Exploring the influence of housing characteristics and sociodemographics on subjective night-time disturbance from transportation noise

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1 Exploring the influence of housing characteristics and sociodemographics on subjective night-time disturbance from transportation noise Owen Douglas Post-Doctoral Researcher University College Dublin Enda Murphy Senior Lecturer University College Dublin Abstract There is growing recognition value of using subjective data to assess night-time disturbance from environmental noise. Accordingly, this paper utilises 208 household questionnaire surveys to assess subjective responses to levels of night-time sleep disturbance from four different residential sites. Each residential site is characterised by a dominant noise source - road, rail, and aircraft noise and these sites are compared to a control site that is relatively free from transportation noise. Specifically, the paper uses multivariate modelling to assess separately the impact of socio-demographic and housing characteristics on subjective disturbance responses. The results show that age and employment status are positively associated with night-time disturbance for railway noise while certain social classes are negatively associated with disturbance for road noise. In addition, the results for housing characteristics demonstrate that dwelling type impacts upon disturbance for both aircraft and railway noise while tenure type and the age dwelling are influential for night-time disturbance from road traffic noise. Introduction and Context Transportation noise is the most prevalent form of noise pollution internationally. The WHO estimate that 90,300 disability-adjusted life years 1 (DALYs) in populations greater than 50,000 are lost to sleep disturbance as a result of environmental noise exposure in the EU[1]. The European Environment Agency (EEA) estimates that almost 20 million adults are annoyed and a further 8 million suffer sleep disturbance due to environmental noise [2]. The general public are also aware that a problem exists; a 2010 Eurobarometer survey found that 44 per cent of Europeans were view that noise affects human health to a large extent [3]. Night-time transportation noise in particular has been shown to have a significant impact on sleep disturbance [4-6] which itself has been shown to precede more serious physical and mental health problems including cardiovascular disease and cognitive impairment [7-10]. Laboratory studies have variously shown that excessive environmental noise exposure can significantly disturb sleep in the form of arousals, awakenings and in reducing the amount of time an individual spends in the deep sleep stages [11-13]. These deep sleep stages slow wave sleep (SWS) and rapid eye movement (REM) are considered to be particularly important for physical recuperation in humans with SWS in particular acting as an energy restoration state for the sleeping body [4]. Excessive exposure to noise during the night has 1 The sum of potential years of life lost due to ill-health, disability or early death and the equivalent years of healthy life lost by virtue of being in states of poor health or disability.

2 1 st - 2 nd September 2016 Proceedings been found to reduce the amount of SWS [11] and annoyance from transportation noise specifically has been found to produce a range of negative emotions [14]. While research has shown that environmental noise in urban areas comes mainly from roadbased transportation, rail and air-based transportation are also important sources. Laboratory-based studies have found that noise-induced sleep disturbance can vary for different modes of transport (road, rail and air) or modes in combination (see [15]). As part of the methodology for Basner et al s study [15], subjects were required to complete questionnaires to subjectively assess their previous night s sleep. The results showed that despite the study subjects being in an unconscious state for most night, they were able to distinguish not only between nights with and without noise but also between nights with low and high degrees of traffic noise exposure. Studies such as these suggest that the administration of questionnaire surveys the morning after a night s sleep is a useful method of assessing the effect of traffic noise from a variety of transport sources on sleep. Whereas related research has typically focused on road traffic noise (e.g. [16-22]), this study applies a standardised methodology to measure and compare subjective responses to nighttime transportation noise in a range of case study areas exposed to dominant road, rail, and aircraft noise as well as a control location where only background noise dominates. As part of a broader research project which is investigating subjective responses to sleep disturbance from transportation sources (see [23]), this paper explores the influence of housing characteristics and socio-demographics on subjective night-time disturbance from transportation noise. Methods Site selection, sampling and survey administration The study area for this research is the Dublin Agglomeration, the largest urban conurbation in the Republic of Ireland with a population of 1,345,402 [24] and accounting for 40% of GDP [25]. Within the study area, four separate residential sites were identified where the dominant transportation noise sources were road, rail and air respectively, as well as a quiet control site (see [23] for selection criteria). Figure 1 shows the geographic location chosen study sites. For the road site, the residential section Rathgar Road was selected. Recent data gathered in the Dublin City Council administrative area identified the route as a black-spot for night-time noise from road traffic [26]. For rail, a row of terraced and semi-detached dwellings proximate to the Luas light rail line was selected. Operating between 05:30 and 00:30 with approximately 30 scheduled tram movements during the night-time period, Monday to Friday, the line runs no more than 20 metres from the rear façade dwellings at any point. For air, the study site identified comprises a private housing development located just over 6 kilometres from the main runway of Dublin airport and positioned directly beneath the primary approach-path in the otherwise undeveloped rural hinterland of North County Dublin 2. Finally, a residential development in the suburb of Clonskeagh was selected as the quiet control site. With no through road and bounded on all sides by established residential areas, this location is not subject to road noise beyond local access traffic and is not located near a rail line or under a major flight-path. 2 Dublin airport operates 24/7, 364 days a year with up to 600 aircraft movements per day. Under normal operating conditions, the study site is only over-flown by inbound flights. While official data is unavailable, the typical weeknight arrivals schedule indicates that there are approximately 20 scheduled inbound passenger flights - in addition to freight and private aircraft - between 11pm and 7am. This is subject to seasonal variation.

