case study of noise pollution

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• Published: 26 January 2024

Noise and mental health: evidence, mechanisms, and consequences

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

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

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

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

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

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

The noise/stress concept

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

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

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

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

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

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

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

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

Epidemiological evidence

Depression and anxiety.

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

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

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

Behavioral problems in children and adolescents

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

Future research needs and conclusions

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

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

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Acknowledgements

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

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These authors contributed equally: Omar Hahad, Marin Kuntic.

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

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

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

Omar Hahad, Marin Kuntic, Andreas Daiber & Thomas Münzel

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

Sadeer Al-Kindi

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

Donya Gilan

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

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

Katja Petrowski

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

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

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A Study of Noise Pollution Measurements and Possible Effects on Public Health in Ota Metropolis, Nigeria

Pelumi e. oguntunde.

1 Department of Mathematics, Covenant University, Ogun State, Ota, Nigeria

Hilary I. Okagbue

Omoleye a. oguntunde.

2 Department of Business Management, Covenant University, Ogun State, Ota, Nigeria

Oluwole O. Odetunmibi

Background:.

Noise pollution has become a major environmental problem leading to nuisances and health issues.

This paper aims to study and analyse the noise pollution levels in major areas in Ota metropolis. A probability model which is capable of predicting the noise pollution level is also determined.

Datasets on the noise pollution level in 41 locations across Ota metropolis were used in this research. The datasets were collected thrice per day; morning, afternoon and evening. Descriptive statistics were performed, and analysis of variance was also conducted using Minitab version 17.0 software. Easy fit software was however used to select the appropriate probability model that would best describe the dataset.

The noise levels are way far from the WHO recommendations. Also, there is no significant difference in the effects of the noise pollution level for all the times of the day considered. The log-logistic distribution provides the best fit to the dataset based on the Kolmogorov Smirnov goodness of fit test.

CONCLUSION:

The fitted probability model can help in the prediction of noise pollution and act as a yardstick in the reduction of noise pollution, thereby improving the public health of the populace.

Introduction

Noise pollution is one of several environmental pollutions across the world. It can be described as the propagation of noise with a harmful impact on the physiological and psychological lives of humans or animals [ 1 ]. Noise or sound pollution is usually not studied compared with other forms of pollution such as air [ 2 ], [ 3 ], [ 4 ], water [ 5 ], soil [ 6 ], light and radioactive. The reason is that the adverse effects of other forms of pollution on humans are more pronounced. Notwithstanding, noise pollution remains a serious health concern in the study area (Ota, Nigeria) in particular and the entire planet [ 7 ], [ 8 ]. Some of the identified sources of noise pollution are loud music from concerts, religious buildings like churches and mosques, noise emitting generators [ 9 ], political rallies, road advertisement, traffic [ 10 ] and air transportation [ 11 ], sporting events, construction and industrial activities. In all the mentioned sources, areas that have high risk of noise pollution are residential places near to major roads [ 12 ] and airports and manufacturing industries [ 13 ]; for example, small scale industries [ 14 ], [ 15 ], steel rolling industries [ 16 ], oil and gas industry [ 17 ], [ 18 ] and so on.

The health effects of noise pollution cannot be over-emphasised. This has prompted the World Health Organization (WHO) and the Federal Environment Protection Agency (FEPA) (Nigeria) to set standards and limits of allowable noise levels. Noise pollution occurs when it is observed that those standards are exceeded as seen in [ 19 ], [ 20 ].

The most common manifestation of noise pollution is hearing loss or impairment [ 21 ]. Hearing impairment is mostly classified as occupational hazards especially when the individual is affiliated with industry that propagates loud sound or noise. Moreover, several physiological and psychological effects of noise pollution exist. The combination of noise and air pollution is associated with respiratory ailments, dizziness and tiredness in school children [ 22 ], [ 23 ]. In adults, noise pollution has been found to be associated with high blood pressure [ 24 ] and cognitive difficulties [ 25 ].

A look at the literature showed the abundance of evidence of the adverse effects of noise pollution on the general public health. The worsening situation of noise pollution is that it has not been upgraded to the level of the other forms of pollution. Also, recommendations suggested by several authors on the different strategies on tackling noise pollution has not been considered and implemented. However, noise pollution continues to impact negatively on fetal development [ 26 ], annoyance and anxiety [ 27 ], mental health crisis [ 28 ], sleep disturbance and insomnia [ 29 ], [ 30 ], cardiovascular disorders in pregnant women [ 31 ], cardiocerebrovascular diseases [ 32 ], type 2 diabetes incidence [ 33 ] and medically unexplained physical symptoms [ 34 ]. Other auditory and non-auditory effects of noise on health are myocardial infarction incidence [ 35 ], peptic ulcers [ 36 ] and disruption of communication and retentive capabilities in children [ 37 ].

Material and Methods

The dataset used in this research was gotten from [ 38 ]. It represents the noise level in 41 major locations in Ota metropolis, Nigeria. These major areas include industrial areas, commercial areas, passenger loading parks, busy roads and junctions. The readings were taken using the SLM (Sound Level Meter). Measurements were taken three different times of the day; morning (7 am to 9 am), afternoon (1 pm to 3 pm) and evening (6 pm to 8 pm). Particularly, the noise pollution level (NLP) was considered and analysed in this present research.

Analysis of Variance

Analysis of variance is conducted in this research to know if there is a significant difference between the effect of noise pollution level in the morning, afternoon and evening in Ota metropolis. The hypothesis tested is:

H 0 : The effects of the noise pollution level are the same for morning, afternoon and evening

H 1 : The effects of the noise pollution level are not the same for at least one of either morning, afternoon or evening.

The level of significance used is 0.05, and the null hypothesis is considered rejected if the p-value is less or equal to the level of significance. The structure of the ANOVA table is such as presented in Table 1 .

A typical example of a one-way ANOVA Table

where, ‘f’ is the number of factors which is 3 according to this research; morning, afternoon and evening. ‘n’ is the overall sample size.

The goodness of Fit Test

The goodness of fit test is performed in this research to select the probability model that best fits the dataset. The Kolmogorov Smirnov (KS) test, the Anderson Darling (AD) test and Chi-square test are examples of the goodness of fit tests.

The KS test was adopted in this research because it is the most popular and others might give similar results. The null hypothesis tests whether the data follow a specified distribution. If represent ordered data points, the KS statistic is:

where are the ordered data and is the cumulative distribution function (cdf) of the continuous distribution tested.

Descriptive Analysis of the Dataset

The summary for the LNP measurements is provided in Figures ​ Figures1 1 to ​ to3 3 while the summary for the mean measurement across the 41 locations is provided in Figure 4 .

Summary report for morning measurements on LNP

Summary report for evening measurements on LNP

Summary report for the mean measurements of LNP across all locations in Ota

Summary report for afternoon measurements on LNP

Result for the Analysis of Variance

The analyses of the means of the various measurements are presented in Table 2 .

Analysis of the Means

The 95% confidence interval (CI) plot for the means is displayed in Figure 5 .

