Digital noise maps produced according to the European Environmental Noise Directive (END) could provide valuable exposure information in noise and health research. However, their usefulness in epidemiological studies has not been evaluated. The objective of this study was to apply and evaluate Swedish END maps for assessments of residential traffic noise exposure. END maps from three Swedish cities were used to assess residential traffic noise exposure for a population sample of 2496 men and women included in a national Environmental Health Survey. For each subject, we assessed noise levels manually and automatically at three geographical points, using survey data to locate dwellings within buildings. Cohen's kappa coefficient (κ) was used to assess agreement between the noise estimates. To evaluate the maps, we compared the observed and predicted proportions of annoyed residents as a function of noise exposure using survey data and already established exposure–response relationships. The root mean square deviation (r.m.s.) was used to assess the precision of observed estimates. The agreement between the noise estimates ranged from κ=0.4 to 0.8. Generally, there was a high correspondence between observed and predicted exposure–response relationships for noise annoyance, regardless of method and if data on dwelling location within building were used. The best precision was, however, found when we manually corrected the noise level according to the location of the dwelling within buildings (r.m.s.=0.029). Noise maps based on the END appear useful for assessing residential traffic noise exposure, particularly if combined with survey data on dwelling location.
Large proportions of the European population live in areas with high noise exposure, primarily from road, railway or aircraft traffic.1 Exposure to noise has been associated with different types of health effects.2, 3 In addition to general annoyance and sleep disturbances, experimental as well as observational studies indicate effects on concentration, memory and learning,4, 5 stress hormone levels,6, 7, 8 arterial blood pressure9 and cardiovascular diseases.10, 11, 12, 13, 14, 15, 16 The impact on health by residential traffic noise may thus be of public health significance, not only with regard to the large number of exposed but also considering the wide range of observed effects.
Methodological aspects concerning the exposure assessment in noise and health research are of importance. Previous epidemiological studies have used various and disparate methods to assess noise exposure, resulting in difficulties to compare the findings. The strategic noise mappings performed according to the Directive 2002/49/EC of the European Parliament and the Council of the European Union, commonly known as the Environmental Noise Directive (END), provides a basis for harmonizing the noise assessments.17 Digital noise maps produced in accordance with the END may thus provide valuable exposure information in noise and health research. However, their usefulness for assessment of residential traffic noise exposure in epidemiological studies has not yet been systematically evaluated.
The objective of this study was to apply and evaluate Swedish END maps for assessments of residential traffic noise exposure to be used in noise and health research. Residential traffic noise exposure was assessed manually and through an automated method at three geographical points based on home addresses for 2496 participants from a national survey. The maps were evaluated by comparing the observed and predicted proportions of annoyed and highly annoyed residents as a function of noise exposure, using survey data and established exposure–response relationships for transportation noise and annoyance.18, 19
MATERIALS AND METHODS
The population sample was based on a National Environmental Health Survey performed in Sweden during 2007. The survey was sent to 43,905 randomly selected Swedish adults, aged 18–80 years, who had lived in Sweden for at least 5 years.20 The survey questionnaire (in Swedish) was answered by 25,851 subjects (59.4%). Out of these, we selected residents in the three largest cities in Sweden, Stockholm, Gothenburg and Malmö, for a detailed noise exposure assessment. These cities have been mapped according to the first phase of the END, which included agglomerates of ≥250,000 inhabitants. In total, we included 2570 subjects (Stockholm n=1242, Gothenburg n=1072 and Malmö n=256). Only 287 participants were exposed to estimated aircraft noise at 45 dB or above and we therefore focused our study on road traffic and railway noise.
The National Environmental Health Survey included questions on health and annoyance in relation to various environmental factors, such as noise annoyance evoked by road and railway traffic. Annoyance was scaled using the five-point category format proposed by ISO21 and based on the question “Thinking about the last 12 months, in or near your home, how much are you disturbed or annoyed by noise or other sounds from… (b) road traffic, (c) railway traffic (subway, tram etc)”. In the following, we quantified noise annoyance as the proportion “annoyed” or “highly annoyed” individuals, using the cutoff definitions proposed by Miedema and Oudshoorn.18, 19 The survey also provided information on building type and dwelling orientation in relation to the nearby environment. In addition, we obtained residential address coordinates from the national Real Property Register, held by the Swedish Mapping, Cadastral and Land Registration Authority.
