This section presented an overview of current regulations, policy and guidance relevant for the mitigation of community noise impacts in relation to UK airspace design (CAA, 2025a), summarised from “Aviation Policy and Regulation Overview” of Gatwick Airport’s Environmental Noise Directive - Noise Action Plan 2019-2014 (END NAP) (Gatwick Airport, 2019). The Airport’s END NAP specifies three main levels of aircraft noise regulation in the UK: International, European and National.

Internationally, the International Civil Aviation Organization (ICAO, 2025a) is the intergovernmental body on aircraft noise, established to develop the principles and techniques of civil air navigation and support the planning and development of international air transport. ICAO establishes the international standards, and recommends best practice and technical information, including for aviation noise. Through the Balanced Approach framework, ICAO (2004) encourages its member states to incorporate four noise control techniques: noise control at source, land use planning, operating procedures, and operational restrictions. The EU and UK have both adopted the Balanced Approach. European regulations are standardised through a common aviation policy approach to aircraft noise management. While the UK has fully left the EU, after the end of the UK/EU Agreement transitional period on 31 December 2020, the CAA (CAA, 2025e, para.1) confirmed “the law that applies to [EU] aviation rights and obligations is now all UK law and includes the retained EU Regulations, as amended by an increasing amount of UK law.”

Regarding NAFs, both the Airports Council International (ACI) and the Civil Air Navigation Services Organization (CANSO) observed, in a presentation to the ICAO Assembly 40th Session Executive Committee ICAO (2019), ss.2.2–2.3, that despite the growing evidence base demonstrating the importance of NAFs in influencing communities’ response to noise and levels of annoyance “national government policy and aircraft noise management strategies to date have focused on measures to reduce noise exposure”. Based on their analysis ACI and CANSO invited the Assembly to “explore further the understanding of non-acoustic factors as a means to potentially support policy development which properly addresses community aircraft noise annoyance” (ICAO, 2019, ES-B).

Nationally, gaps in UK law were identified pertaining to the impact of noise on health. CAP1616 (CAA, 2023) sets out that an airspace change process (ACP) appraisal must use the DfT’s (2024b) TAG module for valuing the impacts of noise, including those from changes in aircraft noise, on health and quality of life. There are gaps in knowledge relating to TAG that are relevant to its use in airspace design and change. One gap is the lack of inclusion of additional health outcomes, which could be considered if a TAG+ approach to the analysis was taken. This would address community concerns about some health outcomes being omitted from TAG. Further concerns relate to the lack of more recent evidence informing the exposure-response relationships used in TAG. Another gap concerns the sensitivity of TAG for evaluating the impacts of airspace change over differing scales and types of geographical area, particularly smaller areas and communities and urban and rural areas. The scale of the assessment can make it difficult for communities to see themselves and their local area represented in a TAG assessment. Assessing the impact of the scheme as a whole limits understanding of changes in impacts at smaller geographical scales. And the focus on the total number of households facing increases or decreases in noise levels can leave communities feeling that the magnitude of the issues faced by those assessed as ‘experiencing no change in exposure’ is being overlooked. It is unclear how the magnitude of change in the total number of households informs the airspace change process. TAG presents results for time-averaged metrics (L_Aeq,16h; L_Aeq,8h) and does not include metrics such as Number Above (NA) or metrics for shorter-time periods which communities can feel better represents their exposure situations. TAG also does not consider populations exposed to levels below 51 dB L_Aeq,16h and 45 dB L_Aeq,8h.

Finally, another gap concerns the inclusion of specific NAFs in policy. The CAA (2018), p.54 reviewed research on NAFs and recommended that: “questions on trust in authorities and perceived fairness in air traffic related decisions should be included in future surveys [of communities], given the importance of these aspects to the annoyance response”.

After the description of the current regulations, policy and guidance relevant for aviation noise, two key aspects should be considered for the inclusion of FED objectives in the development of the Airport’s airspace change proposals: i Environmental noise is a priority below 4,000 ft, where the objective in UK policy is to limit and, where possible, reduce the total adverse effects on people (DfT, 2017; DfT, 2024a); ii TAG as the tool for assessing airspace change proposals should be updated, in dialogue with policymakers to discuss how TAG+ and/or a health dashboard and NAFs analyses could additionally be taken into account in the decision making processes in line with the current and growing evidence base (CAA, 2025b; Schreckenberg et al., 2024).

The DfT (2017) Air Navigation Guidance (ANG) provides the CAA and wider aviation industry with statutory guidance on the environmental objectives that should be considered when conducting air navigation functions, including the development and regulation of Airspace Change Proposals (ACPs). The guidance lays out the altitude-based priorities that should be taken into account when considering the potential environmental impact of an airspace change. Change sponsors must satisfy these priorities when developing design options that seek to optimise the environmental performance of a portion of airspace.