The 95% confidence interval (C.I) plot for the means

The result of the analysis of variance is presented in Table 3 .

Analysis of Variance (ANOVA) Table

The result in Table 3 shows that the generated p-value is 0.997 which is far greater than the level of significance (0.05). Hence, there is no enough evidence to reject the null hypothesis, and it can, therefore, be concluded that there is no significant difference in the means of the noise level measurements taken in the morning, afternoon and evening. This result is further confirmed by Turkey’s post-hoc test which is summarized in Figure 6 .

Summary of Turkey’s post-hoc analysis

It can be observed in Figure 6 that all the intervals contained zero; this is an indication that there is no significant difference in the pair of each of the measurements considered.

Fitting of Probability Models

To determine the appropriate probability model that describes the mean noise pollution level in Ota metropolis, Easyfit (trial version) software was used to select distribution with the best fit. The Kolmogorov-Smirnov (KS) test of goodness of fit was used to select the best model. The software fitted sixty distributions to the dataset, but the best five was reported in this research. The result is presented in Table 4 .

Fitted Distributions

From Table 4 , the best-fitted model is the three-parameter Log-logistic distribution; this selection/decision is based on the Kolmogorov Smirnov statistic. A graph showing the best distribution fitted to the dataset on mean noise pollution level is presented in Figure 7 .

Graph of log-logistic distribution on the histogram of the dataset

In conclusion, further analyses of the noise pollution level in Ota metropolis has been provided in this research. The mean noise level in the morning was 90.78 which is higher than (though very close to) that of afternoon and evening with means 90.6 and 90.72 respectively. This is reasonable as more activities are expected during this time; pupils are going to school, workers going to the office, traffic at some junction and major bus stops. However, the analysis of variance result indicated that the time of the day (morning, afternoon and evening) have the same effect on the environment and populace. Also, the noise pollution level in Ota metropolis can be modelled using the log-logistic distribution as evident from the goodness of fit test. The model can now be used in predicting and managing noise pollution in that area. Furthermore, the model can be used in different geographical settings where noise pollution poses a perceived threat to the public health of the populace.

Funding: This research received financial support from the Covenant University

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

Noise Pollution, Its Sources and Effects: A Case Study of University Students in Delhi

(2018), EPRA International Journal of Economic and Business Review, 6 (2), pp. B15-B23

9 Pages Posted: 26 Mar 2018

Saba Ismail

Jamia Millia Islamia

Shahid Ahmed

Jamia Millia Islamia - Economics

Date Written: 2018

Noise is a type of pollution and impacts on our health and wellness. The prevalence of noise is increasing in magnitude and severity because of urban life style and no or bad governance of noise in NCR region as the rules is flouted routinely. Noise pollution leads to many chronic and socially significant impacts. The present study investigates the level of awareness about noise pollution in Delhi, its causes, its health impacts and solutions among the youth in Delhi. The paper has used primary data collected through a schedule from university/college students in Delhi. The study concludes that the majority of educated youth is aware about noise pollution, its causes and probable health effects but the vast majority of educated youth did not perceive noise pollution as environmental challenge and ranked it as least important threat to the health and environment. The study reveals that the female youth are more sensitive compared to male youth about noise pollution in Delhi. The study identified vehicular pollution as one of the most important causes of noise pollution and loud music as the second most important cause of noise pollution. It implies that the majority of educated youth understand the health related implications of noise pollution in Delhi. Finally, the study suggests of awareness campaign involving citizens and strict enforcement of environment laws by concerned agencies as the appropriate solution to control environment degradation.

Keywords: Environment Sustainability, Noise Pollution, Health Effects

JEL Classification: Q50, Q53, I12

Suggested Citation: Suggested Citation

Jamia Millia Islamia ( email )

Jamia Nagar, New Delhi Delhi, 110025 India

Shahid Ahmed (Contact Author)

Jamia millia islamia - economics ( email ).

Jamia Nagar New Delhi, 110025 India

HOME PAGE: http://jmi.ac.in/economics/faculty-members/Prof_Shahid_Ahmed-1783

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Case study of New Delhi

Loudest city in india.

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

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

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

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

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

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

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

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Metrics details

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

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

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

Review findings

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

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

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

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

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

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

Stakeholder engagement

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

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

Objective of the review

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

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

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

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

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

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

Method enhancements.

We reinforced the search strategy with:

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

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

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

Method downgrades

Because of our resource limitations:

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

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

Search for articles

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

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

Search string

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

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

Comprehensiveness of the search

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

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

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

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

Bibliographic databases

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

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

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

Web-based search engines

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

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

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

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

Specialist websites

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

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

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

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

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

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

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

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

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

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

Supplementary searches

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

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

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

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

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

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

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

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

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

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

Bibliographies from relevant reviews

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

Testing the comprehensiveness of the search results

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

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

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

Manually added articles

Finally, some articles were added manually to the corpus:

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

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

Duplicate removal

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

Article screening and study-eligibility criteria

Screening process.

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

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

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

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

Eligibility criteria

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

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

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

Study-validity assessment

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

Data-coding strategy

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

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

Article description:

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

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

Language (English/French);

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

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

Article characteristics:

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

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

Categories to code the sources of noise (exposure)

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

Categories to code the impacts of noise (outcomes)

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

For studies only:

Country where the study was conducted;

Type of habitat (terrestrial or aquatic);

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

Experimental (causal)/observational (correlative) study;

Origin of noise (artificial, real, recorded).

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

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

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

Data-mapping method

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

Literature searches and screening stages

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

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

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

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

General bibliometrics on the database

Article sources.

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

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

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

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

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

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

Article types and contents

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

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

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

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

Chronological distribution

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

Chronologic number of articles since 1950

Map characteristics on the population, exposure and outcomes

Taxonomic groups.

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

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

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

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

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

Sources of noise

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

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

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

Types of impacts

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

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

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

Knowledge gaps and knowledge clusters

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

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

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

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

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

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

Study characteristics

Study location.

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

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

Noise source and media

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

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

Study context and design

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

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

Reviews and meta-analyses

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

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

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

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

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

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

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

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

“Effects of Noise on Wildlife” [ 77 ];

“Marine Mammals and Noise” [ 78 ];

“Animal Communication and Noise” [ 79 ];

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

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

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

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

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

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

General comments

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

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

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

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

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

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

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

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

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

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

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

Comparison to other evidence syntheses

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

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

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

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

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

Limitations of the systematic map

Search strategy.

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

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

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

Full-text searching

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

Languages accepted at full-text screening stage

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

Coding strategy

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

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

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

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

Implications for policy/management

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

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

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

Implications for research

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

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

Availability of data and materials

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

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Acknowledgements

The map team thanks:

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

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

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

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

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

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

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Home > Books > Noise Control

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

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

DOI: 10.5772/intechopen.104607

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

• noise pollution
• health issues
• equivalent noise level

Author Information

Bijay kumar swain *.