Digital noise maps for the year 2004 and local technical reports were retrieved from the Environment Health Administrations in each city. Digital background data, such as city borders, roads, railways and buildings, were obtained from the local Offices of Urban Development. The cities used different consulting firms for the noise mappings (Ingemansson Technology AB, WSP and Acoustic Control AB), and as a result, various software and reference systems had been used. Noise levels were calculated using CadnaA (DataKustik GmbH in Munich, Germany), SoundPlan (SoundPLAN International LLC) or MapNoise (WSP). To harmonize the data, we converted the noise maps and background information to the Swedish national reference system at the time, (RT90 2.5 GonV). Data management and exposure assessments were performed in ArcGIS Desktop (9.3.1) and MapInfo Professional (9.0).
All three cities used the Nordic Prediction Method22, 23, 24 and the common indicator of residential traffic noise exposure in the European Union, Lden17 (Supplementary Table S1). Lden is defined as the A-weighted 24-h equivalent continuous sound pressure level, with an addition of 5 dB for evening noise events (in Sweden defined as the period 1900–2300 hours) and 10 dB for night-time noise events (in Sweden: 2300–0700 hours). Both the range (35/45 to 75 dB), and resolution (1 or 5 dB) of the maps exceeded the minimum requirement of the END. However, the mappings differed in some respects between the cities, which may have implications for the accuracy and comparability of the data. For example, the quality of the traffic flow information varied between the cities, as did assumptions regarding diurnal vehicle distribution. Other differences include ground surface elevation, which was most detailed in Stockholm (1 m), and grid spacing, also most detailed in Stockholm (2–3 m). In addition, the city of Gothenburg used noise maps at 4 m calculation height, but Stockholm and Malmö provided maps calculated at 2 m.
Manual Exposure Assessment
To estimate the residential noise exposure manually, we superimposed address coordinates for the 2570 participants on the noise maps for road and railway traffic, respectively, using Geographical Information Systems (GISs). Data on roads, railways and buildings were also added. In most cases (94%), the coordinates were located at the entrance of the buildings; however, for some, they were located at the building center point. In this way, 2496 addresses were linked to a building around which the noise levels were assessed and entered into a database. Subjects whose address could not be linked to a building were excluded, n=74 (3%).
To characterize the participants’ exposure to noise, we assessed the Lden level at three different geographical points at their residential address: (1) at the most exposed façade of the building, (2) at the exact address point and (3) at the most exposed façade of the dwelling. In the following text, these are referred to as the “building”, “address” and “dwelling” estimates. The dwelling estimate was based on visual inspection of the noise maps together with survey data on the dwelling's orientation in relation to the nearby environment. The following survey question was used to locate dwellings within apartment buildings: “Does your residence have a window facing… (a) larger street or traffic route; (b) local street; (c) railway (including subway, trams etcetera); (d) industry or industrial area; (e) inner yard or back yard; (f) garden or park; (g) nature (forest, lake, meadow or open field); (h) other than listed, what?”. An example of the manual noise assessment is given in Figure 1. The Lden levels were recorded in 5-dB classes, ranging from <45 to ≥75 dB.
Automated Exposure Assessment
To facilitate noise exposure assessments in large data sets, we developed an automated method for searching and selecting noise estimates from the Swedish END maps through scripts, written in MapInfo Professionals programming language MapBasic (version 9.0). The automated method required some adjustment of the noise maps and creation of buffer zones around each building and address point. Processing of map geometries (topologization) was used to improve script performance. There were no glitches between the maps, but overlapping areas between adjacent maps were removed to assure a single noise estimate at every position. Similarly, noise values beneath buildings were eliminated. To obtain the address point estimates, the coordinates had to be moved to the nearest façade line. In most cases, this was done automatically through a GIS application called “snapping”. However, in some cases the coordinates had to be moved manually. The buffer zones were then used to assess the highest noise level within 2 m from the building facades and around the address points. We did not develop a procedure for including survey data on dwelling orientation in the automated method; thus, only building and address estimates were assessed by the script.
