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Article Contents

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

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

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

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

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

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

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

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

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

Search strategy

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

Inclusion and exclusion criteria

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

Data extraction

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

Quality of systematic review and strength of evidence

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

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

Data analysis

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

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

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

Patient and public involvement

No patients contributed to this research.

Features of meta-analysis

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

Study flowchart

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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The remaining references are listed in the Supplementary Reference .

Author notes

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

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Analysis of Sampling Methodologies for Noise Pollution Assessment and the Impact on the Population

Guillermo rey gozalo.

1 Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, 5 Poniente 1670, Talca 3460000, Chile

Juan Miguel Barrigón Morillas

2 Departamento de Física Aplicada, Escuela Politécnica, Universidad de Extremadura, Avda. de la Universidad s/n, Cáceres 10003, Spain; se.xenu@nogirrab

Today, noise pollution is an increasing environmental stressor. Noise maps are recognised as the main tool for assessing and managing environmental noise, but their accuracy largely depends on the sampling method used. The sampling methods most commonly used by different researchers (grid, legislative road types and categorisation methods) were analysed and compared using the city of Talca (Chile) as a test case. The results show that the stratification of sound values in road categories has a significantly lower prediction error and a higher capacity for discrimination and prediction than in the legislative road types used by the Ministry of Transport and Telecommunications in Chile. Also, the use of one or another method implies significant differences in the assessment of population exposure to noise pollution. Thus, the selection of a suitable method for performing noise maps through measurements is essential to achieve an accurate assessment of the impact of noise pollution on the population.

1. Introduction

A recent publication by the World Health Organization points out that noise pollution, ranked second among a series of environmental stressors for their public health impact and, contrary to the trend for other environmental stressors which are declining, is actually increasing in Europe [ 1 ].

Noise is known to have auditory and non-auditory health impacts [ 2 ]. Environmental noise causes both psychological and physiological non-auditory health effects and the evidence for the non-auditory effects is growing [ 3 ]. Specifically, road traffic is considered to be the main source of community noise pollution. The most important non-auditory effects of traffic noise are annoyance and sleep disturbance [ 4 , 5 , 6 , 7 ]. Annoyance is a feeling of displeasure that can result in adverse emotions including irritability, stress, fear, and even depression [ 8 , 9 , 10 , 11 , 12 ]; it is associated with health-related quality of life [ 13 , 14 , 15 ].

Nighttime noise exposure directly influences sleep disturbance causing body motility, sleep stage changes, delayed sleep onset latency, and nocturnal awakenings [ 2 , 6 , 16 ]. Sleep disturbances can lead to serious long term health effects and there is increasing evidence from epidemiological studies that indicate long-term noise exposure leads to cardiovascular diseases, obesity or diabetes [ 17 , 18 , 19 , 20 , 21 ].

In considering the adverse effects of noise, the European Commission recognised community noise as an important environmental problem and adopted the European Noise Directive to assess and manage environmental noise [ 22 ]. The Directive focuses on noise mapping that aims to evaluate the number of people exposed to environmental noise. The precision of noise maps is essential to an appropriate identification of affected places and for planning suitable control measurements. In addition, a proper management of noise pollution can lead to benefits in reducing air pollutants because of the relation between them [ 23 , 24 ].

The European Noise Directive has not only been applied to European countries, but has also been used as a reference by non-European countries [ 25 , 26 , 27 , 28 ]. For example, in Chile, where this study was developed, over recent years the government has supported a number of projects initiated to gather knowledge about the acoustic situation in the cities [ 29 ]. As in other countries, different methods or strategies have been used for noise mapping, such as computation methods or studies carried out with “ in situ ” measurements. The use of an appropriate sampling method is important for the precision of noise maps, because even computation methods need to be validated and calibrated using “ in situ ” measurements [ 30 , 31 ].

Nowadays the sampling methods more commonly used in noise mapping are based on systematic random sampling using a regular grid or on the stratification of urban roads [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. There are also studies that carry out a stratification of land use after selecting any of the previous sampling strategies [ 40 , 41 ].

The grid method is the only sampling method that is accepted in an international standard, ISO 1996-2, that represents a verified reference for the measurement of noise levels in urban environments [ 42 ]. The grid method is widely used in many scientific fields because its use guarantees the statistical principle of equal probability and, moreover, a uniform coverage of the area under study. However, the grid method has other drawbacks. The standard says that the source of these problems stems from the existence of a high sound level variability in cases of proximity to the noise sources or the existence of large physical obstacles.

The stratification of urban roads is an increasingly popular method [ 34 , 36 ]. It is based on the generally accepted assumption that road traffic is the most important source of noise in cities, and for most streets it can be considered the main cause of the spatial and temporal variability of that noise. The stratification of urban roads used by a great number of researchers is based on information from the relevant ministries of transport [ 27 , 37 , 38 , 39 , 40 ]. These organisations classify the roads according to their main function and especially according to their design features.

In this context, our research group has been working for some years on the development of a sampling method for “ in situ ” noise measurements. We term this method the categorisation method. On the basis of the concept of street functionality, each stratum defined by the categorisation method presents a sound level variability that is lower than the total sound spatial variability in a city. This has produced significant improvements in both the reduction of the number of sampling points and in the estimation of noise levels in unsampled streets. Its usefulness has mainly been studied in Spanish cities with a wide range of populations: from 2000 to 3,250,000 inhabitants [ 43 , 44 , 45 ]. However, the economic development and urban planning of Chilean cities are different from the European cities analysed with the categorisation method in previous studies. Overall, European cities have typically been developed from a medieval historic centre with a complex street structure. Nowadays, shopping centres and administration centres are located in the historic centre. Chilean cities have a grid street plan in which streets run at right angles to each other, forming a grid. Also, another important difference is the fact that Chilean cities classify their roads according to a legislative procedure, whereas no standard classification exists for the roads in Spanish cities. The applicability of both methods based on roads classification has never been previously compared. In view of the above, the following objectives have been set out in this study:

• Compare the applicability and predictive capacity of two sampling methods—the legislative road classification and the categorisation method—in the assessment of urban noise in a Chilean city.
• Compare both sampling methods in terms of the prediction of exposure levels and the percentage of people annoyed.