The ICAO (2008) PBN Manual, Doc 9613 describes PBN specifications. Routes use a combination of satellite technology, ground systems and onboard avionics to improve aircraft track keeping accuracy. PBN routes can be designed with greater precision and flexibility than conventional alternatives, offering more options to mitigate the environmental impacts of aviation. However, the precision with which PBN routes are flown typically concentrates noise impacts as the general dispersion in flight paths following conventional routes is removed. Noise concentration linked to PBN has the potential to degrade the environmental performance of airspace if the positioning of the routes is not carefully managed. The expectation set out in the ANG (DfT, 2017, s.3.2) is that airspace changes limit and, where possible, reduce the number of people in the UK significantly affected by adverse impacts from aircraft noise. In this case the traffic concentration delivered by PBN routes is an asset in avoiding densely populated areas. However, the increased concentration of aircraft may come at the expense of increasing the intensity or frequency of aircraft operations over a smaller area which can negatively affect communities overflown.

For the purpose of assessing airspace changes, according to the ANG (DfT, 2017, fn.18), the UK government wishes the CAA to interpret this objective to mean that the total adverse effects on people as a result of aviation noise should be limited and, where possible, reduced, rather than the absolute number of people in any particular noise contour. Adverse effects are considered to be those related to health and quality of life. There is no threshold at which all individuals are significantly adversely affected by noise. It is possible to set a Lowest Observed Adverse Effect Level (LOAEL) that is regarded as the point at which adverse effects begin to be seen on a community basis (Defra, 2010, s.1.7).

Air traffic controllers often rely on vectoring (FAA, 2025) to manage the traffic using conventional routes as efficiently as possible. Flights are directed to follow a specific compass heading to a particular altitude or way point rather than fly the conventional route as designed. This practice further increases the dispersion of flight paths. The introduction of PBN routes is commonly accompanied by a significant reduction in the use of vectoring. The extent to which vectoring is reduced by the introduction of air traffic onto multiple PBN routes is a key determinant of noise concentration and therefore an important factor when considering the optimal approach to mitigating environmental impacts.

The degree of noise mitigation provided by the dispersion of air traffic onto PBN routes will significantly depend on the lateral separation between the routes (ICAO, 2012), the height of the aircraft and the degree of vectoring that is retained to deliver an efficient sequence of inbound traffic for landing. In CAP1378 (2016, Annex A, p.97; Annex B, p.100), the CAA’s Environmental Research and Consultancy Department (ERCD) presented analysis of the relationship between route spacing, aircraft height and noise on the ground. A summary of these results shows the relationship between lateral spacing and noise level considering four broad thresholds: ‘just perceptible’ (3 dB difference), ‘clearly perceptible’ (5 dB difference), ‘half as loud’ (10 dB difference), and ‘much quieter’ (20 dB difference) (CAA, 2016; fig.A1). The results demonstrate that:

at 3,000 ft relief routes would need to be at least c.1 km away to be perceptibly quieter, c.2 km away to be half as loud and c.4 km away to be considered ‘much quieter’.

The optimal position of routes included in an ACP is highly influenced by the local circumstances associated with each proposal (e.g., NAFs). Engagement and consultation with all stakeholders that may be affected by the change is essential for the sponsor to understand the features of an optimal solution regarding the positioning of routes. For this FED study, the process of optimisation assumed a blank sheet approach that is not constrained by the treatment of existing noise abatement procedures. At times, concentrating flight paths on a smaller number of routes away from densely populated areas may lead towards an optimal solution. In other circumstances, distributing the flight paths using multiple routes to achieve predictable/periods of noise relief and/or respite (see Nomenclature) for those on the ground may be preferrable. This section did not consider any limitation in the number of routes in PBN options with multiple routes. In line with CAA (2016) guidance, the specific number of routes will need to be carefully designed and implemented on the bases of safety considerations, but also accounting for the minimum lateral distance between routes to ensure a meaningful change in noise level on the ground.

Time-averaged metrics are used to deal with the variation of sound pressure over time. They are metrics which give an average sound pressure level integrated over a specific period of time. With respect to aviation noise, time-averaged metrics quantify the average amount of physical aircraft noise over time by taking into account both the average sound level associated with each aircraft flyover event, and the average number of those events over defined time periods. These metrics do not quantify actual annoyance or disturbance (Flindell et al., 2021). A widely used time-averaged metric is the equivalent continuous sound pressure level (L_eq), which when corrected by the A-Weighting curve to be applied to aviation noise, is A-weighted L_eq (L_Aeq). Some benefits of using the L_Aeq for aviation noise is that it can represent the total aircraft noise exposure of a given period and may be used to show variations in sound level from contiguous time periods, for example, hourly. Some drawbacks of the L_eq metric are that the noise level at any given time within an encompassing time period is not represented. Average noise exposure metrics may yield similar results for one area with many quieter flight events, compared to another area with few(er) noisier flights, whereas the actual experience at the two locations will be different with one another. Also, as human response varies over a 24-h period, the heightened sensitivity to noise during the evening and night-time periods are not accounted for with the L_Aeq.

Sometimes it is beneficial to represent individual noise events with discrete-event metrics, such as the maximum sound pressure level, L_Amax. Discrete event metrics such as the sound exposure level, SEL or L_E represent the individual dose of an event, such as an aircraft fly-over, by representing the complete sound energy of the event normalised over 1 s. By adding up the individual SELs from a number of individual events, one can determine the L_eq over a time period, for example, a 16-h daytime period.

