.ac.uk/ ).

The English search string detailed above will be used. If necessary, the search string will be modified as per the search-engine help files (when provided). To minimize bias in favor of published literature in search results provided by Google Scholar [ 14 ], searches will be performed on titles only and the first 300 hits will be screened (based on sorting by relevance of results if possible).

Specialist sources

The following French specialist organizations will be searched for relevant publications, including grey literature, using manual searches of their websites and automatic search facilities using French keywords if possible:

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

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

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

Supplementary searches

A call for literature will be conducted through a professional network to find non peer-reviewed literature, including reports published in French or in English. Specialized organizations will also be requested to amplify the call for literature using their network, their web forum or their mailing list. Social media ( http://www.academia.edu , http://www.researchgate.net and http://www.linkedin.com ) will be used to alert the research community concerning the systematic map and to request that subject experts submit non peer-reviewed publications.

Article screening and study eligibility criteria

Screening process.

Using the predefined inclusion/exclusion criteria detailed below, article selection will proceed according to a three-stage hierarchical process, i.e. first title, then abstract and finally the full text.

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

The three screening stages will be conducted by two or more reviewers. To assess the consistency of the inclusion/exclusion decisions, a Kappa test will be performed. To that end, before the actual screening process, a set of articles will be randomly selected and screened by each of the reviewers independently. The operation will be repeated until reaching a Kappa value higher than 0.6. Whatever the Kappa value, disagreements will be discussed and resolved between the reviewers before beginning the screening process.

During the scoping stage conducted in the “Web of Science Core Collection”, the three stages of the screening process were tested by one reviewer in order to refine the eligibility criteria. For the articles screened during the scoping stage, a second reviewer will examine the rejected articles to assess the consistency of the inclusion/exclusion decision.

Eligibility criteria

Article eligibility will be based on the list of criteria detailed in Table  2 . The list of all articles will be provided, informing the inclusion/exclusion decisions at the three screening stages and, in case, reasons for the exclusion (see the code book in Additional file 5 ).

Data coding strategy

All the studies passing the three screening stages will be included in the mapping database.

Coding strategy

Each article will be coded based on the full text using keywords and expanded comments fields describing various aspects of study (see the code book in Additional file 6 ).

The key variables will include:

Study description:

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

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

Language (English/French);

Publication type (journal article, book, thesis…);

Study content (study, review, meta-analysis, other, etc.);

Study characteristics:

Country where the study was conducted;

Type of population studied (species or species groups);

Type of exposition, source of noise (e.g. urban, transportation, industrial activity, recreation, other), type of environment or media (terrestrial, aerial, aquatic), type of noise (artificial, real, recorded);

Type of impacts, used to describe subtopics of noise pollution (e.g. space use, reproduction, communication, abundance, etc.) in relation to the outcomes;

Information on study quality:

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

Experimental (causal)/Observational (correlative) study;

For experimental studies, the type of comparator (spatial/temporal).

As far as possible, controlled vocabulary will be employed to code the variables (e.g. publication type, dates, country, etc.), using thesaurus or ISO standards (e.g. ISO 639-1 for the language publication variable). To categorize the sources of noise and the outcomes (effects), we will use the review Shannon et al. [ 34 ] that give an example of categorization (see in this publication Table 2, page 988 about the sources of noise and Table 3, page 889 about the impacts of noise).

Each selected article will be double coded by two reviewers. If, due to resource limitations, true double coding is not possible, an a posteriori check will be carried out by a second reviewer and potential disagreements will be discussed until a consensus is reached.

Study map and presentation

Where there is more than one study found in an article, each study will be recorded as a specific entry in the database.

The database will be open access and included as an appendix to the systematic map publication. To ensure reusability and long-term preservation, the database will, if possible, be deposited as a.csv file in a data repository such as Zenodo.

The final systematic map will include summary figures and tables of the study characteristics. Possible 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) will be identified e.g. by cross-tabulating key meta-data variables in heat maps (e.g. biological groups and outcomes). Based on these results, recommendations will be made on priorities for future research and mitigation of noise pollution.

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Authors’ contributions

RS is the project scientific coordinator of the map. RS originated the idea of the systematic map, conducted the scoping stage and wrote the draft manuscript. BL assisted the team concerning methods and CEE guidelines. FF helped with the search strategy. SV brought her expertise about noise pollution public policies. All authors read and approved the final manuscript.

Authors’ information

RS, BL and SV are scientists. FF is an academic librarian. BL works at the French Collaboration for Environmental Evidence Center, hosted at FRB (Paris).

Acknowledgements

RS thanks Nicola Randall for advice.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Data sharing is not applicable to the systematic map protocol in that no datasets were generated for this article. Datasets produced by the systematic map will be shared publicly.

Consent for publication

Not applicable.

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Sordello, R., Flamerie De Lachapelle, F., Livoreil, B. et al. Evidence of the environmental impact of noise pollution on biodiversity: a systematic map protocol. Environ Evid 8 , 8 (2019). https://doi.org/10.1186/s13750-019-0146-6

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The growing movement against noise pollution

Pien Huang

As more research shows how noise pollution can have severely harmful impacts on our health, there is a growing movement looking for ways to make communities quieter and healthier.

PIEN HUANG, HOST:

Noise - it's a part of life.

(SOUNDBITE OF CARS BEEPING)

HUANG: The sounds you hear most may depend on where you live - a rural community versus urban, city or suburbs - but a completely quiet home in America is hard to come by. And according to the experts, no matter where you live, it's getting louder.

JAMIE BANKS: We have more transportation around us. This might be road traffic, rail traffic, air traffic. There's many other sources of noise coming from outdoor power equipment, industry, entertainment venues and so forth.

HUANG: Jamie Banks is founder and president of the nonprofit Quiet Communities, and groups like hers are part of a growing movement that sees chronic noise exposure as not just a nuisance but a health risk.

(SOUNDBITE OF MONTAGE)

UNIDENTIFIED REPORTER #1: The medical community is beginning to notice the magnitude and long-term effects that noise has at the cellular level.

UNIDENTIFIED REPORTER #2: New research published in the Journal of the American College of Cardiology finds such noise pollution may have an effect on your heart health.

HUANG: New York City Council Member Gale Brewer is trying to make one of America's noisiest cities a little quieter.

GALE BREWER: New York City is exciting and noise comes with it. For me, the issue is the noise has to stay within the Department of Environmental Protection guidelines because they exist, and that's the law.

HUANG: She introduced legislation that would require emergency vehicles to use low-frequency sirens. This comes as noise complaints have skyrocketed since the pandemic.

BREWER: In the last year, we've had, you know, 300 complaints about noise, including some of the ones that you just mentioned - sirens, leaf blowers, construction noise is another one. And the city has had 45,000 complaints to 311.

HUANG: Noise is something many of us have learned to live with. We just tune it out. But noise researcher Erica Walker says that that complacency can be a problem, especially in places with chronic noise pollution, because it's affecting our health. I've spent years learning how to block out the din of daily life, and now I wanted to learn how to unblock it to understand just how much noise we live with. So I went on a sound tour with Walker. It's the middle of the day in the middle of the summer.

ERICA WALKER: We're in Kennedy Plaza in downtown Providence, R.I.

HUANG: We're in the middle of the city of Providence, where Walker is a noise researcher at Brown University.

WALKER: You got people, transportation, music. This is just, like, quintessential urban environment.

HUANG: She studies how noise pollution affects people's health. Our first stop is the bus depot, where we meet a woman named Keisha (ph) who asked us to only use her first name because we were discussing what is a contentious issue in the community - which is sound. She doesn't mind the way the city sounds.

KEISHA: Trees, wind, buses, people, birds, public (laughter) - I can't complain. I'm just trying to get to work.

HUANG: Here, the sounds are temporary, but it's the noise at home that's the problem.

KEISHA: Businesses with loud music - it's ridiculous - all hours of the night. It's crazy. Call the police - nothing gets done. I can't sleep with a speaker coming out of a SUV till 7:00 in the morning.

HUANG: Noise pollution is unwanted sound, and it can affect the body in a few different ways. For those who live or work in very loud places, it can damage their hearing. But Walker says it can still affect their health.

WALKER: It's that - yeah, it's that response of calling 311 over and over and over again. It's the - I can't sleep at night. It's the - I feel like I'm going to have to sell my house and move out. It is the - I had to go to the emergency room because I had a panic attack. It's - I can't sleep. I can't hear my children. It's all of those things.

HUANG: Chronic noise exposure in places where you live can put your body in constant fight-or-flight mode. It can lead to hypertension, heart problems and a decline in mental health. Walker came to this work because of her own experience. Years ago, she was living in an apartment in Boston.

WALKER: A family moves in above me with two really small kids. And, of course, those two very small kids ran across their floor, which was my ceiling, for, like, 24 hours a day.

HUANG: While it sounded like joy to their parents, it was a constant stressor in her life. She documented the noise, started recording her stress levels and even collected her saliva to test for stress hormones.

WALKER: When I go hard, I go hard.

HUANG: Her goal was to get the family evicted until a trusted friend channeled her frustrations into the fields of public health, helping communities deal with noise. Next, we head to a residential neighborhood.

WALKER: So we're in a really posh neighborhood off of Blackstone Boulevard in Providence, R.I.

HUANG: We're standing in the shade of a leafy tree next to a beautiful lawn. You can hear the low hum of air conditioning, and you can hear the birds.

WALKER: I just feel like everything just slowed down considerably. You know, you hear an occasional dog barking. Cars drive by slower. You feel like you can just hear yourself think.

HUANG: Walker says that this is the sound of privilege and that this quiet should be something everyone gets in their lives. But we are standing in a neighborhood of million-dollar homes. It's where a lot of professors live, though not Walker.

Erica, should we head to our last stop?

WALKER: Yeah, absolutely. I'm ready.

HUANG: Where are we headed?

WALKER: We're headed to Pawtucket, R.I., which is where I live.

HUANG: The impacts of noise pollution can't be fully captured in decibels. That's what Walker's research shows. A few years ago, she did a study on people living near Fenway Park, which is an open-air baseball stadium in Boston. On game days, there's music. There's announcers. There's military aircraft flyovers.

WALKER: So yeah, they can be extremely loud, but it was something that the community agreed to, right?

HUANG: But when the stadium was used as a concert venue, the neighbors got upset, even though the volume of the sound was about the same.

WALKER: People were like, we didn't sign up for this. The emotional response to the concerts was just outrageous.

HUANG: Walker found that the source of the noise and whether people felt like they had agreed to it matters a lot.

WALKER: I'm more concerned about the emotional responses because I feel like that is what's driving the health impacts.

HUANG: We get to Pawtucket, just north of Providence. It was an early hub for the textile industry, and it still has a lot of manufacturing.

HUANG: We stand on a narrow sidewalk overlooking six lanes of high-speed traffic on Interstate 95.

WALKER: On one side, there's, like, houses. There's a street. There's a little sidewalk, and there's the interstate.

HUANG: It's the view from Walker's home.

WALKER: The traffic is pretty much 24 hours a day.

HUANG: Walker owns a unit in a converted textile mill, and as a noise researcher, she's got some tricks to mask the sounds.

WALKER: At night, I do more brown noise. It sort of offsets the sound from the heavy trucks. But during the day, like, a soundtrack that sounds like waterfalls, that really helps.

HUANG: But it's not just the noise. The things that cause the noise cause other problems, too.

WALKER: I run around here, right? This is my neighborhood. I run. And sometimes, after I get finished running, I definitely can taste, like, a little soot in my mouth. So I know that there are air quality issues.

HUANG: Walker calls noise pollution a canary in a coal mine for air pollution, water pollution, visual pollution. Basically, if it's noisy, that means that there are other contaminants.

WALKER: You know, I know people would ask, well, why would somebody want to live next to Interstate 95? And it's like, for a lot of people, they have no choice. And this was literally the only place I could afford.

HUANG: She says our cities and neighborhoods can be better designed for reducing the stress of noise pollution. One of her favorite quiet places is a park in Boston in the middle of a hospital district with sirens going off and helicopters overhead.

WALKER: But, like, you walk up a little hill. You get to the top of this park, and it is, like, one of the most quiet and serene places I've ever been in.

HUANG: She says that nothing beats the feeling of simply being at peace.

(SOUNDBITE OF MUSIC)

HUANG: Jamie Banks wants more communities to find that peace. She's the founder and president of Quiet Communities. It's a nonprofit that works to reduce the harms of noise pollution. We called her to talk about how far the U.S. has to go in addressing those harms. We started out talking about the health risks that noise pollution poses.

BANKS: When people think of noise, they automatically think about their ears. And when noise is loud enough, it can certainly damage the ears, and chronic noise can also damage the ears. But there's many other non-hearing health effects of noise. So what happens is that each noise event can set off an involuntary stress response in the body. And what happens is that noise can activate what's known as the autonomic nervous system. That's the nervous system that controls involuntary things like our heart rate, blood pressure, breathing and so forth. So when the autonomic nervous system gets activated, stress hormones like cortisol and epinephrine are released. And this increases things like blood pressure, heart rate, blood sugar, these kinds of risk factors. Now, when people are hearing chronic noise, this puts them into a chronic stress state. This can cause, over time, things like heart disease, high blood pressure, anxiety, depression, metabolic disturbances and even increase premature mortality from these types of conditions.

HUANG: I wanted to ask you about the distribution of noise pollution. So there was a 2017 study in the journal Environmental Health Perspectives, and what it found was that noise pollution is worse in segregated cities and neighborhoods with predominantly Black and brown residents. And it's been a few years since that study. So can we say whether pollution - noise pollution has gotten better or worse in these places?

BANKS: That's a good question. There is nothing to suggest that it's gotten better. A lot of the noise pollution that are being experienced by those communities are tied to historic placement of those communities in areas that might be closer to industry, that might be closer to airports and so forth - things that are sources of loud and chronic noise. Those kinds of things are still being perpetuated today in policy decisions that tend to protect wealthier communities from those sorts of exposures and not protect poor communities as well.

HUANG: What are some of the measures that have been used to protect communities from noise? And what can city or federal officials do to address these disparities when it comes to that?

BANKS: Pien, the first thing that's really needed is a greater awareness about noise and its adverse effects. There's very little awareness, and this stems from the fact that the United States today does not have an effective noise control program. In the 1970s, there was a program, and that was doing things like educating people, providing funding for research and so forth, and really making people more aware of the dangers of noise.

HUANG: You know, as we're talking, I'm wondering if there are communities or cities that you have found that have done the best in addressing noise pollution. And I'm wondering how they did it.

BANKS: Unfortunately, a lot of the work has been done over in Europe. And so anecdotally, we know that people that we correspond with have - that have gone over there say, wow, it is much quieter over there. There's a calmer environment, a quieter environment in general. There's even some countries that have, you know, no-noise days, like on Sundays.

HUANG: I mean, what do you think is the difference between, you know, the policies that they have and are able to implement in some of these places in Europe versus what you're able to accomplish here?

BANKS: In the early 2000s, the European Union created a noise directive that gave general guidance for how communities could start to pay attention to noise and mitigate noise. You know, just like we have states in the United States, the European Union has its individual states or countries. Each of those countries are obliged to submit a strategic plan on how they're going to reduce noise. And what they do is identify the most common exposures - transportation is a big one - air, rail and road transportation - and then identify ways to mitigate it.

HUANG: I'm wondering what the ultimate goal for a group like yours is. You know, do you envision cities, you know, like, parts of the country without noise? Like, what is the goal for you?

BANKS: We - our goal is to encourage communities to be aware of noise and to promote quiet as a valuable natural resource. So quiet is important for learning. It's important for health and well-being. It's important for our environment. And of course, we're going to have sources of noise, but what we want to do is prevent the most excessive sources of noise from harming people and the environment.

HUANG: Jamie Banks is the founder and president of Quiet Communities. Thanks so much for joining us.

BANKS: Thank you for having me.

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Noise Could Take Years Off Your Life. Here’s How.

By Emily Baumgaertner ,  Jason Kao ,  Eleanor Lutz ,  Josephine Sedgwick ,  Rumsey Taylor ,  Noah Throop and Josh Williams June 9, 2023

  • Share full article

We used a professional sound meter to measure the din of daily life and talked to scientists about the health risks it can pose.

Bankers Hill, San Diego

On a spring afternoon in Bankers Hill, San Diego, the soundscape is serene: Sea breeze rustles through the trees, and neighbors chat pleasantly across driveways.

Except for about every three minutes, when a jet blazes overhead with an ear-piercing roar.

A growing body of research shows that this kind of chronic noise — which rattles the neighborhood over 280 times a day, more than 105,000 each year — is not just annoying. It is a largely unrecognized health threat that is increasing the risk of hypertension, stroke and heart attacks worldwide, including for more than 100 million Americans .

We’ve all been told to limit the volume on our headphones to protect our hearing. But it is the relentless din of daily life in some places that can have lasting effects throughout the body.

Greenpoint, Brooklyn

Anyone who lives in a noisy environment, like the neighborhoods near this Brooklyn highway, may feel they have adapted to the cacophony. But data shows the opposite: Prior noise exposure primes the body to overreact, amplifying the negative effects.

Even people who live in relatively peaceful rural and suburban communities can be at risk. The sudden blare of trains that run periodically through D’Lo, Miss. (population: less than 400), can be especially jarring to the body because there is little ambient noise to drown out the jolt.

D’Lo, Miss.

We went to neighborhoods in rural Mississippi, New York City, and suburban California and New Jersey to measure residents’ noise exposure and interview them about the commotion in their lives. We consulted more than 30 scientists and reviewed thousands of pages of research and policy to examine the pathology and epidemiology of noise.

What noise does to your body

A siren shrills. A dog barks. Engines thrum. Jackhammers clack.

research articles on noise pollution

Unpleasant noise enters your body through your ears, but it is relayed to the stress detection center in your brain.

This area, called the amygdala , triggers a cascade of reactions in your body. If the amygdala is chronically overactivated by noise, the reactions begin to produce harmful effects.

The endocrine system can overreact, causing too much cortisol, adrenaline and other chemicals to course through the body.

The sympathetic nervous system can also become hyperactivated, quickening the heart rate, raising blood pressure, and triggering the production of inflammatory cells.