Achieving these objectives will facilitate better understanding of the suitability of different noise situation sampling methods in cities. Information about the percentage of the population exposed in a Chilean city will also be provided. Until now this information has not been available in the Chilean cities evaluated. According to the European Noise Directive, the knowledge of the percentage of the population exposed is required for establishing effective preventive and, if necessary, corrective measures.

This study was conducted in the city of Talca (Maule region, Chile). Talca has a population of about 200,000 inhabitants (the population increases during the academic year due to the influx of university students) and is the tenth largest city in the country. The highest percentage of the active population (approximately 55%) works in the service sector, followed by the industrial sector (approximately 36%). This city does not have a historic centre and a high percentage of buildings have only one floor. The mean annual temperature and rainfall are 13 °C and 750 mm, respectively.

Three sampling methods were analysed: the grid method [ 42 ], road types established by the Ministry of Transport and Telecommunications of Chile (MTT) [ 46 ], and the categorisation method [ 45 ]. In order to compare the uncertainties using a similar sampling time the same number of sampling points (52) was selected for each measurement method. The grid method was analysed because it is accepted in an international standard, but its applicability was not compared with the other sampling methods.

2.1. Grid Method

In the grid method, a grid is superimposed over a city map and the measurement points are located at the nodes of the square or at the nearest location when the nodes are inaccessible. The area of Talca is approximately 29 km 2 . A total of 35 squares with 52 sampling points were drawn on the city map using a grid square with 800 m of resolution. A similar square grid resolution has been used in previous studies [ 33 ]. Figure 1 a shows the map of Talca with the grid used for this study.

Sampling methods used in the city of Talca. ( a ) Sampling squares of grid method; ( b ) Ministry of Transport and Telecommunications (MTT) road types; ( c ) and categorisation method.

2.2. Road Types Established by the MTT

The Ministry of Transport and Telecommunications of Chile (MTT) classifies urban roads according to their main function and their urban design features. However, in practice, urban characteristics, such as the width of the roads, are more relevant. Five types of roads are differentiated: highway, trunk, service, collector, and local. A similar classification has been used in recent acoustic assessment studies of cities in Chile and in other countries [ 27 , 37 , 38 , 39 , 40 ].

The sampling points were then randomly selected along the total length of each road type taking into account two factors. First, in the types of roads with a greater length (see Figure 2 ), a greater number of sampling points were selected with a minimum of eight sampling points for each road type. Second, equivalent points (those points located on the same section of a street with no important intersection between them) were discarded. For this reason, only one sampling point was selected in the highway road type. Figure 1 b shows the road types and locations of the sampling points: one point in highways, eight in trunk, twelve in service, eight in collector, and twenty-three in local road types.

Length of road types and road categories in Talca.

2.3. Categorisation Method

As previously mentioned, the categorisation method is based on the concept of street functionality, that is to say, the functionality of the streets of the city as a communication path between different parts of the city and between the city and other urban areas. In addition, other variables such as the flow of vehicles, the type of traffic, the average speed, and urban variables may have a clear relationship with functionality [ 47 ]. The streets of Talca were classified according to the definitions proposed in the categorisation method established in previous work [ 48 ].

A strategy similar to the previous method was used to select the sampling points in each road category. Figure 1 c shows the categorisation of different streets in the city and the locations of sampling points: eight points in Category 1, eight in Category 2, ten in Category 3, twelve in Category 4, and fourteen in Category 5.

2.4. Measurement Procedure

The measurements of different methods were carried out simultaneously from March to July 2015 following the ISO 1996-2 guidelines [ 42 ]. The measurements were performed on different working days and the sampling time for each measurement was 15 min. Previous studies [ 36 , 49 ] showed stability of the daily noise levels in the aforementioned months, and also these studies indicated that the main temporal variability of noise levels was among time-intervals within the day. At each sampling point, for each sampling strategy, at least five measurements were randomly selected in the following time-intervals: diurnal (from 07.00 to 19.00), evening (from 19.00 to 23.00), and nocturnal (from 23.00 to 07.00). A type-I sound level meter (2250 Brüel & Kjaer; Nærum, Denmark) was used with tripod and windshield and it was placed at a height of 1.5 m and at 2 m from the curb.

The A-weighted equivalent sound level ( L Aeq ) was used to analyse the results in the present study at different time-intervals of the day. The L Aeq registered in the diurnal period (from 07:00 to 19.00) and evening period (from 19.00 to 23.00) was very similar. For this reason, L Aeq from 7.00 to 23.00 ( L d ) was analysed. The noise descriptor L den was calculated following the guidelines of the European Noise Directive [ 22 ]. Other relevant information (traffic flow, types of vehicles, meteorological conditions, urban variables, etc. ) was also noted.

2.5. Statistical Analysis

In the acoustic assessment in Talca, the applicability of different sampling methods was analysed using the calculated noise descriptors ( L d , L n and L den ) at each sampling point ( P ij ). The subscript “ i ” refers to the point code and the subscript “ j ” refers to the sampling method.

In the grid method there are no assumptions of the location of sampling points in urban roads. However, the location of the sampling points with respect to the traffic noise source was similar in the different sampling methods. For this reason, the sound values registered in the sampling points of the grid method were used to analyse the predictive capacity of the others two sampling methods. The noise value assigned to each square ( S i ) was the median value of the four nodes of the square. For each square, the interquartile range was calculated from these four values. Moreover, the difference in sound levels between adjacent grid points was calculated. This difference should not be greater than 5 dB according to ISO 1996-2 [ 42 ].