A commonly used group of noise descriptors in aviation noise is the number above metric (NA). A number above metric corresponds to the total number of aircraft sound events above a specific sound pressure level during a specified time period. Some examples are, N60 and N65, which represent the number of aircraft events above 60 dB [during a defined time period, typically the night-time, although some argue for its use for daytime operations (Yu and Hansman, 2019)] and 65 dB (during the daytime) respectively. The NA metric is usually based on single-event metrics such as SEL or L_Amax. The strengths of these metrics are in combining the single-event metrics with the number of aircraft operations and to give an indication as to how airport operations might affect human activities, such as awakenings during the night-time. The NA metrics might also show the frequency of disturbing aircraft flyover events during the daytime or night-time periods. A minor disadvantage to relying solely on this metric would be that it is not a specific indicator of individual level, just an indication of the number of events above a predetermined threshold.

Some attempts have been made to develop combined metrics which account for the issue of subjectivity in human noise perception. The most prominent metric of this class is the Effective Perceived Noise Level (EPNL), a derivative of the Perceived Noise Level (PNL) which was first defined in Kryter (1960). The EPNL went further in trying to quantify the annoyance effect from tonal components within aircraft sound in addition to the effects from the duration of the noise event. Furthermore, the Noise and Number Index (NNI) is another metric used to assess the subjective effects of numerous flights during a given time period (e.g., day or night). A difference of 10 NNI has the effect of quadrupling the number of flights during the integration period or increasing the PNL by 10 dB.

Many epidemiological studies and annoyance surveys have reviewed noise exposure expressed as equivalent continuous levels over longer time periods (e.g., L_dn, L_den, L_night, etc.). It has been repeatedly shown that these energy-based metrics (alone) are limited regarding annoyance or disturbance effects from noise Wunderli et al. (2016). The combined effects of overall energy with the temporal aspects of a given noise event are reviewed here.

In CAP1498 the CAA the defines overflight as an in-flight aircraft passing over an observer at an elevation angle greater than an agreed threshold and at an altitude below 7,000 ft. The elevation angle is the approximate angle between the horizon and the aircraft. These overflights accumulated over a specified period of time, form the overflight metric. Given this definition, further metrics such as onset rate can be used to describe the noise characteristics associated with defined overflights. An additional metric to those most common within aviation noise policy is the onset rate, which aims to quantify the effect of the increase in noise level associated with aircraft flyover. Typically, onset rates of commercial aircraft lie in the region of 10 dB increase in instantaneous sound pressure level per second (dB/s). Despite strong correlations between annoyance and onset rates at various SELs tested on humans both indoors and outdoors, the use of the metric is not often used within policy (Plotkin et al., 1991). Wunderli et al. (2016) conceptualised the intermittency ratio (IR) as a metric for describing the temporal variation of a series of noise events, particularly designed to be applied to highly intermittent traffic noise exposure situations, which means it is suitable for implementation in common aircraft noise prediction models. IR is defined as the proportion of acoustic energy from individual noise events above a defined threshold as a proportion of the overall sound energy. It is particularly aimed at addressing the effects of transportation noise on humans during the night, where acute physical reactions to noise during sleep occur. The probabilities of these reactions clearly correlate with maximum sound pressure level of noise events whereas average noise metrics usually fail to predict noise-induced sleep disturbances sufficiently.

An important consideration is that the currently available metrics are only designed to quantify the aircraft noise and therefore should be used with caution if representing community annoyance and disturbance. At an individual level, there is a significant variability in the relationship between noise exposure and any associated annoyance effects. For this reason, the community impacts of aircraft noise are subject to considerable uncertainty. An example of this is the fact that overall reported annoyance around airports does not seem to be reduced in proportion to the important reductions in noise contour areas that have been achieved with the introduction of quieter aircraft and operational procedures (Guski et al., 2017). Flindell et al. (2021) summarise the sources of uncertainty in aircraft noise annoyance as:

Sound levels measured (or modelled) might not be representative of the actual exposure to aircraft noise.

In sum, the most commonly studied aviation noise metrics have been reviewed within this section. Although time averaged metrics, such as L_Aeq,T are widely studied, relatively simple to understand and are somewhat correlated to annoyance, other metrics such as NA, L_Amax and Intermittency Ratio can provide more accurate information about the number of overflights effectively contributing to the total aircraft noise exposure. Other acoustic and psychoacoustic metrics able to better account for short-term noise exposure and impacts associated with respite, relief or dispersal schemes need further investigation. Reviewing the acoustic metrics is a fundamental framework needed to address the objective effects of aircraft noise on health.

