What is a Single Tone Continuous Noise Called

Continuous Noise

2Identified as continuous noise levels exceeding 45dB(A), or a maximum noise level exceeding 60dB(A) (Construction Noise and Information Sheet, 2011).

From: Environmental Noise Pollution , 2014

Principles of Environmental Noise

Enda Murphy , Eoin A. King , in Environmental Noise Pollution, 2014

2.4.1 Continuous Equivalent Noise Level: L _eq

Probably the most common type of noise descriptor is the equivalent continuous noise level over a time period T, L _eq,T. This metric is an energy-based indicator as it represents the total amount of acoustic energy over the specified time period. It is the continuous steady sound level that would have the same total acoustic energy as the fluctuating noise measured over the same period of time. It may be defined as:

(2.10) $L_{(eq, T)} = 10 {log}_{10} \frac{1}{T} \int_{0}^{T} {(\frac{p (t)}{p_{0}})}^{2} d t [dB]$

where T is the time period over which measurements occur, p(t) is the instantaneous acoustic pressure and p ₀ is the reference sound pressure level (20 μPa).

Graphically it is explained in Figure 2.9. The time-varying noise signal (in blue) is measured over a time T. The amount of energy in this signal is equivalent to the amount of energy contained in a continuous noise level L _eq over this same time period (red).

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Industrial and Construction Type Noise

Enda Murphy , Eoin A. King , in Environmental Noise Pollution, 2014

6.6.2 Hours of Activity

Construction noise is often controlled by restricting the times during which construction can occur. Many authorities restrict construction activities to normal working hours and do not allow activities to take place over weekends or public holidays. For example, in South Australia because construction noise results in an adverse impact on amenity, ² it is restricted from operating on a Sunday or a public holiday. For all other days, it is restricted to hours between 07:00 and 19:00.

In some exceptional circumstances, construction may need to take place outside of these hours. Examples include the delivery of oversized plant structures, emergency work, and maintenance and repair of public infrastructure where work during the standard hours might disrupt essential services. In such cases, regulators might encourage a range of work practices to minimise the construction noise impact rather than focusing on meeting stringent noise criteria.

Noise may be minimised from construction by the operator implementing best practice work methods. Some examples include:

•: scheduling of particularly noisy activities during less sensitive periods of the day;
•: choosing plant and equipment that is the quietest and most suitable for the project. This may include ensuring noise reduction devices are installed on plant (for example, fitting more efficient exhaust sound reduction equipment, use of machines inside acoustic enclosures);
•: ensuring all equipment is well maintained and operating within specifications;
•: making use of mitigation measures where appropriate (e.g. acoustic screens);
▪: employing work practices which minimise noise activities. These may include restricting waste material from being dropped at excessive heights and line chutes and dump trucks with resilient material.

BS 5228 sets out methods and criteria for assessing the significance of noise effects. One such method includes the ABC method. This method sets threshold values to determine if there will be a significant effect at dwellings for three different categories (A, B and C). The threshold values are different for each ABC category and different time periods. The ambient noise level is determined for the appropriate period and then rounded to the nearest 5 dB. This is then compared to the total noise level, including construction noise. If the total noise level exceeds the appropriate category values, a significant effect is deemed to occur.

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Noise Pollution and Its Control

Iyyanki V. Muralikrishna , Valli Manickam , in Environmental Management, 2017

15.4 Effects on Health

Noise pollution can take a severe toll on human health in the long run. These effects will not become apparent immediately, but there could be repercussions later on.

The following is a list of the kinds of effects noise pollution will have on human health after continuous exposure for months, and even years:

•: The most immediate effect is a deterioration of mental health. As an example, people who are living too close to airports will probably be quite jumpy. Continuous noise can create panic episodes in a person and can even increase frustration levels. Also, noise pollution is a big deterrent in focusing the mind to a particular task. Over time, the mind may just lose its capacity to concentrate on things.
•: Another immediate effect of noise pollution is a deterioration of the ability to hear things clearly. Even on a short-term basis, noise pollution can cause temporary deafness. But if the noise pollution continues for a long period of time, there is a danger that the person might go permanently deaf.
•: Noise pollution also takes a toll on the heart. It is observed that the rate at which heart pumps blood increases when there is a constant stimulus of noise pollution. This could lead to side-effects like elevated heartbeat frequencies, palpitations, breathlessness, and the like, which may even culminate into seizures.
•: Noise pollution can cause dilation in the pupils of the eye, which could interfere in ocular health in the later stages of life.
•: Noise pollution is known to increase digestive spasms. This could be the precursor of chronic gastrointestinal problems.
•: Noise can awaken people from sleep, and it can keep them awake, frequently awakening, or awakening for long periods, which can be very disruptive. Even if not awakened by noise, a person's sleep pattern can be significantly disturbed, and a reduced feeling of well-being can result the next day. Frequent and prolonged sleep disturbances can result in physical, mental, or emotional illness.
•: External sounds are able to interfere with conversations and use of the telephone as well as the enjoyment of radios, television programs, and like pastimes. It can thus effect the efficiency of offices, schools, and other place where communication has been of vital importance. The maximum acceptable level of noise under such conditions has been 55 dB. 70 dB is considered very noisy, and serious interference with verbal communication is inevitable.

Table 15.1 lists the effects of high intensity noise on human beings.