For the MTT road types and the categorisation method a similar statistic procedure was carried out. The value assigned to each road type ( R i ) or road category ( C i ) was the average of the sound levels measured at the sampling points ( P ij ). This value was the expected value for all of the other points located in the same road type or road category. The average sound value and its variability will determine whether the stratums formed by road categories or by road types present significant differences. This hypothesis was assessed using the nonparametric tests Kruskal-Wallis and Mann-Whitney U [ 50 , 51 ]. This hypothesis was not tested with an inferential analysis in previous studies that used a legislative road classification [ 27 , 37 , 38 , 39 , 40 ]. The Kruskal-Wallis test was used to compare all the road categories in order to identify any significant differences. When such differences were found, Mann-Whitney U tests were used to compare pairs of road categories. The Mann-Whitney U test evaluates whether two independent samples or observations come from the same distribution. To avoid any errors due to the use of data from the same population rather than randomly selected data, the Holm correction was used [ 52 ].

In contrast to previous statistical tests, the receiver operating characteristics analysis ( ROC ) was used to evaluate the discriminative capacity of the MTT road types and of categorisation method to differentiate the sound values of the sampling points between pairs of strata (stratum i versus stratum j ) [ 45 ]. For the categorisation method and for MTT, the strata are the road categories and road types, respectively. The ROC analysis allows us to establish the upper and lower limits of the sound levels assigned to each stratum, to calculate the sensitivity (capacity to include previously assigned sampling points in the stratum), the non-specificity (proportion of sampling points that were not initially assigned to a certain stratum but that the ROC analysis indicated belonged to that stratum), and the predictive values (proportion of the sampling points that the ROC analysis assigned to a stratum that matched the strata to which they were initially assigned, relative to the total number of sampling points that the ROC analysis determined for the stratum). To do so, the following equations were used:

After studying the functioning of both methods, the predictive capacity of each method was then analysed using the sound values of the sampling points of the other methods as controls [ 53 , 54 ]. The parameter used for this analysis was the prediction error (ε i ), which is the difference between the measured value (control value) and the predicted value. The equations used to calculate the prediction error of the MTT road types (Equation (4)), and categorisation method (Equation (5)), respectively, were as follows:

The subscript “ i ” refers to the sampling point code ( P i ), road type code ( R i ) or road category code ( C i ), and the subscript “ j ” refers to the sampling methods in which the error is not being analysed. Next, the median prediction error obtained for each road category or road type was compared with the null value. For this, the Wilcoxon signed-rank test was applied [ 55 ]. This test determines whether the median of the prediction errors was biased. If the distribution of the prediction errors is unbiased, then a zero value will be obtained for the median.

Prediction errors of the different methods were also compared. To that end, the median absolute error of prediction (|ε i |) was analysed using the Mann-Whitney test [ 51 ]. If there is no significant difference it is assumed that the sampling methods have a similar predictive capacity.

Finally, the population exposed to noise was analysed and the population annoyed by noise was estimated. The demographic data of the geographic information system of the National Statistics Institute of Chile [ 56 ] were used to analyse the population exposed to noise. Noise levels registered in the road categories or road types were assigned to populations that reside in them [ 54 ]. Internationally validated equations were used to estimate the population annoyed by noise. Thus, the percentages of annoyed (% A ) and highly annoyed (% HA ) population were estimated from the L den descriptor with the following equations [ 57 , 58 ]:

With respect to nocturnal noise, the percentages of population with little sleep disturbance (% LSD ), sleep disturbance (% SD ), and those who were highly sleep disturbed (% HSD ) were estimated from L n descriptor using the following equations [ 59 ]:

3.1. Study of the Functioning of Sampling Methods

3.1.1. grid method.

Having calculated the sound values of L d , L n and L den descriptors in the different sampling points, the sound values of the different square grids were calculated. The results are shown in Table 1 . Table 1 shows that the interquartile range of sound values registered in the cells is quite high. Previous studies [ 33 , 48 ] reported high uncertainties in the predictive capacity of the grid squares, due to the high variability of the sound levels among nearby streets with different functionality. Therefore, if the sound differences between adjacent sampling points are analysed, 69%, 49% and 59% are higher than 5 dB for L d , L n and L den descriptors, respectively.

Median (M e ) and interquartile range (IQR) of L d , L n and L den descriptors registered in the square grids.

3.1.2. MTT Road Types

This stratified sampling is based on the hypothesis that different strata—road types in this case—have significant differences in sound values. First, to resolve this hypothesis, a descriptive analysis through a box plot was carried out ( Figure 3 ).

Box plot of L d , L n and L den descriptors registered in each road types.

Figure 3 shows that average values of sound descriptors decrease from trunk to local road type. In highway road types, as previously indicated, only one sampling point was used. In this road type the sound values of 76.4 dB, 70.1 dB and 78.9 dB were registered for the L d , L n and L den descriptors, respectively. Figure 3 also shows the analysis of the variability in mean sound levels. Trunk and service road types have an overlap of interquartile range and local road types have a high variability.

The hypothesis was resolved first by using the Kruskal-Wallis test. This test indicated significant differences ( p -value ≤ 0.001) for all the sound descriptors studied. Thus, the Mann-Whitney U test was then applied to analyse the differences among road type pairs ( Table 2 ).

p -Values with Holm adjustment of pairwise comparisons of road types using Mann-Whitney U test.

As shown in Table 2 , the Mann-Whitney U test found no significant differences ( p -value > 0.05) between trunk and service road types for L d , L n and L den descriptors. Nevertheless, for the remaining pairs of road types, significant differences ( p -value ≤ 0.05) for all sound indicators analysed were found.

In order to corroborate the quality of the previous results and to obtain more information about the MTT road types, the classification capacity of this method was then examined using ROC analysis. The results of this analysis are shown in Figure 4 .

Results of ROC analysis for the different sound descriptors registered in the Ministry of Transport and Telecommunications road types.