The effects of aircraft noise on health could provide further information or metrics to inform considerations of FED. However, the increasing number of individual studies examining the effects of aircraft noise on health in recent years has led to complexity and introduced uncertainty in understanding 1 whether aircraft noise is having a detrimental effect on a specific health outcome; and 2 the magnitude of any effect, as findings often vary across studies. Systematic reviews of the evidence have been undertaken to help stakeholders and policymakers understand the strength of the evidence across studies by statistically pooling estimates for effects of aircraft noise on a health outcome using meta-analysis. Narrative systematic reviews, which qualitatively compare findings, are also available where it is not possible to meta-analyse data. Typically, evidence from systematic reviews is considered more favourably than individual studies, but individual studies that are relevant to the Airport or UK context, and/or that examine novel exposures or health outcomes, and/or that are methodologically robust should also inform considerations of FED. Evidence from longitudinal studies that examine how aircraft noise exposure influences the development/incidence of disease is stronger than evidence from cross-sectional studies which examine exposure and health at one time-point. The following sections summarised evidence to inform discussions about using health as a metric of FED, regarding:

Most recent evidence and/or exposure-response functions;

The term annoyance describes negative reactions to noise such as disturbance, irritation, dissatisfaction and nuisance (Guski, 1999). Exposure-response functions (ERFs) showing the ‘percentage highly annoyed’ (%HA), assessed following Technical Standard (ISO/TS15666, 2021; ISO/TS15666, 2003), plotted against noise exposure increasingly inform policy, guidance and impact assessment.

However, predicting annoyance at any given sound level has uncertainty, with wide-ranging estimates of annoyance being found for the same sound level across studies (Guski et al., 2017; Guski et al., 2018). Uncertainty is associated with methodological differences in survey design (sampling, recruitment, population, range of exposure) but also in terms of how noise exposure is estimated; operational differences between airports (e.g., number of runways, night-flights, availability of respite) and non-acoustic factors (Clark et al., 2021a). There has been much debate about differences between recent annoyance surveys (Independent Commission on Civil Aviation Noise, 2019; Guski et al., 2019; Gjestland, 2018). The uncertainty assessment of annoyance should take a ‘sensitivity’ approach and examine impacts using estimates from different ERFs, and not rely solely on one ERF to estimate annoyance effects (the WHO recommends using a local ERF where available). ERFs can be derived either from pooling individual study ERFs across the evidence base (e.g., WHO) or from individual studies of a particular context (e.g., SoNA 2014). Both approaches have pros and cons and provide different or limited information on the uncertainties in the estimates. Studies rarely report uncertainties in the noise exposure assessment and pooled estimates, depending on the methodology employed, often cannot provide confidence intervals of the lowest and highest estimates for the annoyance relationship. The ‘sensitivity’ approach could take uncertainties into account when examining impacts, where known.

In terms of time-average metrics such as L_Aeq,16h or L_den there are several ERFs from meta-analyses or individual studies that could be used to estimate aircraft annoyance within communities around the Airport: the World Health Organization Environmental Noise Guidelines Exposure Response Function (WHO ENG ERF) (Guski et al., 2017b), the Survey of Noise Attitudes (SoNA) 2014 ERF (Civil Aviation Authority, 2021), and the 2002 EU ERF (often referred to as the Miedema curve) (European Commission, 2002; Miedema and Oudshoorn, 2001).

The WHO ENG estimates a stronger relationship between aircraft noise and annoyance than the SoNA 2014 and 2002 EU ERFs. For example, the WHO ENG ERF suggests that 10% HA would occur at 45 dB L_den, whilst SoNA 2014 suggests that 10% HA would occur at 55 dB L_Aeq,_16h. SoNA 2014 additionally provides an ERF for N65, suggesting an increase in annoyance responses between 50–99 events and 100–199 events for N65.

In considering how to undertake Health Impact Assessment (HIA), the WHO ENG set out that “data and exposure–response curves derived in a local context should be applied whenever possible to assess the specific relationship between noise and annoyance in a given situation. If, however, local data are not available, general exposure–response relationships can be applied, assuming that the local annoyance follows the generalized average annoyance.” (Section 5.5, page 109) (WHO, 2018). This would favour using the SoNA 2014 ERF. However, as previously described there has been much debate about differences between the WHO ENG and SoNA 2014 ERFs.

Another aircraft noise metric that could inform the consideration of FED is the intermittency ratio (IR) (Wunderli et al., 2016), which reflects whether the time-average noise exposure is made up of distinct or a high number of distinct pass-by events (a high IR) or constantly flowing events (a low IR) (Brink et al., 2019). The Swiss SiRENE study found that the ERF between aircraft noise exposure (L_den) and %HA varied slightly with the intermittency of the noise exposure (Brink et al., 2019), suggesting that annoyance was slightly higher for continuous compared with intermittent aircraft noise. To date, there are no annoyance ERFs for the UK context published that consider the IR. Similarly, there are no published ERFs for aircraft noise annoyance that consider how the annoyance response might vary between areas with different characteristics (e.g., urban, suburban, rural) or with differing levels of ambient noise. The lack of evidence makes it challenging to take these factors into account in any impact assessment.

The WHO ENG, SoNA 2014 and EU 2002 ERFs are steady-state relationships, reflecting the relationship between current noise exposure and annoyance. They do not reflect how people may respond to a change in exposure, which has led to criticism of their use in assessments dealing with airport expansion or airspace change including cost-benefit analyses such as TAG (Independent Commission on Civil Aviation Noise, 2019).