Over time, these changes can lead to inflammation, hypertension and plaque buildup in arteries , increasing the risk of heart disease, heart attacks and stroke .

To understand this pathway, researchers broke it down: They scanned the brains of people as they listened to unpleasant sounds — styrofoam rubbing, nails on a chalkboard, a dentist’s drill — and watched live as their amygdalas activated. They also strapped blood pressure monitors and noise dosimeters onto auto assembly plant workers during a shift to see their blood pressures and heart rates rise with their noise exposure.

To simulate relentless nights, scientists played dozens of sporadic recordings of passing trains and planes overhead in healthy volunteers’ bedrooms — recordings taken of real disruptions from people’s homes. They found that the next morning, the volunteers had higher adrenaline levels, stiffened arteries, and spikes in plasma proteins that indicate inflammation.

Is your life noisy? Tell us about the noise in your life and learn an easy way to measure it .

When researchers analyzed the brain scans and health records of hundreds of people at Massachusetts General Hospital, they made a stunning discovery: Those who lived in areas with high levels of transportation noise were more likely to have highly activated amygdalas, arterial inflammation and — within five years — major cardiac events.

The associations remained even after researchers adjusted for other environmental and behavioral factors that could contribute to poor cardiac health, like air pollution, socioeconomic factors, and smoking.

In fact, noise may trigger immediate heart attacks: Higher levels of aircraft noise exposure in the two hours preceding nighttime deaths have been tied to heart-related mortality.

How loud is too loud?

Sound is often measured on a scale of decibels, or dB, in which near total silence is zero dB and a firecracker exploding within a meter of the listener is about 140 dB.

We used a professional device called a sound level meter to record the decibel levels of common sounds and environments.

Busy street

Freight train

Compared with a quiet room , a passing freight train peaks at about four times as many decibels.

But the difference in how loud the train sounds to the ear is much more dramatic: The train sounds more than 500 times as noisy.

That’s because the decibel scale is logarithmic, not linear: With every 10 dB increase, the sense of loudness to the ear generally doubles. And that means regular exposure to even a few more decibels of noise above moderate levels can trigger reactions that are harmful to health.

According to the World Health Organization , average road traffic noise above 53 dB or average aircraft noise exposure above about 45 dB are associated with adverse health effects.

Nearly a third of the U.S. population lives in areas exposed to noise levels of at least 45 dB, according to a preliminary analysis based on models of road, rail and aircraft noise in 2020 from the Department of Transportation.

This chart shows how many people in the United States may be exposed to various outdoor noise levels, on average. Since transportation patterns in 2020 were low because of the pandemic, researchers suspect that current transportation-related noise could be notably higher.

3 million people in the U.S. may live in areas with average outside noise levels above 70 dB

60-70 dB 9 million

232 million

In this Brooklyn apartment, the windows are closed, but indoor sound levels are consistently above the maximum average levels recommended by the W.H.O.

Brooklyn-Queens Expressway

The nighttime noise that a person in such an environment experiences is considered particularly detrimental to health because it can fragment sleep and trigger a stress response, even if the person does not recall being roused.

The W.H.O. has long recommended less than 40 dB as an annual average of nighttime noise outside bedrooms to prevent negative health effects, and less than 30 dB of nighttime noise inside bedrooms for high-quality sleep. That’s even quieter than inside this house in D’Lo, when a train isn’t going by.

D’Lo, Miss., in between trains.

Mounting research suggests that the relationship between noise levels and disease is eerily consistent: A study following more than four million people for more than a decade, for example, found that, starting at just 35 dB, the risk of dying from cardiovascular disease increased by 2.9 percent for every 10 dB increase in exposure to road traffic noise.

The increase in risk of dying from a heart attack was even more pronounced: Also starting at just 35 dB, it increased by 4.3 percent for every 10 dB increase in road traffic noise.

Not all loud noise is equal

At High Tech Middle School in Point Loma, San Diego — less than a mile from the runway of San Diego International Airport — the roofs above classrooms are heavily insulated to mitigate the rumble. But students still have a term for an aircraft interruption so loud that it halts discussion: the Point Loma Pause.

Scientists believe that pronounced fluctuations in noise levels like this might compound the effects on the body. They suspect jarring sounds that break through the ambience — recurring jet engines, a pulsating leaf blower, or the brassy whistle of trains — are more detrimental to health than the continuous whirring of a busy roadway, even if the average decibel levels are comparable.

To visualize the concept, Swiss researchers measured and compared transportation noise along a highway with a railroad track, over the course of a night.

They found that the highway and the railroad had the same average decibel level over the eight-hour night.

But while the hum of the highway remained relatively steady throughout the night…

…the periodic passing of trains caused far more dramatic variation, a sound quality linked to harm.

In a subsequent Swiss study , higher degrees of nighttime “noise intermittency” — or the extent to which sound events were distinguishable from the background levels — were associated with heart disease, heart attacks, heart failure and strokes.

Who is most at risk?

As with so many health issues, poor people and communities of color are more likely to experience excessive noise exposure because they often have fewer housing choices and are more likely to live near high-traffic roads, raucous waste dumps and industrial areas.

According to a study of more than 94,000 schools , students in those estimated to be most highly exposed to road or aviation noise were significantly more likely to be eligible for free or reduced-price meals and to be Hispanic, Black, or Asian/Pacific Islander. Such excess noise in schools is associated with heightened stress hormones , lower reading scores and even hyperactivity among children.

Nighttime noise shows similar inequities. Census data shows that city communities with almost no low-income residents averaged 44 dB at night, compared with about 47 dB in those where half of residents fall below the poverty line. Neighborhoods with almost no Black residents averaged about 42 dB at night, compared with about 46 dB in communities that were three-fourths Black.

The difference of a few dBs might not seem like much, but for every one dB increase, the risk of developing cardiovascular disease climbs by roughly another percentage point, according to a preliminary analysis of more than 100,000 U.S. nurses. And as dBs climb, so too do associations with death because of cardiovascular disease and heart attack.

The disparities in noise exposure are likely to be much larger than the noise model suggests, researchers said, since wealthier households and schools are more likely to install triple-pane windows and more insulation. And the inequities are not unique to the United States: Spatial modeling has revealed similar disparities within various countries across four other continents.

What can be done?

Fifty years ago, under the Noise Control Act of 1972, the newly formed Environmental Protection Agency was a trailblazer in recognizing the danger of noise and addressing it: It educated the public, established safety limits, published deep analyses on various culprits and recommended actions to mitigate harm.

But its office of noise abatement was defunded by the Reagan administration, rendering policies unenforceable and regulatory criteria obsolete. The Occupational Safety and Health Administration’s eight-hour workplace noise limit is still 90 dB.

European countries have far outpaced the rest of the world in regulating noise. The European Union requires member nations to monitor and assess sound levels across regions and to produce new action plans every five years to address communities at greatest risk. The E.U. now mandates quiet brake locks on rail freight fleets and noise labels on outdoor power equipment ; it also requires noise reduction in car manufacturing and mitigation efforts at airports .

Individual cities and countries have taken additional measures. Paris has installed noise cameras that measure the sound level of vehicles and fine drivers who exceed them. Berlin has used new bike lanes to reduce the flow of engine-powered vehicles and move the source of the noise to the center of the road, away from houses. Switzerland has introduced national “quiet hours” — overnight, one midday hour on weekdays, and all day on Sundays.

While scientists say it’s too soon to make a prediction about the effects of these policies on cardiovascular health, several European countries have reported tens of thousands fewer residents exposed to major sources of noise.

Like many health issues, protection against noise would be economically advantageous. Economists who analyzed health care spending and productivity loss because of heart disease and hypertension have argued that a 5 dB reduction in U.S. noise could result in an annual benefit of $3.9 billion .

But unlike most other contributors to heart disease, noise cannot be addressed fully between a patient and a doctor. Protection requires changes in local, state and federal policy.

In the meantime, in D’Lo, Miss., George Jackson has repeatedly jacked his home to decrease the vibration. In Mendenhall, Carolyn Fletcher tried resealing her windows. In Bankers Hill, Ron Allen says all he can do is take vitamin supplements and plug his ears.

Sources and methodology

For the decibel graphic on the videos and the graphic comparing decibel levels, we measured decibels using a SoundAdvisor Model 831C sound level meter from Larson Davis. In both cases, we show A-weighted decibels to emphasize the frequencies that are available to the human ear and that are commonly used in health studies and regulatory requirements. For each video, we positioned the sound level meter next to the camera, which was about shoulder height.

For the decibel graphic, we measured sound levels in an empty room; on the sidewalk of a busy New York City street; and a few inches away from a hair dryer in a quiet room. The videos show decibel changes on a linear scale.

Most research and policy cited in this article used A-weighted measurements.

Estimates of the number of people in the United States exposed to each decibel range do not include U.S. territories and are from Department of Transportation data analyzed by Edmund Seto and Ching-Hsuan Huang at the University of Washington.

The data for the Swiss transportation noise chart was provided by Jean Marc Wunderli at the Swiss Federal Laboratories for Materials Science and Technology, and it was derived from research in the Journal of Exposure Science and Environmental Epidemiology.

Anatomy references are from the third edition of “Anatomische Atlas,” edited by Anne M. Gilroy, Brian R. MacPherson and Jamie C. Wikenheiser.

Additional sources

Jamie Banks , president of Quiet Communities and chair of the Noise & Health Committee at the American Public Health Association

Dr. Mathias Basner , sleep and health researcher, University of Pennsylvania

Stuart Batterman , professor of environmental health sciences, University of Michigan

Rachel Buxton , soundscape ecologist, Carleton University

Joan Casey , assistant professor, University of Washington School of Public Health

Timothy William Collins , professor of geography, University of Utah

Andreas Daiber , molecular cardiologist, University Medical Center Mainz

Gary Evans , environmental and developmental psychologist, Cornell University

Dr. Daniel Fink , board chair, The Quiet Coalition

Kurt Fristrup , affiliate research scientist at Colorado State University, retired sound researcher at the National Park Service

Ching-Hsuan Huang , doctoral candidate, University of Washington

Chandra Jackson , cardiovascular epidemiologist and investigator, National Institutes of Health

Peter James , environmental epidemiologist, Harvard Medical School

Chucri Kardous , retired research engineer, National Institute for Occupational Safety and Health

Nina Lee , doctoral student and research assistant at the Brown Community Noise Lab

Dr. Thomas Münzel , chief of cardiology, University Medical Center Mainz

Dr. Jose V. Pardo , professor of psychiatry, University of Minnesota

Dr. Andrei Pyko , environmental epidemiologist, Karolinska Institutet

Rebecca Rolland , speech-language pathologist and Harvard lecturer

Charlie Roscoe , postdoctoral fellow, Harvard University

Edmund Seto , associate professor of Environmental and Occupational Health Sciences, University of Washington

Ed Strocko , director of the Office of Spatial Analysis and Visualization, Bureau of Transportation Statistics

Dr. Ahmed Tawakol , associate professor of medicine, Harvard Medical School

Danielle Vienneau , group leader, Swiss Tropical and Public Health Institute

Erica Walker , assistant professor of epidemiology, Brown University School of Public Health

Jean Marc Wunderli , chair of the acoustics and noise control lab, Swiss Federal Laboratories for Materials Science and Technology

Special thanks to community members in D’Lo, Mendenhall and Braxton, Miss.; Loma Portal, Ocean Beach and Bankers Hill in San Diego, Calif.; South Orange, N.J.; and Greenpoint, Brooklyn.

What to Know About Heart Health

Heart attacks and strokes are among the leading causes of death around the world, but there are ways to protect yourself..

Coronary artery bypass grafting is the most common cardiac procedure in the United States. New research shows what happens after the operation has a lot to do with a patient’s sex .

About 80% of all cases of cardiovascular disease are preventable. Use our guide to improve your heart health .

In the United States, strokes are more common and serious in women. Here’s how to understand your risk .

A new genetic test, known as a polygenic risk score, could help patients understand whether they really need early treatment for heart disease .

Noise can damage your heart  as well as your hearing. But there are ways to measure your exposure and reduce your risk .

Psychological stress can set in motion a cascade of reactions  that can lead to heart attacks and strokes. Here’s how to reduce the harm of stress .

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Environmental noise in hospitals: a systematic review

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Environmental noise has been growing in recent years, causing numerous health problems. Highly sensitive environments such as hospitals deserve special attention, since noise can aggravate patients’ health issues and impair the performance of healthcare professionals. This work consists of a systematic review of scientific articles describing environmental noise measurements taken in hospitals between the years 2015 and 2020. The researchers started with a consultation of three databases, namely, Scopus, Web of Science, and ScienceDirect. The results indicate that for the most part, these studies are published in journals in the fields of medicine, engineering, environmental sciences, acoustics, and nursing and that most of their authors work in the fields of architecture, engineering, medicine, and nursing. These studies, which are concentrated in Europe, the Americas, and Asia, use as reference values sound levels recommended by the World Health Organization. L eq measured in hospital environments showed daytime values ranging from 37 to 88.6 dB (A) and nighttime values of 38.7 to 68.8 dB (A). L eq values for outdoor noise were 74.3 and 56.6 dB (A) for daytime and nighttime, respectively. The measurements were taken mainly inside hospitals, prioritizing more sensitive departments such as intensive care units. There is a potential for growth in work carried out in this area, but research should also include discussions about guidelines for improvement measures aimed at reducing noise in hospitals.

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Introduction

Over the last few decades, noise pollution has grown mainly due to urban expansion and the increasing size of vehicle fleets, which is considered an aggravating factor for public health (Hanninen et al. 2014 ). Several problems related to human health and cognitive activities are attributed to noise, such as sleep disturbance (Muzet 2007 ; Basner and McGuire 2018 ), annoyance (Miedema and Oudshoorn 2001 ; Licitra et al. 2016 ; Guski et al. 2017 ; Paiva et al. 2019 ), cardiovascular diseases (Babisch et al. 2005 ; Dratva et al. 2012 ; Sorensen et al. 2017 ; Héritier et al. 2018 ; van Kempen et al. 2018 ; Lin et al. 2020 ), perception, and learning (Erickson and Newman 2017 ; Minichilli et al. 2018 ).

There is even a greater concern in areas considered sensitive to noise, such as hospitals, where noise affects the well-being of patients, slowing their recovery, reducing the productivity of professionals, and increasing the occurrence of medical errors (Hsu et al. 2012 ; Loupa et al. 2019 ; Montes-González et al. 2019 ; Loupa 2020 ). Moreover, noise can also have a negative effect on visitors and the hospital as a whole (Zannin et al. 2019 ) and can increase the incidence of rehospitalization (Hagerman et al. 2005 ). The main factors involved in noise audible inside hospitals may originate outdoors, e.g., vehicle traffic, or indoors, e.g., conversations among employees and/or patients (Ravindra et al. 2016 ).

Several studies on environmental noise measurements in hospitals that have been carried out around the world (Busch-Vishniac et al. 2005 ; Fortes-Garrido et al. 2014 ; Zannin and Ferraz 2016 ; Montes-González et al. 2019 ) have revealed noise levels exceeding those recommended for a healthy environment. The World Health Organization (WHO) suggests that sound levels should not exceed L eq 35 dB (A) in the daytime and L eq 30 dB (A) to L max 40 dB (A) at night in hospital environments (Berglund et al. 1999 ). The United States Environmental Protection Agency (USEPA) recommends daytime and nighttime sound levels of less than L eq 45 and 35 dB (A), respectively (USEPA 1974 ).

In practice, even with technological advances in hospital equipment and construction processes, noise levels inside hospitals have gradually increased from the 1960s to the present day (Busch-Vishniac et al. 2005 ; Busch-Vishniac and Ryherd 2019 ). Sound level assessments in hospitals are performed in various ways, given the complexity of hospital environments (Wallis et al. 2019 ).

The purpose of this systematic review was to survey research conducted between the years 2015 and 2020 pertaining noise measurement in hospitals around the world by examining top ranking scientific and academic journals.

The systematic review of the literature in electronic format involved three databases, Scopus, Web of Science, and ScienceDirect. The first filter employed in the search selected articles published in the last 6 years (from 2015 to 2020), only articles in English, research articles (excluding technical and review notes), and keywords “Noise” and “Hospital.” The second filter excluded duplicate articles, while articles found in the Web of Science database were kept as reference (without excluding them). The third filter removed articles containing titles outside the context of the research, such as other types of interventions in hospitals or noise from hospital imaging equipment (e.g., X-ray machines). The fourth filter excluded articles whose abstract did not contain elements that met the objective of this review, such as those that did not measure sound pressure levels outside or inside hospitals. Lastly, in the fifth filter, after the articles were read in full, those stating that their authors had taken measurements using noise dosimeters (that evaluated only the noise dose) were excluded, since this is a special device for measuring individual exposure to sound pressure levels. Research that used class 2 equipment was excluded in order to equalize the work in terms of quality and quantity of resources of class 1 equipment. Also excluded were articles involving only simulations and modeling, but not measurements.

The articles selected after applying the five filters revealed the following information: (a) the databases containing the largest number of published articles; (b) the areas of knowledge of the journals in which the articles are published; (c) the countries whose hospitals have been studied and the laws/standards used as reference; (d) the authors’ profession/area of expertise and the main focus of their studies (areas outside or inside hospitals); (e) measurement methods and parameters that were used; (f) works that adopted/proposed noise mitigation measures; and (g) future perspectives for the area. Figure 1 summarizes the literature review filtering scheme, while Appendix 1 Table 6 provides information about all the final articles selected.

figure 1

Literature review filtering scheme

Results and discussion

Databases and areas of knowledge of journals.

After applying the filters, the database found to contain the largest number of articles was Web of Science, with 73%, followed by Scopus with 21% and ScienceDirect with 6%. Figure 2 shows the areas of knowledge of the journals (SCImago 2020 ) in which the articles were published.

figure 2

Areas of knowledge of scientific journals. Areas of knowledge of scientific journals extracted from the site: https://www.scimagojr.com

Figure 2 shows that 28% of the articles were published in medical journals, followed by 21% in engineering, 18% in environmental sciences, 15% in acoustics, 9% in nursing, 6% in pediatrics, and 3% in multidisciplinary journals, indicating the interdisciplinarity of the subject and its importance in several fields of science.