From the results shown in Figure 4 , the following can be noted:

• Regarding the ROC sensitivity (%), which is a measure of the capacity to include previously assigned sampling points in the stratum, only the collector road type for L n and L den has values above 80%. The sensitivity has low percentages for the sound descriptors analysed, sometimes even lower than 50%, because of the presence of overlaps among trunk and service road types and the high variability of the local road type.
• Regarding the non-specificity (%), which measures the proportion of sampling points that were not initially assigned to a given stratum, but which the ROC analysis indicates belong to that stratum, only the local road type has values lower than 10% for all the sound descriptors. The collector road type also has high non-specificity values for all the sound descriptors, although it has high sensitivity values for L n and L den .
• Finally, with regard to the predictive values of the different road types (which represent the proportion of the sampling points that the ROC analysis assigned to the stratum that matched the road types to which they were initially assigned, relative to the total number of sampling points that the ROC analysis determined for the stratum) only the local road type has values above 80% for all the sound descriptors. The stratum predicted by the ROC analysis for local road types has a high percentage of sampling points that MTT had initially classified in this road type. However, other sampling points of local road types have high values and these points are classified in other road types according to ROC analysis. Therefore, the local road type has low sensitivity values.

3.1.3. Categorisation Method

The different road categories defined by the method are based on the assumption of having significantly different noise levels. Therefore, like the MTT road types method, a descriptive and inferential analysis was conducted to test this hypothesis. The results of the descriptive analysis are shown in Figure 5 .

Box plot of L d , L n and L den descriptors registered in each road categories.

In the box plot, the interquartile ranges of the different road categories and sound descriptors have no overlaps. Category 5 has the greatest variability but it is considerably lower than that presented by the local road type.

An inferential analysis was then conducted using the Kruskal-Wallis and Mann-Whitney tests. The Kruskal-Wallis test indicates significant differences ( p -value ≤ 0.001) for all the sound descriptors studied. Thus, the Mann-Whitney U test with Holm correction was applied to analyse the differences among road category pairs ( Table 3 ).

p -Values with Holm adjustment of pairwise comparisons of road categories using Mann-Whitney U test.

As shown in Table 3 , the Mann-Whitney U test found significant differences ( p -value ≤ 0.01) among all pairs of road categories studied for all sound descriptors analysed. To corroborate the previous results, as carried out for the previous method, the classification capacity of the categorisation method was studied via ROC analysis. The results of this analysis are shown in Figure 6 .

Results of ROC analysis for the different sound descriptors registered in the road categories.

The results presented in Figure 6 show that the sensitivity of different sound descriptors is higher than 80% for all road categories (except the L n in Category 4), and even for the L den descriptor it is 100%. These high percentages are also obtained for the predictive value and therefore the percentages obtained in non-specificity are very low. They are lower than 5% in all sound descriptors.

These results differ from the previous method and it is therefore essential to compare the predictive capacity of both sampling methods. The results of this comparison are shown in the following section.

3.2. Predictive Capacity Analysis

In analysing the predictive capacity of the sampling methods, the sound values registered at the sampling points of the methods that were not being analysed were used.

To evaluate predictive capacity of the MTT road types, the sampling points chosen for the grid and categorisation method were used to compare the predictions of the MTT road types. All 104 sampling points evaluated in the grids and road categories could be associated with one of the road types (only one point was located in the highway road type, therefore, this road was not analysed). The sound values of these sampling points were compared with the mean value of the road type in which they were located and the prediction error was calculated using the difference between them (Equation (4)). The prediction error was analysed according to the road type where the control sampling point ( P ij ) was located. Table 4 shows the median from the error for the analysed sound descriptors.

Prediction errors (ε) of Ministry of Transport and Telecommunications road types for L d , L n and L den descriptors.

No.: Number; * Significant at p ≤ 0.05; ** Significant at p ≤ 0.01; n.s. Non-significant difference ( p > 0.05).

Prediction errors of MTT road types are mostly lower than the 3 dB considered as suitable for estimations on noise maps. However, according to the Wilcoxon signed-rank test, errors by underestimation in trunk and service road types have significant differences with respect to the null value (except for the L den descriptor in the service road type). These two road types, as noted above, showed no significant difference in the average sound values registered. This fact directly affects the predictive capacity of the method.

The predictive capacity of the categorisation method was then analysed. To this end, using a similar procedure to that described above, the sampling points employed for the grid method and MTT road types were used to compare with the predictions of the road categories. All 104 of the sampling points evaluated in the grids and road types could be associated with one of the road categories. The sound values of these sampling points were compared with the mean value of the road category in which they were located and the prediction error was calculated using the difference between them (Equation (5)). The prediction error was analysed according to the road category where the control sampling point was located ( P ij ). Table 5 shows the median from the error for the sound descriptors analysed.

Prediction errors (ε) of the categorisation method for L d , L n and L den descriptors.

No.: Number; n.s. Non-significant difference ( p > 0.05).

The prediction errors of the categorisation method are lower than 2 dB and have no significant differences with respect to the null value for all road categories and sound descriptors analysed (n.s.). These prediction errors are mostly lower compared with those of the MTT road types. However, to produce a detailed analysis of the differences in the estimation errors of the sampling methods, the median absolute errors of prediction were compared (|ε i |) using the Mann-Whitney test. The results are shown in Table 6 .

Absolute values of prediction errors (|ε|) for L d , L n and L den for road types and road categories and comparison to prediction errors of both methods (Categorisation and Ministry of Transport and Telecommunication (MTT)) using Mann-Whitney U test.

No.: Number; Sig.: Significance; * Significant at p ≤ 0.05; ** Significant at p ≤ 0.01; *** Significant at p ≤ 0.001; n.s. Non-significant difference ( p > 0.05).

