The epidemiological analysis of exposure to noise therefore often remains spatially partial, or too aggregated.įor such studies, we aim at leveling data inequities and search for a scalable noise mapping approach. Also, where a large city physically grew outside its administrative boundaries, areas not mapped L den > 55 dB(A) correspond to the two separate END sections k) and n) of article 3 and thus have different semantics as well: inside the boundaries, these areas represent L den < 55 dB(A), whereas in the suburbs, these areas actually were not mapped at all. In regards to environmental health equity though, the incomplete data bases of rural areas distort direct comparisons of affected populations. Therefore, predominantly no noise data are available for smaller urban areas and other roads in peripheral areas. k) and for rural areas along major roads with more than 3,000,000 vehicles per year ( art.3 sec. Road noise, for example, only needs to be mapped in urban agglomerations with more than 100,000 inhabitants ( art.3 sec. Notwithstanding its merits though, the END has limitations as well-particular for consecutive exposure studies on regional or even national scale. Amongst other parameters, these maps include L den, the yearly averaged noise estimate condensing weighted day, evening, and night periods, specific to individual noise emitters (e.g., road traffic) at 4 m above the ground. In accordance with the END, this approach is deployed to generate strategic noise maps every 5 years in Europe. Provided with detailed traffic information and subsequently with data describing the environment, these source–path–receiver-based simulations are known to be very accurate. However, sophisticated engineering methods can be deployed to map simulated noise. Actual field measurements need to be comprehensive and therefore are expensive. Noise, however, is highly complex in its spatial and temporal variability so that quantification and mapping is challenging. In Europe, noise pollution has received increasing societal and political attention leading among others to the establishment of the European Noise Directive (END). In fact, multiple studies in the domain of environmental justice have found that noise exposure is particularly affecting social groups of lower socioeconomic position. Consequently, noise affects the population to a varying degree depending on their place of residence and spatial behavior.
Thereby, noise emitted from infrastructures such as roads, airports or from industries is not spatially distributed equally but confined to specific areas.
Multiple studies have shown its influence on annoyance, stress and subsequent cardiovascular diseases, sleep disturbance, and further impairments, as well as impacts on animals and ecosystems in general. Today, noise is ubiquitous-it is prevalent in and around urban areas (see ) and even pervades remote protect areas. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations. This data is new, particular for small communities that have not been mapped sufficiently in Europe so far.
In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A). Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise ( L den) variability ( R 2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas. As reference, we use the day–evening–night noise level indicator L den. MethodsĬompliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model. Objectiveīased on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters.
Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. It is an annoyance and can have a negative impact on human health as well as on the environment. In modern societies, noise is ubiquitous.
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