Countries where the studies were conducted

Among the 33 studies selected in this review, the countries with the highest participation rates were the USA ( n = 3), Brazil ( n = 3), China ( n = 3), England ( n = 3), Portugal ( n = 3), and Turkey ( n = 3) (9% each), followed by Colombia ( n = 2) and Iran ( n = 2) (6% each one), Germany, Australia, Bosnia and Herzegovina, Canada, South Korea, Spain, Greece, the Netherlands, India, Peru, and Taiwan (each with 1 study, corresponding to approximately 3% each) (see Fig. 3 ).

figure 3

Global map showing the distribution of countries where studies were performed

Approximately 42% of studies are located in Europe (although Turkey is geographically situated between two continents, for the purpose of this review it was considered in Europe), 18% in South America, 12% in North America, 24% in Asia, and 4% in Oceania. Research on the African continent does not exist for the period and the criteria adopted in this review. One of the hypotheses for the paucity of studies on noise in hospitals in the African continent is that there are other more urgent needs, such as access to drinking water and treatment of some diseases, such as HIV and Ebola. Several researchers cite the lack of noise-related research in Africa (Okokon et al. 2018 ; Sieber et al. 2018 ). In a review study on noise pollution, Khan et al. ( 2018 ) found that most research on the subject has been conducted in Europe, demonstrating a potential gap for studies in this area in Africa, Oceania and South America. In a review study on noise in hospitals, Wallis et al. (2020) found that 33% of research was performed in Europe (in twelve countries), 38% in North America (in two countries), 5% in South America (two countries), 17% in Asia (four countries), 5% in Oceania (one country), and 2% in Africa (one country). In addition to the global geographic gap, if one considers, for example, that the USA has 6090 hospitals (AHA 2021 ), there is also a regional/local gap, since hospitals have different configurations, activities, layouts, etc.

Standards/laws used as references in studies

Although several countries have noise pollution laws and/or standards, many studies use other references as a parameter to assess whether or not measured noise levels pose risks to human health. Table 1 describes the main characteristics of laws and standards used as references in the studies.

It was found that 45% of the studies cited the WHO as a reference for noise values, followed by the United States Environmental Protection Agency (EPA) with 15% and the American Academy of Pediatrics (AAP) with 12%. The WHO has more restrictive noise values than the other laws/standards cited in other studies, since it considers overall well-being. Taken together, WHO and EPA are the pioneer institutions in creating standard values for hospital noise (Baqar et al. 2017 ). Other studies have shown that the values recommended by the WHO are widely used as a reference for noise levels in hospitals (Wallis et al. 2019 ), although these levels are often exceeded and unlikely to be achieved (Loupa 2020 ).

Other countries and/or cities had their own particularities when they created their reference laws and considered not only the general health and well-being of the population. Most of these laws/standards are quite old, dating back to the 1970s, 1980s, or 1990s, when cities were less crowded and vehicle fleets smaller. Jahan et al. ( 2016 ) state that noise pollution was not a major concern for the population of Bangladesh in the 1970s and early 1980s, but that the risk of noise pollution increased and exceeded the level of tolerance in response to the growing number of motor vehicles in the country. Urban and demographic growth is not always planned, and it is difficult to adjust external environmental noise emission standards to acceptable levels.

To assess the effect of noise exposure of people in different countries, other factors must be considered as well, such as psychological, economic, social, cultural, climatic, and others not yet identified (Sieber et al. 2018 ).

Table 2 shows the range of equivalent sound pressure levels (L eq ) for indoor and outdoor environments observed in the studies.

The indoor daytime L eq values in hospitals ranged from 37 dB (A) (Cho et al. 2019 ) to 88.6 dB (A) (Pirsaheb et al. 2016 ), while the nighttime levels varied from 38.7 dB (A) (Bevan et al. 2018 ) to 68.8 dB (A) (Filus et al. 2015 ). The outdoor noise level (L eq ) was 74.3 in the daytime and 56.6 dB (A) at night. For measurements lasting 24 hours or longer, the values ​​ranged from 39.7 (Zijlstra et al. 2019 ) to 71.7 dB (A) (Carvalhais et al. 2015 ; Santos et al. 2017 ). As for studies of outdoor noise levels, these varied from 83.3 to 88.6 dB (A) in the daytime (García-Rivero et al. 2020 ), from 58.3 to 65.4 dB (A) at night (Predrag et al. 2018 ), and from 62.7 to 84.7 dB (A) in a 24 h period (Tezel et al. 2019 ).

Even in countries that have technical guidelines for measuring indoor sound levels, such as Brazil through the NBR 10152 standard, it was found that the levels exceeded recommended ones (Filus et al. 2015 ; de Araújo Vieira et al. 2016 ).

Authors’ field of expertise

Like the journals that publish articles on noise in hospitals, the main authors of the publications also have different areas of expertise. In Table 3 , note that the total of 33 selected articles were divided into 21 distinct areas. Areas such as architecture and engineering, which represent 27% of the authors’ specialties, contribute to the quality and interpretation of noise assessments in hospitals. Among the authors’ areas of expertise, 36% are in medicine, nursing and other related areas such as pediatrics, communication disorders, public health, health sciences, health and environment, and experimental clinical sciences. One of the explanations for these numbers is that most of the authors have some connection with hospitals (medical, nursing, pediatrics, and other departments, as well as medical laboratories). This facilitates not only the development of the study, in terms of bureaucracy, but also the methodology (choice of noise measurement points, authorization from specific departments, and analysis in different layouts, among others). The areas of expertise of the remaining 37% of authors are in environment, physics, and acoustics. This indicates that the subject has attracted increasing attention from different areas (Loupa 2020 ), since noise is often underestimated.

Focus of the studies (areas outside and/or inside hospitals)

Table 4 describes the focus of the studies in terms of location, which may be outside or inside the hospital building, as well as the number of studies.

Eighty-two percent of studies measured noise levels inside hospitals, while 15% measured noise outside hospital buildings and 3% took measurements both outdoors and indoors. Indoor measurements are important to evaluate the level of acoustic comfort of patients and medical staff, since a quiet environment is beneficial for both, lowers the physical and mental stress of hospital staff, and contributes to hasten patient recovery (Mousavi and Sohrabi 2018 ). Outdoor measurements are normally taken to draw up acoustic maps, which are useful tools for diagnosing and evaluating urban noise and indicating the noise levels that reach the hospital facade (Fiedler and Zannin 2015 ; Zannin and Ferraz 2016 ). Studies combining outdoor and indoor noise measurements are more laborious, but they can better describe the stressors that may originate outside buildings, such as vehicle traffic, or inside, such as medical equipment and loud conversations (Zannin and Ferraz 2016 ). Hospitals are environments where patients need to rest and recover and should therefore be quiet indoors and outdoors (Ramadhan and Talal 2015 ).

Measurements taken outside hospitals usually evaluate traffic noise generated by light vehicles, e.g., motorcycles and cars, and by heavy vehicles, e.g., buses, trucks, and trains. The studies conducted inside hospital environments in specific locations or in various departments are listed in Fig. 4 .

figure 4

Hospital environments and number of studies conducted

Most of the studies available in the literature that involved measuring noise inside hospitals prioritized departments where patients are most vulnerable. Of the total of studies carried out indoors, 26% were conducted in neonatal ICUs, 22% in various different ICUs, and 19% in multiple locations inside hospitals. In addition, 22% were carried out in private and waiting rooms and 11% in emergency rooms, infusion center, and dental clinic. In another review of the literature on noise in hospitals, the authors found that 70% of the studies involved measurements taken in ICUs, and the remainder in different hospital departments (Wallis et al. 2019 ).

Measurement methods and parameters used

Table 5 describes the criteria adopted in the studies for installing the microphone of the sound level meter, such as height and distance from reflective surfaces, measurement time, and evaluated parameters. Most of the studies examined in this review used L eq dB (A) as a parameter. Other parameters such as L max are present in 39% of the studies, followed by L min with 27%. Some studies consider the statistical indices most commonly employed, such as L 5 , L 10 , L 50 , L 90 , and L 95 (employed in approximately 73% of the studies), and others less common ones, such as L 1 , L 70 , L 30 , and L 33 (used in 12% of the studies). It is noteworthy that 27% of the studies also performed frequency analysis (Carvalhais et al. 2015 ; Chen 2015 ; Lahav 2015 ; Ai et al. 2017 ; Galindo et al. 2017 ; Santos et al. 2017 ; Bliefnick et al. 2019 ; Loupa et al. 2019 ; Hasegawa and Ryherd 2020 ). In addition, 30% performed subjective analysis with questionnaires (Chen 2015 ; Oliveira et al. 2015 ; de Araújo Vieira et al. 2016 ; Ai et al. 2017 ; Santos et al. 2017 ; Cho et al. 2019 ; Wu et al. 2019 ; Zijlstra et al. 2019 ; Astin et al. 2020 ; Tang et al. 2020 ), or used reverberation time measures such as RT 30 , RT 20 (Chen 2015 ; Cho et al. 2019 ), or the Speech Intelligibility Index (SII) (Bliefnick et al. 2019 ).

Distance of measurements from ground height ranged from 0.75 to 4 m, with some studies installing microphones on the ceiling or objects in order to protect the equipment, not disturb the hospital routine, or avoid the Hawthorne effect, enabling them to take long-term noise measurements (D’Souza et al. 2017 ; Cho et al. 2019 ; Zijlstra et al. 2019 ; Astin et al. 2020 ). As for the distance from reflective surfaces, 18% of the studies adopted 1 m.

Measurement times varied widely, ranging from 2.5 min to 52 days of uninterrupted measurement (Filus et al. 2015 ; Astin et al. 2020 ), although 21% of the studies took 24-h sound level measurements.

Studies that adopted/proposed noise mitigation measures

Some studies proposed or adopted measures to mitigate noise in hospitals, as indicated in Fig. 5 . Studies that proposed measures for possible noise mitigation in hospitals represent 36% of the total number of studies analyzed in this review, while 52% of studies did not adopt or propose measures and only took measurements. Studies that adopted measures in order to make “before and after” comparisons represent 3% of the total, while 9% proposed and adopted noise mitigation measures.

figure 5

Studies that proposed or adopted noise mitigation measures

Luetz et al. ( 2016 ) adopted ICU room modification measures, achieving noise reductions in the order of 2.8 dB (A). The main measures proposed by the studies for noise reduction in hospitals involve preventive and educational actions (Filus et al. 2015 ; Disher et al. 2017 ; Santos et al. 2017 ; Astin et al. 2020 ); use of sound absorbing materials (Chen 2015 ); adoption of barriers and architectural designs (Monazzam et al. 2015 ); simulations (Fiedler and Zannin 2015 ; Montes-González et al. 2019 ); changes in equipment; physical installations, and procedures (Pirsaheb et al. 2016 ; Shield et al. 2016 ); and more studies in different locations and hospitals (Hasegawa and Ryherd 2020 ; García-Rivero et al. 2020 ).

Studies that proposed and adopted noise control measures involved a training program (Carvalhais et al. 2015 ) in which the noise levels showed no variation after implementation of the program, a non-talking rule (Zijlstra et al. 2019 ) whose implementation led to a noise reduction of 1.1 dB (A), and a quiet hospital environment (Bliefnick et al. 2019 ).

Vehicle traffic noise is what most affects the modern human lifestyle (Ruiz-Padillo et al. 2016 ). Changes in driver behavior can contribute to reduce noise levels that reach hospital facades. However, some studies have indicated that despite the significant reduction in the number of vehicles circulating during the COVID-19 pandemic, reductions in noise levels were lower than expected, a fact that is attributed the high driving speed of the remaining vehicles (Asensio et al. 2020 ). Other factors that contribute to traffic noise levels and that deserve attention are the types of tires (Licitra et al. 2017 ) and of paving (Praticò and Anfosso-Lédée 2012 ; Praticò 2014 ; Licitra et al. 2015 ; Licitra et al. 2019 ; Del Pizzo et al. 2020 ).

Future perspectives in this field

Noise measurements are extremely important for assessing the level of exposure to which people are subjected, given the risks associated with this type of pollution. In hospitals, these measurements are even more important, given the physical and emotional vulnerability of patients and the stress to which hospital staff are subjected daily. Hence, working in extreme situations while subjected to noise levels exceeding those established by laws, standards, or agencies such as the WHO can delay the recovery of patients and impair the performance of healthcare professionals (Zannin and Ferraz 2016 ; Zannin et al. 2019 ; Busch-Vishniac and Ryherd 2019 ; Loupa 2020 ).

An average of 5.5 articles per year were published in the period studied in this review, as illustrated in Fig. 6 .

figure 6

Number of studies conducted in the last 6 years

In order to improve noise assessments in hospitals, research must contain as much information as possible, e.g., locations where equipment was installed, measurement height from ground level, distance from reflective surfaces, measurement time, noise sources, and measurement period (Wallis et al. 2019 ). As can be seen in Table 5 , the information provided by some studies is insufficient for a careful reproduction or analysis, even those that took measurements for 24 h or more. Studies published in journals in the area of ​​acoustics (e.g., Bliefnick et al. 2019 ; Hasegawa and Ryherd 2020 ) offer more complete information, extracting the maximum quality and diversity of resources provided by the equipment used, thus enabling the analysis of a series of interventions and improvements in the quality of the environment. Given the diversity of ways in which the studies are conducted, perhaps a more comprehensive standardization strategy is needed in order to balance noise measurement procedures in indoor hospital environments, considering that the forms of noise measurements in outdoor environments with validation on maps ensure more reliable results.

Conclusions

Several scientific journals have published studies on environmental noise assessment in hospitals, mainly in the areas of medicine, engineering, environmental sciences, acoustics, and nursing. The areas of expertise of the authors of these studies correspond to those of the journals, since most of them are doctors, nurses, and engineers. The studies are concentrated mainly in Europe, the Americas, and Asia.

Most of the studies use the noise levels recommended by WHO as a reference to determine whether the measured noise levels may be harmful to human health in the hospital environment. However, it should be noted that the levels recommended by WHO are the most restrictive possible and that the noise levels measured in practically all the studies selected for this review were much higher. The L eq levels measured in indoor hospital environments varied from 37 to 88.6 dB (A) in the daytime and from 38.7 to 68.8 dB (A) at night, while the outdoor noise levels were 74.3 in the daytime and 56.6 dB (A) at night.

The main focus of the studies was the internal part of the hospitals, and most of them took measurements in only one hospital. Departments treating more sensitive and vulnerable patients that require greater attention from health care professionals, such as ICUs, were preferred environments for environmental noise measurements.

A considerable number of the studies only indicate if the measured noise levels are in conformity with some reference standard or law, but do not adopt or propose measures to reduce the levels.

This is a field of research with a potential for growth. However, there is a need for a more critical assessment of the quality of studies, aiming at scientific advances and reliable dissemination of information to the community in general.

Data Availability

All data generated or analyzed in this study are included in this published article [and its supplementary information files].

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Cluster pattern analysis of environmental stressors and quantifying their impact on all-cause mortality in Belgium

  • Bram Vandeninden 1 , 2 , 3 ,
  • Eva M. De Clercq 3 ,
  • Brecht Devleesschauwer 2 , 6 ,
  • Martina Otavova 2 , 4 , 5 ,
  • Catherine Bouland 1   na1 &
  • Christel Faes 5   na1  

BMC Public Health volume  24 , Article number:  536 ( 2024 ) Cite this article

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Environmental stress represents an important burden on health and leads to a considerable number of diseases, hospitalisations, and excess mortality. Our study encompasses a representative sample size drawn from the Belgian population in 2016 ( n  = 11.26 million, with a focus on n  = 11.15 million individuals). The analysis is conducted at the geographical level of statistical sectors, comprising a total of n  = 19,794 sectors, with a subset of n  = 18,681 sectors considered in the investigation. We integrated multiple parameters at the finest spatial level and constructed three categories of environmental stress through clustering: air pollution, noise stress and stress related to specific land-use types. We observed identifiable patterns in the spatial distribution of stressors within each cluster category. We assessed the relationship between age-standardized all-cause mortality rates (ASMR) and environmental stressors. Our research found that especially very high air pollution values in areas where traffic is the dominant local component of air pollution (ASMR + 14,8%, 95% CI: 10,4 – 19,4%) and presence of industrial land (ASMR + 14,7%, 95% CI: 9,4 – 20,2%) in the neighbourhood are associated with an increased ASMR. Cumulative exposure to multiple sources of unfavourable environmental stress (simultaneously high air pollution, high noise, presence of industrial land or proximity of primary/secondary roads and lack of green space) is associated with an increase in ASMR (ASMR + 26,9%, 95% CI: 17,1 – 36,5%).

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Introduction

Environmental stress causes a significant burden on our human health. Environmental stress refers to various types of pollution and non-ideal environmental circumstances, such as air pollution (e.g., exposure to nitrogen dioxide (NO 2 ) and particulate matter (PM 2,5 )), noise (due to road traffic, aviation, industries, etc.), lack of green spaces, industrial pollution, or exposure to non-optimal temperatures (extreme heat and extreme cold). WHO estimates that 12–13% mortality is attributable to environmental stressors, in Belgium, close to the EU-28 mean of 13% [ 35 ]. The reduction of environmental stressors exposure could prevent more than 40% of strokes, 35% of ischaemic heart disease, more than 30% of lower respiratory tract infections and 20% of cancers worldwide [ 24 ].

Air pollution is a major global cause of disease, hospitalisations, and death [ 7 ]. In 2020, 364,000 premature deaths in Europe were attributed to air pollution. The cities, with their high traffic, industry, lack of green spaces and residential areas, tend to have the most elevated levels of pollution, which can lead to higher rates of death and disease. For example, Khomenko et al. [ 18 ] found that reducing PM 2.5 and NO 2 concentrations in several Belgian cities to the lowest levels seen in European cities could prevent up to 7% and 6% of deaths, respectively. A literature review of PM 2.5 and NO 2 found an increased risk for both mortality and hospital admissions from short- and long-term exposure [ 1 ]. Noise pollution, especially from road traffic, contributes to cardiovascular disease. [ 11 ]. A 10 dB increment in road noise exposure starting at 50 dB is associated with an 8% increase in the incidence of ischemic heart disease [ 10 ]. In terms of land cover and effects on human health, access to recreational green spaces and forests can reduce mortality with an average 4% reduction in mortality per 0.1 increments in the Normalised Difference Vegetation Index (NDVI) [ 25 ]. Proximity to industrial land can worsen health and increase mortality [ 20 ]. Living near primary and secondary roads negatively affects social wellbeing, physical activity, air pollution and noise exposure. Lower IQs for children and lower cognitive abilities, worse memory and lower verbal skills for elderly living near major roadways are among the documented examples [ 12 , 33 ]. The effects of proximity to agricultural land and pesticide exposure are less clear, with some studies finding decreased mortality [ 27 ] and others finding increased risks for specific diseases such as Parkinson’s from glyphosate exposure [ 6 ].