• DIET, India

Chidananda Prasad Das

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

Shreerup Goswami

• PG Department of Geology, Utkal University, India

*Address all correspondence to: [email protected]

1. Introduction

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

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

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

2. Methodology

2.1 study area.

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

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

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

2.2 Monitoring sites

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

2.3 Sampling and data acquisition

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

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

2.4 Community response

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

3. Results and discussion

3.1 studies related to zone specific noise.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3.2 Community responses

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

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

Personal characteristics of respondents (in percentage).

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

Relation between demographic character and annoyance.

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

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

Participant’s perception towards different aspects of noise pollution.

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

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

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

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

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

Relation between demographic character and drowsy.

Relation between demographic character and relax.

Relation between demographic character and sensitive.

Relation between demographic character and Anexiety.

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

Analysis of ANNOVA between demographic characteristics and annoyance.

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

Analysis of ANNOVA between demographic characteristics and anxiety.

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

Analysis of ANNOVA between demographic characteristics and sensitivity.

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

Analysis of ANNOVA between demographic characteristics and relax.

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

Analysis of ANNOVA between annoyance and environmental factors.

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

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

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

4. Conclusion

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

Acknowledgments

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

Conflicts of interest

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

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Noise Pollution: Environmental Impact and What You Can Do

Noise pollution is bad for humans and awful for wildlife. Here's what it is, how it affects animals, and how you can help.

As a journalist, Gabriella Sotelo covers the environment, climate change, and agriculture. She has a bachelor's in Journalism/Environmental Studies from NYU.

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What is Noise Pollution?

• The Clean Air Act

Effects of Noise Pollution on Wildlife

What can be done.

Have you ever been somewhere truly free from the sounds of humankind? We become immune to the sounds of distant traffic and especially the subtle hum of planes above, but there are few places that don't have some form of noise pollution.

Noise pollution is noise that has surpassed ambient noise levels and has a harmful impact on humans and animals. This type of pollution is generated by humans and is a form of environmental degradation. It can serve as a source of stress on fauna, can have negative effects on animal welfare, and can even cause behavioral changes in birds, according to a study on the impacts of noise pollution on birds.  

Noise pollution can be problematic when frequencies that are produced disrupt information transmission in animals, specifically animals that use similar frequencies to communicate. These disturbances can also lead to higher anti-predator behaviors in situations that don’t require it, as well as change species vocalization, increase stress and stress-related diseases, and have the potential to decrease populations.

Noise Pollution Facts

• The third most common chronic physical condition in the United States is hearing loss. (CDC)
• Over 100 million people in the European Union are exposed to traffic noise above 55 decibels (dB), according to a study looking at noise pollution and its health effects.
• Noise over 70 dB over a long period of time can damage your hearing, and noise above 120 dB can cause immediate damage to your ears. The average sound of a firework is 140 dB, and the average sound of traffic (from inside the car) is 80 to 85 dB. ( CDC )
• Noise pollution threatens the survival of over 100 species.

Noise pollution can also be defined as an unwanted sound. The noise that is studied usually refers to occupational noise instead of social noise or environmental noise like construction.

In the E.U., around 56 million who live in areas with a population size of more than 250,000 people are exposed to more than average traffic noise. In the United States, noise has been shown to be increasing in California due to street traffic and increased at a rate of 6.7 dBA (A-weighted decibels). 

Noise pollution's impact on the environment can be classified as:

• Chronic Contamination / Continuous Noise : Constant exposure to noise; this type of pollution can lead to hearing impairment.
• Temporary Contamination with Physiological Damage : Exposure to a limited source of noise; an example is exposure to explosives.
• Temporary Pollution Without Damage : Continuous noise for a limited period of time, like street noise—this can lead to temporary hearing impairments.

Meanwhile, low-frequency noise is described as the background noise that comes from urban environments like air conditioning systems or vehicles. Traffic accounts for 80% of the environmental impact of noise. In animals, traffic noise can reduce foraging efficiency, and in birds can affect their reproductive system.

Examples of Noise Pollution

Barcelona , Spain

Barcelona is among the top cities exposed to noise pollution. Almost 48% of city blocks had an average noise level over 65dB, and only 5% of city blocks had noise levels under 55dB, according to research on environmental noise inequities in the city. The area with the highest noise level was the Eixample district; this district has high flows of street traffic and is also where the very popular La Sagrada Familia is located. This district, as well as the Sarria-Sant Gervasi district, experience levels over 70dB. In Barcelona, 94% of the population lives in city blocks that experience high-noise levels. In Madrid, 80% of all urban noise comes from road traffic, according to an impact assessment of traffic noise in Madrid . In general, the E.U. has shown that 65% of Europeans live in major urban areas that are exposed to high noise levels.

New York City, United States

Noise has been consistently reported as the number-one quality-of-life issue affecting residents in New York City. Sound pressure levels were reported at 70 to 85 dB in midtown Manhattan, which is above average and is at a level that poses health hazards, according to an assessment of noise pollution in NYC . More than two million people in New York City reported that they were disturbed from sleep by noise once a week; 78% of those people reported being disturbed three or more nights each week, according to a paper on the effects of ambient noise on sleep . Traffic noise caused 53% of sleep disturbances. Measured locations in New York City with noise levels greater than 70dB increased risk of hearing loss. These noise levels were especially high in areas with a lot of traffic, during the morning and evening commuting periods, and all around Manhattan, as reported in an assessment of street-level noise in New York City. The assessment also found that the highest noise measurement occurred when sirens, heavy pedestrian traffic, or construction was present. Street-level noises contribute to 4% of total noise exposed to the NYC public.

Noise Pollution and the Clean Air Act

The Clean Air Act Amendment added Title IV to the document, which relates to noise pollution. This amendment established the EPA Office of Noise Abatement and Control to study the effect of noise on public health and the effect on wildlife, the psychological and physiological effects it may have on people, and the effect of sporadic extreme noise. The sources of noise that are regulated by the EPA include construction equipment, trucks, transport equipment, low-noise emission products, and rail and motor carriers. It also regulates the labeling of hearing protection devices.  During the time this amendment was written, the EPA identified the average exposure to environmental noise to be 70 dB over 24 hours and average levels of 55 dB outdoors. However, the Office of Noise Abatement and Control was closed as the administration thought it was best if issues regarding noise were handled at the local and State level, according to the EPA .

The Noise Control Act of 1972 and the Quiet Communities Act essentially replaced the office and have yet to be rescinded, however the EPA’s website states they are “essentially unfunded.” Since the Clean Air Act and the previously mentioned amendment are no longer enforced, people can look at their state’s regulations. For example, Colorado limits the decibels produced by noise in residential, commercial, light industrial, and industrial zones between a set time. Their statute also considers periodic, intrusive, or shrill noises as a nuisance. The California Noise Control Act reiterates the harm excessive noise can have on physiological and psychological health, and also states that people in California are entitled to having a “peaceful and quiet” environment without noise that could be hazardous to their health.