Several international studies examining change in aircraft noise exposure, including newly overflown communities, airspace change, and runway alternations, have found that there is an excess annoyance response in relation to the change in noise exposure, both for increases and decreases in exposure (Brown and van Kamp, 2017; Brink et al., 2008; Fidell et al., 2002; Nguyen et al., 2018; Quehl et al., 2017). This means that when noise exposure increases, the annoyance response is slightly higher than would be predicted from steady-state ERFs for the actual noise exposure. Studies from Switzerland and the Netherlands suggest that these excess-responses are not short-term but can endure for at least a couple of years, if not longer (Breugelmans et al., 2007; Brink et al., 2008).

A study of respite operational changes to the night-time curfew and redistribution of flights into the shoulder hours around Frankfurt airport also found that residents were poor at describing and perceiving changes in operations (Schreckenberg et al., 2015). Very few residents were aware of quite significant changes in sound levels, with residents experiencing reductions of 4–10 dB L_Aeq,_1h or increases of 1.5–4.5 dB L_Aeq,_1h.

Taken as a whole, evidence suggests annoyance responses are likely to increase above that predicted by steady-state ERFs where there is an increase in aircraft noise exposure. However, there is little UK evidence to estimate the magnitude of the change effect so if used to inform the consideration of FED, it would be necessary to further review the international evidence to inform stakeholder discussions about modifications to the ERFs used in the assessment and any such estimates will have uncertainty and will need to be reviewed if more suitable evidence becomes available.

Sleep disturbance is a key health outcome in relation to aircraft noise exposure. Humans continue to respond to sounds in the environment even when asleep and studies have shown that noise can affect sleep in terms of immediate effects (e.g., arousal responses, sleep state changes, awakenings, body movements, total wake time, autonomic responses), after-effects (e.g., sleepiness, daytime performance, cognitive function) and long-term effects (e.g., self-reported chronic sleep disturbance; cardiovascular effects such as increased blood pressure, heart attacks) (Basner and McGuire, 2018; Elmenhorst et al., 2019; Basner et al., 2006).

Two different and distinct types of sleep outcomes have been examined. Subjective (self-reported) sleep disturbance which is linked to external time-average metrics such as L_night; and objective sleep disturbance which uses polysomnography (PSG) to record biophysiological changes that occur during sleep and changes in sleep stages which has been linked to individual noise events such as indoor or outdoor L_Asmax. Reports between subjective sleep disturbance and objective sleep disturbance can differ as individuals are not always aware of or recall biological awakenings. Time-average metrics may not be best for assessing impacts on sleep disturbance, as night-time aircraft noise events are intermittent, which means that the same L_night value can result from differing numbers of events and maximum levels.

ERFs for aircraft noise effects on objective and subjective sleep disturbance were published to inform the WHO ENG 2018 (Basner and McGuire, 2018) and provide ERFs that could be used to estimate sleep disturbance in communities surrounding the Airport.

For example, the health protection scheme proposed by Basner et al. (2006) for the Leipzig/Halle airport in Germany to manage the risk of sleep disturbances associated with aircraft noise included the recommendation that on average there should be less than one additional EEG awakening induced by aircraft noise per night. This is an annualised metric, so there can be more than one additional EEG awakening per night if there are other nights when no additional EEG awakenings per night occur. The concept has been used to produce awakening contours for airports. This type of information could be used to inform considerations of FED.

However, the evidence for an impact of change in aircraft noise on subjective sleep disturbance is mixed, with some studies finding no change in self-reported sleep disturbance after operational changes (Nguyen et al., 2015; Yano et al., 2015; Breugelmans et al., 2007) and others suggesting that an increase or decrease in noise was associated with subjective sleep disturbance (Fidell et al., 2002). Given the mixed evidence it cannot be discounted that operational changes can cause subjective sleep disturbance.

Noise is hypothesised to increase risk factors for poorer cardiometabolic health such as blood pressure, blood sugar and blood fats which can lead to diabetes, heart attacks and strokes. (Münzel et al., 2018; Münzel et al., 2017). There are several ERFs for aircraft noise and cardiometabolic outcomes available which find statistically significant increases in risk for a range of cardiovascular outcomes including Acute Myocardial Infarction (AMI), coronary heart disease and cardiovascular disease (CVD), as well as on cardiovascular risk factors such as hypertension and diabetes (Cai et al., 2021; Basner et al., 2014; Van Kempen et al., 2018; Vienneau et al., 2019; Vienneau et al., 2015). There are also studies of populations around Heathrow Airport that could be used (Hansell et al., 2013; Jarup et al., 2008). A recent publication on the Swiss National Cohort has assessed the association of aircraft noise (L_den) with cardiovascular mortality over a 15 years period, finding an association with Myocardial Infarction (MI) but not with other cardiovascular mortality outcomes including CVD, ischaemic heart disease, and stroke (Vienneau et al., 2022). This study also found that intermittency of events during the night (assessed across three noise sources of road traffic, aircraft noise and railway noise) was associated with a range of cardiovascular mortality outcomes but that associations for number of events (also assessed across three noise sources) were only found for MI and stroke. This paper also provides age-specific estimates, as some of the associations with (CVD) mortality were stronger for younger (30-64 years) or older (65+) participants and the authors suggest this should be taken into account in HIA.

