Does the Macro-Temporal Pattern of Road Traffic Noise Affect Noise Annoyance and Cognitive Performance?

Noise annoyance is usually estimated based on time-averaged noise metrics. However, such metrics ignore other potentially important acoustic characteristics, in particular the macro-temporal pattern of sounds as constituted by quiet periods (noise breaks). Little is known to date about its effect on noise annoyance and cognitive performance, e.g., during work. This study investigated how the macro-temporal pattern of road traffic noise affects short-term noise annoyance and cognitive performance in an attention-based task. In two laboratory experiments, participants worked on the Stroop task, in which performance relies predominantly on attentional functions, while being exposed to different road traffic noise scenarios. These were systematically varied in macro-temporal pattern regarding break duration and distribution (regular, irregular), and played back with moderate LAeq of 42-45 dB(A). Noise annoyance ratings were collected after each scenario. Annoyance was found to vary with the macro-temporal pattern: It decreased with increasing total duration of quiet periods. Further, shorter but more regular breaks were somewhat less annoying than longer but irregular breaks. Since Stroop task performance did not systematically vary with different noise scenarios, differences in annoyance are not moderated by experiencing worsened performance but can be attributed to differences in the macro-temporal pattern of road traffic noise.

Conversion between noise exposure indicators Leq24h, LDay, LEvening, LNight, Ldn and Lden: Principles and practical guidance.

This article presents empirically derived conversion rules between the environmental noise exposure metrics Leq24h, LDay, LEvening, LNight, Ldn, and Lden for the noise sources road, rail and air traffic. It caters to researchers that need to estimate the value of one (unknown) noise metric from the value of another (known) metric, e.g. in the scope of epidemiological meta-analyses or systematic reviews, when results from different studies are pooled and need to be related to one common exposure metric. Conversion terms are derived using two empirical methods a) based on analyzing the diurnal variation of traffic, and b) by analyzing differences between calculated noise exposure metrics. For a) we collected and analyzed diurnal traffic share data from European and US airports as well as data on the diurnal variation of traffic from roads in several European countries and from railway lines in Switzerland which were derived from counting stations and official records. For b) we calculated differences between noise metrics in over 50'000 stratified randomly sampled dwellings in Switzerland. As a result of this exercise, conversion terms, including uncertainty estimates, are systematically tabulated for all variants of the target metrics. Guidance as to the practical applicability of the proposed conversions in different contexts is provided, and limitations of their use are discussed.