The greatest effect of noise pollution on the environment is on animals. Noise pollution can affect an animal's ability to detect acoustic signals, affect courtship behaviors, cause birds to produce fewer eggs, and cause fewer offspring to reach reproductive age. On detecting acoustic signals, noise can also be produced in the same frequencies in which animals vocalize and can interrupt the transmission of information.

Noise affects many species of animals, from amphibians, arthropods, birds, and fish to mammals, mollusks, and reptiles.

According to the World Health Organization, noise is one of the most hazardous forms of pollution and has become omnipresent in aquatic and terrestrial ecosystems.

How Noise Affects Animals

• It hampers communication . Most animals rely on vocalizations and other acoustic signals to communicate with each other. Interference makes it challenging for animals to find mates, warn of danger, establish territories, and coordinate group activities.
• It disrupts reproduction : Noise pollution is distracting and can disrupt breeding behaviors and lead to diminished reproductive success. For instance, loud noises near nesting sites can cause birds to abandon their nests.
• It compromises dwindling habitat : Noise can reduce the quality of usable habitat, something that is already in critical decline.
• It alters foraging patterns : Noise pollution can change the foraging patterns of animals. For example, ship noise can cause marine mammals to avoid certain feeding grounds.
• It leads to stress and health issues : Just like in human animals, prolonged exposure to loud and constant noise can lead to chronic stress in non-human animals, which can have many adverse effects.
• It drowns out environmental cues : Noise pollution can make it hard to hear important environmental cues that animals have always relied on to navigate and detect predators or prey.
• It disorients and can cause strandings : Particularly in aquatic environments, noise—like that from ships or oil extraction activities—can disorient marine animals and lead to beach strandings or collisions with boats.

These disturbances can have long-term consequences. For example, some species may perform anti-predator behavior due to the confusion noise may create, as is the case with the impact of noise pollution on the saffron finch.

In this case, the noise created by traffic changed the behavior of saffron finches and made them less aggressive. In an environment with heavy noise, the male bird would display less aggressive behaviors when confronted by an intruding bird. This may be because they pay less attention to the intruder if unwanted noise masks the information that dictates the attributes of the intruder. The study predicts that if noise pollution were to continue, this species would continue to exhibit anti-predator behavior, as well as eat and reproduce less. This type of behavioral change was also found in the chipping sparrow.

Trees can be used against noise pollution, according to an investigation on the effects of leaves, branches, and canopies on noise pollution. By decreasing the area in which noise is made and increasing tree presence with tree belts of at least 12 meters, trees could serve as a noise barrier in urban areas. Another study found that tree belts with a width of 30 meters could be planted on the roadside and have more than 6dB reduction of noise than a grassland would. The conclusion was that more trees, branches, and leaves could reduce noise pollution.

Regulations have also been placed in the U.S. statewide and local governments to reduce noise pollution. New York, for example, has a regulation that looks at occupational noise exposure; this ranges from monitoring noise to providing personal protective equipment. Many states and local governments in the U.S. have their own regulations regarding noise pollution; however, many focus on the human impacts that noise pollution has and not the environmental impacts.

How Can You Help?

• Advocate for planting trees and vegetation, or plant trees yourself. Trees can serve as a great noise barrier and have many other benefits as well.
• Turn down the volume on your television, music, and car stereo—especially in urban and residential areas where people can overhear your entertainment.
• If you own a car or motorcycle, ensure it's well-maintained to minimize engine noise.
• Check your road rage and limit unnecessary honking.
• Limit the use of loud machinery or equipment.
• Opt for electric gardening equipment—gas-powered lawnmowers and leafblowers are a bane for your neighbors.
• Opt for an electric car, which reduces engine noise from traffic.
• Use soundproofing in your home or workspace to decrease the noise you hear and the noise you emit.
• Advocate for quiet zones in public places, like parks, libraries, or public transportation.
• Research and support local and national noise-reduction policies and regulations.
• Become active in local community initiatives to reduce noise pollution, such as organizing noise awareness campaigns or supporting noise-reduction projects.
• The Environmental Impact of Ocean Noise
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• Unexpected Species Are Colonizing Rafts of Plastic Debris in the High Seas
• Coastal Lights Are Tricking Coral Reefs and Harming Their Recovery
• Whales Are Ingesting a Shocking Amount of Microplastics
• Noise Pollution Maps Offer a Nightmare Feast for the Senses
• How Does Mercury Get in Fish?
• Ocean Plastic Sees Staggering Increase Since 2005, Study Finds
• Windshield Washer Fluid Has a Dirty Little Secret
• Bolivia's Notorious 'Death Road' Is Now a Wildlife Haven
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• New York Has a Massive Building Air Pollution Problem

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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

Joint first authors

• Article contents
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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

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

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

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

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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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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Urban Noise Pollution Prevention — Tokyo Case Study

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The present chapter provides an exhaustive review on the noise monitoring studies, comparison of the prediction models including physical propagation model, and applications of the artificial intelligence techniques, noise mapping, and noise pollution monitoring in mining sector carried out by various researchers. Most of the noise pollution studies deal with the assessment of traffic noise and some were focused exclusively on noise monitoring for the residential, educational, industrial, and commercial sites noise. The study reveals that early models were based on mathematical prediction models, later machine learning and deep learning methods were generally used for prediction and forecasting of noise levels. A retrospective view on noise mapping and control is presented in the chapter. Also, the noise pollution control and abatement measures are highlighted that shall be indispensable for reducing the ambient noise levels in metropolitan cities of the country.

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Acknowledgments

Authors also express their gratitude toward Director, CSIR-National Physical Laboratory, India, for his constant encouragement and support. Authors also express their sincere thanks to Dr. Sanjay Yadav, Head, Physico-Mechanical Standards and Professor Ravinder Aggarwal, Professor, Thapar University, Patiala for their constant encouragement and support for this chapter. The views and opinions expressed in this article are those of author’s own and do not necessarily reflect the official policy or position of any agency of the Government of India. The content of the papers is solely to present a retrospective and prospective view of noise levels and may not be used or considered for disputes redressal in legal framework.

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Tiwari, S.K., Kumaraswamidhas, L.A., Garg, N. (2023). Strategies and Implications of Noise Pollution Monitoring, Modelling, and Mitigation in Urban Cities. In: Aswal, D.K., Yadav, S., Takatsuji, T., Rachakonda, P., Kumar, H. (eds) Handbook of Metrology and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-1550-5_86-1

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Making marine noise pollution impacts heard: the case of cetaceans in the north sea within life cycle impact assessment.

1. Introduction

2. material and methods, 2.1. choice of impact pathway and affected species, 2.2. constructing the characterization factor, 2.2.1. sound propagation and fate factor, 2.2.2. affected animals and modelling of a midpoint characterization factor, 2.2.3. endpoint modelling, 2.3. verification of the method, 2.4. expansion to other cetacean species, 2.4.1. threshold values, 2.4.2. abundance and population density data, 2.5. case-study, 3.1. sound propagation, 3.2. verification of approach, 3.3. characterization factors, 3.4. comparison with other impact categories, 4. discussion, 4.1. choice of impact pathway, 4.2. characterization factor development, 4.2.1. sound propagation model, 4.2.2. disturbance days, 4.2.3. endpoint characterization factor, 4.3. application to other cetacean species, 4.4. case-study, 5. conclusions, supplementary materials, acknowledgments, author contributions, conflicts of interest.