Table 15.1. Effects of High Intensity Noise on Human Beings

Noise (dB)	Effects Observed
0	Threshold of audibility
150	Significance change in pulse rate
110	Stimulation of reception in skin
120	Pain threshold
130–135	Nausea, vomiting, dizziness, interference with touch and muscle sense
140	Pain in ear, extreme limit of human noise tolerance
150	Prolonged exposure causing burning of skin
160	Minor permanent damage if prolonged
190	Major permanent damage in a short time

Noise hazards are classified into several stages based on the quantum of impact they cause. Table 15.2 lists some of the health issues and the quantum of impact they cause.

Table 15.2. Health Issues Related to Noise Pollution

A. Noise Hazards
Stage: I	Stage: II
Threat to survival	Causing injury
1. Communication interference	1. Neural-humoral stress response
2. Permanent hearing loss	2. Temporary hearing loss
B. Noise Nuisances
Stage: III	Stage: IV
Curbing efficient performance	Diluting comfort and enjoyment
1. Mental stress	1. Invasion of privacy
2. Task interference	2. Disruption of social interaction
3. Sleep interference	3. Hearing loss

Basic noise levels for an industrial zone should not exceed 55 dB at night and 65 dB during the daytime. Noise contributes to development of cardiovascular problems like heart diseases and high blood pressure. Workers exposed to high noise levels are having more circulatory problems, cardiac disturbances, neuro-sensory, motor impairment, and even more social conflicts at home and at work.

15.4.1 Physiological Responses

Physiological responses accompanying a response and other noise exposures include:

1.: a vascular response characteristic by peripheral vaso-constriction, changes in heart rate and blood pressure,
2.: various glandular changes such as increased output of adrenaline evidenced as chemical changes in blood during circulation,
3.: slow, deep breathing,
4.: a change in the electrical resistance of skin with changes in activity of the sweat glands,
5.: brief changes in skeletal muscle tension.

According to environmentalist Thomsa G. Ayles Worth, "constant noise may cause our blood-vessels to contract, our skin to become pale, our muscles to contract and adrenaline to be shot into our blood stream." This adrenaline is responsible for both excretory and inhibitory responses in living beings. This is the reason that factory workers develop abnormal heartbeat rates and suffer from insomnia, nervousness, and impaired motor coordination. The US Government has kept 90 dB as a health hazard for an 8-h-day working environment. It has been proved that high noise is bad particularly for those suffering from hypertension and diabetics. Noise also produces startling effects on babies, and they may even develop a fear psychosis as a result of sharp and sudden noise.

15.4.2 Effects on Communication

External sounds are able to interfere with conversations and use of the telephone as well as the enjoyment of radios, television programs, and like pastimes. It can thus effect the efficiency of offices, schools, and other place where communication has been of vital importance. The maximum acceptable level of noise under such conditions has been 55 dB. 70 dB is considered very noisy and serious interference with verbal communication is inevitable.

15.4.3 Epidemiological Studies

Several researchers have conducted field studies testing industrial workers and/or collating their health records in an attempt or to overcome the limitations of duration and realism in laboratory studies. The difference between very noisy industries and less noisy industries is the two groups indicate a higher incidence of problems among the high noise group than by the low noise group. The high noise group came from the light industries such as textile industries. There are other numerous differences that could have effects on health, such as heat, physical work load, anxiety, and the type of people.

Noise exposure can cause two kinds of health effects: non-auditory effects and auditory effects. Non-auditory effects include stress, related physiological and behavioral effects, and safety concerns. Auditory effects include hearing impairment resulting from excessive noise exposure. Noise-induced permanent hearing loss is the main concern related to occupational noise exposure.

15.4.4 Auditory Health Effects

The main auditory effects include these:

Acoustic trauma: sudden hearing damage caused by short burst of extremely loud noise such as a gunshot,

Tinnitus: ringing or buzzing in the ear,

Temporary hearing loss: also known as temporary threshold shift, which occurs immediately after exposure to a high level of noise; there is gradual recovery when the affected person spends time in a quiet place, and complete recovery may take several hours,

Permanent hearing loss: Permanent hearing loss, also known as permanent threshold shift (PTS), progresses constantly as noise exposure continues month after month and year after year. The hearing impairment is noticeable only when it is substantial enough to interfere with routine activities. At this stage, a permanent and irreversible hearing damage has occurred. Noise-induced hearing damage cannot be cured by medical treatment and worsens as noise exposure continues.

When noise exposure stops, the person does not regain the lost hearing sensitivity. As the employee ages, hearing may worsen as "age-related hearing loss" adds to the existing noise-induced hearing loss.

15.4.5 Characteristics of Noise-Induced Permanent Hearing Loss

The main characteristics of noise-induced hearing loss are these:

•: Noise-induced hearing loss is a cumulative process: both level of noise and exposure time over a worker's work history are important factors.
•: At a given level, low-frequency noise (below 100 Hz) is less damaging compared to noise in the mid-frequencies (1000–3000 Hz).
•: Noise-induced hearing loss occurs randomly in exposed persons.
•: Some individuals are more susceptible to noise-induced hearing loss than others.
•: In the initial stages, noise-induced hearing loss is most pronounced at 4000 Hz, but it spreads over other frequencies as noise level and/or exposure time increases.