To compare the predictive capacity of different sampling methods, the road type or road category where the control sampling point ( P ij ) was located was used as reference. Table 6 shows that the errors were higher for MTT road types for all sound descriptors analysed, regardless of road categories or road types taken as a reference. Taking the road category in which the control sampling point was placed as a reference, the error of L n descriptor showed no significant differences between both sampling methods in Category 3 and 4. Taking the road type where the control sampling point was placed as a reference, the errors of both sampling methods in the collector road type showed no significant differences for all sound descriptors. The error of the night level in trunk and service road types and the error of the day, afternoon and night level in the trunk road type revealed no significant differences. Indeed, the differences in errors of both sampling methods are reduced if road types are taken as a reference. However, it is important to keep in mind that this classification had problems of statistical differentiation.

3.3. Calculation of Exposure Level and the Percentage of Annoyance

In the previous section the predictive capacity of sound values was analysed according to the different sampling methods. A sampling method that presents significant uncertainties of prediction will directly influence the calculation of the exposed population. Therefore, the variation in the level of exposed population and the percentage of annoyance depending on the sampling method used were analysed. In this study, the categorisation and MTT road type methods were analysed.

Figure 7 shows the percentage of exposed population according to the L den descriptors registered in different road categories and road types. Depending on the selected method, the results of population exposed to noise can change significantly. According to the MTT road types method, of the populations that reside in the highway, trunk, service and collector road type areas, 10% are exposed to levels higher than 65 dB. These areas whose L den > 65 dB are referred to as black acoustic zones [ 60 ]. However, in the case of the categorisation method, 23% of the population resides in black acoustic zones. Likewise, if the level of noise exposure in the road type and in the road category where a higher percentage of population resides is compared, the local road type population is in an acoustic grey zone (55 ≤ L den ≤ 65), whereas in Category 5 the population is in a white acoustic zone ( L den < 55). Therefore, the differences in the capacity of sound prediction can clearly be misleading in the calculation of the percentage of exposed population.

Population exposed to noise in different road categories and road types.

Finally, we calculated the percentages of annoyed population and percentages of the population who are sleep disturbed by noise using both the MTT road types and the categorisation method. The results are shown in Figure 8 .

Percentages annoyance indicators (percentages of annoyed (% A ) and highly annoyed (% HA ) population; percentages of lowly sleep disturbed (% LSD ), sleep disturbed (% SD ) and highly sleep disturbed (% HSD ) population) obtained from the proposed equations [ 58 , 59 ] for road types and road categories.

The results show that different road types have percentages of annoyance and sleep disturbed by noise higher than those registered in the different road categories. Those road types that register higher noise levels, and therefore higher levels of noise annoyance, are those that had a higher level of sound prediction uncertainty. The trunk and service road type have similar percentages of annoyance to Categories 2 and 3. However, in previous analysis significant problems of differentiation between these two road types were found. Furthermore, the difference in the percentages of annoyance between the local road type and Category 5 should be noted, being those with lower noise levels. These differences were also detected in the analysis of sound exposure.

4. Discussion

The variability of sound values registered in the grid squares of Talca is quite high. This result indicates a low predictive capacity of the grid method to assess the noise exposure. If the interquartile range obtained in the cells is compared with that obtained in the local road type and in Category 5 (the road type and road category with the highest variability of noise levels), more than 50% and 75% of the grids register a greater value, respectively. Indeed, the grid size is quite high; however, as stated above, in this study has been considered relevant to use the same number of sampling points in each measurement method. Following the instructions of the ISO 1996-2 [ 42 ], if intermediate grid points would be added when the sound differences between adjacent grid points were higher than 5 dB, a new sampling would have carried out with a number of similar points. However, as shown in previous studies [ 33 ], the selection of new sampling points does not guarantee a difference between adjacent points lower than 5 dB. Consequently, this method was not used in order to compare the uncertainties between different sampling methods.

Regarding the MTT road types, the results show an overlap of interquartile range of the sound values registered in the trunk and service road types. Also, the local road type has a high sound variability. These results are similar to those obtained in other studies carried out in cities of Chile with legislative road classification [ 38 ]. Consequently, the ROC analysis indicates that this method has a low percentage of sensitivity and predictive capacity and a high percentage of non-specificity. Nevertheless, the sound values in the different road categories of the categorisation method have highly significant statistical differences. The road categories also have a high percentage of sensitivity and predictive capacity and a very low percentage of specificity.

The prediction errors of the categorisation method are lower than those of the MTT method for the different urban roads analysed. These differences in the prediction of sound values involve differences in the estimation of exposure levels and percentage of annoyance. According to the MTT method, 10% of the population is exposed to L den > 65 dB, whereas this is 23% of population according to the categorisation method. Also, as shown in Figure 8 , road types have percentages of annoyance and sleep disturbed by noise higher than those registered by road categories.

Finally, the exposed population and the percentage of annoyance obtained using the categorisation method were compared with the results obtained in other cities. Lee et al. [ 28 ] carried out a recent acoustic study in Seoul (S, Korea) and the percentage obtained from population that exceeds the level of 65 dB for the L d descriptor and the level of 55 dB for L n descriptor were compared with European cities. In Talca 11% of the population (Category 1, 2 and 3) is exposed to average levels at daytime that are higher than 65 dB and to average levels at night that are higher than 55 dB. For both time periods these percentages are higher than those obtained in the cities of Helsinki (Finland) and Berlin (Germany), and are similar to those obtained in cities such as Frankfurt (Germany). However, these percentages are lower than those obtained the cities of Seoul, Copenhagen (Denmark) and Madrid (Spain). In a further acoustic study recently carried out by Braubach et al. [ 15 ] in the cities of Basel (Switzerland), Rotterdam (The Netherlands) and Thessaloniki (Greece), limit values of 64 dB (annoyance by noise), 67.5 dB (major noise problem), and 65 dB (major noise problem) were found using the L den descriptor. The population of Talca residing in Category 1 to Category 4 is exposed to levels greater than 64 and 65 dB for the L den descriptor and for Category 1 to Category 3 the population is exposed to levels higher than 65.5 dB. Therefore, 23% and 14% of the population is exposed to values greater than 64–65 dB and 67.5 dB respectively. These percentages are much higher than those obtained in the cities of Basel, Rotterdam and Thessaloniki.