3 Figure 1: Study Sites in the context Dublin Agglomeration In order to examine responses to the noise environment in these locations, a questionnaire survey was designed to capture subjective responses to transportation noise and reported sleep disturbance. More specifically, respondents were required to indicate if they had been bothered disturbed or annoyed by transportation noise (road, rail or air) over the past 12 months to any extent. Two survey questions were designed for this purpose based on the recommendations of Fields et al. [27]. The first asked respondents to select from five categorical responses: extremely, very, moderately, slightly or not at all. The second required a scalar response ranging from 0 to 10; 0 represented not at all bothered, disturbed or annoyed while 10 represented extremely bothered, disturbed or annoyed 3. If a positive response was offered for either question, respondents were subsequently asked to indicate whether they were disturbed by night-time noise from specific transportation sources during a typical week, and if so, to what extent using a 0 to 10 point opinion scale. We selected a random sample of residents in each four sites using An Post s GeoDirectory database 4. A target of 50 responses in each study site (200 in total) was deemed appropriate since logistic regression modelling (the primary analysis technique to be employed in this analysis) generally requires a minimum of 10 responses per independent variable and a minimum sample size of 100. Survey administration was undertaken over a period of 4 weeks in June/July Sample households were contacted prior to the study by written notice and surveys were administered by a team of interviewers in each case study site during weekdays between 10am and 8pm. A minimum of one call attempt was made at each address in the pre-selected sample (with a maximum of three) until the target 3 Having more than one scale is consistent with the most basic principles of increasing the reliability of psychometric measurements, it provides some assurance against translation difficulties and provides the strengths of both verbal and numerical scales. Verbal scales allow for clear communication between researcher and subject while numerical/scalar questions provide greater assurance that scale points are equally spaced for analysis purposes. 4 The GeoDirectory is a complete database of every building and address in the Republic of Ireland (see 28. Fahey, D. and F. Finch, GeoDirectory Technical Guide , An Post/Ordnance Survey: Dublin, Ireland.). It not only identifies each building by its use (commercial, residential or both) but also provides its coordinates so that buildings can be mapped using GIS software.

4 1 st - 2 nd September 2016 Proceedings of 50 responses was achieved in each study site. In total, 208 survey questionnaires were completed. Table 1 shows sample statistics for each study site. Table 1: Selected sample statistics (%) Road Rail Air Control Total (N=51) (N=50) (N=54) (N=53) (N=208) Gender Male Female Age or above Occupational Class Professional Managerial and Technical Non-manual Skilled Manual Unskilled All others Type of Dwelling Apartment or flat Mid-terraced house Semi-detached or end of terrace house Detached house or bungalow Dwelling Age (period built) Before or later Bedroom windows double glazed No Yes A large proportion of dwellings on the Rathgar road were built before 1919, many of which are protected structures. This designation helps explain the lower proportion of double glazing when compared to the other study locations.