Hearing sensitivity declines as people become older. This medical condition is called presbycusis. Age-related hearing loss adds to noise-induced hearing loss. Hearing ability may continue to worsen even after a person stops working in a noisy environment. Noise affects the hearing organs (cochlea) in the inner ear. That is why noise-induced hearing loss is a sensory-neural type of hearing loss. Certain medications and diseases may also cause damage to the inner ear resulting in hearing loss. Generally, it is not possible to distinguish sensory-neural hearing loss caused by exposure to noise from sensory-neural hearing loss due to other causes. Medical judgment, in such cases, is based on the noise exposure history. Workers in noisy environments who are also exposed to vibration (e.g., from a jack hammer) may experience greater hearing loss than those exposed to the same level of noise but not to vibration. Some chemicals are ototoxic; that is, they are toxic to the organs of hearing and balance or the nerves that go to these organs. This means that noise-exposed workers who are also exposed to ototoxic chemicals (e.g., toluene and carbon disulfide) may suffer from more hearing impairment than those who have the same amount of noise exposure without any exposure to ototoxic chemicals.

15.4.6 Measurement of Hearing Loss

Hearing loss is measured as a threshold shift in dB units using an audiometer. The 0 dB threshold shift reading of the audiometer represents the average hearing threshold level of an average young adult with disease-free ears. The PTS, as measured by audiometry, is the decibel-level of sounds of different frequencies that are just barely audible to that individual. A positive threshold shift represents hearing loss, and a negative threshold shift means better than average hearing when compared with the standard. Several methods of calculating the percentage of hearing disability are in use. The American Medical Association (AMA)/American Academy of Otolaryngology (AAO) formula is widely accepted in North America. The current method recommended by AMA/AAO is as follows:

1.: The average hearing threshold level at 500, 1000, 2000, and 3000 Hz should be calculated for each ear.
2.: ultiplying, one should calculate the percentage of impairment for each ear (the monaural loss) as 1.5 times the amount by which the aforementioned average exceeds 25 dB (low fence). Hearing impairment is 100% for the 92-dB average hearing threshold level.
3.: The hearing disability (binaural assessment) is calculated by multiplying the smaller percentage (better ear) by 5, adding it to the larger percentage (poorer ear), and dividing the total by 6.

15.4.7 Relationship Between Noise Exposure and Hearing Loss

From the scientific data accumulated to date, it is possible to determine the risk of hearing loss among a group of noise-exposed persons. To do this we need the following data:

•: a measure of daily noise exposure level,
•: duration of noise exposure (months, years),
•: age of person,
•: Hearing loss is defined as average threshold shift at 500, 1000, 2000, and 3000 Hz (Fig. 15.3).

Figure 15.3. Relation between exposure and hearing loss.

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A review of the environmental parameters necessary for an optimal sleep environment

Zachary A. Caddick , ... Erin E. Flynn-Evans , in Building and Environment, 2018

3 Noise

Exposure to noise can disrupt sleep quality and quantity [61]. The magnitude of sleep disruption conferred by noise depends on the decibel level (dB), the frequency and pitch, duration (i.e. continuous, intermittent, or impulsive) and whether the noise is meaningful (e.g. a familiar voice). A World Health Organization working group report on noise determined that there is a causal relationship between nighttime noise exposure and self-reported sleep disturbances, use of pharmaceuticals, self-reported health problems, and insomnia-like symptoms [122]. The same group issued guidelines for noise exposure during sleep, setting the limit for average level between 30 and 40 dBA, citing that higher levels of noise in the sleep environment leads to changes in the duration of sleep stages and increase sleep fragmentation.

Table 2 shows the literature describing the association between noise exposure and sleep disruption. There have been several high quality randomized (category 1A) or quasi-experimental (category 2A) studies using EEG that confirm that the auditory arousal threshold that causes a transition from sleep to wake varies between individuals and sleep stage [70,87]. Awakening due to noise exposure <50 dBA is more likely in shallow stages of sleep (i.e. stages 1 & 2), where louder noises [42,43,69,121] or noises that are in the low frequency range (∼500 Hz [15], are required to cause waking from deeper stages of sleep (i.e. stages 3 & 4. It is notable that the arousal threshold in REM sleep is not easily determined due to the influence of dreaming (described in Ref. [87]). Complete awakening from sleep appears to be dependent on the frequency of repetition of the noise in addition to the volume, with more frequent pulses causing more sleep disruption [70]. Similarly, the auditory arousal threshold has been shown to change with repeated exposure to noise [11] and others have shown that there are large inter-individual differences in noise sensitivity [62]. The awareness that sleeping individuals have of their surroundings can also contribute to awakenings, such as speaking a sleeper's name [78], the sound of human voices [49,50], and household activity [95].

Table 2. Summary of studies describing noise levels and their influence on sleep.