5. Conclusions

The selection of a suitable sampling method is essential to achieve an accurate assessment of the impact of noise pollution on the population. The grid, MTT road types and categorisation methods were analysed in the city of Talca (Chile). The major conclusions drawn from the results are as follows:

The grid squares have a high variability of sound values. This high variability leads to differences in sound values registered at adjacent points of more than 5 dB in 69%, 49% and 59% for L d , L n and L den descriptors, respectively.

The MTT road types have a low percentage of sensitivity and predictive capacity (except for the collector road type for L n and L den that has values above 80% of sensitivity and for the local road type for all the sound descriptors that has values above 80% of predictive capacity) and a high percentage of non-specificity (except for the local road type for all the sound descriptors that has values lower than 10%). This low discrimination and predictive capacity is caused, among other factors, by the lack of significant differentiation of sound values registered in trunk and service road types and by the high variability of the sound values of the local road type.

Average sound values in the different road categories of the categorisation method have highly significant statistical differences. The road categories also have a high percentage of sensitivity (>75%) and predictive capacity (>80%) and a very low percentage of specificity (<5%). Therefore, the functional stratification of noise levels observed in European cities that were studied previously is also found in Chilean cities. These results suggest a great advance in the validity of the categorisation method because of its application in a Chilean city.

The predictive capacity of the categorisation method is higher than that of the MTT method. This difference in the predictive capacity of sound values involves differences in the estimation of exposure levels and in the percentage of annoyance. Consequently, the categorisation method is more accurate than the MTT method to assess the impact of noise pollution on the population.

Talca is a city affected by noise pollution and also by its related problems of public health of its inhabitants. The percentages of population exposed to daytime and nighttime sound levels that are harmful to health are higher than those obtained in Helsinki and Berlin. Furthermore, the percentage of exposed population to L den > 64 dB is much higher than that obtained in the cities of Basel, Rotterdam and Thessaloniki.

Acknowledgments

This research was supported by the National Commission for Scientific and Technological Research (CONICYT) through the Nacional Fund for Scientific and Technological Development (FONDECYT) for research initiation No. 11140043. The authors thank the collaboration of Gonzalo B. Pacheco-Covili in the data collection for this study.

Author Contributions

Both authors contributed substantially to the conception of the study. Guillermo Rey Gozalo was responsible for the design of the study and the analysis of data in collaboration with Juan Miguel Barrigón Morillas. Interpretation of the results was discussed between both authors. Both authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Assessment Methodologies

Various assessment methods using standard acoustical principles and modeling techniques are adopted for various types of environmental noise. Also, there are guidelines or criteria for the planning and control of environmental noise.

Aircraft noise is one example of transportation noise. This picture shows an aircraft in flight following its flight path. Please click on the photo to listen to the sound. Then click on its photo again to stop the sound/noise.

With the forthcoming full commissioning of the Three Runway System, please click here for the NEF contours of 2030.

• to assess the effect of traffic noise from a future road on existing and planned sensitive developments, say, residential buildings; and
• to assess the effect of traffic noise from an existing road on future sensitive developments of, say, a residential estate.

The assessment method gives corrections to adjust the reference noise level for traffic flow (such as traffic volume and speed), road surface correction, distance, ground effect (soft or hard), barrier and reflection effects etc.

Examples of fixed noise sources are power plants, concrete batching plants, electricity substations, pump houses, bus depots, and similar stationary sources. Click on the loudspeaker icons round the middle of the following photographs to listen to the noise from an electricity substation and a concrete batching plant. Then click on the photo again to stop the noise.

The assessment is based on standard acoustical principles. Issued under the Noise Control Ordinance, the following Technical Memorandum on Noise from places other than Domestic Premises, Public Places or Construction Sites is commonly referred to when considering the methodologies and noise limits for assessing noise from fixed sources.

In cases of noise planning, the noise levels are calculated using the available information on types and quantities of plants and machines and their operation patterns.

In Hong Kong, noise from construction sites is controlled under a system of Construction Noise Permit (CNP) under the Noise Control Ordinance (Cap. 400). Essentially, a CNP is required for the following:

• carrying out of percussive piling;
• carrying out of general construction work during restricted hours.

The assessment of noise from construction sites is based on standard acoustical principles and the following Technical Memoranda, which provide guidance on assessment:

• Technical Memorandum on Noise from Percussive Piling,
• Technical Memorandum on Noise from Construction Work other than Percussive Piling, and
• Technical Memorandum on Noise from Construction Work in Designated Areas
• Technical Memorandum on the Environmental Impact Assessment Process

The followings show the cover pages of the Technical Memoranda mentioned above. For details, please click on the covers.

The above Technical Memoranda contain information on the noise from most plants and machines used in construction sites. The information forms the basis for the prediction of noise from them. Where no such information for a particular machine is found in the Technical Memoranda, reference is then made to either :

• Actual measurement results, or
• Other sources of information such as international recognized standards

Railway noise can be assessed by obtaining noise level through noise measurement at the site in question. Or, if a new railway line is being planned or a residential development near an existing railway is being proposed, rail noise can be assessed by considering sound performance of the type of rolling stocks at the respective operating conditions such as train frequency, speed, type of track etc.

Evs Project On Noise Pollution For Class 11th

Table of Contents

Acknowledgement:

I would like to extend my heartfelt gratitude and appreciation to my esteemed Environmental Science teacher, [Name], for their invaluable guidance, support, and encouragement during the completion of this project on Noise Pollution.

Throughout the project, [Name] has provided me with valuable insights and suggestions, helping me refine my research methodology and analysis. Their constructive feedback and guidance have been invaluable in ensuring the accuracy and comprehensiveness of the project.

I would also like to express my gratitude to [Name] for their unwavering support and belief in my abilities. Their constant encouragement has instilled confidence in me and has been pivotal in overcoming challenges during the project’s completion.