5 Logistic regression analysis was employed to identify whether particular socio-demographic or housing characteristics had explanatory power when modelled for subjective night-time disturbance for each transportation noise source. As is standard procedure, an alpha value of 0.05 was set as a threshold for statistical significance. Analyses were performed using IBM SPSS Statistics Version 20 and Microsoft Excel Results and Discussion As set out in Table 1, each site differed in terms of its socio-demographic composition and housing characteristics. Thus, it is conceivable that these factors may either independently or in combination predict the subjective night-time noise experience of respondents. In this regard, 33 factors were grouped thematically into socio-demographic and housing factors for each site and a logistic modelling procedure was undertaken to identify potential predictors of subjective night-time disturbance from individual sources of transportation noise. Tables 2 and 3 display the results logistic modelling with reported night-time disturbance as the independent variable. Table 2: Logistic regression models Socio-Demographic Logistic Regression for reported night-time disturbance - Socio-Demographics Regression - Road Road Noise Model N= 207 Regression - Rail Rail Noise Model N= 207 *P 0.05 Socio- Demographics Road Socio-Demographics Socio- Demographics Rail Socio-Demographics - Rail Univariate Regression - Air Aircraft noise model N= 207 Socio- Socio-Demographics - Air Demographics Air **P 0.01 Block Chi2 = p<.085 Block Chi2 = Block Chi2 = ***P Cox and Snell R2 = Cox and Snell R2 = Cox and Snell R2 = **** P Nagelkerke R2 = Nagelkerke R2 = Nagelkerke R2 = Vindependent ariable Odds Ratio Odds Ratio Odds Ratio Odds Ratio Odds Ratio Odds Ratio Gender - Ref. Category: Female Male 0.973(-) (-) 0.796(-) (-) Age - Ref. Category: Aged 35 to 64 Aged 34 or less (-) 0.495(-) 8.126** 2.971** 0.676(-) Aged 65 or over 0.529(-) (-) ** 0.857(-) Household Type - Ref. Category: Couple One Person 0.784(-) 0.705(-) (-) 0.339(-) Couple and Child/Children 0.582(-) 0.738(-) 0.485(-) ** SingleParent N/A N/A N/A N/A N/A N/A Other (Family) Household * ** 0.714(-) Non Family Household 0.397(-) 0.388(-) 0.216(-) N/A 0.143(-) 0.146(-) Employment Status - Ref. Category: Employed Private Sector Employment Undisclosed 0.924(-) * 63.88* N/A N/A Self-Employed 0.882(-) (-) Employed Public Sector (-) (-) 0.175(-) Student (-) (-) Unemployed 0.693(-) N/A 7.238* * 0.519(-) Home Duties 0.990(-) * ** Unable to Work * * N/A N/A Retired (-)** 0.426(-) Social Class - Ref. Category: Non- Manual Professional Workers 0.583(-) 0.617(-) 0.994(-) 0.860(-) 0.935(-) 0.435(-) Managerial & Technical 0.325(-)* 0.297(-)* 0.316(-) 0.263(-) 3.60** Skilled Manual 0.563(-) 0.231(-) (-) 0.863(-) Unskilled 0.375(-) 0.307(-) 5.8 N/A N/A N/A AllOthers 0.431(-) 0.219(-)** (-) 0.786(-) 0.460(-) Education - Ref. Category: Any 3rd Level Education No 3rd Level Education 2.210* (-) 0.270(-)** 0.340(-)*

6 1 st - 2 nd September 2016 Proceedings Logistic Regression - Housing Characteristics Univariate Regression - Road Road Noise Model N= 208 Univariate Regression - Rail Rail Noise Model N= 208 Univariate Regression - Air Aircraft noise model N= 208 *P 0.05 Housing - Road Housing - Road Housing - Rail Housing - Rail Housing - Air Housing - Air **P 0.01 Block Chi2 = Block Chi2 = Block Chi2 = ***P Cox and Snell R2 = Cox and Snell R2 = 0.24 Cox and Snell R2 = 0.47 **** P Nagelkerke R2 = Nagelkerke R2 = Nagelkerke R2 = 0.68 Variable Odds Ratio Odds Ratio Odds Ratio Odds Ratio Dwelling Type - Ref. Category: Detached House Mid terraced House **** 0.350(-) 4.877** 8.730** 0.05(-)**** 0.008(-)**** Semi-detached or end of terrace 7.543** 0.483(-) 0.416(-) 0.294(-) 0.12(-)**** 0.014(-)**** Apartment or flat ** N/A N/A 0.24(-)** 0.069(-)* Tenure Type - Ref. Category: Private Rented Owned Outright 3.115* ** ** Owned with a Mortgage ** 0.941(-) ** 8.044**** Public Rented N/A N/A Age of Dwelling - Ref. Category: Post 2001 Not included in model Not included in model Pre **** **** 0.488(-) N/A N/A N/A Between 1946 and ** *** 6.774*** N/A N/A N/A Between 1961 and * ** N/A N/A N/A N/A Between 1981 and (p=0.052) 0.344(-) N/A N/A N/A Bedroom Windows Double Glazed - Ref. Category: No Not included in model Bedroom Windows Double Glazed 0.2(-)*** N/A N/A Time in Dwelling - Ref. Category: Over 10 Years 6 months or less 0.281(-) * **** Over 6 months less than 1 year * 0.907(-) to 2 years 0.831(-) (-) Over 2 years less than (-) N/A N/A 3.886** to 10 years **** 4.492* Table 3: Logistic regression models Housing Characteristics For each dominant noise source, the first column shows the results for each variable being regressed independently for night-time disturbance (univariate models). The second column shows the model results for each noise source (multivariate models) which control for other factors. The results from Table 2 the socio-demographic model - revealed no significant predictors of night time disturbance for road noise. Indeed, the overall model Chi-square was not statistically significant for this model. While socio-demographic characteristics did not predict night-time disturbance at the road site, respondents being in the youngest age cohort (less than 34 years old) predicted reported night-time disturbance at the rail site. This suggests that younger people may be more sensitive to rail noise than those in the older age cohorts given that the age profile at the rail site was mixed (see Table 1). Living in a household with related persons (other than standard nuclear family configurations) also strongly predicted night-time rail disturbance. This is somewhat more difficult to explain but may reflect the likelihood of higher occupancy in shared houses, in particular rear bedrooms which are most exposed to the noise source. In addition to the employment undisclosed category, respondents being unemployed, unable to work or carrying out home duties also predicted night-time rail disturbance. These groups are likely to spend more time in the dwelling and are therefore more likely to be highly attuned to their acoustic surroundings and in turn, more