Noise Source	Type of Disruption	Sleep Measure	Noise Level	Type of Noise	Source
Church bells	Dose response between awakening and noise level	EEG questionnaire	30-70 dB	Intermittent	[14]
Rail noise	Higher noise level: increased arousals, awakening, S1, SWS, sleep stage transitions, reduced TST	EEG	30-65 dBA	Intermittent	[6,26,94]
Aircraft noise	Higher modeled aircraft noise = no difference in sleep insufficiency compared to lower modeled noise	Questionnaire	Modeled noise of <60, 60–65, >65 dBA	Intermittent	[34]
Aircraft noise	Reduced sleep quality	Actigraphy self-report	Quiet control, 60 dBA	Intermittent	[96]
Aircraft noise	Higher noise level: increased awakening, arousals sleep-stage transitions, reduced SWS, TST	EEG	30-65 dBA	Intermittent	[5–7]
Traffic noise	Increased latency, awakening, consumption of sleeping pills, reduced sleep quality	Questionnaire self-report	>45–75 dBA	Not reported	[103,114]
Traffic noise	Reduced TST, increased sleep latency, awakening	Actigraphy sleep logs	40-45 dB (inside bedroom)	Not reported	[81]
Traffic noise	Higher noise level: increased awakening, S1, arousals sleep-stage transitions, reduced SWS, TST, WASO	EEG	30-60 dBA	Intermittent	[41,83,107,120]
Traffic and rail noise	Individuals living in a dwelling with a quiet side reported reduced sleep quality	Questionnaire	Modeled noise of <40,40–60, and >60 dBA	Intermittent	[10]
Aircraft, traffic, and rail noise	Increased sleep latency, WASO, S1, reduced TST, sleep efficiency, SWS, REM	EEG	32 dB vs. 39, 44, 50 dBA	Continuous	[60]
Wind turbine noise	Dose response relationship with poorer sleep associated with nearness to wind turbines	PSQI, ESS	40-52 dBA	Continuous	[71]
Wind turbine (pre/post installation)	No differences in EEG sleep, reduced self-reported sleep quality	EEG self-report	37 dBA (pre), 37 dBA (post)	Continuous	[37]
Wind turbine noise	No association between sleep outcomes and level of noise	Actigraphy, PSQI self-report	<25, 25–30, 30–35, 35–40, 40–46 dBA	Continuous	[63]
General noise in the sleep environment	Fragmentation of sleep and impact on duration of various sleep stages	N/A	35 dBL_{Amax, inside}	Not specified	World Health Organization Limit [122]

dBA = A-weighted decibels (where low frequencies are reduced); dBL_{Amax, inside} = maximum levels per event inside a bedroom; dB = decibels; EEG = electroencephalogram; PSQI = Pittsburgh Sleep Quality Index; ESS = Epworth Sleepiness Scale.

There have been few category 1 or EEG studies that have been conducted evaluating the impact of continuous noise on sleep. The two experimental EEG studies (category 1A) that have been conducted suggest that exposure to continuous ambient noise of 39 dBA or greater is associated with increased night waking, shorter sleep duration, and poorer sleep quality, including reduced REM sleep [ 60,97]. In contrast, continuous noise of 62 dB containing a blend of 1–22.05 kHz has been shown to protect sleep by masking the influence of exposure to other intermittent noises [101]. In the natural environment, continuous noise pollution typically emanates from sources such as exposure to wind turbines, however, it is unclear whether exposure to wind turbine noise influences sleep. One study found a dose response relationship with poorer sleep being associated with proximity to wind turbines generating 40–52 dBA using the Pittsburgh Sleep Quality Index (PSQI) and Eppworth Sleepiness Scale (ESS; category 3C) [71]. However, another study found no association between sleep outcomes and level of wind turbine noise ranging from <25 dBA to 46 dBA using PSQI and actigraphy (category 3B) [63]. Similarly, a study that compared EEG-measured sleep before and after installation of wind turbines in a neighborhood found no differences in objective or subjective sleep outcomes (category 1A), however, the noise exposure in that study was the same in both conditions (37 dBA, [37]. These findings suggest that quiet environments are better for sleep, but that continuous noise may be useful in dampening intermittent noises. More research is needed to determine what level and frequency of continuous noise is detrimental to sleep outcomes.

Intermittent noise is generally perceived to be more disruptive to sleep than continuous noise. There are numerous sources of intermittent noise that can infiltrate the bedroom environment. Several randomized studies using EEG in controlled environments (category 1A) have shown that exposure to intermittent noises such as simulated door slamming [115], passing trains [94] [6], aircraft flyovers [5] [6], and traffic noise [6,83] are associated with increased night waking, increased arousals from sleep, increased slow wave sleep, increased sleep stage transitions, and reduced total sleep time. Studies in the field largely confirm these findings. Several high quality observational studies using EEG have shown that intermittent exposure to noise in the environment is associated with sleep disruption. Traffic noise has been shown to increase the frequency of awakening, increased arousals (i.e. EEG defined awakening) and sleep stage transitions, which cause increased stage 1 sleep, reduced slow wave sleep, and reduced total sleep time (category 1A and 2A) [41,107,120]. Similarly, aircraft flyovers [7] and passing trains [26] have been shown to be associated with increased awakening, increased sleep stage transitions, and reduced slow wave sleep and total sleep time (category 3A). These findings are consistent with the results of an observational EEG study (category 3A) that found that overnight bell tolling from church bells is associated with night waking in a dose response manner [14]. Several survey and quasi-experimental studies using questionnaires, self-report, or actigraphy (category 2B, 2C) confirm that exposure to intermittent noise is associated with sleep disruption. Individuals who are exposed to higher levels of traffic noise self-report more disrupted sleep [10,81,103,114] and increased use of sleeping pills [103] relative to individuals with lower nocturnal traffic noise exposure. Similar findings have been reported for actigraphically-measured sleep disruption from aircraft noise (category 2B) [96] and also questionnaire-based measures of sleep disruption from rail noise (category 3C) [10]. In contrast, a questionnaire-based study (category 3C) comparing modeled aircraft noise on sleep found no changes in sleep insufficiency among those living under higher modeled level noises [34], however, that study used the exposure category of <60 dBA as a comparison group, which would include noises loud enough to be disruptive to sleep. Together, these findings largely confirm that intermittent noise is disruptive to sleep outcomes.