Previous studies primarily considered the singular effect of individual environmental stressors on the country level. Nevertheless, only a few studies considered sub-national spatial patterns or cumulative effects of multiple environmental stressors exposure on health. While there is limited evidence indicating environmental stressors can work together to have synergistic effects on human health, more research is needed to understand those interactions and the mechanisms behind them (EEA, Agency and [ 7 ].

In this study, we performed a cluster analysis including, simultaneously, multiple environmental stressors to consider non-linear relationships between the environmental stressors, mortality, and socio-economic factors. It allows us to analyse the spatial pattern of environmental stress in Belgium, identifying patterns between those elements with more coherence. This paper aims to unravel the geographical patterns of environmental stressors in Belgium. We consider air pollution, exposure to agricultural land and associated pesticides, the presence of industrial sites, busy roads and related noise, and the lack of green space.

In the pursuit of an improved understanding of environmental mortality determinants, we examine the relationship between cluster patterns of detected environmental stressors and mortality using an ecological regression model and relevant literature (e.g., meta-analyses of individual-level cohort studies).

Methodology

The analysis is performed at the statistical sector level. This is the smallest administrative subdivision of Belgium. There are 19 794 statistical sectors in Belgium (2020). Their mean population size is 550 inhabitants. The geographical size varies depending on population density. In areas with the highest population density, statistical sectors tend to be very small, while in less populated rural areas, statistical sectors tend to be larger.

Study setting and design

Belgium is a country in Europe with a population of 11.6 million inhabitants (2021). 31% of the population lives in cities, while 56% of the population lives in towns and suburbs and 13% of the population in rural areas according to the Eurostat classification for the year 2021 [ 8 ].

The health and environmental policies in Belgium are in part organised at the federal level, and in part on the level of the 3 administrative regions Brussels, Flanders, and Wallonia.

Statistical sectors are the smallest administrative subdivision in Belgium. There are 19 794 statistical sectors in Belgium with on average 500 inhabitants living in one statistical sector. In terms of geographical area, statistical sectors are smaller in densely populated urban areas and larger in rural areas.

For the clustering analysis, all 19 794 statistical sectors within Belgium are used. For this mortality calculation, all 19 794 statistical sectors in Belgium are used. For the ecological regression analysis, data with missing values are removed. Data with missing values include statistical sectors which less than 5 deaths over the considered period, which are removed from the dataset for privacy concerns. In the final analysis, 18 681 statistical sectors (94.4% of sectors, 99.2% of population) remained available for inclusion.

Mortality data

Observed mortality per statistical sector per age group of 10 years (0-9 year, 10–19 year, 20–29 years, etc.) aggregated for the period 2012–2016 were obtained via the DEMOBEL database, part of the Causineq dataset, also related to Statbel.

Exposure assessment

Data on environmental stressors include high-resolution (10 × 10 m) annual air pollution maps of PM 2.5 , NO 2 , BC and O 3 from the year 2018 (derived from the open-data endpoint https://www.irceline.be/en/documentation/open-data/open-data?set_language=en ), High resolution (100 × 100 m) land cover data from CORINE land cover for the year 2018 (derived from https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 ), data on primary and secondary roads (derived from Statbel, https://statbel.fgov.be/ ), vector data on noise segments for Flanders and Wallonia 2016–2017 (obtained via regional government data) and noise raster data for Brussels (obtained via regional government data, e.g. “omgeving Vlaanderen”, “Leefmilieu Brussels”, “Geoportail Wallonie”).

Environmental stressors data are post-processed (in a GIS environment using ArcMap) into mean values per statistical sector. For Air Pollution, mean annual PM 2.5 , NO 2 , BC and O 3 concentrations per statistical sector for air pollution are calculated (2018) derived from original 10 × 10 m resolution model raster maps for those air pollutants. The original data is a Land-Use-Regression derived air pollution model where local and regional emissions are added to the model, which together is named the RIO-IFDM model chain [ 15 , 19 ]. In recent years, an improved version of the model was launched, named ATMO-Street. The building stones of this model remain similar compared to the model described above. However, in addition, this model takes into account concentration peaks in Street Canyons. Validation showed the model passes the European FAIRMODE quality criteria, deeming the model chain suitable for policy support [ 14 ]. All concentrations are expressed in µg/m 3 . In our analysis, we used the improved ATMO-street model.

For Land Cover, maps from 2018 from CORINE Land Cover are used. Those data are based on remote sensing images using the Sentinel-2 and Landsat-8 satellites. In addition, a GIS-dataset on primary and secondary roads, based on OpenStreetMaps combined with the official road type classifications was used. The spatial resolution of the data from this source are at least 100 m. In our post processing of the data, the mean fraction of agriculture, industry, primary and secondary roads, green and forested areas per statistical sector are calculated. The mean fraction is the percentage of the surface of the considered statistical sector that is characterised by this type of land cover. For example, a statistical sector with a surface area of 2.1km 2 , characterised by 0.2km 2 of primary and secondary roads, 0.8km 2 of agriculture, 0.4km 2 of residential houses and 0.7km 2 of industry area has an agriculture fraction of 0.38, an industry fraction of 0.33 and a primary & secondary road fraction of 0.10. Validation of the CLC2018 land cover data showed average accuracies of > 92.0%, passing the recommended threshold of 85% [ 29 ].

For noise, the percentage of the statistical sector characterised by road, railway, and airplane noise between 55 and 70 dB and above 70 dB is calculated based on original vector files of noise for the Flanders and Wallonia region, which contained noise segments for each category for locations where the threshold of 55 dB is exceeded and calculated based on a raster file containing noise values at high 100-m resolution level for the Brussels area. The Brussels and Flanders data are yearly averages for the year 2016 while the Wallonia data is a yearly average for the year 2017. Those data are post processed into the same data format of each region, containing per statistical sector the area percentage of the statistical sector exceeding respectively 55 and 70 dB noise for the Lden indicator. All noise data are developed in accordance with the CNOSSOS-EU noise guidelines [ 17 ].

The input data described here are subsequently used for hierarchical clustering via the Ward Algorithm, which for each category of environmental data, namely air pollution, land cover and noise, classified the statistical sectors into clusters. The section “statistical analysis” elaborates on this further.

Covariate data

Co-variate data and confounding factors are pivotal in health-environment relationships. Socio-economic disparities, like low education and employment, often align with increased environmental stress, such as elevated air pollution or reduced green spaces [ 2 ].

In our study, Socio-economic were aggregated at the level of the statistical sector. For this we used an index of multiple Deprivation (IMD) for Belgium [ 22 ], consisting out of a weighted average of multiple deprivation domains (health, income, education, crime, employment and housing). The domains in the IMD are based on the aggregation of census data indicating a form of deprivation from the year 2011. For example, the housing domain contains indicators as the proportion of individuals living in dwellings, being tenants, having a living area < 35m 2 , without central heating, without insolation, without kitchen, without toilet, without bathroom and without internet. More information on the IMD, including its domains, parameters, weights and calculation methods can be found in the scientific publication where the index is discussed [ 22 ].

Further, age-specific population data for the year 2016 were obtained via Statbel. This contains the number of people per statistical sector per age group of 10 years (0–9 years, 10–19 years, 20–29 years, etc.)

For the data layer of the regions, the 19 794 statistical sectors are classified into 3 categories: Flanders, Wallonia, and Brussels, corresponding to those administrative regions. As important parts of health and environmental policies are a responsibility of the regions rather than the national government, different policies in those regions may have led to different strategies related to confounding factors such as tobacco and alcohol, for example in the domain of health prevention. For example, adding the regions as a variable in the model reduces the influence of the confounding factor smoking. The factor smoking explains around 19% of mortality in Belgium [ 9 ], with important spatial variations, namely attributable mortality ranging from 10 to 47% between communities [ 23 ]. Smoking prevalence varies geographically, with lower rates in Flanders, higher rates in many Walloon municipalities, and an intermediate position in the Brussels-Capital Region. Urban areas, with higher air pollution and noise, have younger and more educated residents, often exhibiting lower smoking prevalence, potentially mitigating environmental-related mortality [ 26 ],Teughels et al., [ 28 ]. In 2018, daily smoking prevalence was 29% for those without an educational degree and 9% for tertiary education holders.

Statistical analysis

The output of the mapping of environmental stressors per statistical sector was used as an input for the hierarchical clustering. The principle of the hierarchical clustering analysis aims to minimise variance between statistical sectors within a cluster and to maximise variance between statistical sectors belonging to different clusters. The Ward algorithm of the hclust package in R (versions 4.1.2 and 4.1.3) is used to conduct the cluster analysis [ 32 ]. Statistical sectors are merged into clusters, thereby minimizing information loss.

Three separate environmental cluster groups are created: air pollution clusters, noise clusters and land cover clusters. The air pollution clusters are based on the pollutants: NO 2 , PM 2.5 , BC and O 3 . For the noise cluster, road, rail and aeroplane noise between 55 and 70 dB and above 70 dB are considered. For the land cover clusters, the following variables are included: industry, agriculture, forests and green spaces, primary and secondary roads.

In addition, a fourth cluster group consists out of the housing, education and crime dimension of the Index of Multiple Deprivation. The Health and Employment domain were excluded from the analysis, as they contain respectively the age-standardized mortality rate and the percentage of people living with a disability / on long-term sick leave, which kind of outcomes are part of our independent variable in the ecological regression model in the next step. Cluster characteristics for the socio-economic cluster are displayed in Additional file 1 : Figure S1.

To determine the number of clusters, a cut-off value of explaining at least 95% of the variance for all cluster groups was chosen. Based on this, the number of five clusters is chosen for all cluster groups.

Mortality attributable to the air pollution clusters based on Population Attributable Fraction (PAF)

The relative risk (RR) between an increase in air pollution concentrations and age-standardized mortality rate is well-established in existing studies and meta-analysis of cohort studies on the individual level. The ELAPSE meta-analysis [ 5 , 13 ] is the most recent elaborate meta-analysis providing RR estimates for PM 2.5 (1.118 [1.060 – 1.179] per 10 µg/m 3 increase and NO 2 (1.045 [1.026 – 1.065] per 10 µg/m 3 increase). Due to the inclusion of more recent meta-analysis with better exposure assessment methodologies such as higher resolution and a focus on the Europe Region, the ELAPSE meta-review has a comparative advantage over older meta-analysis available. To calculate the PAF, the fraction of mortality attributable to the different air pollutants, based on the following formula:

in which \(p\) represents the prevalence of the exposure and \(RR\) is the relative risk of the exposure.

The RR is recalled from the default unit per 10 µg/m. 3 increase to the relevant exposure unit, using

In which RR10 is the RR for an increase in 10 µg/m 3 and CON is the actual mean concentration of the statistical sector of the air pollutant under consideration. The confidence intervals of our analysis originate from conducting a Monte Carlo Simulation assuming a triangular distribution.

Ecological regression model

The relations between the exposure to environmental stressors through the clusters and total mortality are determined using a generalised linear regression model at the ecological level of the statistical sector. We improved the existing ecological studies by using smaller administrative units, thereby reducing the heterogeneity within the areas, resulting in finer ecological exposure.

A negative binomial model was used as our epidemiological data showed a Poisson distribution with a large amount of overdispersion. A 26% drop in Bayesian Information Criterion (BIC) objectified our choice of a negative binomial model over a Poisson model. The response variable is the number of deaths per statistical sector. Independent variables include cluster groups of air pollution, noise stress and land cover, socio-economic and health variables (more details below). The expected deaths per statistical sector were taken as an offset, to account for the population size and age-distribution in the statistical sector. The ASMR is calculated from the indirect standardised rate with the whole of Belgium as the standard population.

The baseline model included the Air Pollution, Land Cover, and Noise clusters as covariates. A second version of the model also contains a cluster with the housing, crime, education, and income dimensions of the Index of Multiplde Deprivation (IMD). Careful interpretation of the results is necessary as multicollinearity can result in type I and type II errors [ 16 ].

We also investigated if the model fit improved adding the regions correcting for different lifestyles which have major impact on the most important confounding factors such as smoking. The model fit improved considerably, resulting in the inclusion of the regions in the model (Flanders, Brussels, and Wallonia).

In the dataset for the negative binomial regression model, we only used the statistical sectors for which no missing values exist for any of the variables to ensure statistic consistency of the analyses (17 434 statistical sectors). The removed statistical sectors in this procedure are dominantly small statistical sectors for which some of the variable values are not available due to privacy concerns.

This has led to the following regression model:

with \({Y}_{i}\) the total number of deaths occurring in each statistical sector over the considered period, \({E}_{i}\) the expected number of deaths expected to occur in each statistical sector over the considered period, \(R{R}_{i}\) the incidence risk ratio to be estimated and \(k\) and overdispersion parameters. In the regression model, \({Reg}_{i}\) corresponds to the Belgian regions (Flanders, Wallonia and Brussels), \(Ai{r}_{i}\) corresponds to the five air pollution clusters (denoted as clusters A to E), and similarly \(Nois{e}_{i}\) and \(Landcove{r}_{i}\) correspond to the five noise stress and five land cover stress clusters. Finally, \(Socio-{economic}_{i}\) corresponds to the five socio-economic clusters based on the income, education, crime and housing domain of the IMD.

In 2nd model version, we replaced the clusters of environmental stress by an index of cumulative exposure. We create three categories depending on the favourability of the conditions of environmental stress. The three categories are “HIGH” (the combination of clusters for which we expect based on literature the most unfavourable health outcomes: statistical sectors belonging to the cluster with very high air pollution values + high noise values + land cover characterised by considerable amounts of industrial land or high fractions of primary and secondary roads), “LOW” (expected most favourable environmental conditions for health: statistical sectors characterised by the combination of the cluster with the lowest air pollution + cluster with no noise exceedance + cluster with a high fraction of green spaces) and “MEDIUM” (everything in between). To compare the socio-economic variables a similar level, the socio-economic cluster based on the aforementioned dimension of the IMD was added to the model. the. This has led to the following regression model:

With \(Cum{E}_{i}\) a categorical classification of the amount of cumulative environmental stress and \(Socio-Economic{}_{i}\) the index of multiple deprivation (income, education, crime and housing dimension).

Importantly, and this is true for all models: our model's purpose is not centered around predictive capabilities; rather, it serves as a tool for studying associations.

Spatial patterns of environmental stressors through clustering

  • Air pollution

Five clusters of air pollution have been identified in Belgium, as represented in Fig.  1 . Descriptive statistics of the clusters can be found in Fig.  2 . Cluster E, located in southern Wallonia, has the lowest concentrations of NO 2 , BC and PM 2.5 , but the highest concentrations of O 3 . It covers 35% of the territory but only 8% of the population. Cluster A, which encompasses parts of Walloon Brabant, West Flanders, Hainaut, Liège, and Limburg, has intermediate pollutant levels and includes both agricultural and urban areas. Clusters B and D, which include cities in Wallonia and suburban areas in Flanders with heavy traffic, have moderate to high pollutant levels, and intermediate ozone levels. Notably, Cluster C has the highest values of NO 2 , BC and PM 2.5 and while it covers only 2% of the territory, it has 20% of the Belgian population. All clusters exceed the PM 2.5 guideline of 5 µg/m 3 set by the WHO. Cluster E is the only cluster that meets the WHO guideline for NO 2 of < 10 µg/m 3 .

figure 1

Air pollution clusters in Belgium

figure 2

Descriptive statistics (population-weighted boxplots) of the respective air pollution clusters in Belgium

Five land cover clusters have been identified (Fig.  3 ). Descriptive statistics are shown in Fig.  4 . Cluster A, where 50% of the population lives, is primarily a residential cluster characterised by moderate fractions agricultural land, with a lack of industrial areas, green spaces, and limited presence of primary and secondary roads. Cluster B (33% of population, 6% of territory), primarily found in middle-sized and large cities such as Brussels, Antwerp, Liège, Charleroi and Ghent is characterised by low agricultural land, a high presence of primary and secondary roads, and an absence of green areas. Cluster C (only 3% of population and 2% of territory) is characterised by high fractions of industrial land (50–75% of the area) and moderate presence (0–25%) of primary and secondary roads. Cluster D (4% of population, 25% of territory) is primarily found in forested areas of the Ardennes and some parts of Limburg and is covered by abundant forests and recreational green spaces with 50% of the surface in the statistical sectors belonging to this cluster.

figure 3

Descriptive statistics (population-weighted boxplots) of the respective Land Cover Clusters in Belgium

figure 4

Descriptive statistics (population-weighted boxplots) of the respective Noise Clusters in Belgium

Five noise clusters have been identified in Belgium (Fig.  5 ). Descriptive statistics are shown in Fig.  6 . According to the EU’s Environmental Noise Directive, the threshold for excess exposure, defined as Lden, is 55 dB. Lden, measured in decibels (dB), indicates an average level during the day, evening, and night. Cluster A is characterised by high levels of road noise, with over 50% of the area exceeding > 55 dB(< 70 dB) Lden and over 5% exceeding 70 dB Lden. Cluster B is characterised by a combination of high levels of aeroplane noise and road noise, with over 50% of the area exceeding > 55 dB(< 70 dB) Lden from both sources. Cluster C is characterised by high levels of railway noise, with 25% of the area exceeding > 55 dB (< 70 dB) Lden. Cluster D is characterised by high levels of road noise, with 20–25% of the area exceeding > 55 dB Lden. Cluster E does not exceed the 55 dB Lden threshold for road, railway, or airplane noise. This cluster covers 74% of the territory and 55% of the population lives in this cluster without any noise exceedances. 45% of the population lives in one of the other clusters where there are noise exceedances for at least one source of noise.

figure 5

Noise Stress clusters in Belgium

figure 6

Descriptive statistics of the noise clusters in Belgium

Cumulative environmental stress

Figure  7 illustrates the cluster categories used as input for the cumulative regression model where simultaneous exposure to multiple favourable and unfavourable conditions is grouped together. We observe that exposure to simultaneous favourable conditions such as co-exposure to sufficient residential green space, low levels of noise and air pollution, mainly occurs in the southern part of Belgium where the most hotspots of unfavourable simultaneous exposure to multiple stressors happens mainly in some smaller spots in the northern part of Belgium. However, the limited geographical extent of the latter category, 2.1% of the population, around 250 000 residents, live in this category, much more than the 0,8% of the population living in the much more extensive geographical area with simultaneous favourable environmental stress conditions.

figure 7

Cumulative exposure to average vs. very high simultaneous (elevated air pollution + high industry/roads + high noise) and very low simultaneous (abundant green space, low air pollution and low noise) environmental stress: geographical map of Belgium

Environmental stress and mortality

Mortality attributable to the air pollution clusters.

