Abstract Urban traffic noise is associated with the health and living environment quality of residents. As urbanization and population density continually increase, it is vital to understand and predict the impact of urban design behavior on urban traffic noise. Despite the current progress has been made in modeling traffic noise using limited land use types, understanding the complex relationship between various land uses and traffic noise remains challenging for stakeholders. This study used generative adversarial networks with Hong Kong one-hour peak traffic noise map to predict urban traffic noise. The applicability of the training model was evaluated through accuracy analysis and validation. The validated model was used to generate the predicted noise map in multiple scenario experiments by adjusting controlled variables. This approach explores how land use changes effect the noise level, with scenario experiments highlighting both effective strategies and areas requiring further validation.

Abstract Europe is acting to fight noise pollution. The Environmental Noise Directive (2002/49/EC) requires EU Member States to determine the exposure to environmental noise through strategic noise mapping, and elaborate action plans to reduce noise pollution. Road traffic noise is a common environmental noise source; henceforth, EU countries are obliged to produce strategic noise maps for all major roads, railways, airports, and agglomerations, on a 5-year basis. These noise maps are used by national competent authorities to identify priorities for action planning and by the European Commission to globally assess noise exposure across the EU. A thorough investigation is conducted in this article to assess how different road surface types affect road traffic noise levels in selected EU member states which are incorporating CNOSSOS-EU into their national law. It has been done by comparing the nationally published noise data to those published by CNOSSOS-EU in 2021 for various vehicle categories by obtaining the rolling and propulsion noise for each road surface type while ignoring other factors. The aim of this study is to address the deficiency in the assessment and show a comparison between the noise generated from different surface types which can potentially enhance the effectiveness of strategic noise mapping.

Abstract Uruguay is a small country in Latin America. It has 178.500 km2 and approximately 3,400,000 inhabitants. Its environmental legislation is still incomplete; for example, there has been a national act about noise pollution since 2004 but it has never been regulated. Thus, no national regulation on noise but only departmental Ordinances in each of its 19 Departments; in practice, 19 different regulations coexist on such a small surface. Noise maps are not mandatory neither in Uruguay nor in any of its Departments. The Research Group on Environmental Noise at Universidad de la República has developed a research project that seeks the best practical methodology to build noise maps through manual measurements. The fieldwork included the determination of the stabilization time of noise measurements, the comparison between long- and short-time measurements, the comparison between measurements taken at 1.20 m and 3.50 m, and the obtention of a national curve of highly annoyed people (% HA) with a basis in the field measurements and simultaneous survey carried out on site. In this article, we present the results of these works and the proposed methodology for building noise maps throughout the country.

Abstract Determining the land cover (LC) data requirements used as input to noise simulations is essential for planning sustainable urban densifications. This study examines how different LC datasets influence simulated environmental noise levels of road traffic using Nord2000 in an urban area of 1 km2 in southern Sweden. Four LC datasets were used. The first dataset was based on satellite data (spatial resolution 10 m) combined with various other datasets implementing an LC classification algorithm prioritizing vegetation. The second dataset was created by applying an LC majority priority rule over every cell of the first dataset. The third dataset was produced by applying a convolutional neural network over an orthophoto (0.08 m spatial resolution), while the fourth dataset was created by manually digitizing ground surfaces over the same orthophoto also utilizing data from the municipality’s basemap. The results show that LC data impact simulated noise levels, with priority rules in LC classification algorithms having a greater effect than spatial resolution. Statistically significant differences (up to 3 dB(A)) were found when comparing the simulated noise levels generated using the vegetation-prioritizing LC dataset compared to the simulated noise levels of the other LC datasets.

Abstract Background Due to rapid urbanization and industrialization, noise pollution has become a growing global concern, with significant impacts on occupational and environmental health. Unlike earlier times when it received limited attention, its importance has increased due to mounting evidence of its health effects. Research on noise pollution highlights its consequences and helps identify gaps that require further exploration. This systematic review aims to compile and categorize the health effects associated with various sources of noise pollution. Methodology This review focuses on studies published from 2017 to 2022 examining the impact of noise on human health. Eligible studies were identified through comprehensive searches on PubMed and Web of Science. Results Out of 1,042 studies retrieved, 287 met the inclusion criteria. The health effects of noise were categorized into auditory effects (e.g., hearing loss), non-auditory effects (e.g., psychological and physiological impacts), and other effects (e.g. immune dysfunction and DNA damage). Conclusions While substantial research highlights the adverse effects of noise, future studies should explore its emerging impacts, especially on occupational and environmental health, such as links to cancer and genetic damage, to address existing research gaps and provide a broader understanding of its implications.