In summary, we reviewed numerous high quality studies that support the notion that noise pollution causes sleep disruption. Our findings suggest that exposure to intermittent noises above 35 dB are associated with reduced sleep quality and quantity. There were few studies describing the influence of continuous noise exposure on sleep outcomes warranting further research in this area. In general, our findings support the importance of locating bedrooms away from common spaces in order to reduce the impact of household and familiar noise on sleep. In buildings where sleep and common spaces must be co-located, such as in hospitals, hotels, and dormitories, measures to reduce noise emanating from other rooms (such as sound attenuating doors) should confer a positive impact on sleep quality and quality for residents. Similarly, bedrooms should be insulated against exposure to noise pollution from the outside environment, particularly when buildings are situated near highways, railways, and airports. In cases where noise pollution cannot be eliminated through insulation or other sound attenuating measures, the use continuous white noise may be useful in minimizing sleep disruption.

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Marine invertebrate anthropogenic noise research – Trends in methods and future directions

M.A. Wale , ... K. Diele , in Marine Pollution Bulletin, 2021

3.1 Exposure and area of biology (N1, N2, N3)

Research on the impact of anthropogenic noise on marine invertebrates has undergone substantial changes since its beginning (Figs. 3-6), most of which has occurred since 2012 (Fig. 2) . Acute noise exposures have dominated the field throughout its history (Fig. 3C). However, the studies conducted between 2012 and 2021 have increasingly used continuous noise exposures (17% of the literature) and started to include modelled noise exposures (2% of the literature) ( Fig. 3C).

One of the most substantial changes is the increase in the use of noise playbacks, which has risen from 38% to 71% of exposures since 2012. Field exposures were abundant prior to 2012 (48% of publications) and, although their frequency has increased of late due to increased research efforts, their relative proportion within the literature has decreased (34%) (Fig. 3B).

The field has been dominated by behavioural studies from its outset, and this trend has continued until today. Behavioural studies made up 29% of the literature prior to 2012, changing to 45% between 2012 and 2021 (Fig. 3A). Studies focusing on physiology and morphology have fallen from 16% and 23% respectively before 2012 to 10%, and 11%, whereas investigations on larval development have risen from 3% to 6% since 2012. The proportion of experiments investigating biochemical responses has risen from 13% to 16% since 2012, ecological experiments have remained constant (3% prior to 2012, 2% after), and the first experiments on the effects of noise on genetics (Peng et al., 2016) were conducted in 2016 (Fig. 3A). The study of the effects of noise on invertebrate fisheries has decreased in representation falling from 13% to 4% since 2012 (Fig. 3A).

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Gas flaring and resultant air pollution: A review focusing on black carbon

Olusegun G. Fawole , ... A.R. MacKenzie , in Environmental Pollution, 2016

3.2 Emissions measurements around real-world gas flaring sites

Few data of pollutants measurement around gas flaring sites are publicly available. Data from the ACCESS aircraft campaign experiment in the Arctic (Norway) and a couple of ground-based in-situ measurements in Nigeria and the US show significant contributions from gas flaring to ambient air concentrations of these pollutants.

In-situ ground measurements of air pollutants around typical oil and gas facilities where varying degrees of gas flaring take place have been undertaken in the US (Edwards et al., 2013, 2014; Johansson et al., 2014), Mexico (Villasenor et al., 2003), Norway (Roiger et al., 2015) and, Nigeria (Ana et al., 2012; Nwaichi and Uzazobona, 2011; Obanijesu et al., 2009; Sonibare et al., 2010 ). Continuous noise levels higher than the WHO limit of 70 dB were also observed around some gas flaring sites in Nigeria ( Abdulkareem and Odigure, 2006; Avwiri and Nte, 2004).

A 4-month sampling of three air pollutants (SO₂, CO and NO₂) around six flow stations in the Niger Delta area of Nigeria was undertaken by Obanijesu et al. (2009) and Sonibare et al. (2010). The measurements were made at 60, 200 and 500 m downwind of the flaring sites. Although to a varying degree depending on the capacity of the station, gas flaring is a prominent daily activity within the stations. Mean pollutants measurements around the six flow stations are shown in Fig. 6(a), (b) and (c). The variation bar on the bar-plots shows the standard deviation of the measurements over the four-month period. The nature and extent of dispersion of pollutants from a stationary source depend on the local meteorology and topography of the area. As shown in Fig. 6(a)–(c), the trend of the measurements from the flaring sites are similar to observations from dispersion model studies where concentrations of pollutants decreases exponentially with distance from the source (Hodgson et al., 2007). Site 4 is the only place where a significant deviation from this trend, especially for CO and NO₂, was observed, suggesting the likelihood of contributions from other source(s).

A summary of the few available in-situ ground measurements downwind of gas flaring sites in other regions of the world are given in Table 3.

Table 3. Pollutant measurements around several oil and gas facilities.