Few studies have examined the impact of a change in aircraft noise on cardiometabolic health. The NORAH study found that neither an increase or decrease in aircraft noise exposure influenced blood pressure over the course of 1 year (NORAH Knowledge No 11, 2011) but studies with a longer duration may be needed to show effects given the long developmental period for poor cardiometabolic health.

TAG estimates AMI based on the 2006 Babisch ERF (Babisch, 2006) and estimates hypertension and stroke based on the 2009 Babisch and van Kamp ERF for hypertension (Babisch and Kamp, 2009): this hypertension ERF informs calculations for effects on vascular dementia and stroke. Heathrow Expansion planned to additionally calculate the effects using the 2018 WHO ERF for stroke (Van Kempen et al., 2018) and the Vienneau ERF for AMI (Vienneau et al., 2015). Subsequent publications provide further ERFs that could be used (Vienneau et al., 2022).

Residents can report impacts of aircraft noise exposure on mental health, wellbeing and quality of life. Such responses could be the result of noise as a stressor but can also result from impacts of annoyance and sleep disturbance on stress hormones and mood. There is evidence for an effect of aircraft noise on a range of adult mental health, wellbeing and quality of life outcomes such as depression, but recent reviews highlight that the evidence does not consistently suggest an association (Clark et al., 2020; Clark and Paunović, 2018b; Hegewald et al., 2020). There is evidence for a small effect of aircraft noise exposure on children’s hyperactivity symptoms (Clark et al., 2021b; Clark and Paunović, 2018b).

The NORAH study examined the health insurance data of over 655,000 individuals aged over 40 years living near Frankfurt Airport finding a relationship between aircraft noise exposure (L_Aeq,24h) in 2005 and new cases of clinical depression diagnosed between 2006 and 2010.

Odds for depression increased with aircraft noise exposure up to 55–60 dB L_Aeq,24h but decreased and became non-significant at higher noise levels, which the authors speculated could have been due to noise mitigation at higher exposures or vulnerable people having moved out of the noisiest areas. The estimates of odds for depression across the 5 dB exposure bands (40–60 dB L_Aeq,24h) range from increases of 9%–23%. Similar associations were also found for night-time aircraft noise exposure. However, the study had limited statistical power to examine the relationship at the highest noise levels as only 11 individuals had depression in the 60 dB L_Aeq,24h group, which is likely to be influencing the findings. The ERF from the NORAH study could be used to estimate effects of aircraft noise on mental health but the estimates above 60 dB L_Aeq,24h are uncertain.

Analysis of the Swiss SAPALDIA study found longitudinal associations between transportation noise annoyance and noise sensitivity with health-related quality of life (assessed by the SF-36) over 8 years later (Cerletti et al., 2020). However, the study did not find associations between aircraft noise and later health-related quality of life (HRQoL). Further analyses also found an association between aircraft noise exposure and incidence of depression and transportation noise annoyance was also associated with higher risk of depression (Eze et al., 2020). Unfortunately, the SAPALDIA study did not examine aircraft noise annoyance per se and the findings are limited to transportation noise. However, considering recent evidence as a whole, there is support for an effect of aircraft noise on risk for depression and for transportation noise on quality of life.

Beghelli (2018) examined the impact of a flightpath change trial at Heathrow Airport in 2012–2013 on prescription medication. Linking monthly medication prescribing to noise exposure at GP practices during the trial, the study found that there were reductions in prescription spending on nervous and respiratory conditions for regions that experienced a drop in air traffic during the trial.

A recent paper has evaluated the influence of the ‘Schiphol New Deal Policy’ on depressive symptoms in over 1,700 older adults (aged 57–102 years) from the Dutch Longitudinal Aging Study Amsterdam (Li et al., 2021). The policy aimed to reduce noise levels by changing flight routes and influencing night-flights. However, the study found very small changes in noise levels before and after the policy was introduced and no change in depressive symptoms. The study also reports no change in life satisfaction, anxiety, sleep, cognition and loneliness but it is difficult to evaluate these findings as no details are provided as to how these outcomes were assessed. Overall, the study findings are potentially limited by the lack of change in exposure and the lack of detail about operational changes, as well as the focus on an older sub-group of the population.

TAG does not include mental health, wellbeing and quality of life outcomes. The recent paper by Clark et al. (2021b) provides an ERF for hyperactivity suitable for use in TAG that is also designed to inform HIAs.

A number of studies have found an association between aircraft noise exposure at school and children’s cognitive abilities or learning outcomes (Clark et al., 2006; Klatte et al., 2016; Hygge et al., 2002). There is also evidence for effects of a range of different aircraft metrics on children’s SATs test score including L_Amax, Number of Events above a threshold metrics (NA), and Time Above a threshold metrics (TA) (Sharp et al., 2014). There is evidence to suggest that becoming newly exposed to aircraft noise exposure impacts on children’s reading test performance (Hygge et al., 2002). TAG does not include effects of noise on children’s reading comprehension, as whilst published in 2005, the RANCH reading comprehension ERF (Clark et al., 2006; Stansfeld et al., 2005) does not use a dichotomous outcome suitable for monetising the effects of noise on reading comprehension. A recent meta-analysis has re-estimated the RANCH reading comprehension ERF pooling estimates across three studies carried out around Heathrow airport, with analyses additionally presented using a categorical assessment of reading comprehension (scoring well below or below average on the reading test) suitable for use in TAG (Clark et al., 2021b). Reading comprehension is a good marker for general cognitive ability.

