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anxiety states (Generalized Anxiety Disorder and Anxiety Disorder NOS), thus supporting the hypothesis of a sustained central autonomic arousal due to chronic exposure to noise (Hardoy et al., 2005). Children Several epidemiological studies have shown that road traffic noise positively associated with increased risk of arterial hypertension in adults who live in areas with daytime average sound pressure level exceeding 65 dB(A) (Babisch, 2006). However the results of the studies on noise 5 exposure and children’s blood pressure are less consistent. This association was found to be negative and significant in the London and Amsterdam study (van Kempen et al., 2006); positive and borderline significant in the Inn Valley study (Evans, Lercher, Meis, Ising, & Kofler, 2001), and positive and significant in the Belgrade study (Belojevic, Jakovljevic, Stojanov, Paunovic, & Ilic, 2008). The Inn Valley study (Evans et al., 2001) reported marginal and borderline significant effects of noise on elevated resting systolic blood pressure in fourth-grade children who were exposed to high noise level (>60 dB) from road and railway noise, compared to less exposed children (<50 dB). The London and Amsterdam study (Van Kempen et al., 2006) showed negative and significant association between daytime road traffic noise at schools and systolic blood pressure. However, nighttime aircraft noise was significantly and positively associated with blood pressure. A recent study in Belgrade (Belojevic et al., 2008) investigated the effects of urban road- traffic noise on children's blood pressure and heart rate using nighttime noise exposure at children's residences and daytime noise at kindergartens. This is a cross-sectional study performed on 328 pre-school children (174 boys and 154 girls) aged 3–7 years who attended 10 public kindergartens in Belgrade. Equivalent noise levels (Leq) were measured overnight in front of the children's residences and during the day in front of kindergartens. A residence was regarded as noisy if Leq exceeded 45 dB(A) during the night and quiet if the Leq was ≤45 dB (A). Noisy and quiet kindergartens were those with daily LeqN60 dB(A) and ≤60 dB(A), respectively. Children's blood pressure was measured with a mercury sphygmomanometer. Heart rate was counted by radial artery palpitation for one minute. The prevalence of children with hypertensive values of blood pressure was 3.96% (13 children, eight boys and five girls), with a higher prevalence in children from noisy residences (5.70%) compared to children from quiet residences (1.48%). The difference was borderline significant (p=0.054). Systolic pressure was significantly higher (5mmHg, on average) among children from noisy residences and kindergartens, compared to children from both quiet environments (p><0.01). Heart rate was significantly higher (2 beats/min on average) in children from noisy residences, compared to children from quiet residences (p><0.05). Multiple regression, after allowing for possible confounders, showed a significant correlation between noise exposure and children's systolic blood pressure (B=1.056; p=0.009). There are several possible reasons for inconsistency in the results of the studies on road traffic noise and blood pressure in children: noise exposure was assessed in different settings, either at home or at school or at kindergartens; the children were of different ages (ranging from preschool to school age); road traffic noise was sometimes combined with other sources of noise (aircraft, railway); and daytime noise level was predominantly used as a noise exposure indictor at home instead of nighttime noise level. Most evidence in relation to aircraft noise on children is derived from school studies carried out in the Munich airport study (Evans et al., 2001), the Sydney airport study (Job RFS, Carter N, Hatfield J, Morrell S, Peploe P, Taylor R, 2000), and the RANCH study (van Kempen et al., 2006). The cross-sectional study around the old Munich airport revealed a borderline significant effect of two mmHg higher systolic blood pressure readings in schoolchildren from noise-exposed areas (Leq, 24hr = 68 dB(A)), as compared to unexposed children (Leq, 24hr = 59 dB(A)). No noise effect was found with regard to diastolic blood pressure (Evans et al., 2001). Longitudinal studies carried out around the new airport showed a two to four mmHg larger increase in BP readings in exposed children than in their counterparts from the quiet areas 18 months after the opening of the new airport. However, the well-matched children from the exposed and the control group had the same 6 absolute blood pressure. The higher change in blood pressure was due to lower values at the beginning of the follow-up. The cross-sectional study around the Sydney airport revealed non-insignificant relation between aircraft noise and diastolic and systolic blood pressure in children (Job RFS, Carter N, Hatfield J, Morrell S, Peploe P, Taylor R, 2000). In a cross-sectional study carried out around Schiphol and Heathrow airports on schoolchildren (the RANCH study), non-insignificant relationship was found between aircraft exposure at school (LAeq, 7 a.m.-11 pm) and measured systolic blood pressure, diastolic blood pressure and heart rate after adjustment for relevant confounders.(van Kempen et al., 2006). However, aircraft noise at home (expressed as LAeq, 7 a.m.- 11 p.m.) was significantly related to higher systolic (0.10 mmHg/dB(A)) and diastolic (0.19 mmHg/dB(A)) blood pressure. Chronic aircraft noise exposure during the night (LAeq, 11 p.m.