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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.

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 km 2 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.

Uruguay is a small country in Latin America. It has 178.500 km 2 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.

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.

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.