Recent years have seen increasing expansion of the types of health outcomes examined in relation to environmental noise, with limited evidence emerging for effects of aircraft noise on birth outcomes, neurodegenerative outcomes and dementia (Nieuwenhuijsen et al., 2017; Clark et al., 2020), breast cancer (Hegewald et al., 2017), as well as effects on other risk factors for poor health including obesity (Pyko et al., 2017; Foraster et al., 2018). The ICCAN Rapid Evidence Review published in 2020 highlighted the lack of studies for aircraft noise specifically for neurodegenerative outcomes and dementia, birth outcomes and cancer (Natcen, 2020).

There will always be evidence from new systematic reviews, research studies, and on novel health outcomes that could inform Environmental Impact Assessment (EIA), and if health metrics are considered as part of FED, there will be a requirement to keep a watching-brief on the evidence-base to identify and agree potential updates and additions with stakeholders.

The evidence-base has evolved in recent years, both in terms of the number of studies and type of health outcomes examined. In 2019 the Department for Environment, Food and Rural Affairs (Defra) commissioned two evidence updates to inform a revision of the TAG methodology (Clark et al., 2020; van Kamp et al., 2020) which covered annoyance, sleep disturbance, cardiometabolic, mental health wellbeing, quality of life, cancer, dementia, birth, reproductive outcomes, and cognition.

Impact Assessments and estimates of burden of disease tend to focus on a number of key health outcomes including annoyance, sleep disturbance, cardiometabolic and learning (World Health Organization, 2011; European Environment Agency, 2020). TAG does not consider learning. Assessments also differ in terms of which specific cardiometabolic outcomes they include.

In terms of the comprehensiveness of any assessment, current approaches treat each health outcome and noise source individually and there is little knowledge about how to combine effects (Houthuijs et al., 2018). There is debate about how burden of disease estimates can be added together to estimate total health effects due to the potential for double counting. Of particular relevance, is the double counting of health effects that are part of the same causal pathway, e.g., hypertension, AMI and Stroke are assessed as separate outcomes, yet hypertension can be on the earlier pathophysiological pathway to AMI and/or Stroke. Similarly, sleep disturbance can lead to greater annoyance and poorer cardiovascular health. Current methods do not take into account different effects on vulnerable groups within populations (e.g., the elderly, those with pre-existing disease) (see European Environment Agency (2020) for a discussion of the evidence for vulnerable groups). Another type of double counting that could be relevant relates to exposure to multiple noise sources, where effects for one noise source may be modified by the presence of another noise source. Uncertainty in assessments also comes from other data in the calculations, such as disability weights. Issues of double counting are methodologically challenging to resolve but should be acknowledged in assessments. Other issues of uncertainty in burden of disease calculations could be further elucidated using sensitivity analyses.

The 2018 World Health Organization Environmental Noise Guidelines for the European Region (WHO ENG) provide recommendations for protecting human health from exposure to aircraft noise (World Health Organization, 2018). The 2018 WHO ENG partially superseded the WHO Community Noise Guidelines 1999 (World Health Organization, 2000) but do not supersede the Night Noise Guidelines, 2009 (World Health Organization, 2009) (WHO NNG).

Based on a series of systematic evidence reviews, the WHO recommended that, “For average noise exposure, the Guideline Development Group (GDG) strongly recommend reducing noise levels produced by aircraft below 45 dB L_den, as aircraft noise above this level is associated with health effects. For night noise exposure, the GDG strongly recommends reducing noise levels produced by aircraft during night-time below 40 dB L_night, as aircraft noise above this level is associated with adverse effects on sleep.” These levels represent those at which 10% of the population will be ‘highly annoyed’ for L_den and at which 11% of the population would report being ‘highly sleep disturbed’ for L_night.

The night-time guideline of 40 dB L_night matches that in the 2009 WHO NNG which was set to protect the public, including the most vulnerable groups such as children, the chronically ill and the elderly. The WHO NNG additionally provided an interim target of 55 dB L_night, _outside “for the countries where the guidelines cannot be achieved in the short term for various reasons, and where policymakers choose to adopt a stepwise approach.” The 2009 WHO NNG values represent a LOAEL value, identifying levels where there is no risk to health. The 2018 WHO ENG do not represent LOAEL values but “noise exposure levels above which the GDG is confident that there is an increased risk of adverse health effects” [see page 20 World Health Organization (2018)].

If health outcomes are used to inform FED, it will be important to assess effects across a wide-range of aircraft noise exposures (and metrics), including levels in the 40–49 dB range for day-time exposures.