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Middel, H.; Verones, F. Making Marine Noise Pollution Impacts Heard: The Case of Cetaceans in the North Sea within Life Cycle Impact Assessment. Sustainability 2017 , 9 , 1138. https://doi.org/10.3390/su9071138

Middel H, Verones F. Making Marine Noise Pollution Impacts Heard: The Case of Cetaceans in the North Sea within Life Cycle Impact Assessment. Sustainability . 2017; 9(7):1138. https://doi.org/10.3390/su9071138

Middel, Heleen, and Francesca Verones. 2017. "Making Marine Noise Pollution Impacts Heard: The Case of Cetaceans in the North Sea within Life Cycle Impact Assessment" Sustainability 9, no. 7: 1138. https://doi.org/10.3390/su9071138

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Noise pollution: a growing threat to liveability in Mumbai

Tong, who works as an educator with students across postgraduate degree programmes, says her job requires primarily reading, reviewing and writing academic material.

Mumbai: On March 9, city-based environmentalist Sumaira Abdulali visited the Mahim home of Penelope Tong (52), a fieldwork supervisor at the Tata Institute of Social Sciences (TISS). Tong, who is visually impaired and relies on a talking software to conduct her work, has recently found herself at her wit’s end owing to loud drilling and clanking sounds emanating from a private construction site adjacent to her building, located just a couple of lanes down from St. Michael’s Church.

Tong, who works as an educator with students across postgraduate degree programmes, says her job requires primarily reading, reviewing and writing academic material. Writing is also how she has been communicating online with her students since the lockdown in March 2020.

“Concentration on work becomes difficult when there is a constant loud banging in the background. Since I use an assistive technology due to my visual impairment, the noise prevents me from hearing and being able to work efficiently. Nonetheless, irrespective of my visual impairment, the continuing loud noise interrupts meetings, telephone conversations, recording, and listening to audio content,” she said.

“When I first started complaining to the builder, the site in-charge asked me to bear up for only two months more, as that is when the plinth work will get done,” said Tong. It’s been close to five months since that conversation.

After enduring the disturbance for far longer than the promised two months, Tong reached out to Abdulali, director of the Awaaz Foundation, which has spent over a decade monitoring noise levels in Mumbai and campaigning for their reduction.

“We noted a maximum decibel level of 97.3dB outside Tong’s house, which is greatly in excess of the 65dB limit prescribed by the Central Pollution Control Board (CPCB) for residential areas during the day time. This is a punishable offence, yet violations continue across the city. I am approached by people whose lives are marred by sound daily,” Abdulali said.

Ground rules

Existing rules around noise are governed by the Centre’s Noise Pollution (Regulation and Control) Rules, 2000, which mandate that residential and silence zones — areas around schools, hospitals and religious shrines — should have a maximum noise level of 55dB and 50dB in the day, and 45dB and 40dB at night. These rules have been almost entirely ignored by civic authorities, experts said. Prior to these (which are based on a World Health Organisation study specifying maximum safe decibel limits), there were no rules against noise at all.

In October 2015 and September 2016, the BMC mapped decibel levels at 740 locations across Greater Mumbai, and found that a majority of the locations recorded noise levels at an average of 75dB during the day and 65dB at night, irrespective of silence or residential zones. In June 2016, the civic body also allowed construction work to proceed for four more hours beyond the earlier deadline of 6pm, in the interest of “ease of doing business”.

“This was completely baffling because 2016 was such an important year in Mumbai’s anti-noise campaign,” said Abdulali. Responding to a bunch of 10 petitions, the Bombay high court in 2016 finally passed orders directing the state government and civic authorities to take steps in reducing noise pollution from multiple sources.

A court case

Abdulali’s first petition against noise pollution was filed in 2002, when she worked with the Bombay Environment Action Group. It led to the first orders being passed banning loudspeakers in Mumbai’s silence zones in 2003. Abdulali has since then filed multiple cases seeking to mitigate noise from various sources, including horns, firecrackers and construction.

Two decades on, the court is still petitioned over related issues.

Robin Jaisinghani, a resident of Dalamal Tower in Cuffe Parade, has been living with the constant din of construction on the upcoming Metro-3 line since May 2017, when the work first began. At first, he installed noise-proof windows, spending close to ₹ 1 lakh, but they didn’t seem to have much effect in dulling the sound.

“My daughters, 11 and 7 years old, used to fall asleep by 8pm, but construction work has been permitted at all hours of the day and they are not able to follow a uniform, healthy sleep cycle. I myself now get headaches quite frequently, which was not the case earlier,” said Jaisinghani, who works as an advocate.

When he filed a writ petition against the Mumbai Metropolitan Railway Corporation in 2018, the Bombay high court (HC) directed them not to carry out any construction work between 10pm to 6am. “But the order was modified on August 24, 2019, when the High Court permitted the MMRC and its contractors Larsen & Toubro (L&T) to carry out construction and ancillary activities also during the night at Cuffe Parade site.”

“You cannot keep the windows shut all the time,” Jaisinghani said.

Noise is a constant in urban living, but experts worry that unlike more tangible environmental pollutants like dust, sewage and waste, the impacts of noise pollution figure little in the city’s planning and development.

“Noise is dealt with largely in a personal capacity. If you can afford to, it’s relatively easy to install certain acoustic interventions in your house or car. Every second window we install for clients today is a noise-proof window. Materials that are meant to absorb sound, and which would typically be used in specialised environments like recording studios, for example, are making their way into affluent households,” says Alan Abraham, of the city-based architecture firm Abraham John Architects.

Quantifying the problem

Empirical studies have highlighted the prevalence and impact of noise pollution in Mumbai and surrounding areas. The most recent study attempting to quantitatively assess ambient (or outdoor) noise levels dates was conducted in 2020 by the y the Council of Scientific and Industrial Research (CSIR)- National Environmental Engineering Research Institute (NEERI) across nine municipalities in the Mumbai Metropolitan Region, including Greater Mumbai, Bhiwandi-Nizampur, Kalyan-Dombivli, Mira-Bhayandar, Navi Mumbai, Panvel, Thane, Ulhasnagar and Vasai-Virar.

The study used 48 hours of ambient noise data from 153 sampling locations across these cities. The monitoring locations were scattered across four types of land-use classifications, namely commercial, industrial, residential and silence zones.

In 36 commercial zones, noise levels were found to range between 75 and 90 dB on average during the daytime, which is significantly in excess of the 65 dB limit set by CPCB. During the night-time, noise limits were exceeded in 27 out of 36 locations, and ranged between 70 to 80 dB, also in violation of CPCB norms.

