Predicting traffic noise using land-use regression-a scalable approach.

BACKGROUND: In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas. OBJECTIVE: Based 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. 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. METHODS: Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator Lden. 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. RESULTS: The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. 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). SIGNIFICANCE: This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Railway noise annoyance: exposure-response relationships and testing a theoretical model by structural equation analysis.

In some regions the exposure to railway noise is extremely concentrated, which may lead to high residential annoyance. Nonacoustical factors contribute to these reactions, but there is limited evidence on the interrelations between the nonacoustical factors that influence railway noise annoyance. The aims of the present study were (1) to examine exposure-response relationships between long-term railway noise exposure and annoyance in a region severely affected by railway noise and (2) to determine a priori proposed interrelations between nonacoustical factors by structural equation analysis. Residents (n = 320) living close to railway tracks in the Middle Rhine Valley completed a socio-acoustic survey. Individual noise exposure levels were calculated by an acoustical simulation model for this area. The derived exposure-response relationships indicated considerably higher annoyance at the same noise exposure level than would have been predicted by the European Union standard curve, particularly for the night-time period. In the structural equation analysis, 72% of the variance in noise annoyance was explained by the noise exposure (L(den)) and nonacoustical variables. The model provides insights into several causal mechanisms underlying the formation of railway noise annoyance considering indirect and reciprocal effects. The concern about harmful effects of railway noise and railway traffic, the perceived control and coping capacity, and the individual noise sensitivity were the most important factors that influence noise annoyance. All effects of the nonacoustical factors on annoyance were mediated by the perceived control and coping capacity and additionally proposed indirect effects of the theoretical model were supported by the data.