BC (ng kg⁻¹)	O₃ (ppbv)	VOC (ppbv)	PAH (ng m⁻³)	NO (ppbv)	NO₂ (ppbv)	SO₂ (ppbv)	CO (ppbv)	Ref
–	>120	100–350	–	>3.5	>7.5	–	>80	Edwards et al. (2013)
>40	>25	–	–	>1.2	–	>1.2	>90	(Roiger et al., 2015)
–	–	–	0.34–3.3 × 10⁴	–	–	–	–	(Ana et al., 2012)

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Effects of marine noise pollution on Mediterranean fishes and invertebrates: A review

E. Di Franco , ... P. Guidetti , in Marine Pollution Bulletin, 2020

3.2.1 Acute MNP

Roberts et al. (2016) showed that an experimental apparatus generating noise types similar to anthropogenic activities, such as blasting and pile driving, may induce the retraction of antennas and reduce locomotion in adult hermit crabs in aquaria. Solan et al. (2016) investigated the impact of simulated continuous and impulsive noise on the Norwegian lobster. These authors reported a reduction of the burying behavior and, in general, of the locomotor activity.

MNP studies on cephalopods in the Mediterranean Sea focused on 4 species: European squid, common cuttlefish, common octopus and southern shortfin squid. Most of the studies (André et al., 2011; Solé et al., 2013a, 2013b, 2016) used sinusoidal wave sweeps that are not strictly impulsive noises but that share many characteristics such as short duration (≤1 s), a relatively broad spectral density or high amplitude, and they can elicit acute effects. André et al. (2011) exposed adults of these four species to high-intensity low-frequency sounds, mimicking acute sounds produced by human activities such as pile driving and air guns. These authors reported permanent and substantial alterations of the sensory hair cells of the statocysts, responsible for the animals' sense of balance and position in space, with damage increasing with time of exposure. These results were confirmed by Solé et al. (2013a, 2013b) who exposed adults of the same four species to the same sound sweeps, and observed typically noise-induced lesions in the statocysts. Solé et al. (2017) observed the same damage on statocysts by exposing caged adults of common cuttlefish to different noises at different distances from the source in the field. Their study confirmed to some extent that noise exposure conditions in previous studies conducted in the laboratory were representative of field conditions. Furthermore, Solé et al. (2018) observed that the damage to the sensory epithelium happened sooner in hatchling than in adult individuals of common cuttlefish, European squid and shortfin squid. Solé et al. (2019) reported differences in protein expression in the statocyst endolymph, some of these proteins being related to the stress response and cytoskeletal structure. Samson et al. (2014) assessed the response of juvenile and adult common cuttlefish exposed to pure-tone pips in aquaria and reported an escape response from sounds. The intensity of the response was related to the stimulus amplitude and sound frequency. After repeated exposure, some evidence of habituation was observed but a total response inhibition never occurred.

The only available study on bivalve mollusk is the one published by Spiga et al. (2016) on the blue mussel, that highlighted a higher clearance rate after exposure to pile driving noise. It is worth mentioning that Aguilar de Soto et al. (2013) highlighted body malformations and delays in development after exposure to pile driving noise of the larva of Pecten novaezelandiae, a scallop from New Zealand which is very close to the scallops found in the Mediterranean.

The only study focusing on cnidarians is that of Solé et al. (2016), which reported injuries in the statocyst sensory epithelium in two jellyfish species (Mediterranean and barrel jellyfishes) after 2 h of exposure to low frequency and high intensity sweeps.

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Biomonitoring as a prerequisite for sustainable water resources: a review of current status, opportunities and challenges to scaling up in East Africa

Frank O. Masese , ... Kobingi Nyakeya , in Ecohydrology & Hydrobiology, 2013

4.6.1 Scoring criteria

The interval 1, 3, 5 scoring system used in the Lake Victoria basin has been commonly used in developing fish and macroinvertebrate IBIs (Karr, 1981; Kerans and Karr, 1994; Barbour et al., 1999; Raburu et al., 2009a,b). The discrete scores (1, 3, 5) are attributed according to the measured conditions and their deviation from the least-disturbed reference conditions; higher scores are attributed to the best conditions (Karr, 1991). The condition at these least-disturbed sites represents the best-available chemical, physical, and biological habitat conditions given the current state of the catchmnets. However, the discrete scores attributed are in part subjective, because they are largely based on professional judgement (Howe et al., 2007). Discrete scoring can also have the effect of increasing the variability of the final IBI and limit its ability to differentiate among ecological condition categories (Blocksom, 2003; Stoddard et al., 2008). Professional judgement may vary, hindering a standardization of the index and increasing the probability of type I error or other errors associated with metric selection (Norris and Hawkins, 2000). To reduce subjectivity and noise, continuous scoring is recommend (0–1 or 0–10) in which the lowest value is zero and the highest one or ten (McCormick et al., 2001; Hering et al., 2006; Whittier et al., 2007b).