Under the residential category, NEERI monitored a total of 48 locations, observing that “noise levels at all the residential sites are exceeding the day time noise limit of 55 dB and night time noise limit of 45 dB during weekdays and weekends, both to an enormous extent.” Noise levels at 81% of the sites were found to be in the range of 75–85 dB during weekdays as well as weekends. During night time, it was observed that on weekdays, noise levels range from 60 to 90 dB, with the majority of locations exposed to 70-75dB of ambient sound.

“But during the weekend, night time noise levels ranged from 55 to 95 dB and the majority of the locations were exposed to noise above 65 dB, especially in the broad range of 65– 80 dB. This shows that noise intensity during night times of the weekend was higher compared to weekdays,” the researchers noted.

“Our study was a simple quantitative evaluation of noise levels which gives some idea of how the levels differ diurnally. If you want to know what impact it is having on the general population, a controlled study should be carried out by public health experts. There may also be an impact of noise on birds, amphibians and other animals, which can be uncovered further through acoustic ecology. Sadly, there is a dearth of experimental studies on the subject, which are required if one is to influence policymakers,” said one of the researchers, who did not wish to be named.

Can noise harm us?

A 2020 study by an audiologist at Mumbai’s KEM Hospital examined 279 firemen in Mumbai between the ages of 45 and 60, and found that all of them suffered from a certain degree of noise-induced hearing loss (NIHL), either in one ear, or both. Of the cohort studied, 37.5% percent of the firemen suffered from the most common type of hearing loss, which is characterised by what professionals call the “4kHz notch”.

“In an audiogram, which is the visual representation of a hearing test, there is something we call a ‘4kHz notch’. It is a mark that sometimes appears in the graph when a person is subjected to sounds in the range of 4,000 hertz. The appearance of this is a clear indicator that the person has been subject to noise-induced hearing loss, and the phenomenon has become quite prevalent now, as opposed to 20 or 30 years ago,” said Dr Hetal Marfatia, an ENT specialist based in Mumbai, at KEM Hospital.

“Even without measurable hearing loss, noise can have severe consequences for one’s quality of life, including disturbed sleep, irritability, and high levels of stress. It’s well known that these are outcomes of noise, but it’s especially hard to design studies around this because of the intangible nature of the pollutant. It’s not like conducting a health study around cigarettes, for example,” Marfatia said.

Experts warn that exposure to noise pollution above 80 decibels (dB) for eight hours a day for eight years is likely to induce permanent deafness, and that shorter exposure of higher decibel levels also damages the ear drums irreparably.

Another study from Mumbai which examines the larger consequence of noise dates back to October 2018, when researchers at the V. K. K. Menon College of Commerce and S.S. Dighe College of Science in Bhandup, and Thadomal Shahani Engineering College in Bandra, demonstrated the impact of noise pollution from trains on students living in close proximity to railway lines.

In a controlled, experimental study, noise levels were assessed near Bhandup railway station for a period of six months, during which two groups of volunteers (10 each) were selected for monitoring of sleep patterns. The experimental group lived in close proximity (1-2 km away) to Bhandup station, whereas the control group lived a considerable distance away. Researchers noted that, on average, residents living close to the station were subjected to a noise level of about 88dB every time a train entered or left Bhandup station. Noise created by honking trains —at least 250 pass by every day — was far louder, at an average of about 107.8 dB.

The two groups were then interviewed to understand the impact of noise on their daily lives. “Ninety percent of the students in the experimental group showed sleep deprivation, whereas in the control group sleep deprivation was observed only in 10% of students,” the study noted. About 70% of subjects in the experimental group also reported having high levels of stress, as against 15% in the control group. Nearly all students in the experimental group reported feeling sleepy in the daytime, as against just one student in the control group.

Treating the affected

As part of the Awaaz Foundation’s campaign against noise pollution, Abdulali said that they have conducted hearing tests for Mumbai’s traffic policemen over the years. “Most of them also suffer from noise-induced hearing loss, as would be expected. In heavy traffic locations, noise levels can surpass 100db, and unfortunately there is to no protective equipment provided to the workers,” she said.

Despite the widespread prevalence of noise as a pollutant and public health hazard, official documents, like the BMC’s development control regulations and the Mumbai Climate Action Plan, do not see noise as a pollutant.

“The BMC’s own rules were relaxed in 2016 to allow construction between 6am and 10pm, which subjects residents to 16 hours of noise each day. In case of major projects, like the Metro or Coastal Road, Courts have allowed construction work twenty-four seven,” said Abdulali.

Sanjay Pandey, Mumbai’s newly appointed police commissioner has taken a strong stand against noise pollution, allowing citizens to file complaints using a wider range of popular noise monitoring, whereas earlier only data from the official NEERI mobile app was accepted.

“I have held a meeting with Mumbai’s major developers and instructed all of them to install noise cutters by March 31. Curbing noise violations will be a priority for my office, and we will step up patrolling in construction-heavy areas. Under no circumstances will private projects be allowed to use heavy machinery beyond 10pm,” said Pandey.

The Maharashtra Pollution Control Board, which is also mandated to enforce noise control measures, however, remains too resource-strapped to act efficiently. “Noise is trickier to control because unlike air pollution or water pollution, it is very hard to turn off at the source. The MPCB does have the mandate to enforce the Noise Pollution (Control and Regulation) Rules, but the offences under it are non-cognisable, and would require taking the violators to court individually. It is not something we have the resources for,” said a sub-regional officer with the MPCB based in Mumbai, seeking anonymity.

It has now been over four months since Penelope Tong’s troubles began. She still finds herself dialling 100 every few days to log a complaint with the police.

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Blood Pressure Levels Impacted by Chronic Occupational Noise Exposure

Study found high blood pressure risk increased 10% for each year worked on noisy industrial machinery.

Aug 15, 2024

Contact: Katie Glenn, [email protected] ,

New Delhi (Aug 15, 2024) -

Noise exposure is a known occupational hazard in some jobs, particularly for hearing loss, physical and psychological stress, and reduced concentration. A new study presented at the ACC Asia 2024 conference found in adult power loom weavers, chronic noise exposure not only increased their blood pressure overall, but also each year of exposure increased their odds of having high blood pressure by 10%.

“While the mechanism is still not well-explored, it is thought that the stress response by the body to chronic sound exposure causes hormonal imbalances that gradually leads to a permanent elevation of blood pressure,” said Golam Dastageer Prince, MBBS, MPH, medical officer at DGHS Bangladesh and the study’s lead author. “High blood pressure impacts more than a billion people worldwide and just 1 in 5 have it under control, yet it is a major cause of premature death. In addition to treating the high blood pressure through appropriate means, we must find ways to mitigate the exposure to the noise if we want to reduce the cardiovascular risk of these patients.”

Researchers at the Directorate of General Health Services in Bangladesh looked at 289 adult workers in selected weaving factories in the Araihazar sub-district of Narayanganj, Bangladesh, from January to December 2023. Participants took a face-to-face interview to complete a questionnaire covering sociodemographic variables, behavior, dietary habits and family medical history. Blood pressure, height, weight and noise intensity were measured following standard procedures by the researchers.