Evaluation of Exposure Assessments
To evaluate the agreement between the building, address and dwelling estimates, we assessed Cohen's kappa coefficient (κ) and calculated the pairwise differences between the estimates, presented as the number (and proportion) of complete matches in 5 dB categories, as well as within ±5, 10 and ≥15 dB. The performance of the automated assessment method was evaluated by comparison with the manual method.
The comparison of observed and predicted proportions of annoyed and highly annoyed residents, as a function of Lden exposure, was performed using data on noise annoyance from the National Environmental Health Survey in conjunction with established exposure–response relationships for transportation noise annoyance, reported in previous meta-analyses.19, 25 To measure the precision of our estimates, we calculated the root mean square deviation (r.m.s.) between the observed and predicted values. To investigate dissimilarities between the cities, we also assessed city-specific estimates. The analyses were performed using STATA/SE 11.0.
Approximately 23% of the participants in our study population lived in semi-detached or detached houses and 77% in apartment buildings. More subjects were exposed to road traffic than to railway noise; however, the distribution of the noise estimates differed according to the geographical location of the assessment (Tables 1 and 2). Differences in mean exposure levels between the manual and automated methods were 1 dB or less.
The evaluation of agreement between the road traffic noise estimates assessed manually showed the highest agreement between the address and dwelling estimates: κ=0.64 (Table 3). The building estimates, which by definition gives the highest values, was at least one category higher than the address estimates in 51% of the cases and also higher than the dwelling estimate in 39%. The noise levels assessed at the address point were generally lower than those assessed at the dwelling façade (24%). Comparing the automated and manual methods, there was a complete within-category match in 84% for the building estimates and in 88% for the address estimates (κ=0.81 and 0.86, respectively). The noise levels produced by the automated method were higher than those for the manual method in 14% for the building estimates and in 10% for the address estimates, and lower in approximately 2% for both estimates. The agreement between the building and address estimates assessed automatically and the dwelling estimate assessed manually was κ=0.42 and 0.61.
Generally, there was a better agreement between the noise estimates for railway traffic compared with those for road traffic, but the trends were similar (Table 4). Comparing the automated and manual methods, the proportion of complete within-category agreements was high; 96% for building estimates and 97% for address estimates (κ=0.94 and 0.94, respectively).
The comparison of the observed and predicted proportions of annoyed and highly annoyed residents indicated a high agreement for all three estimates of road traffic noise (r.m.s. ranging from 0.029 to 0.064) and there were no systematic differences between the manual and automated methods (Figures 2a–c). The best agreement between observed and predicted data was, however, indicated for the manually derived dwelling estimate. Considering the building estimates, fewer residents than predicted reported noise annoyance at higher noise levels. Furthermore, the proportion of annoyed residents was higher than predicted at noise levels below 50 dB for all three estimates, although most prominent for the address estimates. Similar patterns were also apparent for the proportion of highly annoyed residents (Figures 3a–c).
For railway noise, all three estimates performed equally well in predicting the prevalence of annoyance, with an r.m.s. ranging from 0.004 to 0.022 (Figures 4a–c and 5a–c). Again, there were no systematic differences between the automated and the manual methods.
We derived separate estimates for Stockholm and Gothenburg only, because of too few observations were available for Malmö. The city-specific analyses agreed well with the aggregated analyses shown above, and we found no systematic differences between the two cities (data not shown).
To our knowledge, this is the first study attempting to evaluate digital noise maps produced in accordance with the European Environmental Noise Directive (END) for assessments of residential traffic noise exposure. Our results indicate that END maps of similar detail as the Swedish maps generate valid estimates of residential traffic noise exposure and can be used in noise and health research. The best agreement between observed and predicted proportions of annoyed and highly annoyed residents, in relation to the Lden level, was found when adjusting for dwelling location within buildings. There were no apparent systematic differences between the manual and automated assessment methods.