Air pollution clusters C and E have the highest and lowest PAF values for both NO 2 and PM 2.5 , respectively. Cluster C specifically has an 11,9% [7,2 – 16,9%] and 14,8% [9,1 – 20,2%] PAF for NO 2 and PM 2.5 respectively, meanwhile cluster E has the lowest, with other clusters falling in between (Table  1 ). The diversity of PAFs for NO 2 is substantial, ranging from mean estimates of around 3% in cluster E to 12% in cluster C, a four-fold difference. The difference in PAFs for PM 2.5 , however, is less pronounced, with cluster C having a PAF that is less than double of cluster E.

Negative binomial regression model

The Figs.  8 , 9 and 10 summarise the results of the model considering all clusters of environmental stress (air pollution clusters, land cover clusters and noise clusters). We display both the increased/decreased percentage in ASMR for the model without socio-economic variables and model with socio-economic variables. For all cluster groups, the cluster with the most favourable environmental stress conditions (lowest mortality to be expected based on existing evidence in literature), is taken as a reference and the AMSR – for the model version including socio-economic variables is compared for the other clusters compared to this reference cluster. The more detailed row outputs for the regression models can be found in the appendices of this paper (Additional file 1 : Table S1, S2, S3 & S4).

figure 8

Summary outputs of the regression model investigating relation between ASMR and the clusters of environmental stress. Output estimates for the air pollution clusters. Own Photographs ©Bram Vandeninden

figure 9

Summary outputs of the regression model investigating relation between ASMR and the clusters of environmental stress. Output estimates for the land cover clusters. Own Photographs ©Bram Vandeninden

figure 10

Summary outputs of the regression model investigating relation between ASMR and the clusters of environmental stress. Output estimates for the noise clusters. Own Photographs ©Bram Vandeninden

All air pollution clusters (A, B, C, and D) show an increased ASMR compared to reference cluster E with the lowest mortality, with a sharp increase in ASMR of 14.8% [95% CI: + 10,4—+ 19,4%] for Air Pollution cluster C with the most elevated AP levels of BC, PM 2.5 and NO 2 in the model without socio-economic variables. After correcting for the socio-economic variables, the increase in ASMR diminishes however remains significant for the residential cluster A and the cluster with high traffic-associated air pollution values cluster C who after adjustment have + 2,5% [+ 0,2%—+ 4,8%) and + 4,9% [+ 0,7—+ 8,8%] higher observed ASMR compared to the expected ASMR respectively.

For land cover, we detect no significant difference in ASMR between the most favourable green/forested reference cluster (D) and cluster E dominated by agriculture. The residential cluster (A) and cluster B with a high fraction of primary and secondary roads show a moderately increased ASMR. The cluster covered by industrial land shows the highest mortality with an increase of 14,7% [+ 9,4—+ 20,2%] in ASMR compared to the reference cluster in the model without adjustment for socio-economic factors. Adjustments for socio-economic factors has a small effect. For the industrial cluster, the increase in ASMR remains very high with + 11,7%

For the noise clusters, no increased mortality compared to the most favourable reference cluster (no noise exceedances) were detected. Most estimates tend to find a protective effect of noise on health, however the estimates are not significant. Except for cluster C (railway noise) where noise showed a significant decrease in ASMR of -3,9 [-6,9—-0,9%]. Presence or absence of adjustment for socio-economic factors had little impact in the noise clusters.

For the cumulative exposure model, comparing three classes of total environmental stress represented by cluster combinations, the ASMR was increased by 34,1% [95% CI: 24,2 – 44,6%] in the areas with a high cumulative dose of environmental stress compared to the cluster exposure combinations with the most favourable environmental stress conditions. After inclusion of socio-economic deprivation in the model through the IMD, the corrected increase in ASMR decreased from to 26,9% [95% CI: 17,1 – 36,5%]% (Fig.  11 ).

figure 11

Cumulative Regression model including categories of cumulative environmental stress and cumulative socio-economic deprivation

There are clear spatial patterns in the distribution of environmental stress in Belgium. In all cluster categories, (air pollution clusters, land cover clusters and noise clusters), we observed clusters presenting low exposure and clusters presenting high exposure to environmental stress. At the same time, the clusters showing expected and observed unfavourable human health conditions, such as elevated levels of PM 2.5 and NO 2 and lack of available residential green spaces, are limited in geographical extent, these are often densely populated areas affecting a considerable share of the population. Findings of our ecological regression model are generally in line with hypotheses based on the existing literature: increased (observed > expected) ASMR rates due to exposure to high levels of air pollution, increased ASMR in traffic-associated hotspots, increased ASMR due to exposure to industrial substances other than the traditional air pollutants, increased ASMR in case of presence of primary and secondary roads. Concerning noise, we could not find any negative effects on human health, the elevated levels of railway noise even had a protective effect. We also demonstrated that cumulative exposure to multiple environmental stressors has potentially synergistic negative effects on human health.

Within the air pollution clusters, clusters B and C correspond to areas where the local air pollution component is largely caused by traffic as demonstrated by the higher NO 2 and BC values compared to PM 2.5 . In contrast, cluster D shows similar PM 2.5 values and lower BC, and NO 2 values indicating that the local PM 2.5 component may be partly originating from residential warming activities like wood burning. The increase of 14,8% [95% CI: 10,4 – 19,4%] in ASMR in the air pollution cluster C decreased to 4,9% [95% CI: 0,7 – 8,8%] after adjustments of confounding factors, which corresponds to respectively 12,8% and 4,7% attributable mortality due to air pollution differences between cluster E and C, is comparatively lower than the combined effect we can estimate based on the literature findings where we found around 12% mortality from NO 2 and 15% from PM 2.5 in cluster C. Considering a potential overlap of 30% [ 30 , 34 ] in effects, the combined air pollution effect of NO 2 and PM 2.5 in Cluster C based on literature evidence is still above 20%. Moreover, the estimate from the literature originates from the ELAPSE meta-analysis, which only included studies with adjustments for socioeconomic and confounding factors.

Our ecological regression model did not reveal any significant increase in ASMR for all-cause mortality associated with exposure to high noise levels. For railway noise, we found a significant protective effect on human health. Potentially this could be confounded by the general absence of busy traffic in areas around railways, affecting human health through different pathways including air pollution, physical activity and green space availability [ 21 ].

For the land cover clusters, cluster D, presenting a high fraction of forests and green spaces, is expected to have a beneficial impact on our health and protect against mortality, as there is a negative relationship between the RR of mortality and the fraction of green space and forests within 500 m of people’s residence [ 25 ]. The significant increase in ASMR in the industrial cluster indicates that human health may be negatively impacted by industrial activities through pathways different from the typical air pollutants NO 2 and PM 2.5 which were explicitly considered in this study. Earlier studies hypothesized health effects from industrial activities through amongst other exposure to chemicals and toxic substances such as lead [ 20 ]. Similarly, earlier studies demonstrated that other factors than the traditional air pollutants (e.g. reduced social contacts, lack of green and recreational spaces, …) may as well be involved in explaining increased disease burden in the presence of primary and secondary roads [ 4 , 12 ]. In our ecological regression model we cannot detect any observed ASMR exceeding the expected ASMR related to agriculture, implying that our study fails to detect evidence of harmful effect of proximity of agricultural land and associated practices.

In some areas, where environmental stress accumulates, synergistic effects may occur. A synergistic effect for heat and air pollution is well-established [ 3 ]. However, little research has been conducted yet and uncertainty remains important. Our ecological regression model indicates the presence of such synergistic effects. The simultaneous exposure to elevated environmental stress tends to have an increased ASMR exceeding the sum of the individual effects, which may indicate synergies. These results are crucial for policy makers in public health and the environment. The cluster analysis is usable as a tool to reduce the number of areas where human health is disproportionally affected negatively by environmental stress.

Different confounding factors such as smoking, dietary habits and alcohol intake can occult the relationship between the environmental stressors and the mortality outcomes at the ecological level. Between 10 and 47% of all-cause mortality can be attributable to smoking in Belgian municipalities [ 23 ] making it an important composite of total mortality. Adding the regions as a variable in the model reduces the influence of the confounding factor of smoking and other unaccounted confounding factors to an extent. As referenced in the methodology section, differences in smoking prevalence between the regions in Belgium are well established and recognised in existing research [ 23 , 26 ]. The same is true for other behavioural confounding factors such as alcohol consumption.

Our study presents several strengths. An analysis based on the statistical sector is an approach that has a considerably higher spatial resolution compared to most existing studies, increasing the accuracy in establishing relationships between environmental stressors and all-cause mortality. A cluster analysis allows the inclusion of multiple environmental stressors at once. Few studies have examined the cumulative effects of exposure to multiple stressors. A cluster analysis approach allowed the identification and comparison of spatial patterns between the different domains under consideration. Grouping statistical sectors in clusters simplifies pattern interpretation compared to examining 20,000 individual sectors. It also facilitates the translation of research findings into relevant knowledge for policymakers in the domains of environment and public health. For example, we demonstrate that while the air pollution clusters with the worst concentrations (air pollution cluster C) cover only a small percentage of the geographical area, a considerable share of the population (20%) lives in those areas. This information is important for determining interventions to reduce mortality and improve environmental and public health outcomes. Statistically, the cluster analysis approach also provided advantages as it results in a more stable regression model because the same model including for example air pollutants like PM 2.5 and NO 2 as separate variables, results in severe issues of multicollinearity and therefore type I and type II errors. A cluster approach is, therefore, essential for obtaining results that allow statistical interpretation. We avoided the use of statistical methods for which it is known they can be problematic in terms of multicollinearity. While we made substantial efforts to reduce the impact of multicollinearity, a limited amount of multicollinearity can still be present despite that statistical parameters such as the Variance Inflation Factor (VIF) do not show any multicollinearity [ 16 ].

Disadvantages of the hierarchical cluster analysis approach may include loss of detail and missing outliers. It also assumes no underlying knowledge of the patterns of environmental stressors. There is always a small but real risk of exposure misclassification and inconsistent temporality of different data sets can increase uncertainties. Another limitation of our study is that it was not possible to consider all confounding factors quantitatively. Another disadvantage is that the attributable all-cause mortality for the different clusters based on existing evidence in the literature is focused on only air pollution, as no robust estimates in the literature were found for the other stressors. Additionally, this is an ecological study. Despite some innovations we used to improve performance and reliability; ecological studies are not considered robust enough for epidemiological studies. Issues of bias and fallacy cannot be excluded from being present. An inclusive perspective on all-cause mortality across various age strata, encompassing even those aged 80 and above, was adopted and can attenuate the associations between health and environmental variables. This effect arises from the incorporation of mortality factors beyond intrinsic health conditions. The integration of all causes of death, including external causes, within the context of all-cause mortality, further underscores this phenomenon. Moreover, relationships between health and both the environmental and socio-economic parameters are expected to attenuate as individuals surpass their projected lifespans, aligning with the eventual mortality of all individuals [ 31 ].

The cluster analysis enabled the detection of spatial patterns of environmental stressors in Belgium. There are extensive differences in exposure to environmental stress in all considered domains – air pollution, land cover and noise – in Belgium. This has important implications for policymakers in the domains of environment and public health. With PM 2.5 WHO target values of 5 µg/m 3 exceeded in all clusters (100% of the population exposed) and NO 2 target values of 10 µg/m 3 exceeded in four of five air pollution clusters (92% of the population exposed to > 10 µg/m 3 ), the WHO target value of 25% Green space only reached in one out of five land cover clusters (96% of population exposed to < 25% GA), and noise exceedances in four of five noise clusters (45% of the population exposed to > 55db from any source), the environment in Belgium does in general not qualify as healthy for our human health, with in addition the presence of large spatial variations and inequalities in exposure. The ecological regression model shows the most elevated ASMR rates in clustered areas with very high air pollution values where traffic is the predominant local air pollution component and in industrial areas, with indications industrial land affects human health through other pathways (e.g., chemical/toxic substances) in addition to the well-known air pollutants PM 2.5 and NO 2 . As the increase in Incidence Risk Ration is higher in the cumulative model compared to the addition of the health effects in the single model, it might indicate a high probability of synergistic health effects from exposure to multiple environmental stressors at once. Ecological bias, misclassification and confounding factors can impact the quantitative estimates from the ecological regression model. However, our results are robust and show strong associations. The cluster analysis allows us to consider some areas as spatially homogenous units and, enables us to identify how to reduce inequalities in exposure to environmental stressors thereby decreasing the number of areas where human health is disproportionally affected in a negatively by environmental stress. Eventually, such an approach could support the improvement of overall population health, reduce inequalities, and cut healthcare costs such as hospital treatments and (paid) sick leave.

Availability of data and materials

Environmental data was obtained through standard data request procedures at the respective environmental agencies, and was provided as geographical maps, and did not contain information on individuals. All environmental data can be directly downloaded as open data or requested from the specific data links provided below.

• The air pollution data are open source and retrieved from the website of IRCEL-CELINE: https://www.irceline.be/en/documentation/open-data

• The noise data was obtained through the respective environmental agencies of the different regions in Belgium:

◦ For Brussels: metadata and access request on https://datastore.brussels (more detail : https://datastore.brussels/web/data/dataset/11daaefc-2abc-4b1a-8768-4544464b452d ). Those data can be requested by any organisation or any individual and no administrative permission are required to obtain or access the data.

◦ For Wallonia: metadata and access request on https://geoportail.wallonie.be/ (catalogue-donnees-et-services?search-changed=false+&search-tri=relevance&search-tab=&search-perPage=&search-text=bruit&search-diffusion-service-type=&search-not-obsolete=true&search-not-inspire=true&search-mapDate=&search-sheetDate=#results-area). Those data can be requested by any organisation or any individual and no administrative permission are required to obtain or access the data.

◦ For Flanders: The data are directly downloadable through https://www.geopunt.be

• Download: https://www.vlaanderen.be/datavindplaats/catalogus/wfs-publieke-download-service-van-vlaamse-overheid-beleidsdomein-omgeving-samenwerkingsverband-mercatornet

• Visualisation : https://www.vlaanderen.be/datavindplaats/catalogus/wms-publieke-view-service-van-vlaamse-overheid-beleidsdomein-omgeving-samenwerkingsverband-mercatornet

• The landcover data are open source and downloaded from the CORINE Land Cover website. https://land.copernicus.eu/pan-european/corine-land-cover

• Population data used are open source available from the open-data portal of Statbel. https://statbel.fgov.be/en/themes/population

• Data on the statistical sectors can be downloaded from the Statbel portal. https://statbel.fgov.be/en/open-data/statistical-sectors-2021

Further, aggregated health (all-cause mortality) data was obtained via request procedure. The Belgian statistical office (Statbel) provided us with aggregated mortality data at the level of the statistical sectors, the smallest administrative units in Belgium. All data were anonymized before it’s use. The mortality data used in our study are part of the Causineq database that was obtained from the Belgian statistical office, Statbel, after approval by the Statistical Oversight Committee of the Privacy Commission. The confidentiality contract numbers are STAT-MA-2015–13 and STAT-MA-2016–23. On 31 May 2021 (decision no. 2021/071), the authors obtained the right to update, use, and store the Causineq data until 31 December 2034. The Causineq database is pseudonymised and contains a multi-digit code specific to this database. It therefore does not enable linkages with other administrative databases or databases belonging to other research centres. The data we received based on this dataset for our study were anonymised before it’s use. More specifically, data are aggregated and numbers lower than 5 were replaced by ‘NA’.

No data was generated in this study. The data for analysis was obtained from third parties, for which the authors do not have the licence for further distribution.

More info: https://statbel.fgov.be/en/about-statbel/what-we-do/microdata-research

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Acknowledgements

We wholeheartedly thank all the people and organisations that provided data that were used in this study (IRCEL-CELINE, STATBEL, CORINE LAND COVER, Omgving Vlaanderen, Leefmilieu Brussels, Geoportail Wallonie).

This study has been supported by a project grant (ELLIS project, https://www.brain-ellis.be/ ) from the Belgian Science Policy Office BELSPO (Grant no. B2/191/P3/ELLIS).

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Catherine Bouland and Christel Faes are share the last authorship.

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School of Public Health, Université Libre de Bruxelles, Brussels, Belgium

Bram Vandeninden & Catherine Bouland

Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium

Bram Vandeninden, Brecht Devleesschauwer & Martina Otavova

Department of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium

Bram Vandeninden & Eva M. De Clercq

Center for Demographic Research, UCLouvain, Louvain-La-Neuve, Belgium

Martina Otavova

Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science, Diepenbeek, Hasselt, Belgium

Martina Otavova & Christel Faes

Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium

Brecht Devleesschauwer

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The corresponding author, B.V., conducted the analysis, including data collection, data-analysis for the mapping of the environmental stress, data-analysis for the cluster analysis and data-analysis for the ecological regression model including writing the code for analysis in the statistical software program R and producing maps using ArcGIS; E.(M)D contributed to some parts of the data collection and the mapping/analysis of the noise data that were used as an input for the cluster analysis. C.F. contributed to the coding and methodology for the regression analysis. The main manuscript text was written by the corresponding author, B.V., who also produced and prepared all tables and figures. All authors reviewed the manuscript, including textual improvements by rewriting certain sentences from specific paragraphs. The manuscript was thoroughly discussed and reviewed multiple times amongst all authors. All authors did approve the final version of the manuscript.