The study cohort was predominantly male and married and were about 34 years of age on average. According to the researchers, a notable proportion of the cohort was illiterate. Workplace exposure duration averaged nearly 16 years, with noise intensity ranging from 96-111 decibels. In the United States the National Institute for Occupational Safety and Health has established the recommended exposure limits for occupational noise exposures to be 85 decibels on average over an eight-hour workday. Sounds at or below 70 decibels are generally considered safe.

According to Prince, none of the study population was found to be wearing ear protection personal protective equipment.

“Hopefully we can raise awareness of not only noise-induced hearing loss, but the impact of noise on blood pressure and workers’ behaviors and attitudes towards using personal protective equipment,” Prince said. “Pushing for structural improvements to industries may also help us improve the health safety of these workers.”

The study population had a 31.5% rate of high blood pressure with an additional 53.3% being prehypertensive. The study also found a positive correlation between blood pressure and noise exposure duration. Each year of exposure was found to increase high blood pressure odds by 10%, even after adjusting for age, body mass index and smoking status.

“As the study focused on workers exposed to more than 85 decibels noise for long periods of time, any profession causing workers to experience similar exposure might experience similar blood pressure impacts,” Prince said. “We definitely need more exploratory studies to reveal more information about the potential mechanisms and long-term health outcomes.”

Recent studies have shown that living near noise pollution, including highways, trains and air traffic, can have an impact on cardiovascular health. However, the current study may not apply to noise experienced during daily life. Noise pollution experienced near home typically ebbs and flows, while the industrial exposures in the study are typically continuous in pattern due to the machinery and remain at a constant sound level, according to Prince.

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A lithium mine in Serbia could rev up Europe's e-vehicles, but opposition is fierce

Rob Schmitz

Europe Lithium-Serbia

Vladan Jakovljevic checks his beehives outside the village of Gornje Nedeljice, in the fertile Jadar Valley, in western Serbia, on Aug. 6. Darko Vojinovic/AP hide caption

JADAR VALLEY, Serbia — Vladan Jakovljevic’s bees are angry. As he slowly lifts a cover from atop one of dozens of hives, they swarm, one of them stinging him in the cheek. He calls it a “bee kiss.”

The 63-year-old beekeeper’s hives are scattered along a hillside overlooking the Jadar Valley’s bucolic green hills and red clay tile-roofed villages of western Serbia. Bees, he points out, are sensitive creatures. Changes in the environment can wipe out their hives. That’s why Jakovljevic is worried about plans to build one of Europe’s largest lithium mines in this valley.

“If there is any pollution in the river from this mine, bees in this region will die because they drink the water,” he says. “We’re talking about 10,000 bee communities that pollinate the crops that grow in the valley. This could cause a devastating chain reaction.”

The transition to a lower carbon emissions future depends on electric vehicles, and the batteries in those vehicles depend on lithium — a mineral in short supply and in big demand. But mining and refining lithium can have a big impact on local environments, and the residents of Jadar Valley have fought to protect theirs, spurring a national protest movement that has shed light on the environmental underbelly of the electric vehicle (EV) industry.

How a European law might get companies around the world to cut climate pollution

In the valley below Jakovljevic’s beehives lies the village of Latina, “salty” in English. Hundreds of feet below the surface of the Jadar Valley lie salty mineral deposits that give the drinking water here a distinctive taste.

Decades ago, scientists here discovered a new mineral they named jadarite, one rich in lithium and boron. After the Balkan wars of the 1990s, British Australian mining company Rio Tinto began drilling exploratory wells in Jadar Valley, confirming that it has one of Europe’s largest deposits of lithium. It's so big it could meet an estimated 90% of Europe's lithium needs . It could be a boon for a continent focused on transitioning to electric vehicles to cut emissions, and for mining giant Rio Tinto, guaranteed profits.

Biden announces new tariffs on imports of Chinese goods, including electric vehicles

Scientists warn of potential environmental problems.

The company recently drilled more exploratory wells, but the water that surfaced from deep below the earth has killed surrounding crops and polluted the river, according to a study published last month in the journal Scientific Reports . Scientists found “substantially elevated concentrations of boron, arsenic, and lithium downstream from the wells.”

“With the opening of the mine,” the scientists wrote, “problems will be multiplied by the tailings pond, mine wastewater, noise, air pollution, and light pollution, endangering the lives of numerous local communities and destroying their freshwater sources, agricultural land, livestock, and assets.”

Multinational company Rio Tinto's test drill hole is seen in a field in the Jadar Valley, Serbia, Aug. 6. Darko Vojinovic/AP hide caption

Beekeeper Jakovljevic says he was shocked when he read the article.

“After I read the findings of those scientists, I didn’t need to hear more about this project,” he says. “This mine must be stopped.”

Officials say it will meet strict standards and boost GDP

But officials in Serbia's government say the mine holds great potential for the country. “We are all drinking the same water and we all breathe the same air, and we all have kids living here,” says Dubravka Djedovic Handanovic, Serbia’s mining and energy minister. “So we want the project implemented, yes, but we want it implemented according to environmental standards.”

Djedovic Handanovic insists the proposed mine would adhere to strict European Union environmental standards, even though Serbia has not yet become an EU member.

A house in the Jadar Valley that has been purchased by Rio Tinto is in the process of being demolished to make way for the potential lithium mine. Rob Schmitz/NPR hide caption

And she highlights the mine’s economic benefits. “Around 20,000 people could be employed in the whole value chain,” she says. “And when we are talking about value chain, we are not only talking the exploitation of the mine but actually refining processes, including the production of cathodes, production of batteries and ultimately the production of electric vehicles.”

She says this lithium mine has the potential to increase Serbia’s gross domestic product by 16%.

Protests against the mine are becoming routine

But many Serbs remain unpersuaded. Massive protests against the project have become routine in cities throughout the country since June, when a court decision cleared the path for the government to approve the mine. The decision came two years after a previous prime minister revoked Rio Tinto’s license following similar protests.

Jelena Isevski was one of tens of thousands who recently filled the streets of the capital of Belgrade to protest the mine.

“We are here to say that we are saying no to corporate powers that extract our country, just dig it out and leave trash, literally trash, for future generations,” she said over the chants of protesters.

Thousands of demonstrators gather in Loznica, western Serbia, to protest against a lithium mining project on June 28. Vladimir Zivojinovic/AFP via Getty Images hide caption

Critics of the mine like Isevski also question the political motivations of the Serbian government. Serbia has applied for EU membership, and the EU’s largest economy, Germany — home to the continent's largest electric vehicle companies — has voiced its strong support for this mine. For years, Europe’s wealthiest economies have wanted to shift away from depending on China, which refines 80% of the world’s lithium for EV batteries. Just a few weeks after a legal path had been cleared for the Jadar mine, the European Union signed a memorandum of understanding with Serbia’s government, launching what it called a “strategic partnership” on sustainable raw materials like lithium as well as battery value chains, and electric vehicles.