In previous epidemiological studies, for example, on cardiovascular health, the exposure to noise is commonly modeled using validated calculation models and detailed traffic data.14, 15, 26, 27, 28 However, the models, as well as the quality of the input data, may vary greatly between studies, depending on national standards. An advantage with applying the END maps for exposure assessments in noise and health research is the standardization of methods,29 which facilitates comparison of the findings. However, experiences from the implementation of the END indicate that there is a need to increase the standardization even further.30, 31, 32
The Swedish END maps generally exceed the minimum requirements of the first phase of the END, for example, they included noise levels also below 55 dB (Supplementary Table S1). Our results may therefore not apply to END maps that only fulfilled the minimum requirements. The methodology of the mappings in the three cities differed in some respects; however, all three cities used the Nordic Prediction Methods for road and railway noise to calculate the exposure.22, 23, 24 Furthermore, we did not detect any substantial differences between the cities with regard to the reporting of annoyance in relation to noise exposure extracted from the maps. Generally, there was a better agreement between observed and predicted proportions of annoyance for railway noise than for road traffic noise. We believe that this in part may be explained by the more narrow range of exposure for railway noise.
The results of this study indicate that it is important to identify the location of dwellings within apartment buildings to achieve an accurate noise exposure assessment. For the purposes of the strategic noise mappings of the END, the assessment point must be at the most exposed building façade. This has also been a common way to characterize the noise exposure of the individuals in previous epidemiological studies on noise and health.14, 15, 16, 27 Dwellings are, however, sometimes located toward quiet inner yards, gardens or nature, and in health research, alternative assessment points may be of interest.17 In this study, we accounted for dwelling orientation within buildings using survey data from the residents. From our comparisons of the observed and predicted proportions of annoyed and highly annoyed residents as a function of road traffic noise, it is evident that the dwelling estimate has the highest precision in relation to the established exposure–response relationship. For the building estimates, we observed a lower proportion of annoyed residents than predicted at higher noise levels. A misclassification of exposure, by not taking into account that some dwellings face a quieter side, may have contributed to these results. We also observed a higher proportion of annoyance than predicted for all estimates at noise levels ≤50 dB. This may be related to the calculation model, which has a lower precision at low noise levels, or because traffic from small local roads had been omitted.
To automatically search and select noise values from the END maps, we used a script-based GIS technique. GIS are commonly used in noise mappings33 and have become a valuable tool for linking spatial data to various environmental exposures, such as in epidemiologic research on air pollution34, 35 and, increasingly, also on noise.12, 14, 36 We did not observe any systematic difference between the automated and the manual methods although the automated method produced slightly higher estimates than the manual procedure. The manual method appeared more robust in this respect, neglecting small and unrepresentative areas of high exposure close to the facade.
To some extent, our findings depend on study area characteristics and may therefore not be generalizable to areas with other features. Stockholm, Gothenburg and Malmö are, however, representative for many European cities in their clear urban structure and building types. A majority of our participants (77%) lived in apartment buildings with differences of up to 30 dB between the most and least exposed façade. In areas with more detached or semi-detached housing, the exposure difference between the facades may be smaller.
A limitation with this study is the lack of information on additional exposure modifiers, for example, floor level, noise insulation and number of windows at noise expose façade. It is likely that a more accurate assessment would have been possible if data on these factors could have been taken into account. For example, 3D noise maps could have improved the assessments with regard to floor height.37 The present Swedish END maps are in 2D, but preferably, future strategic mappings should adopt the 3D technique.
The strengths of our study include the possibilities to link exposure data from the Swedish END maps to individuals for whom we also had survey data on annoyance and residential characteristics. Furthermore, the use of GIS techniques in combination with register data on the geographical location of each participant's residential address enabled us to extract noise levels from the END maps, both manually and with an automated method, with high accuracy.
It may be concluded that digital noise maps based on the END appear to be useful for assessing residential traffic noise exposure. To enhance the precision, information on exposure moderating factors, such as dwelling orientation, should be taken into account.
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We thank Magnus Lindqvist, Jörgen Bengtsson (Stockholm), Thomas Hammarlund, Gunnar Blide (Gothenburg), Magnus Hillberg and Håkan Kristersson (Malmö) for providing digital noise maps, urban background data and technical reports, and Rebecka Pershagen for assisting in the manual noise exposure assessment. The study was funded by the Swedish Research Council FORMAS, the Swedish Heart and Lung Foundation, and the Swedish Council for Working life and Social Research.
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website
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Eriksson, C., Nilsson, M., Stenkvist, D. et al. Residential traffic noise exposure assessment: application and evaluation of European Environmental Noise Directive maps. J Expo Sci Environ Epidemiol 23, 531–538 (2013) doi:10.1038/jes.2012.60
- exposure modeling
- population-based studies
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