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Additional file 1: figure s1..

Characteristics of the socio-economic clusters. Average decile score for education, housing, crime and incoe for each of the socio-economic clusters. A decile score of 10 implies “lowest level of deprivation” while a decile score of 1 implies “highest level of deprivation”. Table S1. Negative Binomial Regression model outputs – Model without Socio-economic variables. Table S2. Negative Binomial Regression model outputs – Model with Socio-economic variables. Table S3. Negative Binomial Regression model outputs – CUMULATIVE MODEL - Model without  Socio-economic variables. Table S4. Negative Binomial Regression model outputs – CUMULATIVE MODEL - Model with Socio-economic variables.

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Vandeninden, B., De Clercq, E.M., Devleesschauwer, B. et al. Cluster pattern analysis of environmental stressors and quantifying their impact on all-cause mortality in Belgium. BMC Public Health 24 , 536 (2024). https://doi.org/10.1186/s12889-024-18011-0

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Whales 'cannot out-sing' human noise pollution.

research articles on noise pollution

  • 12:05PM February 22, 2024

Complex whale melodies, first recorded some 50 years ago, are known to play a key role in the social and reproductive communication of these massive marine mammals.

While toothed whales have a nasal vocal organ, filter-feeding baleen whales use a larynx, although scientists had not figured out exactly how it created the vocalisations. 

In a new study published in the journal Nature, a team of scientists in Denmark, Austria, and the United States examined the larynxes of three stranded baleen whales -- the sei, minke, and humpback species -- using scanning and modelling techniques to reconstruct how they produce sound.

They found several differences from land mammals, including a U-shaped structure instead of vocal cords, that allows them to generate their low-frequency songs.

"We've never seen this in any other animal," lead author Coen Elemans, professor of bioacoustics at the University of Southern Denmark, told AFP.

"This is a completely novel adaptation, and we think this allowed these large whales to make sound in the water while basically holding their breath."

But the scientists also found a potentially serious challenge for the whales: the struggle to make themselves heard over noise pollution from ships.

- No escape  -

Computer models showed that baleen whale songs can travel long distances through water, but at a maximum depth of 100 metres (330 feet) and at a frequency of up to 300 Hz -- within the range noise made by shipping vessels.

This means that whales in a noisy ocean soundscape are essentially trying to talk across a busy motorway or at a loud party: the further away you are, the less you'll be able to hear, Elemans said. 

"It's really sad that baleen whale vocalisations exactly overlap with the sounds we make, predominantly with shipping noise, and there is no way for the whales to sing louder, at a higher frequency, or deeper in the water," Elemans said. 

"These animals really can't escape this, and we really need to mitigate the noise we make." 

They cannot even dive down to escape the din. 

The deeper they go, the greater the pressure which reduces the volume of air available for vocalising, said Joy Reidenberg, a professor at the Icahn School of Medicine at Mount Sinai, who was not involved in the study.

Noise pollution can force whales to change their behaviour, such as remaining silent until quiet returns, moving to another location, or trying to communicate over the noise –- the latter two requiring whales to exert extra energy, potentially weakening their body condition and affecting long-term survival, Reidenberg said.

She said that understanding whale vocalisations could aid conservation efforts by helping understand which depths are "critical habitats".

This is particularly important at mating sites where, depending on the season, noise pollution can disrupt reproduction.

"We must be smarter about when and where we put sound into the water," Reidenberg said.

Researchers say there is an urgent need to regulate underwater noise.

The harm goes beyond whales -- there is evidence that scores of marine species are negatively affected by underwater noise pollution, Melanie Lancaster, senior Arctic species specialist at the World Wildlife Fund, who was not involved in the study, told AFP. 

"We know the most about marine mammals, which is why they feature so prominently, yet the impacts are much farther reaching, essentially impacting entire marine ecosystems," she said.

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[Air pollution, noise and hypertension : Partners in crime]

Affiliations.

  • 1 Zentrum für Kardiologie, Kardiologie I, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland. [email protected].
  • 2 Zentrum für Kardiologie, Kardiologie I, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland.
  • PMID: 38321170
  • DOI: 10.1007/s00059-024-05234-5

Abstract in English, German

Air pollution and traffic noise are two important environmental risk factors that endanger health in urban societies and often act together as "partners in crime". Although air pollution and noise often co-occur in urban environments, they have typically been studied separately, with numerous studies documenting consistent effects of individual exposure on blood pressure. In the following review article, we examine the epidemiology of air pollution and noise, especially regarding the cardiovascular risk factor arterial hypertension and the underlying pathophysiology. Both environmental stressors have been shown to lead to endothelial dysfunction, oxidative stress, pronounced vascular inflammation, disruption of circadian rhythms and activation of the autonomic nervous system, all of which promote the development of hypertension and cardiovascular diseases. From a societal and political perspective, there is an urgent need to point out the potential dangers of air pollution and traffic noise in the American Heart Association (AHA)/American College of Cardiology (ACC) prevention guidelines and the European Society of Cardiology (ESC) guidelines on prevention. Therefore, an essential goal for the future is to raise awareness of environmental risk factors as important and, in particular, preventable risk factors for cardiovascular diseases.

Luftverschmutzung und Verkehrslärm sind wichtige Umweltrisikofaktoren, die die Gesundheit in städtischen Gesellschaften gefährden und oft gemeinsam als „Komplizen“ auftreten. Obwohl Luftverschmutzung und Lärm in städtischen Umgebungen häufig gleichzeitig auftreten, wurden sie in der Regel getrennt untersucht, wobei zahlreiche Studien konsistente Auswirkungen individueller Expositionen auf den Blutdruck dokumentieren. Im folgenden Übersichtsartikel beleuchten wir die Epidemiologie von Luftverschmutzung und Lärm mit Hinblick auf den Herz-Kreislauf-Risikofaktor arterielle Hypertonie sowie die zugrunde liegende Pathophysiologie. Beide Umweltbelastungen führen nachweislich zu einer endothelialen Dysfunktion, oxidativem Stress, einer ausgeprägten Gefäßentzündung, zur Störung des zirkadianen Rhythmus und zur Aktivierung des autonomen Nervensystems, die insgesamt die Entwicklung von Bluthochdruck und Herz-Kreislauf-Erkrankungen begünstigen. Aus gesellschaftlicher und politischer Sicht besteht dringender Bedarf, auf das Gefahrenpotenzial von Luftverschmutzung und Verkehrslärm in den Präventionsrichtlinien der American Heart Association (AHA)/des American College of Cardiology (ACC) und in den ESC(European Society of Cardiology)-Richtlinien zur Prävention hinzuweisen. Daher ist es ein wichtiges Ziel für die Zukunft, das Bewusstsein für Umweltrisikofaktoren als bedeutende und insbesondere vermeidbare Risikofaktoren für Herz-Kreislauf-Erkrankungen zu schärfen.

Keywords: Cardiovascular risk; Mortality; Particulate matter; Prevention; Traffic noise.

© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

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Environmental and Health Impacts of Air Pollution: A Review

Ioannis manisalidis.

1 Delphis S.A., Kifisia, Greece

2 Laboratory of Hygiene and Environmental Protection, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece

Elisavet Stavropoulou

3 Centre Hospitalier Universitaire Vaudois (CHUV), Service de Médicine Interne, Lausanne, Switzerland

Agathangelos Stavropoulos

4 School of Social and Political Sciences, University of Glasgow, Glasgow, United Kingdom

Eugenia Bezirtzoglou

One of our era's greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. There are many pollutants that are major factors in disease in humans. Among them, Particulate Matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. Despite the fact that ozone in the stratosphere plays a protective role against ultraviolet irradiation, it is harmful when in high concentration at ground level, also affecting the respiratory and cardiovascular system. Furthermore, nitrogen oxide, sulfur dioxide, Volatile Organic Compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) are all considered air pollutants that are harmful to humans. Carbon monoxide can even provoke direct poisoning when breathed in at high levels. Heavy metals such as lead, when absorbed into the human body, can lead to direct poisoning or chronic intoxication, depending on exposure. Diseases occurring from the aforementioned substances include principally respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD), asthma, bronchiolitis, and also lung cancer, cardiovascular events, central nervous system dysfunctions, and cutaneous diseases. Last but not least, climate change resulting from environmental pollution affects the geographical distribution of many infectious diseases, as do natural disasters. The only way to tackle this problem is through public awareness coupled with a multidisciplinary approach by scientific experts; national and international organizations must address the emergence of this threat and propose sustainable solutions.

Approach to the Problem

The interactions between humans and their physical surroundings have been extensively studied, as multiple human activities influence the environment. The environment is a coupling of the biotic (living organisms and microorganisms) and the abiotic (hydrosphere, lithosphere, and atmosphere).

Pollution is defined as the introduction into the environment of substances harmful to humans and other living organisms. Pollutants are harmful solids, liquids, or gases produced in higher than usual concentrations that reduce the quality of our environment.

Human activities have an adverse effect on the environment by polluting the water we drink, the air we breathe, and the soil in which plants grow. Although the industrial revolution was a great success in terms of technology, society, and the provision of multiple services, it also introduced the production of huge quantities of pollutants emitted into the air that are harmful to human health. Without any doubt, the global environmental pollution is considered an international public health issue with multiple facets. Social, economic, and legislative concerns and lifestyle habits are related to this major problem. Clearly, urbanization and industrialization are reaching unprecedented and upsetting proportions worldwide in our era. Anthropogenic air pollution is one of the biggest public health hazards worldwide, given that it accounts for about 9 million deaths per year ( 1 ).

Without a doubt, all of the aforementioned are closely associated with climate change, and in the event of danger, the consequences can be severe for mankind ( 2 ). Climate changes and the effects of global planetary warming seriously affect multiple ecosystems, causing problems such as food safety issues, ice and iceberg melting, animal extinction, and damage to plants ( 3 , 4 ).

Air pollution has various health effects. The health of susceptible and sensitive individuals can be impacted even on low air pollution days. Short-term exposure to air pollutants is closely related to COPD (Chronic Obstructive Pulmonary Disease), cough, shortness of breath, wheezing, asthma, respiratory disease, and high rates of hospitalization (a measurement of morbidity).

The long-term effects associated with air pollution are chronic asthma, pulmonary insufficiency, cardiovascular diseases, and cardiovascular mortality. According to a Swedish cohort study, diabetes seems to be induced after long-term air pollution exposure ( 5 ). Moreover, air pollution seems to have various malign health effects in early human life, such as respiratory, cardiovascular, mental, and perinatal disorders ( 3 ), leading to infant mortality or chronic disease in adult age ( 6 ).

National reports have mentioned the increased risk of morbidity and mortality ( 1 ). These studies were conducted in many places around the world and show a correlation between daily ranges of particulate matter (PM) concentration and daily mortality. Climate shifts and global planetary warming ( 3 ) could aggravate the situation. Besides, increased hospitalization (an index of morbidity) has been registered among the elderly and susceptible individuals for specific reasons. Fine and ultrafine particulate matter seems to be associated with more serious illnesses ( 6 ), as it can invade the deepest parts of the airways and more easily reach the bloodstream.

Air pollution mainly affects those living in large urban areas, where road emissions contribute the most to the degradation of air quality. There is also a danger of industrial accidents, where the spread of a toxic fog can be fatal to the populations of the surrounding areas. The dispersion of pollutants is determined by many parameters, most notably atmospheric stability and wind ( 6 ).

In developing countries ( 7 ), the problem is more serious due to overpopulation and uncontrolled urbanization along with the development of industrialization. This leads to poor air quality, especially in countries with social disparities and a lack of information on sustainable management of the environment. The use of fuels such as wood fuel or solid fuel for domestic needs due to low incomes exposes people to bad-quality, polluted air at home. It is of note that three billion people around the world are using the above sources of energy for their daily heating and cooking needs ( 8 ). In developing countries, the women of the household seem to carry the highest risk for disease development due to their longer duration exposure to the indoor air pollution ( 8 , 9 ). Due to its fast industrial development and overpopulation, China is one of the Asian countries confronting serious air pollution problems ( 10 , 11 ). The lung cancer mortality observed in China is associated with fine particles ( 12 ). As stated already, long-term exposure is associated with deleterious effects on the cardiovascular system ( 3 , 5 ). However, it is interesting to note that cardiovascular diseases have mostly been observed in developed and high-income countries rather than in the developing low-income countries exposed highly to air pollution ( 13 ). Extreme air pollution is recorded in India, where the air quality reaches hazardous levels. New Delhi is one of the more polluted cities in India. Flights in and out of New Delhi International Airport are often canceled due to the reduced visibility associated with air pollution. Pollution is occurring both in urban and rural areas in India due to the fast industrialization, urbanization, and rise in use of motorcycle transportation. Nevertheless, biomass combustion associated with heating and cooking needs and practices is a major source of household air pollution in India and in Nepal ( 14 , 15 ). There is spatial heterogeneity in India, as areas with diverse climatological conditions and population and education levels generate different indoor air qualities, with higher PM 2.5 observed in North Indian states (557–601 μg/m 3 ) compared to the Southern States (183–214 μg/m 3 ) ( 16 , 17 ). The cold climate of the North Indian areas may be the main reason for this, as longer periods at home and more heating are necessary compared to in the tropical climate of Southern India. Household air pollution in India is associated with major health effects, especially in women and young children, who stay indoors for longer periods. Chronic obstructive respiratory disease (CORD) and lung cancer are mostly observed in women, while acute lower respiratory disease is seen in young children under 5 years of age ( 18 ).

Accumulation of air pollution, especially sulfur dioxide and smoke, reaching 1,500 mg/m3, resulted in an increase in the number of deaths (4,000 deaths) in December 1952 in London and in 1963 in New York City (400 deaths) ( 19 ). An association of pollution with mortality was reported on the basis of monitoring of outdoor pollution in six US metropolitan cities ( 20 ). In every case, it seems that mortality was closely related to the levels of fine, inhalable, and sulfate particles more than with the levels of total particulate pollution, aerosol acidity, sulfur dioxide, or nitrogen dioxide ( 20 ).

Furthermore, extremely high levels of pollution are reported in Mexico City and Rio de Janeiro, followed by Milan, Ankara, Melbourne, Tokyo, and Moscow ( 19 ).

Based on the magnitude of the public health impact, it is certain that different kinds of interventions should be taken into account. Success and effectiveness in controlling air pollution, specifically at the local level, have been reported. Adequate technological means are applied considering the source and the nature of the emission as well as its impact on health and the environment. The importance of point sources and non-point sources of air pollution control is reported by Schwela and Köth-Jahr ( 21 ). Without a doubt, a detailed emission inventory must record all sources in a given area. Beyond considering the above sources and their nature, topography and meteorology should also be considered, as stated previously. Assessment of the control policies and methods is often extrapolated from the local to the regional and then to the global scale. Air pollution may be dispersed and transported from one region to another area located far away. Air pollution management means the reduction to acceptable levels or possible elimination of air pollutants whose presence in the air affects our health or the environmental ecosystem. Private and governmental entities and authorities implement actions to ensure the air quality ( 22 ). Air quality standards and guidelines were adopted for the different pollutants by the WHO and EPA as a tool for the management of air quality ( 1 , 23 ). These standards have to be compared to the emissions inventory standards by causal analysis and dispersion modeling in order to reveal the problematic areas ( 24 ). Inventories are generally based on a combination of direct measurements and emissions modeling ( 24 ).

As an example, we state here the control measures at the source through the use of catalytic converters in cars. These are devices that turn the pollutants and toxic gases produced from combustion engines into less-toxic pollutants by catalysis through redox reactions ( 25 ). In Greece, the use of private cars was restricted by tracking their license plates in order to reduce traffic congestion during rush hour ( 25 ).

Concerning industrial emissions, collectors and closed systems can keep the air pollution to the minimal standards imposed by legislation ( 26 ).

Current strategies to improve air quality require an estimation of the economic value of the benefits gained from proposed programs. These proposed programs by public authorities, and directives are issued with guidelines to be respected.

In Europe, air quality limit values AQLVs (Air Quality Limit Values) are issued for setting off planning claims ( 27 ). In the USA, the NAAQS (National Ambient Air Quality Standards) establish the national air quality limit values ( 27 ). While both standards and directives are based on different mechanisms, significant success has been achieved in the reduction of overall emissions and associated health and environmental effects ( 27 ). The European Directive identifies geographical areas of risk exposure as monitoring/assessment zones to record the emission sources and levels of air pollution ( 27 ), whereas the USA establishes global geographical air quality criteria according to the severity of their air quality problem and records all sources of the pollutants and their precursors ( 27 ).

In this vein, funds have been financing, directly or indirectly, projects related to air quality along with the technical infrastructure to maintain good air quality. These plans focus on an inventory of databases from air quality environmental planning awareness campaigns. Moreover, pollution measures of air emissions may be taken for vehicles, machines, and industries in urban areas.

Technological innovation can only be successful if it is able to meet the needs of society. In this sense, technology must reflect the decision-making practices and procedures of those involved in risk assessment and evaluation and act as a facilitator in providing information and assessments to enable decision makers to make the best decisions possible. Summarizing the aforementioned in order to design an effective air quality control strategy, several aspects must be considered: environmental factors and ambient air quality conditions, engineering factors and air pollutant characteristics, and finally, economic operating costs for technological improvement and administrative and legal costs. Considering the economic factor, competitiveness through neoliberal concepts is offering a solution to environmental problems ( 22 ).

The development of environmental governance, along with technological progress, has initiated the deployment of a dialogue. Environmental politics has created objections and points of opposition between different political parties, scientists, media, and governmental and non-governmental organizations ( 22 ). Radical environmental activism actions and movements have been created ( 22 ). The rise of the new information and communication technologies (ICTs) are many times examined as to whether and in which way they have influenced means of communication and social movements such as activism ( 28 ). Since the 1990s, the term “digital activism” has been used increasingly and in many different disciplines ( 29 ). Nowadays, multiple digital technologies can be used to produce a digital activism outcome on environmental issues. More specifically, devices with online capabilities such as computers or mobile phones are being used as a way to pursue change in political and social affairs ( 30 ).