WHO (2018, p.13) reported that “Nonacoustic factors are an important possible confounder […] between noise levels and critical health effects and the effects of acoustic interventions on health outcomes.” Guski (1999), p.1 found that “In noise annoyance studies nonacoustic factors may explain up to 33% of the variance [of people’s response to noise apart from the level].” Following the discussion in Section 3.5.5, the significance of NAFs and their potential strength of impact on human health, mental health, wellbeing and quality of life is well established (Basner et al., 2014; Benz et al., 2022; Clark et al., 2025; Hahad et al., 2024). How health impacts from NAFs related to FED of aircraft might be avoided, minimised and mitigated requires an effective understanding of locally salient NAFs (Figure 3, stage 1).

In a white paper for ICAO Schreckenberg et al. (2024), p.12 confirmed the ‘consensus view [amongst experts is] that non-acoustical factors could prove useful in examining community response to [aircraft] noise.’ Bartels et al. (2022), pp.198, 200, 204 emphasised the importance of gaining a better understanding of the influence of specific NAFs on annoyance from aviation noise including ‘coping strategies and possibilities, and social aspects related to trust in authorities and perceived fairness’. Therefore, the impact of NAFs on the effective implementation of FED of aircraft requires understanding their contextual relevance. The NAF of perceived fairness has been consistently identified by experts as paramount to communities concerned about the distribution of aircraft noise (Hauptvogel et al., 2021; Liebe et al., 2020; CAA, 2018; ICCAN, 2021; Bartels et al., 2022; Heyes et al., 2022; Schreckenberg et al., 2022). Central to the concept of perceived fairness in this context are the perceptions of costs (e.g., adverse effects of aircraft noise) versus benefits (e.g., the perception of a/the desired intervention/s to support the ability/capacity to cope with the effects of aircraft noise) as perceived, experienced and/or understood by individuals in context. Hauptvogel et al. (2021), pp.1-2,14 explored the concept of annoyance from aircraft noise through the frame of social justice research noting that ‘improved perceived cost-benefit ratios could increase an individual’s sense of perceived fairness’. They posited that these phenomena could be understood as functions of ‘reduced adverse responses to noise through enhanced perceived capacity to cope’. Further, applying social justice theories to practice, Hauptvogel et al. (2021) presented a series of recommendations aimed at increasing individuals’/communities’ sense of perceived fairness, and thereby coping capacity, relative to FED of aircraft and called for empirical research to explore the efficacy of the recommendations.

Bartels et al. (2022), pp.208–10 emphasised the importance of identifying specific locally relevant NAFs as requisite to designing and implementing a range of potential interventions to reduce annoyance from aircraft noise. Given the subjective nature and multi-dimensionality of NAFs, as outlined, what constitutes a desired perceived cost-benefit ratio may vary widely amongst individuals and communities and needs to be understood through the lens of all NAFs categories (Hauptvogel et al., 2023; Schreckenberg et al., 2022). Therefore, by extension, it can be theorised that solutions individuals and communities may perceive as effective and desired cost-benefit ratios for FED of aircraft need first to be informed by an understanding of contextually salient NAFs across these categories. Thus, following the work of, for example, Haubrich et al. (2020) and Hauptvogel et al. (2021), this study proposed the necessity of a stage to assess local NAFs as a basis to inform stakeholder discussions and engagement to develop a workable definition of FED of aircraft (Heyes et al., 2022; Asensio et al., 2017; Haubrich et al., 2019). This approach is presented in the study recommendations (s.4) as part of a Sound, Noise and Health Conceptual Framework (Lavia et al., 2023) (Figure 3). The Framework was designed to enhance transparency by bringing community concerns to the forefront of the consultation process and thus, ideally, the decisions and outcomes.

Stage 1 of the Sound, Noise and Health Conceptual Framework (Figure 3) sets out the categories of NAFs based on ISO 16755-1 (2025) of personal, social, physical and situational factors (see s.3.6) and proposes a stakeholder consultation to determine the local contextually relevant NAFs for agreeing a definition of FED.

During Framework Stage 2, based on the NAFs derived from the Framework Stage 1 data collection and analysis, the selection of acoustic (including psychoacoustic and technology) options, health, and operational metrics are consulted and determined with stakeholders based on the ability of the metrics to fit the design principles reviewed in Stage 1. For example,:

Acoustic, Psychoacoustic and Technology Options–measure the acoustic features and levels of airport operations and flights. These might include the use of a combination of acoustic (e.g., N_xx, L_Amax, L_Aeq) and psychoacoustic metrics (e.g., Zwicker Loudness based on ISO, 2017).

Framework Stage 3 consists of agreeing the Performance Indicators, Incentives and Objectives for the agreed suite of parameters and how these will be monitored, reported, enforced and reviewed. Once again, it is crucial that this stage is reviewed in consultation/co-creation with stakeholders.

Framework Stage 4 links the agreed suite of metrics (Stage 2) and parameters (Stage 3) with an airport’s Noise Action Planning process agreed in consultation/co-creation with stakeholders. Finally, it is recommended that Stages 1, 2 and 3 are reviewed with stakeholders during each round of Noise Action Planning, to ensure that the NAFs (Stage 1) and all subsequent metrics (Stage 2) and parameters (Stage 3) remain relevant to the local environment and stakeholders.