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Isevski says the EU should look within its 27-country bloc for its lithium. “It’s not only Serbia that has lithium,” she says. “Why is this being done in a country like Serbia, that allegedly doesn’t have the right to fight back? There’s lithium in France, right?”

Opponents of the mine also question the track record of mining giant Rio Tinto, which has a checkered history in developing countries throughout the world, including a mine in Papua New Guinea whose environmental destruction spurred a nine-year civil war.

Rio Tinto commits to "transparency" and independent reviews

But Rio Tinto’s manager of its Serbia operations, Chad Blewitt, says times have changed. “We are committed to transparency,” he says. “We've learnt from all those incidents, including where there was a civil war in Papua New Guinea 35 years ago. That created a lot of our local content programs globally because we have to give back to the community.”

Blewitt’s own history at Rio Tinto includes working as a manager at the company’s Simandou iron ore mine in Guinea in 2011, the same year that company officials were found by U.S. Securities and Exchange Commission investigators to have bribed a Guinean political adviser. Last year, Rio Tinto settled with the SEC for $15 million over the violations.

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Blewitt says in the case of the Jadar mine, Rio Tinto would be willing to allow independent experts to complete an environmental review of the project if it would help persuade those who question its impact on the ecosystem. As it stands, Blewitt calls Jadar “the most studied lithium project in Europe,” saying Rio Tinto has spent more than $600 million on it so far and says the studies found it would be safe.

Part of the company’s effort is outreach — Blewitt says Rio Tinto has held 150 information sessions for the local community and Serbia’s mining ministry has set up a call center to try to calm fears about the project, and about his company. “Last year we spent $85 million on community programs globally,” says Blewitt. “We gave back to governments around the world eight and a half billion US dollars in taxes. So I would say don't judge Rio Tinto by what we are in the past.”

Back in the Jadar Valley, beekeeper Jakovljevic says if he and his neighbors can’t judge Rio Tinto by its past, then how, he asks, should they judge the company?

Vladan Jakovljevic looks out onto the Jadar Valley. The beekeeper says his bees, which pollinate crops throughout the valley, are threatened by potential environmental damage from Rio Tinto’s proposed lithium mine in the valley. Rob Schmitz/NPR hide caption

He’s joined by a neighbor, a literature teacher named Marijana Petkovic, and her dog for cool drinks in the shade on a hot summer’s day. Petkovic points to homes across the field behind her property where some neighbors have sold their land to Rio Tinto. Dozens of homes in the valley are cordoned off with tape and are being demolished to make way for the project.

Petkovic has been following this issue closely. She says she thinks that Rio Tinto still needs hundreds more acres of land to build the mine but that the rest of the valley’s residents are not willing to sell. “They’re going house to house asking our neighbors if they need anything or if Rio Tinto can help them in any way,” she says.

The employees work in an information center the company established in the valley, but Petkovic calls it a “disinformation” center. She says the Serbian government has already tried to change the law so it can expropriate land from homeowners, but protests a few years ago put a stop to it.

But she says the local government recently rezoned her and her neighbor’s land from “agricultural” to “construction,” which has her wondering if the government is about to try to take her land again. She says this makes her worry about the future of this valley, and for Serbia.

Clarification Aug. 23, 2024

This story previously said a study was published in Nature . The study was published in the journal Scientific Reports , part of the Nature Portfolio, and was featured on the nature.com website.

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El Paso Matters

Opinion: The ‘deck park’ can be built without widening the highway

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By David C. Stout

I strongly believe that parks and green spaces are essential for a thriving community with high quality of life.

That is why I support the concept, if not the actual proposal, to put a park on top of the Trench, the depressed portion of Interstate 10 of about six blocks on the north edge of Downtown.

However, I know that transportation is the fastest-growing source of climate-altering pollution, and that highways are the largest part of that , and that people who live adjacent to highways are affected by the exhaust, noise, vibrations, and critically, heat emitted from vehicles – especially commercial trucks.

That is why I oppose Downtown 10, a proposed project to expand I-10 and add mini-highways in the form of new frontage roads next to the highway, from Copia to Executive, about a six-mile stretch.  

And that is why, while I’m still carefully considering my wants and those of proponents of the so-called “deck park,” I also am thinking about the rights of my constituents to live in healthier, cleaner neighborhoods.

For background, the deck is a developer-driven project many years in the planning. The project developers have been working for years behind the scenes at the highest levels of the Texas Department of Transportation and state government to gain political support, and now are working on gaining community support.

The same people are also pushing the widening of I-10 Downtown as they continue to ignore the negative environmental impacts and health implications that doing so will bring, due to induced traffic demand. This is a well-recognized concept that applies economic theory of supply and demand to roads. Put simply, if you build it, they will come, even if they hadn’t planned to before.

El Paso, quite frankly, does not have the same traffic problems other major Texas cities have . There are hot spots during peak hours – I-10 around Lee Treviño consistently clogs, as does Loop 375 at Montana and around Zaragoza. 

Downtown 10 does not address those needs, as an independent consultant hired by the County found . It will exacerbate the existing problems around the Spaghetti Bowl, which has horrible congestion on the ramp that heads to Juárez, a direct and severe source of pollution for the San Javier and Chamizal neighborhoods, as well as San Juan and Washington-Delta, and on the eastbound climb up I-10 right before Bassett.

What would benefit all of El Paso, and especially the area I represent, would be to depress the highway between Copia and Downtown, reducing the pollution spewed by highways onto neighborhoods, as well as the noise and vibrations and even heat. TxDOT was considering it, but apparently did not consider El Paso worthy of the expense.

Barring that option, which would require all hands on deck to advocate not only for funding but for community health, there is urgent work to be done.

TxDOT has said for years that the trench could fail. This is because what may be considered patchwork was done in the 1990s, but the roadway was not completely rebuilt as it should have been, and the roadway is at risk of failure. 

It seems negligent not to address that. So a win-win could be to defer Downtown 10 until it can be designed and funded for community health, while addressing the pressing issues in the trench as quickly and efficiently as possible, without taking property. 

This would allow deck proponents the opportunity to make their case to coordinate deck development with the work on the trench, for which I understand, although have not been told directly by TxDOT, there are two options.

One would take all the property on Yandell that sits above the trench, and potentially hand it over to private developers. That raises other issues; there is no need to take those properties in order to reconstruct the trench. The other option, known as Option D, uses the existing footprint of the trench.

This makes the most sense to me. TxDOT fixes the immediate problem, there is no taking property from one set of owners to hand over to others, and deck proponents can focus on convincing the community of the deck’s value.

At this point, they are dangling this shiny object in front of our faces to distract us from what the Downtown 10 project does , which is to harm some of the most vulnerable populations in this community. This is the definition of greenwashing !

We should not compromise the health, wellbeing and quality of life of low-income, historic neighborhoods and take property from small businesses for the benefit of well-connected developers and speculators.

So I say, build the park, but don’t widen the highway. Let’s make this happen together.

David C. Stout is El Paso County commissioner for Precinct 2.

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