In the present paper, we focus on the sources of environmental pollution in relation to public health and propose some solutions and interventions that may be of interest to environmental legislators and decision makers.

Sources of Exposure

It is known that the majority of environmental pollutants are emitted through large-scale human activities such as the use of industrial machinery, power-producing stations, combustion engines, and cars. Because these activities are performed at such a large scale, they are by far the major contributors to air pollution, with cars estimated to be responsible for approximately 80% of today's pollution ( 31 ). Some other human activities are also influencing our environment to a lesser extent, such as field cultivation techniques, gas stations, fuel tanks heaters, and cleaning procedures ( 32 ), as well as several natural sources, such as volcanic and soil eruptions and forest fires.

The classification of air pollutants is based mainly on the sources producing pollution. Therefore, it is worth mentioning the four main sources, following the classification system: Major sources, Area sources, Mobile sources, and Natural sources.

Major sources include the emission of pollutants from power stations, refineries, and petrochemicals, the chemical and fertilizer industries, metallurgical and other industrial plants, and, finally, municipal incineration.

Indoor area sources include domestic cleaning activities, dry cleaners, printing shops, and petrol stations.

Mobile sources include automobiles, cars, railways, airways, and other types of vehicles.

Finally, natural sources include, as stated previously, physical disasters ( 33 ) such as forest fire, volcanic erosion, dust storms, and agricultural burning.

However, many classification systems have been proposed. Another type of classification is a grouping according to the recipient of the pollution, as follows:

Air pollution is determined as the presence of pollutants in the air in large quantities for long periods. Air pollutants are dispersed particles, hydrocarbons, CO, CO 2 , NO, NO 2 , SO 3 , etc.

Water pollution is organic and inorganic charge and biological charge ( 10 ) at high levels that affect the water quality ( 34 , 35 ).

Soil pollution occurs through the release of chemicals or the disposal of wastes, such as heavy metals, hydrocarbons, and pesticides.

Air pollution can influence the quality of soil and water bodies by polluting precipitation, falling into water and soil environments ( 34 , 36 ). Notably, the chemistry of the soil can be amended due to acid precipitation by affecting plants, cultures, and water quality ( 37 ). Moreover, movement of heavy metals is favored by soil acidity, and metals are so then moving into the watery environment. It is known that heavy metals such as aluminum are noxious to wildlife and fishes. Soil quality seems to be of importance, as soils with low calcium carbonate levels are at increased jeopardy from acid rain. Over and above rain, snow and particulate matter drip into watery ' bodies ( 36 , 38 ).

Lastly, pollution is classified following type of origin:

Radioactive and nuclear pollution , releasing radioactive and nuclear pollutants into water, air, and soil during nuclear explosions and accidents, from nuclear weapons, and through handling or disposal of radioactive sewage.

Radioactive materials can contaminate surface water bodies and, being noxious to the environment, plants, animals, and humans. It is known that several radioactive substances such as radium and uranium concentrate in the bones and can cause cancers ( 38 , 39 ).

Noise pollution is produced by machines, vehicles, traffic noises, and musical installations that are harmful to our hearing.

The World Health Organization introduced the term DALYs. The DALYs for a disease or health condition is defined as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences ( 39 ). In Europe, air pollution is the main cause of disability-adjusted life years lost (DALYs), followed by noise pollution. The potential relationships of noise and air pollution with health have been studied ( 40 ). The study found that DALYs related to noise were more important than those related to air pollution, as the effects of environmental noise on cardiovascular disease were independent of air pollution ( 40 ). Environmental noise should be counted as an independent public health risk ( 40 ).

Environmental pollution occurs when changes in the physical, chemical, or biological constituents of the environment (air masses, temperature, climate, etc.) are produced.

Pollutants harm our environment either by increasing levels above normal or by introducing harmful toxic substances. Primary pollutants are directly produced from the above sources, and secondary pollutants are emitted as by-products of the primary ones. Pollutants can be biodegradable or non-biodegradable and of natural origin or anthropogenic, as stated previously. Moreover, their origin can be a unique source (point-source) or dispersed sources.

Pollutants have differences in physical and chemical properties, explaining the discrepancy in their capacity for producing toxic effects. As an example, we state here that aerosol compounds ( 41 – 43 ) have a greater toxicity than gaseous compounds due to their tiny size (solid or liquid) in the atmosphere; they have a greater penetration capacity. Gaseous compounds are eliminated more easily by our respiratory system ( 41 ). These particles are able to damage lungs and can even enter the bloodstream ( 41 ), leading to the premature deaths of millions of people yearly. Moreover, the aerosol acidity ([H+]) seems to considerably enhance the production of secondary organic aerosols (SOA), but this last aspect is not supported by other scientific teams ( 38 ).

Climate and Pollution

Air pollution and climate change are closely related. Climate is the other side of the same coin that reduces the quality of our Earth ( 44 ). Pollutants such as black carbon, methane, tropospheric ozone, and aerosols affect the amount of incoming sunlight. As a result, the temperature of the Earth is increasing, resulting in the melting of ice, icebergs, and glaciers.

In this vein, climatic changes will affect the incidence and prevalence of both residual and imported infections in Europe. Climate and weather affect the duration, timing, and intensity of outbreaks strongly and change the map of infectious diseases in the globe ( 45 ). Mosquito-transmitted parasitic or viral diseases are extremely climate-sensitive, as warming firstly shortens the pathogen incubation period and secondly shifts the geographic map of the vector. Similarly, water-warming following climate changes leads to a high incidence of waterborne infections. Recently, in Europe, eradicated diseases seem to be emerging due to the migration of population, for example, cholera, poliomyelitis, tick-borne encephalitis, and malaria ( 46 ).

The spread of epidemics is associated with natural climate disasters and storms, which seem to occur more frequently nowadays ( 47 ). Malnutrition and disequilibration of the immune system are also associated with the emerging infections affecting public health ( 48 ).

The Chikungunya virus “took the airplane” from the Indian Ocean to Europe, as outbreaks of the disease were registered in Italy ( 49 ) as well as autochthonous cases in France ( 50 ).

An increase in cryptosporidiosis in the United Kingdom and in the Czech Republic seems to have occurred following flooding ( 36 , 51 ).

As stated previously, aerosols compounds are tiny in size and considerably affect the climate. They are able to dissipate sunlight (the albedo phenomenon) by dispersing a quarter of the sun's rays back to space and have cooled the global temperature over the last 30 years ( 52 ).

Air Pollutants

The World Health Organization (WHO) reports on six major air pollutants, namely particle pollution, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. Air pollution can have a disastrous effect on all components of the environment, including groundwater, soil, and air. Additionally, it poses a serious threat to living organisms. In this vein, our interest is mainly to focus on these pollutants, as they are related to more extensive and severe problems in human health and environmental impact. Acid rain, global warming, the greenhouse effect, and climate changes have an important ecological impact on air pollution ( 53 ).

Particulate Matter (PM) and Health

Studies have shown a relationship between particulate matter (PM) and adverse health effects, focusing on either short-term (acute) or long-term (chronic) PM exposure.

Particulate matter (PM) is usually formed in the atmosphere as a result of chemical reactions between the different pollutants. The penetration of particles is closely dependent on their size ( 53 ). Particulate Matter (PM) was defined as a term for particles by the United States Environmental Protection Agency ( 54 ). Particulate matter (PM) pollution includes particles with diameters of 10 micrometers (μm) or smaller, called PM 10 , and extremely fine particles with diameters that are generally 2.5 micrometers (μm) and smaller.

Particulate matter contains tiny liquid or solid droplets that can be inhaled and cause serious health effects ( 55 ). Particles <10 μm in diameter (PM 10 ) after inhalation can invade the lungs and even reach the bloodstream. Fine particles, PM 2.5 , pose a greater risk to health ( 6 , 56 ) ( Table 1 ).

Penetrability according to particle size.

Multiple epidemiological studies have been performed on the health effects of PM. A positive relation was shown between both short-term and long-term exposures of PM 2.5 and acute nasopharyngitis ( 56 ). In addition, long-term exposure to PM for years was found to be related to cardiovascular diseases and infant mortality.

Those studies depend on PM 2.5 monitors and are restricted in terms of study area or city area due to a lack of spatially resolved daily PM 2.5 concentration data and, in this way, are not representative of the entire population. Following a recent epidemiological study by the Department of Environmental Health at Harvard School of Public Health (Boston, MA) ( 57 ), it was reported that, as PM 2.5 concentrations vary spatially, an exposure error (Berkson error) seems to be produced, and the relative magnitudes of the short- and long-term effects are not yet completely elucidated. The team developed a PM 2.5 exposure model based on remote sensing data for assessing short- and long-term human exposures ( 57 ). This model permits spatial resolution in short-term effects plus the assessment of long-term effects in the whole population.

Moreover, respiratory diseases and affection of the immune system are registered as long-term chronic effects ( 58 ). It is worth noting that people with asthma, pneumonia, diabetes, and respiratory and cardiovascular diseases are especially susceptible and vulnerable to the effects of PM. PM 2.5 , followed by PM 10 , are strongly associated with diverse respiratory system diseases ( 59 ), as their size permits them to pierce interior spaces ( 60 ). The particles produce toxic effects according to their chemical and physical properties. The components of PM 10 and PM 2.5 can be organic (polycyclic aromatic hydrocarbons, dioxins, benzene, 1-3 butadiene) or inorganic (carbon, chlorides, nitrates, sulfates, metals) in nature ( 55 ).

Particulate Matter (PM) is divided into four main categories according to type and size ( 61 ) ( Table 2 ).

Types and sizes of particulate Matter (PM).

Gas contaminants include PM in aerial masses.

Particulate contaminants include contaminants such as smog, soot, tobacco smoke, oil smoke, fly ash, and cement dust.

Biological Contaminants are microorganisms (bacteria, viruses, fungi, mold, and bacterial spores), cat allergens, house dust and allergens, and pollen.

Types of Dust include suspended atmospheric dust, settling dust, and heavy dust.

Finally, another fact is that the half-lives of PM 10 and PM 2.5 particles in the atmosphere is extended due to their tiny dimensions; this permits their long-lasting suspension in the atmosphere and even their transfer and spread to distant destinations where people and the environment may be exposed to the same magnitude of pollution ( 53 ). They are able to change the nutrient balance in watery ecosystems, damage forests and crops, and acidify water bodies.

As stated, PM 2.5 , due to their tiny size, are causing more serious health effects. These aforementioned fine particles are the main cause of the “haze” formation in different metropolitan areas ( 12 , 13 , 61 ).

Ozone Impact in the Atmosphere

Ozone (O 3 ) is a gas formed from oxygen under high voltage electric discharge ( 62 ). It is a strong oxidant, 52% stronger than chlorine. It arises in the stratosphere, but it could also arise following chain reactions of photochemical smog in the troposphere ( 63 ).

Ozone can travel to distant areas from its initial source, moving with air masses ( 64 ). It is surprising that ozone levels over cities are low in contrast to the increased amounts occuring in urban areas, which could become harmful for cultures, forests, and vegetation ( 65 ) as it is reducing carbon assimilation ( 66 ). Ozone reduces growth and yield ( 47 , 48 ) and affects the plant microflora due to its antimicrobial capacity ( 67 , 68 ). In this regard, ozone acts upon other natural ecosystems, with microflora ( 69 , 70 ) and animal species changing their species composition ( 71 ). Ozone increases DNA damage in epidermal keratinocytes and leads to impaired cellular function ( 72 ).

Ground-level ozone (GLO) is generated through a chemical reaction between oxides of nitrogen and VOCs emitted from natural sources and/or following anthropogenic activities.

Ozone uptake usually occurs by inhalation. Ozone affects the upper layers of the skin and the tear ducts ( 73 ). A study of short-term exposure of mice to high levels of ozone showed malondialdehyde formation in the upper skin (epidermis) but also depletion in vitamins C and E. It is likely that ozone levels are not interfering with the skin barrier function and integrity to predispose to skin disease ( 74 ).

Due to the low water-solubility of ozone, inhaled ozone has the capacity to penetrate deeply into the lungs ( 75 ).

Toxic effects induced by ozone are registered in urban areas all over the world, causing biochemical, morphologic, functional, and immunological disorders ( 76 ).

The European project (APHEA2) focuses on the acute effects of ambient ozone concentrations on mortality ( 77 ). Daily ozone concentrations compared to the daily number of deaths were reported from different European cities for a 3-year period. During the warm period of the year, an observed increase in ozone concentration was associated with an increase in the daily number of deaths (0.33%), in the number of respiratory deaths (1.13%), and in the number of cardiovascular deaths (0.45%). No effect was observed during wintertime.

Carbon Monoxide (CO)

Carbon monoxide is produced by fossil fuel when combustion is incomplete. The symptoms of poisoning due to inhaling carbon monoxide include headache, dizziness, weakness, nausea, vomiting, and, finally, loss of consciousness.

The affinity of carbon monoxide to hemoglobin is much greater than that of oxygen. In this vein, serious poisoning may occur in people exposed to high levels of carbon monoxide for a long period of time. Due to the loss of oxygen as a result of the competitive binding of carbon monoxide, hypoxia, ischemia, and cardiovascular disease are observed.

Carbon monoxide affects the greenhouses gases that are tightly connected to global warming and climate. This should lead to an increase in soil and water temperatures, and extreme weather conditions or storms may occur ( 68 ).

However, in laboratory and field experiments, it has been seen to produce increased plant growth ( 78 ).

Nitrogen Oxide (NO 2 )

Nitrogen oxide is a traffic-related pollutant, as it is emitted from automobile motor engines ( 79 , 80 ). It is an irritant of the respiratory system as it penetrates deep in the lung, inducing respiratory diseases, coughing, wheezing, dyspnea, bronchospasm, and even pulmonary edema when inhaled at high levels. It seems that concentrations over 0.2 ppm produce these adverse effects in humans, while concentrations higher than 2.0 ppm affect T-lymphocytes, particularly the CD8+ cells and NK cells that produce our immune response ( 81 ).It is reported that long-term exposure to high levels of nitrogen dioxide can be responsible for chronic lung disease. Long-term exposure to NO 2 can impair the sense of smell ( 81 ).

However, systems other than respiratory ones can be involved, as symptoms such as eye, throat, and nose irritation have been registered ( 81 ).

High levels of nitrogen dioxide are deleterious to crops and vegetation, as they have been observed to reduce crop yield and plant growth efficiency. Moreover, NO 2 can reduce visibility and discolor fabrics ( 81 ).

Sulfur Dioxide (SO 2 )

Sulfur dioxide is a harmful gas that is emitted mainly from fossil fuel consumption or industrial activities. The annual standard for SO 2 is 0.03 ppm ( 82 ). It affects human, animal, and plant life. Susceptible people as those with lung disease, old people, and children, who present a higher risk of damage. The major health problems associated with sulfur dioxide emissions in industrialized areas are respiratory irritation, bronchitis, mucus production, and bronchospasm, as it is a sensory irritant and penetrates deep into the lung converted into bisulfite and interacting with sensory receptors, causing bronchoconstriction. Moreover, skin redness, damage to the eyes (lacrimation and corneal opacity) and mucous membranes, and worsening of pre-existing cardiovascular disease have been observed ( 81 ).

Environmental adverse effects, such as acidification of soil and acid rain, seem to be associated with sulfur dioxide emissions ( 83 ).

Lead is a heavy metal used in different industrial plants and emitted from some petrol motor engines, batteries, radiators, waste incinerators, and waste waters ( 84 ).

Moreover, major sources of lead pollution in the air are metals, ore, and piston-engine aircraft. Lead poisoning is a threat to public health due to its deleterious effects upon humans, animals, and the environment, especially in the developing countries.

Exposure to lead can occur through inhalation, ingestion, and dermal absorption. Trans- placental transport of lead was also reported, as lead passes through the placenta unencumbered ( 85 ). The younger the fetus is, the more harmful the toxic effects. Lead toxicity affects the fetal nervous system; edema or swelling of the brain is observed ( 86 ). Lead, when inhaled, accumulates in the blood, soft tissue, liver, lung, bones, and cardiovascular, nervous, and reproductive systems. Moreover, loss of concentration and memory, as well as muscle and joint pain, were observed in adults ( 85 , 86 ).

Children and newborns ( 87 ) are extremely susceptible even to minimal doses of lead, as it is a neurotoxicant and causes learning disabilities, impairment of memory, hyperactivity, and even mental retardation.

Elevated amounts of lead in the environment are harmful to plants and crop growth. Neurological effects are observed in vertebrates and animals in association with high lead levels ( 88 ).

Polycyclic Aromatic Hydrocarbons(PAHs)

The distribution of PAHs is ubiquitous in the environment, as the atmosphere is the most important means of their dispersal. They are found in coal and in tar sediments. Moreover, they are generated through incomplete combustion of organic matter as in the cases of forest fires, incineration, and engines ( 89 ). PAH compounds, such as benzopyrene, acenaphthylene, anthracene, and fluoranthene are recognized as toxic, mutagenic, and carcinogenic substances. They are an important risk factor for lung cancer ( 89 ).

Volatile Organic Compounds(VOCs)

Volatile organic compounds (VOCs), such as toluene, benzene, ethylbenzene, and xylene ( 90 ), have been found to be associated with cancer in humans ( 91 ). The use of new products and materials has actually resulted in increased concentrations of VOCs. VOCs pollute indoor air ( 90 ) and may have adverse effects on human health ( 91 ). Short-term and long-term adverse effects on human health are observed. VOCs are responsible for indoor air smells. Short-term exposure is found to cause irritation of eyes, nose, throat, and mucosal membranes, while those of long duration exposure include toxic reactions ( 92 ). Predictable assessment of the toxic effects of complex VOC mixtures is difficult to estimate, as these pollutants can have synergic, antagonistic, or indifferent effects ( 91 , 93 ).

Dioxins originate from industrial processes but also come from natural processes, such as forest fires and volcanic eruptions. They accumulate in foods such as meat and dairy products, fish and shellfish, and especially in the fatty tissue of animals ( 94 ).