Additionally, I would like to thank my classmates and friends who have assisted me in gathering data and providing valuable inputs during the project’s development. Their collaboration and enthusiasm have enriched the overall quality of this project.

Last but not least, I am deeply grateful to my family for their unwavering support and understanding. Their encouragement and patience have been vital in enabling me to dedicate the necessary time and effort to complete this project successfully.

Once again, I express my deepest gratitude to [Name] and all those who have contributed to the realization of this project on Noise Pollution. Your guidance, support, and encouragement have been indispensable, and I am truly grateful for the opportunity to work on this project under your supervision.

Introduction to Noise Pollution:

Noise pollution is a widespread environmental problem that arises from various sources and has adverse effects on both human health and the environment. It is characterized by excessive or disturbing sounds that disrupt the normal balance of the acoustic environment.

Noise pollution is generated by numerous activities and sources in our daily lives. Transportation, including vehicles on roads, trains, airplanes, and ships, is a major contributor to noise pollution. The constant honking of horns, engine noises, and the sonic boom of airplanes create a high level of noise, particularly in urban areas.

Industrial activities, such as manufacturing processes, power plants, and construction sites, also generate significant amounts of noise. The operation of machinery, heavy equipment, and power tools produces loud and continuous sounds, which can impact both workers and nearby communities.

Construction activities, including drilling, hammering, and demolition work, contribute to noise pollution as well. These activities often take place in residential areas, leading to disturbance and inconvenience for the residents.

Recreational activities can also generate noise pollution, especially in crowded areas. Events like concerts, sporting events, and festivals produce loud music, cheering crowds, and amplified announcements, causing discomfort and potential harm to individuals.

The excessive exposure to noise pollution can have detrimental effects on human health. Prolonged exposure to high levels of noise can lead to hearing loss and damage to the auditory system. It can also cause annoyance, stress, and sleep disturbances, leading to psychological issues such as anxiety, irritability, and reduced concentration.

Furthermore, noise pollution has negative impacts on the environment. It disrupts the natural habitats of wildlife, affecting their behavior, feeding patterns, and reproductive activities. For example, noise pollution from ships and sonar activities can disturb marine animals, leading to changes in migration patterns and communication difficulties.

In conclusion, noise pollution is a pervasive problem resulting from various sources such as transportation, industrial activities, construction, and recreational events. Its detrimental effects on human health and the environment make it a matter of concern that requires attention and effective mitigation strategies. By understanding the causes and consequences of noise pollution, we can work towards creating a quieter and healthier environment for all.

Example of Noise Pollution:

Noise pollution manifests in various forms and can be observed in numerous everyday situations. Two prominent examples of noise pollution are the incessant honking of vehicles in urban areas and the noise generated by construction sites.

In urban areas, the constant honking of vehicles has become a significant source of noise pollution. The honking is primarily due to traffic congestion, aggressive driving behavior, or lack of adherence to traffic rules. The cumulative effect of honking horns from cars, motorcycles, and buses creates a chaotic and stressful environment. Pedestrians, motorists, and residents in the vicinity are exposed to high levels of noise, leading to increased stress levels, annoyance, and a reduced quality of life. Prolonged exposure to such noise pollution can also have long-term impacts on individuals’ hearing abilities, potentially resulting in hearing loss or other auditory issues.

Another notable example of noise pollution is the noise generated by construction sites. Construction activities involve the use of heavy machinery, such as excavators, bulldozers, jackhammers, and concrete mixers, which emit high-intensity noise. The continuous operation of these machines, especially in densely populated areas, can cause significant disturbance and inconvenience to nearby communities. Construction noise disrupts the peace and tranquility of the surroundings, affecting residents’ sleep patterns, concentration levels, and overall well-being. It can also impact vulnerable populations, such as the elderly, young children, and individuals with certain medical conditions.

These examples highlight how noise pollution can arise from common activities and significantly impact individuals and communities. The incessant honking of vehicles in urban areas and the noise generated by construction sites are just a few instances of the widespread issue of noise pollution. It is crucial to address these sources of noise pollution through effective regulations, soundproofing measures, and responsible behavior to ensure a healthier and more peaceful living environment for everyone.

Importance of EVS Project on Noise Pollution:

The EVS project on Noise Pollution holds significant importance for several reasons. Firstly, it raises awareness among individuals about the adverse effects of noise pollution on human health and the environment. By understanding its consequences, people can take necessary measures to minimize their contribution to noise pollution and protect themselves. Secondly, the project highlights the need for effective policies and regulations to control and mitigate noise pollution. Lastly, it emphasizes the role of collective action and responsible behavior in reducing noise pollution and creating a more peaceful environment.

How Can We Make It Happen?

To effectively address noise pollution, we need a collective effort from individuals, communities, and governing bodies. Here are some steps that can be taken:

Public Awareness: Conduct awareness campaigns, seminars, and workshops to educate people about the causes and consequences of noise pollution. Encourage individuals to adopt soundproofing measures in their homes and workplaces.

Implement Noise Regulations: Enforce strict noise regulations and standards for industries, construction sites, and public places. These regulations should limit noise levels and define penalties for non-compliance.

Noise Reduction Measures: Encourage the use of noise-reducing technologies and techniques in transportation, construction, and industrial activities. Promote the adoption of quieter machinery and equipment.

Land Use Planning: Incorporate noise considerations into urban planning by ensuring the appropriate placement of residential areas, schools, and hospitals away from high-noise sources like highways or industrial zones.

The Three Pillars of Addressing Noise Pollution:

Prevention: Focus on reducing noise pollution at its source by employing quieter technologies, controlling noise emissions from industries, and promoting responsible behavior among individuals.

Protection: Implement measures to protect individuals from excessive noise exposure, such as providing sound barriers, noise barriers on highways, and soundproofing buildings near noisy areas.