Abstract Urban environments are characterized by a complex interplay of various sound sources, which significantly influence the overall soundscape quality. This study presents a methodology that combines the intermittency ratio (IR) metric for acoustic event detection with deep learning (DL) techniques for the classification of sound sources associated with these events. The aim is to provide an automated tool for detecting and categorizing polyphonic acoustic events, thereby enhancing our ability to assess and manage environmental noise. Using a dataset collected in the city center of Barcelona, our results demonstrate the effectiveness of the IR metric in successfully detecting events from diverse categories. Specifically, the IR captures the temporal variations of sound pressure levels due to significant noise events, enabling their detection but not providing information on the associated sound sources. To fill this weakness, the DL-based classification system, which uses a MobileNet convolutional neural network, shows promise in identifying foreground sound sources. Our findings highlight the potential of DL techniques to automate the classification of sound sources, providing valuable insights into the acoustic environment. The proposed methodology of combining the two above techniques represents a step forward in automating acoustic event detection and classification in urban soundscapes and providing important information to manage noise mitigation actions.

Abstract In the field of urban design assessment, increasing attention to the nonexpert verbalizations is paid to engage social participation in planning processes, especially those not limited to static visual renderings. However, little has been explored on reproducible methods to collect these vocabularies and their relationship with architectural features. This study presents a hierarchical multifactor analysis to extract the main perceptual nonexpert clusters; and a linear correlation analysis to relate them with 2D isovist measures. In an on-line experiment, participants ( n = 20 n=20 ) elicited individual attributes ( n = 120 n=120 ) to describe their soundscape and visual perception and compared recorded urban environments ( n = 8 n=8 ). The results show that a percentage of nonexpert audio attributes correlate as well as the visual ones with isovist metrics.

Abstract The hospital soundscape is known for high noise levels and a perception of chaos, leading to concerns about its impact on patients, families, professionals, and other hospital staff. This study investigates the relationship between sound, Annoyance, and sleep quality in a multi-patient neurology ward. A mixed-methods approach was employed. Interviews were conducted with medical staff (n = 7) to understand their experiences with sound. Questionnaires and sleep tracking devices (n = 20) assessed patient sleep quality and Annoyance caused by sound events. In addition, listeners (n = 28) annotated 429 nighttime audio recordings to identify sound sources and rate Annoyance level, which we considered the key emotional descriptor for patients. Over 9,200 sound events were analysed. While snoring, a patient-generated sound dominated the nighttime soundscape and was highly rated for Annoyance, and staff-generated sounds such as speech and footsteps were found to contribute more to accumulated Annoyance due to their extended duration. This study suggests that patient sleep quality can be improved by focusing on design interventions that reduce the impact of specific sounds. These might include raising awareness among staff about activities that might produce annoying sounds and implementing strategies to mitigate their disruptive effects.

Abstract Within the current urbanised society, the call for calm and quiet areas seems more pressing than ever. Such tranquil environments like the Woonerf streets in the Netherlands allow a more human-centred design, where traffic has a restricted speed limit of 15 km/h, while pedestrians and cars share the street without segregation. In the past, predictive models have been developed to assess the tranquillity levels based on indices related to noise exposure and the amount of greenery measured through the Green View Index. However, the urban environment encompasses multiple sound sources with people having different reactions towards the auditory stimuli. Because of this complexity, objective sound measurements are examined in combination with the subjective perception of noise through eight perceptual attributes. This is done by collecting audio and visual data in 61 Woonerf streets in the cities of Groningen and Leeuwarden, supported by additional questionnaire data gathered from the corresponding residents of the above-mentioned areas. Within the context of Woonerf streets, results indicate that sound levels are perceived as relatively pleasant and uneventful. Furthermore, a difference is observed between the predicted and subjective tranquillity.