However, the continuous scoring system has not been evaluated to test its suitability in the LVB. To come up with the final total score or IBI value for each study site, each individual metric receives a score depending on its departure from the baseline value set for that metric, which corresponds to least-disturbed conditions for the region. Two criteria are commonly used to come up with the baseline value. The first criterion is used when reference conditions are used and the 25th and 75th percentiles of reference values are used as the upper bound and lower bound, respectively (Table 5). For metrics that decrease with impairment, sites receive a score of: 5 if the value of the metric is >25th percentile of reference site values, 3 if the value lies between the 25th percentile of reference and the 50th percentile of impaired site values, and 1 if the value is >50th percentile of impaired site values. For metrics that increase with impairment, sites are scored a value of: 5 if the value of the metric is <75th percentile of reference site values, 3 if the value lies between the 75th percentile of reference and the 50th percentile of impaired site values, and 1 if the value is >50th percentile of impaired site values. The second criterion applies when reference conditions are not used and the best value obtained, after sampling across all sites in the study, is used as the baseline. This criterion mostly applies when most sites are degraded and it is not possible to establish reference conditions (e.g., Ganasan and Hughes, 1998; Masese et al., 2009a; Raburu and Masese, 2012). For positive metrics (i.e., those that increase with improving conditions), the highest value of a metric across all sites is trisected (Barbour et al., 1999). Values above the upper one-third received a score of 5, those in the middle received a score of 3 while those in the lower one-third received a score of 1, corresponding to unimpaired, intermediate and impaired biota, respectively (Barbour et al., 1999). For negative metrics, which decreased with improving condition, the metric is trisected but scoring is done in reverse, i.e. values above the upper one third received a score of 1, those in the middle range a score of 3 while those in the lower one-third, a score of 5. To obtain the final index score for each site values of the scores for each metric are summed.

Table 5. Method for calculating scores using 1, 3, 5 scoring system based on reference conditions.

Score	Calculation
Value of metric decrease with impairment
5	>25th percentile of reference sites
3	<25th percentile of reference sites and >50th percentile of impaired sites
1	>50th percentile of impaired sites

Value of metric increase with impairment
5	<75th percentile of reference sites
3	>75th percentile of reference sites and <50th percentile of impaired sites
1	>50th percentile of impaired sites

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Seismic interferometry and ambient noise tomography in the British Isles

Heather Nicolson , ... Erica Galetti , in Proceedings of the Geologists' Association, 2012

4.2 Seismic interferometry across the Scottish Highlands

We have applied the ambient noise tomography method to noise data recorded on all 24 RUSH-II broadband seismometers. We mostly follow the data processing procedure as described in detail by Bensen et al. (2007) which is summarised as follows: first, we cut the continuous noise data into files of 24 h in length, then remove each seismometer's instrumental response, and the mean and linear trends from the day-files. To reduce the amount of storage space and computational time required, the data are then decimated to one sample per second. The next step involves applying time domain normalisation in order to remove the influence of large amplitude events such as earthquakes and other non-stationary noise sources from the subsequent cross-correlation. Bensen et al. (2007) describe various methods of applying time domain normalisation, however we decided that computationally less expensive one-bit normalisation (i.e. only the sign of the signal is retained) was satisfactory for our purposes since the British Isles are more or less aseismic. Finally, the single station day-files are spectrally whitened in order to create more broadband ambient noise records, and to reduce the effect of any monochromatic noise sources inherent in the data.

Cross-correlations are computed for each day between as many station pairs as possible, and the results are then stacked over the total time period available for each pair. Cross-correlations between stations with a separation of less than 50 km are rejected since those between stations that are separated by smaller distances do not produce useful results. Fig. 7 shows typical cross-correlations across the Scottish Highlands. The positive and negative lag times represent energy travelling in opposite directions between the pair of stations. Note that the cross-correlation functions can be asymmetric around zero delay time. This occurs when the ambient noise travels predominantly in one direction between the stations, and is a common characteristic of British interferometry due to the proximity of the Atlantic Ocean to the West, which is the dominant noise source. For example in Fig. 7b the arriving energy is predominantly on the positive (or causal) part of the cross-correlation, indicating that the ambient noise travelled dominantly in a direction from station STOR towards CAWD, so generally from West to East. Conversely in Fig. 7c the arriving energy is predominantly on the negative (acausal) component therefore the seismic noise travelled dominantly from KYLE towards RANN. Since asymmetry of the cross-correlations is prevalent in the British data and it is not always clear whether the causal or acausal component is better, we use the symmetric-component of the cross-correlation (i.e. the average of its causal and acausal parts) as our estimate of each inter-station seismogram.

A basic property of Rayleigh and Love waves is that they propagate as a series of different fundamental and higher modes, which are related to solutions of their governing wave equations (Aki and Richards, 2002). We concentrate our efforts on the fundamental modes since they are normally the most easily identified modes in interferometrically constructed surface waves. Once cross-correlations have been computed for a station pair and stacked over time giving an inter-receiver seismogram, a group velocity dispersion curve is estimated for the fundamental mode of the resulting virtual surface wave. A dispersion curve is a plot of the speed of travel of a surface wave versus period which describes the dispersion of each component period in the surface wave, similar to those in Fig. 5. We do this by applying the multiple phase-matched filter method of Herrmann (2005). Here, the fundamental mode is isolated from other unwanted arrivals such as those due to higher mode surface waves or high frequency noise using a standard time-frequency filter. Its dispersion properties can then be computed and group velocities for all possible periods, in this case between 5 and 30 s approximately, are picked interactively on a computer.