7 disturbed by rail noise 6. At the air site, only level of educational attainment predicted nighttime disturbance. More specifically, having any third level education as opposed to none significantly predicted subjective disturbance from aircraft noise. Since higher educational attainment is generally associated with higher income, it may be the case that higher income individuals expect that their home and residential environment will be capable of insulating them from external disturbance and annoyance. As a result, they may be more sensitive to disturbance from aircraft noise. In terms of housing characteristics (Table 3) owning the property outright or with a mortgage (as opposed to renting privately) predicted night-time disturbance from road noise. This indicates that having an interest in a property may influence the propensity of an individual to be disturbed by road noise. Similar to the road noise site, home ownership or, interestingly here, having lived in the dwelling for any period less than one year (as opposed to over 10 years) strongly predicted night-time disturbance from rail noise. This suggests that individuals may habituate to noise exposure over time even though the potential health impacts remain. Living in a mid-terraced house as opposed to a detached house also predicted disturbance from rail noise. However, given the large proportion of terraced houses in the rail site, this does not warrant in depth discussion. Considering night time disturbance from aircraft noise, those living in anything other than a detached house or bungalow were less likely to report night time disturbance from aircraft noise. This may relate to the way aircraft noise interacts with the dwelling whereby multiple facades are exposed as the aircraft passes over. Clearly, detached houses will have more exposed façades than apartments or semi-detached or terraced houses in this instance. Interestingly, those living in their dwelling for 5 to 10 years were more likely to report being disturbed by aircraft noise than those living in their dwelling for a shorter period, or indeed, a longer period (the reference category). This indicates that increased time in dwelling may not in fact result in a reduced propensity for disturbance by aircraft noise. The opposite conclusion was drawn in the rail site indicating that the relationship is non-linear and more complex. Conclusions As societies urbanise globally, human populations are increasingly living at higher densities in close proximity to wide ranging transportation modes. Consequently, a better understanding effects of noise from multiple forms of transportation sources on the health and well-being of urban residents is required. Existing research on subjective responses to transportation noise has overwhelmingly focused on road based sources. Where investigations have considered other types of transportation noise such as rail and aircraft sources, this has generally been limited to the consideration of subjective responses to artificially simulated noise studies in laboratory settings. In response to the weaknesses of studies that focus on a single source - despite the acoustic environment in contemporary developed cities becoming increasingly complex - this study has applied a standardised methodology to consider a comprehensive range of modes through the inclusion of rail and aircraft noise in addition to road noise. In so doing, we identified a range of socio-demographic and housing characteristics as predictors of nighttime disturbance from transportation noise. In this way, we provide evidence to suggest that an understanding of population composition and housing characteristics, in addition to subjective responses is crucial for policymakers, planners and engineers in the development of urban residential areas and, in particular, noise mapping and noise mitigation strategies. Overall, the results of this study suggest that variations in subjective responses to night-time noise by a dominant source, can - at least in part - be explained by the socio-demographic characteristics of residents and the characteristics housing stock. The findings further support the argument that knowledge characteristics of population and housing characteristics, and an understanding of how they affect subjective sleep disturbance, are important for a better understanding of night-time disturbance form transportation noise and dose-effect relationships. 6 These results do come with a caveat in that the number of respondents reporting night time disturbance from rail sources was relatively small, but are nonetheless statistically valid.

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