-7 a.m.) at home was also positively associated with blood pressure. This latter association was significant only for systolic blood pressure. In the pooled data-set, an increase of 0.09 mmHg/dB(A) was found. Due to significant differences in noise effects between the two centers, no unequivocal conclusions about the association between aircraft noise exposure and blood pressure in children could be drawn (van Kempen et al., 2006). Explanations put forward concern differences in flight pattern variation and the aircraft fleets. Also, differences in schooling systems and teachers’ attitudes towards noise might have differential effects on the children’s reactions to noise. None of these could be tested on the available data. Finally, even though the results were adjusted for ethnic differences and diet, residual confounding due to these factors might explain the differences (Babisch & Kamp, 2009). Mediating Effects Stress Noise-induced annoyances are experienced by both children and adults. Noise causes a release of stress hormones that can adversely affect health. Similar to other stressors, noise disturbs the homeostasis of the cardiovascular, endocrine and immune systems in the body to cope with the environmental or perceived demands of the individual. The imbalance between the demand and the individual’s resources to cope determine the individual’s ability to deal with noise-induced stress. The body’s inability to cope with overstimulation can lead to adverse stress reactions (Prasher, 2009). The glucocortcoid hormone, cortisol, is the main secretory product of the neuroendocrine cascade and a valid indicator of stress. The cortisol profile normally shows a diurnal variation, high in the morning and low at night. Studies have shown elevated cortisol level in relation to noise. After long-time stressful exposure, the ability to down-regulate cortisol may be inhibited (Babisch et al., 2009)(Babisch et al., 2009; Bjork et al., 2006; Ohrstrom et al., 2007). In models of noise, stress and disease, cortisol plays a key role in hypothalamic-pituitary-adrenal (HPA) axis activity and was examined in three recent studies of nighttime noise exposure. In an observational study, researchers obtained salivary cortisol samples from 68 children who had had recent physician contact for bronchitis (Ising, Lange-Asschenfeldt, Moriske, Born, & Eilts, 2004). They found that night-time noise levels above 53 dB(A) were associated with increased morning cortisol levels and were thought to lead, in the long term, to the aggravation of bronchitis in children. 7 In a laboratory-based sleep study measuring salivary cortisol, low frequency noise (40 dB(A), ≤125 Hz) was associated with an attenuated cortisol response after waking. Cortisol levels had not yet peaked at 30 minutes post-waking, as it did in controls (NTOT=12) (Waye, Clow, Edwards, Hucklebridge, & Rylander, 2003). In a second laboratory study, exposure to simulated vehicle backup alarms (60-80 dB(A), 1000 Hz) failed to elicit change in cortisol concentration profiles in the days afterward (Michaud et al., 2006). Interpretation of cortisol measurement data remains complex in noise research (Babisch, 2003). However, there may be several factors that influence the variability seen in cortisol response in noise simulation, including timing or measurement, type of stressor, controllability, individual response characteristics and individual psychiatric sequalae (Miller, Chen, & Zhou, 2007). Sleep Disturbance There is both objective and subjective evidence for sleep disturbance by noise. Exposure to noise disturbs sleep proportional to the amount of noise experienced in terms of an increased rate of changes in sleep stages and in number of awakenings (Gitanjali & Ananth, 2003). Noise exposure during sleep may increase blood pressure, heart rate and finger pulse amplitude as well as body movements. There may also be after-effect during the day following disturbed sleep; perceived sleep quality, mood and performance in terms of reaction time all decreased following sleep disturbed by road traffic noise. Studies on noise abatement show that, if indoor noise level can be reduced, the amount of REM sleep and slow wave sleep can be increased (Stansfeld & Matheson, 2003). Exposure to environmental noise is also associated with the increased use of sleep medication (Franssen, van Wiechen, Nagelkerke, & Lebret, 2004). Economic Costs of Noise A large number of studies in Europe have examined the question of the external costs of noise to society, especially transport noise. The estimates range from 0.2% to 2% of gross domestic product (GDP), which represents an annual cost to society of over 12-120 billion euro (€1=$1.36 as of Nov. 11, 2010). A study from Germany showed that, on average, an individual would be prepared to pay around 10 euro per 1 dB(A) improvement per person, per year if the noise levels exceed 43 dB(A). On this basis, the annual costs of traffic noise in Germany were estimated to be 7.8 - 9.6 billion Euro. • Willingness to pay based on surveys • Change of the market value of properties • Cost of the abatement measures • Cost of avoidance or prevention • Cost of medical care and production losses A study (Gjestland, 2007) in Norway took a different approach to assess the economic impact of noise, a noise annoyance index (SPI). SPI is the product of noise annoyance score and number of people exposed to that annoyance. Using simple linear approximation noise annoyance score can be calculated as a function of time-weighted noise level (in dB) and noise source dependent correction factor. They assess the economical cost of noise (by different sources) at community level. For example, a community of 500 residents is exposed to two different noise sources: aircraft noise at 55 dB(A) and road traffic at 60 dB(A). The aircraft noise source at 55 dB(A) can be substituted by an equally annoying road traffic noise source at 61 dB(A), based on the fact that there is a six dB aircraft malus when compared with road traffic noise. 