If the method of ambient noise surface wave estimation is robust, it should be repeatable in time. Therefore, four more estimates of every possible surface wave fundamental mode dispersion curve were picked, where each is constructed by stacking correlations from an equal number of randomly-selected days of noise, and where each individual day can appear in only one random stack. Thus, in total we obtain five completely independent group velocity estimates for each period. The standard deviation of these curves provides an estimate of the uncertainty in the average velocity measurement at each period.

While the output measurement from the multiple phase-matched filter step described above is the average group velocity along a raypath, what is actually measured during the above process is the peak arrival time of the wave packet at each individual frequency. Hence, the quantity measured is the average travel-time and its standard deviation. These inter-receiver travel-time uncertainties are used to weight the relative importance of their associated paths in the tomographic inversion.

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Airport noise and wildlife conservation: What are we missing?

Renata D. Alquezar , Regina H. Macedo , in Perspectives in Ecology and Conservation, 2019

Understanding how noise and stress impact wildlife reproduction

Several types of noise are known to affect animal welfare, including noise generated by aircrafts, helicopters, gas compressor stations, mines, industries, ships, sonars and road traffic (Bayne et al., 2008; Duarte et al., 2015; Gil et al., 2014; Habib et al., 2007; Kruger and Du Preez, 2016; Lampe et al., 2014; Sousa-Lima et al., 2002; Wright et al., 2011). Noise primarily causes sound masking, jeopardizing animal communication and eliciting costly changes in sound production, affecting birds (Gil et al., 2014), cetaceans (Sousa-Lima et al., 2002), insects (Lampe et al., 2014), frogs (Kruger and Du Preez, 2016 ), and other taxonomic groups. Because birds depend upon communication for reproductive purposes, exposure to continuous noise has been reported to cause decreases in nest success, brood size, nestling growth rates, and egg success ( Fairhurst et al., 2013; Halfwerk et al., 2011; Hayward et al., 2011; Kleist et al., 2018; Strasser and Heath, 2013). This reduced reproductive success brings in turn reductions in population sizes and decreased species richness and diversity in areas impacted by noise (Francis et al., 2009; Reijnen et al., 1995), which even results in changes in patterns of seed dispersion and pollination (Francis et al., 2012). Another consequence of noise masking is the change in alertness, increasing vigilance behavior in detriment of other daily activities (Klett-Mingo et al., 2016). This constant state of alertness associated with stress can bring negative physiological consequences (Anderson et al., 2011).

Noise-elicited stress is the key factor that strengthens the argument that wildlife exposure to chronic noise can jeopardize medium and large sized mammal reproduction in conservation areas affected by noise. Sporadic stressful situations (e.g. noise, predation attempts, food shortage) can cause the release of glucocorticoids, which help individuals to deal with novel situations, and even enhance the immune system (Wingfield and Kitaysky, 2002). However, exposure to constant stressful situations can generate a range of physiological responses, including a decline in immune condition (Martin, 2009; Sobrian et al., 1997). In vertebrates, glucocorticoid production and release occur in the hypothalamic–pituitary–adrenal (HPA) axis, and reproduction control occurs in the hypothalamic–pituitary–gonadal (HPG) axis. High levels of glucocorticoid are associated with a suppressed secretion of gonadotrophin releasing hormone (GnRH), luteinizing hormone (LH), and follicle stimulating hormone (FSH) (Breen et al., 2005), all of them critical for mammalian reproduction.

Sexually mature adults, pregnant females and offspring that are chronically exposed to stressful events can suffer severe consequences. Few studies have evaluated the effect of noise stress on mammalian reproduction (see Owen et al., 2004; Sobrian et al., 1997). There are, however, studies that explore how other types of stress affect mammalian reproduction, including the effects of captivity (Owen et al., 2004), laboratory protocols (Smith et al., 2004), farm management (von Borell et al., 2007), habitat fragmentation (Rangel-Negrín et al., 2009), human disturbance and climatic changes (Love et al., 2013).

Stress-related effects prior to copulation include reduced fertility (von Borell et al., 2007), reduction of gonad size (hypogonadism), decreased production of sperm which may be of lower quality, and in females, compromised maturation and reduced fertility of oocytes (Breen et al., 2005; Whirledge and Cidlowski, 2010). If copulation, fertilization and embryo implantation occur successfully, pregnant females exposed to high glucocorticoid levels can experience gestational stress, which will negatively affect offspring development (Smith et al., 2004). Gestational stress can jeopardize the development of fetal brain structures and function, affecting future offspring behavior and ability to deal with novel situations (Love et al., 2013; Smith et al., 2004; Whirledge and Cidlowski, 2010). Studies investigating prenatal stress resulting from noise have shown that offspring may develop less reactive immune systems (Sobrian et al., 1997).

After birth, mammalian offspring are highly dependent on maternal care. The implications of maternal stress in this phase involve a reduced rate of maternal effort in cleaning and protection (Smith et al., 2004), and even the rejection or accidental crushing of newborns (Owen et al., 2004). The exposure of the newborn mammals to stressful conditions can also produce disturbances (Moles et al., 2004), impairing HPA axis functioning, which persists during adult life. Both prenatal and postnatal stress can negatively affect population dynamics by influencing offspring survival, susceptibility to predation and dispersal capacity, which in turn result in consequences across all community interactions (Blas et al., 2007; Love et al., 2013).

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What is a Single Tone Continuous Noise Called

Continuous Noise

Principles of Environmental Noise

2.4.1 Continuous Equivalent Noise Level: L _eq