Public Participation: Encourage active involvement of the public in raising concerns about noise pollution and participating in decision-making processes. Engage community organizations, NGOs, and citizen groups to work collaboratively in addressing noise pollution issues.

Conclusion:

In conclusion, noise pollution poses a significant threat to our environment, health, and overall well-being. Throughout this EVS project, we have delved into the causes, provided examples, and discussed the consequences of noise pollution. It has become evident that raising awareness and taking proactive measures are crucial to address this issue effectively.

Noise pollution is a multifaceted problem caused by various sources, including transportation, industrial activities, construction, and recreational events. It disrupts the harmony of our surroundings, leading to stress, annoyance, and potential health problems such as hearing loss, sleep disturbances, and psychological issues.

Raising awareness about noise pollution is essential. By educating ourselves and others about its causes and consequences, we can foster a sense of responsibility towards reducing noise pollution. Awareness campaigns, seminars, and workshops can play a pivotal role in disseminating information and encouraging individuals to take action.

Implementing effective measures to combat noise pollution requires a three-pronged approach: prevention, protection, and public participation.

Prevention involves addressing noise pollution at its source. This can be achieved by employing quieter technologies, promoting the use of noise-reducing equipment, and encouraging responsible behavior among individuals and industries.

Protection measures focus on safeguarding individuals from excessive noise exposure. Implementing sound barriers, noise barriers, and soundproofing measures in residential areas, schools, hospitals, and workplaces can significantly reduce the impact of noise pollution.

Public participation is crucial in creating a sustainable and peaceful environment. Encouraging active involvement from citizens, community organizations, NGOs, and other stakeholders fosters a sense of ownership and collective responsibility. By engaging in decision-making processes, raising concerns, and advocating for noise regulations and policies, individuals can contribute to meaningful change.

In conclusion, by working together and implementing the three pillars of prevention, protection, and public participation, we can make a positive impact in reducing noise pollution. It is essential for governments, organizations, communities, and individuals to collaborate and take action to create a more peaceful and sustainable environment for everyone.

As responsible citizens, we must recognize the detrimental effects of noise pollution and strive to minimize our contribution to it. Let us work towards a future where tranquility and harmony prevail, promoting a healthier and more enjoyable quality of life for ourselves and future generations.

Certificate of Completion

This is to certify that I, [Student’s Name], a [Class/Grade Level] student, have successfully completed the project on “Evs Project On Noise Pollution For Class 11th.” The project explores the fundamental principles and key aspects of the chosen topic, providing a comprehensive understanding of its significance and implications.

In this project, I delved into in-depth research and analysis, investigating various facets and relevant theories related to the chosen topic. I demonstrated dedication, diligence, and a high level of sincerity throughout the project’s completion.

Key Achievements:

Thoroughly researched and analyzed Evs Project On Noise Pollution For Class 11th. Examined the historical background and evolution of the subject matter. Explored the contributions of notable figures in the field. Investigated the key theories and principles associated with the topic. Discussed practical applications and real-world implications. Considered critical viewpoints and alternative theories, fostering a well-rounded understanding. This project has significantly enhanced my knowledge and critical thinking skills in the chosen field of study. It reflects my commitment to academic excellence and the pursuit of knowledge.

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Statistical and spatio-temporal analyses of noise pollution level and its health impact

• Research Article
• Published: 19 June 2023
• Volume 30 , pages 82951–82963, ( 2023 )

Cite this article

• Neeraj K. Singh 1 ,
• Markandeya   ORCID: orcid.org/0000-0001-7239-1512 2 ,
• Manish K. Manar 3 ,
• Sheo P. Shukla 4 &
• Devendra Mohan 5  

434 Accesses

2 Citations

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Due to rapid urbanization and exponential growth in transportation; traffic noise has become a major area of concern. Noise not only disturbs our day-to-day life but also have severe adverse health effects over humans which further may lead to mortality. This paper focuses on the behavior of noise levels of Lucknow city over a decade and establishes its correlation with impact on human health in terms of annoyance and sleep disturbance. Apart from L eq , different noise parameters like L 10 , L 50 , L 90 , Traffic Noise Index (TNI), Noise Pollution Index (NPI), and Noise Climate (NC) have also been analyzed to understand the variation of noise. At all the locations, the noise level has been found exceeding their prescribed standards during day time and night time except at Amausi. Out of nine locations, TNI was found to be exceeding at three locations during day time and NPI exceeding at one location. However, during night time both values of TNI and NPL were observed within the limit at all the locations. From the noise map of the city during day time and night time, among all sampling locations, Charbagh has been found to be worst affected by noise pollution. A strong positive correlation has been observed among the total population, vehicular count, and day and night time noise data, which directly contribute to a higher percentage of sleep disturbance and annoyance among residents. Due to the increase in noise levels over a period of time, almost four times the population get affected by high annoyance and almost double the population get affected by sleep disturbance.

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Acknowledgements

The corresponding author is highly grateful to the Director of CSIR-IITR, Lucknow, for providing necessary support in data collection.

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Neeraj K. Singh

Ex-Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India

Department of Community Medicine and Public Health, King George’s Medical University, Lucknow, 226003, India

Manish K. Manar

Rajkiya Engineering College, Banda, 210201, India

Sheo P. Shukla

Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India

Devendra Mohan

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The main investigator was Markandeya, who designed the manuscript. The technical drafting of the manuscript was done by NKS, while MKM helped in writing. Moreover, SPS and DM were advisors of the present research. All the authors have read and approved the manuscript.

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Singh, N.K., Markandeya, Manar, M.K. et al. Statistical and spatio-temporal analyses of noise pollution level and its health impact. Environ Sci Pollut Res 30 , 82951–82963 (2023). https://doi.org/10.1007/s11356-023-28264-8

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Received : 11 November 2022

Accepted : 10 June 2023

Published : 19 June 2023

Issue Date : July 2023

DOI : https://doi.org/10.1007/s11356-023-28264-8

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