8 These two road traffic noise sources, at 60 and 61 dB(A), are added (energy) to give a total level of 63.5 dB(A). The annoyance score associated with this level is 0.38, and the total noise annoyance index for this area is (500 x 0.38) = 190 SPI. Considering factors (psycho-physiological effects, stress, sleep disturbances and resulting productivity loss, communication problems and possible hearing damage) that influence the “cost,” in Norway, the "cost" of one extremely annoyed person (1 SPI) has been estimated to be approximately €1600 per year. The annoyance index for Norway caused by road traffic noise, 503,388 SPI, corresponds to a cost of more than 800 million Euros. In a study among U.S. Navy sailors, Tufts, Weathersby and Rodriguez (Tufts, Weathersby, & Rodriguez, 2010), found that the nominal noise-exposure case (93 dB(A) for six years) yielded a total expected lifetime cost of $13,472, with a range of $2,500 to $26,000 per sailor. Starting with the nominal case, a decrease of 50% in exposure level or duration would yield cost savings of approximately 23% and 19%, respectively. A Swiss study (Riethmuller, Muller-Wenk, Knoblauch, & Schoch, 2008) assessing the monetary value of disturbed sleep due to road traffic noise concluded that the value of noise-free sleep was 7.45-23.81 Swiss francs (CHF) per night (CHF 1=$1.02 as of Nov. 11, 2010). A 1999 United Kingdom Department of Transportation review of 64 studies on valuation of noise used three strategies to set a ”price” on noise: cost per decibel, average percentage change in property prices per decibel, and percentage of GDP. The review concluded that the ranges of costs are: • ₤15-₤30 per decibel per household per year • 0.08-2.30% change in property value per decibel • 0.02-2.27% GDP 9 References 1. Babisch, W. (2002). The Noise/Stress concept, risk assessment and research needs. Noise & Health, 4(16), 1-11 2. Babisch, W. (2003). Stress hormones in the research on cardiovasEvidence of the environmental impact of noise pollution on biodiversity: a systematic map protocol Romain Sordello1*, Frédérique Flamerie De Lachapelle2 , Barbara Livoreil3 and Sylvie Vanpeene4 Abstract Background: For decades, biodiversity has sufered massive losses worldwide. Urbanization is one of the major drivers of extinction because it leads to the physical fragmentation and loss of natural habitats and it is associated with related efects, e.g. pollution and in particular noise pollution given that many man-made sounds are generated in cities (e.g. industrial and trafc noise, etc.). However, all human activities generate sounds, even far from any human habitation (e.g. motor boats on lakes, aircraft in the air, etc.). Ecological research now deals increasingly with the efects of noise pollution on biodiversity. Many studies have shown the impacts of anthropogenic noise and concluded that it is potentially a threat to life on Earth. The present work describes a protocol to systematically map evidence of the environmental impact of noise pollution on biodiversity. The resulting map will inform on the species most studied and on the demonstrated impacts. This will be useful for further primary research by identifying knowledge gaps and in view of further analysis, such as systematic reviews. Methods: Searches will include peer-reviewed and grey literature published in English and French. Two online databases will be searched using English terms and search consistency will be assessed with a test list. No geographical restrictions will be applied. The subject population will include all species. Exposures will include all types of man-made sounds (industrial, trafc, etc.) in all types of environments (or media) (terrestrial, aerial, aquatic), including all contexts and sound origins (spontaneous or recorded sounds, in situ or laboratory studies, etc.). All relevant outcomes will be considered (space use, reproduction, communication, abundance, etc.). An open-access database will be produced with all relevant studies selected during the three screening stages. For each study, the database will contain metadata on key variables of interest (species, types of sound, outcomes, etc.). This database will be available in conjunction with a map report describing the mapping process and the evidence base with summary fgures and tables of the study characteristics. Keywords: Noise, Pollution, Man-made sounds, Anthropogenic sounds, Masking auditory, Urban noise, Trafc noise, Impact of urbanization, Species loss, Natural habitats, Ecosystems © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Background For decades, biodiversity has sufered massive losses worldwide. Species are disappearing (e.g. [36]), populations are collapsing (e.g. [15]), species’ ranges are changing (both shrinking and expanding) at unprecedented rates (e.g. [7]) and communities are being displaced by invasive alien species (e.g. [24]). All the above are caused by human activities and scientists regularly alert the international community concerning our responsibility [30]. In particular, urban growth is one of the major reasons for biodiversity loss [21, 29] in that it destroys natural habitats, fragments the remaining ecosystems (e.g. [40]) and also has other impacts, such as pollution. For example, cities produce artifcial light at night that disturbs circadian rhythms, impacting plants and animals [2, 13]. Similarly, many man-made sounds are generated in cities, by trafc and numerous human activities Open Access Environmental Evidence *Correspondence: romain.sordello@mnhn.fr 1 UMS Patrimoine Naturel (PATRINAT), AFB-CNRS-MNHN, 36 rue Geofroy- 

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