Short-period exhibition to high dioxin concentrations may result in dark spots and lesions on the skin ( 94 ). Long-term exposure to dioxins can cause developmental problems, impairment of the immune, endocrine and nervous systems, reproductive infertility, and cancer ( 94 ).

Without any doubt, fossil fuel consumption is responsible for a sizeable part of air contamination. This contamination may be anthropogenic, as in agricultural and industrial processes or transportation, while contamination from natural sources is also possible. Interestingly, it is of note that the air quality standards established through the European Air Quality Directive are somewhat looser than the WHO guidelines, which are stricter ( 95 ).

Effect of Air Pollution on Health

The most common air pollutants are ground-level ozone and Particulates Matter (PM). Air pollution is distinguished into two main types:

Outdoor pollution is the ambient air pollution.

Indoor pollution is the pollution generated by household combustion of fuels.

People exposed to high concentrations of air pollutants experience disease symptoms and states of greater and lesser seriousness. These effects are grouped into short- and long-term effects affecting health.

Susceptible populations that need to be aware of health protection measures include old people, children, and people with diabetes and predisposing heart or lung disease, especially asthma.

As extensively stated previously, according to a recent epidemiological study from Harvard School of Public Health, the relative magnitudes of the short- and long-term effects have not been completely clarified ( 57 ) due to the different epidemiological methodologies and to the exposure errors. New models are proposed for assessing short- and long-term human exposure data more successfully ( 57 ). Thus, in the present section, we report the more common short- and long-term health effects but also general concerns for both types of effects, as these effects are often dependent on environmental conditions, dose, and individual susceptibility.

Short-term effects are temporary and range from simple discomfort, such as irritation of the eyes, nose, skin, throat, wheezing, coughing and chest tightness, and breathing difficulties, to more serious states, such as asthma, pneumonia, bronchitis, and lung and heart problems. Short-term exposure to air pollution can also cause headaches, nausea, and dizziness.

These problems can be aggravated by extended long-term exposure to the pollutants, which is harmful to the neurological, reproductive, and respiratory systems and causes cancer and even, rarely, deaths.

The long-term effects are chronic, lasting for years or the whole life and can even lead to death. Furthermore, the toxicity of several air pollutants may also induce a variety of cancers in the long term ( 96 ).

As stated already, respiratory disorders are closely associated with the inhalation of air pollutants. These pollutants will invade through the airways and will accumulate at the cells. Damage to target cells should be related to the pollutant component involved and its source and dose. Health effects are also closely dependent on country, area, season, and time. An extended exposure duration to the pollutant should incline to long-term health effects in relation also to the above factors.

Particulate Matter (PMs), dust, benzene, and O 3 cause serious damage to the respiratory system ( 97 ). Moreover, there is a supplementary risk in case of existing respiratory disease such as asthma ( 98 ). Long-term effects are more frequent in people with a predisposing disease state. When the trachea is contaminated by pollutants, voice alterations may be remarked after acute exposure. Chronic obstructive pulmonary disease (COPD) may be induced following air pollution, increasing morbidity and mortality ( 99 ). Long-term effects from traffic, industrial air pollution, and combustion of fuels are the major factors for COPD risk ( 99 ).

Multiple cardiovascular effects have been observed after exposure to air pollutants ( 100 ). Changes occurred in blood cells after long-term exposure may affect cardiac functionality. Coronary arteriosclerosis was reported following long-term exposure to traffic emissions ( 101 ), while short-term exposure is related to hypertension, stroke, myocardial infracts, and heart insufficiency. Ventricle hypertrophy is reported to occur in humans after long-time exposure to nitrogen oxide (NO 2 ) ( 102 , 103 ).

Neurological effects have been observed in adults and children after extended-term exposure to air pollutants.

Psychological complications, autism, retinopathy, fetal growth, and low birth weight seem to be related to long-term air pollution ( 83 ). The etiologic agent of the neurodegenerative diseases (Alzheimer's and Parkinson's) is not yet known, although it is believed that extended exposure to air pollution seems to be a factor. Specifically, pesticides and metals are cited as etiological factors, together with diet. The mechanisms in the development of neurodegenerative disease include oxidative stress, protein aggregation, inflammation, and mitochondrial impairment in neurons ( 104 ) ( Figure 1 ).

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Impact of air pollutants on the brain.

Brain inflammation was observed in dogs living in a highly polluted area in Mexico for a long period ( 105 ). In human adults, markers of systemic inflammation (IL-6 and fibrinogen) were found to be increased as an immediate response to PNC on the IL-6 level, possibly leading to the production of acute-phase proteins ( 106 ). The progression of atherosclerosis and oxidative stress seem to be the mechanisms involved in the neurological disturbances caused by long-term air pollution. Inflammation comes secondary to the oxidative stress and seems to be involved in the impairment of developmental maturation, affecting multiple organs ( 105 , 107 ). Similarly, other factors seem to be involved in the developmental maturation, which define the vulnerability to long-term air pollution. These include birthweight, maternal smoking, genetic background and socioeconomic environment, as well as education level.

However, diet, starting from breast-feeding, is another determinant factor. Diet is the main source of antioxidants, which play a key role in our protection against air pollutants ( 108 ). Antioxidants are free radical scavengers and limit the interaction of free radicals in the brain ( 108 ). Similarly, genetic background may result in a differential susceptibility toward the oxidative stress pathway ( 60 ). For example, antioxidant supplementation with vitamins C and E appears to modulate the effect of ozone in asthmatic children homozygous for the GSTM1 null allele ( 61 ). Inflammatory cytokines released in the periphery (e.g., respiratory epithelia) upregulate the innate immune Toll-like receptor 2. Such activation and the subsequent events leading to neurodegeneration have recently been observed in lung lavage in mice exposed to ambient Los Angeles (CA, USA) particulate matter ( 61 ). In children, neurodevelopmental morbidities were observed after lead exposure. These children developed aggressive and delinquent behavior, reduced intelligence, learning difficulties, and hyperactivity ( 109 ). No level of lead exposure seems to be “safe,” and the scientific community has asked the Centers for Disease Control and Prevention (CDC) to reduce the current screening guideline of 10 μg/dl ( 109 ).

It is important to state that impact on the immune system, causing dysfunction and neuroinflammation ( 104 ), is related to poor air quality. Yet, increases in serum levels of immunoglobulins (IgA, IgM) and the complement component C3 are observed ( 106 ). Another issue is that antigen presentation is affected by air pollutants, as there is an upregulation of costimulatory molecules such as CD80 and CD86 on macrophages ( 110 ).

As is known, skin is our shield against ultraviolet radiation (UVR) and other pollutants, as it is the most exterior layer of our body. Traffic-related pollutants, such as PAHs, VOCs, oxides, and PM, may cause pigmented spots on our skin ( 111 ). On the one hand, as already stated, when pollutants penetrate through the skin or are inhaled, damage to the organs is observed, as some of these pollutants are mutagenic and carcinogenic, and, specifically, they affect the liver and lung. On the other hand, air pollutants (and those in the troposphere) reduce the adverse effects of ultraviolet radiation UVR in polluted urban areas ( 111 ). Air pollutants absorbed by the human skin may contribute to skin aging, psoriasis, acne, urticaria, eczema, and atopic dermatitis ( 111 ), usually caused by exposure to oxides and photochemical smoke ( 111 ). Exposure to PM and cigarette smoking act as skin-aging agents, causing spots, dyschromia, and wrinkles. Lastly, pollutants have been associated with skin cancer ( 111 ).

Higher morbidity is reported to fetuses and children when exposed to the above dangers. Impairment in fetal growth, low birth weight, and autism have been reported ( 112 ).

Another exterior organ that may be affected is the eye. Contamination usually comes from suspended pollutants and may result in asymptomatic eye outcomes, irritation ( 112 ), retinopathy, or dry eye syndrome ( 113 , 114 ).

Environmental Impact of Air Pollution

Air pollution is harming not only human health but also the environment ( 115 ) in which we live. The most important environmental effects are as follows.

Acid rain is wet (rain, fog, snow) or dry (particulates and gas) precipitation containing toxic amounts of nitric and sulfuric acids. They are able to acidify the water and soil environments, damage trees and plantations, and even damage buildings and outdoor sculptures, constructions, and statues.

Haze is produced when fine particles are dispersed in the air and reduce the transparency of the atmosphere. It is caused by gas emissions in the air coming from industrial facilities, power plants, automobiles, and trucks.

Ozone , as discussed previously, occurs both at ground level and in the upper level (stratosphere) of the Earth's atmosphere. Stratospheric ozone is protecting us from the Sun's harmful ultraviolet (UV) rays. In contrast, ground-level ozone is harmful to human health and is a pollutant. Unfortunately, stratospheric ozone is gradually damaged by ozone-depleting substances (i.e., chemicals, pesticides, and aerosols). If this protecting stratospheric ozone layer is thinned, then UV radiation can reach our Earth, with harmful effects for human life (skin cancer) ( 116 ) and crops ( 117 ). In plants, ozone penetrates through the stomata, inducing them to close, which blocks CO 2 transfer and induces a reduction in photosynthesis ( 118 ).

Global climate change is an important issue that concerns mankind. As is known, the “greenhouse effect” keeps the Earth's temperature stable. Unhappily, anthropogenic activities have destroyed this protecting temperature effect by producing large amounts of greenhouse gases, and global warming is mounting, with harmful effects on human health, animals, forests, wildlife, agriculture, and the water environment. A report states that global warming is adding to the health risks of poor people ( 119 ).

People living in poorly constructed buildings in warm-climate countries are at high risk for heat-related health problems as temperatures mount ( 119 ).

Wildlife is burdened by toxic pollutants coming from the air, soil, or the water ecosystem and, in this way, animals can develop health problems when exposed to high levels of pollutants. Reproductive failure and birth effects have been reported.

Eutrophication is occurring when elevated concentrations of nutrients (especially nitrogen) stimulate the blooming of aquatic algae, which can cause a disequilibration in the diversity of fish and their deaths.

Without a doubt, there is a critical concentration of pollution that an ecosystem can tolerate without being destroyed, which is associated with the ecosystem's capacity to neutralize acidity. The Canada Acid Rain Program established this load at 20 kg/ha/yr ( 120 ).

Hence, air pollution has deleterious effects on both soil and water ( 121 ). Concerning PM as an air pollutant, its impact on crop yield and food productivity has been reported. Its impact on watery bodies is associated with the survival of living organisms and fishes and their productivity potential ( 121 ).

An impairment in photosynthetic rhythm and metabolism is observed in plants exposed to the effects of ozone ( 121 ).

Sulfur and nitrogen oxides are involved in the formation of acid rain and are harmful to plants and marine organisms.

Last but not least, as mentioned above, the toxicity associated with lead and other metals is the main threat to our ecosystems (air, water, and soil) and living creatures ( 121 ).

In 2018, during the first WHO Global Conference on Air Pollution and Health, the WHO's General Director, Dr. Tedros Adhanom Ghebreyesus, called air pollution a “silent public health emergency” and “the new tobacco” ( 122 ).

Undoubtedly, children are particularly vulnerable to air pollution, especially during their development. Air pollution has adverse effects on our lives in many different respects.

Diseases associated with air pollution have not only an important economic impact but also a societal impact due to absences from productive work and school.

Despite the difficulty of eradicating the problem of anthropogenic environmental pollution, a successful solution could be envisaged as a tight collaboration of authorities, bodies, and doctors to regularize the situation. Governments should spread sufficient information and educate people and should involve professionals in these issues so as to control the emergence of the problem successfully.

Technologies to reduce air pollution at the source must be established and should be used in all industries and power plants. The Kyoto Protocol of 1997 set as a major target the reduction of GHG emissions to below 5% by 2012 ( 123 ). This was followed by the Copenhagen summit, 2009 ( 124 ), and then the Durban summit of 2011 ( 125 ), where it was decided to keep to the same line of action. The Kyoto protocol and the subsequent ones were ratified by many countries. Among the pioneers who adopted this important protocol for the world's environmental and climate “health” was China ( 3 ). As is known, China is a fast-developing economy and its GDP (Gross Domestic Product) is expected to be very high by 2050, which is defined as the year of dissolution of the protocol for the decrease in gas emissions.

A more recent international agreement of crucial importance for climate change is the Paris Agreement of 2015, issued by the UNFCCC (United Nations Climate Change Committee). This latest agreement was ratified by a plethora of UN (United Nations) countries as well as the countries of the European Union ( 126 ). In this vein, parties should promote actions and measures to enhance numerous aspects around the subject. Boosting education, training, public awareness, and public participation are some of the relevant actions for maximizing the opportunities to achieve the targets and goals on the crucial matter of climate change and environmental pollution ( 126 ). Without any doubt, technological improvements makes our world easier and it seems difficult to reduce the harmful impact caused by gas emissions, we could limit its use by seeking reliable approaches.

Synopsizing, a global prevention policy should be designed in order to combat anthropogenic air pollution as a complement to the correct handling of the adverse health effects associated with air pollution. Sustainable development practices should be applied, together with information coming from research in order to handle the problem effectively.

At this point, international cooperation in terms of research, development, administration policy, monitoring, and politics is vital for effective pollution control. Legislation concerning air pollution must be aligned and updated, and policy makers should propose the design of a powerful tool of environmental and health protection. As a result, the main proposal of this essay is that we should focus on fostering local structures to promote experience and practice and extrapolate these to the international level through developing effective policies for sustainable management of ecosystems.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

IM is employed by the company Delphis S.A. The remaining authors declare that the present review paper was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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    Research also reveals how noise pollution connects with climate change. Many contributors to global warming generate noise, chief among them transportation and fossil fuel extraction and processing. Urban sprawl and deforestation destroy natural carbon absorption reservoirs while removing natural sound buffers.

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    The trends in prevalence of tinnitus in Europe are particularly worrying—a 2021 Article published in The Lancet Regional Health - Europe by Roshni Biswas and colleagues estimated that more than one in seven adults in the EU have tinnitus.

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    As more research shows how noise pollution can have severely harmful impacts on our health, there is a growing movement looking for ways to make communities quieter and healthier. PIEN HUANG,...

  13. What are the health effects of noise pollution?

    Summary Noise pollution occurs when unwanted sounds enter the environment. The potential health effects of noise pollution include increased stress levels, sleep disturbance, or hearing damage....

  14. Noise pollution and human cognition: An updated systematic ...

    The evidence so far suggests that noise exposure is associated with cognition, but more good quality research using standardised methodology is required to corroborate these results and to allow for precise risk estimation by larger meta-analyses. There is also a need for more research with older te …

  15. Noise Could Take Years Off Your Life. Here's How.

    Here Are the Health Impacts - The New York Times Noise Could Take Years Off Your Life. Here's How. By Emily Baumgaertner , Jason Kao , Eleanor Lutz , Josephine Sedgwick , Rumsey Taylor , Noah...

  16. Environmental noise in hospitals: a systematic review

    The systematic review of the literature in electronic format involved three databases, Scopus, Web of Science, and ScienceDirect. The first filter employed in the search selected articles published in the last 6 years (from 2015 to 2020), only articles in English, research articles (excluding technical and review notes), and keywords "Noise" and "Hospital."

  17. Noise pollution and annoyance: An urban soundscapes study

    The study was conducted in two steps: Evaluation of noise levels, with the development of noise maps, and health related inquiries. 180 individuals were interviewed, being 60 in each scenario, divided into 30 exposed to high level of noise and 30 to low level.

  18. Full article: The proliferation of noise pollution as an urban social

    This article envisioned to study noise pollution as a social problem in that the causes of the problem are social for human activities causing noise pollution, ... MPH in Reproductive and Family health and is active teaching staff who published more than 20 research articles. Tamirat Mengistu Kolcha (Assistant Prof) has MA in Urban Sociology ...

  19. Construction noise management: A systematic review and directions for

    1. Introduction. Noise pollution is an increasing problem in our modern society [24], [106].People in urban areas are exposed to different types of environmental noise such as traffic noise, train noise, airport noise and industrial noise [93].According to the World Health Organization (WHO), the effects of noise pollution on human health have been accumulating in recent years [141].

  20. Full article: Noise Pollution: A Multi-Step Approach to Assessing the

    An article was deemed irrelevant if it failed to cover climate change and only mentioned it in passing or in terms of comparison; these articles fall under our conceptualization of noise. In total, our sample was balanced—i.e. 56.6% ( n = 283) of the 500 articles were coded as relevant, while 43.4% ( n = 217) were rated as not relevant.

  21. Environmental noise in hospitals: a systematic review

    Methods. The systematic review of the literature in electronic format involved three databases, Scopus, Web of Science, and ScienceDirect. The first filter employed in the search selected articles published in the last 6 years (from 2015 to 2020), only articles in English, research articles (excluding technical and review notes), and keywords "Noise" and "Hospital."

  22. Cluster pattern analysis of environmental stressors and quantifying

    Environmental stress causes a significant burden on our human health. Environmental stress refers to various types of pollution and non-ideal environmental circumstances, such as air pollution (e.g., exposure to nitrogen dioxide (NO 2) and particulate matter (PM 2,5)), noise (due to road traffic, aviation, industries, etc.), lack of green spaces, industrial pollution, or exposure to non ...

  23. Whales 'cannot out-sing' human noise pollution

    AFP. 12:05PM February 22, 2024. Baleen whales have evolved a special voice box to help them to sing underwater -- but this could also make them uniquely vulnerable to being drowned out by human ...

  24. Decibel Hell: The Effects of Living in a Noisy World

    The growing noise pollution problem has many different causes. Booming population growth and the loss of rural land to urban sprawl both play a role.

  25. [Air pollution, noise and hypertension : Partners in crime]

    10.1007/s00059-024-05234-5. Air pollution and traffic noise are two important environmental risk factors that endanger health in urban societies and often act together as "partners in crime". Although air pollution and noise often co-occur in urban environments, they have typically been studied separately, with numerous studies documenting ...

  26. Environmental and Health Impacts of Air Pollution: A Review

    In Europe, air pollution is the main cause of disability-adjusted life years lost (DALYs), followed by noise pollution. The potential relationships of noise and air pollution with health have been studied . ... At this point, international cooperation in terms of research, development, administration policy, monitoring, and politics is vital ...